Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 3;13(4):514.
doi: 10.3390/metabo13040514.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Affiliations

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Sandi L Navarro et al. Metabolites. .

Abstract

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women's Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2-0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Keywords: NMR; confounders; correlates; mass spectrometry; metabolomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Similar articles

Cited by

References

    1. Johnson C.H., Ivanisevic J., Siuzdak G. Metabolomics: Beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 2016;17:451–459. doi: 10.1038/nrm.2016.25. - DOI - PMC - PubMed
    1. Tolstikov V., Moser A.J., Sarangarajan R., Narain N.R., Kiebish M.A. Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics. Metabolites. 2020;10:224. doi: 10.3390/metabo10060224. - DOI - PMC - PubMed
    1. Nagana Gowda G.A., Raftery D. Biomarker Discovery and Translation in Metabolomics. Curr. Metab. 2013;1:227–240. doi: 10.2174/2213235X113019990005. - DOI - PMC - PubMed
    1. Dang N.H., Singla A.K., Mackay E.M., Jirik F.R., Weljie A.M. Targeted cancer therapeutics: Biosynthetic and energetic pathways characterized by metabolomics and the interplay with key cancer regulatory factors. Curr. Pharm. Des. 2014;20:2637–2647. doi: 10.2174/13816128113199990489. - DOI - PubMed
    1. Lindon J.C., Nicholson J.K. The emergent role of metabolic phenotyping in dynamic patient stratification. Expert Opin. Drug Metab. Toxicol. 2014;10:915–919. doi: 10.1517/17425255.2014.922954. - DOI - PubMed

LinkOut - more resources