Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease

Introduction

Ketosis is a prevalent metabolic disease of transition dairy cows that affects milk yield and the development of other periparturient diseases.

Objectives

The objective of this study was to retrospectively metabotype the serum of dairy cows affected by ketosis before clinical signs of disease, during the diagnosis of ketosis, and after the diagnosis of disease and identify potential predictive and diagnostic serum metabolite biomarkers for the risk of ketosis.

Methods

Targeted metabolomics was used to identify and quantify 128 serum metabolites in healthy (CON, n?=?20) and ketotic (n?=?6) cows by DI/LC-MS/MS at ?8 and ?4 weeks prepartum, during the disease week, and at +4 and +8 weeks after parturition.

Results

Significant changes were detected in the levels of several metabolite groups including amino acids, glycerophospholipids, sphingolipids, acylcarnitines, and biogenic amines in the serum of ketotic cows during all time points studied.

Conclusions

Results of this study support the idea that ketosis is preceded and associated and followed by alterations in multiple metabolite groups. Moreover, two sets of predictive biomarker models and one set of diagnostic biomarker model with very high sensitivity and specificity were identified. Overall, these findings throw light on the pathobiology of ketosis and some of the metabolites identified might serve as predictive biomarkers for the risk of ketosis. The data must be considered as preliminary given the lower number of ketotic cows in this study and more research with a larger cohort of cows is warranted to validate the results.

     

Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows

Introduction

Metritis is an uterine pathology that causes economic losses for the dairy industry. It is associated with lower reproductive efficiency, increased culling rates, decreased milk production and increased veterinary costs.

Objectives

To gain a more detailed view of the urine metabolome and to detect metabolite signature in cows with metritis. In addition, we aimed to identify early metabolites which can help to detect cows at risk to develop metritis in the future.

Methods

We used nuclear magnetic resonance spectroscopy starting at 8 and 4 weeks prior to the expected day of parturition, during the week of diagnosis of metritis, and at 4 and 8 weeks after diagnosis of metritis in Holstein dairy cows.

Results

At 8 weeks before parturition, pre-metritic cows had a total of 30 altered metabolites. Interestingly, 28 of them increased in urine when compared with control cows (P?<?0.05). At 4 weeks before parturition, 34 metabolites were altered. At the week of diagnosis of metritis a total of 20 metabolites were altered (P?<?0.05). The alteration continued at 4 and 8 weeks after diagnosis.

Conclusions

The metabolic fingerprints in the urine of pre-metritic and metritic cows point toward excretion of multiple amino acids, tricarboxylic acid cycle metabolites and monosaccharides. Combination of galactose, leucine, lysine and panthotenate at 8 weeks before parturition might serve as predictive biomarkers for metritis.

     

Prepartal overfeeding alters the lipidomic profiles in the liver and the adipose tissue of transition dairy cows

Introduction

Physiological adaptations in the energy metabolism of dairy cows during the periparturient period are partly mediated by insulin resistance (IR), which may subsequently induce metabolic disorders postpartum. The molecular mechanisms underlying IR in dairy cows are largely unknown.

Objective

This study aimed to find a novel insight into the molecular mechanisms underlying IR in dairy cows during the periparturient period by analyzing the effects of prepartal overfeeding on the lipidomic profiles in the liver and adipose tissue (AT).

Methods

Sixteen cows were allocated to controlled-energy and high-energy feeding groups. Lipidomic profiling was conducted on liver and adipose tissue samples collected at 8 days prior to the predicted parturition, and 1 day (only AT) and 9 days after the actual parturition.

Results

Five ceramides (Cers) were identified to be significantly increased by prepartal overfeeding in AT in the analysis of the variance between groups within time points. Principal component-linear discriminant analysis showed that lipidomic profiles between the feeding groups were mainly characterized by phosphatidylcholines (PC), phosphatidylethanolamines (PE), lysophophosphatidylcholines (LysoPC), and lysophosphatidylethanolamines (LysoPE) in the liver, and by Cer, PE, and phosphatidylinositols (PI) in AT. Lipid class levels indicated that prepartal overfeeding elevated the concentration of PE, PI, LysoPC, LysoPE, and sphingomyelin in the liver, and increased the concentration of Cer in AT during the periparturient period.

Conclusion

Prepartal overfeeding significantly altered the concentrations of various sphingolipids, phospholipids, and lysophospholipids in the liver and AT of dairy cows during the periparturient period.

