Serum Gamma-Glutamyl Transferase as a Predictive Biomarker for Metabolic Syndrome and Insulin Resistance: Implications for Early Risk Assessment and Preventive Healthcare
Tasnim Shamrin
Department of Microbiology, City Dental College and Hospital, Bangladesh
Mohamed NurMohamed
Department of Public Health, World University of Bangladesh (WUB), Bangladesh
Abu BakarSiddik
Department of Microbiology, Jagannath University, Bangladesh
Sadia Islam
Department of Laboratory Medicine, Bangladesh Specialized Hospital, Bangladesh
Nayem Sarker
Department of Biochemistry & Cell Biology, Bangladesh University of Health Sciences (BUHS), Bangladesh
Sajib Halder
Department of Biochemistry & Cell Biology, Bangladesh University of Health Sciences (BUHS), Bangladesh
Tanzila Akter
Department of Virology, National Institute of Laboratory Medicine & Referral Center, Bangladesh
Md. AshiqurRahman
Department of Laboratory Medicine, Novus Clinical Research Services Limited (NCRSL), Bangladesh
Keywords: Metabolic Syndrome (MetS); Gamma-Glutamyl Transferase; HOMO-IR; Insulin resistance
Abstract
Background: Metabolic syndrome (MetS) is a significant global public health concern. Each component of MetS is linked to various non-communicable chronic diseases, including type 2 diabetes mellitus (T2DM), coronary artery disease (CAD), cerebrovascular disease, and non-alcoholic fatty liver disease (NAFLD), which collectively are major causes of mortality worldwide. Aims: This study aimed to investigate the association between serum gamma-glutamyl transferase (GGT) levels and the risk of MetS and insulin resistance (IR) in a sample of adults, exploring how GGT levels correlate with MetS components and IR markers. Additionally, we examined whether these associations vary by gender. Methods: A total of 440 participants were selected for this study, including 196 individuals diagnosed with MetS and 244 without. Participants were selected based on predefined criteria from those attending the outpatient department of a biochemistry clinic in Dhaka, Bangladesh. Logistic regression analysis was conducted, adjusting for potential confounders, including age, sex, body mass index (BMI), alanine aminotransferase (ALT), uric acid, and low-density lipoprotein cholesterol (LDL-C), to assess the odds ratios (95% CI) for MetS across GGT tertiles. Results: Logistic regression analysis indicated that, after adjusting for confounders, the odds ratios for MetS increased significantly across GGT tertiles (1, 1.22 (0.36-4.12), p=0.738; 5.09 (2.06-12.58), p
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