The discrimination of dyslipidaemia using anthropometric measures in ethnically diverse populations of the Asia–Pacific Region: The Obesity in Asia Collaboration
Bibliographic record
Abstract
Dyslipidaemia is a major risk factor for cardiovascular disease and is only detectable through blood testing, which may not be feasible in resource-poor settings. As dyslipidaemia is commonly associated with excess weight, it may be possible to identify individuals with adverse lipid profiles using simple anthropometric measures. A total of 222 975 individuals from 18 studies were included as part of the Obesity in Asia Collaboration. Linear and logistic regression models were used to assess the association between measures of body size and dyslipidaemia. Body mass index, waist circumference, waist : hip ratio (WHR) and waist : height ratio were continuously associated with the lipid variables studied, but the relationships were consistently stronger for triglycerides and high-density lipoprotein cholesterol. The associations were similar between Asians and non-Asians, and no single anthropometric measure was superior at discriminating those individuals at increased risk of dyslipidaemia. WHR cut-points of 0.8 in women and 0.9 in men were applicable across both Asians and non-Asians for the discrimination of individuals with any form of dyslipidaemia. Measurement of central obesity may help to identify those individuals at increased risk of dyslipidaemia. WHR cut-points of 0.8 for women and 0.9 for men are optimal for discriminating those individuals likely to have adverse lipid profiles and in need of further clinical assessment.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".