DI/LC-MS/MS-Based Metabolic Profiling for Identification of Early Predictive Serum Biomarkers of Metritis in Transition Dairy Cows

Metabolomics-a powerful tool in livestock research

Advancements in the Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS) along with recent developments in omics sciences have resulted in a better understanding of molecular mechanisms and pathways associated with the physio-pathological state of the animal. Metabolomics is a post-genomics tool that deals with small molecular metabolites in a given set of time which provides clear information about the status of an organism. Recently many researchers mainly focus their research on metabolomics studies due to its valuable information in the various fields of livestock management and precision dairying. The main aim of the present review is to provide an insight into the current research output from different sources and application of metabolomics in various areas of livestock including nutri-metabolomics, disease diagnosis advancements, reproductive disorders, pharmaco-metabolomics, genomics studies, and dairy production studies. The present review would be helpful in understanding the metabolomics methodologies and use of livestock metabolomics in various areas in a brief way.

Mastitis: What It Is, Current Diagnostics, and the Potential of Metabolomics to Identify New Predictive Biomarkers

Periparturient diseases continue to be the greatest challenge to both farmers and dairy cows. They are associated with a decrease in productivity, lower profitability, and a negative impact on cows’ health as well as public health. This review article discusses the pathophysiology and diagnostic opportunities of mastitis, the most common disease of dairy cows. To better understand the disease, we dive deep into the causative agents, traditional paradigms, and the use of new technologies for diagnosis, treatment, and prevention of mastitis. This paper takes a systems biology approach by highlighting the relationship of mastitis with other diseases and introduces the use of omics sciences, specifically metabolomics and its analytical techniques. Concluding, this review is backed up by multiple studies that show how earlier identification of mastitis through predictive biomarkers can benefit the dairy industry and improve the overall animal health.

Plasma Metabolomic Analysis Reveals the Relationship between Immune Function and Metabolic Changes in Holstein Peripartum Dairy Cows

Dairy cows undergo dynamic physiological changes from late gestation to early lactation, including metabolic changes and immune dysfunction. The aim of this study was to investigate the relationship between immune function and metabolic changes in peripartum dairy cows. Fifteen healthy Holstein dairy cows were enrolled 14 days prior to parturition, and plasma was collected on day −7, 0, 7, and 21 relative to calving. Plasma non-esterified fatty acids (NEFAs), glucose, β-hydroxybutyric acid (BHBA), immunoglobulin G (IgG), tumor necrosis factor alpha (TNF-α), and interleukin-2 levels were measured, and metabolic profiles were determined using ultra-high-performance liquid chromatography−quadrupole time-of-flight mass spectrometry. The data were analyzed using Tukey−Kramer adjustment for multiple comparisons, and multivariate and univariate statistical analyses were performed to screen for differential metabolites. The results showed that the concentrations of NEFAs, glucose, BHBA, and TNF-α in the plasma significantly increased and concentrations of IgG and interleukin-2 in plasma significantly decreased from −7 d to the calving day (p < 0.05). Additionally, the concentrations of glucose, IgG, and TNF-α significantly decreased from 0 to +7 d, and concentrations of NEFAs decreased significantly from +7 to +21 d (p < 0.05). The following six primary metabolic pathways were identified in all time point comparisons, and L-glutamate, linoleic acid, taurine, and L-tryptophan were involved in these major metabolic pathways. Correlation and pathway analyses indicated that a negative energy balance during the transition period adversely affects immune responses in cows, and L-tryptophan exerts immunomodulatory effects through the Trp-Kyn pathway, resulting in depletion of Trp and elevation of Kyn.

Multi-Channel Metabolomics Analysis Identifies Novel Metabolite Biomarkers for the Early Detection of Fatty Liver Disease in Dairy Cows

