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ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 38 Issue 10
Oct.  2022
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Article Navigation >  Journal of Clinical Hepatology  > 2022  >  38(10): 2247-2251

Correlation between cardiometabolic index and metabolic-associated fatty liver disease

DOI: 10.3969/j.issn.1001-5256.2022.10.010
  • 1a.

    Department of General Practice, The First Affiliated Hospital of Xi'an Medical College, Xi'an 710000, China

  • 1b.

    Department of General Surgery, The First Affiliated Hospital of Xi'an Medical College, Xi'an 710000, China

  • 2.

    Graduate School of Xi'an Medical College, Xi'an 710000, China

Research funding:

Shaanxi Provincial Key Promotion Project for Scientific and Technological Achievements (2022SF-132)

More Information
  • Corresponding author: WU Yunxia, 1445778990@qq.com(ORCID: 0000-0001-6553-364X); REN Tianshun, rentianshun101@sohu.com(ORCID: 0000-0002-5631-6421)
  • Received Date: 2022-03-30
  • Accepted Date: 2022-04-30
  • Published Date: 2022-10-20
    Abstract
  •   Objective  To analyze association of the cardiometabolic index (CMI) with risk of metabolism-associated fatty liver disease (MAFLD).  Methods  This study recruited 480 individuals from Inpatient Department of Gastroenterology, The First Affiliated Hospital of Xi'an Medical College from January 2018 to December 2020 and divided into MAFLD (n=202) and non-MAFLD groups (n=278) for comparison of the CMI and related biochemical and FibroScan parameters. The independent samples t-test was performed for comparison of normally distributed continuous data, while the Mann-Whitney U test was performed for comparison of non-normally distributed continuous data and the chi-square test was used for comparison of categorical data between these two groups of individuals. These individuals were then divided into Q1-Q4 subgroups based on their CMI and then subjected to the Kruskal-Wallis H test to compare the distribution of the controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) between these two groups of patients. Finally, a Logistic regression analysis was performed to calculate MAFLD risk at different CMI scores (Q1-Q4).  Results  Compared with the non-MAFLD individuals, the MAFLD group had significantly higher levels of CMI, CAP, LSM, body weight, waist circumference, body mass index, systolic and diastolic blood pressure, triglyceride, total cholesterol, low-density lipoprotein cholesterol, alanine aminotransferase, aspartate aminotransferase, γ-glutamyl transferase, serum uric acid, and fasting blood glucose, but significantly lower high-density lipoprotein cholesterol level (all P < 0.01). Moreover, there was significant difference in the CAP among different CMI quartile levels in these individuals (χ2=15.220, P=0.002). The multivariate logistic regression analytic data revealed that the CMI in the Q4 was an independent predictor for MAFLD risk vs. the CMI in the Q1 quantile ones (95% confidence interval, 1.415-8.764, OR=3.521; P < 0.01) after adjustment for related risk factors.  Conclusion  A higher CMI score (Q4) was associated with a MAFLD risk and a gradual increase in the CMI score was with the increased number of MAFLD patients and fibrosis risk, indicating that a routine health check-up and measurement of CMI could help to identify and control MAFLD early.

     

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