Huskies in Michigan Tech's statistics bachelor's degree program don't just participate in impactful research, they take the lead — like spring 2026 graduate Aili Toyli, whose work was recently published in the Journal of the American Heart Association.
In recent years, medical researchers have found correlations between cardiovascular disease and Alzheimer's disease that indicate the two conditions may have some common traits in their heart-brain interaction. Aili Toyli ’26, who earned her bachelor's degree in statistics from Michigan Technological University in April, took a deep dive into data on those correlations as an undergraduate. Her findings were published this month in a peer-reviewed research article in the Journal of the American Heart Association.
"Since both of these conditions are very chronic, long-term diseases, it's hard to untangle how they're tied together," said Toyli. "In this paper, specifically, we looked at different types of cardiovascular disease, and which of those subtypes have the strongest association with Alzheimer's disease."
Toyli was credited as the lead author of the study, titled "Cardiovascular Disease Subtypes and Alzheimer's Disease: Phenotypic and Genetic Associations in the UK Biobank and All of Us Research Program".
One of Toyli's co-authors is Weihua Zhou, associate professor of health informatics and applied computing at Michigan Tech, who introduced Toyli to the research topic after reading a paper in Science magazine.
"The authors of that study analyzed the correlations between cardiovascular disease and Alzheimer's disease, but they didn't specifically analyze the different types of cardiovascular disease," said Zhou.
About the Researcher
Weihua Zhou
whzhou@mtu.edu
906-487-2666
Areas of Expertise
- Image Processing and Computer Vision
- Machine Learning
- Medical Image Analysis
- Health Informatics
- Text Mining
Disease subtypes are specific categories of a broader disease, classified based on underlying causes, genetic profiles or symptom patterns. Because individual cardiovascular diseases have different biological processes, Zhou and Toyli suspected that identifying which subtypes are more strongly associated with Alzheimer's disease could help reveal which factors are tied to dementia risk. Though Toyli's research doesn't identify those risk factors directly, it does point to biological pathways whose potential connection to Alzheimer's merits further research.
"We believe there are so many causes of cardiovascular disease, and each subtype may have specific correlations with Alzheimer's disease," said Zhou. "So Aili investigated it and published the paper."
Collaborating for Statistics' Highest Clinical Value
Toyli's paper in the Journal of the American Heart Association is her third medical research publication on the topic of dementia. It was preceded by her first publication comparing cerebral blood flow in patients with dementia caused by Lewy bodies and Parkinson's disease in the journal Nuclear Medicine Communications in 2024, and her second publication exploring interconnections between cardiovascular disease and Alzheimer's in Frontiers in Cardiovascular Medicine in 2025.
Toyli's interest in Alzheimer's began close to home.
"My family has pretty high rates of Alzheimer's disease. So I am very familiar with the disease and the burden it can place on people and their families," said Toyli.
Her first opportunity to participate in research on the subject began in her first year at Michigan Tech. As a new Husky, she attended an undergraduate research forum where Zhou spoke about how data and statistics are applied to clinical problems in his lab.
"I've always been really driven by an interest in medical applications of statistics, so I connected with Dr. Zhou after that presentation and started getting involved in his lab," said Toyli.
She also connected with Qiuying Sha, a distinguished professor of mathematical sciences at Tech, who has collaborated with Zhou on data mining for medical research since 2019 through the University's Health Research Institute.
Zhou is continuously looking for statistics majors to participate in his research. Sha, as a mathematics professor, supports the intersection between students' need for hands-on experience with statistical interpretation and the large medical datasets necessary for both her work and Zhou's.
"Dr. Sha provided a lot of statistical guidance for methodologies that I should use throughout the project," said Toyli.
"Aili is very proactive," said Sha. "She's amazing at data analysis, statistical methods, and how to interpret results. It's inspiring to see her accomplishments."
About the Researcher
Qing-Hui Chen
qinghuic@mtu.edu
906-487-1629
Research Interests
- Cardiovascular physiology, neurophysiology and exercise science.
- Neural mechanisms of cardiovascular diseases and metabolic disorders.
- Neural mechanisms of exercise training in regulating autonomic and cardiovascular function.
In addition to Sha's mathematical expertise, the research included collaboration with Qing-Hui Chen, a Michigan Tech professor of kinesiology and integrative physiology, who contributed guidance on cardiology, neurology, and the project's clinical value.
