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Changes in biomarkers of metabolic stress during late gestation of dairy cows associated with colostrum volume and immunoglobulin content

Open AccessPublished:November 01, 2022DOI:https://doi.org/10.3168/jds.2022-22240

      ABSTRACT

      The objective of this observational study was to compare the metabolic status of dairy cows during the last 6 wk of gestation based on colostrum volume and immunoglobulin content. For this, healthy Holstein cows were randomly selected from 3 commercial herds in Michigan. In each farm, 4 cohorts of 21 cows (1 per season), stratified by parity, were enrolled (n = 228). Cows were blood sampled weekly during the last 6 wk of gestation, and biomarkers related to nutrient utilization, oxidant status, and inflammation were quantified in serum. Cows were milked within 6 h of calving, and the volume of colostrum produced was recorded and an aliquot collected. Concentration of IgG, IgA, and IgM were measured by radial immunodiffusion. Cows were grouped into high or low colostrum producer, high or low IgG, high or low IgA, and high or low IgM. For volume category, we arbitrarily defined 6 L of colostrum (4 L for first and 2 L for second feeding of calves) as the cutoff point, whereas for IgG we used the industry standard of ≥50 g/L. To create groups of low and high IgM or IgA, we used the median of these immunoglobulin as the cutoff point. Colostrum volume was lowest in winter, but no differences were observed among parity groups. Conversely, colostrum IgG concentration was highest in fall and winter, but colostrum IgM was lowest at these seasons. However, colostrum immunoglobulin content only showed a negative weak correlation with volume (Spearman's correlation coefficient < −0.28). Compared with low colostrum producer, high colostrum producer cows had higher concentrations of antioxidant potential and β-hydroxybutyrate, and lower cholesterol and oxidant status index. Cows with high IgG showed higher concentrations of glucose compared with low IgG. Cows with high IgA had higher concentrations of cholesterol, reactive oxygen and nitrogen species, oxidant status index, and total protein, whereas β-hydroxybutyrate and glucose were lower compared with low IgA. Biomarkers of metabolic stress were not significantly different between high IgM and low IgM. Nevertheless, the differences observed did not result in differences in inflammatory status between animals in any of the colostrum variable categories analyzed, suggesting that physiological homeostasis was not disrupted during late gestation in association with the colostrum variables studied. Overall, the great variability observed in colostrum variables suggests that colostrogenesis is a complex and multifactorial process. However, our results suggest that greater availability of antioxidants during late gestation could support the production of higher volumes of colostrum, which needs to be explored in future trials.

      Key words

      INTRODUCTION

      Despite improvements in dairy calf health management practices over the last decades, preweaning morbidity and mortality incidence risks in US herds are still high, at 33.9 and 5%, respectively (
      • Urie N.J.
      • Lombard J.E.
      • Shivley C.B.
      • Kopral C.A.
      • Adams A.E.
      • Earleywine T.J.
      • Olson J.D.
      • Garry F.B.
      Preweaned heifer management on US dairy operations: Part V. Factors associated with morbidity and mortality in preweaned dairy heifer calves.
      ). One of the major contributors to preweaning disease occurrence is failure of transfer of passive immunity, a problem that still has a high prevalence of 13% in US dairy herds (
      • Raboisson D.
      • Trillat P.
      • Cahuzac C.
      Failure of passive immune transfer in calves: A meta-analysis on the consequences and assessment of the economic impact.
      ). Providing insufficient volume, low quality, or both, of colostrum within the first hours of life is the major contributing factor to failed immunity transfer in calves (
      • Morin D.E.
      • McCoy G.C.
      • Hurley W.L.
      Effects of quality, quantity, and timing of colostrum feeding and addition of a dried colostrum supplement on immunoglobulin G1 absorption in Holstein bull calves.
      ). Traditionally, a volume of colostrum of 10 to 12% of the calves' BW, given in 1 single meal, was recommended as a feeding strategy to transfer passive immunity successfully (
      • Godden S.
      Colostrum management for dairy calves.
      ). However, feeding 4 L of high-quality colostrum within 6 h of birth and 2 L at 12 h of life has been recently recommended to optimize the transfer of passive immunity and calf health (
      • Hammon H.M.
      • Steinhoff-Wagner J.
      • Flor J.
      • Schonhusen U.
      • Metges C.C.
      Lactation biology symposium: Role of colostrum and colostrum components on glucose metabolism in neonatal calves.
      ;
      • Godden S.M.
      • Lombard J.E.
      • Woolums A.R.
      Colostrum management for dairy calves.
      ). In fact, calves that received a second colostrum meal within the first 12 h of birth had greater ADG, and lower failed immunity transfer and preweaning morbidity risks than calves that only received 1 meal (
      • Abuelo A.
      • Cullens F.
      • Hanes A.
      • Brester J.L.
      Impact of 2 versus 1 colostrum meals on failure of transfer of passive immunity, pre-weaning morbidity and mortality, and performance of dairy calves in a large dairy herd.
      ).
      However, there is considerable variability in colostrum production among cows (
      • Morin D.E.
      • McCoy G.C.
      • Hurley W.L.
      Effects of quality, quantity, and timing of colostrum feeding and addition of a dried colostrum supplement on immunoglobulin G1 absorption in Holstein bull calves.
      ;
      • Gavin K.
      • Neibergs H.
      • Hoffman A.
      • Kiser J.N.
      • Cornmesser M.A.
      • Haredasht S.A.
      • Martinez-Lopez B.
      • Wenz J.R.
      • Moore D.A.
      Low colostrum yield in Jersey cattle and potential risk factors.
      ;
      • Kessler E.C.
      • Pistol G.C.
      • Bruckmaier R.M.
      • Gross J.J.
      Pattern of milk yield and immunoglobulin concentration and factors associated with colostrum quality at the quarter level in dairy cows after parturition.
      ), making it difficult to harvest the volume of colostrum needed to sustain this feeding regimen in some cases. Colostrogenesis has been an active area of research and some aspects, such as IgG transfer from bloodstream, have been reviewed extensively (
      • Barrington G.M.
      • McFadden T.B.
      • Huyler M.T.
      • Besser T.E.
      Regulation of colostrogenesis in cattle.
      ;
      • Baumrucker C.R.
      • Bruckmaier R.M.
      Colostrogenesis: IgG1 transcytosis mechanisms.
      ). However, the scientific evidence on factors affecting the volume of colostrum being produced is still limited. Colostrogenesis starts 3 to 4 wk before calving. At this time, cows start experiencing metabolic adaptations in preparation to the onset of lactation. However, cows may develop metabolic stress if they fail to physiologically adapt to the profound increase in nutrient requirements associated with fetal growth and milk production (
      • Sordillo L.M.
      • Mavangira V.
      The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows.
      ). Metabolic stress is characterized by excessive lipid mobilization, oxidative stress, and inflammatory dysfunction (
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      The importance of the oxidative status of dairy cattle in the periparturient period: revisiting antioxidant supplementation.
      ). The negative effect of metabolic stress on the immune function, health, and production of dairy cattle during this period is well established (
      • Kehrli Jr., M.E.
      • Nonnecke B.J.
      • Roth J.A.
      Alterations in bovine neutrophil function during the periparturient period.
      ;
      • Sordillo L.M.
      • Aitken S.L.
      Impact of oxidative stress on the health and immune function of dairy cattle.
      ;
      • Bradford B.J.
      • Yuan K.
      • Farney J.K.
      • Mamedova L.K.
      • Carpenter A.J.
      Invited review: Inflammation during the transition to lactation: New adventures with an old flame.
      ). However, to the best of our knowledge, the association between metabolic stress biomarkers and colostrum production have not yet been examined. Colostrum is more concentrated in nutrients than milk (
      • Godden S.
      Colostrum management for dairy calves.
      ), which might result in important nutritional demands for the cow. We, therefore, hypothesized that cows producing high volumes of colostrum and quality as assessed by immunoglobulin content would exhibit increased metabolic activity during late gestation. Thus, the objective of this observational study was to identify the association between biomarkers of metabolic stress during late gestation, and colostrum volume and concentration of IgG, IgA, and IgM.

