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Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers

BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these method...

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Autores principales: Conde, Susana, Xu, Xiaoguang, Guo, Hui, Perola, Markus, Fazia, Teresa, Bernardinelli, Luisa, Berzuini, Carlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069804/
https://www.ncbi.nlm.nih.gov/pubmed/30066639
http://dx.doi.org/10.1186/s12859-018-2178-2
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author Conde, Susana
Xu, Xiaoguang
Guo, Hui
Perola, Markus
Fazia, Teresa
Bernardinelli, Luisa
Berzuini, Carlo
author_facet Conde, Susana
Xu, Xiaoguang
Guo, Hui
Perola, Markus
Fazia, Teresa
Bernardinelli, Luisa
Berzuini, Carlo
author_sort Conde, Susana
collection PubMed
description BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. RESULTS: We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. CONCLUSIONS: The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2178-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-60698042018-08-03 Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers Conde, Susana Xu, Xiaoguang Guo, Hui Perola, Markus Fazia, Teresa Bernardinelli, Luisa Berzuini, Carlo BMC Bioinformatics Research BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher’s ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. RESULTS: We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. CONCLUSIONS: The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2178-2) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-09 /pmc/articles/PMC6069804/ /pubmed/30066639 http://dx.doi.org/10.1186/s12859-018-2178-2 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Conde, Susana
Xu, Xiaoguang
Guo, Hui
Perola, Markus
Fazia, Teresa
Bernardinelli, Luisa
Berzuini, Carlo
Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title_full Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title_fullStr Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title_full_unstemmed Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title_short Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
title_sort mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6069804/
https://www.ncbi.nlm.nih.gov/pubmed/30066639
http://dx.doi.org/10.1186/s12859-018-2178-2
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