type 1 diabetes case study pdf

• Previous Article
• Next Article

Assessment and Data Collection

Diagnosis and problem identification, planning and intervention, case presentation, case study: a patient with type 1 diabetes who transitions to insulin pump therapy by working with an advanced practice dietitian.

• Split-Screen
• Article contents
• Figures & tables
• Supplementary Data
• Peer Review
• Open the PDF for in another window
• Cite Icon Cite
• Get Permissions

Claudia Shwide-Slavin; Case Study: A Patient With Type 1 Diabetes Who Transitions to Insulin Pump Therapy by Working With an Advanced Practice Dietitian. Diabetes Spectr 1 January 2003; 16 (1): 37–40. https://doi.org/10.2337/diaspect.16.1.37

Download citation file:

• Ris (Zotero)
• Reference Manager

Registered dietitians (RDs) who have earned the Board Certified–Advanced Diabetes Manager (BC-ADM) credential hold a master’s or doctorate degree in a clinically relevant area and have at least 500 hours of recent experience helping with the clinical management of people with diabetes. 1 They work in both inpatient and outpatient settings, including diabetes or endocrine-based specialty clinics, primary care offices, hospitals, and private practices. Advanced practice dietitians provide all components of diabetes care, including advanced assessment (medical history and physical examination), diagnosis, medical management, education, counseling, and overall case management.

The role of RDs in case and disease management was explored in a recent article 2 that included interviews with three dietitians who work as case managers or disease managers. All three reported experiencing challenges in practice and noted that the meaning of “case management” varies from one health care setting to another. This is also true for RD, BC-ADMs. Advanced practice dietitians specializing in diabetes require case management expertise that stresses communication skills, knowing the limits of your own discipline, knowing how to interact with other health care professionals, and knowing when to seek the expertise of other members of the diabetes care team.

Clinical practice includes assessment and data collection, diagnosis and problem identification, planning, and intervention. In many cases, diabetes educators who are dietitians and those who are nurses are cross-trained to perform the same roles. The first one to meet with a client handles that client’s assessment, and cases are discussed and interventions planned at weekly team meetings.

For advanced practice dietitians, the first session with a client often involves a complete physical assessment, not just a nutrition history. This includes a comprehensive medical history of all body systems. The diabetes-focused physical examination, just as performed by clinicians from other disciplines, includes height and weight measurement, body mass index (BMI) calculation, examination of injection sites, assessment of injection technique, and foot assessment.

Assessment also includes reviewing which medications the client is taking, evaluating their effectiveness and side effects, and determining the need for adjustment based on lifestyle, dietary intake, and blood glucose goals.

When carbohydrate counting is added to therapy, dietitians calculate carbohydrate-to-insulin ratios and teach clients how to use carbohydrate counting instead of a sliding-scale approach to insulin. Medications are adjusted based on clients’ lifestyles until blood glucose goals are achieved.

The therapeutic problem solving, regimen management, case management, and self-management training performed by advanced practice dietitians exceeds the traditional role of most dietetics professionals. 3  

A role delineation study for clinical nurse specialists, nurse practitioners, RDs, and registered pharmacists, 4 conducted in 2000 by the American Nurses Credentialing Center, reported equal findings among all four groups for the skills used to identify pathophysiology, analyze diagnostic tests, and list problems. Assessment for medical nutrition therapy typically includes evaluation of food intake, metabolic status, lifestyle, and readiness to change. For people with diabetes, monitoring glucose and measuring hemoglobin A 1c (A1C), lipids, blood pressure, and renal status are essential to evaluating nutrition-related outcomes.

The U.S. Air Force health care system conducted a pilot test giving RDs clinical privileges and evaluating their clinical judgment in patient nutritional care. A protocol was approved, and dietitians were allowed to order and interpret selected outpatient laboratory tests independently. The higher-level clinical judgments and laboratory privileges were linked to additional certifications. 5  

The Diabetes Prevention Program (DPP) also provided a unique opportunity for dietitians to demonstrate advance practice roles. 6 Dietitians served as lifestyle coaches, contacting participants at least once a month to address intervention goals. As case managers, they interviewed potential volunteers, assessed past experience with weight loss, and scheduled quarterly outcome assessments and weekly reviews of each participant’s progress at team meetings. Within the DPP’s central management, dietitians served as program coordinators and served on national study committees related to participant recruitment and retention, quality control, the use of protocols, and lifestyle advisory groups. 7  

Dietitians now play key roles in translating DPP findings and serving as community advocates to reduce the incidence of obesity and the health care burden of type 2 diabetes. This includes serving in a consultative role to other health care team members on issues regarding weight loss and risk factor reduction.

Advanced practice RDs offer comprehensive diabetes patient care services, including identifying patient goals and expected outcomes, selecting nonpharmacological and pharmacological treatments, and developing integrated plans of care. Problems discussed with patients range from acute and chronic diabetes complications to comorbid conditions, other conditions, preventive interventions, and self-management education. Advanced practice RDs also review patients’ health care resources and order laboratory tests if information is not available from referral sources. They provide supportive counseling and referral to specialists, as needed. And, they provide a full report of their findings and any regimen changes and recommendations they make to referring clinicians after each visit.

These activities and responsibilities go beyond the scope and standards of practice for the RDs and for RD, CDEs. 8 They will be included in the scope of practice document for RD, BC-ADMs that is now being developed by the Diabetes Care and Education Practice Group of The American Dietetic Association.

The following case study illustrates the clinical role of advanced practice dietitians in the field of diabetes.

B.C. is a 51-year-old white man who was diagnosed with type 1 diabetes 21 years ago. He believes that his diabetes has been fairly well controlled during the past 20 years and that his insulin needs have increased. He was recently remarried, and his wife is now helping him care for his diabetes.

His endocrinologist referred him to the RD for an urgent visit because 4 days ago he had a hypoglycemic event requiring treatment in the emergency room (ER). He has come to see the dietitian because his doctor and his wife insisted that he do so.

B.C. has had chronic problems with asymptomatic hypoglycemia. His last doctor’s visit was 3–4 weeks ago, when areas of hypertrophy were found. His endocrinologist asked him to change his injection sites from his thigh to his abdomen after the ER incident.

He does not think he needs any diabetes education but would like help in losing 10 lb. His body mass index is 25 kg/m 2 .

His medications include pravastatin (Pravacol), 10 mg daily; NPH insulin, 34 units in the morning and 13 units at bedtime; and regular insulin at breakfast and dinner following a sliding-scale algorithm. He also takes lispro (Humalog) insulin as needed to correct high blood glucose.

Before his ER visit, B.C. monitored his blood glucose only minimally, testing fasting and sometimes before dinner but not keeping records. Since his severe hypoglycemia 4 days ago, he has begun checking his blood glucose four times a day, before meals and bedtime.

Lab Results

B.C.’s most recent laboratory testing results were as follows:

A1C: 8.3% (normal 4.2–5.9%)

Lipid panel

    • Total cholesterol: 207 mg/dl (normal: 100–200 mg/dl)

    • HDL cholesterol: 46 mg/dl (normal: 35–65 mg/dl)

    • LDL cholesterol: 132 mg/dl (normal: <100 mg/dl)

    • Triglycerides: 144 mg/dl (normal: <150 mg/dl)

Creatinine: 0.9 mg/dl (normal: 0.5–1.4 mg/dl)

Microalbumin: 4 μg (normal: 0–29 μg)

At his initial visit with the RD for crisis management of asymptomatic hypoglycemia, she examined his injection sites and asked if he had made the changes recommended by his clinician. She reviewed his injection technique, diet history, incidence of hypoglycemia, and hypoglycemia treatment methods. She discussed with B.C. ways to reduce his risks of hypoglycemia, including food choices, insulin timing, and absorption variations at different injection sites.

The RD reinforced his clinician’s instruction to avoid old injection sites and added a new recommendation to lower insulin doses because of improved absorption at the new sites.

B.C. was now checking his blood glucose and recording results in a handheld electronic device in a form that could be downloaded, e-mailed, or faxed, but he was not recording his food choices. The dietitian asked him to keep food records and started his carbohydrate-counting education. A follow-up visit was scheduled for 1 week later.

At the second visit, B.C.’s mid-afternoon blood glucose was <70 mg/dl. He did not respond to treatment with 15 g carbohydrate from 4 oz. of regular soda. His blood glucose continued to drop, measuring 47 mg/dl 15 minutes later. He drank another 8 oz. of soda, and his blood glucose increased to 63 mg/dl 1 hour later. He then drank another 8 oz. of soda and ate a sandwich before leaving the dietitian’s office. He called in 1 hour later to report that his blood glucose was finally up to 96 mg/dl.

B.C.’s records showed a pattern of mid-afternoon hypoglycemia. He was willing to add a shot of lispro at lunch to his regimen, so the RD recommended reducing his morning NPH to prevent lows later in the day.

The RD also calculated insulin and carbohydrate ratios for blood glucose correction and meal-related insulin coverage using the “1500 rule” and the “500 rule.”

The 1500 rule is a commonly accepted formula for estimating the drop in a person’s blood glucose per unit of fast-acting insulin. This value is referred to as an “insulin sensitivity factor” (ISF) or “correction factor.” To use the 1500 rule, first determine the total daily dose (TDD) of all rapid- and long-acting insulin. Then divide 1500 by the TDD to find the ISF (the number of mg/dl that 1 unit of rapid-acting insulin will lower the blood glucose level). B.C.’s average TDD was 41 units. Therefore, his estimated ISF was 37 mg/dl per 1 unit of insulin. The RD rounded this up to 40 mg/dl to be prudent, given his history of hypoglycemia.

The 500 rule is a formula for calculating the insulin-to-carbohydrate ratio. To use the 500 rule, divide 500 by the TDD. For B.C., the insulin-to-carbohydrate ratio was calculated at 1:12 (1 unit of insulin to cover every 12 g of carbohydrate), but again this was rounded up to 1:14 for safety. Later, his carbohydrate ratio was adjusted down to 1:10 based on blood glucose monitoring results before and 2 hours after meals.

The RD taught B.C. how to use the insulin-to-carbohydrate ratio instead of his sliding scale to adjust his insulin and asked him to try to follow the new recommendations. With his endocrinologist’s approval, she reduced his NPH doses to 34 units and added a shot of lispro at lunchtime, the dose to be based on the amount of carbohydrate in the meal and his before-meal blood glucose level.

The RD asked B.C. to return in 1 week for evaluation and review of his new regimen. However, 3 days later, he returned after having had another severe episode of hypoglycemia.

In the course of these early visits, a good rapport developed between B.C. and the dietitian. B.C. learned that his judgment on how hypo- and hyperglycemia felt was often inaccurate and led him to make insulin adjustments that contributed to his hypoglycemia problems. By improving B.C.’s understanding of insulin doses and blood glucose responses, the RD hoped to help him become more skilled at making insulin dose adjustments. For the time being, however, he was still at risk for asymptomatic hypoglycemia. He had recently filled a prescription for glucagon, but the RD needed to review and encourage its proper use. She also provided literature to support his wife in case she needed to administer glucagon for him.

At this third visit, the RD reduced B.C.’s morning NPH dose to 22 units because of his rapid drop in blood glucose between noon and 1:00 p.m. This reduction finally eliminated his mid-afternoon lows.

B.C. had started using carbohydrate counting to make his decisions about lunchtime insulin doses. He liked carbohydrate counting because it gave him a more viable reason for testing his blood glucose frequently. Over the years, B.C.’s glycemia had become increasingly difficult to control. He had stopped checking his blood glucose because he felt unable to improve the situation once he had the information. In the early 1990s, his endocrinologist had started him self-adjusting insulin doses using the exchange system, but he found that he was always “chasing his blood sugars.” Carbohydrate counting changed everything. He now knew what to do to improve his blood glucose levels, and that made him feel more in charge of his diabetes.

Still, although carbohydrate counting led to more frequent testing and better blood glucose control than his old sliding scale, it was not perfect. At home, he had mastered this technique, but he ate many of his meals in restaurants, where carbohydrate counting was more challenging.

B.C. found it difficult to carry different types of insulin. This and his lifestyle suggested the need to change his multiple daily injections from regular to lispro insulin. He continued checking his blood glucose before and 2 hours after meals. His insulin-to-carbohydrate ratio of 1:10 g and his ISF of 1:40 mg/dl allowed him to stay within his goal of no more than a 30-mg/dl increase in blood glucose 2 hours after meals. He continued to be asymptomatic of hypoglycemia, but lows occurred less frequently. The new goal of therapy was to recover his hypoglycemia symptoms at a more normal level of about 70 mg/dl. He was scheduled for another visit 2 weeks later.

Between visits to the RD, BC-ADM, his clinician identified problems with the timing of his long-acting insulin peak, resulting in early nocturnal lows. Based on the clinician’s clinical experience of lente demonstrating a slightly smoother peak, she changed B.C.’s long-acting insulin unit-for-unit from NPH to lente.

At B.C.’s next visit, he and the RD reviewed his insulin doses of 22 units of lente in the morning and 11 units of lente at night. His TDD including premeal lispro now averaged 49 units. His average blood glucose levels were 130 mg/dl fasting, 100 mg/dl mid-afternoon, 127 mg/dl before dinner, and 200 mg/dl at bedtime.

The bedtime levels were higher because of late meals, the fat content of restaurant meals, his meat food choices, and his inexperience at counting carbohydrates for prepared foods. The dietitian suggested mixing regular and lispro insulin to try and get the average bedtime blood glucose level to 140 mg/dl. Mixing his calculated dose to be one-third regular and two-third lispro would provide coverage lasting a little longer than that of just lispro to cover higher-fat foods that took longer to digest. At the same time, the dietitian encouraged B.C. to choose lower-fat foods to help reduce his LDL cholesterol and assist with weight loss. B.C. now had an incentive to keep accurate food records to help evaluate his accuracy at calculating insulin doses.

B.C. and the RD also reviewed his decisions for treating lows. At his first meeting, B.C. ate anything and everything when he experienced hypoglycemia, which often resulted in blood glucose levels >400 mg/dl. Now, he was appropriately using 15–30 g of quick-acting glucose—usually 4–8 oz. of orange juice. He based this amount on his blood glucose level, expecting about a 40-mg/dl rise over 30 minutes from 10 g of carbohydrate. He checked his glucose level before treating when possible and always checked 15–30 minutes after treating to evaluate the results. Once his glucose reached 80 mg/dl or above, he either ate a meal or ate 15 g of carbohydrate per hour to prevent a recurrence of hypoglycemia until his next meal.

In completing her assessment during the next few meetings with B.C., the RD identified a problem with erectile dysfunction. She notifed his clinician and referred him to a urologist. Eventually, the urologist diagnosed reduced blood flow and started B.C. on sildenafil (Viagra).

B.C. wanted to resume exercise to help his weight loss efforts. Because exercise improves insulin sensitivity and can acutely lower blood glucose, the dietitian taught B.C. how to reduce his insulin doses by 25–50% for planned physical activity to further reduce his risks of hypoglycemia. He learned to carry his blood glucose meter, fluids, and carbohydrate foods during and after exercise. His pre-exercise blood glucose goal was set at 150 mg/dl. The dietitian instructed B.C. to test his blood glucose again after exercise and to eat carbohydrate foods if it was <100 mg/dl.

She also gave instructions for unplanned exercise. He would require additional carbohydrate depending on his blood glucose level before exercise, his previous experience with similar exercise, and the timing of the exercise. Education follow-ups were scheduled with the dietitian for 1 month later and every 3 months thereafter.

At his next annual eye exam, B.C. discovered that he had background retinopathy. He also reported feeling that his daily injection regimen had become too complicated. Still feeling limited in his ability to control his diabetes and looking for an alternative to insulin injections, he wanted to discuss continuous subcutaneous insulin infusion therapy (insulin pump therapy).

He, his endocrinologist, and his dietitian discussed the pros and cons of pump therapy and how it might affect his current situation. They reviewed available insulin pumps and sets and agreed on which ones would best meet his needs. The equipment was ordered, and a training session was scheduled with the dietitian (a certified pump trainer) in 1 month.

B.C. started using an insulin pump 2 years after his first visit with the dietitian. His insulin-to-carbohydrate ratio was adjusted for his new therapy regimen, and a new ISF was calculated to help him reduce high blood glucose levels. His endocrinologist set basal insulin rates at 0.3 units/hour to start at midnight and 0.5 units/hour to start at 3:00 a.m. This more natural delivery of insulin based on B.C.’s body rhythms and lifestyle further improved his diabetes control.

