The effects of lactic acid bacteria and yeast as probiotics on the performance, blood parameters, nutrient digestibility, and carcase quality of rabbits: a meta-analysis

Abstract

A meta-analysis was conducted to determine the effects of probiotics on the performance, blood parameters, nutrient digestibility and carcase quality of domesticated rabbits. A dataset was constructed based on relevant published papers. An algorithm was constructed from 2004 to 2022, with a search in Scopus, Web of Science, Pub Med, and Medline using the MESH terms ‘probiotics’, ‘rabbit’, ‘performance’, ‘blood parameters’, ‘nutrient digestibility’, and ‘carcasses’. After carefully evaluation, the final dataset consisted 35 in-vivo studies comprising 964 treatment units. The data analysis and coding were performed using software R version 4.2.1 ‘Funny-looking kid’ computing with library mode (cowplot); (tidyverse); and (viridis); and (nlme). The results showed the level of probiotics increased body-weight gain with a linear pattern (p < 0.001). With regard to blood parameters, probiotics decreased triglycerides (p < 0.001) and concomitantly decreased albumin (p < 0.01) in domesticated rabbits. In conclusion, probiotics positively affect parameters for production performance and blood metabolites in domesticated rabbits.

Highlights

• Probiotics helps to increased body-weight gain, suppressed triglycerides and albumin

• Elevated to carcase yield after exposure to probiotics

• Probiotic showed protective effects and enhanced immune systems in domesticated rabbit

Keywords:

• Microorganism
• mix model
• probiotic
• rabbit
• systematic review

Introduction

In the early 1990s, researchers were arguing about the dangers of bacterial resistance and debating the use of antibiotic growth promoters (AGPs) as feed supplements (Maron et al. 2013). It had been discovered that adding antibiotics to livestock feed accelerated animal growth and reduced costs more than conventional feed supplements (Sjofjan et al. 2021a). However, researchers found that repeated exposure of bacteria to antibiotics had negative effects on human health. As a result, the European Union (EU) began to ban the use of AGPs for livestock in 2006 in Germany and Denmark and promoted alternatives to antibiotic use such as probiotics (Sjofjan et al. 2021b).

Probiotic use was introduced by the German scientist Werner Kollath, who described active substances as essential for healthy development in cells (Gasbarrini et al. 2016). Moreover, an expert consensus document was published that described the term ‘probiotic’ as applying to organisms which, given in adequate amounts, confer health benefits on cells. Initially, researchers focussed on alternative probiotics for pseudo ruminant animals, and since then research into the use of probiotics in rabbit farming has been conducted worldwide and published in various scientific journals. The number of publications in Scopus on this topic increased from fewer than 50 in 1995 to more than 250 in 2015 (Park et al. 2016).

Results relating to the use of probiotics in domesticated rabbits are inconsistent. Report from Rotolo et al. (2014) stated that the yeast used as a probiotic did not affect the performance or meat quality of rabbits reared in controlled environments in any treatment. Contrast findings from Wang et al. (2017) reported the use probiotic improve the performance, enhance the immune and defense system of rabbit production and breeding.

By the middle of the twentieth century, the sheer volume of quantitative research required solutions that could address varied results of this type and a researcher coined the term ‘meta-analysis’ (Adli 2021). Meta-analysis is the statistical analysis of a large collection of analysis results from previous studies for the purpose of integrating findings (Sauvant et al. 2008). Thus, this study applies meta-analysis strategies that limit the effects of size and can synthesise results regarding the use of probiotics in rabbit farming to provide a quantitative summary of the pooled findings. Accordingly, the aim of this study was to determine the effects of probiotics on the performance, blood parameters, nutrient digestibility and carcases of domesticated rabbits through a meta-analysis of previously published articles.

Materials and methods

Literature Search and database development

A raw database was constructed based on peer-reviewed and published research articles that reported the use of probiotics in rabbit. Articles were selected based on the Preferred Reporting Items for systematic review and meta-analyses (PRISMA). An algorithm literature published from 2004 to 2022, a search was conducted in Scopus, Web of Science, Pub Med, and Medline using the MESH terms ‘probiotics’, ‘rabbit’, ‘performance’, ‘blood parameters’, ‘nutrient digestibility’, and ‘carcasses’. A search from Google Scholar was also undertaken to identify additional studies that may have been relevant to our objectives. The time search was conducted between 01/01/2004 to 31/08/2022.

Study selection criteria

The inclusion criteria applied for the selection of studies included the following: being written in English; available as full text; and reporting on the use of probiotics – both lactic acid bacteria and yeast – in both male and female rabbits of any breed and age. Included observation parameters were performance, blood serum, carcase quality, immunity, and nutrient digestibility. The studies included were recorded in a spreadsheet and the following criteria for an article to be included in the database were as follows: (1) articles published in peer-reviewed format between 2004 and 2022; (2) articles and studies reporting on probiotic use in other animals were not included; (3) research directly on rabbits as the experimental animal; (4) concentrated probiotics both in powder and liquid forms used in methods allowing for calculation and transformation into a logarithmic unit; (5) information on the experimental period and specific ages being provided; and (6) the experiments being conducted in controlled-trial environments.

