Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Caroline A Brorsson; Lotte B Nielsen; Marie-Louise Andersen; Simranjeet Kaur; Regine Bergholdt; Lars Hansen; Henrik B. Mortensen; Flemming Pociot; Joachim Størling; Hvidoere Study Group On Childhood Diabetes

doi:10.1155/2016/9570424

Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Caroline A Brorsson, Lotte B Nielsen, Marie-Louise Andersen, Simranjeet Kaur, Regine Bergholdt, Lars Hansen, Henrik B. Mortensen, Flemming Pociot, Joachim Størling, Hvidoere Study Group On Childhood Diabetes

Department of Clinical Medicine

12 Citations (Scopus)

49 Downloads (Pure)

Abstract

Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

Original language	English
Article number	9570424
Journal	Journal of Diabetes Research
Volume	2016
Number of pages	8
ISSN	2314-6745
DOIs	https://doi.org/10.1155/2016/9570424
Publication status	Published - 2016

Keywords

Adolescent
Adult
Alleles
Child
Cohort Studies
Cytokines
Diabetes Mellitus, Type 1
Disease Progression
Female
Gene Expression Profiling
Genetic Load
Genetic Predisposition to Disease
Genome-Wide Association Study
Genotype
Hemoglobin A, Glycosylated
Humans
Hyperglycemia
Insulin-Secreting Cells
Interferon-gamma
Interleukin-1beta
Islets of Langerhans
Male
Middle Aged
Polymorphism, Single Nucleotide
Risk
Tumor Necrosis Factor-alpha
Young Adult
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

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Brorsson, C. A., Nielsen, L. B., Andersen, M.-L., Kaur, S., Bergholdt, R., Hansen, L., Mortensen, H. B., Pociot, F., Størling, J., & Hvidoere Study Group On Childhood Diabetes (2016). Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control. Journal of Diabetes Research, 2016, Article 9570424. https://doi.org/10.1155/2016/9570424

Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control. / Brorsson, Caroline A; Nielsen, Lotte B; Andersen, Marie-Louise et al.
In: Journal of Diabetes Research, Vol. 2016, 9570424, 2016.

Research output: Contribution to journal › Journal article › Research › peer-review

Brorsson, CA, Nielsen, LB, Andersen, M-L, Kaur, S, Bergholdt, R, Hansen, L, Mortensen, HB, Pociot, F, Størling, J & Hvidoere Study Group On Childhood Diabetes 2016, 'Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control', Journal of Diabetes Research, vol. 2016, 9570424. https://doi.org/10.1155/2016/9570424

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abstract = "Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.",

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author = "Brorsson, {Caroline A} and Nielsen, {Lotte B} and Marie-Louise Andersen and Simranjeet Kaur and Regine Bergholdt and Lars Hansen and Mortensen, {Henrik B.} and Flemming Pociot and Joachim St{\o}rling and {Hvidoere Study Group On Childhood Diabetes}",

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doi = "10.1155/2016/9570424",

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T1 - Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients

T2 - Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

AU - Brorsson, Caroline A

AU - Nielsen, Lotte B

AU - Andersen, Marie-Louise

AU - Kaur, Simranjeet

AU - Bergholdt, Regine

AU - Hansen, Lars

AU - Mortensen, Henrik B.

AU - Pociot, Flemming

AU - Størling, Joachim

AU - Hvidoere Study Group On Childhood Diabetes

PY - 2016

Y1 - 2016

N2 - Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

AB - Genome-wide association studies (GWAS) have identified over 40 type 1 diabetes risk loci. The clinical impact of these loci on β-cell function during disease progression is unknown. We aimed at testing whether a genetic risk score could predict glycemic control and residual β-cell function in type 1 diabetes (T1D). As gene expression may represent an intermediate phenotype between genetic variation and disease, we hypothesized that genes within T1D loci which are expressed in islets and transcriptionally regulated by proinflammatory cytokines would be the best predictors of disease progression. Two-thirds of 46 GWAS candidate genes examined were expressed in human islets, and 11 of these significantly changed expression levels following exposure to proinflammatory cytokines (IL-1β + IFNγ + TNFα) for 48 h. Using the GWAS single nucleotide polymorphisms (SNPs) from each locus, we constructed a genetic risk score based on the cumulative number of risk alleles carried in children with newly diagnosed T1D. With each additional risk allele carried, HbA1c levels increased significantly within first year after diagnosis. Network and gene ontology (GO) analyses revealed that several of the 11 candidate genes have overlapping biological functions and interact in a common network. Our results may help predict disease progression in newly diagnosed children with T1D which can be exploited for optimizing treatment.

KW - Adolescent

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KW - Alleles

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KW - Cohort Studies

KW - Cytokines

KW - Diabetes Mellitus, Type 1

KW - Disease Progression

KW - Female

KW - Gene Expression Profiling

KW - Genetic Load

KW - Genetic Predisposition to Disease

KW - Genome-Wide Association Study

KW - Genotype

KW - Hemoglobin A, Glycosylated

KW - Humans

KW - Hyperglycemia

KW - Insulin-Secreting Cells

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KW - Interleukin-1beta

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KW - Polymorphism, Single Nucleotide

KW - Risk

KW - Tumor Necrosis Factor-alpha

KW - Young Adult

KW - Journal Article

KW - Research Support, N.I.H., Extramural

KW - Research Support, Non-U.S. Gov't

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DO - 10.1155/2016/9570424

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SN - 2314-6745

VL - 2016

JO - Journal of Diabetes Research

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