Alzheimer’s disease, a terminal neurodegenerative disease, has historically been diagnosed based on observing significant memory loss. Recent research has shown that a biological marker associated with the disease, a peptide called amyloid-beta, changes long before any memory-related issues are apparent.
Examining the concentration of the peptide in an individual’s spinal fluid provides an indication of risk decades before any memory related issues occur. Unfortunately, accessing spinal fluid is highly invasive, requires an anaesthetist and is expensive to conduct on large segments of the population. Hence, there is a strong effort in the research community to develop a less invasive test, such as a blood test, that can yield information about Alzheimer’s disease risk.
A recent paper by my team at IBM Research – Australia, published today in Scientific Reports, used machine learning to identify a set of proteins in blood that can predict the concentration of amyloid-beta in spinal fluid. The models we built could one day help clinicians to predict this risk with an accuracy of up to 77%.
– Scientific Reports – A blood-based signature of cerebrospinal fluid Aβ1–42 status (Published: 11 March 2019)