Chronic diseases are the #1 killer worldwide, costing millions of lives and trillions of dollars every year. Chronic systemic inflammation is a central underlying cause of many of these disorders. This is distinct from acute local inflammation, which is short-term and promotes tissue repair. By contrast, chronic systemic inflammation is persistent and damages the tissue.
To search for biomarkers of chronic systemic inflammation, Furman’s lab monitored 1000 people over many years, and applied deep learning approaches (specifically an “autoencoder”) to crunch the data. They generated a GAE-based method for predicting the biological age of a subject based on the levels of inflammatory mediators called cytokines. This “inflammatory age” correlates much better with multi-morbidity (i.e., the propensity to develop multiple life-threatening age-related diseases) than chronological age.
Even in a population with no known risk factors for cardiovascular disease, a single inflammatory factor correlated with cardiovascular aging: CXCL9, produced by aging endothelial cells, which suppresses the pro-longevity protein SIRT3.
Furman is planning follow-up studies in which inflammatory age is compared with other aging “clocks” such as DNA methylation.
Pioneer: David Furman (Visiting Associate Professor/Stanford)
Agent: Chris Patil (Hourglass)