      MATERIALS AND METHODS

      Animals, Feed, Farms, and Management

      All procedures were approved by the Michigan State University Institutional Animal Care and Use Committee (protocol 04/18–065–00) and animals were enrolled with owners' consent. This prospective cohort study was conducted using a convenience sample of 3 commercial Michigan dairy farms, selected based on location within 50 miles to the university and willingness to participate in the study. The study was designed to have 1 cohort per season at each farm, a total of 4 cohorts, to account for the documented changes in colostrum yield associated with season (
      • Gavin K.
      • Neibergs H.
      • Hoffman A.
      • Kiser J.N.
      • Cornmesser M.A.
      • Haredasht S.A.
      • Martinez-Lopez B.
      • Wenz J.R.
      • Moore D.A.
      Low colostrum yield in Jersey cattle and potential risk factors.
      ;
      • Borchardt S.
      • Sutter F.
      • Heuwieser W.
      • Venjakob P.
      Management-related factors in dry cows and their associations with colostrum quantity and quality on a large commercial dairy farm.
      ). Sampling occurred during the period between June 2019 and September 2020.
      The sample size was calculated using an online calculator ( https://epitools.ausvet.com.au ) to achieve 90% confidence and 5% precision of within-herd prevalence, resulting in 21 animals per cohort per farm (n = 252). Healthy Holstein cows were selected using randomization software ( https://www.graphpad.com/quickcalcs/randomSelect1/ ) among those expected to calve 6 to 8 wk after the start of sampling from a list of cows generated by the herd management software. Healthy animals were defined as not being in the sick pen, not currently receiving any medical treatment, and not displaying sick cow behavior based on the subjective interpretation of the research staff. To reflect common demographics of dairy farms, random selection was stratified by parity groups of cows entering their first, second, or third to fifth lactation. Exclusion criteria for enrollment were cows with a successful breeding later than 150 DIM, and BCS under 2 or over 4 on a scale of 1 to 5 (
      • Wildman E.E.
      • Jones G.M.
      • Wagner P.E.
      • Boman R.L.
      • Troutt Jr., H.F.
      • Lesch T.N.
      A dairy-cow body condition scoring system and its relationship to selected production characteristics.
      ). Finally, data from 24 enrolled cows were excluded from analyses due to abortions, injuries resulting in euthanasia, deaths before calving, or failure to obtain colostrum data (samples not collected or yield not recorded). Therefore, the complete data from 228 cows were included in the analyses.
      Housing and characteristics of management practices of late-gestation cows of the 3 farms are reported in Table 1. Cows had ad libitum access to a TMR and water for the entire dry-cow period. Farms A and B had 2 dietary groups for dry cows (far-off and close-up), whereas farm C managed all dry cows in the same diet. Farms A and C had separate groups for heifers and multiparous cows, whereas in farm B heifers and multiparous cows were separated in the far-off group, but not the close-up. Diets were formulated by the farms' nutritionist to meet or exceed
      • NRC (National Research Council)
      Nutrient Requirements of Dairy Cattle.
      recommendations. Samples of all TMR were collected at 2-wk intervals from the feed bunk at the time of distribution and sent to an external laboratory for chemical composition analysis (Cumberland Valley Analytical Services, Waynesboro, PA). The composition and chemical analysis results of the diets are summarized in Supplemental Tables S1 and S2 ( https://www.biorxiv.org/content/10.1101/2022.07.18.500308v1.supplementary-material ;
      • Rossi R.M.
      • Cullens F.M.
      • Bacigalupo P.
      • Sordillo L.M.
      • Abuelo A.
      Changes in biomarkers of metabolic stress during late gestation of dairy cows associated with colostrum volume and immunoglobulin content. Supplemental files.
      ).
      Table 1Description of herd, housing, and management practices for late-gestation cows in the study farms
      ItemFarm AFarm BFarm C
      Close-up cowsClose-up heifersFar-off cowsFar-off heifersClose-up cows/heifersFar-off cowsFar-off heifersClose-up cows/heifersFar-off cowsFar-off heifers
      Pen designFreestallFreestallFreestallFreestallBedded packFreestallFreestallBedded packFreestallFreestall
      Heat abatement137 cm diameter fans on stall areaNoneNone91 cm diameter fans on bedding areasNone61 cm diameter fans on bedding areas
      Target dry period length (d)606045
      Target duration of close- up diet (d)2121Same diet for whole dry period
      Rolling yearly milk yield per cow (kg)15,04914,50814,011
      Rolling yearly average of cows in milk (n)3,7121,0541,608
      Min-max ambient temperature
      Data from the National Environmental Satellite, Data, and Information Service station closest to each farm (https://www.ncei.noaa.gov/access/past-weather/); reported as the average of the daily minimum and maximum temperatures during the sampling period at each farm.
      (°C)
       Summer17.2 to 30.117.8 to 29.415.2 to 30.5
       Fall5.5 to 14.85.5 to 15.111.1 to 22.2
       Winter−6.8 to 1.0−6.7 to 1.1−6.1 to 2.8
       Spring1.3 to 11.01.1 to 12.20.8 to 12.8
      1 Data from the National Environmental Satellite, Data, and Information Service station closest to each farm (https://www.ncei.noaa.gov/access/past-weather/); reported as the average of the daily minimum and maximum temperatures during the sampling period at each farm.

      Sample Collection

      Blood samples were obtained weekly starting 6 wk before expected parturition, taken approximately at the time of feed delivery via puncture of coccygeal vessels using two 10-mL evacuated tubes with serum separator (BD Vacutainer; Becton, Dickinson and Company). Blood samples that were collected within 2 d of actual calving date were not considered as the −1 wk point to avoid changes in blood biomarkers due to the hormonal changes associated with calving, considering the previous week sample instead. Tubes were transported to the laboratory on ice, separated within 1 h by centrifugation at 2,000 × g for 20 min at 4°C, aliquoted, snap-frozen in liquid nitrogen, and stored at −80°C pending analysis within 1 mo of collection.
      Colostrum was harvested by trained farm personnel within 6 h of calving following each farm's routine procedures. The volume of colostrum produced was measured using a graduated bucket (10 Quart Measuring Pail with Handle, United States Plastic Corporation). A 50-mL sample of each cow's colostrum was also collected and kept frozen at −20°C for further analysis.

