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Atlas of Plasma NMR Biomarkers for Health and Disease in 118,461 Individuals from the UK Biobank

Abstract

Blood lipids and metabolites are markers of current health and future disease risk. Here, we describe plasma nuclear magnetic resonance (NMR) biomarker data for 118,461 participants in the UK Biobank. The biomarkers cover 249 measures of lipoprotein lipids, fatty acids, and small molecules such as amino acids, ketones, and glycolysis metabolites. We provide an atlas of associations of these biomarkers to prevalence, incidence, and mortality of over 700 common diseases ( nightingalehealth.com/atlas ). The results reveal a plethora of biomarker associations, including susceptibility to infectious diseases and risk of various cancers, joint disorders, and mental health outcomes, indicating that abundant circulating lipids and metabolites are risk markers beyond cardiometabolic diseases. Clustering analyses indicate similar biomarker association patterns across different disease types, suggesting latent systemic connectivity in the susceptibility to a diverse set of diseases. This work highlights the value of NMR based metabolic biomarker profiling in large biobanks for public health research and translation.

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References
1.
Pietzner M, Stewart I, Raffler J, Khaw K, Michelotti G, Kastenmuller G . Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med. 2021; 27(3):471-479. PMC: 8127079. DOI: 10.1038/s41591-021-01266-0. View

2.
Mayers J, Wu C, Clish C, Kraft P, Torrence M, Fiske B . Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med. 2014; 20(10):1193-1198. PMC: 4191991. DOI: 10.1038/nm.3686. View

3.
Sinnott-Armstrong N, Tanigawa Y, Amar D, Mars N, Benner C, Aguirre M . Genetics of 35 blood and urine biomarkers in the UK Biobank. Nat Genet. 2021; 53(2):185-194. PMC: 7867639. DOI: 10.1038/s41588-020-00757-z. View

4.
Julkunen H, Cichonska A, Slagboom P, Wurtz P . Metabolic biomarker profiling for identification of susceptibility to severe pneumonia and COVID-19 in the general population. Elife. 2021; 10. PMC: 8172246. DOI: 10.7554/eLife.63033. View

5.
Soininen P, Kangas A, Wurtz P, Suna T, Ala-Korpela M . Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics. Circ Cardiovasc Genet. 2015; 8(1):192-206. DOI: 10.1161/CIRCGENETICS.114.000216. View