alcohol research papers

• Search Menu
• Sign in through your institution
• Author Guidelines
• Submission Site
• Open Access
• About Alcohol and Alcoholism
• About the Medical Council on Alcohol
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Contact the MCA
• Journals on Oxford Academic
• Books on Oxford Academic

Editors-in-Chief

Dr Giancarlo Colombo

Dr Lorenzo Leggio

Publish with Alcohol and Alcoholism

Supportive and international Editorial Board, rigorous and constructive peer review, open access options, and much more. Learn why your research is a perfect fit for Alcohol and Alcoholism .

Featured Content

Advanced access, high cited articles, latest posts on x.

The liver collection

Research highlights related to ALD and AUD. 

Browse the papers

10 Reasons to Publish with Alcohol and Alcoholism

Learn more about why your impactful research is the perfect fit for Alcohol and Alcoholism .

Read more here

Promote Your Article

Have you published an article? What should you do now? Read our top tips on promoting your work to reach a wider audience and ensure your work makes an impact.

Find out more

Explore the archive

Alcohol and Alcoholism offers free online access to all content older than 12 months back to Jan 1st 1996.

Trending articles

Discover the Alcohol and Alcoholism  articles that your peers and the public are talking about, and the news stories they have been featured in.

Join global conversations

Committee on Publication Ethics (COPE)

This journal is a member of and subscribes to the principles of the Committee on Publication Ethics (COPE)

publicationethics.org

ISAJE (International Society of Addiction Journal Editors)

Alcohol and Alcoholism is an ISAJE Member Journal. ISAJE is a not-for-profit organization supporting journal editors, authors and reviewers who work in the addiction field.

Looking for a place to publish?

Alcohol and Alcoholism welcomes submissions, publishing papers on the biomedical, psychological, and sociological aspects of alcoholism and alcohol research.

To gain more information please see the Instructions to Authors page.

Recommend to your library

Fill out our simple online form to recommend Alcohol and Alcoholism to your library.

Recommend now

Open Access options for authors

Alcohol and Alcoholism welcomes submissions from Authors wishing to submit open access papers.

The International Impacts of Alcohol Use Disorder and Nicotine and Tobacco

Explore a collaborative collection showcasing crucial research into the global impacts of alcohol use disorder and nicotine and tobacco use.

Related titles

• Recommend to your Library

Affiliations

• Online ISSN 1464-3502
• Copyright © 2024 Medical Council on Alcohol and Oxford University Press
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

• Open access
• Published: 13 November 2019

Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review

• Susan A. Rombouts 1 ,
• James Conigrave 2 ,
• Eva Louie 1 ,
• Paul Haber 1 , 3 &
• Kirsten C. Morley   ORCID: orcid.org/0000-0002-0868-9928 1  

Systematic Reviews volume  8 , Article number:  275 ( 2019 ) Cite this article

7347 Accesses

3 Citations

Metrics details

Alcohol use disorder (AUD) is highly prevalent and accounts globally for 1.6% of disability-adjusted life years (DALYs) among females and 6.0% of DALYs among males. Effective treatments for AUDs are available but are not commonly practiced in primary health care. Furthermore, referral to specialized care is often not successful and patients that do seek treatment are likely to have developed more severe dependence. A more cost-efficient health care model is to treat less severe AUD in a primary care setting before the onset of greater dependence severity. Few models of care for the management of AUD in primary health care have been developed and with limited implementation. This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

We will conduct a systematic review to synthesize studies that evaluate the effectiveness of models of care in the treatment of AUD in primary health care. A comprehensive search approach will be conducted using the following databases; MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present).

Reference searches of relevant reviews and articles will be conducted. Similarly, a gray literature search will be done with the help of Google and the gray matter tool which is a checklist of health-related sites organized by topic. Two researchers will independently review all titles and abstracts followed by full-text review for inclusion. The planned method of extracting data from articles and the critical appraisal will also be done in duplicate. For the critical appraisal, the Cochrane risk of bias tool 2.0 will be used.

This systematic review and meta-analysis aims to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings. The evidence will define which models are most promising and will guide further research.

Protocol registration number

PROSPERO CRD42019120293.

Peer Review reports

It is well recognized that alcohol use disorders (AUD) have a damaging impact on the health of the population. According to the World Health Organization (WHO), 5.3% of all global deaths were attributable to alcohol consumption in 2016 [ 1 ]. The 2016 Global Burden of Disease Study reported that alcohol use led to 1.6% (95% uncertainty interval [UI] 1.4–2.0) of total DALYs globally among females and 6.0% (5.4–6.7) among males, resulting in alcohol use being the seventh leading risk factor for both premature death and disability-adjusted life years (DALYs) [ 2 ]. Among people aged 15–49 years, alcohol use was the leading risk factor for mortality and disability with 8.9% (95% UI 7.8–9.9) of all attributable DALYs for men and 2.3% (2.0–2.6) for women [ 2 ]. AUD has been linked to many physical and mental health complications, such as coronary heart disease, liver cirrhosis, a variety of cancers, depression, anxiety, and dementia [ 2 , 3 ]. Despite the high morbidity and mortality rate associated with hazardous alcohol use, the global prevalence of alcohol use disorders among persons aged above 15 years in 2016 was stated to be 5.1% (2.5% considered as harmful use and 2.6% as severe AUD), with the highest prevalence in the European and American region (8.8% and 8.2%, respectively) [ 1 ].

Effective and safe treatment for AUD is available through psychosocial and/or pharmacological interventions yet is not often received and is not commonly practiced in primary health care. While a recent European study reported 8.7% prevalence of alcohol dependence in primary health care populations [ 4 ], the vast majority of patients do not receive the professional treatment needed, with only 1 in 5 patients with alcohol dependence receiving any formal treatment [ 4 ]. In Australia, it is estimated that only 3% of individuals with AUD receive approved pharmacotherapy for the disorder [ 5 , 6 ]. Recognition of AUD in general practice uncommonly leads to treatment before severe medical and social disintegration [ 7 ]. Referral to specialized care is often not successful, and those patients that do seek treatment are likely to have more severe dependence with higher levels of alcohol use and concurrent mental and physical comorbidity [ 4 ].

Identifying and treating early stage AUDs in primary care settings can prevent condition worsening. This may reduce the need for more complex and more expensive specialized care. The high prevalence of AUD in primary health care and the chronic relapsing character of AUD make primary care a suitable and important location for implementing evidence-based interventions. Successful implementation of treatment models requires overcoming multiple barriers. Qualitative studies have identified several of those barriers such as limited time, limited organizational capacity, fear of losing patients, and physicians feeling incompetent in treating AUD [ 8 , 9 , 10 ]. Additionally, a recent systematic review revealed that diagnostic sensitivity of primary care physicians in the identification of AUD was 41.7% and that only in 27.3% alcohol problems were recorded correctly in primary care records [ 11 ].

Several models for primary care have been created to increase identification and treatment of patients with AUD. Of those, the model, screening, brief interventions, and referral to specialized treatment for people with severe AUD (SBIRT [ 12 ]) is most well-known. Multiple systematic reviews exist, confirming its effectiveness [ 13 , 14 , 15 ], although implementation in primary care has been inadequate. Moreover, most studies have looked primarily at SBIRT for the treatment of less severe AUD [ 16 ]. In the treatment of severe AUD, efficacy of SBIRT is limited [ 16 ]. Additionally, many patient referred to specialized care often do not attend as they encounter numerous difficulties in health care systems including stigmatization, costs, lack of information about existing treatments, and lack of non-abstinence-treatment goals [ 7 ]. An effective model of care for improved management of AUD that can be efficiently implemented in primary care settings is required.

Review objective

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings. We aim to evaluate the effectiveness of the models of care in increasing engagement and reducing alcohol consumption.

By providing this overview, we aim to guide improvement of design and implementation of evidence-based models of care for the treatment of alcohol use disorder in primary health care settings.

The systematic review is registered in PROSPERO international prospective register of systematic reviews (CRD42019120293) and the current protocol has been written according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) recommended for systematic reviews [ 17 ]. A PRISMA-P checklist is included as Additional file  1 .

Eligibility criteria

Criteria for considering studies for this review are classified by the following:

Study design

Both individualized and cluster randomized trials will be included. Masking of patients and/or physicians is not an inclusion criterion as it is often hard to accomplish in these types of studies.

Patients in primary health care who are identified (using screening tools or by primary health care physician) as suffering from AUD (from mild to severe) or hazardous alcohol drinking habits (e.g., comorbidity, concurrent medication use). Eligible patients need to have had formal assessment of AUD with diagnostic tools such as Diagnostic and Statistical Manual of Mental Disorders (DSM-IV/V) or the International Statistical Classification of Diseases and Related Health Problems (ICD-10) and/or formal assessment of hazardous alcohol use assessed by the Comorbidity Alcohol Risk Evaluation Tool (CARET) or the Alcohol Use Disorders Identification test (AUDIT) and/or alcohol use exceeding guideline recommendations to reduce health risks (e.g., US dietary guideline (2015–2020) specifies excessive drinking for women as ≥ 4 standard drinks (SD) on any day and/or ≥ 8 SD per week and for men ≥ 5 SD on any day and/or ≥ 15 SD per week).

Studies evaluating models of care for additional diseases (e.g., other dependencies/mental health) other than AUD are included when they have conducted data analysis on the alcohol use disorder patient data separately or when 80% or more of the included patients have AUD.

Intervention

The intervention should consist of a model of care; therefore, it should include multiple components and cover different stages of the care pathway (e.g., identification of patients, training of staff, modifying access to resources, and treatment). An example is the Chronic Care Model (CCM) which is a primary health care model designed for chronic (relapsing) conditions and involves six elements: linkage to community resources, redesign of health care organization, self-management support, delivery system redesign (e.g., use of non-physician personnel), decision support, and the use of clinical information systems [ 18 , 19 ].

As numerous articles have already assessed the treatment model SBIRT, this model of care will be excluded from our review unless the particular model adds a specific new aspect. Also, the article has to assess the effectiveness of the model rather than assessing the effectiveness of the particular treatment used. Because identification of patients is vital to including them in the trial, a care model that only evaluates either patient identification or treatment without including both will be excluded from this review.

Model effectiveness may be in comparison with the usual care or a different treatment model.

Included studies need to include at least one of the following outcome measures: alcohol consumption, treatment engagement, uptake of pharmacological agents, and/or quality of life.

Solely quantitative research will be included in this systematic review (e.g., randomized controlled trials (RCTs) and cluster RCTs). We will only include peer-reviewed articles.

Restrictions (language/time period)

Studies published in English after 1 January 1998 will be included in this systematic review.

Studies have to be conducted in primary health care settings as such treatment facilities need to be physically in or attached to the primary care clinic. Examples are co-located clinics, veteran health primary care clinic, hospital-based primary care clinic, and community primary health clinics. Specialized primary health care clinics such as human immunodeficiency virus (HIV) clinics are excluded from this systematic review. All studies were included, irrespective of country of origin.

Search strategy and information sources

A comprehensive search will be conducted. The following databases will be consulted: MEDLINE (1946 to present), PsycINFO (1806 to present), Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials (CENTRAL) (1991 to present), and Embase (1947 to present). Initially, the search terms will be kept broad including alcohol use disorder (+synonyms), primary health care, and treatment to minimize the risk of missing any potentially relevant articles. Depending on the number of references attained by this preliminary search, we will add search terms referring to models such as models of care, integrated models, and stepped-care models, to limit the number of articles. Additionally, we will conduct reference searches of relevant reviews and articles. Similarly, a gray literature search will be done with the help of Google and the Gray Matters tool which is a checklist of health-related sites organized by topic. The tool is produced by the Canadian Agency for Drugs and Technologies in Health (CADTH) [ 20 ].

See Additional file  2 for a draft of our search strategy in MEDLINE.

Data collection

The selection of relevant articles is based on several consecutive steps. All references will be managed using EndNote (EndNote version X9 Clarivate Analytics). Initially, duplicates will be removed from the database after which all the titles will be screened with the purpose of discarding clearly irrelevant articles. The remaining records will be included in an abstract and full-text screen. All steps will be done independently by two researchers. Disagreement will lead to consultation of a third researcher.

Data extraction and synthesis

Two researchers will extract data from included records. At the conclusion of data extraction, these two researchers will meet with the lead author to resolve any discrepancies.

In order to follow a structured approach, an extraction form will be used. Key elements of the extraction form are information about design of the study (randomized, blinded, control), type of participants (alcohol use, screening tool used, socio-economic status, severity of alcohol use, age, sex, number of participants), study setting (primary health care setting, VA centers, co-located), type of intervention/model of care (separate elements of the models), type of health care worker (primary, secondary (co-located)), duration of follow-up, outcome measures used in the study, and funding sources. We do not anticipate having sufficient studies for a meta-analysis. As such, we plan to perform a narrative synthesis. We will synthesize the findings from the included articles by cohort characteristics, differential aspects of the intervention, controls, and type of outcome measures.

Sensitivity analyses will be conducted when issues suitable for sensitivity analysis are identified during the review process (e.g., major differences in quality of the included articles).

Potential meta-analysis

In the event that sufficient numbers of effect sizes can be extracted, a meta-analytic synthesis will be performed. We will extract effect sizes from each study accordingly. Two effect sizes will be extracted (and transformed where appropriate). Categorical outcomes will be given in log odds ratios and continuous measures will be converted into standardized mean differences. Variation in effect sizes attributable to real differences (heterogeneity) will be estimated using the inconsistency index ( I 2 ) [ 21 , 22 ]. We anticipate high degrees of variation among effect sizes, as a result moderation and subgroup-analyses will be employed as appropriate. In particular, moderation analysis will focus on the degree of heterogeneity attributable to differences in cohort population (pre-intervention drinking severity, age, etc.), type of model/intervention, and study quality. We anticipate that each model of care will require a sub-group analysis, in which case a separate meta-analysis will be performed for each type of model. Small study effect will be assessed with funnel plots and Egger’s symmetry tests [ 23 ]. When we cannot obtain enough effect sizes for synthesis or when the included studies are too diverse, we will aim to illustrate patterns in the data by graphical display (e.g., bubble plot) [ 24 ].

Critical appraisal of studies

All studies will be critically assessed by two researchers independently using the Revised Cochrane risk-of-bias tool (RoB 2) [ 25 ]. This tool facilitates systematic assessment of the quality of the article per outcome according to the five domains: bias due to (1) the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported results. An additional domain 1b must be used when assessing the randomization process for cluster-randomized studies.

Meta-biases such as outcome reporting bias will be evaluated by determining whether the protocol was published before recruitment of patients. Additionally, trial registries will be checked to determine whether the reported outcome measures and statistical methods are similar to the ones described in the registry. The gray literature search will be of assistance when checking for publication bias; however, completely eliminating the presence of publication bias is impossible.

Similar to article selection, any disagreement between the researchers will lead to discussion and consultation of a third researcher. The strength of the evidence will be graded according to the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach [ 26 ].

The primary outcome measure of this proposed systematic review is the consumption of alcohol at follow-up. Consumption of alcohol is often quantified in drinking quantity (e.g., number of drinks per week), drinking frequency (e.g., percentage of days abstinent), binge frequency (e.g., number of heavy drinking days), and drinking intensity (e.g., number of drinks per drinking day). Additionally, outcomes such as percentage/proportion included patients that are abstinent or considered heavy/risky drinkers at follow-up. We aim to report all these outcomes. The consumption of alcohol is often self-reported by patients. When studies report outcomes at multiple time points, we will consider the longest follow-up of individual studies as a primary outcome measure.

Depending on the included studies, we will also consider secondary outcome measures such as treatment engagement (e.g., number of visits or pharmacotherapy uptake), economic outcome measures, health care utilization, quality of life assessment (physical/mental), alcohol-related problems/harm, and mental health score for depression or anxiety.

This proposed systematic review will synthesize and evaluate differential models of care for the management of AUD in primary health care settings.

Given the complexities of researching models of care in primary care and the paucity of a focus on AUD treatment, there are likely to be only a few studies that sufficiently address the research question. Therefore, we will do a preliminary search without the search terms for model of care. Additionally, the search for online non-academic studies presents a challenge. However, the Gray Matters tool will be of guidance and will limit the possibility of missing useful studies. Further, due to diversity of treatment models, outcome measures, and limitations in research design, it is possible that a meta-analysis for comparative effectiveness may not be appropriate. Moreover, in the absence of large, cluster randomized controlled trials, it will be difficult to distinguish between the effectiveness of the treatment given and that of the model of care and/or implementation procedure. Nonetheless, we will synthesize the literature and provide a critical evaluation of the quality of the evidence.

This review will assist the design and implementation of models of care for the management of AUD in primary care settings. This review will thus improve the management of AUD in primary health care and potentially increase the uptake of evidence-based interventions for AUD.

Availability of data and materials

Not applicable.

Abbreviations

Alcohol use disorder

Alcohol Use Disorders Identification test

Canadian Agency for Drugs and Technologies in Health

The Comorbidity Alcohol Risk Evaluation

Cochrane Central Register of Controlled Trials

Diagnostic and Statistical Manual of Mental Disorders

Human immunodeficiency virus

10 - International Statistical Classification of Diseases and Related Health Problems

Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols

Screening, brief intervention, referral to specialized treatment

Standard drinks

World Health Organization

WHO. Global status report on alcohol and health: World health organization; 2018.

The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016. a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry. 2018;5(12):987–1012.

Article   Google Scholar  

WHO. Global strategy to reduce the harmful use of alcohol: World health organization; 2010.

Rehm J, Allamani A, Elekes Z, Jakubczyk A, Manthey J, Probst C, et al. Alcohol dependence and treatment utilization in Europe - a representative cross-sectional study in primary care. BMC Fam Pract. 2015;16:90.

Morley KC, Logge W, Pearson SA, Baillie A, Haber PS. National trends in alcohol pharmacotherapy: findings from an Australian claims database. Drug Alcohol Depend. 2016;166:254–7.

Article   CAS   Google Scholar  

Morley KC, Logge W, Pearson SA, Baillie A, Haber PS. Socioeconomic and geographic disparities in access to pharmacotherapy for alcohol dependence. J Subst Abus Treat. 2017;74:23–5.

Rehm J, Anderson P, Manthey J, Shield KD, Struzzo P, Wojnar M, et al. Alcohol use disorders in primary health care: what do we know and where do we go? Alcohol Alcohol. 2016;51(4):422–7.

Le KB, Johnson JA, Seale JP, Woodall H, Clark DC, Parish DC, et al. Primary care residents lack comfort and experience with alcohol screening and brief intervention: a multi-site survey. J Gen Intern Med. 2015;30(6):790–6.

McLellan AT, Starrels JL, Tai B, Gordon AJ, Brown R, Ghitza U, et al. Can substance use disorders be managed using the chronic care model? review and recommendations from a NIDA consensus group. Public Health Rev. 2014;35(2).

Storholm ED, Ober AJ, Hunter SB, Becker KM, Iyiewuare PO, Pham C, et al. Barriers to integrating the continuum of care for opioid and alcohol use disorders in primary care: a qualitative longitudinal study. J Subst Abus Treat. 2017;83:45–54.