      Analytical Determinations

      Serum Samples

      Oxidant status was assessed following previously reported methods (
      • Abuelo A.
      • Gandy J.C.
      • Neuder L.
      • Brester J.
      • Sordillo L.M.
      Short communication: Markers of oxidant status and inflammation relative to the development of claw lesions associated with lameness in early lactation cows.
      ). The concentrations of reactive oxygen and nitrogen species (RONS) in serum were determined as indicator of pro-oxidant production using the OxiSelect In Vitro Reactive Oxygen and Nitrogen Species assay kit (Cell BioLabs Inc.). Briefly, free radicals present in the sample bind to a dichlorodihydrofluorescein probe, converting it to a fluorescing product (2',7'-dichlorodihydrofluorescein). Thus, the fluorescence intensity is proportional to the concentration of RONS in the sample. The fluorescence of dichlorofluorescent dye was determined at excitation wavelengths of 480 nm and emission of 530 nm in a Synergy H1 Hybrid plate reader (Biotek). To ensure fluorescence at various concentrations, a standard curve, made by 6 serial dilutions (0–10,000 nM) of the fluorescence probe 2',7'-dichlorodihydrofluorescein diacetate, was included in each plate. All samples and standards were analyzed in duplicate and those with a coefficient of variation greater than 10% were re-assayed. Background fluorescence was eliminated by subtracting blank values from sample values. Results are reported as the average relative fluorescence units between replicates.
      The antioxidant potential (AOP) of serum samples were determined using trolox (synthetic vitamin E analog) equivalents antioxidant capacity, as described previously (
      • Re R.
      • Pellegrini N.
      • Proteggente A.
      • Pannala A.
      • Yang M.
      • Rice-Evans C.
      Antioxidant activity applying an improved ABTS radical cation decolorization assay.
      ). Antioxidant components of serum interact, making it difficult to quantify each antioxidant individually. As a result, this method considers the synergism of all antioxidants present in a sample, including albumin (Alb), thiols, bilirubin, and superoxide dismutase. In brief, based on the standard curve of 0 to 25 g/L, a known volume of trolox standard concentration would result in a similar reduction of the radical 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (Sigma-Aldrich). Samples were analyzed in triplicate, and samples with replicates with coefficients of variation greater than 10% were re-assayed. Changes in oxidative balance may occur because of shifts in RONS, AOP, or both. Thus, the oxidant status index (OSi) was calculated as the ratio between RONS and AOP, as this better characterizes shifts in redox balance in periparturient cows (
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      Oxidative stress index (OSi) as a new tool to assess redox status in dairy cattle during the transition period.
      ).
      The serum concentration of BHB, BUN, calcium (Ca), cholesterol (Chol), glucose (Gluc), magnesium (Mg), nonesterified fatty acids (NEFA), Alb, and total protein (TP) were quantified using commercial reagents from Catachem Inc. as biomarkers of nutrient utilization. As biomarker of inflammation, we determined the concentration of the positive acute phase protein haptoglobin (Hp; phase Haptoglobin Assay TP-801, Tridelta Development Limited). All biomarkers related to nutrient utilization and inflammation were determined using a small-scale biochemistry analyzer (CataChemWell-T; Catachem Inc.) previously validated for cattle (
      • Abuelo A.
      • Brester J.L.
      • Starken K.
      • Neuder L.M.
      Technical note: Comparative evaluation of 3 methods for the quantification of nonesterified fatty acids in bovine plasma sampled prepartum.
      ). The analyzer was calibrated every week using the assay manufacturer's calibrators. Physiological and pathological reference samples were also analyzed at the time of calibration for 2-level quality control. The precision of all biomarkers quantified in this analyzer is reported in Supplemental Table S3 ( https://www.biorxiv.org/content/10.1101/2022.07.18.500308v1.supplementary-material ;
      • Rossi R.M.
      • Cullens F.M.
      • Bacigalupo P.
      • Sordillo L.M.
      • Abuelo A.
      Changes in biomarkers of metabolic stress during late gestation of dairy cows associated with colostrum volume and immunoglobulin content. Supplemental files.
      ).

      Colostrum Samples

      The concentration of IgG, IgA, and IgM in colostrum samples were measured via radial immunodiffusion (Bovine IgG, IgA and IgM test; Triple J Farms) following manufacturer's instructions ( https://kentlabs.com/jjj/triple-j-farms-product-information/rid-plate-procedure/ ). The method is based on the precipitation in agarose gel growing in a circle antigen-antibody complexes, which develop after 10 to 20 h at room temperature and continues to grow until equilibrium is reached. Briefly, the colostrum samples were thawed overnight at 4°C. Dilutions of each sample were performed in 0.9% NaCl. Samples were diluted at 1:6, 1:9, 1:10 for IgG analyses and at 1:2, 1:4, and 1:5 for IgA and IgM quantification. Standards were included in each plate for reference and ranged from 1.8 to 28.03 (IgG), 0.53 to 3.87 (IgA), and 0.62 to 3.81 (IgM) g/L. The diffusion ring through the agarose gel containing mono-specific antibody after 24 h of incubation at room temperature was measured using a caliper with a precision of 0.1 mm (VWR traceable caliper). The values of the sample's ring were read off the standard curve to determine immunoglobulin concentrations in grams per liter. Samples falling outside of the standard curve were re-assayed using a higher or lower dilution as needed.

      Statistical Analyses

      Data were managed in Excel spreadsheets and exported to the statistical software. All statistical analyses were performed with JMP Pro version 15.2 (SAS Institute Inc.) and the criterion for statistical significance was established at P < 0.05. A one-way ANOVA was used to compare colostrum variables among seasons, parity groups (first, second, or third to fifth), and farms. Associations between colostrum variables were examined using Spearman's correlation coefficient. Cows were grouped ex-post into groups based on their colostrum variables to compare the changes in biomarkers of metabolic stress according to the volume and immunoglobulin content of their colostrum. Based on the recent recommendation of colostrum volume to sufficiently feed 2 meals of colostrum for 1 calf (
      • Godden S.M.
      • Lombard J.E.
      • Woolums A.R.
      Colostrum management for dairy calves.
      ), we considered high colostrum producers (HCP) when cows produced ≥6 L, whereas low colostrum producers (LCP) yielded <6 L. Cows were classified as producing low (LIG) or high IgG (HIG) colostrum based on the industry threshold of 50 g/L (
      • Godden S.
      Colostrum management for dairy calves.
      ). However, no industry standard exists for IgM and IgA to date. Thus, we used the median of the IgM and IgA values as the cutoff point to create equal sized groups of low and high immunoglobulin, LIM or HIM, and LIA or high immunoglobulin A producers (HIA), respectively. Linear mixed models with repeated measures were built for the biomarkers Alb, BHBA, BUN, Ca, Chol, Gluc, Hp, Mg, NEFA, TP, RONS, AOP, and OSi as outcome variables. Fixed effects included time (sampling wk −6 to −1 relative to actual calving), groups (LCP vs. HCP, LIG vs. HIG, LIM vs. HIM, or LIA vs. HIA), and their interaction. Cow nested within farm, season, and parity group were used as random effects. For repeated measures, the covariance structures autoregressive 1, compound symmetry, or residual were tested for each variable, and the one with the lowest Akaike information criterion was chosen. Model assumptions were assessed by evaluation of homoscedasticity and normality of residuals. To satisfy these assumptions, some variables were natural log- or square root-transformed and the resulting least squares means estimates were subsequently back transformed and presented as geometric means. All P-values given are those controlled for multiple comparisons with Tukey's honestly significant difference test.

