Milk lipidome alterations in first-lactation dairy cows with lameness: A biomarker identification approach using untargeted lipidomics and machine learning DOI Creative Commons
Ana S. Cardoso, Sandra Martínez‐Jarquín, Robert Hyde

et al.

Journal of Dairy Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Lameness, defined as an impaired gait, impacts cow welfare and performance, compromising future health production, increasing culling risk. Untargeted milk lipidomics, together with the use of machine learning methods, have shown promise in identifying potential biomarkers for early detection lameness, before development visible clinical lameness. Prediction lameness would allow earlier implementation management treatment strategies, ultimately reducing negative consequences. This study aimed to evaluate predictive accuracy differences metabolome identify lipid first-lactation dairy cows. lipidomics approaches were used metabolomic profiles samples collected from heifers during transition period (before lameness) at time first onset. A total 56 32 cows (16 lame, 16 control) analyzed by liquid chromatography-high-resolution mass spectrometry after calving Elastic net regression achieved 83% predicting 100% 10 ions selected different statistical methods showed be considered predictors Pathway analysis revealed significant dysregulation retinol metabolism that go on develop lactation. demonstrated using detection. This, turn, provides insights into pathogenesis, furthering our understanding ultimate goal developing interventions improve farm productivity.

Language: Английский

Milk lipidome alterations in first-lactation dairy cows with lameness: A biomarker identification approach using untargeted lipidomics and machine learning DOI Creative Commons
Ana S. Cardoso, Sandra Martínez‐Jarquín, Robert Hyde

et al.

Journal of Dairy Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Lameness, defined as an impaired gait, impacts cow welfare and performance, compromising future health production, increasing culling risk. Untargeted milk lipidomics, together with the use of machine learning methods, have shown promise in identifying potential biomarkers for early detection lameness, before development visible clinical lameness. Prediction lameness would allow earlier implementation management treatment strategies, ultimately reducing negative consequences. This study aimed to evaluate predictive accuracy differences metabolome identify lipid first-lactation dairy cows. lipidomics approaches were used metabolomic profiles samples collected from heifers during transition period (before lameness) at time first onset. A total 56 32 cows (16 lame, 16 control) analyzed by liquid chromatography-high-resolution mass spectrometry after calving Elastic net regression achieved 83% predicting 100% 10 ions selected different statistical methods showed be considered predictors Pathway analysis revealed significant dysregulation retinol metabolism that go on develop lactation. demonstrated using detection. This, turn, provides insights into pathogenesis, furthering our understanding ultimate goal developing interventions improve farm productivity.

Language: Английский

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