Association of Seroprevalence of Respiratory Pathogens and Herd-Level Management Factors with Inflammatory Markers in Dairy Cattle

Abstract

This cross-sectional study investigated the associations between the acute-phase proteins (APP) serum amyloid A (SAA) and haptoglobin (Hp), herd-level factors, and the seroprevalence of respiratory pathogens in Estonian dairy herds. Serum samples were analysed from 938 cows (95 herds) and 921 heifers (94 herds). Seroprevalence was tested for bovine herpesvirus 1 (BHV-1), bovine respiratory syncytial virus (BRSV), bovine parainfluenza virus 3, bovine viral diarrhoea virus, bovine coronavirus, bovine adenovirus, and Mycoplasma bovis (M. bovis). Farm visits included questionnaires on herd management practices. Linear random-intercept regression models showed higher serum SAA concentrations in cows from farms with BHV-1 seroprevalence of >50% and on BRSV-positive farms (p < 0.05), while farms employing a veterinarian had lower serum SAA concentrations. Cows had higher serum Hp concentrations in M. bovis-positive herds (p = 0.030). In heifers, serum SAA concentrations increased with low to moderate BHV-1 seroprevalence, decreased with higher M. bovis seroprevalence, and were higher in free-stall or mixed housing compared to tie-stall housing. Heifers’ serum Hp concentrations were lower in BHV-1-positive herds, but higher in herds with breeding bulls and larger herd sizes. To conclude, APP may reflect the herd health status and management-related effects on animals, supporting their use in herd-level monitoring.

1. Introduction

Bovine respiratory disease (BRD) represents a significant health concern in the cattle industry, giving rise to substantial morbidity, mortality, and economic challenges. Moreover, it exerts adverse effects on the growth and feed conversion rate [1]. The disease occurs in cattle across all age groups and commonly involve both viral and bacterial pathogens. Environmental stressors, management practices, and weakened host defences also play significant roles in its occurrence [2,3].

Over the years, several viruses have been associated with BRD, including bovine herpesvirus 1 (BHV-1), bovine respiratory syncytial virus (BRSV), bovine parainfluenza virus 3 (PIV-3), bovine viral diarrhoea virus (BVDV), bovine coronavirus (BCV), and bovine adeno-virus (BAV) [4,5]. Major viral respiratory pathogens, such as BHV-1 and BRSV [6,7], along with Mycoplasma spp. [8], generally trigger clinical respiratory diseases by damaging lung tissues, making animals more susceptible to secondary bacterial infections [9]. These viral pathogens, together with bacterial pathogens like Pasteurella multocida [10] and Mannheimia haemolytica [11], have been linked to increased serum acute-phase protein (APP) concentrations in cattle in both naturally occurring respiratory infections and experimental trials.

APPs are components of the broader systemic reaction known as the acute phase response which can be triggered during infectious and inflammatory processes [12]. Haptoglobin (Hp) and serum amyloid A (SAA) are important bovine APPs whose concentrations increase in the blood during an acute phase response [13]. While the serum SAA concentration rises rapidly during acute rather than chronic inflammation [14], the serum Hp concentration increases more slowly and is linked to the severity of the disease and the presence of bacterial pathogens invading the respiratory tract [6,13].

APPs, as diagnostic inflammatory markers and mediators of the immune response, are of great importance both in humans and production animals. In humans, APPs such as the C-reactive protein and SAA are widely used as biomarkers to diagnose and monitor the progress of inflammatory conditions, particularly in atherosclerosis and cardiovascular diseases [15]. In production animals, SAA and Hp can reflect the overall herd health and welfare status of cows [11,16], whereas in pigs, Hp, C-reactive protein, and SAA have been identified as sensitive indicators of infection [17].

As dairy operations grow larger globally, there is an increasing need for tests to assess the overall herd health and welfare. Current diagnostic approaches frequently focus on individual animals or pooled samples for practicality reasons, which may overlook subclinical infections or the cumulative influence of co-infections on herd health. APPs may offer a fast and holistic, integrative measure of the inflammatory status, reflecting ongoing inflammation in the herd due to infections and management-related stressors.

Estonian dairy cattle herds, where BHV-1, BVDV, BRSV, and Mycoplasma bovis (M. bovis) are endemic [18], offer an ideal population for studying co-infections of respiratory pathogens. Moreover, the majority of Estonian dairy herds are large-scale with intensive production systems [18]. These factors make it feasible to investigate how the circulation of respiratory pathogens interacting with farm management practices affect APP profiles in cows and heifers, which can offer clarity into the role of different pathogens and management approaches in herd health and welfare.

