For Tania Huedo-Medina, who was born and raised in Spain, moving to Connecticut in 2006 meant leaving behind warmer weather and fresher foods, but it also meant a new opportunity to engage in high-impact research at UConn. Huedo-Medina, an assistant professor in the Department of Allied Health Sciences (AHS), is is a biostatistician who develops and applies statistical techniques that identify variables and interactions between them that make health-related treatments and prevention interventions effective in reducing disease. She hopes her results will allow medical experts to tailor prevention and treatments to individuals. Huedo-Medina has worked with dozens of health researchers at UConn and is a major resource for those on campus seeking statistical expertise on health-related issues.
In Spain, Huedo-Medina earned her BS in psychology from the University of Murcia and her MS and PhD in biostatistics from a combined program between three Madrid universities, Complutense, Autonoma and UNED. In 2006, she was appointed postdoctoral fellow at UConn’s Center for Health, Intervention, and Prevention (CHIP), and three years later she was appointed research associate. Currently, as assistant professor in AHS, Huedo-Medina is also director of the Synthesis of Individual Participant Evidence Data (SIPED) Lab, where she works with undergraduate and graduate students and collaborates with faculty in the Departments of Allied Health Sciences, Psychology, Statistics, Kinesiology, Nutritional Sciences and other health-related disciplines across the University; at the UConn Health Center; and with other universities nationally and internationally. She believes that “if you want to do research, you need to have a strong, supportive network,” something she was able to easily build at UConn. She adds that this kind of research opportunity, “I cannot imagine . . . in Spain, unfortunately.”
With her collaborators, Huedo-Medina applies statistical methods such as meta-analysis, causality and multilevel modeling to learn about the success of health promotion interventions and treatments of chronic diseases. Meta-analysis is a methodology that combines results from different clinical studies to obtain an evidence-based and more accurate estimate of a treatment’s effectiveness and has the potential to explain health diversity. She develops causal models to analyze, for example, possible biological mediators between environmental or lifestyle characteristics and the onset of improvement of a disease. Multilevel modeling identifies correlations within related groups or over time and helps Huedo-Medina understand why treatments work similarly within the same family, the same region or other possible clusters. She applies these methods to a variety of health-related topics, focusing on, among other chronic diseases, HIV and celiac disease. She and her collaborators build models that identify which behavioral, environmental and/or biological differences between individuals, including genomics, metabolemics and microbiome, make treatments and/or health promotion interventions successful for some and less so for others.
Consider one of Huedo-Medina’s many research interests: the effects of the Mediterranean diet on cardiovascular disease. Huedo-Medina says that researchers “know that [the diet] is very beneficial, but for whom, and when and how much?” Many meta-analyses of the diet’s effects have been published, but they often don’t meet well-accepted standards of data credibility, don’t use appropriate statistical techniques and don’t disclose full methodological characteristics. She and other researchers conducted an “‘up-to-code” study, which revealed that, among other factors, if a Mediterranean diet is conducted as a long-term intervention, as a supervised program, including self-monitoring and social support from other participants, participants are less likely to develop a cardiovascular disease. This Mediterranean diet, Huedo-Medina points out, is “more like a lifestyle” than a short-term intervention.
Huedo-Medina believes this kind of lifestyle approach, where nor only the individual’s behavior but biology and environment for the long term are considered, is effective in preventing other autoimmune and nutrition-related problems, like celiac disease and obesity. Her future research goal is to create personalized models that predict these and related conditions in individuals. She will do this by integrating environmental, behavioral and biological data into her models. Multi-sourced data can indicate why two people who have the same health behaviors but slightly different genomes may have wildly different health-related problems. “You are not exactly the same as me. Everything that works for you (that could be healthy) may not work for me,” she says. Medical practitioners can use these models to identify the variables that make one patient different from another and design a prevention and/or treatment tailored to that patient within his or her community. This approach is called personalized medicine. Huedo-Medina believes that using statistical techniques to learn about individuals’ characteristics and environments are crucial in making personalized medicine more effective.
Huedo-Medina’s work has always been personal for her, too. Health is an important value in her family and her dream since since she was a child has been to bring to all the possibility of health. She says that as a child in Spain, she first realized what it meant to be healthy. On a child’s birthday, “the wish that you always want to ask for is to be healthy. If you are not healthy, you cannot work, you cannot fall in love, you cannot write. If you have your health, you can try to make any dream come true.” When she moved to Connecticut in 2006, these lessons didn’t stay behind. In many ways, Huedo Medina’s dedication to her research in biostatistics and personalized medicine constantly takes her back home and reminds her of the importance of taking care of one’s health and reducing health disparities.
Members of Huedo-Medina’s lab group include undergraduate student Katie Feeney and Ali Corso and graduate students Xiaoran Li, David Dayya, Julia Shook, Nusrat Habib and Marisa Creature.