The immune system is a complex network of cells and proteins, and understanding its overall health status remains challenging. Researchers led by John Tsang at Yale University developed a comprehensive test analyzing gene expression, immune cells, and over 1,300 proteins from blood samples of individuals with immune diseases and healthy volunteers. They employed machine learning to correlate these measurements with health metrics, enabling the creation of an immune health score called the immune health metric (IHM).
The resulting IHM aligns with other health indicators such as disease resistance and response to treatments, suggesting potential for early identification of disease risks including cancer. The complexity and variability of the immune system make defining “healthy” a challenge, but this test attempts to objectively quantify immune health across a spectrum. The use of extensive biomolecular data and machine learning distinguishes this approach from typical clinical blood tests.
Despite promising results published in Nature Medicine, the immune health test is not yet ready for widespread clinical application. Further validation and development are required before it can help explain varied patient responses to vaccines and therapies or serve as a diagnostic tool. Meanwhile, research continues to deepen understanding of immune health assessment and its implications for personalized medicine.
👉 Pročitaj original: MIT Technology Review – AI
