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2,348 results for “type 1 diabetes”
Supporting data for: Type 1 diabetes risk genes mediate pancreatic beta cell survival in response to proinflammatory cytokines
<p><strong>SUMMARY OF THE STUDY</strong></p> <p>We combined functional genomics and human genetics to investigate processes that affect type 1 diabetes (T1D) risk by mediating beta-cell survival in response to proinflammatory cytokines. We mapped 38,931 cytokine-responsive candidate <em>cis-</em>regulatory elements (cCREs) in beta-cells using ATAC-seq and snATAC-seq and linked them to target genes using co-accessibility and HiChIP. Using a genome-wide CRISPR screen in EndoC-βH1 cells we identified 867 genes affecting cytokine-induced survival, and genes promoting survival and up-regulated in cytokines were enriched at T1D risk loci. Using SNP-SELEX, we identified 2,229 variants in cytokine-responsive cCREs altering transcription factor (TF) binding, and variants altering binding of TFs regulating stress, inflammation and apoptosis were enriched for T1D risk. At the 16p13 locus, a fine-mapped T1D variant altering TF binding in a cytokine-induced cCRE interacted with <em>SOCS1</em>, which promoted survival in cytokine exposure. Our findings reveal processes and genes acting in beta-cells during inflammation that modulate T1D risk.</p> <p><strong>DESCRIPTION OF FILES:</strong></p> <ul> <li>Supplementary Data 1. List of islet cCREs annotated with cell type and cytokine response - also in GSE205853</li> <li>Supplementary Data 2. Coaccessible sites in untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 3. Coaccessible sites in cytokine-treated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 4. Coaccessible sites in cytokine treated and untreated beta cells and promoter annotations - also in GSE205853</li> <li>Supplementary Data 5. Chromatin interactions in EndoC-BH1 cells - also in GSE205853</li> <li>Supplementary Data 6. Variants selected for SNP-SELEX assay </li> <li>Supplementary Data 7. Variants with TF binding and allelic binding results from SNP-SELEX</li> <li>Supplementary Data 8. snATAC-seq barcodes and metadata - also in GSE205853</li> <li>Supplementary Data 9. CRISPR-KO screen results - also in GSE205853</li> <li>Supplementary Data 10. Bulk ATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 11. Bulk RNA-seq count matrix - also in GSE205853</li> <li>Supplementary Data 12. Alpha cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 13. Acinar cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 14. Beta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 15. Stellate cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 16. Endothelial cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 17. Delta cells snATAC-seq count matrix - also in GSE205853</li> <li>Supplementary Data 18. Luciferase assay rs10483809</li> <li>Supplementary Data 19. SOCS1 knockdown qPCR results</li> <li>Supplementary Data 20. SOCS1 knockdown Apotracker (flow-cytometry)results</li> </ul> <p><strong>Raw data deposited at GEO, accessions GSE205853 and GSE118725.</strong></p> <p><em>Please refer to publication and GEO for details on methods.</em></p>
T1D-lipidome: Database of lipidomic aberrations during the pathogenesis of type 1 diabetes (T1D)
<p>This is the<strong> living database</strong> of <strong>lipidomic aberrations</strong> during the <strong>pathogenesis of type 1 diabetes</strong> (T1D).</p> <p>The database has been collected from scientific publications that report abnormalities related to the onset of T1D. In practice, this either means:</p> <ol> <li>lipids that are aberrated in blood samples collected from persons, who are later known to have been diagnosed with T1D,</li> <li>lipids that are aberrated in blood samples collected from persons, who are have islet auto-antibodies (IAA-positive), or</li> <li>lipids that are associated with the deterioration of insulin secretion in blood samples collected from persons recently diagnosed with T1D.</li> </ol> <p>This database is described in the following publication. Please cite the publication, if you use the database or related code:</p> <p><strong>Citation</strong></p> <p>Tommi Suvitaival. <strong>Lipidomic Abnormalities During the Pathogenesis of Type 1 Diabetes: a Quantitative Review</strong>. <em>Current Diabetes Reports</em>. 20, 46 (2020). <a href="http://dx.doi.org/10.1007/s11892-020-01326-8">http://dx.doi.org/10.1007/s11892-020-01326-8</a></p> <p><strong>Acknowledgement</strong></p> <p>This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115797 (<a href="https://www.innodia.eu/">INNODIA</a>). This Joint Undertaking receives support from the Union’s Horizon 2020 research and innovation programme and “EFPIA”, ‘JDRF” and “The Leona M. and Harry B. Helmsley Charitable Trust”.</p>
