ArticleMetabolites2025
Longitudinal, Intra-Individual Stability of Untargeted Plasma and Cerebrospinal Fluid Metabolites.
Article in Metabolites, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
BACKGROUND/
objectivesLongitudinal metabolomics analysis offers valuable insights into how metabolic pathways change according to age and health status. However, metabolite levels can fluctuate due to biological factors (e.g., age, diet, and health status) and technical factors (e.g., sample handling, storage times, and instrument performance), with some metabolites exhibiting greater sensitivity to these sources of variability than others. This study aimed to characterize the longitudinal and technical stability of untargeted plasma and cerebrospinal fluid (CSF) metabolites and to identify a subset that remains reliable over the extended time scales required for epidemiological research.
methodsUntargeted ultrahigh-performance liquid chromatography-mass spectrometry (LC-MS) metabolomic profiles were available from multiple visits in the Wisconsin Registry for Alzheimer's Prevention (WRAP) and Wisconsin Alzheimer's Disease Research Center (ADRC) studies. For this analysis, we constructed a subset of generally healthy participants with samples drawn at four time points (~2.5 years apart): two visits analyzed in 2017 and two visits analyzed in 2023, corresponding to two distinct analytical waves. We computed Rothery's intraclass correlation coefficients (ICCs) to quantify intra-wave and inter-wave stability, evaluated pooled quality-control (QC) variation, classified metabolite stability by established thresholds, and developed a composite score integrating longitudinal stability and susceptibility to technical variance.
resultsAcross all metabolites, median stability was classified as 'fair' (Rothery's
conclusionsThis work highlights metabolites suitable for long-term epidemiological studies and informs experimental design and analytical strategies for combining data across cohorts and analytical batches.
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