Cardiometabolic index (CMI) is a well promising indicator for predicting obesity-related diseases, but its predictive value for metabolic associated fatty liver disease (MAFLD) is unclear. This study aimed to investigate the relationship between CMI and MAFLD and to evaluate the predictive value of CMI for MAFLD.A total of 943 subjects were enrolled in this cross-sectional study. CMI was calculated by multiplying the ratio of triglycerides and high-density lipoprotein cholesterol (TG/HDL-C) by waist-to-height ratio (WHtR). Multivariate logistic regression analysis was used to systematically evaluate the relationship between CMI and MAFLD. Receiver operating characteristic (ROC) curves were used to assess the predictive power of CMI for MAFLD and to determine the optimal cutoff value. The diagnostic performance of high CMI for MAFLD was validated in 131 subjects with magnetic resonance imaging diagnosis.Subjects with higher CMI exhibited a significantly increased risk of MAFLD. The odds ratio for a 1-standard-deviation increase in CMI was 3.180 (2.102-4.809) after adjusting for various confounding factors. Further subgroup analysis showed that there were significant additive interactions between CMI and MAFLD risk in gender, age, and BMI (P for interaction < 0.05), and the area under the ROC curve(AUC) of CMI for predicting MAFLD were significantly higher in female, young, and nonobese subgroups than that in male, middle-aged and elderly, and obese subgroups (all P < 0.05). Moreover, among nonobese subjects, the AUC of CMI was significantly higher than that of waist circumference, BMI, TG/HDL-C, and TG (all P < 0.05). The best cutoff values of CMI to diagnose MAFLD in males and females were 0.6085 and 0.4319, respectively, and the accuracy, sensitivity, and specificity of high CMI for diagnosing MAFLD in the validation set were 85.5%, 87.5%, and 80%, respectively.CMI was strongly and positively associated with the risk of MAFLD and can be a reference predictor for MAFLD. High CMI had excellent diagnostic performance for MALFD, which can enable important clinical value for early identification and screening of MAFLD.Copyright © 2022 Duan, Yang, Xia, Ren, Chen and Yao.