Development and validation of a nomogram for predicting metabolic-associated fatty liver disease in the Chinese physical examination population

• Published in: Lipids in health and disease Vol. 22; no. 1; pp. 85 - 12
• Main Authors: Zhou, Bingqian, Gong, Ni, Huang, Xinjuan, Zhu, Jingchi, Qin, Chunxiang, He, Qingnan
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 29.06.2023; BioMed Central; BMC
• Subjects: Alanine transaminase; Analysis; Blood pressure; Body mass index; Body measurements; Care and treatment; Cholesterol; Data collection; Datasets; Diabetes; Diagnosis; Examinations; Fatty liver; Glucose; High density lipoprotein; Hypertension; Liver diseases; MAFLD; Metabolism; Methods; Nomogram; Nomograms; Nomography (Mathematics); Overweight; Periodic health examinations; Physical diagnosis; Physical examination; Population; Regression analysis; Risk prediction; Self report; Triglycerides; Ultrasonic imaging; Uric acid; Validity; Variables; Womens health; China; United States > US; MAFLD; Risk prediction; Nomogram; Physical examination
• ISSN: 1476-511X; 1476-511X
• DOI: 10.1186/s12944-023-01850-y

We aim to develop and validate a nomogram including readily available clinical and laboratory indicators to predict the risk of metabolic-associated fatty liver disease (MAFLD) in the Chinese physical examination population. The annual physical examination data of Chinese adults from 2016 to 2020 were retrospectively analyzed. We extracted the clinical data of 138 664 subjects and randomized participants to the development and validation groups (7:3). Significant predictors associated with MAFLD were identified by using univariate and random forest analyses, and a nomogram was constructed to predict the risk of MAFLD based on a Lasso logistic model. Receiver operating characteristic curve analysis, calibration curves, and decision curve analysis were used to verify the discrimination, calibration, and clinical practicability of the nomogram, respectively. Ten variables were selected to establish the nomogram for predicting MAFLD risk: sex, age, waist circumference (WC), uric acid (UA), body mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting plasma glucose (FPG), triglycerides (TG), and alanine aminotransferase (ALT). The nomogram built on the nonoverfitting multivariable model showed good prediction of discrimination (AUC 0.914, 95% CI: 0.911-0.917), calibration, and clinical utility. This nomogram can be used as a quick screening tool to assess MAFLD risk and identify individuals at high risk of MAFLD, thus contributing to the improved management of MAFLD.