Researchers at the University of California have used algorithms to identify potential blood-based markers of Alzheimer’s disease. The discovery could lead to earlier diagnoses and spur non-invasive ways to track the disease’s progression, they said.

When scientists ran data through an algorithm developed by Assistant Professor Greg Ver Steeg, Ph.D., distinct clusters of relationships emerged. Amyloid and tau were found to be important. But the algorithm also revealed strong relationships with cardiovascular health, hormone levels, metabolism and immune system response.

“In a very large database of health measures, it helped us discover predictive features of Alzheimer’s disease that nobody suspected were there,” said Paul Thompson, Ph.D., a neuroscientist at USC who helped conduct the investigation.

Investigators studied medical data from 829 older adults from the Alzheimer’s Disease Neuroimaging Initiative database to identify predictors of cognitive decline and brain atrophy over a one-year period. Participants fell into three diagnostic categories: cognitively normal, mild cognitive impairment and those with Alzheimer’s disease. The data included more than 400 biomarkers collected from brain imaging, genetics, plasma and demographic information.

Full findings appear in Frontiers in Aging Neuroscience.