Moderate Coffee Consumption and Its Protective Role Against Type 2 Diabetes in Men: A Meta-analysis Study
Bibliographic record
Abstract
Background and Objective: Coffee consumption has been widely studied for its potential effects on the risk of type 2 diabetes mellitus (T2DM), with mixed results across studies. This study aims to evaluate the association between daily coffee intake and the risk of developing T2DM in men. Methods: A comprehensive search of the Scopus, PubMed, and Web of Science databases was conducted up to October 13, 2024, to identify cohort studies that examined coffee consumption and T2DM risk among men. Studies were selected based on predefined inclusion criteria, and data on relative risk (RR), odds ratio (OR), or hazard ratio (HR) were extracted. The quality of studies was assessed using the Newcastle-Ottawa Scale. Statistical analyses were performed using random effects models for dose-response relationships, and potential nonlinear associations were explored using restricted cubic splines. Publication bias was assessed using Egger’s and Begg’s tests. Results: A total of 12 studies with 266,183 participants were included. The dose-response meta-analysis revealed a linear decrease in T2DM risk with each additional cup of daily coffee, with a pooled RR of 0.91 (95% CI: 0.89–0.93) for the linear term. The quadratic term (dose squared) showed a modest upward adjustment in effect, suggesting a nonlinear relationship, with an RR of 1.004 (95% CI: 1.002–1.006). There was no significant publication bias (Egger's test p = 0.39, Begg’s test p = 0.38). Conclusions: This study demonstrated that while moderate coffee consumption provides significant benefits, higher intake does not yield additional advantages, helping to refine dietary recommendations. The identified nonlinear relationship underscores the need for further research to establish the upper limits of coffee intake and its long-term metabolic impact.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.056 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".