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Rulla Alsaedi , Kimberly McKeirnan; Literature Review of Type 2 Diabetes Management and Health Literacy. Diabetes Spectr 1 November 2021; 34 (4): 399–406. https://doi.org/10.2337/ds21-0014

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The purpose of this literature review was to identify educational approaches addressing low health literacy for people with type 2 diabetes. Low health literacy can lead to poor management of diabetes, low engagement with health care providers, increased hospitalization rates, and higher health care costs. These challenges can be even more profound among minority populations and non-English speakers in the United States.

A literature search and standard data extraction were performed using PubMed, Medline, and EMBASE databases. A total of 1,914 articles were identified, of which 1,858 were excluded based on the inclusion criteria, and 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 articles were reviewed in detail.

Patients, including ethnic minorities and non-English speakers, who are engaged in diabetes education and health literacy improvement initiatives and ongoing follow-up showed significant improvement in A1C, medication adherence, medication knowledge, and treatment satisfaction. Clinicians considering implementing new interventions to address diabetes care for patients with low health literacy can use culturally tailored approaches, consider ways to create materials for different learning styles and in different languages, engage community health workers and pharmacists to help with patient education, use patient-centered medication labels, and engage instructors who share cultural and linguistic similarities with patients to provide educational sessions.

This literature review identified a variety of interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy.

Diabetes is the seventh leading cause of death in the United States, and 30.3 million Americans, or 9.4% of the U.S. population, are living with diabetes ( 1 , 2 ). For successful management of a complicated condition such as diabetes, health literacy may play an important role. Low health literacy is a well-documented barrier to diabetes management and can lead to poor management of medical conditions, low engagement with health care providers (HCPs), increased hospitalizations, and, consequently, higher health care costs ( 3 – 5 ).

The Healthy People 2010 report ( 6 ) defined health literacy as the “degree to which individuals have the capacity to obtain, process, and understand basic health information and services needed to make appropriate health decisions.” Diabetes health literacy also encompasses a wide range of skills, including basic knowledge of the disease state, self-efficacy, glycemic control, and self-care behaviors, which are all important components of diabetes management ( 3 – 5 , 7 ). According to the Institute of Medicine’s Committee on Health Literacy, patients with poor health literacy are twice as likely to have poor glycemic control and were found to be twice as likely to be hospitalized as those with adequate health literacy ( 8 ). Associations between health literacy and health outcomes have been reported in many studies, the first of which was conducted in 1995 in two public hospitals and found that many patients had inadequate health literacy and could not perform the basic reading tasks necessary to understand their treatments and diagnoses ( 9 ).

Evaluation of health literacy is vital to the management and understanding of diabetes. Several tools for assessing health literacy have been evaluated, and the choice of which to use depends on the length of the patient encounter and the desired depth of the assessment. One widely used literacy assessment tool, the Test of Functional Health Literacy in Adults (TOFHLA), consists of 36 comprehension questions and four numeric calculations ( 10 ). Additional tools that assess patients’ reading ability include the Rapid Estimate of Adult Literacy in Medicine (REALM) and the Literacy Assessment for Diabetes. Tests that assess diabetes numeracy skills include the Diabetes Numeracy Test, the Newest Vital Sign (NVS), and the Single-Item Literacy Screener (SILS) ( 11 ).

Rates of both diabetes and low health literacy are higher in populations from low socioeconomic backgrounds ( 5 , 7 , 12 ). People living in disadvantaged communities face many barriers when seeking health care, including inconsistent housing, lack of transportation, financial difficulties, differing cultural beliefs about health care, and mistrust of the medical professions ( 13 , 14 ). People with high rates of medical mistrust tend to be less engaged in their care and to have poor communication with HCPs, which is another factor HCPs need to address when working with their patients with diabetes ( 15 ).

The cost of medical care for people with diabetes was $327 billion in 2017, a 26% increase since 2012 ( 1 , 16 ). Many of these medical expenditures are related to hospitalization and inpatient care, which accounts for 30% of total medical costs for people with diabetes ( 16 ).

People with diabetes also may neglect self-management tasks for various reasons, including low health literacy, lack of diabetes knowledge, and mistrust between patients and HCPs ( 7 , 15 ).

These challenges can be even more pronounced in vulnerable populations because of language barriers and patient-provider mistrust ( 17 – 19 ). Rates of diabetes are higher among racial and ethnic minority groups; 15.1% of American Indians and Alaskan Natives, 12.7% of Non-Hispanic Blacks, 12.1% of Hispanics, and 8% of Asian Americans have diagnosed diabetes, compared with 7.4% of non-Hispanic Whites ( 1 ). Additionally, patient-provider relationship deficits can be attributed to challenges with communication, including HCPs’ lack of attention to speaking slowly and clearly and checking for patients’ understanding when providing education or gathering information from people who speak English as a second language ( 15 ). White et al. ( 15 ) demonstrated that patients with higher provider mistrust felt that their provider’s communication style was less interpersonal and did not feel welcome as part of the decision-making process.

To the authors’ knowledge, there is no current literature review evaluating interventions focused on health literacy and diabetes management. There is a pressing need for such a comprehensive review to provide a framework for future intervention design. The objective of this literature review was to gather and summarize studies of health literacy–based diabetes management interventions and their effects on overall diabetes management. Medication adherence and glycemic control were considered secondary outcomes.

Search Strategy

A literature review was conducted using the PubMed, Medline, and EMBASE databases. Search criteria included articles published between 2015 and 2020 to identify the most recent studies on this topic. The search included the phrases “diabetes” and “health literacy” to specifically focus on health literacy and diabetes management interventions and was limited to original research conducted in humans and published in English within the defined 5-year period. Search results were exported to Microsoft Excel for evaluation.

Study Selection

Initial screening of the articles’ abstracts was conducted using the selection criteria to determine which articles to include or exclude ( Figure 1 ). The initial search results were reviewed for the following inclusion criteria: original research (clinical trials, cohort studies, and cross-sectional studies) conducted in human subjects with type 2 diabetes in the United States, and published in English between 2015 and 2020. Articles were considered to be relevant if diabetes was included as a medical condition in the study and an intervention was made to assess or improve health literacy. Studies involving type 1 diabetes or gestational diabetes and articles that were viewpoints, population surveys, commentaries, case reports, reviews, or reports of interventions conducted outside of the United States were excluded from further review. The criteria requiring articles to be from the past 5 years and from the United States were used because of the unique and quickly evolving nature of the U.S. health care system. Articles published more than 5 years ago or from other health care systems may have contributed information that was not applicable to or no longer relevant for HCPs in the United States. Articles were screened and reviewed independently by both authors. Disagreements were resolved through discussion to create the final list of articles for inclusion.

PRISMA diagram of the article selection process.

Data Extraction

A standard data extraction was performed for each included article to obtain information including author names, year of publication, journal, study design, type of intervention, primary outcome, tools used to assess health literacy or type 2 diabetes knowledge, and effects of intervention on overall diabetes management, glycemic control, and medication adherence.

A total of 1,914 articles were collected from a search of the PubMed, MEDLINE, and EMBASE databases, of which 1,858 were excluded based on the inclusion and exclusion criteria. Of the 56 articles that met criteria for abstract review, 46 were excluded because of a lack of relevance to both diabetes management and health literacy. The remaining 10 studies identified various diabetes management interventions, including diabetes education tools such as electronic medication instructions and text message–based interventions, technology-based education videos, enhanced prescription labels, learner-based education materials, and culturally tailored interventions ( 15 , 20 – 28 ). Figure 1 shows the PRISMA diagram of the article selection process, and Table 1 summarizes the findings of the article reviews ( 15 , 20 – 28 ).

Findings of the Article Reviews (15,20–28)

SAHLSA, Short Assessment of Health Literacy for Spanish Adults.

Medical mistrust and poor communication are challenging variables in diabetes education. White et al. ( 15 ) examined the association between communication quality and medical mistrust in patients with type 2 diabetes. HCPs at five health department clinics received training in effective health communication and use of the PRIDE (Partnership to Improve Diabetes Education) toolkit in both English and Spanish, whereas control sites were only exposed to National Diabetes Education Program materials without training in effective communication. The study evaluated participant communication using several tools, including the Communication Assessment Tool (CAT), Interpersonal Processes of Care (IPC-18), and the Short Test of Functional Health Literacy in Adults (s-TOFHLA). The authors found that higher levels of mistrust were associated with lower CAT and IPC-18 scores.

Patients with type 2 diabetes are also likely to benefit from personalized education delivery tools such as patient-centered labeling (PCL) of prescription drugs, learning style–based education materials, and tailored text messages ( 24 , 25 , 27 ). Wolf et al. ( 27 ) investigated the use of PCL in patients with type 2 diabetes and found that patients with low health literacy who take medication two or more times per day have higher rates of proper medication use when using PCL (85.9 vs. 77.4%, P = 0.03). The objective of the PCL intervention was to make medication instructions and other information on the labels easier to read to improve medication use and adherence rates. The labels incorporated best-practice strategies introduced by the Institute of Medicine for the Universal Medication Schedule. These strategies prioritize medication information, use of larger font sizes, and increased white space. Of note, the benefits of PCL were largely seen with English speakers. Spanish speakers did not have substantial improvement in medication use or adherence, which could be attributed to language barriers ( 27 ).

Nelson et al. ( 25 ) analyzed patients’ engagement with an automated text message approach to supporting diabetes self-care activities in a 12-month randomized controlled trial (RCT) called REACH (Rapid Education/Encouragement and Communications for Health) ( 25 ). Messages were tailored based on patients’ medication adherence, the Information-Motivation-Behavioral Skills model of health behavior change, and self-care behaviors such as diet, exercise, and self-monitoring of blood glucose. Patients in this trial were native English speakers, so further research to evaluate the impact of the text message intervention in patients with limited English language skills is still needed. However, participants in the intervention group reported higher engagement with the text messages over the 12-month period ( 25 ).

Patients who receive educational materials based on their learning style also show significant improvement in their diabetes knowledge and health literacy. Koonce et al. ( 24 ) developed and evaluated educational materials based on patients’ learning style to improve health literacy in both English and Spanish languages. The materials were made available in multiple formats to target four different learning styles, including materials for visual learners, read/write learners, auditory learners, and kinesthetic learners. Spanish-language versions were also available. Researchers were primarily interested in measuring patients’ health literacy and knowledge of diabetes. The intervention group received materials in their preferred learning style and language, whereas the control group received standard of care education materials. The intervention group showed significant improvement in diabetes knowledge and health literacy, as indicated by Diabetes Knowledge Test (DKT) scores. More participants in the intervention group reported looking up information about their condition during week 2 of the intervention and showed an overall improvement in understanding symptoms of nerve damage and types of food used to treat hypoglycemic events. However, the study had limited enrollment of Spanish speakers, making the applicability of the results to Spanish-speaking patients highly variable.

Additionally, findings by Hofer et al. ( 22 ) suggest that patients with high A1C levels may benefit from interventions led by community health workers (CHWs) to bridge gaps in health literacy and equip patients with the tools to make health decisions. In this study, Hispanic and African American patients with low health literacy and diabetes not controlled by oral therapy benefited from education sessions led by CHWs. The CHWs led culturally tailored support groups to compare the effects of educational materials provided in an electronic format (via iDecide) and printed format on medication adherence and self-efficacy. The study found increased adherence with both formats, and women, specifically, had a significant increase in medication adherence and self-efficacy. One of the important aspects of this study was that the CHWs shared cultural and linguistic characteristics with the patients and HCPs, leading to increased trust and satisfaction with the information presented ( 22 ).

Kim et al. ( 23 ) found that Korean-American participants benefited greatly from group education sessions that provided integrated counseling led by a team of nurses and CHW educators. The intervention also had a health literacy component that focused on enhancing skills such as reading food package labels, understanding medical terminology, and accessing health care services. This intervention led to a significant reduction of 1–1.3% in A1C levels in the intervention group. The intervention established the value of collaboration between CHW educators and nurses to improve health information delivery and disease management.

A collaboration between CHW educators and pharmacists was also shown to reinforce diabetes knowledge and improve health literacy. Sharp et al. ( 26 ) conducted a cross-over study in four primary care ambulatory clinics that provided care for low-income patients. The study found that patients with low health literacy had more visits with pharmacists and CHWs than those with high health literacy. The CHWs provided individualized support to reinforce diabetes self-management education and referrals to resources such as food, shelter, and translation services. The translation services in this study were especially important for building trust with non-English speakers and helping patients understand their therapy. Similar to other studies, the CHWs shared cultural and linguistic characteristics with their populations, which helped to overcome communication-related and cultural barriers ( 23 , 26 ).

The use of electronic tools or educational videos yielded inconclusive results with regard to medication adherence. Graumlich et al. ( 20 ) implemented a new medication planning tool called Medtable within an electronic medical record system in several outpatient clinics serving patients with type 2 diabetes. The tool was designed to organize medication review and patient education. Providers can use this tool to search for medication instructions and actionable language that are appropriate for each patient’s health literacy level. The authors found no changes in medication knowledge or adherence, but the intervention group reported higher satisfaction. On the other hand, Yeung et al. ( 28 ) showed that pharmacist-led online education videos accessed using QR codes affixed to the patients’ medication bottles and health literacy flashcards increased patients’ medication adherence in an academic medical hospital.

Goessl et al. ( 21 ) found that patients with low health literacy had significantly higher retention of information when receiving evidence-based diabetes education through a DVD recording than through an in-person group class. This 18-month RCT randomized participants to either the DVD or in-person group education and assessed their information retention through a teach-back strategy. The curriculum consisted of diabetes prevention topics such as physical exercise, food portions, and food choices. Participants in the DVD group had significantly higher retention of information than those in the control (in-person) group. The authors suggested this may have been because participants in the DVD group have multiple opportunities to review the education material.

Management of type 2 diabetes remains a challenge for HCPs and patients, in part because of the challenges discussed in this review, including communication barriers between patients and HCPs and knowledge deficits about medications and disease states ( 29 ). HCPs can have a positive impact on the health outcomes of their patients with diabetes by improving patients’ disease state and medication knowledge.

One of the common themes identified in this literature review was the prevalence of culturally tailored diabetes education interventions. This is an important strategy that could improve diabetes outcomes and provide an alternative approach to diabetes self-management education when working with patients from culturally diverse backgrounds. HCPs might benefit from using culturally tailored educational approaches to improve communication with patients and overcome the medical mistrust many patients feel. Although such mistrust was not directly correlated with diabetes management, it was noted that patients who feel mistrustful tend to have poor communication with HCPs ( 20 ). Additionally, Latino/Hispanic patients who have language barriers tend to have poor glycemic control ( 19 ). Having CHWs work with HCPs might mitigate some patient-provider communication barriers. As noted earlier, CHWs who share cultural and linguistic characteristics with their patient populations have ongoing interactions and more frequent one-on-one encounters ( 12 ).

Medication adherence and glycemic control are important components of diabetes self-management, and we noted that the integration of CHWs into the diabetes health care team and the use of simplified medication label interventions were both successful in improving medication adherence ( 23 , 24 ). The use of culturally tailored education sessions and the integration of pharmacists and CHWs into the management of diabetes appear to be successful in reducing A1C levels ( 12 , 26 ). Electronic education tools and educational videos alone did not have an impact on medication knowledge or information retention in patients with low health literacy, but a combination of education tools and individualized sessions has the potential to improve diabetes medication knowledge and overall self-management ( 20 , 22 , 30 ).

There were several limitations to our literature review. We restricted our search criteria to articles published in English and studies conducted within the United States to ensure that the results would be relevant to U.S. HCPs. However, these limitations may have excluded important work on this topic. Additional research expanding this search beyond the United States and including articles published in other languages may demonstrate different outcomes. Additionally, this literature review did not focus on A1C as the primary outcome, although A1C is an important indicator of diabetes self-management. A1C was chosen as the method of evaluating the impact of health literacy interventions in patients with diabetes, but other considerations such as medication adherence, impact on comorbid conditions, and quality of life are also important factors.

The results of this work show that implementing health literacy interventions to help patients manage type 2 diabetes can have beneficial results. However, such interventions can have significant time and monetary costs. The potential financial and time costs of diabetes education interventions were not evaluated in this review and should be taken into account when designing interventions. The American Diabetes Association estimated the cost of medical care for people with diabetes to be $327 billion in 2017, with the majority of the expenditure related to hospitalizations and nursing home facilities ( 16 ). Another substantial cost of diabetes that can be difficult to measure is treatment for comorbid conditions and complications such as cardiovascular and renal diseases.

Interventions designed to address low health literacy and provide education about type 2 diabetes could be a valuable asset in preventing complications and reducing medical expenditures. Results of this work show that clinicians who are considering implementing new interventions may benefit from the following strategies: using culturally tailored approaches, creating materials for different learning styles and in patients’ languages, engaging CHWs and pharmacists to help with patient education, using PCLs for medications, and engaging education session instructors who share patients’ cultural and linguistic characteristics.

Diabetes self-management is crucial to improving health outcomes and reducing medical costs. This literature review identified interventions that had a positive impact on provider-patient communication, medication adherence, and glycemic control by promoting diabetes self-management through educational efforts to address low health literacy. Clinicians seeking to implement diabetes care and education interventions for patients with low health literacy may want to consider drawing on the strategies described in this article. Providing culturally sensitive education that is tailored to patients’ individual learning styles, spoken language, and individual needs can improve patient outcomes and build patients’ trust.

Duality of Interest

No potential conflicts of interest relevant to this article were reported.

Author Contributions

Both authors conceptualized the literature review, developed the methodology, analyzed the data, and wrote, reviewed, and edited the manuscript. R.A. collected the data. K.M. supervised the review. K.M. is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation

Portions of this research were presented at the Washington State University College of Pharmacy and Pharmaceutical Sciences Honors Research Day in April 2019.

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Risk models and scores for type 2 diabetes: systematic review

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• Peer review
• Douglas Noble , lecturer 1 ,
• Rohini Mathur , research fellow 1 ,
• Tom Dent , consultant 2 ,
• Catherine Meads , senior lecturer 1 ,
• Trisha Greenhalgh , professor 1
• 1 Centre for Primary Care and Public Health, Barts and the London School of Medicine and Dentistry, London E1 2AT, UK
• 2 PHG Foundation, Cambridge, UK
• Correspondence to: D Noble d.noble{at}qmul.ac.uk
• Accepted 5 October 2011

Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice.

Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology.

Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes.

Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact.

Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes.

Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse.

Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.

Introduction

The prevalence of diabetes is rising rapidly throughout the world. 1 By 2010 its prevalence in the adult populations of the United Kingdom, the United States, mainland China, and the United Arab Emirates had exceeded 7%, 2 11%, 3 15%, 4 and 17%, 5 respectively. Americans born in 2000 or later have a lifetime risk of more than one in three of developing diabetes. 6 Type 2 diabetes (which accounts for over 95% of diabetes worldwide) results from a complex gene-environment interaction for which several risk factors, such as age, sex, ethnicity, family history, obesity, and hypertension, are well documented. The precise interaction of these and other risk factors with one another is, however, a complex process that varies both within and across populations. 7 8 9 10 11 Epidemiologists and statisticians are striving to produce weighted models that can be presented as scores to reflect this complexity but which at the same time are perceived as sufficiently simple, plausible, affordable, and widely implementable in clinical practice. 12 13

Cohort studies have shown that early detection of established diabetes improves outcome, although the evidence base for screening the entire population is weak. 14 15 The proportion of cases of incident type 2 diabetes in people with impaired glucose tolerance or impaired fasting glucose levels was reduced in landmark trials from China, 16 Finland, 17 and the United States 18 by up to 33%, 50%, and 58%, respectively, through lifestyle changes (increased exercise, weight loss) or pharmacotherapy, or both, although changes may be more modest in a non-trial population. Some have argued that because combining known risk factors predicts incident diabetes at least as effectively as impaired glucose metabolism, a diabetes risk score may be a better and more practical means of identifying people for preventive interventions than either a glucose tolerance test or a fasting blood glucose level. 19 Others favour targeting the assessment of diabetes risk in those with established impaired glucose metabolism on the basis that interventions in this group are particularly effective. 20

Risk models and scores first emerged for cardiovascular disease, and these are widely used in clinical and public health practice. In the United Kingdom, for example, all electronic patient record systems in general practice offer the facility to calculate the Framingham score, a patient’s risk of a cardiovascular event within 10 years. This risk score features in many guidelines and decision pathways (such as the cut-off for statin therapy 21 ), and general practitioners receive financial rewards for calculating it. 22 In contrast, although numerous models and scores have been developed for diabetes risk, we found limited evidence for use of these as part of a formal health policy, guideline, or incentive scheme for practitioners in any country (one Australian scheme incentivises general practitioners’ measurement of the risk of diabetes in adults aged 40-49 23 ). This is perhaps surprising, given that morbidity and mortality from cardiovascular disease has been decreasing in many countries since the 1970s, 24 whereas those from diabetes continue to increase. 3

A diabetes risk score is an example of a prognostic model. 25 Such scores should ideally be developed by taking a large, age defined population cohort of people without diabetes, measuring baseline risk factors, and following the cohort for a sufficiently long time to see who develops diabetes. 26 Although prospective longitudinal designs in specially assembled cohorts are expensive, difficult, and time consuming to execute, cross sectional designs in which risk factors are measured in a population including people both with and without diabetes are methodologically inferior. They use prevalence as a proxy for incidence and conflate characteristics of people with diabetes with risk factors in those without diabetes, and thus are incapable of showing that a putative risk factor predated the development of diabetes. In practice, researchers tend to take one of two approaches: they either study a cohort of people without diabetes, which was assembled some years previously with relevant baseline metrics for some other purpose (for example, the British Regional Heart Study 27 ), or they analyse routinely available data, such as electronic patient records. 8 Both approaches are potentially susceptible to bias.

Some diabetes risk scores are intended to be self administered using questions such as “have you ever been told you have high blood pressure?” Scores that rely entirely on such questions may be hosted on the internet (see for example www.diabetes.org.uk/riskscore ). Some researchers have used self completion postal questionnaires as the first part of a stepwise detection programme. 28 To the extent that these instruments are valid, they can identify two types of people: those who already have diabetes whether or not they know it (hence the questionnaire may serve as a self administered screening tool for undiagnosed diabetes) and those at high risk of developing diabetes—that is, it may also serve as a prediction tool for future diabetes. Prevalence rates for diabetes derived from self assessment studies thus cannot be compared directly with the rate of incident diabetes in a prospective longitudinal sample from which those testing positive for diabetes at baseline have been excluded.

A good risk score is usually defined as one that accurately estimates individuals’ risk—that is, predictions based on the score closely match what is observed (calibration); the score distinguishes reliably between high risk people, who are likely to go on to develop the condition, and low risk people, who are less likely to develop the condition (discrimination); and it performs well in new populations (generalisability). Validating a risk model or score means testing its calibration and discrimination either internally (by splitting the original sample, developing the score on one part and testing it on another), temporally (re-running the score on the same or a similar sample after a time period), or, preferably, externally (running the score on a new population with similar but not identical characteristics from the one on which it was developed). 26 29 Caution is needed when extrapolating a risk model or score developed in one population or setting to a different one—for example, secondary to primary care, adults to children, or one ethnic group to another. 30

Risk scores and other prognostic models should be subject to “impact studies”—that is, studies of the extent to which the score is actually used and leads to improved outcomes. Although most authors emphasise quantitative evaluation of impact such as through cluster randomised controlled trials, 30 much might also be learnt from qualitative studies of the process of using the score, either alone or as an adjunct to experimental trials. One such methodology is realist evaluation, which considers the interplay between context, mechanism (how the intervention is perceived and taken up by practitioners), and outcome. 31 In practice, however, neither quantitative nor qualitative studies of impact are common in the assessment of risk scores. 30

We sought to identify, classify, and evaluate risk models and scores for diabetes and inform their selection and implementation in practice. We wanted to determine the key statistical properties of published scores for predicting type 2 diabetes in adults and how they perform in practice. Hence we were particularly interested in highlighting those characteristics of a risk score that would make it fit for purpose in different situations and settings. To that end we reviewed the literature on development, validation, and use of such scores, using both quantitative data on demographics of populations and statistical properties of models and qualitative data on how risk scores were perceived and used by practitioners, policy makers, and others in a range of contexts and systems.

Theoretical and methodological approach

We followed standard methodology for systematic reviews, summarised in guidance from a previous study and the York Centre for Reviews and Dissemination. 32 33 The process was later extended by drawing on the principles of realist review, an established form of systematic literature review that uses mainly qualitative methods to produce insights into the interaction between context, mechanism, and outcome, hence explaining instances of both success and failure. 34 Our team is leading an international collaborative study, the Realist and Meta-narrative Evidence Synthesis: Evolving Standards (RAMESES) to develop methodological guidance and publication standards for realist review. 35

Search strategy

We identified all peer reviewed cohort studies in adults over age 18 that had derived or validated, or both, a statistically weighted risk model for type 2 diabetes in a population not preselected for known risk factors or disease, and which could be applied to another population. Studies were included that had developed a new risk model based on risk factors and that used regression techniques to weight risk factors appropriately, or validated an existing model on a new population, or did both. Exclusion criteria were cross sectional designs, studies that had not finished recruiting, studies on populations preselected for risk factors or disease, studies that did not link multiple risk factors to form a scoring system or weighted model, screening or early detection studies, genetic studies, case series, studies on under 18s, animal studies, and studies that applied a known risk model or score to a population but did not evaluate its statistical potential.

In January 2011 we undertook a scoping search, beginning with sources known to the research team and those recommended by colleagues. We used the 29 papers from this search to develop the definitive protocol, including search terms and inclusion and exclusion criteria. In February 2011 a specialist librarian designed a search strategy (see web extra) with assistance from the research team. Key words were predict, screen, risk, score, [type two] diabetes, model, regression, risk assessment, risk factor, calculator, analysis, sensitivity and specificity, ROC and odds ratio. Both MESH terms and text words were used. Titles and abstracts were searched in Medline, PreMedline, Embase, and relevant databases in the Cochrane Library from inception to February 2011, with no language restrictions.

Search results from the different databases were combined in an endnote file and duplicates removed electronically and manually. In February and March 2011 two researchers independently scanned titles and abstracts and flagged potentially relevant papers for full text analysis.

Two researchers independently read the interim dataset of full text papers and reduced this to a final dataset of studies, resolving disagreements by discussion. Bilingual academic colleagues translated non-English papers and extracted data in collaboration with one of the research team. To identify recently published papers two researchers independently citation tracked the final dataset of studies in Google Scholar. Reference lists of the final dataset and other key references were also scanned.

Quantitative data extraction and analysis

Properties of included studies were tabulated on an Excel spreadsheet. A second researcher independently double checked the extraction of primary data from every study. Discrepancies were resolved by discussion. Where studies trialled multiple models with minimal difference in the number of risk factors, a judgment was made to extract data from the authors’ preferred models or (if no preferences were stated in the paper) the ones judged by two researchers to be the most complete in presentation of data or statistical robustness. Data extraction covered characteristics of the population (age, sex, ethnicity, etc), size and duration of study, completeness of follow-up, method of diagnosing diabetes, details of internal or external validation, or both, and the components and metrics used by authors of these studies to express the properties of the score, especially their calibration and discrimination—for example, observed to predicted ratios, sensitivity and specificity, area under the receiver operating characteristic curve. We aimed to use statistical meta-analysis where appropriate and presented heterogeneous data in disaggregated form.

Qualitative data extraction and analysis

For the realist component of the review we extracted data and entered these on a spreadsheet under seven headings (box 1).

Box 1: Categories for data entry

Intended users.

Authors’ assumptions (if any) about who would use the risk score, on which subgroups or populations

Proposed action based on the score result

Authors’ assumptions (if any) on what would be offered to people who score above the designated cut-off for high risk

Authors’ hypothesised (or implied) mechanism by which use of the score might improve outcomes for patients

Authors’ adjectives to describe their risk model or score

Relative advantage

Authors’ claims for how and in what circumstances their model or score outperforms previous ones

Authors’ stated concerns about their model or score

Real world use, including citation tracking

Actual data in this paper or papers citing it on use of the score in the real world

One researcher extracted these data from our final sample of papers and another checked a one third sample of these. Our research team discussed context-mechanism-outcome interactions hypothesised or implied by authors and reread the full sample of papers with all emerging mechanisms in mind to explore these further.

Impact analysis

We assessed the impact of each risk score in our final sample using three criteria: any description in the paper of use of the score beyond the population for whom it was developed and validated; number of citations of the paper in Google Scholar and number of these that described use of the score in an impact study; and critical appraisal of any impact studies identified on this citation track. In this phase we were guided by the question: what is the evidence that this risk score has been used in an intervention which improved (or sought to improve) outcomes for individuals at high risk of diabetes?

Prioritising papers for reporting

Given the large number of papers, statistical models, and risk scores in our final sample, we decided for clarity to highlight a small number of scores that might be useful to practising clinicians, public health specialists, or lay people. Adapting previous quality criteria for risk scores, 26 we favoured those that had external validation by a separate research team on a different population (generalisability), statistically significant calibration, a discrimination greater than 0.70, and 10 or fewer components (usability).

Figure 1 ⇓ shows the flow of studies through the review. One hundred and fifteen papers were analysed in detail to produce a final sample of 43. Of these 43 papers, 18 described the development of one or more risk models or scores, 8 27 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 17 described external validation of one or more models or scores on new populations, 9 10 19 52 53 54 55 56 57 58 59 60 61 62 63 64 65 and eight did both. 7 66 67 68 69 70 71 72 In all, the 43 papers described 145 risk models and scores, of which 94 were selected for extraction of full data (the other 51 were minimally different, were not the authors’ preferred model, or lacked detail or statistical robustness). Of the final sample of 94 risk models, 55 were derivations of risk models on a base population and 39 were external validations (of 14 different models) on new populations. Studies were published between 1993 and 2011, but most appeared in 2008-11 (fig 2 ⇓ ). Indeed, even given that weaker cross sectional designs had been excluded, the findings suggest that new risk models and scores for diabetes are currently being published at a rate of about one every three weeks.

Fig 1  Flow of studies through review

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Fig 2  Publication of diabetes risk models and scores 1990-2010. Eleven new risk models and scores had been published in the first five months of 2011

Table 1 ⇓ gives full details of the studies in the sample, including the origin of the study, setting, population, methodological approach, duration, and how diabetes was diagnosed. The studies were highly heterogeneous. Models were developed and validated in 17 countries representing six continents (30 in Europe, 25 in North America, 21 in Asia, 8 in Australasia, 8 in the Middle East, 1 in South America, and 1 in Africa).

 Summary of 43 papers from which 94 diabetes risk models or scores were identified for systematic review

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Comparisons across studies were problematic owing to heterogeneity of data and highly variable methodology, presentation techniques, and missing data. Cohorts ranged in size from 399 to 2.54 million. The same data and participants were often included in several different models in the same paper. Ten research populations were used more than once in different papers. 9 10 27 37 41 42 44 46 47 48 49 51 52 53 54 55 56 63 64 65 66 70 71 In total, risk models were tested on 6.88 million participants, although this figure includes duplicate tests on the same dataset. Participants aged 18 to 98 were studied for periods ranging from 3.15 to 28 years. Completeness of follow-up ranged from 54% to 99% and incidence of diabetes across the time periods studied ranged from 1.3% to 20.9%.

None of the models in the sample was developed on a cohort recruited prospectively for the express purpose of devising it. Rather, all authors used the more pragmatic approach of retrospectively studying a research dataset that had been assembled some years previously for a different purpose. Forty two studies excluded known diabetes in the inception cohort. Diagnosis of diabetes in a cohort at inception and completion of the study was done in different ways, including self report, patient questionnaires, clinician diagnosis, electronic code, codes from the International Classification of Diseases , disease or drug registers, diabetes drugs, dietary treatment, fasting plasma glucose levels, oral glucose tolerance test, and measurement of haemoglobin A 1c . In some studies the method was not stated. Half the studies used different diagnostic tests at inception and completion of the study.

One third of the papers focused almost exclusively on the statistical properties of the models. Many of the remainder had a clinician (diabetologist or general practitioner) as coauthor and included an (often short and speculative) discussion on how the findings might be applied in clinical practice. Three described their score as a “clinical prediction rule.” 45 51 59

Quantitative findings

Table 2 ⇓ gives details of the components of the 94 risk models included in the final sample and their statistical properties—including (where reported) their discrimination, calibration, sensitivity, specificity, positive and negative predictive value, and area under the receiver operating characteristic curve. Many papers offered additional sophisticated statistical analysis, although there was no consistency in the approach used or statistical tests. Heterogeneity of data (especially demographic and ethnic diversity of validation cohorts and different score components) in the primary studies precluded formal meta-analysis.

