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Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results

Hypertension Research volume 46, pages 2280–2292 (2023)Cite this article

A Comment to this article was published on 01 August 2023

Abstract

The renin-angiotensin-aldosterone system (RAAS) is a regulatory mechanism of the endocrine system and is associated with various diseases, including hypertension and renal and cardiovascular diseases. The gut microbiota (GM) have been associated with various diseases, mainly in animal models. However, to our knowledge, no studies have examined the relationship between the RAAS and GM in humans. The present study aimed to assess the association between the systemic RAAS and GM genera and their causal relationships. The study participants were 377 members of the general population aged 40 years or older in Shika-machi, Japan. Plasma renin activity (PRA), plasma aldosterone concentration (PAC), aldosterone-renin ratio (ARR), and GM composition were analyzed using the 16S rRNA method. The participants were divided into high and low groups according to the PRA, PAC, and ARR values. U-tests, one-way analysis of covariance, and linear discriminant analysis of effect size were used to identify the important bacterial genera between the two groups, and binary classification modeling using Random Forest was used to calculate the importance of the features. The results showed that Blautia, Bacteroides, Akkermansia, and Bifidobacterium were associated with the RAAS parameters. Causal inference analysis using the linear non-Gaussian acyclic model revealed a causal effect of Blautia on PAC via SBP. These results strengthen the association between the systemic RAAS and GM in humans, and interventions targeting the GM may provide new preventive measures and treatments for hypertension and renal disease.

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Data availability

Raw sequencing data were registered with the DNA Data Bank of Japan (DDBJ) (Number DRA015515).

References

  1. Ames MK, Atkins CE, Pitt B. The renin-angiotensin-aldosterone system and its suppression. J Vet Intern Med. 2019;33:363–82. https://doi.org/10.1111/jvim.15454.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Te Riet L, van Esch JH, Roks AJ, van den Meiracker AH, Danser AH. Hypertension: renin-angiotensin-aldosterone system alterations. Circ Res. 2015;116:960–75. https://doi.org/10.1161/CIRCRESAHA.116.303587.

    Article  CAS  Google Scholar 

  3. Yang X, Zeng H, Wang L, Luo S, Zhou Y. Activation of Piezo1 downregulates renin in juxtaglomerular cells and contributes to blood pressure homeostasis. Cell Biosci. 2022;12:197. https://doi.org/10.1186/s13578-022-00931-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Jaworska K, Koper M, Ufnal M. Gut microbiota and renin-angiotensin system: a complex interplay at local and systemic levels. Am J Physiol Gastrointest Liver Physiol. 2021;321:G355–66. https://doi.org/10.1152/ajpgi.00099.2021.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Funder JW, Carey RM, Mantero F, Murad MH, Reincke M, Shibata H, et al. The management of primary aldosteronism: case detection, diagnosis, and treatment: an Endocrine Society Clinical Practice Guideline. J Clin Endocrinol Metab. 2016;101:1889–916. https://doi.org/10.1210/jc.2015-4061.

    Article  CAS  PubMed  Google Scholar 

  6. Mulatero P, Monticone S, Deinum J, Amar L, Prejbisz A, Zennaro MC, et al. Genetics, prevalence, screening and confirmation of primary aldosteronism: a position statement and consensus of the Working Group on Endocrine Hypertension of The European Society of Hypertension. J Hypertens. 2020;38:1919–28. https://doi.org/10.1097/HJH.0000000000002510.

    Article  CAS  PubMed  Google Scholar 

  7. Naruse M, Katabami T, Shibata H, Sone M, Takahashi K, Tanabe A, et al. Japan Endocrine Society clinical practice guideline for the diagnosis and management of primary aldosteronism 2021. Endocr J. 2022;69:327–59. https://doi.org/10.1507/endocrj.EJ21-0508.

    Article  PubMed  Google Scholar 

  8. Shreiner AB, Kao JY, Young VB. The gut microbiome in health and in disease. Curr Opin Gastroenterol. 2015;31:69–75. https://doi.org/10.1097/MOG.0000000000000139.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kho ZY, Lal SK. The human gut microbiome—a potential controller of wellness and disease. Front Microbiol. 2018;9. https://doi.org/10.3389/fmicb.2018.01835.

  10. Avery EG, Bartolomaeus H, Maifeld A, Marko L, Wiig H, Wilck N, et al. The gut microbiome in hypertension: recent advances and future perspectives. Circ Res. 2021;128:934–50. https://doi.org/10.1161/CIRCRESAHA.121.318065.

