Body Roundness Index and Waist–Hip Ratio Result in Better Cardiovascular Disease Risk Stratification: Results From a Large Chinese Cross-Sectional Study
Bibliographic record
Abstract
Background: The appropriate optimal anthropometric indices and their thresholds within each BMI category for predicting those at a high risk of cardiovascular disease risk factors (CVDRFs) among the Chinese are still under dispute. Objectives: We aimed to identify the best indicators of CVDRFs and the optimal threshold within each BMI category among the Chinese. Methods: Between 2012 and 2020, a total of 500,090 participants were surveyed in Hunan, China. Six anthropometric indices including waist circumference (WC), a body shape index (ABSI), body roundness index (BRI), waist-hip ratio (WHR), hip circumference (HC), and waist-height ratio (WHtR) were evaluated in the present study. Considered CVDRFs included dyslipidaemia, hypertension, diabetes mellitus (DM), and chronic kidney disease (CKD). The associations of anthropometrics with CVDRFs within each BMI category were evaluated through logistic regression models. The area under the receiver operating characteristic curve (AUROC) was used to assess the predictive abilities. Results: For the presence of at least one CVDRFs, the WHR had the highest AUROC in overweight [0.641 (95%CI:0.638, 0.644)] and obese [0.616 (95%CI:0.609, 0.623)] men. BRI had the highest AUROC in underweight [0.649 (95%CI:0.629, 0.670)] and normal weight [0.686 (95%CI:0.683, 0.690)] men. However, the BRI had the highest discrimination ability among women in all the BMI categories, with AUROC ranging from 0.641 to 0.727. In most cases, the discriminatory ability of WHtR was similar to BRI and was easier to calculate; therefore, thresholds of BRI, WHR, and WHtR for CVDRFs identification were all calculated. In men, BRI thresholds of 1.8, 3.0, 3.9, and 5.0, WHtR thresholds of 0.41, 0.48, 0.53, and 0.58, and WHR thresholds of 0.81, 0.88, 0.92, and 0.95 were identified as optimal thresholds across underweight, normal weight, overweight, and obese populations, respectively. The corresponding BRI values in women were 1.9, 2.9, 4.0, and 5.2, respectively, and WHtR were 0.41, 0.48, 0.54, and 0.59, while the WHR values were 0.77, 0.83, 0.88, and 0.90. The recommended BRI, WHtR, or WHR cut-offs could not statistically differentiate high-risk CKD or hypercholesterolemia populations. Conclusions: We found that BRI and WHR were superior to other indices for predicting CVD risk factors, except CKD or hypercholesterolemia, among the Chinese.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".