Housing system significantly modulates the association of serum levels of essential and toxic trace elements and minerals with milk productivity in dairy cows

Housing system significantly modulates the association of serum levels of essential and toxic trace elements and minerals with milk productivity in dairy cows | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Housing system significantly modulates the association of serum levels of essential and toxic trace elements and minerals with milk productivity in dairy cows Elena Sizova, Sergey Miroshnikov, Svetlana Notova, Anatoly Skalny, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4300973/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Feb, 2025 Read the published version in Environmental Science and Pollution Research → Version 1 posted 5 You are reading this latest preprint version Abstract The objective of the present study was to evaluate serum trace element and mineral levels in low- and high-productive dairy cows during feedlot and pasture periods. Serum trace element and mineral levels were assessed inductively-coupled plasma mass-spectrometry. The obtained data demonstrate that serum Ca, Mg, K, and Na levels increase significantly in the pasture period, and this increase is more evident in high-productive cows. In turn, circulating levels of B, Co, Cr, Fe, I, and Se levels were characterized by a decrease in the pasture period. Despite the lack of group differences in the feedlot period, serum B, Cr, and Fe levels in the pasture period were higher in high-productive cows. In turn, circulating Co and I concentrations in the low-productive cows exceeded those in high-productive animals. Finally, the levels of toxic trace elements in the pasture period were found to be higher in low-productive cows. Discriminant analysis demonstrated that the groups of cows with different milk productivity were clearly discriminated in the pasture but not feedlot period. In addition, multiple regression analysis revealed a significant inverse and positive association of serum Pb and Cr levels with daily milk yield. Taken together, the obtained data demonstrate that the differences in serum trace element and mineral levels between the low- and high-productive cows are more profound in the pasture period. More beneficial trace element and mineral profile in high-productive cows may hypothetically contribute to higher milk yield. However, further more detailed studies are required to elucidate the mechanisms of this association. milk yield heavy metals feedlot pasture minerals Figures Figure 1 Introduction Essential trace elements and minerals play a significant role in the organism due to their involvement in multiple enzymatic systems and metabolic pathways (Yatoo et al. 2013 ). In ruminants, trace element and minerals are known to be involved in reproduction, immunity, and lactation (Overton, Yasui, 2014 ). In turn, deficiency of essential trace elements and minerals is associated with adverse health effects including impaired growth, reproductive dysfunction, hypothyroidism, immunosuppression, hepatic and cardiac diseases to name a few (Graham, 1991 ). At the same time, excessive intake of certain essential elements like Cu (Grace, Knowles, 2015 ) or Fe (Wysocka et al., 2020 ) may also possess adverse effects on dairy cow health. In contrast to essential trace elements and minerals, toxic metals and metalloids possess adverse effects on ruminant health (Raikwar et al. 2008 ), including nephrotoxicity (Tahir, Alkheraije, 2023 ) and reproductive dysfunction (Wrzecińska et al. 2021 ). In view of the effect of general health of dairy cows on milk production (Bareille et al. 2003 ), it has been demonstrated that the intake and body burden of trace elements and minerals are associated with differences in milk yield (Blanco-Penedo et al. 2014 ). In addition, lactation is associated with increased organism’s requirements in essential trace elements and minerals (Erickson, Kalscheur, 2020 ). Therefore, supplementation with essential trace elements and minerals is considered a potential tool for improvement of milk productivity. In contrast, overexposure to toxic metals is known to result in reduced milk production (Afzal, Mahreen, 2024). Specifically, accumulation of toxic metals in dairy cows was inversely associated with milk yield (Miroshnikov et al. 2019 ). In addition, transfer of heavy metals into raw cow milk may pose a significant risk to human health (Boudebbouz et al. 2021 ). Therefore, monitoring of trace element and mineral intake and its improvement is essential for optimal livestock health and productivity (López-Alonso et al. 2012). A number of studies demonstrated that housing system significantly affect trace element and mineral metabolism in dairy cows due to variations in the supply of dietary items (Blanco-Penedo et al. 2009 ). Certain studies demonstrate that grazing on pastures may result in insufficient nutrient intake as compared to indoor housing (Mee, Boyle, 2020 ). Specifically, during indoor housing the cows are routinely supplemented with concentrate feeds, resulting in higher trace element (Cu, Zn, Se) content in milk as compared to cows grazed on pastures (Rey-Crespo et al. 2013 ). The levels of I, Co, and certain minerals are also expected to be low in pasture plants thus increasing the risk of their insufficient intake (Masters et al. 2019 ). The levels of essential trace elements and minerals in dairy cow serum were also different in the feedlot and pasture periods (Sizova et al. 2024 ). The composition of pasture grass was also shown to affect the intake of trace elements and its content in milk (Gulati et al. 2018 ). In addition to essential trace elements and minerals, it has been demonstrated that different housing systems also have a significant impact on the intake of toxic elements. It has been reported that concentrate feeds frequently used during the feedlot period contain high levels of toxic metals (As, Cd, Pb) (Li et al. 2005 ), whereas during pasture grazing significant levels of soil-associated toxic elements including As and Pb may be consumed (Orjales et al. 2018 ). Therefore, it is assumed that variations in micronutrient intake in different housing systems may significantly modulate the association between trace element and mineral levels and milk productivity in dairy cows. To testify this hypothesis, the objective of the present study was to evaluate serum trace element and mineral levels in low- and high-productive dairy cows during feedlot and pasture periods. Materials and methods The protocol of the present study was approved by the Local Ethics Committee of the Orenburg State University (Orenburg, Russia), protocol No. 03.04.2020-2. All studies have been performed in agreement with the ethical standards set down by the World Medical Association in the 1964 Declaration of Helsinki. A total of 40 healthy 5–6 y.o. cows of Red Steppe breed, cultivated by the nurse-cow technique in the Orenburg region were examined. The study involved inly cows weighting 400–450 kg (431 ± 12 kg) having 3–4 lactation period in 30–40 days after calving. Based on the median milk yield values the cows were considered as low ( 10 l/day, n = 16). Blood was collected at the end of the feedlot (indoor) period in the last decade of April and during the pasture period in the last decade of June. The study involved a 2 × 2 factorial design with grouping according to milk productivity (low and high) and housing (indoor/feedlot and outdoor/pasture). Assessment of daily milk yield and collection of milk samples were performed at the milking stations. In addition, milk protein and fat content were assessed using Lactan 700 equipment (SibagroPribor, Russia). Blood samples were obtained from the coccygeal vein prior milking and collected in VACUETTE® CAT serum separator clot activator tubes by a trained technician. The obtained blood samples were subjected to centrifugation at 1000 g for 10 min (CM-6M, Elmi, Latvia) to obtain serum. The collected serum samples were stored frozen in Eppendorf tubes until analysis. Prior analysis, the obtained serum was diluted (1:15; v/v) with an acidified (pH = 2.0) diluent containing 0.07% HNO3 (Sigma-Aldrich, Co., USA), 1% 1-Butanol (Merck KGaA, Germany), and 0.1% Triton X-100 (Sigma-Aldrich, Co., USA), and in distilled deionized water (Merck Millipore, USA). Evaluation of essential (B, Co, Cr, Cu, Fe, I, Li, Mn, Se, Zn) and toxic trace elements (Al, As, Cd, Hg, Pb, Sn), as well as minerals (Ca, K, Mg, Na, P) in serum samples was performed using inductively-coupled plasma mass-spectrometry (ICP-MS) at NexION 300D spectrometer (PerkinElmer Inc., Shelton, CT, USA) equipped with ESI SC-2 DX4 autosampler (Elemental Scientific Inc., Omaha, NE, USA). Prior analysis the ICP-MS system was calibrated using stock solutions from Universal Data Acquisition Standards Kit provided by the manufacturer (PerkinElmer Inc., Shelton, CT, USA). Internal on-line standardization was performed using the 10 µg/l solutions of Yttrium-89 and rhodium-103 prepared from the respective Pure Single-Element Standard (PerkinElmer Inc.). Laboratory quality control of trace element and mineral analysis was performed daily using the commercial certified reference materials of plasma (ClinChek® Plasma Control, lyophil., for Trace Elements, Levels 1 and 2, RECIPE Chemicals + Instruments GmbH, Munich, Germany). The mean recovery rates for all studied elements varied from 92–109%. The obtained data were processed using Statistica 10.0 software (StatSoft, Tulsa, OK, USA). Assessment of data distribution normality was performed using Shapiro-Wilk test. Mean values and the respective standard deviations were used as descriptive statistics. Given the 2*2 design of the study, further processing was performed using two-way ANOVA after log-transformation of the raw data in order to evaluate the influence of the particular factors (milk productivity, housing period) and their interaction on serum trace element levels. Group comparisons were performed using Fisher’s LSD test. Discriminant function analysis was performed in order to assess significant discrimination between the study groups based on serum trace element and mineral levels with subsequent assessment of the distance between group centroids, and contribution of the particular trace elements and minerals into group discrimination. In addition, multiple regression analysis was performed in order to assess the independent association between particular trace elements and minerals and daily milk yield, milk protein and fat content, with adjustment for difference in the housing period. All tests were considered significant at p < 0.05 Results Analysis of daily milk yield demonstrated that high-productive cows were characterized by a nearly twofold higher milk production in both feedlot and pasture periods (all p < 0.001). At the same time, transition from feedlot to pasture was associated with a significant 27% and 24% decrease in daily milk yield in low- and high-productive cows, respectively. Milk fat content tended to increase during pasture feeding. Despite the lack of significant difference in the feedlot period, milk fat content in high-productive cows in the pasture period exceeded that in low-productive animals by 14% (p = 0.046). At the same time, no significant differences in milk protein content was observed between the groups of animals irrespectively of the housing system. Factorial analysis demonstrated that housing system had a significant influence on daily milk yield (p < 0.001) and milk fat content (p = 0.023). In turn, daily milk production (p < 0.001) and fat content (p = 0.054) were significantly and border-significantly impacted by milk productivity of cows, respectively. None of the factors had a significant influence on milk protein content (Table 1 ). Table 1 Daily milk yield, milk fat and protein content in low- and high-productive dairy cows during different housing periods Period Feedlot Pasture Productivity Low High Low High Milk yield, l/day 9.77 ± 1.66 18.63 ± 2.08 1 7.11 ± 1.36 1,2 14.17 ± 1.27 1,2,3 Fat, % 3.54 ± 0.25 3.87 ± 0.71 3.8 ± 0.47 4.33 ± 0.79 1,3 Protein, % 3.06 ± 0.32 3.21 ± 0.24 3.02 ± 0.22 2.95 ± 0.15 Data are expressed as the mean value and the respective standard deviation; 1, 2, 3 – significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher’s LSD test at p < 0.05 The obtained data demonstrate a significant influence of both productivity and housing period on serum mineral content (Table 2 ). Specifically, serum Ca levels in low- and high-productive cows in the pasture period exceeded the respective values in the feedlot period by 12% (p = 0.004) and 29% (p < 0.001). Despite the lack of significant difference in the feedlot period (p = 0.918), serum Ca level in high-productive cows significantly exceeded that in the low-productive cows by 16% in the pasture period (p = 0.001). In the feedlot period serum K levels in high-productive cows was lower than in the low-productive cows by 38% (p < 0.001). At the same time, in the pasture period serum K concentrations in high-productive animals exceeded that in the feedlot period by 60% (p < 0.001), while no difference between feedlot and pasture periods in low-productive cows was observed (p = 0.071). Transition from feedlot to pasture was associated with a significant increase in serum Mg levels in both low- and