This proposal will study falls in older adults with preclinical Alzheimer?s disease (AD). Conversion to symptomatic AD is not an immediate process; rather, it develops slowly over a series of preclinical stages. Although symptomatic AD is characterized by progressive cognitive problems, growing evidence suggests that functional mobility abnormalities and an increase in falls may precede cognitive impairment. Falls are a leading cause of morbidity and mortality in the older adult population. Older AD patients have more than twice the risk of falls compared to the general population. Measures of everyday function are currently not included in the evaluation of cognitively normal individuals with preclinical AD. Within a well-characterized cohort of older, cognitively normal individuals (>65 years old) who are followed by the Knight Alzheimer?s Disease Research Center, we will perform longitudinal in-home assessments to quantitatively evaluate falls and functional mobility. We hypothesize that brain neuropathology (e.g., the presence of amyloid and/or tau proteins, evidence of neurodegeneration) lead to changes in functional connectivity of brain networks. These brain network changes affect both central nervous system markers (e.g., cognitive performance) and peripheral nervous system markers (e.g., balance, strength, vision, and sensation). We suggest that a synergistic interaction between central and peripheral changes leads to altered functional mobility, resulting in an increase in falls. Our findings will advance the field of AD by identifying novel ?real-world? biomarkers in preclinical stages of AD. Assessment of falls and gait instability in the home setting could potentially serve as an inexpensive and non-invasive screening tool to identify individuals at greatest risk for conversion to symptomatic AD. This may have important implications for the timing of interventions in secondary prevention trials in AD.
Alzheimer?s disease (AD) is a progressive dementia affecting more than 5 million Americans and is characterized by a slow progression through a series of preclinical stages. Falls are a leading cause of morbidity and mortality in older adults, the frequency and severity of which may serve as an indicator of preclinical AD. Here, we evaluate whether fall probability and concurrent brain network changes in an elderly population can predict disease progression, enabling earlier therapeutic interventions for those most at risk for developing AD.