One week after starting pump therapy, B.C. called the dietitian to report large urine ketones and a blood glucose level of 317 mg/dl. His endocrinologist had changed his basal rates, but he wanted to meet with the dietitian to review his sites, set insertion, troubleshooting skills, and related issues. Working together, they eventually discovered that problems with his pump sites required using a bent-needle set to resolve absorption issues.

B.C’s relationship with his endocrinologist and dietitian was seamless. He met with the dietitian when his clinician was unavailable or when he needed more time to work through problems.

B.C. has met with the RD 15 times over 3 years. Eventually, he recovered symptoms of hypoglycemia when his blood glucose levels were 70 mg/dl. After 6 months of education meetings, his lab values had reached target ranges. Most recently, his LDL cholesterol was <100 mg/dl, his A1C results were <7%, his hypoglycemia symptoms were maintained at a blood glucose level of 70 mg/dl, and his blood glucose had been stabilized using the square-wave and dual-wave features on his insulin pump.

B.C. learned how to achieve recommended goals and to self-manage his diabetes with the help of his care team: endocrinologist, cardiologist, ophthalmologist, podiatrist, urologist, and advanced practice dietitian.

Advanced practice dietitians in diabetes work in many settings and see clients referred from many different types of medical professionals. They may see clients either before or after their appointments with other members of the health care team, depending on appointment availability and their need for nutrition therapy and diabetes education. Referring clinicians rely on their evaluations and findings. When necessary, clinician approval can be obtained for immediate interventions, enhancing the timeliness of care.

Why would an RD want to obtain the skills and certification necessary to earn the BC-ADM credential? The answer, as illustrated in the case study above, lies in their routine use of two sets of skills and performance of two roles: patient education and clinical management.

Dietitians who specialize in diabetes often find that their role expands beyond provider of nutrition counseling. As part of a multidisciplinary team, they become increasingly involved with patient care. As they move patients toward self-management of their disease, they necessarily participate actively in assessment and diagnosis of patients; planning, implementation, and coordination of their diabetes care regimens; and monitoring and evaluation of their treatment options and strategies. They find that their daily professional activities go beyond diabetes education, crossing over into identifying problems, providing or coordinating clinical care, adjusting therapy, and referring to other medical professionals. They often work independently, providing consultation both to people with diabetes and to other diabetes care team members.

The BC-ADM credential acknowledges this professional autonomy while promoting team collaboration and thus improving the quality of care for people with diabetes. The new certification formally recognizes advanced practice dietitians as they move beyond their traditional roles and into clinical problem solving and case management.

Claudia Shwide-Slavin, MS, RD, BC-ADM, CDE, is a private practice dietitian in New York, N.Y.

Note of disclosure: Ms. Shwide-Slavin has received honoraria for speaking engagements from Medtronic Minimed, which manufactures insulin pumps, and Eli Lilly and Co., which manufactures insulin products for the treatment of diabetes.

Email alerts

• Advanced Practice Care: Advanced Practice Care in Diabetes: Epilogue
• Advanced Practice Care: Advanced Practice Care in Diabetes: Preface
• Online ISSN 1944-7353
• Print ISSN 1040-9165
• Diabetes Care
• Clinical Diabetes
• Diabetes Spectrum
• Standards of Medical Care in Diabetes
• Scientific Sessions Abstracts
• BMJ Open Diabetes Research & Care
• ShopDiabetes.org
• ADA Professional Books

Clinical Compendia

• Clinical Compendia Home
• Latest News
• DiabetesPro SmartBrief
• Special Collections
• DiabetesPro®
• Diabetes Food Hub™
• Insulin Affordability
• Know Diabetes By Heart™
• About the ADA
• Journal Policies
• For Reviewers
• Advertising in ADA Journals
• Reprints and Permission for Reuse
• Copyright Notice/Public Access Policy
• ADA Professional Membership
• ADA Member Directory
• Diabetes.org
• X (Twitter)
• Cookie Policy
• Accessibility
• Terms & Conditions
• Get Adobe Acrobat Reader
• © Copyright American Diabetes Association

This Feature Is Available To Subscribers Only

Sign In or Create an Account

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 16 October 2020

Risk factors for type 1 diabetes, including environmental, behavioural and gut microbial factors: a case–control study

• Deborah Traversi 1 , 8 ,
• Ivana Rabbone 2 , 7 ,
• Giacomo Scaioli 1 , 8 ,
• Camilla Vallini 2 ,
• Giulia Carletto 1 , 8 ,
• Irene Racca 1 ,
• Ugo Ala 5 ,
• Marilena Durazzo 4 ,
• Alessandro Collo 4 , 6 ,
• Arianna Ferro 4 ,
• Deborah Carrera 3 ,
• Silvia Savastio 3 ,
• Francesco Cadario 3 ,
• Roberta Siliquini 1 , 8 &
• Franco Cerutti 1 , 2  

Scientific Reports volume  10 , Article number:  17566 ( 2020 ) Cite this article

6942 Accesses

14 Citations

28 Altmetric

Metrics details

• Microbiology
• Molecular biology
• Risk factors

Type 1 diabetes (T1D) is a common autoimmune disease that is characterized by insufficient insulin production. The onset of T1D is the result of gene-environment interactions. Sociodemographic and behavioural factors may contribute to T1D, and the gut microbiota is proposed to be a driving factor of T1D. An integrated preventive strategy for T1D is not available at present. This case–control study attempted to estimate the exposure linked to T1D to identify significant risk factors for healthy children. Forty children with T1D and 56 healthy controls were included in this study. Anthropometric, socio-economic, nutritional, behavioural, and clinical data were collected. Faecal bacteria were investigated by molecular methods. The findings showed, in multivariable model, that the risk factors for T1D include higher Firmicutes levels (OR 7.30; IC 2.26–23.54) and higher carbohydrate intake (OR 1.03; IC 1.01–1.05), whereas having a greater amount of Bifidobacterium in the gut (OR 0.13; IC 0.05 – 0.34) was a protective factor for T1D. These findings may facilitate the development of preventive strategies for T1D, such as performing genetic screening, characterizing the gut microbiota, and managing nutritional and social factors.

Similar content being viewed by others

Effects of a personalized nutrition program on cardiometabolic health: a randomized controlled trial

Microbiota in health and diseases

Associations of dietary patterns with brain health from behavioral, neuroimaging, biochemical and genetic analyses

Introduction.

Type 1 diabetes (T1D) is a multifactor disease caused by β-cell destruction (which is mostly immune-mediated) and absolute insulin deficiency. At present, the management of T1D has been improved, but the disease remains incurable. T1D onset is most common in childhood. T1D represents approximately 5–10% of all diabetes diagnoses 1 . Between 70 and 90% of T1D patients at diagnosis exhibit evidence of an immune-mediated process with β-cell autoantibodies. T1D onset is preceded by a preclinical period that lasts approximately 3 years, in which autoantibodies appear in the circulatory system 2 . Immune destruction of the β-cells can be detected by the evaluation of some haematic markers 3 . The disease has strong HLA associations, which explain nearly half of the genetic disease predisposition, while the remainder is due to other genetic polymorphisms 3 , 4 .

Analysis of genetic disease susceptibility suggests that there is a greater risk of T1D development when the father is affected by the disease than when the mother is affected 5 . On the other hand, there is evidence that a critical role is played by non-genetic factors, including both environmental and host-related factors, which are considered to play decisive roles in the disease process, leading to the manifestation of clinical T1D 6 .

The worldwide incidence of T1D in the age group of 0–15 years varies considerably by region (from 0.5 to 60 per 100,000 children), and the yearly increase ranges from 0.6% to 9.3%. In Europe, the percentage of cases in the age group of 0–15 years will rise by 70% 7 . In the Piedmont region, up to 2013, there were approximately 8,000 cases in this age group with an incidence of 27 new diagnoses per 100,000 8 . Migrant populations tend to show an incidence of diabetes similar to that of most host populations; therefore, a higher T1D incidence in migrant children was observed in Europe 6 , 9 , 10 . Such a pronounced increase in incidence cannot be attributable to genetic factors alone. Other major risk factors may include the environment, Western lifestyle and nutrition 10 . Other diseases with immune involvement, such as allergies, exhibit a similar trend, suggesting an inductor role for exogenous factors regarding the increased predisposition to autoimmunity 11 . Preventive measures to reduce the incidence of T1D have not been defined to date. Various factors seem to be involved in modulating the incidence of T1D, including birth delivery mode, feeding, birth weight, infections (especially viral), dietary behaviour, and pharmaceutical use (especially antibiotics). Such factors may contribute to T1D development during the early disease stage 12 ; however, compared with genetic factors, environmental factors are less well characterized 13 . β- Cell vulnerability to stress factors has been discussed as the basis of the overload hypothesis 14 . Associations among the microbiome, metabolome, and T1D were shown, highlighting a host-microbiota role in the onset of the disease 12 , 15 . The origin of the disease process was suspected to be gut microbiota dysbiosis (imbalances in the composition and function of intestinal microbes) associated with altered gut permeability and a major vulnerability of the immune system 6 . Accordingly, evidence obtained from both animal models and human studies suggests that the gut microbiota and the immune system interact closely, emphasizing the role of the intestinal microbiota in the maturation and development of immune functions 16 . Recently, mycobiome-bacteriome interactions, as well as intestinal virome and islet autoimmunity, were hypothesized to be drivers of dysbiosis 17 . Several studies have specifically investigated microbiota composition in children with T1D 18 , 19 , 20 , but the results have not been consistent. Interestingly, most studies are in agreement regarding the reduced microbial diversity observed in subjects with T1D compared with controls; moreover, the microbiota structure in T1D subjects was found to be different from that of control subjects 21 , 22 . To date, a typical T1D-associated microbiota has not been identified 23 , 24 , 25 , 26 . The research also determined that T1D clinical management could be improved by in-depth analysis of the partial remission phase 27 ; however, preventive measures are limited and generally focus only on genetic susceptibility 28 and general population screening for islet autoimmunity 29 . The development of an integrated prediction strategy could be useful for increasing early diagnosis while avoiding onset complications by identifying children at risk of T1D to place under observation and, in the future, to treat with preventive methods 10 .

The aim of this study is to identify environmental, behavioural, and microbial risk factors of T1D onset to develop an integrated T1D preventive management strategy that is suitable for paediatricians in the Piedmont region.

Subject description and origin factor analysis

To analyse the origin factor, the study population was subdivided by the children's origins (Italian and migrant, 69 and 27 children, respectively). An analysis of the socio-demographic and behavioural factors examined in the study showed many differences between Italian and migrant children, while other variables appear to be quite homogeneous (Table 1 ). In the studied cohort, migrant status did not produce a significant increase in T1D onset.

Approximately 79% of the children in the cohort had siblings; approximately 40% of the included children lived with a pet in the house, and more than 65% of the children took antibiotics during the first two years of life. The residency zone was notably different between Italians and migrants: the percentage of migrant children living in urban sites was higher but not significant following the adjusted model. Regular sports activities seem to be practised more by Italian children than by migrant children (73.5% vs 51.8%, p = 0.054). A total of 77.9% of Italian children and 55.6% of migrant children were subjected to regular health check-ups (p = 0.017). A significant difference was confirmed for the ages of the migrant mother and father (Table 1 ), meanly 6 years and 4 years younger respectively at recruitment, respect the Italians (p = 0.017 and p = 0.0425). The analysis of eating habits and nutritional intake revealed that the majority of the children were breastfed. Moreover, the weaning age was 6 months, as recommended. Migrant children showed higher total carbohydrate intake (+ 12%, p = 0.044) and simple carbohydrate intake (+ 24%, p = 0.0045). Moreover, among migrants, the children tended to access food by themselves and to consume meals alone. The percentage of migrant children who ate meals while watching TV was higher but not significant. Finally, the one-course meal was more frequent in migrant families (ratio 1:3, p = 0.006).

The analysis of microbiota and bioindicator species displayed no significant differences between Italian and migrant children: the qRT-PCR measurements showed a trend of greater value for the total bacteria (both for the experimental design with and without probe), Bacteroides and M. smithii (both using 16S rDNA and nifH) in migrant children. The DGGE profile and dendrogram analysis did not show a different clustering pattern based on the origin, and the migrant group showed a trend towards greater α-diversity of the faecal microbiota profiles (Shannon index + 5%). Additionally, the α-diversity analyses in next generation sequencing (NGS) showed a difference in taxonomic units (OTUs), i.e., there were more OTUs in migrants than in Italians, but the difference was not significant, though it was close to the limit of significance (p = 0.057). Furthermore, the phylogenetic diversity index (Faith PD) suggested that the origin of the subjects could influence the structure of the microbial community. Although the overall number of OTUs did not change significantly, the phylogenetic distance of the individual OTUs was greater in the migrant group than in the Italian group, as the OTUs occupied a broader ecological niche in the migrant group.

T1D risk factors

Previous results indicated that being a migrant child in the Piedmont region is not a significant risk factor for T1D onset 30 . Table 2 shows single logistic regressions performed to estimate the impact of the different variables on the outcome. Notably, the analysis of socio-demographic, behavioural, and nutritional determinants revealed that having parents with at least a high school certificate seems to be a protective factor for T1D onset, even if not significant after adjusted comparisons.

High total caloric intake, as well as high protein intake and consumption of total carbohydrates, are associated with only a slightly increased risk of T1D onset.

The DGGE gel and the results of the cluster analysis are shown in Fig.  1 . The Pearson similarity clustering showed macro beta-diversity differences between the T1D patients and healthy children, with the main division being in two different clusters.

DGGE banding patterns and the results of the analysis in which the Pearson coefficient (numbers reported near the nodes) was used for measuring similarity in banding patterns. The cluster identifies T1D patients (red lines) and healthy children (green lines).

Firmicutes and Bacteroidetes followed by Proteobacteria and Actinobacteria (Table 3 ) predominantly composed the gut microbiota of all children. In the children with diabetes, an increase in the levels of three members of Bacteroidetes ( Alistipes senegalensis , Bacteroides timonensis , and Barnesiella intestinihominis ) and three members of Firmicutes ( Christensenella timonensis ,

Ruminococcus bromii , and Urmitella timonensis ) was observed by sequencing.

Furthermore, other notable results were obtained by NGS analyses. The taxonomic analysis revealed that the gut microbiota of the study participants was composed of nine relevant phyla: Firmicutes, Bacteroidetes, Actinobacteria, Proteobacteria, Verrucomicrobia, Euryarchaeota, Tenericutes, Cyanobacteria, and an unclassified phylum.

Moreover, beta-diversity analyses were carried out to highlight the differences among the samples based on the structures of their microbial communities. The weighted UniFrac metric showed that the samples were not subdivided into clusters. The intragroup and intergroup distances were comparable, and there was no separation between the clusters. These findings were confirmed by the Permanova test. Finally, analyses of the differential abundance were performed to compare the increase or decrease in the abundance of one or more bacteria in the case and control groups. DeSeq2 showed 48 significantly abundant OTUs (p < 0.001). The most abundant OTU was Rikenellaceae followed by Prevotellaceae ( Prevotella copri ), Barnesiellaceae , Lachnospiraceae, and Ruminococcaceae ( Ruminococcus bromii ), which were significantly more abundant in children with diabetes.

The difference in the results observed between methods is an interesting discussion point. The methods are characterized by different sensitivities; they represent different molecular perspectives regarding the faecal microbiota. When a method with a higher sensibility is used (NGS), a flattening effect is possible. On the other hand, the major abundance of such genera as Ruminococcu s was confirmed by different microbiota study methods, which is in keeping with the qRT-PCR results. A group of 23 samples showed different clusterization compared to the others (Fig.  2 , left). This small group was not different from the main group regarding any characteristics. The only significant difference was observed for the M. smithii presence and the A. muciniphila levels, both of which were higher in the separated group (Fig.  2 , right). A. muciniphila was proposed as a probiotic 31 , while M. smithii has been characterized as the most abundant methanogen in the gut 32 .

Left-Unweighted UniFrac graph of the NGS results. There are two identifiable groups: the blue circle (main group) and the red circle (separated group). No experimental hypothesis was confirmed for the cluster definition. On the Right: box plot of the qRT-PCR results for some microbiological targets ( Akkermansia muciniphila and Methanobrevibacter smithii ), the difference between the groups is significant (t-test p < 0.05).

The qRT-PCR gut microbiota analysis indicated significant differences among T1D patients and healthy children (Table 2 ). The logistic regression analysis showed that the increase in the Margalef index was associated with a decrease in the likelihood of disease onset (OR 0.20; 95% CI 0.09–0.46, p = 0.000). Increased Firmicutes levels and decreased Bacteroidetes levels were significant risk factors for T1D (OR 7.49; 95% CI 3.25–17.28, p = 0.0001; OR 0.28; 95% CI 0.15–0.51 p = 0.0001, respectively). Moreover, Bifidobacterium spp. was a protective factor for T1D onset (OR 0.20; 95% CI 0.10–0.38, p = 0.0001).

