Keywords
Diabetes Mellitus, Type 2 ×
Show more Keywords
Table of contents
  1. 1. Clinical Trial
  2. 2. Routine Documentation
  3. 3. Registry/Cohort Study
  4. 4. Quality Assurance
  5. 5. Data Standard
  6. 6. Patient-Reported Outcome
  7. 7. Medical Specialty
Selected data models

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- 7/15/24 - 2 forms, 8 itemgroups, 147 items, 2 languages
Itemgroups: IG.1, IG.2, IG.3, IG.4, IG.5, IG.6, IG.7, Fragebogen

Optional Set

3 itemgroups 46 items
- 4/28/24 - 5 forms, 1 itemgroup, 1 item, 1 language
Itemgroup: IG.elig
Principal Investigator: Ruth Loos, PhD, The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA MeSH: Cardiovascular Diseases,Obesity,Diabetes Mellitus, Type 2,Glucose,Kidney Failure, Chronic,Cholesterol, HDL,Cholesterol, LDL,Triglycerides,Coronary Disease,Myocardial Infarction,Inflammation,Stroke,Body Height https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000925 The Institute for Personalized Medicine (IPM) Bio*Me* Biobank is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Bio*Me* Biobank populations include 28% African American, 38% Hispanic Latino predominantly of Caribbean origin, 23% Caucasian/White. IPM BioMe Biobank disease burden is reflective of health disparities with broad public health impact. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. This study is part of the Population Architecture using Genomics and Epidemiology (PAGE) study (phs000356).

pht005176.v1.p1

1 itemgroup 4 items

pht005178.v1.p1

1 itemgroup 6 items

pht006203.v1.p1

1 itemgroup 6 items

pht005177.v1.p1

1 itemgroup 5 items
- 3/16/24 - 8 forms, 1 itemgroup, 2 items, 1 language
Itemgroup: IG.elig

pht001126.v2.p1

1 itemgroup 3 items

pht004481.v1.p1

1 itemgroup 3 items

pht004482.v1.p1

1 itemgroup 4 items

pht004483.v1.p1

1 itemgroup 21 items

pht004484.v1.p1

1 itemgroup 3 items

pht004485.v1.p1

1 itemgroup 3 items
- 3/9/24 - 5 forms, 1 itemgroup, 1 item, 1 language
Itemgroup: IG.elig
Principal Investigator: David Altshuler, Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA MeSH: Type 2 Diabetes Mellitus https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000840 The Genetics of Type 2 Diabetes Consortium (GoT2D) is a collaboration between the University of Michigan, the Broad Institute and the Wellcome Trust Centre for Human Genetics. The overall aim is to extend upon recent efforts, such as genome-wide association studies (GWAS) and large scale meta-analyses. While they have proved successful at mapping genomic loci that influence human diseases, like type 2 diabetes, much of the heritability remains unexplained. In this study, we use next generation sequencing and genotyping technologies to query for lower frequency variants in the human genome. Thereby, allowing a deeper characterization of the spectrum of alleles associated with type 2 diabetes risk, and a better assessment of the genes that play a role in the etiology of type 2 diabetes development. We studied 1,326 T2D cases and 1,331 normoglycemic controls from Northern and Central Europe (Sweden, Finland, UK, and Germany). To efficiently characterize the entire genome sequence of each individual, we performed low-coverage (~5x) whole-genome sequencing, augmented by deep coverage (~100x) sequencing of the exome (Fuchsberger et al, 2016), and dense (2.5M) single nucleotide polymorphism (SNP) genotyping using the HumanOmni2.5 array. The data deposited in dbGaP will include all the Swedish, Finnish, and UK samples, but the German data will be deposited in the European Genome-phenome Archive (EGA), by virtue of the project specific funding requirements.

pht005641.v1.p1

1 itemgroup 4 items

pht005643.v1.p1

1 itemgroup 20 items

pht005644.v1.p1

1 itemgroup 6 items

pht005642.v1.p1

1 itemgroup 3 items
- 1/31/24 - 5 forms, 1 itemgroup, 3 items, 1 language
Itemgroup: pht005331

pht005332.v1.p1

1 itemgroup 23 items

pht005333.v1.p1

1 itemgroup 3 items

Eligibility

1 itemgroup 3 items

pht005330.v1.p1

1 itemgroup 4 items
- 12/1/23 - 4 forms, 1 itemgroup, 1 item, 1 language
Itemgroup: IG.elig
Principal Investigator: Vasan Ramachandran, Department of Medicine, Boston University School of Medicine, Boston, MA, USA MeSH: Cardiovascular Diseases,Atherosclerosis,Atrial Fibrillation,Death, Sudden, Cardiac,Diabetes Mellitus, Type 2,Heart Failure,Blood Pressure,Hypertension,Body Mass Index,Adiposity,Lipids,Pulmonary Disease, Chronic Obstructive,Renal Insufficiency, Chronic,Stroke,Osteoporosis,Risk Factors,Biological Markers,Biomarkers, Pharmacological https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000974 The Framingham Heart Study (FHS) is a prospective cohort study of 3 generations of subjects who have been followed up to 65 years to evaluate risk factors for cardiovascular disease. Its large sample of ~15,000 men and women who have been extensively phenotyped with repeated examinations make it ideal for the study of genetic associations with cardiovascular disease risk factors and outcomes. DNA samples have been collected and immortalized since the mid-1990s and are available on ~8000 study participants in 1037 families. These samples have been used for collection of GWAS array data and exome chip data in nearly all with DNA samples, and for targeted sequencing, deep exome sequencing and light coverage whole genome sequencing in limited numbers. Additionally, mRNA and miRNA expression data, DNA methylation data, metabolomics and other 'omics data are available on a sizable portion of study participants. This project will focus on deep whole genome sequencing (mean 30X coverage) in ~4100 subjects and imputed to all with GWAS array data to more fully understand the genetic contributions to cardiovascular, lung, blood and sleep disorders. Comprehensive phenotypic and pedigree data for study participants are available through dbGaP phs000007.

pht004909.v3.p3

1 itemgroup 2 items

pht004910.v4.p3

1 itemgroup 2 items

pht004911.v3.p3

1 itemgroup 9 items

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