Introduction

Common inflammatory diseases, such as mastitis, sole ulcer, uterine infections, and pneumonia [1,2,3,4], cause significant economic losses to the dairy industry. For example, mastitis currently causes substantial milk production losses that translate into an annual loss of about $2 billion to the US dairy industry [5,6,7,8]. It was reported that there are some beneficial effects of using combination therapy in the treatment of calves affected by bovine respiratory disease, including the co-administration of florfenicol and flunixin meglumine in one injection [9]. Therefore, it is important not only to use simple antibiotic therapy for these diseases but to understand the inflammatory state and control inflammation. Inflammation is a complex response to cellular and tissue damage, and excessive inflammation can damage the cells themselves. For example, cows with acute mastitis showed a very different prognosis depending on the severity of the inflammation [10]. In dairy animal practice, it was reported that administration of meloxicam to postpartum cows showed a significant reduction in haptoglobin (HPT), an inflammatory biomarker, compared with untreated cows [11]. In addition, it was revealed that administration of flunixin meglumine improved systemic and local oxidant status and reduced blood isoprostanes, which were shown to be correlated with oxidant stress [12]. Both of the previous studies reported that the cows’ productivity increased after treatment. These results indicated that controlling inflammation can be useful for improving productivity and quality of life. To control inflammation, indicators are required to determine how much inflammation is occurring and how much has been changed by medication. The indicators, such as biomarkers for inflammation, should be easy to use in daily practice. Currently, acute phase proteins (APP) are commonly used as biomarkers for inflammation in medical practice [13].

APPs are used to maintain blood homeostasis against the innate immune system’s systemic response to infection, inflammation, and trauma [14]. Common APPs used in cows are C-reactive protein (CRP), HPT, and serum amyloid A (SAA) [14]. However, the clinical applications of HPT and SAA in dairy animal practice are difficult due to the complicated and expensive measurements [15, 16]. Therefore, it is necessary to develop a clinically applicable inflammatory biomarker that is inexpensive and convenient for bovine practice veterinarians to understand.

Notably, concentrations of blood trace elements can be affected by metabolic changes caused by inflammatory reactions. For example, serum zinc (Zn) concentration is a potential diagnostic marker for detecting inflammation in calves with endotoxin shock [17]. Serum iron (Fe) is known to be stored in cells by hepcidin, one of the hematopoietic proteins, which also inhibits Fe absorption by intestinal macrophages during inflammatory reactions [18]. Thus, Fe concentration appears to decrease in association with inflammatory responses [19, 20]. In addition, Serum Fe concentrations were significantly decreased in beef cattle with respiratory disease and in calves during the first 24 h after dehorning [21, 22]. Moreover, the measurement of the serum Fe concentration is inexpensive, does not require special equipment, and can be performed simultaneously with other biochemical tests, making it less time-consuming. Hence, serum Fe may be an effective substitute for APPs as a field-available marker for inflammation [21, 22]. However, there have not been any reports that reveal the correlation between APPs and serum Fe concentrations. If any relationships between them are found, it may be possible to estimate the concentration of APPs from serum Fe concentrations. The purpose of this study was to clarify the relationship between APPs and serum Fe concentrations to determine whether serum Fe can be biochemically applied to inflammatory biomarkers instead of APPs.

Materials and Methods

Animals and Experimental Design

One hundred five Holstein–Friesian breed dairy cows were enrolled in this study. All cows were milked twice daily and fed a total mixed ration. Otherwise, they were provided free access to water and hay. The cows were divided into 43 control cows and 62 cows with inflammatory diseases (16 subclinical mastitis cows, 15 severe mastitis cows, 15 sole mild ulcer cows, and 16 severe sole ulcer cows), which were diagnosed by a veterinarian. Control cows were enrolled from several commercial farms and blood samples were utilized taken only once at regulatory check-ups. Cows with inflammation were referred patients in the Hokkaido Agricultural Mutual Releef Association (North part of Japan) and veterinary teaching hospital of Rakuno Gakuen University. Blood samples were taken from the jugular vein on the first medical visitation. The severity classification of mastitis is determined by the presence or absence of systemic inflammation. Subclinical mastitis is characterized by abnormal milk and localized inflammation of the udder. On the other hand, if the inflammation spreads throughout the body, it is diagnosed as clinical mastitis [23].