Thus, the objective of this cross-sectional study, conducted on Estonian dairy farms, was to identify the relationship between the herd-level seroprevalence of respiratory pathogens (BHV-1, BRSV, BCV, BAV, PIV-3, BVDV, and Mycoplasma bovis) and farm-level factors, including the herd size, housing, and management practices, with serum concentrations of acute-phase proteins (SAA and Hp) in heifers and cows.

2. Material and Methods

2.1. Study Design

This cross-sectional study is based on samples collected during a study by [19], conducted between September 2006 and April 2008. This study aimed to estimate herd-level and within-herd prevalence of BHV-1 and BVDV, and to identify risk factors for BHV-1 infection in Estonian dairy herds. The study sample consisted of 103 herds with 20 or more dairy cows (Figure 1). These herds were selected randomly from different size categories (20–99; 100–199; 200–399; ≥400 cows). In each of the selected herds, blood samples from a representative sample were collected to estimate within-herd seroprevalence of BHV-1 in cows and young stock older than 6 months. In total, 9637 blood samples were taken. Additionally, during the herd visits, questionnaires were filled out to gather comprehensive herd-level data to understand the risk factors related to farming practices. To minimise bias, all questionnaires were attended and interpreted by a single trained person. A subset of the questionnaire data used included information on herd size, number of livestock units per farm, use of veterinary and insemination services, frequency of animal movement between barns, participation in agricultural shows, purchase history, housing type (cold or warm barns), housing systems for cows and young stock (loose or tied), management of young stock (kept separately from cows, in contact with cows during certain life period, or housed in the same barn with cows), use of bulls for serving cows and heifers, cattle breed(s), grazing practices of cows and/or young stock, vaccination history, employee hygiene practices (change of clothing), and disinfection procedures [19].

Blood (serum) samples collected from individual animals were later used for testing additional BRD pathogens and for APP analysis for the present study, which was completed in 2009. These results have not been published previously.

Of the 103 sampled herds by [19], a subset of 95 herds was used for APP analysis by testing 10 randomly selected serum samples from each herd. Herds were eligible if they had at least 10 heifers (>6 months old; n = 94 herds) and at least 10 cows (n = 95 herds) with usable serum samples (Figure 1). Both heifers and cows were tested from the same herds, except for one herd where only cows were sampled because the heifer selection criterion was not met.

2.2. Sampling of Animals and Sample Analysis

Blood samples were collected from the coccygeal vein of animals (>6 months of age) using vacuum tubes with clotting activator (Vacuette, Kremsmünster, Austria). Serum samples were stored at room temperature for 24 h before transport to the National Centre for Laboratory Research and Risk Assessment (LABRIS) for immediate serology for BHV-1 and BVDV. The left-over serum sample aliquots were stored at −20 °C until further analysis at Estonian University of Life Sciences (EMÜ). Serum samples initially selected for testing were screened for haemolysis on five-level scale (no haemolysis, slight, moderate, marked, and gross haemolysis). Samples classified as grossly haemolysed were excluded from further analyses, resulting in the removal of 31 samples (19 from heifers and 12 from cows). The final sample sizes for APP analysis was 921 heifer (average 9.8, min–max: 5–10 samples per farm) and 938 cow serum samples (average 9.9, min–max: 7–10 samples per farm).

Antibody detection for selected pathogens was performed using commercial ELISA test kits. Antibodies against BHV-1 were detected in serum samples from cows and heifers using the IDEXX IBR gB X3 ELISA (IDEXX Laboratories). Antibodies to BVDV were measured in heifers using the PrioCheck BVDV Ab ELISA (Prionics AG, Schlieren-Zurich, Switzerland). Antibodies against M. bovis and BRSV were detected in cows and heifers using the BIO K 260 ELISA (Bio-X Diagnostics, Rochefort, Belgium) and the SVANOVIR BRSV-Ab ELISA (Boehringer Ingelheim Svanova, Uppsala, Sweden), respectively. In heifers, antibodies to PIV-3, BCV, and BAV were detected using the SVANOVIR PIV3-Ab ELISA, the SVANOVIR BCV-Ab ELISA, and the IDEXX Adenovirus-Ab ELISA (IDEXX, Westbrook, Maine, USA). For within-herd prevalence estimation, the average sample size per herd for BHV-1 was 50 cows (range: 16–116) and 55 heifers (range: 11–237; [19]). Spot testing of the herd was performed to define the herd BVDV status regarding persistent infection; 10 heifers per herd were tested [19]. A herd was considered suspect for containing persistently infected animals if at least 6 out of 10 young animals tested positive for BVDV antibodies [20]. For within-herd prevalence assessment of M. bovis and BRSV, an average of 9.8 cows (range: 8–10) and 18.6 heifers (range: 10–20) were used per herd. For evaluation of within-herd prevalence in heifers of PIV-3, BCV, and BAV, an average of 18.6 samples per herd (range: 11–20) were used.

