Impact of body mass and alcohol consumption on all‐cause and liver mortality in 240 000 adults in the United States
Bibliographic record
Abstract
INTRODUCTION: Nationally representative studies of the combined impact of drinking and body mass (BMI) on mortality outcomes are unavailable. We investigate whether both act together to elevate risk of all-cause or liver mortality. METHODS: We obtained self-reported histories of drinking and BMI from 129 098 women (mean age 47.2 years) and 102 568 men (mean age 45.6 years) ≥18 years interviewed from 1997 to 2004 in the National Health Interview Survey and related these data to the deaths that occurred by 31 December 2006 (women = 8486; men = 7819 deaths). Death hazards among current drinkers in different BMI groups were adjusted for age, education, race and smoking. RESULTS: higher BMI was associated with an elevated all-cause mortality in men (hazard ratios 1.27, 95% confidence interval [CI]: 1.16-1.40) and women (1.12, [1.02-1.24]). The excess risk due to interaction was more pronounced in men (7.30, [3.60-11.00]) than women (2.90, [0.50-5.30]). DISCUSSION AND CONCLUSIONS: Obesity and excess alcohol are both related to all-cause and liver mortality-the latter with evidence of a supra-additive interaction between the risk factors. The presence of both factors in the same population and their impact should inform treatment, public health policies and research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".