high-productive cows by 15% (p = 0.020) and 36% (p < 0.001), respectively. No significant group difference in serum Mg concentrations was observed in the feedlot period (p = 0.567), whereas in the pasture period serum Mg level in high-productive cows was higher than that in low-productive ones by 22% (p = 0.025). Concomitantly, serum Na concentration in low- and high-productive cows in the pasture period was higher than in the feedlot period by 24% (p < 0.001) and 30% (p < 0.001), respectively. Despite the lack of group differences in the feedlot period (p = 0.342), serum Na in high-productive cows was border-significantly higher than in low-productive animals by 8% (p = 0.053). In contrast, serum P level in both low- and high-productive cows in the pasture period was 20% (0.001) and 23% (p = 0.001) lower than the respective values in the feedlot period. Serum P concentrations in high-productive cows exceeded the respective values in low-productive animals in the feedlot and pasture periods by 19% (p = 0.017) and 15% (p = 0.096), although in the latter it did not reach statistical significance. Table 2 Serum levels of minerals in low- and high-productive cows in feedlot and pasture periods Period Feedlot Pasture Productivity Low High Low High Ca, µg/ml 101.5 ± 5.4 102 ± 7.7 113.2 ± 8.4 1.2 131.3 ± 22.4 1,2,3 K, µg/ml 279.1 ± 72 202.6 ± 20.1 1 305 ± 16 2 317 ± 33.5 1,2 Mg, µg/ml 24.5 ± 3.1 25.3 ± 2 28.3 ± 2.6 1 34.4 ± 10.8 1,2,3 Na, µg/ml 3144 ± 381.5 3020.4 ± 464.8 3882.3 ± 214 1,2 4194.8 ± 619.7 1,2 P, µg/ml 169.5 ± 25.5 201.7 ± 36.5 1 135.3 ± 20.6 1,2 155.1 ± 35.4 2 Data are expressed as the mean value and the respective standard deviation; 1, 2, 3 – significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher’s LSD test at p < 0.05 Factorial analysis demonstrated that both milk productivity and especially housing period had a significant impact on the level of all minerals (Table 3 ). In turn, serum Ca and K levels but not other minerals were significantly influenced by factorial interaction between housing period and milk productivity. Table 3 Factorial analysis of the influence of milk productivity and housing system on serum mineral levels in dairy cows Mineral Housing system Milk productivity Factorial interaction Ca < 0.001 * 0.014 * 0.020 * K < 0.001 * 0.014 * 0.002 * Mg < 0.001 * 0.044 * 0.208 Na < 0.001 * 0.041 * 0.441 P < 0.001 * 0.005 * 0.641 Data are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p < 0.05 Serum essential trace element levels were also dependent on housing period and milk productivity (Table 4 ). Specifically, in the pasture period serum B concentrations in low- and high-productive cows were significantly lower than in the feedlot period by 37% (p < 0.001) and 23% (p = 0.007), respectively. In the feedlot and pasture periods serum B level in high-productive cows was and 20% (p = 0.052) and 46% (p = 0.002) higher than that in the low-productive animals. Concomitantly, serum Co concentration in low- and high-productive animals in the pasture period was 28% (p = 0.018) and 71% (p < 0.001) lower than the respective values in the feedlot period. As a result, in the pasture period serum Co in high-productive cows was nearly twofold lower than that in low-productive cows (p = 0.028). Circulating Cr concentrations of low-productive cows in the pasture period were nearly twofold lower than those in the feedlot period (p < 0.001), whereas in the high-productive cows only a 19% (p = 0.026) decrease in the pasture period was observed in relation to the respective values in the feedlot period. Despite the lack of significant group difference in the feedlot period, serum Cr level in the pasture period exceeded that in the feedlot period by 65% (p < 0.001). Transition from feedlot to pasture was associated with a 7% reduction in circulating Fe in the low-productive cows (p = 0.006), whereas no significant differences in the high-productive cows were observed between the periods (p = 0.434). Serum Fe levels of high-productive cows in the pasture period were 26% higher when compared to the respective values in low-productive cows, although this difference was border-significant (p = 0.053). Blood serum I levels in the high-productive animals were 18% (p = 0.021) and 25% (p = 0.001) lower than in the low-productive animals in the feedlot and pasture periods, respectively. The observed trend to lower serum Mn concentrations in cows in the pasture period did not reach statistical significance. It is notable that serum Se levels in low-productive animals in the pasture period was lower than that in the feedlot period by 14% (p = 0.033), while no such difference was observed in high-productive cows (p = 0.600). In turn, no significant differences in serum Cu, Li, and Zn levels were observed between the groups of animals irrespectively of the housing period. Table 4 Comparative analysis of serum levels of essential trace elements in low- and high-productive cows in the feedlot and pasture periods Period Feedlot Pasture Productivity Low High Productivity Low B, µg/ml 0.217 ± 0.056 0.260 ± 0.051 0.136 ± 0.019 1,2 0.199 ± 0.066 2,3 Co, ng/ml 0.958 ± 0.44 1.207 ± 0.925 0.693 ± 0.56 1,2 0.353 ± 0.13 1,2,3 Cr, ng/ml 12.01 ± 2.826 12.98 ± 1.456 6.355 ± 1.113 1,2 10.528 ± 2.282 2,3 Cu, µg/ml 0.694 ± 0.159 0.763 ± 0.199 0.645 ± 0.141 0.663 ± 0.232 Fe, µg/ml 2.703 ± 1.126 2.635 ± 0.5 1.92 ± 0.206 1,2 2.423 ± 0.533 I, µg/ml 0.061 ± 0.015 0.050 ± 0.009 1 0.057 ± 0.008 0.043 ± 0.011 1,3 Li, µg/ml 0.023 ± 0.021 0.034 ± 0.021 0.029 ± 0.011 0.051 ± 0.032 1 Mn, ng/ml 4.23 ± 3.876 4.85 ± 2.119 3.095 ± 0.352 2 3.643 ± 0.519 Se, µg/ml 0.091 ± 0.015 0.086 ± 0.016 0.078 ± 0.016 1 0.092 ± 0.023 Zn, µg/ml 1.034 ± 0.174 1.103 ± 0.159 1.097 ± 0.091 1.17 ± 0.216 1 Data are expressed as the mean value and the respective standard deviation; 1, 2, 3 – significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher’s LSD test at p < 0.05 Being in agreement with the results of group comparisons, the results of factorial analysis (Table 5 ) demonstrated a significant impact of both housing period and milk productivity on serum essential trace element levels. Specifically, serum B, Co, Cr, Fe and Li were significantly influenced by the housing period. Noteworthy, the influence of housing period on circulating I and Mn levels was also nearly significant. In turn, the concentrations of B, Cr, I, Li, and Mn were characterized by a significant impact of variability in milk productivity. Neither housing period, nor milk productivity significantly influenced Cu, Se, and Zn levels. In turn, factorial interaction (housing period*milk productivity) possessed a significant influence on circulating levels of Cr, while nearly significantly affecting Co and Se concentrations. Table 5 Factorial analysis of the influence of milk productivity and housing system, as well as factorial interaction on serum essential trace element concentrations in dairy cows Element Housing system Milk productivity Factorial interaction B < 0.001 * < 0.001 * 0.307 Co < 0.001 * 0.127 0.088 Cr < 0.001 * < 0.001 * 0.004 * Cu 0.121 0.546 0.522 Fe 0.017 * 0.114 0.213 I 0.087 < 0.001 * 0.374 Li 0.032 * 0.013 * 0.906 Mn 0.066 0.049 * 0.674 Se 0.312 0.418 0.074 V < 0.001 * 0.007 * 0.001 * Zn 0.177 0.176 0.829 Data are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p < 0.05 In addition to essential trace elements and minerals, variability of serum toxic metal and metalloid levels was also associated with milk productivity and housing period (Table 6 ). Specifically, in the pasture period high-productive cows are characterized by significantly lower serum As levels in comparison to low-productive animals by 12% (p = 0.002). Serum Cd levels in high-productive cows in the pasture period were nearly twofold lower is comparison to the respective values in the feedlot period (p = 0.001). In turn, no significant differences in circulating Cd between the feedlot and pasture periods in low-productive animals were observed (p = 0.324). Serum Hg concentrations in high-productive cows in the feedlot period exceeded the respective values in low-productive animals by 89% (p < 0.001). At the same time, transition from feedlot to pasture in low-productive cows was associated with a more than twofold increase of circulating Hg levels (p < 0.001), whereas no such increase was observed in high-productive animals (p = 0.851). As a result, no group differences in serum Hg between low- and high-productive cows was observed in the pasture period (p = 0.501). Blood serum Pb level in low-productive animals in the pasture period was 32% lower when compared to the feedlot period (p = 0.035). In turn, Pb concentrations in high-productive cows in the pasture period were more than 3-fold lower than those in the feedlot period (p = 0.001). Thus, in the pasture period the levels of Pb in blood serum of low-productive cows exceeded that in high-productive cows by 74% (p = 0.031). No significant differences in circulating Sn concentrations were observed with respect to milk productivity and housing period. Table 6 Serum toxic trace element levels in cows with different milk productivity in the feedlot and pasture periods Period Feedlot Pasture Productivity Low High Productivity Low Al, µg/ml 0,071 ± 0,082 0,062 ± 0,066 0,064 ± 0,022 0,075 ± 0,048 As, ng/ml 3,96 ± 1,298 3,08 ± 1,106 3,965 ± 0,604 2,718 ± 1,528 1,3 Cd, ng/ml 0,054 ± 0,046 0,055 ± 0,005 0,038 ± 0,014 2 0,030 ± 0,002 2 Hg, ng/ml 0,215 ± 0,112 0,408 ± 0,048 1 0,440 ± 0,037 1 0,573 ± 0,323 1 Pb, ng/ml 0,533 ± 0,161 0,728 ± 0,581 0,363 ± 0,084 1 0,208 ± 0,089 1,2,3 Sn, ng/ml 0,128 ± 0,048 0,133 ± 0,108 0,155 ± 0,058 0,148 ± 0,046 Data are expressed as the mean value and the respective standard deviation; 1, 2, 3 – significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher’s LSD test at p < 0.05 The results of factorial analysis (Table 7 ) demonstrated that the housing period had a significant impact on circulating Cd, Hg, Pb, and Sn levels in cows. In turn, the concentrations of As and Hg were both influenced by differences in milk productivity. Finally, factorial interaction significantly affected the levels of Hg in blood serum of dairy cows, while the influence on serum Cd concentration was only nearly significant. Table 7 Factorial analysis of the impact of housing system, milk productivity, and factorial interaction on circulating levels of toxic trace elements in dairy cows Element Housing system Milk productivity Factorial interaction Al 0.178 0.920 0.667 As 0.340 0.002 * 0.195 Cd 0.002 * 0.417 0.045 * Hg 0.001 * < 0.001 * 0.001 * Pb < 0.001 * 0.075 0.167 Sn 0.045 * 0.474 0.688 Data are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p < 0.05 Correlation analysis was performed in order to estimate the potential negative relationships between accumulation of toxic metals/metalloids and essential trace elements and minerals. A significant inverse correlation between Al and Co (-0.306; p = 0.028), Al and I (-0.321; p = 0.020), As and Li (-0.300; p = 0.031), Cd and K (-0.444; p = 0.001), Cd and Na (-0.336; p = 0.015), Hg and Co (-0.361; p = 0.008), Hg and I (-0.396; p = 0.004), Pb and K (-0.283; p = 0.042), Pb and Na (-0.343; p = 0.013) was revealed. The obtained data demonstrate that differences in accumulation of toxic metals and metalloids may at least partially contribute to variability in serum mineral levels (K, Na, and P), as well as certain essential trace elements like Co, I, and Li. In view of the observed differences in toxic and essential trace elements and minerals in serum of cows, discriminant analysis was performed in order to estimate the contribution of the particular chemical elements into the differences between the groups (Fig. 1 ). Being in agreement with group differences, discriminant analysis failed to reveal complete discrimination between the low- and high-productive cows in the feedlot period, as evidenced by the small distance between the group centroids (MD 2 = 18.2; p = 0.029). At the same time, in the pasture period a complete discrimination between the animals with low and high milk productivity was observed (MD 2 = 70.4; p < 0.001). Moreover, the groups of both low- (MD 2 = 372.5; p < 0.001) and high-productive cows (MD 2 = 373.8; p < 0.001) in the pasture period were clearly discriminated from the respective groups in the feedlot period. Further analysis demonstrated that serum Al (p = 0.005), Ca (p < 0.001), Cd (p = 0.038), Cr (p < 0.001), Cu (p = 0.003), Fe (p = 0.040), Hg (p < 0.001), K (p = 0.003), Mg (p = 0.013), Na (p = 0.013), P (p < 0.001), Pb (p = 0.003), and Sn (p = 0.007) contributed significantly to group discrimination, whereas the contribution of As (p = 0.063), B (p = 0.062), and I (p = 0.084) was nearly significant. In turn, Co (p = 0.269), Li (p = 0.689), Mn (p = 0.534), Se (p = 0.382), Zn (p = 0.616) concentrations did not contribute significantly to the model. Multiple linear regression analysis (Table 8 ) was performed to evaluate the association between circulating levels of essential and toxic trace elements and minerals and daily milk yield in dairy cows with adjustment for the housing period (1 – feedlot; 2 – pasture). The obtained data demonstrate that serum Pb was characterized by inverse association with daily milk production, whereas circulating Cr concentration was positively associated with this parameter. The overall model accounted for 58% variability in daily milk yield. In contrast