Fatty liver disease, a type of metabolic disorder, frequently occurs in dairy cows during the parturition period, causing a high culling rate and, therefore, considerable economic losses in the dairy industry owing to the lack of effective diagnostic methods. Here, metabolite biomarkers were identified and validated for the diagnosis of metabolic disorders. A total of 58 participant cows, including severe fatty liver disease and normal control groups, in the discovery set (liver biopsy tested, n = 18), test set (suspected, n = 20) and verification set (liver biopsy tested, n = 20), were strictly recruited and a sample collected for their feces, urine, and serum. Non-targeted GC-MS-based metabolomics methods were used to characterize the metabolite profiles and to screen in the discovery set. Eventually, ten novel biomarkers involved in bile acid, amino acid, and fatty acid were identified and validated in the test set. Each of them had a higher diagnostic ability than the traditional serum biochemical indicators, with an average area under the receiver operating characteristic curve of 0.830 ± 0.0439 (n = 10) versus 0.377 ± 0.182 (n = 9). Especially, combined biomarker panels via different metabolic pipelines had much better diagnostic sensitivity and specificity than every single biomarker, suggesting their powerful utilization potentiality for the early detection of fatty liver disease. Intriguingly, the serum biomarkers were confirmed perfectly in the verification set. Moreover, common biological pathways were found to be underlying the pathogenesis of fatty liver syndrome in cattle via different metabolic pipelines. These newly-discovered and non-invasive metabolic biomarkers are meaningful in reducing the high culling rate of cows and, therefore, benefit the sustainable development of the dairy industry.

Blood Metabolomic Phenotyping of Dry Cows Could Predict the High Milk Somatic Cells in Early Lactation—Preliminary Results

Subclinical mastitis (SCM) is a very common disease of dairy cows. Currently, somatic cell count (SCC) is used for SCM diagnoses. There are no prognostic tests to detect which cows may develop SCM during the dry-off period. Therefore, the objectives of this study were to identify metabolic alterations in the serum of pre-SCM cows during the dry-off period, at −8 and −4 weeks before calving, through a targeted mass spectrometry (MS) assay. Fifteen cows, free of any disease, and 10 cows affected only by SCM postpartum served as controls (CON) and the SCM group, respectively. Results showed 59 and 47 metabolites that differentiated (p ≤ 0.05) CON and pre-SCM cows at –8 and −4 weeks prior to the expected date of parturition, respectively. Regression analysis indicated that a panel of four serum metabolites (AUC = 0.92, p < 0.001) at −8 weeks and another four metabolites (AUC = 0.92, p < 0.01) at −4 weeks prior to parturition might serve as predictive biomarkers for SCM. Early identification of susceptible cows can enable development of better preventive measurements ahead of disease occurrence.

Recent advances and future directions for uterine diseases diagnosis, pathogenesis, and management in dairy cows

Researchers, veterinarians, and farmers' pursuit of a consistent diagnosis, treatment, and prevention of uterine diseases remains challenging. The diagnosis and treatment of metritis is inconsistent, a concerning situation when considered the global threat of antimicrobial resistance dissemination. Endometritis is an insidious disease absent on routine health programs in many dairy farms and from pharmaceutical therapeutics arsenal in places like the US market. Conversely, a multitude of studies advanced the understanding of how uterine diseases compromise oocyte, follicle, and embryo development, and the uterine environment having long-lasting effects on fertility. The field of uterine disease microbiome also experienced tremendous progress and created opportunities for the development of novel preventives to improve the management of uterine diseases. Activity monitors, biomarkers, genomic selection, and machine learning predictive models are other innovative developments that have been explored in recent years to help mitigate the negative impacts of uterine diseases. Albeit novel tools such as vaccines for metritis, immune modulators, probiotics, genomic selection, and selective antimicrobial therapy are promising, further research is warranted to implement these technologies in a systematic and cost-effective manner.

The Bovine Metabolome

From an animal health perspective, relatively little is known about the typical or healthy ranges of concentrations for many metabolites in bovine biofluids and tissues. Here, we describe the results of a comprehensive, quantitative metabolomic characterization of six bovine biofluids and tissues, including serum, ruminal fluid, liver, Longissimus thoracis (LT) muscle, semimembranosus (SM) muscle, and testis tissues. Using nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography–tandem mass spectrometry (LC–MS/MS), and inductively coupled plasma–mass spectrometry (ICP–MS), we were able to identify and quantify more than 145 metabolites in each of these biofluids/tissues. Combining these results with previous work done by our team on other bovine biofluids, as well as previously published literature values for other bovine tissues and biofluids, we were able to generate quantitative reference concentration data for 2100 unique metabolites across five different bovine biofluids and seven different tissues. These experimental data were combined with computer-aided, genome-scale metabolite inference techniques to add another 48,628 unique metabolites that are biochemically expected to be in bovine tissues or biofluids. Altogether, 51,801 unique metabolites were identified in this study. Detailed information on these 51,801 unique metabolites has been placed in a publicly available database called the Bovine Metabolome Database.

