Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited ...these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). The correlation between the sequencing-based method and the other two were r.sub.ATPas = 0.44 0.13-0.67, 95% CI, and r.sub.myosin = 0.83 0.61-0.93, with p = 5.70 x 10.sup.-3 and 2.00 x 10.sup.-6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. This new method (https://github.com/OlaHanssonLab/PredictFiberType) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.
Although statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters ...are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.AIMS/HYPOTHESISAlthough statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal the heterogeneity of the at-risk population by identifying clinically meaningful clusters are lacking. We aimed to identify and characterise clusters of islet autoantibody-positive individuals who share similar characteristics and type 1 diabetes risk.We tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.METHODSWe tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention study data (n=1123). The outcome of the analysis was the time to development of type 1 diabetes, and variables in the model included demographic characteristics, genetics, metabolic factors and islet autoantibodies. An independent dataset (the Diabetes Prevention Trial of Type 1 Diabetes Study) (n=706) was used for validation.The analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.RESULTSThe analysis revealed six clusters with varying type 1 diabetes risks, categorised into three groups based on the hierarchy of clusters. Group A comprised one cluster with high glucose levels (median for glucose mean AUC 9.48 mmol/l; IQR 9.16-10.02) and high risk (2-year diabetes-free survival probability 0.42; 95% CI 0.34, 0.51). Group B comprised one cluster with high IA-2A titres (median 287 DK units/ml; IQR 250-319) and elevated autoantibody titres (2-year diabetes-free survival probability 0.73; 95% CI 0.67, 0.80). Group C comprised four lower-risk clusters with lower autoantibody titres and glucose levels (with 2-year diabetes-free survival probability ranging from 0.84-0.99 in the four clusters). Within group C, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels and age. A decision rule for assigning individuals to clusters was developed. Use of the validation dataset confirmed that the clusters can identify individuals with similar characteristics.Demographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.CONCLUSIONS/INTERPRETATIONDemographic, metabolic, immunological and genetic markers may be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables.
Although the genetic basis and pathogenesis of type 1 diabetes have been studied extensively, how host responses to environmental factors might contribute to autoantibody development remains largely ...unknown. Here, we use longitudinal blood transcriptome sequencing data to characterize host responses in children within 12 months prior to the appearance of type 1 diabetes-linked islet autoantibodies, as well as matched control children. We report that children who present with insulin-specific autoantibodies first have distinct transcriptional profiles from those who develop GADA autoantibodies first. In particular, gene dosage-driven expression of GSTM1 is associated with GADA autoantibody positivity. Moreover, compared with controls, we observe increased monocyte and decreased B cell proportions 9-12 months prior to autoantibody positivity, especially in children who developed antibodies against insulin first. Lastly, we show that control children present transcriptional signatures consistent with robust immune responses to enterovirus infection, whereas children who later developed islet autoimmunity do not. These findings highlight distinct immune-related transcriptomic differences between case and control children prior to case progression to islet autoimmunity and uncover deficient antiviral response in children who later develop islet autoimmunity.
We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes ...diagnosis.
We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.
Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses.
The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.
OBJECTIVE To explore if oral insulin could delay onset of stage 3 type 1 diabetes (T1D) among patients with stage 1/2 who carry HLA DR4-DQ8 and/or have elevated levels of IA-2 autoantibodies ...(IA-2As). RESEARCH AND METHODS Next-generation targeted sequencing technology was used to genotype eight HLA class II genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, and DPB1) in 546 participants in the TrialNet oral insulin preventative trial (TN07). Baseline levels of autoantibodies against insulin (IAA), GAD65 (GADA), and IA-2A were determined prior to treatment assignment. Available clinical and demographic covariables from TN07 were used in this post hoc analysis with the Cox regression model to quantify the preventive efficacy of oral insulin. RESULTS Oral insulin reduced the frequency of T1D onset among participants with elevated IA-2A levels (HR 0.62; P = 0.012) but had no preventive effect among those with low IA-2A levels (HR 1.03; P = 0.91). High IA-2A levels were positively associated with the HLA DR4-DQ8 haplotype (OR 1.63; P = 6.37 × 10−6) and negatively associated with the HLA DR7–containing DRB1*07:01-DRB4*01:01-DQA1*02:01-DQB1*02:02 extended haplotype (OR 0.49; P = 0.037). Among DR4-DQ8 carriers, oral insulin delayed the progression toward stage 3 T1D onset (HR 0.59; P = 0.027), especially if participants also had high IA-2A level (HR 0.50; P = 0.028). CONCLUSIONS These results suggest the presence of a T1D endotype characterized by HLA DR4-DQ8 and/or elevated IA-2A levels; for those patients with stage 1/2 disease with such an endotype, oral insulin delays the clinical T1D onset.