to daily milk yield, no significant associations between serum trace element and mineral levels and milk fat or protein content were observed. Table 8 Multiple linear regression analysis of the association of serum trace element and mineral levels with milk yield (Model 1), milk fat (Model 2) and protein (Model 3) concentration Element Milk yield Milk fat, % Milk protein, % β p β p β p B 0.323 0.233 0.561 0,122 0,243 0,611 Ca -0.493 0.087 0.090 0,808 -0,279 0,577 Co 0.221 0.186 0.128 0,557 0,031 0,917 Cr 0.828 0.013 * -0.031 0,939 -0,271 0,626 Fe -0.299 0.061 0.016 0,937 0,074 0,787 I 0.106 0.571 -0.298 0,234 0,276 0,410 K -0.067 0.752 -0.138 0,623 0,195 0,608 Mg 0.245 0.356 0.259 0,460 0,126 0,789 Na -0.118 0.577 -0.321 0,258 0,361 0,344 P 0.308 0.246 -0.100 0,772 -0,582 0,221 As 0.330 0.216 0.350 0,320 -0,539 0,258 Cd -0.065 0.665 0.031 0,876 -0,336 0,220 Hg 0.424 0.169 -0.242 0,547 0,529 0,332 Pb -0.723 0.001 * -0.377 0,134 0,178 0,591 Period 0.336 0.626 0.673 0,462 -1,069 0,388 Multiple R 0.864 0.759 0.477 Multiple R 2 0.747 0.576 0.227 Adjusted R 2 0.582 0.287 0.099 p for a model 0.001 * 0.069 0.951 Data are expressed as regression coefficient (β) and the respective p values; * - the association is significant at p < 0.05 Discussion Taken together, the obtained data demonstrate that the differences in serum toxic and essential trace element and mineral levels between the cows with different milk productivity were more obvious in the pasture period when compared to the feedlot period. Specifically, after transition from feedlot to pasture high-productive cows were characterized by lower levels of toxic metals, higher circulating mineral concentrations, as well as a less profound decrease in essential trace element levels when compared to low-productive cows. Furthermore, serum toxic metal and metalloid levels were characterized by an inverse association with certain minerals and essential trace elements, being indicative of the potential antagonistic relationships. Finally, the differences in trace element and mineral levels were found to be associated with daily milk yield, but not milk protein or lipid content. The observed differences in serum metal and trace element and mineral levels in cows between the feedlot and pasture periods may be mediated by feeding regimens. Specifically, indoor housing in winter involves concentrate feed supplementation (Magan et al. 2021 ) that is known to be associated with higher intake of essential and toxic trace elements (Rey-Crespo et al. 2013 ). O’Brien et al. (1999) demonstrated that milk Cu and I were found to be higher during winter periods, whereas no clear trend in the changes of Mn, Mo, Zn, Co, Cr, and Fe levels was observed (O'Brien et al. 1999 ). Correspondingly, administration of a concentrate-based ration was associated with higher blood I level (Lejeune et al. 2010 ). In addition, it has been demonstrated that the intake of Co is also associated with the administration of concentrate feeds (Orjales et al. 2018 ). In contrast to trace elements that are characterized by higher intake during indoor period, consumption of minerals (Ca, Mg, P, Na, K) was shown to be positively associated with time spent on pastures (Morales-Almaráz et al. 2021 ). Grazing outdoors on perennial ryegrass pasture was shown to be associated with higher milk Ca levels as compared to indoor housing on a total mixed ration (Gulati et al. 2018 ). The observed increase in Ca levels in the pasture period corresponds to higher Ca level in pasture grass in comparison to hay, silage, and chow administered during the feedlot period (Sizova et al. 2024 ). The existing data on the variations in heavy metal intake and accumulation in dairy cows in different periods and housing systems are insufficient. However, Pastorelli et al. ( 2023 ) also reported higher milk Cd levels in winter as compared to summer period (Pastorelli et al., 2023 ). In winter higher Pb and Cd levels were observed in raw milk samples in N.R. Macedonia (Limani et al. 2022). Furthermore, the level of Cd, Cr, Pb, and Ni was found to be higher in milk samples from conventional and especially commercial farms as compared to the organic ones (Zwierzchowski et al. 2018). Hypothetically, higher intake of heavy metals may be associated with its higher content in feed concentrates. Specifically, earlier studies demonstrate that animal feeds frequently contain detectable heavy metal levels that sometimes even exceed the upper tolerable level (Dai et al. 2016). Of all components of Wisconsin dairy herd feed ration, mineral mix was shown to contain the highest levels of As, Cd, and Pb (Li et al. 2005 ). Moreover, a study by Li et al. ( 2019 ) originating from China reported that the levels of Cr and Pb in dairy feeds may exceed the recommended limits by a factor of more than 6 and 17, respectively (Li et al. 2019 ). Correspondingly, Pb and Cd levels in cattle tissues significantly correlated with feed heavy metal content (Hashemi et al. 2018). In addition, Al, As, Hg levels in feed also correlated with cow milk concentrations (Zhou et al. 2017 ). However, the particular mechanism underlying relatively lower accumulation of trace elements in high-productive cows in comparison to the low-productive ones despite grazing on the same pastures is unclear. Hypothetically, the observed differences may be mediated by different genetic characteristics of cows with different milk production. Specifically, Upadhyay et al. ( 2015 ) demonstrated that gene ontology categories associated with metal ion transport (GO: 0030001) and metal ion transmembrane transporter activity (GO: 0046873) were found to be enriched in cows characterized by different milk production (Upadhyay et al. 2015 ). Furthermore, high-productive dairy cows were characterized by upregulation of hepatic GSTM4 gene (McCarthy et al., 2010 ), that is also known to be involved in detoxification (Gasmi et al. 2022 ). The assumption of the role of different genetic background in accumulation of metals and other trace elements is also supported by the observation by Denholm et al. ( 2019 ) who revealed a significant association between dairy cow genotype and blood and milk trace element concentrations (Denholm et al. 2019 ). The results of group comparisons demonstrated that high-productive cows are characterized by higher levels of minerals and essential trace elements (except for Co and I), while having lower serum levels of toxic metals and metalloids. Furthermore, regression analysis also supported these findings demonstrating a positive relationship between Cr, Co, Mg, I, and Na levels with milk yield, whereas circulating toxic As and Pb were found to be inversely associated with milk production. Toxic metal exposure was shown to be a risk factor for adverse health effects and lower productivity in dairy cows (Raikwar et al. 2008 ). Specifically, it has been also demonstrated that total accumulation of toxic metals in hair of dairy cows is inversely associated with milk production (Miroshnikov et al. 2021 ). Cd exposure is able to affect reproduction and milk production (Lane et al. 2015). An earlier study by Miller et al. demonstrated that dietary Cd exposure significantly decreases milk production in dairy cows (Miller et al. 1967 ). Serum Pb levels were shown to be inversely associated with body condition scores in dairy cows, although no relationship with milk yield was revealed (Denholm et al. 2022 ). In turn, reduction of Pb and Cd body burden was associated with an increase in daily milk yield in cows from an industrial area (Portiannyk, Mamenko, 2021 ). In contrast to toxic metals, mineral intake is known to be essential for dairy cow health. Specifically, adequate circulating Ca was shown to be critical for postpartum health and reproductive performance in dairy cows (Jeong et al. 2018 ). Supplementation with oral Ca boluses was shown to increase daily milk yield (Oetzel, Miller, 2012 ). Similar effect was observed in multiparous cows of greater production potential, but not those with below average production potential (Martinez et al. 2016 ). Correspondingly, clinical hypocalcemia in multiparous cows was associated with lower daily milk production (Venjakob et al. 2018 ). At the same time, the results of certain studies did not reveal any significant association between serum Ca levels and milk productivity (Østergaard, Larsen, 2000 ). Mg is also an essential mineral for dairy cows (Schonewille, 2013 ). An increase in milk yield is associated with higher Mg requirements with an increase in Mg intake and Mg absorption (Martens, Stumpff, 2019 ), therefore, high-productive cows are considered at higher risk of Mg deficiency (Pinotti et al., 2021 ). Oral Mg supplementation was shown to increase milk production and milk fat content in hypomagnesemic cows (Wilson, 1980 ). Correspondingly, supplementation with Mg butyrate significantly increased daily milk yield and milk protein and fat content (Fébel et al. 2023 ). Reduction of dietary P content was also shown to result in decreased milk yield, while having no significant effect on milk fat and protein content in lactating dairy cows (Wu et al. 2000 ). These findings corroborate earlier data on the association between P deficiency and low milk production (Call et al. 1987 ). It is also proposed that dietary P deficiency may impair rumen microbiota resulting in reduced milk protein content (Elizondo Salazar et al. 2013 ). Previous studies also demonstrated the role of Na and K in regulation of milk production. Specifically, milk yield was shown to be associated with K retention in dairy cows (Silanikove et al. 1997 ). It is proposed that K requirements in high-productive cows may be increased in early lactation period (Dennis et al. 1976 ). Specifically, an increase in milk production was associated with early hypokalemia (Plöntzke et al. 2022 ). Correspondingly, increased dietary potassium in early lactation may improve milk yield and milk fat content (Harrison et al. 2011 ). Na supplementation was shown to increase daily milk yield in cows grazing a tropical grass-legume pasture (Davison et al. 1980 ). In addition to increased milk yield, Na supplementation was shown to improve Ca and Mg metabolism in cows, also possessing beneficial effect on mammary gland health (PHILLIPS et al. 2000 ). Essential trace elements are also involved in maintenance of dairy cattle health and thus milk production. Specifically, adequate B nutrition was shown to be beneficial for dairy cows and other animals (Abdelnour et al. 2018 ). Despite the lack of B supplementation on milk yield, it significantly improved milk composition and mammary gland health (Praveen et al. 2021 ), in parallel with a beneficial effect on metabolic regulation in dairy cows (Basoglu et al. 2017 ). In addition, B was shown to improve Ca and Mg bioavailability in cows (Baspinar et al. 2017 ). The results of a recent meta-analysis demonstrated that Cr supplementation significantly improves milk production but not composition (Malik et al. 2023 ), also significantly influencing metabolic parameters by reducing non-esterified fatty acid levels and increasing glucagon concentrations (Malik et al. 2024 ). Given the role of Cr as an insulin-mimetic, it has been shown that Cr supplementation significantly modulates insulin-signaling pathway, although this effect was different in antepartal than post-partal period (Pantelić et al. 2018 ), being also dependent on the energy balance in these periods (Kegley, Spears, 1999 ). Co deficiency is known to be associated with a wide spectrum of adverse effects including impaired growth and reproduction (Silva et al. 2021 ). Milk secretion is associated with higher B12 and Co requirements (Kincaid et al. 2003 ). In turn, Co supplementation was shown to improve daily milk yield in cows (González-Montaña et al. 2020 ). However, high dietary Co supplementation may affect both milk production and fatty acid composition (Karlengen et al. 2013 ). Correspondingly, we have observed a trend to increased serum Co levels in high-productive cows (Sizova et al. 2024 ). Lactation is associated with decreased plasma Fe and Hb levels, being indicative of higher Fe requirements (Randhawa et al. 2009 ). Correspondingly, simultaneous Fe and Cu deficiency was shown to be associated with reduced milk yield (Abramowicz et al. 2021 ). Fe supplementation in cows consuming a diet adequate in Fe, did not improve daily milk production but significantly reduced somatic cell count in milk (Weiss et al. 2010 ). At the same time, Denholm et al. ( 2022 ) demonstrated that serum Fe levels were inversely associated with milk yield, while being positively correlated with body condition scores (Denholm et al. 2022 ). Although earlier studies demonstrate that insufficient I intake may contribute to reduced milk production, later investigations failed to reveal an association between dietary I intake and milk yield (Niero et al. 2023 ). Specifically, no effect of I supplementation on milk production was observed in cows grazed on pastures with adequate I content (Grace, Waghorn, 2005 ). In addition to lower levels of toxic metals and higher concentration of essential trace elements and minerals in high-productive cows compared to low-productive ones, a significant inverse correlation was observed between certain toxic and essential elements. These findings generally corroborate the earlier data on the