Osteoarthritis (OA) in the knee is a leading cause of disability worldwide, affecting 500 million people and becoming more common as the population ages. By the time symptoms like pain and stiffness appear, the damage is often irreversible. Detecting OA early, before severe joint damage occurs, is crucial to slow its progression and improve outcomes—especially for at-risk groups like those over 40, athletes, or people with obesity.
Current tools like MRIs and X-rays are expensive and only helpful in advanced cases, and affordable early detection methods are lacking. Scientists are exploring biomarkers—molecules like proteins or microRNAs (miRNAs) found in blood or tissue—that can indicate early OA. These biomarkers reflect the molecular changes in the joint before symptoms develop. However, finding reliable biomarkers is challenging because most detection methods focus on abundant, non-specific molecules, making it harder to identify disease-specific ones.
Recent research suggests that proteins and miRNAs may be used as early warning signals for OA, especially when tailored to different types (or phenotypes) of OA, such as those caused by joint injuries, inflammation or metabolic issues. Identifying these molecular patterns can help personalize treatments instead of using a “one-size-fits-all” approach.
The use of cutting-edge technologies like sequencing and machine learning can be very helpful to develop biomarkers in large, long-term studies. Creating affordable, accessible tests for early detection and better patient-specific therapies is also crucial. Researchers are also studying how society perceives AI-driven health tools and working with patient groups to encourage acceptance of these innovations. The ultimate goal is to enable early intervention, reduce the impact of OA, and improve the quality of life for patients.
Osteoarthritis (OA) is a widespread condition causing pain, disability, and significant socioeconomic burdens. This project focuses on reducing OA’s impact by developing tools for early detection and personalized treatment, aiming to improve patient care, reduce healthcare costs, and advance scientific understanding.
Early intervention is a key goal, allowing timely treatments like lifestyle changes, physical therapy, and disease-modifying therapies to slow progression and preserve joint function. By stratifying patients into subgroups based on molecular profiles, targeted therapies can optimize effectiveness and minimize side effects. Monitoring molecular markers in blood ensures treatments are effective and refined over time.
The project aims to improve outcomes by reducing pain, preserving joint function and enhancing quality of life. Addressing OA early can also lower healthcare costs by preventing expensive treatments like joint replacement surgery. Additionally, identifying molecular markers will uncover new insights into OA’s mechanisms, paving the way for innovative treatments.
Public health benefits include improved population health and reduced productivity losses due to OA-related disability. Advanced technologies like AI and machine learning play a crucial role, aiding in disease prediction and treatment optimization. These methods could also extend to other medical conditions.
The project creates economic opportunities by developing diagnostic tools that open new markets for partnering companies. Improved patient management workflows and early detection empower individuals to make informed decisions about their health. Advocacy efforts aim to influence policymakers to prioritize OA care, while community engagement fosters solidarity and support among patients and caregivers.
By combining cutting-edge research, advanced technologies and patient-centred strategies, this initiative seeks to alleviate the burden of OA and create lasting societal impact, improving the lives of millions of people worldwide.
The new technologies developed within CirBioCare project will bring significant benefits to both patients and the medical community by transforming the way osteoarthritis (OA) is understood, detected and treated. These advancements are poised to improve patient outcomes and equip healthcare professionals with innovative tools and insights.
The ability to detect OA in its early or asymptomatic stages will enable timely interventions, preventing disease progression and reducing pain, disability, and long-term complications.
Molecular panels identifying patient subtypes will allow for tailored treatments, ensuring therapies to be more effective and targeted to individual needs.
Insights into the interaction between joint tissues, including the infrapatellar fat pad (IPFP), will lead to the development of new and more effective therapies.
Increased public awareness of OA will empower patients to seek early screening and adopt preventive measures, improving their quality of life.
Early and precise interventions will help patients maintain joint function, reduce suffering and enhance overall well-being.
Clinicians will gain access to cutting-edge technologies for complex molecular characterization, enabling the identification of low-abundance biomarkers associated with OA.
Molecular panels will help predict OA in at-risk patients and stratify existing patients into subtypes, supporting better clinical decisions and treatment planning.
Insights into the molecules involved in OA will reveal potential therapeutic targets, paving the way for innovative treatments.
Enhanced understanding of how to integrate AI-informed OA screening into healthcare systems will optimize patient management and improve efficiency.
Dissemination through publications, conferences, and public outreach will promote the adoption of CircBioCare therapies and encourage its widespread use in clinical practice.
By bridging cutting-edge research with clinical application, this project offers a holistic approach to tackling OA, ultimately benefiting millions of patients while advancing medical knowledge and healthcare practices.