GWAS of random glucose in 476,326 individuals provide insights into diabetes pathophysiology, complications and treatment stratification

Journal article

Conventional measurements of fasting and postprandial blood glucose levels investigated in genome-wide association studies (GWAS) cannot capture the effects of DNA variability on 'around the clock' glucoregulatory processes. Here we show that GWAS meta-analysis of glucose measurements under nonstandardized conditions (random glucose (RG)) in 476,326 individuals of diverse ancestries and without diabetes enables locus discovery and innovative pathophysiological observations. We discovered 120 RG loci represented by 150 distinct signals, including 13 with sex-dimorphic effects, two cross-ancestry and seven rare frequency signals. Of these, 44 loci are new for glycemic traits. Regulatory, glycosylation and metagenomic annotations highlight ileum and colon tissues, indicating an underappreciated role of the gastrointestinal tract in controlling blood glucose. Functional follow-up and molecular dynamics simulations of lower frequency coding variants in glucagon-like peptide-1 receptor (GLP1R), a type 2 diabetes treatment target, reveal that optimal selection of GLP-1R agonist therapy will benefit from tailored genetic stratification. We also provide evidence from Mendelian randomization that lung function is modulated by blood glucose and that pulmonary dysfunction is a diabetes complication. Our investigation yields new insights into the biology of glucose regulation, diabetes complications and pathways for treatment stratification.

Authors: Lagou, V; Jiang, L; Ulrich, A; Zudina, L; González, KSG

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Nature Genetics
• Volume: 55
• Issue: 9
• Pages: 1448-1461
• Publication date: 2023-09-07
• DOI: 10.1038/s41588-023-01462-3
• EISSN: 1546-1718
• ISSN: 1061-4036
• Language: English
• Keywords: Genetics; Single-nucleotide polymorphism; Endocrinology; Bioinformatics; Gene; Genome-wide association study; Genotype; Pathophysiology; Computational biology; Stratification (seeds); Internal medicine; Biology; Medicine; Diabetes mellitus; Risk stratification