Urinary metabolomics fingerprinting around parturition identifies metabolites that differentiate lame dairy cows from healthy ones

Lameness is a very important disorder of periparturient dairy cows with implications on milk production and composition as well as with consequences on reproductive performance. The aetiology of lameness is not clear although there have been various hypotheses suggested over the years. The objective of this study was to metabotype the urine of dairy cows prior to, during and after the onset of lameness by evaluating at weeks -8, -4 pre-calving, the week of lameness diagnosis, and +4 and +8 weeks post-calving. We used a metabolomics approach to analyse urine samples collected from dairy cows around calving (6 cows with lameness v. 20 healthy control cows). A total of 153 metabolites were identified and quantified using an in-house MS library and classified into 6 groups including: 11 amino acids (AAs), 39 acylcarnitines (ACs), 3 biogenic amines (BAs), 84 glycerophospholipids, 15 sphingolipids and hexose. A total of 23, 36, 40, 23 and 49 metabolites were observed to be significantly different between the lame and healthy cows at -8 and -4 weeks pre-calving, week of lameness diagnosis as well as at +4 and +8 weeks post-calving, respectively. It should be noted that most of the identified metabolites were elevated; however, a few of them were also lower in lame cows. Overall, ACs and glycerophospholipids, specifically phosphatidylcholines (PCs), were the metabolite groups displaying the strongest differences in the urine of pre-lame and lame cows. Lysophosphatidylcholines (LysoPCs), although to a lesser extent than PCs, were altered at all time points. Alterations in urinary AA concentrations were also observed during the current study for four time points. During the pre-calving period, there was an observed elevation of arginine (-8 week), tyrosine (-8 week) and aspartate (-4 week), as well as a depression of urinary glutamate (-4 weeks). In the current study, it was additionally observed that concentrations of several sphingomyelins and one BA were altered in pre-lame and lame cows. Symmetric dimethylarginine was elevated at both -8 weeks pre-calving and the week of lameness diagnosis. Data showed that urinary fingerprinting might be a reliable methodology to be used in the future to differentiate lame cows from healthy ones.

Metabolome profiling in skeletal muscle to characterize metabolic alterations in over-conditioned cows during the periparturient period

The transition from late gestation to early lactation is associated with extensive changes in metabolic, endocrine, and immune functions in dairy cows. Skeletal muscle plays an important role in maintaining the homeorhetic adaptation to the metabolic needs of lactation. The objective of this study was to characterize the skeletal muscle metabolome in the context of the metabolic changes that occur during the transition period in dairy cows with high (HBCS) versus normal body condition (NBCS). Fifteen weeks antepartum, 38 pregnant multiparous Holstein cows were assigned to 1 of 2 groups, which were fed differently to reach the targeted BCS and back fat thickness (BFT) until dry-off at -49 d before calving (HBCS: >3.75 and >1.4 cm; NBCS: <3.5 and <1.2 cm). During the dry period and the subsequent lactation, both groups were fed identical diets. The differences in both BCS and BFT were maintained throughout the study. The metabolome was characterized in skeletal muscle samples (semitendinosus muscle) collected on d -49, 3, 21, and 84 relative to calving using a targeted metabolomics approach (AbsoluteIDQ p180 kit; Biocrates Life Sciences AG, Innsbruck, Austria), which allowed for the quantification of up to 188 metabolites from 6 different compound classes (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, and hexoses). On d -49, the concentrations of citrulline and hydroxytetradecadienyl-l-carnitine in muscle were higher in HBCS cows than in NBCS cows, but those of carnosine were lower. Over-conditioning did not affect the muscle concentrations of any of the metabolites on d 3. On d 21, the concentrations of phenylethylamine and linoleylcarnitine in muscle were lower in HBCS cows than in NBCS cows, and the opposite was true for lysophosphatidylcholine acyl C20:4. On d 84, the significantly changed metabolites were mainly long-chain (>C32) acyl-alkyl phosphatidylcholine and di-acyl phosphatidylcholine, along with 3 long-chain (>C16) sphingomyelin that were all lower in HBCS cows than in NBCS cows. These data contribute to a better understanding of the metabolic adaptation in skeletal muscle of dairy cows during the transition period, although the physiological significance and underlying molecular mechanisms responsible for the regulation of citrulline, hydroxytetradecadienyl-l-carnitine, carnosine, and phenylethylamine associated with over-conditioning are still elusive and warrant further investigation. The changes observed in muscle lysophosphatidylcholine and phosphatidylcholine concentrations may point to an alteration in phosphatidylcholine metabolism, probably resulting in an increase in membrane stiffness, which may lead to abnormalities in insulin signaling in the muscle of over-conditioned cows.