"I appreciate the collaborative environment at MTU," said Zhou. "We have a lot of clinical research, and we hope our discoveries can be used to really improve patient management and outcomes. We need clinical help, and we also need the statistical analysis to consolidate our findings."
The Data of Dementia and Cardiovascular Correlation
Supported by an R15 grant of $429,153 from the National Institutes of Health and by Michigan Tech's Undergraduate Research Internship Program, Toyli began her research on cardiovascular disease and Alzheimer's disease correlations using two datasets — the U.K. Biobank and All of Us. The first is considered a gold-standard population-based dataset with many variables and a highly structured study design. However, participants in that dataset are not particularly diverse and are known to be healthier than average. All of Us is an American database, pulled from a more diverse pool of patient health records with identifying information removed.
With her datasets selected, Toyli first determined which cardiovascular disease subtypes had more than 10,000 cases in those sets, wanting to ensure the types she focused on had plenty of data to analyze. Then, she looked through existing papers to find common covariates — variables tied to both heart disease and dementia — to adjust her analysis for the most common factors that affect both diseases.
"I started by building an odds ratio test," said Toyli. "It's a statistical method that looks at how much more likely people are to have Alzheimer's if they have each of these cardiovascular disease subtypes."
As the project progressed, she re-analyzed the data using various models to ensure her methods were robust and to confirm her results. With the support of her faculty co-authors, Toyli read existing studies, decided on statistical methods to use, wrote code to analyze the data, and wrote the draft for the manuscript later published in the Journal of the American Heart Association.
"She moves very confidently from learning concepts to applying them independently in research," said Sha. "She came up with ideas and then asked if they were applicable or if I had any input. When she encountered challenges, she took the initiative to explore the solutions on her own before coming back with thoughtful questions."
About the Researcher
Toyli's work on the project was no small feat for an undergraduate student balancing a full course load, a job tutoring in Tech's Math Learning Center, and a diligent training schedule with the Rowing Club.
"We have multiple successful undergraduate researchers, and Aili definitely is one of our most successful students," said Zhou. "Aili showed independence, maturity, and put in a lot of effort in our discussions with the interdisciplinary team."
Questions Lead to Research; Research Leads to Questions
Toyli's study, published June 10, shows associations between Alzheimer's disease and nearly every type of cardiovascular disease examined, except myocardial infarction, commonly known as a heart attack, when blood flow to the heart is greatly reduced or blocked, leading to heart muscle damage. The strongest correlation revealed in Toyli's research connects Alzheimer's with hypotension, or low blood pressure. A lot of existing research focuses on hypertension — high blood pressure — which her study also strongly associated with dementia.
Toyli's study suggests that more research is needed into the correlation between dementia and low blood pressure. It's been found that lower blood flow to the brain, which could occur from low blood pressure, can cause Alzheimer's disease risk to increase, but also that having Alzheimer's disease can impair the brain's regulation of other parts of the body, which could then cause low blood pressure.
"Our study didn't determine a causal relationship between low blood pressure and Alzheimer's disease," said Toyli. "That's another case of where our research left us with a lot more questions and things that we can look at in the future."
Zhou is interested in taking Toyli's findings from a correlational study to a causal inference study, meaning investigating a possible causal relationship between the two diseases.
"This is a very hot topic, researching cardiovascular disease and Alzheimer's disease and whether there are any associations," said Zhou. "What was the causal factor? We still have lots of ideas that we can work on."
Communicating her findings to other researchers — both during the process and through her study's publication — Toyli is strongly optimistic about the difference this type of collaboration between clinicians and statisticians could make in medical research as a whole. She plans to begin pursuing her master's degree in biostatistics at the University of Iowa this fall, bringing with her the love of collaborative research she fostered at Tech.
"One of the big challenges in the process was learning to communicate the analytical methods we're using to our clinical collaborators so they can understand our findings," said Toyli. "Similarly, we as statisticians have to understand their clinical interpretations and the medical significance of the data we're working with. It's rewarding to work with people from different disciplines and learn from them."
Michigan Technological University is an R1 public research university founded in 1885 in Houghton, and is home to nearly 7,500 students from more than 60 countries around the world. Consistently ranked among the best universities in the country for return on investment, Michigan's flagship technological university offers more than 185 undergraduate and graduate degree programs in science, technology, engineering, mathematics, computing, forestry, business, health professions, robotics, psychology, social sciences, humanities, and the arts. The rural campus is situated just miles from Lake Superior in Michigan's Upper Peninsula, offering year-round opportunities for outdoor adventure.
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