Affiliations 

  • 1 Health Administration Program, Faculty of Business & Management, Universiti Teknologi MARA, Bandar Puncak Alam, Malaysia
  • 2 Department of Health Care Management & Administration, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
  • 3 Department of Preventive Medicine and Community Health, University of Occupational and Environmental Health, Kitakyushu, Japan
  • 4 Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
  • 5 Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Shinjuku-ku, Japan
Metab Syndr Relat Disord, 2024 Feb;22(1):27-38.
PMID: 38350086 DOI: 10.1089/met.2023.0055

Abstract

Background: Serum gamma-glutamyltransferase (γ-GT), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) levels often increase in metabolic diseases. Objective: This study was conducted to determine which liver enzymes are strongly associated with metabolic syndrome (MetS), how they interact to produce different probability estimates, and what cutoff levels should be used to guide clinical decision-making. Methods: The researchers examined the insurance-based medical checkup data of 293,610 employees ≥35 years years of age, who underwent medical checkups between April 1, 2016, and March 31, 2017. Liver enzyme levels were grouped into quartiles. The association and interaction of liver enzymes with MetS were examined using logistic regression, and receiver operating characteristic (ROC) analyses were used to determine the optimal cutoff values for each liver enzyme in detecting the prevalence of MetS. Results: High levels of γ-GT and ALT were more strongly associated with MetS than AST. At various levels, the tested liver enzymes were found interactive, and associated with the likelihood of MetS prevalence. ROC analysis underscored the significance of all liver enzymes in predicting the development of MetS. The cutoff values for each liver enzyme were determined. Conclusion: This findings of this study directly support the identification of MetS risks within the population, prioritize prevention strategies, and potentially inform policy formulation.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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