Key result
Novel 8-variable nomogram accurately identifies MASLD in T2DM with ~0.83 external validation AUC.
Why the study?
Does a diagnostic nomogram based on BMI and routine serological indicators accurately identify metabolic dysfunction-associated steatotic liver disease (MASLD) in hospitalized patients with type 2 diabetes mellitus?
Case-Control (n=770)
Internal validation set remained blinded throughout model construction
Stratified random sampling into training and internal validation sets at a 7:3 ratio (random seed 123)
No
Does a diagnostic nomogram based on BMI and routine serological indicators accurately identify metabolic dysfunction-associated steatotic liver disease (MASLD) in hospitalized patients with type 2 diabetes mellitus?
Answers are grounded in this paper and cite their sources.
A nomogram integrating BMI and seven routine blood biomarkers enables accurate, non-invasive screening for MASLD among hospitalized T2DM patients without requiring specialized imaging or invasive biopsy.
Objective: The prevalence of type 2 diabetes mellitus (T2DM) with concurrent Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) is high. However, simple, efficient and non-invasive diagnostic tools based on routine serological indicators are lacking. This study aims to construct and validate a simple diagnostic model using routine serological indicators. Methods: A retrospective case-control study is conducted, enrolling 470 patients with T2DM. They are randomly stratified into a training set and a validation set at a 7:3 ratio. Independent predictive factors are identified using univariate and multivariate logistic regression. Diagnostic performance is assessed via receiver operating characteristic (ROC) curves. A nomogram diagnostic model is constructed and evaluated using ROC curves, calibration curves and decision curve analysis. For external validation, an additional 300 T2DM patients are recruited. Results: Body mass index (BMI), fasting C-peptide, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), urea and alanine aminotransferase (ALT) are independent predictors of comorbid T2DM and MASLD. The area under the curve (AUC) of this model in the training set, internal validation set and external validation set is 0.921, 0.913 and 0.830, respectively. The calibration curve closely approximates the ideal diagonal line and decision curve analysis (DCA) demonstrates significant clinical net benefit across a broad range of threshold probabilities. Conclusion: This model enables high-precision diagnosis of T2DM with MASLD using routinely available serological indicators, avoiding invasive procedures and exhibiting strong potential for clinical translation. Future studies should validate its generalizability in multicenter prospective cohorts to promote its use as a routine screening tool for MASLD in T2DM patients.
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Wu et al. (2026) conducted a case-control in Type 2 diabetes mellitus comorbid with metabolic dysfunction-associated steatotic liver disease (MASLD) (n=770). Diagnostic nomogram model based on BMI and 7 routine serological indicators (fasting C-peptide, TC, TG, HDL, LDL, urea, ALT) vs. Abdominal ultrasonography diagnosis of MASLD (230 cases vs 240 non-cases in primary cohort; 150 cases vs 150 non-cases in external validation cohort) was evaluated on Diagnostic discrimination (AUC) for comorbid MASLD in T2DM patients (AUC 0.921 (training), 0.913 (internal validation), 0.830 (external validation), 95% CI 0.895-0.947). A nomogram combining BMI and seven routine serological indicators diagnosed MASLD in T2DM patients with an AUC of 0.921 in the training set, 0.913 in internal validation, and 0.830 in external validation.
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