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Gut Microbiome-Augmented Models for Heart Failure Survival in FINRISK 2002

Medicine Group    Start Submission

Hojin Moon*, Nhat Hoang Nguyen, Jaehee Park, Sohyul Ahn and Gyumin Kim

Volume6-Issue9
Dates: Received: 2025-09-07 | Accepted: 2025-09-13 | Published: 2025-09-16
Pages: 1265-1279

Abstract

Background: Conventional Heart‑Failure (HF) risk scores use limited clinical variables and show moderate accuracy. We tested whether gut‑microbiome signatures improve long-term HF survival prediction in a population‑based Finnish cohort.

Methods: We analyzed FINRISK 2002 participants free of HF at baseline (n = 5,212; training 3,471; test 1,741; median follow‑up 13.8 years). Microbiome profiles were prevalence-abundance filtered (≥ 30% prevalence; ≥ 0.01% relative abundance), centered log‑ratio transformed, and reduced to 125 core taxa. We trained penalized Cox (elastic net), Random Survival Forests (RSF), and DeepSurv neural network under two feature sets: nine-clinical covariates alone versus clinical + microbiome. Feature selection was performed using elastic net and RSF. Discrimination was assessed with Harrell’s C-index on a test set, and model comparisons were tested for significance.

Results: Microbiome-enhanced models achieved test-set C-indices of 0.7225 (elastic net), 0.7231 (RSF), and 0.7211 (DeepSurv), modestly exceeding the Cox baseline model (0.7110). The elastic-net model selected 14 predictors, including age, prevalent coronary heart disease, and multiple microbial taxa. Absolute gains were modest yet statistically significant across algorithms.

Conclusion: In a population-based cohort with long follow-up, incorporating gut microbiome taxa with routine clinical variables produced modest but consistent improvements in HF survival prediction. Microbiome-informed models may enhance early risk stratification and support personalized prevention in cardiovascular care.

FullText HTML FullText PDF DOI: 10.37871/jbres2182


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Copyright

© 2025 Moon H, et al. Distributed under Creative Commons CC-BY 4.0

How to cite this article

Moon H, Nguyen NH, Park J, Ahn S, Kim G. Gut Microbiome-Augmented Models for Heart Failure Survival in FINRISK 2002. J Biomed Res Environ Sci. 2025 Sept 16; 6(9): 1265-1279. doi: 10.37871/jbres2182, Article ID: JBRES2182, Available at: https://www.jelsciences.com/articles/jbres2182.pdf


