Implications
In high performance dairy cows, dietary intake is unable to meet the demands of high milk production in particular during early lactation. Cows enter a period of negative energy balance and experience metabolic stress that is linked to reduced immune function and increased health problems. There is an urgent need for predictive markers as a tool to identify animals at risk and to select animals having a high adaptability and robustness.
Introduction
High performance cattle breeds like German Holstein have been selected for improved milk production and thereby also for high ad libitum feed intake and metabolic rates (Kennedy et al., Reference Kennedy, Dillon, Delaby, Faverdin, Stakelum and Rath2003). On the other hand, dairy cows experience distinct metabolic stress during periods of high metabolic load and inadequate energy/fuel availability leading to a negative energy balance (NEB), for example, as a result of infectious or metabolic/digestive disorders, during heat stress, and in particular during the transition period around parturition (Gross et al., Reference Gross, van Dorland, Bruckmaier and Schwarz2011). Subclinical disorders of the energy metabolism facilitate failure of homeorhetic and homoeostatic adaptations resulting in health problems and reduced performance (Mudron et al., Reference Mudron, Rehage, Sallmann, Höltershinken and Scholz2005). Therefore, parameters are needed to assess the metabolic status, stress level and regulatory capacity of individual cows and herds. Because of the well-known interactions between metabolic stress, nutrition and reproduction, various metabolites (i.e. non-esterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), glucose, lactate) and reproductive hormones are commonly used (Chilliard et al., Reference Chilliard, Bocquier and Doreau1998; Mudron et al., Reference Mudron, Rehage, Sallmann, Höltershinken and Scholz2005). In addition, the plasma level of cortisol and the heart rate (HR) are applied to assess a stress response and/or the welfare status, and both are known to be influenced by feeding and by the nutritional status (Beerda et al., Reference Beerda, Kornalijnslijper, van der Werf, Noordhuizen-Stassen and Hopster2004; Davidson and Beede, Reference Davidson and Beede2009; Turbill et al., Reference Turbill, Ruf, Mang and Arnold2011).
The autonomic nervous system (ANS) plays a predominant role in regulating the adaptive response to inadequate energy and/or fuel availability and the resulting metabolic stress (Fröhli and Blum, Reference Fröhli and Blum1988; Chilliard et al., Reference Chilliard, Bocquier and Doreau1998). Particularly, the ANS influences the metabolic rate in organs such as heart, liver and gastrointestinal tract. The sympathetic nervous system (SNS) activity in a variety of tissues/organs is increased in conjunction with high feeding levels and is decreased during starvation (Fröhli and Blum, Reference Fröhli and Blum1988). Also, the SNS and epinephrine are mainly involved in control of protein kinase A-mediated lipolysis during periods of NEB (Chilliard et al., Reference Chilliard, Bocquier and Doreau1998). Together with the adrenocortical axis and behavioural adaptations, the SNS belongs to the main mediators of the stress response (Mudron et al., Reference Mudron, Rehage, Sallmann, Höltershinken and Scholz2005). Different reactivity/activity of the ANS might thus explain part of the considerable variation in the ability of high-yielding dairy cows to adapt successfully to the metabolic load during pregnancy and onset of lactation. If so, parameters linked to ANS activity and sympatho-vagal balance could be possible early markers of metabolic stress that can be used to predict cows with compromised regulatory capacity.
To test this hypothesis, we here investigated autonomic regulation and stress level of dry, pregnant, high-yielding dairy cows in response to a 10-h feed deprivation by using heart rate variability (HRV) analysis. Linear and nonlinear indices of HRV have been identified as non-invasive quantitative markers of autonomic activity and of stress (Mohr et al., Reference Mohr, Langbein and Nürnberg2002; Hagen et al., Reference Hagen, Langbein, Schmied, Lexer and Waiblinger2005; Gygax et al., Reference Gygax, Neuffer, Kaufmann, Hauser and Wechsler2008). The advantage of HRV over traditional measurement of heart rate (HR), body temperature (BT) or hormone concentrations is its better reflection of the status of the central nervous regulations and of the individual capacity to respond to environmental demands (Task Force, 1996). In cattle HRV analysis has been used to determine stressful effects of high temperature, insect harassment and diarrhea (Mohr et al., Reference Mohr, Langbein and Nürnberg2002), of milking dairy cows in automatic or conventional systems (Hagen et al., Reference Hagen, Langbein, Schmied, Lexer and Waiblinger2005; Gygax et al., Reference Gygax, Neuffer, Kaufmann, Hauser and Wechsler2008; Kézér et al., Reference Kézér, Kovács, Jurkovich, Szenci and Tözsér2014), and of transrectal examination of lactating and dry dairy cows (Kézér et al., Reference Kézér, Kovács, Jurkovich, Szenci and Tözsér2014). As far as we know, there is no information on changes of sympathetic and, in particular, of parasympathetic activity pattern in pregnant, high-yielding dairy cows experiencing a defined metabolic load.
Therefore, in the present study we determined a wide range of HRV indices before, during and after a 10-h feed removal. The aims of the experimental work have been: (1) to develop a procedure suitable to identify group-specific or inter-individual differences in the cow’s metabolic stress level and regulatory capacity in response to a 10-h food removal, (2) to identify specific HRV indices reflecting this different status already under control conditions (ad libitum feeding) and (3) can be used as predictive markers.
