Multi-ancestry genome-wide study in >2.5 million individuals reveals heterogeneity in mechanistic pathways of type 2 diabetes and complications

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases. We identify 1,289 independent association signals at genome-wide significance (P&lt;5×10 - 8 ) that map to 611 loci, of which 145 loci are previously unreported. We define eight non-overlapping clusters of T2D signals characterised by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial, and enteroendocrine cells. We build cluster-specific partitioned genetic risk scores (GRS) in an additional 137,559 individuals of diverse ancestry, including 10,159 T2D cases, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned GRS are more strongly associated with coronary artery disease and end-stage diabetic nephropathy than an overall T2D GRS across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings demonstrate the value of integrating multi-ancestry GWAS with single-cell epigenomics to disentangle the aetiological heterogeneity driving the development and progression of T2D, which may offer a route to optimise global access to genetically-informed diabetes care. </p

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Interaction entre variabilité génétique et consommation de lipides dans la survenue du diabète de type 2

La détection des gènes impliqués dans le risque de diabète de type 2 revêt une importance capitale pour une meilleure compréhension de l'étiologie de la maladie et la découverte de nouvelles cibles thérapeutiques. A ce jour, plus d'une soixantaine de loci ont été associés au risque de diabète de type 2. Cependant, ces derniers n'expliquent que faiblement la part imputable à la génétique. L'étude des interactions génétique-nutrition pourrait permettre de mettre évidence de nouveaux gènes impliqués dans la maladie.L'objectif de ce travail consiste à évaluer les effets d'interaction entre la consommation de lipides et le génotype de polymorphismes de gènes candidats, sur le risque de diabète de type 2 dans une cohorte prospective issue de la population générale française, la cohorte D.E.S.I.R. Nous nous sommes notament intéréssé à deux groupes de gènes:- Gènes senseurs de lipides dont l'activité est modulée par les niveaux d'acides gras, tels que les gènes codant les récepteurs nucléaires PPARy et PPARa et le récepteur couplé aux proteines G GPR120. Gènes du transport inverse du cholestérol (TIC) tel que ABCA1, la CETP, et des lipases hépatique (LIPC) et endothéliale (LIPG) dont le rôle dans la régulation de l'homéostasie glucidique reste à déterminer.Nos résultats ont mis en évidence un effet des polymorphismes de PPARG, PPARA, GPR120, ABCA1, LIPG et CETP sur le risque de diabète. Ces effets ne sont détectables que lorsque la consommation de lipides est prise en compte. Ceci souligne l'importance des interactions gène-environnement dans la survenue d'une maladie complexe tel que le diabète de type 2.The detection of the genes associated with type 2 diabetes risk is essential for a better comprehension of the etiology of the disease. To date, more than sixty loci have been associated with the onset of type 2 diabetes. Nevertheless, these loci explain only a small part of the heritability of the disease. The study of interactions between gene and nutrition could help to reveal new genes involved in the onset of type 2 diabetes.The objective of our work was to estimate the interaction between dietary fat intake and the genotype of polymorphisms of candidate genes on type 2 diabetes incidence in a large cohort drawn from the French general population (the D.E.S.I.R study).Given the data available for this cohort (DNA, nutritional habits, glyceamic status) the interactions genotype-fat intake were analyzed for polymorphisms of two groups of genes:Lipid sensor genes, encoding for proteins whose activity is regulated by fatty acidssuch as the nuclear receptors PPARa, y, and the G protein coupled receptor GPR120.Reverse cholesterol transport genes, such as ABCA1, CETP, hepatic and endothelial lipases (LIPC and LIPG) whose implication in the onset of type 2 diabetes needs to be clarified.Our results revealed different associations between polymorphisms of PPARA, PPARG, GPR120, ABCA1 and LIPC on the risk of type 2 diabetes. These effects were detected only when dietary fat intake was considered. These results highlight the importance of gene-environment interactions in the onset of complex diseases such as type 2 diabetes.PARIS7-Bibliothèque centrale (751132105) / SudocSudocFranceF

Use of race, ethnicity, and ancestry data in health research

Race, ethnicity, and ancestry are common classification variables used in health research. However, there has been no formal agreement on the definitions of these terms, resulting in misuse, confusion, and a lack of clarity surrounding these concepts for researchers and their readers. This article examines past and current understandings of race, ethnicity, and ancestry in research, identifies the distinctions between these terms, examines the reliability of race, ethnicity, and ancestry classification, and provides researchers with guidance on how to use these terms. Although race, ethnicity, and ancestry are often treated synonymously, they should be considered as distinct terms in the context of health research. Researchers should carefully consider which term is most appropriate for their study, define and use the terms consistently, and consider how their classification may be used in future research by others. The classification should be self-reported rather than assigned by an observer wherever possible

Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study

peer reviewe

ABCG8 polymorphisms and renal disease in type 2 diabetic patients

International audienc

The genetic risk of gestational diabetes in South Asian women

South Asian women are at increased risk of developing gestational diabetes mellitus (GDM). Few studies have investigated the genetic contributions to GDM risk. We investigated the association of a type 2 diabetes (T2D) polygenic risk score (PRS), on its own, and with GDM risk factors, on GDM-related traits using data from two birth cohorts in which South Asian women were enrolled during pregnancy. 837 and 4372 pregnant South Asian women from the SouTh Asian BiRth CohorT (START) and Born in Bradford (BiB) cohort studies underwent a 75-g glucose tolerance test. PRSs were derived using genome-wide association study results from an independent multi-ethnic study (~18% South Asians). Associations with fasting plasma glucose (FPG); 2 hr post-load glucose (2hG); area under the curve glucose; and GDM were tested using linear and logistic regressions. The population attributable fraction (PAF) of the PRS was calculated. Every 1 SD increase in the PRS was associated with a 0.085 mmol/L increase in FPG ([95% confidence interval, CI=0.07–0.10], p=2.85×10(−20)); 0.21 mmol/L increase in 2hG ([95% CI=0.16–0.26], p=5.49×10(−16)); and a 45% increase in the risk of GDM ([95% CI=32–60%], p=2.27×10(−14)), independent of parental history of diabetes and other GDM risk factors. PRS tertile 3 accounted for 12.5% of the population’s GDM alone, and 21.7% when combined with family history. A few weak PRS and GDM risk factors interactions modulating FPG and GDM were observed. Taken together, these results show that a T2D PRS and family history of diabetes are strongly and independently associated with multiple GDM-related traits in women of South Asian descent, an effect that could be modulated by other environmental factors