Incidence and risk factors of type 2 diabetes mellitus in an overweight and obese population: a long-term retrospective cohort study from a Gulf state
Bibliographic record
Abstract
OBJECTIVES: A high body mass index (BMI) is associated with risk of type 2 diabetes mellitus (DM). The United Arab Emirates (UAE) is experiencing a marked increase in obesity. Nonetheless, no data are available regarding the incidence of type 2 DM in the high-risk adult UAE population. Therefore, this study aimed to evaluate the incidence rate and risk of developing type 2 DM among individuals with above-normal BMI in the UAE. DESIGN: A retrospective cohort study. SETTING: Outpatient clinics at a tertiary care centre in Al Ain, UAE. PARTICIPANTS: Three hundred and sixty-two overweight or obese adult UAE nationals who visited outpatient clinics between April 2008 and December 2008. PRIMARY OUTCOME MEASURE: Patients with type 2 DM were identified based on diagnosis established by a physician or through glycated haemoglobin (HbA1c) levels ≥6.5% during the follow-up period (until April 2018). RESULTS: The overall incidence rate of type 2 DM during the median follow-up time of 8.7 years was 16.3 (95% CI 12.1 to 21.4) cases per 1000 person-years. Incidence rates in men and women were 17.7 (95% CI 11.6 to 25.9) and 15.0 (95% CI 9.8 to 22.2) cases per 1000 person-years, respectively. Multivariable Cox proportional hazard analysis determined older age and obesity in women and pre-diabetes in men to be independent risk factors for developing type 2 DM. CONCLUSIONS: The incidence rate of type 2 DM in overweight and obese UAE nationals is high. In addition to screening, current strategies should strongly emphasise lifestyle modifications to decrease HbA1c and BMI levels in this high-risk population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".