Background
Endogenic biomarkers can reflect lifestyle habits and may mediate associations between lifestyle and cancer risk. Identifying biomarkers that are associated with lifestyle factors could, therefore, help advance our knowledge about the biological mechanisms linking lifestyle to cancer development.
 
Objective
We aim to conduct an exploratory data analysis of body mass index (BMI), tobacco smoking, and alcohol consumption in relation to 249 metabolites and metabolite ratios using the Nightingale Health CoreMetabolomics panel.
 
Methods
The study included 1105 plasma samples from 877 participants in the Northern Sweden Health and Disease Study, who were included as control participants in a well-characterized nested case-control study of colorectal cancer. Spearman's correlation was applied to test for correlations.  Metabolites with similar patterns across different lifestyle factors were clustered using hierarchical clustering with a Spearman correlation-based distance metric.
 
Results
Preliminary results from the Spearman correlation tests revealed most correlations between metabolites and BMI (in both directions). Alcohol consumption demonstrated heterogeneous, moderate correlations (spearman correlation coefficient ≤ 0.3, p ≤ 0.05) across the spectrum of metabolites, whereas smoking status presented more directionally consistent effects within certain subsets of metabolites. Hierarchical clustering [Bv5] revealed that metabolites grouped based on shared association patterns across lifestyle factors; however, BMI emerged as the dominant lifestyle factor associated with circulating metabolite concentrations.
 
Conclusion
Moderate correlations were observed for multiple metabolites, particularly in relation to BMI, but also for smoking[LV6]  and alcohol. The hierarchical clustering results suggest that lifestyle-related metabolic profiles may serve as informative biomarkers that could be used to infer etiological mechanisms. Planned next steps include using linear mixed models, fitted for each metabolite, to identify associations that account for repeated measures and appropriate confounders, as well as to expand the analyses to additional lifestyle factors.