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2017 Grants - Graham
Salivary Biomarkers of Alzheimer's Disease
Stewart Francis Graham, Ph.D.
William Beaumont Hospital
Royal Oak, Michigan
2017 Alzheimer's Association Research Grant (AARG)
Can biological changes in saliva indicate which people are at risk for Alzheimer's disease?
Healthy brains require energy to function properly, and the chief source of this energy is glucose (sugar). When the body breaks down sugar to produce energy, it converts some of the sugar into compounds called metabolites. These compounds are normally transported throughout the body to supply cells to carry out vital functions. In Alzheimer's disease, however, metabolites can accumulate abnormally in certain parts of the body, where they may impair energy production and other brain activities.
Most researchers who study this problem focus on metabolites that collect in the blood, urine, or cerebrospinal fluid (the fluid that surrounds the brain). However, Stewart Francis Graham, Ph.D., and colleagues have found that metabolite accumulation in saliva can represent an increased risk of Alzheimer's disease and mild cognitive impairment (MCI), a condition of subtle memory loss thought to precede Alzheimer's.
For their research grant, the investigators will conduct a larger study to verify and expand on their earlier results. They will acquire saliva samples from people with Alzheimer's and MCI, as well as healthy individuals. They will then use sophisticated imaging and analytical tools to determine which metabolites may act as biomarkers (or biological indicators) that can predict the development of MCI and dementia. The team will also determine whether these "salivary biomarkers" are related to other indicators of dementia risk, such as the Alzheimer's-associated gene variant APOE-e4.
The results of Dr. Graham's efforts could refine our understanding of how metabolite dysfunction affects brain health as we age and suggest new targets for therapies. It could also identify a non-invasive, cost-efficient technique for diagnosing Alzheimer's at its earliest stages — when treatments can be most effective. Additionally, it may be used to track the effectiveness of new treatments before any symptoms occur.