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17,257 results for “Diabetes”
Inter-Chemical Correlation results for the study: HHEARx2017-1962 (SEARCH for Diabetes in Youth (SEARCH))
Title: SEARCH for Diabetes in Youth (SEARCH) <br>Species: Homo sapiens <br>Number of samples: 1796 <br>Number of named analytes: 4 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=34 <br>
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>
Awareness, treatment, and control among adults living with arterial hypertension or diabetes mellitus in two rural districts in Lesotho
<p>These are pseudo-anonymised data from the ComBaCaL survey and belong to the manuscript "Awareness, treatment, and control among adults living with arterial hypertension or diabetes mellitus in two rural districts in Lesotho". </p> <p>The data dictionary explains the critical data available in the dataset. Between November 2021 and August 2022 , 6061 participants over 18 years old were visited in their households in two districts of Lesotho. Of these, data from those who were diagnosed with either hypertension or diabetes were further analysed and are documented here.</p>
Effect of superparamagnetic iron oxide nanoparticles on glucose homeostasis on type 2 diabetes experimental model
<p>The data correspond to figures in the paper by Ali, L.M.A. et al. Life Sciences 245 (2020) 117361. doi:10.1016/j.lfs.2020.117361.</p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</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>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</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>
Diabetic Macular Edema VQA Dataset
<p>Medical VQA dataset built from the <a href="https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid">IDRiD</a> and <a href="https://www.adcis.net/en/third-party/e-ophtha/">eOphta</a> datasets. The dataset contains both healthy and unhealthy fundus images. For each image, a set of pre-defined questions is generated, including questions about regions (e.g. are there hard exudates in this region?), for which an associated mask denotes the location of the region.</p> <p>The motivation for this dataset includes the lack of public medical VQA datasets with related questions. In our dataset, questions are related because there is a high-level question about the DME grade of the image, and associated low-level questions that can lead to the answer of the high-level question. This allows to study the consistency of a VQA model i.e. how often the model produces contradictory answers to questions about a given image. Questions about regions are also a novel feature of this dataset.</p> <p>The dataset can be used for general VQA purposes, and also for the more specific purpose of consistency improvement.</p> <p>Number of images : Train: 433 Val: 112 Test: 134</p> <p>Number of QA pairs: Train: 9779 Val: 2380 Test: 1311</p> <p>More details can be found <a href="https://github.com/sergiotasconmorales/consistency_vqa/blob/master/DATA.md">here</a>.</p> <p>If you use this dataset, please make sure you cite <a href="https://arxiv.org/abs/2206.13296">our paper</a>:</p> <p><em>@inproceedings{tascon2022consistency,<br> title={Consistency-Preserving Visual Question Answering in Medical Imaging},<br> author={Tascon-Morales, Sergio and Márquez-Neila, Pablo and Sznitman, Raphael},<br> booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},<br> pages={386--395},<br> year={2022},<br> organization={Springer}<br> }</em></p> <p>Do you need annotations about logical relations? No problem; check out our <a href="https://zenodo.org/record/7777849">DME VQA dataset with logical relations</a>.</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>
Blood Vessels Dataset obtained from Retina Images of Healthy and Diabetic Retinopathy Individual
<p>This dataset contains blood vessels image files extracted from publicly available fundus retina images</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>
Dataset on preferences of patients with diabetes towards EQ-5D-5L health states in Germany
<p>The dataset includes data on preferences of patients with diabetes toward EQ-5D-5L health states in Germany and adjacent codebooks. </p>
Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis
<p>Genetic studies have identified ≥240 loci associated with risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterize the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into <em>cis</em>- and <em>trans</em>-acting genetic components and mapping <em>cis</em>-acting loci associated with miRNA expression (miRNA-eQTLs). We find (i) 84 heritable miRNAs, primarily regulated by <em>trans</em>-acting genetic effects, and (ii) 5 miRNA-eQTLs. We also use several different strategies to identify T2D-associated miRNAs. First, we colocalize miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersect miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and find 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we perform differential expression analysis and identify 14 miRNAs associated with T2D status—including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p—and 4 miRNAs associated with a polygenic score for HbA1c levels—miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.</p>
Cessation of anti-diabetic medications by 'Daily 2-Only Meals-and- Exercise' lifestyle modification and remission of Type-2 Diabetes Mellitus
<p>This is the dataset describing details of the patient's age, gender, weight, waist circumference, HBA1C levels and Fasting Insulin levels from the date of enrolment in the study and subsequent changes at monthly intervals. </p>
Hypertension and Diabetes Registry Dataset in Addis Ababa, Ethiopia
<p>Dataset collected from the “hypertension and diabetes” national registry of the ministry of health of Ethiopia.<br> </p>
Pterostilbene Protects Cochlea from Ototoxicity in Streptozotocin-Induced Diabetic Rats by Inhibiting Apoptosis
<p>Diabetes mellitus (DM) causes ototoxicity by inducing oxidative stress, microangiopathy, and apoptosis in the cochlear sensory hair cells. The natural anti-oxidant pterostilbene (PTS) (trans-3,5-dimethoxy-4-hydroxystylbene) has been reported to relieve oxidative stress and apoptosis in DM, but its role in diabetic-induced ototoxicity is unclear. This study aimed to investigate the effects of dose-dependent PTS on the cochlear cells of streptozotocin (STZ)-induced diabetic rats. The study included 30 albino male Wistar rats that were randomized into five groups: non-diabetic control (Control), diabetic control (DM), and diabetic rats treated with intraperitoneal PTS at 10, 20, or 40 mg/kg/day during the four-week experimental period (DM + PTS10, DM + PTS20, and DM + PTS40). Distortion product otoacoustic emission (DPOAE) tests were performed at the beginning and end of the study. At the end of the experimental period, apoptosis in the rat cochlea was investigated using caspase-8, cytochrome-c, and terminal deoxyribonucleotidyl transferase-mediated dUTP-biotin end labeling (TUNEL). Quantitative real-time polymerase chain reaction was used to assess the mRNA expression levels of the following genes: CASP-3, BCL-associated X protein (BAX), and BCL-2. Body weight, blood glucose, serum insulin, and malondialdehyde (MDA) levels in the rat groups were evaluated. The mean DPOAE amplitude in the DM group was significantly lower than the means of the other groups (0.9–8 kHz; P < 0.001 for all). A dose-dependent increase of the mean DPOAE amplitudes was observed with PTS treatment (P < 0.05 for all). The Caspase-8 and Cytochrome-c protein expressions and the number of TUNEL-positive cells in the hair cells of the Corti organs of the DM rat group were significantly higher than those of the PTS treatment and control groups (DM > DM + PTS10 > DM + PTS20 > DM + PTS40 > Control; P < 0.05 for all). PTS treatment also reduced cell apoptosis in a dose-dependent manner by increasing the mRNA expression of the anti-apoptosis BCL2 gene and by decreasing the mRNA expressions of both the pro-apoptosis BAX gene and its effector CASP-3 and the ratio of BAX/BCL-2 in a dose-dependent manner (P < 0.05 compared to DM for all). PTS treatment significantly improved the metabolic parameters of the diabetic rats, such as body weight, blood glucose, serum insulin, and MDA levels, consistent with our other findings (P < 0.05 compared to DM for all). PTS decreased the cochlear damage caused by diabetes, as confirmed by DPOAE, biochemical, histopathological, immunohistochemical, and molecular findings. This study reports the first in vivo findings to suggest that PTS may be a protective therapeutic agent against diabetes-induced ototoxicity.</p>
Monocyte class switch and hyperinflammation characterise severe COVID-19 in type 2 diabetes
<p>raw and source data for manuscript EMM-2020-13038 under revision and preprint doi: https://doi.org/10.1101/2020.06.02.20119909</p>
ADIPOQ Gene Variants (rs266729, rs2241766, rs1501299) and Acute Myocardial Infarction in Vietnamese Patients with Type 2 Diabetes Mellitus
<p>This data is from a study project about ADIPOQ Gene Variants (rs266729, rs2241766, rs1501299) and Acute Myocardial Infarction in Vietnamese Patients with Type 2 Diabetes Mellitus. The data contains information from 550 patients with their identification removed to ensure confidentiality.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.