Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies
Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespre...
Gespeichert in:
| Veröffentlicht in: | American journal of epidemiology Jg. 186; H. 9; S. 1084 |
|---|---|
| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.11.2017
|
| Schlagworte: | |
| ISSN: | 1476-6256, 1476-6256 |
| Online-Zugang: | Weitere Angaben |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings. |
|---|---|
| AbstractList | Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings. Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings.Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular associations of established risk factors. A quantitative metabolomics platform based on nuclear magnetic resonance spectroscopy has found widespread use, already profiling over 400,000 blood samples. Over 200 metabolic measures are quantified per sample; in addition to many biomarkers routinely used in epidemiology, the method simultaneously provides fine-grained lipoprotein subclass profiling and quantification of circulating fatty acids, amino acids, gluconeogenesis-related metabolites, and many other molecules from multiple metabolic pathways. Here we focus on applications of magnetic resonance metabolomics for quantifying circulating biomarkers in large-scale epidemiology. We highlight the molecular characterization of risk factors, use of Mendelian randomization, and the key issues of study design and analyses of metabolic profiling for epidemiology. We also detail how integration of metabolic profiling data with genetics can enhance drug development. We discuss why quantitative metabolic profiling is becoming widespread in epidemiology and biobanking. Although large-scale applications of metabolic profiling are still novel, it seems likely that comprehensive biomarker data will contribute to etiologic understanding of various diseases and abilities to predict disease risks, with the potential to translate into multiple clinical settings. |
| Author | Kangas, Antti J Soininen, Pasi Ala-Korpela, Mika Würtz, Peter Davey Smith, George Lawlor, Debbie A |
| Author_xml | – sequence: 1 givenname: Peter surname: Würtz fullname: Würtz, Peter – sequence: 2 givenname: Antti J surname: Kangas fullname: Kangas, Antti J – sequence: 3 givenname: Pasi surname: Soininen fullname: Soininen, Pasi – sequence: 4 givenname: Debbie A surname: Lawlor fullname: Lawlor, Debbie A – sequence: 5 givenname: George surname: Davey Smith fullname: Davey Smith, George – sequence: 6 givenname: Mika surname: Ala-Korpela fullname: Ala-Korpela, Mika |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29106475$$D View this record in MEDLINE/PubMed |
| BookMark | eNpNkMlOwzAURS1URAfY8AHISzahnmIn7KqqDFJLgZZ15DgvxSVxSpwU-vcEARKrd6VzdHX1hqjnKgcInVNyRUnMx3oL47ePT0LlERpQoWQgWSh7_3IfDb3fEkJpHJIT1GcxJVKocID2T612jW10Y_eAV1C3JX5oTQG6xgu9cdBYg5_BV047A3gBjU6roiqt8dg6PNf1BoKV0QXg2c5mUNqObg7XeIIfa1tCjSuHg2Xn4zWYV_dNLfhTdJzrwsPZ7x2hl5vZenoXzJe399PJPDBcRU0QaW14LowiscrzMBKCEIgZyZQSiikmTIcVMzzSmVBRyBRVKQsF8Jym3FA2Qpc_vbu6em_BN0lpvYGi0A6q1ic0lpRwSWPeqRe_apuWkCW7br6uD8nfr9gXEKptFw |
