ArticleProceedings of the National Academy of Sciences of the United States of America2024
Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 3 citations in OpenAlex.
- Article
- Transfer Learning for Moderate-Dimensional Ridge-Regularized Robust Linear Regression.Entropy (Basel, Switzerland) · 2026Article
- Discovery of Hypoxanthine and Inosine as Robust Biomarkers for Predicting the Preanalytical Quality of Human Plasma and Serum for Metabolomics.Analytical chemistry · 2024Article
- NMR-based metabolomics: Where are we now and where are we going?Progress in nuclear magnetic resonance spectroscopyReview
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Authors and funding
7 authors at 3 institutions in 1 country.
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Abstract
Blood metabolite levels are affected by numerous factors, including preanalytical factors such as collection methods and geographical sites. These perturbations have caused deleterious consequences for many metabolomics studies and represent a major challenge in the metabolomics field. It is important to understand these factors and develop models to reduce their perturbations. However, to date, the lack of suitable mathematical models for blood metabolite levels under homeostasis has hindered progress. In this study, we develop quantitative models of blood metabolite levels in healthy adults based on multisite sample cohorts that mimic the current challenge. Five cohorts of samples obtained across four geographically distinct sites were investigated, focusing on approximately 50 metabolites that were quantified using
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