AbstractParturition and lactation stress greatly affect physiological and metabolic status of postpartum cows. Monitoring feeding behavior can help assess the health status of postpartum cows. This study aimed to explore the changes in feeding behavior, milk yield (MY), serum indexes, and metabolites of dairy cows during three weeks postpartum. Furthermore, the relationships between feeding behavior, milk yield and serum metabolites were investigated. One hundred seventy-eight healthy multiparous Holstein dairy cows were enrolled to continuously record feeding behavior variables, including feeding time (FT), frequency of feeding (FF), and average feeding time (AFT), using the Nedap neck collar devices, wherein the milk yield was recorded. Out of 178 Holstein dairy cows, 20 cows with the same parity number (2 parities), body condition score (3.0 ± 0.25, recorded in 7 days before parturition), and with eutocic calvings, were selected to explore the serum indexes changes on week 1, 2, and 3 postpartum. Then, 7 cows were further selected based on similar calving date (± 7 d) for metabolic transition detection. Compared to week 1 postpartum, the FT and AFT normalized values on week 2 and 3 postpartum were significantly increased (P < 0.001). The normalized values of FT and AFT were significantly and positively correlated with MY (P < 0.01). Furthermore, the serum total protein (TP), total cholesterol (T-Chol) and IgA contents on week 2 and 3 postpartum were significantly increased compared to week 1 postpartum, while the β-hydroxybutyric acid (BHBA) concentration was significantly decreased. Serum malondialdehyde (MDA), interleukin-6 (IL-6), and leptin (LEP) contents of the 2nd week postpartum, were significantly declined compared to the 1st week postpartum. The normalized values of FT was negatively correlated with serum BHBA level, while positively correlated with the contents of TP and T-Chol (P < 0.05). According to UPLC-MS/MS and pathway analysis results, the serum levels of glycerophospholipids changed most in first three weeks postpartum, which were mainly related to glycerophospholipid metabolism. Finally, the correlation analysis showed that 29 glycerophospholipids (GPs) and 3 fatty acyls (FAs) were significantly and positively correlated with the FT and AFT normalized values (P < 0.05). Together, the findings suggest that the feeding behavior variables, such as FT and AFT, could serve as reliable indicators of energy metabolism in dairy cows during the first three weeks postpartum.
IntroductionMost dairy farms have established an information management system that captures all kinds of data to achieve fine and intelligent pasture management. Monitoring the behavior of the cows helps evaluate physiological health, especially feeding behavior1. Feeding behavior generally includes daily feeding time, feeding frequency, feeding rate, and feeding intake2,3. Traditional monitoring of feeding behavior usually adopts a manual detection method, which is time-consuming, laborious, and inefficient, and observation results are affected by subjective factors that require more precise analysis4,5. With the rapid development of the artificial intelligence technology, some wearable behavior-monitoring systems can replace manual real-time monitoring. They are integrated with sensors, such as collars, ear tags, and leg bands, which have been used to detect animals’ feeding behaviors2. For example, Wolfger et al. attached an accelerometer to an ear tag of beef cattle to explore the effects of detecting feeding behavior6. They found that the sensitivity and specificity of feeding behavior monitoring were 95% and 76%, and the concordance correlation between observations and the sensor was 0.79 (95% CI: 0.61 to 0.85). In addition, Nogoy et al. collected cattle behavior data from an accelerometer sensor placed on the cattle collar to classify the cattle’s active, eating, and resting behaviors7. They obtained high classification performance in classifying the eating behavior (79% precision, 88% sensitivity, and 83% F1-score) using the combination of a 4-min window size and the long short-term memory (LSTM) algorithm. Therefore, evidence supports that wearable devices with accelerometers are useful monitoring systems for cows’ feeding behavior.The perinatal period, also known as the transition period, is critical in the production cycle of dairy cows, spans from three weeks before and three weeks after calving8. In the last weeks of gestation, cows have to bear the burden of rapid fetal development and activate the lactation mechanism to regenerate udder glandular tissue and colostrum production9. However, dairy cows’ dry matter intake (DMI) decreases three weeks before calving10. During parturition, endocrine changes could force dairy cows to produce physiological stress, leading to loss of appetite and insufficient DMI11. A rapid increase in energy requirements at the onset of lactation results in a negative energy balance (NEB) in dairy cows that begins several days before calving and may continue