Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality
Bibliographic record
Abstract
BACKGROUND: With two well-validated aging measures capturing mortality and morbidity risk, this study examined whether and to what extent aging mediates the associations of unhealthy lifestyles with adverse health outcomes. METHODS: Data were from 405,944 adults (40-69 years) from UK Biobank (UKB) and 9972 adults (20-84 years) from the US National Health and Nutrition Examination Survey (NHANES). An unhealthy lifestyles score (range: 0-5) was constructed based on five factors (smoking, drinking, physical inactivity, unhealthy body mass index, and unhealthy diet). Two aging measures, Phenotypic Age Acceleration (PhenoAgeAccel) and Biological Age Acceleration (BioAgeAccel) were calculated using nine and seven blood biomarkers, respectively, with a higher value indicating the acceleration of aging. The outcomes included incident cardiovascular disease (CVD), incident cancer, and all-cause mortality in UKB; CVD mortality, cancer mortality, and all-cause mortality in NHANES. A general linear regression model, Cox proportional hazards model, and formal mediation analysis were performed. RESULTS: The unhealthy lifestyles score was positively associated with PhenoAgeAccel (UKB: β = 0.741; NHANES: β = 0.874, all p < 0.001). We further confirmed the respective associations of PhenoAgeAccel and unhealthy lifestyles with the outcomes in UKB and NHANES. The mediation proportion of PhenoAgeAccel in associations of unhealthy lifestyles with incident CVD, incident cancer, and all-cause mortality were 20.0%, 17.8%, and 26.6% (all p < 0.001) in UKB, respectively. Similar results were found in NHANES. The findings were robust when using another aging measure-BioAgeAccel. CONCLUSIONS: Accelerated aging partially mediated the associations of lifestyles with CVD, cancer, and mortality in UK and US populations. The findings reveal a novel pathway and the potential of geroprotective programs in mitigating health inequality in late life beyond lifestyle interventions.
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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) | 0.000 | 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".