The multivariable analysis produced a R 2  = 0.6259 (p < 0.001). After adjusting for confounding factors, the likelihood of having diabetes is significantly higher in those with higher amount of Firmicutes, lower amount of Bifidobacterium spp and a higher amount of total carbohydrate intake (Table 4 ).

T1D is an important disease that affects health with onset primarily occurring in childhood. At present, there is no cure for this disease, and only disease management is possible. The disease burden of T1D is immense, especially considering the number of years of life lost due to disability but also the years of life lost due to premature death. The life expectancy for T1D patients is approximately 16 years shorter than that of the comparable healthy population 33 . Even if relevant risk factors are known, to date, such scientific determinants do not include a screening programme for preventive purposes. Of course, preventive action must be considered as a systematic process that focuses on the main risk factors to identify children at higher risk of T1D and to suggest efficacious preventive treatments. In the study, the main T1D onset risk factors seem to be identifiable in the composition of the microbiota and, in particular, the microbiota α-diversity, Firmicutes and Bacteroidetes levels and their ratio, as well as the Bifidobacterium level. Similar evidence was obtained by other studies, which observed both higher Bacteroidetes in T1D patients 34 , 35 and less abundant anti-inflammatory genera in children with multiple islet autoantibodies 36 . Reduced microbial diversity appears to become significant between seroconversion and overt T1D 15 . A significant difference in the Bifidobacterium level was observed in different studies, including both a small cohort of autoimmune children 37 , 38 and a larger population associated with such protective factors as breastfeeding 21 . At the genus level, a significant difference in, for example, Blautia (increased in patients), was observed 39 ; however, in other studies, different single species ( Bacteroides ovatus ) seem to be more abundant in patients than in the controls 18 . However, prior studies suggest the presence of duodenal mucosa abnormalities in the inflammatory profile for T1D patients 22 , 40 and on the T1D-related changes in the gut microbiota, even if proving the causality of these factors has remained challenging 21 .

The characterization of the microbiota is rapidly evolving. Traditional methods that are not as sensitive as PCR-DGGE are still suitable, while NGS methods are expanding. Sophisticated whole-genome sequencing methods integrated with metabolomics and proteomics have been proposed. However, the large amount of data, being affected by multiple confounding factors, has not had a clear impact on T1D prevention strategies. The development of a simple method to describe microbiota modulation using validated biomarkers, which could serve as a rapid screening test, may be warranted.

Another risk factor is the occurrence of stress due to a traumatic or emotional experience. This stress seems to be able to affect the autoimmunity process. Therefore, particular attention could be paid to such risk factors for T1D risk in children.

A high education level of one or both parents could be also protective, suggesting that socioeconomic factors affect the T1D risk. Other factors, identified as significant risk modulators among behavioural and nutritional factors, had minor effects.

The study has some potential limitations, including susceptibility to bias in recollection about exposure and reverse causality. The exposure recollection could be biased, but this issue can be less influential at the onset, as in this study. Moreover, recruitment at the onset guarantees a temporal coherence of the exposure with respect to the disease onset.

T1D is one of the most frequently diagnosed diseases in children; however, it is not a high-incidence disease. The prospective inclusion of a large number of healthy children, which is needed for the observation of enough cases, requires a very long time of observation. Moreover, a restricted age range was necessary in children for the rapid changes in behaviour and microbiota. This requirement resulted in an additional included subject restriction. On the other hand, the study of multifactorial diseases with poorly understood pathogenic pathways is imperative, even if it is at risk for obtaining less conclusive evidence. Of course, such a study alone could not elucidate the causation process, but the evidence obtained could be important for the selection of higher-risk subpopulations, planning of future research, and improving prevention.

Identification of a higher-risk subpopulation is strictly relevant for the subsequent validation of an efficient preventive screening to be produced with a prospective method. Of course, the pathogenesis of type 1 diabetes has not been fully elucidated to date; however, in this study, various factors (associated with both the disease and the microbiota composition) were included, such as the origin of the children, the age of the mother, the age of breastfeeding and the age of weaning. Other possible confounding factors not included in our analysis are viral infections, particularly enteroviruses, and preterm birth; however, there was no clear consensus regarding these novel factors at the beginning of the study.

Concerning the microbiota, the knowledge is still incomplete, and various factors can interact to produce a T1D risk modulation that is not explainable at present. Moreover, the results obtained using different techniques were also dissimilar (for example, clusterization due to β-diversity analysis). This finding is likely due to the different sensitivities of the applied methods 41 . Furthermore, even if the time between the symptom comparison and the diagnosis is very short, there is a danger of biased estimates due to reverse causality.

In conclusion, this study confirmed that T1D onset risk is modulated by compositional changes in the gut microbiota and that such evidence must be employed to devise preventive measure. The results showed that the gut microbial indicators found in children with T1D differ from those found in healthy children. These findings also pave the way for new research attempting to develop strategies to control T1D development by modifying the gut microbiota. However, a better knowledge of gut microbial composition associated with the development of T1D must be obtained to choose the best treatment 10 , 42 , 43 , 44 , 45 .

In brief, direct or indirect manipulations of the intestinal microbiome may provide effective measures for preventing or delaying the disease process leading to the manifestation of clinical T1D. At present, a preventive strategy could be developed that includes the main genetic and microbiome risk factors. Then, this strategy could be applied to healthy children to reduce the burden of T1D.

Study design and participants

The case–control study began in January 2016 46 and ended in September 2018 (case–control phase of clinicaltrial.gov Protocol ID: G12114000080001). The work was conducted following the STROBE Statement for a case–control study. The activity is bicentric and includes the two main paediatric hospitals in the Piedmont region (located in Torino and Novara), which cover the clinical management for cases of T1D in the region. The ethics committees of the two hospitals approved the research activities during 2015 (“Comitato etico interaziendale A.O.U. Ordine Mauriziano di Torino ASLTO1” with record number 0117120 and “Comitato etico Interaziendale A.O.U. “Maggiore della Carità” ASL BI, NO, VCO” record number 631/CE).

The recruitment included 40 paediatric patients with T1D (cases) and 56 healthy children (controls), who were comparable in terms of age, gender, and ethnicity to avoid bias. The included subjects represent the most convenient sample possible. The inclusion criteria were age (5–10 years), normal weight, and residence in Piedmont. Exclusion criteria were celiac disease, chronic disease diagnosis, eating disorders, active infections, use of antibiotics and/or probiotics and/or any other medical treatment that influences intestinal microbiota during the 3 months before recruitment and children with parents of mixed origins (Italian and migrant) for the exclusion of important confounding factors due to genetic and cultural mixed backgrounds 19 .

The T1D children were integrated into the study at disease onset, with hyperglycaemia, with or without ketoacidosis, polyuria symptoms, a high value of glycated haemoglobin (HbA1c > 42 mmol/mol) and T1D-specific autoantibody positivity. Healthy children were contacted by paediatricians in the territory of the acute care system. The guardians of the enlisting children read, understood, and then signed informed consent forms following the declaration of Helsinki. A module is prepared for parents, children, and mature children 47 . All the following methods were carried out following relevant guidelines and regulations when available. A questionnaire was given to the parents containing items and questions to retrieve data on the family contest with particular regards to emotive stressors, such as mourning or separation, anthropometrics, and socio-demographic, nutritional, and behavioural information.

Anthropometric and nutritional data included weight, height, body mass index (BMI), food frequency based on 24-h recall and a food frequency questionnaire (FFQ), neonatal feeding, and age of weaning. The anthropometric parameters (weight and height) were measured according to standard recommendations. The BMI values were interpreted according to the WHO criterion. The 24-h recall technique reconstructed the meals and food intake on a recent "typical" day, estimating the bromatological inputs according to a food composition database for epidemiological studies in Italy (BDA). The FFQ, developed for the study, focused on the consumption of certain food categories (those containing sugars, fibre, omega-3, calcium, vitamin D, condiments, and cereals) and eating habits (e.g., alone or with adults, in front of the TV).

Twenty-eight percent of the involved population is migrants (both parents not Italian). Such data are consistent with the percentage of newborns from non-Italian mothers, which is approximately 30% in northern Italy 48 . The migrant group included children coming mainly from northern Africa and Eastern Europe. The migration involved the parents and sometimes the children; on average, the included children as migrants were residents in Italy for less than 5 years. At the end of recruitment, no significant differences were observed between the case and control groups for age, sex composition, and origins (criteria for pairing) or for height, weight, and BMI (T-test, p > 0.05) (Table 5 ).

Sample collection and DNA extraction

A kit for stool collection was delivered to each study participant following a validated procedure 49 , 50 and using a Fecotainer device (Tag Hemi VOF, Netherlands). Faecal samples were homogenized within 24 h in the laboratory, and five 2 g aliquots were stored at − 80 °C until DNA isolation was performed. Total DNA extractions from the stool samples were performed using the QiaAmp PowerFecal DNA Kit (QIAGEN, Hilden, Germany). The nucleic acids were quantified using a NanoQuant Plate (TECAN Trading AG, Switzerland), which allows quantification using a spectrophotometer read at 260 nm. The spectrophotometer used was the TECAN Infinite 200 PRO, and the software was i-Control (version 1.11.10). The extracted DNA concentrations ranged from 1.1–155.5 ng/μl (mean 41.35 ± 38.70 ng/μL). Samples were stored at –20 °C until molecular analysis was performed.

The PCR products for denaturing gradient gel electrophoresis (DGGE) were obtained by amplifying the bacterial 16S rRNA genes following a marker gene analysis approach 51 . The primer pairs were 357F-GC and 518R (Table 6 ) 52 . All PCRs were performed with the T100 Bio-Rad Thermocycler in a 25-μl reaction volume containing 1X Master Mix (166–5009, Bio-Rad, Berkeley, CA, USA), 0.02 bovine serum albumin (BSA), 0.4 μM of each primer, and 2 μl of DNA diluted 1:10 in sterile DNase-treated water. DGGE was carried out using a DCode System (Bio-Rad) with a 30–50% denaturing gradient of formamide and urea 53 . Electrophoresis ran at 200 V for 5 h at 60 °C in 1X TAE buffer. Gels were stained for 30 min with SYBR Green I nucleic acid gel stain (10.000X in DMSO, S9430, Sigma-Aldrich, USA) and were visualized using the D-Code XR apparatus from Bio-Rad. Then, DGGE bands were excised, incubated overnight at − 20 °C, washed, and crushed in 20 μl of molecular-grade water. The supernatant (2 μl) was used as a template and reamplified, as previously described, without BSA and using modified linker-PCR bacterial primers (357F-GC; 518R-AT-M13) (Table 6 ) 19 , 52 , 54 , 55 , 56 , 57 , 58 , 59 , 60 . The obtained PCR products were sequenced with Sanger sequencing (Genechron-Ylichron S.r.l.). The sequence similarities were obtained by the National Centre for Biotechnology Information (NCBI) database using nucleotide Basic Local Alignment Search Tool (BLASTn) analysis.

High-throughput DNA sequencing and analysis were conducted by BMR Genomics s s.r.l. The V3-V4 region of 16S rDNA was amplified using the MiSeq 300PEPro341F and Pro805R primer pair 6 . The sample reads were above 12*10 6 . The reaction mixture (25 μl) contained 3–10 ng/μl genomic DNA, Taq Platinum HiFi (Invitrogen, Carlsbad, CA), and 10 μM of each primer. The PCR conditions for amplification of DNA were as follows: 94 °C for 1 min (1X), 94 °C for 30 s, 55 °C for 30 s, 68 °C for 45 s (25X), and 68 °C for 7 min (1X). PCR products were purified through Agencourt XP 0.8X Magnetic Beads and amplified shortly with the Index Nextera XT. The amplicons were normalized with SequalPrep (Thermo Fisher) and multiplexed. The pool was purified with Agencourt XP 1X Magnetic Beads, loaded onto MiSeq, and sequenced with the V3 chemistry-300PE strategy.

Starting from the extracted DNA, the following microbial targets were quantified by qRT-PCR using a CFX Touch Real-Time PCR Detection System (Bio-Rad-Hercules, CA) and CFX Manager (3.1 Software): total Bacteria, Bacteroidetes, Bacteroides spp., Firmicutes, Bifidobacterium spp., Akkermansia muciniphila, and Methanobrevibacter smithii . Total bacteria and M. smithii were detected following two reaction designs. For M. smithii , the analysis was performed using as target both the 16S rDNA and then a specific functional gene ( nifH ). For total bacteria, quantification was carried out using a protocol with or without a probe. For the determination of total bacteria (method without probe), Bacteroidetes, Bacteroides spp., Firmicutes, Bifidobacterium spp. and Akkermansia muciniphila , 2 µl of 1:10 extracted DNA was added to a reaction mixture consisting of 10 µl Sso Advance SYBR Green Supermix (172–5261, Bio-Rad), 0.5 µl each of the forward and reverse primers (10 µM final concentration) and 7 µl of ultrapure water in a 20 µl final reaction volume. The reaction conditions were set as follows: 95 °C for 3 min (1X), 95 °C for 10 s, and 59 °C for 15 s (57 °C for Bacteroidetes spp. and 60 °C for Firmicutes), 72 °C for 10 s (39X), 65 °C for 31 s, 65 °C for 5 s + 0.5 °C/cycle, ramp 0.5 °C/s (60X). Moreover, for the determinations of M. smithii and total bacteria (method with probe), the reaction was as follows. Two microlitres of 1:10 extracted DNA was added to a reaction mixture consisting of 10 µl IQ Multiplex PowerMix (Bio-Rad-Hercules, CA), 0.2 µl of the molecular probe (10 µM), 0.5 µl each of the forward and reverse primers (10 µM final concentration) and 6.8 µl of ultrapure water in a 20 µl final reaction volume. The reaction conditions were 95 °C for 3 min (1X), 95 °C for 10 s, 59 °C for 15 s, 72 °C for 15 s (39X), and 72 °C for 5 min. Standard curves were produced with serial six-fold dilutions of genomic DNA from the microorganism target, provided by ATCC (Manassas, Virginia, USA) or DSMZ (Braunschweig, Germany). All PCR tests were carried out in triplicate. Table 6 provides detailed information regarding oligonucleotide sequences and genomic standards 19 , 54 , 55 , 56 , 57 , 58 , 59 , 60 . The PCR efficiencies were always between 90 and 110%. To confirm the amplification of each target, gel electrophoresis was performed on 2% agarose gels.

Data elaboration and statistical analyses

The statistical analysis was performed using STATA version 11.0. Moreover, the data on the included T1D patients and healthy controls were elaborated to highlight the likelihood of having diabetes. A descriptive analysis of the variables was conducted. The data were reported as absolute numbers and percentages for categorical variables and as means and standard deviations for continuous variables. Moreover, the subjects were divided by individual origins into two groups: Italian and migrant, considering the origin of the children and their families, to show differences in the distribution of disease determinants and to assess whether being a migrant could be associated with T1D onset. Differences between Italian and migrant children were assessed using the χ 2 test with Fisher’s correction for categorical variables and Student’s t-test for continuous variables. Univariable logistic regression was then performed to estimate the impact of sociodemographic, nutritional, and microbiota-related variables on the outcome. These associations were expressed as odds ratios (OR) at a 95% confidence interval (CI). Moreover, the adjusted p-value for multiple comparisons was calculated using the Benjamini and Hochberg false discovery rate method. We conducted multivariable analyses including various variables (age, gender, Firmicutes, Bifidobacterium spp ., and total carbohydrate intake) and the risk of type 1 diabetes using logistic regression models. The Spearman rank-order correlation coefficient was also determined to assess the relationships between variables. A p-value p < 0.05 was considered significant for all analyses.

The DGGE gel analysis was performed with Bionumerics 7.2. The hierarchical classification was performed with a UPGMA system (1% tolerance and optimization level) and Pearson correlation. Simpson's diversity index, Shannon’s index, and Margalef index were calculated for each DGGE profile to evaluate alpha diversity.

NGS bioinformatics analysis was performed with the software pipeline Qiime2. The reads were cleaned up by the primers using the software Cutadapt (version 2018.8.0) and processed with the software DADA2. The sequences were trimmed at the 3′ end (forward: 270 bp; reverse 260 bp), filtered by quality, and merged with default values. Subsequently, the sequences were elaborated to obtain unique sequences. In this phase, the chimaeras (denoised-paired) are also eliminated. The sequences were clustered against unique sequences at 99% similarity. The taxonomies of both GreenGenes (version 13–8) and Silva (version 132) were assigned to the OTU sequences. Alpha-diversity analyses were performed on all samples using the observed OTUs, Shannon, Pielou's evenness, and Faith PD indices, and for each index, the Kruskal–Wallis test was used to verify the significance of the comparisons between samples. Beta-diversity analyses were performed on all samples using the Bray–Curtis, Jaccard, and UniFrac metrics (weighted and unweighted). Multivariable statistical analyses were performed using the PERMANOVA, Adonis, and ANOSIM tests; instead, the analysis of the differential abundance was based on the packages of R (MetagenomeSeq, DeSeq2, and ANCOM).

