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2,801 results for “diabetes mellitus”
Placental Remodeling in Gestational Diabetes Mellitus (GDM) Disrupts Lipid Metabolism
GEO Series GSE317191. Homo sapiens. 11 samples. Type: Methylation profiling by genome tiling array.
Gene-expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy
GEO Series GSE189007. synthetic construct; Homo sapiens. 130 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Expression profile of CircRNA in Type2 diabetes mellitus patients with retinopathy
GEO Series GSE276127. Homo sapiens. 10 samples. Type: Non-coding RNA profiling by array.
Human Placental Exosomes in Gestational Diabetes Mellitus Carry a Specific Set of miRNAs Associated with Skeletal Muscle Insulin Sensitivity
GEO Series GSE112168. Homo sapiens. 12 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Exosomal RNA expression profiles and their prediction performance in gestational diabetes mellitus patients with macrosomia
GEO Series GSE194119. Homo sapiens. 6 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Vitamin C supplementation reduces expression of circulating miR-451a in poorly controlled type 2 diabetes mellitus
GEO Series GSE154647. Homo sapiens. 10 samples. Type: Non-coding RNA profiling by array.
Circulating lncRNAs NONHSAT054669.2 and ENST00000525337 can be used as early biomarkers of gestational diabetes mellitus
GEO Series GSE217299. Homo sapiens. 6 samples. Type: Non-coding RNA profiling by array.
METTL3 mediates m6A modification of hsa_circ_0072380 to regulate the progression of gestational diabetes mellitus
GEO Series GSE250374. Homo sapiens. 12 samples. Type: Other.
Next generation sequencing of long non-coding RNAs in diabetes mellitus rats after sleeve gastrectomy
GEO Series GSE162018. Rattus norvegicus. 10 samples. Type: Non-coding RNA 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>
Dataset related to the article : "The Burden of Impaired Serum Albumin Antioxidant Properties and Glyco-Oxidation in Coronary Heart Disease Patients with and without Type 2 Diabetes Mellitus"
<p>This record contains raw data related to the article: The Burden of Impaired Serum Albumin Antioxidant Properties and Glyco-Oxidation in Coronary Heart Disease Patients with and without Type 2 Diabetes Mellitus</p> <p>Abstract</p> <p>Human serum albumin (HSA) has an important antioxidant activity due to the presence of the reduced cysteine at position 34, which represents the most abundant free thiol in the plasma. In oxidative-based diseases, HSA undergoes S-thiolation (THIO-HSA) with changes in the antioxidant function of albumin that could contribute to the progression of the disease. The aim of this study was to verify, for the first time, the different burdens of THIO-HSA, glycated HSA (GLY-HSA), and advanced glycation end products (AGE) accumulation both in type 2 diabetes mellitus (T2DM) patients and in non-diabetic patients, with or without coronary heart disease (CHD). In this study, we assessed the presence of modified forms of HSA, THIO-HSA, and GLY-HSA by means of mass spectrometry in 33 patients with both T2DM and CHD, in 31 patients with T2DM and without CHD, in 30 patients without diabetes with a history of CHD, and 27 subjects without diabetes and CHD. All the patients' anthropometric and clinical data were recorded including age, sex, duration of diabetes, body mass index (BMI), blood pressure, and history of CHD defined with anamnestic data. Metabolic parameters, such as fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), lipids, pentosidine, AGE, receptor for advanced glycation end-products (RAGE) and its soluble form (sRAGE), were measured. AGE and pentosidine are significantly higher in T2DM patients with and without CHD with respect to non-diabetic patients with CHD and control subjects. RAGE levels are significantly higher in T2DM patients with respect to non-diabetic patients, and among T2DM patients, the group with CHD showed significantly higher RAGE levels than those without CHD (217 ± 171 pg/mL and 140 ± 61 pg/mL, respectively). Albumin isoforms discriminate between non-diabetic patients with CHD and T2DM patients with and without CHD and control subjects, with GLY-HSA levels higher in T2DM with and without CHD, and THIO-HSA higher in CHD patients without T2DM. Finally, we demonstrated that the oxidized forms of HSA can increase the expression of the inflammatory cytokine Tumor Necrosis Factor-alpha (TNFα) in monocytic cells. In patients with CHD, GLY-HSA and THIO-HSA have a different prevalent distribution, the first one prevailing in patients with T2DM and the second one in patients without T2DM. These findings suggest that albumin quality and homeostasis balance between glyco-oxidation and thiolation might have an impact on the antioxidant defense system in cardiovascular diseases.</p>
Prevalence of Diabetes Mellitus Among Patients Treated With Atypical and Conventional Antipsychotics
ClinicalTrials.gov study NCT00224276. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Relation Between Serum Level of Amioterminal Propeptide of Type I Procollagen and Diastolic Dysfunction in Hypertensive Patients Without Diabetes Mellitus
ClinicalTrials.gov study NCT00172406. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Effect of Metformin Treatment on Thyroid Hormone Metabolism in Euthyroid Patients With Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT00463502. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Structured Patient Education and Quality of Life of Elderly Patients With Diabetes Mellitus- a Prospective Study
ClinicalTrials.gov study NCT00444483. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Insulin Dependent Gestational Diabetes Mellitus: Randomized Trial of Induction of Labour at 38 and 40 Weeks of Gestation
ClinicalTrials.gov study NCT01256892. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Next generation sequencing of circular RNAs in diabetes mellitus rats after sleeve gastrectomy
GEO Series GSE162013. Rattus norvegicus. 12 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Sitagliptin phosphate ameliorates chronic inflammation in diabetes mellitus via modulating macrophage polarization
GEO Series GSE293313. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Platelet micro-RNA expression in type 2 diabetes mellitus
GEO Series GSE44093. Homo sapiens. 60 samples. Type: Non-coding RNA profiling by array.
miRNA expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy
GEO Series GSE189002. Homo sapiens; synthetic construct. 45 samples. Type: Non-coding RNA profiling by array.
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