Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods
Year: 2026
Type: article
Abstract: Background Diabetes mellitus is the leading cause of end-stage renal disease and confers excess mortality in patients on hemodialysis. However, which diabetes-specific clinical parameters best predict survival remains insufficiently explored. Methods This retrospective cohort study followed 252 patients receiving maintenance hemodialysis (126 with diabetes, 126 without) from three centres in Ankara, Turkey, over 68 months (January 2020–September 2025; 66 deaths). Mortality predictors were assessed using Cox proportional hazards models, propensity score–weighted regression, and gradient-boosted machine-learning survival analysis. Eleven diabetes-specific parameters were examined within the diabetes subgroup. Results Diabetes was associated with reduced survival (log-rank p = 0.038) and a 68% higher mortality hazard (HR 1.68, 95% CI 1.02–2.75). After multivariable adjustment, the association was attenuated but directionally consistent across all specifications (HR 1.52–1.66). Within the diabetes subgroup, HbA1c was the strongest modifiable predictor (adjusted HR 1.43 per 1%, 95% CI 1.17–1.76, p < 0.001), improving model discrimination (C-index 0.679 vs. 0.652). No other diabetes-specific parameter, including retinopathy, diabetic foot, and diabetes duration, independently predicted mortality. Conclusion In this Turkish hemodialysis cohort, diabetes showed a consistent adverse trend toward higher mortality. HbA1c was the dominant modifiable predictor, supporting the integration of structured glycaemic monitoring into routine hemodialysis care.
Source: PLoS ONE
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W7225235157