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References


  1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, Barengo NC, Beaton AZ, Benjamin EJ, Benziger CP, Bonny A, Brauer M, Brodmann M, Cahill TJ, Carapetis J, Catapano AL, Chugh SS, Cooper LT, Coresh J, Criqui M, DeCleene N, Eagle KA, Emmons-Bell S, Feigin VL, Fernández-Solà J, Fowkes G, Gakidou E, Grundy SM, He FJ, Howard G, Hu F, Inker L, Karthikeyan G, Kassebaum N, Koroshetz W, Lavie C, Lloyd-Jones D, Lu HS, Mirijello A, Temesgen AM, Mokdad A, Moran AE, Muntner P, Narula J, Neal B, Ntsekhe M, Moraes de Oliveira G, Otto C, Owolabi M, Pratt M, Rajagopalan S, Reitsma M, Ribeiro ALP, Rigotti N, Rodgers A, Sable C, Shakil S, Sliwa-Hahnle K, Stark B, Sundström J, Timpel P, Tleyjeh IM, Valgimigli M, Vos T, Whelton PK, Yacoub M, Zuhlke L, Murray C, Fuster V; GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010. Erratum in: J Am Coll Cardiol. 2021 Apr 20;77(15):1958-1959. doi: 10.1016/j.jacc.2021.02.039. PMID: 33309175; PMCID: PMC7755038.
  2. Shahim B, Kapelios CJ, Savarese G, Lund LH. Global Public Health Burden of Heart Failure: An Updated Review. Card Fail Rev. 2023 Jul 27;9:e11. doi: 10.15420/cfr.2023.05. PMID: 37547123; PMCID: PMC10398425.
  3. Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K, Fonarow GC, Heidenreich P, Ho JE, Hsich E, Ibrahim NE, Jones LM, Khan SS, Khazanie P, Koelling T, Krumholz HM, Khush KK, Lee C, Morris AA, Page RL 2nd, Pandey A, Piano MR, Stehlik J, Stevenson LW, Teerlink JR, Vaduganathan M, Ziaeian B; Writing Committee Members. Heart Failure Epidemiology and Outcomes Statistics: A Report of the Heart Failure Society of America. J Card Fail. 2023 Oct;29(10):1412-1451. doi: 10.1016/j.cardfail.2023.07.006. Epub 2023 Sep 26. PMID: 37797885; PMCID: PMC10864030.
  4. Taylor CJ, Ordóñez-Mena JM, Roalfe AK, Lay-Flurrie S, Jones NR, Marshall T, Hobbs FDR. Trends in survival after a diagnosis of heart failure in the United Kingdom 2000-2017: population based cohort study. BMJ. 2019 Feb 13;364:l223. doi: 10.1136/bmj.l223. Erratum in: BMJ. 2019 Oct 8;367:l5840. doi: 10.1136/bmj.l5840. PMID: 30760447; PMCID: PMC6372921.
  5. Darvish M, Shakoor A, Feyz L, Schaap J, van Mieghem NM, de Boer RA, Brugts JJ, van der Boon RMA. Heart failure: assessment of the global economic burden. Eur Heart J. 2025 Aug 14;46(31):3069-3078. doi: 10.1093/eurheartj/ehaf323. PMID: 40444781; PMCID: PMC12349942.
  6. Kummen M, Mayerhofer CCK, Vestad B, Broch K, Awoyemi A, Storm-Larsen C, Ueland T, Yndestad A, Hov JR, Trøseid M. Gut Microbiota Signature in Heart Failure Defined From Profiling of 2 Independent Cohorts. J Am Coll Cardiol. 2018 Mar 13;71(10):1184-1186. doi: 10.1016/j.jacc.2017.12.057. PMID: 29519360.
  7. Cui X, Ye L, Li J, Jin L, Wang W, Li S, Bao M, Wu S, Li L, Geng B, Zhou X, Zhang J, Cai J. Metagenomic and metabolomic analyses unveil dysbiosis of gut microbiota in chronic heart failure patients. Sci Rep. 2018 Jan 12;8(1):635. doi: 10.1038/s41598-017-18756-2. PMID: 29330424; PMCID: PMC5766622.
  8. Branchereau M, Burcelin R, Heymes C. The gut microbiome and heart failure: A better gut for a better heart. Rev Endocr Metab Disord. 2019 Dec;20(4):407-414. doi: 10.1007/s11154-019-09519-7. PMID: 31705258.
  9. Louis P, Flint HJ. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol Lett. 2009 May;294(1):1-8. doi: 10.1111/j.1574-6968.2009.01514.x. Epub 2009 Feb 13. PMID: 19222573.
  10. Wang Z, Klipfell E, Bennett BJ, Koeth R, Levison BS, Dugar B, Feldstein AE, Britt EB, Fu X, Chung YM, Wu Y, Schauer P, Smith JD, Allayee H, Tang WH, DiDonato JA, Lusis AJ, Hazen SL. Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. 2011 Apr 7;472(7341):57-63. doi: 10.1038/nature09922. PMID: 21475195; PMCID: PMC3086762.
  11. Organ CL, Otsuka H, Bhushan S, Wang Z, Bradley J, Trivedi R, Polhemus DJ, Tang WH, Wu Y, Hazen SL, Lefer DJ. Choline Diet and Its Gut Microbe-Derived Metabolite, Trimethylamine N-Oxide, Exacerbate Pressure Overload-Induced Heart Failure. Circ Heart Fail. 2016 Jan;9(1):e002314. doi: 10.1161/CIRCHEARTFAILURE.115.002314. Epub 2015 Dec 23. PMID: 26699388; PMCID: PMC4943035.
