A team at the University of Iowa has developed a sophisticated Bayesian joint model to better understand the progression of Leishmania infection. This model integrates longitudinal data and time-to-event data, providing a comprehensive approach to studying the disease. The study has been published in MORE ONE.
Dr. Félix Pabón-Rodríguez and his colleagues, including Dr. Grant Brown, Dr. Breanna Scorza and Dr. Christine Petersen, have used a Bayesian statistical framework to explore the interplay between pathogen load, responses immune responses, including antibody levels, and disease progression.
The Bayesian articulation model developed by the researchers incorporates data from a cohort of dogs naturally exposed to Leishmania infantum. This model considers multiple factors, including inflammatory and regulatory immune responses, providing a dynamic and comprehensive view of disease progression. By including measurements such as CD4+ and CD8+ T cell proliferation, along with expressions of cytokines such as interleukin 10 (IL-10) and interferon gamma (IFN-γ), the model captures the complexity of the immune response during infection.
Dr. Pabón-Rodríguez, who is now an assistant professor of Biostatistics and Health Data Sciences at Indiana University School of Medicine, highlighted the importance of their findings: “Our model not only helps understand the progression of Leishmania infection but also predicts individual diseases. trajectories. “This may be essential to develop specific treatments for canine leishmaniasis.” Furthermore, he emphasized: “By integrating multiple immune response variables, we can more accurately predict disease outcomes, which is crucial for timely and effective intervention.”
Significantly, the researchers' findings revealed that high levels of Leishmania-specific antibodies are observed in subjects with severe forms of the disease, and there is accumulating evidence that B cells and antibodies correlate with disease pathology. “By incorporating CD4+ and CD8+ T cell variables, such as proliferation and cytokine expression, we can closely model real-world disease progression,” said Dr. Pabón-Rodríguez. This detailed modeling approach underscores the importance of elements of the immune response in disease progression and potential treatment outcomes.
The model also uses a longitudinal autoregressive moving average (ARMA) approach to account for within-host variability and pathogen dynamics over time. This allows for a more nuanced understanding of how various factors interact to influence disease progression and survival outcomes. By including inflammatory and regulatory immune responses, the model provides insights into the delicate balance of the immune system in managing chronic infections such as Leishmania.
Dr. Pabón-Rodríguez emphasized the broader implications of his work: “Our approach can be adapted to study other chronic infectious diseases, providing a valuable tool for researchers in the field of infectious disease modeling.” The study demonstrates how advanced statistical models can improve the understanding of complex disease processes and ultimately contribute to the development of better therapeutic strategies.
In conclusion, this research marks a significant advance in the field of infectious disease modeling, particularly for diseases with complex immune responses such as Leishmania. The Bayesian ensemble model developed by the University of Iowa team offers a powerful framework for understanding disease progression and improving predictions of individual disease outcomes.
Magazine reference
Pabón-Rodríguez, FM, Brown, GD, Scorza, BM, Petersen, CA “Bayesian within-host joint modeling of longitudinal and time-to-event data from Leishmania infection.” PLUS ONE (2024).
DOI: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297175
About the Author
Félix Pabón-Rodríguez is an assistant professor in the Department of Biostatistics and Health Data Science at the Indiana University School of Medicine (IUSM). He graduated with his Ph.D. He graduated in Biostatistics from the University of Iowa in May 2023 and joined IUSM in July 2023. He earned his master's and bachelor's degrees at the University of Puerto Rico Mayagüez. Dr. Pabón-Rodríguez chose Indiana University because of the unique research opportunities between the School of Medicine and the Fairbanks School of Public Health.
Felix's biomedical research contributes to advancing the understanding of infectious diseases and immune responses through the application of Bayesian statistical methodologies. Some of his research work includes estimating epidemiological parameters for the Zika virus, studying immune system dynamics in relation to visceral leishmaniasis and Lyme disease, and the impact of co-infections using a Bayesian joint data model. longitudinal and survival. Additionally, he is interested in addressing health disparities with a particular focus on communicable and non-communicable diseases.
Other interests revolve around promoting diversity, equity, and inclusion in STEM education. She is dedicated to addressing the underrepresentation of minority students in STEM disciplines and improving education in statistics and data science.
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