 Key characteristics of 94 diabetes risk models or scores included in systematic review

All 94 models presented a combination of risk factors as significant in the final model, and different models weighted different components differently. The number of components in a single risk score varied from 3 to 14 (n=84, mean 7.8, SD 2.6). The seven risk scores that were classified as having high potential for use in practice offered broadly similar components and had similar discriminatory properties (area under receiver operating characteristic curve 0.74-0.85, table 4). Overall, the areas under the receiver operating characteristic curve ranged from 0.60 to 0.91. Certain components used in some models (for example, biomarkers) are rarely available in some pathology laboratories and potentially too expensive for routine use. Some models that exhibited good calibration and discrimination on the internal validation cohort performed much less well when tested on an external cohort, 62 67 suggesting that the initial model may have been over-fitted by inclusion of too many variables that had only minor contributions to the total risk. 73 Although this study did not seek out genetic components, those studies that had included genetic markers alongside sociodemographic and clinical data all found that the genetic markers added little or nothing to the overall model. 9 10 36 50

Reporting of statistical data in some studies was incomplete—for example, only 40 of the 94 models quantified any form of calibration statistic. Forty three presented sensitivity and specificity, 27 justified the rationale for cut-off points, 22 presented a positive predictive value, 19 presented a negative predictive value, and 26 made some attempt to indicate the percentage of the population that would need clinical follow-up or testing if they scored as “high risk.” Some models performed poorly—for example, there was a substantial gap between expected and observed numbers of participants who developed diabetes over the follow-up period. The false positive and false negative rates in many risk scores raised questions about their utility in clinical practice (for example, positive predictive values ranged from 5% to 42%, negative predictive values from 88% to 99%). However, some scores were designed as non-invasive preliminary instruments, with a recommended second phase involving a blood test. 7 43 52 53 55 58 65

Risk models and scores tended to “morph” when they were externally validated because research teams dropped components from the original (for example, if data on these were not available), added additional components (for example, to compensate for missing categories), or modified what counted in a particular category (for example, changing how ethnicity was classified); in some cases these modifications were not clarified. A key dimension of implementation is appropriate adaptation to a new context. It was considered that this did not negate the external validation.

Qualitative findings

Table 3 ⇓ provides the qualitative findings from the risk scores. Of the 43 papers in the full sample, three did not recommend use of the model tested because the authors believed it had no advantage over existing ones. 50 56 60 Authors of the other 40 papers considered that at least one of their scores should be adopted and used, and to justify this made various claims. The commonest adjective used by authors to describe their score was “simple” (26 of 43); others mentioned “low cost,” “easily implemented,” “feasible,” and “convenient.”

 Summary of authors’ assumptions and claims about their diabetes risk models or scores

Sixteen of the 43 studies that recommended use of a particular risk model or score did not designate an intended user for it. Some authors assigned agency to a risk score—that is, they stated, perhaps inadvertently, that the score itself had the potential to prevent diabetes, change behaviour, or reduce health inequalities. Although most authors did state an intended target group, this was usually given in vague terms, such as “the general population” or “individuals who are likely to develop diabetes.” Eleven of the 43 papers gave a clear statement of what intervention might be offered, by whom, to people who scored above the cut-off for high risk; the other papers made no comment on this or used vague terms such as “preventive measures,” without specifying by whom these would be delivered.

In all, authors of the papers in the full sample either explicitly identified or appeared to presume 10 mechanisms (box 2) by which, singly or in combination, use of the diabetes risk score might lead to improved patient outcomes (see table 3).

Box 2: 10 suggested mechanisms by which diabetes risk scores could help improve patient outcomes

Direct impact —clinicians will pick up high risk patients during consultations and offer advice that leads to change in patients’ behaviour and lifestyle

Indirect impact —routine use of the score increases clinicians’ awareness of risk for diabetes and motivation to manage it

Self assessment

Direct impact —people are alerted by assessing their own risk (for example, using an online tool), directly leading to change in lifestyle

Indirect impact —people, having assessed their own risk, are prompted to consult a clinician to seek further tests or advice on prevention

Technological

Individual impact —a risk model programmed into the electronic patient record generates a point of care prompt in the clinical encounter

Population impact —a risk model programmed into the electronic patient record generates aggregated data on risk groups, which will inform a public health intervention

Public health

Planners and commissioners use patterns of risk to direct resources into preventive healthcare for certain subgroups

Administrative

An administrator or healthcare assistant collects data on risk and enters these onto the patients’ records, which subsequently triggers the technological, clinical, or public health mechanisms

Research into practice

Use of the risk score leads to improved understanding of risk for diabetes or its management by academics, leading indirectly to changes in clinical practice and hence to benefits for patients

Future research

Use of the risk score identifies focused subpopulations for further research (with the possibility of benefit to patients in later years)

Risk models and scores had been developed in a range of health systems. Differences in components could be explained partly in terms of their intended context of use. For example, the QDScore, intended for use by general practitioners, was developed using a database of electronic records of a nationally representative sample of the UK general practice population comprising 2.5 million people. The QDScore is composed entirely of data items that are routinely recorded on general practice electronic records (including self assigned ethnicity, a deprivation score derived from the patient’s postcode, and clinical and laboratory values). 8 Another score, also intended to be derived from electronic records but in a US health maintenance organisation (covering people of working age who are in work), has similar components to the QDScore except that ethnicity and socioeconomic deprivation are not included. In contrast, the FINDRISC score was developed as a population screening tool intended for use directly by lay people; it consists of questions on sociodemographic factors and personal history along with waist circumference but does not include clinical or laboratory data; high scorers are prompted to seek further advice from a clinician. 52 Such a score makes sense in many parts of Finland and also in the Netherlands where health and information literacy rates are high, but would be less fit for purpose in a setting where these were low.

Prioritising scores for practising clinicians

Table 4 ⇓ summarises the properties of seven validated diabetes risk scores which we judged to be the most promising for use in clinical or public health practice. The judgments on which this selection was based were pragmatic; other scores not listed in table 4 (also see tables 1 and 2) will prove more fit for purpose in certain situations and settings. One score that has not yet been externally validated was included in table 4 because it is the only score already being incentivised in a national diabetes prevention policy. 23

 Components of seven diabetes risk models or scores with potential for adaptation for use in routine clinical practice

Studies of impact of risk scores on patient outcomes

None of the 43 papers that validated one or more risk scores described the actual use of that score in an intervention phase. Furthermore, although these papers had been cited by a total of 1883 (range 0-343, median 12) subsequent papers, only nine of those 1883 papers (table 5 ⇓ ) described application and use of the risk score as part of an impact study aimed at changing patient outcomes. These covered seven studies, of which (to date) three have reported definitive results. All three reported positive changes in individual risk factors, but surprisingly none recalculated participants’ risk scores after the intervention period to see if they had changed. While one report on the ongoing FIN-D2D study suggests that incident diabetes has been reduced in “real world” (non-trial) participants who were picked up using a diabetes risk score and offered a package of preventive care, 74 this is a preliminary and indirect finding based on drug reimbursement claims, and no actual data are given in the paper. With that exception, no published impact study on a diabetes risk score has yet shown a reduction in incident diabetes.

 Results of impact citation search (studies using diabetes risk models or scores as part of an intervention to improve outcomes)

Numerous diabetes risk scores now exist based on readily available data and provide a good but not perfect estimate of the chance of an adult developing diabetes in the medium term future. A few research teams have undertaken exemplary development and validation of a robust model, reported its statistical properties thoroughly, and followed through with studies of impact in the real world.

Limitations of included studies

We excluded less robust designs (especially cross sectional studies). Nevertheless, included studies were not entirely free from bias and confounding. This is because the “pragmatic” use of a previously assembled database or cohort brings an inherent selection bias (for example, the British Regional Heart Study cohort was selected to meet the inclusion criteria for age and comorbidity defined by its original research team and oriented to research questions around cardiovascular disease; the population for the QDScore is drawn from general practice records and hence excludes those not registered with a general practitioner).

Most papers in our sample had one or more additional limitations. They reported models or scores that required collection of data not routinely available in the relevant health system; omitted key statistical properties such as calibration and positive and negative predictive values that would allow a clinician or public health commissioner to judge the practical value of the score; or omitted to consider who would use the score, on whom, and in what circumstances. We identified a mismatch between the common assumption of authors who develop a risk model (that their “simple” model can now be taken up and used) and the actual uptake and use of such models (which seems to happen very rarely). However, there has recently been an encouraging—if limited—shift in emphasis from the exclusive pursuit of statistical elegance (for example, maximising area under the receiver operating curve) to undertaking applied research on the practicalities and outcomes of using diabetes risk scores in real world prevention programmes.

Strengths and limitations of the review

The strengths of this review are our use of mixed methodology, orientation to patient relevant outcomes, extraction and double checking of data by five researchers, and inclusion of a citation track to identify recently published studies and studies of impact. We applied both standard systematic review methods (to undertake a systematic and comprehensive search, translate all non-English texts, and extract and analyse quantitative data) and realist methods (to consider the relation between the components of the risk score, the context in which it was intended to be used, and the mechanism by which it might improve outcomes for patients).

The main limitation of this review is that data techniques and presentation in the primary studies varied so much that it was problematic to determine reasonable numerators and denominators for many of the calculations. This required us to make pragmatic decisions to collate and present data as fairly and robustly as possible while also seeking to make sense of the vast array of available risk scores to the general medical reader. We recognise that the final judgment on which risk scores are, in reality, easy to use will lie with the end user in any particular setting. Secondly, authors of some of the primary studies included in this review were developing a local tool for local use and made few or no claims that their score should be generalised elsewhere. Yet, the pioneers of early well known risk scores 49 68 have occasionally found their score being applied to other populations (perhaps ethnically and demographically different from the original validation cohort), their selection of risk factors being altered to fit the available categories in other datasets, and their models being recalibrated to provide better goodness of fit. All this revision and recalibration to produce “new” scores makes the systematic review of such scores at best an inexact science.

Why did we not recommend a “best” risk score?

We have deliberately not selected a single, preferred diabetes risk score. There is no universal ideal risk score, as the utility of any score depends not merely on its statistical properties but also on its context of use, which will also determine which types of data are available to be included. 75 76 Even when a risk model has excellent discrimination (and especially when it does not) the trade-off between sensitivity and specificity plays out differently depending on context. Box 3 provides some questions to ask when selecting a diabetes risk score.

Box 3: Questions to ask when selecting a diabetes risk score, and examples of intended use

What is the intended use case for the score.

If intended for use:

In clinical consultations, score should be based on data on the medical record

For self assessment by lay people, score should be based on things a layperson would know or be able to measure

In prevention planning, score should be based on public health data

What is the target population?

If intended for use in high ethnic and social diversity, a score that includes these variables may be more discriminatory

What is expected of the user of the score?

If for opportunistic use in clinical encounters, the score must align with the structure and timeframe of such encounters and competencies of the clinician, and (ideally) be linked to an appropriate point of care prompt. Work expected from the intended user of the score may need to be incentivised or remunerated, or both

What is expected of the participants?

If to be completed by laypeople, the score must reflect the functional health literacy of the target population

What are the consequences of false positive and false negative classifications?

In self completion scores, low sensitivity may falsely reassure large numbers of people at risk and deter them from seeking further advice

What is the completeness and accuracy of the data from which the score will be derived?

A score based on automated analysis of electronic patient records may include multiple components but must be composed entirely of data that are routinely and reliably entered on the record in coded form, and readily searchable (thus, such scores are only likely to be useful in areas where data quality in general practice records is high)

What resource implications are there?

If the budget for implementing the score and analysing data is fixed, the cost of use must fall within this budget

Given the above, what would be the ideal statistical and other properties of the score in this context of use?

What trade-offs should be made (sensitivity v specificity, brevity v comprehensiveness, one stage v two stage process)?

Risk scores as complex interventions

Our finding that diabetes risk scores seem to be used rarely can be considered in the light of the theoretical literature on diffusion of innovation. As well as being a statistical model, a risk score can be thought of as a complex, technology based innovation, the incorporation of which into business as usual (or not) is influenced by multiple contextual factors including the attributes of the risk score in the eyes of potential adopters (relative advantage, simplicity, and ease of use); adopters’ concerns (including implications for personal workload and how to manage a positive score); their skills (ability to use and interpret the technology); communication and influence (for example, whether key opinion leaders endorse it); system antecedents (including a healthcare organisation’s capacity to embrace new technologies, workflows, and ways of working); and external influences (including policy drivers, incentive structures, and competing priorities). 77 78

Challenges associated with risk scores in use

While the developers of most diabetes risk scores are in little doubt about their score’s positive attributes, this confidence seems not to be shared by practitioners, who may doubt the accuracy of the score or the efficacy of risk modification strategies, or both. Measuring diabetes risk competes for practitioners’ attention with a host of other tasks, some of which bring financial and other rewards. At the time of writing, few opinion leaders in diabetes seem to be promoting particular scores or the estimation of diabetes risk generally—perhaps because, cognisant of the limited impacts shown to date (summarised in table 5), they are waiting for further evidence of whether and how use of the risk score improves outcomes. Indeed, the utility of measuring diabetes risk in addition to cardiovascular risk is contested within the diabetes research community. 79 In the United Kingdom, the imminent inclusion of an application for calculating QDScore on EMIS, the country’s most widely used general practice computer system, may encourage its use in the clinical encounter. But unless the assessment of diabetes risk becomes part of the UK Quality and Outcomes Framework, this task may continue to be perceived as low priority by most general practitioners. Given current evidence, perhaps this judgment is correct. Furthermore, the low positive predictive values may spell trouble for commissioners. Identifying someone as “[possibly] high risk” will inevitably entail a significant cost in clinical review, blood tests, and (possibly) intervention and follow-up. Pending the results of ongoing impact studies, this may not be the best use of scarce resources.

Delivering diabetes prevention in people without any disease requires skills that traditionally trained clinicians may not possess. 80 We know almost nothing about the reach, uptake, practical challenges, acceptability, and cost of preventive interventions in high risk groups in different settings. 12 The relative benefit of detecting and targeting high risk people rather than implementing population-wide diabetes prevention strategies is unknown. 13 Effective prevention and early detection of diabetes are likely to require strengthening of health systems and development of new partnerships among the clinicians, community based lifestyle programmes, and healthcare funders. 81

Mechanisms by which risk scores might have impact

Although most authors of papers describing diabetes risk scores have hypothesised (or seem to have assumed) a clinical mechanism of action (that the score would be used by the individual’s clinician to target individual assessment and advice), the limited data available on impact studies (see table 5) suggest that a particularly promising area for further research is interventions that prompt self assessment—that is, laypeople measuring their own risk of diabetes. The preliminary findings from the impact studies covered in this review also suggest that not everyone at high risk is interested in coming forward for individual preventive input, nor will they necessarily stay the course of such input. It follows that in areas where aggregated data from electronic patient records are available, the diabetes risk scores may be used as a population prediction tool—for example, to produce small area statistics (perhaps as pictorial maps) of diabetes risk across a population, thereby allowing targeted design and implementation of community level public health interventions. 82 Small area mapping of diabetes risk may be a way of operationalising the recently published guidance on diabetes prevention from the National Institute for Health and Clinical Excellence, which recommends the use of “local and national tools . . . to identify local communities at high risk of developing diabetes to assess their specific needs.” 83

Towards an impact oriented research agenda for risk scores

We recommend that funding bodies and journal editors help take this agenda forward by viewing the risk score in use as a complex intervention and encouraging more applied research studies in which real people identified as at “high risk” using a particular risk score are offered real interventions; success in risk score development is measured in terms of patient relevant intermediate outcomes (for example, change in risk score) and final outcomes (incident diabetes and related morbidity) rather than in terms of the statistical properties of the tool; a qualitative component (for example, process evaluation, organisational case study, patient’s experience of lifestyle modification) explores both facilitators and barriers of using the score in a real world setting; and an economic component evaluates cost and cost effectiveness.

Millions of participants across the world have already participated in epidemiological studies aimed at developing a diabetes risk score. An extensive menu of possible scores are now available to those who seek to use them clinically or to validate them in new populations, none of which is perfect but all of which have strengths. Nevertheless, despite the growing public health importance of type 2 diabetes and the enticing possibility of prevention for those at high risk of developing it, questions remain about how best to undertake risk prediction and what to do with the results. Appropriately, the balance of research effort is now shifting from devising new risk scores to exploring how best to use those we already have.

What is already known on this topic

The many known risk factors for type 2 diabetes can be combined in statistical models to produce risk scores

What this study adds

Dozens of risk models and scores for diabetes have been developed and validated in different settings

Sociodemographic and clinical data were much better predictors of diabetes risk than genetic markers

Research on this topic is beginning to shift from developing new statistical risk models to considering the use and impact of risk scores in the real world

Cite this as: BMJ 2011;343:d7163

We thank Helen Elwell, librarian at the British Medical Association Library, for help with the literature search; Samuel Rigby for manually removing duplicates; and Sietse Wieringa, Kaveh Memarzadeh, and Nicholas Swetenham for help with translation of non-English papers. BMJ reviewers Wendy Hu and John Furler provided helpful comments on an earlier draft.

Contributors: DN conceptualised the study, managed the project, briefed and supported all researchers, assisted with developing the search strategy and ran the search, scanned all titles and abstracts, extracted quantitative data on half the papers, citation tracked all papers, checked a one third sample of the qualitative data extraction, and cowrote the paper. TG conceptualised the qualitative component of the study, extracted qualitative data on all papers, independently citation tracked all papers, and led on writing the paper. RM independently scanned all titles and abstracts of the electronic search, extracted quantitative data from some papers, assisted with other double checking, and helped revise drafts of the paper. TD helped revise and refine the study aims, independently double checked quantitative data extraction from all papers, and helped revise drafts of the paper. CM advised on systematic review methodology, helped develop the search strategy, extracted quantitative data from some papers, and helped revise drafts of the paper. TG acts as guarantor.

Funding: This study was funded by grants from Tower Hamlets, Newham, and City and Hackney primary care trusts, by a National Institute of Health Research senior investigator award for TG, and by internal funding for staff time from Barts and the London School of Medicine and Dentistry. The funders had no input into the selection or analysis of data or the content of the final manuscript.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: no support from any organisation for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; and no other relationships or activities that could appear to have influenced the submitted work.

Ethical approval: Not required.

Data sharing: No additional data available.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode .

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• Published: 23 July 2015

Type 2 diabetes mellitus

• Ralph A. DeFronzo 1 ,
• Ele Ferrannini 2 ,
• Leif Groop 3 ,
• Robert R. Henry 4 ,
• William H. Herman 5 ,
• Jens Juul Holst 6 ,
• Frank B. Hu 7 ,
• C. Ronald Kahn 8 ,
• Itamar Raz 9 ,
• Gerald I. Shulman 10 ,
• Donald C. Simonson 11 ,
• Marcia A. Testa 12 &
• Ram Weiss 13  

Nature Reviews Disease Primers volume  1 , Article number:  15019 ( 2015 ) Cite this article

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• Diabetes complications
• Type 2 diabetes

Type 2 diabetes mellitus (T2DM) is an expanding global health problem, closely linked to the epidemic of obesity. Individuals with T2DM are at high risk for both microvascular complications (including retinopathy, nephropathy and neuropathy) and macrovascular complications (such as cardiovascular comorbidities), owing to hyperglycaemia and individual components of the insulin resistance (metabolic) syndrome. Environmental factors (for example, obesity, an unhealthy diet and physical inactivity) and genetic factors contribute to the multiple pathophysiological disturbances that are responsible for impaired glucose homeostasis in T2DM. Insulin resistance and impaired insulin secretion remain the core defects in T2DM, but at least six other pathophysiological abnormalities contribute to the dysregulation of glucose metabolism. The multiple pathogenetic disturbances present in T2DM dictate that multiple antidiabetic agents, used in combination, will be required to maintain normoglycaemia. The treatment must not only be effective and safe but also improve the quality of life. Several novel medications are in development, but the greatest need is for agents that enhance insulin sensitivity, halt the progressive pancreatic β-cell failure that is characteristic of T2DM and prevent or reverse the microvascular complications. For an illustrated summary of this Primer, visit: http://go.nature.com/V2eGfN

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Acknowledgements

The authors acknowledge grants from: the South Texas Veterans Healthcare System to R.A.D.; the National Institutes of Health (grants R01DK24092 to R.A.D.; DK58845 and P30 DK46200 to F.B.H.; R01 DK-040936, R01 DK-049230, R24 DK-085836, UL1 RR-045935, R01 DK-082659 and R24 DK085610 to G.I.S.; P30 DK036836 to C.R.K. Novo Nordisk Foundation for Basic Metabolic Research and the University of Copenhagen to G.I.S. and C.R.K.; DVA-Merit Review grant and VA San Diego Healthcare System to R.H.; National Institute for Diabetes and Digestive and Kidney Disease (grant P30DK092926) to W.H.; the Swedish Research Council (grants 2010–3490 and 2008–6589) and European Council (grants GA269045) to L.G.; Italian Ministry of University & Research (MIUR 2010329EKE) to E.F.; the Patient-Centered Outcomes Research Institute (PCORI) Program Award (CE1304-6756) to D.C.S. and M.A.T.; NovoNordisk Foundation to the NNF Center for Basic Metabolic Research to J.H. W.H. acknowledges the Michigan Center for Diabetes Translational Research and I.R. thanks R. Sprung for editorial assistance.

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Authors and affiliations.

Diabetes Division, Department of Medicine, University of Texas Health Science Center, South Texas Veterans Health Care System and Texas Diabetes Institute, 701 S. Zarzamoro, San Antonio, 78207, Texas, USA

Ralph A. DeFronzo

CNR Institute of Clinical Physiology, Pisa, Italy

Ele Ferrannini

Department of Clinical Science Malmoe, Diabetes & Endocrinology, Lund University Diabetes Centre, Lund, Sweden

University of California, San Diego, Section of Diabetes, Endocrinology & Metabolism, Center for Metabolic Research, VA San Diego Healthcare System, San Diego, California, USA

Robert R. Henry

University of Michigan, Ann Arbor, Michigan, USA

William H. Herman

University of Copenhagen, Kobenhavn, Denmark

Jens Juul Holst

Department of Nutrition, Harvard T.H. Chan School of Public Health and Department of Epidemiology, Harvard T.H. Chan School of Public Health and Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA

Frank B. Hu

Harvard Medical School and Joslin Diabetes Center, Boston, Massachusetts, USA

C. Ronald Kahn

Division of Internal Medicine, Diabetes Unit, Hadassah Hebrew University Hospital, Jerusalem, Israel

Howard Hughes Medical Institute and the Departments of Internal Medicine and Cellular & Molecular Physiology, Yale University School of Medicine, New Haven, Connecticut, USA

Gerald I. Shulman

Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA

Donald C. Simonson

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

Marcia A. Testa

Department of Human Metabolism and Nutrition, Braun School of Public Health, Hebrew University, Jerusalem, Israel

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Contributions

Introduction (R.R.H.); Epidemiology (F.B.H.); Mechanisms/pathophysiology (L.C.G., C.R.K., E.F., G.I.S. and R.A.D.); Diagnosis, screening and prevention (W.H.H.); Management (R.A.D.); Quality of life (D.C.S. and M.A.T.); Outlook (I.R., J.J.H. and R.W.); overview of Primer (R.A.D.).

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Correspondence to Ralph A. DeFronzo .

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The authors declare the following potential COI: (1) R.A.D.: Research Grant Support - AstraZeneca, Bristol Myers Squibb, Janssen; Speaker's Bureau - AstraZeneca, Novo Nordisk, Advisory Board/Consultant - AstraZeneca, Janssen, Novo Nordisk, Boehringer Ingelheim, Lexicon, Intarcia; (2) E.F.: Research Grant Support - Boehringer Ingelheim, Eli Lilly; Consultant/Speaker Bureau-Boehringer Ingelheim, Eli Lilly, Sanofi, Novo Nordisk, Janssen, AstraZeneca, Takeda, Medtronic, Intarcia; (3) C.R.K. serves as a consultant for Medimmune, Merck, Five Prime Therapeutics, CohBar, Antriabio, and Catabasis; (4) L.G. has no conflict of interest; (5) R.H. has received grant support from Hitachi, Janssen, Eli Lilly, Sanofi-Aventis and Viacyte and is a consultant/advisory board member for Alere, Amgen, AstraZeneca, Boehringer Ingelheim, Bristol Myers Squibb, Clin Met, Eisai, Elcelyx, Gilead, Intarcia, Isis, Janssen, Merck, Novo Nordisk, Sanofi-Aventis, and Vivus; (6) W.H.H. has no conflict of interest; (7) J.J.H. has received grant support from Novartis and Merck and is a consultant/advisory board member for Glaxo, Smith, Kline, Novo Nordisk, and Zealand Pharmaceuticals; (8) M.A.T. has no conflict of interest; (9) R.W. serves as a consultant for Medtronics and Kamada and is on the speaker's bureau for Medtronics and Novo Nordisk; (10) F.H. has received research support from California Walnut Commission and Metegenics; (11) G.I.S. serves on scientific advisory boards for Merck and Novartis and he has received research grant support from Gilead Pharmaceuticals; (12) D.C.S. has no conflict of interest; (13) I.R. – Advisory Board: Novo Nordisk, Astra Zeneca/BMS, MSD, Eli Lilly, Sanofi, Medscape Cardiology; Consultant: Astra Zeneca/BMS, Insuline; Speaker's Bureau: Eli Lilly, Novo Nordisk, Astra Zeneca/BMS, J&J, Sanofi, MSD, Novartis, Teva; Shareholder: Insuline, Labstyle.

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DeFronzo, R., Ferrannini, E., Groop, L. et al. Type 2 diabetes mellitus. Nat Rev Dis Primers 1 , 15019 (2015). https://doi.org/10.1038/nrdp.2015.19

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• Published: 13 July 2020

Screening strategies for adults with type 2 diabetes mellitus: a systematic review protocol

• Helen Mearns   ORCID: orcid.org/0000-0002-7971-1256 1 , 2 ,
• Paul Kuodi Otiku 1 , 2 ,
• Mary Shelton 3 ,
• Tamara Kredo 4 , 5 ,
• Benjamin M. Kagina 1 , 2 &
• Bey-Marrié Schmidt 4  

Systematic Reviews volume  9 , Article number:  156 ( 2020 ) Cite this article

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There is limited evidence on whether screening for type 2 diabetes mellitus affects health outcomes. A recent systematic review of randomised clinical trials found only one trial that met their inclusion criteria; therefore, current guidelines for screening interventions for type 2 diabetes mellitus are based on expert opinions and best practice rather than synthesised evidence. This systematic review seeks to collate evidence from non-randomised studies to investigate the effect of screening for adults with type 2 diabetes on outcomes including diabetes-related morbidity, mortality (all-cause and diabetes-related) and harms.

This systematic review will follow Effective Practice and Organisation of Care (EPOC) guidelines for the synthesis of non-randomised studies. We will search PubMed/MEDLINE, Scopus, Web of Science, CINAHL, Academic Search Premier and Health Source Nursing Academic (from inception onwards). We will include non-randomised trials, controlled before-after studies, interrupted time-series studies, repeated measures studies and concurrently controlled prospective cohort studies. The primary outcome will be diabetes-related morbidity (microvascular complications of diabetic retinopathy, nephropathy or neuropathy or macrovascular complications of non-fatal myocardial infarction, peripheral arterial disease or non-fatal stroke). The secondary outcomes will be mortality (all-cause and diabetes-related) and harms of screening strategies to patients (including psychological harms or adverse events following treatments) or to health care system (including resource allocation for false-positives or overdiagnosis). Two reviewers will independently screen all citations and full-text articles. Data will be abstracted by one reviewer and checked by a second. The risk of bias of individual studies will be appraised using the ROBINS-I tool. GRADE will be used to determine the quality of the scientific evidence. If feasible, we will conduct random effects meta-analysis where appropriate. If necessary, analyses will be conducted to explore the potential sources of heterogeneity (e.g. age, sex, socio-economic status, rural versus urban or low-middle income versus high-income country). We will disseminate the findings via publications and through relevant networks.

The protocol outlines the methods for systematically reviewing and synthesising evidence of screening strategies for type 2 diabetes mellitus and their effect on health outcomes associated with the disease. The potential impact of this systematic review is improved evidence-informed decision-making for policies and practice for screening of type-2 diabetes.

Systematic review registration

PROSPERO CRD42020147439

Peer Review reports

Description of the condition

Diabetes mellitus is a disease of increasing global concern. The global prevalence of diabetes was approximately 425 million people in 2017, approximately 8.5% of the adult population, and is expected to double by 2045 [ 1 ]. In high-income countries, type 2 diabetes mellitus accounts for approximately 90% of diabetes cases; there is insufficient data to estimate the ratio of type 2 diabetes mellitus in low- and middle- income countries, but it is assumed to be similar [ 1 , 2 ]. Clinical diabetes is diagnosed through the detection of elevated levels of glucose in the blood (hyperglycemia) [ 3 ]; however, it is estimated that half of the people who have diabetes are not diagnosed [ 1 ].

In addition to those individuals who have clinical diabetes, another 352 million, approximately 7.3% of the adult population, have intermediate blood glucose levels that are considered in between normal and clinically diagnosed diabetes [ 1 , 3 ]. These intermediate blood glucose levels perform as a risk score, where increasing values are associated with an increasing likelihood of progression to diabetes, cardiovascular disease and all-cause mortality [ 2 , 4 ]. Patients who present with intermediate levels of blood glucose are described using a number of terminologies including mild glucose intolerance, non-diabetic hyperglycaemia and prediabetes. The terminology promoted by the World Health Organization (WHO) is impaired glucose tolerance (IGT), impaired fasting glucose (IFG) and intermediate hyperglycaemia [ 3 , 5 ]. The term prediabetes is gaining in popularity even though the WHO has warned its use may lead to disease stigma and detract from the significant cardiovascular risk of this population [ 5 ]. About a third of people with IGT and IFG are young, aged between 20–39 years, meaning they will spend many years at risk of developing diabetes [ 1 ]. Other risk factors, apart from intermediate glucose levels, for the development of diabetes are increasing age of more than 45 years and obesity [ 2 ].

Type 2 diabetes mellitus arises due to defective insulin activity in body tissues, defective insulin secretion from pancreas or a combination of the two [ 2 ]. Type 2 diabetes mellitus usually occurs in older adults, but with a change in lifestyle factors, such as inactivity and obesity, the condition is increasingly being detected in children, adolescents and young adults [ 1 , 2 ]. Current management of type 2 diabetes mellitus involves lifestyle modification: increasing physical activity, improving diet, reaching a healthy body weight and stopping smoking, all monitored by regular screening [ 2 ]. If lifestyle modification does not result in sufficiently decreased blood glucose levels, medication may be prescribed, of which there are a range of treatment options available [ 2 ]. The complication with type 2 diabetes mellitus is the long latency period, often lasting several years, during which time the individual is often asymptomatic and unaware of their condition [ 1 , 2 ]. This prolonged asymptomatic state results in long-term damage to the body’s organs that leads to negative health outcomes including pregnancy complications, oral health problems, disabilities such as blindness, reduced wound healing, foot disease that may require amputation, stroke, heart and kidney disease and death [ 1 , 2 , 3 ].

Description of the intervention

There are many types of screening interventions and strategies that may be used to detect disease in a population often classified as mass, opportunistic and targeted strategies—as presented in Table 1 [ 2 , 6 ]. This systematic review will use these classifications, but if additional strategies are noted, these too will be included.

The biochemical tests commonly used are fasting plasma glucose (FPG), oral glucose tolerance test (OGTT) and detection of glycated haemoglobin A1C (HbA1c) although there are also urine glucose tests available or random blood glucose tests [ 2 , 6 ]. In addition, there are a number of risk scores [ 7 , 8 ], including the Finnish Diabetes Risk Score (FINDRISC) [ 9 ] and the American Diabetes Association’s risk test [ 10 ]; however, these are not commonly used as stand-alone screening tools. Classification of patients post testing can be termed as in the normal range or as having diabetes, impaired glucose tolerance (IGT) or impaired fasting glucose (IFG) (as presented in Table 2 ) [ 1 , 3 ].

How the intervention might work

The theory behind screening for type 2 diabetes mellitus is to identify either disease or associated risk factors to initiate preventative measures that can halt, slow or improve the course of disease [ 11 ]. Therefore, the earlier the disease is detected, especially where there is high risk of disease, theoretically, the better the expected outcomes. The logic model in Fig. 1 describes a complex system in which the intervention interacts with participants, context, implementation and how these affect the outcomes and the impact of this research [ 12 ].

Logic model describing the interactions between screening for diabetes, implementation, context, participants, outcomes and impact

Why it is important to do this review

Guidelines for screening interventions for type 2 diabetes mellitus, such as those released by the UK National Screening Committee [ 13 ], the American Diabetes Association [ 2 ] or the Society for Endocrinology, Metabolism and Diabetes of South Africa [ 14 ], are based on expert opinion and local practice rather than synthesised evidence. This is because there is limited information to provide evidence about best practice for screening interventions for type 2 diabetes mellitus and even less evidence in low- and middle-income countries [ 15 ]. A recently published Cochrane review assessed the effects of any type of screening compared with no screening for type 2 diabetes [ 16 ] and found only one trial, the ADDITION-Cambridge trial [ 17 ], that met their inclusion criteria. The ADDITION-Cambridge trial consisted of 20,184 participants aged 40–69 years from general practices in England who were at risk for diabetes but had no known diabetes. These participants were randomised to screening versus no screening arms, and followed up for a median of 9.6 years (November 2001 to November 2011). The review found moderate certainty evidence that screening for diabetes probably makes little or no difference to all-cause mortality and low certainty evidence that it may make little or no difference to diabetes-related mortality. However, because the review only included one trial, firm conclusions about early diabetes screening on health outcomes cannot be drawn. In consultation with the authors of the unpublished Cochrane review and considering the public health importance of screening and the potential impact on large populations, we propose to assess evidence from non-randomised intervention study designs. The questions for the systematic review will include the following: Does screening for type 2 diabetes mellitus reduce morbidity and/or mortality? Does a particular screening strategy result in a greater reduction of morbidity and/or mortality as compared to another screening strategy? Does screening for type 2 diabetes mellitus result in harms to participants or the health system?

