Blood Biomarkers

Blood biomarkers are being increasingly leveraged for earlier and more personalized disease diagnosis and prognosis, moving beyond population-average reference ranges. Current research focuses on integrating lifestyle data and advanced computational methods, such as machine learning (including deep learning and random forests) and mechanistic modeling, to improve biomarker prediction accuracy and develop personalized risk stratification tools. This work is particularly impactful in areas like Alzheimer's disease and cancer, where early detection is crucial, and holds promise for improving healthcare through earlier interventions and tailored treatment strategies. The use of non-invasive imaging biomarkers, combined with blood-based markers, is also showing promise in improving prognostication.