Aetiological differences between novel subtypes of diabetes derived from genetic associations

Background: Type 2 diabetes (T2D) is a multi-organ disease defined by hyperglycemia resulting from different disease mechanisms. Using clinical parameters measured at diagnosis (age, BMI, HbA1c, HOMA2-B, HOMA2-IR and GAD autoantibodies) adult patients with diabetes have been reproducibly clustered into five subtypes, that differed clinically with respect to disease progression and outcomes.1 In this study we use genetic information to investigate if these subtypes have distinct underlying genetic drivers. Methods: Genome-wide association (GWAS) and genetic risk score (GRS) analysis was performed in Swedish (N=12230) and Finnish (N=4631) cohorts. Family history was recorded by questionnaires. Results: Severe insulin-deficient diabetes (SIDD) and mild obesity-related diabetes (MOD) groups had the strongest family history of T2D. A GRS including known T2D loci was strongly associated with SIDD (OR per 1 SD increment [95% CI]=1.959 [1.814-2.118]), MOD (OR 1.726 [1.607-1.855]) and mild age-related diabetes (MARD) (OR 1.771 [1.671-1.879]), whereas it was less strongly associated with severe insulin-resistant diabetes (SIRD, OR 1.244 [1.157-1.337]), which was similar to severe autoimmune diabetes (SAID, OR 1.282 [1.160-1.418]). SAID showed strong association with the GRS for T1D, whereas the non-autoimmune subtype SIDD was most strongly associated with the GRS for insulin secretion rate (P<7.43x10-9). SIRD showed no association with variants in TCF7L2 or any GRS reflecting insulin secretion. Instead, only SIRD was associated with GRS for fasting insulin (P=3.10x10-8). Finally, a T2D locus, rs10824307 near the ZNF503 gene was uniquely associated with MOD (ORmeta=1.266 (1.170-1.369), P=4.3x10-9). Conclusions: New diabetes subtypes have partially different genetic backgrounds and subtype-specific risk loci can be identified. Especially the SIRD subtype stands out by having lower heritability and less involvement of beta-cell related pathways in its pathogenesis.

[1]  P. Donnelly,et al.  A new multipoint method for genome-wide association studies by imputation of genotypes , 2007, Nature Genetics.

[2]  Matti Pirinen,et al.  Assessing allele-specific expression across multiple tissues from RNA-seq read data , 2015, Bioinform..

[3]  Cassandra N. Spracklen,et al.  Identification of type 2 diabetes loci in 433,540 East Asian individuals , 2019, bioRxiv.

[4]  L. Groop,et al.  Metabolic Consequences of a Family History of NIDDM (The Botnia Study): Evidence for Sex-Specific Parental Effects , 1996, Diabetes.

[5]  Archie Campbell,et al.  Exploration of haplotype research consortium imputation for genome-wide association studies in 20,032 Generation Scotland participants , 2017, Genome Medicine.

[6]  May E. Montasser,et al.  Genome-Wide Association Study of the Modified Stumvoll Insulin Sensitivity Index Identifies BCL2 and FAM19A2 as Novel Insulin Sensitivity Loci , 2016, Diabetes.

[7]  H. Hakonarson,et al.  First Genome-Wide Association Study of Latent Autoimmune Diabetes in Adults Reveals Novel Insights Linking Immune and Metabolic Diabetes , 2018, Diabetes Care.

[8]  A. Morris,et al.  Data quality control in genetic case-control association studies , 2010, Nature Protocols.

[9]  Nicholette D. Palmer,et al.  A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants , 2017, Diabetes.

[10]  L. Groop,et al.  Risk of diabetes-associated diseases in subgroups of patients with recent-onset diabetes: a 5-year follow-up study. , 2019, The lancet. Diabetes & endocrinology.

[11]  B. Shields,et al.  Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data , 2019, The lancet. Diabetes & endocrinology.

[12]  O. Melander,et al.  Risk factors for the progression of carotid intima-media thickness over a 16-year follow-up period: the Malmö Diet and Cancer Study. , 2015, Atherosclerosis.

[13]  Peter Almgren,et al.  Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. , 2007, The Journal of clinical investigation.

[14]  Omar Yaxmehen Bello-Chavolla,et al.  Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach , 2020, BMJ open diabetes research & care.

[15]  L. Groop,et al.  Smoking and the Risk of LADA: Results From a Swedish Population-Based Case-Control Study , 2016, Diabetes Care.

[16]  Gad Getz,et al.  Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis , 2018, PLoS medicine.

[17]  Carson C Chow,et al.  Second-generation PLINK: rising to the challenge of larger and richer datasets , 2014, GigaScience.

[18]  Claude Bouchard,et al.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance , 2012, Nature Genetics.

[19]  Da I Jung,et al.  First genome-wide association study of esophageal atresia identifies three genetic risk loci at CTNNA3, FOXF1/FOXC2/FOXL1, and HNF1B , 2022, HGG advances.

[20]  L. Groop,et al.  Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. , 2018, The lancet. Diabetes & endocrinology.

[21]  T. Greenhalgh,et al.  Efficacy and effectiveness of screen and treat policies in prevention of type 2 diabetes: systematic review and meta-analysis of screening tests and interventions , 2017, British Medical Journal.

[22]  P. Visscher,et al.  Common SNPs explain a large proportion of heritability for human height , 2011 .

[23]  Anthony J. Payne,et al.  Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps , 2018, Nature Genetics.

[24]  Mark I. McCarthy,et al.  A Central Role for GRB10 in Regulation of Islet Function in Man , 2014, PLoS genetics.

[25]  Stephen Burgess,et al.  PhenoScanner V2: an expanded tool for searching human genotype–phenotype associations , 2019, Bioinform..

[26]  Stephen Burgess,et al.  PhenoScanner: a database of human genotype–phenotype associations , 2016, Bioinform..

[27]  Giovanni Malerba,et al.  Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes , 2017, Nature Genetics.