adverse effect of toxic metal exposure on mineral metabolism in cows. Specifically, Cd accumulation in the organism following long-term dietary exposure was shown to affect hepatic and renal levels of essential metals (Smith et al. 1991 ). A significant inverse correlation of milk Pb and Cd with Ca and Mg levels, respectively, was observed in Simmental cows from an organic farm (Pilarczyk et al. 2013 ). In cattle from industrial areas, blood Pb levels was inversely associated with circulating concentrations of Cu, Co, and Fe (Patra et al. 2006 ). In turn, improved mineral intake may possess protective effects against toxic metal overaccumulation. Specifically, administration of mineral blocks containing high levels of P and Ca were shown to reduce bioavailability of Pb in vitro (Pareja-Carrera et al. 2020 ). Correspondingly, mineral block supplementation was shown to reduce Pb accumulation in sheep by increasing its fecal excretion (Pareja-Carrera et al. 2021 ). Noteworthy, dietary Ca was shown to reduce liver Pb content in sheep (Pearl et al. 1983 ). Conclusions Taken together, our novel data demonstrate that serum trace element and mineral levels in dairy cows are associated with milk productivity, although the housing system significantly affects this association. Specifically, the differences between low- and high-productive cows in the feedlot period were negligible. While transition from the feedlot to pasture feeding resulted in a significant increase in serum mineral levels and a decrease in circulating essential trace elements, in the pasture period the serum levels of essential trace elements and minerals (except for Co and I) were higher in high-productive cows. Furthermore, in the pasture period high-productive cows were characterized by significantly lower circulating toxic metal and metalloid levels when compared to low-productive cows. Negative associations between certain essential and toxic elements demonstrate the potential antagonistic relationships between the elements in dairy cows. Taken together, in the pasture period high-productive cows were characterized by a more beneficial trace element and mineral profile of blood serum than the low-productive animals. In view of significant association between essential and toxic trace element and mineral levels with daily milk yield, it is assumed that the observed differences in trace element and mineral metabolism may at least partially contribute to milk productivity in dairy cows. At the same time, further studies are required in order to elucidate the particular mechanisms underlying the association between trace element and mineral metabolism with milk productivity. Declarations STATEMENTS AND DECLARATIONS Ethical approval The ethics committee of the Federal Research Centre for Biological Systems and Agro technologies of the Russian Academy of Sciences approved the experimental design (No. 03.04.2020-2). Consent to Participate Not Applicable Consent to Publish Not Applicable Authors Contribution All authors whose names appear on the submission made substantial contributions to the conception or design of the work. Sergey Miroshnikov and Svetlana Notova and Anatoly Skalny conceived and designed this study, Elena Yausheva and Aina Kamirova performed the most majority of experiments, Elena Sizova and Alexey Tinkov аnalyzed data and authored the final article. Funding This research work was supported by the Russian Science Foundation (project No. 20-16-00078-P). Conflict of interest The authors declare that they have no conflict of interest. References Abd El-Hack ME, Alagawany M, Farag MR, Arif M, Emam M, Dhama K, Sayab M (2017) Nutritional and pharmaceutical applications of nanotechnology: Trends and advances . Int J Pharmacol 13(4):340–350 Abdelnour SA, Abd El-Hack ME, Swelum AA, Perillo A, Losacco C (2018) The vital roles of boron in animal health and production: A comprehensive review. J Trace Elem Med Biol 1:50:296–304 Abramowicz B, Kurek L, Chalabis-Mazurek A, Lutnicki K (2021) Copper and iron deficiency in dairy cattle. 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Sizova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBAC+WYInWB/vPkAkGYmrMXgMFQLw5ljCURqYYBpuZFjQKQWdvaHj3lqGBIYe858k/i5w1rO4ADvMwl8WoB+STbmOcaQwMzeu02y90y6sWQDuxleLQyHGY5JzmBjSGDjObtNgrftcGI/AxsbAS2MbZIz/jEk8EjkPJP823a4vo2wFmY2iY9tDAkSEjls0kBbEvgJaTE4zMZs8LFPIsGA55ixtWxbuuHMZjZmC3xa5PuPP3yQ8M2m3oC9+eHNt23W8gbH2xhv4HUYBIBdwgJxDxFRAwfMH0hQPApGwSgYBSMIAABfvkFHkJj6ywAAAABJRU5ErkJggg==","orcid":"","institution":"Federal Research Centre of Biological System and Agro-technologies of the Russia Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Elena","middleName":"","lastName":"Sizova","suffix":""},{"id":316413278,"identity":"d9d4c327-ab73-456b-ad33-ae79893bddf8","order_by":1,"name":"Sergey Miroshnikov","email":"","orcid":"","institution":"Orenburg State University: Orenburgskij gosudarstvennyj universitet","correspondingAuthor":false,"prefix":"","firstName":"Sergey","middleName":"","lastName":"Miroshnikov","suffix":""},{"id":316413279,"identity":"a10d1b6d-6f48-4f3e-a006-5e9ba8ceb147","order_by":2,"name":"Svetlana Notova","email":"","orcid":"","institution":"Orenburg State University: Orenburgskij gosudarstvennyj universitet","correspondingAuthor":false,"prefix":"","firstName":"Svetlana","middleName":"","lastName":"Notova","suffix":""},{"id":316413280,"identity":"81b5fbcb-d863-4350-a9e5-9bc62298cc4b","order_by":3,"name":"Anatoly Skalny","email":"","orcid":"","institution":"I M Sechenov First Moscow State Medical University: Pervyj Moskovskij gosudarstvennyj medicinskij universitet imeni I M Secenova","correspondingAuthor":false,"prefix":"","firstName":"Anatoly","middleName":"","lastName":"Skalny","suffix":""},{"id":316413281,"identity":"c5fc3158-32a1-47aa-8875-550034ebbd86","order_by":4,"name":"Elena Yausheva","email":"","orcid":"","institution":"FSSI Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences: FGBNU Federal'nyj naucnyj centr biologiceskih sistem i agrotehnologij Rossijskoj akademii nauk","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"","lastName":"Yausheva","suffix":""},{"id":316413282,"identity":"be0ede70-ee8f-49df-8c1d-9d42e43bf73b","order_by":5,"name":"Aina Kamirova","email":"","orcid":"","institution":"FSSI Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences: FGBNU Federal'nyj naucnyj centr biologiceskih sistem i agrotehnologij Rossijskoj akademii nauk","correspondingAuthor":false,"prefix":"","firstName":"Aina","middleName":"","lastName":"Kamirova","suffix":""},{"id":316413283,"identity":"09be5f34-7e84-4db5-a4ad-0780f109bf5b","order_by":6,"name":"Alexey Tinkov","email":"","orcid":"","institution":"FSSI Federal Research Centre of Biological Systems and Agrotechnologies of the Russian Academy of Sciences: FGBNU Federal'nyj naucnyj centr biologiceskih sistem i agrotehnologij Rossijskoj akademii nauk","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Tinkov","suffix":""}],"badges":[],"createdAt":"2024-04-21 13:46:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4300973/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4300973/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11356-025-36021-2","type":"published","date":"2025-02-03T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59749590,"identity":"d6d49875-f82a-4d7f-8a2f-f6408f700036","added_by":"auto","created_at":"2024-07-05 19:31:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88329,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminant analysis score plot of low- (blue and green) and high-productive (red and purple) cows in the feedlot and pasture periods based on the patterns of trace element and mineral levels blood serum\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4300973/v1/21cb8e9e2ebeb61a7f506f68.png"},{"id":75930820,"identity":"ddf04a10-9715-47e0-a9bc-c54b5a82b6fc","added_by":"auto","created_at":"2025-02-10 16:13:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971574,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4300973/v1/9fb126e4-f9f0-4d32-978e-664fe7cd0fc1.pdf"}],"financialInterests":"","formattedTitle":"Housing system significantly modulates the association of serum levels of essential and toxic trace elements and minerals with milk productivity in dairy cows","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEssential trace elements and minerals play a significant role in the organism due to their involvement in multiple enzymatic systems and metabolic pathways (Yatoo et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In ruminants, trace element and minerals are known to be involved in reproduction, immunity, and lactation (Overton, Yasui, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In turn, deficiency of essential trace elements and minerals is associated with adverse health effects including impaired growth, reproductive dysfunction, hypothyroidism, immunosuppression, hepatic and cardiac diseases to name a few (Graham, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). At the same time, excessive intake of certain essential elements like Cu (Grace, Knowles, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or Fe (Wysocka et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) may also possess adverse effects on dairy cow health. In contrast to essential trace elements and minerals, toxic metals and metalloids possess adverse effects on ruminant health (Raikwar et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), including nephrotoxicity (Tahir, Alkheraije, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and reproductive dysfunction (Wrzecińska et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn view of the effect of general health of dairy cows on milk production (Bareille et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), it has been demonstrated that the intake and body burden of trace elements and minerals are associated with differences in milk yield (Blanco-Penedo et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, lactation is associated with increased organism\u0026rsquo;s requirements in essential trace elements and minerals (Erickson, Kalscheur, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, supplementation with essential trace elements and minerals is considered a potential tool for improvement of milk productivity. In contrast, overexposure to toxic metals is known to result in reduced milk production (Afzal, Mahreen, 2024). Specifically, accumulation of toxic metals in dairy cows was inversely associated with milk yield (Miroshnikov et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, transfer of heavy metals into raw cow milk may pose a significant risk to human health (Boudebbouz et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, monitoring of trace element and mineral intake and its improvement is essential for optimal livestock health and productivity (L\u0026oacute;pez-Alonso et al. 2012).\u003c/p\u003e \u003cp\u003eA number of studies demonstrated that housing system significantly affect trace element and mineral metabolism in dairy cows due to variations in the supply of dietary items (Blanco-Penedo et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Certain studies demonstrate that grazing on pastures may result in insufficient nutrient intake as compared to indoor housing (Mee, Boyle, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specifically, during indoor housing the cows are routinely supplemented with concentrate feeds, resulting in higher trace element (Cu, Zn, Se) content in milk as compared to cows grazed on pastures (Rey-Crespo et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The levels of I, Co, and certain minerals are also expected to be low in pasture plants thus increasing the risk of their insufficient intake (Masters et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The levels of essential trace elements and minerals in dairy cow serum were also different in the feedlot and pasture periods (Sizova et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The composition of pasture grass was also shown to affect the intake of trace elements and its content in milk (Gulati et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition to essential trace elements and minerals, it has been demonstrated that different housing systems also have a significant impact on the intake of toxic elements. It has been reported that concentrate feeds frequently used during the feedlot period contain high levels of toxic metals (As, Cd, Pb) (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), whereas during pasture grazing significant levels of soil-associated toxic elements including As and Pb may be consumed (Orjales et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, it is assumed that variations in micronutrient intake in different housing systems may significantly modulate the association between trace element and mineral levels and milk productivity in dairy cows. To testify this hypothesis, the objective of the present study was to evaluate serum trace element and mineral levels in low- and high-productive dairy cows during feedlot and pasture periods.