Data extraction

Two authors screened the articles for suitability for inclusion and three authors agreed on the final papers for inclusion. Studies that met the search criteria were downloaded into Mendeley software (Elsevier) and the data were tabulated as follows: first author, publication year, study, type of rabbit used, duration of the study, probiotic used, and results. Data extraction was completed in accordance with the task analysis to obtain the exact values from graphical data, and the relevant figures from the papers were subjected to an online tool, WebplotDigitizer 4.4 (https://automeris.io/WebPlotDigitizer/), following the Park et al. (2016) method. The final database consisted of 35 in vivo articles with 964 treatment units. The details for the study selection included in this meta-analysis are provided in Figure 1. The summary of the final database is presented in Table 1.

Figure 1. Diagram flow of article selection in the meta-analysis using SYRCLE method.

Table 1. Studies included in the meta-analyses of the effects of probiotics on the performance, blood parameters, nutrient digestibility, and carcases quality of rabbit.

No.	References	Kind of probiotic	Form	Periods (d)	Strain of rabbit
1.	Rotolo et al. 2014	Lactic acid bacteria	Powder	37 to 84	New Zealand White
2.	Seyidoglu et al. 2014	Yeast	Powder	36 to 84	New Zealand White
3.	Phuoc and Jamikorn 2017	Lactic acid bacteria	Powder	28 to 70	New Zealand White
4.	Fathi et al. 2017	Lactic acid bacteria	Powder	56 to 112	Spanish
5.	Attia et al. 2013	Lactic acid bacteria	Powder	16 to 61	New Zealand White
6.	Simonova et al. 2015	Lactic acid bacteria	Powder	21 to 36	Hyplus
7.	Belhaseen et al. 2016	Yeast	Powder	35 to 77	Pannon White
8.	Bhatt et al. 2016	Lactic acid bacteria	Powder	42 to 91	Chincilla
9.	El-Shafei et al. 2019	Lactic acid bacteria	Powder	28 to 84	New Zealand White
10.	El-Sagheer et al. 2014	Lactic acid bacteria	Powder	30 to 72	New Zealand White
11.	Ezema et al. 2012	Yeast	Powder	Not reported	New Zealand White
12.	Wang et al. 2017	Lactic acid bacteria	Powder	35 to 85	Rex
13.	Wallace et al. 2012	Lactic acid bacteria	Powder	30 to 36	New Zealand White
14.	Simonova et al. 2013	Lactic acid bacteria	Powder	35 to 77	Hyplus
15.	Trocino et al. 2005	Lactic acid bacteria	Liquid	37 to 79	Hyla
16.	Liu et al. 2019	Lactic acid bacteria	Powder	60 to 80	Sichuan
17.	El-deep et al. 2021	Yeast	Powder	35 to 85	Not Reported
18.	Nicodemus et al. 2004	Lactic acid bacteria	Liquid	7 to 30	New Zealand White
19.	Simonova et al. 2020	Lactic acid bacteria	Powder	35 to 77	Hycole
20.	Chrastinova et al. 2010	Lactic acid bacteria	Powder	35 to 77	New Zealand White
21.	Kritas et al. 2008	Lactic acid bacteria	Powder	38 to 93	New Zealand White
22.	Kurchaeva et al. 2019	Lactic acid bacteria	Powder	1 to 120	New Zealand White
23.	Dimova et al. 2018	Lactic acid bacteria	Powder	36 to 84	New Zealand White
24.	Simonova et al. 2020	Lactic acid bacteria	Liquid	42 to 77	Hyplus
25.	Kalma et al. 2018	Yeast	Powder	35 to 77	Soviet Chincilla
26.	Helal et al. 2021	Yeast	Powder	Not reported	New Zealand White
27.	El-Dimerdash et al. 2011	Lactic acid bacteria	Powder	10 to 40	New Zealand White
28.	Oso et al. 2013	Lactic acid bacteria	Powder	35 to 70	New Zealand White
29.	Seyidoğlu et al. 2014	Yeast	Powder	30 to 90	New Zealand White
30.	Shah et al. 2020	Lactic acid bacteria	Powder	42 to 92	Not reported
31.	Shehu et al. 2014	Yeast	Powder	36 to 84	New Zealand White
32.	Emmanuel et al. 2019	Yeast	Powder	16 to 81	New Zealand White
33.	Ahmed et al. 2019	Yeast	Powder	36 to 84	New Zealand White
34.	Abd El-Aziz et al. 2021	Yeast	Powder	16 to 81	Rex X V-line
35.	Chowdhury et al. 2022	Lactic acid bacteria and yeast	Powder	42 to 98	New Zealand White