    Article  CAS  PubMed  Google Scholar 

  11. O’Donnell JA, Zheng T, Meric G, Marques FZ. The gut microbiome and hypertension. Nat Rev Nephrol. 2023. https://doi.org/10.1038/s41581-022-00654-0.

  12. Hobby GP, Karaduta O, Dusio GF, Singh M, Zybailov BL, Arthur JM. Chronic kidney disease and the gut microbiome. Am J Physiol Ren Physiol. 2019;316:F1211–17. https://doi.org/10.1152/ajprenal.00298.2018.

    Article  CAS  Google Scholar 

  13. Witkowski M, Weeks TL, Hazen SL. Gut microbiota and cardiovascular disease. Circ Res. 2020;127:553–70. https://doi.org/10.1161/CIRCRESAHA.120.316242.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Nagase S, Karashima S, Tsujiguchi H, Tsuboi H, Miyagi S, Kometani M, et al. Impact of gut microbiome on hypertensive patients with low-salt intake: Shika study results. Front Med. 2020;7:475. https://doi.org/10.3389/fmed.2020.00475.

    Article  Google Scholar 

  15. Lu C-C, Hu Z-B, Wang R, Hong Z-H, Lu J, Chen P-P, et al. Gut microbiota dysbiosis-induced activation of the intrarenal renin-angiotensin system is involved in kidney injuries in rat diabetic nephropathy. Acta Pharm Sin. 2020;41:1111–8. https://doi.org/10.1038/s41401-019-0326-5.

    Article  CAS  Google Scholar 

  16. Shimizu S, Inazumi T, Sogawa Y, Hyvarinen A, Kawahara Y, Washio T, et al. A direct method for learning a linear non-Gaussian structural equation model. J Mach Learn Res. 2011;12:1225–48.

    Google Scholar 

  17. Miyajima Y, Karashima S, Ogai K, Taniguchi K, Ogura K, Kawakami M, et al. Impact of gut microbiome on dyslipidemia in japanese adults: assessment of the Shika-machi super preventive health examination results for causal inference. Front Cell Infect Microbiol. 2022;12:908997. https://doi.org/10.3389/fcimb.2022.908997.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, et al. The Japanese society of hypertension guidelines for the management of hypertension (JSH 2019). Hypertens Res. 2019;42:1235–481. https://doi.org/10.1038/s41440-019-0284-9.

  19. Karashima S, Kometani M, Tsujiguchi H, Asakura H, Nakano S, Usukura M, et al. Prevalence of primary aldosteronism without hypertension in the general population: results in shika study. Clin Exp Hypertens. 2017;40:118–25. https://doi.org/10.1080/10641963.2017.1339072.

    Article  CAS  PubMed  Google Scholar 

  20. Kameoka S, Motooka D, Watanabe S, Kubo R, Jung N, Midorikawa Y, et al. Benchmark of 16S rRNA gene amplicon sequencing using Japanese gut microbiome data from the V1–V2 and V3–V4 primer sets. BMC Genomics. 2021;22:527. https://doi.org/10.1186/s12864-021-07746-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Nat Biotechnol. 2019;37:852–7. https://doi.org/10.1038/s41587-019-0209-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: high-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. https://doi.org/10.1038/nmeth.3869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6. https://doi.org/10.1093/nar/gks1219.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2012;12:2825–30.

    Google Scholar 

  25. Vujkovic-Cvijin I, Sklar J, Jiang L, Natarajan L, Knight R, Belkaid Y. Host variables confound gut microbiota studies of human disease. Nature. 2020;587:448–54. https://doi.org/10.1038/s41586-020-2881-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Oksanen J, Gavin L, Simpson F, Blanchet G, Kindt R, Legendre P, et al. Package ‘vegan’. Community Ecol Package. 12:40:02 UTC. 2022.

  27. Willis AD. Rarefaction, alpha diversity, and statistics. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.02407.

  28. Segata N, Izard J, Waldron L, Gevers D, Miropolsky L, Garrett WS, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. https://doi.org/10.1186/gb-2011-12-6-r60.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Pluznick JL, Protzko RJ, Gevorgyan H, Peterlin Z, Sipos A, Han J, et al. Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proc Natl Acad Sci USA. 2013;110:4410–5. https://doi.org/10.1073/pnas.1215927110.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Wang L, Zhu Q, Lu A, Liu X, Zhang L, Xu C, et al. Sodium butyrate suppresses angiotensin II-induced hypertension by inhibition of renal (pro) renin receptor and intrarenal renin–angiotensin system. J Hypertens. 2017;35:1899–908. https://doi.org/10.1097/HJH.0000000000001378.

