{
  "abstract": "Background\n\nDespite strong evidence supporting the benefits of sodium-glucose cotransporter-2 inhibitor (SGLT2i) for managing type 2 diabetes, heterogeneity of treatment response is rarely assessed. This study aims to demonstrate the heterogeneity effects of SGLT2i for preventing chronic kidney disease (CKD) via a causal machine learning model.Methods\n\nA retrospective cohort study was conducted. Data from 2,280 type 2 diabetes patients were used to develop a double/debiased machine learning (DDML) model for CKD prediction; a conventional treatment effect (TE) model was also constructed for comparison. Model performance was evaluated using area under the receiver operating characteristic curve, and calibration plot. Subsequently, average treatment effects (ATEs) and conditional average treatment effects (CATEs) of SGLT2i versus non-SGLT2i were estimated and compared among models.Results\n\nA neural network (NN) was selected for the DDML framework for CKD prediction. Although the conventional TE model demonstrated slightly better discrimination and calibration, the DDML-NN produced comparable results. The ATEs (95% confidence interval) estimated by DDML-NN and conventional TE models were -0.0399 (-0.0600, -0.0199) and - 0.0424 (- 0.0616, - 0.0232), respectively. Corresponding CATE ranges were -0.3945 to 0.2495 and - 0.6968 to 0.0026. Patients gaining higher benefits from SGLT2i trended to be older, male, and overweight, compared to who were benefited less from SGLT2i.Conclusion\n\nThe protective effect of SGLT2i against CKD was indicated, albeit heterogeneous. Counterfactual models including DDML-NN and conventional TE models demonstrated similar performance. However, data from a larger cohort are still required to draw a firm conclusion.",
  "abstract_shown": true,
  "affiliations": [
    "Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.",
    "Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Mahidol University 4Th Floor Sukho Place Building 218/11 Sukhothai Road, Dusit, Bangkok, 10300, Thailand. panu.loo@mahidol.ac.th.",
    "Research Affairs, Faculty of Medicine, Chulalongkorn University 10, Floor Mongkut-Phetcharat Research and Service Innovation Building, King Chulalongkorn Memorial Hospital, 1873 Ratchadamri Road, Pathum Wan, Bangkok, 10330, Thailand. amarit.t@chula.ac.th.",
    "Department of Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Mahidol University 4Th Floor Sukho Place Building 218/11 Sukhothai Road, Dusit, Bangkok, 10300, Thailand.",
    "Department of Information Technology, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.",
    "Siriraj Informatics and Data Innovation Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.",
    "School of Medicine and Public Health, and Hunter Medical Research Institute, University of Newcastle, New Lambton, NSW, Australia.",
    "Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen's University, Belfast, UK."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Tanawan Kongmalai"
    },
    {
      "affs": [
        1
      ],
      "name": "Panu Looareesuwan"
    },
    {
      "affs": [
        2
      ],
      "name": "Amarit Tansawet",
      "orcid": "0000-0002-2040-7970"
    },
    {
      "affs": [
        3
      ],
      "name": "Sukanya Siriyotha"
    },
    {
      "affs": [
        4
      ],
      "name": "Patratorn Kunakorntham"
    },
    {
      "affs": [
        5
      ],
      "name": "Burin Boonwatcharapai"
    },
    {
      "affs": [
        6
      ],
      "name": "John Attia"
    },
    {
      "affs": [
        7
      ],
      "name": "Gareth J McKay"
    },
    {
      "affs": [
        3
      ],
      "name": "Ammarin Thakkinstian"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1b5dcmaznya8f.bib",
  "cited_by": 0,
  "doi": "10.1186/s13098-026-02249-2",
  "fulltext_url": "https://europepmc.org/articles/PMC13689252",
  "id": "1b5dcmaznya8f",
  "issns": [
    "1758-5996"
  ],
  "landing_url": "https://europepmc.org/article/MED/42908014",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RC620-627"
    ],
    [
      "openalex",
      "2712|Diabetes Treatment and Management"
    ]
  ],
  "openalex": "W7171301069",
  "pdf_url": "https://europepmc.org/articles/PMC13689252?pdf=render",
  "pmcid": "PMC13689252",
  "pmid": "42908014",
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/1b5dcmaznya8f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:57Z",
      "native_id": "MED:42908014",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42908014"
    },
    {
      "first_seen": "2026-10-30T13:46:12Z",
      "native_id": "c8935014c25044f99f87016de8b4b36e",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s13098-026-02249-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2712|Diabetes Treatment and Management",
      "subfield": "endocrinology-metabolism"
    }
  ],
  "title": "Heterogeneity in the treatment effect of sodium-glucose cotransporter-2 inhibitor on chronic kidney disease risk reduction: a prevalent new user cohort study applying double/debiased machine learning.",
  "title_text": "Heterogeneity in the treatment effect of sodium-glucose cotransporter-2 inhibitor on chronic kidney disease risk reduction: a prevalent new user cohort study applying double/debiased machine learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:12Z",
  "url": "https://research.draconitic.com/paper/1b5dcmaznya8f",
  "venue": "Diabetology & metabolic syndrome",
  "versions": []
}