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Post-treatment adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) lifestyle recommendations were associated with health-related quality of life (HRQoL), fatigue, and chemotherapy-induced peripheral neuropathy (CIPN) in colorectal cancer (CRC) survivors. In a prospective cohort among CRC survivors ( n = 459), repeated home-visits were performed at 6 weeks, 6, 12, and 24 months post-treatment. Dietary intake, body composition, sedentary behaviour, and physical activity were assessed to construct a lifestyle score based on adherence to seven 2018 WCRF/AICR recommendations. Longitudinal associations of the lifestyle score with HRQoL, fatigue, and CIPN were analysed by confounder-adjusted linear mixed models. A higher lifestyle score was associated with better physical functioning and less activity-related fatigue, but not with CIPN. Adjustment for physical activity substantially attenuated observed associations, indicating its importance in the lifestyle score with regards to HRQoL. In contrast, adjustment for body composition and alcohol inflated observed associations, indicating that both recommendations had a counteractive influence within the lifestyle score. Our findings suggest that CRC survivors benefit from an overall adherence to the WCRF/AICR lifestyle recommendations in terms of HRQoL and fatigue, but not CIPN. Specific recommendations have a varying influence on these associations, complicating the interpretation and requiring further study.

Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t -tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted., Competing Interests: The authors declare no conflicts of interest. C.M.U. has as cancer center director oversight over research funded by several pharmaceutical companies, but has not received funding directly herself.

The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.