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MIRACLE · Project

AI-Driven Genetic Risk Prediction for Heart Disease and Stroke Prevention

healthPrototypeTRL 3

Imagine if your doctor could see a 'weather forecast' for your heart years before a storm hits. Instead of just looking at your age or smoking habits, this tool scans your DNA and blood markers to find hidden warning signs. It helps doctors treat men and women differently because their bodies react to heart disease in unique ways.

By the numbers
10%
improvement in accurate classification of high-risk patients
17
potential new drug targets
233
genetic regions linked to increased risk
1125
patients analyzed for gene activity in plaques
5
distinct plaque types discovered
The business problem

What needed solving

Current heart disease risk models (like SCORE2) are too generic and miss genetic drivers, leading to unexpected strokes or heart attacks in patients who seemed 'low risk'.

The solution

What was built

A multi-omics prediction model that combines genetic data (PRS) and blood biomarkers to identify high-risk cardiovascular patients.

Audience

Who needs this

Cardiovascular drug developersPrecision medicine diagnostic labsHealth insurance underwritersPersonalized health screening clinics
Business applications

Who can put this to work

Pharmaceuticals
enterprise
Target: Drug Discovery Firm

If you are a drug discovery firm dealing with high failure rates in cardiovascular trials — this project identified 17 potential new drug targets, including HTRA1, that can be used to develop targeted therapies for atherosclerosis.

Diagnostics
mid-size
Target: Clinical Laboratory

If you are a clinical laboratory dealing with outdated risk scores like SCORE2 — this project developed a method integrating polygenic risk scores that provides a 10% improvement in accurately classifying high-risk patients.

Digital Health
SME
Target: Health-Tech App Developer

If you are a health-tech developer dealing with generic health advice — this project identified five distinct plaque types and sex-specific patterns that allow for personalized risk stratification tools.

Frequently asked

Quick answers

What is the cost or pricing for implementing these risk models?

Based on available project data, there is no specific pricing or cost information provided for the implementation of these models.

Can this be scaled to an industrial level for millions of patients?

The project utilizes the globally largest CAD, PAD, and stroke GWAS information, suggesting the underlying data is scalable, though industrial deployment details are not specified.

What is the IP and licensing status of the 17 drug targets?

Based on available project data, the specific licensing terms for the 17 identified drug targets have not been disclosed.

How does this integrate with current clinical workflows?

The project aims to enhance existing tools like SCORE2 by adding polygenic risk and circulating biomarkers to the diagnostic process.

What is the timeline for the final results?

The project period runs from 2023-10-01 to 2027-09-30, with final results expected by September 2027.

Consortium

Who built it

The consortium is heavily academic, consisting of 9 partners across 6 countries, with 8 universities and 1 other organization. There are 0 industry partners and 0 SMEs, indicating the project is currently focused on fundamental discovery and validation rather than immediate commercialization.

How to reach the team

Contact University of Helsinki (SUOMEN YLIOPISTO) in Finland

Next steps

Talk to the team behind this work.

Contact us to bridge the gap between these 17 drug targets and your R&D pipeline.

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