The severity of hoof ulcers in cow can be assessed in the diagnostic criteria of Mobility Quality (MQ) that is the UK Agriculture and Horticulture Development Board (2019) 4-point scale using the following definitions [24].

Blood samples (10 ml) were withdrawn from the jugular vein and stored in serum separator and heparin-2 K-coated vacuum tubes. Serum and plasma were harvested from whole blood by centrifugation at 1,500 × g at room temperature for 15 min and stored at − 80 °C until the assay was performed to avoid repeated thawing and freezing. Refrozen samples were not used in the SAA assay because the SAA concentration is affected by freeze–thaw cycles.

Analysis of APPs and Biochemical Parameters

According to the instructions, HPT and alpha-1 acid glycoprotein (AGP) concentrations were measured using a commercial ELISA kit for HPT (Bovine Haptoglobin ELISA, Immunology Consultants Laboratory, Inc., Portland, OR, USA) and AGP (Bovine alpha-1 acid glycoprotein ELISA, Immunology Consultants Laboratory, Inc.), respectively. The SAA concentrations were measured using an automated latex agglutination turbidimetric immunoassay kit, and the protocol of this assay kit was modified with an SAA assay kit (LZ test ‘Eiken’ SAA, Eiken Chemical Co., Tokyo, Japan) using an automated clinical chemical analyzer (Hitachi 7170S, Hitachi Ltd., Tokyo, Japan). The serum Fe concentration was measured using 2-Nitroso-5-[N–n-propyl-N-(3- sulfopropyl) amino] phenol (Nitroso-PSAP) methods with a commercial kit (N-assay L Fe-H Nittobo, Nitto Boseki, Co., Ltd., Tokyo, Japan) using an automated clinical chemical analyzer (Hitachi 7170S, Hitachi Ltd., Tokyo, Japan). Since APPs were synthesized in the liver, it is important to confirm that there are no significant abnormalities in the liver functions. Aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (γ-GTP), total bilirubin (T-Bil), serum urea (BUN), and creatinine (Cre) were also measured using an automated clinical chemical analyzer (Hitachi 7170S, Hitachi Ltd., Tokyo, Japan) and commercial analysis kits (Eiken Chemical Co., Tokyo, Japan). The mean concentrations of the biochemical parameters were used for further statistical analyses.

Statistical Analysis

All statistical analyses were performed using IBM SPSS Statistics software, v.23 (IBM Co, Somers, NY, USA). The normally distributed and non-normally distributed data were expressed as the means ± SD and medians [minimum–maximum], respectively. The two-sample t-test was employed for the comparison of control and inflammatory disease groups. The effect size between the two groups was also calculated using Cohen d. The multiple logistic regression analysis was used to assess the strength and direction of association between any two assays measured on an interval scale. An odds ratio (OR) was examined in the stepwise selection method between control and inflammatory disease groups. For items for which odds ratios were calculated, receiver operating characteristic (ROC) curves were used to characterize the sensitivity and specificity of each parameter concentration for the inflammatory disease. The optimal cutoff value for a test was calculated based on the Youden index. The Youden index (J) is defined as the maximum vertical distance between the ROC curve and the diagonal or chance line, calculated as J = maximum [sensitivity + specificity—1]. The cutoff value from ROC curves that corresponds to J is taken as the optimal cutoff value. The significance level was p < 0.05.

Results

The plasma HPT, AGP, SAA, serum Fe levels, and other biochemical parameters (AST, ALT, ALP,γ-GTP, T-Bil, BUN, and Cre) in the cows are summarized in Tables 1, 2, and 3. In the present study, the plasma HPT concentrations in the inflammatory diseases and control groups were 47.85 [0.06–303.2] and 0.29 [0.10–0.80] mg/l, respectively (Fig. 1). The plasma HPT concentration in the inflammatory disease group was higher than that in the control group (p < 0.001, 95% CI: − 69.30 ~  − 25.82). In addition, the plasma AGP concentration in the inflammatory disease group was higher (0.4236 [0.14–0.92] mg/ml) than that in the control group (0.33 [0.16–0.72] mg/ml) (p = 0.003, 95% CI: − 0.16 ~  − 0.03). Although HPT and AGP were significantly higher in the diseased cows, no odds ratios were determined in the multiple logistic regression analysis.