Identification of disordered metabolic networks in postpartum dairy cows with left displacement of the abomasum through integrated metabolomics and pathway analyses

High-producing dairy cows are easily affected by left displacement of the abomasum (LDA) within 4 weeks postpartum. Although LDA is highly associated with metabolic disturbances, the related information on comprehensive metabolic changes, with the exception of some blood biochemical parameters, remains limited. In this study, the changes in plasma metabolites and in the metabolic profile of postpartum dairy cows with LDA were investigated through liquid chromatography coupled with quadrupole time of flight mass spectrometry (LC-Q/TOF-MS)-based metabolomics, and the metabolic networks related to LDA were constructed through metabolomics pathway analysis (MetPA). An obvious change in the metabolic profile was reflected by significant variations in 68 plasma metabolites in postpartum dairy cows with LDA, and these variations consequently altered 13 metabolic pathways (histidine metabolism, tyrosine metabolism, valine, leucine and isoleucine biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, arginine and proline metabolism, tryptophan metabolism, synthesis and degradation of ketone bodies, linoleic acid metabolism, arachidonic acid metabolism, citrate cycle, butanoate metabolism, vitamin B₆ metabolism and pyrimidine metabolism). This study shows that the more detailed information obtained by LC-Q/TOF-MS-based metabolomics and MetPA might contribute to a better understanding of the disordered metabolic networks in postpartum dairy cows with LDA.

Serum metabolic fingerprinting of pre-lameness dairy cows by GC-MS reveals typical profiles that can identify susceptible cows

The objectives of this study were to identify metabolite fingerprints in the serum related to amino acid (AA), carbohydrate, and lipid metabolism in transition dairy cows at -8 and -4 wks prior to parturition, at +2 wks postpartum during lameness diagnosis as well as at +4 and +8 wks after parturition. All cases of lameness occurred at around +2 wks after parturition. Out of 100 dairy cows included in this nested case-control study only 6 pregnant multiparous (parity: 3.0 ± 0.6, Mean ± SEM) Holstein dairy cows with lameness only and 20 healthy control cows (CON) were selected for serum GC-MS metabolomics analysis. All cows selected were not injured mechanically and had similar parity (3.3 ± 0.6) and body condition score (BCS). A total of 29 metabolites were identified and quantified in the serum. Results showed that 18 and 15 metabolites differentiated pre-lame cows from CON ones at -8 and -4 wks prior to parturition. Ten metabolites were found altered at the week of lameness diagnosis. Of note: pre-lame cows were characterized by greater concentrations of several amino acids including Gly, Leu, Phe, Ser, Val, D-mannose, Myo-inositol, and phosphoric acid (PA) at -8 and -4 wks prior to lameness and at the week of lameness diagnosis. At +4 wks after parturition 11 metabolites were altered in lameness cows, and at +8 wks there were 13 metabolites that differentiated the two groups. The high accuracy of the top 6 metabolites at -8 wks prior to parturition or approximately 9-11 wks before lameness diagnosis (Glu, Orn, Phe, Ser, Val, and PA) and another 5 metabolites at -4 wks before parturition, or approximately 5-7 wks before lameness diagnosis (Leu, Orn, Phe, Ser, and D-mannose) suggest that those metabolites may serve as potential monitoring biomarkers of lameness prior to lameness diagnosis. Data also showed multiple alterations during the week of lameness as well as at +4 and +8 wks postpartum suggesting lame cows are not metabolically healthy several weeks after the incidence of lameness. SIGNIFICANCE: Lameness is one of the top three health issues of dairy cows in Canada that influences early culling of dairy cows. Despite a few efforts, there is scarcity of data regarding metabolic alterations that precede, associate, and follow lameness. We investigated whether alterations in the metabolite signatures prior, during, and after development of lameness can be used to screen cows for susceptibility to lameness, characterize lameness from the metabolic prospective, and predict the outcome of this economically important health issue of dairy cows. The results demonstrate typical metabotypes as shown by increased serum concentrations of Val, Gly, Ser, Leu, Phe, D-mannose, myo-inositol, and phosphoric acid at -8 and -4 wks prior to parturition (or -6 to -10 wks prior to occurrence of lameness) and at the week of lameness diagnosis.

