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

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      , , , , , # , # , , , , , , , , , , § , , , § , § , # , , , § , , ,
      Environmental Science & Technology
      American Chemical Society
      blood, biomarkers, metabolomics, repeatability, variability, liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) , epidemiology, cohort study , reliability, intraclass correlation coefficient (ICC) , within-individual variability, between-individual variability

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          Abstract

          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.

          Abstract

          Limited insight exists on the repeatability of untargeted metabolomic measurements of human serum samples. This study estimates its repeatability over 100 days with implications for exposome research.

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          Most cited references30

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          brms: An R Package for Bayesian Multilevel Models Using Stan

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Intraclass correlations: Uses in assessing rater reliability.

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                Author and article information

                Journal
                Environ Sci Technol
                Environ Sci Technol
                es
                esthag
                Environmental Science & Technology
                American Chemical Society
                0013-936X
                1520-5851
                15 August 2023
                29 August 2023
                : 57
                : 34
                : 12752-12759
                Affiliations
                []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.
                []Swiss Tropical and Public Health Institute , Allschwil 4123, Switzerland
                []University of Basel , Basel 4001, Switzerland
                [# ]Italian Institute for Genomic Medicine (IIGM) , c/o IRCCS, Turin 10060, Italy
                []National Heart and Lung Institute, Imperial College London , London SW3 6LY, U.K.
                []Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization , Lyon CS 90627, France
                []Centre for Environmental Health and Sustainability & School of Geography, Geology and the Environment, University of Leicester , Leicester LE1 7RH, U.K.
                []NIHR Imperial Biomedical Research Centre , London W2 1NY, U.K.
                Author notes
                Author information
                https://orcid.org/0000-0003-3991-2632
                https://orcid.org/0000-0001-5774-0905
                https://orcid.org/0000-0001-6079-0030
                https://orcid.org/0000-0003-3423-2013
                https://orcid.org/0000-0001-8341-5436
                https://orcid.org/0000-0001-8935-4566
                Article
                10.1021/acs.est.3c03233
                10469440
                37582220
                4c0e2107-fe27-4e95-9405-8ff134970236
                © 2023 The Authors. Published by American Chemical Society

                Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 02 May 2023
                : 28 July 2023
                : 28 July 2023
                Funding
                Funded by: Horizon 2020 Framework Programme, doi 10.13039/100010661;
                Award ID: 874627
                Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek, doi 10.13039/501100003246;
                Award ID: 024.004.017
                Funded by: Ministerie van Onderwijs, Cultuur en Wetenschap, doi 10.13039/501100003245;
                Award ID: NA
                Funded by: Seventh Framework Programme, doi 10.13039/100011102;
                Award ID: 308610
                Categories
                Article
                Custom metadata
                es3c03233
                es3c03233

                General environmental science
                blood,biomarkers,metabolomics,repeatability,variability,liquid chromatography coupled to high-resolution mass spectrometry (lc-hrms),epidemiology,cohort study,reliability,intraclass correlation coefficient (icc),within-individual variability,between-individual variability

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