Mitchell AJ, Meader N, Bird V, Rizzo M. Clinical recognition and recording of alcohol disorders by clinicians in primary and secondary care: meta-analysis. Br J Psychiatry. 2012;201:93–100.

Babor TF, Ritson EB, Hodgson RJ. Alcohol-related problems in the primary health care setting: a review of early intervention strategies. Br J Addict. 1986;81(1):23–46.

Kaner EF, Beyer F, Dickinson HO, Pienaar E, Campbell F, Schlesinger C, et al. Effectiveness of brief alcohol interventions in primary care populations. Cochrane Database Syst Rev. 2007;(2):Cd004148.

O'Donnell A, Anderson P, Newbury-Birch D, Schulte B, Schmidt C, Reimer J, et al. The impact of brief alcohol interventions in primary healthcare: a systematic review of reviews. Alcohol Alcohol. 2014;49(1):66–78.

Bertholet N, Daeppen JB, Wietlisbach V, Fleming M, Burnand B. Reduction of alcohol consumption by brief alcohol intervention in primary care: systematic review and meta-analysis. Arch Intern Med. 2005;165(9):986–95.

Saitz R. ‘SBIRT’ is the answer? Probably not. Addiction. 2015;110(9):1416–7.

Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. Bmj. 2015;350:g7647.

Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness. Jama. 2002;288(14):1775–9.

Bodenheimer T, Wagner EH, Grumbach K. Improving primary care for patients with chronic illness: the chronic care model, part 2. Jama. 2002;288(15):1909–14.

CADTH. Grey Matters: a practical tool for searching health-related grey literature Internet. 2018 (cited 2019 Feb 22).

Higgins JPT. Thompson SG. Quantifying heterogeneity in a meta-analysis. 2002;21(11):1539–58.

Google Scholar  

Higgins JPT, Thompson SG, Deeks JJ. Altman DG. Measuring inconsistency in meta-analyses. 2003;327(7414):557–60.

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ (Clinical research ed). 1997;315(7109):629–34.

Higgins JPT, López-López JA, Becker BJ, Davies SR, Dawson S, Grimshaw JM, et al. Synthesising quantitative evidence in systematic reviews of complex health interventions. BMJ Glob Health. 2019;4(Suppl 1):e000858–e.

Higgins, J.P.T., Sterne, J.A.C., Savović, J., Page, M.J., Hróbjartsson, A., Boutron, I., Reeves, B., Eldridge, S. (2016). A revised tool for assessing risk of bias in randomized trials. In: Chandler, J., McKenzie, J., Boutron, I., Welch, V. (editors). Cochrane methods. Cochrane database of systematic reviews, 10 (Suppl 1). https://doi.org/10.1002/14651858.CD201601 .

Schünemann H, Brożek J, Guyatt G, Oxman A, editor(s). Handbook for grading the quality of evidence and the strength of recommendations using the GRADE approach (updated October 2013). GRADE Working Group, 2013. Available from gdt.guidelinedevelopment.org/app/handbook/handbook.html ).

Download references

Acknowledgements

There is no dedicated funding.

Author information

Authors and affiliations.

Discipline of Addiction Medicine, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

Susan A. Rombouts, Eva Louie, Paul Haber & Kirsten C. Morley

NHMRC Centre of Research Excellence in Indigenous Health and Alcohol, Central Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia

James Conigrave

Drug Health Services, Royal Prince Alfred Hospital, Camperdown, NSW, Australia

You can also search for this author in PubMed   Google Scholar

Contributions

KM and PH conceived the presented idea of a systematic review and meta-analysis and helped with the scope of the literature. KM is the senior researcher providing overall guidance and the guarantor of this review. SR developed the background, search strategy, and data extraction form. SR and EL will both be working on the data extraction and risk of bias assessment. SR and JC will conduct the data analysis and synthesize the results. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kirsten C. Morley .

Ethics declarations

Ethics approval and consent to participate, consent for publication, competing interests.

The authors declare that they have no competing interests.

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..

PRISMA-P 2015 Checklist.

Additional file 2.

Draft search strategy MEDLINE. Search strategy.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Reprints and permissions

About this article

Cite this article.

Rombouts, S.A., Conigrave, J., Louie, E. et al. Evidence-based models of care for the treatment of alcohol use disorder in primary health care settings: protocol for systematic review. Syst Rev 8 , 275 (2019). https://doi.org/10.1186/s13643-019-1157-7

Download citation

Received : 25 March 2019

Accepted : 13 September 2019

Published : 13 November 2019

DOI : https://doi.org/10.1186/s13643-019-1157-7

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

• Model of care
• Primary health care
• Systematic review

Systematic Reviews

ISSN: 2046-4053

• Submission enquiries: Access here and click Contact Us
• General enquiries: [email protected]

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

The NIAAA is the lead agency for U.S. research on the causes, consequences, prevention and treatment of alcohol use disorder and alcohol-related problems.

National Institute on Alcohol Abuse and Alcoholism (NIAAA)

Major research initiatives.

Addressing alcohol-related issues—from basic science to clinical studies.

Extramural Research: Research at Grantee Institutions

Primary areas of research, funding opportunities, and staff listings for Extramural Research Divisions.

Intramural Research: Research in NIAAA Labs

Organization, primary areas of research, and staff listings for Intramural Research Labs.

Guidance and policies for scientists pursuing alcohol research.

The Strategic Plan describes research goals, themes, and programs.

NIAAA’s peer-reviewed scientific journal.

About NIAAA's Small Business Research Funding Programs

NIAAA surveillance reports on alcohol-related problems.

Research on factors that compel youth to begin and continue drinking.

Research on medications in development for treatment of alcohol use disorder.

The NIAAA data archive is a data repository that houses and shares human subjects data generated by NIAAA-funded research. 

Resources include biological specimens, animals, data, materials, tools, or services made available to  any qualified investigato r to accelerate alcohol-related research in a cost-effective manner.

Current and potential alcohol research investigators and trainees are encouraged to subscribe to our new email list to receive NIAAA information and updates relevant to the research community. To sign up, enter your contact information below.

Sign Up for Email Updates

niaaa.nih.gov

An official website of the National Institutes of Health and the National Institute on Alcohol Abuse and Alcoholism

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: 13 May 2024

Single-site iron-anchored amyloid hydrogels as catalytic platforms for alcohol detoxification

• Jiaqi Su   ORCID: orcid.org/0000-0003-2010-8388 1 , 2   na1 ,
• Pengjie Wang   ORCID: orcid.org/0000-0001-9975-6737 3   na1 ,
• Wei Zhou 4 ,
• Mohammad Peydayesh   ORCID: orcid.org/0000-0002-6265-3811 1 ,
• Jiangtao Zhou   ORCID: orcid.org/0000-0003-4248-2207 1 ,
• Tonghui Jin 1 ,
• Felix Donat   ORCID: orcid.org/0000-0002-3940-9183 5 ,
• Cuiyuan Jin 6 ,
• Lu Xia   ORCID: orcid.org/0000-0002-2726-5389 7 ,
• Kaiwen Wang   ORCID: orcid.org/0000-0002-1046-4525 7 ,
• Fazheng Ren   ORCID: orcid.org/0000-0001-6250-0754 3 ,
• Paul Van der Meeren   ORCID: orcid.org/0000-0001-5405-4256 2 ,
• F. Pelayo García de Arquer   ORCID: orcid.org/0000-0003-2422-6234 7 &
• Raffaele Mezzenga   ORCID: orcid.org/0000-0002-5739-2610 1 , 8  

Nature Nanotechnology ( 2024 ) Cite this article

23k Accesses

1 Citations

1251 Altmetric

Metrics details

• Nanoscale materials
• Nanostructures

Constructing effective antidotes to reduce global health impacts induced by alcohol prevalence is a challenging topic. Despite the positive effects observed with intravenous applications of natural enzyme complexes, their insufficient activities and complicated usage often result in the accumulation of toxic acetaldehyde, which raises important clinical concerns, highlighting the pressing need for stable oral strategies. Here we present an effective solution for alcohol detoxification by employing a biomimetic-nanozyme amyloid hydrogel as an orally administered catalytic platform. We exploit amyloid fibrils derived from β-lactoglobulin, a readily accessible milk protein that is rich in coordinable nitrogen atoms, as a nanocarrier to stabilize atomically dispersed iron (ferrous-dominated). By emulating the coordination structure of the horseradish peroxidase enzyme, the single-site iron nanozyme demonstrates the capability to selectively catalyse alcohol oxidation into acetic acid, as opposed to the more toxic acetaldehyde. Administering the gelatinous nanozyme to mice suffering from alcohol intoxication significantly reduced their blood-alcohol levels (decreased by 55.8% 300 min post-alcohol intake) without causing additional acetaldehyde build-up. Our hydrogel further demonstrates a protective effect on the liver, while simultaneously mitigating intestinal damage and dysbiosis associated with chronic alcohol consumption, introducing a promising strategy in effective alcohol detoxification.

Similar content being viewed by others

Preparation, physicochemical characterization, and bioactivity evaluation of berberine-entrapped albumin nanoparticles

Targeting ferroptosis by poly(acrylic) acid coated Mn3O4 nanoparticles alleviates acute liver injury

Amyloid-polysaccharide interfacial coacervates as therapeutic materials

Although widely enjoyed for its social and relaxing effects (Supplementary Fig. 1 ), alcohol consumption consistently poses significant risks to public health. In fact, in 2016 alone, harmful alcohol consumption resulted in nearly three million deaths and 132.6 million disability-adjusted life years 1 , 2 , 3 , 4 . Existing therapies, mainly relying on endogenous enzymes 5 , 6 , 7 , offer only temporary relief from symptoms, such as nausea and headaches, but fail to address other underlying issues, such as drowsiness, exhaustion and chronic alcoholism. Nanocomplexes with multiple complementary hepatic enzymes have emerged as an effective approach for accelerating human alcohol metabolism 8 , 9 . Although promising, a significant obstacle arises from the insufficient activity of commercially available enzymes, leading to the accumulation of a more hazardous intermediate, acetaldehyde, and possibly damage to human organs. Furthermore, natural enzymes possess major disadvantages, such as high cost, poor physicochemical stability and challenging storage, which have so far impeded the practical application of these complexes for alcohol detoxification purposes.

Over the past decades, advances in nanotechnology have facilitated the evolution of artificial enzymes into nanomaterials, that is, nanozymes, which have ignited enormous scientific interest across diverse fields, ranging from in vitro biosensing and detection to in vivo therapeutics 10 , 11 , 12 , 13 . Inspired by natural enzyme frameworks, researchers have predominantly focused on atomically distributed metal catalysts, in which the catalytic centre of natural enzymes is replicated at the atomic level 14 , 15 , 16 . These single-site catalysts, designed with well-defined electronic and geometric architectures, possess excellent catalytic capabilities, holding great potential as viable substitutes for natural enzymes. Given these promising prospects, attempts have been made to develop biomimetic nanozymes for alcohol detoxification by using, for example, natural enzymes on exogenous supports such as graphene oxide quantum dots or metal-organic framework nanozymes 17 , 18 . However, these approaches still either rely on natural enzymes or offer indirect effects, underscoring the potential for substantial design enhancements. The critical, yet challenging, aspect is the design of efficient single-site catalysts that are capable of converting ethanol into less-toxic acetic acid, or further into carbon dioxide and water, while minimizing the generation of acetaldehyde. Additionally, the task also lies in developing an orally administerable nanozyme that can withstand the gastrointestinal environment and which features no additional toxicity.

In this article, we report a biomimetic-nanozyme amyloid hydrogel to alleviate the deleterious effects of alcohol consumption via oral administration. Within this platform, single-site iron-anchored amyloid fibrils, an original kind of atomic-level engineered nanozyme featuring a similar coordination structure to horseradish peroxidase and with remarkable peroxidase-like activity, are used to efficiently catalyse alcohol oxidation. Specifically, the resultant nanozyme exhibits excellent selectivity in favour of acetic acid production. The catalytic activity of the gelatinous nanozyme could largely tolerate the digestive process, leading to a substantial decrease in blood alcohol levels in alcoholic mice, while avoiding the additional build-up of acetaldehyde. We finally demonstrate that this hydrogel also achieves heightened liver protection and substantial alleviation of intestinal damage and dysbiosis, thereby underscoring its potential as an improved therapeutic approach for alcohol-related conditions. By employing atomic-level design and harnessing the capabilities of nanozymes, our study offers promising insights into the development of efficient and targeted alcohol antidotes, with potential benefits for both liver protection and gastrointestinal health.

Synthesis of single-site iron-anchored β-lactoglobulin fibrils

Diverging from conventional methods that use inorganic carriers, in the current work, we sought to utilize a readily available protein material, β-lactoglobulin (BLG) amyloid fibrils, as the supportive framework for atomically dispersed iron. In addition to their intrinsic binding affinity to various metal ions 19 , including iron, the large aspect ratio of protein filaments (Supplementary Fig. 2a ) and tacked-up β-sheet units also enhance the accessibility of potential binding sites, thereby facilitating the high-density loading of iron atoms. Moreover, BLG fibrils can be easily derived from native BLG, a readily available milk protein, and have very recently been demonstrated safe nutrition ingredients by a comprehensive in vitro and in vivo assessment 20 , meeting the requirements for potential oral administration 21 . Moreover, the exceptional gelling property of BLG fibrils allows for the easy production of hydrogels 22 , which anticipates a delayed digestion process and a prolonged action time within the gastrointestinal tract due to their high viscoelasticity 23 , 24 .

The single-site iron-anchored BLG fibrils (Fe SA @FibBLG) catalyst was synthesized by a straightforward wetness impregnation procedure (Fig. 1a ), which involved exposing a dispersion of BLG fibrils in a mixture of ethanol and polyethylene glycol 200 (PEG200) to a Fe(NO 3 ) 3 PEG200 solution. During this process, the natural occurrence of nitrogen in BLG fibrils coordinated with iron ions to form functional Fe–N–C active sites. The resulting precipitate was lyophilized and collected after multiple rounds of centrifugation and washing.

a , Illustration of the synthesis process of Fe SA @FibBLG. b – d , TEM image ( b ), HAADF-STEM image ( c ) and the corresponding EDS mapping images ( d ) of Fe SA @FibBLG. e – g , AFM images of Fe SA @FibBLG ( e, f (I) ) and FibBLG ( f (II) ) on the mica surface and ( g ) the corresponding height profiles of the white auxiliary lines. h , Representative HAADF-STEM image of Fe SA @FibBLG. The images presented in b – f , h are representative of six technical replicates ( n  = 6), each yielding similar results.

Source data

Having synthesized Fe SA @FibBLG, we then performed a comprehensive characterization of the material using multiple analytical techniques. The morphology of Fe SA @FibBLG, which retains a nanometre-scale diameter consistent with pure BLG fibrils (Supplementary Fig. 2b ), suggests minimal structural impact from the integration of iron (Fig. 1b and Supplementary Fig. 2b ). The iron was homogeneously dispersed across the BLG fibril framework, as evidenced by a significant overlap of the Fe K-edge profile with the elemental composition of the BLG fibrils (Fig. 1c,d and Supplementary Fig. 2c ). Atomic force microscopy (AFM) images confirmed a consistent height of approximately 3 nm both before and after iron integration, verifying the negligible presence of crystalline iron or oxide species (Fig. 1e,f,g ). As shown in Fig. 1h and Supplementary Fig. 2d–f , the presence of individual bright dots with a size below 0.2 nm clearly demonstrated the atomic dispersion of single iron atoms over Fe SA FibBLG, indicating that iron, upon participating in the synthetic procedure described above, is present exclusively in single-site form on the BLG fibrils.

Structural analysis of Fe SA @FibBLG

The coordination environment of iron within Fe SA @FibBLG was elucidated by X-ray absorption fine structure (XAFS) spectroscopy 25 . Figure 2a shows that the pre-edge position for Fe SA @FibBLG resided between the positions of iron foil (metallic iron) and Fe 2 O 3 . The white line area located at higher binding energy demonstrates a lower oxidation state and different coordination environments compared with Fe 2 O 3 (ref. 26 ). X-ray absorption near-edge spectroscopy (XANES) features are valuable for discerning site symmetry around iron in macromolecular complexes 27 . A distinct prominent pre-edge feature below 7,120 eV indicates the ferrous iron (Fe 2+ ) square-planar coordination in iron(II) phthalocyanine (FePc), whereas in Fe SA @FibBLG this feature is slightly reduced due to deviations from ideal square-planarity 28 . The XANES spectrum of Fe SA @FibBLG (Fig. 2a , inset) closely resembles that of FePc, implying a positively charged ionic state of iron within Fe SA @FibBLG (Fe δ + , where the average δ is close to 2). Further insights were obtained from extended X-ray absorption fine structure (EXAFS) spectra in R -space (Fig. 2b ), which revealed a single peak at approximately 1.4 Å. From comparison with reference materials this peak was attributable to the backscattering between iron and lighter atoms, primarily nitrogen (Fig. 2b ), supporting the atomic dispersion of iron sites within Fe SA @FibBLG. Wavelet transform analysis differentiated the sample from the iron foil reference by showing a single maximum intensity at approximately 4 Å −1 and 1.4 Å, suggesting significant Fe–N contributions (Fig. 2c and Supplementary Fig. 3 ), with the coordination number of iron estimated to be 4.5 (Fig. 2d and Supplementary Table 1 ). However, given the challenge in distinguishing Fe–N from Fe–O coordination compared to references such as FePc and Fe 2 O 3 , it is crucial to emphasize the potential existence of Fe–O bonds. Collectively, these findings confirmed that iron in Fe SA @FibBLG exists as single-site iron, devoid of any crystalline or oxide iron metal structure and mainly coordinates with nitrogen atoms. X-ray photoelectron spectroscopy (XPS) analysis of Fe SA @FibBLG further identified distinct binding states of carbon, nitrogen, oxygen and iron, demonstrating a majority of single-site iron in the Fe 2+ state and the existence of Fe–N coordination (Supplementary Figs. 4 and 5 ) 29 , 30 , 31 .

a , Normalized XANES spectra at the Fe K-edge of Fe SA @FibBLG along with reference samples. b , Fourier-transformed (FT) magnitudes of the experimental Fe K-edge EXAFS signals of Fe SA @FibBLG along with reference samples. c , Wavelet transform analysis of Fe K-edge EXAFS data. d , Fitting curves of the EXAFS of FeSA@FibBLG in the R -space and k -space (inset). Fitting results are summarized in Supplementary Table 1 . e , Representative snapshots of the assembly structure of 102 amyloid-forming fragments (LACQCL) from BLG in the process of AAMD simulation using the Gromacs54A force field at 10 ns. f , The 3D gradient isosurfaces and corresponding 2D scatter diagram of δg versus sign( λ 2 )ρ for possible non-covalent interactions between a single iron atom and dimer intercepted from BLG fibril segments in e through DFT simulation. δg is a quantitative measure derived from comparing electron density gradients in the presence and absence of interference, highlighting the penetration of electron density from one Bader atom to its neighbor; sign(λ 2 ) ρ is a scalar field value used to describe the product of the sign of the second eigenvalue (λ 2 ) of the Hessian matrix of a scalar field and the scalar field’s density ( ρ ).

