Relationship between energy balance and metabolic profiles in plasma and milk of dairy cows in early lactation

Negative energy balance in dairy cows in early lactation is related to alteration of metabolic status. However, the relationships among energy balance, metabolic profile in plasma, and metabolic profile in milk have not been reported. In this study our aims were: (1) to reveal the metabolic profiles of plasma and milk by integrating results from nuclear magnetic resonance (NMR) with data from liquid chromatography triple quadrupole mass spectrometry (LC-MS); and (2) to investigate the relationship between energy balance and the metabolic profiles of plasma and milk. For this study 24 individual dairy cows (parity 2.5 ± 0.5; mean ± standard deviation) were studied in lactation wk 2. Body weight (mean ± standard deviation; 627.4 ± 56.4 kg) and milk yield (28.1 ± 6.7 kg/d; mean ± standard deviation) were monitored daily. Milk composition (fat, protein, and lactose) and net energy balance were calculated. Plasma and milk samples were collected and analyzed using LC-MS and NMR. From all plasma metabolites measured, 27 were correlated with energy balance. These plasma metabolites were related to body reserve mobilization from body fat, muscle, and bone; increased blood flow; and gluconeogenesis. From all milk metabolites measured, 30 were correlated with energy balance. These milk metabolites were related to cell apoptosis and cell proliferation. Nine metabolites detected in both plasma and milk were correlated with each other and with energy balance. These metabolites were mainly related to hyperketonemia; β-oxidation of fatty acids; and one-carbon metabolism. The metabolic profiles of plasma and milk provide an in-depth insight into the physiological pathways of dairy cows in negative energy balance in early lactation. In addition to the classical indicators for energy balance (e.g., β-hydroxybutyrate, acetone, and glucose), the current study presents some new metabolites (e.g., glycine in plasma and milk; kynurenine, panthothenate, or arginine in plasma) in lactating dairy cows that are related to energy balance and may be of interest as new indicators for energy balance.</p

Relationship between inflammatory biomarkers and oxidative stress with uterine health in dairy cows with different dry period lengths

Earlier studies indicated that the inflammatory status of dairy cows in early lactation could not be fully explained by the negative energy balance (NEB) at that moment. The objective of the present study was to determine relationships between inflammatory biomarkers and oxidative stress with uterine health in dairy cows after different dry period lengths. Holstein-Friesian dairy cows were assigned to one of three dry period lengths (0-, 30-, or 60-d) and one of two early lactation rations (gluco-genic or lipogenic ration). Cows were fed either a glucogenic or lipogenic ration from 10-d before the expected calving date. Part of the cows which were planned for a 0-d dry period dried themselves off and were attributed to a new group (0 → 30-d dry period), which resulted in total in four dry period groups. Blood was collected (N = 110 cows) in weeks -3, -2, -1, 1, 2, and 4 relative to calving to determine bio-markers for inflammation, liver function, and oxidative stress. Uterine health status (UHS) was monitored by scoring vaginal discharge (VD) based on a 4-point scoring system (0, 1, 2, or 3) in weeks 2 and 3 after calving. Cows were classified as having a healthy uterine environment (HU, VD score = 0 or 1 in both weeks 2 and 3), nonrecovering uterine environment (NRU, VD score = 2 or 3 in week 3), or a recovering uterine environment (RU, VD score = 2 or 3 in week 2 and VD score= 0 or 1 in week 3). Independent of dry period length, cows with NRU had higher plasma haptoglobin (P = 0.05) and lower paraoxonase levels (P < 0.01) in the first 4 weeks after calving and lower liver functionality index (P < 0.01) compared with cows with HU. Cows with NRU had lower plasma albumin (P = 0.02) and creatinine (P = 0.02) compared with cows with a RU, but not compared with cows with HU. Independent of UHS, cows with a 0 → 30-d dry period had higher bil-irubin levels compared with cows with 0-, 30-, or 60-d dry period (P < 0.01). Cows with RU and fed a lipogenic ration had higher levels of albumin in plasma compared with cows with NRU and fed a lipogenic ration (P < 0.01). In conclusion, uterine health was related to biomarkers for inflammation (haptoglobin and albumin) and paraoxonase in dairy cows in early lactation. Cows which were planned for a 0-d dry period, but dried themselves off (0 → 30-d dry period group) had higher bilirubin levels, which was possibly related to a more severe NEB in these cows. Inflammatory biomarkers in dairy cows in early lactation were related to uterine health in this period.</p