| CitedBy_id | crossref_primary_10_1002_anie_201804736 crossref_primary_10_1111_sms_14261 crossref_primary_10_1016_j_ebiom_2021_103764 crossref_primary_10_1186_s13148_020_00957_8 crossref_primary_10_1161_ATVBAHA_123_320175 crossref_primary_10_1007_s11695_023_06905_8 crossref_primary_10_1038_s41591_022_01992_z crossref_primary_10_1186_s12933_022_01726_y crossref_primary_10_1371_journal_pone_0282433 crossref_primary_10_1093_ije_dyaf065 crossref_primary_10_1111_dom_15084 crossref_primary_10_1111_acel_14125 crossref_primary_10_1016_j_ebiom_2018_05_011 crossref_primary_10_1038_s41591_022_01686_6 crossref_primary_10_1017_S0007114520002524 crossref_primary_10_1161_JAHA_119_013131 crossref_primary_10_1055_a_1827_7896 crossref_primary_10_1002_advs_202406670 crossref_primary_10_1073_pnas_2500001122 crossref_primary_10_1016_j_atherosclerosis_2022_03_019 crossref_primary_10_1093_ajcn_nqz100 crossref_primary_10_1038_s43856_024_00669_7 crossref_primary_10_1186_s12877_024_05144_5 crossref_primary_10_1038_s41591_022_01980_3 crossref_primary_10_1161_ATVBAHA_121_316341 crossref_primary_10_1186_s12263_025_00763_y crossref_primary_10_1038_s41598_018_26441_1 crossref_primary_10_1053_j_ajkd_2023_05_014 crossref_primary_10_1038_s41366_025_01895_2 crossref_primary_10_1007_s00125_024_06282_6 crossref_primary_10_1017_S0007114519001429 crossref_primary_10_1038_s41598_025_07711_1 crossref_primary_10_1111_aogs_15176 crossref_primary_10_1186_s12916_025_04107_w crossref_primary_10_1371_journal_pone_0223692 crossref_primary_10_1016_j_nut_2021_111440 crossref_primary_10_1186_s12916_023_03115_y crossref_primary_10_1373_clinchem_2018_299222 crossref_primary_10_1016_j_jdiacomp_2022_108203 crossref_primary_10_1016_j_foodhyd_2024_109839 crossref_primary_10_1111_cen_14119 crossref_primary_10_1371_journal_pbio_3000572 crossref_primary_10_1016_j_atherosclerosis_2019_12_009 crossref_primary_10_1111_joim_20105 crossref_primary_10_7554_eLife_87894 crossref_primary_10_3390_ijms23169266 crossref_primary_10_1161_JAHA_121_021995 crossref_primary_10_1016_j_jlr_2025_100778 crossref_primary_10_1093_cei_uxae127 crossref_primary_10_1007_s11306_022_01917_4 crossref_primary_10_1177_00220345241298219 crossref_primary_10_1186_s12933_025_02899_y crossref_primary_10_1002_mnfr_202300338 crossref_primary_10_1007_s11306_025_02259_7 crossref_primary_10_1016_j_diabres_2020_108583 crossref_primary_10_12688_wellcomeopenres_16341_1 crossref_primary_10_1186_s12916_025_03993_4 crossref_primary_10_1111_cpf_12594 crossref_primary_10_12688_wellcomeopenres_16341_2 crossref_primary_10_1038_s42003_024_05977_1 crossref_primary_10_1038_s41598_025_06683_6 crossref_primary_10_1007_s00125_024_06108_5 crossref_primary_10_1093_ije_dyy287 crossref_primary_10_1038_s41591_019_0722_x crossref_primary_10_3233_JAD_190132 crossref_primary_10_3389_fimmu_2020_01527 crossref_primary_10_1016_j_atherosclerosis_2025_120457 crossref_primary_10_1002_ppul_24558 crossref_primary_10_1093_cvr_cvaf076 crossref_primary_10_1007_s12020_020_02235_2 crossref_primary_10_1136_bjophthalmol_2021_320584 crossref_primary_10_1186_s13073_022_01135_6 crossref_primary_10_1016_j_jri_2024_104397 