Data availability

The database includes human data that are available upon reasonable request.

National Center for Chronic Disease Prevention and Health Promotion. National Diabetes Statistics Report, 2017. Estimates of Diabetes and Its Burden in the United States. CDC (2017).

Mikael Knip, Md, P. et al. Prediction of Type 1 Diabetes in the General Population. Diabetes Care 33 , 1206–1212 (2010).

American Diabetes Association. Standard medical care in diabetes - 2018. Diabetes Care 41 , 1–159 (2018).

Article   Google Scholar  

Pociot, F. & Lernmark, Å. Genetic risk factors for type 1 diabetes. Lancet 387 , 2331–2339 (2016).

Article   CAS   PubMed   Google Scholar  

Turtinen, M. et al. Characteristics of familial type 1 diabetes : effects of the relationship to the affected family member on phenotype and genotype at diagnosis. (2019).

Knip, M., Luopajärvi, K. & Härkönen, T. Early life origin of type 1 diabetes. Semin. Immunopathol. 39 , 653–667 (2017).

World Health Organization. Global Report on Diabetes . (2016).

Bruno, G. Il registro diabete Piemonte. Ital. Heal. Policy Br. 1–8 (2016).

Regnell, S. E. & Lernmark, Å. Early prediction of autoimmune (type 1) diabetes. Diabetologia 60 , 1370–1381 (2017).

Knip, M. & Honkanen, J. Modulation of type 1 diabetes risk by the intestinal microbiome. Curr. Diab. Rep. 17 , 4–11 (2017).

Article   CAS   Google Scholar  

Bach, J.-F. & Chatenoud, L. The hygiene hypothesis : an explanation for the increased frequency of insulin. Cold Sping Harb. Perpect. Med. 2 , a007799 (2012).

Google Scholar  

Zununi Vahed, S., Moghaddas Sani, H., Rahbar Saadat, Y., Barzegari, A. & Omidi, Y. Type 1 diabetes: through the lens of human genome and metagenome interplay. Biomed. Pharmacother. 104 , 332–342 (2018).

Butalia, S., Kaplan, G. G., Khokhar, B. & Rabi, D. M. Environmental risk factors and type 1 diabetes: past, present, and future. Can. J. Diabetes 40 , 586–593 (2016).

Article   PubMed   Google Scholar  

Ilonen, J., Lempainen, J. & Veijola, R. The heterogeneous pathogenesis of type 1 diabetes mellitus. Nat. Rev. Endocrinol. 15 , 635–650 (2019).

Siljander, H., Honkanen, J. & Knip, M. Microbiome and type 1 diabetes. EBioMedicine 46 , 512–521 (2019).

Hooper, L. V., Littman, D. R., Macpherson, A. J. & Program, M. P. Interactions between the microbiota and the immune system. 336 , 1268–1273 (2012).

CAS   Google Scholar  

Davis-richardson, A. G. & Triplett, E. W. On the role of gut bacteria and infant diet in the development of autoimmunity for type 1 diabetes. Reply to Hänninen ALM and Toivonen RK [ letter ]. 2197–2198 (2015). doi: https://doi.org/10.1007/s00125-015-3701-x

Giongo, A. et al. Toward defining the autoimmune microbiome for type 1 diabetes. ISME J. 5 , 82–91 (2011).

Murri, M. et al. Gut microbiota in children with type 1 diabetes differs from that in healthy children : a case-control study. 1–12 (2013).

Mejìa-Leòn, M. E., Petrosino, J. F., Ajami, N. J., Domìnguez-Bello, M. G. & Calderòn de la Barca, M. Fecal microbiota imbalance in Mexican children with type 1 diabetes. 4 , 1–5 (2013).

Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562 , 583–588 (2018).

Article   ADS   CAS   PubMed   Google Scholar  

Vatanen, T. et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature 562 , 589–594 (2018).

De Goffau, M. C. et al. Fecal Microbiota Composition Differs Between Children With Beta-Cell Autoimmunity and Those Without. Diabetes 62 , 1238–1244 (2013).

Article   PubMed   CAS   Google Scholar  

Davis-Richardson, A. G. et al. Bacteroides dorei dominates gut microbiome prior to autoimmunity in Finnish children at high risk for type 1 diabetes. Front. Microbiol. 5 , 1–11 (2014).

Kostic, A. D. et al. The Dynamics of the Human Infant Gut Microbiome in Development and in Progression toward Type 1 Diabetes. Cell Host Microbe 17 , 260–273 (2015).

Article   MathSciNet   CAS   PubMed   Google Scholar  

Kemppainen, K. M. et al. Early childhood gut microbiomes show strong geographic differences among subjects at high risk for type 1 diabetes. Diabetes Care 38 , 329–332 (2015).

Zhong, T. et al. The remission phase in type 1 diabetes: changing epidemiology, definitions and emerging immuno-metabolic mechanisms. Diabetes Metab. Res. Rev. https://doi.org/10.1002/dmrr.3207 (2019).

Winkler, C. et al. Identification of infants with increased type 1 diabetes genetic risk for enrollment into Primary Prevention Trials—GPPAD-02 study design and first results. Pediatr. Diabetes https://doi.org/10.1111/pedi.12870 (2019).

Ziegler, A.-G. et al. Screening for asymptomatic β-cell autoimmunity in young children No Title. Lancet Child Adolesc. Heal. May , 288–290 (2019).

Rabbone, I. et al. Microbiota, epidemiological and nutritional factors related to ketoacidosis at the onset of type 1 diabetes. Acta Diabetol. https://doi.org/10.1007/s00592-020-01555-z (2020).

Cani, P. D. Human gut microbiome: hopes, threats and promises. Gut 67 , 1716–1725 (2018).

Dridi, B., Raoult, D. & Drancourt, M. Archaea as emerging organisms in complex human microbiomes. Anaerobe 17 , 56–63 (2011).

Rawshani, A. et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet 392 , 477–486 (2018).

Mejía-León, M. E., Petrosino, J. F., Ajami, N. J., Domínguez-Bello, M. G. & De La Barca, A. M. C. Fecal microbiota imbalance in Mexican children with type 1 diabetes. Sci. Rep. 4 , 1–5 (2014).

Alkanani, A. K. et al. Alterations in intestinal microbiota correlate with susceptibility to type 1 diabetes. Diabetes 64 , 3510–3520 (2015).

Harbison, J. E. et al. Gut microbiome dysbiosis and increased intestinal permeability in children with islet autoimmunity and type 1 diabetes: a prospective cohort study. Pediatr. Diabetes 20 , 574–583 (2019).

CAS   PubMed   Google Scholar  

Maffeis, C. et al. Association between intestinal permeability and faecal microbiota composition in Italian children with beta cell autoimmunity at risk for type 1 diabetes. 700–709 (2016). doi: https://doi.org/10.1002/dmrr

Murri, M. et al. Association between intestinal permeability and faecal microbiota composition in Italian children with beta cell autoimmunity at risk for type 1 diabetes. 1–12 (2013). doi: https://doi.org/10.1002/dmrr

Qi, C. J. et al. Imbalance of fecal microbiota at newly diagnosed type 1 diabetes in Chinese Children. 129 , 1298–1304 (2016).

Pellegrini, S. et al. Duodenal mucosa of patients with type 1 diabetes shows distinctive inflammatory profile and microbiota. J. Clin. Endocrinol. Metab. 102 , 1468–1477 (2017).

Putignani, L., Del Chierico, F., Petrucca, A., Vernocchi, P. & Dallapiccola, B. The human gut microbiota: a dynamic interplay with the host from birth to senescence settled during childhood. Pediatr. Res. 76 , 2–10 (2014).

Regueiro, L. et al. Relationship between microbial activity and microbial community structure in six full-scale anaerobic digesters. Microbiol. Res. 167 , 581–589 (2012).

Uusitalo, U. et al. Association of Early Exposure of Probiotics and Islet Autoimmunity in the TEDDY Study. JAMA Pediatr. 33612 , 1–9 (2015).

Panigrahi, P. Probiotics and prebiotics in neonatal necrotizing enterocolitis: New opportunities for translational research. Pathophysiology 21 , 35–46 (2014).

Brüssow, H. Biome engineering-2020. Microb. Biotechnol. 9 , 553–563 (2016).

Traversi, D. et al. Gut microbiota diversity and T1DM onset: Preliminary data of a case-control study. Hum. Microbiome J. 5–6 , 11–13 (2017).

World Health Organization. ICF Parental Consent-clinicalstudies. (2018).

Ministero della Salute. Certificato di assistenza al parto (CeDAP). Analisi dell’evento nascita - Anno 2015 . (2018).

Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. PNAS 111 , E2329–E2338 (2014).

IHMS Consortium. IHMS-SOP 02 V2: Standard Operating Procedure for Fecal Samples Self ‐ Collection Laboratory Analysis Handled Within 4 To 24 Hours . (2015).

Knight, R. et al. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16 , (2018).

Muyzer, G., Waal, E. C. D. E. & Uitierlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59 , 695–700 (1993).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Webster, N. S. & Negri, A. P. Site-specific variation in Antarctic marine biofilms established on artificial surfaces. Environ. Microbiol. 8 , 1177–1190 (2006).

Dridi, B., Henry, M., El Khechine, A., Raoult, D. & Drancourt, M. High prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol. PLoS ONE 4 , e7063 (2009).

Article   ADS   PubMed   CAS   Google Scholar  

Dao, M. C. et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity : relationship with gut microbiome richness and ecology. Gut Microbiota 65 , 426–436 (2016).

Guo, X. et al. Development of a real-time PCR method for Firmicutes and Bacteroidetes in faeces and its application to quantify intestinal population of obese and lean pigs. Lett. Appl. Microbiol. 47 , 367–373 (2008).

Johnston, C., Ufnar, J. A., Griffith, J. F., Gooch, J. A. & Stewart, J. R. A real-time qPCR assay for the detection of the nifH gene of Methanobrevibacter smithii, a potential indicator of sewage pollution. J. Appl. Microbiol. 109 , 1946–1956 (2010).

Matsuki, T. et al. Quantitative PCR with 16S primers for analysis of human intestinal bifidobacteria. Appl. Environ. Microbiol. 70 , 167–173 (2004).

Nakayama, T. & Oishi, K. Influence of coffee ( Coffea arabica ) and galacto-oligosaccharide consumption on intestinal microbiota and the host responses. FEMS Microbiol. Lett. 343 , 161–168 (2013).

Takahashi, S., Tomita, J., Nishioka, K., Hisada, T. & Nishijima, M. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE 9 , 1–9 (2014).

Download references

Acknowledgements

The authors are grateful to the Italian Ministry of Health (RF-2011-02350617), the University of the Study of Torino and the Città della salute e e della scienza di Torino and the Hospital “Maggiore della Carità" di Novara for co-funding this project. Moreover, the authors wish to thank dr. Barbara Di Stefano (Sanitary Direction AOU Novara) and Mrs Rim Maatoug, Mrs Shpresa Xheka, and Mrs Daniela Elena Zelinschi (cultural intermediaries) at Novara Hospital for the translation of the questionnaire for migrant people. Finally, the authors make a special acknowledgement to the participant children and their families.

Author information

Authors and affiliations.

Department of Public Health and Pediatrics, University of Turin, Piazza Polonia 94, 10126, Torino, Italy

Deborah Traversi, Giacomo Scaioli, Giulia Carletto, Irene Racca, Roberta Siliquini & Franco Cerutti

S.S.V.D. Endocrinology and Diabetology, O.I.R.M., Azienda Ospedaliera Città Della Salute E Della Scienza, Turin, Italy

Ivana Rabbone, Camilla Vallini & Franco Cerutti

Paediatric Endocrinology, Azienda Ospedaliero Universitaria Maggiore Della Carità - Novara, Novara, Italy

Deborah Carrera, Silvia Savastio & Francesco Cadario

S.C.U. Medicina Interna 3, Azienda Ospedaliera Città Della Salute e Della Scienza Di Torino, Torino, Italy

Marilena Durazzo, Alessandro Collo & Arianna Ferro

Department of Veterinary Sciences, University of Turin, Torino, Italy

Dietetic and Clinical Nutrition Department, Azienda Ospedaliero Universitaria Maggiore Della Carità, Novara, Italy

Alessandro Collo

Department of Health Science, University of Eastern Piedmont Amadeo Avogadro - Azienda Ospedaliero Universitaria Maggiore Della Carità - Novara, Novara, Italy

Ivana Rabbone

Department of Public Health and Pediatrics, Hygiene Unit, University of the Study of Turin, via Santena 5 bis, 10126, Torino, Italy

Deborah Traversi, Giacomo Scaioli, Giulia Carletto & Roberta Siliquini

You can also search for this author in PubMed   Google Scholar

Contributions

F.C. and R.S. coordinate the work. F.C., I.R., R.S., D.T.: design the work. F.C., I.R., S.S., and F.C.: patient inclusion and questionnaire administration. C.V., D.C.: clinical data collection, Torino and Novara, respectively. I.R.: patient sample collection and transport, questionnaire elaboration. D.T., G.C.: sample processing and extraction, molecular analysis. G.S., U.A., D.T. : statistical analysis and bioinformatics. M.D., A.C., A.F.: nutritional data elaboration. G.C., G.S.: drafted the work. F.C., I.R., R.S., M.D.: revised the work. D.T.: substantively revised the work.

Corresponding author

Correspondence to Deborah Traversi .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Traversi, D., Rabbone, I., Scaioli, G. et al. Risk factors for type 1 diabetes, including environmental, behavioural and gut microbial factors: a case–control study. Sci Rep 10 , 17566 (2020). https://doi.org/10.1038/s41598-020-74678-6

Download citation

Received : 22 January 2020

Accepted : 30 September 2020

Published : 16 October 2020

DOI : https://doi.org/10.1038/s41598-020-74678-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

• Open access
• Published: 27 May 2022

Breastfeeding, nutrition and type 1 diabetes: a case-control study in Izmir, Turkey

• İpek Çiçekli   ORCID: orcid.org/0000-0003-4717-6145 1 &
• Raika Durusoy   ORCID: orcid.org/0000-0003-1041-8462 2  

International Breastfeeding Journal volume  17 , Article number:  42 ( 2022 ) Cite this article

6038 Accesses

4 Citations

10 Altmetric

Metrics details

The relationship between infant breastfeeding and type 1 diabetes mellitus (DM) is unclear but it has been suggested that there may be a link between many environmental factors, including dietary antigens affecting diabetes epidemiology.

The main objective of this study is to investigate nutritional risk factors, especially breastfeeding early in life that may be associated with the development of type 1 DM and to determine the relationship these factors have with the disease.

This research is a case-control study and was carried out in Ege University Children’s Hospital in İzmir, Turkey between 13 January 2020 and 5 March 2020. A total of 246 children aged between 4 and 14 years were included in the study. The case group consisted of patients diagnosed with type 1 DM followed-up by Ege University Children’s Hospital’s Endocrinology Unit and the control group included non-diabetic children attending the same hospital’s General Pediatric Outpatient Clinic. A structured questionnaire was created by the researchers after reviewing the literature related to nutritional and other risk factors for type 1 DM. The questionnaire was administered by interviewing the parents and it was related to the child, mother and family of the child. In this study, breastfeeding duration was defined as the total duration of breastfeeding and exclusive breastfeeding meant that the child received only breast milk from the mother.

The mean age at diagnosis was 6.30 ± 4.03 years for cases and 7.48 ± 2.56 years for controls. We found that each monthly increase in exclusive breastfeeding duration provided a 0.83-fold (95% CI 0.72, 0.96) decrease in the risk of type 1 DM. Introduction of cereals in the diet at the sixth month or earlier was associated with a 2.58-fold (95% CI 1.29, 5.16) increased risk.

Conclusions

Determining the contribution of exclusive breastfeeding to the disease is important in establishing preventive policies. A longer duration of exclusive breastfeeding may be an important role in preventing the disease. This free intervention that truly works will be cost-effective. Future studies are needed to clarify the role of both exclusive and non-exclusive breastfeeding on the development of type 1 DM.

