Molin Yue, a graduate student in the Department of Biostatistics and Health Data Science, was driven to enter asthma research because of his fascination with biology, a passion for solving real-world clinical problems and a desire to translate his undergraduate biological studies into practical clinical applications. "I wanted to bridge the gap between biology and practical clinical applications, particularly in pediatric asthma diagnosis,” he says about the decision to go to graduate school.
At Pitt Public Health, the opportunity to work both as a graduate student researcher and as a teaching assistant allowed Yue to directly contribute to pressing medical challenges. In particular, he was drawn to a new and evolving area of asthma research: the development of a nasal swab test to diagnose different subtypes of asthma in children. As lead statistician, Yue’s expertise in data analysis and interpretation played a pivotal role in understanding the nasal swab samples. In this project, he was also responsible for guiding clinicians on how to properly interpret the data and apply statistical methods to the results. “The power of data analysis in uncovering medical insights was crucial, and it was rewarding to see how our work could potentially change asthma diagnosis for the better,” Yue says.
Despite the excitement of working on such an innovative project, Yue faced a series of challenges during the research process. One major hurdle was the need to resubmit the study after the initial submission. Feedback indicated that the analysis lacked certain details and required more comprehensive external validation through secondary sources. “Facing feedback that required us to refine our analysis was tough, but it pushed me to be more meticulous. The revision process taught me the importance of validation and thorough data interpretation, and in the end, it made our findings even stronger.” The work was ultimately published in JAMA with Yue as first author on the paper Transcriptomic Profiles in Nasal Epithelium and Asthma Endotypes in Youth.
Among the most rewarding findings of the research was the discovery regarding the prevalence of T helper 2 (T2)-high asthma. Despite T2-high asthma being regarded as the most common form in asthma research, the study found it to be the least prevalent among the youth participants. “That was a surprising and valuable insight,” Molin remembers, suggesting that many T2-high asthma patients may have been misdiagnosed. “It made me realize how critical it is to have accurate, data-driven methods to diagnose asthma subtypes. Misdiagnosis could lead to ineffective treatments, and our findings could change that.” The study also revealed the importance of recognizing asthma’s different endotypes, such as T17-high and T2-low/T17-low, allowing clinicians to better tailor treatments for patients. The use of nasal swabs to identify these asthma subtypes presents a non-invasive diagnostic tool, paving the way for more personalized and effective asthma management.
“This project was a real turning point in my career. It not only sharpened my skills in biostatistics but also deepened my appreciation for how data science can directly impact health care. It’s made me even more passionate about pursuing a career in health data science,” Yue reflected. While his academic coursework equipped him with foundational knowledge, this project challenged him to think critically, refine his skills, and explore the intersection of data science and health care. The study not only helped him grow as a researcher but also reshaped his perspective on his career in science, solidifying the desire to be a health data biostatistician.
“Working alongside experts like Drs. [Juan] Celedón and [Wei] Chen showed me the value of collaboration in medical research. It’s a reminder that progress happens when we bring together diverse expertise to tackle complex issues.”
-Calvin Dziewulski