Abstract #102

# 102
NMR metabolomic analysis of dairy cows reveals milk glycerophosphocholine to phosphocholine ratio as prognostic biomarker for risk of ketosis.
M. S. Klein1, N. Krattenmacher2, S. Wiedemann2, W. Junge2, G. Thaller2, P. J. Oefner1, W. Gronwald*1, 1Institute of Functional Genomics, University of Regensburg, Regensburg, Bavaria, Germany, 2Institute of Animal Breeding and Husbandry, Christian-Albrechts-University, Kiel, Schleswig-Holstein, Germany.

In this contribution we show that nuclear magnetic resonance (NMR)-based analysis of milk allows identifying prognostic biomarkers for risk of ketosis in dairy cows. Diagnostic markers for ketosis such as acetone and β-hydroxybutyric acid (BHBA) are known, but disease prediction remains an unsolved challenge. Milk is a steadily available biofluid and routinely collected on a daily basis. This high availability makes milk superior to blood or urine samples for diagnostic purposes. We demonstrate that high milk glycerophosphocholine (GPC) levels and high ratios of GPC to phosphocholine (PC) allow for the reliable selection of healthy and metabolically stable cows for breeding purposes. Throughout lactation, high GPC values are connected with a low ketosis incidence. During the first month of lactation, molar GPC/PC ratios equal or greater than 2.5 indicate a very low risk for developing ketosis. This threshold was validated for different breeds (Holstein-Friesian, Brown Swiss, and Simmental Fleckvieh) and for animals in different lactations, with observed odds ratios between 1.5 and 2.38. In contrast to acetone and BHBA, these measures are independent of the acute disease status. A possible explanation for the predictive effect is that GPC and PC are measures for the ability to break down phospholipids as a fatty acid source to meet the enhanced energy requirements of early lactation.

Key Words: ketosis, metabolomics, nuclear magnetic resonance (NMR)

Speaker Bio
I was born and grew up in Hamburg, Germany. After completion of high school I studied chemistry at the Technical University of Braunschweig. At that time I became fascinated by the possibilities that computational approaches together with experimental methods offer for the study of biological objects such as three-dimensional protein structures and small organic molecules (metabolites). Something I pursued from that time on through my whole career first as a Ph.D. student, then as a post-doc at the University of Alberta in Canada and now with my own research group at the University of Regensburg in Germany.