To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D).
Next-generation targeted sequencing was used ...to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression.
First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio HR = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression.
These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.
Modeling the course of C-peptide decline in autoantibody (Ab) positive individuals is important for type 1 diabetes (T1D) prediction and implementation of T1D prevention trials. Single nucleotide ...polymorphisms in TCF7L2, JAZF1, SLC30A8, INS, PTPRK, G6CP2, CLEC16, PTPN22 and HLA genes are associated with persistent C-peptide at or after T1D diagnosis. We sought to determine whether 12 SNPs in these genes and the T1D genetic risk score-2 (GRS2) can predict C-peptide trajectory before diagnosis of T1D. We studied Ab positive at-risk participants in the TrialNet Pathway to Prevention Study who had ImmunoChip data (N=1217, age at initial screen (mean±SD) 16.1±12.7 years, 51.5% female, 81.0% non-Hispanic white) . Over a mean follow up of 3.4±2. years, 255 (21.0%) developed multiple Ab and 336 (27.6%) developed clinical T1D. We analyzed the influence of these 12 SNPs and the T1D GRS2 on C-peptide AUC during progression from single to multiple Abs, from single Ab to clinical T1D, and from multiple Abs to clinical T1D. Analyses were adjusted for baseline C-peptide AUC, age, glucose AUC, BMI Z-score and HbA1c; the presence of high-risk HLA haplotypes (DR3 and DR4-DQ8) ; and the first 3 principal components. The type 2 diabetes (T2D) -associated TCF7L2 rs7901695 and rs4506565 SNPs were significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p<0.04) and neared significance for the single-to-multiple Ab transition (p=0.05) . Additionally, lower T1D GRS2 was significantly associated with higher C-peptide AUC during progression from multiple Abs to T1D (p=0.01) . We observed trend associations of lower C-peptide AUC with JAZF1 SNP rs864745 in both single Ab-to-T1D and multiple Ab-to-T1D progression (both p=0.06) , and with SNPs in SLC30A8 and INS in multiple Abs to T1D (p=0.and 0.08, respectively) .
In conclusion, T2D-associated SNPs in the TCF7L2 gene and lower T1D GRS2 predict higher C-peptide particularly in progression from multiple Abs to clinical T1D.
Disclosure
T.M.Triolo: None. M.J.Redondo: Advisory Panel; Provention Bio, Inc. H.M.Parikh: None. M.Tosur: Advisory Panel; Provention Bio, Inc. P.Gottlieb: Advisory Panel; Janssen Research & Development, LLC, ViaCyte, Inc., Other Relationship; IM Therapeutics, Research Support; Caladrius Biosciences, Inc., Immune Tolerance Network, National Institute of Diabetes and Digestive and Kidney Diseases, Novo Nordisk, Precigen, Inc., Tolerion, Inc. R.A.Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. S.Onengut-gumuscu: None. J.Krischer: None. S.S.Rich: None. A.Steck: None.
Funding
NIH K12 DK094712NIH RDK121843NIH RDK124395
Autoimmune loss of beta-cell function (measured by C-peptide) is the hallmark of type 1 diabetes (T1D) targeted by interventions that aim to prevent T1D or its progression after onset. We sought to ...determine whether T1D genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) that have been previously associated with C-peptide preservation after T1D diagnosis (e.g., SNPs in CLEC16A, G6CP2, INS, JAZF1, PTPN22, SLC30A8 and TCF7L2) influence C-peptide levels before diagnosis.
