Does physical activity moderate the association between alcohol drinking and all-cause, cancer and cardiovascular diseases mortality? A pooled analysis of eight British population cohorts
Bibliographic record
Abstract
OBJECTIVE: To examine whether physical activity (PA) moderates the association between alcohol intake and all-cause mortality, cancer mortality and cardiovascular diseases (CVDs) mortality. DESIGN: Prospective study using 8 British population-based surveys, each linked to cause-specific mortality: Health Survey for England (1994, 1998, 1999, 2003, 2004 and 2006) and Scottish Health Survey (1998 and 2003). PARTICIPANTS: 36 370 men and women aged 40 years and over were included with a corresponding 5735 deaths and a mean of 353 049 person-years of follow-up. EXPOSURES: 6 sex-specific categories of alcohol intake (UK units/week) were defined: (1) never drunk; (2) ex-drinkers; (3) occasional drinkers; (4) within guidelines (<14 (women); <21 (men)); (5) hazardous (14-35 (women); 21-49 (men)) and (6) harmful (>35 (women) >49 (men)). PA was categorised as inactive (≤7 MET-hour/week), active at the lower (>7.5 MET-hour/week) and upper (>15 MET-hour/week) of recommended levels. MAIN OUTCOMES AND MEASURES: Cox proportional-hazard models were used to examine associations between alcohol consumption and all-cause, cancer and CVD mortality risk after adjusting for several confounders. Stratified analyses were performed to evaluate mortality risks within each PA stratum. RESULTS: We found a direct association between alcohol consumption and cancer mortality risk starting from drinking within guidelines (HR (95% CI) hazardous drinking: 1.40 (1.11 to 1.78)). Stratified analyses showed that the association between alcohol intake and mortality risk was attenuated (all-cause) or nearly nullified (cancer) among individuals who met the PA recommendations (HR (95% CI)). CONCLUSIONS: Meeting the current PA public health recommendations offsets some of the cancer and all-cause mortality risk associated with alcohol drinking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".