Modeling blood metabolite homeostatic levels reduces sample heterogeneity across cohorts

• 成果类型: Article
• 署名作者: Liu, Danni; Gowda, G. A. Nagana; Jiang, Zhongli; Alemdjrodo, Kangni; Zhang, Min; Zhang, Dabao; Raftery, Daniel
• 署名单位: Purdue University System; Purdue University; University of Washington; University of Washington Seattle; University of California System; University of California Irvine
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10059
• DOI: 10.1073/pnas.2307430121
• 发表日期: 2024-02-20
• 关键词: global reconstruction; metabolomics; serum; RISK; ACID

摘要

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 chal- lenge 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 approxi- mately 50 metabolites that were quantified using 1H NMR spectroscopy. More than one - third of the variation in these metabolite profiles is due to cross - cohort variation. A dramatic reduction in the variation of metabolite levels (90%), especially their site - to - site variation (95%), was achieved by modeling each metabolite using demographic and clinical factors and especially other metabolites, as observed in the top principal com- ponents. The results also reveal that several metabolites contribute disproportionately to such variation, which could be explained by their association with biological pathways including biosynthesis and degradation. The study demonstrates an intriguing network effect of metabolites that can be utilized to better define homeostatic metabolite levels, which may have implications for improved health monitoring. As an example of the potential utility of the approach, we show that modeling gender- related metabolic dif- ferences retains the interesting variance while reducing unwanted (site- related) variance.