      RESULTS

      Descriptive Results of Colostrum Variables

      The distribution of colostrum volume and concentrations of IgG, IgM, and IgA by season and parity group is depicted in Figure 1. Distribution of cows per groups were 137 LCP versus 91 HCP, 16 LIG versus 212 HIG, 112 LIA versus 116 HIA, and 113 LIM versus 115 HIM. The overall average colostrum yield was 5.5 (range = 0.5 to 15.3) L with the highest average yield recorded in summer and lowest in winter (Table 2). Volume of colostrum did not vary among parity groups (P = 0.24) or farms (P = 0.15). The overall colostrum average (range) IgG concentration was 118.7 (8.3 to 261.2) g/L, with cows calving in fall and winter producing more IgG concentrated colostrum than those calving in summer or spring (Table 2). Also, cows entering their third or greater lactation produced colostrum with greater IgG concentration than those entering their first or second lactation. No differences in IgG concentrations were found between first and second lactation animals. Colostrum IgG concentration was statistically different among all 3 farms of the study (P < 0.001).
      Figure thumbnail gr1
      Figure 1Distribution of colostrum (A) volume, (B) IgG concentration, (C) IgM concentration, and (D) IgA concentration per season and parity group. Boxes are delimited by the 15th and 75th percentiles. The horizontal line within the box represents the median. The whiskers of the box plots represent the 5 to 95% percentiles.
      Table 2Distribution of colostrum variables across seasons, parity groups, and study farms
      Colostrum was harvested within 6 h of calving by farm staff; results are expressed as means (95% CI); data were analyzed through one-way ANOVA and means compared with Tukey honest significant difference test.
      ItemVolume (L)IgG (g/L)IgM (g/L)IgA (g/L)
      Season
       Summer6.9 (6.6–7.3)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      110.7 (105.5–115.9)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.8 (5.5–6.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      6.4 (6.0–6.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       Fall5.4 (5.1–5.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      127.7 (122.6–132.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.5 (4.2–4.7)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.6 (5.2–6.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       Winter4.3 (4.0–4.6)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      130.8 (125.9–135.6)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.6 (4.4–4.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      6.4 (6.0–6.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       Spring5.4 (5.0–5.7)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      104.9 (100.0–110.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.0 (4.8–5.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      6.8 (6.4–7.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      Parity group
       First lactation5.3 (5.0–5.6)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      115.9 (111.4–120.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.2 (5.0–5.5)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.4 (4.0–4.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       Second lactation5.4 (5.1–5.7)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      108.5 (104.8–112.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.3 (4.1–4.5)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      6.3 (6.0–6.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       Third to fifth lactation5.7 (5.4–6.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      135.1 (130.8–139.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.4 (5.2–5.6)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      8.1 (7.8–8.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      Farm
       A6.1 (5.7–6.9)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      119.0 (114.7–123.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.2 (5.0–5.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.8 (5.5–6.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       B5.7 (5.4–6.0)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      105.6 (101.0–110.1)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.7 (4.5–4.9)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      5.9 (5.5–6.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
       C5.4 (5.1–5.8)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      130.0 (125.8–134.2)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      4.9 (4.7–5.1)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      7.1 (6.8–7.4)
      Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      a–c Within each group and variable, means with different superscript letters are statistically different (P < 0.05).
      1 Colostrum was harvested within 6 h of calving by farm staff; results are expressed as means (95% CI); data were analyzed through one-way ANOVA and means compared with Tukey honest significant difference test.
      The mean (range) IgM concentration of colostrum was 4.9 (0.6 to 12.3) g/L, with higher concentrations during summer (Table 2). Interestingly, cows entering the second lactation produced colostrum with lower IgM concentration than those entering the first or third or greater (P < 0.023), and we also found differences in colostrum IgM concentration among farms (P = 0.019). For IgA, the overall mean (range) concentration was 6.29 (0.33 to 17.5) g/L, with lower concentrations recorded during fall compared with other months (Table 2). Immunoglobulin A concentration in colostrum increased with lactation number (P = 0.014), and farm C's colostrum had higher IgA concentrations than farm A and B (P < 0.001). Colostrum volume showed a negative but weak correlation with the concentrations of IgG and IgA but did not correlate with IgM (Table 3). Conversely, the concentrations of all immunoglobulin showed a positive correlation with each other. Nevertheless, none of the correlations identified showed a correlation coefficient greater than 0.50.
      Table 3Correlations among colostrum volume and concentrations of IgG, IgM, and IgA
      Results expressed as Spearman's correlation coefficient (ρ) with 95% CI and associated P-values.
      VariableVolume (L)IgG (g/L)IgM (g/L)IgA (g/L)
      Volume
       ρ1.0−0.280.03−0.15
       (95% CI)(−0.38 to −0.15)(−0.10 to 0.17)(−0.28 to −0.02)
      P-value<0.00010.620.021
      IgG
       ρ1.00.260.50
       (95% CI)(0.13 to 0.38)(0.40 to 0.60)
      P-value<0.0001<0.0001
      IgM
       ρ1.00.30
       (95% CI)(0.17 to 0.41)
      P-value<0.0001
      IgA
       ρ1.0
       (95% CI)
      P-value
      1 Results expressed as Spearman's correlation coefficient (ρ) with 95% CI and associated P-values.

