Find research datasets worth reusing

openGEO-OpenFeb 2019View details →

Targeted elimination of senescent beta cells prevents Type 1 Diabetes

GEO Series GSE117770. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJan 2022View details →

HAMSAB supplement enhances SCFA production associated with microbiota and immune modulation in type 1 diabetes

GEO Series GSE176230. Homo sapiens. 34 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenMar 2023View details →

Multi-omics analysis revealed the pathogenetic mechanisms of Mechanical allodynia in type 1 diabetes

GEO Series GSE226315. Rattus norvegicus. 9 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJan 2024View details →

Monocytes in Type 1 diabetes families exhibit high cytolytic activity and subset abundances that correlate with clinical progression

GEO Series GSE239501. Homo sapiens. 4 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJan 2025View details →

A multi-omics integration approach relying on circulating factors does not discern subtypes in childhood type 1 diabetes.

GEO Series GSE287275. Homo sapiens. 103 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenDec 2019View details →

Identified differentially expressed lncRNAs in Type 1 Diabetes Patients

GEO Series GSE130279. Homo sapiens. 12 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.

openGEO-OpenJul 2024View details →

T cell receptor β-chains display abnormal shortening and repertoire sharing in type 1 diabetes

GEO Series GSE272431. Homo sapiens. 88 samples. Type: Other.

openGEO-OpenSep 2025View details →

Single-cell multiome profiling reveals pancreas cell type-specific gene regulatory programs of type 1 diabetes progression [snRNA-seq]

GEO Series GSE273597. Homo sapiens. 33 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenDec 2009View details →

Folic acid supplementation normalizes the endothelial progenitor cell transcriptome of patients with type 1 diabetes

GEO Series GSE17635. Homo sapiens. 32 samples. Type: Expression profiling by array.

zenodo24/100

Primary documentation on the scientific study of indicators of continuous monitoring and flash monitoring of glycemia in children and adolescents with type 1 diabetes mellitus

The main purpose of creating an electronic database was to evaluate the performance of continuous glucose monitoring (CGM) and flash monitoring (FMS) in children and adolescents diagnosed with type 1 diabetes mellitus. The database is intended for entering, systematizing, storing and displaying patient data (date of birth, age, date of diagnosis of type 1 diabetes mellitus, length of illness, date of first visit to an endocrinologist, with the installation of a CGM or FMS), glycated hemoglobin indicators initially , during the study and ultimately, as well as CGM, FMS data (average glucose level, glycemic variability, percentage of cases above the target range, percentage of cases within the target range, percentage of cases below the target range, number of hypoglycemic episodes and their average duration, frequency of daily scans and frequency of sensor readings).The database is the basis for comparative statistical analysis of dynamic monitoring indicators in groups of patients with the presence or absence of diabetic complications (neuropathy, retinopathy and nephropathy). The database presents the results of a prospective, open, controlled, clinical study obtained over a year and a half. The database includes information on 307 patients (adolescent children) aged 3 to 17 years inclusive. During the study, the observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and with no diabetic complications, 155 people. All registrants of the database were assigned individual codes, which made it possible to exclude personal data (full name) from the database.The database is executed in the Microsoft Office Excel program and has the character of a depersonalized summary table, which consists of two blocks-sheets: patients of groups 1 and 2  and is structured according to the following sections: "Patient number"; "Patient code"; "Date of birth"; "Age of the patient"; section "Date of diagnosis of DM1" indicates the date of the official diagnosis of type 1 diabetes mellitus at the first hospitalization of the patient, this information is borrowed from medical information systems; section "Length of service DM1" reflects information about the duration of the patient's illness; the section "Date of the first visit" contains information about the date of the registrant's visit to the endocrinologist with the installation of FMS / CGM devices; the section "Frequency of self-monitoring with a glucometer" contains information about the frequency of measuring blood glucose levels by the patient at home using a glucometer until the establishment of FMS / CGM.Sections "HbA1c initially (GMI)", "HbA1c (GMI)", "HbA1c final (GMI)", display the indicators of the level of glycated hemoglobin from the total for the period of the beginning of the study, at the intermediate stages of the study and at the end of observation.The database structure has a number of sections accumulating information obtained with CGM/FMS, in particular: the section "Average glucose level"; the section "% above the target range", reflecting the percentage of the patient's stay with glycemia above the target indicators during the day; the section "% within the target range", reflecting the percentage of the patient's stay within the target glycemia indicators per day; the section "% below the target range", reflecting the percentage of the patient's stay with glycemia below the target indicators during the day; the section "Hypoglycemic phenomena", reflecting the number of cases of hypoglycemia in patients within 2 weeks; the section "Average duration", reflecting the average duration of hypoglycemic phenomena registered in the patient; the section "Sensor data received", indicating the percentage of time the patient was with an active device sensor; the section "Daily scans" show the frequency of scans of the patient's glycemic level (once a day); the section "%CV" displays the variability of the patient's glycemia recorded by the device. The listed sections are repeated in the database in accordance with the number of follow-up visit.Also in the database there is a section "Mid. values", which contains indicators of the average values of patient data for all of the above sections, both in the first and in the second group of patients.When working with the database, the use of filters (in the "Data" tab) containing the names of indicators allows you to enter information about new registrants in a convenient form or correct existing data, as well as sort and search for one or more specified indicators.The electronic database allows you to systematize a large volume of results, distribute data into categories, search for any field or set of fields in the input format, systematize the selected array, makes it possible to directly use this data for statistical analysis, as well as to view and print information on specified conditions with the location of fields in a convenient sequence.