Serum SAA concentration was measured with a commercially available ELISA kit (Phase SAA kit, Tridelta Ltd., Maynooth, Ireland), according to the manufacturer’s instructions for cattle. Initially, all samples were diluted to 1:1000 (for heifers) and 1:500 (for cows). Highest standard curve concentrations of 150 mg/L were used. Detection limit of the kit was 0.3 mg/L.

The concentration of Hp was determined using the haemoglobin binding assay described by [21], with the modification of tetramethylbenzidine (0.06 mg/mL) used as chromogen [22]. Pooled and lyophilised aliquots of bovine acute-phase serum were used to produce standard curves for assay by serial dilution. To calibrate the assay, a bovine sample with a known Hp concentration provided by the European Commission Concerted Action Project (number QLK5-CT-1999-0153) was used. The range of the standard curve was 0.03–2.10 g/L. The intra- and inter-assay coefficients of variations were <11% and <14% for SAA and <12% and <14% for Hp, respectively.

2.3. Statistical Analysis

To understand the association between seroprevalence of respiratory pathogens, herd-level factors (Tables S3 and S4), and APP, four linear random-intercept regression models were built, with log-transformed SAA and inverse square root-transformed Hp as outcome variables for cows and heifers. Logarithmic transformation for SAA and inverse square root-transformation for Hp were used to achieve normal distributions of these outcome variables. A causal diagram (Figure 2) was composed using DAGitty to identify causal relationships between variables and potential confounders [23].

For each pathogen, the 95% confidence intervals for prevalence estimates were calculated by using Exact (Clopper–Pearson) proportion method using Stata/IC 14.2 software (StataCorp LP, College Station, TX, USA). To understand between-herd variability of serum APP concentrations, intra-class correlation coefficients (ICC) were calculated. Farm was included as a random effect in the models to account for variability across farms, assuming an exchangeable covariance structure. The within-herd prevalences of respiratory pathogens (Tables S1 and S2) and other herd- and management-level factors (Tables S3 and S4) were included as predictors.

The within-herd prevalences of the tested pathogens (BHV-1, BRSV, BVDV, BCV, and BAV) were categorised into three groups (0%, 1–50%, and >50%) to perform group comparisons across herds with different infection levels and sufficient herd numbers in each group for statistical analyses; these variables were included in the models as fixed effects. For M. bovis and PIV-3, the number of herds with 0% prevalence was very low. To form three prevalence categories, a low-prevalence group (0–30%) was created. Herd-level-management-related variables for cows included whether the veterinarian or artificial insemination technician was employed by the farm (Yes/no) and whether the technician also provided services to other farms (Yes/no), the use of a breeding bull in heifers (Yes/no), recent animal purchases within the previous three years (Yes/no), whether young stock were housed together with cows or in a separate building from six months of age until pregnancy, housing system for cows (tie-stall or free-stall), and herd size (<50, 50–99, 100–199, 200–399, and >399). For heifers, herd-level-management-related variables included the use of a breeding bull in heifers, recent animal purchases, vaccination history for BHV-1 (Yes/no), grazing practices (Yes/no), and housing system for heifers (tie-stall, free-stall, or mixed).

The age of the animal was included as a continuous predictor in the models, and a squared term for age was added to the SAA models to capture non-linear relationships. Haemolysis can influence APP concentrations, thus categorised as none, slight, or marked, and so was included in all models to control for sample quality by including this as fixed effect. The herd size, housing system, age of the animal, and haemolysis were considered as confounders and added to the model as fixed effects to clarify associations between predictors and APP concentrations. If herd-level prevalence of a particular infection was not associated with APP, the animal-level antibody status was included in the model (based on the causal diagram; Figure 2) and retained in the models if significant.

