Fighting Alzheimer’s Disease with Statistics
Hopkins researcher develops a statistical approach to shed light on Alzheimer’s disease.
The United States is facing unprecedented growth in its population of older adults. In 2040, people aged 65 and older are expected to account for 22% of the American population, up from 14% in 2013. This aging of the US society will require novel ways to meet the associated increase in health needs. Zheyu Wang, assistant professor of oncology at Johns Hopkins, is committed to addressing one of those needs – better care for Alzheimer’s disease – using a sophisticated statistical approach.
Alzheimer’s disease, a debilitating form of dementia affecting approximately 5 million Americans, is the sixth leading cause of death in the US. Memory loss is often the first symptom of the disease, and, over time, patients can lose the ability to have conversations or perform basic tasks. Alzheimer’s disease is a particularly worrisome condition because its prevalence will only grow as the US population ages and because there is no cure. Treatments help manage the disease and its manifestations, but do not stop its progression.
Dr. Wang’s research is intended to lay the groundwork for more effective treatment of Alzheimer’s disease. Currently, the condition has an asymptomatic phase, followed by a stage of mild cognitive impairment that ultimately culminates as Alzheimer’s dementia. Treatment begins after the onset of clinically recognizable symptoms, even though deleterious changes in the brain occur during the asymptomatic phase. Believing that treatment might be more effective if begun before symptoms emerge, Dr. Wang studies several imaging and cerebrospinal biomarkers that may be able to reveal abnormal changes that lead to mild cognitive impairment in the brains of asymptomatic individuals.
To do this, Dr. Wang developed an innovative statistical approach that reflects that brain changes during the asymptomatic phase cannot be measured directly and that each biomarker provides only partial information that may be prone to error. She then built a model that combines known Alzheimer’s risk factors, cognitive tests, as well as the imaging and cerebrospinal biomarkers, to determine which people with normal cognition are likely to have asymptomatic disease. The pattern of disease-associated biomarkers and risk factors derived from the model becomes the basis from which Dr. Wang assigns a personalized risk score that predicts the likelihood an asymptomatic individual will be diagnosed with mild cognitive impairment.
If successful, Dr. Wang’s approach could result in a breakthrough for the earlier diagnosis of Alzheimer’s disease. It can also accelerate treatment development by enabling clinical trials that feature the recruitment of groups of individuals at high risk for Alzheimer’s and the use of biomarkers as intermediate outcomes.