Next, we performed a density functional theory (DFT) calculation for the process of anchoring a ferric ion onto the BLG fibril structure. Since the formation of BLG fibrils involved the participation of multiple peptides assembling in a random manner, here a model nanofibre structure was generated in silico based on repetitive amyloid-forming fragments (LACQCL) from BLG, using an all-atom molecular dynamics (AAMD) simulation (Fig. 2e ) 19 . An evident periodic nanofibril was formed at 10 ns containing 102 repetitive fragments, where a peptide dimer with verified thermodynamic stability was intercepted for DFT calculation (Supplementary Fig. 6 ). As shown in Fig. 2f , the blue isosurface observed between the iron atom and surrounding nitrogen atoms corresponds to strong attractive interactions between iron and nitrogen, potentially arising from the sharing of electron pairs between the iron and nitrogen atoms (Supplementary Fig. 7 ). This was further verified by the existence of the prominent peak at approximately −0.03 in the scatter plot (Fig. 2f ). These results clearly demonstrate that the BLG fibrils possessed effective binding sites that were capable of capturing iron atoms through Fe–N coordination, enabling the formation of active iron centres in Fe SA @FibBLG.

Peroxidase-like activity of Fe SA @FibBLG

The coordination structure of the catalytic sites in our Fe SA @FibBLG was similar to that of the horseradish peroxidase enzyme (Supplementary Fig. 8a ) 32 . Inspired by this similarity, we characterized the peroxidase-like activities of Fe SA @FibBLG by studying the facilitated chromogenic reactions through catalysing artificial substrates of peroxidase (for example, 3,3′,5,5′-tetramethylbenzidine (TMB), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) or o -phenylenediamine) in the presence of H 2 O 2 (Supplementary Fig. 8b ). By using the general method described in the current work, two comparison catalysts, namely, single-site iron-anchored BLG (Fe SA @BLG), and iron-nanoparticle-anchored BLG fibrils (FeNP@FibBLG), were synthesized and then used to characterize the enzymatic activity (Supplementary Figs. 9 and 10 and Supplementary Table 2 ). Using TMB as a substrate, the specific activity (SA) values (U mg −1 ) of these nanozymes were measured: the SA of Fe SA @FibBLG was markedly superior, at 95.0 U mg −1 , approximately 1.7 and 10.1 times higher than the SAs of Fe SA @BLG (57.3 U mg −1 ) and FeNP@FibBLG (9.38 U mg −1 ), respectively (Fig. 3a ). Steady-state kinetic assays revealed that Fe SA @FibBLG exhibited superior catalytic performance among the tested nanozymes in oxidizing TMB, with remarkable kinetic parameters including maximum reaction rate ( V max  = 0.788 μM s −1 ), turnover number ( K cat  = 21.9 min −1 ), catalytic efficiency ( K cat / K m  = 5.47 × 10 8  M −1  min −1 ) and selectivity ( K m  = 4.00 × 10 –2  mM) (Fig. 3b and Supplementary Table 3 ). We also determined the kinetic parameters for the H 2 O 2 substrate, which further substantiated the exceptional catalytic performance of Fe SA @FibBLG (Supplementary Table 4 ).

a – f , Typical Michaelis–Menten curves of Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG by varying the TMB ( a ), ethanol ( c ) and acetaldehyde ( e ) concentrations in the presence of H 2 O 2 . Comparison of the SAs (U mg −1 ) of Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG on TMB ( b ), ethanol ( d ) and acetaldehyde ( f ) oxidation in the presence of H 2 O 2 . One nanozyme activity unit (U) is defined as the amount of nanozyme that catalyses 1 µmol of product per minute. The SAs (U mg −1 ) were determined by plotting the nanozyme activities against their weight and measuring the gradients of the fitting curves. 1 H NMR spectrum of the reaction products of Fe SA @FibBLG-catalysed ethanol (inset d ) and acetaldehyde (inset f ) oxidation. Data are presented as the mean ± s.d. from n  = 3 independent experiments. g , EPR spectra of 5,5-dimethyl-pyrroline- N -oxide/H 2 O 2 solution upon the addition of nanozymes. h , Schematic illustration of the peroxidase-like activities of Fe SA @FibBLG when exposed to various substrates.

Interestingly, Fe SA @FibBLG also exhibited a notable capacity for catalytically oxidizing ethanol and acetaldehyde in the presence of H 2 O 2 (Fig. 3c–f ). The SA of Fe SA @FibBLG achieved a value of 7.90 U mg −1 when ethanol was used as the substrate, remarkably surpassing the other two reference catalysts. The superior catalytic efficacy of Fe SA @FibBLG with respect to ethanol was further confirmed by determining its kinetic parameters, which indicate it achieves a catalytic efficiency ( K cat / K m  = 4.11 × 10 5  M −1  min −1 ) that exceeds that of Fe SA @BLG ( K cat / K m  = 8.66 × 10 4  M −1  min −1 ) by 4.7 times and FeNP@FibBLG ( K cat / K m  = 9.25 × 10 3  M −1  min −1 ) by 44.4 times (Supplementary Table 5 ). Fe SA @FibBLG also manifested the lowest K m value when ethanol was the substrate, signifying its excellent affinity towards ethanol. It is important to note that Fe SA @FibBLG could directly oxidize ethanol to acetic acid, yielding formic acid as the only by-product, without generating any detectable acetaldehyde intermediate, as evidenced by 1 H NMR (Fig. 3d , inset).

To explain this, we performed a steady-state kinetic analysis of Fe SA @FibBLG participating in acetaldehyde oxidation. We found Fe SA @FibBLG to have the lowest K m value of the evaluated nanozymes, signifying its superior substrate affinity towards acetaldehyde. The K cat / K m for this reaction (3.89 × 10 5  M −1  min −1 ) was very close to that for ethanol oxidation (4.11 × 10 5  M −1  min −1 ) (Supplementary Tables 5 and 6 ). Upon the reaction between these nanozymes and H 2 O 2 , the electron paramagnetic resonance (EPR) spectrum exhibited characteristic peaks associated with 5,5-dimethyl-pyrroline- N -oxide–OH · , with Fe SA @FibBLG displaying the strongest EPR signal, indicating the highest production of OH · (Fig. 3g ). The same characteristic peaks were observed in the EPR spectrum of the Fe SA @FibBLG/H 2 O 2 /ethanol reaction system (Supplementary Fig. 17 ), confirming the existence of OH · in ethanol oxidation—a finding that agrees with numerous studies demonstrating the efficacy of OH · in oxidizing diverse organic compounds, including ethanol and acetaldehyde 33 , 34 . Nevertheless, it is essential to emphasize that our investigation serves as a preliminary exploration of the free radicals involved in this reaction; a more comprehensive mechanistic investigation is required for an in-depth understanding of the catalytic process.

Additionally, the catalytic stability of Fe SA @FibBLG was assessed by high-resolution transmission electron microscopy (TEM), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), energy-dispersive spectroscopy elemental analysis, X-ray diffraction and XPS (Supplementary Figs. 18 and 19 ). Fe SA @FibBLG did not exhibit substantial morphological or oxidation state alterations and effectively preserved the high atomic dispersion of iron active sites throughout the catalysis. It is also worth mentioning that Fe SA @FibBLG retained at least 95.2% and 84.1% of its activity after undergoing 3 h of digestion in simulated gastric and intestinal fluids, respectively (Supplementary Fig. 20 ). The robust stability observed in Fe SA @FibBLG may be due to the reduction effects of BLG fibril support 21 .

Protective potential on acute alcohol intoxication

Even a single new onset of blood alcohol that exceeds the detoxifying capability of the hepatic system can induce individual symptoms of acute alcohol intoxication, such as hepatocyte destruction, stress response and cognitive deficits 35 , 36 . To mitigate potential damage to the human digestive tract from direct H 2 O 2 ingestion, a biomimetic cascade catalysis system was designed by integrating gold nanoparticles (AuNPs) for onsite and sustainable H 2 O 2 generation 37 , 38 , 39 . AuNPs have demonstrated exceptionally efficient and enduring catalytic activity similar to glucose oxidase, which allows the conversion of glucose into gluconic acid, accompanied by the production of adequate H 2 O 2 (Supplementary Fig. 21 ). Because protein fibrils transiently remained and were mostly digested (generally within 4 h) in the gastrointestinal tract 20 , where the majority of alcohol was absorbed, a salt-induced technique 40 ( Methods ) was followed to fabricate the AuNP-attached Fe SA @FibBLG amyloid hydrogel (Fe SA @AH) (Supplementary Fig. 22 ) to achieve prolonged retention within the gastrointestinal tract, and, thereby, an enhanced overall capacity for ethanol oxidation. The resultant Fe SA @AH showed typical self-standing ability, obvious nanofibril structures (exceptional birefringence under polarized light) and good syringability (Fig. 4a ). We then labelled Fe SA @AH with [ 18 F]fluoro-2-deoxyglucose ([ 18 F]FDG) and visualized its transportation in C57BL/6 mice by using micro positron emission tomography (PET)–computed tomography (CT) scanning. The metabolism of Fe SA @AH took more than 6 h in the upper gastrointestinal tract after gavage, which indicated an extended retention time in vivo due to the hydrogel nature of the compound 20 .

a , Visualization (1) and microstructures (2) of Fe SA @AH under polarized light, and injectability test (3). b , Time-series images of gastrointestinal translocation of [ 18 F]FDG-loaded Fe SA @AH in mice (0–6 h). c , Schematic of acute alcohol intoxication model construction ( Methods ). Created with BioRender.com. d , Effect of different treatment (PBS, AH and Fe SA @AH) on alcohol tolerance time and sobering-up time in C57BL/6 mice. e , Representative trajectory of search strategies of mice with different treatments. f , g , Escape latencies ( f ) and path length ( g ) of four groups of mice. h , i , Mean concentrations of blood alcohol ( h ) and acetaldehyde ( i ) in alcohol-intoxicated mice treated with PBS, AH and Fe SA @AH. j , Serum levels of ALT and AST enzyme levels in four groups of mice. Data are obtained for n  = 8 independent biological replicates, mean ± s.e.m. P values in d , f , g , h , j were tested by one-way analysis of variance followed by Tukey–Kramer test. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001.

The prophylactic benefits of Fe SA @AH administration were assessed in an alcohol-treated murine model 41 (Fig. 4c ). A group of ethanol-free, but PBS-gavaged mice served as a negative control; all the ethanol-gavaged mice were asleep for alcohol intoxication. Although they tolerated alcohol intake for a longer period of time ( ∼ 40 min), the Fe SA @AH mice were awoken significantly earlier ( ∼ 2 h) than other intoxicated groups (Fig. 4d ). We then conducted the Morris water maze (MWM) test 6 h post-alcohol intake to quantitatively assess murine spatial reference memory (Fig. 4e ). Grouped mean swimming speeds of alcohol-exposed mice were comparable to those of the blank group, indicating recovery of fundamental activities (Supplementary Fig. 25a ). However, PBS- and AH-treated mice showed increased search time and distance to locate the hidden platform, whereas the mice given Fe SA @AH demonstrated markedly improved navigational efficiency (Fig. 4f,g ). Additionally, distinct search strategies were observed, with PBS and AH groups favouring less efficient patterns, in contrast to the strategic approaches of the Fe SA @AH and control groups (Supplementary Fig. 25b ).

Aetiologically, behavioural abnormalities were attributed to alcohol and its in vivo intermediate metabolite, acetaldehyde 42 , and the liver played a core role in ethanolic metabolism. Prophylactic Fe SA @AH immediately and persistently reduced the mice blood alcohol (BA) concentration by a significant amount (Fig. 4h ). The BA in Fe SA @AH mice decreased by 41.3%, 40.4%, 42.0%, 46.6% and 55.8%, respectively, 30, 60, 120, 180 and 300 min post-gavaging. Importantly, the above-mentioned process induced no additional acetaldehyde (BAce) accumulation in blood (Fig. 4i ), which plays a crucial role in safeguarding the liver, as the build-up of acetaldehyde is known to be a catalyst for liver cirrhosis and hepatocellular carcinoma. Stress responses of liver were definitely mitigated, which was revealed by the significantly decreased blood alanine aminotransferase (ALT), aspartate aminotransferase (AST), malondialdehyde (MDA) and glutathione (GSH) levels in the Fe SA @AH group (Fig. 4j and Supplementary Fig. 26a,b ).

Prophylactic effect on chronic alcohol intoxication

The NIAAA model (mouse model of chronic and binge ethanol feeding) was conducted to confirm the long-term beneficial effects of Fe SA @AH 43 . After model constructions (Fig. 5a and Methods ), the PBS mice showed a significantly decreased body weight, increased liver injury (ballooning degeneration and multifocal inflammatory cell infiltration) and hepatic lipid accumulation compared with the blank (Fig. 5b,c ). Notably, Fe SA @AH-rescued mice showed a significantly decreased loss in body weight, less liver damage and re-regulated hepatic lipid metabolism (Fig. 5b,c ) from intoxication. Moreover, mice treated with Fe SA @AH had lower BA than those with PBS and AH (Supplementary Fig. 27a ). It is worth noting, however, that Fe SA @AH also decreased the BAce concentration (Supplementary Fig. 27b ), indicating its dominant competitive role in ethanol elimination to endogenous ADH. Significant lower blood ALT and AST levels further confirmed the inflammation alleviation effect of Fe SA @AH on the liver (Fig. 5d ). Additionally, administration of Fe SA @AH also significantly suppressed triglyceride and total cholesterol accumulation in ethanol-fed mice (Supplementary Fig. 28e–j ).

a , Schematic of the chronic alcohol intoxication model construction ( Methods ). Created with BioRender.com. b , Body weight changes in the four groups of mice during the feeding period. c , Representative H&E-stained images of liver in the four groups. d , Serum ALT and AST levels in mice. e , H&E images of colon (left part) and its assessed scores (right histogram) in different groups of mice. f , Immunofluorescence staining of the tight junction proteins in the colon (left part, 30× magnification). The tight junction proteins (Claudin-1, occludin and ZO-1) were stained green whereas the 4,6-diamidino-2-phenylindole (DAPI) was blue. The histograms (right) show the mean density of the normalized levels of occludin and ZO-1. IOD, integrated optical density. g , Taxonomic and phylogenetic tree of the top 21 most affected genera (genus with >10% mean abundance change in at least one group compared to others) by different treatments generated by GraPhlAn 4.0. Outer circles show the grouped mean relative abundance of each genus. h , Metabolic processes of alcohol to acetate and further in mice. The left colour blocks indicate the endogenous organs, liver, intestine and gut microbiota involved in alcohol decomposition, and the right shows the path in which Fe SA @AH participated. The box-plot shows the relative levels of ko00770 pantothenate and CoA biosynthesis among groups (minimum–maximum). The heatmap shows 83 significantly changed pathways compared with those in the PBS group. Source data are provided as a Source Data file. i , LPS concentrations of mice in the four groups. Data are shown in the form of mean ± s.e.m. from n  = 8 biological replicates. In c , e , f , the images displayed are representative of three independent biological replicates ( n  = 3), each producing consistent results. For histopathological, physiological and biochemical indexes ( c – f , i ), P values were tested by one-way analysis of variance followed by Tukey–Kramer test whereas the pairwise Wilcoxon test with Bonferroni–Holm correction was used for microbial taxa ( h , i ). * P  < 0.05, ** P  < 0.01, *** P  < 0.001.

The gut and its symbionts (the microbiota) are important, but usually overlooked, alcohol-metabolizing organs 44 , 45 , 46 . Chronic alcohol consumption caused histopathological changes in the colon, destroyed epithelial cells, atrophied goblet cells and resulted in inflammatory cell infiltration (Fig. 5e ), and also weakened permeability (Fig. 5f ), which may cause more microbial components to enter the bloodstream 47 . Alcohol also induced significant compositional shifts (β-diversity) in the gut microbiota of mice (Supplementary Fig. 29a ), but showed limited effects on the Shannon index and percentage of Gram-negative bacteria (Supplementary Fig. 29b,c ). Consistently 48 , the mean abundance of Bacteroidota increased in all alcohol-treated groups. Another dominant phylum, Firmicutes , decreased significantly in the PBS group compared with the blank group (Supplementary Fig. 29d ). Interestingly, a significant loss of functional murine-mucoprotein-degrading bacteria, Akkermansia ( verrucomicrobiota ), and transitions of Ileibacterium and Allobaculum (blank) to Bacteroides and Prevotellaceae_UCG-001 (PBS), were identified (Fig. 5g ).

In terms of functional profiles, we found no significant intergroup gut microbial function changes due to ethanol-related processes (Supplementary Table 10 ). In accordance with previous research 47 , gut microbiota were determined to be indirectly involved in ethanol metabolism, especially acetate-induced microbial anaerobic respiration, such as the glycolysis/gluconeogenesis (ko00010) and pentose phosphate pathway (ko00030) (Supplementary Table 10 ). Alcohol consumption also induced significantly overexpressed pantothenate. Moreover, CoA biosynthesis (ko00770) and the citrate cycle (TCA) (ko00020) constituted important carbon unit donors for further processes (Fig. 5h ), such as lipopolysaccharide (LPS) biosynthesis (ko00540)—LPS is widely recognized as an endotoxin that can induce hepatic inflammation 49 . This epithelial pathophysiological damage and intraluminal dysbiosis were significantly mitigated by Fe SA @AH compared with other AHs (Fig. 5e–h ). Furthermore, as one of the final beneficial outputs, the concentration of blood LPS was significantly decreased in Fe SA @AH-treated mice (Fig. 5i ).

In aggregate, we have demonstrated the design of a single-site iron-anchored amyloid hydrogel with remarkable catalytic oxidation capacity for alcohol as a highly efficient catalytic platform for in vivo alcohol metabolism. This work provides compelling evidence for the viability of a biomimetic-nanozyme-based hydrogel as an orally applied antidote for alcohol intoxication. Fe SA @AH demonstrates exceptional preference for acetic acid production, enabling a rapid decrease in blood alcohol levels while simultaneously mitigating the risk of excessive acetaldehyde accumulation, and markedly surpasses the effectiveness of existing alcohol intoxication antidotes that rely on a combination of natural enzymes. Unlike the predominantly liver-centric human intrinsic alcohol metabolism, orally administered Fe SA @AH directs this process towards the gastrointestinal tract, providing increased safety for the liver. In addition, despite this shift in the site of alcohol metabolism, there is no manifestation of additional adverse gastrointestinal symptoms; in fact, Fe SA @AH shows a remarkable alleviation of intestinal damage and dysbiosis induced by alcohol consumption, further demonstrating its potential for clinical translation.

The findings from our study outline a general and efficient strategy for synthesizing a diverse group of orally applied biomimetic nanozymes, and establish the foundation for future investigations aimed at maximizing the potential of artificial enzyme design in different therapeutic applications.