Predictors were initially screened through univariable analyses, and those with a p-value ≤ 0.2 were considered for multivariable modelling. A step-wise backward elimination approach was used to develop the final multivariable models, assessing the significance of each predictor and systematically removing the least significant ones. The significance threshold for retaining predictors in the multivariable model was set at a p-value of ≤0.05. No significant interactions were found in any of the models.

Confounding effects of the herd factors were assessed by testing whether removing a variable led to a change of ≥15% in the coefficient estimates of other predictors. Therefore, potential confounders were retained in the multivariable model. Model linearity assumptions were verified using scatter plots and normality plots of standardised residuals. All statistical analyses were conducted using Stata/IC 14.2 software.

3. Results

Serum concentrations of SAA and Hp varied between cows and heifers, with an increased concentration observed in cows for both measured APPs. The mean serum SAA concentration was 16.6 mg/L in cows and 8.8 mg/L in heifers, and a similar trend was observed for Hp, with mean concentrations of 0.23 g/L in cows and 0.13 g/L in heifers.

The herd-level and mean within-herd prevalence (%) of respiratory viruses and Mycoplasma bovis antibodies in cows and heifers are presented in Supplementary Tables S1 and S2.

According to the multivariable linear regression model, the cows’ serum SAA concentrations were higher on farms with BHV-1 seroprevalence over 50% (p < 0.001;

Table 1. Results of linear regression mixed model (farm included as random factor) of associations between herd-level variables and cows’ serum amyloid A (SAA) concentrations (mg/L) after logarithmical transformation. Data from 938 cows from 95 dairy farms were included (n = cows/farms).

Variable	Coeff.	SE	p-Value	Wald Test p-Value
Age of cow (year)	−0.242	0.109	0.027
Age of cow squared (year)	0.023	0.009	0.011
Within-herd prevalence of BHV-1 in cows:				<0.001
0% (n = 365/37)	0
1–50% (n = 256/26)	0.219	0.160	0.172
>50% (n = 317/32)	0.729	0.169	<0.001
Within-herd prevalence of BRSV in cows:				0.021
0% (n = 178/18)	0
1–50% (n = 209/21)	0.404	0.195	0.038
>50% (n = 551/56)	0.504	0.182	0.006
Veterinarian is an employee of the farm:
No (n = 741/72)	0
Yes (n = 197/20)	−0.542	0.184	0.003
Herd size:				0.011
<50 (n = 237/24)	0
50–99 (n = 140/14)	0.541	0.198	0.006
100–199 (n = 140/14)	0.043	0.203	0.831
200–399 (n = 220/22)	0.369	0.190	0.052
>399 (n = 201/21)	0.570	0.217	0.009
Constant	1.228	0.333	<0.001

Intra-class correlations coefficient (ICC) for farm = 0.338 and p < 0.002 for random effect. Abbreviations: SE—standard error; BHV-1—bovine herpesvirus 1; BRSV—bovine respiratory syncytial virus. The superscript ^{“squared”} indicates that the cow age variable is squared (Age of cow × Age of cow).

) and on farms positive for BRSV (1–50%, p = 0.038; >50%, p = 0.006; Table 1) compared to negative farms. Additionally, herd sizes of 50–99 (p = 0.006; Table 1) and >399 cows (p = 0.009; Table 1) were associated with a higher serum SAA concentration in cows, compared to small herds with less than 50 cows. SAA concentrations were lower on farms that employ their own veterinarian (Table 1). The cows’ serum Hp concentrations were increased in M. bovis-positive cow herds (p = 0.030;

Table 2. Results of linear regression mixed model (farm included as random factor) of associations between herd-level variables and cows’ haptoglobin (Hp) concentrations (g/L) after 1/square root transformation ¹. Data from 938 cows from 95 dairy farms were included (n = cows/farms).

Variable	Coeff.	SE	p-Value	Wald Test p-Value
Age of cow (years)	−0.021	0.011	0.056
Cow M. bovis status:
Negative (n = 664)	0
Positive (n = 274)	−0.105	0.048	0.030
Haemolysis in serum sample:				<0.001
No haemolysis (n = 763)	0
Slight haemolysis (n = 120)	−0.436	0.066	<0.001
Marked haemolysis (n = 55)	−0.898	0.093	<0.001
Herd size:				0.291
<50 (n = 237/24)	0
50–99 (n = 140/14)	−0.015	0.083	0.852
100–199 (n = 140/14)	0.107	0.082	0.193
200–399 (n = 220/22)	0.027	0.072	0.706
>399 (n = 201/21)	−0.077	0.074	0.302
Constant	2.744	0.078	<0.001

Intra-class correlations coefficient (ICC) for farm = 0.130 and p < 0.012 for random effect. ¹ Due to inverse transformation, minus coefficient means positive effect. Abbreviations: SE—standard error; Mycoplasma (M.) bovis.

