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5,946 results for “diabetes type 2”
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>
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>
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>
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>
CONCEPT-DIABETES DATA MODEL TO ANALYSE HEALTHCARE PATHWAYS OF TYPE 2 DIABETES
<p><strong>Technical notes and documentation on the common data model of the project CONCEPT-DM2. </strong></p> <p>This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes, version v0.2.0.</p> <p><strong>Aims of the CONCEPT-DM2 project: </strong></p> <p>General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.</p> <p>Main specific aims:</p> <ul> <li>To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations</li> <li>To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines</li> <li>To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.</li> <li>To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.</li> </ul> <p><strong>Study Design: </strong>It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.</p> <p><strong>Cohort definition: </strong>All patients with code of Type 2 Diabetes in the clinical health records</p> <ul> <li>Inclusion criteria: patients that, at 2017-01-01 or during the follow-up from 2017-01-01 to 2022-12-31 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)</li> <li>Exclusion criteria: <ul> <li>patients with no contact with the health system from 2017-01-01 to 2022-12-31</li> <li>patients that had a T1D (DM1) code opened after the T2D code during the follow-up.</li> </ul> </li> <li>Study period. From 2017-01-01 to 2022-12-31</li> </ul> <p><strong>Files included in this publication: </strong></p> <ul> <li>Datamodel_CONCEPT_DM2_diagram_v0.2.0.jpg</li> <li>Common data model specification (Datamodel_CONCEPT_DM2_v.0.2.0.xlsx)</li> <li>Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data_v0.2.0) <ul> <li>sample_data1_dm_patient.csv</li> <li>sample_data2_dm_param.csv</li> <li>sample_data3_dm_patient.csv</li> <li>sample_data4_dm_param.csv</li> <li>sample_data5_dm_patient.csv</li> <li>sample_data6_dm_param.csv</li> <li>sample_data7_dm_param.csv</li> <li>sample_data8_dm_param.csv</li> </ul> </li> <li>Datamodel_CONCEPT_DM2_explanation_v0.2.0.pptx</li> </ul> <p><strong>CHANGE-LOG from version v0.1.0 to v0.2.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Missing data is now identified leaving the field empty</li> <li> <p>All ICD diagnosis given in the Datamodel refer to the root code, so that all codes and subcodes that start with the given codes need to be considered. For instance, if in the data model appears I21, then all codes I21.x should be included.</p> </li> <li> <p>All admissions registered in the CMBD will be included to facilitate the extraction procedure (annex 8 disappears).</p> </li> <li> <p>Three diagnosis codes and three procedure codes are now included in table dm_cmbd.</p> </li> </ul> <p><strong>CHANGE-LOG from version v0.2.0 to v0.3.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Variable 'copayment' (annex 2) change: cod 002.01 =>0; cod 002.02 =>1</li> <li>Variable 'visit_service' (annex 8) change: APR refers to Primary Care and APA refers to Pathological Anatomy</li> <li>Variable 'filglom' (annex 4) change: non numerical values compatible with '> 60' => 999</li> </ul>
Dataset Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study
<p>This dataset is related to "Validation of seven type 2 diabetes mellitus risk scores in a population-based cohort. The CoLaus Study".</p> <p>Vanessa Kraege*, Janko Fabecic*, Pedro Marques Vidal, Gérard Waeber and Marie Méan</p> <p>*Contributed equally; co-first authors</p>
Recognition of symptoms, mitigating mechanisms and self-care experiences of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia
<p>Compliance of patients with self-care practices is the mainstay of measures to manage diabetes. Thus, the study explored self-care practices of type 2 diabetes patients receiving insulin treatment in North-East Ethiopia.</p>
The short-term cost-effectiveness of once-weekly semaglutide versus once-weekly dulaglutide for the treatment of type 2 diabetes mellitus in Colombian adults
<p>Dataset used for the study titled "A relative cost of control analyses of once weekly semaglutide versus dulaglutide for the treatment of type 2 diabetes mellitus in Colombian adults"</p>
Safety, Pharmacokinetics and Efficacy of Bimagrumab in Overweight and Obese Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT03005288. IPD Sharing: YES. Countries: 2. Publications: 1.
Comparison of a New Formulation of Insulin Glargine With Lantus in Patients With Type 2 Diabetes Mellitus on Basal Plus Mealtime Insulin
ClinicalTrials.gov study NCT01499082. IPD Sharing: YES. Countries: 13. Publications: 3.
Efficacy and Safety of the Insulin Glargine/Lixisenatide Fixed Ratio Combination (FRC) Versus GLP-1 Receptor Agonist in Patients With Type 2 Diabetes, With a FRC Extension Period
ClinicalTrials.gov study NCT02787551. IPD Sharing: YES. Countries: 9. Publications: 5.
Effect of Sotagliflozin on Cardiovascular Events in Participants With Type 2 Diabetes Post Worsening Heart Failure (SOLOIST-WHF Trial)
ClinicalTrials.gov study NCT03521934. IPD Sharing: YES. Countries: 32. Publications: 6.
Personalizing Sleep Interventions to Prevent Type 2 Diabetes in Community Dwelling Adults With Pre-Diabetes
ClinicalTrials.gov study NCT03398902. IPD Sharing: YES. Countries: 1. Publications: 16.
Assessment of Glycemic Control in Patients With Type 2 Diabetes Mellitus and Late Stage Chronic Kidney Disease
ClinicalTrials.gov study NCT03383627. IPD Sharing: NO. Countries: 1. Publications: 4.
Effect of Sotagliflozin on Cardiovascular and Renal Events in Participants With Type 2 Diabetes and Moderate Renal Impairment Who Are at Cardiovascular Risk
ClinicalTrials.gov study NCT03315143. IPD Sharing: YES. Countries: 45. Publications: 5.
Comparison of a New Formulation of Insulin Glargine With Lantus in Patients With Type 2 Diabetes on Basal Insulin With Oral Antidiabetic Therapy
ClinicalTrials.gov study NCT01499095. IPD Sharing: YES. Countries: 13. Publications: 6.
A Study to Assess the Safety and Efficacy of SAR425899 in Patients With Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT02973321. IPD Sharing: YES. Countries: 8. Publications: 2.
VERIFY:A Study to Compare Combination Regimen With Vildagliptin & Metformin Versus Metformin in Treatment-naïve Patients With Type 2 Diabetes Mellitus
ClinicalTrials.gov study NCT01528254. IPD Sharing: YES. Countries: 34. Publications: 5.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.