The Relationship Between Alcohol Consumption, BMI, and Type 2 Diabetes: A Systematic Review and Dose-Response Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Moderate alcohol use may be associated with lower risk of type 2 diabetes mellitus (T2DM). Previous reviews have reached mixed conclusions. PURPOSE: To quantify the dose-response relationship between alcohol consumption and T2DM, accounting for differential effects by sex and BMI. DATA SOURCES: Medline, Embase, Web of Science, and one secondary data source. STUDY SELECTION: Cohort studies on the relationship between alcohol use and T2DM. DATA EXTRACTION: Fifty-five studies, and one secondary data source, were included with a combined sample size of 1,363,355 men and 1,290,628 women, with 89,983 and 57,974 individuals, respectively, diagnosed with T2DM. DATA SYNTHESIS: Multivariate dose-response meta-analytic random-effect models were used. For women, a J-shaped relationship was found with a maximum risk reduction of 31% (relative risk [RR] 0.69, 95% CI 0.64-0.74) at an intake of 16 g of pure alcohol per day compared with lifetime abstainers. The protective association ceased above 49 g per day (RR 0.82, 95% CI 0.68-0.99). For men, no statistically significant relationship was identified. When results were stratified by BMI, the protective association was only found in overweight and obese women. LIMITATIONS: Our analysis relied on aggregate data. We included some articles that determined exposure and cases via self-report, and the studies did not account for temporal variations in alcohol use. CONCLUSIONS: The observed reduced risk seems to be specific to women in general and women with a BMI ≥25 kg/m2. Our findings allow for a more precise prediction of the sex-specific relationship between T2DM and alcohol use, as our results differ from those of previous studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".