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2,348 results for “type 1 diabetes”
Gene expression Fulminant type 1 diabetes vs classical type 1A diabetes
GEO Series GSE44313. Homo sapiens. 10 samples. Type: Expression profiling by array.
Modulation of ER Stress in Human Type 1 Diabetic Islets Through Combined Use of Stress Inducer and Inhibitor Treatments
GEO Series GSE275138. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing.
Response of peripheral blood mononuclear cells from 20 healthy donor and 15 patients with type 1 diabetes to type 1 diabetogenic protein (IGRP, PPI) derived peptide stimulation.
GEO Series GSE72377. Homo sapiens. 165 samples. Type: Expression profiling by array.
Latent EBV impairs immune cell signaling and enhances the efficacy of anti-CD3 mAb in Type 1 Diabetes.
GEO Series GSE296549. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus
<h2><strong>T1DiabetesGranada</strong></h2> <p>A longitudinal multi-modal dataset of type 1 diabetes mellitus</p> <p>Documented by:</p> <p><strong>Rodriguez-Leon, C., Aviles-Perez, M. D., Banos, O., Quesada-Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Munoz-Torres, M. (2023). T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus.<em> Scientific Data, 10(1), 916.</em> <a href="https://doi.org/10.1038/s41597-023-02737-4">https://doi.org/10.1038/s41597-023-02737-4</a></strong></p> <p> </p> <h3><strong>Background</strong></h3> <p>Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.</p> <p> </p> <h3><strong>Data Records</strong></h3> <p>The data are stored in four comma-separated values (CSV) files which are available in <em><strong>T1DiabetesGranada.zip</strong></em>. These files are described in detail below.</p> <p> </p> <h4><strong>Patient_info.csv</strong></h4> <p><em>Patient_info.csv</em> is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Sex</strong> – Sex of the patient. Values: F (for female), masculine (for male) </p> <p><strong>Birth_year</strong> – Year of birth of the patient. Format: YYYY.</p> <p><strong>Initial_measurement_date</strong> – Date of the first blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_measurement_date</strong> – Date of the last blood glucose level measurement of the patient in the <em>Glucose_measurements.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_days_with_measures</strong> – Number of days with blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 8 to 1463.</p> <p><strong>Number_of_measurements</strong> – Number of blood glucose level measurements of the patient, extracted from the <em>Glucose_measurements.csv</em> file. Values: ranging from 400 to 137292.</p> <p><strong>Initial_biochemical_parameters_date</strong> – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Final_biochemical_parameters_date</strong> – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Format: YYYY-MM-DD.</p> <p><strong>Number_of_biochemical_parameters</strong> – Number of biochemical parameters measured on the patient, extracted from the <em>Biochemical_parameters.csv</em> file. Values: ranging from 4 to 846.</p> <p><strong>Number_of_diagnostics</strong> – Number of diagnoses realized to the patient, extracted from the <em>Diagnostics.csv</em> file. Values: ranging from 1 to 24.</p> <p> </p> <h4><strong>Glucose_measurements.csv</strong></h4> <p><em>Glucose_measurements.csv</em> is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Measurement_date</strong> – Date of the blood glucose level measurement. Format: YYYY-MM-DD. </p> <p><strong>Measurement_time</strong> – Time of the blood glucose level measurement. Format: HH:MM:SS.</p> <p><strong>Measurement</strong> – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.</p> <p> </p> <h4><strong>Biochemical_parameters.csv</strong></h4> <p><em>Biochemical_parameters.csv</em> is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Reception_date</strong> – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.</p> <p><strong>Name</strong> – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.</p> <p><strong>Value</strong> – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.</p> <p> </p> <h4><strong>Diagnostics.csv</strong></h4> <p><em>Diagnostics.csv</em> is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:</p> <p><strong>Patient_ID</strong> – Unique identifier of the patient. Format: LIB19XXXX.</p> <p><strong>Code</strong> – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).</p> <p><strong>Description</strong> – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (<a href="https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes">https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes</a>).</p> <p> </p> <h3><strong>Technical Validation</strong></h3> <p>Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.</p> <p>Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the <em>Glucose_measurements.csv </em>file as they should be.</p> <p> </p> <h3><strong>Usage Notes</strong></h3> <p>For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.</p> <p>The files that compose the dataset are CSV type files delimited by commas and are available in <em><strong>T1DiabetesGranada.zip</strong></em>. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in <em><strong>UsageNotes.zip</strong></em>.</p> <p> </p> <h4><strong>Graphs_and_stats.ipynb</strong></h4> <p>The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.</p> <p> </p> <h3><strong>Code Availability</strong></h3> <p>The dataset was generated using some custom code located in <em><strong>CodeAvailability.zip</strong></em>. The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.</p> <p> </p> <h4><strong>Original_patient_info_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.