restrictedcc-by-4.0Jun 2023View details →

zenodo24/100

Primary documentation on the scientific study of glycemic variability indicators in children and adolescents with diabetes mellitus type 1 diabetes

The main purpose of creating an electronic database was to assess indicators of glycemic variability in adolescents and children diagnosed with diabetes mellitus type 1. The database presents the results of prospective, open, controlled, clinical study obtained over a period of one and a half years. Base data includes information on 307 patients (children and adolescents) aged 3 to 17 years inclusive. The observed patients were divided into two groups: Group 1 - patients diagnosed with type 1 diabetes mellitus and with diabetic complications, 152 people, Group 2 – patients diagnosed with type 1 diabetes mellitus and without diabetic complications, 155 people. All database registrants were assigned individual codes, which made it possible to exclude personal data (full name) from the database.During the study, an analysis of carbohydrate metabolism was carried out with an assessment HbA1c (glycated hemoglobin), CGMS (continuous glucose monitoring), FMG (flash glucose monitoring) with face-to-face consultations with an endocrinologist and analysis of the data obtained. Obtained monitoring results were processed using a specialized variability calculator glycemia (EasyGV®, ver. 9), the following indicators and indices were calculated variability: average glycemic level (Mean), standard deviation (SD); index prolonged increase in glycemia (CONGA); glycemic lability index (LI); index risk of hypoglycemia (LBGI); hyperglycemia risk index (HBGI), average value overall risk (ADRR), average amplitude of glycemic fluctuations (MAGE); grade inter-day GV (MODD), rate of change in glycemia (MAG), J-index - indicator quality of glycemic control. The database is intended for entering, organizing, storing and displaying all of the above data.

restrictedcc-by-4.0Jul 2023View details →

zenodo24/100

Increasing Contribution of Adolescent Type 1 Diabetes Drives Incidence Rates in Poland - a 40-year-long Observational Study

