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Association between human blood metabolome and the risk of pre-eclampsia

Hypertension Research volume 47, pages 1063–1072 (2024)Cite this article

Abstract

Pre-eclampsia is a complex multi-system pregnancy disorder with limited treatment options. Therefore, we aimed to screen for metabolites that have causal associations with preeclampsia and to predict target-mediated side effects based on Mendelian randomization (MR) analysis. A two-sample MR analysis was firstly conducted to systematically assess causal associations of blood metabolites with pre-eclampsia, by using metabolites related large-scale genome-wide association studies (GWASs) involving 147,827 European participants, as well as GWASs summary data about pre-eclampsia from the FinnGen consortium R8 release data that included 182,035 Finnish adult female subjects (5922 cases and 176,113 controls). Subsequently, a phenome-wide MR (Phe-MR) analysis was applied to assess the potential on-target side effects associated with hypothetical interventions that reduced the burden of pre-eclampsia by targeting identified metabolites. Four metabolites were identified as potential causal mediators for pre-eclampsia by using the inverse-variance weighted method, including cholesterol in large HDL (L-HDL-C) [odds ratio (OR): 0.88; 95% confidence interval (95% CI): 0.83–0.93; P = 2.14 × 10−5), cholesteryl esters in large HDL (L-HDL-CE) (OR: 0.88; 95% CI: 0.83–0.94; P = 5.93 × 10−5), free cholesterol in very large HDL (XL-HDL-FC) (OR: 0.88; 95% CI: 0.82–0.94; P = 1.10 × 10−4) and free cholesterol in large HDL (L-HDL-FC) (OR: 0.89; 95% CI: 0.84–0.95; P = 1.45 × 10−4). Phe-MR analysis showed that targeting L-HDL-CE had beneficial effects on the risk of 24 diseases from seven disease chapters. Based on this systematic MR analysis, L-HDL-C, L-HDL-CE, XL-HDL-FC, and L-HDL-FC were inversely associated with the risk of pre-eclampsia. Interestingly, L-HDL-CE may be a promising drug target for preventing pre-eclampsia with no predicted detrimental side effects.

The study consists of a two-stage design that conducts MR at both stages. First, we assessed the causality for the associations between 194 blood metabolites and the risk of pre-eclampsia. Second, we investigated a broad spectrum of side effects associated with the targeting identified metabolites in 693 non-preeclampsia diseases. Our results suggested that Cholesteryl esters in large HDL may serve as a promising drug target for the prevention or treatment of pre-eclampsia with no predicted detrimental side effects.

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References

  1. Chappell LC, Cluver CA, Kingdom J, Tong S. Pre-eclampsia. Lancet. 2021;398:341–54.

    Article  CAS  PubMed  Google Scholar 

  2. Dimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV, et al. Pre-eclampsia. Nat Rev Dis Prim. 2023;9:22.

    Google Scholar 

  3. Espinoza J, Vidaeff A, Pettker CM, Simhan H, Amer Coll Obstet G. Gestational hypertension and preeclampsia. Obstet Gynecol. 2020;135:E237–60.

    Article  Google Scholar 

  4. Magee LA, Nicolaides KH, von Dadelszen P. Preeclampsia. N Engl J Med. 2022;386:1817–32.

    Article  CAS  PubMed  Google Scholar 

  5. Atallah A, Lecarpentier E, Goffinet F, Doret-Dion M, Gaucherand P, Tsatsaris V. Aspirin for prevention of preeclampsia. Drugs. 2017;77:1819–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, Davis EM, et al. Aspirin use to prevent preeclampsia and related morbidity and mortality US preventive services task force recommendation statement. J Am Med Assoc. 2021;326:1186–91.

    Article  Google Scholar 

  7. Henderson JT, Vesco KK, Senger CA, Thomas RG, Redmond N. Aspirin use to prevent preeclampsia and related morbidity and mortality: updated evidence report and systematic review for the US preventive services task force. JAMA. 2021;326:1192–206.

    Article  PubMed  Google Scholar 

  8. Johnson CH, Ivanisevic J, Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol. 2016;17:451–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Onuh JO, Qiu HY. Metabolic profiling and metabolites fingerprints in human hypertension: discovery and potential. Metabolites. 2021;11:17.

    Article  Google Scholar 

  10. Arnett DK, Claas SA. Omics of blood pressure and hypertension. CircRes. 2018;122:1409–19.

    CAS  Google Scholar 

  11. McGarrah RW, Crown SB, Zhang GF, Shah SH, Newgard CB. Cardiovascular metabolomics. CircRes. 2018;122:1238–58.

    CAS  Google Scholar 

  12. Ussher JR, Elmariah S, Gerszten RE, Dyck JRB. The emerging role of metabolomics in the diagnosis and prognosis of cardiovascular disease. J Am Coll Cardiol. 2016;68:2850–70.

