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LC-MS-Based Population Metabolomics: A Mini-Review of Recent Studies and Challenges from Sample Collection to Data Processing

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Clinical Metabolomics Applications in Genetic Diseases

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Abstract

Metabolomics aims to identify and quantify metabolites in biological samples to understand better biological changes resulting from lifestyle, environment, or disease. This is challenging due to the structural diversity of the metabolites and the complexity of samples of interest, such as blood and urine, useful in population studies to study biological changes in large cohorts. The limited number of commercially available standards and incomplete metabolite spectral databases impedes the identification of many metabolites. Furthermore, the need for more standardization in sample preparation, analysis, and interpretation of data is an important issue that can influence results in large cohort studies. Variations or errors occurring during the pre-analytical stage can highly affect levels of metabolites. In this mini-review, we outline the challenges associated with population metabolomic studies and show an overview of current practices in the field with some case studies.

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Abbreviations

APCI:

Atmospheric pressure chemical ionization

ASQ:

Age and stage questionnaire

BMI:

Body mass index

CI:

Chemical ionization

COMETS:

COnsortium of METabolomic Studies

CV:

Coefficient of variation

DNA:

Deoxyribonucleic acid

EDIH:

Empirical dietary index of hyperinsulinemia

EDTA:

Ethylenediaminetetraacetic acid

EI:

Electron ionization (electron impact)

ESI:

Electrospray ionization

FDR:

False discovery rate

GC-MS:

Gas chromatography-mass spectrometry

HDL:

High-density lipoprotein

HMDB:

Human metabolome database

IS:

Internal standard

LC-MS:

Liquid chromatography-mass spectrometry

LOD:

Limit of detection

LTR:

Long-term reference

MALDI:

Matrix-assisted laser desorption/ionization

MAR:

Missing at random

MCAR:

Missing completely at random

MNAR:

Missing not at random

MS:

Mass spectrometry

NIST:

National Institute of Standards and Technology

NMR:

Nuclear magnetic resonance

OPLS-DA:

Orthogonal partial least-squares discriminant analysis

PBS:

Phosphate-buffered saline

PCA:

Principal component analysis

QA:

Quality assurance

QC:

Quality control

QqQ:

Triple quadrupole

QqTOF:

Quadrupole time of flight

QRILC:

Quantile regression imputation of left-censored data

RNA:

Ribonucleic acid

ROC:

Receiver operating characteristic curve

SRM:

Standard reference material

VLDL:

Very-low-density lipoprotein

WGCNA:

Weighted gene co-expression network analysis

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Authors and Affiliations

  1. Chemistry Department/CERMO-FC, Université du Québec à Montréal (UQAM), Montreal, QC, Canada

    Myriam Mireault & Lekha Sleno

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  1. Myriam Mireault
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  2. Lekha Sleno
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Correspondence to Lekha Sleno .

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  1. Metabolomics Section, Department of Clinical Genomics, Center for Genome Medicine, King Faisal Specialist Hospital & Research Center (KFSHRC), Riyadh, Saudi Arabia

    Anas M. Abdel Rahman

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Mireault, M., Sleno, L. (2023). LC-MS-Based Population Metabolomics: A Mini-Review of Recent Studies and Challenges from Sample Collection to Data Processing. In: Abdel Rahman, A.M. (eds) Clinical Metabolomics Applications in Genetic Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-99-5162-8_13

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  • DOI: https://doi.org/10.1007/978-981-99-5162-8_13

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