Primary objective

To assess the effectiveness of targeted, opportunistic or mass screening for type 2 diabetes mellitus on reduction of diabetes-associated morbidity in adults

Secondary objectives

To assess the effectiveness of targeted, opportunistic or mass screening for type 2 diabetes mellitus on reduction of mortality (all cause as well as diabetes-associated) in adults

To assess the harms of targeted, opportunistic or mass screening for type 2 diabetes mellitus in adults

The present protocol has been registered within the PROSPERO database (CRD42020147439). This manuscript is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement [ 18 ] (see checklist in Additional file 1 ).

Study and source eligibility

Types of studies.

As existing reviews have found limited randomised evidence addressing this question [ 15 , 19 , 20 ], we will focus on non-randomised intervention studies (NRIS). We will employ the Cochrane EPOC criteria [ 21 ], and NRIS of interest will include non-randomised trials, controlled before-after studies, interrupted time-series study, repeated measures study and concurrently controlled prospective cohort study. The difficulty associated with labelling NRIS is well-documented in the literature; several of these designs, for example, have been used interchangeably; we will make use of the EPOC definitions and flow diagram to assist in study design identification ( Appendix 1 ):

Non-randomised trial (NRT) is a study design in which individual participants, or clusters of participants, are allocated to intervention or comparator in a quasi-random or non-random manner. If there is an allocation rule, it is often by, for example, alternation, day of the week, odd/even hospital, or identification number.

Controlled before-after (CBA) is a study design that estimates intervention effectiveness by comparing pre- and post-intervention outcomes in individuals or clusters that receive the intervention and those that do not.

Interrupted time series (ITS) studies design uses multiple observations from individuals or clusters pre-intervention to establish the pre-existing outcome trend; intervention effectiveness is then estimated by measuring post-intervention changes in the expected outcome trend associated with the introduction of an intervention (the ‘interruption’). An ITS study can identify both immediate and long-term changes associated with the intervention. The interrupted time-series studies will be required to have a clearly defined point in time when the intervention occurred and a minimum of 3 time points before and 3 time points after the intervention [ 21 ].

A repeated measures (RM) study is an interrupted time-series study but where the outcomes of interest are measured in the same participants at each point in time.

Concurrently controlled prospective cohort study (PCS) is where subjects are identified prospectively as having received an intervention or comparator and are then followed over time. The allocation rule is often in relation to organizational factors such as ward, clinic, doctor or provider organisation. Control arms should be contemporaneous, we will not include retrospective control arms.

‘PICO’ eligibility

Types of participants.

We will include adults aged 18 years and older without documented diabetes mellitus or pregnancy.

Types of interventions

We will include studies comparing one of the screening strategies, targeted, opportunistic or mass screening interventions for the detection of type 2 diabetes mellitus, against no screening or another of the screening strategies (Table 1 ). There will be a 6-month minimum follow-up time required for the primary clinical outcome of morbidity.

Types of outcome measures

Primary outcomes.

Clinical outcomes

Diabetes-related morbidity defined as study-reported microvascular complications (diabetic retinopathy, diabetic nephropathy, diabetic neuropathy) or macrovascular complications (non-fatal myocardial infarction, peripheral arterial disease, non-fatal stroke) and measured from 6 months after screening

Secondary outcomes

Mortality (all-cause and diabetes-related) defined as death due to any-cause including diabetes or other cardiovascular causes (including acute myocardial infarction, ischemic heart disease, stroke or any cardiovascular disorder that lead to death) and measured at any time after screening

Harms of diabetes screening

Harms to patients is defined as event/s reported in the study at any time after screening.

Psychological harms such as anxiety or stigma that impacts on quality of life due to a false-positive test

Number of days of work lost

Side-effects from treatment

Loss of health insurance benefits

Harms to health care system is defined as event/s reported in the study at any time after screening.

False-positive test resulting in human, physical and financial resource allocation to patients who are not in need

Overdiagnosis may lead to over-extension of human, physical and financial resources for patients who end up in prolonged treatment and engagement with the health system even if they never develop disease

The rationale for prioritisation of outcomes: Primary outcome serves to inform whether screening alters the course of disease as assumed per screening theory [ 11 ] and depicted in Fig. 1 . Secondary outcome of mortality contributes to the current data outlining no reduction in mortality following screening intervention [ 19 ] while also assessing harms that may arise from screening intervention [ 3 ] and therefore contribute to evidence to substantiate policy and practice recommendations.

Search methods for identification of studies

Electronic searches.

The University of Cape Town Health Sciences Reference Librarian (MS) assisted the first author (HM) in developing the search strategy and will provide advice and guidance in conducting the searches for the review.

Electronic Database Search (from inception onwards)

PubMed (MEDLINE)

Scopus (includes majority of EMBASE contents)

Web of Science Platform (Web of Science Core Collection, Biological Abstracts, SciELO Citation Index)

Academic Search Premier (on the EBSCOhost platform)

CINAHL (on the EBSCOhost platform)

Health Source Nursing Academic (on the EBSCOhost platform)

A draft search strategy for PubMed/MEDLINE, based on the original search strategy utilised by the Cochrane Review team and revised by an information specialist, is provided in Appendix 2 (see Appendix 2 ). We will include all studies regardless of publication status; however, we will only include English language studies. We are aware that this decision may lead to language bias [ 22 ], but due to capacity and resource limitation of the systematic review team, we are restricted to English only. We will search all databases from inception to the date of search. The search syntax will first be tested and optimised in PubMed. We will thereafter replicate the searches in the other databases adapting search syntax as necessary for those databases.

Grey literature search

We will conduct a grey literature search to identify studies not indexed in the databases listed above.

OpenGrey (multidisciplinary European database, covering science, technology, biomedical science, economics, social science and humanities)

Conference abstracts from The American Diabetes Association (ADA), the European Association for the Study of Diabetes (EASD) meeting and Diabetologia will be used to track down full text articles.

National Institute for Health Research Economic Evaluation Database (NHS EED)

Cost-Effectiveness Analysis Registry (CEA) (www.healtheconomics)

We will search key references, such as systematic reviews, by cross-checking reference lists for additional potentially eligible primary studies [ 23 ]. We will also contact experts in the field to check if we have missed any relevant studies. We may contact authors of included studies to clarify reported published information and to seek unpublished data.

Methods for screening search results

Screening methods.

We will collate and transfer search results to the Rayyan screening software [ 24 ] and remove duplicate records. At least two review authors will independently screen titles and abstracts of every record retrieved. Outcome measures will not be used to exclude studies during title and abstract screening. The potentially eligible records will be retrieved for full text screening. The two review authors will independently review full text records for compliance of studies with eligibility criteria of the review. A decision tree based on the eligibility criteria will be used to assist in decision making for exclusion of studies (see Appendix 3 ). Two review authors will resolve any disagreements through discussion or, if required, will consult a third review author. A study must meet all inclusion criteria to be included. We will list excluded studies at the full text screening stage in the ‘Characteristics of excluded studies’ table. We will collate multiple reports of the same study so that each study rather than each report is the unit of interest in the review. We will provide any information we can obtain about ongoing studies. We will record the selection process in sufficient detail to complete a PRISMA flow diagram [ 25 ].

Data collection and analysis

Data extraction.

We will use a standard data extraction form in Microsoft Excel to capture study characteristics and outcome data [ 22 , 26 ]; we will pilot the form on at least one eligible study. One review author will extract the following study characteristics from the included studies, and an independent review author will check the extraction:

Source: study ID (created by review author), review author ID (created by review author), citation and contact details

Eligibility: confirm eligibility for review, reason for exclusion

Methods: study design, number of study centres and location, study setting, withdrawals, date of study, follow-up, confounding factors considered, and the methods used to control for confounding, aspects of risk of bias specific for NRIS (see “Assessment of risk of bias in included studies” below), how missing data was handled

Participants: number, mean/median age, age range, gender, severity of condition, diagnostic criteria, inclusion criteria, exclusion criteria, screening criteria, diagnostic criteria, presence of known risk factors for type 2 diabetes mellitus (obesity, family history), co-morbidity (hypertension, dyslipidaemia), socio-demographics

Interventions: intervention components, comparison, fidelity assessment using the Template for Intervention Description and Replication (TIDieR) as a guide [ 27 ]

Outcomes: primary and secondary outcomes specified above in the section “Types of outcome measures”.

Miscellaneous: funding source, notable conflicts of interest of study authors, ethical approval, key conclusions of the study authors, miscellaneous comments from the study authors, references to other relevant studies, correspondence required, miscellaneous comments by the review authors.

One review author will extract outcome data from included studies, and an independent review author will check extracted data. We will note in the ‘Characteristics of included studies’ table if outcome data were reported in an unusable way. We will resolve disagreements by consensus or by involving a third review author.

Assessment of risk of bias in included studies

Two review authors will independently assess risk of bias for each study using the ROBINS-I tool [ 28 ]. Any disagreement will be resolved by discussion or by involving a third review author.

We will assess the risk of bias according to the following domains:

Pre-intervention: bias due to confounding

Pre-intervention: bias in selection of participants into the study

At intervention: bias in classification of interventions

Post-intervention: bias due to deviations from intended interventions

Post-intervention: bias due to missing data

Post-intervention: bias in measurement of outcomes

Post-intervention: bias in selection of the reported result

We will judge each potential source of bias as low risk, moderate risk, serious risk, critical risk of bias or no information. We will summarise the ‘Risk of bias’ judgements across different studies for each of the domains listed. We will consider blinding separately for different key outcomes where necessary (e.g. for unblinded outcome assessment, risk of bias for all-cause mortality may be very different than for a patient reported pain scale). Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the ‘Risk of bias’ table. We will not exclude studies on the grounds of their risk of bias but will clearly report the risk of bias when presenting the results of the studies. When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome. We will conduct the review according to this published protocol and report any deviations from it in the ‘Differences between protocol and review’ section of the systematic review.

Dealing with missing data

Authors will be contacted, and missing data will be requested. If only returned in part and data can be logically imputed, such as standard errors, this will occur. All missing data will be clearly reported in the data extraction forms and risk of bias table and as such be assessed in the sensitivity analysis.

Data management

EndNote X9 and Microsoft Excel will be used for data management. If there is a conflict between data reported across multiple sources for a single study (e.g. between a published article and a trial registry record), we will report the data from the first peer-reviewed published article.

Data synthesis

Preparation for data synthesis.

In preparation for synthesis (either meta-analyses or synthesis without meta-analysis), we will assess how much data are available for each of our objectives by creating a table to compare the PICO elements and the study design features as well as the extracted numerical data for the compilation of a meta-analysis.

Measures of treatment effect

We will estimate the effect of the intervention using risk ratio for dichotomous data, and mean difference or standardised mean difference for continuous data. Time to event outcomes will be reported as hazard ratios. If other effect estimates are provided, we will convert between estimates where possible. Measures of precision will be 95% confidence intervals. We will ensure that an increase in scores for continuous outcomes can be interpreted in the same way for each outcome, explain the direction to the reader, and report where the directions were reversed if this was necessary. Interrupted time series data will be analysed and, if required, a statistical comparison of time trends before and after the intervention will be performed. For ITS studies, the guideline as outlined in Analysis in EPOC reviews will be followed with assistance of a statistician to ensure integrity of analysis [ 29 ].

Unit of analysis issues

To avoid unit of analysis errors we will consider the unit used to cluster the intervention (such as a ward, clinic, doctor or provider organisation) or in the case of repeated measures that there will be multiple observations for the same outcome. For instance, multiple screening intervention events per participant may occur over time that may cause a unit-of-analysis error. In order to calculate the confidence intervals, the participants per treatment group rather than the number of intervention attempts will be used [ 22 ]. Multiple intervention groups could create unit-of-analysis issues especially if different screening interventions are compared against no screening intervention and use the same participants with no screening intervention in both comparisons [ 22 ]. If there is more than one comparison in the study design, we will combine groups into a single pairwise comparison. If there is a unit of analysis error in the reported analysis for a study and there is insufficient information to reanalyse the results, the study authors will be contacted to obtain necessary data. If these data are not available, we will not report confidence intervals or p values for which there is a unit of analysis error [ 30 ].

Quantitative synthesis

We will undertake meta-analyses only where this is meaningful, i.e. if the interventions, participants and the underlying clinical question are similar enough for pooling to make sense. If feasible and appropriate, outcome data from primary studies will be used to perform random effects meta-analyses. Since heterogeneity is expected a priori, we will estimate the pooled treatment effect estimates and its 95% confidence interval using the random effects model. The random effects model assumes that the effect estimates follow a normal distribution, considering both within-study and between-study variation.

Assessment of heterogeneity

Forest plots will be used to visualise the extent of heterogeneity among studies. We will quantify statistical heterogeneity by estimating the variance between studies using I 2 statistic. The I 2 is the proportion of variation in effect estimates that is due to genuine variation rather than sampling (random) error. I 2 ranges between 0 and 100% (with values of 0–25% and 75–100% taken to indicate low and considerable heterogeneity, respectively) [ 22 ]. We will also calculate the chi-squared test where a p value < 0.1 indicates statistically significant heterogeneity.

Assessment of publication bias

If we include more than 10 studies investigating a particular outcome, we will use a funnel plot to explore possible publication bias, interpreting the results with caution [ 31 ].

Subgroup analysis and investigation of heterogeneity

We expect the following population characteristics may introduce clinical heterogeneity: age, sex, socio-economic status [ 6 ].

We expect the following contexts may introduce health system heterogeneity: study setting of rural or urban or in a low-middle income country or a high-income country (as defined by the World Bank) [ 6 ].

We will use the following outcomes in subgroup analysis:

Diabetes-associated morbidity

Mortality (all-cause and diabetes-associated)

Sensitivity analysis

We may conduct a sensitivity analysis to explore the influence of various factors on the effect size of the primary outcomes of the review only. We will stratify studies according to:

Restricting the analysis to published studies.

Restricting the analysis to studies with a low risk of bias, as specified in “Assessment of risk of bias in included studies”

Imputing missing data.

Any post hoc sensitivity analyses that may arise during the review process will be justified in the final report.

Assessment of certainty of evidence using the GRADE approach

Two review authors will independently assess the certainty of the evidence (high, moderate, low and very low) for each outcome using the five GRADE considerations for downgrading the certainty of evidence (risk of bias, consistency of effect, imprecision, indirectness, and publication bias) and the three criteria for upgrading the certainty of evidence (large effect, dose response and residual confounding opposing the observed effect) [ 32 ]. We will use the GRADEpro software GDT [ 33 ] to create the ‘Summary of findings’ tables for the main intervention comparisons and include the following outcomes: diabetes-associated morbidity, mortality (all-cause and diabetes-associated), harms (see Appendix 4 for SoF). We will resolve disagreements on certainty ratings by discussion and provide justification for decisions to down- or upgrade the ratings using footnotes in the SoF table and make comments to aid readers’ understanding of the review where necessary. We will use plain language statements to report these findings in the review [ 34 ]. The SoF tables will be used to draw conclusions about the certainty of the evidence within the text of the review. If during the review process, we become aware of an important outcome that we failed to list in our planned ‘SoF’ tables, we will include the relevant outcome and explain the reasons for this is the section ‘Differences between protocol and review’.

Systematic reviews of screening for type 2 diabetes have found no evidence that this intervention saves lives [ 15 , 19 , 20 ]; therefore, this review will primarily focus on the impact of screening on the reduction of diabetes-associated morbidities. The impact of this review is synthesised data for the provision of evidence-based decision-making for informing policy and practice around screening strategies for type 2 diabetes mellitus. Important protocol amendments will be documented and noted in the discussion.

Limitations

The potential limitations of this review at a study (outcome) level include the following: the potential finding of insufficient studies of similar study design and clinical question to synthesise abstracted study data; the overall completeness and applicability of evidence and quality of evidence especially due to the limitation to non-randomised studies due to the lack of randomised studies and therefore the lower quality of evidence; the limitation to English studies only and therefore the potential to miss published research; the limitation of not being able to discern between all-cause mortality and diabetes-related mortality and therefore combining this outcome under one mortality outcome. The potential limitation of this review at a systematic review process level includes the potential biases in the review process such as post hoc analysis and focus of outcome objectives.

Availability of data and materials

Not applicable.

Abbreviations

American Diabetes Association

Controlled before-after

Cost-Effectiveness Analysis Registry

Collaboration for Evidence-Based Healthcare and Public Health in Africa

European Association for the Study of Diabetes

Effective Practice and Organisation of Care

Fasting plasma glucose

Grading of Recommendations Assessment, Development and Evaluation

Detection of glycated haemoglobin A1C

Interrupted time series

NHS Economic Evaluation Database

Non-randomised trial

Non-randomised intervention studies

Oral glucose tolerance test

Prospective cohort study

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Repeated measures

Summary of findings

Template for Intervention Description and Replication

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Acknowledgements

The authors would like to thank the following people who have supported this protocol:

• The authors of an unpublished Cochrane systematic review titled “Screening for type 2 diabetes mellitus”: Nasheeta Peer and Solange Durao, South African Medical Research Council, for providing their protocol to form the basis of this protocol, as well as a summary of their findings to form the rationale of the protocol and a quote to summarise the findings of their unpublished review.

• Information specialist Maria-Inti Metzendorf, Cochrane Metabolic and Endocrine Disorders Group, provided search strategy for the unpublished Cochrane review by Peer and Durao upon which the current search strategy is based.

• CEBHA+ Methodological Support Group Jake Burns and Peter Philipsborn, Ludwig-Maximillians Universitat Munchen, are internal reviewers for CEBHA+ which funds this review and assisted by providing critical review of the draft protocol.

• Prof Naomi Levitt, Chronic Disease for Africa, UCT, is a clinical Endocrinologist/Diabetologist who contributed to the PICO of this review when conceptualising the project and gave critical feedback to assist with responding to reviewers comments on the protocol before resubmission.

The work reported herein was made possible through Cochrane South Africa, South African Medical Research Council under the Collaboration for Evidence-Based Healthcare and Public Health in Africa (CEBHA+) Scholarship Programme. CEBHA+ receives funding from the Federal Ministry for Education and Research (Bundesministerium fur Bildung und Forschung, BMBF), Germany, through the BMBF funding of Research Networks for Health Innovation in Sub-Saharan Africa. The content hereof is the sole responsibility of the authors and does not necessarily represent the official views of SAMRC or the funders. The funding body, the Federal Ministry for Education and Research (Bundesministerium fur Bildung und Forschung, BMBF), Germany, played no role in the design and writing of the protocol; however, members of the Collaboration for Evidence-Based Healthcare and Public Health in Africa (CEBHA+) provided critical review and expert advice on the penultimate version of the protocol.

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Helen Mearns, Paul Kuodi Otiku & Benjamin M. Kagina

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Mary Shelton

Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa

Tamara Kredo & Bey-Marrié Schmidt

Division of Clinical Pharmacology, Department of Medicine, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa

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Contributions

Conceiving the protocol: BS, TK. Designing the protocol: HM, BS. Co-ordinating the protocol: HM. Designing search strategies: MS, HM. Writing the protocol: HM. Providing general advice on the protocol and approving the final version: PKO, MS, BK, TK, BS. Securing funding for the protocol: BS, TK. Performing previous work that was the foundation of the current study: NP, SD. Guarantor of the review: BS. The authors read and approved the final manuscript.

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Correspondence to Helen Mearns .

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Supplementary information

Additional file 1..

PRISMA Checklist.

Flow diagram to assist with identifying the type of study (modified from [ 34 ])

Search Strategy for PubMed:

Set 1: Diabetes Mellitus, Type 2 [MeSH] OR [Text Word field:] Adult onset diabetes OR late onset diabetes OR latent diabetes OR mature onset diabetes OR MODY OR NIDDM OR noninsulin-dependent diabetes OR slow onset diabetes OR stable onset diabetes OR type 2 diabetes OR type II diabetes OR T2DM OR T2D

Set 2: Diabetes Insipidus [MeSH] OR [Text Word field:] diabetes insipidus

Set 3 : 1 NOT 2

Set 4: Mass screening [MeSH] OR [Text Word field:] screening

Set 5: 3 AND 4

Set 6: Animals [MeSH] NOT Humans [MeSH]

Set 7: 5 NOT 6

Set 8: [All fields:] Trial OR trials OR before-and-after study OR before-and-after studies OR cohort OR comparative study OR comparative studies OR Controlled OR evaluation study OR evaluation studies OR follow-up study OR follow-up studies OR interrupted time series OR longitudinal study OR longitudinal studies OR non-randomised OR non-randomized OR nonrandomised OR nonrandomized OR non randomised OR non randomized OR program evaluation OR programme evaluation OR prospective study OR prospective studies OR quantitative study OR quantitative studies OR quasi experimental OR repeated measures

Set 9: 7 AND 8

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Mearns, H., Otiku, P.K., Shelton, M. et al. Screening strategies for adults with type 2 diabetes mellitus: a systematic review protocol. Syst Rev 9 , 156 (2020). https://doi.org/10.1186/s13643-020-01417-3

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Type 1 and type 2 diabetes mellitus: Clinical outcomes due to COVID-19. Protocol of a systematic literature review

Contributed equally to this work with: Juan Pablo Pérez Bedoya, Alejandro Mejía Muñoz

Roles Conceptualization, Investigation, Methodology, Project administration, Writing – original draft

* E-mail: [email protected]

Current address: National Faculty of Public Health, University of Antioquia, Medellin, Antioquia, Colombia

Affiliation Epidemiology Group, National Faculty of Public Health, University of Antioquia, Medellín, Colombia

Affiliation Biology and Control of Infectious Diseases Group, Faculty of Exact and Natural Sciences, University of Antioquia, Medellín, Colombia

Roles Supervision, Validation, Writing – review & editing

¶ ‡ NCB and PADV also contributed equally to this work.

Affiliation Department of Translational Medicine, Herbert Wertheim College of Medicine & Department of Global Health, Robert Stempel College of Public Health and Social Work, Florida International University, Miami, FL, United States of America

• Juan Pablo Pérez Bedoya, 
• Alejandro Mejía Muñoz, 
• Noël Christopher Barengo, 
• Paula Andrea Diaz Valencia

• Published: September 9, 2022
• https://doi.org/10.1371/journal.pone.0271851
• See the preprint
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Introduction

Diabetes has been associated with an increased risk of complications in patients with COVID-19. Most studies do not differentiate between patients with type 1 and type 2 diabetes, which correspond to two pathophysiological distinct diseases that could represent different degrees of clinical compromise.

To identify if there are differences in the clinical outcomes of patients with COVID-19 and diabetes (type 1 and type 2) compared to patients with COVID-19 without diabetes.

Observational studies of patients with COVID-19 and diabetes (both type 1 and type 2) will be included without restriction of geographic region, gender or age, whose outcome is hospitalization, admission to intensive care unit or mortality compared to patients without diabetes. Two authors will independently perform selection, data extraction, and quality assessment, and a third reviewer will resolve discrepancies. The data will be synthesized regarding the sociodemographic and clinical characteristics of patients with diabetes and without diabetes accompanied by the measure of association for the outcomes. The data will be synthesized regarding the sociodemographic and clinical characteristics of patients with diabetes and without diabetes accompanied by the measure of association for the outcomes.

Expected results

Update the evidence regarding the risk of complications in diabetic patients with COVID-19 and in turn synthesize the information available regarding type 1 and type 2 diabetes mellitus, to provide keys to a better understanding of the pathophysiology of diabetics.

Systematic review registry

This study was registered at the International Prospective Registry for Systematic Reviews (PROSPERO)— CRD42021231942 .

Citation: Pérez Bedoya JP, Mejía Muñoz A, Barengo NC, Diaz Valencia PA (2022) Type 1 and type 2 diabetes mellitus: Clinical outcomes due to COVID-19. Protocol of a systematic literature review. PLoS ONE 17(9): e0271851. https://doi.org/10.1371/journal.pone.0271851

Editor: Alok Raghav, GSVM Medical College, INDIA

Received: July 7, 2022; Accepted: August 23, 2022; Published: September 9, 2022

Copyright: © 2022 Pérez Bedoya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.

Funding: This research was developed within the framework of the project "Repository for the surveillance of risk factors for chronic diseases in Colombia, the Caribbean and the Americas" and has the financial support of the Ministry of Science, Technology and Innovation of Colombia—Minciencias 844 (grant number 111584467754). The opinions expressed are those of the authors and not necessarily of Minciencias.

Competing interests: The authors have declared that no competing interests exist.

The Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV-2), the causal viral agent of coronavirus disease 2019 (COVID-19), currently has the world in one of the greatest public health crises of recent times since its appearance at the end of 2019 in the city of Wuhan, China [ 1 ]. The infection has a mild or even asymptomatic course in most cases, but in elderly patients (over 60 years-of-age) and in those with pre-existing chronic comorbidities, it can progress severe complications such as pneumonia, acute respiratory distress (ARDS) with hyperinflammatory involvement and multi-organ failure, leading in some cases to death [ 2 ].

Different studies have reported that patients diagnosed with diabetes who suffer from COVID-19 disease have higher morbidity and mortality compared with people without diabetes [ 3 ]. An analysis by Gude Sampedro et al. using prognostic models found that diabetic patients had greater odds of being hospitalized (OR 1.43; 95% CI: 1.18 to 1.73), admitted to the intensive care unit (OR 1.61; 95% CI: 1.12 to 2.31) and dying from COVID-19 (OR 1.79; 95% CI %: 1.38 to 2.32) compared with patients without diabetes [ 4 ]. However, it is difficult to establish whether diabetes alone directly contributed to the increase likelihood of complications.

Several studies using secondary data have emerged during the course of the pandemic that seek to determine the association of diabetes with mortality and other clinical outcomes in patients with COVID-19, such as, for example, a meta-analysis carried out by Shang et al. of severe infection and mortality from COVID-19 in diabetic patients compared with those without diabetes. They reported that patients with COVID-19 and diabetes had higher odds of serious infection (OR = 2.38, 95% CI: 2.05 to 2.78) and mortality (OR = 2, 21, 95% CI: 1.83 to 2.66) than patients without diabetes [ 5 ]. Despite the fact that there are several primary studies that attempt to explain the association between diabetes and COVID-19, most studies lack epidemiological rigor in the design and methodology used [ 6 ]. In addition, many of them did not distinguish between type 1 and type 2 diabetes, which are two very different conditions with different clinical development and pathophysiological mechanisms [ 7 ]. This may lead to different degrees of clinical complications from COVID-19. Currently, there is a gap in knowledge about the complications in patients with COVID-19 according to the type of diabetes. Moreover, only limited information exist how COVID-19 affects type 1 patients [ 8 , 9 ].

The objective of this systematic literature review will be to identify whether there are differences in the clinical outcomes of both type 1 and type 2 diabetes patients diagnosed with COVID-19 compared with patients with COVID-19 without a diagnosis of diabetes. This study will provide scientific evidence regarding the risk of complications in diabetic patients with COVID-19 and, in turn, synthesize the available information regarding to type 1 and type 2 diabetes.

Study design

This systematic literature review protocol was prepared according to the Preferred Reporting Elements for Systematic Review and Meta-Analysis Protocols (PRISMA-P) [ 10 ] ( S1 Appendix ). The results of the final systematic review will be reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA 2020) [ 11 , 12 ]. In the event of significant deviations from this protocol, they will be reported and published with the results of the review.

Eligibility criteria

Participants (population)..

Patients with a confirmed diagnosis of COVID-19 without restriction of geographic region, sex, or age. For the diagnosis of COVID-19, the operational definition of confirmed case of the World Health Organization in its latest update will be used as a reference. Confirmed case of SARS-CoV-2 infection: a person with a positive Nucleic Acid Amplification Test (NAAT), regardless of clinical criteria OR epidemiological criteria or a person meeting clinical criteria AND/OR epidemiological criteria (suspect case A) with a positive professional- use or self-test SARS-CoV-2 Antigen RDT [ 13 ].

Patients with COVID-19 and concomitant diagnosis of unspecified diabetes mellitus, differentiated into type 1 diabetes mellitus or type 2 diabetes mellitus, without restriction of geographic region, gender, or age of the patients, who present definition of clinical criteria and /or paraclinical tests used by researchers to classify patients according to their diabetes status.

The operational definition of a confirmed case of diabetes mellitus provided by the American Diabetes Association will be used as a guide. The reference diagnostic criteria for diabetes are fasting plasma glucose ≥126 mg/dL (7.0 mmol/L). Fasting is defined as no caloric intake for at least 8 h or 2-h plasma glucose ≥ 200 mg/dL (11.1 mmol/L) during OGTT or hemoglobin A1C ≥6.5% (48 mmol/mol) or in a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, at random plasma glucose ≥200 mg/dL [ 14 ].

In selected primary studies, identification of diabetes status may be based on medical history and International Classification of Diseases codes for type 1 or type 2 diabetes, use of antidiabetic medications, or previously defined diagnostic criteria.

Comparator.

Patients with COVID-19 who do not have a concomitant diagnosis of diabetes mellitus.

The main endpoint is all-cause mortality (according to the definitions of each primary study) and the secondary outcomes are hospitalization and admission to the ICU, where the authors specify a clear definition based on clinical practice guidelines and provide a well-defined criteria for patient outcomes.

Type of study.

Primary observational original research studies (prospective or retrospective cohort, case-control design, and cross-sectional studies) will be included in this systematic review.

Exclusion criteria

Clinical trials, editorials, letters to the editor, reviews, case reports, case series, narrative reviews or systematic reviews and meta-analyses, as well as research in the field of basic sciences based on experimental laboratory models, will be excluded. Original research articles that only include other types of diabetes, such as monogenic diabetes, gestational diabetes, latent autoimmune diabetes in adults, ketosis-prone diabetes, among others, or articles with publication status prior to publication will not be considered. In addition, articles whose main hypothesis is not diabetes and do not have the established outcomes will be excluded.

Information sources and search strategy

Electronic bibliographic databases..

For the preparation of the search strategy, the recommendations of the PRISMA-S guide [ 15 ] will be adopted. Relevant articles will be identified by electronic search applying the equation previously developed by the researchers and validated by an expert librarian ( S2 Appendix ). The following electronic bibliographic databases will be used: MEDLINE, EMBASE, LILACS, OVID MEDLINE, WHO (COVID-19 Global literature on coronavirus disease) and SCOPUS with a publication date from December 2019 to August 15, 2022, without language restriction.

The search for potential primary studies published in gray literature will be performed through the World Health Organization database for COVID-19 (WHO COVID-19 Global literature on coronavirus disease). This database contains different electronic bibliographic databases incorporated into its browser, including Web of Science, EuropePMC and Gray literature, among others.

Unlike electronic bibliographic databases.

To identify other potentially eligible studies, the references of relevant publications will be reviewed to perform a snowball manual search. This technique consists of searching for new articles from the primary studies already selected in order to guarantee exhaustiveness in the search.

Study selection process

Two researchers will independently evaluate all the titles and abstracts of the retrieved articles, using the free access Rayyan® software [ 16 ] with previously established selection criteria. Disagreements will be resolved in first instance through discussion and in the second instance through a third reviewer. Subsequently, the full text of the articles selected in the eligibility phase will be read independently by two researchers, both using the same instrument previously validated in Excel according to predefined criteria. Discrepancies will be resolved by discussion or a third reviewer. The process of identification, selection and inclusion of primary studies will be described and presented using the flowchart recommended by the PRISMA statement in its latest version 2020 [ 11 , 12 ].

Data collection and extraction

Standardized and validated forms will be used to collect the data extracted from the primary studies, accompanied by a detailed instruction manual to specify the guiding questions, and avoid the introduction of bias. Data will be extracted from those articles in full text format. If the full text is not available, contact the author or search for the manuscript with the help of the library system. This process will be carried out by two researchers independently. A third investigator will verify the extracted data to ensure the accuracy of the records. The authors of the primary studies will be contacted to resolve any questions that may arise. The reviewers will resolve the disagreements through discussion and one of the two referees will adjudicate the discrepancies presented through discussion and consensus.

In specific terms, the following data will be collected both for the primary studies that report diabetes and COVID-19 and for those that differentiate between DMT1 and DMT2: author, year and country where the study was carried out; study design; general characteristics of the population, sample size, demographic data of the participants (sex, age, ethnicity), percentage of patients with diabetes, percentage of patients with type 1 and/or type 2 diabetes, percentage of patients without diabetes, frequency of comorbidities in diabetics and non-diabetics, percentage of diabetic and non-diabetic patients who presented the outcomes (hospitalization, ICU admission and mortality) and association measures reported for the outcomes. Data extraction will be done using a Microsoft Excel 365 ® spreadsheets.