    Article  CAS  PubMed  Google Scholar 

  13. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. J Am Med Assoc. 2017;318:1925–6.

    Article  Google Scholar 

  14. Bennett DA, Holmes MV. Mendelian randomisation in cardiovascular research: an introduction for clinicians. Heart. 2017;103:1400–7.

    Article  CAS  PubMed  Google Scholar 

  15. King EA, Davis JW, Degner JF. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 2019;15:e1008489.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nelson MR, Tipney H, Painter JL, Shen JD, Nicoletti P, Shen YF, et al. The support of human genetic evidence for approved drug indications. Nat Genet. 2015;47:856.

    Article  CAS  PubMed  Google Scholar 

  17. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18:15.

    Article  Google Scholar 

  18. Kettunen J, Demirkan A, Wurtz P, Draisma HHM, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:9.

    Article  Google Scholar 

  19. Salomaa V. Nightingale Health and UK Biobank announces major initiative to analyse half a million blood samples to facilitate global medical research. https://www.ukbiobank.ac.uk/learn-more-about-uk-biobank/news/nightingale-health-and-uk-biobank-announces-major-initiative-to-analyse-half-a-million-blood-samples-to-facilitate-global-medical-research. Accessed April 4.

  20. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ardissino M, Slob EAW, Rajasundaram S, Reddy RK, Woolf B, Girling J, et al. Safety of beta-blocker and calcium channel blocker antihypertensive drugs in pregnancy: a Mendelian randomization study. BMC Med. 2022;20:10.

    Article  Google Scholar 

  22. Li PS, Wang HY, Guo L, Gou XY, Chen GD, Lin DX, et al. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study. BMC Med. 2022;20:10.

    Article  CAS  Google Scholar 

  23. Kurki MI, Karjalainen J, Palta P, Sipilä TP, Kristiansson K, Donner K, et al. FinnGen: unique genetic insights from combining isolated population and national health register data. Genet Genom Med. 2022. https://doi.org/10.1101/2022.03.03.22271360.

  24. Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50:1335.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chong M, Sjaarda J, Pigeyre M, Mohammadi-Shemirani P, Lali R, Shoamanesh A, et al. Novel drug targets for ischemic stroke identified through Mendelian randomization analysis of the blood proteome. Circulation. 2019;140:819–30.

    Article  CAS  PubMed  Google Scholar 

  26. Hemani G, Zhengn J, Elsworth B, Wade KH, Haberland V, Baird D, et al. The MR-Base platform supports systematic causal inference across the human phenome. eLife. 2018;7:29.

    Article  Google Scholar 

  27. Cai JH, Chen X, Wang HX, Wei ZX, Li M, Rong XM, et al. Iron status may not affect amyotrophic lateral sclerosis: a Mendelian randomization study. Front Genet. 2021;12:8.

    Article  Google Scholar 

  28. Burgess S, Thompson SG, Collaboration CCG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40:755–64.

    Article  PubMed  Google Scholar 

  29. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37:658–65.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Br Med J. 2003;327:557–60.

    Article  Google Scholar 

  31. Zhao QY, Wang JS, Hemani G, Bowden J, Small DS. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat. 2020;48:1742–69.

    Article  MathSciNet  Google Scholar 

  32. Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32:377–89.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Barter PJ, Rye KA. HDL cholesterol concentration or HDL function: which matters? Eur Heart J. 2017;38:2487–9.

    Article  PubMed  Google Scholar 

  34. Otvos JD. Measurement of lipoprotein subclass profiles by nuclear magnetic resonance spectroscopy. Clin Lab. 2002;48:171–80.

    CAS  PubMed  Google Scholar 

  35. Michos ED, McEvoy JW, Blumenthal RS. Lipid management for the prevention of atherosclerotic cardiovascular disease. N Engl J Med. 2019;381:1557–67.

    Article  CAS  PubMed  Google Scholar 

  36. Lincoff AM, Nicholls SJ, Riesmeyer JS, Barter PJ, Brewer HB, Fox KAA, et al. Evacetrapib and cardiovascular outcomes in high-risk vascular disease. N Engl J Med. 2017;376:1933–42.

    Article  PubMed  Google Scholar 

  37. Pappa E, Elisaf MS, Kostara C, Bairaktari E, Tsimihodimos VK. Cardioprotective properties of HDL: structural and functional considerations. Curr Med Chem. 2020;27:2964–78.