The open D1NAMO dataset: A multi-modal dataset for research on non-invasive type 1 diabetes management
<p>The description of the dataset is available at <a href="https://doi.org/10.1016/j.imu.2018.09.003">https://doi.org/10.1016/j.imu.2018.09.003</a></p> <p>The usage of wearable devices has gained popularity in the latest years, especially for health-care and well being. Recently there has been an increasing interest in using these devices to improve the management of chronic diseases such as diabetes. The quality of data acquired through <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/wearable-sensor">wearable sensors</a> is generally lower than what medical-grade devices provide, and existing datasets have mainly been acquired in highly controlled clinical conditions. In the context of the <em>D1NAMO</em> project — aiming to detect <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/glycemic">glycemic</a> events through non-invasive <a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/ecg-abnormality">ECG pattern</a> analysis — we elaborated a dataset that can be used to help developing health-care systems based on wearable devices in non-clinical conditions. This paper describes this dataset, which was acquired on 20 healthy subjects and 9 patients with type-1 diabetes. The acquisition has been made in real-life conditions with the <em>Zephyr BioHarness 3</em> wearable device. The dataset consists of <em>ECG</em>, <em>breathing</em>, and <em><a href="https://www.sciencedirect.com/topics/medicine-and-dentistry/accelerometer">accelerometer</a></em> signals, as well as <em>glucose</em> measurements and annotated <em>food pictures</em>. We open this dataset to the scientific community in order to allow the development and evaluation of diabetes management algorithms.</p>
Combined unsupervised and semi-automated supervised analysis of flow cytometry data reveals cellular fingerprint associated with newly diagnosed pediatric type 1 diabetes
<p>Type 1 diabetes is a chronic autoimmune disease resulting in an immune-mediated loss of pancreatic β-cells; however, an unbiased and reproducible profiling of type 1 diabetes-specific circulating immunome at disease onset has yet to be explored. In this study, fresh whole blood was collected from a pediatric cohort of 107 patients with new-onset type 1 diabetes, 85 relatives of patients with type 1 diabetes with 0-1 islet autoantibodies, 58 patients with celiac disease or autoimmune thyroiditis and 76 healthy controls. Up to 6 mL of blood was collected from each subject into a VACUETTE® TUBE 6 ml ACD-B (Greiner). Fresh whole blood underwent red blood cell lysis, was washed and stained with specific monoclonal antibodies. Fresh whole blood samples were stained with five panels of antibodies labelled as T cells, T&NK cells, B cells, Tregs and DCs/monos encompassing main subsets of T cells, NK cells, B cells, Tregs, DCs and monocytes detected using 26 surface markers and the intracellular marker forkhead box P3 (FoxP3); for the Treg panel, intracellular staining was performed after fixation and permeabilization. Cells were acquired on a BD FACSCanto-II flow cytometer equipped with FACSDiva software (Becton Dickinson, Franklin Lakes, NJ). </p>
Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes
<p>This is the dataset for the publication "Assessment of skin autofluorescence and its association with glycated hemoglobin, cardiovascular risk markers and concomitant chronic diseases in children with type 1 diabetes".</p>
Supplemental data for: Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study
