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Prebiotic intervention with HAMSAB in untreated essential hypertensive patients assessed in a phase II randomized trial

Nature Cardiovascular Research volume 2, pages 35–43 (2023)Cite this article

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Abstract

Fibers remain undigested until they reach the colon, where some are fermented by gut microbiota, producing metabolites called short-chain fatty acids (SCFAs), such as acetate and butyrate1. SCFAs lower blood pressure in experimental models2,3,4,5, but their translational potential is unknown. Here we present the results of a phase II, randomized, placebo-controlled, double-blind cross-over trial (Australian New Zealand Clinical Trials Registry ACTRN12619000916145) using prebiotic acetylated and butyrylated high-amylose maize starch (HAMSAB) supplementation6. Twenty treatment-naive participants with hypertension were randomized to 40 g per day of HAMSAB or placebo, completing each arm for 3 weeks, with a 3-week washout period between them. The primary endpoint was a reduction in ambulatory systolic blood pressure. Secondary endpoints included changes to circulating cytokines, immune markers and gut microbiome modulation. Patients receiving the HAMSAB treatment showed a clinically relevant reduction in 24-hour systolic blood pressure independent of age, sex and body mass index without any adverse effects. HAMSAB increased levels of acetate and butyrate, shifted the microbial ecosystem and expanded the prevalence of SCFA producers. In summary, a prebiotic intervention with HAMSAB could represent a promising option to deliver SCFAs and lower blood pressure in patients with essential hypertension.

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Fig. 1: Recruitment summary.
Fig. 2: HAMSAB diet reduces in 24-hour ambulatory SBP and increases plasma SCFAs.
Fig. 3: HAMSAB promotes the expansion of SCFA-producing commensal bacteria.

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Data availability

The microbiome data described in this article are available at the GenBank Nucleotide Database (BioProject ID PRJNA903679). We did not obtain patient consent for all the data to be available publicly. However, the data underlying this article can be shared for selected research questions upon reasonable request to the corresponding author. Please email F.Z.M. at francine.marques@monash.edu, who will respond within 4 weeks.

Code availability

The microbiome coding used is described at https://github.com/michael-nakai/waterway.

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Acknowledgements

We would like to acknowledge the Monash Proteomics and Metabolomics Facility for SCFA measurement and the Monash Bioinformatics Platform for access to M3 servers. We also would like to acknowledge M. Schlaich and J. Sesa-Ashton for their help with recruitment and T. Veitch for help developing the recipes used in the trial. This work was supported by a National Heart Foundation Vanguard grant (102182), a National Health & Medical Research Council (NHMRC) of Australia project grant (GNT1159721) and NHMRC fellowships to D.M.K., G.A.H., J.M. and R.E.C. F.Z.M. is supported by a Senior Medical Research Fellowship from the Sylvia and Charles Viertel Charitable Foundation Fellowship and by National Heart Foundation Future Leader Fellowships (101185 and 105663). The Baker Heart & Diabetes Institute is supported, in part, by the Victorian Government’s Operational Infrastructure Support Program.

Author information

Authors and Affiliations

  1. Hypertension Research Laboratory, School of Biological Sciences, Faculty of Science, Monash University, Clayton, VIC, Australia

    Hamdi A. Jama, Dakota Rhys-Jones, Michael Nakai & Francine Z. Marques

  2. Department of Gastroenterology, Central Clinical School, Monash University, Melbourne, VIC, Australia

    Dakota Rhys-Jones, Chu K. Yao & Jane Muir

  3. Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia

    Rachel E. Climie

  4. Sports Cardiology Laboratory, Clinical Research Domain, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    Rachel E. Climie

  5. Neuropharmacology Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    Yusuke Sata & Geoffrey A. Head

  6. Monash Institute of Pharmaceutical Sciences, Monash University, Melbourne, VIC, Australia

    Dovile Anderson, Darren J. Creek & Francine Z. Marques

  7. Department of Pharmacology, Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia

    Geoffrey A. Head

  8. Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia

    David M. Kaye

  9. Department of Cardiology, Alfred Hospital, Melbourne, VIC, Australia

    David M. Kaye

  10. Central Clinical School, Faculty of Medicine Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia

    David M. Kaye

  11. Department of Microbiology, Biomedical Discovery Institute, Faculty of Medicine, Nursing and Health, Monash University, Clayton, VIC, Australia

    Charles R. Mackay

  12. School of Pharmaceutical Sciences, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China

    Charles R. Mackay

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  1. Hamdi A. Jama
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Contributions

F.Z.M., D.M.K., C.R.M. and J.M. conceived and designed the study. H.A.J. wrote the first draft of the report, with input from F.Z.M. Both H.A.J. and F.Z.M. accessed and verified the data and performed the statistical analyses. D.R.-J. (supervised by F.Z.M. and J.M.) coordinated the trial, recruited participants and collected samples and data. Y.S. helped with recruitment. M.N. (microbiome), R.E.C. and G.A.H. (blood pressure), D.A. and D.J.C. (metabolites) and H.A.J. (cytokines) contributed with methods. All authors had full access to all data in the study, revised the manuscript critically, approved the version to be published and had final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Francine Z. Marques.

