SALIVARY MICROBIOME SIGNATURE FOR PRECISION CARDIOVASCULAR DISEASE RISK PREDICTION

LICENSING & PARTNERING OPPURTUNITIES


Traditional cardiovascular disease (CVD) diagnostics rely on invasive blood-based markers, presenting a hurdle to early detection. In a quest for non-invasive precision medicine diagnostics tools, Sidra Medicine’s inventors have uncovered a game-changing solution which predicts a high risk to develop CVD using a Salivary Microbiome Signature.

The Problem

The intricate interplay between our microbiome and health has emerged as a frontier in medical research. In the realm of cardiovascular diseases (CVD), where the stakes are high, traditional diagnostic methods often fall short in capturing the nuanced indicators of risk. While blood-based biomarkers offer insights, they are invasive and often lack the precision required to unravel the complexity of CVD and its interconnectedness with overall health.

As our understanding deepens, the need to harness the potential of the microbiome as a marker for various diseases, including CVD, becomes increasingly apparent. The conventional methods, focusing on invasive blood tests, struggle to provide a comprehensive picture, leaving a critical gap in early detection and preventive strategies.

The Solution

Our innovative solution lies in the uncharted territory of saliva—the Salivary Microbiome Signature. This groundbreaking approach capitalizes on the richness of microbial diversity in saliva to predict the risk of developing CVD. Pioneering the use of machine learning, our Salivary Microbiome Signature sets a new standard for precision diagnostics.

This non-invasive signature, specifically designed for the Arab population, not only propels early detection but also opens avenues for targeted interventions.

The Advantages

  • Non-Invasive Precision: Sidestep the discomfort of invasive procedures with a simple saliva test.
  • Arab Population Focus: Tailored microbial markers cater to the unique genetic makeup of the Arab population.
  • Machine Learning Mastery: Move beyond traditional diagnostics with machine learning algorithms, ensuring unparalleled accuracy.

Inventors

Annalisa Terrengra

Sidra Medicine

Mohamed El Anbari

Sidra Medicine

Selvasankar Murugesan

Sidra Medicine

Souhaila Alkhodor

Sidra Medicine