Synthesis of catalysts

BLG (>98%) was purchased from Davisco Foods International and purified using a previously reported protocol 50 . For a detailed description of BLG fibril preparation, see ref. 51 . For the synthesis of Fe SA @FibBLG, 100 mg lyophilized BLG fibril powder was dispersed in a mixture of 8.0 ml ethanol and 1.9 ml PEG200. The dispersion was then subjected to argon bubbling for 30 min to remove the dissolved oxygen, followed by irradiation under a xenon lamp with an ultraviolet filter (250–380 nm, 27.9 mW cm −2 , PLS-SXE300CUV) for 10 min to generate free radicals. Subsequently, 0.1 ml of 108.21 mg ml −1 Fe(NO 3 ) 3 ·9H 2 O EDTA solution was added dropwise to the dispersion of BLG fibrils under magnetic stirring for 12 h at 25 °C. Fe SA @BLG was prepared by the same synthesis procedure as for Fe SA @FibBLG, except that the BLG fibril powder was replaced by an equal amount of BLG powder. For the synthesis of FeNP@FibBLG, the as-obtained Fe SA @FibBLG dispersion was further ultraviolet-irradiated for 18 min under anaerobic conditions to reduce the iron ions. Finally, samples were collected by centrifugation at 4 °C, 11,100 g for 10 min, washed by ethanol (10.0 ml × 6) and resuspended in 5.0 ml deionized water (pH 2). The powdered Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG were obtained by lyophilization and stored at 4 °C.

Characterizations

The high-resolution TEM images and elemental mappings were recorded with an FEI Talos F200X microscope at accelerating voltages of 80 kV and 200 kV, respectively. AFM images were obtained using a Bruker Multimode 8 scanning probe microscope. HAADF-STEM images were captured using an FEI Titan Themis G2 microscope equipped with a probe spherical aberration corrector and operated at 300 keV. The crystalline structure and phase purity were detected by a powder diffractometer (Siemens D500 with Cu Kα radiation (λ = 1.5406 Å)). The iron loadings on catalysts were analysed by inductively coupled plasma mass spectrometry (Elan DRC-e, Perkin Elmer). The X-ray absorption structure spectra (Fe K-edge) were collected at beamline BL44B2 of the SPring-8 synchrotron (Japan), operated at 8.0 GeV with a maximum current of 250 mA. Data were collected in transmission mode using a Si(111) double-crystal monochromator. The EXAFS data were analysed using the ATHENA module implemented in IFEFFIT software (CARS). XPS measurements were performed using a multipurpose spectrometer (Sigma Probe, Thermo VG Scientific) with a monochromatic Al Kα X-ray source. EPR spectra were acquired using a Bruker X-band (9.4 GHz) EMXplus 10/12 spectrometer equipped with an Oxford Instruments ESR-910 liquid helium cryostat. All spectra were collected under ambient conditions. Solution 1 H NMR spectra were collected on a Bruker DRX 300 spectrometer (7.05 T; Larmor frequency, 300 MHz ( 1 H)) in deuterated water (D 2 O) at room temperature.

MD simulations

All of the AAMD simulations were performed on a GROMACS 2018 package using a gromacs54A force field 52 . The box size of the initial model was 12 × 12 × 30 nm 3 , including an SPC/E water model and 102 peptide chains (sequence, LACQCL) 19 under three-dimensional periodic boundary conditions. A spherical cut-off of 1.0 nm was used for the summation of van der Waals interactions and short-range Coulomb interactions, and the particle-mesh Ewald method 53 . The temperature and pressure of the system were controlled by means of a velocity rescaling thermal thermostat and a Berendsen barostat. At first, the energy of the system was minimized in small steps to balance the initial velocity of the molecules. Then, the NPT ensemble using a leapfrog integrator with a time step of 1.0 fs was used to simulate the system for 8 ns at 300 K, which is sufficient for the balance of the system. Dynamic snapshot images were generated in Visual Molecular Dynamics 1.9.3 54 .

DFT calculations

To investigate the interaction between iron ions and the system, one iron ion was inserted into the peptide dimer, and the structure was optimized by DFT using the CP2K software package 55 . The Perdew–Burke-Ernzerhof generalized gradient approximation functional was adopted to describe the electronic exchange and correlation, in conjunction with the DZVP-MOLOPT-SR-GTH basis set for all atoms (C, H, O, N, Fe). The structure was optimized with the spin multiplicity to treat the doublet spin state and the charge of the iron ion was set to +2 e . The convergence criterion for the absolute value of the maximum force was set to 4.5 × 10 −4  a.u. and the r.m.s. of all forces to 3 × 10 −4  a.u. Grimme’s DFT-D3 method was adopted for correcting van der Waals interactions 56 .The interaction of the system was characterized by the independent gradient model method, and the based isosurface maps were rendered by Visual Molecular Dynamics from the cube files exported from Multiwfn 3.8 (ref. 57 ).

Peroxidase-like activity

The peroxidase-like activities of nanozymes were assessed at 37 °C using 350 μl of HAc–NaAc buffer (0.1 M, pH 4.0) with varied nanozyme concentrations, using TMB as the substrate. Following the addition of 20 μl of TMB solution (20 mM in dimethylsulfoxide) and 20 μl of H 2 O 2 solution (2 M), 10 μl of nanozymes with varying concentrations was introduced into the system. The catalytic oxidation of TMB (oxTMB) was quantified by measuring the absorbance at 652 nm via an ultraviolet–visible spectrometer. The steady-state kinetics analysis was executed by modifying the concentrations of TMB and H 2 O 2 . To derive the Michaelis–Menten constant, we performed Lineweaver–Burk plot analysis using the double reciprocal of the Michaelis–Menten equation, ν  =  ν max  × [ S ]/( K m  + [S]), where ν denotes the initial velocity, ν max represents the maximum reaction velocity, [ S ] indicates the substrate concentration and K m is the Michaelis constant. Additionally, the catalytic rate constant ( k cat ) was computed as k cat = ν max /[ E ], where [ E ] signifies the molar concentration of metal within the nanozymes. By employing diverse pH buffer solutions, we explored the pH dependency of the peroxidase-like activity of nanozymes, spanning a range from pH 2 to 9. Similarly, we investigated its temperature sensitivity by observing its activity at various temperatures, progressively increasing from 20 °C to 60 °C.

Catalytic oxidation activity on alcohol and acetaldehyde

The catalytic oxidation activities of nanozymes on both alcohol and acetaldehyde were carried out at 37 °C in 350 μl of HAc–NaAc buffer (0.1 M, pH 4.0), with varying nanozyme concentrations (10 μl). Subsequent to adding 20 μl of H 2 O 2 solution (2 M), 20 μl of ethanol or acetaldehyde solution (2 mM) was introduced into separate tubes containing the reaction mixture. Quantification of the catalytic oxidation of ethanol or acetaldehyde was performed using the Ethanol Assay Kit (ab65343) and Acetaldehyde Assay Kit (ab308327) from Abcam Biotechnology. Through altering the concentrations of ethanol or acetaldehyde, steady-state kinetics analysis was carried out, and the Michaelis–Menten constant was determined by analysing Lineweaver–Burk plots involving the double reciprocal of the Michaelis–Menten equation. Additionally, the identification of the reaction products was confirmed by 1 H NMR spectrometry.

Catalytic activity assessment of nanozymes during in vitro simulation of the digestion process

We adhered to the INFOGEST standard protocol for nanozyme digestion to replicate the physiological human gastrointestinal digestion process 58 . In this methodology, stock solutions of simulated gastric fluid and simulated intestinal fluid were prepared and equilibrated at 37 °C prior to use. For gastric digestion, 2 ml of the nanozyme (1 mg ml −1 ) was mixed with 2 ml of simulated gastric fluid stock solution, and porcine pepsin solution was added to achieve a final enzyme activity of 500 U per mg of protein. CaCl 2 (H 2 O) 2 was then introduced into the mixture to reach a final concentration of 0.15 mM prior to adjusting the pH to 3 using 5 M HCl. The mixture was transferred to a water bath shaker (VWR 462-0493) at 37 °C and sampled at 30 and 60 min, after which NaOH solution was used to deactivate the enzyme. Following the gastric digestion, pancreatin (0.1 mg ml −1 ) was dissolved in simulated intestinal fluid containing 0.6 mM CaCl 2 and added to the gastric digests in a 1:1 (v/v) ratio to initiate intestinal digestion, which lasted for 120 min at 37 °C with regular sampling every 30 min. The samples were freeze-dried immediately after collection for enzyme activity evaluation experiments using TMB as a substrate, in which the amount of nanozyme after digestion was normalized.

Hydrogel formation

Gelation of Fe SA @FibBLG dispersion containing AuNPs (Fe SA @AH) was achieved following our previously reported procedure with some modifications 40 . For the synthesis of AuNPs, all glassware was cleaned with freshly prepared aqua regia (HCl:HNO 3  = 3:1 vol/vol) and then thoroughly rinsed with water. A 2 ml solution of BLG fibrils (2.0 wt%, pH 2.0) was mixed with a 40 mM HAuCl 4 solution to reach a final protein:gold mass ratio of 14.7:1. The mixture underwent a chemical reduction through the dropwise addition of a NaBH 4 solution (0.8 ml) under a nitrogen atmosphere. The resulting solution was then dialysed to remove any remaining NaBH 4 and concentrated to 2 ml with a dialysis membrane (Spectra/Por, molecular weight cut-off, 6–8 kDa, Spectrum Laboratories) against a 6 wt% PEG solution ( M r  ≈ 35,000, Sigma-Aldrich) at pH 2.0. TEM imaging of AuNPs stabilized by BLG fibrils revealed three-dimensional particles with an average size of 1.32 nm (Supplementary Fig. 21a ), determined by analysing six TEM images using ImageJ software v.1.8.0. For the preparation of Fe SA @AH, 2 g of Fe SA @FibBLG powder was dissolved in the resulting AuNP-attached BLG fibril solution (2 ml). The mixture was then transferred into a plastic syringe, the top part of which had been previously cut. The plastic syringe was covered with a section of a dialysis tube (Spectra/Por, molecular weight cut-off, 6–8 kDa), and the head of the syringe was positioned in direct contact with an excess of 300 mM NaCl solution at pH 7.4 for at least 16 h in a 4 °C cold room to facilitate gelation. The resulting hydrogel sample was kept under 4 °C. The working hydrogel was freshly prepared by mixing the aforementioned hydrogel with 0.1 ml of a glucose solution (8.0 M) immediately before further characterization or detoxification use. A BLG fibril hydrogel was obtained using the same procedure, except that the Fe SA @FibBLG was replaced with an equal amount of BLG fibril dispersion.

Murine models

Male wild-type C57BL/6 mice, 20–25 g and 8–10 weeks old, were purchased from Beijing Vital River Laboratory Animal Technology. All of the murine experiments in the current study were approved by the Regulations of Beijing Laboratory Animal Management (approval number AW40803202-5-1) and conducted according to the guidelines set forth in the Institutional Animal Care and Use Committee of China Agricultural University.

Acute model

Thirty-two male C57BL/6 mice were randomly divided into four groups after 12 h fasting. Mice were orally gavaged with AH and Fe SA @AH (at doses of 10 ml per kg (body weight)), and two groups of mice received the same volume of PBS (as controls, the blank and the PBS groups), respectively. After 20 min of adaptation, mice from the AH, Fe SA @AH, and PBS groups were orally administered an alcohol liquid diet (10 g per kg (body weight)), while the same volume of PBS was administered for the blank group. All the mice were killed 6 h later.

Chronic model

A mouse model of chronic and binge ethanol feeding (NIAAA model) was conducted following the protocol proposed by Bertola et al. 43 . In brief, after 5 days of ad libitum Lieber–DeCarli diet adaptation, 32 mice were randomly divided into four groups: (1) a control group (Con) of mice were pair-fed with the control diet; (2) an ethanol diet group (EtOH); (3) an ethanol diet group with additional 10 ml per kg (body weight) AH; and (4) an ethanol diet group with additional 10 ml per kg (body weight) Fe SA @AH. The ethanol-fed groups were granted unrestricted access to the ethanol Lieber–DeCarli diet containing 5% (vol/vol) ethanol for 10 days, and additionally received daily morning (9:00) gavage of PBS, AH or Fe SA @AH, respectively. The control group was pair-fed with an isocaloric control diet and daily control-liquid gavage. All animals were maintained in specific pathogen-free conditions, at a temperature of 23 ± 1 °C and 50–60% humidity, under a 12 h light/dark cycle, with access to autoclaved water. On day 16, both the ethanol-fed and pair-fed mice were orally administered a single dose of ethanol (5 g per kg (body weight)) or isocaloric maltose dextrin at 9:20, respectively, and killed 6 h later. The body weight of mice was recorded every 2 days.

After overnight fasting, mice were gavaged with 0.1 ml [ 18 F]FDG-labelled Fe SA @AH. Then, mice were anaesthetized with oxygen containing 2% isoflurane, and placed in and fixed in a prone position in an imaging chamber. Time-series images were obtained with an Inveon microPET/CT scanner (Siemens); the scanner parameters were a 15 min CT scan (80 kVp, 500 μA, 1,100 ms exposure time) followed by a 10 min PET acquisition. Quantification of images was performed by AMIDE software 3.0.

Alcohol tolerance test

Approximately 10 µl of blood was collected from the submandibular vein at 30, 60, 90, 120, 180 and 300 min after alcohol exposure. In the chronic model, sampling was conducted after the binge ethanol feeding. Blood alcohol concentration (BAC) was determined using a test kit from Abcam Biotechnology (ab65343). BACs were normalized to mice body weights as previously described 8 . Normalized BAC, BAC nor , was calculated using the equation: BAC nor  = BAC i  × (BWT i /BWT ave ), where BAC i and BWT i denote the blood alcohol level and body weight of mice, respectively, and BWT ave represents the average weight of all mice in each set of experiments. The quantification of the BAce concentration was carried out using a test kit obtained from Abcam Biotechnology (ab308327), and the normalization process was conducted using the same method as for the BAC.

Alcohol tolerance time was the duration between alcohol administration and the absence of righting reflex, while the duration of the absence of righting reflex was recorded as the sobering-up time. Mice that became ataxic were considered to have lost their righting reflex, and were then placed face up. The time point at which the mice returned to their normal upright position signified they had regained their righting reflex.

An MWM test 59 was conducted by Anhui Zhenghua Biologic Apparatus Facilities, as described previously. Specifically, the MWM apparatus comprised a large circular pool (120 cm diameter and 40 cm height) which was filled with TiO 2 -dyed, 25 °C thermostatic water, and a 10-cm-diameter platform was positioned and fixed 2 cm below the water surface. Before acute ethanol exposure, mice received four rounds of daily training for 6 days. Each trial was limited to 60 s, and the time that it took for the mice to successfully locate the platform was recorded. On day 7, mice were retested (no platform condition) 5 h after ethanol feeding (the time point by which all mice regained their consciousness and mobility). The tested items included trajectory, path length, escape latency and swimming speed (MWM animal behaviour video tracking system, Morris v.2.0).

Biochemical assays

Blood samples were collected through cardiac puncture from anaesthetized mice 6 h after alcohol gavage. Prior to testing, samples were maintained at ambient temperature for 4 h, and then centrifuged (864.9 g , 4 °C) for 20 min. Supernatants were suctioned and stored at −80 °C for further analysis. Serum ALT, AST, triglycerides and total cholesterol were measured by a Hitachi Biochemistry Analyzer 7120 (Hitachi High-Tech).

Weighed liver tissues were collected and immersed immediately in 10% neutral buffered formaldehyde. After overnight fixation, tissues were embedded in paraffin and cut into 5 μm sections for further haematoxylin and eosin (H&E) and oil red O (Sigma) staining. Images were captured by a Nikon Eclipse TI-SR fluorescence microscope. Fresh liver was homogenized in chilled normal saline and centrifuged (1,500 g , 4 °C) for 15 min. GSH and MDA levels of the resultant supernatant were detected using the GSH assay kit (ab65322) and the lipid peroxidation (MDA) assay kit (ab118970), respectively. Hepatic and cellular lipid content was isolated using the chloroform/methanol-based method 60 , and quantified by using the triglyceride assay kit (ab65336) and the mouse total cholesterol ELISA kit (ab285242, SSUF-C), respectively.

Colon histology and immunohistochemistry

Colon length, caecum to anus, was measured, and the distal colon was washed with saline, with one-half being fixed with 4% paraformaldehyde, and the other half stored at −80 °C. Histological measurements of the colon were the same as those for the liver.

For immunofluorescence, colon tissues were treated with EDTA buffer and boiled to expose the antigens. Tissues were then incubated overnight at 4 °C with primary antibody and washed three times for 5 min each with PBS. Subsequently, colon tissues were covered with secondary antibody and incubated at room temperature in the dark for 50 min, followed by another set of three 5 min washes with PBS. The resultant sections were mounted with a mounting medium and stained with 4,6-diamidino-2-phenylindole. Slides were then covered, and the images were captured using a Nikon Eclipse Ti inverted fluorescence microscope.

Microbiota changes

Faecal samples were collected within 5 min after defecation into a sterile tube and stored at −80 °C. Microbial genome DNA was extracted from faeces by using the DNeasy PowerSoil Pro Kit (QIAGEN) according to the manufacturer’s instructions, and the variable 3–4 (V4-v4) region of the 16S rRNA gene was PCR-amplified using barcoded 338F-806R primers (forward primer, 5′-ACTCCTACGGGAGGCAGCAG-3′; reverse primer, 5′-GGACTACHVGGGTWTCTAAT-3′). PCR components contained 25 μl of Phusion High-Fidelity PCR Master Mix, 3 μl (10 μM) of each forward and reverse primer, 10 μl of the DNA template, 3 μl of DMSO and 6 μl of double-distilled H2O. The following cycling conditions were used: initial denaturation at 98 °C for 30 s, followed by 25 cycles of denaturation at 98 °C for 15 s, annealing at 58 °C for 15 s, and extension at 72 °C for 15 s, and a final extension of 1 min at 72 °C. PCR amplicons were purified using Agencourt AMPure XP Beads (Beckman Coulter) and quantified using a PicoGreen dsDNA Assay Kit (Invitrogen). After quantification, amplicons were pooled in equal amounts, and 2 × 150 bp paired-end sequencing was performed using the Illumina Miseq PE300 platform at GUHE Info Technology. Amplicon sequence variants (ASVs) were denoised and clustered by the UNOISE algorithm. Taxa bar plots, and α- and β-diversity analysis, were performed with QIIME 2 v.2020.6 and the R package v.3.6.3. Metabolic function was predicted using PICRUSt2, and the output file was further analysed using the STAMP software package (v.2.1.3).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All the data that validates the outcomes of this study are included in the Article and its Supplementary Information files. For any other relevant source data, interested parties can obtain them from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Simulation files and code used for modelling iron-anchored BLG fibril segments can be accessed via Zenodo at: https://doi.org/10.5281/zenodo.10819612 (ref. 61 ).

GBD 2016 Alcohol Collaborators. Alcohol use and burden for 195 countries and territories, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 392 , 1015–1035 (2018).

Article   PubMed Central   Google Scholar  

Rehm, J. et al. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 373 , 2223–2233 (2009).

