Metabotype Concept: Flexibility, Usefulness and Meaning in Different Biological Populations

Semmar, Nasredine

doi:10.5772/33303

Cited by 5 publications

(4 citation statements)

References 97 publications

(94 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Metabolic phenotype or metabotype (also called chemotype) concept is often used to describe a particular metabolic state of biological system through its metabolic signature represented as different level of individual metabolites [38]. Metabotypes can be defined as absence or presence of a particular metabolites, absolute or relative concentration of metabolites in a sample or as metabolite profiles or metabolic signatures.…”

Section: Metabotypes and Precision Medicinementioning

confidence: 99%

Metabolomics technology and bioinformatics for precision medicine

Azad¹,

Shulaev²

2018

Briefings in Bioinformatics

101

View full text Add to dashboard Cite

Precision medicine is rapidly emerging as a strategy to tailor medical treatment to a small group or even individual patients based on their genetics, environment and lifestyle. Precision medicine relies heavily on developments in systems biology and omics disciplines, including metabolomics. Combination of metabolomics with sophisticated bioinformatics analysis and mathematical modeling has an extreme power to provide a metabolic snapshot of the patient over the course of disease and treatment or classifying patients into subpopulations and subgroups requiring individual medical treatment. Although a powerful approach, metabolomics have certain limitations in technology and bioinformatics. We will review various aspects of metabolomics technology and bioinformatics, from data generation, bioinformatics analysis, data fusion and mathematical modeling to data management, in the context of precision medicine.

show abstract

Section: Metabotypes and Precision Medicinementioning

confidence: 99%

Metabolomics technology and bioinformatics for precision medicine

Azad¹,

Shulaev²

2018

Briefings in Bioinformatics

101

View full text Add to dashboard Cite

show abstract

“…Metabolism is a principal driver of human health, and metabolic phenotypes (metabotypes) are influenced by a variety of internal and external factors, including genetics, diet and the environment. [ 34 ] In the present study, we investigated the ability to detect and identify metabolites associated with (poly)phenolic rich diets using an untargeted UHPLC‐HR‐MS platform. The phytochemicals investigated were selected from prior reports on the composition of phytochemical‐dense foods and/or their metabolites post consumption, and confirmation via quantitative targeted analysis of nutrition intervention study sets (including published [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ] and unpublished study sets).…”

Section: Discussionmentioning

confidence: 99%

Exploring the Contribution of (Poly)phenols to the Dietary Exposome Using High Resolution Mass Spectrometry Untargeted Metabolomics

Li¹,

Rushing²,

Schroder³

et al. 2022

Molecular Nutrition Food Res

View full text Add to dashboard Cite

Scope This study presents a workflow to construct a Dietary Exposome Library (DEL) comprised of phytochemicals and their metabolites derived from host and gut microbiome metabolism for use in peak identification/annotation of untargeted metabolomics datasets. Methods and Results An evidence mapping initiative established target analytes related to the consumption of phytochemical‐rich foods. Analytes were confirmed by ultra‐performance liquid chromatography–mass spectrometry (UPLC‐MS(n)) analysis of human biospecimens from dietary intervention studies of (poly)phenol‐rich diets. One hundred and sixty six verified compounds were subsequently analyzed on an untargeted metabolomics platform to acquire chromatographic and high‐resolution mass spectral data for construction of a DEL. The DEL facilitated identification/annotation of 123 metabolites associate with exposure to (poly)phenol enriched diets, which included aromatic ketones, benzoic acids, ellagic acids, caffeoylquinic acids, catecholamines, coumarins, hippuric acid, hydroxytoluenes, phenylamines, stilbenes, urolithins, valerolactones, and xanthonoids, in untargeted metabolomics datasets acquire from human plasma and urine reference materials. Conclusions The DEL focusing on (poly)phenols and their metabolites of dietary exposure facilitated identification/annotation of ingested food components and their associated pathways in untargeted metabolomics datasets acquired from human biospecimens. The DEL continues to expand with the aim to provide evidence‐based data for dietary metabolites in exposome research and inform the development of dietary intervention strategies.

show abstract

“…An organism’s metabolome varies with perturbation of the system by environmental factors, drugs, or other modalities [ 174 ]. The metabolomic analysis can yield a metabolic fingerprint or metabotype that discriminates an individual’s response to the perturbation, such as association of metabolic biomarkers of drug response with variable platelet reactivity on clopidogrel therapy [ 11 , 175 ]. Thus, metabolomics or pharmacometabolomics can partner with pharmacogenomics to potentially predict cardiovascular outcomes on clopidogrel therapy [ 11 ].…”

Section: Multi-omic Precision Medicine Approach To Response Regulamentioning

confidence: 99%

Pharmacogenomic Impact of CYP2C19 Variation on Clopidogrel Therapy in Precision Cardiovascular Medicine

Brown

Pereira

2018

JPM

View full text Add to dashboard Cite

Variability in response to antiplatelet therapy can be explained in part by pharmacogenomics, particularly of the CYP450 enzyme encoded by CYP2C19. Loss-of-function and gain-of-function variants help explain these interindividual differences. Individuals may carry multiple variants, with linkage disequilibrium noted among some alleles. In the current pharmacogenomics era, genomic variation in CYP2C19 has led to the definition of pharmacokinetic phenotypes for response to antiplatelet therapy, in particular, clopidogrel. Individuals may be classified as poor, intermediate, extensive, or ultrarapid metabolizers, based on whether they carry wild type or polymorphic CYP2C19 alleles. Variant alleles differentially impact platelet reactivity, concentration of plasma clopidogrel metabolites, and clinical outcomes. Interestingly, response to clopidogrel appears to be modulated by additional factors, such as sociodemographic characteristics, risk factors for ischemic heart disease, and drug-drug interactions. Furthermore, systems medicine studies suggest that a broader approach may be required to adequately assess, predict, preempt, and manage variation in antiplatelet response. Transcriptomics, epigenomics, exposomics, miRNAomics, proteomics, metabolomics, microbiomics, and mathematical, computational, and molecular modeling should be integrated with pharmacogenomics for enhanced prediction and individualized care. In this review of pharmacogenomic variation of CYP450, a systems medicine approach is described for tailoring antiplatelet therapy in clinical practice of precision cardiovascular medicine.

show abstract

Metabotype Concept: Flexibility, Usefulness and Meaning in Different Biological Populations

Cited by 5 publications

References 97 publications

Metabolomics technology and bioinformatics for precision medicine

Metabolomics technology and bioinformatics for precision medicine

Exploring the Contribution of (Poly)phenols to the Dietary Exposome Using High Resolution Mass Spectrometry Untargeted Metabolomics

Pharmacogenomic Impact of CYP2C19 Variation on Clopidogrel Therapy in Precision Cardiovascular Medicine

Contact Info

Product

Resources

About