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe protocol of the present study was approved by the Local Ethics Committee of the Orenburg State University (Orenburg, Russia), protocol No. 03.04.2020-2. All studies have been performed in agreement with the ethical standards set down by the World Medical Association in the 1964 Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eA total of 40 healthy 5\u0026ndash;6 y.o. cows of Red Steppe breed, cultivated by the nurse-cow technique in the Orenburg region were examined. The study involved inly cows weighting 400\u0026ndash;450 kg (431\u0026thinsp;\u0026plusmn;\u0026thinsp;12 kg) having 3\u0026ndash;4 lactation period in 30\u0026ndash;40 days after calving. Based on the median milk yield values the cows were considered as low (\u0026lt;\u0026thinsp;10 l/day, n\u0026thinsp;=\u0026thinsp;24) or high-productive (\u0026gt;\u0026thinsp;10 l/day, n\u0026thinsp;=\u0026thinsp;16). Blood was collected at the end of the feedlot (indoor) period in the last decade of April and during the pasture period in the last decade of June. The study involved a 2 \u0026times; 2 factorial design with grouping according to milk productivity (low and high) and housing (indoor/feedlot and outdoor/pasture).\u003c/p\u003e \u003cp\u003eAssessment of daily milk yield and collection of milk samples were performed at the milking stations. In addition, milk protein and fat content were assessed using Lactan 700 equipment (SibagroPribor, Russia).\u003c/p\u003e \u003cp\u003eBlood samples were obtained from the coccygeal vein prior milking and collected in VACUETTE\u0026reg; CAT serum separator clot activator tubes by a trained technician. The obtained blood samples were subjected to centrifugation at 1000 g for 10 min (CM-6M, Elmi, Latvia) to obtain serum. The collected serum samples were stored frozen in Eppendorf tubes until analysis.\u003c/p\u003e \u003cp\u003ePrior analysis, the obtained serum was diluted (1:15; v/v) with an acidified (pH\u0026thinsp;=\u0026thinsp;2.0) diluent containing 0.07% HNO3 (Sigma-Aldrich, Co., USA), 1% 1-Butanol (Merck KGaA, Germany), and 0.1% Triton X-100 (Sigma-Aldrich, Co., USA), and in distilled deionized water (Merck Millipore, USA).\u003c/p\u003e \u003cp\u003eEvaluation of essential (B, Co, Cr, Cu, Fe, I, Li, Mn, Se, Zn) and toxic trace elements (Al, As, Cd, Hg, Pb, Sn), as well as minerals (Ca, K, Mg, Na, P) in serum samples was performed using inductively-coupled plasma mass-spectrometry (ICP-MS) at NexION 300D spectrometer (PerkinElmer Inc., Shelton, CT, USA) equipped with ESI SC-2 DX4 autosampler (Elemental Scientific Inc., Omaha, NE, USA). Prior analysis the ICP-MS system was calibrated using stock solutions from Universal Data Acquisition Standards Kit provided by the manufacturer (PerkinElmer Inc., Shelton, CT, USA). Internal on-line standardization was performed using the 10 \u0026micro;g/l solutions of Yttrium-89 and rhodium-103 prepared from the respective Pure Single-Element Standard (PerkinElmer Inc.). Laboratory quality control of trace element and mineral analysis was performed daily using the commercial certified reference materials of plasma (ClinChek\u0026reg; Plasma Control, lyophil., for Trace Elements, Levels 1 and 2, RECIPE Chemicals\u0026thinsp;+\u0026thinsp;Instruments GmbH, Munich, Germany). The mean recovery rates for all studied elements varied from 92\u0026ndash;109%.\u003c/p\u003e \u003cp\u003eThe obtained data were processed using Statistica 10.0 software (StatSoft, Tulsa, OK, USA). Assessment of data distribution normality was performed using Shapiro-Wilk test. Mean values and the respective standard deviations were used as descriptive statistics. Given the 2*2 design of the study, further processing was performed using two-way ANOVA after log-transformation of the raw data in order to evaluate the influence of the particular factors (milk productivity, housing period) and their interaction on serum trace element levels. Group comparisons were performed using Fisher\u0026rsquo;s LSD test. Discriminant function analysis was performed in order to assess significant discrimination between the study groups based on serum trace element and mineral levels with subsequent assessment of the distance between group centroids, and contribution of the particular trace elements and minerals into group discrimination. In addition, multiple regression analysis was performed in order to assess the independent association between particular trace elements and minerals and daily milk yield, milk protein and fat content, with adjustment for difference in the housing period. All tests were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAnalysis of daily milk yield demonstrated that high-productive cows were characterized by a nearly twofold higher milk production in both feedlot and pasture periods (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the same time, transition from feedlot to pasture was associated with a significant 27% and 24% decrease in daily milk yield in low- and high-productive cows, respectively. Milk fat content tended to increase during pasture feeding. Despite the lack of significant difference in the feedlot period, milk fat content in high-productive cows in the pasture period exceeded that in low-productive animals by 14% (p\u0026thinsp;=\u0026thinsp;0.046). At the same time, no significant differences in milk protein content was observed between the groups of animals irrespectively of the housing system.\u003c/p\u003e \u003cp\u003eFactorial analysis demonstrated that housing system had a significant influence on daily milk yield (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and milk fat content (p\u0026thinsp;=\u0026thinsp;0.023). In turn, daily milk production (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fat content (p\u0026thinsp;=\u0026thinsp;0.054) were significantly and border-significantly impacted by milk productivity of cows, respectively. None of the factors had a significant influence on milk protein content (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDaily milk yield, milk fat and protein content in low- and high-productive dairy cows during different housing periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFeedlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMilk yield, l/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.08 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.17\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27 \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79 \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as the mean value and the respective standard deviation; 1, 2, 3 \u0026ndash; significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher\u0026rsquo;s LSD test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe obtained data demonstrate a significant influence of both productivity and housing period on serum mineral content (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, serum Ca levels in low- and high-productive cows in the pasture period exceeded the respective values in the feedlot period by 12% (p\u0026thinsp;=\u0026thinsp;0.004) and 29% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Despite the lack of significant difference in the feedlot period (p\u0026thinsp;=\u0026thinsp;0.918), serum Ca level in high-productive cows significantly exceeded that in the low-productive cows by 16% in the pasture period (p\u0026thinsp;=\u0026thinsp;0.001). In the feedlot period serum K levels in high-productive cows was lower than in the low-productive cows by 38% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the same time, in the pasture period serum K concentrations in high-productive animals exceeded that in the feedlot period by 60% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while no difference between feedlot and pasture periods in low-productive cows was observed (p\u0026thinsp;=\u0026thinsp;0.071). Transition from feedlot to pasture was associated with a significant increase in serum Mg levels in both low- and high-productive cows by 15% (p\u0026thinsp;=\u0026thinsp;0.020) and 36% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. No significant group difference in serum Mg concentrations was observed in the feedlot period (p\u0026thinsp;=\u0026thinsp;0.567), whereas in the pasture period serum Mg level in high-productive cows was higher than that in low-productive ones by 22% (p\u0026thinsp;=\u0026thinsp;0.025). Concomitantly, serum Na concentration in low- and high-productive cows in the pasture period was higher than in the feedlot period by 24% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 30% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively. Despite the lack of group differences in the feedlot period (p\u0026thinsp;=\u0026thinsp;0.342), serum Na in high-productive cows was border-significantly higher than in low-productive animals by 8% (p\u0026thinsp;=\u0026thinsp;0.053). In contrast, serum P level in both low- and high-productive cows in the pasture period was 20% (0.001) and 23% (p\u0026thinsp;=\u0026thinsp;0.001) lower than the respective values in the feedlot period. Serum P concentrations in high-productive cows exceeded the respective values in low-productive animals in the feedlot and pasture periods by 19% (p\u0026thinsp;=\u0026thinsp;0.017) and 15% (p\u0026thinsp;=\u0026thinsp;0.096), although in the latter it did not reach statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSerum levels of minerals in low- and high-productive cows in feedlot and pasture periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFeedlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4 \u003csup\u003e1.2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e131.3\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4 \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279.1\u0026thinsp;\u0026plusmn;\u0026thinsp;72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202.6\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e305\u0026thinsp;\u0026plusmn;\u0026thinsp;16 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e317\u0026thinsp;\u0026plusmn;\u0026thinsp;33.5 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8 \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3144\u0026thinsp;\u0026plusmn;\u0026thinsp;381.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3020.4\u0026thinsp;\u0026plusmn;\u0026thinsp;464.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3882.3\u0026thinsp;\u0026plusmn;\u0026thinsp;214 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4194.8\u0026thinsp;\u0026plusmn;\u0026thinsp;619.7 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.5\u0026thinsp;\u0026plusmn;\u0026thinsp;25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e201.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.5 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135.3\u0026thinsp;\u0026plusmn;\u0026thinsp;20.6 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155.1\u0026thinsp;\u0026plusmn;\u0026thinsp;35.4 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as the mean value and the respective standard deviation; 1, 2, 3 \u0026ndash; significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher\u0026rsquo;s LSD test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFactorial analysis demonstrated that both milk productivity and especially housing period had a significant impact on the level of all minerals (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In turn, serum Ca and K levels but not other minerals were significantly influenced by factorial interaction between housing period and milk productivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactorial analysis of the influence of milk productivity and housing system on serum mineral levels in dairy cows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMineral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousing system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilk productivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactorial interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.041 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSerum essential trace element levels were also dependent on housing period and milk productivity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Specifically, in the pasture period serum B concentrations in low- and high-productive cows were significantly lower than in the feedlot period by 37% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 23% (p\u0026thinsp;=\u0026thinsp;0.007), respectively. In the feedlot and pasture periods serum B level in high-productive cows was and 20% (p\u0026thinsp;=\u0026thinsp;0.052) and 46% (p\u0026thinsp;=\u0026thinsp;0.002) higher than that in the low-productive animals. Concomitantly, serum Co concentration in low- and high-productive animals in the pasture period was 28% (p\u0026thinsp;=\u0026thinsp;0.018) and 71% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) lower than the respective values in the feedlot period. As a result, in the pasture period serum Co in high-productive cows was nearly twofold lower than that in low-productive cows (p\u0026thinsp;=\u0026thinsp;0.028). Circulating Cr concentrations of low-productive cows in the pasture period were nearly twofold lower than those in the feedlot period (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas in the high-productive cows only a 19% (p\u0026thinsp;=\u0026thinsp;0.026) decrease in the pasture period was observed in relation to the respective values in the feedlot period. Despite the lack of significant group difference in the feedlot period, serum Cr level in the pasture period