Association between the prevalence of metabolic syndrome and coffee consumption among Korean adults: results from the Health Examinees study
Bibliographic record
Abstract
The aim of this study was to evaluate the association between the frequency and quantity of coffee consumption and metabolic syndrome (MetS) in the Health Examinees study. A total of 130 420 participants (43 682 men and 86 738 women) were included in our study. Coffee consumption was categorized into 5 categories (0, <1, 1, 2–3, and ≥4 cups/day). We calculated odds ratios (ORs) and 95% confidence intervalS (CIs) using multivariate logistic regression. In this study population, the prevalence of MetS was 12 701 (29.1%) in men and 21 338 (24.6%) in women. High coffee consumption (≥4 cups/day) was associated with a lower prevalence of MetS compared with non-coffee consumers (OR = 0.79, 95% CI = 0.70–0.90, p for trend <0.0001 in men; OR = 0.70, 95% CI = 0.62–0.78, p for trend <0.0001 in women). The multivariable-adjusted ORs for high fasting glucose decreased with increasing levels of coffee consumption in men (OR = 0.60, 95% CI = 0.54–0.67, p for trend <0.0001) and women (OR = 0.70, 95% CI = 0.63–0.79, p for trend <0.0001). For women, the multivariable-adjusted ORs for hypertriglyceridemia (OR = 0.84, 95% CI = 0.75–0.93, p for trend = 0.0007) decreased with increasing levels of coffee consumption. We found that coffee consumption was inversely associated with the prevalence of metabolic syndrome among Korean men and women. Our study warrants further prospective cohort 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".