Quality evaluation

The study quality assessment tool provided by the National Institutes of Health (NIH) [ 17 ] will be used for observational studies such as cohort, case-control, and cross-sectional. Two tools will be sued: one for cohort and cross-sectional studies (14 questions/domains) and one for case-control studies (12 questions/domains). These tools are aimed at detecting elements that allow evaluation of possible methodological problems, including sources of bias (for example, patient selection, performance, attrition and detection), confounding, study power, the strength of causality in the association between interventions and outcomes, among other factors. The different tools that will be used reflect a score of "1" or "0" depending on the answer "yes" or "no", respectively for each question or domain evaluated, or failing that, the indeterminate criterion option. For observational cohort studies, which consist of 14 risk of bias assessment domains, the studies will be classified as having good quality if they obtain ≥10 points, of fair quality if they obtain 8 to 9 points, and of poor quality if they obtain less than 8 points. On the other hand, in the case of case-control studies that consist of 12 bias risk assessment domains, the studies will be classified as good quality if they obtained ≥8 points, regular quality if they obtained 6 to 7 points and of poor quality if they obtained less than 6 points. However, the internal discussion between the research team will always be considered as the primary quality criterion.

Data synthesis

A narrative synthesis with summary tables will be carried out according to the recommendations adapted from the Synthesis Without Meta-analysis (SWiM) guide to describe in a structured way the methods used, and the findings found in the primary studies, as well as the criteria for grouping of the studies [ 18 ]. A narrative synthesis will be presented in two sections, one for patients with COVID-19 and diabetes and another for patients with COVID-19 and type 1 or type 2 diabetes.

Assessment of clinical and methodological heterogeneity will determine the feasibility of the meta-analysis. Possible sources of heterogeneity identified are the clinical characteristics of the study population, the criteria used to define the outcomes in the groups of patients, the time period of the pandemic in which the study was carried out, and the availability of measurement and control for potential confounding factors. For this reason, it is established a priori that this diversity of findings will make it difficult to carry out an adequate meta-analysis [ 19 ]. However, if meta-analysis is considered feasible, the random effects model will be used due to the high probability of heterogeneity between studies. Statistical heterogeneity will be assessed using the X 2 test and the I 2 statistic, and publication bias assessed using funnel plots if there are sufficient (>10) studies [ 20 ].

Exploratory ecological analysis

An exploratory ecological analysis of the association between the frequency of clinical outcomes of diabetic patients with COVID-19 and the indicators related to the health care dimension, reported for the different countries analyzed by means of the correlation coefficient, will be carried out. The open public databases of the World Bank (WB) [ 21 ], the World Health Organization (WHO) [ 22 ] and Our World In Data [ 23 ] will be used to extract population indicators related to health care, among those prioritized, universal health coverage, hospital beds per 1,000 people, doctors per 1,000 people, current health spending as a percentage of gross domestic product (GDP), percentage of complete vaccination coverage for COVID-19.

Since the first epidemiological and clinical reports were released from the city of Wuhan regarding the clinical characteristics of patients with COVID-19, a high incidence of chronic non-communicable diseases has been observed in Covid-19 patients. Current scientific evidence has shown that certain comorbidities increase the risk for hospitalization, severity of illness or death from COVID-19, such as hypertension, cardiovascular disease, chronic kidney disease, chronic respiratory disease, diabetes, among others [ 24 ].

One of the main chronic comorbidities affected by the COVID-19 pandemic is diabetes. Multivariate analysis of several observational epidemiological studies have revealed that COVID-19 patients with diabetes were at increased risk of hospitalization, ICU admission, and mortality compared with patients without diabetes [ 4 ].

For this reason, it is expected that this systematic literature review will provide scientific support regarding the outcomes and complications that patients diagnosed with COVID-19 with type 1 or type 2 diabetes present compared with patients without diabetes. This information will be useful for healthcare personnel, public health professionals and epidemiologists involved in patient care or decision making, generating epidemiological evidence. Thus, highlighting the decisive role of epidemiological research in the context of the pandemic, especially in the field of diabetes epidemiology may improve comprehensive management and care of diabetic patients. This study may also provide important information that can be used to update of clinical practice guidelines.

Limitations

There are some potential limitations to the proposed systematic review. Firstly, both type 1 and type 2 diabetes may have different key search terms and some studies may be missed. To minimize this limitation, different search equations have been designed for each database in an exhaustive and sensitive manner. In addition to reading references and level ball as an additional strategy. Another limitation is that observational studies evaluating the effect of an intervention may be susceptible to significant confounding bias and may present high heterogeneity in the findings. To report these possible biases, an adequate quality assessment will be carried out, with highly sensitive and previously validated tools, exclusive for each type of observational design. The review is intended for publication in a peer-reviewed journal.

The status of the study

The study is in the selection phase of the records by applying the eligibility criteria to the titles and abstracts. Completion of the project is expected in September 2022 with the publication of the results.

Conclusions

This report describes the systematic review protocol that will be utilized to update the evidence regarding the risk of complications in diabetic patients with COVID-19 and in turn synthesize the information available regarding DM1 and DM2, to provide keys to a better understanding of the pathophysiology of diabetics.

Supporting information

S1 appendix. prisma-p (preferred reporting items for systematic review and meta-analysis protocols) 2015 checklist: recommended items to address in a systematic review protocol..

https://doi.org/10.1371/journal.pone.0271851.s001

S2 Appendix. Search string details for each database.

https://doi.org/10.1371/journal.pone.0271851.s002

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Affiliations.

• 1 . [email protected].
• 2 . [email protected].
• 3 . [email protected].
• 4 . [email protected].
• 5 . [email protected].
• 6 . [email protected].
• 7 . [email protected].
• 8 . [email protected].
• 9 . [email protected].
• 10 . [email protected].
• 11 . [email protected].
• PMID: 36043961
• PMCID: PMC9534249
• DOI: 10.23750/abm.v93i4.13324

T2DM is a multifactorial disease, and it is considered a worldwide challenge for its increasing prevalence and its negative impact on patients' wellbeing. Even if it is known that self-care is a key factor in reaching optimal outcomes, and males and females implement different self-care behaviors, sex-related differences in self-care of patients with T2DM have been poorly investigated. Especially, an overall view of the available evidence has not yet been done. Accordingly, this review aims to summarize, critically review, and interpret the available evidence related to the sex-related differences in self-care behaviors of patients with T2DM. An extensive literature review was performed with a narrative synthesis following the PRISMA statement and flowchart through four databases: PubMed, CINAHL, Scopus, and Embase. From the 5776 identified records by the queries, only 29 articles were included, having a high-quality evaluation. Both females and males with T2DM must improve their self-care: more males reported performing better behaviors aimed at maintaining health and clinical stability (i.e., self-care maintenance) than females, but mainly in relation to physical activity. On the other hand, more females reported performing adequate behaviors aimed at monitoring their signs and symptoms (i.e., self-care monitoring) but with worse glycemic control and diabetic complications (i.e., self-care management). This review firstly provides an overall view of different self-care behaviors implemented by males and females with T2DM, showing that self-care management should be improved in both sexes. Health education must include the problems related to the diabetic pathology and the patient's own characteristics, such as sex.

Publication types

• Diabetes Complications* / complications
• Diabetes Mellitus, Type 2* / epidemiology
• Diabetes Mellitus, Type 2* / therapy

REVIEW article

A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum.

• 1 Grossman School of Medicine, New York University (NYU) Langone Health, New York, NY, United States
• 2 Division of Cardiovascular Disease, Grossman School of Medicine, New York University (NYU) Langone Health, New York, NY, United States
• 3 Division of Endocrinology, Grossman School of Medicine, New York University (NYU) Langone Health, New York, NY, United States

The prevalence of diabetes is estimated to reach almost 630 million cases worldwide by the year 2045; of current and projected cases, over 90% are type 2 diabetes. Air pollution exposure has been implicated in the onset and progression of diabetes. Increased exposure to fine particulate matter air pollution (PM 2.5 ) is associated with increases in blood glucose and glycated hemoglobin (HbA1c) across the glycemic spectrum, including normoglycemia, prediabetes, and all forms of diabetes. Air pollution exposure is a driver of cardiovascular disease onset and exacerbation and can increase cardiovascular risk among those with diabetes. In this review, we summarize the literature describing the relationships between air pollution exposure, diabetes and cardiovascular disease, highlighting how airborne pollutants can disrupt glucose homeostasis. We discuss how air pollution and diabetes, via shared mechanisms leading to endothelial dysfunction, drive increased cardiovascular disease risk. We identify portable air cleaners as potentially useful tools to prevent adverse cardiovascular outcomes due to air pollution exposure across the diabetes spectrum, while emphasizing the need for further study in this particular population. Given the enormity of the health and financial impacts of air pollution exposure on patients with diabetes, a greater understanding of the interventions to reduce cardiovascular risk in this population is needed.

1 Introduction

Since antiquity, physicians have suspected that air quality could alter human health. Indeed, the Hippocratic Corpus details the importance of clean air, and the philosopher Seneca noted the deleterious health effects of Rome’s contaminated air ( 1 ). Research in the past few decades has implicated air pollution in the development of non-communicable diseases, with a strong body of observational and experimental studies establishing a link between air pollution and cardiovascular disease (CVD), encompassing coronary heart disease, heart failure, stroke, peripheral artery disease, and hypertension ( 2 ). For example, airborne co-pollutants have been observed to increase hospital admissions for CVD ( 3 , 4 ). More recently, evidence has implicated air pollution in the onset and progression of type 2 diabetes mellitus (hereafter referred to as diabetes), a widely recognized and significant cardiovascular risk factor ( 5 , 6 ). Converging lines of evidence in a growing body of literature support the notion that air pollution, especially fine particulate matter (PM 2.5 ), can markedly exacerbate CVD risk in patients with diabetes and prediabetes, referred to as the “diabetes spectrum” in this review.

Globally, exposure to air pollution is the fourth leading risk factor for early death and the fourth leading modifiable risk factor for cardiovascular disease (CVD) ( 7 , 8 ). In the US, exposure to fine particulate matter (PM 2.5 ) has been estimated to result in 8.2 million healthy life-years lost annually from diabetes ( 9 ). The magnitude of this ongoing and ubiquitous risk factor for diabetes and CVD would be difficult to overstate. Yet, the problem remains absent from most discussions of risk in health education ( 10 – 12 ). As such, in this review we aim to summarize this existing evidence supporting the relationship between air pollution, diabetes and CVD ( Figure 1 ), including the biological mechanisms underlying this phenomenon. Furthermore, this review will discuss potential interventions to reduce air pollution exposure among patients with diabetes and barriers to effective implementation of such interventions. Lastly, this review will identify gaps in the current research landscape and suggest future directions.

Figure 1 Relationships between air pollution, diabetes and CVD. Multiple physiologic pathways are affected by exposures and drive numerous subclinical and clinical outcomes. Created with BioRender.com .

2 Scope of the problem

2.1 diabetes and cvd.

Globally, an estimated 536 million adults have diabetes, either diagnosed or undiagnosed, a number that will increase to 783 million by 2045, driven by expanding populations in middle-income countries ( 13 ). In the United States, approximately 37.3 million people have diagnosed or undiagnosed diabetes, with this number expected to increase to over 54.9 million by 2030 ( 14 , 15 ). Similarly, impaired glucose tolerance has a global prevalence of 464 million that is projected to increase to 638 million by 2045 ( 16 ). Whereas in the United States, 96 million adults have prediabetes, estimated to increase to 107 million in 2030 ( 14 , 15 ). The projected increase in diabetes and prediabetes is partially driven by an aging population in conjunction with climbing obesity rates; increased body mass index (BMI) is strongly related to increased diabetes risk ( 17 ). The annual total cost of diagnosed diabetes in the United States is estimated at $327 billion, taking into account healthcare utilization and lost productivity ( 18 ). These data make evident the pressing need to identify ways to minimize the incidence and progression of diabetes spectrum disorders, including attention to emerging modifiable risk factors such as environmental exposures.

Like diabetes, CVD is on the rise worldwide, with ischemic heart disease now the second leading cause of morbidity and mortality globally ( 19 ). In the United States, nearly 128 million adults live with at least one manifestation of CVD, and 928,741 deaths were attributed to CVD in 2020 alone ( 20 ). This heavy burden of CVD in the United States comes at a substantial price, necessitating $407 billion in direct and indirect costs in 2018 ( 20 ). By 2060, an estimated 234 million Americans will have CVD, with racial and ethnic minorities bearing the majority of this increased burden ( 21 ). As these statistics demonstrate, CVD is and will remain a tremendous problem that will require a multimodal approach to prevention and treatment.

These two chronic conditions separately affect massive populations worldwide with substantial economic and quality of life impact. However, we know there is significant interplay between diabetes and CVD. A long history of prospective cohort studies dating back to the first Framingham Heart Study has established diabetes as a major risk factor for CVD ( 22 ). In 2010, the Emerging Risk Factors Collaboration published a meta-analysis of 102 prospective cohort studies, concluding that diabetes confers an approximate 2-fold risk increase for coronary heart disease (95% CI [1.83, 2.19]), with similar risk increases for ischemic (2.27 [1.95, 2.65]) and hemorrhagic stroke (1.84 [1.59, 2.13]) ( 23 ). Subsequently, a competing risks analysis using data from 12 Spanish prospective cohorts followed for a median of 10 years found that diabetes increased cumulative risk of cardiovascular death by 1.5-2.5% in both men and women ( 24 ). In addition, diabetes appears to not only confer its own risk, but also to accelerate the age-related increase in CVD. A retrospective cohort study found that adults with diabetes develop a high risk of CVD on average 14.6 years sooner versus their counterparts without diabetes ( 25 ).

In contrast with older thinking that diabetes increases disease risk only beyond a certain threshold of HbA1c, it appears that CVD risk increases across the continuum of glucose intolerance. An international prospective cohort study of nearly 19,000 adults without diabetes at baseline found that the risk of incident CV events increased by 1.16 [1.11, 1.22] per 1 mmol/L increase in fasting plasma glucose ( 26 ). These findings suggest that glucose intolerance should be considered along a continuum, similar to blood pressure ( 27 ). There is also evidence linking insulin resistance to CVD. For example, in a prospective cohort study of elderly men in Sweden, insulin resistance was associated with an increased risk of developing congestive heart failure over 7-12 years of follow-up (HR 1.44, 95% CI 1.08-1.93 per 1-SD increase in oral glucose tolerance test glucose level) ( 28 ). After 20 years of follow-up in the same cohort, insulin resistance at age 50 was associated with left ventricular dysfunction at age 70 ( 29 ). Further supporting a link between diabetes and heart failure, a Swedish cohort study of over 270,000 adults demonstrated that, even with other risk factors in target ranges, patients with diabetes still had an excess risk for hospitalization due to heart failure (HR 1.45 [1.34, 1.57]) ( 30 ). Given the significant CVD risk that increases across the diabetes spectrum, developing personal and public strategies to mitigate glucose intolerance at every stage is paramount to preventing excess morbidity and mortality worldwide.

3 Air pollution as a risk factor

3.1 background on air pollution.

The World Health Organization (WHO) has defined air pollution as “contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere” ( 31 ). Air pollution is a heterogeneous mixture of particles and gases, much of which is anthropogenic in origin. Nitrogen oxides (NO x ), including nitrogen dioxide (NO 2 ), and carbon monoxide (CO) are generated by fossil fuel combustion, with traffic as a major source. Sulfur dioxide (SO 2 ) is generated by fossil fuel combustion for heating homes and generating power ( 32 ). Ozone (O 3 ) forms in reactions between light and various compounds, including CO and NO x . Particulate matter (PM) air pollution is composed of sulfates, nitrogen oxides, ammonia, sodium chloride, black carbon, mineral dust, organic compounds, and products of incomplete combustion of petroleum. PM is generated from many sources, including traffic, industrial, construction, fires, and trash burning, and is typically described in terms of particle size. Coarse PM (PM 10 ) is defined as PM with a diameter between 2.5 and 10 μm and fine PM (PM 2.5 ) with a diameter less than 2.5 μm; ultrafine PM is PM smaller than 0.1 μm (PM 0.1 ). Most studies examine the effects of PM 2.5 , since this is the most widely available data. Thus, the most conclusive effects are seen for this particular pollutant.

3.2 Epidemiologic studies on air pollution and diabetes

As early as 1967, researchers probed Public Health Service data to investigate the relationship between air quality and diabetes death rate in urban populations across the US ( 33 ). Since then, the body of research on air pollution and diabetes has expanded, especially in the past two decades. This research is summarized in Table 1 . A 2002 ecological study demonstrated a significant positive correlation between industrial air emissions and diabetes prevalence by state in the US (r = 0.54, p = 5.7x 10 -5 ) ( 34 ). Another ecological study in 2010 conducted a county-level analysis across the US, showing a 1% increase in county diabetes prevalence per 10 μg/m 3 per average county increase in PM 2.5 (β = 0.81 [0.48, 1.07], p < 0.001) ( 35 ). Many observational studies over the following decade supported the early ecological findings. A cross-sectional analysis of a Swiss cohort study showed a positive association between 10-year average PM 10 and NO 2 exposure and diabetes prevalence, even at levels below the World Health Organization air quality guidelines (OR: 1.40 [1.17 – 1.67], 1.19 [1.03 – 1.38] per 10 μg/m 3 increase in pollutant, respectively) ( 45 ). A cross-sectional study of 69,000 adults in China without a prior history of diabetes demonstrated that for each standard deviation increase in 3-year average concentration of PM 2.5 there were increased odds of diagnosed diabetes (OR: 1.04 [1.01, 1.07]) by fasting blood ( 46 ). Another cross-sectional study of 11,847 adults in China found that annual average PM 2.5 exposure was associated with diabetes prevalence (PR: 1.14 [1.08, 1.20] for a 41.1 μg/m 3 increase in PM 2.5 ), with a greater effect seen in subjects who were male, smoking, elderly, or had high BMI or less education ( 38 ).

Table 1 Studies of links between air pollution and DM incidence and prevalence.

The findings from these cross-sectional studies have been recapitulated in prospective data with mixed results. Particulate matter-associated diabetes incidence was investigated in a prospective cohort study of over 61,000 elderly Hong Kong residents without diabetes at baseline followed from 1998 to 2010. The analysis showed an increased risk of incident diabetes (HR: 1.15 [1.05, 1.25]) per 3.2 μg/m 3 increase in average annual PM 2.5 exposure ( 39 ). A Canadian cohort study followed 62,000 adults without diabetes in Ontario for up to 15 years, during which time a 10 μg/m 3 increase in average PM 2.5 exposure was associated with an increased risk of incident diabetes (HR 1.11 [1.02, 1.21]) ( 47 ). A Danish prospective cohort study from 1993 until 2013 found that annual average PM 2.5 was significantly associated with increased diabetes incidence (HR: 1.11 [1.02, 1.22]), especially in patients with obesity ( 40 ). Additionally, a 16-year-long cohort study of women in Germany without diabetes at baseline demonstrated a 15% [4%, 27%] increase in the risk of incident diabetes per 1 interquartile range increase in traffic-related PM (a composite of particles derived from traffic) and NO 2 exposure, but no significant risk increase due to PM 10 exposure ( 41 ). In contrast, an analysis of participants in the Nurses’ Health Study and the Health Professionals Follow-Up Study found no significant association between 1-year average PM 2.5 or PM 10 and incident diabetes, although the direction of effect was weakly positive (1.03 [0.96, 1.10] and 1.04 [0.99, 1.09] for PM 2.5 and PM 10 , respectively) ( 42 ). Differences across these studies may be due to varying exposure assessments or particle compositions, or due to differences in cohort characteristics (e.g., diet, BMI). Thus, despite conflicting results, the majority of studies support an association between particulate matter air pollution exposure and diabetes incidence.

Similar to particulate matter, there are differing reports on associations between NO 2 exposure and diabetes. NO 2 exposure was studied in a prospective study of 2,631 Swiss adults without baseline diabetes followed from 2002 to 2011; results showed no significant association between average annual NO 2 exposure and diabetes incidence (RR: 0.87 [0.60, 1.22]). However, few incident diabetes cases in the cohort may have diminished the ability to detect an effect ( 36 ). A similar negative finding for NO 2 was found in a prospective cohort of African American women residing in US cities (HR: 0.90 [0.82 – 1.00] for a 9.7 ppb increase in NO 2 in the fully adjusted model); this negative result was purported to be due to confounding by socioeconomic status (SES) given the inverse correlation between neighborhood SES and NO 2 in this cohort ( 43 ). Among women in the Nurses’ Health Study, there was, however, a significant association between increased proximity to a roadway and developing diabetes (HR: 1.14 [1.03, 1.27] for living < 50 m vs. ≥00 m from a roadway). Because motor vehicles are a major generator of NO 2 , and proximity to roadways has been used as a surrogate marker for exposure, the authors suggest this result may support a link between NO 2 and diabetes in females ( 42 ). Similar to the Nurses’ Health Study results, a Danish prospective cohort study using a national public register found a nonsignificant but positive association between NO 2 exposure and confirmed diabetes (HR: 1.04 [1.00, 1.08] per 4.9 μg/m 3 increase in average NO 2 ), with the strongest associations in women and subjects with elevated waist-to-hip ratio ( 44 ). A cross-sectional study of respiratory clinic patients in two Canadian cities found a positive association between NO 2 exposure and diabetes diagnosis but only for women (OR: 1.04 [1.00, 1.08]) ( 48 ). A comparable cross-sectional study in the Netherlands showed a non-significant association between increasing levels of NO 2 exposure and diabetes diagnosis, with the direction of effect stronger in women (OR: 1.48 [1.07, 2.04]) ( 49 ). Given that air pollution is a complex mixture of particles and gases, it is challenging to interpret studies of the health effects of individual component pollutants. Regardless, evidence continues to mount in support of the association between increasing air pollution exposure and increases in incident and prevalent diabetes.

3.2.1 Short-term effects of air pollution on diabetes and glucose homeostasis

In addition to the literature supporting associations between air pollution exposure and incidence of diabetes, there is growing literature showing worsening of glucose metabolism with air pollution exposure for patients that already have a diabetes diagnosis. Furthermore, short-term exposures to air pollution, over days to weeks, seem to cause dysregulated glucose homeostasis, even among those without diabetes. The following studies examining short-term air pollution exposures and diabetes are summarized in Table 2 .

Table 2 Studies of short-term effects of air pollution on glucose homeostasis.

A cross-sectional study of 2,840 patients with diabetes hospitalized from 2013-2016 in Chongqing, China, investigated the impact of short-term, 15-day average air pollution exposure on length of stay and cost of admission. The study authors found a positive correlation between a 10 μg/m 3 increase in sulfur dioxide (SO 2 ) and carbon monoxide (CO) exposure and prolonged length of stay, increased by 0.487 days [0.318, 0.656] and 0.013 days [0.003, 0.022], respectively, with a concordant increase in the cost of hospitalization ( 50 ). In Israel, a retrospective study between 2001-2012 of over 1 million fasting blood glucose tests from approximately 130,000 patients found a significant positive association between fasting blood glucose and 24-72 hour averages for NO 2 and SO 2 in all patients regardless of diabetes status. A 6.36 ppb increase in NO 2 was associated with a 0.40% [0.31%, 0.50%] increase in fasting glucose in patients without diabetes, 0.56% [0.40%, 0.71%] in those with prediabetes, and 1.08% [0.86%, 1.29%] in those with diabetes.; for a 1.17 ppb increase in SO 2 fasting glucose increased by 0.29% [0.22%, 0.36%], 0.20% [0.10%, 0.31%], 0.33% [0.14%, 0.52%], in these same groups ( 51 ). A similar retrospective study in the same Israeli population found that 12-week average PM 10 and PM 2.5 exposure was associated with increased fasting blood glucose in all patients (0.30% [0.153%, 0.452%]; 0.02% [-0.12%, 0.18%], respectively) and this increase was more pronounced in those with diabetes (0.57% increase [0.29%, 0.85%], 0.41% increase [0.12%, 0.69%]). Also, HbA1c increases were found in patients with diabetes (3.58% [1.03%, 6.20%]; 2.93% [0.35%, 5.59%] for PM 10 and PM 2.5 respectively). The 1-7 day average PM 10 and PM 2.5 exposure windows had no or negligible association with fasting blood glucose and HbA1c ( 55 ).

Prospective data have corroborated the retrospective data suggesting short-term effects of air pollution on blood glucose. A German prospective cohort study between 2000-2008 of 7,108 adults without diabetes at baseline evaluated short-term associations between air pollution exposure and fasting blood glucose levels and HbA1c. Increases in 28-day average accumulation mode particle number (PN AM , PM between 0.1-1μm in aerodynamic diameter) and PM 2.5 concentrations were both positively associated with increasing blood glucose (0.64 mg/dL [0.07, 1.21] per 2,142.3 n/mL increase and 0.91 mg/dL [0.38, 1.44] per 5.7 μg/m 3 increase, respectively) ( 52 ). In the US Framingham Heart Study, increased 7-day moving average BC and NO x exposures were positively associated with higher fasting glucose among adults without diabetes. In contrast, an increased short-term O 3 exposure was inversely associated with blood glucose (exact numbers not provided by the study authors) ( 56 ). A prospective cohort study of approximately 28,000 adults in China followed from 2006-2008 found that a 100 μg/m 3 increase in the 4-day average of NO 2 , SO 2 , or PM 10 exposure was associated with elevated fasting blood glucose (0.53 mmol/L [0.42, 0.65], 0.17 mmol/L [0.15, 0.19], 0.11 mmol/L [0.07, 0.15], respectively), with increased elevations among female, elderly, or overweight subjects ( 53 ). A recent study of 2 large Indian cities (Chennai and Delhi) found that a 10 μg/m 3 difference in 1-month average exposure to PM 2.5 was associated with a 0.40 mg/dL increase in fasting plasma glucose (95% CI 0.22 to 0.58) and 0.021 unit increase in HbA1c (95% CI 0.009 to 0.032) ( 57 ).

Some studies have been conducted on short-term air pollution exposure and glucose metabolism using the homeostasis model assessment of insulin resistance (HOMA-IR). In one study, 25 adults without diabetes who resided in rural Michigan were exposed to urban ambient air for 4-5 hours per day for 5 days. HOMA-IR was measured before, during, and after the air pollution intervention. A positive correlation was found between each 10 μg/m 3 increase in measured PM 2.5 exposure and study subjects’ HOMA-IR (+0.7 [0.1, 1.3]). A 3.5 μg/m 3 increase in PM 2.5 was associated with worsening HOMA-IR (+0.25 [0.04, 0.46], indicating potential adverse effects even at low concentrations of PM 2.5 ( 54 ). A cross-sectional analysis of Mexican American women with a personal or family history of gestational diabetes but without diabetes at the time of the study revealed that up to 40 days of daily PM 2.5 exposure and up to 37 days of daily NO 2 exposure were associated with increased HOMA-IR (β = 6.99, p = 0.002 for PM 2.5 ; β= 6.63, p = 0.009 for NO 2 ). However, no significant associations were found for O 3 exposure ( 58 ). Last, a clinical trial testing 48 hours of portable air cleaner (PAC) intervention in healthy college students in China showed an approximately 10% increase in HOMA-IR per 10 μg/m3 increase in PM 2.5 (exact numbers not provided) ( 59 ).

In summary, the collective evidence supports short-term associations between air pollution exposure and fasting glucose and dysregulated glucose metabolism evidenced by HOMA-IR, but not HbA1c.

3.2.2 Long-term effects of air pollution on diabetes and glucose metabolism

Long-term exposures to air pollution also have been shown to affect glucose homeostasis. Studies examining this phenomenon have been summarized in Table 3 . Cross-sectional analyses of a Chinese cohort found 1-year average PM 2.5 to be positively associated with both elevated fasting glucose (0.26 mmol/L increase [0.19, 0.32]) and HbA1c (0.08% increase [0.06%, 0.10%]) for a large, 41.1 μg/m 3 increase in PM 2.5 ( 38 ). Furthermore, a secondary analysis of a Taiwanese cohort found 1-year average PM 2.5 , PM 10 , O 3 , and NO 2 to be positively associated with fasting glucose and HbA1c; a 20.42 μg/m 3 increase in PM 2.5 was associated with 34.6 mg/dL [16.5, 52.7] increase in fasting glucose and 2.1% [1.5, 2.7] increase in HbA1c ( 60 ). The air pollution exposures experienced by this cohort were substantially above the WHO guidelines. A subsequent cross-sectional study of 2,895 adults in the Dunkirk and Lille areas of France, regions with relatively low concentrations of air pollution, found that HbA1c was 0.044% higher [0.021%, 0.067%] with a 2 μg/m 3 increase in annual mean PM 10 and 0.031% higher [0.010%, 0.053%] with a 5 μg/m 3 increase in annual mean NO 2 . However, neither NO 2 nor PM 10 were significantly associated with diabetes prevalence, likely due to a low number of patients with diabetes in the study sample. Moreover, neither pollutant had an association with fasting blood glucose ( 61 ).

Table 3 Studies of long-term effects of air pollution on glucose homeostasis.

Large prospective cohort studies repeatedly demonstrate the long-term effects of air pollution exposure on diabetes outcomes. A German prospective cohort study without baseline diabetes demonstrated that 91-day average exposure to PN AM and PM 2.5 was associated with increased random blood glucose and, more strongly, with increased HbA1c. There was a 0.67 mg/dL [0.10, 1.24] and 0.81 mg/dL [0.05, 1.58] increase in random blood glucose (adjusted for time since last meal) per interquartile range (IQR) increase in PN AM (1,352.7 n/mL) and PM 2.5 (4.0 μg/m 3 ), respectively. In this German study, HbA1c increased by 0.09% [0.07, 0.11] and 0.07% [0.04, 0.10] per IQR increase of each pollutant, respectively) ( 52 ). A census data analysis of 2.1 million randomly selected Canadian adults, followed from 1991 to 2001, found that a 10 μg/m 3 increase in average PM 2.5 exposure over a 5-year period was associated with a hazard ratio of 1.49 [1.37, 1.62] for diabetes-related mortality. This association was consistent across subgroups of age, sex, education, income, community, and at low concentrations of PM 2.5 (<5 μg/m 3 ). The risk of diabetes-related mortality was most pronounced in participants with lower SES as well as aboriginal ancestry ( 62 ). Similar effects on HbA1c have been observed in U.S. cohort studies. For example, in a probability sample of U.S. adults with diabetes over 57 years of age (n= 4121) followed from 2005 to 2011, a 3.7 μg/m 3 increase in 2-year moving average PM 2.5 was associated with an increase in HbA1c of 1.8% ± .6% (p<0.01). In subjects without diabetes, a significant positive association with HbA1c was found for NO 2 exposure (0.8% ± .2%, p<0.01) ( 64 ). The prospective cohort study from Chennai and Delhi showed that a 1-year increase in PM 2.5 exposure of 10 μg/m 3 was associated with increased HR for incident diabetes (1.22 [1.09, 1.36]), with similar significant estimates for 1.5-year and 2-year exposures as well ( 57 ).

In addition to the long-term associations with HbA1c, long-term exposures have been associated with measures of insulin resistance. A cross-sectional study of long-term air pollution exposure in Korea found that the relationship between HOMA-IR and PM 10 observed in studies of short-term air pollution exposures retained significance even with rigorous adjustment for visceral adiposity, with a dose-dependent increase in HOMA-IR by 14% [8%, 21%] for men and 14% [7%, 21%] for women, per 11 μg/m 3 increase in PM 10 ( 63 ). Other cross-sectional studies have found similar associations with HOMA-IR. A cross-sectional study investigating the effect of PM 10 exposure in young-onset (before age 46) patients with diabetes at a clinic in India found that a 43.83 μg/m 3 increase in 1-year average PM 10 exposure was associated with increased HOMA-IR of 4.89% [0.59%, 9.37%], with a significantly greater effect in female and patients with obesity ( 65 ). In the cross-sectional analysis of Mexican American women discussed previously, annual PM 2.5 exposure was associated with increased HOMA-IR (beta coefficient 5.81, p = 0.016), without significant associations for annual NO 2 or O 3 exposures ( 58 ). A German prospective cohort study of nearly 3,000 adults with and without diabetes/prediabetes found that a 7.9 μg/m 3 increase in 2-year average PM 2.5 exposure was associated with increased HOMA-IR (15.6% [4.0%, 28.6%]) and insulin (14.5% [3.6%, 26.5%]). In contrast, an 11.9 μg/m 3 increase in 2-year average NO 2 exposure was associated with increases in HOMA-IR by 19.2% [7.7%, 31.6%], insulin by 17.2% [6.6%, 29.0%], glucose by 1.7% [0.1%, 3.3%], and leptin by 15.3% [6.8%, 24.5%]. However, there was no association between either pollutant and HbA1c ( 66 ).

While effect sizes have varied across cohorts and pollutants, the directionality of the relationships between long-term air pollution exposures and HbA1c remains generally consistent. If short-term air pollution exposure induces hyperglycemia, then we would expect increases in medium- and long-term exposure to have the effect of raising HbA1c. As expected, most studies to date support that months-long exposures are more strongly associated with HbA1c, indicating a potential cumulative effect of shorter-term air pollution exposure.

