» Articles » PMID: 32437301

Metabolomics and Proteomics in Type 2 Diabetes

Overview
Journal Circ Res
Date 2020 May 22
PMID 32437301
Citations 67
Authors
Affiliations
Soon will be listed here.
Abstract

The persistent increase in the worldwide burden of type 2 diabetes mellitus (T2D) and the accompanying rise of its complications, including cardiovascular disease, necessitates our understanding of the metabolic disturbances that cause diabetes mellitus. Metabolomics and proteomics, facilitated by recent advances in high-throughput technologies, have given us unprecedented insight into circulating biomarkers of T2D even over a decade before overt disease. These markers may be effective tools for diabetes mellitus screening, diagnosis, and prognosis. As participants of metabolic pathways, metabolite and protein markers may also highlight pathways involved in T2D development. The integration of metabolomics and proteomics with genomics in multiomics strategies provides an analytical method that can begin to decipher causal associations. These methods are not without their limitations; however, with careful study design and sample handling, these methods represent powerful scientific tools that can be leveraged for the study of T2D. In this article, we aim to give a timely overview of circulating metabolomics and proteomics findings with T2D observed in large human population studies to provide the reader with a snapshot into these emerging fields of research.

Citing Articles

Energy metabolism in health and diseases.

Liu H, Wang S, Wang J, Guo X, Song Y, Fu K Signal Transduct Target Ther. 2025; 10(1):69.

PMID: 39966374 PMC: 11836267. DOI: 10.1038/s41392-025-02141-x.


Untargeted metabolomics analysis of glycerophospholipid metabolism in very low birth weight infants administered multiple oil lipid emulsions.

Zeng X, Yu T, Xia L, Ruan Z BMC Pediatr. 2024; 24(1):849.

PMID: 39736612 PMC: 11686898. DOI: 10.1186/s12887-024-05343-4.


Novel type 2 diabetes prediction score based on traditional risk factors and circulating metabolites: model derivation and validation in two large cohort studies.

Xie R, Herder C, Sha S, Peng L, Brenner H, Schottker B EClinicalMedicine. 2024; 79:102971.

PMID: 39720612 PMC: 11667638. DOI: 10.1016/j.eclinm.2024.102971.


Changes in the gut microbiota and derived fecal metabolites may play a role in tacrolimus-induced diabetes in mice.

Qian M, Jiang Z, Xu C, Wang L, Hu N Future Microbiol. 2024; 20(3):237-246.

PMID: 39711145 PMC: 11812427. DOI: 10.1080/17460913.2024.2444761.


Microbial and proteomic signatures of type 2 diabetes in an Arab population.

Murugesan S, Yousif G, Djekidel M, Gentilcore G, Grivel J, Al Khodor S J Transl Med. 2024; 22(1):1132.

PMID: 39707404 PMC: 11662572. DOI: 10.1186/s12967-024-05928-8.


References
1.
Randle P, Garland P, Hales C, Newsholme E . The glucose fatty-acid cycle. Its role in insulin sensitivity and the metabolic disturbances of diabetes mellitus. Lancet. 1963; 1(7285):785-9. DOI: 10.1016/s0140-6736(63)91500-9. View

2.
Gieger C, Geistlinger L, Altmaier E, de Angelis M, Kronenberg F, Meitinger T . Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 2008; 4(11):e1000282. PMC: 2581785. DOI: 10.1371/journal.pgen.1000282. View

3.
Wang T, Ngo D, Psychogios N, Dejam A, Larson M, Vasan R . 2-Aminoadipic acid is a biomarker for diabetes risk. J Clin Invest. 2013; 123(10):4309-17. PMC: 3784523. DOI: 10.1172/JCI64801. View

4.
Bales J, Higham D, Howe I, Nicholson J, Sadler P . Use of high-resolution proton nuclear magnetic resonance spectroscopy for rapid multi-component analysis of urine. Clin Chem. 1984; 30(3):426-32. View

5.
Ference B, Robinson J, Brook R, Catapano A, Chapman M, Neff D . Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes. N Engl J Med. 2016; 375(22):2144-2153. DOI: 10.1056/NEJMoa1604304. View