In 2021, my PhD advisor, Nicholas Jewell, put me in contact with Dr. Ben Caplin of University College of London and Dr. Marvin Gonzalez-Quiroz and their larger DEGREE study team to help them study the potential causes of chronic kidney disease of unexplained cause (CKDu) among young, apparently healthy individuals in Nicaragua.
For more information about the epidemic of CKDu in Central America, see the Cycle of Death video created by the team members and a Lancet article on the topic.
The research team have been collecting longitudinal data on cohorts of young, healthy people in Nicaragua. After a two-year follow up, they applied longitudinal growth mixture models (GMM) to detect any associations with declining eGFR patterns. While this statistical approach worked well for a short follow up, it was not as applicable for detecting the underlying cause with a longer 5-year follow up since individuals may start to decline at any point during the study.
In collaboration with nephrologists, epidemiologists, and statisticians, I came up with an alternative methodology (details forthcoming in publication) to detect an early kidney injury, the time at which an individual might switch from healthy kidney function to declining, unhealthy kidney function before being clinically diagnosed as having CKD.
Macalester students, Alex McCreight and Connie Zhang, worked with me to implement this method to investigate potential exposures with early kidney injury. The first paper using this method is currently in review.
Connie Zhang, in her Honors Thesis, worked to expand the novel method to allow for a bivariate distribution based on eGFR. Continued work is needed to refine this method for early kidney disease detection.
Using simulation studies, I am compare the potential conclusions from the original GMM methodology with this alternative method.
Product
- First paper submitted after revise and resubmit decision
- Further analysis is in progress
