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- Ahlqvist, Emma, et al.
(författare)
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Novel subgroups of adult-onset diabetes and their association with outcomes : a data-driven cluster analysis of six variables
- 2018
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Ingår i: The Lancet Diabetes and Endocrinology. - 2213-8587 .- 2213-8595. ; 6:5, s. 361-369
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundDiabetes 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.MethodsWe 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, HbA1c, and homoeostatic model assessment 2 estimates of β-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.FindingsWe 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.InterpretationWe 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.
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| 2. |
- Söderholm, M, et al.
(författare)
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Exome array analysis of ischaemic stroke : results from a southern Swedish study
- 2016
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Ingår i: European Journal of Neurology. - : Wiley. - 1351-5101 .- 1468-1331. ; 23:12, s. 1722-1728
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND AND PURPOSE: Genome-wide association (GWA) studies have identified a few risk loci for ischaemic stroke, but these variants explain only a small part of the genetic contribution to the disease. Coding variants associated with amino acid substitutions or premature termination of protein synthesis could have a large effect on disease risk. We performed an exome array analysis for ischaemic stroke.METHODS: Patients with ischaemic stroke (n = 2385) and control subjects (n = 6077) from three Swedish studies were genotyped with the Illumina HumanOmniExpressExome BeadChip. Single-variant association analysis and gene-based tests were performed of exome variants with minor allele frequency of < 5%. A separate GWA analysis was also performed, based on 700 000 genotyped common markers and subsequent imputation.RESULTS: No exome variant or gene was significantly associated with all ischaemic stroke after Bonferroni correction (all P > 1.8 × 10(-6) for single-variant and >4.15 × 10(-6) for gene-based analysis). The strongest association in single-variant analysis was found for a missense variant in the DNAH11 gene (rs143362381; P = 5.01 × 10(-6) ). In gene-based tests, the strongest association was for the ZBTB20 gene (P = 7.9 × 10(-5) ). The GWA analysis showed that the sample was homogenous (median genomic inflation factor = 1.006). No genome-wide significant association with overall ischaemic stroke risk was found. However, previously reported associations for the PITX2 and ZFHX3 gene loci with cardioembolic stroke subtype were replicated (P = 7 × 10(-15) and 6 × 10(-3) ).CONCLUSIONS: This exome array analysis did not identify any single variants or genes reaching the pre-defined significance level for association with ischaemic stroke. Further studies on exome variants should be performed in even larger, well-defined and subtyped samples.
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