crossref_primary_10_1111_vcp_12954 crossref_primary_10_1186_s12916_021_01929_2 crossref_primary_10_1038_s41576_023_00674_x crossref_primary_10_1136_openhrt_2020_001554 crossref_primary_10_1186_s12986_024_00882_0 crossref_primary_10_1210_clinem_dgaa729 crossref_primary_10_3390_nu17010008 crossref_primary_10_1093_ajcn_nqaa413 crossref_primary_10_1186_s12872_023_03394_6 crossref_primary_10_3390_metabo9010004 crossref_primary_10_1016_j_heliyon_2021_e07114 crossref_primary_10_1249_MSS_0000000000002003 crossref_primary_10_1007_s11306_025_02334_z crossref_primary_10_1111_liv_14174 crossref_primary_10_3390_jcm9040970 crossref_primary_10_7554_eLife_98709_3 crossref_primary_10_1002_ange_201804736 crossref_primary_10_1016_j_atherosclerosis_2019_05_011 crossref_primary_10_1111_jcpe_13902 crossref_primary_10_3389_fvets_2023_1105113 crossref_primary_10_1016_j_diabet_2024_101584 crossref_primary_10_1371_journal_pone_0304966 crossref_primary_10_1016_j_jhazmat_2024_136498 crossref_primary_10_3389_fnut_2024_1462300 crossref_primary_10_1186_s12916_024_03529_2 crossref_primary_10_1038_s41467_023_44459_6 crossref_primary_10_3390_nu12113309 crossref_primary_10_1002_ejhf_3226 crossref_primary_10_1016_j_atherosclerosis_2019_05_023 crossref_primary_10_1186_s12916_023_02742_9 crossref_primary_10_1016_j_biopsych_2019_08_016 crossref_primary_10_1136_bmjopen_2017_020900 crossref_primary_10_3390_metabo13121181 crossref_primary_10_1017_S0007114522000241 crossref_primary_10_1038_s41366_020_0565_z crossref_primary_10_1038_s41598_023_34598_7 crossref_primary_10_1111_cns_70507 crossref_primary_10_1016_j_atherosclerosis_2024_119094 crossref_primary_10_1097_EJA_0000000000001591 crossref_primary_10_1177_17474930241293408 crossref_primary_10_1186_s12916_021_01959_w crossref_primary_10_1016_j_lanepe_2025_101223 crossref_primary_10_1016_j_clnu_2024_07_027 crossref_primary_10_1186_s12916_018_1008_8 crossref_primary_10_1016_j_jacc_2017_12_006 crossref_primary_10_1161_JAHA_122_027934 crossref_primary_10_1111_dom_15935 crossref_primary_10_1161_JAHA_118_011852 crossref_primary_10_1186_s12916_022_02449_3 crossref_primary_10_1002_dmrr_3734 crossref_primary_10_1186_s12979_025_00527_7 crossref_primary_10_1161_ATVBAHA_118_312021 crossref_primary_10_1093_clinchem_hvae222 crossref_primary_10_1016_j_ebiom_2024_105279 crossref_primary_10_1038_s41380_019_0640_9 crossref_primary_10_1038_s41598_018_36450_9 crossref_primary_10_1016_j_plefa_2020_102099 crossref_primary_10_1016_j_atherosclerosis_2020_01_020 crossref_primary_10_3389_fnut_2022_962787 crossref_primary_10_1016_j_jtemb_2018_09_003 crossref_primary_10_1038_s41467_024_54357_0 crossref_primary_10_3390_metabo9040064 crossref_primary_10_1093_eurheartj_ehaa972 crossref_primary_10_2337_dc21_1415 crossref_primary_10_1038_s41598_024_69594_y crossref_primary_10_3389_fnut_2021_664939 crossref_primary_10_3390_metabo11090614 crossref_primary_10_1007_s10654_025_01284_z crossref_primary_10_3390_metabo12121172 crossref_primary_10_1002_advs_202413491 crossref_primary_10_1038_s41467_020_20750_8 crossref_primary_10_1186_s12933_022_01493_w crossref_primary_10_1038_s41598_021_90644_2 