to 2–3 weeks later12. The NEB in dairy cows is a physiological state of undernutrition that disrupts metabolic and immune functions13. This condition increases the risk of early metabolic diseases14, compromising animal welfare, production, and profitability15. To meet its energy demands, the organism mobilizes fat and protein for hepatic gluconeogenesis, resulting in increased blood levels of non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHBA) into the circulation16,17. These blood indexes and metabolites reflect the metabolic status; however, blood sampling is invasive and time-consuming. Feeding behavior can be easily monitored using automated sensor devices. Tracking variables such as feeding time and meal frequency has been used to identify cows at risk of postpartum-related disorders or diseases18,19. Monitoring the feeding behavior of dairy cows after delivery can help us better understand postpartum cows’ health status. We hypothesized that automatic monitoring feeding behavior might be used for prediction metabolic status of healthy dairy cows in early lactation.Metabonomics is an ideal tool to further measure the dynamic metabolic response of dairy cows after delivery20. Examining the altered expression metabolites of dairy cows resulting from week 1 to 3 w postpartum, makes it possible to verify and elucidate the serum biomarker and interpret the functional pathways associated with the health status of postpartum cows. Therefore, the objective of this study was to explore the changes in feeding behavior, milk yield (MY), serum indexes, and metabolites in dairy cows during the first three weeks postpartum. Furthermore, the relationships between feeding behavior, milk yield, and metabolic status were examined to clarify the correlation between feeding behavior and serum metabolic markers.Materials and methodsAll animal use and experimental procedures in experiments were approved by the local commercial Holstein dairy farm and the Inner Mongolia Agricultural University Research Ethics Committee (NND2023118). All experiments were performed in accordance with relevant guidelines and regulations and authors complied with the ARRIVE guidelines.Animals, diets, and feeding behavioral variablesThe experiment was conducted from October 2023 to November 2023 on a commercial dairy farm (Hohhot, China). The cows had to be multiparous and clinically healthy to be included in the study. The veterinarian and clinicians of the Department of Farm Animals assessed their health status. Any cow that contracted an illness or transition disease except for calving difficulties was removed from the experiment, and the associated data was omitted. One hundred and seventy-eight healthy Holstein dairy cows (2, 3, and 4 parities) with similar milk yields (305-day lactation) (total milk yield of 10,750 to 12,021 kg of the last lactation period), and body condition score (BCS 3.0 ± 0.5) were enrolled and kept in a free-stall barn with free access to drinking water. According to Edmonson’s method21, the same person performed the BCS on all test cows at 7 days before parturition. The study lasted 3 weeks, beginning on parturition and continuing until 21 d of postpartum. Throughout the study, all cows were fed exactly the same diet and kept in the same enclosed and scattered hurdle after calving. Each cow has a separate bed with chopped wheat straw. Cows were milked twice daily, at 8:00 a.m. and 4:00 p.m., and fed with a total mixed ration (TMR) after milking. Feed residues were removed from the fed bunk before each new TMR delivery. Ingredient and nutrient composition of diet for postpartum dairy cows is presented in Table 1.Table 1 Ingredient and nutrient composition of diet for postpartum dairy cows (%, dry matter basis).Full size tableAll test cows were equipped with a Nedap SmartTag Neck (Nedap Livestock Management, Groenlo, Netherlands) to continuously monitor feeding behavior and collect complete data from parturition to 21 days postpartum. The Nedap SmartTag Neck was validated by Borchers et al.22. The Nedap recorder calculated and summarized the data every 2 h and reported the feeding time (FT) and feeding frequency (FF) data from 0 to 24 h every day to Nedap Livestock Management System software as the daily FT(min/d), FF (time/d). The average feeding time (AFT, min/time) was calculated by dividing FT by FF. Data processing and characteristics of feeding behavioral variables are shown in Table 2.Table 2 Details of feeding behavior data features (n = 178).Full size tableSample collection and measurementThe sample size was determined based on previous studies with similar designs and methods in dairy cows23,24; however, a power analysis was not conducted. The farm used a WestfaliaSurge milking system (GEA Farm Technologies, Cambridge, New Zealand) and associated DairyPlan C21 (version 5.2) herd and parlor management software (GEA Farm Technologies). The MY of 178 Holstein dairy cows during the 3 weeks postpartum was recorded.Out of 178 Holstein dairy cows, 20 cows with the same parity