CNC

Overview

Project Summary

Obstructive Sleep Apnea Syndrome (OSA) is one of the most common sleep disorders and its prevalence is expected to continue increasing. However, 80-90% of OSA cases remain undiagnosed. Diagnosis typically involves expensive and time-consuming sleep studies, burdening patients and leading to long waiting lists. Left untreated, OSA contributes to the development of age-related diseases such as cancer, cardiovascular, metabolic and neurodegenerative disorders. This Project addresses the pressing need for improved detection and management of OSA. Leveraging bioinformatics tools and machine learning, we have identified a set of blood molecular markers distinguishing OSA patients from healthy individuals and treated from untreated patients. The aim is to establish a highly sensitive and specific algorithm based on the previously identified alterations, enabling OSA identification, disease progression assessment and treatment response monitoring. In collaboration with the Sleep Medicine Center at the Coimbra University Hospital Center, we will apply and validate this algorithm on patient samples, facilitating early OSA detection, disease monitoring and treatment response assessment.

Main Goals

Our goal is to to establish the OSAscreener algorithm as a reliable tool for OSA diagnosis and monitoring. Specifically, we aim to validate the algorithm's performance using blood samples from healthy individuals and OSA patients, before and after treatment. We seek to demonstrate the algorithm's effectiveness in reducing diagnosis wait times and associated costs. Furthermore, we antecipate to pave the way for its implementation in public and private hospital centers, with the ultimate goal of improving patient outcomes and contributing to the development of novel therapeutic strategies for OSA.