).

Heifers’ serum concentrations of SAA were increased in herds with low-to-moderate seroprevalence (1–50%) of BHV-1 antibodies (p = 0.004;

Table 3. Results of linear regression mixed model (farm included as random factor) of associations between herd-level variables and heifers’ serum amyloid A (SAA) concentrations (mg/L) after logarithmical transformation. Data from 921 heifers from 94 dairy farms were included (n = heifers/farms).

Variable	Coeff.	SE	p-Value	Wald Test p-Value
Age of heifer (months)	−0.082	0.025	0.001
Age of heifer squared (months)	0.002	0.0006	0.001
Number of heifers at farm × 100	0.052	0.027	0.058
Haemolysis in serum sample:				<0.001
No haemolysis (n = 661)	0
Slight haemolysis (n = 181)	−0.171	0.098	0.083
Marked haemolysis (n = 79)	−0.572	0.138	<0.001
Within-herd prevalence of BHV-1 in heifers:				0.007
0% (n = 543/56)	0
1–50% (n = 269/27)	0.386	0.132	0.004
>50% (n = 109/11)	−0.036	0.206	0.861
Within-herd prevalence of M. bovisin heifers:				0.003
0–30% (n = 119/13)	0
31–50% (n = 356/36)	−0.065	0.182	0.719
>50% (n = 446/45)	−0.446	0.179	0.013
Housing system for heifers:				0.043
Tie stall (n = 240/24)	0
Free stall (n = 232/24)	0.350	0.16	0.029
Mixed (n = 449/46)	0.319	0.14	0.023
Constant	1.864	0.287	<0.001

Intra-class correlations coefficient (ICC) for farm = 0.275 and p < 0.001 for random effect. Abbreviations: SE—standard error; BHV-1—bovine herpesvirus 1; Mycoplasma (M.) bovis. The superscript “^squared” indicates that the heifer age variable is squared (Age of heifer × Age of heifer).

) compared to herds that were seronegative to BHV-1. Heifers’ serum SAA concentrations were decreased (p = 0.013; Table 3) in herds with high (>50%) seroprevalence of M. bovis antibodies compared to herds with low (0–30%) seroprevalence. Additionally, free-stall (p = 0.029) and mixed housing systems (p = 0.023) were associated with increased serum SAA concentrations in heifers compared to tie-stall housing (Table 3). Heifers’ serum Hp concentrations were decreased in herds which were positive for BHV-1 antibodies (1–50%, p = 0.040; >50%, p = 0.018;

Table 4. Results of linear regression mixed model (farm included as random factor) of associations between herd-level variables and heifers’ serum haptoglobin (Hp) concentrations (g/L) after 1/square root transformation ¹. Data from 921 heifers and 94 farms were included (n = heifers/farms).

Variable	Coeff. 1	SE	p-value	Wald test p-value
Age of heifer (months)	−0.013	0.005	0.007
Number of heifers at farm × 100	−0.069	0.022	0.001
Haemolysis of sample:				<0.001
No haemolysis (n = 661)	0
Slight haemolysis (n = 181)	−0.843	0.082	<0.001
Marked haemolysis (n = 79)	−1.551	0.116	<0.001
Within-herd prevalence of BHV-1 in heifers:				0.023
0% (n = 543/56)	0
1–50% (n = 269/27)	0.224	0.109	0.040
>50% (n = 109/11)	0.398	0.169	0.018
Use of a breeding bull in heifers:
No (n = 594/60)	0
Yes (n = 327/34)	−0.205	0.098	0.038
Constant	4.061	0.119	<0.001

Intra-class correlations coefficient (ICC) for farm = 0.275 and p < 0.001 for random effect. ¹ Due to inverse transformation, minus coefficient means positive effect. Abbreviations: SE—standard error; BHV-1—bovine herpesvirus 1.

) compared to herds with heifers negative for BHV-1 antibodies. The heifers’ serum Hp concentration was increased (p = 0.038; Table 4) if a breeding bull was used with heifers in the herd. The larger number of heifers in the herd was associated with increased serum Hp concentrations (p = 0.001; Table 4).

