» Articles » PMID: 30585652

The Metabolome As a Biomarker of Mortality Risk in the Common Marmoset

Overview
Journal Am J Primatol
Date 2018 Dec 27
PMID 30585652
Citations 5
Authors
Affiliations
Soon will be listed here.
Abstract

Recently, the common marmoset has been proposed as a non-human primate model of aging. Their short lifespan coupled with pathologies that are similar to humans make them an ideal model to understand the genetic, metabolic, and environmental factors that influence aging and longevity. However, many of the underlying physiological changes that occur with age in the marmoset are unknown. Here, we attempt to determine if individual metabolites are predictive of future death and to recapitulate past metabolomic results after a change in environment (move across the country) was imposed on a colony of marmosets. We first determined that low levels of tryptophan metabolism metabolites were associated with risk of death in a 2-year follow-up in the animals, suggesting these metabolites may be used as future biomarkers of mortality. We also discovered that betaine metabolism and methionine metabolism are associated with aging regardless of environment for the animals, or of metabolomic assay technique. These two metabolic pathways are therefore of particular interest to examine as future targets for health and lifespan extending interventions. Many of the pathways associated with age in our first study of marmoset metabolomics were not found to have significant age effects in our second study, suggesting more work is needed to understand the reproducibility of large scale metabolomic studies in mammalian models. Overall, we were able to show that while several metabolomics markers show promise in understanding health and lifespan relationships with aging, it is possible that choice of technique for assay and reproducibility in these types of studies are still issues that need to be examined further.

Citing Articles

Rationale and design of the Dog Aging Project precision cohort: a multi-omic resource for longitudinal research in geroscience.

Prescott J, Keyser A, Litwin P, Dunbar M, McClelland R, Ruple A Geroscience. 2025; .

PMID: 40038157 DOI: 10.1007/s11357-025-01571-3.


Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway.

Lokhov P, Balashova E, Maslov D, Trifonova O, Archakov A Metabolites. 2024; 14(11).

PMID: 39590829 PMC: 11597009. DOI: 10.3390/metabo14110593.


The Potential of Metabolomics in Biomedical Applications.

Gonzalez-Covarrubias V, Martinez-Martinez E, Del Bosque-Plata L Metabolites. 2022; 12(2).

PMID: 35208267 PMC: 8880031. DOI: 10.3390/metabo12020194.


Comparative Metabolomic Study of Species with Different Lifespans.

Maslov D, Zemskaya N, Trifonova O, Lichtenberg S, Balashova E, Lisitsa A Int J Mol Sci. 2021; 22(23).

PMID: 34884677 PMC: 8657752. DOI: 10.3390/ijms222312873.


Extending human healthspan and longevity: a symposium report.

DeVito L, Barzilai N, Cuervo A, Niedernhofer L, Milman S, Levine M Ann N Y Acad Sci. 2021; 1507(1):70-83.

PMID: 34498278 PMC: 10231756. DOI: 10.1111/nyas.14681.


References
1.
Yao K, Fang J, Yin Y, Feng Z, Tang Z, Wu G . Tryptophan metabolism in animals: important roles in nutrition and health. Front Biosci (Schol Ed). 2011; 3(1):286-97. DOI: 10.2741/s152. View

2.
Menni C, Kastenmuller G, Petersen A, Bell J, Psatha M, Tsai P . Metabolomic markers reveal novel pathways of ageing and early development in human populations. Int J Epidemiol. 2013; 42(4):1111-9. PMC: 3781000. DOI: 10.1093/ije/dyt094. View

3.
Go Y, Uppal K, Walker D, Tran V, Dury L, Strobel F . Mitochondrial metabolomics using high-resolution Fourier-transform mass spectrometry. Methods Mol Biol. 2014; 1198:43-73. PMC: 4318503. DOI: 10.1007/978-1-4939-1258-2_4. View

4.
Layne D, Power R . Husbandry, handling, and nutrition for marmosets. Comp Med. 2003; 53(4):351-9. View

5.
Xia J, Wishart D . Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis. Curr Protoc Bioinformatics. 2016; 55:14.10.1-14.10.91. DOI: 10.1002/cpbi.11. View