Association of Age-Related Cataract Risk with High Polygenetic Risk Scores Involved in Galactose-Related Metabolism and Dietary Interactions

Abstract

Introduction: Cataracts are associated with the accumulation of galactose and galactitol in the lens. We determined the polygenetic risk scores for the best model(PRSBM) associated with age-related cataract(ARC) risk and their interaction with diets and lifestyles in 40,262 Korean adults aged over 50 years belonged to a hospital-based city cohort. Methods: The genetic variants for ARC risk were selected in lactose and galactose metabolism-related genes with multivariate logistic regression using the PLINK 1.9 version. PRSBM from the selected genetic variants was estimated by generalized multifactor dimensionality reduction (GMDR) after adjusting covariates. The interactions between the PRSBM and each lifestyle factor were determined to modulate ARC risk. Results: The genetic variants for ARC risk related to lactose- and galactose metabolism were SLC2A1_rs3729548, ST3GAL3_rs3791047, LCT_rs2304371, GALNT5_rs6728956, ST6GAL1_rs2268536, GALNT17_rs17058752, CSGALNACT1_rs1994788, GALNTL4_rs10831608, B4GALT6_rs1667288, and A4GALT_ rs9623659. In GMDR, the best model included all ten genetic variants. The highest odds ratio (OR) for a single SNP in the PRSBM was 1.26. However, subjects with a high-PRSBM had a higher ARC risk by 2.1-fold than a low-PRSBM after adjusting for covariates. Carbohydrate, dairy products, kimchi, and alcohol intake interacted with PRSBM for ARC risk: the participants with high-PRSBM had a much higher ARC risk than those with low-PRSBM when consuming diets with high carbohydrate and low dairy product and kimchi intake. However, only with low alcohol intake, the participants with high-PRSBM had a higher ARC risk than those with low-PRSBM. Conclusion: Adults aged >50 years having high-PRSBM may modulate dietary habits to reduce ARC risk.

Susceptibility Loci for Type 2 Diabetes in the Ethnically Endogamous Indian Sindhi Population: A Pooled Blood Genome-Wide Association Study

“…Published evidence implicated these 18 genes directly through population studies (GBP6, GSTO1, HHIPL1, INTS10, RGS16 and RNU6-679P), indirectly through association with other metabolic conditions (CCDC69, DNAH2, GALNT17, HERC5, KIF5C, MARCH2 and PRDM14) or through association with complications of T2D (CASC15, CCDC107, MIR147A, PFKFB3 and RNF166). Detailed annotation of the published literature in this regard is provided in Supplementary Table S3 ) [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. From the remaining genes identified to be significantly associated with T2D in this study, there were two open reading frames, three long intergenic non-coding elements and a pseudogene.…”