     

Cinnamon: does it hold its promises in cows? Using non-targeted blood serum metabolomics profiling to test the effects of feeding cinnamon to dairy cows undergoing lactation-induced insulin resistance

Introduction

Cinnamon exerts insulin-enhancing activity in vitro and was demonstrated to improve blood glucose and lipid profiles in several human studies. Such effects may have an impact on metabolically stressed cows.

Objective

To study the effects of cinnamon supplementation during the transition from late pregnancy to early lactation on the metabolism in dairy cows.

Methods

Twenty-four Holstein cows (n?=?8/group) were assigned to either the control group (CTR; without supplementation) or the supplementation groups [supplemental cinnamon at 20 (LCIN) or 40 (HCIN) g/cow per day (d)] from 28 d before calving until 21 d thereafter. Blood samples were assayed for glucose, nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and insulin; an index estimating insulin sensitivity (RQUICKI) was calculated. The serum metabolome was characterized in the samples collected from d 14 using a non-targeted approach.

Results

The serum concentrations of glucose and insulin did not differ among groups and followed a similar pattern over time. The serum NEFA concentrations were greater in LCIN (d 2, 7, and 14) and HCIN (d 14) than in CTR. On d 14 and 21, LCIN and HCIN had greater serum BHBA concentrations than CTR cows. The top 10 metabolites identified with significantly higher levels in the supplemented than the CTR cows were related to fatty acid metabolism.

Conclusion

The data suggest lipolytic and ketogenic effects of cinnamon supplementation in dairy cows during the transition from late gestation to early lactation. The fatty acid metabolites found elevated in the supplemented cows point towards impaired mitochondrial fatty acid β-oxidation.

     

Metallotyping of ketotic dairy cows reveals major alterations preceding,associating, and following the disease occurrence

Introduction

Ketosis is a common metabolic disorder, which is characterized by elevated concentrations of ketone bodies or ketoacids in three body fluids including blood, urine, and milk. Two of the ketones including β-hydroxybutyric acid and acetoacetic acid are strong acids which at high concentrations trigger ketoacidosis influencing physiological functions of various tissues and organs.

Objectives

The objectives of this study were to: (1) investigate mineral alterations in both serum and urine of preketotic, ketotic, and postketotic cows, (2) identify potential predictive and diagnostic mineral biomarkers for ketosis in serum and urine, and (3) better understand the role of minerals in the pathobiology of the disease.

Methods

Inductively coupled plasma mass spectrometry metallotyping was performed in the serum and urine of six cases of ketosis and 20 healthy controls cows at ?8 and ?4 weeks prepartum, at disease diagnosis week, and at +4 and +8 weeks postpartum.

Results

Data showed that concentrations of aluminum (Al), iron (Fe), manganese (Mn), and arsenic (As) were greater (P?<?0.001) in the serum of preketotic, ketotic and postketotic cows at most of the tested time points. Moreover, boron (B) and Al as well as calcium (Ca), phosphorus (P), potassium (K), and magnesium (Mg) were found to be elevated in the urine of preketotic and postketotic cows (P?<?0.001).

Conclusions

It is concluded that alterations of mineral elements observed in the serum and urine of preketotic, ketotic, and postketotic cows might be related to the state of chronic acidosis in those cows. The mineral elements identified in both serum and urine can be used as biomarkers to early diagnose ketosis at its pre-subclinical state and develop preventive interventions in the future.

     

A metabolomics approach to characterize phenotypes of metabolic transition from late pregnancy to early lactation in dairy cows

Introduction

Dairy cows experience metabolic stress during the transition from late pregnancy to early lactation, due to the complex adaptation processes affecting energy homeostasis in support of milk production, collectively referred to as homeorhesis. According to the individual efficiency of this adaptation, some cows develop severe metabolic diseases while others are able to maintain metabolic health.

Objectives

This study aimed to characterize patterns and changes of metabolic phenotype during the transition period, and to identify how far different metabolic pathways are affected by or contributing to the complex system of homeorhesis.

Methods

Blood samples were collected from 26 German Holstein cows, repeatedly during the transition period: 42 and 10 days before calving and 3, 21 and 100 days after calving. Blood serum samples were subjected to a liquid chromatography–mass spectrometry based targeted metabolomics analysis using the AbsoluteIDQ p180 Kit of Biocrates Life Science AG (Innsbruck, Austria). Processed metabolomics data were evaluated by multivariate data analysis techniques such as principal component analysis (PCA) and partial least squares-discriminant analysis and by heatmap visualization.

Results

The PCA revealed a clear separation according to sampling days, indicating a notable shift of the metabolic phenotype during the transition period. The heatmap showed that acylcarnitines provided a consistent clustering within sampling days, while the concentration of glycerophospholipids and sphingolipids were remarkably decreased 10 days before and 3 days after calving than earlier and later in the transition period.