Article   PubMed   Google Scholar  

GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet 396 , 1204–1222 (2020).

GBD 2020 Alcohol Collaborators. Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020. Lancet 400 , 185–235 (2022).

Xie, L. et al. The protective effects and mechanisms of modified Lvdou Gancao decoction on acute alcohol intoxication in mice. J. Ethnopharmacol. 282 , 114593 (2022).

Article   CAS   PubMed   Google Scholar  

Chen, X. et al. Protective effect of Flos puerariae extract following acute alcohol intoxication in mice. Alcohol. Clin. Exp. Res. 38 , 1839–1846 (2014).

Guo, J., Chen, Y., Yuan, F., Peng, L. & Qiu, C. Tangeretin protects mice from alcohol-induced fatty liver by activating mitophagy through the AMPK-ULK1 pathway. J. Agric. Food Chem. 70 , 11236–11244 (2022).

Liu, Y. et al. Biomimetic enzyme nanocomplexes and their use as antidotes and preventive measures for alcohol intoxication. Nat. Nanotechnol. 8 , 187–192 (2013).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Xu, D. et al. A hepatocyte-mimicking antidote for alcohol intoxication. Adv. Mater. 30 , e1707443 (2018).

Article   PubMed   PubMed Central   Google Scholar  

Wang, H., Wan, K. & Shi, X. Recent advances in nanozyme research. Adv. Mater. 31 , e1805368 (2019).

Jiang, D. et al. Nanozyme: new horizons for responsive biomedical applications. Chem. Soc. Rev. 48 , 3683–3704 (2019).

Yu, Z., Lou, R., Pan, W., Li, N. & Tang, B. Nanoenzymes in disease diagnosis and therapy. Chem. Commun. 56 , 15513–15524 (2020).

Article   CAS   Google Scholar  

Cao, C. et al. Biomedicine meets nanozyme catalytic chemistry. Coord. Chem. Rev. 491 , 215–245 (2023).

Article   Google Scholar  

Peng, C., Pang, R., Li, J. & Wang, E. Current advances on the single-atom nanozyme and its bio-applications. Adv. Mater. 36 , e2211724 (2023).

Jiao, L. et al. When nanozymes meet single-atom catalysis. Angew. Chem. Int. Ed. 59 , 2565–2576 (2020).

Zhang, S. et al. Single-atom nanozymes catalytically surpassing naturally occurring enzymes as sustained stitching for brain trauma. Nat. Commun. 13 , 4744 (2022).

Sun, A., Mu, L. & Hu, X. Graphene oxide quantum dots as novel nanozymes for alcohol intoxication. ACS Appl. Mater. Interfaces 9 , 12241–12252 (2017).

Geng, X. et al. Confined cascade metabolic reprogramming nanoreactor for targeted alcohol detoxification and alcoholic liver injury management. ACS Nano 17 , 7443–7455 (2023).

Bolisetty, S. & Mezzenga, R. Amyloid–carbon hybrid membranes for universal water purification. Nat. Nanotechnol. 11 , 365–371 (2016).

Xu, D. et al. Food amyloid fibrils are safe nutrition ingredients based on in-vitro and in-vivo assessment. Nat. Commun. 14 , 6806 (2023).

Shen, Y. et al. Amyloid fibril systems reduce, stabilize and deliver bioavailable nanosized iron. Nat. Nanotechnol. 12 , 642–647 (2017).

Cao, Y. & Mezzenga, R. Design principles of food gels. Nat. Food 1 , 106–118 (2020).

Hu, B. et al. Amyloid–polyphenol hybrid nanofilaments mitigate colitis and regulate gut microbial dysbiosis. ACS Nano 14 , 2760–2776 (2020).

Peydayesh, M. et al. Amyloid–polysaccharide interfacial coacervates as therapeutic materials. Nat. Commun. 14 , 1848 (2023).

Scheffen, M. et al. A new-to-nature carboxylation module to improve natural and synthetic CO 2 fixation. Nat. Catal. 4 , 105–115 (2021).

Wang, C. et al. Atomic Fe hetero-layered coordination between g-C 3 N 4 and graphene nanomeshes enhances the ORR electrocatalytic performance of zinc–air batteries. J. Mater. Chem. A 7 , 1451–1458 (2019).

Kim, S. et al. In situ XANES of an iron porphyrin irreversibly adsorbed on an electrode surface. J. Am. Chem. Soc. 113 , 9063–9066 (1991).

Shui, J. L., Karan, N. K., Balasubramanian, M., Li, S. Y. & Liu, D. J. Fe/N/C composite in Li–O 2 battery: studies of catalytic structure and activity toward oxygen evolution reaction. J. Am. Chem. Soc. 134 , 16654–16661 (2012).

Bagus, P. S. et al. Combined multiplet theory and experiment for the Fe 2 p and 3 p XPS of FeO and Fe 2 O 3 . J. Chem. Phys. 154 , 094709 (2021).

Nelson, G. W., Perry, M., He, S. M., Zechel, D. L. & Horton, J. H. Characterization of covalently bonded proteins on poly(methyl methacrylate) by X-ray photoelectron spectroscopy. Colloids Surf. B 78 , 61–68 (2010).

Vanea, E. & Simon, V. XPS study of protein adsorption onto nanocrystalline aluminosilicate microparticles. Appl. Surf. Sci. 257 , 2346–2352 (2011).

Ji, S. et al. Matching the kinetics of natural enzymes with a single-atom iron nanozyme. Nat. Catal. 4 , 407–417 (2021).

Chamarro, E., Marco, A. & Esplugas, S. Use of Fenton reagent to improve organic chemical biodegradability. Water Res. 35 , 1047–1051 (2001).

Meyerstein, D. Re-examining Fenton and Fenton-like reactions. Nat. Rev. Chem. 5 , 595–597 (2021).

Vonghia, L. et al. Acute alcohol intoxication. Eur. J. Intern. Med. 19 , 561–567 (2008).

Schweizer, T. A. et al. Neuropsychological profile of acute alcohol intoxication during ascending and descending blood alcohol concentrations. Neuropsychopharmacology 31 , 1301–1309 (2006).

Chen, J. et al. Glucose-oxidase like catalytic mechanism of noble metal nanozymes. Nat. Commun. 12 , 3375 (2021).

Comotti, M., Della|Pina, C., Falletta, E. & Rossi, M. Aerobic oxidation of glucose with gold catalyst: hydrogen peroxide as intermediate and reagent. Adv. Synth. Catal. 348 , 313–316 (2006).

Ishida, T. et al. Influence of the support and the size of gold clusters on catalytic activity for glucose oxidation. Angew. Chem. Int. Ed. 47 , 9265–9268 (2008).

Usuelli, M. et al. Polysaccharide-reinforced amyloid fibril hydrogels and aerogels. Nanoscale 13 , 12534–12545 (2021).

Dolganiuc, A. & Szabo, G. In vitro and in vivo models of acute alcohol exposure. World J. Gastroenterol. 15 , 1168–1177 (2009).

Zakhari, S. Overview: how is alcohol metabolized by the body? Alcohol Res. Health 29 , 245–254 (2006).

PubMed   PubMed Central   Google Scholar  

Bertola, A., Mathews, S., Ki, S. H., Wang, H. & Gao, B. Mouse model of chronic and binge ethanol feeding (the NIAAA model). Nat. Protoc. 8 , 627–637 (2013).

Mutlu, E. A. et al. Colonic microbiome is altered in alcoholism. Am. J. Physiol. Gastrointest. 302 , G966–G978 (2012).

Canesso MCC et al. Comparing the effects of acute alcohol consumption in germ-free and conventional mice: the role of the gut microbiota. BMC Microbiol. 14 , 1–10 (2014).

Google Scholar  

Keshavarzian, A. et al. Evidence that chronic alcohol exposure promotes intestinal oxidative stress, intestinal hyperpermeability and endotoxemia prior to development of alcoholic steatohepatitis in rats. J. Hepatol. 50 , 538–547 (2009).

Horowitz, A., Chanez-Paredes, S. D., Haest, X. & Turner, J. R. Paracellular permeability and tight junction regulation in gut health and disease. Nat. Rev. Gastroenterol. Hepatol. 20 , 417–432 (2023).

Martino, C. et al. Acetate reprograms gut microbiota during alcohol consumption. Nat. Commun. 13 , 4630 (2022).

Han, Y. H. et al. Enterically derived high-density lipoprotein restrains liver injury through the portal vein. Science 373 , eabe6729 (2021).

Jung, J.-M., Savin, G., Pouzot, M., Schmit, C. & Mezzenga, R. Structure of heat-induced β-lactoglobulin aggregates and their complexes with sodium-dodecyl sulfate. Biomacromolecules 9 , 2477–2486 (2008).

Jung, J.-M. & Mezzenga, R. Liquid crystalline phase behavior of protein fibers in water: experiments versus theory. Langmuir: ACS J. Surf. Colloids 26 , 504–514 (2010).

Kutzner, C. et al. Best bang for your buck: GPU nodes for GROMACS biomolecular simulations. J. Comput. Chem. 36 , 1990–2008 (2015).

Wennberg, C. L. et al. Direct-space corrections enable fast and accurate Lorentz–Berthelot combination rule Lennard–Jones lattice summation. J. Chem. Theory Comput. 11 , 5737–5746 (2015).

Humphrey, W., Dalke, A. & Schulten, K. VMD—Visual Molecular Dynamics. J. Mol. Graph. 14 , 33–38 (1996).

Kuhne, T. D. et al. CP2K: An electronic structure and molecular dynamics software package—Quickstep: efficient and accurate electronic structure calculations. J. Chem. Phys. 152 , 194103 (2020).

Grimme, S., Antony, J., Ehrlich, S. & Krieg, H. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H–Pu. J. Chem. Phys. 132 , 154104 (2010).

Lu, T. & Chen, F. Multiwfn: a multifunctional wavefunction analyzer. J. Comput. Chem. 33 , 580–592 (2012).

Brodkorb, A. et al. INFOGEST static in vitro simulation of gastrointestinal food digestion. Nat. Protoc. 14 , 991–1014 (2019).

Vorhees, C. V. & Williams, M. T. Morris water maze: procedures for assessing spatial and related forms of learning and memory. Nat. Protoc. 1 , 848–858 (2006).

Folch, J., Lees, M. & Sloane Stanley, G. H. A simple method for the isolation and purification of total lipides from animal tissues. J. Biol. Chem. 226 , 497–509 (1957).

Su, J. Code for MD and DFT. Zenodo https://doi.org/10.5281/zenodo.10819612 (2024).

Download references

Acknowledgements

The authors thank I. Kutzli for the purification of BLG, W. Wang for 1 H NMR measurements, and M. Wörle for X-ray diffraction experiments. Bruna F. G. L. is gratefully acknowledged for the help in analysing XAFS data. Appreciation is also extended to C. Zeder for the inductively coupled plasma mass spectrometry measurements. Support from R. Schäublin during electron microscopy observations is gratefully acknowledged. J.S. acknowledges financial support from the Special Research Fund of Ghent University (BOF.PDO.2021.0050.01) and the Research Foundation–Flanders (FWO V420422N). ICFO authors thank CEX2019-000910-S (MCIN/AEI/10.13039/501100011033), Fundació Cellex, Fundació Mir-Puig, Generalitat de Catalunya through CERCA and the La Caixa Foundation (100010434, EU Horizon 2020 Marie Skłodowska-Curie grant agreement 847648).

Open access funding provided by Swiss Federal Institute of Technology Zurich.

Author information

These authors contributed equally: Jiaqi Su, Pengjie Wang.

Authors and Affiliations

Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

Jiaqi Su, Mohammad Peydayesh, Jiangtao Zhou, Tonghui Jin & Raffaele Mezzenga

Particle and Interfacial Technology Group, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

Jiaqi Su & Paul Van der Meeren

Department of Nutrition and Health, Beijing Higher Institution Engineering Research Center of Animal Products, China Agricultural University, Beijing, China

Pengjie Wang & Fazheng Ren

Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland

Institute of Energy and Process Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

Felix Donat

Institute of Translational Medicine, Zhejiang Shuren University, Zhejiang, China

Cuiyuan Jin

ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain

Lu Xia, Kaiwen Wang & F. Pelayo García de Arquer

Department of Materials, ETH Zurich, Zurich, Switzerland

Raffaele Mezzenga

You can also search for this author in PubMed   Google Scholar

Contributions

R.M. and J.S. conceived the idea, designed the experiments, co-wrote the manuscript and coordinated the overall research project. J.S. developed the fabrication procedure of protein-fibril-based single-atom nanozymes, characterized the enzymatic activities of nanozymes, collected and analysed the data, and performed the computational analysis. L.X. and K.W. performed the XAFS measurements of samples and analysed the data. T.J and M.P. assisted in the analysis of enzyme kinetics data. W.Z. performed XPS and 1 H NMR measurements of samples and analysed the data. J.Z. coordinated the AFM characterization of samples. P.W. and F.R. designed the in vitro and in vivo experiments on cells and animals. P.W. and J.S. carried out cell and animal studies. C.J. contributed to the microbiota test and data analysis. P.V.d.M., F.P.G.d.A. and F.D. contributed to interpreting the data and revised the manuscript. All the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Jiaqi Su or Raffaele Mezzenga .

Ethics declarations

Competing interests.

J.S. and R.M. are the inventors of a patent filed jointly by Ghent University and ETH Zurich (EP24153321).

Peer review

Peer review information.

Nature Nanotechnology thanks Marco Frasconi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Supplementary information

Supplementary information.

Supplementary Methods, Discussion, Figs. 1–29, Tables 1–10, Gating strategy for flow cytometry and References.

Reporting Summary

Source data fig. 1.

Source data for Fig. 1.

Source Data Fig. 2

Source data for Fig. 2.

Source Data Fig. 3

Source data for Fig. 3.

Source Data Fig. 4

Source data for Fig. 4.

Source Data Fig. 5

Source data for Fig. 5.

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.

Su, J., Wang, P., Zhou, W. et al. Single-site iron-anchored amyloid hydrogels as catalytic platforms for alcohol detoxification. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01657-7

Download citation

Received : 10 October 2023

Accepted : 21 March 2024

Published : 13 May 2024

DOI : https://doi.org/10.1038/s41565-024-01657-7

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

This article is cited by

How cheesemaking could cook up an antidote for alcohol excess.

Nature (2024)

Quick links

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

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

The independent source for health policy research, polling, and news.

A Look at the Latest Alcohol Death Data and Change Over the Last Decade

Heather Saunders and Robin Rudowitz Published: May 23, 2024

Alcohol use disorder (AUD) is often an underrecognized substance use disorder (SUD) despite its substantial consequences . Over half of US adults (54%) say that someone in their family has struggled with an alcohol use disorder, making it the most prevalent non-tobacco substance use disorder. Yet, only one-third of adults view alcohol addiction as a crisis, compared to over half who see opioids as such. Federal data show that 1 in 10 people had an alcohol use disorder in the past year, over 4 in 10 alcohol users report binge drinking in the past month, and per capita alcohol consumption is higher than the decade prior. Treatment rates for alcohol use disorders are notably low, especially for the use of medication , a recommended AUD treatment component. Although the opioid crisis has been declared a public health emergency by the U.S. Department of Health and Human Services since 2017, no similar declaration exists regarding alcohol deaths. However, HHS has set a priority goal of reducing emergency department visits for acute alcohol use, mental health conditions, suicide attempts, and drug overdoses by 10% by 2025.

This analysis focuses on the narrowest definition of alcohol deaths known as “alcohol-induced deaths” (referred to as “alcohol deaths” throughout the brief). These alcohol deaths are caused by conditions directly attributable to alcohol consumption, such as alcohol-associated liver diseases . Broader definitions of alcohol deaths extend this definition to also encompass cases where an alcohol-induced condition was a contributing factor, but not the underlying cause of death. Key takeaways from this analysis of CDC WONDER data from 2012 to 2022 include the following:

• Alcohol deaths increased steadily over the past decade with sharp rises during the pandemic years. Overall, the national alcohol death rate has risen 70% in the past decade, accounting for 51,191 deaths in 2022, up from 27,762 deaths in 2012.
• Alcohol deaths in 2022 were highest among people aged 45 to 64, American Indian and Alaska Native (AIAN) people, and males. Alcohol death rates for AIAN people are the highest–5 times higher than death rates for White people, the racial group with the next highest prevalence. Deaths are rising fastest among adults aged 26 to 44, AIAN people, and females–with these groups experiencing nearly or more than a 100% rise in alcohol mortality rates in the last decade.
• Rates of alcohol deaths varied considerably across states in 2022. While all states and D.C. experienced increases in deaths rates over the past decade and during the pandemic, the rate of change varied by state and year, with some states’ death rates rising most sharply during the pandemic and other state experiencing rises more evenly before and during the pandemic. Rural areas have a higher rate of alcohol deaths and experienced greater growth in death rates both over the past decade.
• The number of alcohol-related deaths rises to 105,308 under a broader definition that counts deaths where alcohol-induced conditions are either the underlying cause or a contributing factor. This exceeds the numbers for opioid and suicide deaths, which also use this broader definition, totaling 83,437 and 49,594, respectively.  

What are the trends in alcohol deaths?

Alcohol deaths have steadily climbed over the past decade, a trend that accelerated during the pandemic (Figure 1). When adjusted for population growth and age, the alcohol death rate has risen by 70% from 2012 to 2022, moving from 7.97 to 13.53 deaths per 100,000 people. Although deaths fell somewhat in 2022, they remain far higher than a decade ago. From 2012 to 2019, the year over year rise in deaths rates averaged about 4% per year, and then jumped during early pandemic years, with the biggest rise from 2019 to 2020. Other data mirror this trend – emergency department (ED) visits for SUD are on the rise and account for twice the number of ED visits compared to opioids. Alcohol related ED visits account for nearly half of all SUD related visits (45%), far higher than the next highest group, opioids, accounting for 13% of ED visits.

How do alcohol death rates vary and how have they changed across demographics groups?

Alcohol deaths in 2022 were highest among people aged 45 to 64, males, people living in rural areas, and AIAN people. Alcohol death rates for AIAN people are by far the highest–5 times higher than death rates for White people, the racial group with the next highest prevalence. Across age groups, people aged 45 to 64 have the highest alcohol death rate, followed by 65+. Death rates in males are more than double that of females and people who reside in rural areas have death rates higher than those who live in urban areas (Figure 2).

Over the past decade (2012-2022), alcohol death rates grew fastest among people 26 to 44, AIAN people, and females (Figure 3) . Overall alcohol consumption has risen somewhat in recent years, but increases may have been concentrated among certain populations as well as other risk factors.

