Future Directions

10. Future Directions

Myome's roadmap includes several advanced capabilities:

Multi-User Federated Learning

While Myome is local-first for privacy, users can opt into federated learning—training shared models without exposing individual data. This enables:

• More accurate predictive models trained on millions of person-years of data
• Discovery of rare biomarker patterns invisible in single-user datasets
• Population-level insights (e.g., air pollution impacts across regions)

Implementation uses differential privacy and secure aggregation to ensure individual privacy while benefiting from collective intelligence.

AI-Powered Health Coaching

Integration with large language models to provide natural language health insights:

• "Your glucose has been more variable this week. I notice you've had less sleep and more stress—these correlate in your data (r=0.58, p<0.01). Would you like suggestions for stress reduction?"
• Answering questions: "What happened to my HRV during that business trip last month?"
• Proactive recommendations: "Based on your father's health trajectory at this age, consider getting a coronary calcium scan."

Clinical Trial Recruitment and N-of-1 Trials

Myome's rich phenotype data enables:

• Precise clinical trial matching (find studies targeting your specific biomarker profile)
• Self-experimentation through N-of-1 trials (test supplement effectiveness using randomized crossover design)
• Real-world evidence generation for therapies

Longevity Optimization

Extending healthspan, not just lifespan, through:

• Biological age tracking and reversal (epigenetic clocks, biomarker panels)
• Rapamycin, metformin, NAD+ precursor monitoring
• Senescent cell burden assessment
• Mitochondrial function testing