3.3 Diabetes may confer increased vulnerability to the cardiovascular effects of air pollution

Patients with diabetes appear to be more vulnerable to the vasculotoxic effects of air pollution exposure. Studies examining this potential predisposition have been summarized in Table 4 . A cross-sectional analysis using Illinois Medicare data from 1988-1994 found that a 10 μg/m 3 increase in ambient PM 10 exposure in the 24 hours prior to admission was associated with a 2.01% [1.40%, 2.62%] increase in hospital admission for CVD in patients with diabetes, a two-fold higher increase in CVD hospitalizations than that observed for adults without diabetes ( 67 ). A case-crossover study examining emergency department visits in Atlanta reported increased odds of visits for dysrhythmia with increasing NO 2 exposure for people with diabetes (OR 1.158 [1.046, 1.282]) compared to people without diabetes (1.014 [0.988, 1.040]; p<0.05 for regression coefficient difference between diabetes vs. no diabetes) ( 72 ). However, this study did not find significant associations for other pollutants such as PM 10 and O 3 , perhaps due to estimating air pollution exposures using central monitors rather than patient residential addresses.

Table 4 Studies examining the increased CVD risk for people with diabetes exposed to air pollution.

In addition to clinical outcomes, exposure to air pollutants has also been associated with subclinical effects. In 2005, a cross-sectional study of 270 adults in Boston found that, among subjects with diabetes, PM 2.5 exposure was associated with a 7.6% decrease in nitroglycerin-mediated vascular reactivity [-12.8%, -2.1%], while black carbon exposure was associated with a 10.7% decrease in flow-mediated reactivity [-17.3, -3.5]. However, there was no such association among subjects without diabetes ( 68 ). That same year, a cross-sectional study of participants in the Veterans Administration Normative Aging Study observed that the association between PM 2.5 exposure and reduced heart rate variability (HRV) was more pronounced in participants with diabetes. Indeed, the percent change in the standard deviation of normal-to-normal intervals (a measure of HRV) due to PM 2.5 exposure, although not significant, was nearly 4-fold higher in participants with diabetes (-16.6% [-36.3, 9.2] compared to -4.7% [-11.4%, 2.6%]) ( 69 ). A double-blind, crossover exposure study of 17 never-smoker adults with diabetes found that 2 hours of controlled exposure to PM 0.1 reduced heart rate variability (p = 0.014) and also increased average heart rate by approximately 8 beats per minute over a day after the exposure ( 74 ). These data point to the synergistic interaction between diabetes and air pollution in driving CVD.

Not all studies support an interaction between diabetes and air pollution. A case-crossover study examining emergency department visits for acute coronary syndrome in Utah found little difference in the PM 2.5 risk estimate for people with diabetes compared to those without ( 71 ). A similar case-crossover study of death records from 20 cities in the United States found no significant effect modification of the PM 10 -CVD death association by diabetes status, although the point estimate for the association between PM 10 and all-cause mortality was higher for people with diabetes compared to those without ( 70 ). Moreover, in an analysis of 22 years of follow up in the American Cancer Society Cancer Prevention Study II cohort, people with diabetes had a higher risk of CVD mortality at both high (HR 2.4 [2.3, 2.5]) and low PM 2.5 exposure (2.2 [2.1, 2.3]) compared to people without diabetes. However, when comparing high to low PM 2.5 exposure, the CVD mortality risk increase was similar in both groups, and formal tests of interaction between diabetes status and PM 2.5 exposure were nonsignificant ( 73 ).

3.4 Environmental inequities contribute to unequal diabetes and cardiovascular disease risk

Consistently, research in the United States has shown that racial/ethnic minority communities, and individuals with low education and income, have greater diabetes prevalence ( 75 ) and mortality ( 76 ). Racial/ethnic minorities also develop diabetes at lower BMIs compared to white people, and the strength of the association between BMI and diabetes is weaker in racial/ethnic minorities, highlighting the complexity of factors that may contribute to this disproportionate burden ( 77 ). Recent work in the US and limited research in Asia and Africa has shown that low-SES communities are subject to higher air pollution exposures ( 78 ). In the United States, black and Hispanic minority groups are disproportionately exposed to the air pollution generated by the white majority ( 79 ). This disproportionate exposure translates into a greater burden of death due to air pollution. A retrospective cohort study of US Veterans Administration patients found that excess death due to PM 2.5 exposure was disproportionately borne by black patients (55.2 deaths per 100,000 [50.5, 60.6]) compared to nonblack patients (51.0 [46.4, 56.1]), as well as by patients living in low SES counties (65.3 [56.2, 75.4]) compared to those living in high SES counties (46.1 [42.3, 50.4]). Notably, 99% of these excess deaths were due to PM 2.5 concentrations below the US Environmental Protection Agency (EPA) recommended limit of 12μg/m 3 ( 80 ). These findings are put into historical context when considering that historically redlined neighborhoods face greater PM 2.5 and NO 2 exposures compared to other communities in the same cities. Even within redlined neighborhoods, racial and ethnic disparities in air pollution exposure persist ( 81 ). Thus, multiple traditional and non-traditional risk factors are disproportionately concentrated in minority communities and may act in concert to further widen health disparities.

3.5 Inconsistencies in the data

As noted previously in this review, some studies have not detected an association between air pollution and incident or prevalent diabetes. Moreover, associations between long-term air pollution exposure and HbA1c were, though not entirely consistent, generally positive. Results for gaseous pollutants appear to be more heterogeneous than particulates; however, since there are correlations between these pollutants, it can be challenging to parse out outcomes for individual components in epidemiological studies. Some associations appear to be only in, or stronger in, subgroups; additionally, some effects appear to be attenuated by other factors such as medications ( 45 ). Regional, cultural, gender, socioeconomic or other differences in work or lifestyle can influence how people spend time in different geographical areas, thereby changing both pollution sources and exposures. Given the temporal and spatial heterogeneity of air pollution concentration and composition, accurate and precise exposures are extremely difficult to assess making some degree of variation in these results unsurprising. Lastly, although experimental studies suggested that having diabetes can exacerbate the cardiovascular derangements induced by air pollution exposure, and epidemiologic observations often reported greater associations between air pollution and death for people with diabetes, the evidence of an effect modification on air pollution-CVD death by diabetes status or an air pollution-diabetes interaction on CVD death remains lacking. The lack of detectable effect modification could be due to inaccurate reporting on surveys and death records. Alternatively, the majority of the effect of air pollution on CVD death may be attributable to air pollution promoting a cardiometabolic disease state. Thus, some of this effect could be lost when stratifying by diabetes status ( 73 ).

3.6 A word on the exposome

Although this review is concerned with the health effects of air pollution exposure, it is important to note that such exposure does not occur in a vacuum. Instead, air pollution exposure often co-occurs with a variety of other environmental exposures, such as noise pollution, nighttime light, and temperature, especially in urban areas ( 82 ). Originally conceived as a complement to the genome ( 83 ), the term “exposome” aims to capture the host of biological responses to the myriad environmental exposures experienced throughout the life course. In recent years, there has been a growing call to consider each environmental exposure in context of the entire exposome. Much of the prior observational analysis has focused on single exposures in isolation. To completely understand the health implications of environmental exposures, including air pollution, experts have argued in favor of advanced analytic methods that consider diverse, simultaneous exposures, their interactions, and their measurable biological effects ( 84 ). Such analytic methods will need to venture beyond standard univariate and multivariate regression models to tease out the likely non-linear effects of a multitude of co-occurring exposures ( 85 ). Taking a page from studies of genetic associations, a novel method known as environment-wide association study might identify the effects of mixtures of exposures ( 86 ). Future observational studies examining the air pollution, diabetes, and CVD link should take into account the exposome concept to more accurately reflect the reality of how people experience these exposures.

3.7 The global variability of air pollution and its health implications

It is worth noting that although air pollution is experienced by nearly all people globally, the burden of air pollution varies by region. Over the past two decades, average airborne PM 2.5 concentrations have declined in North America, Europe, and East Asia, whereas the opposite has occurred in the Middle East, Africa, and South Asia ( 87 , 88 ). Despite this increase in ambient PM concentration in the Middle East and North Africa, morbidity and mortality rates due to air pollution have decreased in these regions, which might be due to a declining rate of indoor fuel burning ( 89 ). Nevertheless, ambient air pollution remains an urgent public health concern, with approximately 22% of deaths due to ischemic heart disease and 21% of deaths due to diabetes attributed to air pollution in the Middle East and North Africa ( 89 ).

Among global regions, substantial differences have been noted in the magnitude of the association between higher PM exposure and increased mortality ( 90 ). Such differences are likely due to a variety of factors. Countries vary in the relative contributions of traffic, industry, and biomass burning to the generation of PM ( 91 ). These pollution sources differ in the exact chemical composition PM ( 92 ), which might explain the global differences in mortality risk due to PM exposure. Moreover, indoor burning of biomass fuel, such as wood, crops, and manure, for heating and cooking can drastically worsen indoor air quality ( 93 ). The greater indoor burning of such fuels in lower- and middle-income countries (LMIC) could alter the observed association between outdoor air pollution and mortality while also placing people in these countries at higher risk ( 90 ). As discussed previously in this review, LMIC face a greater projected increase in prediabetes and diabetes compared to high-income countries. Many of these countries also face trends of worsening air quality. Evidently, the combined epidemics of air pollution exposure and diabetes represent an urgent threat to global public health.

3.8 Conclusions from epidemiological data

Overall, the studies to date indicate that PM 2.5 , PM 10 , PN AM , black carbon, SO 2 , and CO may have deleterious effects on glucose homeostasis in the short- and long-term. Consistently, PM 2.5 had the most consistently demonstrated effect on glucose across multiple populations. The inconsistent results for NO 2 may be due to low diabetes event rate in the study subjects and/or low overall levels of NO 2 leading to small effect size. Acute air pollution exposure is more strongly associated with increased fasting blood glucose, whereas chronic exposure has a stronger association with worsened HbA1c. Susceptible subgroups demonstrate stronger effects of air pollution on glucose metabolism as well as diabetes prevalence and incidence among those with overweight and obesity. Consequently, air pollution appears to have stronger effects on adverse CV outcomes among those with dysregulated glucose homeostasis.

While there is a growing body of work in this field, most epidemiological studies to date have been conducted in populations residing in the US, Canada, western Europe, and East Asia. Most of these regions have experienced improvements in air pollution levels in recent decades, while LMIC have experienced an increase in morbidity and mortality due to air pollution ( 94 ). With diabetes prevalence on the rise worldwide, more studies are needed to investigate the effects of worsening pollution on the metabolic health of people living in LMIC. Results from wealthier countries cannot be extrapolated to LMIC, given the known differences in air pollution exposures and population characteristics between these regions.

4 Shared mechanisms

While human epidemiological studies can identify associations, mechanistic evidence is important to define the affected biological pathways. Such data can assist in identifying susceptibility factors, specific pollutants to target with regulation, or molecular targets for pharmaceutical interventions. To date, mechanistic studies in this field include known exposure studies in cell lines and in animal models, exposure chamber studies, and natural experiments with humans. In this section, we will review these types of studies and summarize the identified mechanisms that underlie the associations between air pollution, dysregulated glucose metabolism, and increased CVD risk.

4.1 Mechanisms in animal studies

There are multiple convening pathways by which air pollution, diabetes, and cardiovascular disease interact ( Figure 2 ). Much of the data to date suggests two primary culprits are inflammation and oxidative stress, themselves intertwined, which often form self-perpetuating feedback loops.

Figure 2 Multiple overlapping mechanisms by which air pollution, diabetes, and cardiovascular disease interact. (AGEs, advanced glycosylation end products; CRP, C-reactive Protein; CVD, cardiovascular disease; IL-6, Interleukin-6; NFκB, Nuclear factor kappa-light-chain-enhancer of activated B cells; NO, nitric oxide; RhoA, Ras homolog family member A; ROS, reactive oxygen species; TNFα, tumor necrosis factor alpha). Created with BioRender.com .

4.1.1 Inflammation in animal models

4.1.1.1 hypothalamic inflammation, diabetes, and air pollution.

Inflammation plays a dominant role in the development and progression of diabetes ( 95 , 96 ). Hypothalamic inflammation is proposed to be a major driver of disorders of glucose homeostasis due to its role in regulating energy intake and expenditure via insulin and leptin ( 97 ). Animal models show that recurrent hypothalamic inflammation, via diet-induced increases in TNFα expression ( 98 – 100 ), leads to a dysregulated body weight set-point, driving increased energy intake and decreased energy expenditure. These behaviors serve to increase adiposity that further increases inflammation ( 101 ). These appear to be independent, yet synergistic, effects on inflammation. In fact, a study in a mouse model of diabetes and prediabetes suggested that differences in hypothalamic inflammation could be to blame for the observed variation in the onset and progression of prediabetes to diabetes within the group of mice ( 102 ).

The hypothalamus appears to be susceptible to the inflammatory effects of particulate matter pollution. A mouse model was exposed to PM 2.5 or clean air, and inhibitor of nuclear factor kappa-B kinase subunit beta (IKK2), an NF-kB inhibitor that interferes with inflammatory signal transduction. Pollution-exposed mice treated with cerebral IKK2 demonstrated an attenuation of the insulin resistance shown in pollution-exposed mice not treated with IKK2; they also had evidence of inhibition of hepatic gluconeogenesis enzymes. Overall, this suggests that PM likely increases the production of these enzymes in part via inflammatory signaling through NF-kB ( 103 ). In a separate study, mice were fed a normal chow diet and exposed to PM 2.5 or filtered air. After five days of PM 2.5 exposure, there was chemical and histologic evidence of a heightened inflammatory response within the hypothalamus, with accompanying food-seeking, exercise-avoidant behavior changes, and adipose gain. After exposure to PM 2.5 for twelve weeks, the mice developed increased toll-like receptor 4 (TLR4) and Ikbke (related to NF-kB) expression, leptin and insulin resistance, and a worsening of their energy homeostasis and development of frank obesity. In this study, knockdown of TLR4 and Ikbke completely attenuated the effects of PM 2.5 exposure on leptin and insulin ( 104 ). Together, these suggest that hypothalamic inflammation could lie along a potential causal pathway between air pollution exposure and dysregulated glucose homeostasis.

4.1.1.2 Inflammation, vascular disease, and air pollution

In addition to the hypothalamic inflammation, air pollution has been shown to exacerbate inflammation systemically. In airway epithelial cells and macrophages, O 3 exposure has been shown to induce the production of inflammatory cytokines, interleukin-6 (IL-6), and interleukin-8 (IL-8) ( 105 ). Animal experiments show that excess glucose and triglycerides cause inflamed adipose tissue to secrete adipokines, driving insulin resistance and pancreatic β cell exhaustion, thereby exacerbating nutrient excess and leading to further inflammation ( 106 ). In a C57BL/6 mouse model, male mice fed a high-fat diet were randomly assigned to PM 2.5 exposure or clean, filtered air. Compared to the control, clean air group, the mice in the exposed group developed elevated insulin resistance, increased visceral fat, and increased adipose inflammation ( 107 ). Furthermore, the exposed mice exhibited decreased vascular relaxation in response to insulin and acetylcholine, indicating insulin resistance ( 107 ). In mice, co-exposure to SO 2 , NO 2 , and PM 2.5 increased circulating levels of the inflammatory molecules TNF-α, IL-6, and cyclooxygenase-2, while also dose-dependently increasing endothelin-1 and decreasing endothelial nitric oxide synthase, which reflect impaired endothelial function ( 108 ). This finding is consistent with prior animal research that implicates inflammatory cytokines in the impairment of vascular tone ( 109 , 110 ).

Air pollution exposure may also potentiate atherosclerosis progression. PM 2.5 exposure in mouse models accelerates atherosclerosis and increases inflammation compared to filtered air, with stronger effects in mice on a high-fat diet ( 111 , 112 ). Notably, inflammasome activation plays a central role in this process ( 113 ). These results demonstrate the indirect effects of air pollution on vascular function. Interestingly, inhaled nanoparticles in rats accumulate in areas of vascular inflammation, including atherosclerotic plaques, suggesting the direct effects of PM exposure on vascular tissue may also be relevant. Together, these studies support that particulate matter pollution can accumulate in, and worsen the inflammation of, adipose and vascular tissue, potentially worsening already impaired vascular and endothelial function ( 114 ). Overall, animal experiments to date suggest that air pollution exposure may independently promote the development and worsening of both diabetes and atherosclerosis with a central role for inflammation in each of these processes.

4.1.2 Oxidative stress

Separate from inflammation, air pollution exposure induces oxidative stress, which refers to the state of imbalance in reactive oxygen species (ROS) and antioxidant mechanisms such that ROS may induce damage to cellular structures or other biomolecules of importance ( 115 ). PM 2.5 and PM 0.1 were shown to accumulate in mitochondria, causing damage to the mitochondria and possibly potentiating the effect of ROS ( 37 ). Transition metals, present in PM 2.5 and PM 0.1 , generate ROS at the particle surface, causing oxidative stress and mitochondrial damage ( 37 ). Furthermore, polycyclic aromatic hydrocarbons, quinones, and peroxyacetyl nitrate found in the organic carbon fraction of PM are potent inducers of oxidative stress ( 116 , 117 ). Even O 3 , when dissolved in plasma, or serum, or saline, generates H 2 O 2 ( 118 ).

Multiple studies in vivo and in vitro have shown that oxidative stress drives many of the vascular complications of diabetes ( 119 ). In particular, hyperglycemia appears to promote mitochondrial generation of superoxide ( 120 ), while interfering with this production attenuates the damaging effects of hyperglycemia on the endothelium ( 121 ). ROS also produce nitrotyrosine, which has been shown to accumulate in necrotic and apoptotic cardiac myocytes from patients with diabetes and from a rat model of diabetes ( 122 ). Generation of superoxide by NADPH oxidase also appears to play a significant role in the micro- and macro-vascular complications of diabetes ( 123 ).

Oxidative stress is extensively implicated in the development of CVD ( 124 ) and has been shown in animal models to play a key role in mediating the cardiovascular effects of air pollution exposure ( 125 ). After 10 weeks of exposure to 14.1 μg/m 3 PM 2.5 and PM 0.1 , rats had increased superoxide concentrations in their aortas with signs of oxidative stress due to both impaired endothelial nitric oxide synthase and impaired hepatic synthetic function. In these rats, ROS generation from both PM 2.5 and PM 0.1 activated RhoA, a known mediator of vasoconstriction and acute hypertension. RhoA activation correlated with an increased mean arterial pressure of the rats exposed to the PM versus control ( 126 ). Compared to clean air controls, rats exposed to concentrated PM for 5 hours had twice the amount of oxidative stress in cardiac tissue ( 127 ).

4.1.3 Conclusions on mechanisms from animal studies

Numerous studies show increases in both inflammation and oxidative stress. The specific vascular impact of air pollution-related oxidative stress in animal models of diabetes has not been extensively studied. However, evaluating the evidence discussed in this section, it is reasonable to hypothesize that air pollution can exacerbate vascular complications of diabetes via increased oxidative stress. Air pollution-induced inflammation and diabetes may then synergistically exacerbate cardiac and vascular dysfunction, providing plausible causal explanations for the links between air pollution, diabetes, and cardiovascular disease in epidemiological studies.

4.2 Mechanisms in human studies

Human studies are limited in the ability to test exposures and outcomes ethically. Some experimental exposure studies in healthy volunteers have investigated the molecular mechanisms underpinning the adverse health effects associated with air pollution exposure. There have also been some epidemiological studies with molecular testing that is suggestive of mechanisms. While limited, these studies can confirm animal studies and validate these pathways in humans.

4.2.1 Inflammation in humans

4.2.1.1 inflammation and diabetes.

Inflammation appears to play a role in the pathogenesis of diabetes. Studies investigating this relationship have been outlined in Table 5 . Multiple nested case-control studies have investigated the role of inflammatory cytokines in the development of diabetes. In the Women’s Health Study, there was an increased risk of developing diabetes for those in the highest vs. lowest quartile of baseline IL-6 (RR: 2.3 [0.9, 5.6]) and CRP (RR: 4.2 [1.5 – 12.0]) ( 128 ). Similarly, in the EPIC-Potsdam study, IL-6 and CRP were not only significantly correlated with HbA1c (0.099, p = 0.019, and 0.1, p = 0.017, respectively), but also with odds of developing diabetes (OR: 2.57 [1.24 – 5.47] and 1.9 [1.2 – 3.2], respectively) in models adjusting for traditional diabetes risk factors and HbA1c ( 129 ). A nested case-control analysis within a prospective study of 3,842 Swiss adults without baseline diabetes followed for 5.5 years on average showed that diabetes risk increased with highest vs. lowest quartile of baseline IL-6 (OR 1.58 [1.02 – 2.45]) and CRP (OR 4.63 [2.85 – 7.53]) ( 130 ). Finally, the Cardiovascular Health Study in the United States showed that having baseline CRP in the highest quartile was associated with increased odds of developing diabetes (OR 2.03 [1.44 - 2.86]) versus the lowest quartile ( 131 ). Related to metabolism and inflammation, the Framingham Heart Study showed a positive correlation between exposure to PM 2.5 and SO 4 2- with adipokines adiponectin and resistin, respectively. 1

Table 5 Cited literature regarding the link between inflammation and diabetes in humans.

The hypothalamic inflammation linked with obesity and diabetes in mouse models recapitulates in humans with multiple magnetic resonance imaging (MRI) studies ( 100 , 132 – 135 ). However, while growing evidence implicates hypothalamic inflammation in abnormal glucose homeostasis in humans, we are aware of no human studies investigating this as a direct result of air pollution exposure. Such investigation, using quantitative MRI methods similar to the other studies in this section, may confirm the air pollution and hypothalamic inflammation link observed in animal studies.

4.2.1.2 Inflammation, atherosclerosis, and air pollution

There is a well-known association between inflammation and clinical atherosclerosis ( 136 , 137 ). Multiple recent reviews ( 138 – 142 ), as well as a meta-analysis ( 143 ), discuss the associations between inflammatory biomarkers and air pollution exposure. The most commonly studied inflammatory biomarkers include CRP, IL-6, and TNF-α, but others demonstrate associations with pollution exposure. Literature exploring air pollution promoting CVD via inflammation has been listed in Table 6 . Experimental exposure studies in healthy volunteers have shown increases in biomarkers of inflammation with controlled exposure to urban air pollution ( 147 , 148 ), but not wood smoke ( 149 ), indicating the importance of pollution composition. Gene expression studies show increased activation of anti-inflammatory pathways that support inflammation as a mediator for air pollution exposure-related adverse effects ( 150 ). A study of traffic-related air pollution exposure in adolescents and young adults with type 1 diabetes showed increases in IL-6 and CRP as well ( 151 ).

Table 6 Cited studies investigating the impact of air pollution driving CVD via inflammation.

Several recent studies have investigated the role of inflammation as a mediator of air pollution-linked CVD risk. Inflammation and CVD biomarkers were examined in a cross-sectional analysis of 6,103 participants with and without CVD in a Swiss cohort. A 5 μg/m 3 increase in annual mean PM 2.5 exposure was associated with increased ceruloplasmin (β = 0.1328 [0.0898, 0.1757]) alpha-1-antitrypsin (β = 0.105 [0.0564, 0.1537]), Lp-PLA 2 (β = 0.085 [0.0303, 0.1397]), neutrophil-leukocyte ratio (0.074 [0.0054, 0.14]), C3 (β = 0.1618 [0.1302, 0.2071]), haptoglobin (β = 0.0981 [0.0075, 0.1886]), and orosomucoid (β = 0.205 [0.1505, 0.2595]) ( 144 ).

Increased exposure to metals from measured PM 2.5 is associated with increased inflammatory biomarker sCD36, which in turn has a significant mediating effect on the association of these metals with pulse pressure, providing further evidence that inflammation appears to occur upstream of CVD in the context of PM 2.5 exposure. No significant associations were found for total PM 2.5 , highlighting that the individual components of PM 2.5 likely have differential effects on inflammation and oxidative stress in humans, which should prompt further investigation ( 146 ).

Vascular inflammation is seen in subclinical atherosclerosis using PET/MRI hybrid imaging techniques ( 152 ), implying that inflammation has a role early in the development of the disease. Inhalable particles likely have direct local effects on arterial disease and indirect systemic effects. For example, when human volunteers at risk for stroke were exposed to inhalable gold nanoparticles, these particles were then detected in their diseased carotid arteries ( 153 ). Advanced imaging techniques have been implemented to investigate a direct link between PM, inflammation, arterial damage, and CVD outcomes in humans ( 145 ), supporting likely direct vascular inflammatory effects of air pollution exposure. These studies suggest air pollutants may act via both systemic and direct vascular inflammatory actions to promote CVD, corroborating the epidemiological studies and animal models. Additional research may more fully elucidate which specific components of pollutants may act on which pathways to identify potential targets for intervention.

4.2.2 Oxidative stress in humans

The effect of air pollution on oxidative stress, demonstrated robustly in animal models, has had inconsistent evidence in humans, likely because measuring oxidized DNA and lipids in humans can be a technological challenge ( 154 , 155 ). However, a meta-analysis of studies examining oxidized DNA and lipids in subjects exposed to air pollution that had minimal measurement error demonstrated a consistent association between PM 2.5 and these measures of oxidative stress ( 156 ).

In particular, a study in healthy adults and adults with diabetes found that inducing labile blood glucose via clamp resulted in elevations of the markers of oxidative stress plasma 3-nitrotyrosine and PGF2α, a decrease in NO synthesis, as well as impaired endothelial function in the presence of vasodilating agents ( 157 ). Furthermore, peroxynitrite, generated by the reaction of superoxide and endothelial NO, has been detected at elevated concentrations ( 158 ) and shown to induce platelet damage in the blood of patients with diabetes ( 159 ).

4.2.3 Inflammation and oxidative stress promote endothelial dysfunction

Endothelial dysfunction mainly refers to the impairment of endothelium-mediated relaxation of vascular tone. Human studies have consistently reported an association between traditional cardiovascular risk factors and endothelial dysfunction ( 160 ). Furthermore, endothelial dysfunction can predict progression and long-term outcomes of coronary heart disease ( 161 ).

Inflammation is known to promote endothelial dysfunction in humans ( 110 , 162 ) in a variety of disease states, including obesity and diabetes ( 163 ). This link between inflammation and endothelial dysfunction appears to be present in the context of air pollution exposure. In a study of healthy volunteers with experimental exposure to PM 2.5 , higher TNF-α just after air pollution exposure was associated with poorer endothelial function a day later, suggesting that endothelial dysfunction seen with air pollution may be mediated by an inflammatory cascade that begins acutely during and after exposure, even in people that are free of diagnosed disease ( 164 ). Research in animals has demonstrated that the production of ROS by inflammatory cytokines, as well as the formation of advanced glycation end products (AGEs), alter the availability of endothelial NO, leading to endothelial dysfunction ( 110 ). ROS occurs in hypertension, hyperlipidemia, and diabetes, providing a likely explanation for the link between these traditional risk factors and endothelial dysfunction ( 165 ). The mechanistic inclusion of AGEs in this pathway is of special interest, as diabetic hyperglycemia promotes the endogenous production of AGE by irreversibly glycating tissue proteins and lipids ( 166 ).

Recently, endothelial dysfunction of arterioles and microvessels was demonstrated to predict the development and progression of diabetes in a German prospective cohort study of 15,000 adults without baseline diabetes or prediabetes ( 167 ). Mechanistically, microvascular endothelial dysfunction may impair insulin action in skeletal muscle and favor blood flow to nonnutritive tissues, thereby promoting hyperglycemia ( 168 ). Moreover, experiments in animal models have demonstrated that microvascular disease in pancreatic ß cells may drive the pathogenesis of diabetes ( 169 , 170 ).

Lastly, accumulating evidence has demonstrated a link between air pollution and endothelial dysfunction mediated by inflammation and oxidative stress ( 171 ). Taken together, it appears that endothelial dysfunction may partly explain the air pollution-diabetes link while also providing a mechanism for the accelerated development of CVD in people with diabetes exposed to air pollution.

4.3 Conclusions from mechanistic data

Systemic inflammation and oxidative stress, converging at endothelial dysfunction, represent common mechanisms whereby air pollution induces glucose dysregulation and exacerbates CVD risk. Due to its role in the onset and progression of both diseases, air pollution represents a critical target in promoting the health of the public and individuals. The next section will explore interventions directed toward mitigating the effects of inflammation and oxidative stress.

5 An eye toward prevention

Despite inconsistencies in the observational literature, the overall balance of evidence across all the tiers of evidence quality supports a deleterious effect of short- and long-term air pollution exposure on metabolic health. Given that the global population will remain exposed to air pollution, for which a completely benign dose has yet to be established, efforts have been made to counter these adverse effects. This section will detail the research concerning such interventions, with close attention paid to barriers to effective implementation.

5.1 Interventions to reduce air pollution as driver of CVD risk in people with diabetes

There is a burgeoning body of literature demonstrating that the use of portable air cleaners (PAC), which are known to reduce PM 2.5 exposure, can reduce serum concentrations of CRP, IL-6, and TNFα. An overview of this research can be found in a recent systematic review and meta-analysis written by our group ( 172 ). PACs may also reduce blood pressure ( 173 , 174 ). Given the importance of inflammation in CVD progression in diabetes, interventions to attenuate the upregulation of these pathways are appealing. However, most of the trials that intervened with PACs were conducted in healthy volunteers for short periods of time and in very controlled settings. Functioning similarly to PACs, a series of trials that used N95 respirators on participants in China demonstrated benefits on systolic blood pressure, HRV, and IL-1 ( 175 ).

The testing of interventions to ameliorate the adverse effects of air pollution exposure on glucose metabolism has been limited. A randomized, double-blind crossover trial in 55 healthy college students residing in Shanghai placed sham or real air purifiers in participants’ dormitories for 1 week, followed by a 17-day washout period, then 1 week of the alternate treatment. The investigators found that serum glucose, glucose-6-phosphate, insulin, and HOMA-IR were lower during the real air purification period compared to the sham period ( 59 ).

Diet patterns and nutritional supplementation with antioxidants or vitamins have been examined for their potential to protect against the adverse cardiometabolic effects of air pollution. Numerous studies have investigated the anti-inflammatory effects of dark chocolate ( 176 – 181 ). Supplementation with L-arginine has been shown to mitigate air pollution-related blood pressure increases among adults with hypertension ( 182 ), while in adults with diabetes, L-arginine was shown to improve glucose control, blood pressure, and forearm blood flow ( 183 ). Vitamin E has also been studied and shown in vitro to reduce inflammatory biomarker expression after PM2.5 exposure to endothelial cells ( 184 ) and to reduce oxidative stress in humans with occupational exposures to air pollutants ( 185 , 186 ). In a cross-sectional study of 47,000 adults, those in the highest quartile of compliance to the Dietary approaches to stop hypertension (DASH) diet had no significantly increased risk of PM 2.5 -associated hypertension. In contrast, the lowest quartile had significantly increased risk (OR: 1.20 [1.10, 1.30]) ( 187 ). While generally low-risk, none of these studies were adequate to conclusively recommend specific dietary interventions for protection against the adverse cardiometabolic effects of air pollution. However, they are suggestive of potential options that warrant further investigation.

5.2 Other interventions to reduce the cardiovascular harms of air pollution

Although the literature investigating interventions to reduce the cardiovascular harms of air pollution exposure in persons with diabetes may be limited, there are additional interventions that are low-risk and readily accessible. These other interventions may provide protection against air pollution exposure or mitigate its harms, despite a weaker evidence base compared to PACs.

First, observational evidence suggests that central air conditioning might mitigate the adverse cardiovascular effects of PM exposure ( 188 – 191 ), even though the filters commonly used in air conditioning systems are less efficient at removing airborne particulate matter compared to HEPA filters. Thus, people with prediabetes or diabetes could be encouraged to use central air conditioning, if accessible and affordable, instead of electric fans and especially instead of opening windows for indoor temperature regulation. The results of a few experimental studies also support the use of in-vehicle air conditioning to reduce air pollution exposure while driving ( 192 – 194 ).

Second, the use of cigarettes and other combustible tobacco products indoors generates smoke that reduces indoor air quality ( 195 , 196 ). Residue from cigarette smoke can adhere to indoor surfaces, creating thirdhand smoke that may continue to harm health after a smoking session has ended ( 197 ). Furthermore, although the health effects of electronic cigarettes are still under active investigation, electronic cigarette vapors contain some of the same pollutants as tobacco smoke ( 198 ) and therefore may also worsen air quality when used indoors. Tobacco product and electronic cigarette cessation should be strongly encouraged in all people, especially those with diabetes. However, if a person with diabetes is unable to quit, they should be counseled to avoid smoking indoors.

Other practical advice includes limiting outdoor activities during periods of poor air quality. The Air Quality Index, an easily understandable scale that describes outdoor air quality ( 199 ), is available on the internet for many cities around the world, especially in North America, East Asia, and Europe. People with diabetes can be advised to regularly check the air quality index (AQI) for their location and adjust activity accordingly. Keeping windows closed can also mitigate exposure to poor outdoor air quality, as can the avoidance of walking beside roads with heavy traffic.

5.3 Policy implications and public health initiatives for prevention

Given the worldwide contributions of traffic, industry, and biomass burning to the generation of ambient PM, policies that address these sources would reduce the ambient air pollution in urban environments ( 91 ). Furthermore, policies aimed at improving capture of industrial air pollution, developing more efficient industrial and agricultural systems, promoting electrification of motor vehicles, decreasing meat consumption, and reducing carbon emissions have been identified as feasible ways to improve global air quality within the next few decades if sufficient political will is generated ( 200 ).

