Association between weight-adjusted waist index and cardiometabolic multimorbidity in older adults: Findings from the English Longitudinal Study of Ageing
Bibliographic record
Abstract
The weight-adjusted waist index (WWI) is a novel anthropometric measure designed to better reflect central obesity than traditional indices such as body mass index and waist circumference (WC). This study examined the prospective association between WWI and cardiometabolic multimorbidity (CMM) and evaluated its predictive utility. We included 3,348 participants (mean age 63 years; 45.1% male) from the English Longitudinal Study of Ageing who were free from hypertension, coronary heart disease, diabetes, and stroke at baseline (wave 4: 2008-2009). WWI was calculated as WC (cm) divided by the square root of body weight (kg). CMM was defined as the presence of ≥ 2 of the following conditions at wave 10 (2021-2023): hypertension, cardiovascular disease, diabetes, or stroke. Multivariable logistic regression and measures of discrimination were used to assess associations and predictive value. Over 15 years, 197 participants developed CMM. Restricted cubic spline analysis indicated a linear dose-response relationship between WWI and CMM risk (p for nonlinearity = .44). Each 1 SD increase in WWI was associated with higher odds of CMM (odds ratio, OR = 1.30; 95% CI: 1.12-1.51), persisting after adjustment for physical activity (OR = 1.28; 95% CI: 1.10-1.49). Similar associations were observed across WWI tertiles. Adding WWI to conventional risk models slightly improved discrimination (ΔC-index = 0.0065; p = .29), with a significant improvement in model fit (-2 log likelihood, p = .001). Higher WWI levels were independently and linearly associated with increased CMM risk in older adults. WWI also improved CMM risk prediction beyond conventional risk factors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".