number (2 parities), body condition score (3.0 ± 0.25, recorded in 7 days before parturition), and with eutocic calvings, were selected to explore the serum indexes changes on week 1, 2, and 3 postpartum. Blood samples were collected from the coccygeal vein and placed into evacuated serum tubes before the morning feeding at d 7, 14 and 21 postpartum. All the tubes were centrifuged at 4,000 × g for 10 min to obtain serum and stored at − 80 °C for later analysis of serum indexes and metabolome profiles. Serum glucose (Glu), total protein (TP), and total cholesterol (T-Chol) were analyzed on a Mindray BS-460 automated hematology analyzer (BS-460, Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China). β-Hydroxybutyric acid (BHBA) concentration was analyzed using ELISA kits (Ruixin Biotech, Quanzhou, China). Malondialdehyde (MDA), immunoglobulin A (IgA), immunoglobulin M (IgM), interleukin-6 (IL-6), cortisol (COR), and leptin (LEP) contents in serum were determined using commercial kits according to the manufacturer’s instructions (Jiyinmei Co., Ltd, Wuhan, China).UPLC-MS/MS compound identification and quantificationOut of 20 cows, 7 cows were further selected based on similar calving date (± 7 d) for metabolic transition detection on week 1, 2, and 3 postpartum. Ultra-performance liquid chromatography coupled with chromatography with tandem mass spectrometry (UPLC-MS/MS) was used to detect serum metabolites qualitatively and quantitatively. The sample stored at -80 °C refrigerator was thawed on ice and vortexed for 10 s. 50 μL of sample and 300 μL of extraction solution (ACN: Methanol = 1:4, V/V) containing internal standards were added into a 2 mL microcentrifuge tube. The sample was vortexed for 3 min and then centrifuged at 12,000 rpm for 10 min (4 °C). 200 μL of the supernatant was collected and placed at -20 °C for 30 min and then centrifuged at 12,000 rpm for 3 min (4 °C). A 180 μL aliquots of supernatant were transferred for LC–MS analysis.The sample extracts were analyzed using an LC–ESI–MS/MS system (UPLC, ExionLC AD, https://sciex.com.cn/; MS, QTRAP® System, https://sciex.com/). The analytical conditions were as follows, UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 µm, 2.1 mm*100 mm); column temperature, 40 °C; flow rate, 0.4 mL/min; injection volume, 2 μL; solvent system, water (0.1% formic acid): acetonitrile (0.1% formic acid); solvent B gradient program, 5% to 20% in 2 min, increased to 60% in the following 3 min, increased to 99% in 1 min and held for 1.5 min, then come back to 5% within 0.1 min, held for 2.4 min.ESI-QTRAP-MS/MSLIT and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (QTRAP), QTRAP® LC–MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in positive and negative ion mode and controlled by Analyst 1.6.3 software (Sciex). The ESI source operation parameters were as follows: source temperature 500 °C; ion spray voltage (IS) 5500 V (positive), -4500 V (negative); ion source gas I (GSI), gas II (GSII), curtain gas (CUR) were set at 55, 60, and 25.0 psi, respectively; the collision gas (CAD) was high. Instrument tuning and mass calibration were performed with 10 and 100 μmol/L polypropylene glycol solutions in QQQ and LIT modes. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period.Data analysisThe MY, FT, FF, and AFT were exported into an Excel spreadsheet (Microsoft Corp., Redmond, WA). The GraphPad Prism version 9.5 Graphpad Software) was the data processing and statistical analysis software used in this experiment. Kolmogorov–Smirnov test was used to analyze the normal distribution of data. When the data fit the normal distribution, the one-way ANOVA was used for variance analysis. If data did not fit normal distribution, non-parametric tests such as Mann–Whitney and Kruskal–Wallis was used for analysis. Data was presented as least squares means and the standard error of mean (SEM). P < 0.05 was used as the cut-off for statistical significance. Pearson’s correlation analysis was generated using the chi-square and Fisher’s test of SPSS version 25.0 (IBM Corp.). P < 0.05 was significant correlations.Differences in serum metabolites on week 1, 2, and 3 postpartum (i.e., W1 vs. W2 vs. W3 ) were used in multivariate statistical analysis methods, including principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA). Significantly different metabolites were identified using variable importance in projection (VIP) scores (VIP no less than 1.0) obtained from the OPLS-DA model and P-values (P value less than 0.05). The KEGG (Kyoto Encyclopedia of Genes and Genomes) database annotated and enriched differential metabolites25,26.ResultsOverall distribution of feeding behavioral variablesThe distribution of feeding behavior variables is shown in Fig. 1. As it can be seen in Fig. 1a, the range of FT data was mainly between 120 and 450 min, accounting for 91.50% of all the FT data. The range of FF data was mainly between 6 and 20 time/d, accounting for 94.66% of all the FF data (Fig. 1b). The range of AFT data was mainly between 10 and 40 