Material and methods
Animal experiment
This study was part of a larger joint research project (Deutsche Forschungsgemeinschaft (DFG) project: KU 1956/3-1; HA 4372/6-1; SCHW 642/6-1) and was conducted with the approval of the Animal Care Committee of the Ministry of Nutrition, Agriculture, Forestry, and Fishery, Schwerin, State of Mecklenburg-Vorpommern, Germany (No. VI-522a-7221.31-1-002/99).
Cows and diet
Experiments were performed with 10 multiparous dried-off German Holstein cows (4 to 6 years old, mean body mass: 726±56 kg) born and raised at the farm of Griepentrog KG (Steinhagen, Germany), during week 4 antepartum (ap). Two of the cows (number 3 and 10) were halfsiblings having the same father. All cows had a milk yield of ⩾10 000 kg/305 days during the prior lactation and had been dried off at 7 weeks before expected calving.
Cows were fed a far-off total mixed ration (TMR) twice daily at ~0700 and 1500 h and had free access to water. The TMR was formulated to meet the nutrient recommendations of the German Society for Nutrition Physiology (2001), and its ingredients and chemical composition are given in Table 1.
Table 1 Ingredients and chemical composition of the total mixed ration
DM=dry matter.
1 Concentrate MF 2000 (Vollkraft Mischfutterwerke GmbH, Güstrow, Germany): 33% extracted soy meal, 20% corn, 17% wheat gluten, 13% wheat, 8% extracted rapeseed meal, 5% sugar beet pulp, 2% sodium hydrogen carbonate, 1.3% calcium carbonate, 0.2% sodium chloride, 8.0 MJ of NEL/kg of DM, 204 g of utilizable protein/kg of DM.
2 Molassed sugar beet pulp (Arp; Thordsen, Rautenberg GmbH & Co. KG, Sollerupmühle, Germany): minerals, 7.3 MJ of NEL/kg of DM, 153 g of utilizable protein/kg of DM.
3 Rinderstolz 9235 far-off (Salvana Tiernahrung GmbH, Sparrieshoop, Germany): 75% crude ash, 4.5% calcium, 6% phosphorus, 10% sodium, 12% magnesium, vitamins.
Experimental design
During weeks −7 to −5 ap, cows were adapted to handling and to staying in respiration chambers (see the ‘Indirect calorimetry and behavioural data’ section) in which the experimental trials were performed. Habituation (criteria: eating, drinking, ruminating, lying down, BT) was performed at least three times and the duration of stay was increased from 1 h on day 1 to 3 to 4 h on day 4. No animal needs longer than 4 days to habituate. At the same time points cows were adapted to wear a fixing belt (criteria: scrubbing, licking, looking to the belt, restlessness), which was tied around the thorax behind the forelegs and is needed for HRV measurements.
The experimental trial was started one day after the cows were transferred to the respiration chambers. Heart rate and interbeat intervals (IBI) computed from the intervals between consecutive R-peaks were continuously measured for 48 h starting at 0630 h. In addition, O2 consumption, CO2 and CH4 production, food intake, and physical activity including standing–lying behaviour were monitored. After 24 h of ad libitum feeding (period 1, P1), feed was removed for 10 h (period 2, P2) to challenge the energy metabolism of the cows. Thereafter, the cows were provided with food ad libitum for a 14-h (1630 to 0630 h) period of re-feeding (period 3, P3). The time course and the experimental periods (P1 to P3) of the trial are shown in Figure 1. The cows were weighed immediately before entering and after leaving the chambers on balances in front of the chambers. The continuous measurements were interrupted for 0.5 h (0630 to 0700 h) on day 2 to clean the chambers and to measure their BT. The latter was also measured after the second feeding (1500 h) during P1 and P3 and at 1630 h during P2.
Figure 1 Time schedule and experimental design. From one cow representative 48 h original recordings of heat production (HP) and of standing–lying behaviour are also shown. Standing periods (ST) are displayed by grey coloured columns and the right y-axis labelling gives the standing time per 6-min measuring interval in min; 0 min=lying position. The 20-min time periods selected for heart rate variability analysis have been encircled.
Heart rate variability measurement and analysis
Heart rate and R-R interval data were taken noninvasively by using the Polar Equine RS800CX monitor (Polar Electro Oy, Kempele, Finland), a newly developed fixing belt for large animals (FBN utility model, case number: DE 20 2012 100 735.5) and the equine belt with transmitters and two integrated electrodes (WearLink® W.I.N.D.; Polar Electro Oy, Kempele, Finland). A few days before the experimental period, the electrode site, an area of about 10×15 cm localized directly behind the left shoulder of the cow, was shaved. To optimize conductivity, the electrodes were made moist before the measuring belt with integrated electrodes has placed on this region.
After measurement, the data were transferred from the monitor to a computer (Polar IrDA USB-Adapter W.I.N.D.; Polar Electro Oy), and relevant data sets from the three experimental periods (P1 to P3) were selected according to heat production (HP). Moreover, in order to minimize the additional effects of physical activity, only those data sets that were recorded during periods when the cows were lying down were taken into consideration. During P1, an interval after the last meal characterized by a stable maximum HP was chosen and compared with an interval with consistently low HP occurring at the end of P2. In P3, a rapid increase of HP was observed after re-submission of food. For data analysis an interval was selected were HP has stabilized. Figure 1 shows typical original traces of HP and standing–lying behaviour obtained from an individual cow. In addition, the periods chosen for HRV analysis are given.
