Identification of the serum metabolites associated with cow milk consumption in Chinese Peri-/Postmenopausal women

Abstract

Cow milk consumption (CMC) and downstream alterations of serum metabolites are commonly considered important factors regulating human health status. Foods may lead to metabolic changes directly or indirectly through remodelling gut microbiota (GM). We sought to identify the metabolic alterations in Chinese Peri-/Postmenopausal women with habitual CMC and explore if the GM mediates the CMC-metabolite associations. 346 Chinese Peri-/Postmenopausal women participants were recruited in this study. Fixed effects regression and partial least squares discriminant analysis (PLS-DA) were applied to reveal alterations of serum metabolic features in different CMC groups. Spearman correlation coefficient was computed to detect metabolome-metagenome association. 36 CMC-associated metabolites including palmitic acid (FA(16:0)), 7alpha-hydroxy-4-cholesterin-3-one (7alphaC4), citrulline were identified by both fixed effects regression (FDR < 0.05) and PLS-DA (VIP score > 2). Some significant metabolite-GM associations were observed, including FA(16:0) with gut species Bacteroides ovatus, Bacteroides sp.D2. These findings would further prompt our understanding of the effect of cow milk on human health.

Keywords:

• Cow milk consumption
• metabolomic
• serum metabolites
• module
• functional pathways
• gut microbiota

Acknowledgements

We thank the National Institutes of Health for financial support and LC-Bio Technologies (Hangzhou) CO., LTD. (Hangzhou, China, www.lc-bio.com) for technical supports for our research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability

The data that support the findings of this study are available on request from the corresponding author, Hong-Wen Deng. The data are not publicly available due to the privacy of research participants.

Additional information

Funding

Hong-Mei Xiao was partially supported by the National Key R&D Program of China (2016YFC1201805 and 2017YFC1001100). Jie Shen was partially supported by grants from the Science and Technology Program of Guangzhou, China (201604020007), and the National Natural Science Foundation of China (81770878).