exceeded that in the feedlot period by 65% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Transition from feedlot to pasture was associated with a 7% reduction in circulating Fe in the low-productive cows (p\u0026thinsp;=\u0026thinsp;0.006), whereas no significant differences in the high-productive cows were observed between the periods (p\u0026thinsp;=\u0026thinsp;0.434). Serum Fe levels of high-productive cows in the pasture period were 26% higher when compared to the respective values in low-productive cows, although this difference was border-significant (p\u0026thinsp;=\u0026thinsp;0.053). Blood serum I levels in the high-productive animals were 18% (p\u0026thinsp;=\u0026thinsp;0.021) and 25% (p\u0026thinsp;=\u0026thinsp;0.001) lower than in the low-productive animals in the feedlot and pasture periods, respectively. The observed trend to lower serum Mn concentrations in cows in the pasture period did not reach statistical significance. It is notable that serum Se levels in low-productive animals in the pasture period was lower than that in the feedlot period by 14% (p\u0026thinsp;=\u0026thinsp;0.033), while no such difference was observed in high-productive cows (p\u0026thinsp;=\u0026thinsp;0.600). In turn, no significant differences in serum Cu, Li, and Zn levels were observed between the groups of animals irrespectively of the housing period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparative analysis of serum levels of essential trace elements in low- and high-productive cows in the feedlot and pasture periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFeedlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.217\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.260\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.136\u0026thinsp;\u0026plusmn;\u0026thinsp;0.019 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.199\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066 \u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.958\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.207\u0026thinsp;\u0026plusmn;\u0026thinsp;0.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.693\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.353\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13 \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.355\u0026thinsp;\u0026plusmn;\u0026thinsp;1.113 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.528\u0026thinsp;\u0026plusmn;\u0026thinsp;2.282 \u003csup\u003e2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.694\u0026thinsp;\u0026plusmn;\u0026thinsp;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763\u0026thinsp;\u0026plusmn;\u0026thinsp;0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.645\u0026thinsp;\u0026plusmn;\u0026thinsp;0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.663\u0026thinsp;\u0026plusmn;\u0026thinsp;0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.703\u0026thinsp;\u0026plusmn;\u0026thinsp;1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.635\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.206 \u003csup\u003e1,2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.423\u0026thinsp;\u0026plusmn;\u0026thinsp;0.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.061\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.043\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011 \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.023\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.034\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.032 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.85\u0026thinsp;\u0026plusmn;\u0026thinsp;2.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.095\u0026thinsp;\u0026plusmn;\u0026thinsp;0.352 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.643\u0026thinsp;\u0026plusmn;\u0026thinsp;0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.091\u0026thinsp;\u0026plusmn;\u0026thinsp;0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.092\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.034\u0026thinsp;\u0026plusmn;\u0026thinsp;0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.103\u0026thinsp;\u0026plusmn;\u0026thinsp;0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.097\u0026thinsp;\u0026plusmn;\u0026thinsp;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.216 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as the mean value and the respective standard deviation; 1, 2, 3 \u0026ndash; significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher\u0026rsquo;s LSD test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBeing in agreement with the results of group comparisons, the results of factorial analysis (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) demonstrated a significant impact of both housing period and milk productivity on serum essential trace element levels. Specifically, serum B, Co, Cr, Fe and Li were significantly influenced by the housing period. Noteworthy, the influence of housing period on circulating I and Mn levels was also nearly significant. In turn, the concentrations of B, Cr, I, Li, and Mn were characterized by a significant impact of variability in milk productivity. Neither housing period, nor milk productivity significantly influenced Cu, Se, and Zn levels. In turn, factorial interaction (housing period*milk productivity) possessed a significant influence on circulating levels of Cr, while nearly significantly affecting Co and Se concentrations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactorial analysis of the influence of milk productivity and housing system, as well as factorial interaction on serum essential trace element concentrations in dairy cows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousing system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilk productivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactorial interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition to essential trace elements and minerals, variability of serum toxic metal and metalloid levels was also associated with milk productivity and housing period (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Specifically, in the pasture period high-productive cows are characterized by significantly lower serum As levels in comparison to low-productive animals by 12% (p\u0026thinsp;=\u0026thinsp;0.002). Serum Cd levels in high-productive cows in the pasture period were nearly twofold lower is comparison to the respective values in the feedlot period (p\u0026thinsp;=\u0026thinsp;0.001). In turn, no significant differences in circulating Cd between the feedlot and pasture periods in low-productive animals were observed (p\u0026thinsp;=\u0026thinsp;0.324). Serum Hg concentrations in high-productive cows in the feedlot period exceeded the respective values in low-productive animals by 89% (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). At the same time, transition from feedlot to pasture in low-productive cows was associated with a more than twofold increase of circulating Hg levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas no such increase was observed in high-productive animals (p\u0026thinsp;=\u0026thinsp;0.851). As a result, no group differences in serum Hg between low- and high-productive cows was observed in the pasture period (p\u0026thinsp;=\u0026thinsp;0.501). Blood serum Pb level in low-productive animals in the pasture period was 32% lower when compared to the feedlot period (p\u0026thinsp;=\u0026thinsp;0.035). In turn, Pb concentrations in high-productive cows in the pasture period were more than 3-fold lower than those in the feedlot period (p\u0026thinsp;=\u0026thinsp;0.001). Thus, in the pasture period the levels of Pb in blood serum of low-productive cows exceeded that in high-productive cows by 74% (p\u0026thinsp;=\u0026thinsp;0.031). No significant differences in circulating Sn concentrations were observed with respect to milk productivity and housing period.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSerum toxic trace element levels in cows with different milk productivity in the feedlot and pasture periods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFeedlot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePasture\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl, \u0026micro;g/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,071\u0026thinsp;\u0026plusmn;\u0026thinsp;0,082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,062\u0026thinsp;\u0026plusmn;\u0026thinsp;0,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,064\u0026thinsp;\u0026plusmn;\u0026thinsp;0,022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,075\u0026thinsp;\u0026plusmn;\u0026thinsp;0,048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,96\u0026thinsp;\u0026plusmn;\u0026thinsp;1,298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,08\u0026thinsp;\u0026plusmn;\u0026thinsp;1,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,965\u0026thinsp;\u0026plusmn;\u0026thinsp;0,604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,718\u0026thinsp;\u0026plusmn;\u0026thinsp;1,528 \u003csup\u003e1,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,054\u0026thinsp;\u0026plusmn;\u0026thinsp;0,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,055\u0026thinsp;\u0026plusmn;\u0026thinsp;0,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,038\u0026thinsp;\u0026plusmn;\u0026thinsp;0,014 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,030\u0026thinsp;\u0026plusmn;\u0026thinsp;0,002 \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,215\u0026thinsp;\u0026plusmn;\u0026thinsp;0,112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,408\u0026thinsp;\u0026plusmn;\u0026thinsp;0,048 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,440\u0026thinsp;\u0026plusmn;\u0026thinsp;0,037 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,573\u0026thinsp;\u0026plusmn;\u0026thinsp;0,323 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,533\u0026thinsp;\u0026plusmn;\u0026thinsp;0,161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,728\u0026thinsp;\u0026plusmn;\u0026thinsp;0,581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,363\u0026thinsp;\u0026plusmn;\u0026thinsp;0,084 \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,208\u0026thinsp;\u0026plusmn;\u0026thinsp;0,089 \u003csup\u003e1,2,3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSn, ng/ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,128\u0026thinsp;\u0026plusmn;\u0026thinsp;0,048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,133\u0026thinsp;\u0026plusmn;\u0026thinsp;0,108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,155\u0026thinsp;\u0026plusmn;\u0026thinsp;0,058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,148\u0026thinsp;\u0026plusmn;\u0026thinsp;0,046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as the mean value and the respective standard deviation; 1, 2, 3 \u0026ndash; significant group difference in comparison to low- (1) and high-productive cows (2) in the feedlot period, and low-productive cows in the pasture period (3), respectively, according to two-way ANOVA Fisher\u0026rsquo;s LSD test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of factorial analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) demonstrated that the housing period had a significant impact on circulating Cd, Hg, Pb, and Sn levels in cows. In turn, the concentrations of As and Hg were both influenced by differences in milk productivity. Finally, factorial interaction significantly affected the levels of Hg in blood serum of dairy cows, while the influence on serum Cd concentration was only nearly significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactorial analysis of the impact of housing system, milk productivity, and factorial interaction on circulating levels of toxic trace elements in dairy cows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousing system\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMilk productivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactorial interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.045 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as p values of the influence of the particular factors or their interaction on serum element levels according to two-way ANOVA; * - factorial influence is significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorrelation analysis was performed in order to estimate the potential negative relationships between accumulation of toxic metals/metalloids and essential trace elements and minerals. A significant inverse correlation between Al and Co (-0.306; p\u0026thinsp;=\u0026thinsp;0.028), Al and I (-0.321; p\u0026thinsp;=\u0026thinsp;0.020), As and Li (-0.300; p\u0026thinsp;=\u0026thinsp;0.031), Cd and K (-0.444; p\u0026thinsp;=\u0026thinsp;0.001), Cd and Na (-0.336; p\u0026thinsp;=\u0026thinsp;0.015), Hg and Co (-0.361; p\u0026thinsp;=\u0026thinsp;0.008), Hg and I (-0.396; p\u0026thinsp;=\u0026thinsp;0.004), Pb and K (-0.283; p\u0026thinsp;=\u0026thinsp;0.042), Pb and Na (-0.343; p\u0026thinsp;=\u0026thinsp;0.013) was revealed. The obtained data demonstrate that differences in accumulation of toxic metals and metalloids may at least partially contribute to variability in serum mineral levels (K, Na, and P), as well as certain essential trace elements like Co, I, and Li.