Prediction of Liver Triglyceride Content in Early Lactation Multiparous Holstein Cows Using Blood Metabolite, Mineral, and Protein Biomarker Concentrations.

Abstract

Simple SummaryBovine fatty liver syndrome is a metabolic disorder in transition dairy cows that has been associated with adverse consequences such as lower milk production and fertility. Fatty liver syndrome is difficult to monitor and diagnose in applied practice and research settings because it requires a liver tissue biopsy to determine liver triglyceride content. This study aimed to develop and validate a panel of blood metabolite, protein, and mineral biomarkers as a less invasive and more accessible tool to assess liver triglyceride content. We investigated a variety of panels using blood measurements from a single timepoint or multiple timepoints, as well as different combinations of biomarkers based on their perceived accessibility. Both the single and multiple timepoint biomarker panels accurately classified cows with high liver triglyceride content (top 33.3% vs. lower 66.7%), but accuracy was lower for classifying cows with or without maximum liver triglyceride in the top 50% or top 66.7% of liver triglyceride content. We suggest that the blood biomarker models predicting high triglyceride content may be useful for monitoring fatty liver in research and applied practice, as well as enable larger scale research studies investigating fatty liver in dairy cows.Bovine fatty liver syndrome (bFLS) is difficult to diagnose because a liver tissue biopsy is required to assess liver triglyceride (TG) content. We hypothesized that a blood biomarker panel could be a convenient alternative method of liver TG content assessment and bFLS diagnosis. Our objectives were to predict liver TG using blood biomarker concentrations across days in milk (DIM; longitudinal, LT) or at a single timepoint (ST; 3, 7, or 14 DIM), as well as different biomarker combination based on their perceived accessibility. Data from two separate experiments (n = 65 cows) was used for model training and validation. Response variables were based on the maximum liver TG observed in 1 and 14 DIM liver biopsies: Max TG (continuous), Low TG (TG > 13.3% dry matter; DM), Median TG (TG > 17.1% DM), and High TG (TG > 22.0% DM). Model performance varied but High TG was well predicted by sparse partial least squares—discriminate analysis models using LT and ST data, achieving balanced error rates ≤ 15.4% for several model variations during cross-validation. In conclusion, blood biomarker panels using 7 DIM, 14 DIM, or LT data may be a useful diagnostic tool for bFLS in research and field settings.