{"doi":"10.1016/j.aca.2015.12.009","title":"Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal","abstract":null,"journal":"Analytica Chimica Acta","year":2016,"id":590748,"datarank":1.6178482384130768,"base_score":3.5263605246161616,"endowment":3.5263605246161616,"self_citation_contribution":0.5289540786924243,"citation_network_contribution":1.0888941597206525,"self_endowment_contribution":0.5289540786924243,"citer_contribution":1.0888941597206525,"corpus_percentile":null,"corpus_rank":null,"citation_count":33,"citer_count":30,"citers_with_citation_signal":28,"citers_with_endowment":28,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1511380,"name":"Izabella Surowiec","orcid":null,"position":1,"is_corresponding":false},{"id":286157,"name":"Junfang Wu","orcid":"0000-0003-3338-7291","position":2,"is_corresponding":false},{"id":1511381,"name":"Sandra Gouveia-Figueira","orcid":null,"position":3,"is_corresponding":false},{"id":368655,"name":"Rui Pinto","orcid":"0000-0002-8527-4873","position":4,"is_corresponding":false},{"id":1511382,"name":"Johan Trygg","orcid":null,"position":5,"is_corresponding":false},{"id":256658,"name":"Angela M. Zivkovic","orcid":"0000-0002-2828-7862","position":6,"is_corresponding":false},{"id":1511383,"name":"Malin L. Nording","orcid":null,"position":7,"is_corresponding":false},{"id":1511379,"name":"Masoumeh Karimpour","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Postprandial metabolomics: A pilot mass spectrometry and NMR study of the human plasma metabolome in response to a challenge meal","abstract":"The study of postprandial metabolism is relevant for understanding metabolic diseases and characterizing personal responses to diet. We combined three analytical platforms - gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) - to validate a multi-platform approach for characterizing individual variation in the postprandial state. We analyzed the postprandial plasma metabolome by introducing, at three occasions, meal challenges on a usual diet, and 1.5 years later, on a modified background diet. The postprandial response was stable over time and largely independent of the background diet as revealed by all three analytical platforms. Coverage of the metabolome between NMR and GC-MS included more polar metabolites detectable only by NMR and more hydrophobic compounds detected by GC-MS. The variability across three separate testing occasions among the identified metabolites was in the range of 1.1-86% for GC-MS and 0.9-42% for NMR in the fasting state at baseline. For the LC-MS analysis, the coefficients of variation of the detected compounds in the fasting state at baseline were in the range of 2-97% for the positive and 4-69% for the negative mode. Multivariate analysis (MVA) of metabolites detected with GC-MS revealed that for both background diets, levels of postprandial amino acids and sugars increased whereas those of fatty acids decreased at 0.5 h after the meal was consumed, reflecting the expected response to the challenge meal. MVA of NMR data revealed increasing postprandial levels of amino acids and other organic acids together with decreasing levels of acetoacetate and 3-hydroxybutanoic acid, also independent of the background diet. Together these data show that the postprandial response to the same challenge meal was stable even though it was tested 1.5 years apart, and that it was largely independent of background diet. This work demonstrates the efficacy of a multi-platform metabolomics approach followed by multivariate and univariate data analysis for a broad-scale screen of the individual metabolome, particularly for studies using repeated measures to determine dietary response phenotype.","is_dataset_classified":null,"base_score":3.5263605246161616,"endowment":3.5263605246161616,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26826694","pmcid":null,"openalex_id":"https://openalex.org/W2216089388","authors":[],"funders":[{"funder_name":"Swedish Research Council Formas","grant_id":"2010-303","title":null},{"funder_name":"Sweden's innovation Agency","grant_id":"2010-02070","title":null},{"funder_name":"Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning","grant_id":"unidentified","title":"unidentified"},{"funder_name":"Kempe Foundations","grant_id":"","title":null}],"total_grants":4,"fwci":1.7904,"citation_percentile":0.85208714,"influential_citations":0,"citation_trend":[{"year":2016,"count":4},{"year":2017,"count":3},{"year":2018,"count":6},{"year":2019,"count":5},{"year":2020,"count":4},{"year":2021,"count":5},{"year":2022,"count":3},{"year":2023,"count":1},{"year":2025,"count":2}],"oa_status":"closed","license":"Elsevier TDM","oa_locations":[{"url":"https://api.elsevier.com/content/article/PII:S0003267015300404?httpAccept=text/xml","host_type":"publisher"},{"url":"https://api.elsevier.com/content/article/PII:S0003267015300404?httpAccept=text/plain","host_type":"publisher"},{"url":"https://doi.org/10.1016/j.aca.2015.12.009","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/26826694","host_type":"repository"},{"url":"https://dx.doi.org/10.1016/j.aca.2015.12.009","host_type":""},{"url":"https://publications.scilifelab.se/publication/948d8dfb2ca6423f9e87b43384490726","host_type":""}],"fields_of_study":["Metabolomics and Mass Spectrometry Studies","Advanced Chemical Sensor Technologies","Biochemical Analysis and Sensing Techniques","0301 basic medicine","0303 health sciences","03 medical and health sciences","Blood","Chromatography, Liquid","Gas Chromatography-Mass Spectrometry","Humans","Magnetic Resonance Spectroscopy","Metabolomics","Pilot Projects","Postprandial Period"],"mesh_terms":["Blood","Chromatography, Liquid","Humans","Gas Chromatography-Mass Spectrometry","Magnetic Resonance Spectroscopy","Pilot Projects","Postprandial Period","Metabolomics"],"keywords":["Postprandial","Metabolome","Chemistry","Metabolomics","Meal","Gas chromatography–mass spectrometry","Chromatography","Mass spectrometry","Liquid chromatography–mass spectrometry","Food science","Internal medicine","Medicine","Data analysis","Nucleic Magnetic Resonance","Multi-platform Analysis","Blood","Magnetic Resonance Spectroscopy","Humans","Pilot Projects","Postprandial Period","Gas Chromatography-Mass Spectrometry","Chromatography, Liquid"],"sdg_mappings":[{"sdg_number":2,"sdg_label":"2. Zero hunger"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-25T11:50:29.196418Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}