Table 1 The serum biochemistry and APPs in inflammatory diseases cows
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Table 2 The serum biochemistry and APPs in control and mastitis cows
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Table 3 The serum biochemistry and APPs in control and sole ulcer cows
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Fig. 1
figure 1

The serum HPT concentration in the control group and inflammatory disease group. The horizontal line in each box represents the median value. The boxes represent the interquartile range (25 to 75%)

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Significant differences in SAA concentration were observed between the inflammatory disease (35.40 [1.00–166.50] mg/l) and control groups (1.81 [0.40–9.90] mg/l, p = 0.000, 95% CI: − 46.72 ~  − 20.46 Fig. 2A). The SAA in the inflammatory disease group had an odds ratio of 1.565 (95% CI: 1.219 ~ 1.869) and showed a significantly higher value due to inflammation (p < 0.001).

Fig. 2
figure 2

Comparison of plasma SAA concentrations in the control group and inflammatory disease group. A The plasma SAA concentration in the control group and inflammatory disease group. The horizontal line in each box represents the median value. The boxes represent the interquartile range (25 to 75%). *Values with asterisks are significantly different between groups (p < 0.05). B The ROC curves of the plasma SAA concentration, used to identify dairy cattle with inflammatory disease

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In contrast, the concentration of serum Fe in the inflammatory disease group (97.84 [8.00–192.00] mg/l) was significantly lower than that in the control group (151.42 [92.0–205.00] mg/l, p = 0.000, 95% CI: 36.41–70.74, Fig. 3A). The serum Fe concentration in the inflammatory disease group had an odds ratio of 0.967 (95% CI: 0.952 ~ 0.958) and showed a significantly lower value due to inflammation (p < 0.001).

Fig. 3
figure 3

Comparison of serum Fe concentrations in the control group and inflammatory disease group. A The serum Fe concentration in the control group and inflammatory disease group. The horizontal line in each box represents the median value. The boxes represent the interquartile range (25 to 75%). *Values with asterisks are significantly different between groups (p < 0.05). B The ROC curves of the serum Fe concentration, used to identify dairy cattle with inflammatory disease

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The biochemical parameters, ALT, ALP, T-Bil, and BUN concentrations in the inflammatory disease group were higher than those in the control group (p < 0.05); however, there was no significant difference in other biochemical parameters. In addition, no odds ratios were determined in the multiple logistic regression analysis. According to these results, SAA and Fe were significant markers for inflammation.

ROC Analysis of Plasma SAA and Serum Fe Concentrations for Inflammatory Disease

ROC curves were used to characterize the sensitivity and specificity of plasma SAA and serum Fe concentrations for inflammatory diseases. The proposed diagnostic cutoff value for plasma SAA concentrations to identify dairy cows with inflammatory diseases based on analyses of ROC curves was set at > 3.65 mg/l (Fig. 2B). In contrast, the proposed diagnostic cutoff value for serum Fe concentrations to identify dairy cows with inflammatory diseases based on analyses of ROC curves was set at < 120.50 µg/dl (Fig. 3B). Figure 4 shows the ROC curves of the serum Fe concentration used to identify the cows that were diagnosed positive based on the cutoff value of SAA was set at < 112.00 µg/dl. The sensitivity and specificity were 90.2 and 64.0%, respectively. If the cutoff value was set at < 120.50 µg/dl, the sensitivity and specificity were 80.4 and 66%, respectively.

Fig. 4
figure 4

The ROC curves of the serum Fe concentration used to identify cows diagnosed positive based on the cutoff value of SAA

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Discussion

The Relationship Between Serum SAA and Serum Fe Concentrations

This study aimed to clarify the relationship between serum APP and serum Fe concentrations to determine whether serum Fe can be biochemically applied as a substitute for APPs. The results revealed that serum Fe concentration may potentially be used as a substitute for SAA in cows when evaluating inflammatory disease. These results closely matched the cutoff value of serum Fe. Therefore, serum Fe could be a simple alternative to APPs for estimating inflammatory disease in cows.