Conclusion

Analyzing longitudinal changes of the blood metabolome and identifying new biomarkers by this approach can help understanding the multifaceted metabolic adaptation of transition dairy cows.

     

Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

Introduction

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.

Objectives

The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.

Methods

A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.

Results

Using partial least squares-discriminant analysis, we observed significant separation between all groups (p?<?0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI)?=?0.940 and 0.929 for HG and LG, respectively.

Conclusion

These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

     

Optimization of fecal sample preparation for untargeted LC-HRMS based metabolomics

Introduction

Collecting feces is easy. It offers direct outcome to endogenous and microbial metabolites.

Objectives

In a context of lack of consensus about fecal sample preparation, especially in animal species, we developed a robust protocol allowing untargeted LC-HRMS fingerprinting.

Methods

The conditions of extraction (quantity, preparation, solvents, dilutions) were investigated in bovine feces.

Results

A rapid and simple protocol involving feces extraction with methanol (1/3, M/V) followed by centrifugation and a step filtration (10 kDa) was developed.

Conclusion

The workflow generated repeatable and informative fingerprints for robust metabolome characterization.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Follicular fluid lipidomics reveals lipid alterations by LH addition during IVF cycles

Introduction

Ovulation induction protocols are key components for performing assisted reproduction treatments successfully. The importance of adding exogenous LH to the controlled ovarian stimulation protocols in young women is still a matter of discussion in the clinical practice.

Objective

To estimate how LH addition to controlled ovarian stimulation protocols may affect the follicular fluid lipid profile of women undergoing in vitro fertilization treatment.

Methods

We conducted the study using 28 self-paired samples, 14 per group. The patients received FSH during their first cycle of ovarian stimulation (FSH group). If the treatment did not result in pregnancy, the same patients returned for a new cycle and received stimulus with the addition of LH to the previous protocol (Low-dose-LH group). Lipidomics analysis was performed by UPLC-MS^E mass spectrometry. The software Progenesis QI was used to identify potential lipid biomarkers. Statistical analysis was performed using the SPSS 18.0 and MetaboAnalyst 2.0 software.

Results

The analysis of clinical data showed no statistically significant differences between groups, in contrast to lipidomic analysis. Concerning lipidomic profile, the lipids differentially expressed in FSH group belong to the following subclasses:triacylglycerols; branched fatty acids and diacylglycerophosphoethanolamines; while in Low-dose-LH group the subclasses are gangliosides; acylglycerophosphoethanolamines; triacylglycerols; acylglycerophosphoserines; GalNAcβ1-3Galα1-4Galβ1-4Glc-(Globo series); amino fatty acids; triacylglycerols and prostaglandins.

Conclusion

The differences found between the groups may contribute to the establishment of potential therapeutical targets based on LH-associated lipid biomarkers aiming to individualize treatments and obtain reproductive success.

     

Serum metabolomics using ultra performance liquid chromatography coupled to mass spectrometry in lactating dairy cows following a single dose of sporidesmin

Introduction

Photosensitization is a common clinical sign in cows suffering from liver damage caused by the mycotoxin sporidesmin. This disease, called facial eczema (FE), is of major importance in New Zealand. Current techniques for diagnosing animals with subclinical sporidesmin-induced liver damage (i.e. without photosensitization) are nonspecific. In addition, little is known of the mechanisms involved in sporidesmin resistance, nor the early effects seen following low-dose sporidesmin intoxication.

Objective

The objective of this study was to identify individual metabolites or metabolic profiles that could be used as serum markers for early stage FE in lactating cows.

Methods

Results are presented from a 59-day sporidesmin challenge in Friesian-cross dairy cows. Serum metabolite profiles were obtained using reversed phase ultra-performance liquid chromatography (UPLC) electrospray ionization mass spectrometry (MS) and UPLC tandem MS. Multivariate and time series analyses were used to assess the data.

Results

Statistical analysis, both with and without the temporal component, could distinguish the profiles of animals with clinical signs from the others, but not those affected subclinically. An increase in the concentrations of a combination of taurine- and glycine-conjugated secondary bile acids (BAs) was the most likely cause of the separation. This is the first time that MS methods have been applied to FE and that bile acids changes have been detected in cattle exposed to sporidesmin.

Conclusions

It is well known that BA concentrations increase during cholestasis due to damage to bile ducts and leakage of the bile. This is the first study to investigate metabolomic changes in serum following a sporidesmin challenge. Further work to establish the significance of the elevation of individual BAs concentrations in the serum of early-stage sporidesmin-poisoned cows is necessary.

