Overweight and abdominal obesity as determinants of undiagnosed diabetes and pre-diabetes in Bangladesh
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes and pre-diabetes are an increasing pandemic globally and often remain undiagnosed long after onset in low-income settings. The objective of this study is to assess the determinants and prevalence of undiagnosed diabetes and pre-diabetes among adults in Bangladesh. METHODS: In an exploratory study, we performed oral glucose tolerance test on 1243 adults ≥20 years of age from urban Mirpur, Dhaka (n = 518) and rural Matlab, Chandpur (n = 725) who had never been diagnosed with diabetes or pre-diabetes. We collected data on socioeconomic, demographic, past medical history, physical activity, and measured weight, height, waist and hip circumferences, and blood pressure. Risk factors associated with undiagnosed diabetes and pre-diabetes were examined using a multiple logistic regression model. RESULTS: Overall prevalence of diabetes and pre-diabetes was 6.6 % (95 % CI 5.3, 8.1) and 16.6 % (14.5, 18.7) respectively, with both being significantly higher in urban than rural populations (diabetes 12.2 % vs 2.6 % respectively, p < 0.000; pre-diabetes 21.2 % vs 13.2 %, p < 0.001). After adjustment the variables, urban residence (OR 2.5 [95 % CI 1.02, 5.9]), age group 40-59 y (2.9 [1.7-5.2]), ≥60 y (8.1 [2.8-23.8]), overweight (2.2 [1.3-3.9]), abdominal obesity (3.3 [1.8-6.0]) and high WHR 5.6 (2.7-11.9) were all significant predictors of diabetes. Significant predictors of pre-diabetes included age group 40-59 (1.6 [1.1-2.2]), female sex (1.5 [1.0-2.2]), abdominal obesity (1.7 [1.2-2.4]) and high WHR (1.6 [1.2-2.3]). CONCLUSION: Both overweight and abdominal obesity contribute to the hidden public health threat of undiagnosed diabetes and pre-diabetes. Awareness raising and screening of high risk groups combined with a tailored approach are essential for halting the epidemic of diabetes and pre-diabetes in Bangladesh.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".