Active molecular network discovery links lifestyle variables to breast cancer in the Long Island Breast Cancer Study Project

28 December 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Background/Aim: Healthy lifestyle has been associated with decreased risk of developing breast cancer. Using untargeted metabolomics profiling, which provides unbiased information regarding lifestyle choices such as diet and exercise, we aim to identify the molecular mechanisms connecting lifestyle and breast cancer through network analysis. Methods: A total of 100 post-menopausal women, 50 with breast cancer and 50 cancer-free controls were selected from the Long Island Breast Cancer Study Project (LIBCSP). We measured untargeted plasma metabolomics using liquid chromatography-high resolution mass spectrometry (LC-HRMS). Using the ‘enet’ package, we retained highly correlated metabolites representing active molecular network (AMN) clusters for analysis. A typical machine learning workflow (LASSO) was used to examine associations between cancer status and AMN metabolites and covariates such as BMI, age, and reproductive factors. LASSO was then repeated to examine associations between AMN metabolites and 10 lifestyle related variables including smoking, physical activity, alcohol consumption, meat consumption, fruit and vegetables consumption, and supplemental vitamin use. Results were displayed as a network to uncover biological pathways linking lifestyle factors to breast cancer status. Results: After filtering, there were 1797 metabolomics peaks in the plasma samples. Of these, 851 “active” metabolites were retained in 197 correlation AMN clusters. Using LASSO, breast cancer status was associated with 71 “active” metabolites. Several of these metabolites were associated with lifestyle variables including meat consumption, alcohol consumption, and supplemental β-carotene, B12 and folate use. No individual lifestyle factors were significantly associated with breast cancer status using LASSO, suggesting that metabolites may act as biological intermediaries between healthy lifestyle factors and breast cancer. In particular, DiHODE, a metabolite linked with inflammation, was associated with breast cancer status and connected to β-carotene supplement usage through an AMN. Conclusions: We found several plasma metabolites associated with lifestyle factors and breast cancer status. Future studies investigating the mechanistic role of inflammation in linking supplement usage to breast cancer status are warranted.

Keywords

metabolomics
exposome
machine learning
network analysis
public health

Supplementary materials

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Title
SI for Active molecular network discovery links lifestyle variables to breast cancer in the Long Island Breast Cancer Study Project
Description
Annotation of dihydroxyoctadeca dienoate (DiHODE) Table S1 Table S2 Table S3
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