</p> <p> </p> <h4><strong>Glucose_measurements_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.</p> <p> </p> <h4><strong>Biochemical_parameters_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the measured biochemical parameter is translated.</p> <p> </p> <h4><strong>Diagnostic_curation.ipynb</strong></h4> <p>In the Jupyter Notebook is preprocessed the original file with patient data of the diagnoses of diabetes mellitus complications or other diseases that patients have in addition to T1D.</p> <p> </p> <h4><strong>Get_patient_info_variables.ipynb</strong></h4> <p>In the Jupyter Notebook it is coded the feature extraction process from the files <em>Glucose_measurements.csv</em>, <em>Biochemical_parameters.csv</em> and <em>Diagnostics.csv</em> to complete the file <em>Patient_info.csv</em>. It is divided into six sections, the first three to extract the features from each of the mentioned files and the next three to add the extracted features to the resulting new file.</p> <p> </p> <h3><strong>Data Usage Agreement</strong></h3> <p>The conditions for use are as follows:</p> <ol> <li>You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.</li> <li>You commit to keeping the T1DiabetesGranada dataset confidential and secure and will not redistribute data or Zenodo account credentials.</li> <li>You will require anyone on your team who utilizes these data to comply with this Data Use Agreement by accessing the data themselves through Zenodo. Each user wishing to access controlled data must individually agree to the Conditions for Use.</li> <li>You understand that these data may not be used for commercial use or to re-contact research participants.</li> <li>You agree to report any misuse, unauthorized access, or data release, intentional or inadvertent within 5 business days. For this purpose, please write an email to icarsecretaria@ugr.es explaining the detected data breach.</li> <li>You agree to cite the research paper under which T1DiabetesGranada dataset was published on all publications or presentations which result from using the T1DiabetesGranada dataset.</li> </ol> <p> </p> <p><strong>Please note that in order to request access to the dataset you need to be authenticated on Zenodo, accept the Data Usage Agreement and specify in the request your full name, email, and the justification of the data use.</strong> </p>
Immunogenicity Safety Study of Wockhardt's Recombinant Insulin Analogue in Type 1 Diabetic Patients
ClinicalTrials.gov study NCT01352663. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Severe Hypoglycemia in Pregnant Women With Type 1 Diabetes
ClinicalTrials.gov study NCT00435565. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of Gut Absorption Rate of Glucose After a Large Evening Meal in Type 1 Diabetes
ClinicalTrials.gov study NCT02782195. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Evaluation of Efficacy and Safety of Vardenafil in Patients With Erectile Dysfunction and Type 1 Diabetes
ClinicalTrials.gov study NCT00660998. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Single-cell RNA sequencing analysis reveals the relationship of bone marrow and osteopenia in type 1 diabetic mice
GEO Series GSE182986. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Differential expression of microRNA profiles from the peripheral blood of type 1 and type 2 diabetic patients
GEO Series GSE27645. Homo sapiens; Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Murid betaherpesvirus 1; Human immunodeficiency virus 1; Murid gammaherpesvirus 4; Betapolyomavirus hominis; human gammaherpesvirus 4; JC polyomavirus; Human gammaherpesvirus 8; Betapolyomavirus macacae; Rattus norvegicus. 35 samples. Type: Non-coding RNA profiling by array.
Single-cell transcriptome analysis reveals phenotypically diverse islet-infiltrating T cell subsets in type 1 diabetes
GEO Series GSE125456. Mus musculus. 13 samples. Type: Expression profiling by high throughput sequencing.
Increased β-cell proliferation prior to immune-cell invasion prevents progression of type 1 diabetes
GEO Series GSE128315. Mus musculus. 23 samples. Type: Expression profiling by array.
Role of tRNA-derived fragments in the cross-talk between immune cells and beta cells during type 1 diabetes pathogenesis (Cyt-treated)
GEO Series GSE242566. Mus musculus. 8 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Gene expression in the peripheral whole blood of established Type 1 diabetes patients
GEO Series GSE89022. Homo sapiens. 10 samples. Type: Expression profiling by array.
Role of tRNA-derived fragments in the cross-talk between immune cells and beta cells during type 1 diabetes pathogenesis (NOD islets)
GEO Series GSE242567. Mus musculus. 7 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Self-renewing islet TCF1+ CD8 T cells undergo interleukin-27 controlled differentiation to become TCF1- terminal effectors during the progression of type 1 diabetes
GEO Series GSE155595. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Expression data from salivary gland cells of NOD/shiLtJ mice and NOD/shiLtJ mice with type 1 diabetes
GEO Series GSE179654. Mus musculus. 10 samples. Type: Expression profiling by array.
Type 1 Diabetes Human Enteroid Studies Reveal Major Changes In The Intestinal Epithelial Compartment
GEO Series GSE264102. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing.
Role of tRNA-derived fragments in the cross-talk between immune cells and beta cells during type 1 diabetes pathogenesis (NOD EVs)
GEO Series GSE242565. Mus musculus. 3 samples. Type: Non-coding RNA profiling by high throughput sequencing.
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