Relationship between inflammatory biomarkers and oxidative stress with uterine health in dairy cows with different dry period lengths

Earlier studies indicated that the inflammatory status of dairy cows in early lactation could not be fully explained by the negative energy balance (NEB) at that moment. The objective of the present study was to determine relationships between inflammatory biomarkers and oxidative stress with uterine health in dairy cows after different dry period lengths. Holstein-Friesian dairy cows were assigned to one of three dry period lengths (0-, 30-, or 60-d) and one of two early lactation rations (gluco-genic or lipogenic ration). Cows were fed either a glucogenic or lipogenic ration from 10-d before the expected calving date. Part of the cows which were planned for a 0-d dry period dried themselves off and were attributed to a new group (0 → 30-d dry period), which resulted in total in four dry period groups. Blood was collected (N = 110 cows) in weeks -3, -2, -1, 1, 2, and 4 relative to calving to determine bio-markers for inflammation, liver function, and oxidative stress. Uterine health status (UHS) was monitored by scoring vaginal discharge (VD) based on a 4-point scoring system (0, 1, 2, or 3) in weeks 2 and 3 after calving. Cows were classified as having a healthy uterine environment (HU, VD score = 0 or 1 in both weeks 2 and 3), nonrecovering uterine environment (NRU, VD score = 2 or 3 in week 3), or a recovering uterine environment (RU, VD score = 2 or 3 in week 2 and VD score= 0 or 1 in week 3). Independent of dry period length, cows with NRU had higher plasma haptoglobin (P = 0.05) and lower paraoxonase levels (P < 0.01) in the first 4 weeks after calving and lower liver functionality index (P < 0.01) compared with cows with HU. Cows with NRU had lower plasma albumin (P = 0.02) and creatinine (P = 0.02) compared with cows with a RU, but not compared with cows with HU. Independent of UHS, cows with a 0 → 30-d dry period had higher bil-irubin levels compared with cows with 0-, 30-, or 60-d dry period (P < 0.01). Cows with RU and fed a lipogenic ration had higher levels of albumin in plasma compared with cows with NRU and fed a lipogenic ration (P < 0.01). In conclusion, uterine health was related to biomarkers for inflammation (haptoglobin and albumin) and paraoxonase in dairy cows in early lactation. Cows which were planned for a 0-d dry period, but dried themselves off (0 → 30-d dry period group) had higher bilirubin levels, which was possibly related to a more severe NEB in these cows. Inflammatory biomarkers in dairy cows in early lactation were related to uterine health in this period.</p

Relationship between inflammatory biomarkers and oxidative stress with uterine health in dairy cows with different dry period lengths