      Biomarkers of Metabolic Stress

      The mean (SE) concentration of the cow biomarkers is presented by group and sampling point for the colostrum volume, IgG, IgM, and IgA variables in Tables 4, 5, Table 6, Table 7, respectively. High colostrum producers cows had higher concentrations of AOP, BHB, and lower Chol and OSi than LCP. For IgG group, HIG cows showed higher concentrations of Alb and Gluc compared with LIG. High immunoglobulin A producers cows had higher concentrations of Chol, RONS, OSi, and TP, whereas BHB and Gluc were lower compared with LIA. We also found a tendency for lower NEFA concentrations (P = 0.096) in the HIA group. For the IgM group, biomarkers of the colostrum variables were not significantly different between HIM and LIM. Also, no significant differences were found on the biomarkers of inflammation Hp and Alb for any of the colostrum variable categories analyzed.
      Table 4Estimated means and significance of the main effects from the generalized linear mixed models
      Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      of blood biomarkers of late-gestation dairy cows comparing groups of high colostrum producers (HCP, n = 91) and low colostrum producers (LCP, n = 137)
      Outcome
      NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      Estimated means of the different time points relative to calving
      Week −6 through wk -1 represent sample points relative to actual calving. HCP ≥ 6 L; LCP < 6 L.
      SEP-value
      Wk −6Wk −5Wk −4Wk −3Wk −2Wk −1
      HCPLCPHCPLCPHCPLCPHCPLCPHCPLCPHCPLCPGroup (G)Time (T)G × T
      NEFA
      Variable natural log transformed for statistical analysis.
      (mM)
      0.220.240.210.210.200.220.190.220.190.210.230.230.080.4910.0270.555
      BHB
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      4.234.094.394.054.324.034.513.904.653.785.154.170.30<0.001<0.0010.001
      Cholesterol
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      176198160175143154131139112125991101.020.002<0.0010.214
      Glucose (mg/dL)7376.674.374.872.673.072.873.372.074.571.773.21.010.2220.0030.087
      Total protein
      Variable natural log transformed for statistical analysis.
      (g/dL)
      7.547.417.627.377.457.247.407.237.147.106.976.820.290.153<0.0010.268
      BUN (mg/dL)11.410.811.811.0011.7211.2211.4611.8511.9912.3212.9412.860.370.646<0.0010.081
      Albumin
      Variable natural log transformed for statistical analysis.
      (g/dL)
      4.214.134.234.054.093.964.133.984.063.994.043.950.270.051<0.0010.343
      Haptoglobin
      Variable natural log transformed for statistical analysis.
      (g/L)
      0.500.460.490.480.470.450.500.430.480.440.460.420.050.2290.0190.205
      Ca (mg/dL)9.299.169.279.189.039.029.289.069.109.179.219.110.110.5330.0160.213
      Mg (mg/dL)2.872.763.002.813.022.802.942.843.012.862.972.890.070.0650.3230.613
      AOP
      Variable natural log transformed for statistical analysis.
      (TE/mL)
      8.197.498.427.358.187.238.207.327.977.117.676.770.330.002<0.0010.690
      RONS
      Variable natural log transformed for statistical analysis.
      (RFU)
      44.849.346.849.848.251.552.655.354.959.253.761.71.050.239<0.0010.572
      OSi
      Variable natural log transformed for statistical analysis.
      (arbitrary units)
      5.386.535.466.735.797.076.317.506.788.276.889.050.060.006<0.0010.583
      1 Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      2 NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      3 Week −6 through wk -1 represent sample points relative to actual calving. HCP ≥ 6 L; LCP < 6 L.
      4 Variable natural log transformed for statistical analysis.
      Table 5Estimated means and significance of the main effects from the generalized linear mixed models
      Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      of blood biomarkers of late-gestation dairy cows comparing groups of high IgG (HIG, n = 212) and low IgG (LIG, n = 16)
      Outcome
      NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      Estimated means at the different time points relative to calving
      Week −6 through wk −1 represent sample points relative to actual calving; HIG ≥ 50 g/L IgG; LIG < 50 g/L IgG.
      SEP-value
      Wk −6Wk −5Wk −4Wk −3Wk −2Wk −1
      HIGLIGHIGLIGHIGLIGHIGLIGHIGLIGHIGLIGGroup (G)Time (T)G × T
      NEFA
      Variable natural log transformed for statistical analysis.
      (mM)
      0.230.130.220.150.210.150.200.180.200.180.230.240.090.3040.3280.415
      BHB
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      4.123.754.193.524.134.194.133.884.113.904.515.080.710.6050.0040.249
      Cholesterol (mg/dL)19619817615415414113912912411010993.38.40.369<0.0010.583
      Glucose
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      75.568.774.966.973.464.773.469.673.966.472.967.41.110.0130.4640.548
      Total protein
      Variable natural log transformed for statistical analysis.
      (g/dL)
      7.487.207.517.027.356.987.327.137.146.766.916.510.180.165<0.0010.649
      BUN (mg/dL)11.012.111.212.811.313.711.513.812.113.612.813.81.140.0890.1010.766
      Albumin
      Variable natural log transformed for statistical analysis.
      (g/dL)
      4.173.844.133.854.023.804.043.884.023.864.003.580.020.0430.0180.172
      Haptoglobin
      Variable natural log transformed for statistical analysis.
      (g/L)
      0.480.560.480.450.460.450.460.540.450.430.440.450.100.8160.2420.367
      Ca (mg/dL)9.238.929.229.099.048.609.149.059.158.879.159.000.210.4300.2630.820
      Mg
      Variable square root transformed for analysis.
      (mg/dL)
      2.772.872.862.922.862.962.852.862.883.042.912.570.150.9410.2700.122
      AOP
      Variable natural log transformed for statistical analysis.
      (TE/mL)
      7.857.097.847.217.676.827.756.857.526.737.216.180.360.2130.0010.914
      RONS
      Variable natural log transformed for statistical analysis.
      (RFU)
      47.734.148.741.450.537.854.347.257.550.358.257.22.110.282<0.0010.186
      OSi
      Variable natural log transformed for statistical analysis.
      (arbitrary units)
      6.014.816.155.736.515.536.946.897.577.478.009.261.130.771<0.0010.155
      1 Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      2 NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      3 Week −6 through wk −1 represent sample points relative to actual calving; HIG ≥ 50 g/L IgG; LIG < 50 g/L IgG.
      4 Variable natural log transformed for statistical analysis.
      5 Variable square root transformed for analysis.
      Table 6Estimated means and significance of the main effects from the generalized linear mixed models
      Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      of blood biomarkers of late-gestation dairy cows comparing groups of high IgM (HIM, n = 115) and low IgM (LIM, n = 113)
      Outcome
      NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      Estimated means at the different time points relative to calving
      Week −6 through wk −1 represent sample points relative to actual calving. HIM ≥ 4.6 g/L IgM; LIM < 4.6 g/L IgM).
      SEP-value
      Wk −6Wk −5Wk −4Wk −3Wk −2Wk −1
      HIMLIMHIMLIMHIMLIMHIMLIMHIMLIMHIMLIMGroup (G)Time (T)G × T
      NEFA
      Variable natural log transformed for statistical analysis.
      (mM)
      0.230.230.230.200.210.210.210.200.190.210.230.230.080.9250.0400.477
      BHB
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      4.014.234.114.224.204.094.274.004.174.074.474.600.290.966<0.0010.206
      Cholesterol
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      1871881671701501471351351181211051052.150.885<0.0010.461
      Glucose
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      75.374.874.374.573.172.672.273.973.173.671.873.21.010.7090.0040.437
      Total protein
      Variable natural log transformed for statistical analysis.
      (g/dL)
      7.737.507.507.457.357.37.327.297.137.106.946.830.590.752<0.0010.558
      BUN (mg/dL)11.011.111.411.111.810.912.111.212.412.012.912.80.360.315<0.0010.272
      Albumin
      Variable natural log transformed for statistical analysis.
      (g/dL)
      4.194.144.144.104.024.004.014.054.014.023.993.981.010.841<0.0010.643
      Haptoglobin
      Variable natural log transformed for statistical analysis.
      (g/L)
      0.460.510.480.480.450.470.480.450.450.450.430.450.230.7140.0070.098
      Ca (mg/dL)9.129.319.139.298.979.089.069.229.089.209.089.200.410.2510.0250.992
      Mg (mg/dL)2.812.792.932.853.012.782.962.802.992.862.992.860.070.0890.2600.394
      AOP
      Variable natural log transformed for statistical analysis.
      (TE/mL)
      7.977.667.817.847.667.617.767.667.557.437.237.110.030.706<0.0010.775
      RONS
      Variable natural log transformed for statistical analysis.
      (RFU)
      50.044.051.745.153.446.656.851.257.156.960.655.61.450.156<0.0010.072
      OSi
      Variable natural log transformed for statistical analysis.
      (arbitrary units)
      6.195.716.535.736.886.087.236.657.477.618.287.770.060.323<0.0010.081
      1 Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      2 NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      3 Week −6 through wk −1 represent sample points relative to actual calving. HIM ≥ 4.6 g/L IgM; LIM < 4.6 g/L IgM).
      4 Variable natural log transformed for statistical analysis.
      Table 7Estimated means and significance of the main effects from the generalized linear mixed models
      Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      of blood biomarkers of late-gestation dairy cows comparing groups of high IgA (HIA, n = 116) and low IgA (LIA, n = 112)
      Outcome
      NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      Estimated means at the different time points relative to calving
      Week −6 through wk −1 represent sample points relative to actual calving; HIA ≥ 5.52 g/L IgA; LIA < 5.52 g/L IgA.
      SEP-value
      Wk −6Wk −5Wk −4Wk −3Wk −2Wk −1
      HIALIAHIALIAHIALIAHIALIAHIALIAHIALIAGroup (G)Time (T)G × T
      NEFA
      Variable natural log transformed for statistical analysis.
      (mM)
      0.230.240.190.250.190.230.180.230.180.220.230.230.080.0960.0300.046
      BHB
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      3.894.294.024.253.994.234.154.053.734.474.124.930.030.007<0.001<0.001
      Cholesterol
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      2031691791561541411371311221151071012.020.002<0.001<0.001
      Glucose
      Variable natural log transformed for statistical analysis.
      (mg/dL)
      73.876.373.175.771.074.870.875.272.074.870.874.21.130.0050.0030.663
      Total protein
      Variable natural log transformed for statistical analysis.
      (g/dL)
      7.667.247.697.227.467.117.397.137.296.867.016.660.09<0.001<0.0010.090
      BUN (mg/dL)11.110.911.511.011.711.112.011.312.611.812.912.70.360.222<0.0010.682
      Albumin
      Variable natural log transformed for statistical analysis.
      (g/dL)
      4.184.144.134.114.014.013.994.074.043.994.023.950.010.782<0.0010.061
      Haptoglobin
      Variable natural log transformed for statistical analysis.
      (g/L)
      0.490.470.490.480.430.490.450.470.440.460.430.440.050.6280.0110.040
      Ca (mg/dL)9.209.239.219.219.029.039.099.199.179.129.189.110.100.9670.0260.758
      Mg (mg/dL)2.772.792.822.922.842.892.842.872.882.912.832.960.020.4090.1890.747
      AOP
      Variable natural log transformed for statistical analysis.
      (TE/mL)
      7.947.677.717.927.797.477.757.657.597.377.247.080.050.6274<0.0010.083
      RONS
      Variable natural log transformed for statistical analysis.
      (RFU)
      54.240.856.041.858.442.662.447.065.350.166.750.91.05<0.001<0.0010.928
      OSi
      Variable natural log transformed for statistical analysis.
      (arbitrary units)
      6.755.297.195.257.425.667.976.098.516.769.127.130.06<0.001<0.0010.739
      1 Generalized linear mixed models with repeated measurements were built for the blood biomarkers as outcomes with the group, time, their interaction (G × T) as fixed effects.
      2 NEFA = nonesterified fatty acids; AOP = antioxidant potential; TE = trolox equivalent; RONS = reactive oxygen and nitrogen species; RFU = relative fluorescence units; OSi = oxidant status index.
      3 Week −6 through wk −1 represent sample points relative to actual calving; HIA ≥ 5.52 g/L IgA; LIA < 5.52 g/L IgA.
      4 Variable natural log transformed for statistical analysis.