\u003c/p\u003e \u003cp\u003eIn view of the observed differences in toxic and essential trace elements and minerals in serum of cows, discriminant analysis was performed in order to estimate the contribution of the particular chemical elements into the differences between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Being in agreement with group differences, discriminant analysis failed to reveal complete discrimination between the low- and high-productive cows in the feedlot period, as evidenced by the small distance between the group centroids (MD\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;18.2; p\u0026thinsp;=\u0026thinsp;0.029). At the same time, in the pasture period a complete discrimination between the animals with low and high milk productivity was observed (MD\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;70.4; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the groups of both low- (MD\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;372.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and high-productive cows (MD\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;373.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the pasture period were clearly discriminated from the respective groups in the feedlot period. Further analysis demonstrated that serum Al (p\u0026thinsp;=\u0026thinsp;0.005), Ca (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cd (p\u0026thinsp;=\u0026thinsp;0.038), Cr (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Cu (p\u0026thinsp;=\u0026thinsp;0.003), Fe (p\u0026thinsp;=\u0026thinsp;0.040), Hg (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), K (p\u0026thinsp;=\u0026thinsp;0.003), Mg (p\u0026thinsp;=\u0026thinsp;0.013), Na (p\u0026thinsp;=\u0026thinsp;0.013), P (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Pb (p\u0026thinsp;=\u0026thinsp;0.003), and Sn (p\u0026thinsp;=\u0026thinsp;0.007) contributed significantly to group discrimination, whereas the contribution of As (p\u0026thinsp;=\u0026thinsp;0.063), B (p\u0026thinsp;=\u0026thinsp;0.062), and I (p\u0026thinsp;=\u0026thinsp;0.084) was nearly significant. In turn, Co (p\u0026thinsp;=\u0026thinsp;0.269), Li (p\u0026thinsp;=\u0026thinsp;0.689), Mn (p\u0026thinsp;=\u0026thinsp;0.534), Se (p\u0026thinsp;=\u0026thinsp;0.382), Zn (p\u0026thinsp;=\u0026thinsp;0.616) concentrations did not contribute significantly to the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultiple linear regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) was performed to evaluate the association between circulating levels of essential and toxic trace elements and minerals and daily milk yield in dairy cows with adjustment for the housing period (1 \u0026ndash; feedlot; 2 \u0026ndash; pasture). The obtained data demonstrate that serum Pb was characterized by inverse association with daily milk production, whereas circulating Cr concentration was positively associated with this parameter. The overall model accounted for 58% variability in daily milk yield. In contrast to daily milk yield, no significant associations between serum trace element and mineral levels and milk fat or protein content were observed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis of the association of serum trace element and mineral levels with milk yield (Model 1), milk fat (Model 2) and protein (Model 3) concentration\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMilk yield\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMilk fat, %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMilk protein, %\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,577\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,608\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,344\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,221\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0,336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0,178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0,388\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiple R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep for a model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.001 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eData are expressed as regression coefficient (β) and the respective p values; * - the association is significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTaken together, the obtained data demonstrate that the differences in serum toxic and essential trace element and mineral levels between the cows with different milk productivity were more obvious in the pasture period when compared to the feedlot period. Specifically, after transition from feedlot to pasture high-productive cows were characterized by lower levels of toxic metals, higher circulating mineral concentrations, as well as a less profound decrease in essential trace element levels when compared to low-productive cows. Furthermore, serum toxic metal and metalloid levels were characterized by an inverse association with certain minerals and essential trace elements, being indicative of the potential antagonistic relationships. Finally, the differences in trace element and mineral levels were found to be associated with daily milk yield, but not milk protein or lipid content.\u003c/p\u003e \u003cp\u003eThe observed differences in serum metal and trace element and mineral levels in cows between the feedlot and pasture periods may be mediated by feeding regimens. Specifically, indoor housing in winter involves concentrate feed supplementation (Magan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) that is known to be associated with higher intake of essential and toxic trace elements (Rey-Crespo et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). O\u0026rsquo;Brien et al. (1999) demonstrated that milk Cu and I were found to be higher during winter periods, whereas no clear trend in the changes of Mn, Mo, Zn, Co, Cr, and Fe levels was observed (O'Brien et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Correspondingly, administration of a concentrate-based ration was associated with higher blood I level (Lejeune et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In addition, it has been demonstrated that the intake of Co is also associated with the administration of concentrate feeds (Orjales et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast to trace elements that are characterized by higher intake during indoor period, consumption of minerals (Ca, Mg, P, Na, K) was shown to be positively associated with time spent on pastures (Morales-Almar\u0026aacute;z et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grazing outdoors on perennial ryegrass pasture was shown to be associated with higher milk Ca levels as compared to indoor housing on a total mixed ration (Gulati et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The observed increase in Ca levels in the pasture period corresponds to higher Ca level in pasture grass in comparison to hay, silage, and chow administered during the feedlot period (Sizova et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe existing data on the variations in heavy metal intake and accumulation in dairy cows in different periods and housing systems are insufficient. However, Pastorelli et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also reported higher milk Cd levels in winter as compared to summer period (Pastorelli et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In winter higher Pb and Cd levels were observed in raw milk samples in N.R. Macedonia (Limani et al. 2022). Furthermore, the level of Cd, Cr, Pb, and Ni was found to be higher in milk samples from conventional and especially commercial farms as compared to the organic ones (Zwierzchowski et al. 2018). Hypothetically, higher intake of heavy metals may be associated with its higher content in feed concentrates. Specifically, earlier studies demonstrate that animal feeds frequently contain detectable heavy metal levels that sometimes even exceed the upper tolerable level (Dai et al. 2016). Of all components of Wisconsin dairy herd feed ration, mineral mix was shown to contain the highest levels of As, Cd, and Pb (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Moreover, a study by Li et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) originating from China reported that the levels of Cr and Pb in dairy feeds may exceed the recommended limits by a factor of more than 6 and 17, respectively (Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Correspondingly, Pb and Cd levels in cattle tissues significantly correlated with feed heavy metal content (Hashemi et al. 2018). In addition, Al, As, Hg levels in feed also correlated with cow milk concentrations (Zhou et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the particular mechanism underlying relatively lower accumulation of trace elements in high-productive cows in comparison to the low-productive ones despite grazing on the same pastures is unclear. Hypothetically, the observed differences may be mediated by different genetic characteristics of cows with different milk production. Specifically, Upadhyay et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) demonstrated that gene ontology categories associated with metal ion transport (GO: 0030001) and metal ion transmembrane transporter activity (GO: 0046873) were found to be enriched in cows characterized by different milk production (Upadhyay et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, high-productive dairy cows were characterized by upregulation of hepatic GSTM4 gene (McCarthy et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), that is also known to be involved in detoxification (Gasmi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The assumption of the role of different genetic background in accumulation of metals and other trace elements is also supported by the observation by Denholm et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) who revealed a significant association between dairy cow genotype and blood and milk trace element concentrations (Denholm et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of group comparisons demonstrated that high-productive cows are characterized by higher levels of minerals and essential trace elements (except for Co and I), while having lower serum levels of toxic metals and metalloids. Furthermore, regression analysis also supported these findings demonstrating a positive relationship between Cr, Co, Mg, I, and Na levels with milk yield, whereas circulating toxic As and Pb were found to be inversely associated with milk production.\u003c/p\u003e \u003cp\u003eToxic metal exposure was shown to be a risk factor for adverse health effects and lower productivity in dairy cows (Raikwar et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Specifically, it has been also demonstrated that total accumulation of toxic metals in hair of dairy cows is inversely associated with milk production (Miroshnikov et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cd exposure is able to affect reproduction and milk production (Lane et al. 2015). An earlier study by Miller et al. demonstrated that dietary Cd exposure significantly decreases milk production in dairy cows (Miller et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1967\u003c/span\u003e). Serum Pb levels were shown to be inversely associated with body condition scores in dairy cows, although no relationship with milk yield was revealed (Denholm et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In turn, reduction of Pb and Cd body burden was associated with an increase in daily milk yield in cows from an industrial area (Portiannyk, Mamenko, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to toxic metals, mineral intake is known to be essential for dairy cow health. Specifically, adequate circulating Ca was shown to be critical for postpartum health and reproductive performance in dairy cows (Jeong et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Supplementation with oral Ca boluses was shown to increase daily milk yield (Oetzel, Miller, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similar effect was observed in multiparous cows of greater production potential, but not those with below average production potential (Martinez et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Correspondingly, clinical hypocalcemia in multiparous cows was associated with lower daily milk production (Venjakob et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). At the same time, the results of certain studies did not reveal any significant association between serum Ca levels and milk productivity (\u0026Oslash;stergaard, Larsen, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMg is also an essential mineral for dairy cows (Schonewille, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An increase in milk yield is associated with higher Mg requirements with an increase in Mg intake and Mg absorption (Martens, Stumpff, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), therefore, high-productive cows are considered at higher risk of Mg deficiency (Pinotti et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Oral Mg supplementation was shown to increase milk production and milk fat content in hypomagnesemic cows (Wilson, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). Correspondingly, supplementation with Mg butyrate