ID

45249

Description

Principal Investigator: Stacey Gabriel, Broad Institute, Cambridge, MA, USA MeSH: Myocardial Infarction https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000279 The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. In the Grand Opportunities Exome Sequencing Program Early MI Project (GO ESP - EOMI), we are sequencing cases with extremely early-onset MI drawn from 8 cohorts. These cohorts include five hospital or community-based studies that ascertained individuals based on MI status. These include PennCATH, Cleveland Clinic Genebank, Massachusetts General Hospital Premature Coronary Artery Disease Study (MGH-PCAD), Heart Attack Risk in Puget Sound (HARPS), and Translational Research Investigating Underlying Disparities in Myocardial Infarction Patients' Health Status (TRIUMPH). Cases were selected based on MI occurring in men aged ≤50 years and women aged ≤60 years. In addition, early-MI cases are being drawn from three population-cohort studies including the Framingham Heart Study, the Women's Health Initiative, and the Atherosclerosis Risk in Communities Study. MI-free controls are being drawn from five population-based cohort studies including the Framingham Heart Study, the Women's Health Initiative, Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, and the Jackson Heart Study. Controls were selected based on two factors: (1) highest predicted risk for MI based on Framingham risk score; and (2) absence of prevalent or incident MI despite a high predicted risk.

Lien

dbGaP study = phs000279

Mots-clés

  1. 04/07/2022 04/07/2022 - Chiara Middel
  2. 12/10/2022 12/10/2022 - Adrian Schulz
Détendeur de droits

Stacey Gabriel, Broad Institute, Cambridge, MA, USA

Téléchargé le

12 octobre 2022

DOI

Pour une demande vous connecter.

Licence

Creative Commons BY 4.0

Modèle Commentaires :

Ici, vous pouvez faire des commentaires sur le modèle. À partir des bulles de texte, vous pouvez laisser des commentaires spécifiques sur les groupes Item et les Item.

Groupe Item commentaires pour :

Item commentaires pour :

Vous devez être connecté pour pouvoir télécharger des formulaires. Veuillez vous connecter ou s’inscrire gratuitement.

dbGaP phs000279 NHLBI GO-ESP: Early-Onset Myocardial Infarction (Broad EOMI)

This subject phenotype data table contains sociodemography variables, race and gender, and myocardial infarction status and age of MI occurrence.

pht001437
Description

pht001437

Subject ID
Description

SUBJID

Type de données

text

Alias
UMLS CUI [1,1]
C2348585
HeartGO cohort (ARIC, CARDIA, CHS, FHS, JHS, MESA)
Description

STUDY

Type de données

text

Alias
UMLS CUI [1,1]
C5237785
European American; African American
Description

ESP_RACE

Type de données

text

Alias
UMLS CUI [1,1]
C0085756
UMLS CUI [1,2]
C0683983
Male; Female
Description

ESP_SEX

Type de données

text

Alias
UMLS CUI [1,1]
C0086582
UMLS CUI [1,2]
C0086287
Clinic or field center within cohort
Description

ESP_STUDY_SITE

Type de données

text

Alias
UMLS CUI [1,1]
C0442592
UMLS CUI [1,2]
C0565990
EOMI_Case; EOMI_Control; LDL_High; LDL_Low; Stroke; BP_High; BP_Low; DPR
Description

ESP_PHENOTYPE

Type de données

text

Alias
UMLS CUI [1,1]
C4014367
UMLS CUI [1,2]
C1698493
UMLS CUI [2,1]
C4014367
UMLS CUI [2,2]
C0009932
UMLS CUI [3,1]
C4747855
UMLS CUI [4,1]
C3807409
UMLS CUI [5,1]
C0038454
UMLS CUI [6,1]
C3843080
UMLS CUI [7,1]
C0020649
UMLS CUI [8,1]
C3846158
MI status at baseline (0/1)
Description

ESP_MI_BASELINE

Type de données

text

Alias
UMLS CUI [1,1]
C1442488
UMLS CUI [1,2]
C0027051
UMLS CUI [1,3]
C0449438
Age at incident MI. For HeartGO, this is only filled in (i.e. non-missing) for those with adjudicated MI that occurred during the study. It is missing for those that are censored.
Description

