Deriving Ethnic-Specific BMI Cutoff Points for Assessing Diabetes Risk
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: The definition of obesity (BMI ≥ 30 kg/m(2)), a key risk factor of diabetes, is widely used in white populations; however, its appropriateness in nonwhite populations has been questioned. We compared the incidence rates of diabetes across white, South Asian, Chinese, and black populations and identified equivalent ethnic-specific BMI cutoff values for assessing diabetes risk. RESEARCH DESIGN AND METHODS: We conducted a multiethnic cohort study of 59,824 nondiabetic adults aged ≥ 30 years living in Ontario, Canada. Subjects were identified from Statistics Canada's population health surveys and followed for up to 12.8 years for diabetes incidence using record linkages to multiple health administrative databases. RESULTS: The median duration of follow-up was 6 years. After adjusting for age, sex, sociodemographic characteristics, and BMI, the risk of diabetes was significantly higher among South Asian (hazard ratio 3.40, P < 0.001), black (1.99, P < 0.001), and Chinese (1.87, P = 0.002) subjects than among white subjects. The median age at diagnosis was lowest among South Asian (aged 49 years) subjects, followed by Chinese (aged 55 years), black (aged 57 years), and white (aged 58 years) subjects. For the equivalent incidence rate of diabetes at a BMI of 30 kg/m(2) in white subjects, the BMI cutoff value was 24 kg/m(2) in South Asian, 25 kg/m(2) in Chinese, and 26 kg/m(2) in black subjects. CONCLUSIONS: South Asian, Chinese, and black subjects developed diabetes at a higher rate, at an earlier age, and at lower ranges of BMI than their white counterparts. Our findings highlight the need for designing ethnically tailored prevention strategies and for lowering current targets for ideal body weight for nonwhite populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it