    Article  CAS  PubMed  Google Scholar 

  31. Lymperopoulos A, Suster MS, Borges JI. Short-chain fatty acid receptors and cardiovascular function. Int J Mol Sci. 2022;23:3303. https://doi.org/10.3390/ijms23063303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Onyszkiewicz M, Gawrys-Kopczynska M, Konopelski P, Aleksandrowicz M, Sawicka A, Koźniewska E, et al. Butyric acid, a gut bacteria metabolite, lowers arterial blood pressure via colon-vagus nerve signaling and GPR41/43 receptors. Pflug Arch. 2019;471:1441–53. https://doi.org/10.1007/s00424-019-02322-y.

    Article  CAS  Google Scholar 

  33. Mirzaei R, Afaghi A, Babakhani S, Sohrabi MR, Hosseini-Fard SR, Babolhavaeji K, et al. Biomed Pharmacother. 2021;139:111619. https://doi.org/10.1016/j.biopha.2021.111619.

    Article  CAS  PubMed  Google Scholar 

  34. Louis P, Flint HJ. Formation of propionate and butyrate by the human colonic microbiota. Environ Microbiol. 2017;19:29–41. https://doi.org/10.1111/1462-2920.13589.

    Article  CAS  PubMed  Google Scholar 

  35. Liu Y, Jiang Q, Liu Z, Shen S, Ai J, Zhu Y, et al. Alteration of gut microbiota relates to metabolic disorders in primary aldosteronism patients. Front Endocrinol. 2021;12:667951. https://doi.org/10.3389/fendo.2021.667951.

    Article  Google Scholar 

  36. Mirzaei R, Afaghi A, Babakhani S, Sohrabi MR, Hosseini-Fard SR, Babolhavaeji K, et al. Role of microbiota-derived short-chain fatty acids in cancer development and prevention. Biomed Pharmacother. 2021;139:111619. https://doi.org/10.1016/j.biopha.2021.111619.

    Article  CAS  PubMed  Google Scholar 

  37. Yan X, Jin J, Su X, Yin X, Gao J, Wang X, et al. Intestinal flora modulates blood pressure by regulating the synthesis of intestinal-derived corticosterone in high salt-induced hypertension. Circ Res. 2020;126:839–53. https://doi.org/10.1161/CIRCRESAHA.119.316394.

    Article  CAS  PubMed  Google Scholar 

  38. Sun S, Lulla A, Sioda M, Winglee K, Wu MC, Jacobs DR Jr, et al. Gut microbiota composition and blood pressure. Hypertension. 2019;73:998–1006. https://doi.org/10.1161/HYPERTENSIONAHA.118.12109.

    Article  CAS  PubMed  Google Scholar 

  39. Silveira-Nunes G, Durso DF, Oliveira LRA Jr, Cunha EHM, Maioli TU, Vieira AT, et al. Hypertension is associated with intestinal microbiota dysbiosis and inflammation in a Brazilian population. Front Pharm. 2020;11:258. https://doi.org/10.3389/fphar.2020.00258.

    Article  CAS  Google Scholar 

  40. Everard A, Belzer C, Geurts L, Ouwerkerk JP, Druart C, Bindels LB, et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc Natl Acad Sci USA. 2013;110:9066–71. https://doi.org/10.1073/pnas.1219451110.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO, et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016;65:426–36. https://doi.org/10.1136/gutjnl-2014-308778.

    Article  CAS  PubMed  Google Scholar 

  42. Plovier H, Everard A, Druart C, Depommier C, Hul MV, Geurts L, et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat Med. 2017;23:107–13. https://doi.org/10.1038/nm.4236.

    Article  CAS  PubMed  Google Scholar 

  43. Robles-Vera I, Visitación N, Toral M, Sánchez M, Romero M, Gómez-Guzmán M, et al. Probiotic Bifidobacterium breve prevents DOCA-salt hypertension. FASEB J. 2020;34:13626–40. https://doi.org/10.1096/fj.202001532R.