• People aged 26 to 44 . Individuals aged 26 to 44 experienced the fastest increase in alcohol death rates, with a rise of 144% over the past decade and over 50% during the pandemic. While this younger age group showed the steepest rate of increase, the largest overall growth in the number of deaths occurred among those aged 45 to 64. This somewhat older group already had the highest death rates and experienced the largest increase in death rates (12 additional deaths/100,000) in the past decade, more than any other group.
• AIAN people. Alcohol deaths for AIAN people have nearly doubled in the last 10 years. During the pandemic years, alcohol death rates increased by almost 25 deaths per 100,000 AIAN people. Increases in alcohol deaths among AIAN people follows worsening trends in other areas related to behavioral health, where AIAN have both the highest rate and fastest growing suicide and overall drug overdose death rates.
• Although males die of alcohol causes more often than females, the relative growth was faster for females over the past 10 years, increasing by 86% for females compared to 61% for males. Impact of heavier drinking may impact women more quickly than men, which may result in the faster development of serious health consequences that contribute to death.

How do alcohol death rates vary and how have they changed across geography?

In 2022 there was wide variation in alcohol death rates. In 2022, New Mexico’s death rate was the highest at 42.7 per 100,000 people, which was more than six times higher than Hawaii, the state with the lowest rate at 7.1 per 100,000 people (Figure 4).

While all states experienced an increase in alcohol deaths, those rates varied widely.  Nationally, alcohol death rates increased by 70% over the past decade, including a 30% rise during the pandemic years alone (2019-2022). However, the extent of these increases varied substantially across states. For instance, the District of Columbia saw a relatively low increase of 24% over the decade, whereas Connecticut experienced a much larger rise of 167%. During the pandemic, increases ranged from 9% in Wyoming and New Jersey to 86% in Mississippi. Some states, like Vermont, had most of their rises in alcohol death rates before the pandemic, with only 12% of the growth occurring during pandemic years. In contrast, Mississippi’s rates more than doubled over the past decade, and over half of that increase happened during pandemic years. Many factors may contribute to the differences in alcohol mortality rates across states, some of which may include differences in alcohol consumption and cultural attitudes, state-specific alcohol policies , and treatment rates (Figure 4).

Rural areas experienced faster growth in alcohol deaths than urban areas, driven by sharp rises during the pandemic. Deaths grew across both rural and urban areas in the past decade; however growth was fastest in rural areas–nearly doubling in the past decade and increasing by 35% during pandemic years. Existing shortages of mental health and substance use treatment professionals may make it particularly difficult to access care in rural areas, where the supply of behavioral health workforce is even more scarce . During the pandemic, telehealth services for behavioral health and other care may have been more accessible to those living in urban areas, where an internet connection is more likely to be available or reliable (Figure 5).

What factors may contribute to the increases in alcohol deaths in the past 10 years?

Alcohol contributes to more deaths than opioids and suicides when the alcohol conditions that contribute to death are included. Defining alcohol deaths can be complex due to the gradual onset of many conditions caused by or linked to alcohol and its ability to exacerbate or increase the risk of developing other health conditions. This analysis adopts the strictest definition of alcohol deaths, focusing on deaths that were directly caused by conditions directly due to alcohol, such as alcohol-related liver diseases. However, if deaths where alcohol conditions are a contributing factor listed on the death certificate —termed ‘ alcohol-related deaths’—are included, the number of deaths increases to 105,308 in 2022, though some cases may overlap. This exceeds the numbers for opioid and suicide deaths, which also use this broader definition, totaling 83,437 and 49,594, respectively. Unlike the immediate effects of opioid overdoses or suicides, alcohol-related conditions often develop slowly over many years. These conditions can directly cause death or worsen other illness. For instance, it may take many years of heavy drinking before alcohol-associated liver diseases , the most common cause of alcohol deaths, to develop. This slower disease progression as well as the role of alcohol in exacerbating other conditions may contribute to the higher number of deaths counted under the expanded definition. The number of alcohol deaths rise even more when the criteria are broadened to include alcohol’s role in increasing the risk of death by other conditions or events, such as cancer or car accidents involving alcohol (Figure 6).

Rises in alcohol deaths may be attributed to a variety of factors including, in part, increases in drinking and low treatment rates. Alcohol consumption and some indicators of binge drinking have been on the rise in recent years , particularly among some demographic groups . Excessive alcohol consumption is tied to the development of alcohol-related diseases, which can be fatal. A variety of factors may have contributed to increases in drinking including a growing social acceptability of alcohol and loosening of alcohol policies at a state level. Other factors, such as increased stressors due to the pandemic and other issues may have increased drinking behaviors.

Treatment rates for alcohol use disorder are very low. Federal survey data show that in 2022, only 7.6% of people (12+) with a past year alcohol use disorder received any treatment. Although medications for alcohol use disorder have been shown to reduce or stop drinking, uptake of these medications is extremely low; with only 2.1% of people who meet criteria for an alcohol use disorder (diagnosed or not) receive medication treatment. Treatment rates are slightly higher among those who do receive a diagnosis–for instance, 10% of Medicaid enrollees diagnosed with an alcohol use disorder received medication, 34% received counseling services, and 74% received some type of interaction with a treatment, such as therapy, medication, assessment, or supportive service.

Barriers to alcohol use disorder treatment include a combination of provider, patient, financial, and infrastructure factors. Providers often lack confidence or knowledge in treating alcohol use disorder and are uncomfortable with medication and other treatment options, which may decrease the likelihood that they will manage treatment or make referrals . To address this, recent initiatives are enhancing education for both practicing and training providers through mandatory training programs and curriculum enhancements in medical schools . Further, recent changes to SUD confidentiality regulations are expected to simplify the diagnosis and coordination of care for individuals with substance use disorders (SUD). Insufficient treatment infrastructure or a shortage of a skilled workforce to staff facilities and deliver care can also play a role in treatment rates.

From the patient perspective, limited understanding of what constitutes problematic drinking and attitudes towards seeking treatment can hinder recognition of the need for help . For example, among those who meet the criteria for SUD—which may include symptoms like increased tolerance, repeated attempts to quit or control use, or social problems related to use– 95% of adults did not seek treatment and didn’t think they needed it. Initiatives aimed at early screening in non-traditional settings, such as schools may help early detection and lead to more timely linkages of individuals to treatment resources. When people think they might need treatment, practical issues such as insurer coverage of services, locating a provider that will accept the patient’s insurance, availability of time off from work, childcare, and the affordability of treatment/out of pocket costs can also influence decisions about seeking or staying in treatment.

This work was supported in part by Well Being Trust. KFF maintains full editorial control over all of its policy analysis, polling, and journalism activities.

• Mental Health

Also of Interest

• A Look at the Latest Suicide Data and Change Over the Last Decade
• COVID-19 Cases and Deaths by Race/Ethnicity: Current Data and Changes Over Time
• Recent Trends in Mental Health and Substance Use Concerns Among Adolescents

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
• HHS Author Manuscripts

The effects of alcohol use on academic achievement in high school

Ana i. balsa.

a Research Professor, Center for Applied Research on Poverty, Family, and Education, Department of Economics, Universidad de Montevideo; Prudencio de Pena 2440, Montevideo, 11600, Uruguay; Phone: (+598 2) 707 4461 ext 300; Fax: (+598 2) 707 4461 ext 325; yu.ude.mu@aslaba

Laura M. Giuliano

b Assistant Professor, Department of Economics, University of Miami, Coral Gables, FL 33124, United States; [email protected]

Michael T. French

c Professor of Health Economics, Health Economics Research Group, Department of Sociology, Department of Economics, and Department of Epidemiology and Public Health, University of Miami, Coral Gables, FL 33124, United States; ude.imaim@hcnerfm

This paper examines the effects of alcohol use on high school students’ quality of learning. We estimate fixed-effects models using data from the National Longitudinal Study of Adolescent Health. Our primary measure of academic achievement is the student’s GPA abstracted from official school transcripts. We find that increases in alcohol consumption result in small yet statistically significant reductions in GPA for male students and in statistically non-significant changes for females. For females, however, higher levels of drinking result in self-reported academic difficulty. The fixed-effects results are substantially smaller than OLS estimates, underscoring the importance of addressing unobserved individual heterogeneity.

1. Introduction

In the United States, one in four individuals between the ages of 12 and 20 drinks alcohol on a monthly basis, and a similar proportion of 12 th graders consumes five or more drinks in a row at least once every two weeks ( Newes-Adeyi, Chen, Williams, & Faden, 2007 ). Several studies have reported that alcohol use during adolescence affects educational attainment by decreasing the number of years of schooling and the likelihood of completing school ( Chatterji & De Simone, 2005 ; Cook & Moore, 1993 ; Gil-Lacruz & Molina, 2007 ; Koch & McGeary, 2005 ; McCluskey, Krohn, Lizotte, & Rodriguez, 2002 ; NIDA, 1998 ; Renna, 2007 ; Yamada, Kendrix, & Yamada, 1996 ) Other research using alternative estimation techniques suggests that the effects of teen drinking on years of education and schooling completion are very small and/or non-significant ( Chatterji, 2006 ; Dee & Evans, 2003 ; Koch & Ribar, 2001 ).

Despite a growing literature in this area, no study has convincingly answered the question of whether alcohol consumption inhibits high school students’ learning. Alcohol consumption could be an important determinant of how much a high school student learns without having a strong impact on his or her decision to stay in school or attend college. This question is fundamental and timely, given recent research showing that underage drinkers are susceptible to the immediate consequences of alcohol use, including blackouts, hangovers, and alcohol poisoning, and are at elevated risk of neurodegeneration (particularly in regions of the brain responsible for learning and memory), impairments in functional brain activity, and neurocognitive defects ( Zeigler et al., 2004 ).

A common and comprehensive measure of high school students’ learning is Grade Point Average (GPA). GPA is an important outcome because it is a key determinant of college admissions decisions and of job quality for those who do not attend college. Only a few studies have explored the association between alcohol use and GPA. Wolaver (2002) and Williams, Powell, and Wechsler (2003) have studied this association among college students, while DeSimone and Wolaver (2005) have investigated the effects of underage drinking on GPA during high school. The latter study found a negative association between high school drinking and grades, although it is not clear whether the effects are causal or the result of unobserved heterogeneity.

Understanding the relationship between teenage drinking and high school grades is pertinent given the high prevalence of alcohol use among this age cohort and recent research on adolescent brain development suggesting that early heavy alcohol use may have negative effects on the physical development of brain structure ( Brown, Tapert, Granholm, & Delis, 2000 ; Tapert & Brown, 1999 ). By affecting the quality of learning, underage drinking could have an impact on both college admissions and job quality independent of its effects on years of schooling or school completion.

In this paper, we estimate the effects of drinking in high school on the quality of learning as captured by high school GPA. The analysis employs data from Waves 1 and 2 of the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative study that captures health-related behaviors of adolescents in grades 7 through 12 and their outcomes in young adulthood. Our analysis contributes to the literature in several ways. First, we focus on the effect of drinking on academic achievement during high school. To date, and to the best of our knowledge, only one other study in the literature has analyzed the consequences of underage drinking on high school GPA. Second, rather than rely on self-reported GPA, we use objective GPA data from academic transcripts, reducing the potential for systematic biases in the estimation results. Third, we take advantage of the longitudinal nature of the Add Health data and use fixed-effects models to purge the analysis of time invariant unobserved heterogeneity. Fixed-effects techniques are superior to instrumental variables (IV) estimation when the strength and reliability of the instruments are suspect ( French & Popovici, 2009 ). Finally, we explore a variety of mechanisms that could underlie a detrimental effect of alcohol use on grades. In addition to analyzing mediators related to exposure to education (days of school skipped), we investigate the effect of drinking on students’ ability to focus on and adhere to academic objectives.

2. Background and significance

Behavioral research has found that educational performance is highly correlated with substance abuse (e.g., Bukstein, Cornelius, Trunzo, Kelly, & Wood, 2005 ; Hawkins, Catalano, & Miller, 1992 ). Economic studies that look at the link between alcohol use and educational outcomes have customarily focused on measures of educational attainment such as graduation (from high school or college), college matriculation, and years of school completed (e.g., Bray, Zarkin, Ringwalt, & Qi, 2000 ; Chatterji, 2006 ; Cook & Moore, 1993 ; Dee & Evans 2003 ; Koch & Ribar, 2001 ; Mullahy & Sindelar, 1994 ; Renna, 2008 ; Yamada et al., 1996 ). Consistent with the behavioral research, early economic studies found that drinking reduced educational attainment. But the most rigorous behavioral studies and the early economic studies of attainment both faced the same limitation: they were cross-sectional and subject to potential omitted variables bias. Some of these cross-sectional economic studies attempted to improve estimation by using instrumental variables (IV). Cook and Moore (1993) and Yamada et al. (1996) found that heavy or frequent drinking in high school adversely affects high school and college completion. Nevertheless, the validity and reliability of the instruments in these studies are open to debate ( Chatterji, 2006 ; Dee & Evans, 2003 ; French & Popovici, 2009 ).

By contrast, more recent economic studies that arguably use better estimation methods have found that drinking has modest or negligible effects on educational attainment. Dee and Evans (2003) studied the effects of teen drinking on high school completion, college entrance, and college persistence. Employing changes in the legal drinking age across states over time as an instrument, they found no significant effect of teen drinking on educational attainment. Koch and Ribar (2001) reached a similar conclusion applying family fixed effects and instrumental variables to NLSY data. Though they found that drinking had a significant negative effect on the amount of schooling completed among men, the effect was small. Finally, Chatterji (2006) used a bivariate probit model of alcohol use and educational attainment to gauge the sensitivity of the estimates to various assumptions about the correlation of unobservable determinants of these variables. She concluded that there is no evidence of a causal relationship between alcohol use and educational attainment when the correlation coefficient is fixed at plausible levels.

Alcohol use could conceivably affect a student’s quality of learning and academic performance regardless of its impact on school completion. This possibility is suggested by Renna (2008) , who uses a research design similar to that used by Dee and Evans (2003) and finds that although binge drinking does not affect high school completion rates, it does significantly increase the probability that a student graduates with a GED rather than a high school diploma. Drinking could affect learning through a variety of mechanisms. Recent neurological research suggests that underage drinking can impair learning directly by causing alterations in the structure and function of the developing brain with consequences reaching far beyond adolescence ( Brown et al., 2000 ; White & Swartzwelder, 2004 ). Negative effects of alcohol use can emerge in areas such as planning and executive functioning, memory, spatial operations, and attention ( Brown et al., 2000 ; Giancola & Mezzich, 2000 ; Tapert & Brown, 1999 ). Alcohol use could also affect performance by reducing the number of hours committed to studying, completing homework assignments, and attending school.

We are aware of five economic studies that have examined whether drinking affects learning per se. Bray (2005) analyzed this issue indirectly by studying the effect of high school students’ drinking on subsequent wages, as mediated through human capital accumulation. He found that moderate high school drinking had a positive effect on returns to education and therefore on human capital accumulation. Heavier drinking reduced this gain slightly, but net effects were still positive. The other four studies approached the question directly by focusing on the association between drinking and GPA. Three of the GPA studies used data from the Harvard College Alcohol Study. Analyzing data from the study’s 1993 wave, both Wolaver (2002) and Williams et al. (2003) estimated the impact of college drinking on the quality of human capital acquisition as captured by study hours and GPA. Both studies found that drinking had a direct negative effect on GPA and an indirect negative effect through reduced study hours. Wolaver (2007) used data from the 1993 and 1997 waves and found that both high school and college binge drinking were associated with lower college GPA for males and females. For females, however, study time in college was negatively correlated with high school drinking but positively associated with college drinking.

To our knowledge, only one study has looked specifically at adolescent drinking and high school GPA. Analyzing data from the Youth Risk Behavior Survey, DeSimone and Wolaver (2005) used standard regression analysis to estimate whether drinking affected high school GPA. Even after controlling for many covariates, they found that drinking had a significant negative effect. Their results showed that the GPAs of binge drinkers were 0.4 points lower on average for both males and females. They also found that the effect of drinking on GPA peaked for ninth graders and declined thereafter and that drinking affected GPA more by reducing the likelihood of high grades than by increasing the likelihood of low grades.

All four GPA studies found that drinking has negative effects on GPA, but they each faced two limitations. First, they relied on self-reported GPA, which can produce biased results due to recall mistakes and intentional misreporting ( Zimmerman, Caldwell, & Bernat, 2006 ). Second, they used cross-sectional data. Despite these studies’ serious efforts to address unobserved individual heterogeneity, it remains questionable whether they identified a causal link between drinking and GPA.

In sum, early cross-sectional studies of educational attainment and GPA suggest that drinking can have a sizeable negative effect on both outcomes. By contrast, more recent studies of educational attainment that use improved estimation methods to address the endogeneity of alcohol use have found that drinking has negligible effects. The present paper is the first study of GPA that controls for individual heterogeneity in a fixed-effects framework, and our findings are consistent with the more recent studies of attainment that find small or negligible effects of alcohol consumption.

Add Health is a nationally representative study that catalogues health-related behaviors of adolescents in grades 7 through 12 and associated outcomes in young adulthood. An initial in-school survey was administered to 90,118 students attending 175 schools during the 1994/1995 school year. From the initial in-school sample, 20,745 students (and their parents) were administered an additional in-home interview in 1994–1995 and were re-interviewed one year later. In 2001–2002, Add Health respondents (aged 18 to 26) were re-interviewed in a third wave to investigate the influence of health-related behaviors during adolescence on individuals when they are young adults. During the Wave 3 data collection, Add Health respondents were asked to sign a Transcript Release Form (TRF) that authorized Add Health to identify schools last attended by study participants and request official transcripts from the schools. TRFs were signed by approximately 92% of Wave 3 respondents (about 70% of Wave 1 respondents).

The main outcome of interest, GPA, was abstracted from school transcripts and linked to respondents at each wave. Because most of the in-home interviews during Waves 1 and 2 were conducted during the Spring or Summer (at the end of the school year) and alcohol use questions referred to the past 12 months, we linked the in-home questionnaires with GPA data corresponding to the school year in which the respondent was enrolled or had just completed at the time of the interview.

The in-home questionnaires in Waves 1 and 2 offer extensive information on the student’s background, risk-taking behaviors, and other personal and family characteristics. These instruments were administered by computer assisted personal interview (CAPI) and computer assisted self-interview (CASI) techniques for more sensitive questions such as those on alcohol, drug, and tobacco use. Studies show that the mode of data collection can affect the level of reporting of sensitive behaviors. Both traditional self-administration and computer assisted self-administered interviews have been shown to increase reports of substance use or other risky behaviors relative to interviewer-administered approaches ( Azevedo, Bastos, Moreira, Lynch, & Metzger, 2006 ; Tourangeau & Smith, 1996 ; Wright, Aquilino, & Supple, 1998 ). Several measures of alcohol use were constructed on the basis of the CAPI/CASI questions: (1) whether the student drank alcohol at least once per week in the past 12 months, (2) whether the student binged (drank five or more drinks in a row) at least once per month in the past 12 months, (3) the average number of days per month on which the student drank in the past 12 months, (4) the average number of drinks consumed on any drinking day in the past 12 months, and (5) the total number of drinks per month consumed by the student in the past year.