Earlier studies indicated that the inflammatory status of dairy cows in early lactation could not be fully explained by the negative energy balance (NEB) at that moment. The objective of the present study was to determine relationships between inflammatory biomarkers and oxidative stress with uterine health in dairy cows after different dry period lengths. Holstein-Friesian dairy cows were assigned to one of three dry period lengths (0-, 30-, or 60-d) and one of two early lactation rations (gluco-genic or lipogenic ration). Cows were fed either a glucogenic or lipogenic ration from 10-d before the expected calving date. Part of the cows which were planned for a 0-d dry period dried themselves off and were attributed to a new group (0 → 30-d dry period), which resulted in total in four dry period groups. Blood was collected (N = 110 cows) in weeks -3, -2, -1, 1, 2, and 4 relative to calving to determine bio-markers for inflammation, liver function, and oxidative stress. Uterine health status (UHS) was monitored by scoring vaginal discharge (VD) based on a 4-point scoring system (0, 1, 2, or 3) in weeks 2 and 3 after calving. Cows were classified as having a healthy uterine environment (HU, VD score = 0 or 1 in both weeks 2 and 3), nonrecovering uterine environment (NRU, VD score = 2 or 3 in week 3), or a recovering uterine environment (RU, VD score = 2 or 3 in week 2 and VD score= 0 or 1 in week 3). Independent of dry period length, cows with NRU had higher plasma haptoglobin (P = 0.05) and lower paraoxonase levels (P < 0.01) in the first 4 weeks after calving and lower liver functionality index (P < 0.01) compared with cows with HU. Cows with NRU had lower plasma albumin (P = 0.02) and creatinine (P = 0.02) compared with cows with a RU, but not compared with cows with HU. Independent of UHS, cows with a 0 → 30-d dry period had higher bil-irubin levels compared with cows with 0-, 30-, or 60-d dry period (P < 0.01). Cows with RU and fed a lipogenic ration had higher levels of albumin in plasma compared with cows with NRU and fed a lipogenic ration (P < 0.01). In conclusion, uterine health was related to biomarkers for inflammation (haptoglobin and albumin) and paraoxonase in dairy cows in early lactation. Cows which were planned for a 0-d dry period, but dried themselves off (0 → 30-d dry period group) had higher bilirubin levels, which was possibly related to a more severe NEB in these cows. Inflammatory biomarkers in dairy cows in early lactation were related to uterine health in this period.</p

Relationship between energy balance and metabolic profiles in plasma and milk of dairy cows in early lactation

Negative energy balance in dairy cows in early lactation is related to alteration of metabolic status. However, the relationships among energy balance, metabolic profile in plasma, and metabolic profile in milk have not been reported. In this study our aims were: (1) to reveal the metabolic profiles of plasma and milk by integrating results from nuclear magnetic resonance (NMR) with data from liquid chromatography triple quadrupole mass spectrometry (LC-MS); and (2) to investigate the relationship between energy balance and the metabolic profiles of plasma and milk. For this study 24 individual dairy cows (parity 2.5 ± 0.5; mean ± standard deviation) were studied in lactation wk 2. Body weight (mean ± standard deviation; 627.4 ± 56.4 kg) and milk yield (28.1 ± 6.7 kg/d; mean ± standard deviation) were monitored daily. Milk composition (fat, protein, and lactose) and net energy balance were calculated. Plasma and milk samples were collected and analyzed using LC-MS and NMR. From all plasma metabolites measured, 27 were correlated with energy balance. These plasma metabolites were related to body reserve mobilization from body fat, muscle, and bone; increased blood flow; and gluconeogenesis. From all milk metabolites measured, 30 were correlated with energy balance. These milk metabolites were related to cell apoptosis and cell proliferation. Nine metabolites detected in both plasma and milk were correlated with each other and with energy balance. These metabolites were mainly related to hyperketonemia; β-oxidation of fatty acids; and one-carbon metabolism. The metabolic profiles of plasma and milk provide an in-depth insight into the physiological pathways of dairy cows in negative energy balance in early lactation. In addition to the classical indicators for energy balance (e.g., β-hydroxybutyrate, acetone, and glucose), the current study presents some new metabolites (e.g., glycine in plasma and milk; kynurenine, panthothenate, or arginine in plasma) in lactating dairy cows that are related to energy balance and may be of interest as new indicators for energy balance.</p

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.</p

Prediction of metabolic status of dairy cows in early lactation with on-farm cow data and machine learning algorithms