ESP_AGE_AT_MI

Type de données

text

Unités de mesure
  • years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C0011008
UMLS CUI [1,3]
C0027051
UMLS CUI [2,1]
C0027051
UMLS CUI [2,2]
C0347984
UMLS CUI [2,3]
C2603343
UMLS CUI [3,1]
C1705492
UMLS CUI [3,2]
C3889990
years
Framingham Risk Score predicted prob. (only calcultated in HeartGO for those with no baseline or incident MI)
Description

ESP_FRS_CHDPROB

Type de données

text

Alias
UMLS CUI [1,1]
C0033204
UMLS CUI [1,2]
C5442395
Age at FRS calculation
Description

ESP_AGE_AT_FRS

Type de données

text

Unités de mesure
  • years
Alias
UMLS CUI [1,1]
C0011008
UMLS CUI [1,2]
C0001779
UMLS CUI [1,3]
C5442395
UMLS CUI [1,4]
C1441506
years
Baseline calculated LDL in mg/dL (missing where trigs>400)
Description

ESP_LDL

Type de données

text

Alias
UMLS CUI [1,1]
C0428474
UMLS CUI [1,2]
C1442488
Age in years at baseline LDL
Description

ESP_AGE_AT_LDL

Type de données

text

Unités de mesure
  • years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C1442488
years
Estimated untreated LDL (In HeartGO, it is currently baseline LDL divided by 0.75 for those on lipid lowering meds)
Description

ESP_LDL_UNTREATED

Type de données

text

Alias
UMLS CUI [1,1]
C0750572
UMLS CUI [1,2]
C0428474
UMLS CUI [1,3]
C0332155
Any lipid lower med at baseline
Description

ESP_LIPID_MED

Type de données

text

Alias
UMLS CUI [1,1]
C0011008
UMLS CUI [1,2]
C1442488
UMLS CUI [1,3]
C0585943
Baseline BMI in kg/m2
Description

ESP_BMI

Type de données

text

Unités de mesure
  • kg/m2
Alias
UMLS CUI [1,1]
C1442488
UMLS CUI [1,2]
C1305855
kg/m2
Age at baseline BMI
Description

ESP_AGE_AT_BMI

Type de données

text

Unités de mesure
  • years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C0011008
UMLS CUI [1,3]
C1442488
UMLS CUI [1,4]
C1305855
years
Type 2 diabetes status at baseline (0/1)
Description

ESP_T2DIABETES

Type de données

text

Alias
UMLS CUI [1,1]
C0011860
UMLS CUI [1,2]
C0449438
UMLS CUI [1,3]
C0011008
UMLS CUI [1,4]
C1442488
Age at last follow-up for MI (missing for anyone with a baseline or incident MI)
Description

ESP_AGE_AT_LAST_FU

Type de données

text

Unités de mesure
  • years
Alias
UMLS CUI [1,1]
C0001779
UMLS CUI [1,2]
C0011008
UMLS CUI [1,3]
C1517741
UMLS CUI [1,4]
C1522577
years

Similar models

This subject phenotype data table contains sociodemography variables, race and gender, and myocardial infarction status and age of MI occurrence.