<div> <div> Aims/hypothesis: 40-year-long longitudinal observation of long-term trends of type 1 diabetes incidence and prevalence in children in Central Poland   </div> <div> Methods: This was a prospective observational study performed by a reference regional center for pediatric diabetes care for Lodz Province (currently 2·4M inhabitants, 360K children). We registered each case of new-onset type 1 diabetes admitted to regional pediatric diabetes centers between the years 1983 and 2022 in children between 0 and 14 y.o. The diagnosis was based on currently available guidelines. Cases of other types of diabetes (e.g., monogenic) were excluded upon identification from incidence and prevalence rates. Yearly data on the at-risk population were acquired from Poland`s General Statistical Office. Sex-specific data on population structure were available from 1989 onwards.   </div> <div> Results: In the analyzed period, the incidence rate of type 1 diabetes increased tenfold from 3·29/100,000 (95%CI: 1·85-4·73) in 1983 to 32.43 (26·42-38·44) in 2022, with an average annual percentage change of 5·73% (95%CI: 4·99%-6·44%). Joinpoint analysis detected two distinct periods of increase: rapid in 1983-2005 (annual percentage increase of 7·38%, 95%CI: 6·30-10·52%) and a slower one in 2005-2022 (3·65%, 95%CI: -0·86-5·13%). Incidence rates among the youngest children (0-4 y.o.) were significantly lower than in 5-9 y.o. (β±SE: -0·567±0·059, p<0·0001) and 10-14 y.o. (β±SE: -0·520±0·060, p<0·0001). The incidence growth dynamic for the two older groups showed a consistent increase, whereas the incidence in 0-4 year-olds plateaued after 2007. Incidence rates varied seasonally, with the most cases diagnosed during the winter months (December, January, and February; mean difference from remaining seasons of 29±11·6 percentage points, p<0·0001). Corresponding with increasing incidence rate, estimated prevalence of type 1 diabetes increased over the years and reached 177·21/100,000 (95%CI: 163·18-191·24) for children 0-14 y.o., and  17·11 (95%CI: 9·2-25·02), 190·54 (95%CI: 165·03-215·75), 238·73 (95%CI: 211·7-265·76) for 0-4, 5-9, and 10-14 y.o., respectively.  </div> <div> Conclusion/interpretation: Over the past 40 years, the incidence of type 1 diabetes in children in Central Poland has increased significantly, but the rate of increase appears to be slowing. As majority of patients with type 1 diabetes are 10 years old or older, with the most new cases occurring in that age group the healthcare systems should prepare for care of young adults who are extensive users of new diabetes technologies.  </div> </div>

restrictedcc-by-4.0Aug 2024View details →

restrictedIPD-UNDECIDEDFeb 2026View details →

Serum 1,25-dihydroxyvitamin D Levels in Type 2 Diabetes Mellitus Patients With Different Levels of Albuminuria

ClinicalTrials.gov study NCT01845870. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Blood Glucose Target Before and During Exercise in Adults With Type 1 Diabetes Using an Artificial Pancreas

ClinicalTrials.gov study NCT05821322. IPD Sharing: NO. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Accessing Care, Clinical Trials and Screening for Underserved Children and Adults With Type 1 Diabetes (ACCESS-T1D)

ClinicalTrials.gov study NCT06908057. IPD Sharing: Not stated. Countries: 1. Publications: 0.

Multicenter Study of Fulminant Type 1 Diabetes in China

ClinicalTrials.gov study NCT05593081. IPD Sharing: NO. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

Health Education During Ramadan Fasting in Type 1 Diabetes

ClinicalTrials.gov study NCT03501511. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

Continuous Glucose Monitoring During Intecorse in Young Adults With Type 1 Diabetes

ClinicalTrials.gov study NCT02750111. IPD Sharing: YES. Countries: 1. Publications: 0.

Intermediate and Long Acting Insulin Young Children Type 1 Diabetes.

ClinicalTrials.gov study NCT04664764. IPD Sharing: NO. Countries: 1. Publications: 0.

Effect of Fasting on Hypoglycemic Counterregulation in Type 1 Diabetes

ClinicalTrials.gov study NCT05973799. IPD Sharing: NO. Countries: 1. Publications: 0.