Subsequently, by using the software ‘Polar ProTrainer 5 Equine Edition’ Version 5.35.161 (Polar Electro Oy), an automatic correction for artefacts was performed. Only data sets that were at least 20 min long and had a corrected fault rate of <10% for each 5-min interval were included in the analysis (Mohr et al., Reference Mohr, Langbein and Nürnberg2002).
Corrected 20-min data sets were converted into text files and saved, and HRV parameters in the time, frequency, and nonlinear domains were calculated (Table 2) from an adjacent 5-min window that moved over the data set by use of Kubios HRV software Version 2.0.
Table 2 Glossary for time domain, frequency domain and nonlinear domain measures of heart rate variability (Mohr et al., Reference Mohr, Langbein and Nürnberg2002; Borell von et al., Reference Borell von, Langbein, Despres, Hansen, Leterrier, Marchant-Forde, Marchant-Forde, Minero, Mohr, Prunier, Valance and Veissier2007)
HRV=heart rate variability; RMSSD=root-mean square differences of successive R-R intervals; SDNN=the mean of the standard deviations for all R-R intervals.
* Quantitative parameters derived from recurrence plots by nonlinear mathematical analysis of HRV (Recurrence Quantification Analysis)
The dissimilar respiratory frequencies in cattle and humans were taken into consideration by setting the limits of the high frequency (HF), low frequency (LF) and very low frequencies bands to 0.2 Hz (lower limit) and 0.58 Hz (upper limit), 0.0133 and 0.2 Hz, and 0.0033 and 0.0133 Hz, respectively (Borell von et al., Reference Borell von, Langbein, Despres, Hansen, Leterrier, Marchant-Forde, Marchant-Forde, Minero, Mohr, Prunier, Valance and Veissier2007). Recurrence quantification analysis (RQA) was used to calculate nonlinear parameters of HRV with the Kubios software Version 2.0. RQA was performed with an embedding dimension m=10, lag of 1, and a threshold distance (radius) r of
$\sqrt m \,{\rm SD}$
, with SD as the standard deviation of the R-R time series.
Indirect calorimetry and behavioural data
Gas exchange of the cows was measured continuously at 6-min intervals in climate-controlled (15 °C, 70% humidity) open-circuit respiration chambers with a volume of 16 m3. All chambers (dimension 4×2×2 m) contained a stanchion allowing the individual animal to stand or lie down. Standing and lying times of the cows were registered by a photoelectric switch (SA1E; Idec Elektrotechnik GmbH, Hamburg, Germany). Other physical activity was detected by a modified IR-based motion detector (IS 120; STEINEL, Herzebrock-clarholz, Germany) converting movements of the animal into impulses.
Feed intake was assessed automatically by measuring feed disappearance from the chamber feed bin (maximum capacity: 40 kg organic substance) via a scale connected to an electronic registration device (PAARI, Erfurt, Germany).
Gas samples were passed through IR absorption based analysers (UNOR 610; MAIHAK AG, Hamburg, Germany) for the determination of CO2 and CH4 content and through a paramagnetic analyser (OXYGOR 610; MAIHAK) for measurement of O2 content. Based on these data, HP was estimated according to Brouwer (Reference Brouwer1965): HP (KJ)=16.18 O2 (l)+5.02 CO2 (l)−2.17 CH4 (l)−5.99 N (g).
All measured variables (gas concentrations for O2, CO2 and CH4, air flow rate, feed disappearance from the feed bin, temperature and relative humidity in and behind the chamber, standing and lying time, activity counts, air pressure) were sent to an acquisition system (Simatic; Siemens, München, Germany) and collected by purpose-adapted software (WinCC, Version 5.1, SP 2; Siemens). DELPHI-based (Delphi 2007, San Francisco, CA, USA) software was programmed in our group (Copyright H. Scholze, FBN) to allow for the automatic calculation of HP and collection of all measured data in EXCEL files.
To obtain accurate information on the cows energy status and rumen fermentation activity, the energy balance (EB) and fermentative CO2 (CO2(ferm)) for P1, P2 and P3 were calculated from the measured data by using the following equations: EB (KJ)=ME Intake (KJ)−HP (KJ) and CO2(ferm) (l)=1.7×CH4 production (l).
Blood sampling and analysis
Cows were equipped with indwelling jugular catheters the day before the trial starts. Extension tubing was used to take blood samples from outside the respiration chambers into Fe-Fluoride monovettes (Sarstedt, Nümbrecht, Germany) and immediately put on ice. Blood samples were centrifuged (2700 r.p.m. (4000×g), 4°C) for 20 min and the supernatants were stored at −80°C until analysis for NEFA, BHBA, total ghrelin and cortisol. Plasma concentrations of NEFA and BHBA were measured by routine analysis (Cobas Mira, Clinic for Cattle, Stiftung Tierärztliche Hochschule Hannover, Hannover, Germany) using kits from Wako Chemicals (Neuss, Germany) (NEFA kit 434–91795) and Randox Laboratories (Wülfrath, Germany) (BHBA kit RB 998), respectively. Total ghrelin (acyl+desacyl ghrelin) was determined in 400-µl freeze-dried plasma samples by using the RIA method described previously by ThidarMyint et al. (Reference ThidarMyint, Yoshida, Ito and Kuwayama2006). Plasma cortisol concentrations were determined by radioimmunoassay at the Veterinary Physiology, Vetsuisse Faculty, University of Bern as described previously by Thun et al. (Reference Thun, Eggenberger, Zerobin, Luscher and Vetter1981).