Individual characteristics obtained from the in-home interviews included age, race, gender, grade in school, interview date, body mass index, religious beliefs and practices, employment status, health status, tobacco use, and illegal drug use. To capture environmental changes for respondents who changed schools, we constructed indicators for whether the respondent attended an Add Health sample school or sister school (e.g. the high school’s main feeder school) in each wave. We also considered family characteristics such as family structure, whether English was spoken at home, the number of children in the household, whether the resident mother and resident father worked, whether parents worked in blue- or white-collar jobs, and whether the family was on welfare. Finally, we took into account a number of variables describing interview and household characteristics as assessed by the interviewer: whether a parent(s) or other adults were present during the interview; whether the home was poorly kept; whether the home was in a rural, suburban, or commercial area; whether the home environment raised any safety concerns; and whether there was evidence of alcohol use in the household.

Respondents to the in-home surveys were also asked several questions about how they were doing in school. We constructed measures of how often the respondents skipped school, whether they had been suspended, and whether they were having difficulties paying attention in school, getting along with teachers, or doing their homework. We analyzed these secondary outcomes as possible mediators of an effect of alcohol use on GPA.

Our fixed-effects methodology required high school GPA data for Waves 1 and 2. For this reason, we restricted the sample to students in grades 9, 10, or 11 in Wave 1 (N=22,792) who were re-interviewed in Waves 2 and 3 (N=14,390), not mentally disabled (N=13,632), and for whom transcript data were available at Wave 3 (N=10,430). In addition, we excluded 1,846 observations that had missing values on at least one of the explanatory or control variables. 1 The final sample had 8,584 observations, which corresponded to Wave 1 and Wave 2 responses for 4,292 students with no missing information on high school GPA or other covariates across both waves. Male respondents accounted for 48% of the sample.

Table 1 shows summary statistics for the analysis sample by wave and gender. Abstracted GPA averages 2.5 for male students and 2.8 for female students, 2 with similar values in Waves 1 and 2. Approximately 9% of males and 6% of females reported drinking alcohol at least one time per week in Wave 1. The prevalence of binge drinking (consuming five or more drinks in a single episode) at least once a month is slightly higher: 11% among males and 7% among females. On average, the frequency of drinking in Wave 1 is 1.34 days per month for male respondents and 0.94 days per month for female respondents, while drinking intensity averages 2.8 drinks per episode for males and 2.2 drinks per episode for females. By Wave 2, alcohol consumption increases in all areas for both males and females. The increases for males are larger, ranging from an 18% increase in the average number of drinks per episode to a 55% increase in the fraction who binge monthly.

Summary Statistics

Note : Based on responses to survey questions regarding most recently completed school year.

Of the Wave 1 respondents, 87% of males and 90% of females had skipped school at least once in the past year, with males averaging 1.47 days skipped and females averaging 1.37 days. Further, 11% of males and 7% of females had been suspended at least once. Regarding the school difficulty measures, 50% of male respondents in Wave 1 reported at least one type of regular difficulty with school: 32% had difficulty paying attention, 15% did not get along with their teachers, and 35% had problems doing their homework. Among females, 40% had at least one difficulty: 25% with paying attention, 11% with teachers, and 26% with homework.

Table 2 tabulates changes in dichotomous measures of problem drinking by gender. Among males, 82.6% did not drink weekly in either wave; 8.1% became weekly drinkers in Wave 2; 4.8% stopped drinking weekly in Wave 2; and the remaining 4.5% drank weekly in both waves. Among females, 88.5% did not drink weekly in either wave; 5.3% became weekly drinkers in Wave 2; 3.7% stopped drinking weekly in Wave 2; and 2.5% drank weekly in both waves. The trends in monthly binging were similar, with the number of students who became monthly bingers exceeding that of students who stopped bingeing monthly in Wave 2. The proportion of respondents reporting binge-drinking monthly in both waves (6.6% and 3.4% for men and women, respectively) was higher than the fraction of students who reported drinking weekly in both waves.

Tabulation of Changes in Dichotomous Measures of Alcohol Use By Gender

4. Empirical methods and estimation issues

We examined the impact of adolescent drinking on GPA using fixed-effects estimation techniques. The following equation captures the relationship of interest:

where GPA it is grade point average of individual i during the Wave t school year, A it is a measure of alcohol consumption, X it is a set of other explanatory variables, c i are unobserved individual effects that are constant over time, ε it is an error term uncorrelated with A it and X it , and α, β a , and β x are parameters to estimate.

The coefficient of interest is β a , the effect of alcohol consumption on GPA. The key statistical problem in the estimation of β a is that alcohol consumption is likely to be correlated with individual-specific unobservable characteristics that also affect GPA. For instance, an adolescent with a difficult family background may react by shirking responsibilities at school and may, at the same time, be more likely to participate in risky activities. For this reason, OLS estimation of Equation (1) used with cross-sectional or pooled longitudinal data is likely to produce biased estimates of β a . In this paper, we took advantage of the two high school-administered waves in Add Health and estimated β a using fixed-effects techniques. Because Waves 1 and 2 were only one year apart, it is likely that most unobserved individual characteristics that are correlated with both GPA and alcohol use are constant over this short period. Subtracting the mean values of each variable over time, Equation (1) can be rewritten as:

Equation (2) eliminates time invariant individual heterogeneity ( c i ) and the corresponding bias associated with OLS estimation of Equation (1) .

We estimated Equation (2) using different sets of time-varying controls ( X it ). 3 We began by controlling only for unambiguously exogenous variables and progressively added variables that were increasingly likely to be affected by alcohol consumption. The first set of controls included only the respondent’s grade level, indicators for attending the sample school or sister school, and the date of the interview. In a second specification, we added household characteristics and interviewer remarks about the household and the interview. This specification includes indicators for the presence of parents and others during the interview and thus controls for a potentially important source of measurement error in the alcohol consumption variables. 4 The third specification added to the second specification those variables more likely to be endogenous such as BMI, religious beliefs/practices, employment, and health status. A fourth specification included tobacco and illegal drug use. By adding these behavioral controls, which could either be mediators or independent correlates of the drinking-GPA association, we examined whether the fixed-effects estimates were influenced by unmeasured time variant individual characteristics.

The fifth and sixth specifications were aimed at assessing possible mechanisms flowing from changes in alcohol use to changes in GPA. Previous research has found that part of the association between alcohol consumption and grades can be explained by a reduction in study hours. Add Health did not directly ask respondents about study effort. It did, however, ask about suspensions and days skipped from school. These school attendance variables were added to the set of controls to test whether an effect of alcohol use on human capital accumulation worked extensively through the quantity of, or exposure to, schooling. Alternatively, an effect of alcohol use on grades could be explained by temporary or permanent alterations in the structure and functioning of an adolescent’s developing brain with resulting changes in levels of concentration and understanding (an intensive mechanism). To test for the mediating role of this pathway, we added a set of dichotomous variables measuring whether the student reported having trouble at least once a week with each of the following: (i) paying attention in school, (ii) getting along with teachers, and (iii) doing homework.

Finally, we considered the number of days the student skipped school and the likelihood of having difficulties with school as two alternative outcomes and estimated the association between these variables and alcohol use, applying the same fixed-effects methodology as in Equation (2) . To analyze difficulties with school as an outcome, we constructed a dichotomous variable that is equal to one if the student faced at least one of the three difficulties listed above. We estimated the effect of alcohol use on this variable using a fixed-effects logit technique.

Separate regressions were run for male and female respondents. The literature shows that males and females behave differently both in terms of alcohol use ( Ham & Hope, 2003 ; Johnston, O’Malley, Bachman, & Schulenberg, 2007 ; Schulenberg, O’Malley, Bachman, Wadsworth, & Johnston, 1996 ; Wechsler, Davenport, Dowdall, Moeykens, & Castillo, 1994 ) and school achievement ( Dwyer & Johnson, 1997 ; Jacob, 2002 ; Kleinfeld, 1998 ). These gender differences are clearly evident in the summary statistics presented in Table 1 . Furthermore, the medical literature suggests that there may be gender differences in the impact of alcohol consumption on cognitive abilities (e.g. Hommer, 2003 ).

In addition to examining differential effects by gender, we tested for differential effects of alcohol use along three other dimensions: age, the direction of change in alcohol use (increases vs. decreases), and initial GPA. These tests, as well as other extensions and robustness checks, are described in Section 6.

Table 3 shows the fixed-effects estimates for β a from Equation (2) . Each cell depicts a different model specification defined by a particular measure of alcohol use and a distinctive set of control variables. Rows (a)-(d) denote the alcohol use variable(s) in each specification, and Columns (1)-(6) correspond to the different sets of covariates. Control variables are added hierarchically from (1) to (3). We first adjusted only by grade level, sample school and sister school indicators, and interview date (Column (1)). We then added time-varying household characteristics and interviewer assessments (Column (2)), followed by other individual time-varying controls (Column (3)). Column (4) adds controls for the use of other substances, which could either be correlates or consequences of alcohol use. Columns (5) and (6) consider other potential mediators of the effects found in (1)-(3) such as days skipped, suspensions from school, and academic difficulties.

Fixed effects Estimates; Dependent Variable = GPA

Notes : See Table 1 for list of control variables in each model specification. Robust standard errors in parentheses;

The results for males provide evidence of a negative yet small effect of alcohol use on GPA. No major changes were observed in the estimates across the different specifications that incrementally added more controls, suggesting that the results are probably robust to unmeasured time-varying characteristics. In what follows, therefore, we describe the results in Column (3), which controls for the greatest number of individual time-varying factors (with the exception of tobacco and illicit drug use). Weekly drinking and monthly binge drinking are both negatively associated with GPA, but neither of these coefficients is statistically significant (Rows (a) and (b)). The continuous measure of alcohol consumption has a statistically significant coefficient (Row (c)), suggesting that increasing one’s alcohol intake by 100 drinks per month reduces GPA by 0.07 points, or 2.8% relative to the mean. The results in Row (d) suggest that variation in both the frequency and the intensity of alcohol use contributes to the estimated effect on grades. An increase of one day per month in drinking frequency reduces GPA by 0.005 points, and consumption of one additional drink per episode reduces GPA by 0.004 points.

Columns (4)-(6) report the estimates of interest after controlling for use of other substances, days skipped or suspended from school, and difficulties with school. Relative to the effects identified in Column (3), controlling for tobacco and illegal drug use reduces the negative effect of total number of drinks on GPA by 9% or 0.006 GPA points (see row (c), Column (4)). Adding the school attendance variables to the set of controls in Column (3) results in a point estimate of −0.06 or 0.01 GPA points below the coefficient in Column (3) (see Column (5)). Adding the school difficulty variables results in a reduction in GPA of 0.007 GPA points or a 10% decrease relative to the estimate in Column (3). While not shown in the table, the inclusion of both school difficulty and attendance variables as controls explains approximately 20% of the effect of alcohol use on grades, with the alcohol use estimates remaining statistically significant at the 10% level.

For females, the estimated coefficients are much smaller than those for males, and for two measures (binge-drinking and drinking frequency), the estimates are actually positive. However, none of the coefficients are statistically significant at conventional levels. 5 Interestingly, after controlling for substance use, difficulties with school, and school attendance, the estimates become less negative or more positive. But they remain statistically non significant.

Table 4 shows the effect of alcohol use on the number of school days skipped during the past year. These results are qualitatively similar to the findings for GPA, suggesting some small and statistically significant effects for males but no significant effects for females. For males, increasing the number of drinks per month by 100 leads to an additional 0.72 days skipped (p<0.10) when controlling for household features, interviewer comments, and individual characteristics such as body mass index, religiosity, employment, and health status (see Column (3), Row (c)). Controlling for tobacco and illegal drug use reduces the coefficient slightly to 0.69 days. The results in Row (d) suggest that this effect is driven mainly by variation in drinking intensity, with an additional drink per episode resulting in an increase of 0.06 days skipped.

Fixed-effects Estimates; Dependent Variable = School Days Skipped

Notes : Robust standard errors in parentheses;

Table 5 contains estimates of the relationship between alcohol use and our dichotomous measure of having difficulty in school. For males, we found one small but statistically significant effect: consumption of an additional 100 drinks per month is associated with a 4% increase in the probability of having trouble in school. For females, the estimated coefficients are all positive and larger than those found for males, and four out of five are statistically significant. The probability of having trouble in school is roughly 11% higher for females who drink weekly relative to those who do not, and there is a similar effect for monthly binge drinking (Rows (a) and (b)). Furthermore, the likelihood of difficulties increases by 7% with an additional 100 drinks per month (Row (c)). These findings suggest that female students suffer adverse consequences from alcohol consumption, even if these effects do not translate into lower grades. Finally, in Row (d), we see that these adverse effects are driven by increases in drinking frequency rather than drinking intensity.

Fixed-effects Logit Estimates; Dependent Variable = Difficulty with School

Notes : Dependent variable is a dummy variable equal to one if respondent had trouble at least once a week with one or more of the following: (1) paying attention in school, (2) getting along with teachers, or (3) doing homework. Robust standard errors in parentheses;

Our main results thus far point to two basic conclusions. After controlling for individual fixed effects, alcohol use in high school has a relatively minor influence on GPA. But there are also some interesting gender differences in these effects. For males, we find small negative effects on GPA that are partially mediated by increased school absences and difficulties with school-related tasks. For females, on the other hand, we find that alcohol use does not significantly affect GPA, but female drinkers encounter a higher probability of having difficulties at school.

Our basic estimates of the effects of drinking on GPA complement those of Koch and Ribar (2001) , who find small effects of drinking on school completion for males and non-significant effects for females. However, our analysis of school-related difficulties suggests that females are not immune to the consequences of drinking. Namely, females are able to compensate for the negative effects of drinking (e.g., by working harder or studying more) so that their grades are unaffected. This interpretation is consistent with Wolaver’s (2007) finding that binge drinking in college is associated with increased study hours for women but with reduced study hours for men. It is also reminiscent of findings in the educational psychology and sociology literatures that girls get better grades than boys, and some of this difference can be explained by gender differences in classroom behavior ( Downey & Vogt Yuan, 2005 ) or by greater levels of self-discipline among girls ( Duckworth & Seligman, 2006 ).

When interpreting our results, there are some important caveats to keep in mind. First, we must emphasize that they reflect the contemporaneous effects of alcohol use. As such, they say nothing about the possible cumulative effects that several years of drinking might have on academic performance. Second, we can only examine the effect of alcohol use on GPA for those students who remain in school. Unfortunately, we cannot address potential selection bias due to high school dropouts because of the high rate of missing GPA data for those students who dropped out after Wave 1. 6 Third, we acknowledge that our fixed-effects results could still be biased if we failed to account for important time-varying individual characteristics that are associated with GPA differentials across waves. It is reassuring, however, that our results are generally insensitive to the subsequent inclusion of additional time-varying (and likely endogenous) characteristics, such as health status, employment, religiosity, tobacco use, and illicit drug use. Finally, we cannot rule out possible reverse causality whereby academic achievement affects alcohol use. Future research using new waves of the data may provide further insight on this issue. In the next section, we discuss some additional issues that we are able to explore via robustness checks and extensions.

6. Robustness checks and extensions

6.1. ols versus fixed effects.

In addition to running fixed-effects models, we estimated β a using OLS. Separate regressions were run by gender and by wave. We first regressed GPA on measures of alcohol use and the full set of time-varying controls used in the fixed-effects estimation (see Column (3), Table 3 ). Next, we added other time-invariant measures such as demographics, household characteristics, and school characteristics. Finally, we controlled for tobacco and illegal drug use. The comparison between fixed-effects and OLS estimates (Appendix Table A1 ) sheds light on the extent of the bias in β ^ a OLS . For males, OLS estimates for Wave 1 were 3 to 6 times larger (more negative) than fixed-effects estimates (depending on the measure of alcohol use), and OLS estimates in Wave 2 were 3 to 4 times larger than those from the fixed-effects estimation. The bias was even more pronounced for females. Contrary to the results in Table 3 , OLS estimates for females were statistically significant, quantitatively large, and usually more negative than the estimates for males.

OLS Cross-sectional Estimates; Dependent Variable = GPA

6.2. Outlier analysis

Concerns about misreporting at the extreme tails of the alcohol use distributions led us to re-estimate the fixed-effects model after addressing these outliers. A common method for addressing extreme outliers without deleting observations is to “winsorize” ( Dixon, 1960 ). This technique reassigns all outlier values to the closest value at the beginning of the user-defined tail (e.g., 1%, 5%, or 10% tails). For the present analysis, we used both 1% and 5% tails. As a more conventional outlier approach, we also re-estimated the models after dropping those observations in the 1% tails. In both cases we winsorized or dropped the tails using the full Wave 1 and Wave 2 distribution (in levels) and then estimated differential effects.

After making these outlier corrections, the estimates for males became larger in absolute value and more significant, but the estimates for females remained statistically non-significant with no consistent pattern of change. 7 For males, dropping the 1% tails increased the effect of 100 drinks per month on GPA to −0.15 points (from −0.07 points when analyzing the full sample). Winsorizing the 5% tails further increased the estimated effect size to −0.31 points.

We offer two possible interpretations of these results for males. First, measurement error is probably more substantial among heavier drinkers and among respondents with the biggest changes in alcohol consumption across waves, which could cause attenuation bias at the top end. 8 Second, the effect of drinks per month on GPA could be smaller among male heavier drinkers, suggesting non-linear effects. Interestingly, neither of these concerns appears to be important for the analysis of females.

6.3. Differential effects

Thus far we have reported the differential effects of alcohol use on GPA for males and females. Here, we consider differential effects along three other dimensions: age, direction of change in alcohol use (increases vs. decreases), and initial GPA. To examine the first two of these effects, we added to Equation (2) interactions of the alcohol use measure with dichotomous variables indicating (i) that the student was 16 or older, and (ii) that alcohol use had decreased between Waves 1 and 2. 9 For males, the negative effects of drinking on GPA were consistently larger among respondents who were younger than 16 years old. None of the interaction terms, however, were statistically significant. We found no consistent or significant differences in the effect of alcohol consumption between respondents whose consumption increased and those whose consumption decreased between Waves 1 and 2. All results were non-significant and smaller in magnitude for females. It should be noted, however, that the lack of significant effects could be attributed, at least in part, to low statistical power as some of the disaggregated groups had less than 450 observations per wave.

To examine whether drinking is more likely to affect low achievers (those with initial low GPA) than high achievers (higher initial GPA), we estimated two fixed-effects linear probability regressions. The first regression estimated the impact of alcohol use on the likelihood of having an average GPA of C or less, and the second regression explored the effect of drinking on the likelihood of having a GPA of B- or better. For males, we found that monthly binging was negatively associated with the probability of obtaining a B- or higher average and that increases in number of drinks per month led to a higher likelihood of having a GPA of C or worse. Frequency of drinking, rather than intensity, was the trigger for having a GPA of C or worse. For females, most coefficient estimates were not significant, although the frequency of drinking was negatively associated with the probability of having a GPA of C or worse.

6.4 Self-reported versus abstracted GPA

One of the key advantages of using Add Health data is the availability of abstracted high school grades. Because most educational studies do not have such objective data, we repeated the fixed-effects estimation of Equation (2) using self-reported GPA rather than transcript-abstracted GPA. To facilitate comparison, the estimation sample was restricted to observations with both abstracted and self-reported GPA (N=2,164 for males and 2,418 for females).

