{
  "abstract": "BackgroundTraditional real-world studies comparing SGLT2 and DPP4 inhibitors on renal outcomes rely on propensity score matching, which causes high-dimensional data loss. We used causal machine learning (Causal ML) to unmask heterogeneous treatment effects in diabetic kidney disease (DKD).\n\nMethodsUsing data from 4,588 patients within the Japanese J-CKD-DB-Ex registry, we implemented a doubly robust (DR) learning framework (Linear DR-learner with XGBoost) to compare SGLT2 and DPP4 inhibitors. Outcomes included the chronic eGFR slope and a composite renal endpoint ( 50% eGFR decline or end-stage kidney disease). Heterogeneity was explored via causal SHAP and decision trees.\n\nResultsAt the population level, SGLT2 inhibitors modestly slowed chronic eGFR decline (average treatment effect [ATE] = 0.14 [95% CI: -0.86, 1.15] mL/min/1.73m{superscript 2}/year) and reduced composite endpoint risk by 9% (ATE: -0.09 [-0.11, -0.08]) versus DPP4 inhibitors. However, individual-level counterfactual analysis suggested that for the chronic eGFR slope, non-glinide users with stable pre-treatment trajectories who were also taking ACE inhibitors had a greater benefit from SGLT2 inhibitors (ATE: 2.95 [-0.68, 6.58]). Conversely, glinide users with steep pre-treatment decline had a greater benefit from DPP4 inhibitors (ATE: -8.98 [-16.11, -1.85]). For composite renal events, SGLT2 inhibitors had a 28% absolute risk reduction within the algorithmically identified high-risk subgroup (eGFR 28.1 mL/min/1.73 m{superscript 2} and positive proteinuria; ATE: -0.28 [-0.33, -0.23]). Even non-proteinuric decliners demonstrated a 8% risk reduction with SGLT2 inhibitors (ATE: -0.08 [-0.10, -0.06]).\n\nConclusionCausal ML advances precision medicine in DKD, shifting from uniform prescribing to individualized, data-driven therapy targeting distinct intrarenal pathways.",
  "abstract_shown": true,
  "affiliations": [
    "Juntendo University Faculty of Medicine"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Y. Yano"
    },
    {
      "name": "H. Nagasu"
    },
    {
      "name": "K. Hiroshi"
    },
    {
      "name": "M. Ohashi"
    },
    {
      "name": "Y. Isaka"
    },
    {
      "name": "H. Okada"
    },
    {
      "name": "M. Nangaku"
    },
    {
      "name": "N. Kashihara"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dstee495xassg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.30.26361750",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361750v1.full",
  "id": "dstee495xassg",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361750v1",
  "language": null,
  "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": [
    [
      "medrxiv",
      "health informatics"
    ],
    [
      "openalex",
      "2727|Chronic Kidney Disease and Diabetes"
    ]
  ],
  "openalex": "W7208735589",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361750v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-30",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dstee495xassg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:39Z",
      "native_id": "10.64898/2026.08.30.26361750",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.08.30.26361750v1"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "medrxiv:health informatics",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Individual-Level Counterfactual Analysis of SGLT2 Inhibitors Versus DPP4 Inhibitors in Diabetic Kidney Disease Using Causal Machine Learning",
  "title_text": "Individual-Level Counterfactual Analysis of SGLT2 Inhibitors Versus DPP4 Inhibitors in Diabetic Kidney Disease Using Causal Machine Learning",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:39Z",
  "url": "https://research.draconitic.com/paper/dstee495xassg",
  "venue": "medRxiv",
  "versions": []
}