Statistical analysis
The statistical analyses were carried out by using SAS software, Version 9.4 for Windows (Copyright; SAS Institute Inc., Cary, NC, USA).
Differences of the HRV variables (Table 3) and of parameters related to the energy, nutrient and activity status (Table 4) between various periods (P1, P2 and P3) were analysed by one way repeated measurement ANOVA. With the exception of BT, all parameters from Table 4 were evaluated as 24 h-means for the ad libitum feeding period (P1: 240 data sets) and as means for the fasting period (P2: 0630 to 1630 h, 99 data sets) and for the ad libitum re-feeding period (P3: 1630 to 0630 h, 140 data sets). Data obtained in P2 and P3 were converted into 24-h values.
Table 3 Heart rate variability indices determined for cows under control conditions (P1=ad libitum feeding) and during fasting (P2) or re-feeding (P3=food ad libitum)
LSM=least square means; Min=minimum value; Max=maximum value; HR, heart rate; HRV=heart rate variability; LF=low frequency; HF=high frequency; ShanEn=Shannon Entropy.
Data are given as LSM ± SE; n=10.
a,b Significant differences between periods (P<0.05).
* LF/HF has been calculated from the non-normalized values of HF and LF (not shown). P1=control (ad libitum feeding), P2=fasting, and P3=re-feeding (food ad libitum).
Table 4 Response of parameters related to the energy, nutrient and activity status to a 10-h fasting (P2) and 14 h re-feeding (P3) period
P1=control (ad libitum feeding), P2=fasting; P3=re-feeding (food ad libitum); LSM=least square means; Min.=minimum value; Max=maximum value; BT=body temperature; HP=heat production; EB=energy balance; DMI=dry matter intake; WI=water intake; NEFA=non-esterified fatty acids; BHBA=β-hydroxybutyrate.
Data are given as LSM ± SE; n=10.
a,b,cSignificant differences between periods (P<0.05).
The response of HF (an indicator of parasympathetic activity) to fasting (HFP1−HFP2=ΔHFP1−P2) was evaluated for individual cows allowing for separation of two groups (HF+(increase of HF in response to fasting) and HF− (decrease of HF in response to fasting) c.f. Figure 2). Then HRV data were analysed by two-way repeated measurement ANOVA with the MIXED procedure of SAS/STAT software. The ANOVA model contained the fixed effects Group (levels: HF+ and HF−) and period (levels: P1, P2, P3) and the interaction Group×Period. Repeated measurements on the same animal were taken into account by the repeated statement of the MIXED procedure by using an unstructured residual covariance matrix.
Figure 2 Response of the high frequency domain (HF) of heart rate variability to fasting. It mainly reflects the activity of the parasympathetic branch of the autonomic nervous system. The response of HF to fasting (HF during ad libitum feeding (P1) minus HF during fasting (P2)=ΔHFP1−P2) is shown for individual cows. Note the increase in vagal tone (ΔHFP1−P2 increase) in five out of 10 cows (defined as group HF+) and an impaired vagal activation (ΔHFP1−P2 decrease) in another five cows (assigned to group HF−).
In a further analysis the relationship between ΔHFP1−P2 and HR, R-R interval, and L MAX at P1 was investigated by linear regression using the REG procedure of SAS/STAT software with the aim to select possible biomarker(s) that predict the sensitivity of individual cows for metabolic stress, and to define a threshold for such biomarker. Of the investigated HRV parameters only L MAX fulfilled the criteria for a possible biomarker and two groups (<L MAX (L MAX lower than threshold in P1: control conditions with feed ad libitum) and >L MAX (L MAX higher than threshold in P1: control conditions with feed ad libitum) c.f. Figure 3a) were defined. After grouping the cows according to the L MAX threshold one way ANOVAs were done for the variables BT, HP, EB, CO2(ferm), dry matter intake (DMI) and water intake, plasma concentrations of NEFA, BHBA, glucose, cortisol, ghrelin (total) and insulin, standing time, activity and milk parameters (energy corrected milk (ECM), milk fat, milk protein, milk lactose and fat/protein quotient) to test the group effect (test for biomarker).
Figure 3 Prediction of group differences in autonomic control by the nonlinear domain heart rate variability (HRV) component Maxline (L MAX). (a) Regression analysis was performed with ΔHFP1−P2 as dependent, and L MAX as independent variable. The obtained regression model (R 2=0.76) allows for calculation of a threshold value (TS=258) for L MAX and assignment of cows to groups having L MAX values above (>L MAX) or below (<L MAX) the TS. (b) Under control conditions (P1: ad libitum feeding) cows of the >L MAX (n=7) and <L MAX (n=3) groups differ in L MAX (*P<0.001), high-frequency (HF, *P<0.002), and low-frequency (LF, *P<0.002) components of HRV.
The first ANOVA model contained the fixed effects Group (levels: <L MAX and >L MAX) and Day (levels: day 1=P1 and day 2=P2+P3 ap, day 3=P1 and day 4=P2+P3 postpartum) and the interaction Group×Day (Table 5). The second ANOVA model contained the fixed effects Group (levels: <L MAX and >LMAX) and Week (levels: weeks −5 to −2 antepartum and weeks +2 to +5 postpartum) and the interaction Group×Week (Table 6). Repeated measurements on the same animal were taken into account by the repeated statement of the MIXED procedure by using an unstructured residual covariance.
