Major Report Update and Omic Age Algorithm

Introduction

• Speaker: Hannah Went, Director of Operations at True Diagnostics
• Purpose: Discuss logistics of accessing and understanding new report updates

New Reports

• For Direct Consumers & Patients:
  • 12 Cell Immunity Convolution Method
  • Physical Fitness Report (VO2 Max, F1 Grip Strength, Gate Speed)
• For Healthcare Providers:
  • Above two reports
  • Inflammation Cognition Report (D-methylation, CRP, IL-6)

Accessing Reports

• Direct Consumers: Upgrade via account login, purchase via Shopify
• Healthcare Providers: Upgrade via portal (click message in bottom left), option for email support

Pricing

• Single Test: $199.99
• Multiple Tests: $29.99 for upgrades

Transcript Highlights: Ryan Smith's Presentation

Development and Importance of Omic Age Algorithm

• Team Effort: Collaboration with Dr. Jessica Luisu and Qingwen Chen at Harvard
• Why Measure Aging:
  • Aging is the biggest risk factor for chronic diseases
  • Objective is to measure and then alter risk of disease via accurate aging metrics

History of Age-Related Algorithms

• First Gen Clocks: Predicted chronological age using simple metrics, e.g., Horvath clock
• Second Gen: Pheno age, predicted biological aging using more complex biomarkers
• Third Gen: DunedinPACE, used longitudinal data and more biomarkers
• Omic Age: Integrates multi-ome data (proteomics, genomics, metabolomics) to predict health outcomes

Methodology of Omic Age

• Step 1: Developed EMR age (19 blood biomarker panel) by training it on 60,000 samples to predict time until death
• Step 2: Created DNA methylation algorithm to predict bioage
• Step 3: Integrated additional omic levels (107 proteins, 21 clinical variables, 267 metabolites) into omic age

Performance and Benefits

• Precision and Accuracy:
  • High ICC value (0.995)
  • Predicts 10-year survival rate with 92% accuracy
• Immune Deconvolution: Adjusts for immune cell variations, independent of recent illnesses or sampling differences
• Risk Assessment: Shows impact of biological aging on disease risk, offers personalized guidance

Report Interpretation

• Clinical Factors:
  • Identifies biomarker impacts on aging (e.g., fasting glucose, hba1c)
  • Uses population level graphs for context
• Metabolomic, Proteomic Data: Provides actionable insights into supplements and lifestyle changes
• Epigenetic Biomarkers Proxies: Approximate clinical biomarkers without traditional tests

Additional Information

• Resources: Annotated sample report, introduction materials, and FAQs available
• Future Plans: New algorithms for specific organ aging expected to release
• Cost: License fee implications necessitate charging for new algorithm includes additional free reports in portal

Q&A and Raffle

• Answered questions about report access and practical uses
• Winners announced: William Stanford, Nita Jane, Rick Cohen
• Support contact info: [email protected], [email protected]

Conclusion

• Encouragement for attendees to explore all provided resources and contact True Diagnostic for support
• Emphasis on validating biological age algorithms with scientific proof, not just assumptions