Research Article

The Effect of Selenium as a Feed Additive on Blood Parameters Antioxidant Activity in Dairy Goat: Meta-Analysis

Dwi Putri Nurmala1, Tri Eko Susilorini1, Osfar Sjofjan2*, Danung Nur Adli2

1Department of Animal Production, Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia; 2Department of Animal Nutrition and Feed Science, Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia.

Received | May 30, 2024; Accepted | June 22, 2024; Published | July 12, 2024

*Correspondence | Osfar Sjofjan, Department of Animal Nutrition and Feed Science, Faculty of Animal Science, Universitas Brawijaya, Malang, East Java, Indonesia; Email: osfar@ub.ac.id

Citation | Nurmala DP, Susilorini TE, Sjofjan O, Adli DN (2024). The effect of selenium as a feed additive on blood parameters antioxidant activity in dairy goat: Meta-analysis. Adv. Anim. Vet. Sci., 12(8):1588-1595.

DOI | https://dx.doi.org/10.17582/journal.aavs/2024/12.8.1588.1595

ISSN (Online) | 2307-8316

Copyright: 2024 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

INTRODUCTION

Feed is very important to support livestock growth and productivity. Feed holds the largest financing, between 60-70% of the total livestock production costs (Suroso et al., 2023). Yadav et al. (2018) stated that livestock requires five categories of nutrients, namely carbohydrates, proteins, lipids, minerals, and vitamins. Ruminants can experience poor growth if fed feeds with low protein, low energy, and unbalanced minerals. Twenty-one minerals are considered essential for animals. These minerals are classified into macro and micro mineral.

Selenium (Se) is one of the important minerals (trace minerals) in livestock production because of its diverse physiological roles (Arshad et al., 2020). Some of the benefits of selenium in the animal body include playing a role in thyroid gland synthesis and thyroid hormone function, reducing oxidative stress, ant mutagenic, ant carcinogenic, antimicrobial and ant parasitic, increasing immune function and providing protection against oxidative brain lipid damage (Hosnedlova et al., 2017).

Selenium is a mineral that is only a small part of feed, but is very important for livestock. The optimal amount of selenium in feed must be considered to maintain normal animal physiology, however, excess or deficiency can cause toxicity and deficiency. Schone et al. (2013) reported that Se toxicity is not considered a life threat to animals or humans. Selenium deficiency can affect reproductive efficiencies such as placental retention, abortion, premature embryo death and infertility, besides it can interfere with growth performance (white muscle disease) and skeletal muscle necrosis (Mehdi and Dufrasne, 2016). Blood profiles have been extensively utilized to assess animal stress and welfare levels, as well as to track nutritional status, metabolism, and health status (Astuti et al., 2021). Blood parameters in ruminants are correlated with the season, the herd management techniques, and the physiological state (Mekroud et al., 2021). However, the studies reported regarding the blood profiles are critical because they provide information regarding the animal’s health status (Gao et al., 2022).

Meta-analysis is a systematic review of a specific topic in the literature that provides quantitative estimates of the impact of a treatment (Russo, 2007). Meta-analysis is the analysis of statistical studies by analyzing data from primary studies. The results of the primary study analysis are used as a basis for correction factors, accepting and supporting existing research (Adli et al., 2024). Meta-analysis overcomes the limitations of small sample sizes of individual studies, detecting effects of interest and reducing the risk of false-negative results. Combining data from separate studies can statistically provide more precise estimates than single studies (Lee, 2019). Therefore, this research aims to provide more precise conclusions about the effect of selenium as a feed additive on blood parameters and antioxidant activity in dairy goats through meta-analysis methods.

MATERIALS AND METHODS

Eligibility criteria, search strategy, and data extraction

A dataset comprising literature on the use of selenium as a feed additive in dairy goats was compiled, covering publications from 2005 to 2023. Literature searches were conducted on Scopus (https://www.sciencedirect.com/), PubMed (https://pubmed.ncbi.nlm.nih.gov/), and Google Scholar (https://scholar.google.com/). The keywords used included selenium, dairy goat, feed additive, red blood cell, white blood cell, haemoglobin, haematocrit, cholesterol, glucose, total protein, GSH-Px, selenium concentration, and blood.

