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Professor Mingfei Li awarded NIH grant to analyze data about the degenerative brain disease
On any given day, roughly 1,300 Americans develop Alzheimer’s, a degenerative brain disease characterized by memory loss, personality changes and dementia. With more than 5.8 million Americans currently living with the disease, Alzheimer’s now ranks as the sixth-leading cause of death in the nation — and the only one without a definitive way to prevent or stop its progression.
Mathematical Sciences Professor Mingfei Li is hoping to change that. A sought-after statistician, Li is part of a team of researchers recently awarded a $2 million, five-year grant from the National Institutes of Health (NIH) to study the disease. Working with colleagues at the Alzheimer’s Disease Center at Boston University, Li hopes to harness the power of Big Data — larger, more complex and highly diversified data sets — to determine whether current Food and Drug Administration-approved medications may actually play a role in averting or delaying the onset of Alzheimer’s.
Li’s study will focus on four drugs widely used by Americans ages 50 and older: angiotensin-converting enzyme (ACE) inhibitors, which treat hypertension by dilating blood vessels; metformin, an oral diabetes drug; beta blockers, which regulate epinephrine levels to treat high blood pressure; and statins, which not only inhibit the production of cholesterol but also increase the liver’s ability to rid itself of the artery-clogging fat. Why these particular drugs? “There are so many medications, we can’t test them all,” Li explains, noting that there are currently more than 20,000 FDA-approved drugs on the market. “So what we’ve done is take an ocean of information and narrow it down to a trickle” by focusing on the most commonly prescribed medications for older Americans.
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With these parameters in place, Li next turns to Big Data. Since 2011, Li has worked with the U.S. Department of Veterans Affairs (VA) as a senior statistician and co-investigator, mining complex data sets to support research into bipolar disorder, schizophrenia, and PTSD and dementia, among other mental conditions. Her new study will also utilize VA statistics, employing the agency’s national database, which “contains information about tens of millions, if not hundreds of millions, of individuals across the country.” This is significant, Li says, because the VA system features more extensive patient histories: “Every inpatient or outpatient visit, every medication prescribed, every lab test ordered — it’s all there.”
Specifically, Li will search patient records to identify people diagnosed with traumatic brain injuries. Most commonly caused by falls, motor vehicle-related collisions and sports injuries, serious head trauma increases a person’s risk of developing Alzheimer’s. Next, Li will isolate data for patients who are prescribed ACE inhibitors, metformin, beta blockers and/or statins, and search for patterns connecting drug dosage and duration with diagnosis.
“Alzheimer’s typically develops gradually, so it’s been difficult for researchers to trace a complete patient history,” Li explains. “We’ll be doing that here, though, looking specifically to see if any combination of these four drugs can prevent or prolong the onset of Alzheimer’s.” She further notes that these medications are just the tip of her analytical iceberg; as her research progresses, Li anticipates expanding the pool to include additional FDA-approved drugs.
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Her approach is unusual, in that it will “break the usual research process.” As Li notes, statistical analysis tends to be introduced in later stages of therapeutic drug development, after a medication has already been tested on animals in a lab or through clinical trials. On this project, however, the data takes center stage: Li’s findings will provide the foundation upon which her co-grant recipients will frame their subsequent lab experiments.
In the past five years alone, the NIH has spent nearly $16 billion on research for Alzheimer’s and related dementias, with scientists today still struggling to identify a cause, let alone a cure. Li is hopeful that, by demonstrating therapeutic benefits of existing medications, her study will provide a focal point for researchers. “I have such sympathy for patients with mental disease,” she says. “I hope our results will be helpful.”