| 1. |
- Dwivedi, Om Prakash, et al.
(författare)
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Genome-wide mRNA profiling in urinary extracellular vesicles reveals stress gene signature for diabetic kidney disease
- 2023
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Ingår i: iScience. - 2589-0042. ; 26:5
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Tidskriftsartikel (refereegranskat)abstract
- Urinary extracellular vesicles (uEV) are a largely unexplored source of kidney-derived mRNAs with potential to serve as a liquid kidney biopsy. We assessed ∼200 uEV mRNA samples from clinical studies by genome-wide sequencing to discover mechanisms and candidate biomarkers of diabetic kidney disease (DKD) in Type 1 diabetes (T1D) with replication in Type 1 and 2 diabetes. Sequencing reproducibly showed >10,000 mRNAs with similarity to kidney transcriptome. T1D DKD groups showed 13 upregulated genes prevalently expressed in proximal tubules, correlated with hyperglycemia and involved in cellular/oxidative stress homeostasis. We used six of them (GPX3, NOX4, MSRB, MSRA, HRSP12 and CRYAB) to construct a transcriptional “stress score” that reflected long-term decline of kidney function and could even identify normoalbuminuric individuals showing early decline. We thus provide workflow and web-resource for studying uEV transcriptomes in clinical urine samples and stress-linked DKD markers as potential early non-invasive biomarkers or drug targets.
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| 2. |
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| 3. |
- 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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| 4. |
- Ahlqvist, Emma, et al.
(författare)
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Subtypes of type 2 diabetes determined from clinical parameters
- 2020
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Ingår i: Diabetes. - : American Diabetes Association. - 0012-1797 .- 1939-327X. ; 69:10, s. 2086-2093
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Forskningsöversikt (refereegranskat)abstract
- Type 2 diabetes (T2D) is defined by a single metabolite, glucose, but is increasingly recognized as a highly heterogeneous disease, including individuals with varying clinical characteristics, disease progression, drug response, and risk of complications. Identification of subtypes with differing risk profiles and disease etiologies at diagnosis could open up avenues for personalized medicine and allow clinical resources to be focused to the patients who would be most likely to develop diabetic complications, thereby both im-proving patient health and reducing costs for the health sector. More homogeneous populations also offer increased power in experimental, genetic, and clinical studies. Clinical parameters are easily available and reflect relevant disease pathways, including the effects of both genetic and environmental exposures. We used six clinical parameters (GAD autoantibodies, age at diabetes onset, HbA1c, BMI, and measures of insulin resistance and insulin secretion) to cluster adult-onset diabetes patients into five subtypes. These sub-types have been robustly reproduced in several populations and associated with different risks of complications, comor-bidities, genetics, and response to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group had the highest risk for diabetic kidney disease (DKD) and fatty liver, empha-sizing the importance of insulin resistance for DKD and hepatosteatosis in T2D. In conclusion, we believe that sub-classification using these highly relevant parameters could provide a framework for personalized medicine in diabetes.
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| 5. |
- Ahlqvist, Emma, et al.
(författare)
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Towards improved precision and a new classification of diabetes mellitus
- 2022
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Ingår i: Journal of Endocrinology. - 1479-6805. ; 252:3, s. 59-70
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Forskningsöversikt (refereegranskat)abstract
- Type 2 diabetes (T2D) is one of the fastest increasing diseases worldwide. Although it is defined by a single metabolite, glucose, it is increasingly recognized as a highly heterogeneous disease with varying clinical manifestations. Identification of different subtypes at an early stage of disease when complications might still be prevented could hopefully allow for more personalized medicine. An important step towards precision medicine would be to target the right resources to the right patients, thereby improving patient health and reducing health costs for the society. More well-defined disease populations also offer increased power in experimental, genetic and clinical studies. In a recent study, we used six clinical variables (GAD autoantibodies, age at onset of diabetes, HbA1c, BMI, and simple measures of insulin resistance and insulin secretion (so called HOMA estimates) to cluster adult-onset diabetes patients into five subgroups. These subgroups have been robustly reproduced in several populations worldwide and are associated with different risks of diabetic complications and responses to treatment. Importantly, the group with severe insulin-deficient diabetes (SIDD) had increased risk of retinopathy and neuropathy, whereas the severe insulin-resistant diabetes (SIRD) group has the highest risk for diabetic kidney disease (DKD) and fatty liver. This emphasizes the key role of insulin resistance in the pathogenesis of DKD and fatty liver in T2D. In conclusion, this novel sub-classification, breaking down T2D in clinically meaningful subgroups, provides the prerequisite framework for expanded personalized medicine in diabetes beyond what is already available for monogenic and to some extent type 1 diabetes.
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| 6. |
- Arora, Geeti P, et al.