4. Discussion

This study examined associations between serum APP concentrations in heifers and cows and the seroprevalence of respiratory pathogens in dairy herds. It also provides insight into the spread of respiratory pathogens in Estonian dairy farms at the time of sampling. However, the prevalence data related to respiratory pathogens were collected between 2006 and 2008, and do not reflect the current epidemiological situation of respiratory pathogens in Estonian dairy herds. Nonetheless, disease prevalence was not the primary focus of this study; rather, the objective was to identify potential associations between herd-level and management-related factors and the overall herd inflammatory status, as reflected by changes in the APP concentrations, thereby supporting their use in herd-level monitoring.

4.1. Seroprevalence of Infections Associated with APP in Cows and Heifers

Cows from herds with a high BHV-1 seroprevalence (>50%) had increased serum SAA concentrations compared to cows from herds with low-to-moderate BHV-1 seroprevalence (1–50%) or those from BHV-1-negative herds. Following exposure to viral pathogens, SAA concentrations in blood rise rapidly, signalling an acute-phase response as the immune system quickly responds to the infection [7,24]. When SAA concentrations remain high for extended periods, it may indicate a more severe and sustained inflammatory response [6,7]. In herds where BHV-1 is widespread, the virus can become dormant and then reactivate, especially under stressful conditions [25]. This reactivation and shedding may lead to repeated virus circulation, driving a continuous inflammatory response that is measurable in the increased SAA concentrations in animals.

In cows, increased serum SAA concentrations were significantly lower in cows from BRSV-negative herds compared to those from herds with BRSV presence, supporting findings from earlier studies on BRSV infection and SAA [6,7]. BRSV is widespread in Europe and is one of the most important causes of respiratory disease in Estonian dairy cows [26]. In previously unexposed herds, BRSV can trigger acute respiratory disease outbreaks in young and adult cattle [27]. The virus can circulate within the herd and rapidly spread, probably in the case of low herd immunity, followed by a temporary decline in the spread of cases as immunity builds up again [26,28]. The virus induces clinical respiratory disease by damaging lung tissue and triggering a prolonged immune response [29], as indicated by increased SAA concentrations.

Cows in M. bovis seropositive herds had increased serum Hp concentrations. M. bovis infection in dairy cows often leads to bronchopneumonia, mastitis, and arthritis [30]. After infection, cows may become subclinical carriers, with bacteria colonising the mucosal surfaces of the respiratory and reproductive tract or mammary glands [31], potentially triggering an inflammatory response.

In heifers, increased serum SAA concentrations were associated with herds having low-to-moderate BHV-1 seroprevalence (1–50%). However, no significant difference in SAA concentrations was observed in high BHV-1 seroprevalence herds (>50%) compared to BHV-1-negative herds. This difference may reflect the dynamics of the virus spread: in herds with low-to-moderate prevalence, there is a substantial proportion of naïve animals, creating optimal conditions for viral circulation and thus higher inflammatory responses. Conversely, in high-prevalence herds (>50%), earlier widespread exposure of the virus may result in potentially reduced susceptibility over time. Since this was a cross-sectional study, samples may have been collected during the virus’s latent phase, leading to lower SAA concentrations among heifers at the time of sampling.

In heifers, decreased serum Hp concentrations were associated with both low-to-moderate (1–50%) and high (>50%) BHV-1 seroprevalence herds. Hp binds free haemoglobin to mitigate its effects, and minor haemorrhagic injuries from respiratory infections like BHV-1 may drive this binding, reducing serum Hp concentrations [13,32]. In herds with active BHV-1 circulation, Hp binding may be more pronounced, as seen in similar studies where mild stressors like transport or minor infections led to decreased Hp concentrations [33,34].

In herds with high M. bovis seroprevalence (>50%), heifers had decreased serum SAA concentrations compared to heifers from herds with seroprevalence of 50% or less. Previous studies have shown that M. bovis antibodies can be detected in calves as young as two months with respiratory disease and transmission among young stock is likely [35,36]. Heifers can also carry M. bovis subclinically, testing positive without visible symptoms [37]. Since the SAA concentration in the blood rises rapidly after respiratory infection [7], chronic or subclinical infections in the herd may not trigger a pronounced response, and serum concentrations of SAA may remain low in these herds. Additionally, the higher herd-level M. bovis seroprevalence observed in heifers in our study reflects a higher respiratory disease burden in this age group than in cows (Table S2). Similar results were reported by [38], where heifers were seropositive to M. bovis in 60.0% of the herds studied, whereas cows were positive in 33.0% of the herds. In a separate study by Mõtus et al. [18] in large-scale Estonian dairy herds, 48.3% of the herds were positive to M. bovis. Moreover, differences in the testing methods and clinical manifestations in the M. bovis infection (respiratory in heifers, mastitis in cows; [39]) may produce a different APP response.

