Comparative evaluation of relative fat mass and body mass index in predicting cardiometabolic multimorbidity in older adults: results from the English Longitudinal Study of Ageing
Bibliographic record
Abstract
Relative fat mass (RFM) is a more accurate measure of body fat percentage than body mass index (BMI). However, its association with cardiometabolic multimorbidity (CMM) and its predictive value have not been examined. This study evaluated and compared the associations and predictive utility of RFM and BMI for CMM. We analyzed data from 3,348 adults (mean age 64 years; 45.1% male) in the English Longitudinal Study of Ageing who were free of hypertension, coronary heart disease, diabetes, and stroke at wave 4 (2008-2009). RFM was derived from height and waist circumference. CMM was defined at wave 10 (2021-2023) as the presence of two or more of hypertension, cardiovascular disease, diabetes, or stroke. Odds ratios (ORs) with 95% confidence intervals (CIs) and measures of discrimination were estimated. Over 12-15 years of follow-up, 197 participants developed CMM. Restricted cubic spline models showed linear dose-response relationships for both RFM and BMI (p for nonlinearity > .05). Higher RFM was strongly associated with CMM (per 1-SD increase: OR 1.66, 95% CI 1.29-2.15; top vs bottom tertile: OR 2.70, 95% CI 1.46-4.99). Associations for BMI were weaker (per 1-SD increase: OR 1.28, 95% CI 1.12-1.47; top vs bottom tertile: OR 1.88, 95% CI 1.24-2.85). Adding RFM to a conventional risk model modestly increased discrimination (ΔC-index = 0.0088, p = .29) and significantly improved model fit (-2 log likelihood, p < .001). Corresponding values for BMI were ΔC-index = 0.0049 (p = .46) and -2 log likelihood (p < .001). The C-index gain from RFM was 0.0039 greater than BMI (p = .39). In an older UK population, RFM was a stronger indicator and predictor of CMM risk than BMI.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".