  12. Zhu W, Gregory JC, Org E, Buffa JA, Gupta N, Wang Z, Li L, Fu X, Wu Y, Mehrabian M, Sartor RB, McIntyre TM, Silverstein RL, Tang WHW, DiDonato JA, Brown JM, Lusis AJ, Hazen SL. Gut Microbial Metabolite TMAO Enhances Platelet Hyperreactivity and Thrombosis Risk. Cell. 2016 Mar 24;165(1):111-124. doi: 10.1016/j.cell.2016.02.011. Epub 2016 Mar 10. PMID: 26972052; PMCID: PMC4862743.
  13. Marques FZ, Mackay CR, Kaye DM. Beyond gut feelings: how the gut microbiota regulates blood pressure. Nat Rev Cardiol. 2018 Jan;15(1):20-32. doi: 10.1038/nrcardio.2017.120. Epub 2017 Aug 24. PMID: 28836619.
  14. Witkowski M, Weeks TL, Hazen SL. Gut Microbiota and Cardiovascular Disease. Circ Res. 2020 Jul 31;127(4):553-570. doi: 10.1161/CIRCRESAHA.120.316242. Epub 2020 Jul 30. PMID: 32762536; PMCID: PMC7416843.
  15. Brown JM, Hazen SL. Microbial modulation of cardiovascular disease. Nat Rev Microbiol. 2018 Mar;16(3):171-181. doi: 10.1038/nrmicro.2017.149. Epub 2018 Jan 8. PMID: 29307889; PMCID: PMC5885760.
  16. Kelly TN, Bazzano LA, Ajami NJ, He H, Zhao J, Petrosino JF, Correa A, He J. Gut Microbiome Associates With Lifetime Cardiovascular Disease Risk Profile Among Bogalusa Heart Study Participants. Circ Res. 2016 Sep 30;119(8):956-64. doi: 10.1161/CIRCRESAHA.116.309219. Epub 2016 Aug 9. PMID: 27507222; PMCID: PMC5045790.
  17. Rajkomar A, Dean J, Kohane I. Machine Learning in Medicine. N Engl J Med. 2019 Apr 4;380(14):1347-1358. doi: 10.1056/NEJMra1814259. PMID: 30943338.
  18. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Series B Stat Methodol. 2005;67:301-20. doi: 10.1111/j.1467-9868.2005.00503.x.
  19. Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. Ann Appl Stat. 2008;2(3):841-60. doi: 10.1214/08-AOAS169.
  20. Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Med Res Methodol. 2018 Feb 26;18(1):24. doi: 10.1186/s12874-018-0482-1. PMID: 29482517; PMCID: PMC5828433.
  21. Borodulin K, Tolonen H, Jousilahti P, Jula A, Juolevi A, Koskinen S, Kuulasmaa K, Laatikainen T, Männistö S, Peltonen M, Perola M, Puska P, Salomaa V, Sundvall J, Virtanen SM, Vartiainen E. Cohort Profile: The National FINRISK Study. Int J Epidemiol. 2018 Jun 1;47(3):696-696i. doi: 10.1093/ije/dyx239. PMID: 29165699.
  22. Erawijantari PP, Kartal E, Liñares-Blanco J, Laajala TD, Feldman LE; FINRISK Microbiome DREAM Challenge and ML4Microbiome Communities; Carmona-Saez P, Shigdel R, Claesson MJ, Bertelsen RJ, Gomez-Cabrero D, Minot S, Albrecht J, Chung V, Inouye M, Jousilahti P, Schultz JH, Friederich HC, Knight R, Salomaa V, Niiranen T, Havulinna AS, Saez-Rodriguez J, Levinson RT, Lahti L. Microbiome-based risk prediction in incident heart failure: a community challenge. medRxiv [Preprint]. 2023 Oct 12:2023.10.12.23296829. doi: 10.1101/2023.10.12.23296829. PMID: 37873403; PMCID: PMC10593042.
  23. Cox DR. Regression models and life-tables. J R Stat Soc. 1972;34(2):187-220. doi: 10.1111/j.2517-6161.1972.tb00899.x
  24. Breiman L. Random forests. Mach Learn. 2001;45(1):5-32. doi: 10.1023/A:1010933404324.
  25. Nelson W. Theory and applications of hazard plotting for censored failure data. Technometrics. 1969;11(3):945-65.
  26. Aalen OO. Nonparametric inference for a family of counting processes. Ann Stat. 1978;6(4):701-26.
  27. Ren Z, Zhu J, Wang Z, Li X, Hu W. Deep learning-based survival analysis for cancer prognosis. IEEE Trans Neural Netw Learn Syst. 2019;30(9):2739-48. doi: 10.1186/s12920-020-0686-1.
  28. Kingma DP, Ba J. Adam.A method for stochastic optimization. arXiv. 2014. doi: 10.48550/arXiv.1412.6980.
  29. Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. Adv Neural Inf Process Syst. 2012;25:2951-2959. doi: 10.48550/arXiv.1206.2944.
  30. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996 Feb 28;15(4):361-87. doi: 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO;2-4. PMID: 8668867.
  31. Tang WH, Kitai T, Hazen SL. Gut Microbiota in Cardiovascular Health and Disease. Circ Res. 2017 Mar 31;120(7):1183-1196. doi: 10.1161/CIRCRESAHA.117.309715. PMID: 28360349; PMCID: PMC5390330.
  32. Din AU, Hassan A, Zhu Y. Inhibitory effect of Bacteroides uniformis on inflammation and metabolic disorder. Front Immunol. 2021;12:653948.


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