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Volume 42 Issue 5
May  2026
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Article Navigation >  Journal of Clinical Hepatology  > 2026  >  42(5): 1056-1066

Value of metabolic markers combined with anthropometric indicators in predicting and risk stratification of metabolic dysfunction-associated fatty liver disease and establishment of a nomogram model

DOI: 10.12449/JCH260510
  • 1.

    The First Clinical Medical College of Lanzhou University，Lanzhou 730000，China

  • 2.

    Department of Hepatology & Infectious Diseases Research Laboratory，The First Hospital of Lanzhou University，Lanzhou 730000，China

  • 3.

    Institute of Portal Hypertension，The First Hospital of Lanzhou University，Lanzhou 730000，China

Research funding:

National Natural Science Foundation of China (82360132);

Gansu Provincial Key R&D Program (22YF7FA085);

Gansu Provincial Joint Research Fund (23JRRA1489);

Gansu Provincial Key Project on Traditional Chinese Medicine (GZKZ-2022-7);

Gansu Provincial Key Talent Program (Gan Zu Tong Zi［2024］No.4);

Lanzhou University The First Hospital Internal Fund (ldyyyn2020-02);

Lanzhou University The First Hospital Internal Fund (ldyyyn2020-14);

Beijing iGandan Foundation Artificial Liver Special Fund (iGandanF-1082024-RGG130)

More Information
  • Corresponding author: LI Junfeng， junfenglee@126.com （ORCID： 0000-0002-5638-706X）; ZHANG Liting， lcheneye@163.com （ORCID： 0009-0005-1259-5747）
  • Received Date: 2025-11-21
  • Accepted Date: 2026-02-24
  • Published Date: 2026-05-25
    Abstract
  •   Objective  To develop a novel clinical predictive model for metabolic dysfunction-associated fatty liver disease （MAFLD） based on metabolic markers and anthropometric indicators， and to provide a more effective tool for the early screening and intervention of MAFLD.  Methods  A retrospective analysis was performed for 2 824 individuals who underwent abdominal color Doppler ultrasound at Health Examination Center of The First Hospital of Lanzhou University from January 1， 2024 to January 1， 2025， and at a ratio of 7∶3， they were randomly divided into training set with 1 976 patients and validation set with 848 patients. Clinical data， serological markers， and abdominal ultrasound results were collected from all patients， and triglyceride-glucose （TyG） index， triglyceride-to-high-density lipoprotein cholesterol （TG/HDL-C） ratio， and anthropometric indicators were calculated. The independent samples t-test was used for comparison of normally distributed or approximately normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between groups. The multivariate logistic regression analysis was used to identify independent predictive factors for MAFLD and intermediate- to high-risk MAFLD. Five risk prediction models were established for MAFLD based on the independent influencing factors， and a nomogram was plotted. The receiver operating characteristic （ROC） curve was plotted to assess model performance， and the area under the ROC curve （AUC） was calculated. The calibration curve was used to evaluate the predictive accuracy of the models， and decision curve analysis was used to assess the clinical practicability of the models. These models were then compared with traditional models.  Results  Among the 1 976 individuals in the training set， 937 （47.42%） were diagnosed with MAFLD， and 423 （21.41%） were diagnosed with intermediate- to high-risk MAFLD； among the 848 individuals in the validation set， 406 （47.88%） were diagnosed with MAFLD. The multivariate logistic regression analysis showed that male sex （odds ratio ［OR］=0.23， 95% confidence interval ［CI］： 0.13 — 0.39， P<0.05）， waist circumference （OR=1.11， 95%CI： 1.06 — 1.17， P<0.05）， alanine aminotransferase （ALT） >40 U/L （OR=2.24， 95%CI： 1.44 — 3.51， P<0.05）， high-density lipoprotein cholesterol （HDL-C） （OR=0.07， 95%CI： 0.04 — 0.15， P<0.05）， TyG index （OR=8.27， 95%CI： 5.09 — 13.44， P<0.05）， TG/HDL-C ratio （OR=0.84， 95%CI： 0.71 — 0.99， P<0.05）， A Body Shape Index （ABSI） （OR=0.45， 95%CI： 0.39 — 0.52， P<0.05）， and body roundness index （BRI） （OR=2.31， 95%CI： 1.50 — 3.55， P<0.05） were independent influencing factors for MAFLD， and male sex （OR=0.17， 95%CI： 0.10 — 0.31， P<0.05）， age （OR=1.09， 95%CI： 1.07 — 1.11， P<0.05）， hemoglobin （OR=0.98， 95%CI： 0.97 — 0.98， P<0.05）， platelet count （OR=0.81， 95%CI： 0.70 — 0.93， P<0.05）， fasting blood glucose （OR=0.80， 95%CI： 0.71 — 0.89， P<0.05）， triglycerides （OR=0.14， 95%CI： 0.07 — 0.29， P<0.05）， TG/HDL-C ratio （OR=0.78， 95%CI： 0.67 — 0.91， P<0.05）， TyG index （OR=5.26， 95%CI： 3.32 — 8.33）， waist circumference （OR=2.50， 95%CI： 1.72 — 3.61， P<0.05）， ABSI （OR=0.58， 95%CI： 0.51 — 0.66， P<0.05）， and BRI （OR=0.01， 95%CI： 0.00 — 0.21， P<0.05） were independent influencing factors for intermediate- to high-risk MAFLD. Among the five models established， model 5 （incorporating sex， ALT elevation， HDL-C， TyG index， TG/HDL-C ratio， waist circumference， and ABSI） had the best performance， with an AUC of 0.917 （95%CI： 0.905 — 0.929） in the training set and 0.911 （95%CI： 0.892 — 0.930） in the validation set. The calibration curve showed that model 5 had good predictive accuracy， and the decision curve analysis confirmed its clinical practicability.  Conclusion  The predictive model for MAFLD constructed based on metabolic markers and anthropometric indicators has good discriminatory ability and can be used to assess the risk of MAFLD. In addition， this study shows that waist circumference， TyG index， TG/HDL-C ratio， ABSI， and BRI are independently associated with intermediate- to high-risk MAFLD， but further studies are needed to confirm their value in predicting liver fibrosis progression.

     

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