Regular use of screening tests such as HbA1c and fasting plasma glucose alone do not appear sufficient to identify all people at risk for diabetes and its complications ( 201 ), therefore, diabetes prevention would likely benefit from population-level interventions. Cross-sectional evidence suggests that policies and public health initiatives that aim to improve the walkability of urban spaces and access to green space should be pursued ( 202 ). Although enhancing access to healthy food might theoretically reduce the population risk for diabetes ( 203 ), the evidence supporting such an initiative is limited due to relatively few studies and heterogenous measures of the food environment ( 202 , 204 ).

5.4 Prevention conclusions

Overall, there is a dearth of data on individual-level interventions to prevent PM-related CVD in people with diabetes. PACs have the most robust experimental evidence to support their use to lower blood pressure, reduce inflammation, and potentially improve glucose control. However, whether PACs can reduce the macrovascular or microvascular complications of diabetes is unknown. Efforts are ongoing to regulate pollutant concentrations on a societal level, but more research is needed to identify susceptible subgroups and effective interventions for them. Avoiding traffic exposure, closing windows, and using air conditioning at home and in vehicles are commonsense actions unsuited for a clinical trial. Thus, data on these preventive strategies are limited ( 205 ). However, that should not preclude recommending these low-risk interventions, particularly for those at increased risk.

6 Summary of key points

● Air pollution exposure, especially fine particulate matter, is known to increase the risk of incident CVD and worsened CVD outcomes. There is no known safe dose of air pollution.

● Air pollution exposure increases the risk of incident diabetes and prediabetes in diverse populations and perturbs glucose homeostasis.*

● Prediabetes and diabetes confer an increased susceptibility to the cardiovascular harms of air pollution exposure.

● Air pollution exposure promotes local and systemic inflammation, which exacerbates atherosclerosis progression as well as endothelial dysfunction. In animal experiments, air pollution contributes to ROS formation and excess oxidative stress, as well as hypothalamic inflammation which may promote excess nutrient intake and resultant diabetes.

● Inflammation and oxidative stress are associated with dysregulated glucose metabolism in humans and animals. Hyperglycemia promotes further oxidative stress and inflammation, which may explain the progression from prediabetes to diabetes as well as the well-known increased CVD risk observed in people with diabetes.

● * Mechanistic evidence supports the role of inflammation, oxidative stress, and hyperglycemia in the development of endothelial dysfunction. Air pollution and diabetes are both associated with endothelial dysfunction, which has been shown to predict CVD outcomes and incident diabetes.

● The study of interventions in people with diabetes to reduce the CVD risk due to air pollution has been limited. Some evidence points to the potential usefulness of portable air cleaners. Suggestive evidence supports further research into the effects of certain dietary and nutritional supplement interventions.

7 Conclusion

The importance of minimizing the impact of air pollution on a global scale cannot be overstated. The impact of air pollution on driving both the development of diabetes and exacerbating CVD risk in patients with diabetes is a topic that needs more research to reach a complete understanding of the interactions and mechanisms at play. Although there is heightened awareness of the adverse health effects of air pollution, further study on preventive strategies in people across the spectrum of dysregulated glucose homeostasis is greatly needed. An improved understanding of the mechanisms by which air pollution, diabetes, and cardiovascular disease interact would hasten the development of interventions to minimize the risks of exposure and slow disease progression. Furthermore, insights from this would greatly benefit a range of parties, including individuals concerned about their risks, healthcare providers wanting to provide optimal care and recommendations, and governments aiming to promote public health.

Author contributions

LJB: Writing – original draft, Writing – review & editing, Visualization. SW: Writing – original draft, Writing – review & editing, Conceptualization, Visualization, Supervision. CL: Visualization, Writing – original draft. JA: Conceptualization, Writing – review & editing. JN: Conceptualization, Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work of the authors was supported by UL1TR001445 (LJB), Grant 2023-0214 from the Doris Duke Foundation (SW), NIDDK K08DK117064-04S1, NHLBI R01HL160891, NHLBI P01HL160470-1A (JA) and R01HL168597 (JN).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: air pollution, cardiovascular risk, environmental exposure, inflammation, oxidative stress, particulate matter, prevention

Citation: Bonanni LJ, Wittkopp S, Long C, Aleman JO and Newman JD (2024) A review of air pollution as a driver of cardiovascular disease risk across the diabetes spectrum. Front. Endocrinol. 15:1321323. doi: 10.3389/fendo.2024.1321323

Received: 13 October 2023; Accepted: 26 March 2024; Published: 11 April 2024.

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Copyright © 2024 Bonanni, Wittkopp, Long, Aleman and Newman. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sharine Wittkopp, [email protected]

† These authors have contributed equally to this work and share first authorship

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• Open access
• Published: 02 April 2023

The roles of dietary lipids and lipidomics in gut-brain axis in type 2 diabetes mellitus

• Duygu Ağagündüz   ORCID: orcid.org/0000-0003-0010-0012 1 ,
• Mehmet Arif Icer 2 ,
• Ozge Yesildemir 3 ,
• Tevfik Koçak 1 ,
• Emine Kocyigit 4 &
• Raffaele Capasso 5  

Journal of Translational Medicine volume  21 , Article number:  240 ( 2023 ) Cite this article

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Type 2 diabetes mellitus (T2DM), one of the main types of Noncommunicable diseases (NCDs), is a systemic inflammatory disease characterized by dysfunctional pancreatic β-cells and/or peripheral insulin resistance, resulting in impaired glucose and lipid metabolism. Genetic, metabolic, multiple lifestyle, and sociodemographic factors are known as related to high T2DM risk. Dietary lipids and lipid metabolism are significant metabolic modulators in T2DM and T2DM-related complications. Besides, accumulated evidence suggests that altered gut microbiota which plays an important role in the metabolic health of the host contributes significantly to T2DM involving impaired or improved glucose and lipid metabolism. At this point, dietary lipids may affect host physiology and health via interaction with the gut microbiota. Besides, increasing evidence in the literature suggests that lipidomics as novel parameters detected with holistic analytical techniques have important roles in the pathogenesis and progression of T2DM, through various mechanisms of action including gut-brain axis modulation. A better understanding of the roles of some nutrients and lipidomics in T2DM through gut microbiota interactions will help develop new strategies for the prevention and treatment of T2DM. However, this issue has not yet been entirely discussed in the literature. The present review provides up-to-date knowledge on the roles of dietary lipids and lipidomics in gut-brain axis in T2DM and some nutritional strategies in T2DM considering lipids- lipidomics and gut microbiota interactions are given.

Type 2 diabetes mellitus (T2DM) is an endocrine metabolic disorder characterized by dysfunctional pancreatic β-cells and peripheral insulin resistance, resulting in abnormalities of glucose metabolism, dyslipidemia, and chronic inflammation [ 1 ]. T2DM is commonly associated with poor blood glucose control, dyslipidemia, and obesity, and has become a global public health concern due to the rising prevalence and implications of the disease [ 2 , 3 ]. In 2021, about 537 million people will be diagnosed with diabetes (DM), and this number is estimated to increase to 643 million by 2030; 90–95 percent of DM diagnoses are T2DM [ 4 , 5 ].

Genetic, metabolic, lifestyle, and dietary patterns are among the most influential determinants of T2DM [ 6 ]. Although ethnicity and family history/genetic predisposition have a substantial genetic basis for developing T2DM, epidemiological studies indicate that T2DM can be prevented by improving modifiable risk factors (obesity, low physical activity, and unhealthy diet) [ 7 , 8 ]. In genome-wide association studies, the variants in zinc finger gene 1 (JAZF1), insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2), transcription factor 7 like 2 ( TCF7L2), melanocortin 4 receptor (MC4R), cell division cycle 123 (CDC123), potassium voltage-gated channel subfamily Q member 1 (KCNQ1), insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2), solute carrier family 16 member 11 (SLC16A11), and PHD finger protein 2 (PHF2) have all been previously were involved in lipid metabolism and were associated with T2DM in adults [ 9 , 10 ].

Dyslipidemia, caused by elevated plasma triglyceride (TG), increased low density lipoprotein cholesterol (LDL), and reduced high dense lipoprotein cholesterol (HDL), is typically observed in T2DM [ 11 ]. Mitochondrial dysfunction, ER stress, inflammation, abnormal fatty acid regulation, and β-oxidation may all be involved in dyslipidemia [ 12 , 13 ]. Excessive dietary intake of free fatty acids (FFA) in patients with T2DM induces the synthesis of phospholipids, glycerolipids, and sphingolipids, hence exacerbating insulin resistance and resulting in lipotoxicity [ 14 , 15 ]. Currently, lipidomics will help in the understanding of changes in lipid metabolism and elucidate the metabolic pathways underlying the relationship between diet, dyslipidemia, and T2DM.

Lipidomics is a branch of metabolomics that identifies and quantifies the lipids produced by cells in response to pathogenic stimuli [ 16 ]. The purpose of lipidomics research is to identify and quantitatively determine the spectrum of intact lipid molecules found in cells and biological fluids, as well as to relate their composition to genetics, proteomics, nutrition, and disease [ 16 , 17 ]. Recently, increased lipidomic research has been conducted to identify biomarkers for and explain the cellular pathogenic processes of lipid metabolism abnormalities, which play a role in the pathogenesis of several diseases including T2DM, cancer, Alzheimer's disease, cardiovascular disease, nonalcoholic fatty liver disease, and obesity [ 18 , 19 , 20 , 21 , 22 ]. Since the 2000s, lipidomics has been used to identify potential predictive and diagnostic parameters of T2DM [ 23 , 24 ].

Population-based studies have indicated that phospholipids containing glycine, lysophosphatidylcholine acyl, acetylcarnitine, α-hydroxybutyrate, and choline are precursors of glucose tolerance abnormalities. Due to the lack of information on plasma lipidome changes during the shift from prediabetes to T2DM, the plasma lipid that may be used to diagnose T2DM has not been precisely determined. These molecules can influence the interaction along the gut-brain axis, hence contributing to the pathogenesis of T2DM. Furthermore, it has been shown that some lipids such as triglycerides and sphingolipids modulate insulin resistance and T2DM. [ 25 , 26 , 27 , 28 ]. The current review summarizes the effect of dietary fats and lipidomics on the gut-brain axis, the role of lipidomics, and possible mechanisms of action in T2DM.

Dietary lipids

Dietary fats are divided into three subgroups: unsaturated FA, saturated fatty acids (SFA), and trans fatty acids (TFA). Unsaturated FA consists of monounsaturated fatty acids (MUFA) and polyunsaturated fatty acids (PUFA) [ 29 ]. Although the fatty acid composition of the diet plays a significant role in enhancing insulin sensitivity and reducing T2DM and T2DM-related complications, the underlying mechanisms remain unclear. In terms of dietary fat, the Food and Agriculture Organization of the United Nations (FAO) concluded in 2010 that a high dietary intake of SFA is a "possible" risk factor for T2DM, while a high intake of TFA is also a "possible" risk factor for T2DM. It has been claimed that PUFA intake was a "possible" positive effect on T2DM, whereas the available evidence for MUFA was insufficient [ 30 ]. The widespread view is that the fatty acid composition of the diet might alter cell membrane function [ 31 ]. The fatty acid content of the cell membrane regulates several physiological systems, including membrane fluidity, cellular activities, ion permeability, insulin receptor affinity, translocation of glucose transporters interacting with second messengers, and membrane fluidity. All of these alterations can alter the tissue and organ insulin sensitivity [ 32 ].

The literature has shown the processes through which FAs exert a direct regulatory influence on gene expression and enzyme function [ 33 ]. In vitro , while PUFA (arachidonic acid (AA) > eicosapentaenoic acid (EPA) > docosahexaenoic acid (DHA) > linoleic acid (LA)) activates nuclear receptors, including peroxisome proliferator-activated receptor (PPAR), SFA and MUFA have only a minimal influence on lipogenic gene expression [ 34 , 35 ]. Furthermore, SFA alters glucose metabolism by regulating inflammatory gene expression, transcription factor activity, and enzyme activity [ 36 ]. LA prevents DM through its anti-inflammatory properties. Omega 3 and omega 6 FAs rich in diet can increase insulin sensitivity by suppressing hepatic lipogenesis and promoting FAs oxidation [ 37 ].

Many foods contain SFA, but only in low amounts; those of animal origin, including milk, butter, cheese, and meat, are the main sources of SFA in the typical diet. Exceptions include fats derived from tropical plants, which are usually low in SFA but high in the palm and coconut oil. Lauric acid (C12:0), myristic acid (C14:0), palmitic acid, and stearic acid (C18:0) are the majority of dietary SFAs [ 38 ]. Palmitic acid and foods derived from animals induce inflammation, oxidative stress, and inflict irreversible harm to cardiometabolic health by interfering with nitric oxide and insulin signaling [ 39 , 40 ].

A high dietary intake of SFA adversely impacts glucose and lipid metabolism by increasing hyperglycemia, hyperinsulinemia, and insulin resistance in metabolic organs such as the liver, pancreas, adipose tissue, and kidney. A diet rich in SFAs decreases the number of large islets in the pancreas, resulting in a more intense insulin response to a glucose load. The structure and function of the islets are altered, resulting in glucose sensitivity and T2DM [ 41 , 42 ]. It impacts lipid homeostasis, differentiation of adipocytes, fat cell volume and number, and promotes weight gain by increasing the white adipose tissue (WAT) in adipose tissue. All of these alterations accelerate the development of inflammation and leukocyte infiltration in adipose tissue. The accumulation of hepatic triacylglycerol, glucose intolerance, elevated blood sugar, and elevated insulin levels are all consequences of this condition. Increased blood lipid concentrations result in insulin resistance in peripheral tissues, impaired glucose absorption and usage and lipid metabolism, blood circulation and lipid aggregation in various tissues, and metabolic signaling pathways that regulate insulin secretion in pancreatic β-cells. Maintaining the homeostasis of the organism requires tight regulation of glucose and lipid catabolism [ 43 , 44 , 45 ].

Epidemiological, case–control, and prospective cohort studies have demonstrated that an increase in dietary SFA consumption is associated with the development of T2DM [ 46 , 47 , 48 ]. Short-term intervention studies indicate that a high SFA diet raises insulin, fasting blood glucose, and Hba1c levels relative to a high MUFA or PUFA diet [ 49 , 50 ]. However, there are other research suggests that there is no relationship between SFA intake and the onset of T2DM [ 51 , 52 , 53 ]. It is considered that dietary SFAs influence glucose metabolism and T2DM by inducing insulin resistance.

Trans fatty acids (TFA) are found in ruminant milk and meats, as well as in partially hydrogenated vegetable oils [ 54 ]. Some of the metabolic impacts of TFA include an increase in obesity, and insulin resistance, inflammation, and a reduction in endothelial function. TG and lipoprotein (a) levels are increased, whereas total cholesterol (TC):HDL ratio, apoB: apoA ratio, and TC: HDL ratio are all decreased by TFAs [ 55 , 56 ]. TFA inhibits the antilipolytic impact of insulin in adipose tissue and insulin-mediated glucose transport in animal models. Resistin reduces insulin sensitivity by increasing messenger RNA (mRNA) expression and decreasing PPAR and lipoprotein lipase expression [ 57 , 58 ].

Olive oil, canola oil, and some plant oils are among the richest sources of MUFA in the diet, whereas red meat, milk, and dairy products also contain MUFA. Olive oil, which is rich in oleic acid (C18:1n-9), and erucic acid (C22:1n-9) are the two most prevalent dietary sources of MUFA in regions where rapeseed oil consumption is prominent [ 38 ]. Oleic acid enhances FA oxidation by protein kinase A (PKA)-mediated deacetylation of the Sirtuin1 (SIRT1)-peroxisome proliferator-activated receptor co-activator-1α (PGC1α) complex. Oleic acid exerts an anti-inflammatory effect by participating in several metabolic pathways. Upregulation of M2 expression, the elevation of adiponectin level, the diminution of phosphate and tensin homolog, downregulation of protein phosphatase 2A, and induction of macrophage polarization are all effects of E-selectin, soluble intercellular adhesion molecule-1 (ICAM-1), interleukin (IL)-6, and tumor necrosis factor alpha (TNF-α). Additionally, oleic acid regulates insulin resistance and T2DM by lowering glucolipotoxicity, oxidative stress, and enhancing β-cell function and endothelial and hypothalamus function [ 45 , 59 , 60 ]. A diet that is rich in MUFA increases insulin sensitivity by lowering the glycemic load and insulin requirement. Through an increase in the number of hepatic LDL receptors, it accelerates the turnover of LDL cholesterol. Many beneficial compounds, including phenolic compounds, phytochemicals, and fat-soluble vitamins, are concentrated in foods high in MUFA [ 61 , 62 , 63 ].

LA and alpha-linoleic acid (ALA) are the only types of necessary FAs that may be obtained from food due to the absence of desaturase enzymes in the human body [ 64 ]. Both LA, which is the precursor of omega 6 FAs, and ALA, which is the precursor of omega 3 FAs, are converted to other PUFAs through the addition of double bonds and acyl chains by the enzymes desaturase and elongases, respectively [ 65 , 66 ]. Omega 3 and omega 6 are both types of FAs that are found in PUFA. Omega 3 FAs consist of long-chain EPA and DHA, both of which are often found in fish, and ALA, which is present in some plant oils such as flaxseed, rapeseed, and canola oil. Canola oil, soybean oil, corn oil, and sunflower oil are just a few of the various plant oils rich in omega 6, particularly LA and dihomo γ LA [ 67 ].

Gene expression, the metabolism of prostaglandin and leukotriene, and the synthesis of interleukin (IL)-1 are all significantly impacted by the ratio of omega 6 to omega 3 FAs that exist in the organism under physiological conditions. Omega 3 and omega 6 FAs are in constant competition for desaturation enzymes. In enzymatic processes, fatty acid desaturases 1 (FADS1) and 2 (FADS2) prefer ALA to LA.

A disruption in the ratio of omega 6 and omega 3 initiates a prothrombotic and proinflammatory process that favors omega 6 in the organism and leads to the development of atherosclerosis, obesity, and DM [ 68 , 69 ]. Long-chain PUFAs improve adipocyte membrane fluidity, GLUT4 RNA and protein levels, hence increasing the number of insulin receptors. Insulin stimulates the activities of desaturases 5 and 6. This increases the amount of insulin receptors on the cell membrane and the affinity of insulin for its receptor, so enhancing the effects of insulin on the organism. However, in the presence of an excess of omega 6 and a deficiency of omega 3, external stimuli release AA from the cell membrane and the formation of proinflammatory mediators [ 70 ]. It has been reported that the ratio of omega 6 to omega 3 in the diet should be 1:1 or 2:1 for optimal health [ 71 ].

Omega 3 FAs improve insulin sensitivity by reducing ER stress in mitochondria and enhancing the β-oxidation of FAs, thus decreasing the accumulation of lipids and reactive oxygen species (ROS) [ 59 ]. Additionally, omega-3 FAs have a beneficial impact on mitofusin 2, a protein implicated in mitochondrial dynamics, homeostasis, and the preservation of the membrane integrity of mitochondria [ 72 ]. EPA and DHA modulate insulin sensitivity via Akt phosphorylation, AMP-activated protein kinase, and activating PPARγ [ 73 ]. Also, omega-3 FAs regulate pancreatic β-cell insulin secretion by acting on the function and structure of lipid rafts and indirectly reducing the development of proinflammatory mediators in adipose tissue and increasing adipokine synthesis. Omega 3 FAs inhibit inflammatory cytokines, induce adipose tissue to produce adipokines, and increase insulin secretion by directly influencing β-cell activity by binding to PPARs, G protein-coupled receptor 40 (GPR40), and GPR120. PUFA binds to GPR120 in adipose tissue, resulting in increased GLUT4 translocation and glucose uptake in adipose tissue [ 74 , 75 ].

In randomized controlled trials examining the impact of omega 3 FAs on glycemic control [EPA (2 g/day, 95% pure EPA), fish oil per day (2 g/day EPA + DHA), omega 3 FAs (1.6 g/day EPA and 0.8 g/day DHA)], fasting plasma glucose, HbA1c, and HOMA-IR were reported to be reduced [ 76 , 77 , 78 ]. Prospective cohort studies have shown that consuming lean fish reduces the incidence of T2DM. It has been observed that consuming lean seafood and fish improves insulin sensitivity and lowers insulin resistance in individuals with insulin resistance [ 79 , 80 , 81 , 82 ]. Studies have revealed that EPA and DHA play a significant part in lowering the risk of lipotoxicity and preserving insulin sensitivity [ 83 , 84 , 85 ]. In several investigations, there was no significant correlation between omega 3 FAs and glycemic indicators such as fasting plasma glucose, insulin, and HbA1c [ 86 , 87 , 88 ].

There was not enough evidence on the relationship between dietary omega 6 intake, desaturase enzymes, and the incidence of T2DM [ 67 ]. In dietary intervention and prospective cohort studies using omega 6 biomarkers/food consumption frequency forms, however, an inverse association was observed between LA and T2DM prevalence [ 40 , 89 ]. It has been determined that whereas high levels of γ-LA and Di-homo-γ-LA increase the risk of T2DM, high levels of LA diminish this risk. According to research findings, omega 6 may be an indicator of hyperinsulinemia rather than a risk or protective factor for T2DM [ 90 , 91 ]. In intervention studies that replaced dietary SFA with MUFA, one research revealed that insulin sensitivity was enhanced [ 92 ], Other studies have shown that fasting insulin and insulin sensitivity are unchanged [ 93 , 94 ]. Table 1 shows the classification and nutritional importance of dietary lipids in T2DM. To sum up, it was shown that hepatic fat deposition was reduced and insulin sensitivity increased in interventions involving the consumption of omega 6 instead of dietary SFA [ 95 , 96 ].

Molecular regulations, and homeostasis of dietary lipids in T2DM

T2DM as a systemic disease as is characterized by hyperinsulinemia, insulin resistance, and relative insulin deficiency [ 6 ]. The pathogenesis of diabetic complications involves genetic and epigenetic changes, dietary factors, and a sedentary lifestyle [ 97 , 98 ].

The metabolism of proteins, lipids, and glucose are all significantly regulated by insulin. It participates in the regulation of glucose uptake in muscle, adipose tissue lipolysis, and muscle proteolysis, as well as the metabolism of hepatic glucose and triglycerides [ 99 ]. The regulation of lipid metabolism is impacted by increased oxidative and ER stress, hyperglycemia, lipidemia, insulin resistance, and impaired pancreatic beta-cell function in T2DM [ 100 ]. Lipid metabolism includes both the biosynthesis and breakdown of lipids like cholesterol, triglycerides, and FAs. Specialized lipoproteins carry lipids from the intestine to the liver (where most lipid conversion occurs) and from the liver to peripheral tissues [ 101 ]. Dyslipidemia, T2DM, and obesity are all linked to abnormalities in lipid metabolism, which frequently result in metabolic complications like insulin resistance, DM, ectopic lipid accumulation, non-alcoholic fatty liver disease, and atherosclerosis [ 102 ]. The regulation of lipid metabolism is greatly influenced by nutrients, particularly sugars and FAs [ 103 ]. Triglycerides account for 90.0% of all dietary lipids (TG). Phospholipids are also present in cholesterol and cholesterol esters in the diet, in addition to triglycerides [ 104 ]. The lingual lipase enzyme and gastric lipase enzymes in the mouth and stomach emulsify the digestion of dietary lipids in adults, but digestion hardly ever occurs. Chyme, a form of lipid, enters the small intestine [ 105 , 106 ]. The mixture, known as Chyme, causes intestinal cells to secrete the hormone secretin. Secretin facilitates the breakdown of lipids by encouraging the pancreas to secrete bicarbonate, pancreatic lipase, cholesterol esterase, and phospholipase A2. Additionally, cholecystokinin increases bile secretion by promoting gallbladder contraction. Lipids are emulsified in the duodenum with bile salts released from the gallbladder, increasing the surface area of the lipid droplet and improving the efficiency of digestive enzymes [ 107 ]. Non-esterified fatty acids (NEFA), CD36, and Niemann-Pick C1-like 1 protein (NPC1L1) for cholesterol are three specific transporters that enable enterocytes to absorb dietary lipids. Triacylglycerols (triglycerides), cholesteryl esters, and other lipids (phospholipids and trace amounts of unesterified cholesterol) come together in enterocytes to form chylomicrons, which are then combined with apolipoprotein (Apo)B-48 (also ApoA-IV and ApoA-I) [ 108 ]. Liquid triglycerides make up 90% of the chylomicron mass. These chylomicrons travel from the intestinal mucosa to the lymphatic system via exocytosis, and the large chylomicrons travel from the blood to the liver and adipose tissue [ 109 ]. In particular, lipoprotein lipase (LPL) mediates intravascular lipolysis of triglyceride-rich lipoproteins (TGs in chylomicrons are broken down into FFA and glycerol) and forms chylomicron remnants, which are important for lipid homeostasis and chylomicron clearance [ 110 ]. Insulin increases the amount of LPL mRNA, which stimulates LPL. As a result, they control apoC-II and LPL activities in concert through both transcriptional and post-translational mechanisms [ 111 ]. The liver cells' ApoE receptors can detect chylomicron remnants. Chylomicrons are more likely to bind to heparan sulfate proteoglycans (HSPGs) on hepatocyte surfaces and be taken up into the liver by LDL receptor-associated protein (LRP) or LDL-R when ApoE is present. In the process of removing lipoprotein residues, apoE is stored in heparan sulfate proteoglycans. In apoB-48, the ligand binding site is missing [ 112 ]. Insulin increases the uptake and clearance of chylomicron residues by inducing the translocation of lipoprotein receptor-related protein to the plasma membrane [ 113 ]. By increasing the expression and activity of LDL-receptors, it also encourages LDL clearance [ 114 ]. When the 125I-labeled activated α2M (α2M  ∗ ) pathway is activated by insulin secretion increases LRP and LDL-R's function by 2–3 times. Insulin increased the liver's LRP-specific uptake of chylomicron residues, according to a rat study. Postprandial lipoprotein metabolism may suffer if insulin-mediated signaling pathways are disrupted [ 115 ]. In HepG2 cells, insulin forms heterodimers with ER small subunit protein disulfide Isomerase to catalyze the transfer of lipid to nascent apoB, a rate-limiting step in the production of hepatic very low-density lipoprotein (VLDL). Additionally, insulin controls the hepatic microsomal triglyceride transfer protein (MTP) metabolism by separating the complex formed by the mitogen-activated protein kinase (MAPK) pathway, Foxa2, which is phosphorylated in response to insulin action, and PGC-1β [ 116 ]. In this study, it was determined that subjects who were insulin resistant had excessive VLDL production and unrestricted MTP expression [ 117 ].

The free cholesterol that HDL absorbs from tissues is esterified using lecithin cholesterol acyl transferase (LCAT). Free cholesterol is quickly esterified by LCAT after being absorbed by HDL. They serve as an Apo C and Apo E circulating store for HDL, VLDL, and chylomicrons. They use scavenger receptor B1 (SR-B1) receptors to remove and esterify free cholesterol from extra hepatic tissues and transport it to the liver [ 118 ]. Small size HDL (commonly known as HDL3) grows via ester transfer (usually called HDL2) [ 119 ]. Also, HDL demonstrates anti-inflammatory, antioxidant, anti-thrombotic, and anti-apoptotic properties [ 120 ]. Additionally, VLDL and LDL can exchange lipids via the cholesteryl ester transfer protein (CETP) provided by HDL. In this manner, triacylglycerols are reciprocally transferred from VLDL to HDL and cholesterol esters from HDL to VLDL [ 121 ]. The physiological control of HDL cholesterol metabolism depends on insulin. It directly affects the liver, encouraging the transition from HDL2 to HDL3 (through its action on hepatic lipase) [ 122 ].

Circulating carbohydrates are transformed into FAs through the intricate process known as de novo lipogenesis (DNL), which is then used to create triglycerides or other lipid molecules. The primary carbon source for the synthesis of FAs comes from the glucose metabolites created during glycolysis [ 123 ]. After consuming a large of carbohydrates, circulating glucose is absorbed by adipocytes via insulin-stimulated GLUT4, where it is converted to pyruvate by glycolysis in the cytosol and then transported to the mitochondria for further oxidation in the tricarboxylic acid cycle (TCA). In the cytosol, novo lipogenesis uses citrate, an intermediate of the TCA cycle, as a substrate. Adipocyte lipogenesis is significantly regulated at the transcriptional level by the protein carbohydrate response element-binding protein (ChREBP). In addition to activating ATP-citrate lyase (ACLY), acetyl-CoA carboxylases 1 (ACC1), fatty acid synthase (FASN), and stearoyl-CoA desaturase-1 (SCD1), insulin activates Max-like protein X (MLX) and ChREBP-, which in turn promote the expression of target genes that support the synthesis of FAs [ 123 ]. Disruptions in lipid and glucose metabolism and a decline in hepatic glycogen synthesis are brought on by problems with insulin secretion and insulin resistance. Ectopic lipid-induced muscle insulin resistance comes first, followed by liver insulin resistance, which directs ingested glucose to the liver, increasing hepatic de novo lipogenesis and hyperlipidemia [ 124 ]. The active role of insulin in normoglycemia and normolpidemia is presented in Fig.  1 .

The active role of insulin in normoglycemia and normolipidemia. An overview of Interaction between impared glucose and lipids metabolisim inT2DM. (1) increased chylomicron production, (2) reduced catabolism of both chylomicrons and VLDLs (diminished LPL activity), (3)increased VLDL production (mostly VLDL1), (4) reduced LDL turnover (5) increased production of large VLDL (VLDL1) preferentially taken up by macrophages; LDL (qualitative and kinetic abnormalities): (6) low plasma adiponectin favouring the increase in HDL catabolism. (7) increased number of glycated LDLs, small, dense LDLs (TAG-rich) and oxidised LDLs, which are preferentially taken up by macrophages; (8) increased CETP activity (increased transfer of triacylglycerols from TAG-rich lipoproteins to LDLs and HDLs), (9) increased TAG content of HDLs, promoting HL activity and HDL catabolism, (10) İmpaired glucose metabolisim. (11) İmpaired de novo lipid metabolisim (Acetyl CoA and NADPH inhibit pyruvate dehydrogenase as a result of B oxidation. The lactate and alalnin thus formed increase hyperglycemia because of gluconeogenesis (ketone bodies formation increases) in the liver.) CE cholesterol ester, CETP cholesteryl ester transfer protein, HDLn nascent HDL, HL hepatic lipase, HSL hormone-sensitive lipase, LPL lipoprotein lipase, SR-B1 scavenger receptor B1, TAG triacylglycerol, PP protein phosphatase, PK protein kinase, NEFA non-esterified fatty acids DNL : de novo lipogenesis, LCAT Lesitin-kolesterol acil transferaz, G3P gliserol 3-fosfat protein kinase

The metabolism of glucose and lipids are interconnected in numerous ways. Diabetic dyslipidemia, which is characterized by elevated triglycerides, low HDL-C, and a predominance of small-dense LDL particles, is the most significant clinical manifestation of this interaction [ 125 ]. Triglyceride-rich lipoproteins (TRLs) tend to accumulate more frequently in diabetic dyslipidemia [ 126 ]. The combination of a defect, excessive production of TRL-apoB-100, also known as very low-density lipoprotein (VLDL, primarily VLDL1) from the liver, and removal of TRL-apoB-48, also known as chylomicrons, from the gut results in diabetic dyslipidemia [ 127 ]. Phosphatidylinositol 4,5-biphosphate (PIP2) is converted to phosphatidylinositol 3,4,5-triphosphate (PIP3) when insulin binds to its receptor, which causes tyrosine phosphorylation, activation of PI3K, and other processes (PIP3). Serine/threonine kinase Akt is activated by PI3K activation, which also inhibits the insulin-mediated inhibition of phospholipase D1 and ARF-1, two components involved in the formation of VLDL1, and decreases the synthesis of ApoB [ 128 ]. Both TNF-receptor 1 (TNFR1) and TNF-receptor 2 (TNFR2), as well as the Src homology 2 domain containing RS-1, Akt S473, and T308, and the (Shc) adapter, are less phosphorylated and more insulin resistant because of TNFα induction [ 129 ]. Impaired insulin metabolism results in dyslipidemia, which is accompanied by increased apoB stability, MTP, and TNFα release [ 130 ]. LPL activity is increased by insulin and decreased by insulin resistance. Chylomicron levels are typically higher in T2DMs with insulin resistance because of both chylomicron overproduction and decreased catabolism. Additionally, through the activation of FOXO1, insulin resistance promotes the production of apoC3, an inhibitor of LPL. The decreased VLDL catabolism is impacted by rising apoC3 serum levels and their impact on LPL metabolism [ 131 ].

