Abstract 10535: Relation of Multiple Low-Risk Lifestyle Behaviors With Cardiovascular Disease and All-Cause Mortality: A Systematic Review and Dose-Response Meta-Analysis of Prospective Cohort Studies
Bibliographic record
Abstract
Introduction: The association of combined low-risk lifestyle behaviors (LRLBs) with cardiovascular disease (CVD) and all-cause mortality has not been systematically quantified. Objective: We undertook a systematic review and dose-response meta-analysis to assess the association of combined LRLBs with CVD and all-cause mortality. Methods: MEDLINE, EMBASE and Cochrane were searched up to December 29, 2021. Prospective cohort studies reporting the association between a minimum of 3 combined LRLBs (including healthy diet) with CVD, coronary heart disease (CHD) and stroke incidence and mortality were included. Independent reviewers extracted data and assessed study quality. Highest vs. lowest LRLB score was pooled using random effects. Heterogeneity was assessed (Cochran Q) and quantified (I 2 ). Global dose response meta-analysis (DRM) for maximum adherence was estimated using one-stage linear mixed model. The certainty of the evidence was assessed using GRADE. Results: 116 cohort comparisons (n=9,775,191) involving 382,922 cases were included. Comparing highest with lowest adherence LRLBs were associated with lower risk of CHD incidence (RR, 0.29 [95% CI, 0.21, 0.42]), stroke incidence (0.56 [0.50, 0.62]), CVD incidence (0.47 [0.37, 0.58]), CHD mortality (0.32 [0.25, 0.41]), stroke mortality (0.37 [0.30, 0.46]), CVD mortality (0.41 [0.34, 0.49]) and all-cause mortality (0.46 [0.41 to 0.52]). DRM analysis showed a linear association between LRLBs and all outcomes reaching a global DRM between 59-76% protection. LRLBs were defined with variable ranges as a healthy body weight (body mass index <30kg/m 2 ), healthy diet (healthy diet score >median), regular physical activity (1/week to >30 minutes/day), smoking cessation (never smoked or smoking cessation), light alcohol intake (≤30g/day) and adequate sleep (5.5-9 hours). The certainty of the evidence was graded as moderate to high owing to downgrades for inconsistency and/or upgrades for a large magnitude of effect and significant dose-response gradient. Conclusions: Pooled analyses show that the combination of LRLBs was associated with a substantial lower risk of CVD outcomes and all-cause mortality. The available evidence provides a very good indication of the benefit of combined LRLBs.
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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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".