      DISCUSSION

      Colostrum Variables

      The averages and ranges in colostrum yield and immunoglobulin isotype concentrations found in our study are in line with previous reports (
      • Kruse V.
      Yield of colostrum and immunoglobulin in cattle at the first milking after parturition.
      ;
      • Larson B.L.
      • Heary Jr., H.L.
      • Devery J.E.
      Immunoglobulin production and transport by the mammary gland.
      ;
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ;
      • Quigley J.D.
      • Lago A.
      • Chapman C.
      • Erickson P.
      • Polo J.
      Evaluation of the Brix refractometer to estimate immunoglobulin G concentration in bovine colostrum.
      ;
      • Borchardt S.
      • Sutter F.
      • Heuwieser W.
      • Venjakob P.
      Management-related factors in dry cows and their associations with colostrum quantity and quality on a large commercial dairy farm.
      ). Based on the group distributions, 93% of the cows in this study produced colostrum, meeting the industry standard of IgG content (50 g/L), similar to the 96% documented by
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      . Conversely, only 40% of cows produced sufficient first-milked colostrum to support a second meal of colostrum to calves (6 L total yield). Thus, suggesting that the volume of colostrum produced is a potential bottleneck for optimal colostrum feeding regimens in commercial dairy farms nowadays.
      Studies reporting factors affecting colostrum volume are scarce in the literature. In agreement with 2 previous studies (
      • Gavin K.
      • Neibergs H.
      • Hoffman A.
      • Kiser J.N.
      • Cornmesser M.A.
      • Haredasht S.A.
      • Martinez-Lopez B.
      • Wenz J.R.
      • Moore D.A.
      Low colostrum yield in Jersey cattle and potential risk factors.
      ;
      • Borchardt S.
      • Sutter F.
      • Heuwieser W.
      • Venjakob P.
      Management-related factors in dry cows and their associations with colostrum quantity and quality on a large commercial dairy farm.
      ), we found the lowest colostrum yield during winter. Nevertheless, contrary to previous reports (
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ;
      • Gavin K.
      • Neibergs H.
      • Hoffman A.
      • Kiser J.N.
      • Cornmesser M.A.
      • Haredasht S.A.
      • Martinez-Lopez B.
      • Wenz J.R.
      • Moore D.A.
      Low colostrum yield in Jersey cattle and potential risk factors.
      ), we did not note differences on colostrum yield by parity group. A potential explanation for these differences is the broad range of time interval from calving to milking (0 to 21 h) in the previous studies, compared with our study, in which all cows were milked within the first 6 h after calving. It is known that colostrum IgG concentration decreases with time to harvest greater than 6 to 8 h postcalving (
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ;
      • Quigley J.D.
      • Lago A.
      • Chapman C.
      • Erickson P.
      • Polo J.
      Evaluation of the Brix refractometer to estimate immunoglobulin G concentration in bovine colostrum.
      ). This is believed to be due to the dilution of colostrum due to the start of lactogenesis. Because multiparous cows produce more milk than primiparous cows, it is possible that this dilution effect is more marked in older cows, which could explain the differences in colostrum yield across parities, observed in the
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      study as time of colostrum harvest is delayed.
      Precalving nutrition is also known to affect colostrum volume (
      • Mann S.
      • Leal Yepes F.A.
      • Overton T.R.
      • Lock A.L.
      • Lamb S.V.
      • Wakshlag J.J.
      • Nydam D.V.
      Effect of dry period dietary energy level in dairy cattle on volume, concentrations of immunoglobulin G, insulin, and fatty acid composition of colostrum.
      ). Recently,
      • Borchardt S.
      • Sutter F.
      • Heuwieser W.
      • Venjakob P.
      Management-related factors in dry cows and their associations with colostrum quantity and quality on a large commercial dairy farm.
      also reported an association between duration in close-up diets and colostrum yield in a large dairy farm. Although we did not intend to evaluate nutrition factors influencing colostrum production, we purposely selected farms with different dry-cow nutrition and management protocols, finding no differences in colostrum yield among study farms. Overall, there was a marked individual variability in colostrum yield within seasons and parity groups as noted by the broad confidence intervals in Figure 1. Thus, suggesting that many factors might influence colostrum yield production. Therefore, large multi-herd studies that investigate the epidemiology of colostrum production are urgently needed to identify which animals are more likely to produce sufficient amounts of high-quality colostrum.
      The colostrum immunoglobulin concentration also showed important individual variability for all isotypes measured, but the ranges were similar to previous studies (
      • Newby T.J.
      • Stokes C.R.
      • Bourne F.J.
      Immunological activities of milk.
      ;
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ). Immunoglobulin content varied by season. In agreement with previous reports, the lowest IgG concentration was documented in the spring (
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ). The referenced study was conducted in a pasture-based dairy system and the authors speculated that the observed differences could be attributed to differences in dry period lengths or diet composition. However, we observed the same differences in IgG concentrations in farms that followed year-round calving patterns with a targeted dry period length and similar TMR composition throughout the year; thus, indicating that other underlying factors are likely involved, and further research is needed to elucidate the changes in IgG concentration associated with season of calving. Interestingly, during the fall and winter seasons, when colostrum showed the highest IgG concentrations, the concentrations of IgM were the lowest (Table 2), despite observing an overall positive correlation between IgG and IgM in colostrum samples (Table 3). Also, colostrum produced in the fall showed lower IgA content than at other seasons. To our knowledge, this is the first report evaluating changes in colostrum IgM and IgA concentrations across seasons and no research is available on factors affecting the colostrum concentration of these immunoglobulin isotypes.
      Colostrum immunoglobulin composition also varied by parity. Cows entering their third to fifth lactation produced colostrum with greater IgG concentration compared with cows entering their first and second lactation. Interestingly, no differences were observed between parity 1 and 2 animals. Despite earlier studies recommending to discharge colostrum from first lactation heifers due to low IgG content (
      • Selman I.E.
      • McEwan A.D.
      • Fisher E.W.
      Absorption of immune lactoglobulin by newborn dairy calves. Attempts to produce consistent immune lactoglobulin absorptions in newborn dairy calves using standardised methods of colostrum feeding and management.
      ), the results from this and previous studies do not support this (
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ). The mean IgG concentration of colostrum for heifers in this study was twice that considered to be the threshold for good quality colostrum (50 g/L), and only 5% of the colostrum samples obtained from heifers were below that threshold. The higher IgG concentration in older (parity 3–5) cows is consistent with the existing literature (
      • Kruse V.
      Yield of colostrum and immunoglobulin in cattle at the first milking after parturition.
      ;
      • Pritchett L.C.
      • Gay C.C.
      • Besser T.E.
      • Hancock D.D.
      Management and production factors influencing immunoglobulin G1 concentration in colostrum from Holstein cows.
      ). Older cows are likely to have been exposed to a greater number of antigens in their lifetime, resulting in greater antibodies in serum and, subsequently, in colostrum (
      • Larson B.L.
      • Heary Jr., H.L.
      • Devery J.E.
      Immunoglobulin production and transport by the mammary gland.
      ). Although IgG are transferred from the bloodstream across the mammary barrier into colostrum and IgA and IgM are largely derived from local synthesis by plasma cells in the mammary gland (
      • Larson B.L.
      • Heary Jr., H.L.
      • Devery J.E.
      Immunoglobulin production and transport by the mammary gland.
      ), differences in antigenic stimulation associated with age might also justify the observed increase in IgA concentration as parity increases. However, colostrum from parity 2 cows had lower IgM content than colostrum from parities 1 or 3 to 5. We cannot explain this finding based on the current evidence available of IgM content in colostrum. Colostrum immunoglobulin composition varied also by farm. Nevertheless, these differences could be attributed to differences in management across farms (diet, housing, dry period length, and so on) as these factors are known to influence IgG content of colostrum (
      • Godden S.
      Colostrum management for dairy calves.
      ).
      Correlation analyses revealed a weak yet statistically significant negative association between colostrum volume and IgG (ρ = −0.28) and A (ρ = −0.15) concentrations. The negative association between colostrum yield and IgG concentration had also been previously reported (
      • Quigley III, J.D.
      • Martin K.R.
      • Dowlen H.H.
      • Wallis L.B.
      • Lamar K.
      Immunoglobulin concentration, specific gravity, and nitrogen fractions of colostrum from Jersey cattle.
      ;
      • Conneely M.
      • Berry D.P.
      • Sayers R.
      • Murphy J.P.
      • Lorenz I.
      • Doherty M.L.
      • Kennedy E.
      Factors associated with the concentration of immunoglobulin G in the colostrum of dairy cows.
      ;
      • Mann S.
      • Leal Yepes F.A.
      • Overton T.R.
      • Lock A.L.
      • Lamb S.V.
      • Wakshlag J.J.
      • Nydam D.V.
      Effect of dry period dietary energy level in dairy cattle on volume, concentrations of immunoglobulin G, insulin, and fatty acid composition of colostrum.
      ). Nevertheless, the variation in colostrum volume only explained and 7.8 and 2.3% of the variation in colostrum IgG and IgA concentrations, respectively (Table 3). Thus, indicating a weak association between colostrum volume and immunoglobulin concentration. This suggests that the processes of synthesis and transfer of IgG and IgA to colostrum might be largely independent of the volume of colostrum produced. For example, colostral IgG are derived from those circulating in plasma and are actively taken up by the mammary gland through binding to FcRn receptors (
      • Zhang R.
      • Zhao Z.
      • Zhao Y.
      • Kacskovics I.
      • Eijk M.
      • Groot N.
      • Li N.
      • Hammarstrom L.
      Association of FcRn heavy chain encoding gene (FCGRT) polymorphisms with igg content in bovine colostrum.
      ), whereas the volume of produced milk depends on the osmotic equilibrium of the blood–milk barrier, regulated mainly by lactose (
      • Costa A.
      • Lopez-Villalobos N.
      • Sneddon N.W.
      • Shalloo L.
      • Franzoi M.
      • De Marchi M.
      • Penasa M.
      Invited review: Milk lactose—Current status and future challenges in dairy cattle.
      ). We speculate that the amount of absorbed water in the mammary gland alveoli during colostrogenesis, and thus, the colostrum volume, is also dependent on this equilibrium, which is affected my many other solutes beyond Ig.
      Traditionally, colostrum quality has been evaluated based solely on the concentration of IgG as this is the most abundant immunoglobulin isotype in bovine colostrum (
      • Larson B.L.
      • Heary Jr., H.L.
      • Devery J.E.
      Immunoglobulin production and transport by the mammary gland.
      ). However, we found weak correlations (defined as Spearman's coefficient ≤ 0.50) among the different isotypes of immunoglobulins in colostrum. Thus, optimization of passive immunity transfer for IgG might not necessarily result in optimal transfer of other immunological factors of colostrum, such as other immunoglobulin isotypes. However, the relevance of the transfer of IgA and IgM to the calf via colostrum on calf health and productivity remains unexplored to date and more research is needed to unravel the effect that all colostrum immunological components have on calf health.

