Association between obstructive sleep apnea risk and type 2 diabetes among Emirati adults: results from the UAE healthy future study
Bibliographic record
Abstract
Introduction: Obstructive sleep apnea (OSA) can have negative impacts on the health outcomes of individuals with type 2 diabetes. However, in the United Arab Emirates (UAE), there is a lack of understanding regarding the relationship between OSA and type 2 diabetes despite the significant implications it has on health. The primary objective of this study is to investigate the association between OSA risk and type 2 diabetes, associated risk factors, and gender differences in OSA symptoms among Emirati adults. Methods: We conducted a cross-sectional analysis of the baseline data from the UAE Healthy Future Study (UAEHFS) collected between February 2016 and March 2023. Our sample consisted of 4578 participants aged 18-71 who completed the STOP-BANG survey, provided body measurements and blood samples. We stratified the patients according to their OSA risk and diabetes. We used univariate and multivariate logistic regression models to analyze the relationship between OSA risk and type 2 diabetes and to identify factors associated with risk for OSA and type 2 diabetes. We estimated odds ratios (ORs) with corresponding 95% confidence intervals (95% CI). Results: The mean age was 27.5 years (± 8.35), and 55.81% (n=2555) were men. The overall prevalence of high risk for OSA was 16.58% and was higher in men compared to women (26.46% vs 4.10%). Women reported feeling tired more often than men (68.02% vs 48.96%). Both genders have similar rates of stop breathing and BMI ≥ 35. There was a significant association between the OSA risk and type 2 diabetes in the unadjusted model (OR=2.44; 95% CI: 1.78-3.35; p-value <0.0001) and (OR=6.44; 95% CI: 4.32-9.59; p-value < 0.0001) among those who reported intermediate and high OSA risk, respectively. After adjusting the model for education attainment, marital status, waist circumference, and smoking, the association remained significant between diabetes and OSA risk, with an OR of 1.65 (95%CI: 1.18-2.32; p-value =0.004) for intermediate OSA risk and 3.44 (95%CI: 2.23-5.33; p-value <0.0001) for high OSA risk. Conclusions: This study conducted in the UAE found a significant correlation between OSA risk and type 2 diabetes. We suggest introducing routine screening of OSA for individuals with diabetes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".