The treatment of disorders of lipid and insulin metabolism depends on maintaining the ideal dietary balance. Oleic acid consumption that is adequate and well-balanced lowers leukotriene B4 levels and boosts insulin sensitivity, improving insulin sensitivity [ 60 ]. LDL-R activity declines and LDL turnover deterioration because of disruption of insulin metabolism. A decline in LDL B/E receptors may be the cause of impaired LDL catabolism (LDL receptor capable of binding apoB and apoE) [ 132 ]. In a study, insulin plays a significant role in the expression of LDL-R in vivo and that, in patients with T2DM and poor metabolic control compared with non-diabetic patients, LDL-R expression decreases despite oral anti-diabetic therapy and returns to normal after 3 months of insulin therapy [ 133 ]. While macrophages are essential for preserving normal tissue homeostasis, they also play a significant role in the emergence of low-grade inflammation. Insulin resistance results from low-grade inflammation, and low-grade inflammation develops and progresses because of both insulin resistance and hyperinsulinemia [ 134 , 135 ]. The activation of macrophages with the IL-4 and interferon-γ (INFγ) signaling pathways is influenced by insulin-activated IRS-MAPK-PI3K and its modulating protein kinase B (PKB)/Akt kinase pathway [ 136 ]. IL-1, IL-2, IL-4, IL-5, IL-6, IL-12, IFN-γ, TNF-α, IL-10, and lipopolysaccharide are other proinflammatory cytokines that are linked to increased FoxO1 activity brought on by impaired insulin metabolism. It also influences how macrophages are stimulated [ 137 ]. There is various oxidation levels in LDL particles in people with T2DM, from minimally oxidized LDL (MM-LDL) to fully oxidized LDL (Ox-LDL). Due to its negative effects on -cells, an elevated Ox-LDL concentration is also linked, and this creates a vicious cycle, to an increased risk of DM. Initial oxidative changes in LDL lipids occur when apoB-100, also referred to as MM-LDL, is not present. LDL lipids undergo oxidation at a later stage, where they turn cytotoxic and proapoptotic [ 138 ]. Because of impaired insulin metabolism, induced regulation of glycated and oxidized LDLs (PI3K/PKB/PPARγ) results in the formation of lipid peroxidation products, cytokine release, ROS production, and inflammation [ 139 , 140 ]. With their hypolipidemic effect, omega 3 FAs in the diet control how VLDLs are formed in the liver and lower lipogenesis by releasing less TAG [ 141 ]. EPA and DHA acids, two long chain omega 3 PUFA, are beneficial for controlling lipid metabolism. apo B is degraded and fatty acid β-oxidation is increased, while TRL apoB-48 secretion and diacylglycerol acyltransferase, fatty acid synthase, and de novo lipogenesis are inhibited, increased, and acetyl CoA carboxylase is carboxylated, respectively, by omega 3 PUFAs [ 142 ]. A randomized controlled study found that supplementing with -3 PUFAs (4 g/day, 46% EPA and 38% DHA) significantly decreased the release of TRL apoB-48 [ 143 ]. Another randomized controlled trial found that in people with impaired glucose regulation, omega 3 FAs or their combination (2-g fish oil (1000-mg EPA + 400 mg DHA)) significantly reduced inflammation, reduced insulin resistance, and improved glucose and lipid metabolism [ 144 ]. Through the inhibition of DNL, an increase in fatty acid oxidation, and a reduction in ApoB synthesis, dietary omega 3 PUFAs prevent the secretion of VLDL [[ 145 ]. Additionally, omega 3 PUFAs reduces the production of the proinflammatory cytokines IL-1, IL-6, TNF-α, and TNF- β in response to an inflammatory stimulus, indirectly regulating lipid metabolism [ 146 ]. Because they bind to PPARs, GPR40, and GPR120, omega 3 PUFAs have a direct impact on -cell function and increase insulin secretion by inhibiting the production of inflammatory cytokines and eicosanoids and adipokines from adipose tissue [ 74 ]. According to the study, supplementing omega 3 PUFAs improves insulin sensitivity and glucose homeostasis regulation, thereby reducing the risk of developing T2DM [ 147 ]. White adipose tissue (WAT) inflammation is increased by high-fat diet models, which also decreases insulin sensitivity and activates Toll-like receptor 4 signaling [ 148 ]. A systematic review of the evidence from HFD interventions found that administering HDF for 2 days to 6 weeks to both lean individuals and those with a fat intake of 45–83% as well as overweight or obese individuals increased fatty acid oxidation and maintained impaired insulin sensitivity [ 149 ]. In a rat experiment, giving non-glandular Goto-Kakizaki rats HFD increased beta cell dysfunction [ 150 ].

Adipocytokines are signaling proteins that play key roles in the regulation of lipid and glucose metabolism, the neuroendocrine system, and the immune system, as well as energy homeostasis [ 151 ]. Adiponectin is also an anti-inflammatory adipokine that regulates the synthesis of FAs, glucose uptake, and fatty acid β-oxidation [ 152 ]. The expression of adiponectin, its induction by insulin, and its modulation of β-cell function form the basis of the association between adiponectin and insulin resistance/hyperinsulinemia [ 153 ]. According to studies, high TNF-α levels and hypoadiponectinemia both contribute to insulin resistance [ 154 ]. The study found that people with T2DM have significantly lower serum adiponectin concentrations, which can be a therapeutic parameter for treating people with T2DM [ 155 ]. Serum HDL concentrations and circulating adiponectin levels exhibit a strong positive correlation [ 156 ]. The major HDL ApoA-I and the ATP-binding cassette transporter A1 (ABCA1), which has the nuclear receptors liver X receptor and PPARγ, are produced more often in the liver when adiponectin is present. Additionally, the formation of large HDL particles (HDL2) from small HDL particles, which is adiponectin's mechanism for reducing HL activity, increases HDL-C (HDL3). In particular, triglyceride hydrolysis in VLDL particles may be increased by adiponectin via upregulation of LPL, reducing triglyceride transfer to HDL [ 157 ]. Adiponectin release is influenced by impaired insulin metabolism, which in turn affects lipid metabolism. The development of low HDL-C concentrations is influenced by insulin resistance through many mechanisms. First off, as IR's influence on CETP, which controls insulin, declines, HDL-C levels rise, possibly because of decreased apo A-I production and secretion from the liver and intestine and increased HL. The formation of TG-rich lipoprotein-derived HDL particles is reduced, and HL formation is increased, because of decreased LPL activity. This is because less TG is hydrolyzed from chylomicrons and VLDL [ 158 ]. The pathogenesis of T2DM in relation to glucose and lidipi metabolism may also be influenced by adipokines like chemerin, leptin, fetuin-A, retinal binding protein 4, vaspin apelin, nesfatin-1, and dipeptidyl peptidase-4 [ 159 ]. Impaired insulin metabolism in T2DM increases lipogenesis, which disrupts lipid metabolism by decreasing glycogen synthesis and glucose metabolism via the TCA cycle [ 160 ]. The effect of the regulation of impaired insulin on glucose and lipid metabolism is presented in Fig.  2 .

Interaction between impared glucose and lipids metabolism in T2DM. An overview of Interaction between impared glucose and lipids metabolisim inT2DM. (1) increased chylomicron production, (2) reduced catabolism of both chylomicrons and VLDLs (diminished LPL activity), (3)increased VLDL production (mostly VLDL1), (4) reduced LDL turnover (5) increased production of large VLDL (VLDL1) preferentially taken up by macrophages; LDL (qualitative and kinetic abnormalities): (6) low plasma adiponectin favouring the increase in HDL catabolism. (7) increased number of glycated LDLs, small, dense LDLs (TAG-rich) and oxidised LDLs, which are preferentially taken up by macrophages; (8) increased CETP activity (increased transfer of triacylglycerols from TAG-rich lipoproteins to LDLs and HDLs), (9) increased TAG content of HDLs, promoting HL activity and HDL catabolism, (10) İmpaired glucose metabolisim. (11) İmpaired de novo lipid metabolisim (Acetyl CoA and NADPH inhibit pyruvate dehydrogenase as a result of B oxidation. The lactate and alalnin thus formed increase hyperglycemia because of gluconeogenesis (ketone bodies formation increases) in the liver.) CE , cholesterol ester, CETP cholesteryl ester transfer protein, HDLn nascent HDL, HL hepatic lipase, HSL hormone-sensitive lipase, LPL lipoprotein lipase, SR-B1 scavenger receptor B1, TAG triacylglycerol, PP protein phosphatase, PK protein kinase, NEFA non-esterified fatty acids, DNL de novo lipogenesis, LCAT Lesitin-kolesterol açil transferaz, G3P gliserol 3-fosfat protein kinase

Lipidomics, or, more generally, metabolomics, is sensitive to various variables, including the host genotype, s gut microbiota, and diet [ 161 ]. The immune system and many other processes, including inflammation, incretin secretion, glucose homeostasis, production of short-chain fatty acids (SCFAs), and bile acid metabolism, are regulated by the intestinal microbiota. Intestinal barrier integrity, pancreatic β-cell proliferation, and short-chain fatty acid synthesis, which supports insulin biosynthesis, can all be negatively impacted by the dysbiosis of the gut microbiota, which can disrupt glucose homeostasis and lead to the onset of T2DM [ 162 , 163 ]. Different microorganisms can produce different products in metabolomics and lipidomics because each has unique properties [ 164 ]. The development of insulin resistance and T2DM may be significantly influenced by FAs. FAs' long-term impact on T2DM is not yet been fully understood, though. Although lipidomics is a relatively underutilized tool, it is used more frequently than genomics, transcriptomics, and proteomics to advance our understanding of obesity and T2DM. A subfield of metabolomics called lipidomics may help us better understand how FAs and lipids, particularly insulin resistance and T2DM, contribute to the emergence of health-related complications [ 165 ]. Research on T2DM-related gut flora disorders will advance with the integration of gut metabolomics and metagenomics [ 166 ].

Dietary lipids and lipidomics in T2DM via gut-brain axis

Although dietary lipids and lipidomics play an important role in the development of type T2DM, their effect mechanisms have not yet been fully elucidated. However, the gut-brain axis plays a role in glucose homeostasis [ 167 ]. The gut-brain axis is a bidirectional communication pathway. Signals from the brain communicate with the gut via the autonomic nervous system and the hypothalamic-pituitary axis to regulate many physiological processes. Signals from the gut to the brain are mediated by vagal and spinal afferent neurons [ 168 ]. The gut-brain axis contains numerous components, containing highly specialized cells responsible for transmitting the information. These are the enteroendocrine cells (EEC), central nervous system (CNS), enteric nervous system (ENS), vagus nerve, and gut microbiota. EECs are specialized trans-epithelial cells found throughout the gut [ 169 ]. The ENS, the nervous system of the gastrointestinal tract, consists of various types of neurons, including intrinsic primary afferent and motor neurons [ 170 ]. It contains 200 to 400 million neurons and enteric glial cells. It also extends throughout the gastrointestinal tract from the esophagus to the anus [ 169 ]. Glucose homeostasis is provided via the CNS and ENS in the gut-brain axis. Ghrelin and glucagon-like peptide-1 (GLP-1) have emerged as the key factors that can transmit metabolic information to the brain and stimulate endogenous glucose production and usage [ 171 ]. The vagus nerve transmits information related to food intake to the brain to regulate energy and glucose homeostasis [ 169 ]. Therefore, it is thought that changes in the gut-brain axis may cause T2DM [ 170 ].

Gut microbiota is also considered an important part of the gut-brain axis, so the microbiota-gut-brain axis is emerging as a prominent factor nowadays [ 169 ]. Gut microbiota is a collection of more than 100 trillion microorganisms (bacteria, fungi, protozoa, and viruses) and their genomes found in the gastrointestinal tract [ 172 ]. Intestinal microorganisms colonize the gastrointestinal tract and contribute to homeostatic balance under healthy physiological conditions [ 173 ]. While the host provides a nutrient-rich environment for microorganisms, microorganisms also affect host physiology, immunology, and metabolism [ 174 ]. Gut microbiota is the main mediator of the gut-brain axis in the regulation of glucose homeostasis. It contributes to glucose homeostasis by regulating the immune system, inflammatory response, modulation of incretin secretion, production of SCFA, and metabolism of bile acids [ 171 ]. Gut microbiota produces several metabolites that directly and indirectly affect the gut-brain axis. GLP-1 is an endocrine factor that may be involved in the control of the gut-brain axis by the gut microbiota [ 170 ]. Cani et al. (2006) demonstrated that the modulation of gut microbiota improves glucose metabolism via a GLP-1-dependent mechanism [ 175 ]. Gut microbiota can affect serotonin (5-HT) production by EECs, altering ENS vagal afferent activation. Bile acids can also alter the expression of Takeda-G-protein-receptor-5 (TGR5) and affect intestinal peptide release from EECs. SCFAs can alter nutrient receptor expression and intestinal peptide production by EECs or can directly activate vagal afferents. Lipopolysaccharides (LPS), a products of pathogenic microorganisms, can impair gut-brain signaling by preventing the activation of vagal afferents or the ENS [ 168 ].

Microbiota dysbiosis is the disruption of intestinal homeostasis by altering the composition of the gut microbiota [ 172 ]. A direct causal relationship between gut dysbiosis and the development of has not been identified. However, immunomodulatory mechanisms mediated by microbiota-derived lipids have been discovered. The most well-known is the release of LPS and decreased SCFA production, which have pro-inflammatory effects. Other suggested mechanisms include altered bile acid metabolism, altered secretion of incretin hormones such as GLP-1, altered circulating branched-chain amino acids, and impaired adipose tissue, liver, or skeletal muscle functions [ 163 , 176 ]. This results in increased colonic permeability, colon, liver, and adipose tissue inflammation, impaired insulin secretion, the occurrence of insulin resistance, impaired glucose and lipid metabolism, and the development of T2DM [ 163 ]. Microbiota dysbiosis in T2DM is characterized with decreased numbers of SCFA-producing gram-positive bacteria and increased numbers of LPS-producing gram-negative opportunistic pathogens [ 176 ]. In a recent study, T2DM patients exhibited a higher Firmicutes/Bacteroidetes ratio than healthy individuals. Bacterial diversity in the gut microbiota was reduced in patients with prediabetes or T2DM compared with healthy individuals [ 163 ]. In another study, it was observed that T2DM patients had a decreased concentration of Faecalibacterium prausnitzii . It has anti-inflammatory properties, promotes the proliferation and growth of epithelial cells and increases the synthesis of tight junction proteins. Ruminococcus bromii , which contributes to the production of SCFAs, was also reduced in T2DM patients [ 177 ]. A study showed that the colonization of germ-free (GF) mice with healthy gut microbiota caused the restoration of neuronal GLP-1 and ENS signaling pathways in the gut. However, it was indicated that this effect was eliminated by colonization with the diabetic gut microbiota [ 178 ]. Another study showed that transplantation of fecal microbiota from lean donors to individuals with metabolic syndrome caused improved insulin sensitivity in the recipients. This suggests that healthy gut microbiota can improve metabolic outcomes [ 179 ]. Furthermore, the improvement in glucose homeostasis is associated with GLP-1R signaling, indicating that prebiotic-induced changes in the microbiota restore the gut-brain axis [ 175 ]. Promoting the growth of beneficial bacteria using indigestible carbohydrates can improve glucose tolerance [ 180 ]. The gut-brain axis pathways related to T2DM was shown in Fig.  3 .

Gut-brain axis pathways related to T2DM. ENS enteric nervous system, SCFA short-chain fatty acid, LPS lipopolysaccharide

Dietary lipids in T2DM via gut-brain axis

Dietary lipids affect the gut microbiota composition, metabolic-end products, other enzymatic markers, and all microbiota-related diseases [ 181 ]. However, the mechanisms of the interplay between dietary lipids and gut microbiota in glucose homeostasis have not been well defined. Many dietary FAs are absorbed in the small intestine; however, some of them directly affect the colonic microbiota composition. It has been shown that gut microbiota can affect glucose and lipid metabolism and may even cause T2DM by disrupting the balance between pro-inflammatory and anti-inflammatory effects in the liver. A diet rich in saturated FAs can adversely affect the microbiota composition and reduce insulin sensitivity [ 182 ]. A high-fat diet increases the amount of Firmicutes and decreases the amount of Bacteroidetes , which may lead to the development of T2DM [ 183 ]. Conversely, a diet high in omega 3 FAs and MUFA may promote a favorable alteration of microbiota composition in T2DM patients [ 87 , 184 ]. Table 2 summarizes the role of dietary lipids on gut microbiota and T2DM.

High-fat diets cause decreased gut microbiota diversity, increased number of gram-negative bacteria, increased LPS translocation, increased intestinal permeability, systemic inflammation, and impaired immune system [ 184 ].

High-fat diets cause the loss of beneficial microorganisms and disrupt the symbiotic relationship between the gut microbiota and the host [ 185 ]. High-fat diets are associated with lower Bifidobacterium species and higher plasma LPS concentration. Likewise, it has been observed that the growth of Desulfovibrio bacteria, which are gram-negative, opportunistic pathogens and produce LPS, during high-fat feeding in mice [ 148 ]. LPS, also known as endotoxin, is a structural compound in the outer membrane of gram-negative bacteria [ 103 ]. LPS initiates low-grade inflammation by activating Toll-Like Receptor 4 (TLR-4), which is expressed in macrophages, hepatocytes, and adipocytes [ 172 ]. This mechanism includes several steps. In the first step, LPS binds to lipopolysaccharide-binding proteins (LBP) and interacts with a cluster-of-differentiation 14 (CD14), which is a glycosyl-phosphatidylinositol-anchored protein. In the second step, TLR-4 is activated, triggering a signaling cascade that ends with phosphorylation and activation of focal adhesion kinase (FAK) in enterocytes. In the third step, FAK increases intestinal permeability by regulating IL-1R-associated kinase 4 (IRAK4)-related myeloid differentiation primary response gene 88 (MyD88) activation. In the final step, LPS translocates into the systemic circulation and causes the release of interleukins. IL-6 causes a low-grade inflammation that affects insulin signaling and triggers insulin resistance. This step constitutes the onset of pancreatic β-cell dysfunction [ 177 ]. Additionally, LPS stimulates the innate immune signaling cascade by enhancing the expression of inducible nitric oxide synthase (iNOS), which is an effector of the innate immune system and synthesizes nitric oxide. iNOS disrupts insulin receptor substrate-1 (IRS-1) expression. Thus, this can result in hyperinsulinemia, insulin resistance, and T2DM [ 172 ].

A healthy intestinal epithelium acts as a barrier to preventing the migration of bacterial-derived factors and prevents LPS from entering the systemic circulation [ 103 ]. However, high-fat diets disrupt intestinal barrier function, allowing LPS translocation. This condition, called metabolic endotoxemia, can cause decreased pancreatic β-cell function, insulin resistance, and T2DM [ 184 ].

Animal studies have revealed that long-term high-fat feeding is associated with increased circulating LPS [ 186 , 187 ]. Cani et al. (2007, 2008) reported that endotoxins were approximately 1.5 times higher in lean and obese mice fed a high-fat diet (72% of total energy) than in mice fed a normal diet for four weeks [ 180 , 188 ]. In previous studies, mice fed a high-fat diet developed hyperglycemia, hyperinsulinemia, glucose intolerance, and insulin resistance, which are associated with decreased intestinal mucus thickness and increased intestinal permeability [ 180 , 189 , 190 ]. In an animal study, mice were fed a low-fat (10% of total energy) or high-fat (60% of total energy) diet for eight weeks. High-fat diets increased proinflammatory cytokines, plasma endotoxin levels, and Firmicutes/Bacteriodetes ratio, resulting in intestinal dysbiosis. Additionally, fasting blood glucose and insulin concentrations were higher in high-fat-fed mice than in low-fat-fed mice [ 191 ]. Richards et al. (2016) showed that GLP-1 release was reduced in mice fed a high-fat diet (60% of total energy) for two weeks. They also found that the expression of L-cell-specific genes decreased in mice fed a high-fat diet for sixteen weeks. This suggests a disruption in enteroendocrine cell function and gut-brain axis [ 169 ].

High-fat diets have been associated with increased LPS levels in humans [ 192 , 193 ]. Ghanim et al. (2009) showed that a high-fat and high-carbohydrate meal increased the LPS levels and TLR-4 expression [ 194 ]. Liang et al. (2013) reported that high LPS levels were negatively associated with skeletal muscle insulin sensitivity in obese individuals with or without T2DM [ 195 ]. A study conducted in humans found that endotoxin levels increased in individuals with glucose intolerance and T2DM (respectively, 20% and 125%) [ 193 ]. Gomes et al. (2017) reported that LPS levels in patients with diabetes were 66.4% higher than in nondiabetic patients. Additively, it was shown that TLR-4 expression was higher in patients with diabetes compared to nondiabetic patients [ 196 ]. It was evidenced that mice lacking CD14, a co-receptor of TLR-4, were more hyperinsulinemia-resistant and insulin resistance induced by a high-fat diet or LPS [ 188 ]. Therefore, deletion or mutation of the gene encoding TLR-4 may protect against fatty acid-induced insulin resistance and T2DM [ 197 , 198 ]. Hulston et al. (2015) reported that a high-fat diet (65% of total energy) impaired insulin sensitivity in healthy and non-obese individuals. Besides, probiotic supplementation ( Lactobacillus casei ) for four weeks maintained normal insulin sensitivity [ 199 ]. Animal and human studies indicate that high-fat diets may cause the development of T2DM by affecting the gut-brain axis and gut microbiota.

Saturated fatty acids

SFAs have been associated with increased non-commensal bacteria ( Firmicutes and Proteobacteria ), intestinal barrier dysfunction, decreased gut microbiota diversity, thinning of the mucus layer, decreased butyrate-producing bacteria, chronic inflammation, and development of T2DM [ 181 ]. addition SFAs cause ER stress. ER stress-associated systemic inflammation also induces disruptions in insulin signaling pathways. Systemic inflammation is considered a precursor to the development and progression of insulin resistance [ 172 ].

Animal studies have found that a high-fat diet increased Firmicutes and decreases Bacteroidetes in the gut microbiota. This is associated with insulin resistance resulting from intestinal inflammation [ 188 , 200 , 201 ]. An animal study showed that a saturated fat diet increased insulin resistance, intestinal permeability, and mesenteric fat inflammation [ 202 ]. Caesar et al. (2015) compared rats fed a high-fat diet rich in SFAs as lard to an isocaloric high-fat diet rich in omega 3 FAs as fish oil. It was found that Akkermansia muciniphila , Lactobacillus , and Bifidobacterium , which are beneficial bacteria, were less in the microbiota of the mice fed a high-fat lard diet. Moreover, a high-fat lard diet activates TLR-4 signaling, reducing insulin sensitivity and increasing inflammation in white adipose tissue [ 203 ]. In an animal study comparing lard-based and palm oil-based diets, lard-based diets were associated with impaired glucose tolerance. Lard-based diets also alter gut microbiota composition and function, acting on lipid metabolism [ 204 ]. Although there is a limited number of human intervention studies on SFAs, data from animal studies suggest that SFAs causes intestinal dysbiosis, leading to insulin resistance and T2DM.

Omega 3 fatty acids

Omega 3 FAs can prevent insulin resistance and T2DM development by increasing the diversity of the gut microbiota, reducing LPS and proinflammatory cytokines, and increasing SCFA production. Omega 3 FAs may exert beneficial effects on the gut microbiota by reducing the proliferation of Enterobacteriaceae , increasing the proliferation of Bifidobacterium , and subsequently inhibiting the inflammatory response associated with metabolic endotoxemia [ 205 ]. Besides, it was reported that a high intake of omega 3 FAs was associated with an increased translocation of commensal bacteria ( Bifidobacterium and Akkermansia ) and a decreased Firmicutes/Bacteroidetes ratio [ 181 ]. Omega 3 FAs inhibit LPS-induced pro-inflammatory cytokine production in human blood monocytes and attenuate intestinal inflammation [ 206 ]. However, omega 3 fatty acid supplementation has not yet been demonstrated to affect insulin metabolism and gut microbiota [ 87 ].

Patterson et al. (2014) showed that a flaxseed/fish oil diet for sixteen weeks significantly increased the intestinal population of Bifidobacterium in mice [ 207 ]. Administration of omega 3 FAs to Salmonella -infected mice increased the amount of SCFAs, altered the gut microbiota, and supported host resistance to pathogens [ 208 ]. In a prospective study, thirty-five patients with T2DM were randomly assigned to a standard T2DM diet group and a standard T2DM diets enriched with 100 g of sardines group. There was no significant difference between the glycemic controls of the patients in either groups. Plasma insulin and insulin resistance (HOMA-IR) decreased from baseline in both groups. Firmicutes decreased in both groups after six months of intervention. Patients fed a standard T2DM diet enriched with sardines had a decreased Firmicutes/Bacteroidetes ratio [ 87 ]. The dietary intake of fish oil has increased the diversity of intestinal flora more than sunflower oil [ 209 ]. A diet high in omega 3 FAs has been observed to increase the number of several SCFA-producing bacteria in the human gut microbiota, including Blautia , Bacteroides , Roseburia , and Coprococcus [ 210 ]. An animal study showed that alpha-linolenic acid-rich flaxseed oil significantly reduced fasting blood glucose, HbA1c, LPS, IL-1β, TNF-α, and IL-6 levels. Additionally, flaxseed oil intervention increased acetate, propionate and butyrate levels. Flaxseed oil eased T2DM by suppressing inflammation and regulating the gut microbiota. Firmicutes and Firmicutes/Bacteroidetes ratios decreased, while Bacteroidetes increased [ 211 ]. All these studies suggest that omega 3 FAs reduces the risk of T2DM development by increasing SCFA production. However, data from both animal models and human interventions are not enough and uncertain.

Omega 6 fatty acids

Animal studies have shown that high-fat diets containing large amounts of omega 6 FAs increase Firmicutes , Actinobacteria , and Proteobacteria and decrease Bacteroidetes [ 201 , 212 ]. Thus, omega 6 FAs may cause T2DM, raising intestinal dysbiosis. Miao et al. (2020) showed that both dietary omega 6 FAs and circulating omega 6 FAs (gamma-linolenic acid) could increase the risk of T2DM through a mechanism that alters the diversity and composition of the gut microbiota. Additionally, gamma-linolenic acid was inversely related to butyrate-producing bacteria in this study. Omega 6 FAs can increase the secretion of bile acid, which may act as an important signaling molecule linking omega 6 FAs and the gut microbiota. Bile acids can be metabolized in the gut to deoxycholic acid, which can disrupt hepatic ER stress. Therefore, it can disrupt glucose homeostasis and cause T2DM [ 213 ]. Although omega 6 FAs may play a role in the development of T2DM by the microbiota-gut-brain axis, more animal and human studies are needed in this field.

Lipidomics in T2DM via gut-brain axis

Lipidomics plays a major role in glucose homeostasis and the development of T2DM. It has been shown that the gut microbiota composition is altered in individuals with T2DM. However, the relationship between gut microbiota and lipidomics needs to be clarified in T2DM [ 214 ]. Animal studies suggest that the gut microbiota may modulate the lipidomics of the host. Homeostasis between lipidomics and gut microbiota is also known to affect the metabolic state of the host [ 204 ]. Furthermore, gut microbiota can synthesize lipids and their metabolites that may impact human health. Changes in gut microbiota and host lipidome appear to be associated with the development of T2DM [ 215 ]. However, there are few studies on lipidomics and its relationship to the gut-brain axis in T2DM [ 216 ].

Sphingolipids

Sphingolipids are bioactive lipids that regulate cellular processes such as cell differentiation, proliferation, apoptosis, and inflammation. Sphingolipids can be obtained either by diet or de novo synthesis [ 215 ]. However, it has been reported recently that the commensal gut microbiota ( Bacteroides , Prevotella , and Porphyromonas ) also produces sphingolipids. Ceramide phosphoinositol and deoxy-sphingolipids are synthesized by gut microbiota. These sphingolipids can exacerbate intestinal inflammation and regulate the amount of ceramide in animals. Sphingolipids may be an early marker of impaired glucose metabolism, but there are limited human studies with conflicting results [ 217 ].

Ceramides are precursors to sphingolipids [ 218 ]. Ceramides may be associated with insulin resistance because they may interfere with insulin signaling. It has been reported that ceramide levels are increased in serum, liver, and skeletal muscle in T2DM patients. Ceramides can trigger adipose tissue inflammation and diabetic pathology, activating pro-inflammatory cytokines [ 219 ]. In another study, ceramides were found to be positively associated with HOMA-IR in patients with T2DM [ 220 ]. Holland et al. (2011) showed that ceramides mediated saturated FAs-induced insulin resistance by TLR-4 signaling in skeletal muscle [ 218 ]. In 435 American-Indian participants in the Strong Heart Family Study, higher levels of ceramides, including stearic acid, arachidic acid, and behenic acid, were associated with T2DM [ 221 ]. In another study conducted in China, ceramides (18:1/18:1, 18:1/20:0, 18:1/20:1, 18:1/22:1), saturated sphingomyelins (C34:0, C36:0, C38:0, C40:0), unsaturated sphingomyelins (C34:1, C36:1, C42:3), hydroxyl-sphingomyelins (C34:1, C38:3) and hexosylceramide (d18:1/20: 1) was associated with T2DM. This study demonstrated that high levels of ceramides and sphingomyelins accompany pancreatic β-cell dysfunction [ 23 ].

Many lipidomics other than ceramides has been defined in T2DM patients. Thirty-five newly diagnosed T2DM patients had higher levels of sphingomyelins (d18:1/18:0, d18:1/18:1), ceramides (d18:1/18:0, d18:1/16:0, d18:1/20:0, d18:1/24:1), lysophosphatidylcholines (15:0, 16:0, 17:0), phosphatidylcholines (19:0/19:0), lysophosphatidylethanolamines (18:0), and phosphatidylethanolamines (16:0/22:6, 18:0/22:6) than the control group. Conversely, phosphatidylethanolamines (17:0/17:0) and phosphatidylcholines (18:1/18:0) were lower in T2DM patients. Levels of serum sphingomyelins (d18:1/18:0, d18:1/18:1), lysophosphatidylcholine (16:0), and lysophosphatidylethanolamines (18:0) decreased after administration of GLP-1 analog [ 222 ]. Floegel et al. (2013) associated diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 levels with an increased risk of T2DM. On the contrary, sphingomyelin C16:1, acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, C44:5 and lysophosphatidylcholine C18:2 were associated with a reduced risk of T2DM [ 223 ]. Lysophosphatidylcholine, phosphatidylcholine, phosphatidylethanolamine, and diacylglycerol have been associated with an increased risk of T2DM. Unlike, sphingomyelins have been related to a reduced risk of T2DM [ 26 ]. In the Metabolic Syndrome in Men (METSIM) cohort consisting of 10.197 men in total, it was found that levels of triacylglycerol and di-acyl-phospholipids were higher in T2DM patients [ 224 ]. Lysophosphatidylcholine was determined to stimulate glucose uptake in 3T3-L1 adipocytes in a dose- and time-dependent manner [ 225 ]. Prada et al. (2020) informed a negative correlation between lysophosphatidylcholine and T2DM risk in females. There was a positive correlation between lysophosphatidylcholine and T2DM risk in males. In addition, cholesteryl esters, monoacylglycerols, and diacylglycerols showed an inverse association with T2DM. A positive correlation was found between FFA and T2DM risk [ 226 ].

Although sphingolipids seem to be related to glucose metabolism, the effect of the gut-brain axis on this relationship is not yet known. Liu et al. (2019) presented that gestational diabetes was strongly associated with a specific gut microbiota composition and lipidomics. This study showed that Faecalibacterium and Prevotella species were related to lysophosphatidylethanolamine and phosphatidylglycerol [ 214 ]. Further and larger studies are required to investigate the effect of the gut-brain axis on lipidomics and glucose metabolism.

Bile acids and derivatives

Primary bile acids are synthesized from cholesterol in hepatocytes [ 168 ]. Primary bile acids are conjugated with glycine or taurine to form bile salts. About 95% of bile acids are reabsorbed in the ileum into the hepatic portal vein and then into the liver sinusoids. About 400–600 mg of bile salts reach the colon. They are converted to secondary bile acids such as deoxycholic acid, lithocholic acid, and ursodeoxycholic acid by the colonic microbiome [ 176 ]. However, secondary bile acids are produced or biotransformed by the gut microbiota. Conjugated primary bile acids are deconjugated by Bacteroides , Clostridium , Lactobacillus , and Bifidobacterium species. The gut microbiota plays an important role in bile acid metabolism. In addition, both primary and secondary bile acids affect host metabolism [ 215 ]. Bile acids regulate glucose homeostasis, lipid metabolism, and gut microbiota [ 227 ].

It has been suggested that secondary bile acids may cause T2DM through the gut microbiota. Taurine-conjugated bile acids have been associated with insulin resistance in nondiabetic individuals. They have been significantly higher in patients with T2DM than those without T2DM [ 228 , 229 ]. In a study, insulin resistance was associated with increased levels of secondary bile acids [ 230 ]. In addition, deoxycholic acid, a secondary bile acid, was positively correlated with Firmicutes levels [ 231 ]. In particular, a high-fat diet alters the gut microbiota, increasing intestinal, brain, and blood levels of taurine-conjugated bile acids such as taurochenodeoxycholic acid, a Farnesoid X receptor (FXR) agonist. Increased taurochenodeoxycholic acid may cause insulin resistance by increasing TCDCA-FXR signaling [ 232 ]. There is evidence that microbiota dysbiosis can cause T2DM by increasing secondary bile acids; however further animal and human studies are required.

Endocannabinoids

The endocannabinoid system consists of lipid-derived endogenous ligands, enzymes involved in their synthesis and degradation, and cannabinoid receptors [ 215 ].