crossref_primary_10_1097_HEP_0000000000000879 crossref_primary_10_1186_s12916_018_1248_7 crossref_primary_10_1111_joim_13306 crossref_primary_10_1177_00220345231203562 crossref_primary_10_1016_j_jad_2023_11_070 crossref_primary_10_1186_s12933_025_02581_3 crossref_primary_10_1016_j_atherosclerosis_2023_117316 crossref_primary_10_1186_s12916_022_02688_4 crossref_primary_10_1007_s00125_020_05162_z crossref_primary_10_1542_peds_2019_3666 crossref_primary_10_1038_s41598_025_16493_5 crossref_primary_10_1177_1744806920923885 crossref_primary_10_7554_eLife_75170 crossref_primary_10_1017_S0007114517002768 crossref_primary_10_1111_jsr_13245 crossref_primary_10_1186_s12916_020_01819_z crossref_primary_10_1038_s41467_023_37729_w crossref_primary_10_1093_aje_kwy221 crossref_primary_10_1038_s41586_024_07148_y crossref_primary_10_1186_s12916_022_02653_1 crossref_primary_10_1016_j_tjnut_2025_04_034 crossref_primary_10_7554_eLife_72909 crossref_primary_10_3390_ijms232112947 crossref_primary_10_1186_s12969_024_01041_8 crossref_primary_10_1038_s41598_024_67177_5 crossref_primary_10_1016_j_jhep_2025_05_026 crossref_primary_10_1038_s41598_018_28793_0 crossref_primary_10_1210_clinem_dgad032 crossref_primary_10_1038_s43856_022_00140_5 crossref_primary_10_1039_D3FO03575A crossref_primary_10_1093_clinchem_hvaa290 crossref_primary_10_1016_j_jacc_2020_09_610 crossref_primary_10_1159_000542468 crossref_primary_10_3389_fnut_2024_1479800 crossref_primary_10_1016_j_ijcard_2018_11_020 crossref_primary_10_1038_s41598_025_12305_y crossref_primary_10_1038_s41598_022_19159_8 crossref_primary_10_1093_clinchem_hvab024 crossref_primary_10_1186_s12916_023_03188_9 crossref_primary_10_1038_s41467_024_46663_4 crossref_primary_10_1017_S0007114518000673 crossref_primary_10_1016_j_ajog_2022_06_009 crossref_primary_10_1093_hmg_ddad097 crossref_primary_10_7554_eLife_90132_3 crossref_primary_10_3390_metabo11020121 crossref_primary_10_1007_s11306_022_01954_z crossref_primary_10_3390_nu14245306 crossref_primary_10_1097_HJH_0000000000002310 crossref_primary_10_1515_znc_2018_0214 crossref_primary_10_1007_s11306_021_01803_5 crossref_primary_10_1186_s13073_024_01395_4 crossref_primary_10_3390_nu12123610 crossref_primary_10_1111_jnc_15659 crossref_primary_10_1017_S0007114524000138 crossref_primary_10_1111_rssc_12490 crossref_primary_10_3389_fnut_2024_1454364 crossref_primary_10_1186_s13054_023_04589_1 crossref_primary_10_1016_j_jad_2023_05_033 crossref_primary_10_1080_01635581_2021_1957947 crossref_primary_10_1038_s41598_020_61801_w crossref_primary_10_1038_s41598_018_33507_7 crossref_primary_10_1186_s12933_023_01815_6 crossref_primary_10_1093_ije_dyab156 crossref_primary_10_1111_joim_13479 crossref_primary_10_1161_CIRCGENETICS_117_001759 crossref_primary_10_1093_aje_kwae445 crossref_primary_10_1093_ije_dyaa188 crossref_primary_10_1186_s12874_024_02181_x crossref_primary_10_1016_j_jacc_2022_10_019 crossref_primary_10_1093_ajcn_nqab132 crossref_primary_10_1111_aogs_14750 crossref_primary_10_7554_eLife_87894_3 crossref_primary_10_1007_s10654_024_01114_8 crossref_primary_10_1093_eurjpc_zwac252 