Tana Jo Almand
University of California, Davis, USA
Abstract Title: Prepartum and Postpartum Biomarkers Associated with Transition Disease Risk in Dairy Cows
Biography:
T. J. Almand is a PhD candidate in Animal Biology at the University of California, Davis, specializing in dairy herd health, antimicrobial stewardship, and biomarker-based disease predictions. Her research focuses on improving selective dry cow therapy (SDCT) through integration of epidemiology, diagnostics, and data-driven decision-making. She is currently completing the California–Denmark Innovator Fellowship at Aarhus University, where her work emphasizes translational approaches to precision livestock health and sustainable dairy production systems. Her long-term goal is to develop practical, field-ready tools that improve animal health, reduce antimicrobial use, and support global agricultural sustainability.
Research Interest:
The transition period in dairy cows involves substantial metabolic changes that increase susceptibility to disease. This study evaluated associations between prepartum and early postpartum biomarkers and the risk of transition diseases. Blood and milk samples were collected from commercial Danish dairy herds using a standardized protocol. Prepartum blood samples were obtained within 42 d before calving, and postpartum milk samples were collected within the first 21 d in milk. Biomarkers included indicators of energy balance, liver function, and mineral status, including nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHB), glucose, cholesterol, calcium, magnesium, point-of-care blood analytes, and milk metabolites. Data were filtered using biologically plausible ranges, and complete-case datasets were analyzed. Associations with mastitis, metritis, ketosis, and lameness within 60 d in milk were evaluated using mixed-effects regression models with herd as a random effect. Between-herd variability was observed across all biomarker groups. Lifetest analytes showed the greatest clustering (ICC = 0.10–0.42), with blood urea nitrogen highest (ICC = 0.42). Serum biomarkers also demonstrated moderate clustering, particularly log-cholesterol (ICC = 0.31), NEFA (ICC = 0.29), and BHB (ICC = 0.22). Milk metabolites showed lower but notable clustering (ICC = 0.06–0.22). Elevated NEFA and BHB were associated with increased odds of transition diseases, consistent with impaired energy balance. These findings support the use of blood- and milk-based biomarkers, including point-of-care analytes, to identify at-risk cows. Accounting for herd-level variability is essential for developing reliable biomarker-based monitoring strategies to improve dairy cow health and antimicrobial stewardship.