The criteria for including articles in the database were: (1) the article was published between 2005 and 2023, (2) the treatment included the dose of selenium used, (3) the article reported on the use of selenium in dairy goats, excluding other animals, (4) the dairy goats were adult females, and (5) selenium was administered by mixing it into feed. Data from articles meeting these criteria were tabulated in Excel, including details such as author, year of publication, type of goat used, lactation phase of the goat, type of selenium used, amount of selenium used, and the values of each parameter. All data were converted into consistent units of measurement to facilitate direct analysis within specific parameters. The final database included 16 articles with a total of 129 data units. Figure 1 details the selection process of the studies used in this meta-analysis, while Table 1 provides a summary of the completed dataset, adhering to PRISMA-P guidelines (Adli et al., 2024).

 

Table 1: Studies selected to be included in the meta-analysis.

No	Reference	Source of selenium	Period	Level (mg/day)	Strain dairy goat
1	Zhang et al. (2017)	Se-enriched yeast and sodium selenite	L	0-1,12	Guanzhong
2	Rashnoo et al. (2020)	N/A	D	0-0,25	N/A
3	Petrera et al. (2009)	Sodium selenite and se yeast	L	0-0,26	Saanen
4	Kachuee et al. (2019)	Sodium selenite and selenomethionine	L	0-0,6	Khalkhali
5	Silveira et al. (2019)	Se yeast	L	0-0,4	Saanen
6	Shi et al. (2018)	Se enriched yeast	D	0-4,63	Taihang black
7	Barcelos et al. (2022)	Se yeast	L	0-11,2	Saanen X Pardo Alpine
8	Vasconcelos et al. (2023)	Se yeast	L	0-40	Saanen X Toggenburg
9	Ziaei et al. (2015)	-	D	0-0,86	Rainei
10	Shareef et al. (2019)	Se yeast	L	0,0,03	Local Iraqi does
11	Tozzi et al. (2016)	Sodium selenite and se yeast	L	0-0,2	Alpine chamois
12	Misurova et al. (2009)	Sodium selenite and lactate protein complex	D	0-0,28	White shorthair
13	Antuvonic et al. (2013)	Se yeast	L	0-0,03	Alpine
14	Pavlata et al. (2011)	Sodium selenite and lactate protein complex	D	0-0,3	White shorthair
15	Pavlata et al. (2012)	Sodium selenite, se-enriched yeast, selenium proteinate and lactate protein complex	D	0-0,24	Shite shorthair
16	Tsiplakou et al. (2021)	Se yeast	L	0-0,12	Alpine X local breed

 

Note: L= Lactation; D= Days open; N/A= Not available.

 

 

Coding and statistical analysis

The meta-analysis equation follows the following formula (St-Pierre, 2001; Sauvant et al., 2008).

Yijk represents the dependent variable, μ stands for the overall mean value (intercept value), Si denotes the random effect of the ith study, assumed to be ~ Niid (0, σS2), τj represents the fixed effect of the jth of τ factor, and Sτij represents the random interaction effect between the ith and jth dosage of the τ factor, also assumed to follow a normal distribution with mean 0 and variance σSr2. β1 represents the overall value of the linear regression coefficient for Y in relation to X, serving as a fixed effect or slope, β2 denotes the general coefficient of the quadratic regression for Y concerning X, functioning as a fixed effect or slope, Xij dan X2ij represent the continuous values of the predictor variable in both linear and quadratic forms, respectively. The bi stands for the random effect specific to each study on the regression coefficient of Y with respect to X, assumed to be ~ Niid (0, σS2). Finally, eijk represents the residual value arising from unpredictable error. In case, the quadratic form presented insignificant different, the models changes into linear form.

The validation test was carried out utilizing the root mean square error (RMSE) and coefficient of determination (R2) as metrics. The following equation represents RMSE and R2.

In this scenario, where O represents the actual value, P stand for the estimated value, NDP denotes the number of data point, σ2f represents the variant of a fixed factor, ∑(σ2l ) is the sum of component variances, σ2e signifies the variance attributed to predictor dispersion, and σ2d characterizes the specific distribution of the variance. All meta-regression analyses were carried out using R software 4.3.3 (2024-02-29 ucrt).