crossref_primary_10_1016_j_jacc_2018_09_066 crossref_primary_10_1002_alz_12180 crossref_primary_10_1039_D4FO00987H crossref_primary_10_1038_s41467_023_42404_1 crossref_primary_10_3390_nu15133002 crossref_primary_10_1186_s12916_023_03198_7 crossref_primary_10_1017_S0033291721001471 crossref_primary_10_1161_ATVBAHA_120_315639 crossref_primary_10_1016_j_jacl_2019_10_012 crossref_primary_10_1016_j_cca_2024_119671 crossref_primary_10_1093_ajcn_nqab269 crossref_primary_10_1161_CIRCRESAHA_121_319272 crossref_primary_10_1161_JAHA_124_039750 crossref_primary_10_1016_j_diabres_2025_112420 crossref_primary_10_1093_cvr_cvac194 crossref_primary_10_12688_wellcomeopenres_15087_2 crossref_primary_10_1093_eurjpc_zwaf305 crossref_primary_10_1016_j_atherosclerosis_2020_03_028 crossref_primary_10_1186_s12937_025_01077_w crossref_primary_10_1210_clinem_dgae204 crossref_primary_10_1016_j_jhep_2024_10_015 crossref_primary_10_1161_ATVBAHA_120_315766 crossref_primary_10_1016_j_ebiom_2023_104503 crossref_primary_10_1210_clinem_dgae318 crossref_primary_10_3390_metabo12060537 crossref_primary_10_1016_j_jrras_2024_101278 crossref_primary_10_1093_eurjpc_zwaf543 crossref_primary_10_1111_bph_15433 crossref_primary_10_1038_s41366_023_01281_w crossref_primary_10_1038_s43856_023_00382_x crossref_primary_10_1016_j_psyneuen_2017_10_005 crossref_primary_10_7554_eLife_90132 crossref_primary_10_1093_ije_dyab051 crossref_primary_10_1017_thg_2020_53 crossref_primary_10_1016_j_jlr_2022_100313 crossref_primary_10_1016_j_jad_2025_119594 crossref_primary_10_1111_dom_16533 crossref_primary_10_1038_s41598_022_09056_5 crossref_primary_10_2337_dc19_2348 crossref_primary_10_1002_mnfr_201801095 crossref_primary_10_3389_fmed_2022_923746 crossref_primary_10_1016_j_tjpad_2025_100067 crossref_primary_10_1016_j_ebiom_2023_104884 crossref_primary_10_1038_s42003_024_06724_2 crossref_primary_10_1038_s41467_023_40245_6 crossref_primary_10_1186_s40364_023_00555_9 crossref_primary_10_1038_s41598_021_00531_z crossref_primary_10_3389_fimmu_2024_1514977 crossref_primary_10_1136_jech_2023_221245 crossref_primary_10_1016_j_jpsychores_2018_02_016 crossref_primary_10_1093_ajcn_nqac025 crossref_primary_10_1093_aje_kwae402 crossref_primary_10_1007_s11306_025_02264_w crossref_primary_10_1158_1055_9965_EPI_25_0196 crossref_primary_10_1007_s11306_025_02308_1 crossref_primary_10_1016_j_jacl_2021_01_011 crossref_primary_10_1038_s41416_024_02906_1 crossref_primary_10_1038_s41467_019_11311_9 crossref_primary_10_1136_annrheumdis_2021_220168 crossref_primary_10_1161_CIRCULATIONAHA_118_034942 crossref_primary_10_1161_CIRCRESAHA_118_314642 crossref_primary_10_7554_eLife_63033 crossref_primary_10_1111_dom_15782 crossref_primary_10_3390_nu12020310 crossref_primary_10_1002_mrc_70026 crossref_primary_10_1007_s11695_023_07042_y crossref_primary_10_1038_s41467_025_60058_z crossref_primary_10_1007_s11357_023_00778_6 crossref_primary_10_1007_s11306_019_1474_9 crossref_primary_10_1161_ATVBAHA_120_315321 crossref_primary_10_3390_biom13030470 crossref_primary_10_1039_D5FO01906K crossref_primary_10_1016_j_mito_2024_101991 crossref_primary_10_1136_bmjopen_2021_049231 