Genetic characterization of a dysmetabolic and obese population of Southern Italy. (March 2023)

• Record Type: Journal Article
• Authors: Motta, B.M.; Masarone, M.; Torre, P.; Calabrese, P.; Aquino, M.; Belladonna, F.; Pilone, V.; Persico, M.
• Is Part Of: Digestive and liver disease. Volume 55(2023)Supplement 1
• Page Start: S56
• Page End: S57
• Publication Date: 2023-03
• Subjects: Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; 616.33005
• DOI: 10.1016/j.dld.2023.01.110
• Languages: English
• ISSNs: 1590-8658

Abstract

Abstract : Background and Aims: Non-alcoholic fatty liver disease (NAFLD) is the leading cause of liver diseases, and the susceptibility to develop NAFLD is highly variable and influenced by environmental and genetic factors. The gold standard for diagnosis and staging of NAFLD is liver biopsy, however, it is an invasive procedure, subject to sampling errors and inter-observer variability. Several non-invasive methods aim at diagnosing hepatic steatosis and predicting significant/advanced fibrosis. GWAS studies identified genetic risk factors, and genetic risk scores (GRS) were developed for risk stratification. NAFLD susceptibility is associated with four genetic variants: PNPLA3 rs738409, TM6SF2 rs58542926, rs641738 close to MBOAT7 locus, GCKR rs1260326. Our aim was to evaluate how these variants are distributed in an obese and dysmetabolic population of Southern Italy and how GRS correlates with biochemical phenotypes. Method: We enrolled 184 patients attending our Hepatology Clinic and Surgery department, which were genotyped for rs738409, rs58542926, rs641738, rs1260326. We calculated a weighted GRS by multiplying beta-coefficient of NAFLD phenotype by respective risk alleles and summing the products. Anthropometric data, FibroScan, and blood test results were collected. Results: In our cohort, we observed a higher MAF of PNPLA3 and GCKR variants compared to the ones reported in European population of 1000Genomes Project (p<0.0001). The rs738409 G allele frequency is 35, 1% vs. 23%. The rs1260326 T allele frequency is 52, 7% vs. 41%. In a sub-cohort of 65 patients, we recorded Fibroscan parameters and biochemical data and evaluated the effect of the 4 variants together by GRS. GRS increased proportionally with ALT levels (p=0.01), and AST, total cholesterol, triglycerides, and CAP (p=N.S). Considering a CAP value of 275 as discriminant for moderate/severe steatosis, GRS is significantly higher in the CAP>275 subjects (p<0.05). Conclusion: This study shows that in our population, PNPLA3 and GCKR risk alleles frequency are higher, and GRS correlates with biochemical and Fibroscan parameters, identifying the most dysmetabolic subjects.