{
  "abstract": "Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is a leading chronic liver disorder closely linked to diabetes mellitus (DM) and its cardiovascular and renal complications. Early identification of diabetes risk in this population is essential for timely intervention.Objective To develop machine learning (ML) models to predict diabetes risk in individuals with MASLD and to identify key predictive factors using a nationally representative dataset.Methods Data from 6310 MASLD participants (2007–2018) were analysed and classified into DM and non-DM groups. Feature selection was performed using Random Forest, Least Absolute Shrinkage and Selection Operator and Support Vector Machine Recursive Feature Elimination. Based on selected features, nine ML models were developed. Model performance was evaluated using accuracy, sensitivity, area under the curve, F1 score, Rank Score and Brier Score. SHapley Additive exPlanations (SHAP) were used for interpretability.Results Eight key variables (age, urinary albumin (Ualb), total cholesterol (TC), lipid accumulation product (LAP), urinary creatinine, white blood cell count, uric acid and Visceral Adiposity Index) were identified and used for model construction. Among nine algorithms, the Light Gradient Boosting Machine (LightGBM) model showed superior predictive performance. SHAP analysis revealed that Ualb, age, TC and LAP were the most influential predictors.Conclusion Our ML-based model effectively identifies individuals with MASLD at high risk for developing DM. The LightGBM algorithm outperformed other models in both accuracy and interpretability. Key predictors such as Ualb and LAP highlight the importance of renal and metabolic markers in early diabetes risk prediction, offering a new approach for individualised intervention and clinical decision-making.",
  "authors": [
    {
      "affiliations": [
        "People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China"
      ],
      "name": "Sijie Yang"
    },
    {
      "affiliations": [
        "Department of Orthopaedic Surgery, Guangxi Diabetic Foot Salvage Engineering Research Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China"
      ],
      "name": "Ruiqing Mo"
    },
    {
      "affiliations": [
        "Department of Orthopaedic Surgery, Guangxi Diabetic Foot Salvage Engineering Research Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China"
      ],
      "name": "Wenqiang Wang"
    },
    {
      "affiliations": [
        "Department of Orthopaedic Surgery, Guangxi Diabetic Foot Salvage Engineering Research Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China",
        "Department of Orthopedics, Xianning Central Hospital, The First Affiliated Hospital of Hubei University of Science and Technology, Xianning, Hubei, China"
      ],
      "name": "Puxiang Zhen"
    },
    {
      "affiliations": [
        "School of Mathematical Science, South China Normal University, Guangzhou, Guangdong, China"
      ],
      "name": "Wentian Han"
    }
  ],
  "title": "Development of a machine learning-based model for prediction of diabetes risk in patients with metabolic dysfunction-associated steatotic liver disease (MASLD)",
  "uid": "dd9a824e-febe-5cd6-b831-5b4ae8afb1d4"
}