Equine pituitary pars intermedia dysfunction (PPID) is a common disease of aged horses, ponies and donkeys. This endocrine disorder is associated with the development of many clinical signs, including incomplete or late shedding, loss of muscle mass, change in behavior, exercise intolerance and laminitis (painful inflammation within the hoof). Treatment can be very effective, particularly if started before advanced disease, highlighting the need for early diagnosis. Unfortunately, currently available PPID diagnostic tests fail to consistently identify early disease. To address this issue, researchers will use a computer modeling approach known as machine learning to create a new way to diagnose early stages of PPID in affected horses. Specifically, they will teach the computer model to identify a specific peptide signature in plasma of PPID-affected horses. Peptides are small fragments of proteins and changes in the amount and type of peptides traveling in the blood stream when disease is present. Studies have successfully generated peptide signatures indicative of disease in human plasma. The team hopes this same approach will be successful in horses and enable improved and earlier diagnosis of PPID in aged horses.
Grant amount awarded
University of Florida
Dianne McFarlane, DVM, PhD, DACVIM-LA