      Nutrient Utilization and Colostrum Variables

      We evaluated the association between colostrum variables and biomarkers of energy (NEFA, BHB, Gluc, Chol), protein (BUN, TP), and macromineral (Ca, Mg) status in blood samples collected throughout the last 6 wk of gestation. The higher BHB concentration exhibited by HCP cows compared with LCP indicates that cows producing >6 L of colostrum had greater nutrient demands than those producing less than 6 L, as BHB is commonly used as an indicator of energy deficit (
      • McArt J.A.
      • Nydam D.V.
      • Oetzel G.R.
      • Overton T.R.
      • Ospina P.A.
      Elevated non-esterified fatty acids and beta-hydroxybutyrate and their association with transition dairy cow performance.
      ). However, the lack of differences in NEFA concentration between colostrum volume groups suggests that cows were able to cope with these increased energy demands without a marked increase in lipid mobilization. Furthermore, HCP cows exhibited lower Chol concentrations than LCP cows, which can be associated with decreased hepatic Chol export due to increased ketogenesis (
      • Kessler E.C.
      • Gross J.J.
      • Bruckmaier R.M.
      • Albrecht C.
      Cholesterol metabolism, transport, and hepatic regulation in dairy cows during transition and early lactation.
      ;
      • Gross J.J.
      • Schwinn A.C.
      • Müller E.
      • Münger A.
      • Dohme-Meier F.
      • Bruckmaier R.M.
      Plasma cholesterol levels and short-term adaptations of metabolism and milk production during feed restriction in early lactating dairy cows on pasture.
      ), and supports the finding of increased energy demands in association with the volume of colostrum produced, despite no marked lipid mobilization.
      Changes in nutrient utilization biomarkers were also identified between the colostrum IgG and IgA groups, but not for IgM. Colostral IgG are derived from those circulating in plasma and are taken up by the mammary gland via action of receptors (
      • Zhang R.
      • Zhao Z.
      • Zhao Y.
      • Kacskovics I.
      • Eijk M.
      • Groot N.
      • Li N.
      • Hammarstrom L.
      Association of FcRn heavy chain encoding gene (FCGRT) polymorphisms with igg content in bovine colostrum.
      ). Changes in colostrum IgG concentrations could, therefore, be caused by changes in circulating blood concentrations in the dam, a change in transfer capability, a difference in the rate of water inclusion, or a combination of these. Cows producing colostrum above the 50 g/L IgG cutoff showed greater Gluc concentrations than those producing colostrum below this threshold, but no other differences in energy-related metabolites were identified. In a recent study,
      • Immler M.
      • Failing K.
      • Gartner T.
      • Wehrend A.
      • Donat K.
      Associations between the metabolic status of the cow and colostrum quality as determined by Brix refractometry.
      also found no relationship the Brix value of the colostrum (an estimate of IgG concentration) and the biomarkers Chol or NEFA. However, these authors did not include Gluc in their panel of biomarkers. Glucose is the primary fuel for immune cells (
      • Calder P.C.
      • Dimitriadis G.
      • Newsholme P.
      Glucose metabolism in lymphoid and inflammatory cells and tissues.
      ), and therefore it is plausible that greater availability of Gluc as energy source allowed blood lymphocytes to synthetize more IgG that would have been subsequently transported into the mammary gland for colostrum synthesis. In fact, a positive correlation between plasma Gluc and IgG has been documented in goats (
      • Hefnawy A.-E.
      • Youssef S.
      • Shousha S.
      Some immunohormonal changes in experimentally pregnant toxemic goats.
      ). However, studies in human and bovine lymphocytes described a reduction in lymphocyte function under high Gluc concentrations in vitro (
      • Franklin S.T.
      • Young J.W.
      • Nonnecke B.J.
      Effects of ketones, acetate, butyrate, and glucose on bovine lymphocyte proliferation.
      ;
      • Jennbacken K.
      • Ståhlman S.
      • Grahnemo L.
      • Wiklund O.
      • Fogelstrand L.
      Glucose impairs B-1 cell function in diabetes.
      ). However, these studies used Gluc concentrations (11.1 mM) exceeding normal blood concentrations in cattle (∼3.2–4.4 mM), limiting the translation of their findings to the live animal. To our knowledge, the biological significance of Gluc concentrations on immunoglobulin synthesis has not been investigated in cattle to date. Furthermore, we did not assess blood IgG concentrations in the cows in the present study, which limits our ability to discern whether the changes in IgG colostrum content is associated with differences in blood IgG concentrations or due to other steps involved in the translocation of IgG from bloodstream into colostrum.
      Cows with higher IgA colostrum concentration showed a metabolic profile suggestive of a better energy status (lower concentrations of BHB and a tendency toward lower NEFA, and higher concentrations of Chol and TP). Unlike IgG, IgA are primarily synthetized locally in the mammary gland and not translocated from bloodstream (
      • Larson B.L.
      • Heary Jr., H.L.
      • Devery J.E.
      Immunoglobulin production and transport by the mammary gland.
      ). However, it is possible that the greater energy balance would allow B lymphocytes in the mammary gland to secrete more IgA, as producing IgA is a considerable energy expense for the body of mammals (
      • Woof J.M.
      • Kerr M.A.
      The function of immunoglobulin A in immunity.
      ). However, it could also be possible that the greater IgA concentration in colostrum was due to increased recruitment of B cells into the mammary gland, increased class switch to IgA+ cells, or both. Lower serum Gluc concentrations were also found in cows with higher IgA colostrum. Because the mammary gland uptakes 60 to 85% of blood Gluc (
      • Annison E.F.
      • Linzell J.L.
      The oxidation and utilization of glucose and acetate by the mammary gland of the goat in relation to their over-all metabolism and milk formation.
      ;
      • Rigout S.
      • Lemosquet S.
      • van Eys J.E.
      • Blum J.W.
      • Rulquin H.
      Duodenal glucose increases glucose fluxes and lactose synthesis in grass silage-fed dairy cows.
      ), lower glycemia might be indicative of higher availability of Gluc for the mammary gland IgA-producing immune cells, as Gluc is the main fuel used by these cells (
      • Calder P.C.
      • Dimitriadis G.
      • Newsholme P.
      Glucose metabolism in lymphoid and inflammatory cells and tissues.
      ). We are unaware of any research in the bovine species studying the effect of energy status on the colostrogenesis mechanisms, and, therefore, more research is needed in this area to be able to modulate colostrum production in the dairy cow.
      We detected no association between any of the colostrum variables studied and concentrations of the macrominerals Ca or Mg.
      • Immler M.
      • Failing K.
      • Gartner T.
      • Wehrend A.
      • Donat K.
      Associations between the metabolic status of the cow and colostrum quality as determined by Brix refractometry.
      , however, found a negative association between serum Ca concentration and colostrum Brix value, but were unable to clarify the physiological background behind this observation. Thus, the differences between their study and ours underscores the need for further research in this area, given that different Ca management nutritional strategies resulted in differences in colostrum IgG concentrations (
      • Diehl A.L.
      • Bernard J.K.
      • Tao S.
      • Smith T.N.
      • Marins T.
      • Kirk D.J.
      • McLean D.J.
      • Chapman J.D.
      Short communication: Blood mineral and gas concentrations of calves born to cows fed prepartum diets differing in dietary cation-anion difference and calcium concentration.
      ).

