Defining Obesity Cut Points in a Multiethnic Population
Bibliographic record
Abstract
BACKGROUND: Body mass index (BMI) is widely used to assess risk for cardiovascular disease and type 2 diabetes. Cut points for the classification of obesity (BMI >30 kg/m2) have been developed and validated among people of European descent. It is unknown whether these cut points are appropriate for non-European populations. We assessed the metabolic risk associated with BMI among South Asians, Chinese, Aboriginals, and Europeans. METHODS AND RESULTS: We randomly sampled 1078 subjects from 4 ethnic groups (289 South Asians, 281 Chinese, 207 Aboriginals, and 301 Europeans) from 4 regions in Canada. Principal components factor analysis was used to derive underlying latent or "hidden" factors associated with 14 clinical and biochemical cardiometabolic markers. Ethnic-specific BMI cut points were derived for 3 cardiometabolic factors. Three primary latent factors emerged that accounted for 56% of the variation in markers of glucose metabolism, lipid metabolism, and blood pressure. For a given BMI, elevated levels of glucose- and lipid-related factors were more likely to be present in South Asians, Chinese, and Aboriginals compared with Europeans, and elevated levels of the blood pressure-related factor were more likely to be present among Chinese compared with Europeans. The cut point to define obesity, as defined by distribution of glucose and lipid factors, is lower by approximately 6 kg/m2 among non-European groups compared with Europeans. CONCLUSIONS: Revisions may be warranted for BMI cut points to define obesity among South Asians, Chinese, and Aboriginals. Using these revised cut points would greatly increase the estimated burden of obesity-related metabolic disorders among non-European populations.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".