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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. |
- Ottosson-Laakso, Emilia, et al.
(författare)
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Glucose-induced Changes in Gene Expression in Human Pancreatic Islets - Causes or Consequences of Chronic Hyperglycemia
- 2017
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Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 66:12, s. 3013-3028
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Tidskriftsartikel (refereegranskat)abstract
- Dysregulation of gene expression in islets from type 2 diabetic patients might be causally involved in the development of hyperglycemia or it could develop as a consequence of hyperglycemia, i.e. glucotoxicity. To separate the genes potentially causally involved in pathogenesis from those likely to be secondary to the hyperglycemia we exposed islets from human donors to normal or high glucose concentrations for 24 hours and analyzed gene expression. We compared these findings with gene expression in islets from donors with normal glucose tolerance (NGT) and hyperglycemia (HG, including T2D). The genes whose expression changed in the same direction after short-term glucose exposure as in T2D were considered most likely to be a consequence of hyperglycemia. Genes whose expression changed in HG but not after short-term glucose exposure, in particular genes that also correlated with insulin secretion, were considered the strongest candidates for causal involvement in T2D. E.g. ERO1LB, DOCK10, IGSF11 and PRR14L were down-regulated in HG and correlated positively with insulin secretion suggesting a protective role while TMEM132C was up-regulated in HG and correlated negatively with insulin secretion suggesting a potential pathogenic role.This study provides a catalogue of gene expression changes in human pancreatic islets after exposure to glucose.
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| 3. |
- Taneera, Jalal, et al.
(författare)
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A Systems Genetics Approach Identifies Genes and Pathways for Type 2 Diabetes in Human Islets
- 2012
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Ingår i: Cell Metabolism. - : Elsevier BV. - 1550-4131 .- 1932-7420. ; 16:1, s. 122-134
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Tidskriftsartikel (refereegranskat)abstract
- Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors. Using 48 genes located near T2D risk variants, we identified gene coexpression and protein-protein interaction networks that were strongly associated with islet insulin secretion and HbA(1c). We integrated our data to form a rank list of putative T2D genes, of which CHL1, LRFN2, RASGRP1, and PPM1K were validated in INS-1 cells to influence insulin secretion, whereas GPR120 affected apoptosis in islets. Expression variation of the top 20 genes explained 24% of the variance in HbA(1c) with no claim of the direction. The data present a global map of genes associated with islet dysfunction and demonstrate the value of systems genetics for the identification of genes potentially involved in T2D.
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