Optimal lifestyle behaviors and 10‐year progression of arterial stiffness: The Multi‐Ethnic Study of Atherosclerosis
Bibliographic record
Abstract
Abstract Majority of previous studies showed no association between a single health behavior and arterial stiffness, but the benefit of simultaneously having multiple healthy behaviors (optimal lifestyle) on the progression of arterial stiffness is unknown. Among 2810 individuals (age 60.0 ± 9.4, 46.5% male), optimal lifestyle marker (yes/no) on four health behaviors (ie, BMI < 25 kg/m2, never or former smoker, never or moderate drinker, exercised > 500 METS min/week) across four visits (≈ 5 years) were summed to create an optimal lifestyle score. Carotid arterial stiffness was measured using distensibility coefficient (DC) and Young's elastic modulus (YEM) at visit 1 and after a mean of 9.5 years (visit 5). The association of optimal lifestyle with 10‐year percent change in DC and YEM was assessed using multiple linear regression. DC decreased by 5.3% and YEM increased by 24.4% over 10 years. Mean optimal lifestyle score was 9.4 ± 3.1 (range: 0–16). Individuals in quintiles 2–5 of optimal lifestyle score compared to quintile 1 (with the least optimal lifestyle score) did not show slower deceleration of DC [Q2, −0.3% (95% CI: −6.0, 5.4); Q3, −0.01% (−4.5, 4.5); Q4, −0.6% (−5.2, 3.9); Q5, −0.4% (−5.3, 4.4)], trend p‐value = .82] or slower progression of YEM [Q2, 0.1% (−7.1, 7.3); Q3, −0.8% (−8.0, 6.5); Q4, 4.5% (−2.3, 11.3); Q5, −0.2% (−8.3, 7.9)], trend p‐value = .49] after adjusting for risk factors. The association remained non‐significant when stratified by categories of age, sex, race, BP control, and diabetes. Our findings indicate that optimal score on multiple health behaviors may not independently slow arterial stiffness progression.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".