<div> <p>The dataset was used in the paper “Mapping lifestyle factors in blood glucose variability in adolescents with Type 1 Diabetes Mellitus- A pilot study”. The article is currently under review for publication. DOI to be inserted.</p> </div> <div> <p>A data-in-brief article is to be published to give in-depth information about the data collected to improve reproducibility "Dataset for: Lifestyle Factors and Blood Glucose Variability in Adolescents with Type 1 Diabetes Mellitus". DOI to be inserted. </p> <p> </p> <p>The aim of the study was to assess whether adolescents with T1D in Ireland meet current nutrition and physical activity (PA) guidelines and to explore the impact of nutrition and PA on glycaemic variability (GV). The dataset includes continuous glucose monitoring (CGM) data, dietary intake records, and PA metrics, providing a comprehensive view of the participants' glucose levels and associated lifestyle behaviours.</p> </div>
The Relationship between LRP5 (rs556442 and rs638051) Polymorphisms and Mutation with Bone Metabolism in Xinjiang women with Type 2 Diabetes after Menopause(Table 1 and Table 2 Statistical Values of Analysis Process)
<p>The Relationship between LRP5 (rs556442 and rs638051) Polymorphisms and Mutation with Bone Metabolism in Xinjiang women with Type 2 Diabetes after Menopause(Table 1 and Table 2 Statistical Values of Analysis Process)</p>
Structural Homology of Epitope Pair Candidates for Molecular Mimicry Trigger of Type 1 Diabetes Mellitus
<p><strong><em><span>Background:</span></em></strong><span> </span><span>Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E<sub>INF</sub>) and T1DM epitopes (E<sub>T1D</sub>) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potential surfaces of the epitope pairs identified in previous work from our laboratory. </span></p> <p><strong><span> </span></strong><strong><em><span>Results:</span></em></strong><span> </span><span> For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold software program. </span><span>Of the 52 epitope pairs considered here only 10 do not exhibit any matching (i.e. less than 3 residues overlap). When considering all residues the RMSD ranges from 0.33 Å to 11.66 Å with an average of 2.68 Å. Twenty-two pairs (42%) have RMSD of less than 1.5 Å and 30 (58%) less than 3 Å. Even some of the matching pairs show some electrostatic similarities that need to be considered. In general there is good agreement between the folding predicted for the isolated </span><span>E<sub>INF</sub></span><span> and E<sub>T1D</sub> epitopes and the folding of the corresponding amino acid sequence in the parent antigen, but in some cases there are deviation that need to be considered, even when the RMDS is small.</span></p> <p><span> </span><strong><em><span>Conclusions:</span></em></strong><span> </span><span>Despite differences, most of the E<sub>INF</sub><span>/</span>E<sub>T1D </sub>pairs selected by sequence homology show similar structural and electrostatic distributions, indicating that the E<sub>INF</sub> may bind to the same protein targets, the major histocompatibility complex molecules, for T1DM, leading to molecular mimicry onset of the disease. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for molecular mimicry that should be considered for further study. Still structure and full docking calculations will be necessary to advance the in-silico molecular mimicry predictions. </span> Here we presnt the following files:</p> <p><span><span>·<span> </span></span></span>Fasta files of all epitopes studied.</p> <p><span><span>·<span> </span></span></span>Alphafold calculated Structures of all epitopes.</p> <p><span><span>·<span> </span></span></span>Antigen structures.</p> <p><span><span>·<span> </span></span></span>Epitope pair structure comparison and their electrostatics.</p> <p> </p> <p> </p>
Meta-GWAS of Age at Type 1 Diabetes Diagnosis
<p>7,923 subjects with type 1 diabetes from five studies (SDRNT1BIO, DCCT, CACTI, WESDR and EDC) were included in this analysis. This dataset includes summary stats for 8,154,711 autosomal SNPs.</p>
Meta-GWAS of C-peptide in Type 1 Diabetes
<p>7,252 subjects with type 1 diabetes from four studies (SDRNT1BIO, DCCT, CACTI and WESDR) were included in this analysis. This dataset includes summary stats for 8,150,646 autosomal SNPs.</p>
Comparison of the Safety and Efficacy of HOE901-U300 With Lantus in Children and Adolescents With Type 1 Diabetes Mellitus
ClinicalTrials.gov study NCT02735044. IPD Sharing: YES. Countries: 24. Publications: 1.
Comparison of SAR341402 to NovoLog in Adult Patients With Type 1 Diabetes Mellitus Also Using Insulin Glargine
ClinicalTrials.gov study NCT03874715. IPD Sharing: YES. Countries: 1. Publications: 2.
"MyPlan" - Individualized Planned Eating Patterns for Adolescents With Type 1 Diabetes
ClinicalTrials.gov study NCT05147324. IPD Sharing: YES. Countries: 1. Publications: 20.