Table 5 Prepartal and postpartal <L max und >L max group differences in parameters related to metabolic status and stress level
Day 1/3 (P1)=control (ad libitum feeding) antepartum/postpartum, Day 2/4 (P2+P3)=fasting and re-feeding (food ad libitum) antepartum/postpartum; LSM=least square means; BT=body temperature; Ns=not significant; HP=heat production; EB=energy balance; ECM=energy corrected milk.
Data are given as LSM ± SE, n=16. Significant differences between <L max and >L max groups (P<0.05).
Table 6 Prepartal and postpartal <L max und >L max group differences in cows kept under normal housing conditions
LSM=least square means; ap=weeks −5 to −2 antepartum; pp=weeks +5 to +2 postpartum; DMI=dry matter intake; ECM=energy corrected milk.
n=16.
Least square means and their SE were calculated and pairwise tested for each effect in each model by using the Tukey–Kramer procedure for pairwise multiple comparisons. Effects and differences were considered significant if P<0.05.
Results
Response of heart rate and heart rate variability indices to a 10-h feed deprivation and subsequent re-feeding
Table 3 summarizes the effects of the 10-h feed deprivation (P2) and subsequent re-feeding (P3) on HRV indices. The mean HR and the resulting R-R interval were 72±2 beats/min and 844±19 ms, respectively, under control conditions (ad libitum feeding, P1). Heart rate and R-R intervals showed a significant reduction (15±2%) or increase (18±3%) in P2 compared with P1 and returned to baseline levels during P3 (Table 3). During all experimental periods HR was positively correlated with HP (P1: r=0.58, P=0.08; P2: r=0.78, P=0.007; P3: r=0.72, P=0.02). L MAX values were significantly higher during the re-feeding period (313±29) compared with P2 (236±17). Over all cows none of the other HRV parameters were significantly influenced by the 10-h feed deprivation.
Characterization of the energy and metabolic status, and the behavioural response of the cows
Parameters related to the energy, metabolic and behavioural status of cows are depicted in Table 4 showing significant effects of the 10-h feed deprivation on HP, EB, fermented carbon dioxide (CO2(ferm)), NEFA, total ghrelin and physical activity. The measured EB was already negative in P1. As expected, compared with P1, the cows EB switched to strongly negative values during P2 and recovered to significantly more positive values during P3. This was accompanied by reductions of HP (18±1%, P<0.05), physical activity (33±3%, P<0.05), standing : lying ratio (40±7%, P<0.05) and production of CO2(ferm) (41±2%, P<0.05) in P2 and recovery of these parameters to ad libitum levels in P3. NEFA plasma concentrations increased 1.8-fold (P<0.05) and total ghrelin concentrations 2.8-fold (P<0.001) during P2 and normalized during P3. A compensatory increase of DMI amounting to 48% was seen in P3 compared with P1.
Mean BT of cows was 38.4°C during all feeding periods. In addition, cortisol levels reacted only marginally to the feed removal (P2) or re-feeding (P3).
Analysis of heart rate variability responses to feed removal in individual cows
The minimum and maximum values of calculated HRV indices show a wide range (Table 3) pointing to inter-individual differences. Therefore, we evaluated the behaviour of frequency domain parameters (HF, LF, LF/HF), known indicators of autonomic control, in response to the 10-h feed deprivation (ΔP1 to P2) for individual cows and were able to define two groups. As shown in Figure 2, after feed removal, the power in the HF band which reflects the parasympathetic control increased in five cows (HF+), but in the other five cows, a decrease (HF−) was observed. A reverse response, that is, a decrease in the HF+ group and an elevation in HF− group, was observed for LF (−12±3% v. 11±6%) and the LF/HF ratio (−73±11% v. 500±312%), respectively. Cows retrospectively assigned to these two groups were shown to differ significantly in their HR (HF+: 76±2 beats per min (bpm), HF−: 68±2.4 bpm), R-R interval (HF+: 796±18 ms, HF−: 892±33 ms) and L MAX (HF+: 357±26, HF−: 187±52) under ad libitum control conditions (P1). Thus, we tested a possible link between these parameters and ΔHFP1−P2 by performing regression analysis. The coefficient of determination (R 2) was low for HR (0.372) and R-R interval (0.325). However, a regression model with L MAX as independent variable reveal an R 2 of 0.76 (Figure 3a), suggesting that it explains the variation in ΔHFP1−P2 to a high extent. From this regression model we calculated a threshold (TS=0=−23.14+0.0897×L MAX) for L MAX (TSLMAX=258) and re-assigned our 10 cows to groups having L MAX values below (<L MAX) or above (>L MAX) this TS (Figure 3a). According to TSLMAX three HF− cows (numbers 1, 5 and 10) were grouped as <L MAX whereas all HF+ and two HF− (numbers 2 and 6) cows were grouped as >L MAX. As shown in Figure 3b, <L MAX and >L MAX groups differ significantly in their P1 values for HF (19.6±4.0 v. 4.7±2.6 n.u., P<0.002), LF (80.4±4.0 v. 95.3±2.6 n.u., P<0,002) and L MAX (109.3±26.1 v. 348.2±17.1, P<0.001).
Characterization of phenotypic differences between cows assigned to <L MAX and >L MAX groups
Results from trials in respiratory chambers
To uncover possible phenotypic differences between <L MAX and >L MAX groups all parameters listed in Table 4 were re-analysed for the day of ad libitum feeding (P1) and for day 2 of the experiment (P2+P3). In addition, data from a second trial performed during week 2 of lactation (postpartum (pp)) under the same conditions were used giving us the possibility to explore milk parameters (fat, protein, fat/protein ratio, lactose and ECM).