Lipidomic profiling of dairy cattle oocytes by high performance liquid chromatography-high resolution tandem mass spectrometry for developmental competence markers

A comparative lipidomic profiling analysis of dairy cattle oocytes with different developmental competences was performed using a combination of high performance liquid chromatography-high resolution tandem mass spectrometry and multivariate statistical analysis. Significant lipidomic changes were identified in degenerating oocytes. Total triacylglycerol in the degenerating oocytes was 1.8-fold higher than that in the normal oocytes; however, total cardiolipin was 53.5% lesser than that in the normal oocytes, which indicated attenuation of energy metabolism. Compared to those in the normal oocytes, triacylglycerols in the degenerating oocytes were composed of longer and more unsaturated acyl chains. In contrast, the acyl chains in free fatty acids present in the degenerating oocytes were shorter and with lesser degree of unsaturation compared to those in the normal oocytes. Moreover, a significant decrease in degenerating oocytes were found in total phosphatidylinositol (14.8 ± 7.6 pmol vs. 24.8 ± 5.5 pmol), total phosphatidylcholine (20.8 ± 8.7 pmol vs. 33.5 ± 7.2 pmol), and total plasmalogen ethanolamine (9.0 ± 4.7 pmol vs. 16.8 ± 5.2 pmol), which indicated dysfunction of lipid-metabolizing enzymes in oocytes during degeneration. Thus, increase of triacylglycerols together with the decrease of certain phospholipid species could be potential markers of oocyte developmental competence. In addition to providing a new approach to investigate the lipidomic changes in oocyte development, the lipidomic profiling in the present study has revealed insights that hold potential to unravel the role of lipid metabolism in oocyte developmental competence in cattle.

Metabolomics meets machine learning: Longitudinal metabolite profiling in serum of normal versus overconditioned cows and pathway analysis

This study aimed to investigate the differences in the metabolic profiles in serum of dairy cows that were normal or overconditioned when dried off for elucidating the pathophysiological reasons for the increased health disturbances commonly associated with overconditioning. Fifteen weeks antepartum, 38 multiparous Holstein cows were allocated to either a high body condition (HBCS; n = 19) group or a normal body condition (NBCS; n = 19) group and were fed different diets until dry-off to amplify the difference. The groups were also stratified for comparable milk yields (NBCS: 10,361 ± 302 kg; HBCS: 10,315 ± 437 kg; mean ± standard deviation). At dry-off, the cows in the NBCS group (parity: 2.42 ± 1.84; body weight: 665 ± 64 kg) had a body condition score (BCS) <3.5 and backfat thickness (BFT) <1.2 cm, whereas the HBCS cows (parity: 3.37 ± 1.67; body weight: 720 ± 57 kg) had BCS >3.75 and BFT >1.4 cm. During the dry period and the subsequent lactation, both groups were fed identical diets but maintained the BCS and BFT differences. A targeted metabolomics (AbsoluteIDQ p180 kit, Biocrates Life Sciences AG, Innsbruck, Austria) approach was performed in serum samples collected on d -49, +3, +21, and +84 relative to calving for identifying and quantifying up to 188 metabolites from 6 different compound classes (acylcarnitines, AA, biogenic amines, glycerophospholipids, sphingolipids, and hexoses). The concentrations of 170 metabolites were above the limit of detection and could thus be used in this study. We used various machine learning (ML) algorithms (e.g., sequential minimal optimization, random forest, alternating decision tree, and naïve Bayes-updatable) to analyze the metabolome data sets. The performance of each algorithm was evaluated by a leave-one-out cross-validation method. The accuracy of classification by the ML algorithms was lowest on d 3 compared with the other time points. Various ML methods (partial least squares discriminant analysis, random forest, information gain ranking) were then performed to identify those metabolites that were contributing most significantly to discriminating the groups. On d 21 after parturition, 12 metabolites (acetylcarnitine, hexadecanoyl-carnitine, hydroxyhexadecenoyl-carnitine, octadecanoyl-carnitine, octadecenoyl-carnitine, hydroxybutyryl-carnitine, glycine, leucine, phosphatidylcholine-diacyl-C40:3, trans-4-hydroxyproline, carnosine, and creatinine) were identified in this way. Pathway enrichment analysis showed that branched-chain AA degradation (before calving) and mitochondrial β-oxidation of long-chain fatty acids along with fatty acid metabolism, purine metabolism, and alanine metabolism (after calving) were significantly enriched in HBCS compared with NBCS cows. Our results deepen the insights into the phenotype related to overconditioning from the preceding lactation and the pathophysiological sequelae such as increased lipolysis and ketogenesis and decreased feed intake.

