Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

Autor: Oosterwegel MJ; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands., Ibi D; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands., Portengen L; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands., Probst-Hensch N; Swiss Tropical and Public Health Institute, Allschwil 4123, Switzerland.; University of Basel, Basel 4001, Switzerland., Tarallo S; Italian Institute for Genomic Medicine (IIGM), c/o IRCCS, Turin 10060, Italy., Naccarati A; Italian Institute for Genomic Medicine (IIGM), c/o IRCCS, Turin 10060, Italy., Imboden M; Swiss Tropical and Public Health Institute, Allschwil 4123, Switzerland.; University of Basel, Basel 4001, Switzerland., Jeong A; Swiss Tropical and Public Health Institute, Allschwil 4123, Switzerland.; University of Basel, Basel 4001, Switzerland., Robinot N; Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon CS 90627, France., Scalbert A; Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon CS 90627, France., Amaral AFS; National Heart and Lung Institute, Imperial College London, London SW3 6LY, U.K.; NIHR Imperial Biomedical Research Centre, London W2 1NY, U.K., van Nunen E; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands., Gulliver J; Medical Research Council-Public Health England Center for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2AZ, U.K.; Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester, Leicester LE1 7RH, U.K., Chadeau-Hyam M; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands.; Medical Research Council-Public Health England Center for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2AZ, U.K., Vineis P; Medical Research Council-Public Health England Center for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2AZ, U.K.; Italian Institute for Genomic Medicine (IIGM), c/o IRCCS, Turin 10060, Italy., Vermeulen R; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands.; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands.; Medical Research Council-Public Health England Center for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, London SW7 2AZ, U.K., Keski-Rahkonen P; Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon CS 90627, France., Vlaanderen J; Division of Environmental Epidemiology, Institute for Risk Assessment Sciences, Utrecht University, Utrecht 3584 CM, The Netherlands.
Jazyk: angličtina
Zdroj: Environmental science & technology [Environ Sci Technol] 2023 Aug 29; Vol. 57 (34), pp. 12752-12759. Date of Electronic Publication: 2023 Aug 15.
DOI: 10.1021/acs.est.3c03233
Abstrakt: Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.
Databáze: MEDLINE
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