Skip to content
DraconiticResearch
Search Sources API
197,768 studies31 sourcesUpdated 5 min ago
Peer-reviewedCC BY 4.0PLOS One

Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods

  • Berrak Itır Aylı1
  • Çiğdem Ikhlef2
  • Ebru Gök Oğuz3
  • Nüket Paksoy4
4 affiliations
  1. 1Faculty of Life Sciences, University of Westminster, London, United Kingdom
  2. 2Department of Nephrology, Etlik City Hospital, Ankara, Türkiye
  3. 3Department of Nephrology, University of Health Sciences, Etlik City Hospital, Ankara, Türkiye
  4. 4Department of Public Health, Faculty of Medicine Hacettepe University, Ankara, Türkiye
Read full textDOICite

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.

Related

  1. Blood and saliva provide complementary but non-redundant host transcriptomic responses to cancer
    Srinivas V Koduru, Mark Kidd, Abdel Halim
    Scientific ReportsPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  2. Mapping the Nonhistone Interactome of the TRIM24 Bromodomain Using Genetically Encoded Complementary Photocrosslinkers
    I. Bardhan, S. Chakraborty, N. S. Ponnaganti +1
    bioRxivPreprintAll rights reservedFull textMolecular biology & gene regulationbioRxiv
  3. Complementary single-nucleus and single-cell RNA sequencing reveal tumor state evolution and microenvironmental rewiring in NSCLC
    Gyeong-Jin Shin, Huiram Kang, Areum Jo +4
    Communications BiologyPeer-reviewedCC BY-NC-ND 4.0Full textMolecular biology & gene regulationCrossref
  4. Complementary roles of human protamine 1 and spermine in giant DNA compaction
    T. Nishio, Y. Kato
    bioRxivPreprintCC BYFull textBiophysicsbioRxiv
  5. Leukocyte Regulation by Exercise and Complementary Lifestyle Factors in Cardiovascular Health
    Xiaoya Xu, Shiyi Wang, Kangning Liu +1
    MetabolitesPeer-reviewedCC BY 4.0Proteomics & metabolomicsCrossref

Cite

BibTeXRIS
BibTeX
@article{aylı2026diabetes,
  title = {{Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods}},
  author = {Berrak Itır Aylı and Çiğdem Ikhlef and Ebru Gök Oğuz and Nüket Paksoy},
  journal = {PLOS One},
  year = {2026},
  month = nov,
  doi = {10.1371/journal.pone.0349639},
  url = {https://doi.org/10.1371/journal.pone.0349639},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods
AU  - Berrak Itır Aylı
AU  - Çiğdem Ikhlef
AU  - Ebru Gök Oğuz
AU  - Nüket Paksoy
PY  - 2026
DA  - 2026/11/04
JO  - PLOS One
DO  - 10.1371/journal.pone.0349639
UR  - https://doi.org/10.1371/journal.pone.0349639
AB  - 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.
SN  - 1932-6203
ER  -