Recent-Onset Type 1 Diabetes Trial Evaluating Efficacy and Safety of Teplizumab
ClinicalTrials.gov study NCT03875729. IPD Sharing: YES. Countries: 9. Publications: 3.
PRISM for Adolescents With Type 1 Diabetes
ClinicalTrials.gov study NCT03847194. IPD Sharing: NO. Countries: 1. Publications: 2.
Comparison of Glucose Values and Variability Between TOUJEO and TRESIBA During Continuous Glucose Monitoring in Type 1 Diabetes Patients
ClinicalTrials.gov study NCT04075513. IPD Sharing: YES. Countries: 7. Publications: 1.
DiaBetter Together for Young Adults With Type 1 Diabetes
ClinicalTrials.gov study NCT04247620. IPD Sharing: YES. Countries: 1. Publications: 1.
Technology-delivered Physical Activity Program for Adolescents With Type 1 Diabetes
ClinicalTrials.gov study NCT05319600. IPD Sharing: NO. Countries: 1. Publications: 9.
Risk factors for cardiovascular disease (CVD) in adults with type 1 diabetes: findings from prospective real-life T1D exchange registry
<p>Context</p> <p>Cardiovascular disease (CVD) is a major cause of mortality in adults with type 1 diabetes.</p> <p>Objective</p> <p>We prospectively evaluated CVD risk factors in a large, contemporary cohort of adults with type 1 diabetes living in the United States.</p> <p>Design</p> <p>Observational study of CVD and CVD risk factors over a median of 5.3 years.</p> <p>Setting</p> <p>The T1D Exchange clinic network.</p> <p>Patients</p> <p>Adults (age ≥18 years) with type 1 diabetes and without known CVD diagnosed before or at enrollment.</p> <p>Main Outcome Measure</p> <p>Associations between CVD risk factors and incident CVD were assessed by multivariable logistic regression.</p> <p>Results</p> <p>The study included 8,727 participants (53% female, 88% non-Hispanic white, median age 33 years [IQR=21, 48], type 1 diabetes duration 16 years [IQR=9, 26]). At enrollment, median HbA1c was 7.6% (66 mmol/mol) [IQR=6.9 (52), 8.6 (70)], 33% used a statin, and 37% used blood pressure medication. Over a mean follow-up of 4.6 years, 325 (3.7%) participants developed incident CVD. Ischemic heart disease was the most common CVD event. Increasing age, BMI, HbA1c, presence of hypertension and dyslipidemia, increasing duration of diabetes, and diabetic nephropathy were associated with increased risk for CVD. There were no significant gender differences in CVD risk.</p> <p>Conclusion</p> <p>HbA1c, hypertension, dyslipidemia and diabetic nephropathy are important risk factors for CVD in adults with type 1 diabetes. A longer follow-up is likely required to assess the impact of other traditional CVD risk factors on incident CVD in the current era.</p>
Data from: A genome-wide functional genomics approach uncovers genetic determinants of immune phenotypes in type 1 diabetes
<p><strong>Background: </strong>The large inter-individual variability in immune-cell cell composition and function determines immune responses in general and susceptibility to immune-mediated diseases in particular. While much has been learned about the genetic variants relevant for type 1 diabetes (T1D), the pathophysiological mechanisms through which these variations exert their effects remain unknown.</p> <p><strong>Methods:</strong> Blood samples were collected from 243 patients with T1D of Dutch descent. We applied genetic association analysis on > 200 immune cell traits and >100 cytokine production profiles in response to stimuli measured to identify genetic determinants of immune function, and compared the results obtained in T1D to healthy controls.</p> <p><strong>Results:</strong> Genetic variants that determine susceptibility to T1D significantly affect T cell composition. Specifically, the CCR5+ regulatory T cells associate with T1D through the CCR region, suggesting a shared genetic regulation. Genome-wide quantitative trait loci (QTL) mapping analysis of immune traits revealed 15 genetic loci that influence immune responses in T1D, including 12 that have never been reported in healthy population studies, implying a disease-specific genetic regulation.</p> <p><strong>Conclusion:</strong> This study provides new insights into the genetic factors that affect immunological responses in T1D.</p>
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