The results are summarized in Table 5. In ap cows, BT was significantly higher in <L MAX compared with >L MAX cows during feed deprivation (P2+P3). In addition, pregnant <L MAX cows had higher cortisol levels than those of the >L MAX group during the control ad libitum feeding at day 1 (Table 5). Throughout the complete ap experiment (P1 to P3) cortisol levels differ significantly between <L MAX and >L MAX groups (6.7±0.5 v. 5.1±0.3 nM/l, P<0.03).
During the pp experiment L MAX group differences were found at day 2 (P2+P3) for the parameters cortisol peak (maximum value measured at the end of P2), total ghrelin and ECM. Cortisol peak and ghrelin (total) responses, and ECM were all higher in >L MAX compared with <L MAX cows (Table 5).
Results from experimental trials under normal housing conditions
To further test the possibility that L MAX could predict different phenotypes we used data obtained from other subprojects of the joint research project (Schäff et al., Reference Schäff, Börner, Hacke, Kautzsch, Albrecht, Hammon, Röntgen and Kuhla2012, Börner et al., Reference Börner, Albrecht, Schäff, Hacke, Kautzsch, Derno, Hammon, Röntgen, Sauerwein and Kuhla2013) during weeks −5 to −2 (ap) and weeks 2 to 5 (pp). Results of these data re-analysis (n=16 cows) are given in Table 6 that summarizes parameters differing significantly between >L MAX and <L MAX cows. Of the parameters analysed, only serum insulin concentrations differ during the complete ap period and were much higher (227%) in >L MAX cows. In addition, for >L MAX cows higher DMI (16%) and ECM (13%) were found during the postnatal period. NEFA concentrations however, were different at week +2 only (<L MAX: 548±145 µM/l, >L MAX; 931±84 µM/l; P=0.0242).
Discussion
General adaptive response of cows to feed deprivation
Compared with the period of ad libitum feeding (P1), in all cows HP was significantly reduced during the 10 h feed deprivation (P2) to save energy (Derno et al., Reference Derno, Jentsch, Schweigel, Kuhla, Metges and Matthes2005; Freetly et al., Reference Freetly, Nienaber and Brown-Brandl2006; Brosh Reference Brosh2007). A reduced blood supply to the portal-drained viscera, mainly the rumen and liver, and thus, a decreased metabolic rate of these organs presumably contribute markedly to energy conservation (Chilliard et al., Reference Chilliard, Bocquier and Doreau1998). All cows also lowered physical activity (reduction of movements, shorter standing times) during P2 which is contrary to experimental results showing that steers (Derno et al., Reference Derno, Jentsch, Schweigel, Kuhla, Metges and Matthes2005) and calves (Schrama et al., Reference Schrama, Roefs, Gorssen, Heetkamp and Verstegen1995) spend more time standing during energy restriction. Our data suggest a reduction of activity-related HP to be a main component of at least short-term behavioural adaptation to feed deprivation in dairy cows. In accordance with findings showing that the HR of dairy cows must be considered in relation to its metabolic und behavioural status (Brosh, Reference Brosh2007), it was positively correlated with HP during all experimental periods. Our data reveal that under conditions of ad libitum feed intake (P1), the mean HR (72±2 beats/min) was similar to levels reported previously for pregnant, non-lactating cows (Mohr et al., Reference Mohr, Langbein and Nürnberg2002; Hagen et al., Reference Hagen, Langbein, Schmied, Lexer and Waiblinger2005; Davidson and Beede, Reference Davidson and Beede2009). In all cows, a strong and immediate HR decrease occurs in response to feed removal in P2 and is known to result from a reduced sympathetic activity to the heart (Young and Landsberg, Reference Young and Landsberg1977). In addition, reductions in intrinsic heart rate and/or an increased vagal tone can contribute to this effect (Clabough and Swanson, Reference Clabough and Swanson1989; Després et al., Reference Després, Veissier and Boissy2002).
In concert with these energy–saving mechanisms, NEFA plasma concentrations are increased indicating that nutrients are provided by lipolysis (Gross et al., Reference Gross, van Dorland, Bruckmaier and Schwarz2011; Weber et al., Reference Weber, Hametner, Tuchscherer, Losand, Kanitz, Otten, Singh, Bruckmaier, Becker, Kanitz and Hammon2013). In addition, a marked elevation (179%) of the growth hormone-releasing and orexigenic peptide hormone ghrelin (Wertz-Lutz et al., Reference Wertz-Lutz, Knight, Pritchard, Daniel, Clapper, Smart, Trenkle and Beitz2006; Bradford and Allen, Reference Bradford and Allen2008) has been observed in all cows.
During NEB, a reduction of BT and elevated plasma levels of cortisol are physiological mechanisms to reduce energy expenditure and to ensure glucose supply to tissues (Samuelsson et al., Reference Samuelsson, Uvnäs-Moberg, Gorewit and Svennersten-Sjaunja1996; Turbill et al., Reference Turbill, Ruf, Mang and Arnold2011). However, BT and blood cortisol levels were unchanged by fasting suggesting that under our experimental conditions the metabolic load was not strong enough to induce a response in all cows.