 

Table 2: Descriptive statistics of the effect selenium as feed additive on the blood profile of dairy goat.

No	Response	Unit	N	Mean	SD	Min	Max
1	RBC	x 1012/L	12	11,02	1,07	9	12,46
2	WBC	x 109/L	12	13,34	3,59	9,24	21,73
3	Hemoglobin	g/dL	12	7,96	0,84	6,82	9,79
4	Hematocrit	%	15	33,20	18,58	20,25	72
5	Cholesterol	mg/dL	8	12,85	18,65	29,19	77,02
6	Glucose	mg/dL	9	57,59	4,51	48,29	61
7	Total protein	g/L	11	59,23	23,49	20,4	80,3
8	Selenium	µg/L	32	185,73	111,94	36,18	463,33
9	GSH-Px	µkat/L	18	329,44	385,89	45,5	1154,6

 

Note: RBC= Red Blood Cell; WBC= White Blood Cell.

 

RESULT AND DISCUSSION

The results of the meta-analysis on the relationship between selenium sources and blood profiles are presented in Table 2. The meta-analysis indicates that different selenium sources have significantly varied effects on white blood cells (WBC), glucose, and glutathione peroxidase (GSH-Px) activity in the blood (P<0.05), with selenium concentration showing a highly significant effect (P<0.01). Conversely, there was no significant effect on red blood cells (RBC), haemoglobin, haematocrit, cholesterol, or total protein (P>0.05). In dairy goats, the white blood cell (WBC) count ranges from 9.24 x 109/L to 21.73 x 109/L. Their glucose levels vary between 48.29 and 61 mg/dL, and the activity of glutathione peroxidase (GSH-Px) ranges from 45.5 to 1154.6 µkat/L. Post hoc analysis indicated that Se-enriched yeast was the most effective selenium source for improving WBC and glucose levels. Both Se-enriched yeast and selenomethionine were effective in increasing total se, whereas sodium selenite and lactate protein complex were most effective for enhancing GSH-Px activity. The study found that organic sources of selenium, such as Se-enriched yeast (782.2 µkat/L), Selenium proteinate (904.2 µkat/L), and lactate protein complex (926.47 µkat/L), were the most effective. The data supporting these findings is presented in Table 3.

According to Sevcikova et al. (2006), organic selenium is utilised more efficiently than inorganic selenium sources. Organic selenium is actively absorbed via amino acid

 

Table 3: Regression linear model of effect source of selenium on blood profile of dairy goat.

No	Response	Unit	Average							F value	Pr > F
			Control	Sodium selenite	Se yeast	Se-enriched yeast	Seleme thionine	Sele-nium protei-nate	Lactate protein complex
1	RBC	x 1012/L	10.69	N/A	11.38	11.93	N/A	N/A	N/A	5.48	0.05
2	WBC*	x 109/L	12.94a	N/A	11.64a	18.59b	N/A	N/A	N/A	7.97	0.01
3	Hemoglobin	g/dL	7.72	N/A	8.31	8.12	N/A	N/A	N/A	1.74	0.25
4	Hematocrit	%	32.16	N/A	24.98	34.63	N/A	N/A	N/A	4.33	0.08
5	Cholesterol	g/dL	48.83	N/A	59.74	34.84	N/A	N/A	N/A	1.29	0.39
6	Glucose*	mg/dL	54.81a	N/A	49.75a	58.79b	N/A	N/A	N/A	26.54	0.03
7	Total protein	g/L	58.37	N/A	71.70	70.75	N/A	N/A	N/A	1.01	0.46
8	Selenium**	µg/L	128.36a	238.6ab	168.09ab	340.97b	435.86b	167.2ab	154.2ab	5.98	0.001
9	GSH-Px*	µkat/L	325.77a	972.72b	170.29ab	782.2ab	N/A	904.2ab	926.47b	6.23	0.025

 

RBC= Red Blood Cell; WBC= White Blood Cell; N/A= Not available. *= significant different (P<0,05); **=significantly (P<0,01).