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The authors declare no competing interests.

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Nature Cardiovascular Research thanks Levi Waldron, Noel T. Mueller and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 HAMSB diet reduces in home systolic and diastolic blood pressure (BP).

a, Mean home systolic BP over 21-day HAMSAB intervention. b, Mean change in systolic BP relative to baseline and the mean difference in HAMSAB treated participants at b, Day1–3 and Day 17–20 and c, Day 1–3 and Day 14. d, Mean change in systolic BP relative to baseline and the mean difference in placebo treated participants at e, Day1–3 and Day 17-20 and f, Day 1-3 and Day 14. g, Overall drop in systolic BP relative to baseline and placebo-subtracted mean difference. h, Mean home diastolic BP over 21-day HAMSAB intervention. mean change in diastolic BP relative to baseline and the mean difference in HAMSAB treated participants at i, Day1-3 and Day 17-20 and j, Day 1-3 and Day 14. k, Mean change in diastolic BP relative to baseline and the mean difference in placebo treated participants at l, Day1-3 and Day 17-20 and m, Day 1-3 and Day 14. n, overall drop in diastolic BP relative to baseline and placebo-subtracted mean difference. n = 20/treatment group. Error bars represent ±SEM. Two-tail paired t-test.

Extended Data Fig. 2 Propionate, changes to the gut microbiome and plasma cytokines between placebo and HAMSAB.

a, HAMSAB diet did not change plasma propionate levels. n = 20/treatment group. Error bars represent ±SEM. Two-tail Wilcoxon test. b, Bray Curtis β diversity test showing principal coordinate analysis plot between baseline and the placebo arm. n = 18-19/treatment group. Shaded ellipsis representing the 95% confidence interval for each group. c-f, In participants randomised to the HAMSAB arm first, Ruminococcus gauvreauii (c-d) and Parabacteroides distasonis (e-f) prevalence was significantly reduced after the 3-week washout period. Showing n = 11 after data from 3 participants randomised into HAMSAB first were removed due to low number of reads. Mean ±SEM. Two-tail Wilcoxon test. Plasma levels of g, IL-6; h, IL-17A; i, IL-10; and j, IL-1b in placebo and HAMSAB treated participants. n = 20/treatment group. Error bars represent mean ± SEM. Two-tail paired t-test for panels g and i, two-tail Wilcoxon test for panels h and j.

Extended Data Fig. 3 The impact of HAMSAB diet on gastrointestinal pH and transit times measured by Smart Pill.

We observed no difference in a, gastric emptying time; b, small intestinal transit time; c, colonic transit, and d, whole gut transit time in hours. We observed no difference in colonic e, minimum and f, median pH, but observed a decrease in g, maximum pH. h, Summary of maximum colonic pH in placebo and HAMSAB relative to baseline. Placebo data from a participant who had antibiotics between visits 3 and 4 was removed, resulting in n = 6 for placebo and n = 7 for baseline and HAMSAB groups. One-way ANOVA adjusted for multiple comparisons, showing adjusted P-values. Error bars represent mean ± SEM.

Extended Data Table 1 Baseline characteristics of the cohort
Full size table
Extended Data Table 2 Comparison between baseline randomization arms
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Extended Data Table 3 Comparison of changes in biochemical parameters, day and night BP from baseline in the placebo and HAMSAB diet arm
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Extended Data Table 4 Summary of the composition of the placebo and HAMSAB interventions per food item provided to the participants
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Extended Data Table 5 Dietary intake of participants during the trial, measured by 3-day food diaries
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Extended Data Table 6 Summary of gastrointestinal symptoms based on the 100-mm VAS responses for stool habits
Full size table
Extended Data Table 7 Taxonomic changes in the fecal microbiome between placebo and HAMSAB
Full size table

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Jama, H.A., Rhys-Jones, D., Nakai, M. et al. Prebiotic intervention with HAMSAB in untreated essential hypertensive patients assessed in a phase II randomized trial. Nat Cardiovasc Res 2, 35–43 (2023). https://doi.org/10.1038/s44161-022-00197-4

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