Frequency domain heart rate variability analysis reveals regulatory differences between cows
Frequency domain analysis of HRV has been shown to be a sophisticated tool for the detection of ANS regulation of the heart (Yang et al., Reference Yang, Chao, Kuo, Yin and Chen2000). However, the distribution of the power and the central frequency of the HRV spectral components also depend on the state of the central nervous system (Cabiddu et al., Reference Cabiddu, Cerutti, Viardot, Werner and Bianchi2012) and reflect the ANS regulatory capacity and activity in response to psychophysiological stress (Borell von et al., Reference Borell von, Langbein, Despres, Hansen, Leterrier, Marchant-Forde, Marchant-Forde, Minero, Mohr, Prunier, Valance and Veissier2007). With regard to its oscillating frequency and underlying mechanism it is categorized into high-frequency (HF) and low-frequency (LF) components (Yang et al., Reference Yang, Chao, Kuo, Yin and Chen2000). The LF component jointly represents both parasympathetic and sympathetic tonus (Borell von et al., Reference Borell von, Langbein, Despres, Hansen, Leterrier, Marchant-Forde, Marchant-Forde, Minero, Mohr, Prunier, Valance and Veissier2007) whereas the HF component reflects the parasympathetic control (Després et al., Reference Després, Veissier and Boissy2002; Kézér et al., Reference Kézér, Kovács, Jurkovich, Szenci and Tözsér2014). The ratio of LF and HF components (LF/HF) mirrors sympatho-vagal balance and is also considered to reflect sympathetic modulation (Yang et al., Reference Yang, Chao, Kuo, Yin and Chen2000; Stuart et al., Reference Stuart, Stafford, Dowling, Schaefer and Webster2008). In our study, by analysing the behaviour of frequency domain HRV parameters we were able to separate cows showing different autonomic regulation in response to fasting. Cows retrospectively assigned to the HF+ group responded to fasting with increased activity of the parasympathetic branch of the ANS characterized by an HF increase and reduction of the LF/HF ratio (Clabough and Swanson, Reference Clabough and Swanson1989; Després et al., Reference Després, Veissier and Boissy2002). In contrast, cows of the HF− group showed a reduction of the HF power accompanied by a 200% increase of the LF/HF ratio. Thus, they reacted to the food removal with a reduction of vagal tone and a shift of their sympatho-vagal balance towards a much stronger dominance of the sympathetic branch of the ANS. In various studies (Mohr et al., Reference Mohr, Langbein and Nürnberg2002; Hagen et al., Reference Hagen, Langbein, Schmied, Lexer and Waiblinger2005; Gygax et al., Reference Gygax, Neuffer, Kaufmann, Hauser and Wechsler2008; Stuart et al., Reference Stuart, Stafford, Dowling, Schaefer and Webster2008; Kézér et al., Reference Kézér, Kovács, Jurkovich, Szenci and Tözsér2014), a decreased parasympathetic activity has been shown to be associated with stress, reduced well-being, and regulatory capacity. Our data indicate that cows retrospectively assigned to the HF− group experience a higher stress level when food was removed and had a restricted regulatory capacity compared with HF+ cows. Having defined these two groups retrospectively, we further investigated whether the observed differences could have been predicted by specific HRV indices during control conditions (P1).
We found that under ad libitum feeding (P1) HF+ and HF− cows differed significantly in the interdependent variables HR and IBI duration and, much more interesting, in L MAX. HR and/or mean R-R interval duration are average values based on a 5-min period integrating the influence of various factors such as ambient temperature, metabolic and motoric activity. Short-term fluctuations, trends or changes in regulation during this time span are masked which limits their usefulness as predictive markers. In accord regression analysis with ΔHFP1−P2 revealed low R 2 values for HR (0.37) and R-R interval (0.33). In contrast to HR and R-R interval, L MAX describes the dynamics of the regulation processes during this 5-min period. The states of natural systems typically change in time. Those changes can be described by the recurrence plot analysis (RP), where vectors (trajectories) describe the behaviour of elements (points) in a phase space. L MAX describes the longest diagonal line found in the RP. The length of this diagonal line is determined by the duration of similar local evolution of the trajectory segments. The faster the trajectory segments diverge, the shorter are the diagonal lines (Marwan et al., Reference Marwan, Carmen Romano, Thiel and Kurths2007), meaning the system changes between different states. Therefore L MAX is more suitable to describe differences in central autonomic regulation. Indeed, regression analysis with ΔHFP1–P2 results in a high value (0.76) of R 2 and allows for calculation of TSLMAX (=258), which is prerequisite to use L MAX for predictive purposes. Of the ten cows used in the present study, seven cows had L MAX values above 258 (348±17, >L MAX group) and three cows had L MAX values below the threshold (109±26, <LMAX group). A shorter L MAX means a higher fluctuation in control of a system, whereas a longer L MAX corresponds to a more deterministic-chaotic character of the time series (Mohr et al., Reference Mohr, Langbein and Nürnberg2002). In our case, <L MAX cows are characterised by a less stable regulation during P1 and the demand of very strong regulation during the metabolic stress of fasting in P2 indicating a restricted regulatory capacity of these animals compared with >L MAX cows. Therefore, it seems conceivable that L MAX can be used to detect alterations in autonomic regulation that might precede metabolic disturbances or a compromised immune function in pregnant and lactating cows in energy deficit.
L max as a possible predictor of disturbed autonomic regulation in response to metabolic stress
In cows grouped by L MAX several phenotypic differences were observed, most of them during the lactation period and in conjunction with the additional stress of fasting (Tables 5 and 6).
