Bayesian Approach Enhances Understanding and Prediction of Leishmania Disease

A team from The University of Iowa have 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 study the disease. The study has been published in PLOS ONE.

Dr. Felix Pabon-Rodriguez and his colleaques including Dr. Grant Brown, Dr. Breanna Scorza, and Dr. Christine Petersen, have utilized a Bayesian statistical framework to explore the interaction between pathogen load, immune responses including antibody levels, and disease progression.

The Bayesian joint 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 cytokine expressions like interleukin 10 (IL-10) and interferon-gamma (IFN-γ), the model captures the complexity of the immune response during infection.

Dr. Pabon-Rodriguez, who is now an assistant professor of Biostatistics and Health Data Science at Indiana University School of Medicine, highlighted the significance of their findings: “Our model not only helps in understanding the progression of Leishmania infection but also predicts individual disease trajectories. This can be instrumental in developing targeted treatments for canine leishmaniasis.” He further emphasized, “By integrating multiple immune response variables, we can more accurately forecast 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 both CD4+ and CD8+ T cell variables, such as proliferation and cytokine expressions, we are able to closely model real-world disease progression,” said Dr. Pabon-Rodriguez. This detailed modeling approach underscores the importance of immune response elements in disease progression and potential treatment outcomes.

The model also utilizes 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 both inflammatory and regulatory immune responses, the model provides insights into the delicate balance of the immune system in managing chronic infections like Leishmania.

Dr. Pabon-Rodriguez emphasized the broader implications of their 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 modeling can enhance the understanding of complex disease processes, ultimately contributing to the development of better therapeutic strategies.

In conclusion, this research marks a significant advancement in the field of infectious disease modeling, particularly for diseases with complex immune responses such as Leishmania. The Bayesian joint model developed by the University of Iowa team offers a powerful framework for understanding disease progression and improving predictions of individual disease outcomes.

Journal Reference

Pabon-Rodriguez, F.M., Brown, G.D., Scorza, B.M., Petersen, C.A. “Within-host bayesian joint modeling of longitudinal and time-to-event data of Leishmania infection.” PLOS ONE (2024).

DOI: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297175

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