Adipokine (adiponectin-rs1501299) Gene Variant and Patient Characteristics in Relation to Metabolic-associated Fatty Liver Disease

Abstract

Background: Several genetic and metabolic variables, most notably the variation in the adipokine gene rs1501298, have been linked to metabolic-associated fatty liver disease etiopathogenesis (MAFLD). Liver biopsy, the gold standard for diagnosing MAFLD, is an invasive procedure; therefore, alternative diagnostic methods are required. Consequently, the integration of these metabolic variables with some of the patients’ characteristics may facilitate the development of noninvasive diagnostic methods that aid in the early detection of MAFLD, identification of at-risk individuals and planning of management strategies. Methods: This study included 224 Egyptians (107 healthy individuals and 117 MAFLD patients). Age, sex, BMI, clinical and laboratory characteristics, and rs1501299 adipokine gene polymorphisms were examined. The rs1501299 variant, insulin resistance, hypertension, obesity, blood pressure, lipid profile, hemoglobin A1C level, and hepatic fibrosis predictors were evaluated for MAFLD risk. The feasibility and effectiveness of developing non-invasive MAFLD diagnostic models will be investigated. Results: The +276G/T (rs1501299) polymorphism (GG vs GT/TT) was linked with MAFLD (OR: 0.43, CI: 0.26–0.69, P = 0.002). The GG variants had lower MAFLD rates than those of the GT and TT variants. In addition to altered lipid profiles, patients with MAFLD showed increased gamma-glutamyl transferase levels (GGT: 56 IU/L vs. 36 IU/L). Genetic diversity also affects the accuracy of hepatic fibrosis and steatosis prediction. Hepatic fibrosis and steatosis predictors had receiver operating characteristic (ROC) AUCs of 0.529%, 0.846%, and 0.700–0.825%, respectively. We examined a diagnostic model based on these variables and demonstrated its effectiveness. Conclusion: The Adipokine variant rs1501299 increased the risk of MAFLD. Identifying and genotyping this variation and other metabolic variables allow for a noninvasive diagnostic model for early MAFLD diagnosis and identification of those at risk. This study illuminates the prevention and management of MAFLD. Further research with more participants is needed to verify these models and to prove their MAFLD diagnostic efficacy.