ISImatsuda as a potential predictor of metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus - Liu, Jing; Wang, Yueqiu; Zhou, Xinghang; Wen, Zaixin; Chen, Yu; Sun, Yiqiong; Su, Shuaiying; Lin, Weiwei; Shen, Ruiting; Sun, Xiaoyu; Li, Hongru; Yu, Xia; et al. (Frontiers in Medicine, 2025)
BackgroundThis study aimed to determine the most efficacious insulin resistance (IR) indices to predict metabolic dysfunction-associated steatotic liver disease (MASLD) in patients with type 2 diabetes mellitus (T2DM).MethodsThis cross-sectional study included 1,587 patients with T2DM. MASLD was defined by abdominal ultrasound findings. Liver fibrosis risk was assessed with FIB-4. All participants underwent a 100 g standard steamed bread meal test. We analyzed basal IR indices (HOMA-IR, QUICKI, IAI, Bennett ISI) and post-stimulation IR indices (ISImatsuda, ISI0,120) to explore their associations with MASLD and liver fibrosis.ResultsParticipants were categorized into four groups according to IR indices quartiles. Among post-stimulation IR indices, MASLD detection rates in ISImatsuda Q1–Q4 groups were 65.7, 54.2, 37.0, and 22.2%, respectively. Logistic regression analysis revealed significantly increased odds ratios (ORs) for MASLD in ISImatsuda Q1-Q3 groups compared to the Q4 group (OR = 3.63, 2.53, and 1.53, respectively; all p < 0.05). Similar results were observed across other IR indices (all p < 0.05). There were no statistically significant differences in the detection rates of liver fibrosis or the ORs among the quartile groups of the IR indices (all p > 0.05). ROC curve analysis showed that ISImatsuda had superior predictive power for MASLD in patients with T2DM (AUC = 0.701). Based on these findings, a risk prediction model for MASLD in the T2DM population was constructed using age, body mass index (BMI), alanine aminotransferase (ALT), triglycerides (TG), and 2-h postprandial C-peptide (2 h CP).ConclusionAmong the IR indices, ISImatsuda demonstrated the strongest correlation and highest predictive value for MASLD in T2DM.