Prevalence and factors associated with prediabetes and diabetes mellitus among adults: Baseline findings of PURE Malaysia cohort study
Bibliographic record
Abstract
Background Diabetes mellitus (DM) is a silent killer that is responsible for almost 1.6 million deaths annually, particularly among those who are undiagnosed during its early stage. Prediabetes has become a growing public health concern due to its potential to progress to DM. This study thus aimed to determine the prevalence of prediabetes and DM and their associated factors among Malaysian adults. Methods A cross-sectional study was conducted among adults 35–70 years of age residing in rural and urban areas in Malaysia. Blood samples (finger prick test) and physical examinations were conducted on 4982 participants who consented to participate in this study. A pre-validated questionnaire consisting of the International Physical Activity Questionnaire and medical history was used to assess physical activity level and family history of DM, respectively. Multinomial logistic regression models were used to identify the factors associated with prediabetes and DM. Results The prevalence of prediabetes and DM were 10.8% and 11.9%, respectively. Participants who were ≥50 years old, male, Malay, or physically inactive or had hypertension or a family history of DM had higher odds of having prediabetes and DM. Unique to DM, individuals with a lower educational level were more likely to have DM. Conclusions Prediabetes health screening is critical in the Malaysian population. Early detection of prediabetes promotes early intervention, including lifestyle modifications, to prevent progression to DM. The factors associated with prediabetes and DM identified in this study will assist in disease prevention and facilitate more efficient management strategies in this population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".