Concerning to clinical impact of biomarkers, APPs are used in human, feline, and canine medicine to determine the plan of treatment and prognosis. For example, in human medicine, CRP is used to determine the efficacy of treatment and prognosis of sepsis. In dogs, CRP is usually elevated in surgery, rheumatoid arthritis, inflammatory bowel disease, and acute pancreatitis [14, 25,26,27]. However, CRP was not included in the analysis in the present study because it presents different concentration responses to inflammation among various species. In cow, CRP increases only 2- to tenfold above normal levels in response to inflammation and is not known to be specific for inflammatory responses [14]. Therefore, in this study, HPT and SAA were investigated as biomarkers for inflammation in cow. HPT and SAA are common APPs in cow and are known to be useful markers in acute coliform mastitis [14], and they are predominantly elevated in uteritis, sole ulcer, and pneumonia. Further studies reported that SAA has diagnostic potential in subclinical mastitis [1, 28,29,30].

HPT is an alpha-globulin that binds to free hemoglobin to reduce oxidative stress induced by hemolysis, and SAA is an acute phase protein. Although the physiological effects of APPs have not yet been elucidated, there have been many reports of their role in inflammation, such as detoxification of endotoxin and inhibition of platelet aggregation [26]. Blood levels of these APPs are known to be significantly elevated in inflammatory diseases, and this result was consistent with the present study.

APPs, such as HPT and SAA, are difficult to apply widely in clinical animal practice because of the time and resources required to measure them, including antibodies for the target APPs and specialized equipment. It is necessary to establish a simple and cost-effective biomarker for inflammation that can be applied clinically. Particularly, trace elements in the blood are one of the possible alternatives to APPs.

With regard to the clinical significance of trace elements, the concentrations of trace elements, such as Fe and Zn, decreased significantly in cows with inflammatory diseases. However, the concentration of Zn is typically measured by atomic absorption spectrometry, which requires sample preparation and is difficult to perform in daily operations [31, 32]. On the other hand, the measurement of Fe can readily be obtained with a biochemical analyzer and measured simultaneously with other parameters [17]. Hence, serum Fe may be an efficient alternative to APPs. Fe also functions as a catalyst for various enzymatic reactions, for example, in bacterial growth. Hepcidin is produced in the liver by the inflammatory cytokine IL-6 and lipopolysaccharide, which are released in the event of inflammation. Hepcidin inhibits Fe absorption by intestinal macrophages and stores Fe in cells. This process has a bacteriostatic effect on bacteria [19, 33, 34], and this function may explain the decrease in serum Fe levels associated with inflammatory diseases. Furthermore, in companion animal medicine, serum Fe levels were shown to be significantly lower in dogs and cats with inflammatory diseases by about 60% and 90%, respectively, and serum Fe concentrations were decreased in horses 24 h after surgery[35, 36]. Thus, the serum Fe concentration has been demonstrated as a potential indicator for clinical applications across species. Regarding cows, Shimamori et al. (2015). observed a decrease in serum Fe concentrations and an increase in SAA in calves after dehorning, and serum Fe was also negatively correlated with SAA and HPT in adult cattle [22]. This result is supported by Tsukano’s finding that serum Fe, as well as HPT and SAA, exhibited diagnostic potential for respiratory disease in Japanese black cows [21].

Since the inflammatory markers used in human medicine are expensive as in cow medicine, they do not match medical income. In this study, it was clarified that the inflammatory state of cow can be assumed to some extent by measuring serum iron concentration. In other words, instead of using expensive inflammatory markers to evaluate the inflammatory state at the first treatment day for inflammatory diseases in cows, it shows the useful for screening with serum Fe concentration that can be measured easily and inexpensively as an alternative.

However, it is assumed that serum iron levels are easily changed by feeding and administration of iron preparation, and are easily lowered by bleeding. In the future, it will be necessary to construct a supported system for diagnosing inflammation using serum iron that considers the effects of these internal and external factors.

Conclusion

The results showed that HPT and SAA were elevated in all cow with inflammatory diseases, whereas the serum Fe concentration decreased, and thus, a negative correlation was observed between Fe and SAA. Additionally, the sensitivity and specificity of cutoff values for serum Fe matched closely or SAA. Therefore, it was suggested that serum Fe concentrations could be used as an alternative biomarker of inflammation to APP and as a mentor for cow treatment.