crossref_primary_10_1038_s41467_025_59964_z crossref_primary_10_1016_j_jacl_2022_02_007 crossref_primary_10_1021_acs_analchem_4c03229 crossref_primary_10_1093_eurjpc_zwad285 crossref_primary_10_1371_journal_pmed_1003636 crossref_primary_10_1093_eurjpc_zwad160 crossref_primary_10_1016_j_abb_2021_108987 crossref_primary_10_1016_j_freeradbiomed_2020_10_020 crossref_primary_10_1016_j_clnu_2025_01_007 crossref_primary_10_1016_j_chest_2024_06_3809 crossref_primary_10_1093_ije_dyad162 crossref_primary_10_3390_nu13020533 crossref_primary_10_1093_brain_awae257 crossref_primary_10_1007_s00125_019_05001_w crossref_primary_10_1016_j_jad_2025_02_109 crossref_primary_10_1186_s12916_022_02354_9 crossref_primary_10_1371_journal_pone_0284570 crossref_primary_10_1016_j_jacl_2024_12_007 crossref_primary_10_1161_CIRCGEN_124_004978 crossref_primary_10_1038_s41390_021_01672_7 crossref_primary_10_1136_rmdopen_2023_003560 crossref_primary_10_3390_metabo9070123 crossref_primary_10_1002_mnfr_70059 crossref_primary_10_1016_j_cca_2022_06_002 crossref_primary_10_1161_CIRCULATIONAHA_121_056892 crossref_primary_10_1016_j_pnmrs_2025_101564 crossref_primary_10_1093_ageing_afae256 crossref_primary_10_1186_s12944_020_01417_1 crossref_primary_10_1016_j_diabet_2024_101527 crossref_primary_10_1007_s11357_024_01142_y crossref_primary_10_1038_s41586_019_1457_z crossref_primary_10_1038_s41467_019_12703_7 crossref_primary_10_1016_j_cclet_2023_109276 crossref_primary_10_1093_eurheartj_ehad330 crossref_primary_10_1111_dom_15224 crossref_primary_10_1038_s41538_023_00233_y crossref_primary_10_1161_CIRCRESAHA_123_323973 crossref_primary_10_1038_s41416_025_02997_4 crossref_primary_10_1038_s41366_023_01361_x crossref_primary_10_12688_wellcomeopenres_14974_1 crossref_primary_10_3390_ijms22063010 crossref_primary_10_1016_j_ebiom_2025_105881 crossref_primary_10_12688_wellcomeopenres_14974_2 crossref_primary_10_7554_eLife_98709 crossref_primary_10_1038_s41596_020_00475_0 crossref_primary_10_3389_fmolb_2021_676349 crossref_primary_10_1016_j_jacc_2018_10_016 crossref_primary_10_1016_j_atherosclerosis_2022_12_008 crossref_primary_10_1177_0004563220961753 crossref_primary_10_1007_s11306_021_01856_6 crossref_primary_10_1093_annweh_wxad018 crossref_primary_10_1210_clinem_dgac412 crossref_primary_10_1093_ije_dyz244 crossref_primary_10_1111_jnc_15845 crossref_primary_10_1186_s12967_024_05868_3 crossref_primary_10_3390_metabo14070380 crossref_primary_10_1007_s11357_025_01544_6 crossref_primary_10_1016_j_ebiom_2025_105868 crossref_primary_10_1136_bmjdrc_2020_002022 crossref_primary_10_1007_s11357_022_00657_6 crossref_primary_10_1111_acel_70033 crossref_primary_10_1111_imm_13739 crossref_primary_10_1002_gps_70138 crossref_primary_10_1016_j_clnesp_2022_04_022 crossref_primary_10_1093_ije_dyae055 crossref_primary_10_1093_bioadv_vbad123 crossref_primary_10_1210_jc_2018_01165 crossref_primary_10_1186_s12916_022_02399_w crossref_primary_10_1038_s41467_023_36231_7 crossref_primary_10_3390_metabo15070434 crossref_primary_10_1007_s00125_018_4619_x crossref_primary_10_3390_metabo9090191 crossref_primary_10_1093_ecco_jcc_jjy162 crossref_primary_10_1186_s12916_019_1440_4 