Trans-ethnic estimation and implications of genetic impact on continuous glycemic profiles.

Abstract

The genetic architecture of glycemic dynamic metrics derived from continuous-glucose monitoring (CGM) across different populations remains poorly understood. Here, we conducted a trans-ethnic genome-wide association study (GWAS) meta-analysis of 20 CGM-derived glycemic traits, building upon a previously established European-ancestry CGM dataset and extending it through the inclusion of additional cohorts, in up to 9677 individuals originating from 2051 Chinese, 901 Dutch, and 6725 Israelis. Across 20 glycemic traits, we identified 18 genome-wide significant associations, of which 9 met study-wide significance, and three variants were novel. These variants indicated a shared genetic basis for continuous glycemic regulation and exhibited consistent patterns with those of sequential fingerstick glucose tests. Our findings further demonstrated that the identified genetic variants were enriched in pathways related to the nervous system. These findings were further supported by observed associations with brain magnetic resonance imaging (MRI) metrics, high CGM-related gene expression and co-regulation of quantitative trait loci in brain tissues. Additionally, we observed a positive relationship between genetic liability for the coefficient of variation (CV) and total cholesterol and a bi-directional putative causal relationship between hyperglycemia and type 1 diabetes across trans-ethnic populations. Moreover, we established a polygenic risk score (PRS) for additional participants and reported that certain glycemic traits were significantly associated with the risk of diabetes or pre-diabetes. These variants constituting the PRS demonstrated high transferability across general populations and pregnant women. Overall, our study yields unique insights into the high trans-ethnic and generalizable genetic architecture of CGM-derived glycemic profiles, supporting improved characterization of interindividual differences in glycemic dynamics and underscoring the potential for more personalized glucose management.