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Abstract :
[en] In healthcare, most metabolomics studies focus on pathologies by studying inter-individual and/or inter-group variations in metabolite concentrations. However, with patient care shifting toward a more personalized approach, integrating metabolomics requires understanding normal intra-individual variations to detect pathological deviations. Currently, these “normal” variations remain poorly characterized, making it essential to study the metabolome of healthy individuals over time.
For this purpose, we conducted a one-year longitudinal study involving 30 healthy volunteers. Serum was collected weekly for ten weeks and then monthly for ten months, following European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Biological Variation recommendations. Questionnaires were also completed at each visit to report habit changes and lifestyle events potentially affecting the metabolome.
As quantification is required, NMR appeared as the most suitable analytical tool. Over the ten-week period, 41 metabolites were classified based on their intra-individual variability, from the least to the most fluctuating. A similar pattern emerged from monthly samples, confirming variability consistency over time. Additionally, we applied the Metabolomic Informative Content (MIC) approach to stratify individuals based on their metabolome variation. Finally, using questionnaire data, linear mixed models identified which events induced significant longitudinal differences at the individual level.
Our results enable the stratification of blood metabolites according to their short- and long-term within-subject variability. Moreover, questionnaire analyses revealed that several lifestyle events significantly modify the metabolome, meaning such factors must be considered when interpreting metabolomics data. Together, these results represent a key step toward integrating reliable metabolomics profiling into routine clinical practice.
