[Predictive value of a Chinese visceral adiposity index for metabolic associated fatty liver disease]

Abstract

Objective: To explore the correlation between Chinese visceral adiposity index (CVAI) and metabolic associated fatty liver disease (MAFLD) so as to evaluate its predictive value for MAFLD. Methods: Six hundred and thirteen cases admitted to the Department of Gastroenterology, Zhongshan Hospital Affiliated to Dalian University from June 2022 to August 2023 were selected and divided into the MAFLD group (n=312) and the non-MAFLD group (n=301) according to the diagnostic criteria of MAFLD. The clinical data differences between the two groups were compared. The MAFLD group was divided into a mild MAFLD group (n=243) and a moderate to severe MAFLD group (n=69) according to the liver/spleen CT value. The differences in body fat indices such as CVAI, visceral fat index (VAI), and visceral fat area (VFA) were compared between subjects with different degrees of MAFLD. The Spearman test was used to analyze the correlation between CVAI, VAI, and various clinical indicators. The subjects were divided into groups (Q1-Q4) according to the quartile levels of CVAI and VAI, and the distribution of MAFLD conditions among the groups was compared. Logistic regression analysis was used to determine the occurrence risk of MAFLD at different CVAI and VAI levels. The receiver operating characteristic curve was drawn. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the predictive value of CVAI, VAI, VFA, waist circumference, and body mass index for MAFLD. The DeLong test was used to compare the differences in the AUC of each predictive index. Results: The prevalence of hypertension and type 2 diabetes mellitus, and the levels of systolic blood pressure, diastolic blood pressure, CVAI, VAI, VFA, subcutaneous fat area, waist circumference, body mass index, total cholesterol, triglycerides, low-density lipoprotein cholesterol, fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance, high-sensitivity C-reactive protein, alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transferase, and serum uric acid were higher in the MAFLD group than the non-MAFLD group (P<0.05), while the level of high-density lipoprotein cholesterol was lower than the non-MAFLD group (P<0.001). The levels of CVAI, VAI, VFA, waist circumference, and body mass index were higher in the mild and the moderate to severe MAFLD group than those in the non-MAFLD group (P<0.001). The detection rate of MAFLD gradually increased(χ²=176.953, 133.659, P<0.001) with the increase of CVAI and VAI levels. Correlation analysis showed that CVAI was positively correlated with VFA (r=0.755, P<0.001) and the homeostasis model assessment of insulin resistance (r=0.579, P<0.001). Multivariate logistic regression analysis showed that after adjusting for various risk factors, the risk of MAFLD in the Q4 group of the CVAI subgroup was still 7.159 times that of the Q1 group (95%CI:3.126-16.392, P<0.001), and the risk of MAFLD in the Q4 group of the VAI subgroup was still 4.667 times that of the Q1 group (95%CI: 2.187-9.962, P<0.001). The receiver operating characteristic curve results showed that the AUC of CVAI for predicting MAFLD was similar to that of VFA (0.822 vs. 0.826), and higher than that of VAI (AUC 0.772), waist circumference (AUC 0.796), and body mass index (AUC 0.755). The optimal critical value of CVAI for predicting the risk of MAFLD was 125.50, with sensitivity and specificity at 70.5% and 79.1%, respectively. Conclusion: The patient's risk of MAFLD increases with the rise of CVAI level, and CVAI has a favorable predictive value for the occurrence of MAFLD.