crossref_primary_10_1093_bioinformatics_btaf065 crossref_primary_10_1038_s41598_024_54569_w crossref_primary_10_1093_cvr_cvz219 crossref_primary_10_3390_cells10112832 crossref_primary_10_3390_metabo9090190 crossref_primary_10_1210_jendso_bvaa026 crossref_primary_10_1016_j_biopsych_2023_01_027 crossref_primary_10_1007_s41666_025_00208_6 crossref_primary_10_1016_j_bone_2025_117460 crossref_primary_10_1002_nbm_4638 crossref_primary_10_1186_s12916_020_01700_z crossref_primary_10_1111_jocn_16090 crossref_primary_10_1007_s11306_019_1626_y crossref_primary_10_1186_s12916_020_01855_9 crossref_primary_10_1007_s11357_023_00918_y crossref_primary_10_1111_acel_14164 crossref_primary_10_1161_JAHA_124_036573 crossref_primary_10_1016_j_jad_2025_119784 crossref_primary_10_1016_j_clnu_2022_12_012 crossref_primary_10_1016_j_vph_2020_106804 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. |
| Copyright_xml | – notice: The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. |
| DBID | CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1093/aje/kwx016 |
| DatabaseName | Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | no_fulltext_linktorsrc |
| Discipline | Medicine Public Health |
| EISSN | 1476-6256 |
| ExternalDocumentID | 29106475 |
| Genre | Journal Article Review |
| GrantInformation_xml | – fundername: Wellcome Trust – fundername: Medical Research Council grantid: MC_UU_12013/1 – fundername: Medical Research Council grantid: MC_UU_12013/5 |
| GroupedDBID | --- -DZ -E4 -~X ..I .2P .I3 .XZ .ZR 0R~ 1TH 23M 2WC 4.4 482 48X 5GY 5RE 5VS 5WA 5WD 6J9 70D 85S AABZA AACZT AAILS AAJKP AAJQQ AAMVS AAOGV AAPNW AAPQZ AAPXW AARHZ AAUAY AAUQX AAVAP AAWTL ABDFA ABEJV ABEUO ABGNP ABIXL ABJNI ABKDP ABLJU ABNHQ ABNKS ABOCM ABPTD ABQLI ABVGC ABXVV ABZBJ ACGFO ACGFS ACGOD ACPRK ACUFI ACUTJ ACUTO ADBBV ADCFL ADEYI ADEZT ADGHP ADGZP ADHKW ADHZD ADIPN ADMHG ADNBA ADOCK ADQBN ADRTK ADVEK ADYVW ADZXQ AEGPL AEHKS AEJOX AEKSI AEMDU AENEX AENZO AEPUE AETBJ AEWNT AFFZL AFIYH AFOFC AFRAH AFYAG AGINJ AGKEF AGORE AGSYK AHMBA AHMMS AHXPO AIAGR AIJHB AJBYB AJEEA AJNCP ALMA_UNASSIGNED_HOLDINGS ALUQC ALXQX APIBT APWMN ATGXG AXUDD BAWUL BAYMD BCRHZ BEYMZ BHONS BTRTY BVRKM C45 CDBKE CGR CS3 CUY CVF CZ4 DAKXR DIK DILTD D~K E3Z EBS ECM EE~ EIF EJD EMOBN F5P F9B FLUFQ FOEOM FOTVD FQBLK GAUVT GJXCC GX1 H13 H5~ HAR HW0 HZ~ IH2 IOX J21 JXSIZ KAQDR KBUDW KOP KQ8 KSI KSN L7B M-Z ML0 N9A NGC NOMLY NOYVH NPM NVLIB O9- OAWHX OCZFY ODMLO OHH OJQWA OJZSN OK1 OPAEJ OVD OWPYF P2P P6G PAFKI PEELM PQQKQ Q1. Q5Y R44 RD5 ROL ROX ROZ RUSNO RW1 RXO TCURE TEORI TJX TR2 UHB UPT W8F WOQ X7H YAYTL YF5 YKOAZ YOC YROCO YSK YXANX ZKX ~91 7X8 |
| ID | FETCH-LOGICAL-c378t-8aac3f4c7097ff584400e920d77472724cc3f72c38ad47852717b254e3f1b3c12 |
| IEDL.DBID | 7X8 |
| ISICitedReferencesCount | 426 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000414354000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1476-6256 |
| IngestDate | Sun Nov 09 11:18:06 EST 2025 Mon Jul 21 06:04:07 EDT 2025 |
| IsDoiOpenAccess | false |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Keywords | fatty acids metabolomics biomarkers amino acids drug development Mendelian randomization serum nuclear magnetic resonance |
