A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Personalized Profiling of Lipoprotein and Lipid Metabolism Based on 1018 Measures from Combined Quantitative NMR and LC-MS/MS Platforms
Tekijät: Zhao, Siyu; Giles, Corey; Huynh, Kevin; Kettunen, Johannes; Järvelin, Marjo-Riitta; Kähönen, Mika; Viikari, Jorma; Lehtimäki, Terho; Raitakari, Olli T.; Meikle, Peter J.; Mäkinen, Ville-Petteri; Ala-Korpela, Mika
Kustantaja: AMER CHEMICAL SOC
Kustannuspaikka: WASHINGTON
Julkaisuvuosi: 2024
Journal: Analytical Chemistry
Tietokannassa oleva lehden nimi: ANALYTICAL CHEMISTRY
Lehden akronyymi: ANAL CHEM
Vuosikerta: 96
Numero: 52
Aloitussivu: 20362
Lopetussivu: 20370
Sivujen määrä: 9
ISSN: 0003-2700
eISSN: 1520-6882
DOI: https://doi.org/10.1021/acs.analchem.4c03229
Verkko-osoite: https://doi.org/10.1021/acs.analchem.4c03229
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/477941551
Applications of advanced omics methodologies are increasingly popular in biomedicine. However, large-scale studies aiming at clinical translation are typically siloed to single technologies. Here, we present the first comprehensive large-scale population data combining 209 lipoprotein measures from a quantitative NMR spectroscopy platform and 809 lipid classes and species from a quantitative LC-MS/MS platform. These data with 1018 molecular measures were analyzed in two population cohorts totaling 7830 participants. The association and cluster analyses revealed excellent coherence between the methodologically independent data domains and confirmed their quantitative compatibility and suitability for large-scale studies. The analyses elucidated the detailed molecular characteristics of the heterogeneous circulatory macromolecular lipid transport system and the underlying structural and compositional relationships. Unsupervised neural network analysis-the so-called self-organizing maps (SOMs)-revealed that these deep molecular and metabolic data are inherently related to key physiological and clinical population characteristics. The data-driven population subgroups uncovered marked differences in the population distribution of multiple cardiometabolic risk factors. These include, e.g., multiple lipoprotein lipids, apolipoprotein B, ceramides, and oxidized lipids. All 79 structurally unique triglyceride species showed similar associations over the entire lipoprotein cascade and indicated systematically increased risk for carotid intima media thickening and other atherosclerosis risk factors, including obesity and inflammation. The metabolic attributes for 27 individual cholesteryl ester species, which formed six distinct clusters, were more intricate with associations both with higher-e.g., CE(16:1)-and lower-e.g., CE(20:4)-cardiometabolic risk. The molecular details provided by these combined data are unprecedented for molecular epidemiology and demonstrate a new potential avenue for population studies.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The NFBC has been supported, for instance, by EU, Academy of Finland, PREcisE, Joint Programming Initiative a Healthy Diet for a Healthy Life (no. 655), UK Medical Research Council, Biotechnology and Biological Sciences Research Council (MR/S03658X/1), and European Regional Development Fund Grant no. 539/2010 A31592. The Young Finns Study has been financially supported by the Academy of Finland: grants 356405, 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117797 (Gendi), and 141071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; The Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association; EU Horizon 2020 (grant 755320 for TAXINOMISIS and grant
848146 for To Aition); European Research Council (grant 742927 for MULTIEPIGEN project); Tampere University Hospital Supporting Foundation; Finnish Society of Clinical Chemistry; the Cancer Foundation Finland; pBETTER4U_EU (Preventing obesity through Biologically and bEhaviorally Tailored inTERventions for you; project number: 101080117); CVDLink (EU grant nro.) and the Jane and Aatos Erkko Foundation. C.G., K.H., and P.J.M.
were supported by Investigator grants (2027256, 1197190, and 2009965, respectively) from the National Health and Medical Research Council of Australia. In addition, their work was supported by the Victorian Government’s Operational Infrastructure Support Program. M.A.-K. was supported by a research grant from the Sigrid Juselius Foundation, the Finnish Foundation for Cardiovascular Research, and the Research Council of Finland (grant no. 357183).