     

Best serum biomarker combination for ovarian cancer classification

Background

Screening test using CA-125 is the most common test for detecting ovarian cancer. However, the level of CA-125 is diverse by variable condition other than ovarian cancer. It has led to misdiagnosis of ovarian cancer.

Methods

In this paper, we explore the 16 serum biomarker for finding alternative biomarker combination to reduce misdiagnosis. For experiment, we use the serum samples that contain 101 cancer and 92 healthy samples. We perform two major tasks: Marker selection and Classification. For optimal marker selection, we use genetic algorithm, random forest, T-test and logistic regression. For classification, we compare linear discriminative analysis, K-nearest neighbor and logistic regression.

Results

The final results show that the logistic regression gives high performance for both tasks, and HE4-ELISA, PDGF-AA, Prolactin, TTR is the best biomarker combination for detecting ovarian cancer.

Conclusions

We find the combination which contains TTR and Prolactin gives high performance for cancer detection. Early detection of ovarian cancer can reduce high mortality rates. Finding a combination of multiple biomarkers for diagnostic tests with high sensitivity and specificity is very important.

     

Comparisons of gene coexpression network modules in breast cancer and ovarian cancer

Background

Breast cancer and ovarian cancer are hormone driven and are known to have some predisposition genes in common such as the two well known cancer genes BRCA1 and BRCA2. The objective of this study is to compare the coexpression network modules of both cancers, so as to infer the potential cancer-related modules.

Methods

We applied the eigen-decomposition to the matrix that integrates the gene coexpression networks of both breast cancer and ovarian cancer. With hierarchical clustering of the related eigenvectors, we obtained the network modules of both cancers simultaneously. Enrichment analysis on Gene Ontology (GO), KEGG pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) in the identified modules was performed.

Results

We identified 43 modules that are enriched by at least one of the four types of enrichments. 31, 25, and 18 modules are enriched by GO terms, KEGG pathways, and DO terms, respectively. The structure of 29 modules in both cancers is significantly different with p-values less than 0.05, of which 25 modules have larger densities in ovarian cancer. One module was found to be significantly enriched by the terms related to breast cancer from GO, KEGG and DO enrichment. One module was found to be significantly enriched by ovarian cancer related terms.

Conclusion

Breast cancer and ovarian cancer share some common properties on the module level. Integration of both cancers helps identifying the potential cancer associated modules.

     

Integrated analysis of microRNA-target interactions with clinical outcomes for cancers

Background

Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.

Methods

Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.

Results

We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.

Conclusions

This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.

     

A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer

Background

Over 500,000 women worldwide are diagnosed with ovarian or endometrial cancer each year. We have used a two-step strategy to identify plasma proteins that could be used to improve the diagnosis of women with an indication of gynecologic tumor and in population screening.

Methods

In the discovery step we screened 441 proteins in plasma using the proximity extension assay (PEA) and five Olink Multiplex assays (CVD II, CVD III, INF I, ONC II, NEU I) in women with ovarian cancer (n?=?106), endometrial cancer (n?=?74), benign ovarian tumors (n?=?150) and healthy population controls (n?=?399). Based on the discovery analyses a set of 27 proteins were selected and two focused multiplex PEA assays were developed. In a replication step the focused assays were used to study an independent set of cases with ovarian cancer (n?=?280), endometrial cancer (n?=?228), women with benign ovarian tumors (n?=?76) and healthy controls (n?=?57).

Results

In the discovery step, 27 proteins that showed an association to cancer status were identified. In the replication analyses, the focused assays distinguished benign tumors from ovarian cancer stage III–IV with a sensitivity of 0.88 and specificity of 0.92 (AUC?=?0.92). The assays had a significantly higher AUC for distinguishing benign tumors from late stage ovarian cancer than using CA125 and HE4 (p?=?9.56e?22). Also, population controls could be distinguished from ovarian cancer stage III–IV with a sensitivity of 0.85 and a specificity of 0.92 (AUC?=?0.89).

Conclusion

The PEA assays represent useful tools for identification of new biomarkers for gynecologic cancers. The selected protein assays could be used to distinguish benign tumors from ovarian and endometrial cancer in women diagnosed with an unknown suspicious pelvic mass. The panels could also be used in population screening, for identification of women in need of specialized gynecologic transvaginal ultrasound examination.