 

transport mechanisms, whereas inorganic selenium is absorbed passively through simple diffusion (Korzeniowska et al., 2018). Khalili et al. (2019) reported that organic selenium in the form of selenium yeast is more effective than inorganic selenium in the form of sodium selenite at increasing mean corpuscular hemoglobin (MCH), reproductive parameters, and health parameters. Additionally, Huang et al. (2023) found that organic selenium supplements, such as selenomethionine and selenium yeast, are more effective at enhancing the immune and antioxidant capacities of Chinese Xiangzhong Black beef cattle.

The bioavailability and toxicity of selenium are linked to its chemical form, with organic selenium being reported as more bioavailable and less toxic than inorganic selenium (Jin et al., 2018). Organic selenium compounds, such as selenomethionine and selenocysteine, are actively absorbed through amino acid transport mechanisms. On the other hand, inorganic selenium, like selenate and selenite, is passively absorbed through simple diffusion processes (Pavlata et al., 2011). The bioavailability of selenium in dairy goats is essential for their health and productivity. Studies have shown that organic selenium sources have higher oral bioavailability due to greater rumen microorganism incorporation and reduced formation of elemental selenium by rumen microorganisms (McDermott et al., 2024). Additionally, organic selenium supplementation has been linked to improved milk production in dairy goats (Dara et al., 2018). Conversely, inorganic selenium is quickly transformed into metabolically available selenide in the organism, which is then converted into functional selenoproteins containing selenocysteine (Mehdi and Dufrasne, 2016). The rapid metabolism of inorganic selenium and its difficulty in absorption contribute to its potential toxicity, emphasizing the importance of considering the chemical form of selenium to mitigate adverse effects (Zhang et al., 2023).

The key differences in study design, animal species, and dosage of supplementation between the two studies likely account for these contrasting results. The theoretical implications of our findings suggest that selenium plays a more critical role in glucose metabolism in dairy goats than previously understood. This aligns with theories proposing selenium’s involvement in antioxidant defense and metabolic regulation. Our study’s significant findings support the hypothesis that adequate selenium levels can enhance metabolic health and glucose homeostasis in dairy goats.

Long-term, these findings suggest that selenium supplementation could be a valuable strategy in managing metabolic health in dairy goats, potentially improving productivity and overall health. Future research should further explore the optimal selenium dosage and its effects on various metabolic parameters, considering different breeds and environmental conditions to generalize these findings.

The meta-analysis results regarding selenium levels and blood profiles are shown in Table 4. The illustration regression line between the level of selenium and blood profile is shown in Figures 2, 3, and 4. Different selenium levels had a highly significant effect on GSH-Px (µkat/L) in the blood (P<0.01), with the regression function y=329.44+1223.944x, Figure 5. Meanwhile, RBC, WBC, hemoglobin, hematocrit, cholesterol, glucose, total protein, and selenium concentration showed no significant effect (P>0.05). Each regression function, where RBC (x 1012/L): y= 11.02 + 0.022x, WBC (x 109/L): y= 13.34 - 0.002x, hemoglobin (g/dL): y= 7.96 + 0.007x, hematrocit (%): y= 33.2 + 7.745x, cholesterol (mg/dL): y=49.47 + 12.854x; glucose (mg/dL): y= 57.59 +1.759x, total protein (g/L): y= 59.23 + 12.855x and selenium (µg/L): y= 185.73 + 0.104x.

Even though the research results are like that, feeding dairy cows a supra-nutritional selenium-yeast supplement during late gestation resulted in improved antioxidant status

 

Table 4: Regression linear model of effect level selenium on blood profile of dairy goat.

No	Response	Unit	Model	N	Inter-cept	SE inter-cept	Slope	SE slope	P value	RMSE	AIC
1	RBC	x 1012/L	L	12	11.02	0.53	0.022	0.02	0.28	0.85	43.33
2	WBC	x 109/L	L	12	13.34	1.46	-0.002	0.08	0.98	0.99	69.72
3	Hemoglobin	g/dL	L	12	7.96	0.39	0.007	0.02	0.68	0.91	41.33
4	Hematocrit	%	L	15	33.20	7.74	7.745	0.06	0.48	0.95	96.43
5	Cholesterol	g/dL	L	8	49.47	12.85	12.854	0.11	0.31	0.78	57.16
6	Glucose	mg/dL	L	9	57.59	1.75	-0,479	0.43	0.31	1.07	55.62
7	Total protein	g/L	L	11	59.23	12.85	-0,043	0.06	0.48	0.87	71.19
8	Selenium	µg/L	L	32	185.73	30,03	0.104	2.49	0.97	1.52	379.32
9	GSH-Px**	µkat/L	L	18	329.44	133,83	1223.944	230,98	<0.001	0.86	219.59

 

Note: RBC= Red Blood Cell; WBC= White Blood Cell; N= amount of data; SE= standart error; RMSE= root mean squares error. AIC= akaike information criteria. **= significantly (P<0.01).