(författare)
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Phenotypic and genotypic differences between Indian and Scandinavian women with gestational diabetes mellitus
- 2019
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Ingår i: Journal of Internal Medicine. - : Wiley. - 1365-2796 .- 0954-6820. ; 286:2, s. 192-206
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Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE: Gestational diabetes mellitus (GDM) is a transient form of diabetes characterized by impaired insulin secretion and action during pregnancy. Population-based differences in prevalence exist which could be explained by phenotypic and genetic differences. The aim of this study was to examine these differences in pregnant women from Punjab, India and Scandinavia.METHODS: 85 GDM/T2D loci in European and/or Indian populations from previous studies were assessed for association with GDM based on Swedish GDM criteria in 4018 Punjabi Indian and 507 Swedish pregnant women. Selected loci were replicated in Scandinavian cohorts, Radiel (N=398, Finnish), STORK/STORK-G (N=780, Norwegian).RESULTS: Punjabi Indian women had higher GDM prevalence, lower insulin secretion and better insulin sensitivity than Swedish women. There were significant frequency differences of GDM/T2D risk alleles between both populations. rs7178572 at HMG20A, previously associated with GDM in South Indian and European women was replicated in North Indian women. The T2D risk SNP rs11605924 in the CRY2 gene was associated with increased GDM risk in Scandinavian but decreased risk in Punjabi Indian women. No other overlap was seen between GDM loci in both populations.CONCLUSIONS: GDM is more common in Indian than Swedish women, which partially can be attributed to differences in insulin secretion and action. There was marked heterogeneity in the GDM phenotypes between the populations which could only partially be explained by genetic differences. This article is protected by copyright. All rights reserved.
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| 7. |
- Asplund, Olof, et al.
(författare)
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Islet Gene View-a tool to facilitate islet research
- 2022
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Ingår i: Life Science Alliance. - : Life Science Alliance, LLC. - 2575-1077. ; 5:12
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Tidskriftsartikel (refereegranskat)abstract
- Characterization of gene expression in pancreatic islets and its alteration in type 2 diabetes (T2D) are vital in understanding islet function and T2D pathogenesis. We leveraged RNA sequencing and genome-wide genotyping in islets from 188 donors to create the Islet Gene View (IGW) platform to make this information easily accessible to the scientific community. Expression data were related to islet phenotypes, diabetes status, other islet-expressed genes, islet hormone-encoding genes and for expression in insulin target tissues. The IGW web application produces output graphs for a particular gene of interest. In IGW, 284 differentially expressed genes (DEGs) were identified in T2D donor islets compared with controls. Forty percent of DEGs showed cell-type enrichment and a large proportion significantly co-expressed with islet hormone-encoding genes; glucagon (GCG, 56%), amylin (IAPP, 52%), insulin (INS, 44%), and somatostatin (SST, 24%). Inhibition of two DEGs, UNC5D and SERPINE2, impaired glucose-stimulated insulin secretion and impacted cell survival in a human beta-cell model. The exploratory use of IGW could help designing more comprehensive functional follow-up studies and serve to identify therapeutic targets in T2D.
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| 8. |
- Asplund, Olof, et al.
(författare)
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MuscleAtlasExplorer : a web service for studying gene expression in human skeletal muscle
- 2020
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Ingår i: Database: the journal of biological databases and curation. - : Oxford University Press (OUP). - 1758-0463. ; 2020
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Tidskriftsartikel (refereegranskat)abstract
- MuscleAtlasExplorer is a freely available web application that allows for the exploration of gene expression data from human skeletal muscle. It draws from an extensive publicly available dataset of 1654 skeletal muscle expression microarray samples. Detailed, manually curated, patient phenotype data, with information such as age, sex, BMI and disease status, are combined with skeletal muscle gene expression to provide insights into gene function in skeletal muscle. It aims to facilitate easy exploration of the data using powerful data visualization functions, while allowing for sample selection, in-depth inspection and further analysis using external tools. Availability: MuscleAtlasExplorer is available at https://mae.crc.med.lu.se/mae2 (username 'muscle' and password 'explorer' pre-publication).
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| 9. |
- Atla, Goutham, et al.
(författare)
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Genetic regulation of RNA splicing in human pancreatic islets
- 2022
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Ingår i: Genome Biology. - : Springer Science and Business Media LLC. - 1474-760X. ; 23, s. 1-28
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundNon-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown.ResultsWe examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3.ConclusionsThese data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.
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| 10. |
- Balboa, Diego, et al.
(författare)
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Genome editing of human pancreatic beta cell models : problems, possibilities and outlook
- 2019
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 62:8, s. 1329-1336
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Forskningsöversikt (refereegranskat)abstract
- Understanding the molecular mechanisms behind beta cell dysfunction is essential for the development of effective and specific approaches for diabetes care and prevention. Physiological human beta cell models are needed for this work. We review the possibilities and limitations of currently available human beta cell models and how they can be dramatically enhanced using genome-editing technologies. In addition to the gold standard, primary isolated islets, other models now include immortalised human beta cell lines and pluripotent stem cell-derived islet-like cells. The scarcity of human primary islet samples limits their use, but valuable gene expression and functional data from large collections of human islets have been made available to the scientific community. The possibilities for studying beta cell physiology using immortalised human beta cell lines and stem cell-derived islets are rapidly evolving. However, the functional immaturity of these cells is still a significant limitation. CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein 9) has enabled precise engineering of specific genetic variants, targeted transcriptional modulation and genome-wide genetic screening. These approaches can now be exploited to gain understanding of the mechanisms behind coding and non-coding diabetes-associated genetic variants, allowing more precise evaluation of their contribution to diabetes pathogenesis. Despite all the progress, genome editing in primary pancreatic islets remains difficult to achieve, an important limitation requiring further technological development.
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