To investigate the risk factors for significant liver fibrosis in patients with subclinical hypothyroidism (SCH) and metabolic associated fatty liver disease (MASLD), and to develop and validate a nomogram prediction model.

A retrospective cohort of 242 patients diagnosed with SCH and MASLD at Yongzhou Central Hospital between December 2022 and December 2025 was enrolled. All patients underwent transient elastography (FibroScan) to obtain liver stiffness measurements (LSM) and were accordingly divided into significant liver fibrosis group (n=68) and non-significant liver fibrosis group (n=174). Clinical data of the two groups were subjected to Lasso regression for dimensionality reduction, followed by multivariable logistic regression to identify independent predictors of significant liver fibrosis, and a nomogram prediction model was subsequently constructed. The discrimination and calibration of the model were evaluated using receiver operating characteristic (ROC) curves, calibration plots, and the Hosmer-Lemeshow test. Internal validation was performed using the Bootstrap method (1 000 resamples) to assess model stability.

Body mass index (BMI), thyroidstimulating hormone (TSH), triglyceride (TG), homeostasis model assessment of insulin resistance (HOMAIR), gammaglutamyltransferase (GGT), controlled attenuation parameter (CAP), and platelet count in the significant liver fibrosis group were higher than those in the nonsignificant liver fibrosis group, and the proportion of type 2 diabetes mellitus was also increased (all P<0.05). TSH, HOMA-IR, GGT, platelet count, and CAP were identified as independent influencing factors for significant liver fibrosis (all P<0.05). The nomogram model integrating the above five indicators achieved an AUC of 0.955, which was superior to each individual indicator (all P<0.05), with a sensitivity of 89.7% and a specificity of 92.0%.

The nomogram incorporating TSH, HOMA-IR, GGT, platelet count, and CAP provides an accurate estimate of the probability of significant liver fibrosis in patients with SCH and MASLD. The ROC and calibration analyses confirm its satisfactory discrimination and goodness-of-fit. This model offers good clinical applicability and may serve as a practical reference tool for early screening and risk stratification in this population.