Diabetes mellitus (DM) is a chronic metabolic disease characterized by hyperglycemia due to impairments in either insulin secretion and / or insulin effect [ 1 ]. As of today, 537 million people worldwide have diabetes [ 2 ]. This number is estimated to reach 643 million in 2030 and 783 million in 2045, which can be considered alarming levels [ 2 ].

Type 1 DM is characterized by insulin deficiency and hyperglycemia, usually starting in childhood, when the beta-cells of the pancreas are destroyed by autoimmune or non-autoimmune processes [ 2 ]. In individuals with genetic predisposition (human leukocyte antigen or HLA groups at risk), autoimmunity is triggered by the effect of environmental factors (viruses, toxins, emotional stress, others) and progressive beta-cell damage begins. Clinical symptoms of diabetes occur when beta-cell reserves are reduced by 80–90% [ 3 ].

It has been suggested that there are many environmental factors, including dietary antigens [ 4 , 5 , 6 ], as well as genetic risk factors [ 7 , 8 , 9 , 10 , 11 ] that affect the epidemiology of type 1 DM [ 12 ]. Although not all genotypes with risk have yet been identified, only about 10–15% of individuals at genetic risk develop type 1 DM [ 5 ]. In studies conducted on migrants, it has been shown that the incidence of type 1 DM increases in those who migrate from a region where the incidence of type 1 DM is low to a region with high incidence, and the effect of environmental conditions has been emphasized [ 13 ]. These data were found to be consistent with the results of studies finding that environmental triggers increase and accelerate the development of clinical type 1 DM despite lower genetic predisposition [ 13 ].

Some nutritional factors contribute to the development of the disease. Studies in 40 countries worldwide have shown that dietary patterns may impact the development of type 1 DM [ 14 ]. Vitamin D, another nutritional factor, may have a protective effect on glycemic control in patients with type 1 DM [ 15 ] and according to a birth cohort study, the provision of vitamin D supplementation for infants early in life could help to reduce the risk of the disease [ 16 ]. The introduction of cow’s milk-based infant formulas in the first three postnatal months was found to be associated with an increase in pancreatic beta-cell auto-antibodies [ 17 ]. However, another study had shown that cow’s milk did not play an important role in the development of type 1 DM [ 18 ].

Although many studies have been performed to investigate the role of nutrition in pregnancy and early in life on type 1 DM, the results have been inconsistent. Breastfeeding [ 19 ], probiotic supplementation [ 20 ], vitamin C, and zinc supplementation [ 21 ] have been shown as possible protective factors against type 1 DM whereas early exposure to eggs, gluten [ 22 , 23 ] and vegetables [ 24 ] might increase the risk.

Studies with school-age children have shown that diabetic children are significantly more prone to stress and depression compared to non-diabetic children [ 25 ]. Beyond the psychological and somatic effects of the disease on the individuals, diabetic individuals also encounter socio-economic consequences affecting their families and entire societies [ 26 ]. Frequent co-morbidities further increase negative socioeconomic consequences, especially in low- and middle-income countries [ 26 ].

According to the Social Security Institution’s data in Turkey, the costs of diabetes and its complications amount to approximately 23% of the total health expenditure [ 27 ]. In addition, indirect costs such as the loss of productivity of diabetics, the persons caring for the patient and their family are not included in these cost estimates. Therefore the cost does not reflect the psychosocial effects of the losses of quality-adjusted life years. Knowledge of modifiable environmental risk factors in type 1 DM can assist authorities in planning and implementing preventive policies to reduce the burden of the disease. It is as yet uncertain how and which nutritional or other environmental factors are important in the development of type 1 DM. Moreover, epigenetic mechanisms are not clearly defined.

The main objective of this study is to investigate potential nutritional risk factors, especially breastfeeding early in life, that may be associated with the development of type 1 DM and to determine the relationship of these factors with the disease, independent of other established risk factors.

Participants

A case-control study was carried out at Ege University Children’s Hospital, İzmir City, Turkey, over a period of two months from January to March 2020.

A minimum sample size of 105 cases and 105 controls with a total of 210 participants was calculated with G-Power using the t-test group, with an effect size of 0.5, an error margin of 0.05, and a power of 95%. About 20% more sample size was added to account for possible non-response and a total of 246 children (120 cases and 126 controls) were included in the study.

The study data were collected at Ege University Faculty of Medicine Children’s Hospital in Bornova, Izmir between 13 January 2020 and 5 March 2020. Children and their parents who attended the general pediatrics and endocrinology / metabolic diseases outpatient clinics of the hospital and who met the study criteria were examined. The case group consisted of 120 children in the age group of 4–14 years who were diagnosed with type 1 DM based on World Health Organization and International Diabetes Federation guidelines [ 28 ] and who were being followed-up at Ege University Children’s Hospital Endocrinology / metabolic diseases outpatient clinic.

The diabetes outpatient clinic is held once a week (on Thursdays) and on the first Monday of every month. The mean number of diabetic patients attending the research was 15 patients per day. The control group comprised 126 non-diabetic children selected from the general pediatric outpatient clinic of the same hospital. A questionnaire was applied face-to-face to the parents of the children. All questions in the study were asked to the parents and separately written informed consent was obtained from children and their parents. In addition, the files of the case group were examined and the date of diagnosis, height, body weight and HbA1c levels at the time of diagnosis were collected as data.

Children who were followed up in the Endocrine and Metabolic Diseases Outpatient Clinic, diagnosed with type 1 DM and aged between 4 and 14 years were included in the case group. Children who attended the General Pediatrics Outpatient Clinic, were not diagnosed with type 1 DM, and aged 4–14 years were included in the control group. Those who did not want to share their information and could not remember answers to the study questions were excluded. The response rates were 96 and 91% among cases and controls, respectively, for all eligible cases and controls attending the hospital.

Questionnaires

A structured questionnaire was created by the researchers after reviewing the literature related to nutritional and other risk factors for type 1 DM [ 21 , 29 , 30 , 31 , 32 , 33 , 34 ]. The questionnaire was administered by interviewing the parents and its content was related to the child, mother and family of the child. For children: anthropometric data, breastfeeding duration, infant formula consumption, the introduction of some foods into the diet, infections, supplementations (vitamin D and probiotic) early in life and physical activity were questioned; for mothers, anthropometric data and history during pregnancy; for family, socio-demographic characteristics such as education, whether the child lived with parents, and family history were asked. In addition, the case group was examined about the age at diagnosis of the disease, the HbA1c level and the percentiles at diagnosis.

In this study, breastfeeding duration was defined as the total duration of breastfeeding and exclusive breastfeeding meant that the child received only breast milk (no other liquids or solids given, not even water with the exception of oral rehydration solution, or drops / syrups of vitamins, minerals or medicines) from the mother [ 35 ].

The percentiles were calculated based on the percentile values table of Neyzi et al. [ 36 ]. Parents’ body mass index (BMI) was classified according to the World Health Organization’s obesity scale [ 37 ]. Finally, high-intensity physical activity was defined as “physical activities that increase the maximum heart rate by 70 − 85%” [ 38 ]. Examples of physical activities were given (running, basketball, football, tennis, swimming, skipping rope) by the researcher.

Statistical analysis

The data were analyzed by using SPSS software. The quality of the data had been checked prior to analysis. Descriptive variables of cases and controls were compared with Student t-tests (continuous variables), Mann Whitney U tests (non-parametric) and chi-square tests (categorical variables). In order to reveal the relationship between significant parameters and the development of type 1 DM independently from other factors, age and sex-adjusted logistic regression analysis were performed. Since the difference in mean ages of the two groups was found to be significant (both age of enrolment in the study and age at diagnosis type 1 DM), other variables were evaluated adjusting for age and gender.

General pediatric outpatient clinic admissions are due to newly developing acute conditions and 85–90% are first visits to the hospital. Ten to 15 % are invited for follow-up one month later, so the follow-up is also at the same age. If they also have a chronic condition, they are referred to pediatric specialization clinics and start follow-up in those clinics.

Among the cases, six were diagnosed with type 1 DM at zero years, three of whom were excluded from the multivariate analysis since they were diagnosed in their first month of life, so the diagnosis would be before the environmental exposures could happen. The remaining three children were diagnosed at 10, 11 and 11 months, thus they were kept in the analysis since they could be exposed to potential nutritional risk factors in question.

Sex and age-adjusted multivariable logistic analysis, adjusted odds ratios (aORs) and 95% confidence intervals (95% CIs) were used to identify possible risk factors of the disease. In all analyses, p  <  0.05 was considered statistically significant. The dependent variable was having type 1 DM. Maternal factors, family history, family characteristics, nutritional characteristics early in life were the independent variables.

Population Attributable Risk (PAR) and Population Attributable Risk Percent (PAR %) were calculated to estimate the proportion of cases for whom the disease is attributable to exclusive breastfeeding and to estimate the excess rate of type 1 DM in the study population of both exposed and non-exposed children that is attributable to being non-breastfed exclusively up to the first six months. This measure was calculated as [ 39 ]:

Characteristics of children

A total of 246 children were included in the study, with 120 cases and 126 controls. The mean age of the case and control groups was 10.43 ± 3.31 and 7.48 ± 2.56 years, respectively ( p  <  0.05). The cases’ mean age at diagnosis was 6.30 ± 4.03 years and was found to be significantly lower than the control group. The mean duration of their disease was 4.2 ± 3 .85 years. The mean height percentile was higher in controls (means 45.66 ± 31.16 and 58.00 ± 31.88, p  = 0.003) and the mean BMI percentile was higher in the case group (means 55.20 ± 29.86 and 40.32 ± 35.02, p  < 0.001). A significant difference was found in the family history of type 1 DM. There was a type 1 DM history in 10.7% of the case group and 0.8% in the control group ( p  = 0.001). No significant difference was found in the child’s living status with parents and parents’ education level. However, a significant difference was found in physical activity levels ( p  = 0.014). There was no difference between the duration of vitamin D use. In both groups, no infant was supplemented with probiotics in the first year postpartum. The controls’ rate of living in urban areas was found to be significantly higher (Table  1 ).

Maternal characteristics

The mean birth interval was higher in the case group. A significant difference was found in the birth intervals with univariate analysis ( p  = 0.036) but not in multivariate analysis. Those with a birth interval of more than six years constituted 20.7% of the cases and 8.0% of controls (Table  2 ). In the case group, no mother was supplemented with probiotics during pregnancy and 98.4% of the control group were not supplemented with probiotics during pregnancy.

Nutritional profiles of children

The mean duration of exclusive breastfeeding was higher in the control group ( p  = 0.009). In the case group, the rate of exclusive breastfeeding for less than one month was 47.8, and 30.6% in the controls (Table  3 ). This difference was statistically significant ( p  = 0.037). No statistically significant differences were found between colostrum consumption, total breastfeeding duration, infant formula consumption and formula preferences.

No statistically significant difference was observed between which month the cow’s milk, eggs, fruits, vegetables, and berry fruits were introduced. However the introduction of cereals was statistically significant and the cases’ introduction to them was earlier ( p  = 0.008). For the case group 5.5% were introduced to cereals before the sixth month as compared to 3.2% of controls, while 44.2% of controls were introduced to cereals after the eighth month, compared to 24.7% of cases (Table  4 ).

Multivariate analysis

According to non-parametric correlation analyses, exclusive breastfeeding duration and total breastfeeding duration were not found to be associated with age when type 1 DM was diagnosed. The birth interval was found to be significant in the age and sex-adjusted regression analysis. In addition, regardless of age and gender, it was observ ed. that the risk of type 1 DM decreased 0.85 ( p  = 0.007; 95% CI0.76, 0.96) times with each monthly increase in the duration of exclusive breastfeeding (Fig.  1 ). Having a birth interval of more than six years increased the risk of the disease by 2.79 ( p  = 0.018; 95% CI 1.19, 6.54) times.

Sex and age adjusted (the cases’ age at diagnosis) logistic regression analysis of risk factors independent of other risk factors

According to results of the multivariate logistic regression, longer exclusive breastfeeding duration, living in a rural area and not consuming infant formula were identified as protective factors. Although there was no significant difference found in type 1 DM risk with introduction to cereals at 12 months and after, it was found that the introduction to cereals at the sixth month and earliere increased the risk of type 1 DM by 2.58 ( p  = 0.008; 95% CI 1.29, 5.16) times compared to between months7–11, independent of other risk factors. Similarly, infant formula consumption after the sixth month was associated with an increased risk of type 1 DM compared to no infant formula consumption (Table  5 ).

Sensitivity analyses

The potential impacts on our results of age at which the cases developed DM, (whether including or excluding data for the three children who were diagnosed at the first month after birth) and with data missing for the father’s education level variable, was assessed using multiple imputations, as described in the Supplementary Data . Among the cases, six of them were diagnosed with type 1 DM in the first year of life, three of whom were excluded from the multivariate analysis since they were diagnosed in their first month of life, so the diagnosis would have occurred before the environmental exposures could happen. The remaining one child was diagnosed at ten months and two children at 11 months, thus they were kept in the analysis since they could have been exposed to the potential nutritional risk factors in question.

The main analyses were repeated after adjusting for age and the father’s education level. Multiple imputations changed some of the conclusions based on the research sample as attached (Supplementary Table  1 ). The missing data on the father’s education level in the study was assessed using multiple imputations and this did not change the conclusions. So the model excluding the father’s education level and cases that developed type 1 DM before the first month was used.

PAR was calculated as 0.111 in the study and PAR% was calculated as 38.3% .

Elimination of preventable environmental risk factors associated with type 1 DM is an important step in the prevention of the disease. However, it has not been precisely explained which factors play a key role and when and in which situations the factors should be eliminated [ 22 ]. In this research we have explored possible preventable environmental triggers and determinants, especially breastfeeding early in life.

We found that each monthly increase in the duration of exclusive breastfeeding but not total breastfeeding provides a reduction in type 1 DM risk. However, introducing the cereals before the sixth month was found to be an important risk factor. The birth interval which was significant in univariate analyzes, lost its significance in multivariate analysis.

• Breastfeeding

The effect of breast milk, the first food of the newborn, on type 1 DM is a controversial issue. There are many studies in the literature that show no effect [ 40 ], a protective effect [ 19 ] and an effect [ 21 ]. It has been suggested that the protective effect of breastmilk is through reducing neonatal intestinal permeability [ 41 ]. The World Health Organization recommends feeding exclusively breast milk in the first six months of life and breastmilk up to the age of two, because feeding children with exclusively breast milk for the first six months after birth prevents diarrhea, respiratory diseases and provides all the nutrients and fluids the infant needs for optimal growth and development [ 42 ]. For participants in our study, it was observed that the rates of those who did not receive breast milk at all or those who were exclusively breastfed for less than a month were quite high. It has been observed that the accomplishment rate of the World Health Organization target for six months exclusive breastfeeding is low.

According to the Turkey Demographic and Health Survey data 2018, approximately two in five children were exclusively breastfed up to six months old and the proportion of children who are exclusively breastfed decreases with age; from 59% among 0–1 month-old infants to 14% among 4–5 months old infants [ 43 ]. On the other hand, the National Immunization Survey results indicated that only one in four children was breastfed exclusively through six months in the U.S. [ 44 ]. In our study, only the median month of breastfeeding was close to the Turkey Demographic and Health Survey data. The exclusive breast milk receiving rates through six months were found to be lower than the worldwide, National Immunization Survey and Turkey Demographic and Health Survey data in both the case and control groups. This was not surprising because our sample was quite low compared to the aforementioned samples, and the data mentioned reflected a population of children younger than two years old in 2018. So the mean age of the children in our study was higher. This result may be different in studies to be conducted with a larger population and adjusted for age. There are large differences in breastfeeding rates between regions, between and within countries. But unfortunately, these rates are insufficient both in the world and in Turkey. We can estimate that 38.3% of type 1 DM cases would be avoided by an increase in the proportion of infants exclusively breastfed to six months. Keep in mind that almost two in five infants who are not breastfed exclusively for the first six months will have type 1 DM so any intervention that can promote breastfeeding may have a big impact in preventing the disease.

In the study of Çarkçı and Altuğ (2020), conducted in the same city as this study (İzmir) the rate of children with type 1 DM who received exclusive breast milk up to the first six months was found to be more than four times compared to our study [ 45 ]. While asking the duration of exclusive breastfeeding, the definition of exclusive breastfeeding was explained as “the total time in which the baby takes only breast milk, and no other liquid (including water) or solids other than oral rehydration solution or vitamins, minerals or drugs/syrups are given” in this study. While making a statement, after the parents answered, “Have you ever given water during this period?” was asked again to be sure. In this process, there were parents who changed their answers after the second question. Therefore, different results may have been obtained in studies where this distinction was not made clear.

