Visceral Adiposity Index Is Associated With Cardiometabolic Multimorbidity and Improves Risk Prediction: The English Longitudinal Study of Ageing
Bibliographic record
Abstract
OBJECTIVE: To investigate the prospective association of the visceral adiposity index (VAI) with the risk of cardiometabolic multimorbidity (CMM) and to evaluate its utility in CMM risk prediction. METHODS: We analyzed data from 3348 adults (mean age, 63 years; 45.1% male) participating in the English Longitudinal Study of Ageing who were free from hypertension, coronary heart disease, diabetes, and stroke at wave 4 (2008-2009). Visceral adiposity index was calculated with anthropometric and metabolic parameters. Cardiometabolic multimorbidity was defined as the presence of 2 or more of the following conditions at wave 10 (2021-2023): hypertension, cardiovascular disease, diabetes, or stroke. Multivariable logistic regression models were used to estimate odds ratios and 95% CIs. RESULTS: During a 12- to 15-year follow-up period, CMM developed in 197 participants. Restricted cubic spline analysis found a predominantly linear pattern between the VAI and CMM risk (P value for nonlinearity, .062). Each 1 SD increment in VAI was associated with higher odds of CMM (odds ratio, 1.33; 95% CI, 1.19 to 1.50) after adjustment for age, sex, smoking, alcohol intake, systolic blood pressure, total cholesterol level, and handgrip strength. This association persisted after further adjustment for physical activity. The associations were qualitatively similar across VAI tertiles. The VAI significantly improved risk discrimination beyond established risk factors (C-index change, 0.0205 [P=.021] and P value for difference in -2 log likelihood <.001). CONCLUSION: There is a positive association between VAI and CMM risk that is independent of established risk factors and consistent with a linear dose-response pattern. The VAI provides significant improvement in CMM risk prediction beyond established 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".