Sarcopenic-obesity and cardiovascular disease risk in the elderly
Bibliographic record
Abstract
OBJECTIVES: To determine: 1) whether sarcopenic-obesity is a stronger predictor of cardiovascular disease (CVD) than either sarcopenia or obesity alone in the elderly, and 2) whether muscle mass or muscular strength is a stronger marker of CVD risk. DESIGN: Prospective cohort study. PARTICIPANTS: Participants included 3366 community-dwelling older (>or= 65 years) men and women who were free of CVD at baseline. MEASUREMENTS: Waist circumference (WC), bioimpedance analysis, and grip strength were used to measure abdominal obesity, whole-body muscle mass, and muscular strength, respectively. Subjects were classified as normal, sarcopenic, obese, or sarcopenic-obese based on measures of WC and either muscle mass or strength. Participants were followed for 8 years for CVD development and proportional hazard regression models were used to compare risk estimates for CVD in the four groups after adjusting for age, sex, race, income, smoking, alcohol, and cognitive status. RESULTS: Compared with the normal group, CVD risk was not significantly elevated within the obese, sarcopenic, or sarcopenic-obese groups as determined by WC and muscle mass. When determined by WC and muscle strength, CVD risk was not significantly increased in the sarcopenic or obese groups, but was increased by 23% (95% confidence interval: 0.99-1.54, P=0.06) within the sarcopenic-obese group. CONCLUSION: Sarcopenia and obesity alone were not sufficient to increase CVD risk. Sarcopenic-obesity, based on muscle strength but not muscle mass, was modestly associated with increased CVD risk. These findings imply that strength may be more important than muscle mass for CVD protection in old age.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".