significantly increased daily milk yield and milk protein and fat content (F\u0026eacute;bel et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReduction of dietary P content was also shown to result in decreased milk yield, while having no significant effect on milk fat and protein content in lactating dairy cows (Wu et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). These findings corroborate earlier data on the association between P deficiency and low milk production (Call et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). It is also proposed that dietary P deficiency may impair rumen microbiota resulting in reduced milk protein content (Elizondo Salazar et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies also demonstrated the role of Na and K in regulation of milk production. Specifically, milk yield was shown to be associated with K retention in dairy cows (Silanikove et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). It is proposed that K requirements in high-productive cows may be increased in early lactation period (Dennis et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Specifically, an increase in milk production was associated with early hypokalemia (Pl\u0026ouml;ntzke et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Correspondingly, increased dietary potassium in early lactation may improve milk yield and milk fat content (Harrison et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Na supplementation was shown to increase daily milk yield in cows grazing a tropical grass-legume pasture (Davison et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). In addition to increased milk yield, Na supplementation was shown to improve Ca and Mg metabolism in cows, also possessing beneficial effect on mammary gland health (PHILLIPS et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEssential trace elements are also involved in maintenance of dairy cattle health and thus milk production. Specifically, adequate B nutrition was shown to be beneficial for dairy cows and other animals (Abdelnour et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the lack of B supplementation on milk yield, it significantly improved milk composition and mammary gland health (Praveen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), in parallel with a beneficial effect on metabolic regulation in dairy cows (Basoglu et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, B was shown to improve Ca and Mg bioavailability in cows (Baspinar et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of a recent meta-analysis demonstrated that Cr supplementation significantly improves milk production but not composition (Malik et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), also significantly influencing metabolic parameters by reducing non-esterified fatty acid levels and increasing glucagon concentrations (Malik et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Given the role of Cr as an insulin-mimetic, it has been shown that Cr supplementation significantly modulates insulin-signaling pathway, although this effect was different in antepartal than post-partal period (Pantelić et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), being also dependent on the energy balance in these periods (Kegley, Spears, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCo deficiency is known to be associated with a wide spectrum of adverse effects including impaired growth and reproduction (Silva et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Milk secretion is associated with higher B12 and Co requirements (Kincaid et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In turn, Co supplementation was shown to improve daily milk yield in cows (Gonz\u0026aacute;lez-Monta\u0026ntilde;a et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, high dietary Co supplementation may affect both milk production and fatty acid composition (Karlengen et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Correspondingly, we have observed a trend to increased serum Co levels in high-productive cows (Sizova et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLactation is associated with decreased plasma Fe and Hb levels, being indicative of higher Fe requirements (Randhawa et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Correspondingly, simultaneous Fe and Cu deficiency was shown to be associated with reduced milk yield (Abramowicz et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fe supplementation in cows consuming a diet adequate in Fe, did not improve daily milk production but significantly reduced somatic cell count in milk (Weiss et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). At the same time, Denholm et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) demonstrated that serum Fe levels were inversely associated with milk yield, while being positively correlated with body condition scores (Denholm et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough earlier studies demonstrate that insufficient I intake may contribute to reduced milk production, later investigations failed to reveal an association between dietary I intake and milk yield (Niero et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, no effect of I supplementation on milk production was observed in cows grazed on pastures with adequate I content (Grace, Waghorn, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to lower levels of toxic metals and higher concentration of essential trace elements and minerals in high-productive cows compared to low-productive ones, a significant inverse correlation was observed between certain toxic and essential elements. These findings generally corroborate the earlier data on the adverse effect of toxic metal exposure on mineral metabolism in cows. Specifically, Cd accumulation in the organism following long-term dietary exposure was shown to affect hepatic and renal levels of essential metals (Smith et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). A significant inverse correlation of milk Pb and Cd with Ca and Mg levels, respectively, was observed in Simmental cows from an organic farm (Pilarczyk et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In cattle from industrial areas, blood Pb levels was inversely associated with circulating concentrations of Cu, Co, and Fe (Patra et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn turn, improved mineral intake may possess protective effects against toxic metal overaccumulation. Specifically, administration of mineral blocks containing high levels of P and Ca were shown to reduce bioavailability of Pb in vitro (Pareja-Carrera et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Correspondingly, mineral block supplementation was shown to reduce Pb accumulation in sheep by increasing its fecal excretion (Pareja-Carrera et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Noteworthy, dietary Ca was shown to reduce liver Pb content in sheep (Pearl et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1983\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTaken together, our novel data demonstrate that serum trace element and mineral levels in dairy cows are associated with milk productivity, although the housing system significantly affects this association. Specifically, the differences between low- and high-productive cows in the feedlot period were negligible. While transition from the feedlot to pasture feeding resulted in a significant increase in serum mineral levels and a decrease in circulating essential trace elements, in the pasture period the serum levels of essential trace elements and minerals (except for Co and I) were higher in high-productive cows. Furthermore, in the pasture period high-productive cows were characterized by significantly lower circulating toxic metal and metalloid levels when compared to low-productive cows. Negative associations between certain essential and toxic elements demonstrate the potential antagonistic relationships between the elements in dairy cows. Taken together, in the pasture period high-productive cows were characterized by a more beneficial trace element and mineral profile of blood serum than the low-productive animals. In view of significant association between essential and toxic trace element and mineral levels with daily milk yield, it is assumed that the observed differences in trace element and mineral metabolism may at least partially contribute to milk productivity in dairy cows. At the same time, further studies are required in order to elucidate the particular mechanisms underlying the association between trace element and mineral metabolism with milk productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSTATEMENTS AND DECLARATIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics committee of the Federal Research Centre for Biological Systems and Agro technologies of the Russian Academy of Sciences approved the experimental design (No.\u0026nbsp;03.04.2020-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors whose names appear on the submission made substantial contributions to the conception or design of the work.\u003c/p\u003e\n\u003cp\u003eSergey Miroshnikov and\u0026nbsp;Svetlana Notova\u0026nbsp;and\u0026nbsp;Anatoly Skalny conceived and designed this study, Elena Yausheva\u0026nbsp;and\u0026nbsp;Aina Kamirova performed the most majority of experiments, Elena Sizova and\u0026nbsp;Alexey Tinkov\u0026nbsp;аnalyzed data and authored the final article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research work was supported by the Russian Science Foundation (project No. 20-16-00078-P).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbd El-Hack ME, Alagawany M, Farag MR, Arif M, Emam M, Dhama K, Sayab M (2017) Nutritional and pharmaceutical applications of nanotechnology: \u003cem\u003eTrends and advances\u003c/em\u003e. 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J Agric Food Chem 13(26):6877\u0026ndash;6888\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"milk yield, heavy metals, feedlot, pasture, minerals","lastPublishedDoi":"10.21203/rs.3.rs-4300973/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4300973/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of the present study was to evaluate serum trace element and mineral levels in low- and high-productive dairy cows during feedlot and pasture periods. Serum trace element and mineral levels were assessed inductively-coupled plasma mass-spectrometry. The obtained data demonstrate that serum Ca, Mg, K, and Na levels increase significantly in the pasture period, and this increase is more evident in high-productive cows. In turn, circulating levels of B, Co, Cr, Fe, I, and Se levels were characterized by a decrease in the pasture period. Despite the lack of group differences in the feedlot period, serum B, Cr, and Fe levels in the pasture period were higher in high-productive cows. In turn, circulating Co and I concentrations in the low-productive cows exceeded those in high-productive animals. Finally, the levels of toxic trace elements in the pasture period were found to be higher in low-productive cows. Discriminant analysis demonstrated that the groups of cows with different milk productivity were clearly discriminated in the pasture but not feedlot period. In addition, multiple regression analysis revealed a significant inverse and positive association of serum Pb and Cr levels with daily milk yield. Taken together, the obtained data demonstrate that the differences in serum trace element and mineral levels between the low- and high-productive cows are more profound in the pasture period. More beneficial trace element and mineral profile in high-productive cows may hypothetically contribute to higher milk yield. However, further more detailed studies are required to elucidate the mechanisms of this association.\u003c/p\u003e","manuscriptTitle":"Housing system significantly modulates the association of serum levels of essential and toxic trace elements and minerals with milk productivity in dairy cows","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-05 19:31:21","doi":"10.21203/rs.3.rs-4300973/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-11-25T08:45:08+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-10-21T06:02:59+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-19T12:55:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-09T04:19:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Science and Pollution Research","date":"2024-05-07T08:07:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b8ccbd75-6368-475a-82bc-1368ad657cfb","owner":[],"postedDate":"July 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-10T16:07:49+00:00","versionOfRecord":{"articleIdentity":"rs-4300973","link":"https://doi.org/10.1007/s11356-025-36021-2","journal":{"identity":"environmental-science-and-pollution-research","isVorOnly":false,"title":"Environmental Science and Pollution Research"},"publishedOn":"2025-02-03 15:57:35","publishedOnDateReadable":"February 3rd, 2025"},"versionCreatedAt":"2024-07-05 19:31:21","video":"","vorDoi":"10.1007/s11356-025-36021-2","vorDoiUrl":"https://doi.org/10.1007/s11356-025-36021-2","workflowStages":[]},"version":"v1","identity":"rs-4300973","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4300973","identity":"rs-4300973","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}