Mid Infrared Milk Testing for Evaluation of Health Status in Dairy Cows METABOLIC STATUS. Use of Biomarkers for Negative Energy Balance and Ketosis

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1 Mid Infrared Milk Testing for Evaluation of Health Status in Dairy Cows H. M. Dann, D. M. Barbano, A. Pape, & R. J. Grant 2018 Cornell Nutrition Conference The cowman in me knew this young 2 year old cow could be headed for a train wreck. After talking with other team members on the farm, they agreed and told me she already had a physical by the herd veterinarian earlier in the week but could not identify her problem. That s when I turned my attention to the computer records as this herd was able to collect milk records, components, somatic cell count, rumination and even each cow s weight. Use of Biomarkers for Negative Energy Balance and Ketosis Milk beta hydroxybutyrate (BHB) and acetone most researched to date Fast, low cost, noninvasive relative to blood Use of Mid Infrared Spectra to Detect Health Issues in Dairy Cows METABOLIC STATUS Milk BHB for screening of subclinical ketosis MIR prediction better for BHB (80% sensitivity) than fat to protein (66% sensitivity) (van Knegsel et al., 2010) Threshold of 0.2 mmol/l detect ketosis in cow, proposed in herd level surveillance programs (Denis Robichaud et al., 2014)

2 Use of DHI Samples to Determine Prevalence of Herd Level Elevated Milk BHB Higher for older cows Higher during spring and fall Possibility to Improve Milk BHB Model Performance by Inclusion of Test day Information Too many false positive results with test day milk BHB Inclusion of cow test day information (parity, season, fat to protein) improved marginally the model performance (van der Drift et al., 2012) Individual test day milk and performance variables were poor predictors of hyperketonemia but combining those variables using logistic and multiple linear models improved predictive ability (Chandler et al., 2018) Limitation frequency of sampling Santschi et al., 2016 Herd prevalence > detection of individual cows Milk BHB for Fresh Cows (4 to 21 DIM; 1 milking/d) Use of MIR Spectra from Milk for Prediction of Blood Biomarkers (Metabolites and Hormones) Blood BHB predicted from MIR spectra, milk composition, and producer reported variables Good but not great models with AUC 0.83 AUC 0.90 Previous research yielded poor models No model achieved the sensitivity and specificity of cow side blood testing Pralle et al., 2018

3 Model to Predict Blood NEFA Directly from Milk MIR Spectra Novel approach Not by calculation from milk fatty acids as others have proposed (Jorjong et al., 2014) PLS model developed using fresh Holstein cows from a herd and validated with cows from another herd Reference mean: 713 ueq/l, predicted mean 703 ueq/l with a SD of difference of 218 ueq/l Blood NEFA measured on blood is a snapshot of the NEFA concentration at an instant in time, while blood NEFA predicted from milk analysis represents a time average for the total time between milkings Model available commercially Barbano et al., 2015 Milk Predicted Blood NEFA, ueq/l Milk Predicted Blood NEFA Highest in Early Lactation DIM Milk Predicted Blood NEFA Fresh Period Milk Predicted Blood NEFA: Deviation from Expected Herd Pattern

4 Milk Predicted Blood NEFA for Fresh Cows (4 to 21 DIM; 1 milking/d) Milk Predicted Blood NEFA for Fresh Cows (4 to 21 DIM; 1 milking/d) Fresh Pen NEFA from NE and MW Herds Fresh Pen NEFA is Related to Milk Preformed Fatty Acids

5 Fresh Pen NEFA is Related to Fatty Acids (g/100 g FA) Commercial Herds Group Samples Commercial Herds Group Samples Performance of Models Predicting Blood Components from MIR Spectra of Dairy Cows Grelet et al., 2018

6 Cross Validation for Models Predicting Blood Components Metabolic Status Clusters Glucose IGF 1 Metabolic Status Healthy NEFA BHB 63% of high values predicted high Threshold = % of low values predicted low Grelet et al., 2018 Moderately Impacted Imbalanced Grelet et al., 2018 Discrimination of Metabolic Status Clusters Milk Composition Changes for a Cow Before and After DA Surgery NEFA Fat Fresh 07 Oct De Novo FA Preformed FA Grelet et al., 2018

7 MIR Methodology and Implementation of Machine Learning Models May Allow for Early Detection of Health Issues Data: 1436 observations for ketosis dataset and 1240 observations for DA, with at least 10 healthy samples for every sick sample Predictors: Milk-estimated blood NEFA (μeq/l) De novo fatty acid (g/100 g FA) Preformed fatty acid (g/100 g FA) Ratio of fat to protein BHB Response: occurrence of ketosis or DA Model type: random forests, logistic regression Evaluation: AUC from 10 replicates of 10-fold cross-validation Milk Composition Predicts a Displaced Abomasum with Moderate Accuracy Pape et al., 2018 Milk Composition Predicts Clinical Ketosis with Moderate Accuracy Real Time Predication of Health Issues: Pilot System for Miner Herd Objective: provide general purpose alerts for health issues in fresh cows to farm staff Approach: Collect and analyze milk samples (M1) from cows in fresh pen Use machine learning models based on milk composition to predict health issues (e.g. ketosis, DA, metritis, mastitis ) Provide information to farm staff Pape et al., 2018

