Body Mass Index Is Inversely Related to Mortality in Older People After Adjustment for Waist Circumference
Bibliographic record
Abstract
OBJECTIVES: To examine the individual and combined influence of body mass index (BMI) and waist circumference (WC) on mortality risk in older people. DESIGN: Longitudinal cohort study. SETTING: Cardiovascular Health Study, a longitudinal study of cardiovascular disease and its risk factors in older people. PARTICIPANTS: Five thousand two hundred men and women aged 65 and older. MEASUREMENTS: BMI and WC were measured at baseline. The risks of all-cause mortality associated with BMI and WC were examined using Cox proportional hazards models over 9 years of follow-up. RESULTS: When examined individually, BMI and WC were both negative predictors of mortality, but when BMI and WC were examined simultaneously, BMI was a negative predictor of mortality, whereas WC was a positive predictor of mortality. After controlling for WC, mortality risk decreased 21% for every standard deviation increase in BMI. After controlling for BMI, mortality risk increased 13% for every standard deviation increase in WC. The patterns of associations were consistent by sex, age, and disease status. CONCLUSION: Higher BMI values indicated a lower mortality risk once the risk attributable to WC was accounted for, whereas higher WC values indicate a higher mortality risk once the risk attributable to BMI was accounted for. Both BMI and WC should be measured in the clinical setting, but in older adults higher BMI is associated with lower mortality rates.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".