We studied islet autoantibody (Ab)-positive participants in the TrialNet Pathway to Prevention Study who had T1DExomeChip data and assessed the influence of these 12 SNPs and the T1D-GRS2 on area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted between 0-9 months prior to the diagnosis of T1D. Participants (n=702) had a mean age of 13.5±10.3 years, 47% were female, mean BMI was 20.7±6.0 kg/m2, and mean HbA1c 5.4±0.4%. The T1D high-risk HLA-DR3-DQ2 haplotype was present in 47% and the high-risk HLA-DR4-DQ8 haplotype was present in 67% of participants. We performed univariate and multivariate analyses adjusting for BMI, age, sex, number of positive Ab, and the first 3 principal components of ancestry.
A higher T1D-GRS2 was associated with lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, p=0.008) analyses. Participants with the JAZF1 rs864745 G allele had lower C-peptide AUC 0-9 months prior to T1D diagnosis in univariate (β=-0.10, p=0.003) and multivariate (β=-0.05, p=0.047) analysis.
In conclusion, the JAZF1 rs864745 G allele (which has also been associated with type 2 diabetes risk) and higher T1D-GRS2 predict lower C-peptide AUC prior to the diagnosis of T1D. Studies on their effect on response to trials to prevent or delay T1D onset are warranted.
Disclosure
T. M. Triolo: None. S. S. Rich: None. A. Steck: None. M. J. Redondo: None. H. M. Parikh: None. M. Tosur: Advisory Panel; Provention Bio, Inc. L. A. Ferrat: Consultant; Johnson & Johnson. L. You: None. P. Gottlieb: Advisory Panel; ViaCyte, Inc., Board Member; ImmunoMolecular Therapeutics, Research Support; Imcyse, Hemsley Charitable Trust, Novartis, National Institute of Diabetes and Digestive and Kidney Diseases, Precigen, Inc., Dompé, Nova Pharmaceuticals, Provention Bio, Inc. R. A. Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. S. Onengut-gumuscu: None. J. Krischer: None.
Funding
National Institutes of Health (R01DK121843, R01DK124395)
Abstract
Context
Some individuals present with forms of diabetes that are “atypical” (AD), which do not conform to typical features of either type 1 diabetes (T1D) or type 2 diabetes (T2D). These ...forms of AD display a range of phenotypic characteristics that likely reflect different endotypes based on unique etiologies or pathogenic processes.
Objective
To develop an analytical approach to identify and cluster phenotypes of AD.
Methods
We developed Discover Atypical Diabetes (DiscoverAD), a data mining framework, to identify and cluster phenotypes of AD. DiscoverAD was trained against characteristics of manually classified patients with AD among 278 adults with diabetes within the Cameron County Hispanic Cohort (CCHC) (Study A). We then tested DiscoverAD in a separate population of 758 multiethnic children with T1D within the Texas Children's Hospital Registry for New-Onset Type 1 Diabetes (TCHRNO-1) (Study B).
Results
We identified an AD frequency of 11.5% in the CCHC (Study A) and 5.3% in the pediatric TCHRNO-1 (Study B). Cluster analysis identified 4 distinct groups of AD in Study A: cluster 1, positive for the 65 kDa glutamate decarboxylase autoantibody (GAD65Ab), adult-onset, long disease duration, preserved beta-cell function, no insulin treatment; cluster 2, GAD65Ab negative, diagnosed at age ≤21 years; cluster 3, GAD65Ab negative, adult-onset, poor beta-cell function, lacking central obesity; cluster 4, diabetic ketoacidosis (DKA)–prone participants lacking a typical T1D phenotype. Applying DiscoverAD to the pediatric patients with T1D in Study B revealed 2 distinct groups of AD: cluster 1, autoantibody negative, poor beta-cell function, lower body mass index (BMI); cluster 2, autoantibody positive, higher BMI, higher incidence of DKA.
Conclusion
DiscoverAD can be adapted to different datasets to identify and define phenotypes of participants with AD based on available clinical variables.
Insulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying ...molecular mechanisms are still poorly understood.
To explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR).
We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A.
The muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.
PDF