8 General Purpose Health Alert Model Performance Any Event First Event Use of Mid Infrared Spectra to Detect Health Issues in Dairy Cows IMMUNOLOGICAL BIOMARKERS AND INFLAMMATION Acute Phase Protein (Haptoglobin and Serum Amyloid A) Prediction 420 serum samples matched with milk spectral and compositional data Samples from cows that were: Healthy No record in Dairy Comp 305 of health problem within 1 wk of sampling and SCC < 100,000 cells/ml Sick Recorded in Dairy Comp 305 as having at least 1 health problem within 1 wk of sampling Possibly experiencing sublinical health issue(s) SCC > 100,000 cells/ml and milk predicted blood NEFA > 250 ueq/ml) Regression modeling Serum Haptoglobin Prediction Model Evaluation (R 2 Values from 10-Fold Cross-Validation were < 0.42 Relatively Weak)

9 Mediocre Results for Regression so Classification (Low vs. High) was Explored Haptoglobin Classification (Random Forest Model) Observations below the nth percentile were assigned to one class and those above the (100 n)th percentile were assigned to the other class Validation of Immunogenic Biomarkers in Milk that Indicated Inflammatory Diseases Healthy and diseased (e.g mastitis or systemic) Holstein cows Receiver operator characteristic analysis with 94% specificity Haptoglobin: 82% sensitivity Secretory component: 59% sensitivity Lactoferrin: 55% sensitivity Vascular endothelial growth factor: 67% sensitivity Zoldan et al., 2017 Use of Mid Infrared Spectra to Detect Health Issues in Dairy Cows DIGESTIVE DISORDERS

10 Milk Composition and Subacute Ruminal Acidosis (SARA) Ratio of fat to protein not specific enough to detect low ruminal ph values (Guegan et al., 2015) Ruminal ph mean and variation related to specific milk fatty acid changes measured by GC (Colman et al., 2012) Susceptibility of cows to SARA is reflected in milk fatty acid proportions measure by GC (Jing et al., 2018) C18:1 trans 10, C15:0, C18:1 trans 11 Milk Fatty Acid Composition by GC and MIR Analysis to Predict SARA in Dairy Goats An index of short and medium chain fatty acids ( C13) to long chain fatty acids from gas chromatography was positively related (r = 0.60) to ruminal ph Index calculated from MIR analysis was not correlated Overestimated the short and medium chain fatty acids Underestimated the long chain fatty acid MIR analysis will not a viable method for prediction of SARA until the inaccuracy in the prediction of fatty acids is addressed Giger Reverdin et al., 2018 Use of MIR Milk Spectra to Predict Rumen ph and SARA Evaluated several ph metrics: mean ph, time below ph 6, area under the ph curve between milkings and ph at the time of milk sample collection along with milk spectra from ruminal cannulated Australian dairy cows The partial least squares models to predict ph metrics from milk spectra had poor to moderate accuracy with R 2 values between 0.22 and 0.59 Discriminant analysis models categorized cows as either having or not having SARA with a sensitivity of 81% and a specificity of 72% Use of Mid Infrared Spectra to Detect Health Issues in Dairy Cows CHALLENGES WITH MODEL DEVELOPMENT AND IMPLEMENTATION Luke et al., 2018

11 Frequency of Milk Testing Monthly DHI milk testing program Prevalence of hyperketonemia at the herd level Need a higher frequency for detection and treatment of individual cows Development of on farm MIR milk analyzers is needed Garbage In, Garbage Out Accuracy of a prediction model based on milk MIR spectra depends on the precision of measurement of the validated method of the biomarker of interest Blood based biomarkers vs. traditional milk components Validated method and participate in an external quality assurance program (interlaboratory proficiency tests) Quality is more important than quantity Barbano et al., 2018 Model Implementation Takes Time Take Home Messages Development and validation Milk analyzer equipment manufacturers implement Calibration samples needed regularly Processing and reporting of information Use/interpretation of information Mid infrared spectroscopy of milk is a tool that can predict and identify health issues in cows Noninvasive, milk available several time per day, cost effective Useful for prediction of energy balance and metabolic status, inflammation, and digestive issues Herd level hyperketonemia Real advantage of tool will be realized when it can be used to provide real time information to dairy producers

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