<scp>BMI</scp> as a predictor of high fasting blood glucose among people living with <scp>HIV</scp> in the Asia‐Pacific region
Bibliographic record
Abstract
Abstract Background Non‐Asian body mass index (BMI) classifications are commonly used as a risk factor for high fasting blood glucose (FBG). We investigated the incidence and factors associated with high FBG among people living with HIV in the Asia‐Pacific region, using a World Health Organization BMI classification specific to Asian populations. Methods This study included people living with HIV enrolled in a longitudinal cohort study from 2003 to 2019, receiving antiretroviral therapy (ART), and without prior tuberculosis. BMI at ART initiation was categorized using Asian BMI classifications: underweight (<18.5 kg/m 2 ), normal (18.5–22.9 kg/m 2 ), overweight (23–24.9 kg/m 2 ), and obese (≥25 kg/m 2 ). High FBG was defined as a single post‐ART FBG measurement ≥126 mg/dL. Factors associated with high FBG were analyzed using Cox regression models stratified by site. Results A total of 3939 people living with HIV (63% male) were included. In total, 50% had a BMI in the normal weight range, 23% were underweight, 13% were overweight, and 14% were obese. Median age at ART initiation was 34 years (interquartile range 29–41). Overall, 8% had a high FBG, with an incidence rate of 1.14 per 100 person‐years. Factors associated with an increased hazard of high FBG included being obese (≥25 kg/m 2 ) compared with normal weight (hazard ratio [HR] = 1.79; 95% confidence interval [CI] 1.31–2.44; p < 0.001) and older age compared with those aged ≤30 years (31–40 years: HR = 1.47; 95% CI 1.08–2.01; 41–50 years: HR = 2.03; 95% CI 1.42–2.90; ≥51 years: HR = 3.19; 95% CI 2.17–4.69; p < 0.001). Conclusion People living with HIV with BMI >25 kg/m 2 were at increased risk of high FBG. This indicates that regular assessments should be performed in those with high BMI, irrespective of the classification used.
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How this classification was reachedexpand
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 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".