There are many studies on the relationship between breastfeeding and type 1 DM. Holmberg et al. (2007) found that the duration of total breastfeeding for less than four months is a risk factor for the development of beta-cell autoimmunity in children under five years old. The same study reported that the duration of exclusive breastfeeding for less than four months increased the risk of developing beta-cell autoantibodies two times [ 17 ]. In another study, it was shown that the risk of type 1 DM in childhood can be reduced by 15%, even by breastfeeding exclusively in the early weeks of life. However, the observed relationship between exclusive breastfeeding and type 1 DM could not be explained independently of certain risk factors for DM such as gestational DM, birth weight, gestational age, maternal age, birth order and mode of delivery [ 19 ]. However in a series of prospective and birth cohort studies investigating the relationship between breastfeeding and the development of islet autoimmunity, no effect of breastfeeding has been reported [ 46 , 47 ]. Similarly, a series of prospective studies investigating the relationship between breastfeeding and the development of type 1 DM reported that breastfeeding had no effect [ 48 , 49 ].

We found that longer exclusive breastfeeding duration was a significant protective factor against type 1 DM but the same effect was not observed with total breastfeeding duration. In some studies, exclusive breastfeeding and total breastfeeding duration were not compared and the duration of total or any breastfeeding was researched. Therefore the differences in studies’ results can be attributed to their methods.

In addition to distinguishing between exclusive breastfeeding and total breastfeeding, there are also differences between studies in defining exclusive breastfeeding. For instance, in two Large Scandinavian Birth Cohorts, breastfed infants were found to be at doubled risk of type 1 DM compared to infants who did not receive breast milk at all but no evidence indicated that longer duration of breastfeeding was associated with a reduced risk of the disease [ 50 ].

Similarly, in two large cohort studies, breastfeeding duration was not associated with type 1 DM [ 51 , 52 ]. Infants classified as exclusively breastfed were allowed water-based drinks in the aforementioned study and duration of exclusive breastfeeding was not taken into account.

As can be seen, many studies only look at total breastfeeding duration without making any distinction between exclusive breastfeeding duration and total breastfeeding duration. In addition, striking differences in the breastfeeding practices of governments and health authorities may be a confounding factor in the results of the studies. Positive social norms that support and encourage breastfeeding, including in public spaces, encourage mothers to breastfeed [ 53 ]. As observed in our study, the duration of exclusive breastfeeding of children after birth is quite low and it has been observed that parents do not attach sufficient importance to the period of exclusive breastfeeding.

Support from trained counselors and peers, including mothers and other family members, is as important as postpartum health care in maintaining breastfeeding in communities. The support of men, spouses and partners should not be ignored in this process [ 53 ]. In studies on breastfeeding, the mother has always been at the center and studies on the role of fathers / partners are insufficient. Tohotoa et al. highlighted the importance of the role of fathers in encouraging and supporting a successful breastfeeding process [ 54 ]. Moreover, paternal practical, physical and emotional support could make a difference [ 54 ].

When challenges experienced by mothers are shared with their partners, babies might have a better chance of receiving exclusive breast milk for the recommended six months and could keep going on breastfeeding for up to two years. In this way, the early introduction of complementary foods, especially cereals, which we found a significant risk factor for type 1 DM in our study, could be prevented.

Cow’s milk and infant formula consumption

Early exposure to cow’s milk proteins has been studied in terms of beta-cell autoimmunity and the risks of clinical disease development [ 55 ]. Early introduction of cow’s milk proteins into the diet may trigger inflammation of the intestinal mucosa and increase intestinal permeability [ 56 ]. The introduction of infant formula reflects the total duration of the exclusive breastfeeding [ 31 ]. Therefore, it should be considered together with the duration of exclusive breastfeeding. These may have led to contradictory results [ 31 ].

Some studies have shown that early exposure to cow’s milk proteins increases the risk of beta-cell autoimmunity [ 57 ] and type 1 DM [ 58 ] while others found no relationship between type 1 DM and cow’s milk proteins [ 31 , 59 ]. We also did not find an association with the timing of cow’s milk introduction. It has been observed that consumption of infant formula at six months and later increased the risk of type 1 DM in this research. However, while the risk of type 1 DM was expected to increase with the consumption of infant formula at six months and earlier compared to those who did not consume it, a statistically significant change was not observed.

This result may be explained in three ways: First, there may be a bias in choosing the control group from the same tertiary care and university hospitals with type 1 DM patients. Considering the socioeconomic status of children attending a university hospital, infant formula may have been introduced earlier than the general community and might not represent the healthy control group clearly. Second, there may have been a response bias. Third, since the mean age of children with type 1 DM is significantly higher, parents may have recall bias. Although it is easy to identify potential sources of bias, it is not possible to predict the true impact of these biases on results.

Introduction to cereals

Gluten, a protein found in barley, wheat and rye has been hypothesized to be one of the nutritional risk factors related to the development of type 1 DM [ 60 ]. A study in non-obese diabetic rats concluded that the intra-epithelial infiltration of T cells, the incidence of autoimmune type 1 DM and enteropathies decreased with a gluten-free diet compared to the controls [ 61 ]. Introduction of gluten before four months of age was associated with an increased risk of type 1 DM in another study [ 62 ]. These results were explained by the hypothesis that the gluten-free diet may prevent gliadin peptides from crossing the intestinal barrier by reducing intestinal permeability, thus preventing the development of pancreatic autoimmunity [ 63 ]. Our study supports these arguments since introducing cereals before the sixth month was found to be an important risk factor.

However in our study, the cereals were not questioned for their gluten content, they were questioned overall, but wheat production and consumption ranks in the first place among cereals in Turkey [ 64 ]. So wheat-containing cereals (including gluten) are expected to be added into the diet of infants in the transition to complementary foods at first. Nevertheless, it could not be confirmed specifically that gluten exposure was earlier in cases, but it was found that cereal introduction prior to the sixth month was associated with an increased risk of type 1 DM.

Limitations

We have many limitations in the study. As in our study, case-control studies always have the potential for bias. It is not easy to collect accurate and unbiased data on past exposures. Therefore case-control studies are prone to some sources of bias like recall bias or the control group’s selection from the hospital. Many of the established risk factors were questioned, in order to overcome confounding. However, the gestation week was not questioned at birth, so it could not be evaluated whether the birth weight was normal for gestational age. It was questioned whether they drank water during exclusive breastfeeding, but we did not collect data on when they started to consume water. Therefore this variable maybe could provide a better comparison for exclusive breastfeeding duration in future studies. In addition, the vaccination status of the children was not asked and abortion was not researched while questioning the birth interval and birth order so they may be confounding factors. Since infections in the first three years were questioned by anamnesis, their bacterial / viral status could not be determined and their relationship with enteroviruses could not be investigated.

Longer exclusive breastfeeding duration may prevent the early introduction of certain nutrients in the diet. Determining the contribution of exclusive breastfeeding and its interactions with protective factors to the disease is important in establishing preventive policies. Breastfeeding is cost-effective and may be a free intervention for the prevention of type 1 DM. Support from partners is a key factor in maintaining breastfeeding in communities. Considering the limitations of the study, systematic reviews with meta-analysis are needed in determining the role of both exclusive and non-exclusive breastfeeding on the development of type 1 DM.

Availability of data and materials

The dataset could be obtained from the corresponding author upon reasonable request.

Abbreviations

Human leukocyte antigen

Population attributable risk

Population attributable risk percent

National Diabetes Consensus Group. TURKDIAB Diabetes Diagnosis and Treatment Guideline 2017. 7th edition. İstanbul; 2017. http://www.turkdiab.org/admin/PICS/webfiles/Diyabet_tani_ve_tedavi__kitabi.pdf . Accessed 25 May 2021.

International diabetes federation. Diabetes atlas 2021. 10th ed. Brussels: International Diabetes Federation; 2021. https://diabetesatlas.org/idfawp/resource-files/2021/07/IDF_Atlas_10th_Edition_2021.pdf .

Turkey Endocrinology and Metabolism Society. Endocrinology and Metabolism Association Guideline 2019. 14th edition. Ankara; 2019. https://temd.org.tr/admin/uploads/tbl_kilavuz/20200625154506-2020tbl_kilavuz86bf012d90.pdf . Accessed 29 Apr 2020.

Åkerblom HK, Vaarala O, Hyöty H, Ilonen J, Knip M. Environmental factors in the etiology of type 1 diabetes. Am J Med Genet Semin Med Genet. 2002;115:18–29.

Article   Google Scholar  

Esposito S, Toni G, Tascini G, Santi E, Berioli MG, Principi N. Environmental factors associated with type 1 diabetes. Front Endocrinol. 2019;10:592. https://doi.org/10.3389/fendo.2019.00592 .

D’Angeli MA, Merzon E, Valbuena LF, Tirschwell D, Paris CA, Mueller BA. Environmental factors associated with childhood-onset type 1 diabetes mellitus: an exploration of the hygiene and overload hypotheses. Arch Pediatr Adolesc Med. 2010;164:732–8. https://doi.org/10.1001/archpediatrics.2010.115 .

Article   PubMed   PubMed Central   Google Scholar  

Redondo MJ, Concannon P. Genetics of type 1 diabetes comes of age. Diabetes Care. 2020;43:16–8. https://doi.org/10.2337/dci19-0049 .

Article   PubMed   Google Scholar  

DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet. 2018;391:2449–62. https://doi.org/10.1016/S0140-6736(18)31320-5 .

Pociot F, Akolkar B, Concannon P, Erlich HA, Julier C, Morahan G, et al. Genetics of type 1 diabetes: What’s next? Diabetes. 2010;59:1561–71. https://doi.org/10.2337/db10-0076 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Howson JMM, Rosinger S, Smyth DJ, Boehm BO, Aldinger G, Aufschild J, et al. Genetic analysis of adult-onset autoimmune diabetes. Diabetes. 2011;60:2645–53. https://doi.org/10.2337/db11-0364 .

Soltész G. Diabetes in the young: A paediatric and epidemiological perspective. Diabetologia. 2003;46:447–54. https://doi.org/10.1007/s00125-003-1101-0 .

Piescik-Lech M, Chmielewska A, Shamir R, Szajewska H. Systematic review: early infant feeding and the risk of type 1 diabetes. J Pediatr Gastroenterol Nutr. 2017;64:454–9.

Bodansky HJ, Staines A, Stephenson C, Haigh D, Cartwrigth R. Evidence for an environmental effect in the aetiology of insulin dependent diabetes in a transmigratory population. BMJ. 1992;304:1020–2. https://doi.org/10.1136/BMJ.304.6833.1020 .

Muntoni S, Cocco P, Aru G, Cucca F, Muntoni S. Nutritional factors and worldwide incidence of childhood type 1 diabetes. Am J Clin Nutr. 2000;71:1525–9.

Article   CAS   Google Scholar  

Aljabri KS, Bokhari SA, Khan MJ. Glycemic changes after vitamin D supplementation in patients with type 1 diabetes mellitus and vitamin D deficiency. Ann Saudi Med. 2010;30:454. https://doi.org/10.4103/0256-4947.72265 .

Hyppönen E, Läärä E, Reunanen A, Järvelin MR, Virtanen SM. Intake of vitamin D and risk of type 1 diabetes: A birth-cohort study. Lancet. 2001;358:1500–3.

Holmberg H, Whalberg J, Vaarala O, Ludvigsson J. Short duration of breast-feeding as a risk-factor for β-cell autoantibodies in 5-year-old children from the general population. Br J Nutr. 2007;97:111–6.

Knip M, Åkerblom HK, Altaji E, Becker D, Bruining J, Castano L, et al. Effect of hydrolyzed infant formula vs conventional formula on risk of type 1 diabetes the TRIGR randomized clinical trial. JAMA. 2018;319:38–48. https://doi.org/10.1001/jama.2017.19826 .

Cardwell CR, Stene LC, Ludvigsson J, Rosenbauer J, Cinek O, Svensson J, et al. Breast-feeding and childhood-onset type 1 diabetes: A pooled analysis of individual participant data from 43 observational studies. Diabetes Care. 2012;35:2215–25. https://doi.org/10.2337/dc12-0438 .

Xiao L, Van’t Land B, Engen PA, Naqib A, Green SJ, Nato A, et al. Human milk oligosaccharides protect against the development of autoimmune diabetes in NOD-mice. Sci Rep. 2018;8:1–15.

Google Scholar  

Virtanen SM, Knip M. Nutritional risk predictors of β cell autoimmunity and type 1 diabetes at a young age. Am J Clin Nutr. 2003;78:1053–67.

Esposito S, Toni G, Tascini G, Santi E, Berioli MG, Principi N. Environmental factors associated with type 1 diabetes. Front Endocrinol. 2019;10:592.

Norris JM, Barriga K, Klingensmith G, Hoffman M, Eisenbarth GS, Erlich HA, et al. Timing of initial cereal exposure in infancy and risk of islet autoimmunity. J Am Med Assoc. 2003;290:1713–20. https://doi.org/10.1001/jama.290.13.1713 .

Virtanen SM, Takkinen HM, Nevalainen J, Kronberg-Kippilä C, Salmenhaara M, Uusitalo L, et al. Early introduction of root vegetables in infancy associated with advanced ß-cell autoimmunity in young children with human leukocyte antigen-conferred susceptibility to type 1 diabetes. Diabet Med. 2011;28:965–71. https://doi.org/10.1111/j.1464-5491.2011.03294.x .

Article   CAS   PubMed   Google Scholar  

Streisand R, Monaghan M. Young children with type 1 diabetes: challenges, research, and future directions. Curr Diabetes Rep. 2014;14:520.

World Health Organization. Classification of Diabetes Mellitus 2019. Geneva; 2019.  https://www.who.int/publications/i/item/classification-of-diabetes-mellitus . Accessed 11 Apr 2020.

Social Security Institution of Turkey. Diabetes from the Social Security Institution’s Perspective. Ankara; 2014. https://veri.sgk.gov.tr . Accessed 11 Apr 2020.

World Health Organization; International Diabetes Federation. Definition and diagnosis of diabetes mellitus and intermediate hyperglycemia Report of a WHO/IDF Consultation. Geneva; 2006. https://apps.who.int/iris/bitstream/handle/10665/43588/9241594934_eng.pdf?sequence=1&isAllowed=y . Accessed 1 Jan 2021.

Awadalla NJ, Hegazy AA, El-Salam MA, Elhady M. Environmental factors associated with type 1 diabetes development: A case control study in Egypt. Int J Environ Res Public Health. 2017;14:615. https://doi.org/10.3390/ijerph14060615 .

Article   PubMed Central   Google Scholar  

Muntoni S, Mereu R, Atzori L, Mereu A, Galassi S, Corda S, et al. High meat consumption is associated with type 1 diabetes mellitus in a Sardinian case-control study. Acta Diabetol. 2013;50:713–9.

Knip M, Simell O. Environmental triggers of type 1 diabetes. Cold Spring Harb Perspect Med. 2012;2:1–15.

Knip M, Virtanen SM, Becker D, Dupre J, Krischer JP, A HK. Early feeding and risk of type 1 diabetes: experiences from the trial to reduce insulin-dependent diabetes mellitus in the genetically at risk (TRIGR) 1-6. Am J Clin Nutr. 2011;94:1814S–20.

Nygren M, Carstensen J, Koch F, Ludvigsson J, Frostell A. Experience of a serious life event increases the risk for childhood type 1 diabetes : the ABIS population-based prospective cohort study. Diabetologia. 2015;58:1188–97.

Rewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet. 2016;387:2340–8. https://doi.org/10.1016/S0140-6736(16)30507-4 .

World Health Organization. Exclusive breastfeeding for optimal growth, development and health of infants. 2019. https://www.who.int/elena/titles/exclusive_breastfeeding/en/ . Accessed 2 Jan 2021.

Neyzi O, Günöz H, Furman A, Bundak R, Gökçay G, Darendeliler F, et al. Body weight, height, head circumference and body mass index reference values in Turkish children. Cocuk Sagligi ve Hastalikari Dergisi. 2008;51(1):1.

World Health Organization. Health Topics: Obesity. https://www.who.int/health-topics/obesity#tab=tab_1 . Accessed 2 Jan 2021.

Republic of Turkey Ministry of Health, General Directorate of Public Health. Turkey Physical Activity Guidelines. 2nd edition. Ankara; 2014. https://hsgm.saglik.gov.tr/depo/birimler/saglikli-beslenme-hareketli-hayat-db/Fiziksel_Aktivite_Rehberi/Turkiye_Fiziksel_Aktivite_Rehberi.pdf .