Invited review: Advances and challenges in application of feedomics to improve dairy cow production and health

Dairy cattle science has evolved greatly over the past century, contributing significantly to the improvement in milk production achieved today. However, a new approach is needed to meet the increasing demand for milk production and address the increased concerns about animal health and welfare. It is now easy to collect and access large and complex data sets consisting of molecular, physiological, and metabolic data as well as animal-level data (such as behavior). This provides new opportunities to better understand the mechanisms regulating cow performance. The recently proposed concept of feedomics could help achieve this goal by increasing our understanding of interactions between the different components or levels and their impact on animal production. Feedomics is an emerging field that integrates a range of omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, metabolomics, metagenomics, and metatranscriptomics) to provide these insights. In this way, we can identify the best strategies to improve overall animal productivity, product quality, welfare, and health. This approach can help research communities elucidate the complex interactions among nutrition, environment, management, animal genetics, metabolism, physiology, and the symbiotic microbiota. In this review, we summarize the outcomes of the most recent research on omics in dairy cows and highlight how an integrated feedomics approach could be applied in the future to improve dairy cow production and health. Specifically, we focus on 2 topics: (1) improving milk yield and milk quality, and (2) understanding metabolic physiology in transition dairy cows, which are 2 important challenges faced by the dairy industry worldwide.

Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics

The metabolic responses of cows undergo substantial changes during the transition from late pregnancy to early lactation. However, the molecular mechanisms associated with these changes in physiological metabolism have not been clearly elucidated. The objective of this study was to investigate metabolic changes in transition cows from the perspective of plasma metabolites. Plasma samples collected from 24 multiparous dairy cows on approximately d 21 prepartum and immediately postpartum were analyzed using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry in positive and negative ion modes. In conjunction with multidimensional statistical methods (principal component analysis and orthogonal partial least squares discriminant analysis), differences in plasma metabolites were identified using the t-test and fold change analysis. Sixty-seven differential metabolites were identified consisting of AA, lipids, saccharides, and nucleotides. The levels of 32 plasma metabolites were significantly higher and those of 35 metabolites significantly lower after parturition than on d 21 prepartum. Pathway analysis indicated that the metabolites that increased from late pregnancy to early lactation were primarily involved in lipid metabolism and energy metabolism, whereas decreased metabolites were related to AA metabolism.

Identification of serum metabolites associated with the risk of metritis in transition dairy cows

In this study, we aimed to identify metabolite signatures that characterize metritis prior to, during, and after the disease incidence. Blood samples were collected from 100 Holstein cows at five time points before and after parturition. Six cows that developed metritis and 20 controls were selected for metabolomics analysis in a nested case-control study. Twenty nine serum metabolites were quantified using gas chromatography–mass spectroscopy. Results showed that similar panels of metabolites differentiated pre-metritic and control cows at 8 and 4 wk prepartum. The top most important metabolites that differentiated the two groups of cows at 8 wk prepartum were oxalate, ornithine, pyroglutamic acid, d-mannose, and glutamic acid, and at 4 wk prepartum were ornithine, pyroglutamic acid, d-mannose, glutamic acid, and phosphoric acid, suggesting their potential use as risk biomarkers for metritis. Area under the curve with values of 1.0 and 0.969 at 8 and 4 wk, respectively, indicated that those panels of metabolites have a very high sensitivity and specificity to be used as risk biomarkers for metritis. Overall, results showed that specific serum metabolite signatures can be used to screen cows for susceptibility to metritis during the dry off period, and to better understand the etiopathobiology of the disease.