To forge this path, larger efforts will have to be made to deepen our understanding of biomarkers and unlock their full potential. These subtle hints can help predict disease, infection or environmental exposure by indicating that certain processes in the body are functioning abnormally. For example, elevated cholesterol levels are a marker for higher risk of heart disease. However, while routine screening of cholesterol levels is common in clinical practice, the assessment of our genetic footprint, or DNA profile, in the clinical setting is still limited to very specific conditions. Interestingly, our genes may indicate a higher risk for certain diseases, especially when environmental or behavioral factors are added to the equation. Improving our understanding of the genetic profile in different populations will expand our capacity to educate patients and prevent disease in a personalized manner.
Moreover, digital technology like wearables, smart monitoring and connected devices generate an enormous amount of round-the-clock data that can be stored and analyzed for the discovery of biomarkers. The traditional definition of biomarkers referring to biological molecules in tissue, blood, or other body fluids, has been expanding to include imaging and other kinds of data that healthcare professionals can gather predictive insights from. That’s where artificial intelligence comes in, helping to organize and analyze all of this data. Once the clinical understanding of this population data from wearables is incorporated into clinical practice, clinicians will have a wider range of options to improve their understanding of the individual patient, helping them predict and consequently prevent specific diseases.