With the aging of the US population and high costs associated with caring for the cognitively impaired elderly, identifying older individuals at greatest risk for developing Alzheimer's disease (AD) is of utmost societal and clinical importance. Currently no treatments effectively slow the progression of AD or other causes of cognitive decline, but billions of dollars are being invested to develop effective treatments. Development of inexpensive, genomic methods that go beyond APOE to identify individuals at risk for cognitive decline has the potential to greatly accelerate the development of new treatments. The overall significance of this project is that it will use a validated polygenic score to more comprehensively understand when older men and women are at highest risk for cognitive decline and Alzheimer's neurodegeneration. Using an age-dependent approach, we have recently developed and validated a novel `polygenic hazard score' (PHS) for quantifying AD dementia age of onset, even among APOE E3/3 individuals, who constitute the majority of all US individuals with AD dementia. In this proposal, our objective is to comprehensively assess whether sex differences influence how PHS predicts cognitive decline and AD neurodegeneration and how age influences this relationship. Leveraging genotypic and multi-modal phenotypic data from several existing NIH-funded cohorts (total n > 7,000), we will investigate whether age-dependent sex differences modify cognitive decline, postmortem and in vivo AD neuropathology and medial temporal lobe volume loss.
This study will improve our understanding of how sex differences impact AD pathobiology. Beyond APOE, it is anticipated that the results of this study will facilitate identification and recruitment of participants into clinical studies aimed at slowing cognitive decline and development of AD and dementia. Furthermore, the methods developed by this highly innovative project have direct application to those providing healthcare for older adults, and these validated genetic measures can be deployed for widespread use in many healthcare settings to screen for AD risk in the future.