No association was found between the serum SAA or Hp concentrations in cows and heifers and the herd antibody status of BVDV, BAV, PIV-3, and BCV. This finding suggests that these pathogens may have a lesser impact on the APP response in herds where BHV-1, BRSV, or M. bovis are present. Similar results were reported by Nikunen et al. [10], who found no associations between the tested viruses in their study (PIV-3, BRSV, BCV, bovine adenovirus-3, and bovine adenovirus-7) and APPs, despite viral seroconversions in calves; however, the results were based on individual-level seroconversion data rather than the herd level.

In both heifers and cows, a higher age was associated with decreased serum SAA concentrations, while Hp concentrations showed an increase with age in heifers, and cows showed only marginal evidence of a serum Hp increase with age. Therefore, age-related variation should be controlled in the model to account for its possible confounding effects.

4.2. Herd-Level Factors Associated with APP in Cows and Heifers

In our study, a larger herd size (>399) was associated with increased serum SAA concentrations in cows. Large herds carry a greater risk of introducing infections to previously unexposed cows compared to smaller herds [40] because young stock is often raised separately and introduced to the main herd around the time of first calving. This practice creates a continuous influx of susceptible animals, which can sustain ongoing pathogen circulation [26]. Moreover, larger herds typically involve more frequent animal movements and group rearrangements, increasing contact between animals with varying immunity and thereby facilitating virus transmission. Frequent regrouping can also induce social and environmental stress, increasing cows’ susceptibility to infections and potentially triggering the reactivation of latent viruses [41].

The presence of a veterinarian employed on the farm was associated with decreased serum SAA concentrations in cows, likely due to the earlier detection of health issues and the implementation of more timely and effective treatments.

Heifers in free and mixed stalls had increased serum SAA concentrations compared to those reared in tie stalls. In free and mixed stalls, increased contact between animals likely facilitates the spread of respiratory pathogens. Frequent regrouping in these systems may support infection transmission, while larger group sizes and potential overcrowding can contribute to increased stress. Moreover, serum APP concentrations are non-specific markers of inflammation [13] and can increase due to factors like physical stress, transport, and weaning [42]. Other unmeasured factors may have influenced the serum APP concentrations in heifers.

We further found that the use of breeding bulls in heifers was associated with increased serum Hp concentrations. The presence of breeding bulls may cause more stress for heifers compared to artificial insemination [43]. While no specific studies comparing Hp concentrations between breeding methods were found, the correlation between increased cortisol and Hp concentrations suggests that the heightened stress from natural mating could potentially lead to higher Hp concentrations [44]. Furthermore, the use of a breeding bulls often involves frequent regrouping, which can facilitate the spread of infections. Venereal infections, such as IBR, BVDV, and M. bovis, may also be transmitted from bull to heifer during breeding as well as between animals through contact with an infected bull [45].

A limitation of this study is that the potential presence of unmeasured infections and non-infectious factors could influence APP concentrations. Also, due to the cross-sectional study design, we cannot draw any conclusions about the causal associations between the seroprevalence of respiratory pathogens and serum SAA and Hp concentrations. Furthermore, the time of seroconversion is unknown, and antibodies against certain pathogens can persist for extended periods, so seropositivity does not necessarily mean a recent infection [46].

5. Conclusions

The presence of respiratory pathogens such as BHV-1, BRSV, and M. bovis in herds, was associated with changes in APP concentrations in dairy cows and heifers, indicating that APPs may reflect the herd health status and management-related effects. Moreover, cows from farms with an in-house veterinarian had lower serum SAA concentrations, whereas free- or mixed-stall-housed heifers showed higher serum SAA than tie-stall heifers. Serum Hp concentrations were higher in heifers from farms that used a breeding bull compared to heifers that were inseminated artificially. These results emphasise that herd-level factors can influence the APP concentration, thus providing valuable insights into the herd disease status and overall herd health.