| Language | English |
| License | The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c378t-8aac3f4c7097ff584400e920d77472724cc3f72c38ad47852717b254e3f1b3c12 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| OpenAccessLink | https://pubmed.ncbi.nlm.nih.gov/PMC5860146 |
| PMID | 29106475 |
| PQID | 1961036193 |
| PQPubID | 23479 |
| ParticipantIDs | proquest_miscellaneous_1961036193 pubmed_primary_29106475 |
| PublicationCentury | 2000 |
| PublicationDate | 2017-11-01 |
| PublicationDateYYYYMMDD | 2017-11-01 |
| PublicationDate_xml | – month: 11 year: 2017 text: 2017-11-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | American journal of epidemiology |
| PublicationTitleAlternate | Am J Epidemiol |
| PublicationYear | 2017 |
| SSID | ssj0011950 |
| Score | 2.672473 |
| SecondaryResourceType | review_article |
| Snippet | Detailed metabolic profiling in large-scale epidemiologic studies has uncovered novel biomarkers for cardiometabolic diseases and clarified the molecular... |
| SourceID | proquest pubmed |
| SourceType | Aggregation Database Index Database |
| StartPage | 1084 |
| SubjectTerms | Biomarkers - blood Cardiovascular Diseases - blood Cardiovascular Diseases - epidemiology Cardiovascular Diseases - genetics Cardiovascular Diseases - metabolism Cause of Death Epidemiologic Methods Genome-Wide Association Study Humans Magnetic Resonance Spectroscopy Mendelian Randomization Analysis - methods Metabolomics - instrumentation Metabolomics - methods Risk Assessment - methods |
| Title | Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29106475 https://www.proquest.com/docview/1961036193 |
| Volume | 186 |
| WOSCitedRecordID | wos000414354000011&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bS8MwFA7qRATxMm_zRgRfw9Yma1pfZMjEB1snKuytpEkq85LOdZv67z1pO_ckCL70pQ2U5MvJd87JOR9CZ4qmOqVKECqFRxjnbSKAVRClpNAioI7UqhCb4FHk9_tBrwq45dW1yplNLAy1yqSNkTcBKQ5YW-AbF8N3YlWjbHa1ktBYRDUKVMaimvfnWQQrcVpUF3GPAM_3Zu1JA9oUz7r58vHZcrzfqWVxxFxt_PfnNtF6RS5xp0TDFlrQpo5Wwip9XkdrZZAOl7VH22h6NxGmqDIDm4fBbEzecGQbHIsRDsWTsQWO2Ab4bVcOjUM9Bsi82jrmHA8MvrG3yMk9rLLG3bnS7Nc57uCelQ0Y4cxgcgvf458QPnjmO-jxqvtweU0qIQYiKffHxBdC0pRJ3gp4mgJlgY2vA7elgDvaRC6T8Jq7kvpCMe63XfARE_A8NU2dhErH3UVLJjN6H2HKwGooJplUgknKfOq7Mkk4UBHV0i5toNPZDMcAdJu9EEZnkzyez3ED7ZXLFA_LjhyxC6THY7x98IfRh2jVtUdzUU94hGopbHN9jJbldDzIRycFguAZ9cJv97fR6A |
| linkProvider | ProQuest |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Quantitative+Serum+Nuclear+Magnetic+Resonance+Metabolomics+in+Large-Scale+Epidemiology%3A+A+Primer+on+-Omic+Technologies&rft.jtitle=American+journal+of+epidemiology&rft.au=W%C3%BCrtz%2C+Peter&rft.au=Kangas%2C+Antti+J&rft.au=Soininen%2C+Pasi&rft.au=Lawlor%2C+Debbie+A&rft.date=2017-11-01&rft.issn=1476-6256&rft.eissn=1476-6256&rft.volume=186&rft.issue=9&rft.spage=1084&rft_id=info:doi/10.1093%2Faje%2Fkwx016&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1476-6256&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1476-6256&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1476-6256&client=summon |