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Ahlqvist, E.

Publications and source records attributed to Ahlqvist, E..

3 recordsLinked to original sources
bioRxiv2018

Loss of ZnT8 function protects against diabetes by enhanced insulin secretion

A rare loss-of-function variant p.Arg138* in SLC30A8 encoding the zinc transporter 8 (ZnT8) enriched in Western Finland protects against type 2 diabetes (T2D). We recruited relatives of the identified carriers and showed that protection was associated with better insulin secretion due to enhanced glucose responsiveness and proinsulin conversion, especially compared with individuals matched for the genotype of a common T2D risk variant in SLC30A8, p.Arg325. In genome-edited human IPS-derived {beta}-like cells, we establish that the p.Arg138* variant results in reduced SLC30A8 expression due to haploinsufficiency. In human {beta}-cells loss of SLC30A8 leads to increased glucose responsiveness and reduced KATP channel function, which was also seen in isolated islets from carriers of the T2D-protective allele p.Trp325. These data position ZnT8 as an appealing target for treatment aiming at maintaining insulin secretion capacity in T2D.

genetics↗
bioRxiv2018

Exome-wide association study on Albuminuria identifies a novel rare variant in CUBN and additional genes, in 33,985 Europeans with and without diabetes

Identifying rare coding variants associated with albuminuria may open new avenues for preventing chronic kidney disease (CKD) and end-stage renal disease which are highly prevalent in patients with diabetes. Efforts to identify genetic susceptibility variants for albuminuria have so far been limited with the majority of studies focusing on common variants.\n\nWe performed an exome-wide association study to identify coding variants in a two phase (discovery and replication) approach, totaling to 33,985 individuals of European ancestry (15,872 with and 18,113 without diabetes) and further testing in Greenlanders (n = 2,605). We identify a rare (MAF: 0.8%) missense (A1690V) variant in CUBN (rs141640975, {beta}=0.27, p=1.3 x 10-11) associated with albuminuria as a continuous measure in the combined European meta-analyses. Presence of each rare allele of the variant was associated with a 6.4% increase in albuminuria. The rare CUBN variant had 3 times stronger effect in individuals with diabetes compared to those without (pinteraction: 5.4 x 10-4, {beta}DM: 0.69, {beta}nonDM: 0.20) in the discovery meta-analyses. Geneaggregate tests based on rare and common variants identify three additional genes associated with albuminuria (HES1, CDC73, and GRM5) after multiple testing correction (P_bonferroni<2.7 x 10-6).\n\nThe current study identifies a rare coding variant in the CUBN locus and other potential genes associated with albuminuria in individuals with and without diabetes. These genes have been implicated in renal and cardiovascular dysfunction. These findings provide new insights into the genetic architecture of albuminuria and highlight novel target genes and pathways for prevention of diabetes-related kidney disease.\n\nSignificance statementIncreased albuminuria is a key manifestation of major health burdens, including chronic kidney disease and/or cardiovascular disease. Although being partially heritable, there is a lack of knowledge on rare genetic variants that contribute to albuminuria. The current study describes the discovery and validation, of a new rare gene mutation (~1%) in the CUBN gene which associates with increased albuminuria. Its effect multiplies 3 folds among diabetes cases compared to non diabetic individuals. The study further uncovers 3 additional genes modulating albuminuria levels in humans. Thus the current study findings provide new insights into the genetic architecture of albuminuria and highlight novel genes/pathways for prevention of diabetes related kidney disease.

genetics↗
bioRxiv2017

Clustering of adult-onset diabetes into novel subgroups guides therapy and improves prediction of outcome

BackgroundDiabetes is presently classified into two main forms, type 1 (T1D) and type 2 diabetes (T2D), but especially T2D is highly heterogeneous. A refined classification could provide a powerful tool individualize treatment regimes and identify individuals with increased risk of complications already at diagnosis.\n\nMethodsWe applied data-driven cluster analysis (k-means and hierarchical clustering) in newly diagnosed diabetic patients (N=8,980) from the Swedish ANDIS (All New Diabetics in Scania) cohort, using five variables (GAD-antibodies, BMI, HbA1c, HOMA2-B and HOMA2-IR), and related to prospective data on development of complications and prescription of medication from patient records. Replication was performed in three independent cohorts: the Scania Diabetes Registry (SDR, N=1466), ANDIU (All New Diabetics in Uppsala, N=844) and DIREVA (Diabetes Registry Vaasa, N=3485). Cox regression and logistic regression was used to compare time to medication, time to reaching the treatment goal and risk of diabetic complications and genetic associations.\n\nFindingsWe identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease, but had been prescribed similar diabetes treatment compared to the less susceptible individuals in clusters 4 and 5. The insulin deficient cluster 2 had the highest risk of retinopathy. In support of the clustering, genetic associations to the clusters differed from those seen in traditional T2D.\n\nInterpretationWe could stratify patients into five subgroups predicting disease progression and development of diabetic complications more precisely than the current classification. This new substratificationn may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.\n\nFundingThe funders of the study had no role in study design, data collection, analysis, interpretation or writing of the report.\n\nResearch in contextEvidence before this study\n\nThe current diabetes classification into T1D and T2D relies primarily on presence (T1D) or absence (T2D) of autoantibodies against pancreatic islet beta cell autoantigens and age at diagnosis (earlier for T1D). With this approach 75-85% of patients are classified as T2D. A third subgroup, Latent Autoimmune Diabetes in Adults (LADA,<10%), is defined by presence of autoantibodies against glutamate decarboxylase (GADA) with onset in adult age. In addition, several rare monogenic forms of diabetes have been described, including Maturity Onset Diabetes of the Young (MODY) and neonatal diabetes. This information is provided by national guidelines (ADA,WHO, IDF, Diabetes UK etc) but has not been much updated during the past 20 years and very few attempts have been made to explore heterogeneity of T2D. A topological analysis of potential T2D subgroups using electronic health records was published in 2015 but this information has not been implemented in the clinic.\n\nAdded value of this study\n\nHere we applied a data-driven cluster analysis of 5 simple variables measured at diagnosis in 4 independent cohorts of newly-diagnosed diabetic patients (N=14755) and identified 5 replicable clusters of diabetes patients, with significantly different patient characteristics and risk of diabetic complications. Particularly, individuals in the most insulin-resistant cluster 3 had significantly higher risk of diabetic kidney disease.\n\nImplications of the available evidence\n\nThis new sub-stratification may help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes

epidemiology↗