Age-Dependent Relationship of Physical Inactivity With Incident Cardiovascular Disease: Analysis of a Large Japanese Cohort
Bibliographic record
Abstract
BACKGROUND: There have been limited studies examining age-dependent associations between physical inactivity and cardiovascular disease (CVD). We aimed to clarify the age-dependent relationship of physical inactivity with incident CVD. METHODS: We analyzed 1,097,424 participants, aged 18 to 105 years, without histories of CVD, enrolled in the DeSC database (median age, 63 years; 46.4% men). We categorized participants into the following 4 groups based on age: ≤ 44 years (n = 203,835); 45 to 64 years (n = 403,619); 65 to 79 years (n = 437,236); and ≥ 80 years (n = 52,734). We used 3 physical inactivity components gained from the self-reported questionnaire during a health checkup. The outcomes were composite CVD events including myocardial infarction, stroke, heart failure, and each CVD event. RESULTS: During a mean follow-up of 3.2 ± 1.9 years, 81,649 CVD events were observed. The hazard ratios of 3 physical inactivity components for CVD events increased with age category (P for interaction < 0.001). For example, the hazard ratio (95% confidence interval) of physical inactivity defined as not doing light sweaty exercise for 30 minutes at least twice a week for incident CVD in the groups aged ≤ 44 years, 45 to 64 years, 65 to 79 years, and ≥ 80 years were 0.97 (0.88-1.05), 1.08 (1.05-1.12), 1.12 (1.10-1.15), and 1.17 (1.12-1.21), respectively (P for interaction < 0.001). This association was consistent across subtypes of CVD including heart failure, myocardial infarction, and stroke. CONCLUSIONS: The association of physical inactivity with a higher risk of developing CVD increased with age. Preventive efforts for physical activity optimization may be more valuable in older people.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".