Subject ID, race, age, gender, BMI, consent form(s) signed by participants, age of type 2 diabetes Mellitus diagnosis, age of end stage renal disease diagnosis, age of hypertension diagnosis, duration of type 2 diabetes Mellitus since diagnosis, individual has reported that he/she is on dialysis, individual has reported hypertension, individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation, HBA1C, pc1, pc2, pc3, pc4, pc5, pc6, pc7, pc8, pc9, and pc10 of participants with T2D and involved in the "A Whole Genome Association Search for Type 2 Diabetes Genes in African Americans" project.

8 Formulieren

StudyEvent: SEV1

Name

Type

Description | Question | Decode (Coded Value)

Datatype

Alias

pht000630

Item Group

pht000630

C3846158 (UMLS CUI [1,1])

SUBJID

Item

Subject ID

text

C2348585 (UMLS CUI [1,1])

Race

Item

Race, ethnicity of participant

text

C5441552 (UMLS CUI [1,1])

Race

Code List

Race, ethnicity of participant

CL Item

African American (AA)

Age

Item

Age at recruitment

text

C0001779 (UMLS CUI [1,1])
C0242800 (UMLS CUI [1,2])

Gender

Item

Gender of participant has been confirmed with genotyping: 1 = Male, 2 = Female

text

C0079399 (UMLS CUI [1,1])

Gender

Code List

Gender of participant has been confirmed with genotyping: 1 = Male, 2 = Female

CL Item

Male (1)

C0086582 (UMLS CUI [1,1])

CL Item

Female (2)

C0086287 (UMLS CUI [1,1])

BMI

Item

Body Mass Index

text

C1305855 (UMLS CUI [1,1])

Consent

Item

Consent form(s) signed by participant

text

C0009797 (UMLS CUI [1,1])
C0742766 (UMLS CUI [1,2])

Consent

Code List

Consent form(s) signed by participant

CL Item

BG91-0129 (A)

C0009797 (UMLS CUI [1,1])

CL Item

BG91-0129, Deceased participant (A,X)

C0009797 (UMLS CUI [1,1])
C1555024 (UMLS CUI [1,2])

CL Item

BG92-0090 (B)

C0009797 (UMLS CUI [1,1])

CL Item

BG92-0090, Deceased participant (B,X)

C0009797 (UMLS CUI [1,1])
C1555024 (UMLS CUI [1,2])

CL Item

BG95-109 (C)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-028 (D)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427 (E)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_001 (E_001)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_001, Reconsent form (E_001, H)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_001, Reconsent form (E_001,H)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_001, Deceased participant (E_001,X)

C0009797 (UMLS CUI [1,1])
C1555024 (UMLS CUI [1,2])

CL Item

BG00-427_002 (E_002)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_003; (E_003)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_004 (E_004)

C0009797 (UMLS CUI [1,1])

CL Item

BG00-427_005 (E_005)

C0009797 (UMLS CUI [1,1])

CL Item

BG01-059 (F)

C0009797 (UMLS CUI [1,1])

CL Item

BG01-299 (G)

C0009797 (UMLS CUI [1,1])

CL Item

BG01-299-001 (G_001)

C0009797 (UMLS CUI [1,1])

CL Item

BG01-299-001, Deceased participant (G_001,X)

C0009797 (UMLS CUI [1,1])
C1555024 (UMLS CUI [1,2])

CL Item

BG01-299-002 (G_002)

C0009797 (UMLS CUI [1,1])

CL Item

BG01-299-002, Reconsent form (G_002, H)

C0009797 (UMLS CUI [1,1])

CL Item

Reconsent form (H)

C0009797 (UMLS CUI [1,1])

CL Item

Deceased participant (X)

C1555024 (UMLS CUI [1,1])

T2DM Age of Onset

Item

Age of T2DM (Type 2 Diabetes Mellitus) diagnosis

text

C1828181 (UMLS CUI [1,1])
C4014362 (UMLS CUI [1,2])

ESRD Age of Onset

Item

Age of ESRD (End Stage Renal Disease) diagnosis, start of dialysis

text

C1828181 (UMLS CUI [1,1])
C0022661 (UMLS CUI [1,2])
C0011946 (UMLS CUI [1,3])
C0439659 (UMLS CUI [1,4])

