Metabolomics is extensively used in large-scale population studies to explore the associations between circulating metabolites and various health outcomes. However, translating metabolomics findings to clinical settings is challenging due to substantial inter-individual variability, the dynamic nature of metabolites, and their sensitivity to sample handling. Factors such as genes, sex, age, diet, lifestyle, the gut microbiome, sample storage and preprocessing all influence metabolic profiles. It is however largely unknown how to manipulate the levels of specific circulating metabolites.

Previous research has demonstrated that maintaining a healthy metabolome can reduce the risk of cardiovascular disease, particularly in obese individuals, and that adherence to a healthy lifestyle improves metabolic profiles in diabetic patients. We thus hypothesize that breast cancer prevention may be partially achievable by maintaining a healthy metabolic profile. Therefore, characterizing lifestyle-related factors that explain inter-individual variations in the circulating metabolome could inform cancer-preventive lifestyle interventions.

In this cross-sectional study, we analyzed metabolic profiles of 2283 healthy female participants from the Trøndelag Health Study (HUNT study), for which detailed information on breast-cancer related lifestyle factors were available. This allowed us to investigate the extent to which lifestyle-related factors explain the variance in individual metabolites, and correlations between lifestyle factors and metabolite levels. We also clustered the study cohort into three distinct groups based on lifestyle-related factors to identify cluster-specific metabolic signatures. Two of the three clusters were characterized by high body weight and high age, respectively, while the third cluster consisted of middle-aged women of an average body weight.

Our findings indicate that lifestyle-related factors can explain up to 30% variance in individual metabolites. Age and obesity were the key factors influencing serum metabolic profiles, both associated with elevated levels of triglyceride-rich very low-density lipoproteins (VLDLs) and intermediate-density lipoproteins (IDLs), amino acids, and glycolysis-related metabolites, as well as decreased levels of high-density lipoproteins (HDLs). This study revealed metabolic similarities between obese and older individuals, suggesting accelerated metabolic aging with obesity. Although causality cannot be determined from our findings, they suggest that IDL and VLDL levels reflect overall lifestyle and health status, and may be modified by weight reduction and/or blood pressure management.