{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Life Sciences, University of Westminster, London, United Kingdom",
    "Department of Nephrology, Etlik City Hospital, Ankara, Türkiye",
    "Department of Nephrology, University of Health Sciences, Etlik City Hospital, Ankara, Türkiye",
    "Department of Public Health, Faculty of Medicine Hacettepe University, Ankara, Türkiye"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
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        0
      ],
      "name": "Berrak Itır Aylı",
      "orcid": "0000-0002-0834-900X"
    },
    {
      "affs": [
        1
      ],
      "name": "Çiğdem Ikhlef"
    },
    {
      "affs": [
        2
      ],
      "name": "Ebru Gök Oğuz"
    },
    {
      "affs": [
        3
      ],
      "name": "Nüket Paksoy"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/88qjqqeyb87ht.bib",
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  "doi": "10.1371/journal.pone.0349639",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13734485/",
  "id": "88qjqqeyb87ht",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0349639",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Albumins"
    ],
    [
      "heading",
      "Anatomy"
    ],
    [
      "heading",
      "Biochemistry"
    ],
    [
      "heading",
      "Biology and Life Sciences"
    ],
    [
      "heading",
      "Biology and life sciences"
    ],
    [
      "heading",
      "Body Fluids"
    ],
    [
      "heading",
      "Body Weight"
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      "heading",
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    [
      "heading",
      "Diabetes Mellitus"
    ],
    [
      "heading",
      "Diabetes diagnosis and management"
    ],
    [
      "heading",
      "Diabetic Retinopathy"
    ],
    [
      "heading",
      "Diagnostic medicine"
    ],
    [
      "heading",
      "Endocrine Disorders"
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      "heading",
      "Endocrinology"
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      "Eye Diseases"
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      "HbA1c"
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      "Hemoglobin"
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    ],
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      "heading",
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    ],
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    ],
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  "pmcid": "PMC13734485",
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  "published": "2026-11-04",
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      "field": "biochemistry",
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  "title": "Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods",
  "title_text": "Diabetes and all-cause mortality in hemodialysis: A comparative survival analysis with complementary machine-learning methods",
  "type": "peer-reviewed",
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