Metabolite-Investigator: Shiny-Tool for investigation of metabolite-covariate relationships
Version 1

The main goal is to provide a principled analysis workflow addressing specific issues of mass-spectrometry metabolite measurements in the context of testing in multiple studies with a high number of hypotheses

Shiny-Application of an analysis pipeline for preprocessing, association and covariate selection of metabolite data with clinical and lifestyle factors in one or more seperate studies. Preprocessing steps include transformation, outlier filtering and batch-adjustment. Analyses include uni- as well as multivariable association and backwards-selection of covariates based on variance explained by each factor in the metabolites. A range of visualization options, metabolite/factor annotations and downloadable results are provided.

LHA ID: 7WDQD8NHCV-5

1 item is associated with this Model:

Human Disease: Disease of metabolism

Model type: Metabolic network

Model format: R package

Execution or visualisation environment: Shiny


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Created: 24th May 2019 at 13:00

Last updated: 16th Oct 2019 at 13:34

Last used: 29th Dec 2024 at 23:18

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Version 1 Created 24th May 2019 at 13:00 by Carl Beuchel

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Genetical Statistics and Systems Biology

The group is interested in omic-level studies including the genome, methylome, transcriptome, and metabolome of the general population as well as of various diseases including cardiovascular diseases, pneumonia, sepsis, obesity, and brain cancers. Additionally, we aim at transfering results of biomathematical model simulations into clinical practice e.g. by haematopoietic growth-factor opimization during cytotoxic chemotherapy and optimization of EPO applications in chronic kidney disease.

The ...

Programme: This Project is not associated with a Programme

Public web page: http://www.imise.uni-leipzig.de/en/Groups/GenStat/

Start date: 1st Jan 2013

Metabolite-Investigator: an integrated user-friendly workflow for metabolomics multi-study analysis
Genetical Statistics and Systems Biology

Abstract (Expand)

Motivation Many diseases have a metabolic background, which is increasingly investigated due to improved measurement techniques allowing high-throughput assessment of metabolic features in several body … fluids. Integrating data from multiple cohorts is of high importance to obtain robust and reproducible results. However, considerable variability across studies due to differences in sampling, measurement techniques and study populations needs to be accounted for. Results We present Metabolite-Investigator, a scalable analysis workflow for quantitative metabolomics data from multiple studies. Our tool supports all aspects of data pre-processing including data integration, cleaning, transformation, batch analysis as well as multiple analysis methods including uni- and multivariable factor-metabolite associations, network analysis and factor prioritization in one or more cohorts. Moreover, it allows identifying critical interactions between cohorts and factors affecting metabolite levels and inferring a common covariate model, all via a graphical user interface. Availability and implementation We constructed Metabolite-Investigator as a free and open web-tool and stand-alone Shiny-app. It is hosted at https://apps.health-atlas.de/metabolite-investigator/, the source code is freely available at https://github.com/cfbeuchel/Metabolite-Investigator. Supplementary information Supplementary data are available at Bioinformatics online.

Authors: Carl Beuchel, Holger Kirsten, Uta Ceglarek, Markus Scholz

Date Published: 16th Nov 2020

Publication Type: Journal article

Clinical and lifestyle related factors influencing whole blood metabolite levels -- A comparative analysis of three large cohorts
Genetical Statistics and Systems Biology

Abstract (Expand)

Objective Human blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights …s into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels. Methods 63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N~=~16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses. Results Inverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained {\textgreater}1{\%} of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies. Conclusions We introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

Authors: Carl Beuchel, Susen Becker, Julia Dittrich, Holger Kirsten, Anke Toenjes, Michael Stumvoll, Markus Loeffler, Holger Thiele, Frank Beutner, Joachim Thiery, Uta Ceglarek, Markus Scholz

Date Published: 17th Aug 2019

Publication Type: Not specified

disease of metabolism
LIFE Child, LIFE - Leipzig Research Center for Civilization Diseases

Taxonomy URI: http://purl.obolibrary.org/obo/DOID_0014667

Synonyms: metabolic disease

Definitions: A disease that involving errors in metabolic processes of building or degradation of molecules.

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