Posted December 19, 2019

If we look at how the blood changes during aging and in Alzheimer’s disease, could we learn something about the brain?

New research shows that measuring proteins in the blood may predict both health and lifespan. The research was published in Nature Medicine with support from a grant from Cure Alzheimer’s Fund. Tony Wyss-Coray, PH.D. and a team of scientists have used advances in protein profiling technology to measure thousands of proteins in the plasma at points across the human lifespan in order to determine what profiles are associated with healthy aging and with disease.

Blood has cells that transport oxygen, fight infectious disease, and carry messenger molecules with information across organ systems. The blood contains hormone-like factors that promote growth and survival; the composition of these factors changes during aging and with disease. The hormone-like factors involved in cell injury, repair, and inflammation increase during aging while those involved in the maintenance and development of tissue decrease with age. Blood tests are beginning to be used as a diagnostic and prognostic tool for many diseases including cancer, brain trauma, and heart failure, as well as amyloid plaque levels in the brain.

Using a technology called SOMAmer the researcher team measured the levels of nearly 3,000 different plasma proteins from more than 4,000 healthy individuals ages 18 – 95. As a key part of the study, the team identified 373 proteins in the blood that showed consistent changes with age across the lifespan of both mice and humans. Using machine learning techniques, this protein change data was used to train an artificial intelligence tool to predict biological age based on a sample donor’s protein profile.  In validation testing, the tool predicted a biological age that was younger than a person’s chronological age for individuals who were indeed healthier both cognitively and physically than individuals of the same chronological age but for whom the tool predicted a correct or older biological age.  The authors hope that this proteomic clock may someday be used to identify individuals at risk for disease.