    Article  CAS  PubMed  Google Scholar 

  38. Leon LJ, McCarthy FP, Direk K, Gonzalez-Izquierdo A, Prieto-Merino D, Casas JP, et al. Preeclampsia and cardiovascular disease in a large UK pregnancy cohort of linked electronic health records A CALIBER Study. Circulation. 2019;140:1050–60.

    Article  PubMed  Google Scholar 

  39. Shahid R, Bari MF, Hussain M. Serum biomarkers for the prediction and diagnosis of preeclampsia: a meta-analysis. J Taibah Univ Med Sci. 2022;17:14–27.

    PubMed  Google Scholar 

  40. Hosier H, Lipkind HS, Rasheed H, DeWan AT, Rogne T. Dyslipidemia and risk of preeclampsia: a multi ancestry mendelian randomization study. Hypertension. 2023;80:1067–76.

    Article  CAS  PubMed  Google Scholar 

  41. Holmes MV, Millwood IY, Kartsonaki C, Hill MR, Bennett DA, Boxall R, et al. Lipids, lipoproteins, and metabolites and risk of myocardial infarction and stroke. J Am Coll Cardiol. 2018;71:620–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Li JJ, Zhang Y, Li S, Cui CJ, Zhu CG, Guo YL, et al. Large HDL subfraction but not HDL-C is closely linked with risk factors, coronary severity and outcomes in a cohort of nontreated patients with stable coronary artery disease: a prospective observational study. Med (Baltim). 2016;95:e2600.

    Article  Google Scholar 

  43. Zhang Y, Gordon SM, Xi H, Choi S, Paz MA, Sun R, et al. HDL subclass proteomic analysis and functional implication of protein dynamic change during HDL maturation. Redox Biol. 2019;24:101222.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Tosi MR, Tugnoli V. Cholesteryl esters in malignancy. Clin Chim Acta. 2005;359:27–45.

    Article  CAS  PubMed  Google Scholar 

  45. Shrestha S, Wu BJ, Guiney L, Barter PJ, Rye KA. Cholesteryl ester transfer protein and its inhibitors. J Lipid Res. 2018;59:772–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Girona J, Amigo N, Ibarretxe D, Plana N, Rodriguez-Borjabad C, Heras M, et al. HDL triglycerides: a new marker of metabolic and cardiovascular risk. Int J Mol Sci. 2019;20:10.

    Article  Google Scholar 

  47. Blauw LL, Noordam R, Soidinsalo S, Blauw CA, Li-Gao RF, de Mutsert R, et al. Mendelian randomization reveals unexpected effects of CETP on the lipoprotein profile. Eur J Hum Genet. 2019;27:422–31.

    Article  CAS  PubMed  Google Scholar 

  48. Martinez LO, Najib S, Perret B, Cabou C, Lichtenstein L. Ecto-F1-ATPase/P2Y pathways in metabolic and vascular functions of high density lipoproteins. Atherosclerosis. 2015;238:89–100.

    Article  CAS  PubMed  Google Scholar 

  49. Abe RJ, Abe JI, Nguyen MTH, Olmsted-Davis EA, Mamun A, Banerjee P, et al. Free cholesterol bioavailability and atherosclerosis. Curr Atheroscler Rep. 2022;24:323–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Gillard BK, Rosales C, Xu B, Gotto AM, Jr., Pownall HJ, Rethinking reverse cholesterol transport and dysfunctional high-density lipoproteins. J Clin Lipido. 2018;12:849–56.

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Acknowledgements

We thank the authors and participants of all GWASs that we have used, for making their results publicly available. Full acknowledgment and funding statements for each of these resources are available via the relevant cited reports.

Funding

This study was supported by the National Natural Science Fund of China (grant number: 82273635).

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Authors and Affiliations

  1. Department of Epidemiology and Health Statistic, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, 215123, P. R. China

    Yaling Ding, Mengxin Yao, Jiafeng Liu, Wanyi Fu, Yelin He & Jieyun Yin

  2. Suzhou Center for Disease Prevention and Control, Suzhou, Jiangsu, China

    Xiaoyan Zhu

  3. School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China

    Xiaoyan Zhu

  4. Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of Taicang, 58 Changsheng Road, Suzhou, Jiangsu, 215413, China

    Qiuping Ma

  5. The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, 215000, China

    Chunhua Zhang

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Ding, Y., Yao, M., Liu, J. et al. Association between human blood metabolome and the risk of pre-eclampsia. Hypertens Res 47, 1063–1072 (2024). https://doi.org/10.1038/s41440-024-01586-x

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