Development of a liver stiffness measurement-based nomogram model to identify early CKD risk in patients with MAFLD

TL;DR

A new model using liver stiffness and other factors helps identify early kidney disease risk in patients with fatty liver disease.

Contribution

A novel nomogram model integrating liver stiffness measurement and clinical factors for early CKD risk detection in MAFLD patients.

Findings

Age, liver stiffness, cholesterol, and diabetes are independent risk factors for CKD in MAFLD patients.

The nomogram model achieved an AUC of 0.899 for early CKD detection in MAFLD patients.

The model showed good discrimination and calibration in both training and validation sets.

Abstract

To identify independent relevant factors for chronic kidney disease (CKD) in patients with metabolic dysfunction-associated fatty liver disease (MAFLD) and to develop and validate a nomogram-based risk diagnostic model for MAFLD-CKD incorporating liver stiffness measurement (LSM).

Clinical data from 3,154 patients with fatty liver disease attending our hospital between January 2024 and September 2025 were collected. According to the 2024 guidelines for the prevention and treatment of metabolic dysfunction-associated (non-alcoholic) fatty liver disease, a total of 1,328 MAFLD patients were ultimately included. Body mass index (BMI) was calculated, and controlled attenuation parameter (CAP) and LSM were measured using transient elastography (TE). Logistic regression analysis was employed to screen for independent relevant factors identifying MAFLD-CKD risk, which were used to construct a nomogram model. The study subjects were randomly divided into a training set (n = 930) and a validation set (n = 398) at a 7:3 ratio for internal validation of the model’s feasibility. The model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and the Hosmer-Lemeshow goodness-of-fit test.

Age, LSM, ALT, AST, total cholesterol (TC), triglycerides (TG), diabetes mellitus (DM), and fasting plasma glucose (FPG) were significantly higher in the MAFLD-CKD group compared to the MAFLD-only group. Multivariate logistic regression revealed that Age, LSM, TC, and the presence of DM were independent relevant factors for CKD in MAFLD patients (all P < 0.01). The diagnostic model combining Age, LSM, TC, and DM status achieved an AUC of 0.899 (95% CI: 0.882–0.917) for early identification of MAFLD-CKD, with a sensitivity of 0.71 and a specificity of 0.80, significantly outperforming any single indicator. A nomogram diagnostic model was successfully developed based on these variables. In the diagnostic model, the AUC for identifying MAFLD-CKD occurrence was 0.91 (95% CI: 0.89–0.93) in the training set and 0.88 (95% CI: 0.85–0.92) in the validation set. The Hosmer-Lemeshow test indicated no statistically significant difference between the training and validation sets (P > 0.05).

Age, LSM, TC, and the presence of DM are independent factors associated with CKD in patients with MAFLD. The risk assessment model integrating these factors significantly improves the ability to differentiate MAFLD patients with existing early-stage CKD risk, demonstrating good discriminatory performance. As a non-invasive marker of liver fibrosis, LSM can serve as a practical clinical indicator for identifying the subgroup of MAFLD patients associated with an elevated risk of CKD.

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