Name
Type
Description | Question | Decode (Coded Value)
Type de données
Alias
Item Group
pht001437
SUBJID
Item
Subject ID
text
C2348585 (UMLS CUI [1,1])
STUDY
Item
HeartGO cohort (ARIC, CARDIA, CHS, FHS, JHS, MESA)
text
C5237785 (UMLS CUI [1,1])
ESP_RACE
Item
European American; African American
text
C0085756 (UMLS CUI [1,1])
C0683983 (UMLS CUI [1,2])
ESP_SEX
Item
Male; Female
text
C0086582 (UMLS CUI [1,1])
C0086287 (UMLS CUI [1,2])
ESP_STUDY_SITE
Item
Clinic or field center within cohort
text
C0442592 (UMLS CUI [1,1])
C0565990 (UMLS CUI [1,2])
ESP_PHENOTYPE
Item
EOMI_Case; EOMI_Control; LDL_High; LDL_Low; Stroke; BP_High; BP_Low; DPR
text
C4014367 (UMLS CUI [1,1])
C1698493 (UMLS CUI [1,2])
C4014367 (UMLS CUI [2,1])
C0009932 (UMLS CUI [2,2])
C4747855 (UMLS CUI [3,1])
C3807409 (UMLS CUI [4,1])
C0038454 (UMLS CUI [5,1])
C3843080 (UMLS CUI [6,1])
C0020649 (UMLS CUI [7,1])
C3846158 (UMLS CUI [8,1])
ESP_MI_BASELINE
Item
MI status at baseline (0/1)
text
C1442488 (UMLS CUI [1,1])
C0027051 (UMLS CUI [1,2])
C0449438 (UMLS CUI [1,3])
ESP_AGE_AT_MI
Item
Age at incident MI. For HeartGO, this is only filled in (i.e. non-missing) for those with adjudicated MI that occurred during the study. It is missing for those that are censored.
text
C0001779 (UMLS CUI [1,1])
C0011008 (UMLS CUI [1,2])
C0027051 (UMLS CUI [1,3])
C0027051 (UMLS CUI [2,1])
C0347984 (UMLS CUI [2,2])
C2603343 (UMLS CUI [2,3])
C1705492 (UMLS CUI [3,1])
C3889990 (UMLS CUI [3,2])
ESP_FRS_CHDPROB
Item
Framingham Risk Score predicted prob. (only calcultated in HeartGO for those with no baseline or incident MI)
text
C0033204 (UMLS CUI [1,1])
C5442395 (UMLS CUI [1,2])
ESP_AGE_AT_FRS
Item
Age at FRS calculation
text
C0011008 (UMLS CUI [1,1])
C0001779 (UMLS CUI [1,2])
C5442395 (UMLS CUI [1,3])
C1441506 (UMLS CUI [1,4])
ESP_LDL
Item
Baseline calculated LDL in mg/dL (missing where trigs>400)
text
C0428474 (UMLS CUI [1,1])
C1442488 (UMLS CUI [1,2])
ESP_AGE_AT_LDL
Item
Age in years at baseline LDL
text
C0001779 (UMLS CUI [1,1])
C1442488 (UMLS CUI [1,2])
ESP_LDL_UNTREATED
Item
Estimated untreated LDL (In HeartGO, it is currently baseline LDL divided by 0.75 for those on lipid lowering meds)
text
C0750572 (UMLS CUI [1,1])
C0428474 (UMLS CUI [1,2])
C0332155 (UMLS CUI [1,3])
ESP_LIPID_MED
Item
Any lipid lower med at baseline
text
C0011008 (UMLS CUI [1,1])
C1442488 (UMLS CUI [1,2])
C0585943 (UMLS CUI [1,3])
ESP_BMI
Item
Baseline BMI in kg/m2
text
C1442488 (UMLS CUI [1,1])
C1305855 (UMLS CUI [1,2])
ESP_AGE_AT_BMI
Item
Age at baseline BMI
text
C0001779 (UMLS CUI [1,1])
C0011008 (UMLS CUI [1,2])
C1442488 (UMLS CUI [1,3])
C1305855 (UMLS CUI [1,4])
ESP_T2DIABETES
Item
Type 2 diabetes status at baseline (0/1)
text
C0011860 (UMLS CUI [1,1])
C0449438 (UMLS CUI [1,2])
C0011008 (UMLS CUI [1,3])
C1442488 (UMLS CUI [1,4])
ESP_AGE_AT_LAST_FU
Item
Age at last follow-up for MI (missing for anyone with a baseline or incident MI)
text
C0001779 (UMLS CUI [1,1])
C0011008 (UMLS CUI [1,2])
C1517741 (UMLS CUI [1,3])
C1522577 (UMLS CUI [1,4])

Utilisez ce formulaire pour les retours, les questions et les améliorations suggérées.

Les champs marqués d’un * sont obligatoires.

Do you need help on how to use the search function? Please watch the corresponding tutorial video for more details and learn how to use the search function most efficiently.

Watch Tutorial