Microbiome-based risk prediction in incident heart failure: a community challenge

Area of Science:

• Microbiome research
• Cardiovascular disease epidemiology
• Computational biology

Background:

• Heart failure (HF) poses a significant public health challenge, necessitating early risk identification for effective interventions.
• Prospective population studies are crucial for understanding disease pathogenesis and developing predictive models.

Approach:

• The FINRISK Microbiome DREAM challenge utilized shotgun metagenomics data from fecal samples of 7231 Finnish adults to predict incident HF risk over 15 years.
• Machine learning models, including Cox regression, were trained on synthetic data and evaluated on real-world data, with an ensemble model developed from top-performing submissions.
• Models were refined by removing phylum information and evaluated at intermediate time points, revealing superior accuracy for 10-year HF incidence prediction.

Key Points:

• Bacterial species, particularly those associated with inflammation, were identified as significant predictors of incident HF.
• The gut microbiome plays a potential role in HF pathophysiology, possibly through inflammatory pathways.
• Ensemble and refined machine learning models demonstrated improved prediction of HF incidence.

Conclusions:

• Incorporating gut microbiome data into risk models offers valuable biological insights into HF pathogenesis.
• Machine learning approaches applied to microbiome data hold promise for enhancing cardiovascular disease risk prediction.
• This study underscores the gut microbiome's relevance in understanding and potentially mitigating heart failure risk.