{
  "abstract": "Background We aimed to develop a prediction model for LREs in patients with T2DM, given the close relationship between diabetes and liver disease progression.Methods This retrospective cohort study included patients with T2DM from Hong Kong (HK-T2DM cohort) and patients with T2DM and metabolic dysfunction-associated steatotic liver disease (MASLD) from an international multicentre cohort undertaking vibration-controlled transient elastography (VCTE) examination (VCTE-Prognosis-T2DM cohort). The HK-T2DM cohort was randomly divided in an 80:20 ratio to form the development and internal validation cohorts. The VCTE-prognosis-T2DM cohort was used for external validation. The DM-LRE score was derived from 11 features including cirrhosis and routine serum markers at baseline ( IDDF2026-ABS-0242 Figure 1. SHAP importance and honeycomb diagram of DM-LRE score). GBM demonstrated the highest concordance index (C-index) and time-dependent area under the receiver operating characteristic curve among 3 potential machine learning algorithms in predicting LREs. The score was applied in risk stratification and a two-step algorithm followed by liver stiffness measurement (LSM) using cutoffs at 80% sensitivity and specificity.Results 575,000 patients were included in the HK-T2DM cohort (mean age 61.9 years; 52.3% males), while 3,587 patients were included in the VCTE-Prognosis-T2DM cohort (mean age 56.0 years; 56.8% males). In the development and external validation cohorts, the C-indices of the DM-LRE score in predicting 5-year LRE were 0.752 (95% CI: 0.742-0.763) and 0.864 (95% CI: 0.806-0.926), respectively, outperforming the Fibrosis-4 index (FIB-4, C-indices: 0.674 and 0.830) and XgBoost and RandomForest models. The DM-LRE score displayed a comparable C-index to LSM in the external cohort (0.864 vs. 0.866).Patients were stratified into low-, intermediate- and high-risk categories by the DM-LRE score with distinct 5-year cumulative incidence (p<0.001, IDDF2026-ABS-0242 Figure 2. 5-year risk-stratified metrics and two-step algorithms using DM-LRE cut-offs at 80% sensitivity and specificity). For the two-step algorithms in the VCTE-prognosis-T2DM cohort, DMLRE demonstrated a comparable negative predictive value to FIB4 (5 years: 99.5% vs. 99.6%), while enabling a larger proportion of patients to be classified as low-risk at the primary step (68.6% vs. 58.3%) and ultimately (83.9% vs. 78.8%), with less than a 0.1% increase in the 5-year LRE incidence in the final low-risk group (IDDF2026-ABS-0242 Figure 2. 5-year risk-stratified metrics and two-step algorithms using DM-LRE cut-offs at 80% sensitivity and specificity).Conclusions The DM-LRE score could identify individuals at risk of LREs in patients with T2DM and facilitate early referral to specialty care.Abstract IDDF2026-ABS-0242 Figure 1Abstract IDDF2026-ABS-0242 Figure 2",
  "authors": [
    {
      "affiliations": [
        "Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Sherlot Juan Song"
    },
    {
      "affiliations": [
        "Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Vincent Wai-Sun Wong"
    },
    {
      "affiliations": [
        "Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Grace Lai-Hung Wong"
    },
    {
      "affiliations": [
        "Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Jimmy Che-To Lai"
    },
    {
      "affiliations": [
        "MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China"
      ],
      "name": "Ming-Hua Zheng"
    },
    {
      "affiliations": [
        "Yonsei University School of Medicine, Korea, South"
      ],
      "name": "Seung Up Kim"
    },
    {
      "affiliations": [
        "MAFLD Research Center, Department of Hepatology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China"
      ],
      "name": "Xiao-Dong Zhou"
    },
    {
      "affiliations": [
        "Section of Gastroenterology, PROMISE, University of Palermo, Italy"
      ],
      "name": "Salvatore Petta"
    },
    {
      "affiliations": [
        "Department of Medical Sciences, Division of Gastroenterology, University of Torino, Italy"
      ],
      "name": "Elisabetta Bugianesi"
    },
    {
      "affiliations": [
        "Yokohama City University, Japan"
      ],
      "name": "Masato Yoneda"
    },
    {
      "affiliations": [
        "UCM Digestive Diseases. Virgen del Rocio University Hospital. SeLiver Group. Institute of Biomedicine of Seville. Ciberehd. Department of Medicine. University of Seville. Seville, Spain"
      ],
      "name": "Manuel Romero-Gomez"
    },
    {
      "affiliations": [
        "Royal Free Hospital, United Kingdom"
      ],
      "name": "Emmanuel Tsochatzis"
    },
    {
      "affiliations": [
        "Roger Williams Institute of Liver Studies, School of Immunology & Microbial Sciences, Faculty of Life Sciences and Medicine, King’s College London, Foundation for Liver Research and King’s College Hospital, London, United Kingdom"
      ],
      "name": "Philip Newsome"
    },
    {
      "affiliations": [
        "Karolinska University Hospital, Sweden"
      ],
      "name": "Hannes Hagström"
    },
    {
      "affiliations": [
        "Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore"
      ],
      "name": "George Goh"
    },
    {
      "affiliations": [
        "Gastroenterology and Hepatology Unit, Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia"
      ],
      "name": "Wah Kheong Chan"
    },
    {
      "affiliations": [
        "Department of Gastroenterology and Hepatology, Hospital Universitario Puerta de Hierro, Universidad Autonoma de Madrid, Madrid, Spain"
      ],
      "name": "José-Luis Calleja"
    },
    {
      "affiliations": [
        "Departement of Hepato-Gastroenterology and Digestive Oncology, Angers University Hospital, Angers, France"
      ],
      "name": "Jerome Boursier"
    },
    {
      "affiliations": [
        "Stravitz-sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, United States"
      ],
      "name": "Arun J Sanyal"
    },
    {
      "affiliations": [
        "Center for Fatty Liver, Department of Gastroenterology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China"
      ],
      "name": "Jiangao Fan"
    },
    {
      "affiliations": [
        "University of Paris-VII, France"
      ],
      "name": "Laurent Castera"
    },
    {
      "affiliations": [
        "Echosens, France"
      ],
      "name": "Victor de Ledinghen"
    },
    {
      "affiliations": [
        "Medical Data Analytics Centre, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China"
      ],
      "name": "Terry Cheuk-Fung Yip"
    }
  ],
  "title": "IDDF2026-ABS-0242 DM-LRE score: gradient boosting machine (GBM)-based machine learning model in predicting liver-related events (LRES) in type 2 diabetes mellitus (T2DM)",
  "uid": "51e1ff4d-c171-5bcb-b1eb-f4422efb5b95"
}