Funding

The Swedish Cancer Foundation, Vinnova (SWELIFE), The Foundation for Strategic Research (SSF), Assar Gabrielsson Foundation.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

Using donor human milk to feed vulnerable term infants: a case series in KwaZulu Natal,South Africa

Background

Donor human milk is the World Health Organization’s recommendation for infant feeding when the mother’s own breast milk is unavailable. Breast milk has been shown to reduce morbidity and mortality and in low birthweight infants, donor milk reduces the incidence of necrotising enterocolitis, late onset sepsis and improves outcomes. There is a paucity of literature documenting outcomes of using donor human milk in older children who need additional support for a variety of health issues.

Case presentation

A series of seven case studies is presented of orphaned and abandoned children, many of whom were either HIV exposed or positive. All children were fed with pasteurised donor human milk at a transition home and their progress reported.

Conclusions

Although detailed medical records were not always available, the case studies provide anecdotal evidence of the protective effects of donor human milk against failure to thrive, diarrhoea, atopic dermatitis, and opportunistic infections.

     

Infant feeding information,attitudes and practices: a longitudinal survey in central Nepal

Background

Infant feeding is governed by environmental as well as cultural factors. Breastfeeding knowledge and attitudes are known to be associated with breastfeeding duration. This study investigated breastfeeding information, attitudes and supplementary feeding in the central hills district of Nepal.

Methods

A community-based prospective cohort study of 701 pregnant women was conducted. Information on breastfeeding attitudes, feeding practices and supplementary feeding was sought from the cohort at 4 weeks, 12 weeks and 22 weeks postpartum through repeated interviews using validated questionnaires.

Results

Average duration of intended breastfeeding was 28 months (SD 7.9) and average target time to introduce solid foods was 6.1 months (SD 1.2). About 80% of women reported their husband, mother/mother-in-law preferred breastfeeding. Eleven percent of the cohort said that breastfeeding was not enjoyable. At 12 weeks and 22 weeks after birth, about a quarter (24.8%) and half (52.8%) of the infants were introduced cow/buffalo milk, respectively, while only 6.3% and 13.4% of them were given infant formula. Overall, any breastfeeding rate remained high at over 98% throughout the follow up period.

Conclusions

Breastfeeding attitudes were encouraging in this population. Breastfeeding was almost universal. Use of infant formula was quite low, whereas cow or buffalo milk appeared to be popular supplementary foods.

     

Metabolomic profiling of maternal hair suggests rapid development of intrahepatic cholestasis of pregnancy

Introduction

Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.

Objective

Investigate the maternal hair metabolome for predictive biomarkers of ICP.

Methods

The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.

Results

Of 105 metabolites detected in hair, none were significantly associated with ICP.

Conclusion

Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.

     

Extent and pattern of pregnancy losses and progesterone levels during gestation in Swedish Red and Swedish Holstein dairy cows

Background

Pregnancy loss is a major source of infertility in dairy cows. Despite a fertilization rate after insemination (AI) of approximately 90%, calving rates are 30%–50%, indicating the occurrence of extensive embryonic and foetal losses. The aim of this study was to establish the extent and pattern of embryonic and foetal loss in Swedish Red (SR) and Swedish Holstein (SH) dairy cows, as well as, the relationship to oestrus intensity (OI) and progesterone (P4) concentration. In total, 2130 AIs and 16,176 milk P4 samples from 359 SR and 212 SH dairy cows were included in the study. Pregnancy losses were estimated using data from P4 values combined with AI information and calving data.

Results

Total pregnancy loss from AI to the day of calving was 65%. Early embryonic loss, late embryonic loss and foetal loss were estimated to be 29, 14 and 13%, respectively. There is strong evidence in the literature that P4 concentrations at different time points are associated with pregnancy loss. In the present study, cows with pregnancy losses had significantly higher P4 levels at the day of AI and significantly lower P4 concentration at days 10, 21 and 30 after AI compared to pregnant cows. Swedish Red cows had significantly lower total pregnancy losses compared to SH cows (62% and 68% respectively, P?=?0.017). Early embryonic loss was 6.7% points lower for cows inseminated at a stronger OI (OI?=?3) compared to at a weaker OI (OI?=?2, P?=?0.006). Cows inseminated at ovulation number?≥?5 had significantly lower early pregnancy losses compared to cows inseminated at first or second ovulation (11.5 and 8% points, respectively, P?<?0.05). With an increase of one SD of milk (448 kg ECM) during the first 60 days in milk, early embryonic loss increased by 4.7% points (P?=?0.006).

Conclusions

It is important to increase the number of cows calving per insemination by reducing embryo/foetal loss. This outcome can be achieved by management and breeding for optimal P4 levels at critical time points, and by considering oestrus expression in the breeding programmes to facilitate the correct timing of insemination.