Physician Briefing: TrueYouOmics Proteomic Risk Stratification
Clinical Rationale
Biological coverage. DNA conveys fixed predisposition; RNA reflects pathway activation; plasma proteins translate and modulate both and show the earliest systemic shifts before organ damage.
Lead‑time advantage. Large‑scale cohorts demonstrate that plasma proteomic signatures anticipate diagnosis of multiple solid tumours by several years (AUROC 0.91 in stage I–II disease), hepatocellular carcinoma in chronic HBV carriers, breast cancer in pre‑diagnostic serum, incipient chronic kidney disease, and diverse age‑related disorders via a 5,000‑analyte ageing clock (C‑index 0.79 for 10‑year morbidity).
Population data. UK Biobank high‑throughput profiling (>50 000 participants, >3 000 proteins) confirms strong, independent proteomic predictors for cardiovascular, neuro‑degenerative and metabolic diseases and has triggered the world’s largest 5 400‑protein expansion in 600 000 samples.
Internal Case Study: Risk Stratification Through Proteomics
Using a dataset comprising over 50,000 individuals from the UK Biobank, we developed a machine-learning model that categorizes them into low and high-risk groups for lung cancer.
To validate these risk groupings, we calculated the percentage of individuals in each category who were diagnosed with lung cancer within five years of their blood sample collection, effectively measuring the incidence rate. We then compare it to the overall lung cancer incidence observed in the general population.
Low-risk group
0.01%
General incidence
0.38%
High-risk group
1.03%
The green box (0.01%) represents the subset identified by our model as low-risk, they exhibited an exceptionally low rate of developing lung cancer.
The blue box (0.38%) displays the lung cancer incidence in the overall population, independent of risk stratification.
The red box (1.03%) indicates the proportion among individuals classified as high-risk, demonstrating a considerably higher likelihood of disease development.
This indicates that individuals we classified as high-risk are 100 times more likely to develop lung cancer over five years compared to those identified as low-risk.
Survival Matters: What the Data Shows
Our Kaplan-Meier survival analysis reveals what this means over time:
• People in the low-risk group show almost perfect survival probability over 5 years.
• Those in the high-risk group see a noticeable decline.
Can You Trust the Model?
Yes. Here’s Why.
We rigorously validated our model across cross-validation folds, demonstrating its strong predictive performance:
• The Area Under the Curve (AUC) remains consistently high over the entire five-year period, confirming the model's sustained ability to distinguish between risk groups.
• We observed a concordance index of approximately 0.88. This is notably above the 0.5 expected from random assignment (i.e., flipping a coin) and close to the ideal value of 1.0, indicating excellent model performance.
Why Knowing Your Risk Matters
If you’re at high risk and don’t know it, chances are you’ll only be diagnosed once symptoms appear, and by then, treatment options are limited.
Knowing your risk early gives you the power to take action:
• Stop smoking and avoid passive smoke).
• Measure radon in your home, as 6.7% of US homes have elevated levels.
• Avoid exposure to asbestos, as the World Health Organization (WHO) estimates about 125 million people are still exposed occupationally to asbestos.
• Eat more fruits and vegetables (20% risk reduction).
• Stay physically active (20% risk reduction).
Small changes. Big difference.
Workflow: Consumer journey
Pricing & Access
Current pricing options would include a private and a research option.
Research: The research option is a way on how to decrease the costs for consumers and make the TYO analysis affordable to anyone. This option implies that the consumer gives consent to research groups to train their models on their biological & lifestyle information to advance their internal research. Consumer data never leaves the secure & compliant TYO cloud environment during this process.
Both price points are based on a high end analysis including multi-omic. Decreasing the depth of the analysis would allow for an even more affordable pricing.
Private
2000.-
p/year
2900 Plasma Proteins
Full report
Recurrent updates
No data licensing
Research
200.-
p/year
2900 Plasma Proteins
Full report
Recurrent updates
Data licensing to research partners
Key Publications for Further Review