» Articles » PMID: 37468430

An Epidemiological Introduction to Human Metabolomic Investigations

Abstract

Metabolomics holds great promise for uncovering insights around biological processes impacting disease in human epidemiological studies. Metabolites can be measured across biological samples, including plasma, serum, saliva, urine, stool, and whole organs and tissues, offering a means to characterize metabolic processes relevant to disease etiology and traits of interest. Metabolomic epidemiology studies face unique challenges, such as identifying metabolites from targeted and untargeted assays, defining standards for quality control, harmonizing results across platforms that often capture different metabolites, and developing statistical methods for high-dimensional and correlated metabolomic data. In this review, we introduce metabolomic epidemiology to the broader scientific community, discuss opportunities and challenges presented by these studies, and highlight emerging innovations that hold promise to uncover new biological insights.

Citing Articles

Challenges in Metabolite Biomarkers as Avenues of Diagnosis and Prognosis of Cancer.

Sharma N, Sarode S, Sekar G, Sonawane K, Bomle D J Cancer Prev. 2025; 29(4):105-112.

PMID: 39790219 PMC: 11706722. DOI: 10.15430/JCP.24.015.


Uncovering the sex steroid hormone secrets in alcohol.

Rodriguez Franco G, Hsu C Alcohol Clin Exp Res (Hoboken). 2024; 49(1):95-98.

PMID: 39523479 PMC: 11740176. DOI: 10.1111/acer.15479.


DHA-enriched phosphatidylserine alleviates bisphenol A-induced liver injury through regulating glycerophospholipid metabolism and the SIRT1-AMPK pathway.

Tian J, Lu Y, Zhao Q, Pu Q, Jiang S, Tang Y Heliyon. 2024; 10(14):e34835.

PMID: 39148994 PMC: 11325772. DOI: 10.1016/j.heliyon.2024.e34835.


A metabolomics study on carcinogenesis of ground-glass nodules.

Zhang X, Tong X, Chen Y, Chen J, Li Y, Ding C Cytojournal. 2024; 21:12.

PMID: 38628288 PMC: 11021118. DOI: 10.25259/Cytojournal_68_2023.


Metabolomic data presents challenges for epidemiological meta-analysis: a case study of childhood body mass index from the ECHO consortium.

Prince N, Liang D, Tan Y, Alshawabkeh A, Angel E, Busgang S Metabolomics. 2024; 20(1):16.

PMID: 38267770 PMC: 11099615. DOI: 10.1007/s11306-023-02082-y.

References
1.
Krumsiek J, Suhre K, Illig T, Adamski J, Theis F . Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst Biol. 2011; 5:21. PMC: 3224437. DOI: 10.1186/1752-0509-5-21. View

2.
Burgess S, Butterworth A, Thompson S . Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013; 37(7):658-65. PMC: 4377079. DOI: 10.1002/gepi.21758. View

3.
Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C . Human serum metabolic profiles are age dependent. Aging Cell. 2012; 11(6):960-7. PMC: 3533791. DOI: 10.1111/j.1474-9726.2012.00865.x. View

4.
Wishart D . Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov. 2016; 15(7):473-84. DOI: 10.1038/nrd.2016.32. View

5.
Higgins J, Thompson S . Quantifying heterogeneity in a meta-analysis. Stat Med. 2002; 21(11):1539-58. DOI: 10.1002/sim.1186. View