{"doi":"10.1089/omi.2014.0019","title":"Medium-Term Variability of the Human Serum Metabolome in the Atherosclerosis Risk in Communities (ARIC) Study","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Metabolomics is a systems biology tool providing small molecule signatures of disease etiology. In order to estimate the biologic variability of the human serum metabolome, this study calculated intraclass correlation coefficients (ICCs) for 178 stably-detected metabolites measured by untargeted chromatography/mass spectrometry. We studied a subsample of 60 participants (57% males, 70% Caucasians, aged 73.77±5.3 years) in the Atherosclerosis Risk in Communities (ARIC) Study who provided two fasting serum samples 4–6 weeks apart. The median ICC across all metabolites was 0.60, and 82% of metabolites had at least fair variability (i.e., ICC&gt;= 0.40). There was variation in the medium-term variability among metabolites, with those in the pathways of amino acid and lipid metabolism showing relatively high ICCs, and those in the carbohydrate pathway showing relatively low ICCs. The results of this study provide a valuable resource for future study design and outcome interpretation of mass spectrometry-based metabolomic studies in epidemiology.</jats:p>","journal":"OMICS: A Journal of Integrative Biology","year":2014,"id":637117,"datarank":0.4636563680037475,"base_score":3.091042453358316,"endowment":3.091042453358316,"self_citation_contribution":0.4636563680037475,"citation_network_contribution":0.0,"self_endowment_contribution":0.4636563680037475,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":21,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"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":1205528,"name":"Bing Yu","orcid":"0000-0002-1273-0452","position":1,"is_corresponding":false},{"id":551056,"name":"Danny Alexander","orcid":null,"position":2,"is_corresponding":false},{"id":687126,"name":"David J. Couper","orcid":null,"position":3,"is_corresponding":false},{"id":245381,"name":"Eric Boerwinkle","orcid":"0000-0001-8813-0544","position":4,"is_corresponding":false},{"id":549935,"name":"Yan Zheng","orcid":"0000-0003-1780-6134","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Medium-Term Variability of the Human Serum Metabolome in the Atherosclerosis Risk in Communities (ARIC) Study","abstract":"<jats:title>Abstract</jats:title>\n                  <jats:p>Metabolomics is a systems biology tool providing small molecule signatures of disease etiology. In order to estimate the biologic variability of the human serum metabolome, this study calculated intraclass correlation coefficients (ICCs) for 178 stably-detected metabolites measured by untargeted chromatography/mass spectrometry. We studied a subsample of 60 participants (57% males, 70% Caucasians, aged 73.77±5.3 years) in the Atherosclerosis Risk in Communities (ARIC) Study who provided two fasting serum samples 4–6 weeks apart. The median ICC across all metabolites was 0.60, and 82% of metabolites had at least fair variability (i.e., ICC&gt;= 0.40). There was variation in the medium-term variability among metabolites, with those in the pathways of amino acid and lipid metabolism showing relatively high ICCs, and those in the carbohydrate pathway showing relatively low ICCs. The results of this study provide a valuable resource for future study design and outcome interpretation of mass spectrometry-based metabolomic studies in epidemiology.</jats:p>","is_dataset_classified":null,"base_score":3.091042453358316,"endowment":3.091042453358316,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"24910946","pmcid":"PMC4048570","openalex_id":"https://openalex.org/W2333179791","authors":[],"funders":[{"funder_name":"NHGRI NIH HHS","grant_id":"U01 HG004402","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100005C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100012C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100007C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100008C","title":null},{"funder_name":"NHGRI NIH HHS","grant_id":"3U01HG004402-02S1","title":"GWA for Gene-Environment Interaction Effects Influencing CHD"},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100011C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100006C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100009C","title":null},{"funder_name":"NHLBI NIH HHS","grant_id":"HHSN268201100010C","title":null}],"total_grants":10,"fwci":0.78,"citation_percentile":0.71432432,"influential_citations":0,"citation_trend":[{"year":2014,"count":1},{"year":2015,"count":2},{"year":2016,"count":2},{"year":2017,"count":1},{"year":2018,"count":1},{"year":2019,"count":1},{"year":2020,"count":4},{"year":2021,"count":1},{"year":2022,"count":1},{"year":2023,"count":2},{"year":2024,"count":2},{"year":2025,"count":2},{"year":2026,"count":1}],"oa_status":"green","license":"https://journals.sagepub.com/page/policies/text-and-data-mining-license","oa_locations":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4048570","host_type":"repository"},{"url":"https://europepmc.org/articles/pmc4048570?pdf=render","host_type":"GREEN"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/4048570","host_type":"repository"},{"url":"https://journals.sagepub.com/doi/full-xml/10.1089/omi.2014.0019","host_type":"publisher"},{"url":"https://journals.sagepub.com/doi/pdf/10.1089/omi.2014.0019","host_type":"publisher"},{"url":"https://doi.org/10.1089/omi.2014.0019","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/24910946","host_type":"repository"},{"url":"https://dx.doi.org/10.17615/h378-0974","host_type":""},{"url":"https://dx.doi.org/10.1089/omi.2014.0019","host_type":""}],"fields_of_study":["Metabolomics and Mass Spectrometry Studies","Gut microbiota and health","Diet and metabolism studies","Medicine","Biology","Chemistry","0301 basic medicine","0303 health sciences","03 medical and health sciences"],"mesh_terms":["Chromatography, Liquid","Female","Humans","Male","Gas Chromatography-Mass Spectrometry","Middle Aged","Mass Spectrometry","Serum","Atherosclerosis","Metabolome"],"keywords":["Atherosclerosis Risk in Communities","Metabolome","Term (time)","Environmental health","Biology","Metabolomics","Medicine","Internal medicine","Bioinformatics","Coronary heart disease","Male","Serum","Middle Aged","Atherosclerosis","Gas Chromatography-Mass Spectrometry","Mass Spectrometry","Humans","Female","Chromatography, Liquid"],"sdg_mappings":[{"sdg_number":2,"sdg_label":"2. Zero hunger"},{"sdg_number":3,"sdg_label":"3. Good health"},{"sdg_number":0,"sdg_label":"Good health and well-being"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-06T18:20:58.576552Z","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":[]}