Dataset Information

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

ABSTRACT: Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progression to T1D onset remain elusive. The Environmental Determinants of Diabetes in the Young (TEDDY) consortium is a prospective cohort study aiming to determine genetic and environmental interactions causing T1D. Here, we analyzed longitudinal blood transcriptomes of 2013 samples from 400 individuals in the TEDDY study before both T1D and islet autoimmunity. We identified and interpreted age-associated gene expression changes in healthy infancy and age-independent changes tracking with progression to both T1D and islet autoimmunity, beginning before other evidence of islet autoimmunity was present. We combined multivariate longitudinal data in a Bayesian joint model to predict individual risk of T1D onset and validated the association of a natural killer cell signature with progression and the model's predictive performance on an additional 356 samples from 56 individuals in the independent Type 1 Diabetes Prediction and Prevention study. Together, our results indicate that T1D is characterized by early and longitudinal changes in gene expression, informing the immunopathology of disease progression and facilitating prediction of its course.

SUBMITTER: Xhonneux LP

PROVIDER: S-EPMC8447843 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

Xhonneux Louis-Pascal LP Knight Oliver O Lernmark Åke Å Bonifacio Ezio E Hagopian William A WA Rewers Marian J MJ She Jin-Xiong JX Toppari Jorma J Parikh Hemang H Smith Kenneth G C KGC Ziegler Anette-G AG Akolkar Beena B Krischer Jeffrey P JP McKinney Eoin F EF

Science translational medicine 20210301 587

Type 1 diabetes (T1D) is a disease of insulin deficiency that results from autoimmune destruction of pancreatic islet β cells. The exact cause of T1D remains unknown, although asymptomatic islet autoimmunity lasting from weeks to years before diagnosis raises the possibility of intervention before the onset of clinical disease. The number, type, and titer of islet autoantibodies are associated with long-term disease risk but do not cause disease, and robust early predictors of individual progres ...[more]

PMID: 33790023

Similar Datasets

Project description:ObjectiveWe tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.Research design and methodsWe studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.ResultsHigher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002).ConclusionsThe T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Project description:The unfolding of type 1 diabetes involves a number of steps: defective immunological tolerance, priming of anti-islet autoimmunity, and destruction of insulin-producing beta-cells. A number of genetic loci contribute to susceptibility to type 1 diabetes, but it is unclear which stages of the disease are influenced by the different loci. Here, we analyzed the frequency of type 1 diabetes-risk alleles among individuals from the Diabetes Prevention Trial-Type 1 (DPT-1) clinical trial, which tested a preventive effect of insulin in at-risk relatives of diabetic individuals, all of which presented with autoimmune manifestations but only one-third of which eventually progressed to diabetes.In this study, 708 individuals randomized into DPT-1 were genotyped for 37 single nucleotide polymorphisms in diabetes susceptibility loci.Susceptibility alleles at loci expected to influence immunoregulation (PTPN22, CTLA4, and IL2RA) did not differ between progressors and nonprogressors but were elevated in both groups relative to general population frequencies, as was the INS promoter variant. In contrast, HLA DQB1*0302 and DQB1*0301 differed significantly in progressors versus nonprogressors (DQB*0302, 42.6 vs. 34.7%, P = 0.0047; DQB*0301, 8.6 vs. 14.3%, P = 0.0026). Multivariate analysis of the factors contributing to progression demonstrated that initial titers of anti-insulin autoantibodies (IAAs) could account for some (P = 0.0016) but not all of this effect on progression (P = 0.00038 for the independent effect of the number of DQB*0302 alleles). The INS-23 genotype was most strongly associated with anti-IAAs (median IAA levels in TT individuals, 60 nU/ml; AT, 121; and AA, 192; P = 0.000037) and only suggestively to the outcome of oral insulin administration.With the exception of HLA, most susceptibility loci tested condition the risk of autoimmunity rather than the risk of failed immunoregulation that results in islet destruction. Future clinical trials might consider genotyping INS-23 in addition to HLA alleles as disease/treatment response modifier.

Project description:Aims/hypothesisType 2 diabetes is a complex and heterogeneous disease and the aetiological components underlying the heterogeneity remain unclear in the Chinese and East Asian population. Therefore, we aimed to investigate whether specific pathophysiological pathways drive the clinical heterogeneity in type 2 diabetes.MethodsWe employed newly developed type 2 diabetes hard-clustering and soft-clustering pathway-specific polygenic risk scores (psPRSs) to characterise individual genetic susceptibility to pathophysiological pathways implicated in type 2 diabetes in 18,217 Chinese patients from Hong Kong. The 'total' type 2 diabetes polygenic risk score (PRS) was summed by genome-wide significant type 2 diabetes signals (n=1289). We examined the associations between psPRSs and cardiometabolic profile, age of onset, two glycaemic deterioration outcomes (clinical requirement of insulin treatment, defined by two consecutive HbA1c values ≥69 mmol/mol [8.5%] more than 3 months apart during treatment with two or more oral glucose-lowering drugs, and insulin initiation), three renal (albuminuria, end-stage renal disease and chronic kidney disease) outcomes and five cardiovascular outcomes.ResultsAlthough most psPRSs and total type 2 diabetes PRS were associated with an earlier and younger onset of type 2 diabetes, the psPRSs showed distinct associations with clinical outcomes. In particular, individuals with normal weight showed higher psPRSs for beta cell dysfunction and lipodystrophy than those who were overweight. The psPRSs for obesity were associated with faster progression to clinical requirement of insulin treatment (adjusted HR [95% CI] 1.09 [1.05, 1.13], p<0.0001), end-stage renal disease (1.10 [1.04, 1.16], p=0.0007) and CVD (1.10 [1.05, 1.16], p<0.0001) while the psPRSs for beta cell dysfunction were associated with reduced incident end-stage renal disease (0.90 [0.85, 0.95], p=0.0001) and heart failure (0.83 [0.73, 0.93], p=0.0011). Major findings remained significant after adjusting for a set of clinical variables.Conclusions/interpretationBeta cell dysfunction and lipodystrophy could be the driving pathological pathways in type 2 diabetes in individuals with normal weight. Genetic risks of beta cell dysfunction and obesity represent two major genetic drivers of type 2 diabetes heterogeneity in disease progression and diabetic complications, which are shared across ancestry groups. Type 2 diabetes psPRSs may help inform patient stratification according to aetiology and guide precision diabetes care.

Dataset Information

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

Publications

Transcriptional networks in at-risk individuals identify signatures of type 1 diabetes progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets