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Metabolomic epidemiology offers insights into disease aetiology

Nature Metabolism volume 5, pages 1656–1672 (2023)Cite this article

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An Author Correction to this article was published on 08 January 2024

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Abstract

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer’s disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

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Fig. 1: Summary of the process and applications of metabolomic epidemiology studies.
Fig. 2: Number of metabolomic publications across commonly studied health-related traits over the years.
Fig. 3: Summary of consistently reported circulating metabolites associated with traits discussed in this Review.
Fig. 4: Significant associations identified in circulating metabolomic GWAS.

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References

  1. Oliver, S. G., Winson, M. K., Kell, D. B. & Baganz, F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 16, 373–378 (1998).

    Article  CAS  PubMed  Google Scholar 

  2. Lasky-Su, J., Kelly, R. S., Wheelock, C. E. & Broadhurst, D. A strategy for advancing for population-based scientific discovery using the metabolome: the establishment of the Metabolomics Society Metabolomic Epidemiology Task Group. Metabolomics 17, 45 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Buergel, T. et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 28, 2309–2320 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yu, B. et al. The Consortium of Metabolomics Studies (COMETS): metabolomics in 47 prospective cohort studies. Am. J. Epidemiol. 188, 991–1012 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Ahola-Olli, A. V. et al. Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four Finnish cohorts. Diabetologia 62, 2298–2309 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tikkanen, E. et al. Metabolic biomarker discovery for risk of peripheral artery disease compared with coronary artery disease: lipoprotein and metabolite profiling of 31,657 individuals from 5 prospective cohorts. J. Am. Heart Assoc. 10, e021995 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Julkunen, H. et al. Atlas of plasma NMR biomarkers for health and disease in 118,461 individuals from the UK Biobank. Nat. Commun. 14, 604 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kirwan, J. A. et al. Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management. Metabolomics 18, 70 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ahlbom, A. Modern Epidemiology, 4th edition. TL Lash, TJ VanderWeele, S Haneuse, KJ Rothman. Wolters Kluwer, 2021. Eur. J. Epidemiol. 36, 767–768 (2021).

  10. Chu, S. H. et al. Integration of metabolomic and other omics data in population-based study designs: an epidemiological perspective. Metabolites 9, 117 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Fiehn, O. Metabolomics by gas chromatography-mass spectrometry: combined targeted and untargeted profiling. Curr. Protoc. Mol. Biol. 114, 30.4.1–30.4.32 (2016).

    Article  PubMed  Google Scholar 

  12. Cirulli, E. T. et al. Profound perturbation of the metabolome in obesity is associated with health risk. Cell Metab. 29, 488–500 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wolfert, A. I. & Oken, D. E. Glomerular hemodynamics in established glycerol-induced acute renal failure in the rat. J. Clin. Invest. 84, 1967–1973 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Rangel-Huerta, O. D., Pastor-Villaescusa, B. & Gil, A. Are we close to defining a metabolomic signature of human obesity? A systematic review of metabolomics studies. Metabolomics 15, 93 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Neeland, I. J., Poirier, P. & Després, J.-P. Cardiovascular and metabolic heterogeneity of obesity: clinical challenges and implications for management. Circulation 137, 1391–1406 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Koay, Y. C. et al. Metabolomics and lipidomics signatures of insulin resistance and abdominal fat depots in people living with obesity. Metabolites 12, 1272 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Windaus, A. Über den Gehalt normaler und atheromatöser Aorten an Cholesterin und Cholesterinestern. Hoppe Seylers Z Physiol. Chem. 67, 174–176 (1910).

  18. Goldstein, J. L. & Brown, M. S. A century of cholesterol and coronaries: from plaques to genes to statins. Cell 161, 161–172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Centers for Disease Control and Prevention (CDC). Decline in deaths from heart disease and stroke–United States, 1900–1999. MMWR Morb. Mortal. Wkly. Rep. 48, 649–656 (1999).

    Google Scholar 

  20. Shah, S. H. et al. Baseline metabolomic profiles predict cardiovascular events in patients at risk for coronary artery disease. Am. Heart J. 163, 844–850 (2012).

    Article  CAS  PubMed  Google Scholar 

  21. Wang, Z. et al. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature 472, 57–63 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Jiang, X. et al. Metabolites associated with the risk of incident venous thromboembolism: a metabolomic analysis. J. Am. Heart Assoc. 7, e010317 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Qi, J. et al. Circulating trimethylamine N-oxide and the risk of cardiovascular diseases: a systematic review and meta-analysis of 11 prospective cohort studies. J. Cell. Mol. Med. 22, 185–194 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Jia, J. et al. Assessment of causal direction between gut microbiota-dependent metabolites and cardiometabolic health: a bidirectional Mendelian randomization analysis. Diabetes 68, 1747–1755 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Holmes, M. V. et al. Lipids, lipoproteins and metabolites and risk of myocardial infarction and stroke. J. Am. Coll. Cardiol. 71, 620–632 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Jiang, W. et al. Mendelian randomization analysis provides insights into the pathogenesis of serum levels of branched-chain amino acids in cardiovascular disease. Metabolites 13, 403 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Trainor, P. J. et al. Systems characterization of differential plasma metabolome perturbations following thrombotic and non-thrombotic myocardial infarction. J. Proteom. 160, 38–46 (2017).

    Article  CAS  Google Scholar 

  29. Lind, L., Fall, T., Ärnlöv, J., Elmståhl, S. & Sundström, J. Large-scale metabolomics and the incidence of cardiovascular disease. J. Am. Heart Assoc. 12, e026885 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ament, Z. et al. Nucleosides associated with incident ischemic stroke in the REGARDS and JHS cohorts. Neurology 98, e2097–e2107 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Sun, D. et al. A prospective study of serum metabolites and risk of ischemic stroke. Neurology 92, e1890–e1898 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ottosson, F. et al. Dimethylguanidino valerate: a lifestyle-related metabolite associated with future coronary artery disease and cardiovascular mortality. J. Am. Heart Assoc. 8, e012846 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Wali, J. A. et al. Nutritional and metabolic regulation of the metabolite dimethylguanidino valeric acid: an early marker of cardiometabolic disease. Am. J. Physiol. Endocrinol. Metab. 319, E509–E518 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Zheng, Y., Ley, S. H. & Hu, F. B. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14, 88–98 (2018).

    Article  PubMed  Google Scholar 

  35. Morze, J. et al. Metabolomics and type 2 diabetes risk: an updated systematic review and meta-analysis of prospective cohort studies. Diabetes Care 45, 1013–1024 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Wang, T. J. et al. Metabolite profiles and the risk of developing diabetes. Nat. Med. 17, 448–453 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Merino, J. et al. Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose. Diabetologia 61, 1315–1324 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shi, L. et al. Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort. Diabetologia 61, 849–861 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Long, J. et al. Metabolite biomarkers of type 2 diabetes mellitus and pre-diabetes: a systematic review and meta-analysis. BMC Endocr. Disord. 20, 174 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ren, M. et al. Potential novel serum metabolic markers associated with progression of prediabetes to overt diabetes in a chinese population. Front. Endocrinol. 12, 745214 (2021).

    Article  Google Scholar 

  41. Tomofuji, Y. et al. Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes. Commun. Med. 3, 5 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Shin, S. -Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Suhre, K. et al. Human metabolic individuality in biomedical and pharmaceutical research. Nature 477, 54–60 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Mootha, V. K. & Hirschhorn, J. N. Inborn variation in metabolism. Nat. Genet. 42, 97–98 (2010).

    Article  CAS  PubMed  Google Scholar 

  45. Long, T. et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat. Genet. 49, 568–578 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Sliz, E. et al. Metabolomic consequences of genetic inhibition of PCSK9 compared with statin treatment. Circulation 138, 2499–2512 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Du, Z. et al. Low-density lipoprotein receptor genotypes modify the sera metabolome of patients with homozygous familial hypercholesterolemia. iScience 25, 105334 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. McGregor, T. L. et al. Characterising a healthy adult with a rare HAO1 knockout to support a therapeutic strategy for primary hyperoxaluria. Elife 9, e54363 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Mandal, R., Chamot, D. & Wishart, D. S. The role of the Human Metabolome Database in inborn errors of metabolism. J. Inherit. Metab. Dis. 41, 329–336 (2018).

    Article  CAS  PubMed  Google Scholar 

  50. Steinbusch, L. K. M. et al. Targeted urine metabolomics with a graphical reporting tool for rapid diagnosis of inborn errors of metabolism. J. Inherit. Metab. Dis. 44, 1113–1123 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Hampe, M. H., Panaskar, S. N., Yadav, A. A. & Ingale, P. W. Gas chromatography/mass spectrometry-based urine metabolome study in children for inborn errors of metabolism: an Indian experience. Clin. Biochem. 50, 121–126 (2017).

    Article  CAS  PubMed  Google Scholar 

  52. Rahnavard, A., Mann, B., Giri, A., Chatterjee, R. & Crandall, K. A. Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity. Sci. Rep. 12, 12204 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Roberts, I. et al. Untargeted metabolomics of COVID-19 patient serum reveals potential prognostic markers of both severity and outcome. Metabolomics 18, 6 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Danlos, F. -X. et al. Metabolomic analyses of COVID-19 patients unravel stage-dependent and prognostic biomarkers. Cell Death Dis. 12, 258 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Blasco, H. et al. The specific metabolome profiling of patients infected by SARS-CoV-2 supports the key role of tryptophan–nicotinamide pathway and cytosine metabolism. Sci. Rep. 10, 16824 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Thomas, T. et al. COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. JCI Insight 5, e140327 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Shen, B. et al. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 59–72 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Barberis, E. et al. Large-scale plasma analysis revealed new mechanisms and molecules associated with the host response to SARS-CoV-2. Int. J. Mol. Sci. 21, 8623 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Danchin, A. & Marlière, P. Cytosine drives evolution of SARS-CoV-2: cytosine drives evolution of SARS-CoV-2. Environ. Microbiol. 22, 1977–1985 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kornhuber, J., Hoertel, N. & Gulbins, E. The acid sphingomyelinase/ceramide system in COVID-19. Mol. Psychiatry 27, 307–314 (2022).

    Article  CAS  PubMed  Google Scholar 

  61. Warburg, O. On the origin of cancer cells. Science 123, 309–314 (1956).

    Article  CAS  PubMed  Google Scholar 

  62. Cori, C. F. & Cori, G. T. The carbohydrate metabolism of tumors. J. Biol. Chem. 64, 11–22 (1925).

    Article  CAS  Google Scholar 

  63. Hui, S. et al. Glucose feeds the TCA cycle via circulating lactate. Nature 551, 115–118 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Faubert, B. et al. Lactate metabolism in human lung tumors. Cell 171, 358–371 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. DeBerardinis, R. J. & Chandel, N. S. We need to talk about the Warburg effect. Nat. Metab. 2, 127–129 (2020).

    Article  PubMed  Google Scholar 

  66. Chen, P. -H. et al. Metabolic diversity in human non-small-cell lung cancer cells. Mol. Cell 76, 838–851 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Santos, C. R. & Schulze, A. Lipid metabolism in cancer. FEBS J. 279, 2610–2623 (2012).

    Article  CAS  PubMed  Google Scholar 

  68. Huang, J., Zhao, B., Weinstein, S. J., Albanes, D. & Mondul, A. M. Metabolomic profile of prostate cancer-specific survival among 1812 Finnish men. BMC Med. 20, 362 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Reichard, C. A. et al. Gut microbiome-dependent metabolic pathways and risk of lethal prostate cancer: prospective analysis of a PLCO cancer screening trial cohort. Cancer Epidemiol. Biomarkers Prev. 31, 192–199 (2022).

    Article  CAS  PubMed  Google Scholar 

  70. Richman, E. L. et al. Choline intake and risk of lethal prostate cancer: incidence and survival. Am. J. Clin. Nutr. 96, 855–863 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Bae, S. et al. Plasma choline metabolites and colorectal cancer risk in the Women’s Health Initiative Observational Study. Cancer Res. 74, 7442–7452 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Huang, J. et al. Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal and ovarian cancer screening trial. Br. J. Cancer 115, 1087–1095 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Mondul, A. M. et al. Metabolomic analysis of prostate cancer risk in a prospective cohort: the alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study. Int. J. Cancer 137, 2124–2132 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Schmidt, J. A. et al. Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: a prospective study of 3,057 matched case–control sets from EPIC. Int. J. Cancer 146, 720–730 (2020).

    Article  CAS  PubMed  Google Scholar 

  75. Shu, X. et al. Prospective study of blood metabolites associated with colorectal cancer risk. Int. J. Cancer 143, 527–534 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Kühn, T. et al. Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study. BMC Med. 14, 13 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Lu, M. -S. et al. Choline and betaine intake and colorectal cancer risk in Chinese population: a case–control study. PLoS ONE 10, e0118661 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zhang, C. -X. et al. Choline and betaine intake is inversely associated with breast cancer risk: a two-stage case-control study in China. Cancer Sci. 104, 250–258 (2013).

    Article  CAS  PubMed  Google Scholar 

  79. Zeng, F. -F. et al. Choline and betaine intakes are associated with reduced risk of nasopharyngeal carcinoma in adults: a case–control study. Br. J. Cancer 110, 808–816 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. Sun, S. et al. Choline and betaine consumption lowers cancer risk: a meta-analysis of epidemiologic studies. Sci. Rep. 6, 35547 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Goldberg, A. A., Titorenko, V. I., Beach, A. & Sanderson, J. T. Bile acids induce apoptosis selectively in androgen-dependent and -independent prostate cancer cells. PeerJ 1, e122 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Stepien, M. et al. Metabolic perturbations prior to hepatocellular carcinoma diagnosis: findings from a prospective observational cohort study. Int. J. Cancer 148, 609–625 (2021).

    Article  CAS  PubMed  Google Scholar 

  83. Stepien, M. et al. Prediagnostic alterations in circulating bile acid profiles in the development of hepatocellular carcinoma. Int. J. Cancer 150, 1255–1268 (2022).

    Article  CAS  PubMed  Google Scholar 

  84. Thomas, C. E. et al. Association between pre-diagnostic serum bile acids and hepatocellular carcinoma: The Singapore Chinese Health Study. Cancers 13, 2648 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Yuan, J. -M., Gao, Y. -T., Ong, C. -N., Ross, R. K. & Yu, M. C. Prediagnostic level of serum retinol in relation to reduced risk of hepatocellular carcinoma. J. Natl Cancer Inst. 98, 482–490 (2006).

    Article  CAS  PubMed  Google Scholar 

  86. Wang, Y., Jacobs, E. J., Carter, B. D., Gapstur, S. M. & Stevens, V. L. Plasma metabolomic profiles and risk of advanced and fatal prostate cancer. Eur. Urol. Oncol. 4, 56–65 (2021).

    Article  PubMed  Google Scholar 

  87. Meller, S. et al. Integration of tissue metabolomics, transcriptomics and immunohistochemistry reveals ERG- and gleason score-specific metabolomic alterations in prostate cancer. Oncotarget 7, 1421–1438 (2016).

    Article  PubMed  Google Scholar 

  88. Dudka, I. et al. Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status. BMC Cancer 20, 437 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Feng, X. et al. Association of prediagnostic blood metabolomics with prostate cancer defined by ERG or PTEN molecular subtypes. Cancer Epidemiol. Biomarkers Prev. 30, 1000–1008 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Xiao, Y. et al. Comprehensive metabolomics expands precision medicine for triple-negative breast cancer. Cell Res. 32, 477–490 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Jack, C. R. Jr et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain 132, 1355–1365 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Toledo, J. B. et al. Metabolic network failures in Alzheimer’s disease: a biochemical road map. Alzheimers Dement. 13, 965–984 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  93. Varma, V. R. et al. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: a targeted metabolomics study. PLoS Med. 15, e1002482 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  94. Wang, G. et al. Plasma metabolite profiles of Alzheimer’s disease and mild cognitive impairment. J. Proteome Res. 13, 2649–2658 (2014).

    Article  CAS  PubMed  Google Scholar 

  95. Serrano-Pozo, A., Frosch, M. P., Masliah, E. & Hyman, B. T. Neuropathological alterations in Alzheimer disease. Cold Spring Harb. Perspect. Med. 1, a006189 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  96. Dong, R. et al. CSF metabolites associate with CSF tau and improve prediction of Alzheimer’s disease status. Alzheimers Dement. 13, e12167 (2021).

    Google Scholar 

  97. Darst, B. F., Lu, Q., Johnson, S. C. & Engelman, C. D. Integrated analysis of genomics, longitudinal metabolomics, and Alzheimer’s risk factors among 1,111 cohort participants. Genet. Epidemiol. 43, 657–674 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Wörheide, M. A. et al. An integrated molecular atlas of Alzheimer’s disease. Preprint at bioRxiv https://doi.org/10.1101/2021.09.14.21263565 (2021).

  99. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  100. Nikolaus, S. et al. Increased tryptophan metabolism is associated with activity of inflammatory bowel diseases. Gastroenterology 153, 1504–1516 (2017).

    Article  CAS  PubMed  Google Scholar 

  101. Scoville, E. A. et al. Alterations in lipid, amino acid, and energy metabolism distinguish crohn’s disease from ulcerative colitis and control subjects by serum metabolomic profiling. Metabolomics 14, 17 (2018).

    Article  PubMed  Google Scholar 

  102. Dawiskiba, T. et al. Serum and urine metabolomic fingerprinting in diagnostics of inflammatory bowel diseases. World J. Gastroenterol. 20, 163–174 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Gallagher, K., Catesson, A., Griffin, J. L., Holmes, E. & Williams, H. R. T. Metabolomic analysis in inflammatory bowel disease: a systematic review. J. Crohns. Colitis 15, 813–826 (2021).

    Article  PubMed  Google Scholar 

  104. Hua, X. et al. Inflammatory bowel disease is associated with prediagnostic perturbances in metabolic pathways. Gastroenterology 164, 147–150 (2023).

    Article  CAS  PubMed  Google Scholar 

  105. Coresh, J. et al. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am. J. Kidney Dis. 39, 920–929 (2002).

    Article  CAS  PubMed  Google Scholar 

  106. Peng, H. et al. Identification of metabolite markers associated with kidney function. J. Immunol. Res 2022, 6190333 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Yu, B. et al. Serum metabolomic profiling and incident CKD among African Americans. Clin. J. Am. Soc. Nephrol. 9, 1410–1417 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Chen, D. -Q. et al. Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan. Nat. Commun. 10, 1476 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Fonseca, R. I. D. et al. Untargeted plasma 1H NMR-based metabolomic profiling in different stages of chronic kidney disease. J. Pharm. Biomed. Anal. 229, 115339 (2023).

    Article  CAS  PubMed  Google Scholar 

  110. Zhang, F. et al. Untargeted serum metabolomics and tryptophan metabolism profiling in type 2 diabetic patients with diabetic glomerulopathy. Ren. Fail. 43, 980–992 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. McClellan, W. M. & Flanders, W. D. Risk factors for progressive chronic kidney disease. J. Am. Soc. Nephrol. 14, S65–S70 (2003).

    Article  PubMed  Google Scholar 

  112. Hu, D. -Y. et al. Metabolomics analysis of human plasma reveals decreased production of trimethylamine N-oxide retards the progression of chronic kidney disease. Br. J. Pharmacol. 179, 4344–4359 (2022).

    Article  CAS  PubMed  Google Scholar 

  113. Goek, O. -N. et al. Serum metabolite concentrations and decreased GFR in the general population. Am. J. Kidney Dis. 60, 197–206 (2012).

    Article  CAS  PubMed  Google Scholar 

  114. Benito, S. et al. Untargeted metabolomics for plasma biomarker discovery for early chronic kidney disease diagnosis in pediatric patients using LC-QTOF-MS. Analyst 143, 4448–4458 (2018).

    Article  CAS  PubMed  Google Scholar 

  115. Fuller, H., Iles, M., Moore, J. B. & Zulyniak, M. A. Unique metabolic profiles associate with gestational diabetes and ethnicity in low- and high-risk women living in the UK. J. Nutr. 152, 2186–2197 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  116. Nobakht M Gh, B. F. Application of metabolomics to preeclampsia diagnosis. Syst. Biol. Reprod. Med. 64, 324–339 (2018).

    Article  CAS  PubMed  Google Scholar 

  117. Ciborowski, M. et al. Potential first trimester metabolomic biomarkers of abnormal birth weight in healthy pregnancies. Prenat. Diagn. 34, 870–877 (2014).

    Article  CAS  PubMed  Google Scholar 

  118. Bot, M. et al. Metabolomics profile in depression: a pooled analysis of 230 metabolic markers in 5283 cases with depression and 10,145 controls. Biol. Psychiatry 87, 409–418 (2020).

    Article  CAS  PubMed  Google Scholar 

  119. MacDonald, K. et al. Biomarkers for major depressive and bipolar disorders using metabolomics: a systematic review. Am. J. Med. Genet. B Neuropsychiatr. Genet. 180, 122–137 (2019).

    Article  PubMed  Google Scholar 

  120. Zhu, Y. et al. Psychological distress and metabolomic markers: a systematic review of posttraumatic stress disorder, anxiety, and subclinical distress. Neurosci. Biobehav. Rev. 143, 104954 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Winning, A., Glymour, M. M., McCormick, M. C., Gilsanz, P. & Kubzansky, L. D. Psychological distress across the life course and cardiometabolic risk: findings from the 1958 British Birth Cohort Study. J. Am. Coll. Cardiol. 66, 1577–1586 (2015).

    Article  PubMed  Google Scholar 

  122. Hagenbeek, F. A. et al. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies. Nat. Commun. 11, 39 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Yousri, N. A. et al. Whole-exome sequencing identifies common and rare variant metabolic QTLs in a Middle Eastern population. Nat. Commun. 9, 333 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Darst, B. F., Koscik, R. L., Hogan, K. J., Johnson, S. C. & Engelman, C. D. Longitudinal plasma metabolomics of aging and sex. Aging 11, 1262–1282 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Chen, Y. et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat. Genet. 55, 44–53 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  126. Speed, D. & Balding, D. J. SumHer better estimates the SNP heritability of complex traits from summary statistics. Nat. Genet. 51, 277–284 (2019).

    Article  CAS  PubMed  Google Scholar 

  127. Wright, F. A. et al. Heritability and genomics of gene expression in peripheral blood. Nat. Genet. 46, 430–437 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Lloyd-Jones, L. R. et al. The genetic architecture of gene expression in peripheral blood. Am. J. Hum. Genet. 100, 228–237 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Ouwens, K. G. et al. A characterization of cis- and trans-heritability of RNA-seq-based gene expression. Eur. J. Hum. Genet. 28, 253–263 (2020).

    Article  CAS  PubMed  Google Scholar 

  130. Smith, C. J. et al. Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation. Elife 11, e79348 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Yin, X. et al. Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci. Nat. Commun. 13, 1644 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Tahir, U. A. et al. Whole genome association study of the plasma metabolome identifies metabolites linked to cardiometabolic disease in black individuals. Nat. Commun. 13, 4923 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Harshfield, E. L. et al. Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci. BMC Med. 19, 232 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Yu, B. et al. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet. 10, e1004212 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Feofanova, E. V. et al. A genome-wide association study discovers 46 loci of the human metabolome in the hispanic community health study/study of latinos. Am. J. Hum. Genet. 107, 849–863 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Wang, Z. et al. Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci. Clin. Transl. Med. 11, e290 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Jung, T. et al. Integrative pathway analysis of SNP and metabolite data using a hierarchical structural component model. Front. Genet. 13, 814412 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Fuller, H., Iles, M. M., Moore, J. B. & Zulyniak, M. A. Metabolic drivers of dysglycemia in pregnancy: ethnic-specific GWAS of 146 metabolites and 1-sample Mendelian randomization analyses in a UK multi-ethnic birth cohort. Front. Endocrinol. 14, 1157416 (2023).

    Article  Google Scholar 

  139. Vriens, K. et al. Evidence for an alternative fatty acid desaturation pathway increasing cancer plasticity. Nature 566, 403–406 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  140. Yuan, S. et al. Plasma phospholipid fatty acids, FADS1 and risk of 15 cardiovascular diseases: a Mendelian randomisation study. Nutrients 11, 3001 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Brayner, B., Kaur, G., Keske, M. A. & Livingstone, K. M. FADS polymorphism, omega-3 fatty acids and diabetes risk: a systematic review. Nutrients 10, 758 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  142. Mathias, R. A., Pani, V. & Chilton, F. H. Genetic variants in the FADS gene: implications for dietary recommendations for fatty acid intake. Curr. Nutr. Rep. 3, 139–148 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Sollis, E. et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 51, D977–D985 (2023).

    Article  CAS  PubMed  Google Scholar 

  144. Chang, L., Zhou, G., Ou, H. & Xia, J. mGWAS-Explorer: linking SNPs, genes, metabolites, and diseases for functional insights. Metabolites 12, 526 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Staley, J. R. et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Gagliano Taliun, S. A. et al. Exploring and visualizing large-scale genetic associations by using PheWeb. Nat. Genet. 52, 550–552 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Fang, S., Holmes, M. V., Gaunt, T. R., Davey Smith, G. & Richardson, T. G. Constructing an atlas of associations between polygenic scores from across the human phenome and circulating metabolic biomarkers. Elife 11, e73951 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  148. Manor, O. et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat. Commun. 11, 5206 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Zierer, J. et al. The fecal metabolome as a functional readout of the gut microbiome. Nat. Genet. 50, 790–795 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Wang, M. et al. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. Nat. Biotechnol. 34, 828–837 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  152. Folz, J. S., Shalon, D. & Fiehn, O. Metabolomics analysis of time-series human small intestine lumen samples collected in vivo. Food Funct. 12, 9405–9415 (2021).

    Article  CAS  PubMed  Google Scholar 

  153. Patel, C. J., Bhattacharya, J. & Butte, A. J. An environment-wide association study (EWAS) on type 2 diabetes mellitus. PLoS ONE 5, e10746 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Price, E. J. et al. Merging the exposome into an integrated framework for ‘omics’ sciences. iScience 25, 103976 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Huang, S. -Y. et al. Investigating causal relations between circulating metabolites and alzheimer’s disease: a Mendelian randomization study. J. Alzheimers Dis. 87, 463–477 (2022).

    Article  CAS  PubMed  Google Scholar 

  156. Yu, X. -H., Cao, R. -R., Yang, Y. -Q. & Lei, S. -F. Identification of causal metabolites related to multiple autoimmune diseases. Hum. Mol. Genet. 31, 604–613 (2022).

    Article  CAS  PubMed  Google Scholar 

  157. Porcu, E. et al. Triangulating evidence from longitudinal and Mendelian randomization studies of metabolomic biomarkers for type 2 diabetes. Sci. Rep. 11, 6197 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Feng, Y. et al. Causal effects of genetically determined metabolites on cancers included lung, breast, ovarian cancer, and glioma: a Mendelian randomization study. Transl. Lung Cancer Res 11, 1302–1314 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Dreyfuss, J. M. et al. High-throughput mediation analysis of human proteome and metabolome identifies mediators of post-bariatric surgical diabetes control. Nat. Commun. 12, 6951 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Camacho, D., de la Fuente, A. & Mendes, P. The origin of correlations in metabolomics data. Metabolomics 1, 53–63 (2005).

    Article  CAS  Google Scholar 

  161. Kelly, R. S. et al. An integrative transcriptomic and metabolomic study of lung function in children with asthma. Chest 154, 335–348 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  162. Krumsiek, J., Suhre, K., Illig, T., Adamski, J. & Theis, F. J. Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst. Biol. 5, 21 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Krumsiek, J. et al. Gender-specific pathway differences in the human serum metabolome. Metabolomics 11, 1815–1833 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Playdon, M. C. et al. Nutritional metabolomics and breast cancer risk in a prospective study. Am. J. Clin. Nutr. 106, 637–649 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Lawlor, D. A., Tilling, K. & Davey Smith, G. Triangulation in aetiological epidemiology. Int. J. Epidemiol. 45, 1866–1886 (2016).

    PubMed  Google Scholar 

  166. Nemet, I. et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell 180, 862–877 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Adam, J. et al. Metformin effect on nontargeted metabolite profiles in patients with type 2 diabetes and in multiple murine tissues. Diabetes 65, 3776–3785 (2016).

    Article  CAS  PubMed  Google Scholar 

  168. Wu, Z. E. et al. Metabolomic signatures for visceral adiposity and dysglycaemia in Asian Chinese and Caucasian European adults: the cross-sectional TOFI_Asia study. Nutr. Metab. 17, 95 (2020).

    Article  CAS  Google Scholar 

  169. Taylor, K. et al. Differences in pregnancy metabolic profiles and their determinants between White European and South Asian women: findings from the Born in Bradford Cohort. Metabolites 9, 190 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Chevli, P. A. et al. Plasma metabolomic profiling in subclinical atherosclerosis: the Diabetes Heart Study. Cardiovasc. Diabetol. 20, 231 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Saravanan, P., Diabetes in Pregnancy Working Group, Maternal Medicine Clinical Study Group & Royal College of Obstetricians and Gynaecologists, UK. Gestational diabetes: opportunities for improving maternal and child health. Lancet Diabetes Endocrinol. 8, 793–800 (2020).

    Article  PubMed  Google Scholar 

  172. Geidenstam, N. et al. Using metabolite profiling to construct and validate a metabolite risk score for predicting future weight gain. PLoS ONE 14, e0222445 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Li, C. et al. Development and validation of a metabolite score for red meat intake: an observational cohort study and randomized controlled dietary intervention. Am. J. Clin. Nutr. 116, 511–522 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Li, J. et al. The Mediterranean diet, plasma metabolome, and cardiovascular disease risk. Eur. Heart J. 41, 2645–2656 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Fernández-Ochoa, Á. et al. Metabolic disturbances in urinary and plasma samples from seven different systemic autoimmune diseases detected by HPLC-ESI-QTOF-MS. J. Proteome Res. 19, 3220–3229 (2020).

    Article  PubMed  Google Scholar 

  176. Liu, N. et al. Comparison of Untargeted metabolomic profiling vs traditional metabolic screening to identify inborn errors of metabolism. JAMA Netw. Open 4, e2114155 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  177. Goossens, N., Nakagawa, S., Sun, X. & Hoshida, Y. Cancer biomarker discovery and validation. Transl. Cancer Res. 4, 256–269 (2015).

    CAS  PubMed  Google Scholar 

  178. Braisted, J. et al. RaMP-DB 2.0: a renovated knowledgebase for deriving biological and chemical insight from metabolites, proteins, and genes. Bioinformatics 39, btac726 (2023).

    Article  CAS  PubMed  Google Scholar 

  179. Wolfe, D., Dudek, S., Ritchie, M. D. & Pendergrass, S. A. Visualizing genomic information across chromosomes with PhenoGram. BioData Min. 6, 18 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  180. VanderWeele, T. Explanation in Causal Inference: Methods for Mediation and Interaction (Oxford Univ. Press, 2015).

  181. Hemani, G., Tilling, K. & Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  182. Verbanck, M., Chen, C. -Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat. Genet. 50, 693–698 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Burgess, S. & Thompson, S. G. Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am. J. Epidemiol. 181, 251–260 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  184. Davey Smith, G. & Hemani, G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23, R89–R98 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Zhang, B. & Horvath, S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genet. Mol. Biol. 4, 17 (2005).

  186. Shutta, K. H., De Vito, R., Scholtens, D. M. & Balasubramanian, R. Gaussian graphical models with applications to omics analyses. Stat. Med. 41, 5150–5187 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  187. Dettori, J. R., Norvell, D. C. & Chapman, J. R. Fixed-effect vs random-effects models for meta-analysis: 3 points to consider. Glob. Spine J. 12, 1624–1626 (2022).

    Article  Google Scholar 

  188. Fitzmaurice, G. M., Laird, N. M. & Ware, J. H. Applied Longitudinal Analysis (Wiley, 2011).

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Acknowledgements

This work was supported by the National Cancer Institute at the National Institutes of Health (grant R00 CA246063 to B.F.D.), the National Science Foundation (grant 2109688 to A.R.) and an award from the Andy Hill Cancer Research Endowment Distinguished Researchers Program (to B.F.D.). K.H.S. was supported by the National Heart Lung and Blood Institute at the National Institutes of Health (grant 2T32HL007427). H.A.C. was supported in part by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (ZICTR000410-03). This work was conducted on the behalf of the COMETS Early Career Scientist Working Group.

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Authors and Affiliations

  1. Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Harriett Fuller, Nasa Sinnott-Armstrong & Burcu F. Darst

  2. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Yiwen Zhu

  3. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jayna Nicholas & Laura M. Raffield

  4. National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA

    Haley A. Chatelaine

  5. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Emily M. Drzymalla

  6. Computational Biology Institute, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, The George Washington University, Washington, DC, USA

    Afrand K. Sarvestani & Ali Rahnavard

  7. Department of Public Health, University of Massachusetts Lowell, Lowell, MA, USA

    Sachelly Julián-Serrano

  8. Department of Cardiology, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Usman A. Tahir

  9. Department of Cardiology, Peking University Third Hospital, Beijing, China

    Xinwei Hua

  10. Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Katherine H. Shutta

  11. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Katherine H. Shutta

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All authors conducted literature searches for article content, contributed substantially to discussions around article content and drafted, reviewed and revised the manuscript. H.F., H.A.C. and A.R. led the creation of manuscript figures with input and assistance from all other authors.

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Fuller, H., Zhu, Y., Nicholas, J. et al. Metabolomic epidemiology offers insights into disease aetiology. Nat Metab 5, 1656–1672 (2023). https://doi.org/10.1038/s42255-023-00903-x

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