min/time, accounting for 93.82% of all the AFT data (Fig. 1c). The FT, FF, and AFT data were highly dispersed, indicating great differences in feeding behavioral variables among individual cows.Fig. 1Data distribution of feeding behavior variables. (a) FT. (b) FF. (c) AFT (n = 178).Full size imageChanges on feeding behavioral variables with postpartum daysThe box plots of normalized feeding behavioral variables are shown in Fig. 2. The normalized FT value initially decreased and then increased as postpartum days progressed, reaching its lowest point on day 4 postpartum, and gradually stabilizing from day 7 postpartum (Fig. 2a). As shown in Fig. 2b, the normalized FF value initially decreased and then increased between days 1 and 21 postpartum, reaching its lowest point on day 4 postpartum, and gradually increasing from day 7 onward. From parturition to day 21 postpartum, the normalized AFT values were lowest on day 1 postpartum, gradually increased, and then stabilized (Fig. 2c).Fig. 2The box plots of normalized feeding behavioral variables. (a) FT. (b) FF. (c) AFT (n = 178).Full size imageDifferential analysis of feeding behavioral variables and MY during the three weeks postpartumIt can be seen in Fig. 3a that compared with week 1 postpartum, the normalized values of FT on week 2 and 3 postpartum were significantly increased (P < 0.0001; 21.94% and 28.40%, respectively). No differences were observed in the normalized values of FF among week 1 , 2, and 3 postpartum (Fig. 3b, P > 0.05). As shown in Fig. 3c, the normalized values of AFT on week 1 postpartum were significantly lower than those in week 2 and 3 postpartum (P < 0.001). The normalized values of AFT on week 2 and 3 postpartum increased by 22.21% and 31.39%, respectively, compared to week 1 postpartum (P < 0.001). The MY of dairy cows increased with postpartum weeks, and reached 44.34 ± 0.45 kg·d-1 on week 3 postpartum (Fig. 3d). Compared to week 1 postpartum, milk yield (MY) in weeks 2 and 3 postpartum significantly increased, rising by 33.99% and 49.80%, respectively (P < 0.0001). MY in week 3 postpartum was significantly higher than in week 2 postpartum, with an increase of 11.80% (P < 0.05).Fig. 3Differential analysis of feeding behavioral variables and MY of dairy cows in 3 week postpartum (n = 178). (a) FT. (b) FF. (c) AFT. (d) MY. Significant differences between week 1, 2 and 3 postpartum are noted ****P < 0.0001;***P < 0.001; **P < 0.01; *P < 0.05 .Full size imageCorrelation analysis between feeding behaviour and MYThe correlation between feeding behavioral variables and MY of dairy cows during the three weeks postpartum were shown in Table 3. The normalized values of FT were positively correlated with MY (R = 0.949, P < 0.01). The normalized values of AFT had a significantly correlation with MY (R = 0.977, P < 0.01).Table 3 Pearson correlation analysis between feeding behavioral variables and MY in 3 week postpartum (n = 178).Full size tableDifferential analysis of serum indexes of dairy cows during the three weeks postpartumResults of serum indexes of dairy cows during the three weeks postpartum are shown in Table 4. Compared to week 1 postpartum, the levels of TP, T-Chol, and IgA in week 2 postpartum significantly increased 4.15%, 25.40%, and 11.23%, respectively (P < 0.05). In contrast, the levels of BHBA, MDA, IL-6, and LEP significantly decreased 20.11%, 13.57%, 25.43%, and 25.85%, respectively (P < 0.05). The levels of TP, T-Chol, IgA, IgM, and COR in week 3 postpartum were significantly higher than those in week 1 postpartum, increasing by 7.33%, 60.08%, 16.49%, 16.06%, and 14.16%, respectively (P < 0.05). In contrast, the BHBA level significantly decreased by 16.62% (P < 0.05).Table 4 Differential analysis of serum indexes of dairy cows in 3 week postpartum (n = 20).Full size tableAssociation analysis of serum indexes with FT and AFTTable 5 showed the correlation between serum indexes with FT and AFT. As shown in Table 5, serum BHBA level has a significant negative correlation with the normalized values of FT (R = -0.484, P < 0.05). The content s of TP and T-Chol were positively correlated with the normalized values of FT and AFT ( P < 0.05).Table 5 Pearson correlation analysis between feeding behavioral variables and serum indexes during three weeks postpartum (n = 20).Full size tableMetabolite profiles of serum samples and data analysisOverview of the metabolitesAs shown in Fig. 4, a total of 733 metabolites were identified in the week 1, 2 and 3 postpartum. More than 75% of metabolites can be classified into six groups: amino acid and its metabolites (163), organic acid and its derivatives (118), glycerophospholipids (GPs, 79), benzene and substituted derivatives (76), fatty acyls (FAs, 66) and nucleotide, and its metabolites (53). The PCA result of the W1 vs. W2 vs. W3 showed that the serum metabolites with different time points after delivery tended to separate (Supplementary Fig. S1 online). The OPLS-DA score plot (Supplementary Fig. S2 online) of metabolomics data revealed a clear separation between W2 