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Future Perspectives of Metabolomics: Gaps, Planning, and Recommendations

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Metabolomics

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Abstract

Metabolomics is a rapidly evolving field that has the potential to revolutionize our understanding of human health and disease. Recent advances in analytical technologies, coupled with the increasing availability of large-scale datasets, have enabled researchers to identify novel biomarkers and pathways that are associated with a wide range of diseases. In this chapter, we discuss some of the key trends and future directions in metabolomics research, including precision medicine, personalized nutrition, multi-omics integration, the role of artificial intelligence and machine learning in metabolomics data analytics, applications of metabolomics in translational biology, and its relationship with the drug development and futuristic wearable for faster disease detections and surveillance. We also highlight some of the challenges and recommendations to fully realize the potential of metabolomics for improving human health.

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Notes

  1. 1.

    Breathalyzer tests have also been designed for diagnosis of viral and bacterial infections through volatile organic compound detection. Secondary electrospray ionization-mass spectrometry (SESI-MS) on mouse breath could detect infection as well as distinguish between different pathogens and strains [6], and a diagnostic breath test using gas chromatography-mass spectrometry (GC-MS) was approved for emergency use in the Covid-19 pandemic [7, 8].

  2. 2.

    Metabolomics analysis has been performed on an isolated mouse-embryonic fibroblast cell by sucking a cell’s contents into a nano-electrospray ionization tip and sent through a mass spectrometer to measure compounds of low molecular weight [165].

Abbreviations

AD:

Alzheimer’s disease

ANN:

Artificial neural network

AUC:

Area under the curve

CE-MS:

Capillary electrophoresis - Mass Spectrometry

CGM:

Continuous glucose monitor

CNN:

Convolutional neural network

DI-MS:

Direct infusion-mass spectrometry

DL:

Deep learning

EI-MS :

Electron ionization mass spectrometry

ELISA:

Enzyme-linked immunosorbent assay

FDR:

False discovery rate

GC-FID:

Gas chromatography-flame ionization detection

GC-MS:

Gas chromatography-mass spectrometry

hCG:

Human chorionic gonadotropin

HDL:

High-density lipoprotein

IEM:

Inborn errors of metabolism

LAESI:

Laser ablation electrospray ionization

LC-HRMS:

Liquid chromatography coupled to high-resolution mass spectrometry

LC-MS:

Liquid chromatography-mass spectrometry

LDL:

Low-density lipoprotein

LDTs:

Laboratory-developed tests

MALDI:

Matrix-assisted laser desorption/ionization

mGWAS:

Metabolic genome-wide association studies

ML:

Machine learning

mQTL:

Metabolites and quantitative trait loci

MSI:

Metabolomics Standards Initiative

NMR:

Nuclear magnetic resonance

PMRN:

Pharmacometabolomics Research Network

QSP:

Quantitative and systems pharmacology

SIMS:

Secondary ion mass spectrometry

SNPs:

Single nucleotide polymorphisms

VOC:

Volatile organic compound

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  1. Authors: “Vijay Soni” and “Nicholas Bartelo” have equally contributed to this chapter.

Authors and Affiliations

  1. Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Vijay Soni

  2. Department of Physiology and Biophysics, Weill Cornell Medicine, Institute for Computational Biomedicine, Englander Institute for Precision Medicine, New York, NY, USA

    Nicholas Bartelo

  3. The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA

    Annalise Schweickart

  4. Tri-Institutional Computational Biology and Medicine Program, Weill Cornell Medicine, New York, NY, USA

    Annalise Schweickart

  5. Department of Hematology, Mayo Clinic, Rochester, MN, USA

    Yogesh Chawla

  6. International AIDS Vaccine Initiative, Gurugram, Haryana, India

    Ankita Dutta

  7. Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USA

    Sonia Jain

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Correspondence to Vijay Soni .

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  1. Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Vijay Soni

  2. Division of Infectious Diseases, Weill Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Travis E. Hartman

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Soni, V., Bartelo, N., Schweickart, A., Chawla, Y., Dutta, A., Jain, S. (2023). Future Perspectives of Metabolomics: Gaps, Planning, and Recommendations. In: Soni, V., Hartman, T.E. (eds) Metabolomics. Springer, Cham. https://doi.org/10.1007/978-3-031-39094-4_14

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