Assessing the prevalence of obesity in a Russian adult population by six indices and their associations with hypertension, diabetes mellitus and hypercholesterolaemia
Bibliographic record
Abstract
The anthropometric index that best predicts cardiometabolic risk remains inconclusive. This study therefore assessed the prevalence of obesity using six indices and compared their associations with obesity-related cardiometabolic disorders. We determined obesity prevalence according to body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio (WHtR), body fat percentage and fat mass index (FMI) using data from the Know Your Heart study (n = 4495, 35–69 years). The areas under the receiver operating characteristic curves (AUCs) provided predictive values of each index for detecting the presence of hypertension, hypercholesterolaemia and diabetes. Age-standardised obesity prevalence significantly varied according to anthropometric index: from 17.2% (FMI) to 75.8% (WHtR) among men and from 23.6% (FMI) to 65.0% (WHtR) among women. WHtR had the strongest association with hypertension (AUC = 0.784; p < 0.001) and with a combination of disorders (AUC = 0.779; p < 0.001) in women. In women, WHtR also had the largest AUCs for hypercholesterolaemia, in men – for hypertension, diabetes and a combination of disorders, although not all the differences from other obesity indices were significant. WHtR exhibited the closest association between hypertension and a combination of disorders in women and was non-inferior compared to other indices in men.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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 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".