    Article  CAS  PubMed  Google Scholar 

  44. Wu IW, Lin CY, Chang LC, Lee CC, Chiu CY, Hsu HJ. Gut microbiota as diagnostic tools for mirroring disease progression and circulating nephrotoxin levels in chronic kidney disease: discovery and validation study. Int J Biol Sci. 2020;16:420–34. https://doi.org/10.7150/ijbs.37421.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. 2020;16:223–37. https://doi.org/10.1038/s41581-019-0244-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Kaufman JS, Dolman L, Rushani D, Cooper RS. The contribution of genomic research to explaining racial disparities in cardiovascular disease: a systematic review. Am J Epidemiol. 2015;181:464–72. https://doi.org/10.1093/aje/kwu319.

    Article  PubMed  Google Scholar 

  47. Whelton PK, Einhorn PT, Muntner P, Appel LJ, Cushman WC, Diez Roux AV, et al. Research needs to improve hypertension treatment and control in African Americans. Hypertension. 2016;68:1066–72. https://doi.org/10.1161/HYPERTENSIONAHA.116.07905.

    Article  CAS  PubMed  Google Scholar 

  48. Syromyatnikov M, Nesterova E, Gladkikh M, Smirnova Y, Gryaznova M, Popov V. Characteristics of the gut bacterial composition in people of different nationalities and religions. Microorganisms. 2022;10:1866. https://doi.org/10.3390/microorganisms10091866.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Nishijima S, Suda W, Oshima K, Kim SW, Hirose Y, Morita H, et al. The gut microbiome of healthy Japanese and its microbial and functional uniqueness. DNA Res. 2016;23:125–33. https://doi.org/10.1093/dnares/dsw002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Nakayama J, Watanabe K, Jiang J, Matsuda K, Chao SH, Haryono P, et al. Diversity in gut bacterial community of school-age children in Asia. Sci Rep. 2015;5:8397. https://doi.org/10.1038/srep08397.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank Editage (Tokyo, Japan; www.editage.jp) for the English language editing.

Funding

This study was supported by grants from JSPS KAKENHI [grant numbers JP19K17956 and JP21K10392 to SK] and the Yakult Bioscience Foundation. The funder financed the experiments as well as the writing and proofreading of this manuscript.

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Authors and Affiliations

  1. School of Biological Science and Technology, College of Science and Engineering, Kanazawa University, Kanazawa, Japan

    Ren Mizoguchi

  2. Institute of Liberal Arts and Science, Kanazawa University, Kanazawa, Japan

    Shigehiro Karashima

  3. Department of Clinical Laboratory Science, Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University, Kanazawa, Japan

    Yuna Miyajima & Shigefumi Okamoto

  4. Advanced Health Care Science Research Unit, Institute for Frontier Science Initiative, Kanazawa University, Kanazawa, Japan

    Kohei Ogura & Shigefumi Okamoto

  5. Department of Endocrinology and Metabolism, Kanazawa University Hospital, Kanazawa, Japan

    Mitsuhiro Kometani, Daisuke Aono, Seigo Konishi & Takashi Yoneda

  6. Department of Hygiene, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan

    Masashi Demura

  7. Department of Public Health, Graduate School of Advanced Preventive Medical Sciences, Kanazawa University, Kanazawa, Japan

    Hiromasa Tsujiguchi, Akinori Hara & Hiroyuki Nakamura

  8. Department of Health Promotion and Medicine of the Future, Kanazawa University, Kanazawa, Japan

    Takashi Yoneda

  9. Faculty of Transdisciplinary Sciences for Innovation, Institute of Transdisciplinary Sciences for Innovation, Kanazawa University, Kanazawa, Japan

    Takashi Yoneda & Kenji Satou

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  1. Ren Mizoguchi
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Contributions

SK and KS designed the study and evaluated and edited the manuscript. KO, MK, DA, MD, TY, HT, AH, and HN supervised consultations. YM and SO collected the samples and performed the experiments. YM and SK performed statistical analyses. YM, SK, and HN contributed to data analysis and interpretation. YM and SK wrote the manuscript. SK and SO acquired funding and supervised the study. All the authors have checked and approved the final version of the manuscript.

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Correspondence to Shigehiro Karashima or Kenji Satou.

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This study was approved by the Human Research Ethics Committee of Kanazawa University (approval number: 1491) and was conducted in accordance with the principles of the Declaration of Helsinki. An overview of the study was provided to all participants at the time of physical examination. Written informed consent was obtained prior to fecal sample collection.

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Mizoguchi, R., Karashima, S., Miyajima, Y. et al. Impact of gut microbiome on the renin-aldosterone system: Shika-machi Super Preventive Health Examination results. Hypertens Res 46, 2280–2292 (2023). https://doi.org/10.1038/s41440-023-01334-7

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