Novel subgroups of adult-onset diabetes and their association with outcomes : a data-driven cluster analysis of six variables

Background Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis. Methods We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA(1c), and homoeostatic model assessment 2 estimates of beta-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations. Findings We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes. Interpretation We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.Peer reviewe

Type 2 diabetes susceptibility gene variants predispose to adult-onset autoimmune diabetes

Aims/hypothesis Latent autoimmune diabetes in adults (LADA) is phenotypically a hybrid of type 1 and type 2 diabetes. Genetically LADA is poorly characterised but does share genetic predisposition with type 1 diabetes. We aimed to improve the genetic characterisation of LADA and hypothesised that type 2 diabetes-associated gene variants also predispose to LADA, and that the associations would be strongest in LADA patients with low levels of GAD autoantibodies (GADA). Methods We assessed 41 type 2 diabetes-associated gene variants in Finnish (phase I) and Swedish (phase II) patients with LADA (n=911) or type 1 diabetes (n=406), all diagnosed after the age of 35 years, as well as in non-diabetic control individuals 40 years or older (n=4,002). Results Variants in the ZMIZ1 (rs12571751, p=4.1 x 10(-5)) and TCF7L2 (rs7903146, p=5.8 x 10(-4)) loci were strongly associated with LADA. Variants in the KCNQ1 (rs2237895, p=0.0012), HHEX (rs1111875, p=0.0024 in Finns) and MTNR1B (rs10830963, p=0.0039) loci showed the strongest association in patients with low GADA, supporting the hypothesis that the disease in these patients is more like type 2 diabetes. In contrast, variants in the KLHDC5 (rs10842994, p=9.5 x 10(-4) in Finns), TP53INP1 (rs896854, p=0.005), CDKAL1 (rs7756992, p=7.0 x 10(-4); rs7754840, p=8.8 x 10(-4)) and PROX1 (rs340874, p=0.003) loci showed the strongest association in patients with high GADA. For type 1 diabetes, a strong association was seen for MTNR1B (rs10830963, p=3.2 x 10(-6)) and HNF1A (rs2650000, p=0.0012). Conclusions/interpretation LADA and adult-onset type 1 diabetes share genetic risk variants with type 2 diabetes, supporting the idea of a hybrid form of diabetes and distinguishing them from patients with classical young-onset type 1 diabetes

Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver

Correction: Article Numbere3001442 DOI10.1007/s00125-021-05620-2 Early AccessNOV 2021Aim/hypothesis Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes. Methods We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205). Results Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy. Conclusions /interpretation Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.Peer reviewe

Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (&gt; 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.Erratum in: Scientific Data, volume 5, Article number: 180002, 2018Doi:10.1038/sdata.2018.2</p

Sequence data and association statistics from 12,940 type 2 diabetes cases and controls (vol 4, 170179, 2017)

This corrects the article DOI: 10.1038/sdata.2017.179

Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals

Abstract Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency &gt; 0.05). In a meta-analysis of up to similar to 1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (P &lt; 5 x 10(⁻⁸)), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were similar to 8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.A Publisher Correction to this article was published on 16 March 2021