Cardiometabolic Index, BMI, Waist Circumference, and Cardiometabolic Multimorbidity Risk in Older Adults
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background/Objectives: The cardiometabolic index (CMI) is a simple anthropometric–metabolic indicator that has recently gained attention as a marker of cardiometabolic risk. This study compared the associations and predictive utility of CMI, body mass index (BMI), and waist circumference (WC) for cardiometabolic multimorbidity (CMM). Methods: Data were drawn from 3348 adults (mean age 63.5 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). CMI was calculated using the triglyceride-to-HDL-cholesterol ratio and the waist-to-height ratio. Incident CMM at wave 10 (2021–2023) was defined as the presence of ≥2 of these conditions: hypertension, cardiovascular disease, diabetes, or stroke. Odds ratios (ORs) with 95% confidence intervals (CIs) and measures of discrimination were estimated. Results: During 12–15 years of follow-up, 197 CMM cases were recorded. CMI, BMI, and WC were each linearly related to CMM. Higher CMI was associated with increased CMM risk (per 1-SD increase: OR 1.25, 95% CI 1.08–1.44; highest vs. lowest tertile: OR 1.88, 95% CI 1.09–3.25), with similar effect sizes for BMI. WC showed stronger associations (per 1-SD increase: OR 1.46, 95% CI 1.25–1.71; highest vs. lowest tertile: OR 2.16, 95% CI 1.35–3.44). Adding CMI to a base model resulted in a small, non-significant improvement in discrimination (ΔC-index = 0.0032; p = 0.55) but significantly improved model fit (−2 log-likelihood p = 0.004), with comparable effects for BMI and greater improvements for WC. Conclusions: In this older UK cohort, higher CMI levels were associated with increased long-term risk of CMM but did not outperform traditional adiposity measures such as BMI and WC.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it