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.