Background: Human brain aging has received special attention in part because of the elevated risks of neurodegenerative disorders such as Alzheimer's disease in seniors. Recent technological advances enable us to investigate whether similar mechanisms underlie aging and neurodegeneration, by quantifying the similarities and differences in their genome-wide gene expression profiles.
Principal findings: We have developed a computational method for assessing an individual's "physiological brain age" by comparing global mRNA expression datasets across a range of normal human brain samples. Application of this method to brains samples from select regions in two diseases--Alzheimer's disease (AD, superior frontal gyrus), frontotemporal lobar degeneration (FTLD, in rostral aspect of frontal cortex ∼BA10)--showed that while control cohorts exhibited no significant difference between physiological and chronological ages, FTLD and AD exhibited prematurely aged expression profiles.
Conclusions: This study establishes a quantitative scale for measuring premature aging in neurodegenerative disease cohorts, and it identifies specific physiological mechanisms common to aging and some forms of neurodegeneration. In addition, accelerated expression profiles associated with AD and FTLD suggest some common mechanisms underlying the risk of developing these diseases.