Exploring the diabesity characteristics and associated all-cause mortality at a population level: results from a small European island state
Bibliographic record
Abstract
Abstract Aim Diabesity, the co-occurrence of diabetes and obesity, presents a global health crisis. Understanding its prevalence, associated risk factors, and mortality outcomes is crucial for effective public health interventions. This study aims to investigate the prevalence of diabesity and diabetes, assess associated risk factors, and analyze mortality outcomes over a 7-year period in the diabetogenic country of Malta. Subject and methods A nationwide health examination survey (2014–16) was conducted involving 3947 adults aged 18–70 years. Sociodemographic data, anthropometric measurements, and blood samples were collected. Relationships between different adiposity indices were explored. Mortality data was obtained by cross-referencing with the national mortality register. Statistical analyses included chi-square tests, logistic regression, and Cox proportional hazard models. Results Prevalence of obesity was 34.08%, diabetes 10.31%, and diabesity 5.78%. Sociodemographic characteristics were similar across all three cohorts. Multivariable regression identified increasing age (OR 1.10 CI95% 1.07–1.12; p ≤ 0.001), male gender (OR 0.53 CI95% 0.30–0.93; p = 0.03), and low educational level (OR 2.19 CI95% 1.39–3.45; p = 0.001) as significant predictors of diabesity. Only diabetes showed a significant increase in mortality risk (HR 3.15 CI95% 1.31–7.62; p = 0.02) after adjustment, with gender (HR 3.17 CI95% 1.20–8.37) and body adiposity index (HR 1.08 CI95% 1.01–1.16) also significant ( p ≤ 0.05). Conclusion Diabesity represents a substantial public health challenge in Malta, with implications for mortality outcomes. Targeted interventions addressing sociodemographic disparities and promoting healthy lifestyles are essential to mitigate its impact. The findings underscore the need for comprehensive healthcare strategies and policy initiatives to combat diabesity and reduce associated mortality rates.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".