HTN Age of Onset

Item

Age of hypertension diagnosis

text

C1828181 (UMLS CUI [1,1])
C0020538 (UMLS CUI [1,2])

Duration T2DM

Item

Duration of T2DM (Type 2 Diabetes Mellitus) since diagnosis

text

C0872031 (UMLS CUI [1,1])
C4014362 (UMLS CUI [1,2])

ESRD

Item

Individual has reported that he/she is on dialysis

text

C0681906 (UMLS CUI [1,1])
C0011946 (UMLS CUI [1,2])

ESRD

Code List

Individual has reported that he/she is on dialysis

CL Item

Yes (ESRD)

C1705108 (UMLS CUI [1,1])

CL Item

No (no)

C1298908 (UMLS CUI [1,1])

CL Item

Yes (yes)

C1705108 (UMLS CUI [1,1])

HTN

Item

Individual has self reported hypertension (yes, no, unknown)

text

C0681906 (UMLS CUI [1,1])
C0020538 (UMLS CUI [1,2])

HTN

Code List

Individual has self reported hypertension (yes, no, unknown)

CL Item

Yes (HTN)

C1705108 (UMLS CUI [1,1])

CL Item

No (no)

C1298908 (UMLS CUI [1,1])

CL Item

Unknown (unknown)

C0439673 (UMLS CUI [1,1])

CL Item

Yes (yes)

C1705108 (UMLS CUI [1,1])

Retinopathy

Item

Individual has diabetic retinopathy reported by ophthalmologist or laser treatment with photocoagulation

text

C0011884 (UMLS CUI [1,1])
C1704292 (UMLS CUI [1,2])
C0700287 (UMLS CUI [1,3])
C0011884 (UMLS CUI [2,1])
C0441510 (UMLS CUI [2,2])

HBA1C

Item

Hemoglobin1AC, test

text

C0850989 (UMLS CUI [1,1])

HBA1C

Code List

Hemoglobin1AC, test

CL Item

Unknown (.)

pc1

Item

Principal component 1: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

C1882460 (UMLS CUI [1,1])
C0489829 (UMLS CUI [1,2])
C0871424 (UMLS CUI [1,3])
C1707511 (UMLS CUI [1,4])
C1707520 (UMLS CUI [1,5])
C0871342 (UMLS CUI [2,1])
C0871161 (UMLS CUI [2,2])
C1705241 (UMLS CUI [2,3])
C0220825 (UMLS CUI [2,4])
C0237589 (UMLS CUI [2,5])
C1882460 (UMLS CUI [3,1])
C1947906 (UMLS CUI [3,2])
C1265611 (UMLS CUI [3,3])
C2348152 (UMLS CUI [3,4])
C4553182 (UMLS CUI [3,5])
C0443228 (UMLS CUI [3,6])
C1264664 (UMLS CUI [3,7])
C0025663 (UMLS CUI [4,1])
C1148554 (UMLS CUI [4,2])
C0449961 (UMLS CUI [4,3])
C0936012 (UMLS CUI [4,4])
C1518422 (UMLS CUI [4,5])
C0205375 (UMLS CUI [4,6])
C0870442 (UMLS CUI [4,7])

pc2

Item

Principal component 2: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc3

Item

Principal component 3: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc4

Item

Principal component 4: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc5

Item

Principal component 5: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc6

Item

Principal component 6: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc7

Item

Principal component 7: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc8

Item

Principal component 8: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc9

Item

Principal component 9: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

pc10

Item

Principal component 10: a mathematical tool commonly used in statistical analysis. It seeks to identify an orthogonal coordinate system that transforms a number of possibly correlated variables into a smaller number of uncorrelated variables called principal components (PC). This method allows for the separation of individuals on the basis of differences in their properties and can also be used to evaluate the properties that contribute the most to these separations. The principal components are ordered in terms of the amount of variation in the dataset that they explain such that the first PC explains the largest fraction of the total variance, and so on. There exist a number of methods to determine the number of PC to be retained for a specific analysis. However, none of them uniformly dominates the others. Following a practice largely adopted in the field, we present the first 10 PC computed on this dataset.

text

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