Hennekens CH, Buring JE. Measures of Disease Frequency and Association. In: Mayrent SL, editor. Epidemiology in Medicine. 1st ed. Boston: Little Brown; 2012. p. 96.

Ziegler AG, Schmid S, Huber D, Hummel M, Bonifacio E. Early infant feeding and risk of developing type 1 diabetes-associated autoantibodies. JAMA. 2003;290:1721–8. https://doi.org/10.1001/JAMA.290.13.1721 .

Catassi C, Bonucci A, Coppa GV, Carlucci A, Giorgi PL. Intestinal permeability changes during the first month: effect of natural versus artificial feeding. J Pediatr Gastroenterol Nutr. 1995;21:383–6. https://doi.org/10.1097/00005176-199511000-00003 .

World Health Organization. Health Topics: Breastfeeding. https://www.who.int/health-topics/breastfeeding#tab=tab_1 . Accessed 25 Jan 2022.

Hacettepe University Institute of Population Studies. Turkey Demographic and Health Survey 2018 Main Report. Ankara/Turkey; 2019. www.hips.hacettepe.edu.tr . Accessed 9 Nov 2021.

Centers for Disease Control and Prevention. Results: Breastfeeding Rates. https://www.cdc.gov/breastfeeding/data/nis_data/results.html . Accessed 11 Nov 2021.

Çarkçı NŞ, Altuğ ÖS. Studying the epidemiologic characteristics of children with type 1 diabetes followed in İzmir. J Educ Res Nurs. 2020;17:24–31.

Ziegler AG, Schmid S, Huber D, Hummel M, Bonifacio E. Early infant feeding and risk of developing type 1 diabetes-associated autoantibodies. J Am Med Assoc. 2003;290:1721–8. https://doi.org/10.1001/jama.290.13.1721 .

Virtanen SM, Kenward MG, Erkkola M, Kautiainen S, Kronberg-Kippilä C, Hakulinen T, et al. Age at introduction of new foods and advanced beta cell autoimmunity in young children with HLA-conferred susceptibility to type 1 diabetes. Diabetologia. 2006;49:1512–21. https://doi.org/10.1007/s00125-006-0236-1 .

Viner RM, Hindmarsh PC, Taylor B, Cole TJ. Childhood body mass index (BMI), breastfeeding and risk of type 1 diabetes: findings from a longitudinal national birth cohort. Diabet Med. 2008;25:1056–61. https://doi.org/10.1111/J.1464-5491.2008.02525.X .

Frederiksen B, Kroehl M, Lamb MM, Seifert J, Barriga K, Eisenbarth GS, et al. Infant exposures and development of type 1 diabetes mellitus: the diabetes autoimmunity study in the young (DAISY). JAMA Pediatr. 2013;167:808–15. https://doi.org/10.1001/JAMAPEDIATRICS.2013.317 .

Lund-Blix NA, Sander SD, Størdal K, Nybo Andersen AM, Rønningen KS, Joner G, et al. Infant feeding and risk of type 1 diabetes in two large Scandinavian birth cohorts. Diabetes Care. 2017;40:920–7. https://doi.org/10.2337/DC17-0016/-/DC1 .

Ponsonby AL, Pezic A, Cochrane J, Cameron FJ, Pascoe M, Kemp A, et al. Infant anthropometry, early life infection, and subsequent risk of type 1 diabetes mellitus: a prospective birth cohort study. Pediatr Diabetes. 2011;12:313–21. https://doi.org/10.1111/J.1399-5448.2010.00693.X .

Savilahti E, Saarinen KM. Early infant feeding and type 1 diabetes. Eur J Nutr. 2009;48:243–9. https://doi.org/10.1007/S00394-009-0008-Z .

UNICEF. Breastfeeding: A Mother’s Gift, for Every Child. New York; 2018. https://www.unicef.org/media/48046/file/UNICEF_Breastfeeding_A_Mothers_Gift_for_Every_Child.pdf . Accessed 2 Feb 2022.

Tohotoa J, Maycock B, Hauck YL, Howat P, Burns S, Binns CW. Dads make a difference: an exploratory study of paternal support for breastfeeding in Perth, Western Australia. Int Breastfeed J. 2009;4:15. https://doi.org/10.1186/1746-4358-4-15 .

Šipetić S, Vlajinac H, Kocev N, Bjekić M, Sajic S. Early infant diet and risk of type 1 diabetes mellitus in Belgrade children. Nutrition. 2005;21:474–9. https://doi.org/10.1016/J.NUT.2004.07.014 .

Knip M, Virtanen SM, Åkerblom HK. Infant feeding and the risk of type 1 diabetes. Am J Clin Nutr. 2010;91:1506S. https://doi.org/10.3945/AJCN.2010.28701C .

Virtanen SM, Nevalainen J, Kronberg-Kippilä C, Ahonen S, Tapanainen H, Uusitalo L, et al. Food consumption and advanced β cell autoimmunity in young children with HLA-conferred susceptibility to type 1 diabetes: a nested case-control design. Am J Clin Nutr. 2012;95:471–8. https://doi.org/10.3945/AJCN.111.018879 .

Wahlberg J, Vaarala O, Ludvigsson J. Dietary risk factors for the emergence of type 1 diabetes-related autoantibodies in 21/2 year-old Swedish children. Br J Nutr. 2006;95:603–8. https://doi.org/10.1079/BJN20051676 .

Patterson C. Rapid early growth is associated with increased risk of childhood type 1 diabetes in various European populations. Diabetes Care. 2002;25:1755–60. https://doi.org/10.2337/DIACARE.25.10.1755 .

Barbeau WE, Bassaganya-Riera J, Hontecillas R. Putting the pieces of the puzzle together - a series of hypotheses on the etiology and pathogenesis of type 1 diabetes. Med Hypotheses. 2007;68:607–19. https://doi.org/10.1016/J.MEHY.2006.07.052 .

Maurano F, Mazzarella G, Luongo D, Stefanile R, D’Arienzo R, Rossi M, et al. Small intestinal enteropathy in non-obese diabetic mice fed a diet containing wheat. Diabetologia. 2005;48:931–7. https://doi.org/10.1007/S00125-005-1718-2 .

Lund-Blix NA, Dong F, Mårild K, Seifert J, Barón AE, Waugh KC, et al. Gluten intake and risk of islet autoimmunity and progression to type 1 diabetes in children at increased risk of the disease: the diabetes autoimmunity study in the young (DAISY). Diabetes Care. 2019;42:789. https://doi.org/10.2337/DC18-2315 .

Haupt-Jorgensen M, Holm LJ, Josefsen K, Buschard K. Possible prevention of diabetes with a gluten-free diet. Nutrients. 2018;10. https://doi.org/10.3390/NU10111746 .

Republic of Turkey Ministry of Food Agriculture and Livestock. Republic of Turkey Ministry of Food Agriculture and Livestock GAP International Agricultural Research and Training Center for Grain Report. Diyarbakır; 2013. https://arastirma.tarimorman.gov.tr/gaputaem/Belgeler/tarımsalveriler/gaputaemgncel/TahılRaporu.pdf . Accessed 29 Jan 2022.

Download references

Acknowledgments

The authors are grateful for help and support provided by Prof. Dr. Damla Gökşen Şimşek, Assoc. Prof. Dr. Aslı Aslan, Spec. Dr. Eren Er and all health professionals from Ege University who enabled contact with potential cases and controls during the data collection.

Not applicable.

Author information

Authors and affiliations.

Department of Nutrition and Dietetics, Faculty of Health Sciences, Acibadem University, İstanbul, Turkey

İpek Çiçekli

Department of Public Health, Faculty of Medicine, Ege University, İzmir, Turkey

Raika Durusoy

You can also search for this author in PubMed   Google Scholar

Contributions

R.D., and İ.Ç. contributed to the design and implementation of the research and to the analysis of the results. İ.Ç. contributed to the writing of the manuscript. R.D. encouraged İ.Ç. to investigate and she supervised this work. All authors read and approved the final manuscript for submission.

Corresponding author

Correspondence to İpek Çiçekli .

Ethics declarations

Ethics approval and consent to participate.

The research was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ege University Faculty of Medicine Clinical Research Ethics Committee (decision no.10059, dated 9 January 2020). Written approval was obtained from the administration of the Department of Child Health and Diseases before starting the study.

Consent for publication

Competing interests.

We have no known conflict of interest to disclose.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Çiçekli, İ., Durusoy, R. Breastfeeding, nutrition and type 1 diabetes: a case-control study in Izmir, Turkey. Int Breastfeed J 17 , 42 (2022). https://doi.org/10.1186/s13006-022-00470-z

Download citation

Received : 09 June 2021

Accepted : 27 March 2022

Published : 27 May 2022

DOI : https://doi.org/10.1186/s13006-022-00470-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Diabetes mellitus
• Risk factors

International Breastfeeding Journal

ISSN: 1746-4358

• General enquiries: [email protected]

• Diabetes Care for Children & Young People

Vol:05 | No:01

Children and young people’s diabetes care: Case study

• 12 Jul 2016

This case study demonstrates the physical and psychological difficulties faced by many young people with type 1 diabetes. Over the year following her diagnosis, Max had a deterioration in glycaemic control despite reporting that little had changed in her management. Detailed assessment revealed a number of psychosocial factors that were preventing her from achieving good control. However, working with her multidisciplinary team, she was able to address these issues and improve her blood glucose levels. This article outlines these issues and the action plan that Max and her diabetes team drew up to overcome them.

Share this article + Add to reading list – Remove from reading list ↓ Download pdf

This case study represents the challenges and issues, both physical and psychological, faced by a young person with type 1 diabetes and the support given by her diabetes multidisciplinary team (MDT). Implications for practice are addressed using current evidence-based research. The names of the child and family have been anonymised to protect their identity.

Case study Max (a pseudonym) is a 17-year-old girl who was diagnosed with type 1 diabetes 4 years ago at the age of 13 years. She and her mother were shocked and upset by the diagnosis, and both felt its management would be too great a task to take on by themselves.

Max is an only child and lives with her mother, a single parent. She attends the local state comprehensive school and is popular with her peer group. Her mother was very involved in her care and diabetes management from the onset. Despite this, her diabetes control deteriorated over time ( Table 1 ). In October 2012, her HbA 1c was 56 mmol/mol (7.3%); however, over the next year, this increased to 84 mmol/mol (9.8%) in July 2013. She found it difficult to count the carbohydrate portions in her food and her injections were hurting much more than when she was first diagnosed. She also expressed a fear of hypoglycaemia and of “looking stupid” in front of her friends.

Max and her MDT discussed treatment options to improve her glycaemic control. She refused insulin pump therapy but agreed to a blood glucose monitor and bolus advisor to assist with her regimen of multiple daily insulin injections (MDI). She is now using the bolus advisor confidently and has had regular one-to-one sessions with a psychologist. She is having fewer hypoglycaemic episodes and her HbA 1c has improved; in January 2016 it was 69 mmol/mol (8.5%) and in April 2016 it was 58 mmol/mol (7.5%).

Discussion Diagnosis Max and her mother were extremely shocked and upset by the diagnosis of type 1 diabetes and the potential severity of the condition and intense management required. Both felt it would be too great a task to take on by themselves.

Kübler-Ross and Kessler (2005) suggested that a diagnosis of diabetes is a life-changing event comparable to the experience of loss, and that children and families will often go through the five stages of grief defined by Kübler-Ross (1970) and outlined in Box 1 . They use this as a coping strategy to enable them to eventually acknowledge the condition. However, many families never reach the fifth stage of acceptance and many will fluctuate between the stages.

Although Max and her mum did accept the diagnosis eventually, at times both of them reverted to the earlier stages of grief. The diabetes MDT supported the family from diagnosis and will continue to support them throughout their time within the paediatric diabetes service, through the transition period with both paediatric and young people’s teams, until discharged to adult diabetes care.

The diabetes MDT was established after the Best Practice Tariff was introduced in 2012. It consists of doctors, nurses, dietitians, a psychologist and a personal assistant. It is well recognised that the MDT needs to work together in close cooperation to achieve good practice, and this can be strengthened by using written protocols, guidelines and targets (Brink, 2010). Logic would suggest that centres with MDTs and the same approaches and treatment regimens would have similar outcomes, yet the Hvidøre Childhood Diabetes Study Group has shown this is not the case (de Beaufort et al, 2013). In terms of glycaemic control, there were notable differences in patient outcomes across 21 diabetes clinics, all of which were committed to MDT-based practice. Although factors such as age, type of insulin regimen and socioeconomic status were shown to have some influence over specific outcomes, they did not explain the apparent differences between these clinics.

Family/social history Max is an only child and lives with her mother, a single parent. East et al (2006) suggested that rapid social change over the past 20 years has seen a marked increase in the number of mother-headed single-parent families. Max attends the local state comprehensive school, where she is generally doing well. She is popular with her peer group. La Greca et al (1995) suggested that peer relationships are important in diabetes management, as children and young people (CYP) may receive considerable emotional support from their friends. However, on occasions, Max’s peer relationships have had a counterproductive effect on her, and she feels she is different from her friends as the only one who has diabetes. This at times affects her self-esteem and impacts her diabetes control.

Max’s mother was very involved in her care and diabetes management from the onset. Anderson and Brackett (2005) suggested that parents typically take on most of the responsibility for management of diabetes when children are young or newly diagnosed.

Deterioration in diabetes control Max’s diabetes control had deteriorated since her diagnosis ( Table 1 ). In October 2012, her HbA 1c was 56 mmol/mol (7.3%), which indicated a good level of diabetes control and a reduced risk of diabetes complications, as suggested by the DCCT (Diabetes Control and Complications Trial; DCCT Research Group, 1994). At her subsequent diabetes clinic appointments up to July 2013, she reported that “nothing had really changed,” except she “didn’t have time to think about her diabetes,” although she felt guilty because she knew she could make herself ill and her mum would get upset. She stated that it was hard counting the carbohydrate portions in her food and her injections were hurting much more than when she was first diagnosed. Her height and weight remained static.

Diabetes care is greatly influenced by psychosocial factors when they obstruct people’s ability to manage their diabetes and achieve good metabolic control. A team-based approach to addressing an individual’s ability to cope is critical (Kent et al, 2010). It is important for healthcare professionals to be aware of how CYP think at the different stages of their development, as their understanding of illness and chronic health conditions is often greater than that of their peers. Jean Piaget (1896–1980) investigated cognitive processes in children, calling them “schemas”. By the time children reach around 12 years of age, they can describe illness in terms of non-functioning or malfunctioning of an internal organ or process. Later in development they can appreciate that a person’s thoughts or feelings can affect the way the body functions, which demonstrates an awareness of psychological factors (Taylor et al, 1999).

Spear (2013) proposed that we can begin to understand how young people with type 1 diabetes think, feel and behave if we consider the cognitive and biological changes that occur during adolescence. Glasper and Richardson (2005) suggested there is now a growing awareness that CYP are able to make their own decisions if given information in an age-appropriate manner. Gillick competence identifies children aged under 16 years as having the capacity to consent to their own treatment if they understand the consequences (NSPCC, 2016).

Butler et al (2007) suggest that adolescence is a time of upheaval when young people have to deal with the influence of peers, school life and developing their own identity, as well as all the physiological changes that occur. Young people with type 1 diabetes have the added responsibility of developing autonomy regarding the self-management of their condition. Hanas (2006) suggests that parents should continue to take part in their child’s diabetes care into adolescence and not hand the responsibility to the young person too early. Snoek and Skinner (2002) suggest that intensive self-management of diabetes is complex and time-consuming, and creates a significant psychosocial burden on children and their families.

There are significant challenges for CYP to engage in effective diabetes self-management. Several of these were identified with Max and her mother:

• Deterioration in diabetes control.
• Difficulty with carbohydrate counting.
• Insulin omission.
• Fear of hypoglycaemia.
• Painful injections.

Action plan An action plan was discussed between Max and the MDT. As she was on an MDI regimen (a long-acting insulin at bedtime and rapid-acting insulin with meals), a bolus advisor/blood glucose monitor was demonstrated and discussed with her and her mum. Max felt she would be able to use this to help eliminate the calculations which, although she was capable of doing them, she often lacked time to do so. With further discussion, Max said she was “scared of getting it wrong and having a hypo”. Insulin pump therapy was discussed but she did not want to “have a device attached to my body because it would remind me all the time that I have diabetes”. Insulin pump therapy is recommended as a treatment option for adults and children over 12 years of age with type 1 diabetes whose HbA 1c levels remain above 69 mmol/mol (8.5%) on MDI therapy despite a high level of care (NICE, 2015a).