vs. W1, W3 vs. W1, and W3 vs. W2.Fig. 4Classification of the 733 metabolites of samples.Full size imageDifferential metabolite and pathway analysisUnder the criterion of VIP > 1.0 and P < 0.05, we identified 66 differential metabolites between the week 2 postpartum (W2) and week 1 postpartum (W1), of which 52 increased and 14 decreased in W2 (Fig. 5a). Compared with W1, GPs increased most in W2 (Supplementary Table S1 online). A total of 213 differential metabolites between week 3 postpartum (W3) and week 1 postpartum (W1) were identified in the volcano plot (Fig. 5b), with 184 metabolites showing increased levels in W3, as detailed in Supplementary Table S2 (online). Compared with W1, GPs increased most in W3 (Supplementary Table S2 online). There are 107 differential metabolites identified between W3 and W2, 84 differential metabolites were increased in the W3 (Fig. 5c, Supplementary Table S3 online). Compared with W2, GPs increased most in W3 (Supplementary Table S3 online).Fig. 5The volcano plots of pairwise comparisons of differential metabolites. (a) W2 vs. W1. (b) W3 vs. W1. (c) W3 vs. W2.Full size imageIn the week 2 postpartum (W2) vs. week 1 postpartum (W1) comparison, top 20 enriched pathways according to KEGG enrichment analysis are showed in Fig. 6a. The most significant metabolic pathway between the 2nd and 1st week postpartum was glycerophospholipid metabolism (P = 0.04), which was associated with 13 glycerophospholipids (GPs), including phosphatidylcholine (PC) (PC(8:0/8:0)), lysophosphatidylethanolamine (LPE) (LPE(17:1/0:0), LPE(16:0/0:0), LPE(16:1/0:0), LPE(20:3/0:0), LPE(22:5/0:0)), lysophosphatidylcholine (LPC) (LPC(O-16:1), LPC(18:0/0:0)), and lysophosphatidic acid (LPA) (LPA(0:0/18:2)) (Fig. 6b).Fig. 6The pathway analysis of differential metabolites recognized in W2 vs. W1. (a) Bubble diagram of top 20 pathways of KEGG enrichment analysis. (b) Differential metabolite clustering heat map of glycerophospholipid metabolism.Full size imageIn the week 3 postpartum (W3) vs. week 1 postpartum (W1) comparison, the upregulated metabolites in W3 activated multiple biological pathways (Fig. 7a), including glycerophospholipid metabolism (P = 0.00) and arachidonic acid metabolism (P = 0.00). A total of 55 GPs were associated with glycerophospholipid metabolism, including PC(8:0/8:0), PC(12:0/12:0), PC(O-1:0/O-16:0), LPE(14:0/0:0), LPE(0:0/15:0), LPE(16:0/0:0), LPE(16:1/0:0), LPE(18:2/0:0), LPE(17:1/0:0), LPE(18:3/0:0), LPE(20:3/0:0), LPE(20:4/0:0), LPC(13:0/0:0), LPC(14:0/0:0), LPC(14:1/0:0), LPC(15:0/0:0), LPC(16:0/0:0), LPC(16:1/0:0), LPC(16:2/0:0), LPC(17:1/0:0), LPC(17:2/0:0), LPC(O-16:1), LPC(O-18:2), LPC(O-18:3), LPC(O-20:2), LPC(18:0/0:0), LPC(18:1/0:0), LPC(18:2/0:0), LPC(18:3/0:0), LPC(19:2/0:0), LPC(20:2/0:0), LPC(20:3/0:0), LPA(16:0/0:0), LPA(16:1), LPA(0:0/18:2), and lysophosphatidylglycerol (LPG)(LPG(0:0/18:2)) (Fig. 7b). Three GPs (PC(8:0/8:0), PC(12:0/12:0), PC(O-1:0/O-16:0)) and three FAs (arachidonic acid (AA), TXB2, prostaglandin E2) were associated with arachidonic acid metabolism (Fig. 7c).Fig. 7The pathway analysis of differential metabolites recognized in W3 vs.W1. (a) Bubble diagram of top 20 pathways of KEGG enrichment analysis. (b) Differential metabolite clustering heat map of glycerophospholipid metabolism. (c) Differential metabolite clustering heat map of arachidonic acid metabolism.Full size imageAs shown in Fig. 8a, the differential metabolites between W3 and W2 were involved in multiple biological pathways, including glycerophospholipid metabolism (P = 0.00), arachidonic acid metabolism (P = 0.01), and linoleic acid metabolism (P = 0.05). Additionally, 21 upregulated glycerophospholipids (GPs) were associated with glycerophospholipid metabolism, including PC(12:0/12:0), PC(O-1:0/O-16:0), LPE(14:0/0:0), LPC(13:0/0:0), LPC(14:1/0:0), LPC(15:0/0:0), LPC(16:0/0:0), LPC(17:1/0:0), LPC(18:2/0:0), LPC(20:2/0:0), LPC(O-16:1), LPC(O-18:2), LPC(O-18:3), LPA(16:1), LPA(0:0/18:2) and LPG(0:0/18:2) (Fig. 8b). Four up-regulated differential metabolites were associated with arachidonic acid metabolism, including AA, TXB2, PC(12:0/12:0), and PC(O-1:0/O-16:0). In addition, PC(12:0/12:0), PC(O-1:0/O-16:0) and AA were also associated with linoleic acid metabolism.Fig. 8The pathway analysis of differential metabolites recognized in W3 vs.W2. (a) Bubble diagram of top 20 pathways of KEGG enrichment analysis. (b) Differential metabolite clustering heat map of glycerophospholipid metabolism.Full size imageAssociation analysis of serum key differential metabolites with FT and AFTTable 6 showed the correlation between serum key differential metabolites with FT and AFT. As shown in Table 6, the normalized values of FT and AFT were positively correlated with three serum fatty acyls and 29 glycerophospholipids, including AA, TXB2, prostaglandin E2, PC(8:0/8:0), PC(12:0/12:0), LPA(0:0/18:2), LPC(13:0/0:0), LPC(14:0/0:0), LPC(15:0/0:0), LPC(16:0/0:0), LPC(17:1/0:0), LPC(18:0/0:0), LPC(19:2/0:0), LPC(20:2/0:0), LPE(14:0/0:0), LPE(0:0/15:0), LPE(16:0/0:0), LPE(17:1/0:0), LPE(18:2/0:0), LPE(20:3/0:0)(P < 0.05).Table 6 Pearson correlation analysis between feeding behavioral variables and serum key differential metabolites in 3 week postpartum (n = 7).Full size tableDiscussionMany ranch managers and dairy farmers recognize that the transition period is crucial in a cow’s life cycle. The physiological and metabolic changes of dairy cows during this period have attracted extensive attention from many scholars27,28. The daily observation of the feeding behavior, characterized by feed intake and feeding time, constitutes a prominent tool for monitoring health status29. Both feed intake and MY increase during the first weeks of lactation30, and feeding behavior might be related to milk yield31. The aim of this study was to analyze the changes of feeding behavior variables, milk yield, serum indexes, and metabolites of dairy cows during three weeks postpartum. Then the correlation analysis was used to correlate feeding behavior variables and milk yield, and differential metabolites.Termination of pregnancy and initiation of lactation is the most tumultuous in a cow’s life, accompanied by endocrine and metabolic changes32. Close monitoring of the feeding of cows during this critical period can reflect the change in the degree of energy mobilization in early lactation and be used to evaluate disease risk33. In general, monitoring individual feed intake in commercial herds relies on automatic feeding systems, which are still not widely used in commercial herds34. Relatively speaking, data on feeding time can be collected more easily and cheaper. The research of Pahl et al. showed that average fresh matter intake (FMI) of dairy cows could be estimated by FT, and the correlation between FMI and FT was 0.89135. A previous study showed that the FT for 300 cows (4 to 20 weeks in milk) was 265 ± 54 min36. Other studies revealed that the FT of 20 healthy cows (3 to 20d in milk) was distributed 197–249 min/d37. DeVries et al. measured the feeding behavior of lactating dairy cows in early to peak lactation. They found that meal criteria varied among cows, ranging from 8.4 min to 52.7 min38,which indicated that a considerable individual variation in feeding time of cows. Consistent with the above results, our data showed the range of FT was mainly in 120–450 min and AFT data was mainly in 10–40 min/time. Among them, the lower FT and AFT were in parturition to 4 d postpartum. The causes of decreased FT and AFT were probably cows feeling tired after calving, weak limbs, and unable to adapt well to the food competition in the group38. Additionally, various inflammatory reactions cows produce after postpartum, including the release of cytokines, may lead to a decrease in feed intake39.The feeding behavior of cows changed with the postpartum time. An earlier study reported that the DMI of dairy cows increased continuously from 1 to 4 week postpartum40. Huzzey et al. found that eating time declined from 87 to 62 min/d from the pre-calving 10 d to the post-calving 10 d41. It has been reported that the average DMI during the first postpartum week is significantly lower than in the subsequent postpartum weeks42. In accordance with the above results, the normalized value of FT and AFT on week 1 postpartum was significantly lesser than that of week 2 and 3 postpartum. It is commonly accepted that the MY of dairy cows gradually increases after calving and reaches its peak 50–90 days in milk43. Similarly, our results showed that the MY increased with weeks postpartum.There is a strong correlation between feeding behavior and DMI of dairy cows44. Johnston and DeVries found that DMI and MY were associated with the FT45. Similarly, Krpálková et al. demonstrated that the most efficient cows with the lowest feed conversion ratio had the highest FT46. FT and AFT normalized values had a highly positive correlation with MY, the correlation coefficient R was 0.949 and 0.977, respectively. It is worth noting that cows with a greater FT or AFT in first three weeks postpartum, have a higher milk yield. Therefore, monitoring of FT or AFT of dairy cows in first three weeks postpartum may be useful to predict the milk yield in early lactation.The glucose and metabolizable energy requirements of postpartum cows are approximately 2 to 3 times higher than those during the pre-production period47. One of the main markers of lipomobilization in dairy cows is BHBA48. The higher concentration of BHBA on week 1 postpartum suggesting an intensive mobilisation of fat. In addition, the lower serum TP and T-Chol contents were observed on week 1 postpartum, coupled with lower FT and AFT, confirmed that NEB of cows was presented on week 1 postpartum, the proteolysis processes could occur in parallel to lipolysis. Piccione et al. found the serum TP content of Holstein Friesian cows on week 1 postpartum was lower than week 2, 5 and 15 postpartum49. Kessler et al. revealed that plasma T-Chol concentrations decreased in week 1 postpartum and then gradually increased until week 14 postpartum50.An excessive fat