ArticlePLoS genetics2024
Disentangling the consequences of type 2 diabetes on targeted metabolite profiles using causal inference and interaction QTL analyses.
Article in PLoS genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Unravelling the molecular mechanisms causal to type 2 diabetes across global populations and disease-relevant tissues.Nature metabolism · 2026Article
- Integration of clinical and proteomic risk factors enhances prognostic modelling of incident vascular complications in type 2 diabetes.Cardiovascular diabetology · 2026Article
- Linking the plasma proteome to genetics in individuals from continental Africa provides insights into type 2 diabetes pathogenesis.Nature genetics · 2026Article
- Metabolomic signature of ultraprocessed food consumption and microvascular complications among individuals with type 2 diabetes.NPJ science of food · 2025Article
- Metabolites mediate the effects of healthy lifestyles on the risks of common age-related diseases.Frontiers in endocrinology · 2025Article
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13 authors.
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Abstract
Circulating metabolite levels have been associated with type 2 diabetes (T2D), but the extent to which T2D affects metabolite levels and their genetic regulation remains to be elucidated. In this study, we investigate the interplay between genetics, metabolomics, and T2D risk in the UK Biobank dataset using the Nightingale panel composed of 249 metabolites, 92% of which correspond to lipids (HDL, IDL, LDL, VLDL) and lipoproteins. By integrating these data with large-scale T2D GWAS from the DIAMANTE meta-analysis through Mendelian randomization analyses, we find 79 metabolites with a causal association to T2D, all spanning lipid-related classes except for Glucose and Tyrosine. Twice as many metabolites are causally affected by T2D liability, spanning almost all tested classes, including branched-chain amino acids. Secondly, using an interaction quantitative trait locus (QTL) analysis, we describe four metabolites consistently replicated in an independent dataset from the Estonian Biobank, for which genetic loci in two different genomic regions show attenuated regulation in T2D cases compared to controls. The significant variants from the interaction QTL analysis are significant QTLs for the corresponding metabolites in the general population but are not associated with T2D risk, pointing towards consequences of T2D on the genetic regulation of metabolite levels. Finally, through differential level analyses, we find 165 metabolites associated with microvascular, macrovascular, or both types of T2D complications, with only a few discriminating between complication classes. Of the 165 metabolites, 40 are not causally linked to T2D in either direction, suggesting biological mechanisms specific to the occurrence of complications. Overall, this work provides a map of the consequences of T2D on Nightingale targeted metabolite levels and on their genetic regulation, enabling a better understanding of the T2D trajectory leading to complications.
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