Gut microbiota has close linked to the endocannabinoid system. It has been suggested that the gut microbiota and endocannabinoids may communicate with signaling pathways involving the gut-brain axis for homeostasis of energy, lipid, and glucose metabolism. N-acyl phosphatidylethanolamine-specific phospholipase D regulates lipid absorption and metabolism. Its disruption causes insulin resistance, glucose tolerance, and intestinal dysbiosis [ 215 ]. Muccioli et al. (2010) demonstrated a relationship between the gut microbiota and the endocannabinoid system that modulates host adipogenesis [ 233 ]. Everard et al. (2013) showed that Akkermansia muciniphila intervention in obese mice increased intestinal levels of 2-oleoylglycerol, 2-arachidonoylglycerol, and 2-palmitoyl-glycerol [ 189 ]. More recently, it was reported that the endocannabinoid-like molecule N-acyl-3-hydroxypalmitoyl-glycine was synthesized by Bacteroides species [ 234 ]. These studies suggest that the gut microbiota generates lipidomics that influences signaling pathways of host metabolism. The mechanisms related to the gut microbiota that mediate the increase in intestinal permeability have not been fully elucidated. However, overactivation of the endocannabinoid system may play an important role in intestinal permeability [ 235 ]. In addition, endocannabinoids can affect the composition of the gut microbiota. Deletion of the endocannabinoid-synthesizing enzyme in adipose tissue may cause obesity, glucose intolerance, altered lipid metabolism, and adipose tissue inflammation [ 236 ].

Specialized pro-resolving mediators

Specialized pro-resolving mediators (SPMs) are synthesized from AA, eicosapentaenoic acid, docosahexaenoic acid, and docosapentaenoic acid during inflammation. These endogenous lipids promote the clearance of pathogenic bacteria and macrophages associated with microbiota dysbiosis. They also enhance tissue regeneration by increasing the secretion of anti-inflammatory mediators and inhibiting proinflammatory cytokines. These lipid mediators play a crucial role in eliminating inflammation, maintaining intestinal integrity, and preventing T2DM [ 181 ].

Short chain fatty acids

SCFA, which are organic FAs containing 2–6 carbon atoms, are synthesized in the host's colon by the microbiota following fermentation of indigestible dietary fibers, proteins, and glycoproteins. Acetate, propionate, and butyrate account for 95% of SCFAs present in the colon [ 170 ]. SCFA-producing commensal bacteria include Lachnospira , Akkermansia , Bifidobacterium , Lactobacillus , Ruminococcus , Roseburia , Clostridium , Faecalibacterium , and Dorea . SCFAs, which are used as substrates for energy production, lipogenesis, gluconeogenesis, and cholesterol synthesis, affect host metabolism. SCFAs act as signaling molecules by stimulating G protein-coupled receptors GPR43/FFAR2 and GPR41/FFAR3. GPR43 increases GLP-1 expression by affecting L cells. Acetate regulates glucose and lipid metabolism via GPR43 [ 215 ]. Acetate and propionate exert anti-inflammatory effects by FFAR2. This receptor signaling results in the inhibition of NF-kB nuclear translocation and decreased expression of proinflammatory cytokines such as TNF-α, IL-1, and IL-6. Preventing inflammation can also reduce the risk of T2DM [ 177 ].

Microbiota dysbiosis can cause T2DM development by decreasing SCFA concentrations [ 237 ]. The composition of the gut microbiota and diet affect the type and amount of SCFA. High-fat diets and some FAs can result in decreased SCFA production and increased harmful secondary metabolite production. Increased intestinal permeability can cause low-grade inflammation and metabolic endotoxemia. Metabolic endotoxemia can also cause insulin resistance, β-cell dysfunction, hyperglycemia, and T2DM [ 238 ]. High-level endotoxemia has been found to increase TNF-α and IL-6 concentrations and insulin resistance [ 239 ]. Recent evidence suggests that dietary lipids and lipidomics can cause T2DM by reducing SCFA production. However, the effect of SCFAs on the pathophysiology of T2DM has generally been observed in animal studies, but these results should be confirmed in larger, long-term studies with clinical endpoints.

Nutritional strategies modulating gut microbiota through lipidomics for T2DM

To promote health, it is now essential to understand how nutrients impact metabolic control [ 240 ]. It is well known that a wide range of exogenous and intrinsic factors affect the intestinal microbiota. The human gut microbiota can be shaped and modified by a number of factors, but diet is one of the most potent [ 241 ]. Food is the primary source of energy for microorganisms in the gut, and changes in the host's diet can result in rapid changes in the microbiota's composition [ 241 , 242 ]. Up to 57% of changes in the gut microbiota may be attributed to dietary factors, but host genes are only thought to be responsible for 12% or less of those changes [ 243 ]. To combat noncommunicable diseases like T2DM, including understanding how diet and dietary nutrient intake affect the gut microbiome, is crucial [ 244 ]. This makes it crucial to identify the dietary components that influence the gut microbiome, where dietary lipids play a significant role [ 181 , 244 ]. Roles and mechanisms of lipidomics in T2DM were discussed in the sections before this one. It is possible that T2DM can be prevented and treated by modulating lipidomics, according to this. A key role in the modulation of lipidomics is played by nutritional status and dietary nutrients. It is stated that especially dietary lipid intake may have anti-diabetic effects with the changes it has created on the composition of the intestinal microbiota through metabolic end products [ 211 , 245 , 246 , 247 ]. The possible roles that some nutritional strategies may play in T2DM through lipidomics and gut microbiota were shown in Fig.  4 .

Some nutritional strategies in T2DM considering lipidomics and gut microbiota interaction

Fatty acids

High consumption of fish and seafood is associated with a significantly lower risk of T2DM [ 248 , 249 ]. It is reported that high omega 3 FAs content may be effective in this situation through different mechanisms such as anti-inflammatory and antioxidant [ 249 , 250 , 251 ]. Another potential mechanism is that it lowers the risk of developing T2DM by influencing the intestinal microbiota through lipidomics, which is a substance produced in the intestine as a result of the metabolism of omega 3 FAs [ 248 , 252 ].

It is known that the intestinal microbiota can be modulated through SCFAs, and in this way, the microbial diversity of the intestine can be changed [ 253 ]. SCFAs increase insulin signaling and insulin sensitivity with their curative effects on systemic inflammation and endotoxemia by decreasing intestinal permeability [ 248 ]. Additionally, SCFA enhances glucose uptake in skeletal muscle and adipose tissue by boosting GLUT4 expression via AMP Kinase (AMPK) activity [ 254 ]. Bacteria that produce butyric acid contribute significantly to maintaining health by converting unfermented dietary fibers into SCFAs such as butyrate [ 248 ]. It has been reported that supplementation of omega 3 FAs helps modulate the intestinal microbiota by increasing the level of SCFAs in the intestine [ 252 ]. In addition, a diet rich in omega 3 FAs is noted to significantly increase butyrate-producing bacteria, including Blautia , Bacteroides , Roseburia, and Coprococcus [ 248 , 252 ].

Another of the main mechanisms of the protective function of omega 3 FAs is their influence on the lipidome, including eicosanoids and membrane lipids [ 255 ]. Yan et al. (2020) found that omega 3 fatty acid supplementation in healthy individuals added glycerophospholipidome levels [ 255 ]. In a study carried out with non-obese T2DM rats and Wistar rats, it was detected that dysbiosis occurred and higher glycerophospholipids were present in non-obese T2DM rats when compared with Wistar rats. Another outcome of the study was that omega 3 supplementation significantly affected plasma glycerophospholipids [ 256 ]. Considering their effects on the lipidome, these data suggest that omega 3 FAs may influence the intestinal microbiota and subsequently T2DM. In research, the effects of supplements containing varying doses of EPA + DHA and dietary sources of omega 3 such as fish and flaxseed oil on intestinal microbiota were investigated. The research outcomes are influenced by sample size, methodological variations, the study group, and the dose and amount of the supplement or nutritional source utilized.

Vegetable oils, nuts, and seeds are the main dietary sources of omega 6 PUFAs. The essential omega 6 FAs in the diet are LA and AA. LA makes up the majority (about 90%) of dietary PUFAs, while AA has a lower intake [ 257 ]. In the body, the majority of LA is transformed into AA [ 258 ]. For several enzymes, AA serves as a substrate [ 181 ]. Different biosynthetic pathways (i.e., cyclooxygenases (COX), lipoxygenases (LOX), or cytochrome P450) of these enzymes are used to produce various eicosanoids, including various prostaglandins, thromboxanes, and leukotrienes [ 259 ]. According to recent research, the majority of immune cells and intestinal epithelial cells synthesize eicosanoids [ 181 , 260 ]. Additionally, it is claimed that by metabolizing AA, intestinal bacteria can produce eicosanoid metabolites [ 181 ]. Particularly, it has been reported that proteobacterial lipoxygenases can generate a variety of prostaglandins in the intestine by fermenting other bacterial byproducts, primarily SCFAs [ 181 , 261 ].

Cyclooxygenase pathway metabolites of AA, especially prostaglandins, increase the risk of T2DM by causing pancreatic β-cell destruction and β-cell dysfunction [ 262 ]. In line with such data, dietary omega 6 FAs may be considered to role in the development of T2DM through enzyme systems and eicosanoid metabolites produced as a result of their metabolism by intestinal bacteria.

Dietary sterols

Sterols are lipophilic substances that can be divided into synthetic sterols, phytosterols (derived from plants), zoosterols (derived from animals), and mycosterols (derived from fungi) [ 263 ]. About 30–60% of dietary cholesterol is absorbed, while the rate of absorption for plant sterols is much lower (2–3%) [ 264 ]. This means that unabsorbed sterols can reach the colon and be further processed by the gut microbiota [ 181 , 264 ]. Among the common phytosterols, β-sitosterol, stigmasterol, campesterol, and brassicasterol can be found in both plant cells and cytoplasmic membranes of many fruits, vegetables, nuts, cereals, and vegetable oils [ 263 ]. Western nations consume about 250 mg of phytosterols per day on average [ 265 ].

Metabolites produced as a result of transformation by the gut microbiota of unabsorbed sterols, including cholesterol and plant sterols, have significant effects on the host's health, including in T2DM, through modulation of the gut microbiota [ 264 ]. Coprostanol, coprostanone and cholestanol are cholesterol metabolites formed in the gut by the gut microbiota [ 264 , 266 ]. Coprostanol is the most abundant animal sterol in feces, followed by coprostanone and cholesterol, which can be converted to lesser cholestanol [ 264 ].

Plant sterol supplementation was found to increase the production of SCFAs and decrease the production of coprostanol and ethylcoprostanol in a study where the researchers evaluated the effects of plant sterols on the intestinal microbiota (acetate and butyrate). Additionally, it was reported that this supplement could improve the gut microbial profile by raising the percentage of some genera from the phylum Firmicutes [ 267 ]. In another study, in which half of 40 postmenopausal women were given a 2 g/day plant sterol-enriched beverage and the remaining half a placebo for six weeks, it was found that the coprostanol levels were lower in the feces of the plant sterol group, and it was concluded that plant sterols could be used to modulate the gut microbiota [ 268 ]. In addition, Weststrate et al. discovered that plant sterol-enriched margarine (8.6 g/day) decreased the amount of cholesterol that was converted to coprostanol during metabolism [ 269 ]. In line with the data obtained from these studies, it can be concluded that while plant sterols decrease the production of some lipidomics such as coprostanol, they increase the production of SCFAs. As mentioned in earlier sections, SCFAs lower the risk of developing T2DM via a variety of mechanisms, including modulation of the gut microbiome. Furthermore, the gut microbiota and glucose homeostasis are known to be influenced by cholesterol metabolites like coprostanol [ 268 , 270 , 271 , 272 , 273 ]. These results in the literature suggest that some sterols (especially plant sterols) may improve glucose homeostasis and reduce the risk of T2DM by the modulating gut microbiota through lipidomics.

Fat-soluble vitamins

Vitamin A (retinol) and its enzymatic oxidation product, retinoic acid, play a role in gut health through interactions with the gut microbiome [ 181 , 274 , 275 ]. It has been reported that significant changes in the gut microbiome profile in acute vitamin A deficiency and the abundance of Bacteroides vulgatus increase rapidly [ 276 ]. Lv et al. (2016) concluded in their study that children with vitamin A deficiency have less microbial diversity in their gut microbiota and are more likely to experience resistant diarrhea. Another outcome of the study was the detection of less butyrate-producing Clostridia bacteria in the stools of children with vitamin A deficiency [ 277 ]. Moreover, it is stated that bacterial diversity may decrease with the decrease in butyrate-producing bacteria and the increase in opportunistic pathogens in case of vitamin A deficiency [ 277 ]. It may be considered due to the aforesaid roles that adequate dietary vitamin A intake may reduce the risk of T2DM by increasing butyrate production, which is attributed to lipidomic. To draw firm conclusions, however, is challenging due to the paucity of studies on this topic in the literature.

Vitamin D3 (cholecalciferol), belonging to the secosteroid family, has received increasing attention in recent years for its potential pleiotropic effects on lipid metabolism [ 278 ]. Vitamin D, which is known to regulate calcium and phosphate metabolism, also has important effects on blood glucose homeostasis and gut microbiota [ 279 , 280 ]. Thomas et al. (2020) found out that strong correlations between the vitamin D metabolites 1,25-dihydroxyvitamin D (1,25(OH) 2 D), and 24,25-dihydroxyvitamin D (24,25(OH) 2 D) and intestinal microbial diversity. It has also been reported in the study that the increase in vitamin D metabolites was positively correlated with the increase in butyrate-producing bacteria, including Firmicutes [ 281 ]. Oral vitamin D3 supplementation increases the levels of Bacteroides and Parabacteroides in the intestine [ 282 ]. In a study, it was determined that vitamin D supplementation increased the levels of probiotic taxa Akkermansia and Bifidobacterium, which are known to have health-improving effects in the gut microbiome, and the ratio of Bacteroidetes/Firmicutes [ 283 ]. Haro et al. (2015) also revealed that an increase in Parabacteroides levels in the gut microbiome reduces the risk of T2DM [ 284 ]. Moreover, it has been reported that the Firmicutes/Bacteroidetes ratio is higher in T2DM individuals than in healthy individuals [ 285 ]. According to these data, it can be considered that vitamin D may reduce the risk of T2DM by affecting the intestinal microbiome through its metabolites.

Vitamin E is a nutrient that can act as an antioxidant and is present in a number of foods, such as wheat germ oil, extra virgin olive oil, hazelnuts, peanuts, fish, oysters, eggs, and butter [ 244 ]. There are eight types of vitamin E: α-, β-, γ-, and δ-tocopherol; and α-, β-, y- and δ-tocotrienol. A common form of vitamin E, α-tocopherol, is the most biologically active form in humans [ 286 ]. Natural antioxidants are said to be able to regulate the gut microbiome by scavenging too many free radicals and enhancing the immune response [ 287 , 288 ].

According to a study, mice that consumed high levels of vitamin E had a lower Firmicutes to Bacteroidetes ratio than mice in the control group and mice that consumed low levels of vitamin E [ 286 ]. In another study, vitamin E intake was linked to a relative decrease in Proteobacteria [ 289 ]. Contrarily, Tang et al. (2016) discovered that supplementing with vitamin E along with iron increased the relative abundance of the butyrate-producing genus Roseburia (phylum Firmicutes) [ 290 ]. Yang et al. (2021) came to the conclusion that δTE-13′-carboxychromanol, a metabolite of vitamin E δ-tocotrienol (δTE), prevents Roseburia depletion, which is known to be reduced in people with inflammatory bowel diseases [ 291 ]. In an in vitro study in which vitamin E was added to the rumen fluid, acetate and propionate concentrations were increased, but butyrate levels decreased in the rumen fluid [ 292 ]. Another in vitro study found that adding vitamin E to the diet increased production of total SCFA, propionate, and tended to increase production of acetate and butyrate [ 293 ]. These findings lead to the conclusion that vitamin E plays a role in T2DM by affecting the abundance of some bacteria and indirectly the production of SCFAs.

There are two molecular variations of vitamin K found in nature: vitamin K1 (phylloquinone) and vitamin K2 (menaquinone). The main dietary source of vitamin K is phylloquinone, which is primarily present in green leafy vegetables [ 294 ]. Meat, eggs, curd, cheese, fermented soybeans, and dairy products are the main dietary sources of menaquinone. As well as its dietary sources, many bacteria in the human gut also synthesize menaquinone [ 294 , 295 ]. It has been suggested that vitamin K supplementation may lower the risk of T2DM due to its benefits for improving insulin sensitivity, glucose tolerance, and preventing insulin resistance [ 294 , 296 ].

Vitamin K plays a role in the proliferation of Bacteroides melaninogenicus that contain sphingolipids in addition to sphingolipid metabolism [ 297 ]. It was found that Bacteroides melaninogenicus grown on vitamin K-deficient medium had lower synthase activity than bacteria grown on vitamin K-supplemented medium [ 297 , 298 ]. It is stated that inhibition of sphingolipid metabolism (decreased sphingolipid metabolism) impairs pancreatic β-cell function and may contribute to the risk of T2DM [ 299 ]. On the contrary, there are studies reporting that sphingolipids may contribute to the risk of T2DM by disrupting the insulin signaling pathway and contributing to mitochondrial dysfunction [ 300 , 301 ].

Obese people with insulin resistance have different gut microbiota compositions, particularly a rise in the Firmicutes/Bacteroidetes ratio [ 302 ]. Ellis et al. (2021) found that dietary vitamin K levels affect the gut microbial community composition [ 303 ]. Menaquinones, in particular, play a crucial role in gut microbiota homeostasis by encouraging the growth of symbiotic bacteria [ 181 ]. Based on the findings of these studies, it is hypothesized that vitamin K may contribute to prevent of T2DM through its effects on the microbiome and sphingolipid metabolism, a lipidomic process. To be certain about the existence and direction of the relationship, new studies are necessary.

Dietary sphingolipids

Sphingolipids are lipids that, along with cholesterol, phospholipids, and proteins, play a role in the structure of the cell membrane. There are more than 4000 different subtypes of sphingolipids [ 304 ]. Sphingolipids are present in foods in various amounts, ranging from a few micromoles per kilogram in rich sources like dairy products, eggs, and soybeans to a few millimoles per kilogram in fruits. Furthermore, it is known that foods such as beef, pork, chicken, turkey and fish have different levels of sphingolipid content. Sphingolipid consumption per person in the United States is estimated to be between 150 and 180 mmol ( ∼ 115–140 g) annually, or between 0.3 and 0.4 g/day, based on the scant information currently available [ 305 ]. Western diets contain 200–400 mg/day of sphingolipids and the largest contribution comes from animal sources [ 305 ]. The amount of sphingolipids consumed daily from plant sources is thought to be around 50 mg in western countries, but for vegetarians, that amount can be much higher [ 304 ]. Milk contains a variety of different types of lipids, such as triglycerides, diglycerides, glycerophospholipids, sphingolipids, NEFA, cholesterols, and glycolipids. Sphingomyelins are the most prevalent of the four types of sphingolipids found in milk, which also includes gangliosides, cerebrosides, and ceramides [ 306 ]. Although dietary sphingolipids are not essential for the continuation of life, they play a key role in health, especially their effects on the composition of the gut microbiome [ 304 ].

Sphingolipid production by bacteria exists in addition to dietary consumption. Prokaryotic sphingolipid production was found to occur first in members of the Bacteroidetes phylum (e.g. Bacteroides, Prevotella, Porphyromonas, Sphingobacterium ) and later in some Proteobacteria (eg Sphingomonas, Bdellovibrio, Acetobacter) [ 304 ]. The abundance of the Bacteroidetes phylum, which comprises 30–40% of the human gut microbiome, further increases the potential effects of bacterial sphingolipid production on the host [ 306 ]. Nutritional status can affect bacterial sphingolipid production by altering the abundance of these bacteria in the gut microbiome [ 248 , 304 , 307 ].

Sphingolipids, which are known to affect the intestinal microbiome, are reported to play a role in the development of insulin resistance and T2DM [ 300 , 301 , 304 ]. These findings suggest that diet may influence T2DM risk by influencing lipidomic sphingolipid intake and indirectly influencing bacterial production.

Conclusion and future directions

In conclusion, dietary lipids and lipidomics seem to play a major role in glucose homeostasis, the development, and the progression of T2DM. Actually, it has been known for a long time that some dietary and plasma lipids like SFAs do not have positive effects on the development and prognosis of T2DMs, while PUFA and MUFA may have positive effects when intake in balance. The new subject we learned is the effects and mechanisms of action of metabolomics, which we call lipidomics on host health and the gut microbiome. Recent literature has noted that gut microbiota may modulate the lipid profiles and lipidomics in T2DM, however, the gut microbiota is altered in individuals with T2DM. Moreover, lipids and lipidomics may influence the gut-brain axis with certain mechanisms, hence contributing to the pathogenesis of T2DM. Furthermore, some studies in the literature suggest that diet may influence T2DM risk by influencing lipid metabolism and lipidomics and directly or indirectly influencing microbiota composition and forming metabolites because of microbiota modulation. In line with the data obtained from the literature, increasing omega-3 fatty acids, dietary sterols, fat-soluble vitamins, and dietary sphingolipids in the diet may be a promising nutritional strategy to reduce the risk of T2DM through the mechanisms specified. However, it can be said that there are still few studies on the relationship between lipidomics and the gut-brain axis in T2DM because lipidomics are relatively new technology products. It is yet early to decide on the uses of lipidomics in diet plans in T2DMs. So, future investigations should be addressed to clarify the relationship between diet, gut microbiota and lipidomics in T2DM. Examining the these dietary components in future studies in different model morganisms and clinical trials at different doses and times will help to reveal possible relationships.

Availability of data and materials

Not applicable.

Abbreviations

1,25-Dihydroxyvitamin D

24,25-Dihydroxyvitamin D

Arachidonic acid

ATP-binding cassette transporter A1

Acetyl-CoA carboxylases 1

Activating ATP-citrate lyase

Alpha-linoleic acid

Apolipoprotein

Cluster-of-differentiation 14

Cholesteryl ester transfer protein

Carbohydrate response element-binding protein

Central nervous system

Cyclooxygenases

Docosahexaenoic acid

De novo lipogenesis

Enteroendocrine cells

Enteric nervous system

Eicosapentaenoic acid

Endoplasmic reticulum

Fatty acid desaturases 1

Fatty acid desaturases 2

Focal adhesion kinase

Food and Agriculture Organization

Fatty acid synthase

Free fatty acids

Farnesoid X receptor

Glucagon-like peptide-1

G proteincoupled receptor 40

High dense lipoprotein cholesterol

Heparan sulfate proteoglycans

Intercellular adhesion molecule-1

Interleukin

Interferon-γ

Inducible nitric oxide synthase

Insulin resistance

IL-1R-associated kinase 4

Insulin receptor substrate-1

Linoleic acid

Lipopolysaccharide-binding proteins

Lecithin cholesterol acyl transferase

Low density lipoprotein cholesterol

Lipoxygenases

Lipoprotein lipase

Lipopolysaccharides

LDL receptor-associated protein

Mitogen-activated protein kinase

Metabolic Syndrome in Men

Insulin activates Max-like protein X

Minimally oxidized LDL

Messenger RNA

Microsomal triglyceride transfer protein

Monounsaturated fatty acids

Noncommunicable diseases

Non-esterified fatty acids

Niemann-Pick C1-like 1 protein

Fully oxidized LDL

Peroxisome proliferator-activated receptor co-activator-1α

Phosphatidylinositol 4,5-biphosphate

Phosphatidylinositol 3,4,5-triphosphate

Protein kinase A

Protein kinase B

Peroxisome proliferator-activated receptor

Polyunsaturated fatty acids

Reactive oxygen species

Stearoyl-CoA desaturase-1

Short-chain fatty acids

Scavenger receptor B1

• Type 2 diabetes mellitus

Total cholesterol

Tricarboxylic acid cycle

Trans fatty acids

Triglyceride

Takeda-G-protein-receptor-5

Toll-Like Receptor 4

TNF-receptor 1

TNF-receptor 2

Tumor necrosis factor alpha

Triglyceride-rich lipoproteins

Very low-density lipoprotein

White adipose tissue

δ-Tocotrienol

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The paper was funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3-Call for tender No. 341 of 15 March 2022 of Italian Ministry of University and Research funded by the European Union—NextGenerationEU; Award Number: Project code PE00000003, Concession Decree No. 1550 of 11 October 2022 adopted by the Italian Ministry of University and Research, CUP D93C22000890001, Project title “ON Foods-Research and innovation network on food and nutrition Sustainability, Safety and Security—Working ON Foods.”

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Department of Nutrition and Dietetics, Faculty of Health Sciences, Gazi University, 06490, Ankara, Turkey

Duygu Ağagündüz & Tevfik Koçak

Department of Nutrition and Dietetics, Faculty of Health Sciences, Amasya University, 05100, Amasya, Turkey

Mehmet Arif Icer

Department of Nutrition and Dietetics, Faculty of Health Sciences, Bursa Uludag University, 16059, Bursa, Turkey

Ozge Yesildemir

Department of Nutrition and Dietetics, Faculty of Health Sciences, Ordu University, 52200, Ordu, Turkey

Emine Kocyigit

Department of Agricultural Sciences, University of Naples Federico II, Portici, 80055, Naples, Italy

Raffaele Capasso

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MAI, OY, TK, EK; writing original draft preparation, writing review and editing, visualization. DA and RC; conceptualization, writing original draft preparation, writing review and editing, supervision. All authors read and approved the final manuscript.

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Ağagündüz, D., Icer, M.A., Yesildemir, O. et al. The roles of dietary lipids and lipidomics in gut-brain axis in type 2 diabetes mellitus. J Transl Med 21 , 240 (2023). https://doi.org/10.1186/s12967-023-04088-5

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Received : 20 February 2023

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DOI : https://doi.org/10.1186/s12967-023-04088-5

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Effectiveness of diabetes self-management education (DSME) in type 2 diabetes mellitus (T2DM) patients: Systematic literature review

Diabetes mellitus is a chronic disease characterized by high glucose levels (hyperglycemia) due to metabolic disorders that prevent patients from producing sufficient amounts of insulin. This research aims to test the effectiveness of implementing diabetes self-management education in patients with Type 2 diabetes mellitus. The search for relevant articles was carried out through Google Scholar, PubMed, ProQuest, and Science Direct using the keywords diabetes mellitus, management education, self-care, diabetes self-management education, DSME, T2DM. The articles were then selected based on inclusion and exclusion criteria. Furthermore, the data were extracted, grouped, and concluded. Based on 15 articles, diabetes self-management education intervention provides significant effectiveness to lifestyle changes and the self-care of T2DM patients. In conclusion, diabetes self-management education intervention has been shown to be effective in dealing with type 2 diabetes mellitus. Furthermore, DSME has a positive effect on lifestyle changes and the self-care of T2DM patients.

Significance for public health

Globally, there are various pillars of diabetes mellitus management. One of the important pillars for the prevention and management is education. When properly carried out, it provides benefits to people with diabetes mellitus. Furthermore, the Association of Diabetes Care and Education (AADE) has guidelines for diabetes self-management education (DSME). In reality, there are many health workers that provide education without paying attention to these guidelines. Therefore, this study on the effectiveness of diabetes self-management education (DSME) would provide information regarding the importance of using these guidelines.

Introduction

Diabetes mellitus (DM) is a chronic disease characterized by high glucose levels (hyperglycemia) due to metabolic disorders that prevent the patient from producing sufficient amounts of insulin. The disease can be prevented and controlled by engaging in certain behaviors and lifestyles such as regular exercise, healthy eating patterns, avoiding smoking, and controlling fat and glucose in the blood. 1 The World Health Organization stated that the number of people living with diabetes mellitus (DM) worldwide reached 422 million, and every year 1.6 million deaths are recorded. 2 The prevalence of the disease in the world is estimated to reach 642 million people by 2040. In 2019, the countries with the highest number of DM sufferers were China, India, the United States, Pakistan, Brazil, Mexico, and Indonesia, with an estimated number of 10 million patients. 3 The number of people living with diabetes could be much greater than the prevalence described, because most sufferers only seek medical help after complications occur. The rising prevalence of diabetes mellitus is due to several factors, such as unhealthy behavior. 1 This behavior is still rampart in Indonesian society, and is evidenced by the results of the Basic Health Research 2018, 1 where 13.6% of the residents were overweight, 21.8% had obesity, and 31% central obesity. Other unhealthy habits include the use of tobacco by men (62.9%) and smoking by adolescents (10-18 years) (23.91%). 4 There are seven major behaviors related to diabetes self-care management, they include diet, physical activity, monitoring blood glucose levels, adherence to proper medication consumption, good problem solving, coping skills, and risk reduction behavior. 5 Continuous selfcare will reduce the incidence of DM complications. However, most DM sufferers do not practice adequate self-care techniques such as controlling fasting blood glucose levels. 6

DM management focuses on several aspects, namely education, meal planning, changes in lifestyle, physical activity, habits. 7 One study explained that educational interventions influence knowledge, physical activity, food intake, self-efficacy, and health literacy. 8 Diabetes self-management education (DSME) plays a key role in empowering people with diabetes to engage and sustain lifestyle changes, which have been shown to improve health outcomes. DSME is the process of facilitating the knowledge, attitudes, and abilities necessary for self-management. 9 In addition to this, DSME play an important role in influencing the self-care practices of patients with diabetes mellitus. Based on this phenomenon, a literature review was prepared to highlight effectiveness of DSME on T2DM.

Design and Methods

The collection and review of articles was carried during the month of October 2020. Furthermore, published articles were obtained through several electronic databases, such as Google Scholar, PubMed, ProQuest, and ScienceDirect using the keywords diabetes mellitus, self-care, diabetes self-management education, and DSME. The articles obtained from these databases were then selected based on the inclusion and exclusion criteria in order to obtain relevant articles. In addition to this, articles designs were selected using cross-sectional, randomized controlled trials (RCT), systematic reviews, and quasi-experimental studies. Subsequently, the data was then extracted, grouped, and concluded; 137 articles were obtained through the selection process (inclusion and exclusion criteria) ( Table 1 ). These articles were then assessed for criticism and 15 were found to be relevant to the criteria.

Results and discussions

The effectiveness of DSME in T2DM is the main focus of this literature review. The heterogeneity of DSME implementation were seen based on the number of sessions, the time span, and the methods used. The study presented 6 articles with homogeneous results showing that DSME has a good effect on T2DM patients ( Table 2 ).

Inclusion and exclusion criteria.

Article review result.

The DSME intervention given to T2DM patients in Ethiopia had a positive impact, such as an increase in knowledge and adherence to diet therapy, exercise, glucose monitoring, and wound care. 10 In line with that, another study explained that DSME significantly improved medication adherence, self-management behavior, knowledge, self-efficacy, and quality of life. 11-13 Several studies show that DSME interventions improve the quality of life. 14-17 Through these interventions, bad behavior such as smoking and alcohol consumption can also be avoided or reduced. 10

Several interventions are also able to influence lifestyle changes such as increasing the duration of exercises (cycling, walking, aerobics), reducing smoking habits, and increasing the consumption of fruits and vegetables. 18 Lifestyle changes caused by DSME interventions are expected to improve the clinical and health status of T2DM patients. One study proved this showing DSME’s effectiveness in controlling fasting blood glucose, random blood glucose, total cholesterol, and triglycerides. 11 In line with that, other studies also showed that DSME can influence glycemic control, body weight and BMI control. 11 , 19-21 Apart from data homogeneity in the article, another difference was found regarding the effect of DSME on HbA1c. Cunningham 14 states that DSME does not significantly affect HbA1c. This is in contrast with other studies which explain that this intervention can significantly affect HbA1c. 21-26 After reviewing several studies, it is proven that DSME has a positive effect on the lifestyle and clinical or health status of T2DM patients. However, the implementation process could be influenced by several factors, namely: i) limited resources, ii) culture, iii) relationship with diabetes, and iv) relationship with clinic.

This systematic review focuses on the effectiveness of DSME on T2DM disease progression. It is known that the DSME intervention provides benefits to the development of T2DM disease. The demonstrated benefits point to efforts to increase T2DM development through lifestyle changes and self-care for T2DM patients. Lifestyle changes such as exercising diligently, increasing consumption of fruits and vegetables, and avoiding smoking can improve the patient’s clinical condition and the patient’s health status. 18 The clinical condition can be seen from the levels of blood glucose and HbA1c.

DSME has a positive effect on T2DM patients to improve their knowledge, behavior, self-efficacy, and clinical conditions of patients such as blood glucose levels, HbA1c, lipid profiles. 10 , 11 , 19-21 However, there were differences in results in studies involving HbA1c levels. The difference that lies in the presence or absence of this effect on HbA1c can be a concern in future studies to consider the determining factors that can influence it. Several studies in this review show that the effectiveness of DSME is influenced by education providers and support systems. 17 , 22 , 25 , 26

Conclusions

Based on the 15 articles reviewed, it was found that DSME intervention provides significant effectiveness to lifestyle changes and the self-care of T2DM patients. Therefore, it improves the clinical or health status of T2DM patients.

Acknowledgment

The authors are grateful to the co-authors and reviewers for this research.

Funding Statement

Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.