 

 

postpartum, indicating a potentially positive impact on red blood cell, hemoglobin, and hematocrit (Żarczyńska et al., 2018). In addition, increasing RBC count, hemoglobin, and hematocrit by selenium supplementation was also reported in lactating donkeys (Tong et al., 2024).

The lack of a significant impact of selenium as a feed additive on glucose levels in dairy goats aligns with the findings of Żarczyńska et al. (2021), their study indicated that selenium supplementation, particularly in organic forms like selenite-triglycerides, did not significantly affect glucose levels in dairy cows. Despite the analytical results of this study, theoretically, there is a hypothesized influence of selenium on glucose metabolism; studies in rats and humans revealed that selenium might stimulate glucose intake and regulation of metabolic processes such as glycolysis, gluconeogenesis, fatty acid synthesis, or pentose phosphate pathway (Fontenelle et al., 2018). Total protein levels are a good indicator to describe the osmotic state, and nutrient transportation through extracellular fluid (blood plasma). In addition, total protein is also a good indication of the physiologic and biochemical function of liver tissue. Good liver function can fulfil the availability of nutrient precursors, both amino acids, glucose, and fatty acids for the biosynthesis of milk in the mammary gland (Januardani et al., 2023). However, in the research of Reczyńska et al. (2019) reported that selenium can increase the concentration of total blood protein in goats, but the effect was observed during a longer study, namely after 160 days of oral selenium supplementation.

 

 

 

There was an increase in the amount of Glutathione Peroxidase in the blood of dairy goats supplemented with selenium, the same as in cows in the study by Salman et al. (2013) has shown that dietary supplementation with selenium enhances the activity of GSH-Px in the blood. Arshad et al. (2020) the significant effects of selenium (Se) in dairy animals are largely due to the various functions performed by selenoproteins. The cellular redox system and the body’s antioxidant defense depend on selenoenzymes (such as Glutathione Peroxidase) and selenoprotein. Therefore, an adequate dietary intake of Se is essential to provide sufficient Se-Cys and Se-Met for selenoprotein synthesis. Supplementing dairy animals’ diets with Se is considered a potential strategy to enhance immune response and reduce metabolic and oxidative stress. Enzyme Glutathione peroxidase contains selenium as an integral structural component and plays an important role in animal physiology and health (Qazi et al., 2019).

CONCLUSIONS AND RECOMMENDATIONS

Results from the meta-analysis showed that feeding dairy goats with selenium as a feed additive significantly increase the activity of glutathione peroxidase (GSH-Px) in their blood. The regression function y = 329.44 + 1223.944x indicates that giving 1 mg of selenium per goat per day will raise the glutathione peroxidase activity in the blood by 1223.944 µkat/L. GSH-Px is a selenoenzyme that plays a role in protecting cells from oxidative damage by catalyzing the breakdown of hydrogen peroxide and lipid peroxide, thus acting as a major antioxidant defense mechanism. An increase in glutathione peroxidase activity in the blood means improved antioxidant status in dairy goats.

ACKNOWLEDGeMENTS

The authors thanks to Universitas Brawijaya and LPDP (Lembaga Pengelola Dana Pendidikan) for funding this research.

NOVELTY STATEMENT

The novelty of this research is by using meta-analysis to evaluate the impact of selenium as a feed additive on the blood profile of dairy goats.

AUTHOR’S CONTRIBUTION

Dwi Putri Nurmala contributed to data collection, data analysis and manuscript preparation. Tri Eko Susilorini, Osfar Sjofjan and Danung Nur Adli contributed to the research design, supervision and revision of the manuscript. All authors read and approved the final version of the manuscript in the journal at this time.

Conflict of interest

The authors have declared no conflict of interest.

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