mobilization due to a NEB, can increase the body’s oxidative stress and inflammatory response16. The primary source of MDA, which reflects the degree of lipid peroxidation to a certain extent, is the peroxides of polyunsaturated fatty acids51. Interleukine—6 (IL-6) is a pro-inflammatory innate immune cytokine. Cows exhibited oxidative stress and inflammation on week 1 postpartum, reflected by higher MDA and IL-6 levels in serum compare to week 2 postpartum. Then the IL-6 content was increased on week 3 postpartum. From parturition to lactation, a moderate inflammation has a fundamental role in eradicating the pathogens, resolving infections, regulating the metabolic changes during transition52. The increase of IL-6 on week 3 postpartum may be useful to activate the adaptive mechanisms in early lactation, ensuring nutrient requirements for the mammary gland in order to support a copious milk synthesis52.Immunoglobulin plays an important role in immune regulation and mucosal defense, inculding IgA, IgG and IgM53. IgA has the ability to agglutinate antigens, neutralize viral and bacterial toxins54. We found the serum IgA content on week 2 and 3 postpartum were significantly higher than that of week 1 postpartum, the IgG content on week 3 postpartum was significantly higher than that week 1 postpartum, indicating the immunity of dairy cows was increased with the weeks postpartum. In line with our results, LeBlanc et al. reported that most dairy cows experience a substantial decline in immune function for several weeks before and after calving, usually reaching its lowest point around week 1 postpartum55.In response to stress, the adrenal cortex produces the glucocorticoid COR, which plays a role in glucose regulation56. Arfuso et al. assessed blood COR levels of cows from d 21 pre to d 21 postpartum. They found that COR concentrations at day 0 and day 1 postpartum were significantly higher than those at days 7 and 21 postpartum, with no significant difference between days 7 and 2157, which was inconsistent with our results. Compared to weeks 1 and 2 postpartum, serum COR levels in dairy cows increased in week 3 postpartum, indicating a mild stress response that may be associated with increased milk production58. In addition, LEP plays a vital role in metabolic adaptation during transition by coordinating feed intake, energy expenditure, and nutrient use in tissues59,60. The amount of secretion of LEP is proportional to the degree of fat in the body61. Kadokawa et al. found that LEP concentrations declined after parturition, reached a nadir at 10 d after parturition, and then increased and became stable near ovulation62. Our results for LEP showed consistency with the results of Arfuso et al.63 who observed that serum LEP levels was significantly decreased in week 2 postpartum in mares, compared to week 1 postpartum. Then the content of LEP was increased in week 3 postpartum, which may be related to the accumulation of body fat61.According to UPLC-MS/MS and pathway analysis results, differential metabolites of pairwise comparison (W2 vs. W1, W3 vs. W1, W3 vs. W2) were mainly related to lipid metabolism (glycerophospholipid metabolism, arachidonic acid metabolism, and linoleic acid metabolism). In the animal body, glucose is primarily metabolized through two pathways, anaerobic glycolysis and the aerobic tricarboxylic acid (TCA) cycle, to generate energy for meeting normal physiological demands64. Fat is the main form of energy storage in animals. When the body’s primary energy source is unavailable, it mobilizes fat as an alternative energy supply. The pairwise comparison of metabolite contents between week 1, 2, and 3 postpartum revealed that the majority of the significantly changing GPs were lysophosphatidylcholine (LPC), lysophosphatidyl ethanolamine (LPE), lysophosphatidic acid (LPA), and phosphatidylcholines (PC) with different chain lengths. These significantly upregulated GPs were involved in glycerophospholipid metabolism. Consistent with our findings, Imhasly et al. reported a steady increase in phospholipid concentrations as early lactation progressed postpartum65. Kenéz et al. reported that the concentration of GPs in dairy cows undergoes significant changes over time after delivery, with metabolic adaptation during the transition period being largely associated with GPs66.In early lactation, dairy cows typically have a negative energy balance67. Van Hoeij et al. analyzed the associations between plasma indexes and behavioral traits in dairy cows in week 4 of lactation23. They found that energy balance was negatively correlated with the plasma BHBA and NEFA, and NEFA was negatively correlated with the DMI and FT. In agreement with above research, our data also showed the serum BHBA level was negative correlation with the normalized values of FT. Xu et al. reported that milk choline had a high correlation with energy balance in lactation week 2. Specifically, cows with severe negative energy balance have low levels of choline in milk67. Similarly, our results showed the FT and