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Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

Udler, Miriam S ; Kim, Jaegil ; von Grotthuss, Marcin ; Bonàs-Guarch, Sílvia ; Cole, Joanne B ; Chiou, Joshua ; Boehnke, Michael ; Laakso, Markku ; Atzmon, Gil ; Glaser, Benjamin ; Mercader, Josep M ; Gaulton, Kyle ; Flannick, Jason ; Getz, Gad ; Florez, Jose C Langenberg, Claudia

PLoS medicine, 2018-09, Vol.15 (9), p.e1002654-e1002654 [Periódico revisado por pares]

United States: Public Library of Science

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Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis
Autor: Udler, Miriam S ; Kim, Jaegil ; von Grotthuss, Marcin ; Bonàs-Guarch, Sílvia ; Cole, Joanne B ; Chiou, Joshua ; Boehnke, Michael ; Laakso, Markku ; Atzmon, Gil ; Glaser, Benjamin ; Mercader, Josep M ; Gaulton, Kyle ; Flannick, Jason ; Getz, Gad ; Florez, Jose C
Langenberg, Claudia
Assuntos: Adiponectin ; Algorithms ; Analysis ; Bayes Theorem ; Bayesian analysis ; Beta cells ; Biological markers ; Biology and Life Sciences ; Biomarkers ; Blood pressure ; Body mass ; Body mass index ; Care and treatment ; Cholesterol ; Cluster Analysis ; Cohort Studies ; Coronary artery ; Coronary artery disease ; Cross-Sectional Studies ; Databases, Genetic ; Development and progression ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - classification ; Diabetes Mellitus, Type 2 - genetics ; Diabetics ; Female ; Founder Effect ; Gene loci ; Genetic analysis ; Genetic aspects ; Genetic diversity ; Genetic Loci ; Genetic Markers ; Genetic Predisposition to Disease ; Genetic variance ; Genetics ; Genome-wide association studies ; Genome-Wide Association Study ; Genomes ; Heart diseases ; Heterogeneity ; High density lipoprotein ; Hospitals ; Humans ; Insulin ; Insulin - deficiency ; Insulin - genetics ; Insulin resistance ; Insulin Resistance - genetics ; Lipid metabolism ; Lipodystrophy ; Liver diseases ; Male ; Management ; Medical treatment ; Medicine ; Medicine and Health Sciences ; Metabolic disorders ; Metabolic syndrome ; Metabolism ; Multigene Family ; Patient care ; Patients ; Pediatrics ; Phenotype ; Phenotypes ; Prospective Studies ; Risk Factors ; Supervision ; Treatment outcome ; Triglycerides ; Type 2 diabetes
É parte de: PLoS medicine, 2018-09, Vol.15 (9), p.e1002654-e1002654
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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The authors have declared that no competing interests exist.
Descrição: Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four separate subsets of individuals with T2D. In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta cell function, differing from each other by high versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), "lipodystrophy-like" fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry. Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.
Editor: United States: Public Library of Science
Idioma: Inglês

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Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

Udler, Miriam S ; Kim, Jaegil ; von Grotthuss, Marcin ; Bonàs-Guarch, Sílvia ; Cole, Joanne B ; Chiou, Joshua ; Boehnke, Michael ; Laakso, Markku ; Atzmon, Gil ; Glaser, Benjamin ; Mercader, Josep M ; Gaulton, Kyle ; Flannick, Jason ; Getz, Gad ; Florez, Jose C Langenberg, Claudia

United States: Public Library of Science

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