The Relationship between Diabetes and Obesity across Different Ethnicities
Bibliographic record
Abstract
The relationship between diabetes and obesity, major contributors to cardiovascular disease, varies with ethnicity; however, only limited information is available regarding Aboriginal and South Asian populations.Objective: This investigation aimed to identify and compare the relationship between diabetes and dysglycemia, and obesity across several ethnicities.Methods: White (n=3593), Aboriginal (n=999), East Asian (n=448), and South Asian (n=222) adults were measured directly for body mass index (BMI) and waist circumference.Individuals were identified for diabetes and dysglycemia through reported diagnosis, measured random blood glucose and A1C values.The risk ratios of diabetes and dysglycemia were compared across measures of BMI and waist circumference.Results: Across all ethnic groups, individuals with greater BMI and waist circumference demonstrated greater risk ratios for diabetes and dysglycemia.Aboriginal and South Asian individuals demonstrated greater risks for diabetes relative to White adults regardless of age, gender, physical activity and body composition.Risks for dysglycemia were greater among East and South Asian adults regardless of covariates, while the increased risk among Aboriginal adults appears to be mediated by waist circumference.Conclusions: Overall, increased risks of diabetes and dysglycemia were observed across all ethnic groups with increased body composition measures.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".