In pregnant cows the stress parameters BT and cortisol (Willett and Erb, Reference Willett and Erb1972; Kataoka et al., Reference Kataoka, Hioki, Kaneko and Nakamura2014) differ between groups, and both were higher in <L MAX compared with >L MAX cows. For the BT a significant difference between groups were found at day 2 (P2+P3) of the ap experiment, pointing to development of a stress-induced hyperthermia in fasting <L MAX cows. Stress-induced hyperthermia means a rise in BT that occurs prior to and during exposure to stress and is different from fever (Vinkers et al., Reference Vinkers, Penning, Ebbens, Hellhammer, Verster, Kalkman and Olivier2010). An ACTH-independent increase in eye temperature has been observed in calves disbudded without local anaesthetic (Stuart et al., Reference Stuart, Stafford, Dowling, Schaefer and Webster2008). Stress-induced hyperthermia is known to be mediated by the dorsomedial hypothalamus and sympathetic premotor neurons in the rostral medullar raphe region that induce thermogenesis and peripheral vasoconstriction (Kataoka et al., Reference Kataoka, Hioki, Kaneko and Nakamura2014) which is in accord with activation of the sympathetic branch of the ANS in pregnant, fasting <L MAX cows. The plasma level of cortisol is influenced by feeding and by the nutritional status (Samuelsson et al., Reference Samuelsson, Uvnäs-Moberg, Gorewit and Svennersten-Sjaunja1996, Chilliard et al., Reference Chilliard, Bocquier and Doreau1998), and has been shown to increase as an anticipatory response to forthcoming food (Willett and Erb, Reference Willett and Erb1972) and in feed-deprived cows (Mills and Jenny, Reference Mills and Jenny1979; Samuelsson et al., Reference Samuelsson, Uvnäs-Moberg, Gorewit and Svennersten-Sjaunja1996). Elevated levels of cortisol are important for glucose supply in animals being in NEB (Samuelsson et al., Reference Samuelsson, Uvnäs-Moberg, Gorewit and Svennersten-Sjaunja1996), but a noticeable increase was only seen in lactating >L MAX cows at day 2 (P2+P3) of the experiment. In addition, peak cortisol levels measured at the end of P2, and reflecting the cortisol response to fasting, were also shown to be significantly higher in >L MAX cows (210% v. 35% in <L MAX cows).
At the same time point <L MAX and >L MAX cows differ in serum concentrations of total ghrelin. Interestingly, in rodents and humans, ghrelin is possibly involved in the neuroendocrine and behavioural responses to stress (Asakawa et al., Reference Asakawa, Inui, Kaga, Yuzuriha, Nagata, Fujimiya, Katsuura, Makino, Fujino and Kasuga2001; Lambert et al., Reference Lambert, Lambert, Ika-Sari, Dawood, Lee, Chopra, Straznicky, Eikelis, Drew, Tilbrook, Dixon, Esler and Schlaich2011). The peptide hormone acts at centres of the central nervous system to reduce sympathetic activity (Matsumura et al., Reference Matsumura, Tsuchihashi, Fujii, Abe and Iida2002; Krapalis et al., Reference Krapalis, Reiter, Machleidt, Iwen, Dodt, Lehnert and Sayk2012), and has been suggested to prevent central stress-induced sympathoactivation (Asakawa et al., Reference Asakawa, Inui, Kaga, Yuzuriha, Nagata, Fujimiya, Katsuura, Makino, Fujino and Kasuga2001; Lambert et al., Reference Lambert, Lambert, Ika-Sari, Dawood, Lee, Chopra, Straznicky, Eikelis, Drew, Tilbrook, Dixon, Esler and Schlaich2011). Moreover, ACTH, cortisol and epinephrine, but not norepinephrine a global marker of overall SNS activity, increase after ghrelin application (Matsumura et al., Reference Matsumura, Tsuchihashi, Fujii, Abe and Iida2002; Krapalis et al., Reference Krapalis, Reiter, Machleidt, Iwen, Dodt, Lehnert and Sayk2012). Higher total ghrelin levels as observed in >L MAX cows might thus have a sympatholytic effect.
The results confirm a higher stress level and instable regulatory processes in <L MAX cows which is also in accord with the marked reduction (about 10 kg/day) of ECM yield that has been observed.
In this context it is interesting to note that L MAX grouping of cows (n=16) and re- analysis of data obtained under normal housing conditions (Schäff et al., Reference Schäff, Börner, Hacke, Kautzsch, Albrecht, Hammon, Röntgen and Kuhla2012, Börner et al., Reference Börner, Albrecht, Schäff, Hacke, Kautzsch, Derno, Hammon, Röntgen, Sauerwein and Kuhla2013) also reveal differences between <L MAX and >L MAX groups. Compared with cows of the >L MAX group, cows of the <L MAX group had lower blood insulin levels during weeks −5 to −2 ap and showed constantly lower DMI and ECM during weeks 2 to 5 of lactation. Further evaluation in a larger number of cows under field conditions is needed to assess whether L MAX can be used as a predictive tool identifying animals at risk and selecting highly adaptable and robust animals.
Acknowledgements
The authors are grateful to the staff at the cattle facility and FBN ‘Tiertechnikum’ for assistance with animal care. In particular, we are grateful for the help of the Cattle Breeding Organization Mecklenburg-West Pommerania (RMV) and the Griepentrog farm for the selection of cows. This study was supported by the Deutsche Forschungsgemeinschaft (DFG; SCHW 642/6-1, KU 1956/3-1; HA 4372/6-1). This research did not receive any grants from the commercial sector. The publication of this article was funded by the Open Access Fund of the Leibniz Institute for Farm Animal Biology (FBN).
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