The National Service Framework standard 3 (Department of Health, 2001) recommends empowering people with diabetes and encourages them and their carers to gain the knowledge and skills to be partners in decision-making, and giving them more personal control over the day-to-day management of their diabetes, ensuring the best possible quality of life. However, if a diabetes management plan is discussed in partnership with a (Gillick-competent) young person but they elect not to comply with the plan despite full awareness of the implications of their actions, then the diabetes team should support them whilst trying to encourage them to maintain the treatment plan. This can be very difficult and frustrating at times, as a healthcare professional is an advocate for the patient, and promotion of the best interests of the patient is paramount.

Psychology involvement Max was reviewed by the psychologist to assess her psychological health and wellbeing. The psychologist used the Wellbeing in Diabetes questionnaire (available from the Yorkshire and Humber Paediatric Diabetes Network) to assess her and identify an optimal plan of care.

The psychology sessions were focussed on her issues around the following:

• Worry about deterioration in control.
• The consequences of insulin omission.

Max had a series of one-to-one appointments and some joint sessions with the paediatric diabetes specialist nurse and/or dietitian, so this linked into other team members’ specialities.

Carbohydrate counting and use of a bolus advisor The dietitian assessed Max and her mother’s ability to carbohydrate count using a calculator, food diagrams and portion sizes, and both of them were able to demonstrate competency in this task. Garg et al (2008) have shown that the use of automated bolus advisors is safe and effective in reducing postprandial glucose excursions and improving overall glycaemic control. However, this can only be true if the bolus advisor is being used correctly and is confirmed as such by comparing blood glucose and HbA 1c results before and after initiation of the bolus advisor, and observing the patient using the device to ensure it is being used safely and correctly.

Barnard and Parkin (2012) propose that, as long as safety and lifestyle are taken into consideration, advanced technology will benefit CYP, as inaccurate bolus calculation can lead to persistent poor diabetes control. These tools can help with removing the burden of such complex maths and have the potential to significantly improve glycaemic control.

Insulin omission and fear of hypoglycaemia Max also expressed her fear of hypoglycaemia and of “looking stupid” in front of her friends. She admitted to missing some of her injections, especially at school. Wild et al (2007) suggest that a debilitating fear of hypoglycaemia can result in poor adherence to insulin regimens and subsequent poor metabolic control. Crow et al (1998) describe the deliberate omission or reduced administration of insulin, which results in hyperglycaemia and subsequent rapid reduction in body weight. Type 1 diabetes predisposes a person to a high BMI. Adolescent girls and adult women with type 1 diabetes generally have higher BMI values than their peers without the condition (Domargård et al, 1999). Affenito et al (1998) observed that insulin misuse was the most common method of weight control used by young women with type 1 diabetes. However, Max’s weight remained stable and there was no clinical indication that she was missing insulin to lose weight; rather, it was her fear of hypoglycaemia that drove her to omitting insulin at school. With the use of the bolus calculator, she was reassured about her calculations for insulin-to-carbohydrate ratios, but it was reinforced with her that the device would only work efficiently if she used it correctly with each meal.

Painful injections Max also highlighted that her injections were now more painful than when she was first diagnosed, and this was causing her distress each time she had to inject. Injection technique was discussed with her and demonstrated using an injection model, and her injection technique was observed and appeared satisfactory. She was using 5-mm insulin needles and so was switched to 4-mm needles, as recommended by Forum for Injection Technique (2015) guidelines.

Appropriate technique when giving injections is key to optimal blood glucose control; however, evidence suggests that injection technique is often imperfect. Studies by Strauss et al (2002) and Frid et al (2010) revealed disturbing practices in relation to injection technique, with little improvement over the years. Current diabetes guidelines do not include detailed advice on injection technique, and only the guidance on type 2 diabetes in adults (NICE, 2015b) makes any reference to providing education about injectable devices for people with diabetes. However, the older Quality Standard for diabetes in adults (NICE, 2011) recommends a structured programme of education, including injection site selection and care (Diggle, 2014).

Conclusion The issues and concerns this young girl had were identified and addressed by the diabetes MDT. She was assessed by several members of the team, and a credible, evidence-based action plan was put into place to assist her and her mother to manage her diabetes at this difficult time. Max is now using the bolus advisor confidently and having fewer hypoglycaemic episodes, and her HbA 1c has improved. She prefers using the 4-mm injection pen needles, although she remains hesitant when giving injections; she will still not consider insulin pump therapy. Her one-to-one sessions with the psychologist have now ceased, but she is aware she can access a psychologist at clinic on request, or if the MDT assesses that her psychological health has deteriorated.

When a child in a family develops a chronic condition such as type 1 diabetes, effective communication is vitally important to address issues with the family at the earliest stage so that problems can be discussed and, hopefully, resolved before they escalate out of control. Upon reflection, the team could have become more intensely involved at an earlier stage to prevent Max’s diabetes management issues and stop her HbA 1c from reaching such a high level. Furthermore, the new NICE (2015a) guideline has set the target HbA 1c at ≤48 mmol/mol (6.5%), so there is still some work to be done. However, the outcome of this case appears to be favourable at present.

Affenito SG, Rodriguez NR, Backstrand JR et al (1998) Insulin misuse by women with type 1 diabetes mellitus complicated by eating disorders does not favorably change body weight, body composition, or body fat distribution. J Am Diet Assoc 98 : 686–8 Anderson BJ, Brackett J (2005) Diabetes in children. In: Snoek FJ, Skinner TC (eds). Psychology in Diabetes Care (2nd edition). John Wiley & Sons, Chichester Barnard K, Parkin C (2012) Can automated bolus advisors help alleviate the burden of complex maths and lead to optimised diabetes health outcomes? Diabetes Care for Children & Young People 1 : 6–9 Brink SJ (2010) Pediatric and adolescent multidisciplinary diabetes team care. Pediatr Diabetes 11 : 289–91 Butler JM, Skinner M, Gelfand D et al (2007) Maternal parenting style and adjustment in adolescents with type I diabetes. J Pediatr Psychol 32 : 1227–37 Crow SJ, Keel PK, Kendall D (1998) Eating disorders and insulin-dependent diabetes mellitus. Psychosomatics 39 : 233–43 de Beaufort CE, Lange K, Swift PG et al (2013) Metabolic outcomes in young children with type 1 diabetes differ between treatment centers: the Hvidoere Study in Young Children 2009. Pediatr Diabetes 14 : 422–8 Department of Health (2001) National Service Framework: Diabetes . DH, London. Available at: http://bit.ly/18OpAzL (accessed 24.02.16) Diabetes Control and Complications Trial Research Group (1994) Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial. J Pediatr 125 : 177–88 Diggle J (2014) Are you FIT for purpose? The importance of getting injection technique right . Journal of Diabetes Nursing 18 : 50–7 Domargård A, Särnblad S, Kroon M et al (1999) Increased prevalence of overweight in adolescent girls with type 1 diabetes mellitus. Acta Paediatr 88 : 1223–8 East L, Jackson D, O’Brien L (2006) Father absence and adolescent development: a review of the literature. J Child Health Care 10 : 283–95 Forum for Injection Technique (2015) The UK Injection Technique Recommendations (3rd edition). Available at: http://bit.ly/1QeZU2E (accessed 24.02.16) Frid A, Hirsch L, Gaspar R et al (2010) The Third Injection Technique Workshop in Athens (TITAN). Diabetes Metab 36 (Suppl 2): 19–29 Garg SK, Bookout TR, McFann KK et al (2008) Improved glycemic control in intensively treated adult subjects with type 1 diabetes using insulin guidance software. Diabetes Technol Ther 10 : 369–75 Glasper EA, Richardson J (2005) A Textbook of Children’s and Young People’s Nursing . Churchill Livingston, London Hanas R (2006) Type 1 Diabetes in Children, Adolescents and Young Adults (3rd edition). Class Publishing, London: 329, 349–50 Kent D, Haas L, Randal D et al (2010) Healthy coping: issues and implications in diabetes education and care. Popul Health Manag 13 : 227–33 Kübler-Ross E (1970) On Death and Dying: What the Dying Have to Teach Doctors, Nurses, Clergy and Their Own Families . Tavistock Publications, London Kübler-Ross E, Kessler D (2005) On Grief and Grieving: Finding the Meaning of Grief Through the Five Stages of Loss . Simon & Schuster UK, London La Greca AM, Auslander WF, Greco P et al (1995) I get by with a little help from my family and friends: adolescents’ support for diabetes care. J Pediatr Psychol 20 : 449–76 NICE (2011) Diabetes in adults (QS6). NICE, London. Available at: www.nice.org.uk/guidance/qs6 (accessed 24.02.16) NICE (2015a) Diabetes (type 1 and type 2) in children and young people: diagnosis and management (NG18). NICE, London. Available at: www.nice.org.uk/guidance/ng18 (accessed 24.02.16) NICE (2015b) Type 2 diabetes in adults: management (NG28). NICE, London. Available at: www.nice.org.uk/guidance/ng28 (accessed 24.02.16) NSPCC (2016) A Child’s Legal Rights: Gillick Competency and Fraser Guidelines . NSPCC, London. Available at: http://bit.ly/1Tj6DcF (accessed 24.02.16) Snoek FJ, Skinner TC (2002) Psychological counselling in problematic diabetes: does it help? Diabet Med 19 : 265–73 Spear LP (2013) Adolescent neurodevelopment. J Adolesc Health 52 (Suppl 2): 7–13 Strauss K, De Gols H, Hannat I et al (2002) A pan-European epidemiologic study of insulin injection technique in patients with diabetes. Practical Diabetes International 19 : 71–76 Taylor J, Müller D, Wattley L, Harris P (1999) The development of children’s understanding. In: Nursing Children: Psychology, Research and Practice . Stanley Thornes, Cheltenham Wild D, von Maltzahn R, Brohan E et al (2007) A critical review of the literature on fear of hypoglycemia in diabetes: implications for diabetes management and patient education. Patient Educ Couns 68 : 10–5

Do youth workers have a role in improving diabetes transition services?

Cgm for children and young people with type 1 diabetes: nice criteria and effects of decision fatigue and alarm fatigue  , improving paediatric diabetes in england: areas of focus, delays in accessing continuous glucose monitoring in people with type 1 diabetes, celebrating may ng: the woman behind the obe, fiona campbell awarded an obe for services to paediatric diabetes, diabetes transition: a time to act.

Can the involvement of youth workers improve diabetes care for young people transitioning to adult diabetes services?

The impact of decision fatigue and alarm fatigue in children and young people using continuous glucose monitoring

NHSEI National Clinical Lead for Diabetes in Children and Young People, Fulya Mehta, outlines the areas of focus for improving paediatric diabetes care.

16 Nov 2022

NICE guidance urges local trusts to improve processes and advocate for CGM use in children and young people.

Sign up to all DiabetesontheNet journals

• CPD Learning
• Diabetes & Primary Care
• Journal of Diabetes Nursing
• The Diabetic Foot Journal
• Diabetes Digest

Useful information

• Terms and conditions
• Privacy policy
• Editorial policies and ethics

By clicking ‘Subscribe’, you are agreeing that DiabetesontheNet.com are able to email you periodic newsletters. You may unsubscribe from these at any time. Your info is safe with us and we will never sell or trade your details. For information please review our Privacy Policy .

Are you a healthcare professional?  This website is for healthcare professionals only. To continue, please confirm that you are a healthcare professional below.

We use cookies  responsibly to ensure that we give you the best experience on our website. If you continue without changing your browser settings, we’ll assume that you are happy to receive all cookies on this website.  Read about how we use cookies .

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Indian J Endocrinol Metab
• v.19(Suppl 1); 2015 Apr

Type 1 diabetes mellitus-common cases

Surender kumar.

Department of Endocrinology, Sir Ganga Ram Hospital, New Delhi, India

Tight glycemic control in type 1 diabetes mellitus patients is associated with the risk of hypoglycemia. Diabetic patients are forced to change their lifestyle to adjust to the disease condition and survive it. The best way to manage diabetes would be to develop a therapy, which could adjust to the patient's conditions. Here, I present few cases wherein switching to a long-acting basal insulin analog helped combat recurrent hypoglycemic episodes experienced by the patients.

I NTRODUCTION

Tight glycemic control in type 1 diabetes mellitus (T1DM) patients is not possible because of hypoglycemia. Diabetic patients are forced to change their lifestyle to adjust to the disease condition and survive it. The best way to manage diabetes would be to develop a therapy, which could adjust to the patient's conditions.[ 1 ]

A 6-year-old boy presented with classic features of diabetic ketoacidosis, that is, weight loss and extreme weakness and osmotic features. The fasting blood sugar level was 300 mg/dL, postprandial glucose level was 467 mg/dL and hemoglobin A1c (HbA1c) was 7.2%. He was administered with standard intravenous insulin and fluid, which finally brought down the fasting blood glucose level to around 120 mg/dL. He was administered basal-bolus therapy and was discharged. Patient had two episodes of severe hypoglycemia. His parents were worried due to frequent checking of blood glucose levels many times in a day. The challenge was also to avoid urination in bed at night by the child. Otherwise he would get a common cold. The patient remained unconscious in the middle of the night and was fed up with the frequent monitoring of blood sugar. The patient and the parents had severe anxiety, depression, frustration, and disgust. The parents considered diabetes as a curse on their family. He was informed about degludec/injection tresiba, which is not yet approved in children because of lack of experience. The physician explained to them that there was nothing wrong in administering it and is not contra-indicated in T1DM.[ 2 ] The parents were also explained that insulin degludec may even help the child to convert from four injections to one injection a day, and from very frequent monitoring to once in a day. After reviewing the literature about insulin degludec, the parents were finally convinced about it. The patient was then put from basal-bolus to 2 bolus plus 1 basal and finally degludec at 16 U. Over the period of time, blood sugar level came to normal at around 110 mg/dL-pre meal. The patient was trained very well that if he wanted to reduce the frequency of monitoring of blood sugar level, then he had to follow small frequent meals. This made him felt happy because once the sugar was controlled then small amount of sweets was also given. The techniques resulted in good compliance from the patient. The patient did not report any hypoglycemic event over a period of 3 months. This was a big relief for the patient and his parents. Later parents were told that the child may require basal-bolus therapy. The outcomes of this case study were that in case of T1DM the physician should not be very aggressive except during the first 2 weeks of admission. The physician should also try to convince the parents about line of treatment, and educate both the patients and the child. The dose may be gradually stabilized without being aggressive, and this also prevents frequent episodes of hypoglycemia. Hence, gradual tightening of glycemic control is very important. The doctor should analyze the psyche of the patient and his parents.

A 57-year-old female presented with a 13 year history of diabetes. Due to the failure of oral hypoglycemic agents (OHAs) in controlling her sugar levels, for the last 3 years, she was treated with biphasic insulin aspart 30/70. She was a very frequent flier, a regular swimmer and socially very active, and this led her to have irregular meals. Hence, she often go into frequent hypoglycemia and during the last 6 months the patient's average blood glucose level during fasting were 170 mg/dL and postprandial glucose levels varied from 230 to 280 mg/dL. Even after high sugar levels, she fortunately had normal kidney functions. Patient was able to afford an insulin pump, so she was put on one. With the pump, her blood glucose was in control and patient was happy. However she soon realized the limitation of carrying it everywhere she went. These were the true feelings of a patient who was very active while she was on an insulin pump. The physician, after discussing with the patient, started her on insulin degludec and lifestyle modification, especially the diet component. Patient understood these problems and followed the diet. She followed the dietary modification and over 2 months of time, fasting blood glucose was 110 mg/dL, post meals values were around 180 mg/dL. She had only one episode of minor hypoglycemia which was due to delayed meal. The doctor later reduced degludec from 44 U to 40 U and blood glucose was still improving without any episode of hypoglycemia in the last 3 months. The outcome of this case is that with this therapy and dietary modification, a desired level of blood glucose can be achieved, without hypoglycemic risk.

An 80-year-old retired army officer, staying alone, has type 2 diabetes for the last 12 years and renal function test was normal and patient was on insulin along with other OHAs. Despite this, the patient was getting attacks of hypoglycemia, which scared the patient of unconsciousness and even death. The limiting factors were that the patient was staying alone and was dependent upon an attendant to get injections. During the weekends or holidays, the attendant was not on a regular time, and this led to irregular insulin injections, causing hypoglycemic episode to patient. This patient as well was put on insulin degludec and over a period the dose of degludec was also increased. His HbA1c and fasting blood glucose level improved without any episode of hypoglycemia. The outcomes of this case are that degludec along with dietary modifications gave desired diabetes control without any hypoglycemia.

The main barrier to tight glycemic control is hypoglycemia. This can be adjusted with slight dietary modification without changing the therapy.[ 3 ]

Source of Support: Nil

Conflict of Interest: None declared.

R EFERENCES