Publication

Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females

Zhang, X., Rimbert, A., Balder, W., Zwinderman, A. H., Kuivenhoven, J. A., Dallinga-Thie, G. M. & Groen, A. K., Nov-2018, In : Journal of Lipid Research. 59, 11, p. 2174-2180 7 p.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Zhang, X., Rimbert, A., Balder, W., Zwinderman, A. H., Kuivenhoven, J. A., Dallinga-Thie, G. M., & Groen, A. K. (2018). Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females. Journal of Lipid Research, 59(11), 2174-2180. https://doi.org/10.1194/jlr.M088930

Author

Zhang, Xiang ; Rimbert, Antoine ; Balder, Willem ; Zwinderman, Aeilko H ; Kuivenhoven, Jan Albert ; Dallinga-Thie, Geesje M ; Groen, Albert K. / Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females. In: Journal of Lipid Research. 2018 ; Vol. 59, No. 11. pp. 2174-2180.

Harvard

Zhang, X, Rimbert, A, Balder, W, Zwinderman, AH, Kuivenhoven, JA, Dallinga-Thie, GM & Groen, AK 2018, 'Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females' Journal of Lipid Research, vol. 59, no. 11, pp. 2174-2180. https://doi.org/10.1194/jlr.M088930

Standard

Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females. / Zhang, Xiang; Rimbert, Antoine; Balder, Willem; Zwinderman, Aeilko H; Kuivenhoven, Jan Albert; Dallinga-Thie, Geesje M; Groen, Albert K.

In: Journal of Lipid Research, Vol. 59, No. 11, 11.2018, p. 2174-2180.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Zhang X, Rimbert A, Balder W, Zwinderman AH, Kuivenhoven JA, Dallinga-Thie GM et al. Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females. Journal of Lipid Research. 2018 Nov;59(11):2174-2180. https://doi.org/10.1194/jlr.M088930


BibTeX

@article{4f443dd40f204acdb5c9aab4f4ed4761,
title = "Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females",
abstract = "Hypercholesterolemia is characterized by high plasma LDL cholesterol and often caused by genetic mutations in LDL receptor (LDLR), APOB, or proprotein convertase subtilisin/kexin type 9 (PCSK9). However, a substantial proportion of hypercholesterolemic subjects do not have any mutations in these canonical genes, leaving the underlying pathobiology to be determined. In this study, we investigated to determine whether combining plasma metabolomics with genetic information increases insight in the biology of hypercholesterolemia. For this proof of concept study, we combined plasma metabolites from 119 hypercholesterolemic females with genetic information on the LDL canonical genes. Using hierarchical clustering, we identified four subtypes of hypercholesterolemia, which could be distinguished along two axes represented by triglyceride and large LDL particle concentration. Subjects with mutations in LDLR or APOB preferentially clustered together, suggesting that patients with defects in the LDLR pathway show a distinctive metabolomics profile. In conclusion, we show the potential of using metabolomics to segregate hypercholesterolemic subjects into different clusters, which may help in targeting genetic analysis.",
keywords = "hypercholesterolemia, triglyceride, low density lipoprotein, genetics, metabolomics, DENSITY-LIPOPROTEIN CHOLESTEROL, FAMILIAL HYPERCHOLESTEROLEMIA, POPULATION, LIPIDS, HOMEOSTASIS, INHIBITION, GENETICS, PATHWAY, DISEASE, GENES",
author = "Xiang Zhang and Antoine Rimbert and Willem Balder and Zwinderman, {Aeilko H} and Kuivenhoven, {Jan Albert} and Dallinga-Thie, {Geesje M} and Groen, {Albert K}",
note = "Published under license by The American Society for Biochemistry and Molecular Biology, Inc.",
year = "2018",
month = "11",
doi = "10.1194/jlr.M088930",
language = "English",
volume = "59",
pages = "2174--2180",
journal = "Journal of Lipid Research",
issn = "0022-2275",
publisher = "AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC",
number = "11",

}

RIS

TY - JOUR

T1 - Use of plasma metabolomics to analyze phenotype-genotype relationships in young hypercholesterolemic females

AU - Zhang, Xiang

AU - Rimbert, Antoine

AU - Balder, Willem

AU - Zwinderman, Aeilko H

AU - Kuivenhoven, Jan Albert

AU - Dallinga-Thie, Geesje M

AU - Groen, Albert K

N1 - Published under license by The American Society for Biochemistry and Molecular Biology, Inc.

PY - 2018/11

Y1 - 2018/11

N2 - Hypercholesterolemia is characterized by high plasma LDL cholesterol and often caused by genetic mutations in LDL receptor (LDLR), APOB, or proprotein convertase subtilisin/kexin type 9 (PCSK9). However, a substantial proportion of hypercholesterolemic subjects do not have any mutations in these canonical genes, leaving the underlying pathobiology to be determined. In this study, we investigated to determine whether combining plasma metabolomics with genetic information increases insight in the biology of hypercholesterolemia. For this proof of concept study, we combined plasma metabolites from 119 hypercholesterolemic females with genetic information on the LDL canonical genes. Using hierarchical clustering, we identified four subtypes of hypercholesterolemia, which could be distinguished along two axes represented by triglyceride and large LDL particle concentration. Subjects with mutations in LDLR or APOB preferentially clustered together, suggesting that patients with defects in the LDLR pathway show a distinctive metabolomics profile. In conclusion, we show the potential of using metabolomics to segregate hypercholesterolemic subjects into different clusters, which may help in targeting genetic analysis.

AB - Hypercholesterolemia is characterized by high plasma LDL cholesterol and often caused by genetic mutations in LDL receptor (LDLR), APOB, or proprotein convertase subtilisin/kexin type 9 (PCSK9). However, a substantial proportion of hypercholesterolemic subjects do not have any mutations in these canonical genes, leaving the underlying pathobiology to be determined. In this study, we investigated to determine whether combining plasma metabolomics with genetic information increases insight in the biology of hypercholesterolemia. For this proof of concept study, we combined plasma metabolites from 119 hypercholesterolemic females with genetic information on the LDL canonical genes. Using hierarchical clustering, we identified four subtypes of hypercholesterolemia, which could be distinguished along two axes represented by triglyceride and large LDL particle concentration. Subjects with mutations in LDLR or APOB preferentially clustered together, suggesting that patients with defects in the LDLR pathway show a distinctive metabolomics profile. In conclusion, we show the potential of using metabolomics to segregate hypercholesterolemic subjects into different clusters, which may help in targeting genetic analysis.

KW - hypercholesterolemia

KW - triglyceride

KW - low density lipoprotein

KW - genetics

KW - metabolomics

KW - DENSITY-LIPOPROTEIN CHOLESTEROL

KW - FAMILIAL HYPERCHOLESTEROLEMIA

KW - POPULATION

KW - LIPIDS

KW - HOMEOSTASIS

KW - INHIBITION

KW - GENETICS

KW - PATHWAY

KW - DISEASE

KW - GENES

U2 - 10.1194/jlr.M088930

DO - 10.1194/jlr.M088930

M3 - Article

VL - 59

SP - 2174

EP - 2180

JO - Journal of Lipid Research

JF - Journal of Lipid Research

SN - 0022-2275

IS - 11

ER -

ID: 65801539