Predictive Values of Anthropometric Measurements for Cardiometabolic Risk Factors and Cardiovascular Diseases Among 44 048 Chinese
Bibliographic record
Abstract
Background The predictive value of adiposity indices and the newly developed index for cardiometabolic risk factors and cardiovascular diseases (CVDs) remains unclear in the Chinese population. This study aimed to compare the predictive value of A Body Shape Index with other 5 conventional obesity-related anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio) in Chinese population. Methods and Results A total of 44 048 participants in the study were derived from the baseline data of the PURE-China (Prospective Urban and Rural Epidemiology) study in China. All participants' anthropometric parameters, CVDs, and risk factors (dyslipidemia, abnormal blood pressure, and hyperglycemia) were collected by standard procedures. Multivariable logistic regression models and receiver operator characteristic curve analysis were used to evaluate the predictive values of obesity-related anthropometric indices to the cardiometabolic risk factors and CVDs. A positive association was observed between each anthropometric index and cardiometabolic risk factors and CVDs in all models (P<0.001). Compared with other anthropometric indices (body mass index, waist circumference, hip circumference, waist-to-hip ratio, and A Body Shape Index), waist-to-height ratio had significantly higher areas under the curve (AUCs) for predicting dyslipidemia (AUCs: 0.646, sensitivity: 65%, specificity: 44%), hyperglycemia (AUCs: 0.595, sensitivity: 60%, specificity: 45%), and CVDs (AUCs: 0.619, sensitivity: 59%, specificity: 41%). Waist circumference showed the best prediction for abnormal blood pressure (AUCs: 0.671, sensitivity: 66%, specificity: 40%) compared with other anthropometric indices. However, the new body shape index did not show a better prediction to either cardiometabolic risk factors or CVDs than that of any other traditional obesity-related indices. Conclusions Waist-to-height ratio appeared to be the best indicator for dyslipidemia, hyperglycemia, and CVDs, while waist circumference had a better prediction for abnormal blood pressure.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".