AFT were positively correlated with serum phosphatidylcholines, lysophosphatidylcholine, lysophosphatidyl ethanolamine, such as PC(8:0/8:0), PC(12:0/12:0), LPC(15:0/0:0), LPC(16:0/0:0), LPE(18:2/0:0), LPE(20:3/0:0). These results imply that feeding behavior variables, such as FT and AFT, could serve as reliable indicators of the energy metabolism of dairy cows during the first three weeks postpartum.In addtion, reduced feed intake during the transition period has been identified as a key indicator of cows at risk for inflammation68. Wang et al. reported that glycerophospholipid metabolism may be involved in inflammation69. In addition to forming biofilms, GPs comprised one of the components of bile and active membrane surface substances, participating in membrane protein recognition and signal transduction70. Lysophosphatidylcholine (LPC) is a major component of all lipoprotein fractions, mediates multiple biological functions, including the release of inflammatory factors, oxidative stress, and apoptosis71. In cardiac myoblasts, only the saturated LPC 16:0 and 18:0 evoked the highest AA release as well as an activation of protein kinase C and a Ca2 + flux into the cells72. AA is an integral constituent of biological cell membrane that has a regulatory role in the inflammatory response and cell metabolism73. Dong et al. observed that AA can regulate the expression of genes such as ARPC3, ARPC4 following lipoteichoic acid induction in bovine mammary epithelial cells, inhibition of apoptosis, and increase in cell metabolism and immune response capacity74. We found that the levels of AA was significantly increased in week 3 postpartum, compared with week 1 and 2 postpartum. Meanwhile, AA was also positively correlated with FT and AFT. In current study, there is no significant correlation between FT and serum IL-6. Further research will be required to determine whether feeding behavior variables can be used as indicators of inflammation in dairy cows during the first three weeks postpartum.ConclusionIn conclusion, the feeding time was positively correlated with milk yield, serum total protein and total cholesterol, while negatively correlated with β-hydroxybutyric acid. Furthermore, the serum levels of glycerophospholipids changed most in first three weeks postpartum, which mainly involved in glycerophospholipid metabolism. There are 29 glycerophospholipids and 3 fatty acyls were significantly and positively correlated with the feeding time and average feeding time. These results suggested that the feeding behavior variables, such as feeding time and average feeding time could serve as reliable indicators of energy metabolism in dairy cows during first three weeks postpartum.
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All data generated or analysed during this study are included in this article.
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Download referencesAcknowledgementsThis work was supported by the National Center of Technology Innovation for Dairy Program (grant number 2023-QNRC-10, 2022-scientific research-2, 2023-JSGG-4), Inner Mongolia Education Department Special Research Project For First Class Disciplines (grant no.YLXKZX-NND-007) and Inner Mongolia Autonomous Region graduate research innovation project.FundingNational Center of Technology Innovation for Dairy Program,2022-scientific research-2,2023-QNRC-10,Inner Mongolia Education Department Special Research Project For First Class Disciplines,YLXKZX-NND-007.Author informationAuthors and AffiliationsCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaNa Liu, Jingwei Qi, Xiaoping An, Yuan Wang, Xia Li, Zhalaga Zhang & Xu HuoCollege of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, ChinaBuyu WangNational Center of Technology Innovation for Dairy-Breeding and Production Research Subcenter, Hohhot, 010018, ChinaNa Liu, Jingwei Qi, Xiaoping An, Yuan Wang, Buyu Wang, Xia Li, Zhalaga Zhang & Xu HuoKey Laboratory of Smart Animal Husbandry at Universities of Inner Mongolia Autonomous Region, Integrated Research Platform of Smart Animal Husbandry at Universities of Inner Mongolia, Inner Mongolia Herbivorous Livestock Feed Engineering Technology Research Center, Hohhot, 010018, ChinaNa Liu, Jingwei Qi, Xiaoping An, Yuan Wang, Buyu Wang, Xia Li, Zhalaga Zhang & Xu HuoAuthorsNa LiuView author publicationsYou can also search for this author in
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PubMed Google ScholarContributionsN.L. wrote the main manuscript text and prepared all the figures, J.Q. and X.A. conceived the experiments and research questions, X.L., Z.Z. and X.H. conducted the experment(s), Y.W. and B.W. analysed the results. J.Q. and X.A. supervised the project. All authors reviewed the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleLiu, N., Qi, J., An, X. et al. Changes in feeding behavior, milk yield, serum indexes, and metabolites of dairy cows in three weeks postpartum.
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KeywordsFeeding behaviorMilk yieldSerum indexesMetabolomics