Metabolic status of dairy cows in early lactation can be evaluated using the concentrations of plasma β-hydroxybutyrate (BHB), free fatty acids (FFA), glucose, insulin, and insulin-like growth factor 1 (IGF-1). These plasma metabolites and metabolic hormones, however, are difficult to measure on farm. Instead, easily obtained on-farm cow data, such as milk production traits, have the potential to predict metabolic status. Here we aimed (1) to investigate whether metabolic status of individual cows in early lactation could be clustered based on their plasma values and (2) to evaluate machine learning algorithms to predict metabolic status using on-farm cow data. Through lactation wk 1 to 7, plasma metabolites and metabolic hormones of 334 cows were measured weekly and used to cluster each cow into 1 of 3 clusters per week. The cluster with the greatest plasma BHB and FFA and the lowest plasma glucose, insulin, and IGF-1 was defined as poor metabolic status; the cluster with the lowest plasma BHB and FFA and the greatest plasma glucose, insulin, and IGF-1 was defined as good metabolic status; and the intermediate cluster was defined as average metabolic status. Most dairy cows were classified as having average or good metabolic status, and a limited number of cows had poor metabolic status (10–50 cows per lactation week). On-farm cow data, including dry period length, parity, milk production traits, and body weight, were used to predict good or average metabolic status with 8 machine learning algorithms. Random Forest (error rate ranging from 12.4 to 22.6%) and Support Vector Machine (SVM; error rate ranging from 12.4 to 20.9%) were the top 2 best-performing algorithms to predict metabolic status using on-farm cow data. Random Forest had a higher sensitivity (range: 67.8–82.9% during wk 1 to 7) and negative predictive value (range: 89.5–93.8%) but lower specificity (range: 76.7–88.5%) and positive predictive value (range: 58.1–78.4%) than SVM. In Random Forest, milk yield, fat yield, protein percentage, and lactose yield had important roles in prediction, but their rank of importance differed across lactation weeks. In conclusion, dairy cows could be clustered for metabolic status based on plasma metabolites and metabolic hormones. Moreover, on-farm cow data can predict cows in good or average metabolic status, with Random Forest and SVM performing best of all algorithms.</p

Effect of Prepartum Dietary Energy Level on Production and Reproduction in Nili Ravi Buffaloes

The objective of this study was to evaluate the effect of prepartum dietary energy level on postpartum production and reproduction in Nili Ravi buffaloes (n = 21). The buffaloes were offered low energy (LE: 1.31 Mcal/kg DM NEL (net energy for lactation)), medium energy (ME: 1.42 Mcal/kg DM NEL) or high energy (HE: 1.54 Mcal/kg DM NEL) diet for 63 days prepartum, and received the same lactation diet (LD: 1.22 Mcal/kg DM NEL) during 14 weeks postpartum. The effects of dietary energy level and week were analyzed with Proc GLIMMIX model. Dry matter intake (DMI) was lower in buffaloes fed the LE diet compared with buffaloes fed the ME or HE diet. Calf birth weight (CBW) was higher in buffaloes fed the HE diet compared with buffaloes fed the ME or LE diet. Milk production was similar in buffaloes fed the HE, ME or LE diet within 14 weeks postpartum and throughout the lactation. Milk fat was higher in buffaloes fed the LE diet compared with buffaloes fed the ME or HE diet. Milk protein and lactose yields was high in buffaloes fed the HE diet compared with buffaloes fed the ME or LE diet. Body condition score was high in HE and was affected by diet × week interactions during pre and postpartum period. The concentrations of β-hydroxybutyrate (BHBA) and triglycerides in serum was lowest in buffaloes fed the HE diet compared with the buffaloes fed the ME or LE diet. The buffaloes fed the HE diet had early uterine involution (UI), first estrus, short dry days, and calving interval (CI) compared with buffaloes fed the ME or LE diet. None of buffaloes fed the LE diet exhibited estrus during the first 14 weeks postpartum compared with buffaloes fed the ME or HE diet. In conclusion, prepartum feeding of high energy diet can be helpful in improving the postpartum productive and reproductive performance in Nili Ravi buffaloes