      Inflammatory Status and Colostrum Variables

      Based on the biomarkers Hp and Alb, positive and negative acute phase proteins respectively, we observed no differences in the inflammatory status of cows based on colostrum volume or immunoglobulin content. This suggests that the cows' physiological homeostasis was not disrupted during late gestation in association with the colostrum variables studied. Although exacerbated inflammation is often seen in transition cows (
      • Bradford B.J.
      • Yuan K.
      • Farney J.K.
      • Mamedova L.K.
      • Carpenter A.J.
      Invited review: Inflammation during the transition to lactation: New adventures with an old flame.
      ), and is one of the hallmarks of metabolic stress (
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      Redox biology in transition periods of dairy cattle: Role in the health of periparturient and neonatal animals.
      ), changes in biomarkers of inflammation usually occur after parturition (
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      A comparative study of the metabolic profile, insulin sensitivity and inflammatory response between organically and conventionally managed dairy cattle during the periparturient period.
      ;
      • Burfeind O.
      • Sannmann I.
      • Voigtsberger R.
      • Heuwieser W.
      Receiver operating characteristic curve analysis to determine the diagnostic performance of serum haptoglobin concentration for the diagnosis of acute puerperal metritis in dairy cows.
      ;
      • Pohl A.
      • Burfeind O.
      • Heuwieser W.
      The associations between postpartum serum haptoglobin concentration and metabolic status, calving difficulties, retained fetal membranes, and metritis.
      ). Thus, even though we are unable to determine cause-effect relationships in this observational study, the lack of association between acute phase proteins and colostrum yield or immunoglobulin isotype content let us to speculate that the colostrogenesis process might not increase inflammation in the prepartum cow.

      Oxidant Status and Colostrum Variables

      Oxidative stress is also a common feature of the transition period of dairy cattle (
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      The importance of the oxidative status of dairy cattle in the periparturient period: revisiting antioxidant supplementation.
      ), known to impair functional capabilities of immune cell populations, including the lymphocytes responsible for immunoglobulin synthesis (
      • Lacetera N.
      • Scalia D.
      • Bernabucci U.
      • Ronchi B.
      • Pirazzi D.
      • Nardone A.
      Lymphocyte functions in overconditioned cows around parturition.
      ;
      • Sordillo L.M.
      • Aitken S.L.
      Impact of oxidative stress on the health and immune function of dairy cattle.
      ;
      • Cuervo W.
      • Sordillo L.M.
      • Abuelo A.
      Oxidative stress compromises lymphocyte function in neonatal dairy calves.
      ). Thus, we anticipated that lower colostrum immunoglobulin content or volume would be associated with a more pro-oxidant systemic redox balance. In line with our hypothesis, LCP cows showed greater OSi values than HCP cows. This was due to differences in AOP as RONS remained similar between colostrum yield groups (Table 4). Therefore, it is possible that higher AOP might have supported HCP cows to produce more colostrum, rendering availability of antioxidants a potential limiting factor in colostrum volume production for cows. Our study is, to our knowledge, the first one to examine relationships between cow oxidant status and colostrum production, but given the observed relationship between lower AOP and colostrum volume, the extent to which increasing antioxidant capacity in late-gestation cows enhances colostrum yield warrants further research.
      We found no differences in oxidant status between cows in the IgG or IgM groups. However, HIA cows showed higher concentration of RONS and greater OSi values compared with LIA. This finding is contrary to our initial hypothesis linking a pro-oxidant status to lower immunoglobulin content. How systemic redox balance might influence the production of the mucosa-derived IgA but not IgG or IgM remains unexplained and asserts the complexity of redox regulation of biological processes. Certainly, the role of oxidative balance in the colostrogenesis process needs to be elucidated further.

      Study Limitations

      Given the observational nature of this study, we were only able to show associations among colostrum variables and biomarkers of metabolic stress, and did not explore cause-effect relationships. However, this is the first study to investigate changes in biomarkers of metabolic stress in association with colostrum variables and the associations identified can be examined further via controlled interventional studies. Another limitation of the study is that using the industry standard of colostrum IgG concentration (50 g/L) resulted in unbalanced groups sizes (212 vs. 16 cows in HIG and LIG), which might have influenced our ability to detect differences. In fact, post hoc power calculations revealed that only powers of 0.13, 0.21, 0.11, and 0.19 for detecting differences in NEFA concentrations between groups of colostrum volume, IgG, IgM, and IgA, respectively. Therefore, we cannot exclude associations between metabolic stress and IgG content that we were not identified in this study.

      CONCLUSIONS

      This study evaluated, for the first time, the metabolic status of dairy cows during the last 6 wk of gestation based on the volume and immunoglobulin concentration of colostrum. We observed marked individual variability in colostrum yield and immunoglobulin isotype concentration, suggesting that colostrum production is a complex and multifactorial process. Also, we detected differences in nutrient utilization and oxidant status biomarkers in association with the volume and IgG and IgA concentration of colostrum. Among all changes detected, our results suggest that increasing availability of antioxidants during late gestation could support the production of higher volumes of colostrum, which warrants further investigation through supplementation trials.

      ACKNOWLEDGMENTS

      This research was funded by the USDA National Institute of Food and Agriculture Animal Health project number 1016161 and a grant from the Michigan Alliance for Animal Agriculture (East Lansing, MI). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. The authors have not stated any conflicts of interest.

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