The results reveal another interesting contrast between males and females. For males, the results based on self-reported grades were fairly consistent with the results based on abstracted grades, although the estimated effects of binging and drinking intensity were somewhat larger (i.e., more negative) when based on self-reported grades. But for females, the results based on self-reported grades showed positive effects of alcohol consumption that were statistically significant at the 10% level for three out of five consumption measures (monthly binging, total drinks per month, and drinks per episode). Furthermore, with the exception of the frequency measure (drinking days per month), the estimated effects were all substantially larger (i.e., more positive) when based on self-reported GPA. This suggests that females who drink more intensively tend to inflate their academic performance in school, even though their actual performance is not significantly different from that of those who drink less. Males who drink more intensely, on the other hand, may tend to deflate their academic accomplishments.

6.5. Analysis of dropouts

In Table 3 , we estimated the effects of alcohol consumption on GPA conditional on being enrolled in school during the two observation years. While increased drinking could lead an adolescent to drop out of school, reduced drinking could lead a dropout to re-enroll. Our GPA results do not address either of these possible effects. Of those who were in 9 th grade in Wave 1, roughly 2.3% dropped out before Wave 2. Of those who were in 10 th and 11 th grades in Wave 1, the dropout rates were 3.7% and 5.0%, respectively. Our core estimates would be biased if the effect of alcohol use on GPA for non-dropouts differed systematically from the unobserved effect of alcohol use on GPA for dropouts and re-enrollers in the event that these students had stayed in school continuously.

To determine whether dropouts differed significantly from non-dropouts, we compared GPA and drinking patterns across the two groups. Unfortunately, dropouts were much more likely to have missing GPA data for the years they were in school, 10 so the comparison itself has some inherent bias. Nevertheless, for those who were not missing Wave 1 GPA data, we found that mean GPA was significantly lower for dropouts (1.11) than for those students who stayed in school at least another year (2.66). Dropouts were also older in Wave 1 (16.9 vs. 15.9 years old) and more likely to be male (54% vs. 48%). They also consumed alcohol more often and with greater intensity in the first wave. While there is evidence of differences across the two groups in Wave 1, it is unclear whether dropouts would have differed systematically with respect to changes in GPA and in drinking behavior over time if they had stayed in school. Due to the small number of dropout observations with Wave 1 GPA data, we could not reliably estimate a selection correction model.

6.6. Attrition and missing data

As described in the data section, a large fraction of the Add Health respondents who were in 9th, 10th, or 11th grade in Wave 1 were excluded from our analysis either because they did not participate in Waves 2 or 3, did not have transcript data, or had missing data for one or more variables used in the analysis. (The excluded sample consisted of 7,104 individuals out of a total of 11,396 potentially eligible.) Mean characteristics were compared for individuals in the sample under analysis (N=4,292) and excluded respondents (N=7,104) in Wave 1. Those in the analysis sample had higher GPAs (both self-reported and abstracted, when available) and were less likely to have difficulties at school, to have been suspended from school, or to have skipped school. They were less likely to drink or to drink intensively if they drank. They were more likely to be female and White, speak English at home, have highly educated parents, have a resident mother or father at home, and be in good health. They were less likely to have parents on welfare, live in commercial areas or poorly kept buildings, and smoke and use drugs.

The above comparisons suggest that our estimates are representative of the sample of adolescents who participated in Waves 2 and 3 but not necessarily of the full 9 th , 10 th , and 11 th grade sample interviewed at baseline. To assess the magnitude and sign of the potential attrition bias in our estimates, we considered comparing fixed-effects estimates for these two samples using self-reported GPA as the dependent variable. But self-reported GPA also presented a considerable number of missing values, especially for those in the excluded sample at Wave 2. Complete measures of self-reported GPA in Waves 1 and 2 were available for 60% of the individuals in the analysis sample and for less than 30% of individuals in the excluded sample.

As an alternative check, we used OLS to estimate the effects of alcohol use on self-reported GPA in Wave 1 for the excluded sample, and compared these to OLS coefficients for our analysis sample in Wave 1. The effects of alcohol use on self-reported grades were smaller for individuals excluded from our core analysis. Because the excluded individuals tend to consume more alcohol, the finding of smaller effects for these individuals is consistent with either of the two explanations discussed in Section 6.2 above. First, the effect of consuming alcohol on GPA could be smaller for those who drink more. And second, measurement error is probably more serious among heavier drinkers, potentially causing more attenuation bias in this sample.

To summarize, the analysis described above suggests that some caution should be exercised when extrapolating the results in this paper to other populations. Due to missing data, our analysis excludes many of the more extreme cases (in terms of grades, substance use, and socioeconomic status). However, our analysis suggests that the effects of alcohol use on grades are, if anything, smaller for these excluded individuals. It therefore supports our main conclusions that the effects of alcohol use on GPA tend to be small and that failure to account for unobserved individual heterogeneity is responsible for some of the large negative estimates identified in previous research.

7. Conclusion

Though a number of investigations have studied the associations between alcohol use and years of schooling, less is known about the impact of adolescent drinking on the process and quality of learning for those who remain in school. Moreover, studies that have examined the impact of drinking on learning have faced two important limitations. First, they have relied on self-reported grades as the key measure of learning and are therefore subject to potential biases that result from self-reporting. Second, they have relied on cross-sectional data and suffer from potential biases due either to unobserved individual heterogeneity or to weak or questionable instrumental variables.

In the present study, we contribute to the existing literature by exploiting several unique features of the nationally representative Add Health survey. First, we measure learning with grade point averages obtained from the respondents’ official school transcripts. Second, we exploit Add Health’s longitudinal design to estimate models with individual fixed effects. This technique eliminates the bias that results from time-invariant unobserved individual heterogeneity in the determinants of alcohol use and GPA. Finally, we explore a variety of pathways that could explain the association between alcohol use and grades. In particular, we examine the effects of alcohol consumption on both the quantity of schooling—as measured by days of school skipped—and the quality—as measured by difficulties with concentrating in school, getting along with teachers, or completing homework.

The main results show that, in general, increases in alcohol consumption result in statistically significant but quantitatively small reductions in GPA for male students and in statistically non-significant changes for females. For both males and females, comparisons of the fixed-effects models with standard cross-sectional models suggest that large biases can result from the failure to adequately control for unobserved individual heterogeneity. Our findings are thus closely aligned with those of Koch and Ribar (2001) and Dee and Evans (2003) , who reach a similar conclusion regarding the effects of drinking on school completion.

Our analysis also reveals some interesting gender differences in how alcohol consumption affects learning in high school. Our results suggest that for males, alcohol consumption has a small negative effect on GPA and this effect is partially mediated by increased school absences and by difficulties with school-related tasks. For females, however, we find that alcohol use does not significantly affect GPA, even though it significantly increases the probability of encountering difficulties at school. Gender differences in high school performance are well documented in the educational psychology and sociology literatures, yet no previous studies have estimated gender differences in high school learning that are directly associated with alcohol use. Our study is therefore unique in that regard.

Finally, our study also highlights the potential pitfalls of using self-reported grades to measure academic performance. Not only do we find evidence that use of self-reports leads to bias; we also find that the bias differs by gender, as drinking is associated with grade inflation among females and grade deflation among males. Hence, the conceptual discoveries uncovered in this research may be as important for future investigations as the empirical results are for current educational programs and policies.

Acknowledgements

Financial assistance for this study was provided by research grants from the National Institute on Alcohol Abuse and Alcoholism (R01 AA15695, R01 AA13167, and R03 AA016371) and the National Institute on Drug Abuse (RO1 DA018645). This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website ( http://www.cpc.unc.edu/addhealth ). No direct support was received from grant P01-HD31921 for this analysis. We gratefully acknowledge the input of several colleagues at the University of Miami. We are also indebted to Allison Johnson, William Russell, and Carmen Martinez for editorial and administrative assistance. The authors are entirely responsible for the research and results reported in this paper, and their position or opinions do not necessarily represent those of the University of Miami, the National Institute on Alcohol Abuse and Alcoholism, or the National Institute on Drug Abuse.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

1 Due to a significant fraction of missing responses, we imputed household income and household welfare status using both predicted values on the basis of other covariates and the sample mean for households that were also missing some of the predicting covariates. We added dummy variables to indicate when an observation was imputed.

2 Grades and numerical grade-point equivalents have been established for varying levels of a student’s academic performance. These grade-point equivalents are used to determine a student’s grade-point average. Grades of A, A-, and B+ with respective grade-point equivalents of 4.00, 3.67, and 3.33 represent an “excellent” quality of performance. Grades of B, B−, and C+ with grade-point equivalents of 3.00, 2.67, and 2.33 represent a “good” quality of performance. A grade of C with grade-point equivalent of 2.00 represents a “satisfactory” level of performance, a grade of D with grade-point equivalent of 1.00 represents a “poor” quality of performance, and a grade of F with grade-point equivalent of 0.00 represents failure.

3 Note that some demographics (e.g., race, ethnicity) and other variables that are constant over time do not appear in Equation (2) because they present no variation across waves.

4 Of particular concern is the possibility that measurement error due to misreporting varies across waves—either because of random recall errors or because of changes in the interview conditions. (For example, the proportion of interviews in which others were present declined from roughly 42% to 25% between Wave 1 and Wave 2.) Such measurement error could lead to attenuation bias in our fixed-effects model. On the other hand, reporting biases that are similar and stable over time are eliminated by the fixed-effects specification.

5 We tested the significance of these differences by pooling males and females and including an interaction of a gender dummy with the alcohol consumption measure in each model. We found statistically significant differences in the effects of monthly bingeing, drinks per month, and drinking days per month.

6 If alcohol use has small or negligible effects on school completion - as found by Chatterji (2006) , Dee and Evans (2003) , and Koch and Ribar (2001) - then such selection bias will also be small.

7 These results are not presented in the tables but are available from the authors upon request.

8 Examination of the outliers showed that only 15% of those who reported a total number of drinks above the 95th percentile of the distribution did so in both waves.

9 These fixed-effects regressions were adjusted by the same set of controls as in Table (3) , Column (3).

10 More than two-thirds of those who dropped out between Waves 1 and 2 were missing Wave 1 GPA data

• Azevedo Simoes A, Bastos FI, Moreira RI, Lynch KG, Metzger DS. A randomized trial of audio computer and in-person interview to assess HIV risk among drug and alcohol users in Rio De Janeiro, Brazil. Journal of Substance Abuse Treatment. 2006; 30 :237–243. [ PubMed ] [ Google Scholar ]
• Bray JW. Alcohol use, human capital, and wages. Journal of Labor Economics. 2005; 23 (2):279–312. [ Google Scholar ]
• Bray JW, Zarkin GA, Ringwalt C, Qi J. The relationship between marijuana initiation and dropping out of high school. Health Economics. 2000; 9 (1):9–18. [ PubMed ] [ Google Scholar ]
• Brown SA, Tapert SF, Granholm E, Delis DC. Neurocognitive functioning of adolescents: effects of protracted alcohol use. Alcoholism: Clinical and Experimental Research. 2000; 24 (2):164–171. [ PubMed ] [ Google Scholar ]
• Bukstein OG, Cornelius J, Trunzo AC, Kelly TM, Wood DS. Clinical predictors of treatment in a population of adolescents with alcohol use disorders. Addictive Behaviours. 2005; 30 (9):1663–1673. [ PubMed ] [ Google Scholar ]
• Chatterji P. Does alcohol use during high school affect education attainment? Evidence from the National Education Longitudinal Study. Economics of Education Review. 2006; 25 :482–497. [ Google Scholar ]
• Chatterji P, DeSimone J. Adolescent drinking and high school droupout. NBER Working Paper #11337. Cambridge, MA: 2005. Available online at SSRN: http://ssrn.com/abstract=723306 . [ Google Scholar ]
• Cook PJ, Moore MJ. Drinking and schooling. Journal of Health Economics. 1993; 12 (4):411–419. [ PubMed ] [ Google Scholar ]
• Dee TS, Evans WN. Teen drinking and educational attainment: evidence from two-sample instrumental variables estimates. Journal of Labor Economics. 2003; 21 (1):178–209. [ Google Scholar ]
• DeSimone J, Wolaver A. Drinking and academic performance in high school. NBER Working Paper #11035. Cambridge, MA: 2005. [ Google Scholar ]
• Dixon WJ. Simplified estimation from censored normal samples. The Annals of Mathematical Statistics. 1960; 31 (2):385–391. [ Google Scholar ]
• Downey DB, Vogt Yuan AS. Sex differences in school performance during high school: Puzzling patterns and possible explanations. The Sociological Quarterly. 2005; 46 :29–321. [ Google Scholar ]
• Duckworth AL, Seligman MEP. Self-discipline gives girls the edge: Gender in self-discipline, grades, and achievement test scores. Journal of Educational Psychology. 2006; 98 (1):198–208. [ Google Scholar ]
• Dwyer CA, Johnson LM. Grades, accomplishments, and correlates. In: Willimgham W, Cole NS, editors. Gender and fair assessment. Mahwah, NJ: Lawrence Erlbaum Associates; 1997. pp. 127–156. [ Google Scholar ]
• French MT, Popovici I. That instrument is lousy! In search of agreement when using instrumental variables estimation in substance use research. Health Economics. 2009 (– On line) DOI: 10.1002/hec.1572. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Giancola PR, Mezzich AC. Neuropsychological deficits in female adolescents with a substance use disorder: better accounted for conduct disorder. Journal of Studies on Alcohol. 2000; 61 (6):809–817. [ PubMed ] [ Google Scholar ]
• Gil-Lacruz AI, Molina JA. Human development and alcohol abuse in adolescence. Applied Economics. 2007; 39 (10):1315–1323. [ Google Scholar ]
• Ham LS, Hope DA. College students and problematic drinking: a review of the literature. Clinical Psychology Review. 2003; 23 (5):719–759. [ PubMed ] [ Google Scholar ]
• Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: implications for substance abuse prevention. Psychological Bulletin. 1992; 112 (1):64–105. [ PubMed ] [ Google Scholar ]
• Hommer DW. Male and female sensitivity to alcohol-induced brain damage. Alcohol Research and Health. 2003; 27 (2):181–185. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Jacob BA. Where the boys aren’t: non-cognitive skills, returns to school and the gender gap in higher education. Economic Education Review. 2002; 21 :589–598. [ Google Scholar ]
• Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future national results on adolescent drug use: overview of key findings, 2006. NIH Publication No. 07-6202. Bethesda, MD: National Institute on Drug Abuse; 2007. [ Google Scholar ]
• Kleinfeld J. The myth that schools shortchange girls: social science in the service of deception. Women’s Freedom Network Document number ED 423 210. Washington, DC: Education Research Information Clearinghouse (ERIC); 1998. [ Google Scholar ]
• Koch SF, Ribar DC. A siblings analysis of the effects of alcohol consumption onset on educational attainment. Contemporary Economic Policy. 2001; 19 (2):162–174. [ Google Scholar ]
• Koch SF, McGeary KA. The effect of youth alcohol initiation on high school completion. Economic Inquiry. 2005; 43 (4):750–765. [ Google Scholar ]
• McCluskey CP, Krohn MD, Lizotte AJ, Rodriguez ML. Early substance use and school achievement: an examination of Latino, white, and African-American youth. The Journal of Drug Issues. 2002; 32 :921–944. [ Google Scholar ]
• Mullahy J, Sindelar JL. Alcoholism and income: the role of indirect effects. The Milbank Quarterly. 1994; 72 (2):359–375. [ PubMed ] [ Google Scholar ]
• National Institute on Drug Abuse (NIDA) National survey results on drug use from the Monitoring the Future study, 1975–1997. Volume 1: Secondary School Students. Rockville, MD: National Institutes of Health; 1998. [ Google Scholar ]
• Newes-Adeyi G, Chen CM, Williams GD, Faden VB. Trends in underage drinking in the United States, 1991–2005. Surveillance Report #81. Bethesda, MD: Division of Epidemiology and Prevention Research, National Institute on Alcohol Abuse and Alcoholism, U.S. Department of Health and Human Services; 2007. [ Google Scholar ]
• Renna F. The economic cost of teen drinking: late graduation and lowered earnings. Health Economics. 2007; 16 (4):407–419. [ PubMed ] [ Google Scholar ]
• Renna F. Teens’ alcohol consumption and schooling. Economics of Education Review. 2008; 27 :69–78. [ Google Scholar ]
• Schulenberg J, O’Malley PM, Bachman JG, Wadsworth KN, Johnston LD. Getting drunk and growing up: trajectories of frequent binge drinking during the transition to young adulthood. Journal of Studies on Alcohol. 1996; 57 (3):289–304. [ PubMed ] [ Google Scholar ]
• Tapert SF, Brown SA. Neuropsychological correlates of adolescent substance abuse: four-year outcomes. Journal of the International Neuropsychological Society. 1999; 5 :481–493. [ PubMed ] [ Google Scholar ]
• Tourangeau R, Smith TW. Asking sensitive questions: The impact of data collection mode, question format, and question context. Public Opinion Quarterly. 1996; 60 :275–304. [ Google Scholar ]
• Wechsler H, Davenport A, Dowdall GW, Moeykens B, Castillo S. Health and Behavioral Consequences of Binge Drinking at Colleges: a national survey of students at 140 campuses. Journal of the American Medical Association. 1994; 272 (21):1672–1677. [ PubMed ] [ Google Scholar ]
• White AM, Swartzwelder HS. Hippocampal function during adolescence: a unique target of ethanol effects. Annals of the New York Academy of Sciences. 2004; 1021 :206–220. [ PubMed ] [ Google Scholar ]
• Williams J, Powell LM, Wechsler H. Does alcohol consumption reduce human capital accumulation? Evidence from the College Alcohol Study. Applied Economics. 2003; 35 (1):1227–1239. [ Google Scholar ]
• Wolaver A. Effects of heavy drinking in college on study effort, grade point average, and major choice. Contemporary Economic Policy. 2002; 20 (4):415–428. [ Google Scholar ]
• Wolaver A. Does drinking affect grades more for women? Gender differences in the effects of heavy episodic drinking in college. The American Economist. 2007; 51 (2):72–88. [ Google Scholar ]
• Wright DL, Aquilino WS, Supple AJ. A comparison of computer assisted and paper-and-pencil self- administered questionnaires in a survey on smoking alcohol and drug use. Public Opinion Quarterly. 1998; 62 :331–353. [ Google Scholar ]
• Yamada T, Kendrix M, Yamada T. The impact of alcohol consumption and marijuana use on high school graduation. Health Economics. 1996; 5 (1):77–92. [ PubMed ] [ Google Scholar ]
• Zeigler DW, Wang CC, Yoast RA, Dickinson BD, McCaffree MA, Rabinowitz CB, Sterling ML. The neurocognitive effects of alcohol on adolescents and college students. Preventive Medicine. 2004; 40 (1):23–32. [ PubMed ] [ Google Scholar ]
• Zimmerman MA, Caldwell CH, Bernat DH. Discrepancy between self-report and school-record grade point average: correlates with psychosocial outcomes among African American adolescents. Journal of Applied Social Psychology. 2006; 32 (1):86–109. [ Google Scholar ]