Data analysis

Prior to statistical meta-analysis, data analysis and coding were performed using R software version 4.2.1 (2022-06-23 UCRT) – ‘Funny-Looking Kid’ x86_64-W64-ming32/X64 computing with library mode (cow plot); (tidy verse); and (veridic); and (nlme) (Pinheiro et al. 2020; R Core Team 2020). The modelling used as follows: (1) $$Y_{\mathit{ijk}}=\mu +S_i+{\tau }_j+{S\tau }_{ij}+{\beta }_1{X}_{ij}+b_i{X}_{ij}+{\beta }_2{X^2}_{ij}+b_i{X^2}_{ij}+e_{\mathit{ijk}}$$(1)

Where: $Y_{\mathit{ijk}}$ = dependent variable, $\mu$ = overall mean value, $S_i$ = random effect of the ith study, assumed to be $\sim N_{\mathit{iid}} (0, {\sigma }_S^2),$ ${\tau }_j$ = fixed effect of the jth of $\tau$ factor, ${S\tau }_{ij}$ = random interaction between the ith and jth level of $\tau$ factor, also assumed to be $\sim N_{\mathit{iid}} (0, {\sigma }_{S\tau }^2),$ ${\beta }_1$ = overall value of the linear regression coefficient of Y to X (a fixed effect), ${\beta }_2$ = overall coefficient value of the quadratic regression of Y to X (a fixed effect), $X_{ij}$ and ${X^2}_{ij}$= continuous values of the predictor variable (in linear and quadratic form, respectively), $b_i$ = random effect of the study on the regression coefficient of Y to X, assumed to be $\sim N_{\mathit{iid}} (0, {\sigma }_b^2),$ and $e_{\mathit{ijk}}$ = residual value from unpredictable error. ${S\tau }_{ij}$ and $S_i$ are taken to be independent variables that are chosen at random. A validation and significance test was conducted on the model. The significance of the values was determined using a one-way analysis of variance. It is significant if the p-value (p or p-val) < 0.05 and tends to be significant if the p-value is between 0.05 and 0.1. As a result, P_l represents the p-value for the linear constant (${\mathit{\beta }}_1$) and P_q represents the p-value of the quadratic constant (${\mathit{\beta }}_2$). Therefore, the validation test was conducted using the root mean square error (RMSE) and Nakagawa determination coefficient (R2) or ${R_{\mathit{GLMM}}(c)}^2$ (2) $$\mathit{RMSE}=\sqrt{\frac{\sum {(O-P)}^2}{\mathit{NDP}}}$$(2) (3) $${R_{\mathit{GLMM}}\left(c\right)}^2= \frac{({{\sigma }^2}_f+\sum \left({{\sigma }^2}_l\right))}{({{\sigma }^2}_f+\sum \left({{\sigma }^2}_l\right)+ {{\sigma }^2}_e+ {{\sigma }^2}_d)}$$(3)

Note: $O$ = actual value, $P$ = estimated value, $\mathit{NDP}$ = number of data point, ${{\sigma }^2}_f$ is the variant of a fixed factor, $\sum \left({{\sigma }^2}_l\right)$ is the sum of all variants of the component, ${{\sigma }^2}_e$ is the variant due to the predictor dispersion and ${{\sigma }^2}_d$ is the specific distribution of the variant.

In the end, we constructed a linear-regression to explore the source of Hedges’g in each experimental effect. A linear-regression was performed using the restricted maximum likelihood (REML; Metareg, R Studio) as follows: (4) $${\theta }_k=\theta +{\beta }_1{x}_i+{ϵ}_k+{\zeta }_k$$(4)

Where ${\theta }_k$ is observed effect size, θ = identical to the true overall effect size, ${ϵ}_k$ = the sampling error through which the effect size of a study deviates from its true effect, ${\zeta }_k$ = denotes that even the true effect size of the study is only sampled from an overarching distribution of effect sizes. In this case, we added the illustration using add-on ‘magick’; ‘ggplot’; ‘heatmap annotation’; ‘ggplot2’; and ‘cowplot’ based on the meta-analysis result of this research.

In addition, the differences between lactic acid bacteria and yeast on each article were determined using principal component analysis (PCA) using the following model: (5) $$Y_{ij}=\mu +S_i+{\tau }_j+{S\tau }_{ij}+e_{ij}$$(5)

Where $Y_{ij}$ = the expected output for dependent variable Y, $\mu$ = overall mean, $S_i$ = random effect of I study, ${\tau }_j$ = fixed effect of the j level, ${S\tau }_{ij}$ = random interaction between i study and the j level, and $e_{ij}$ =residual error. A significant effect was declared at p < 0.05 or there is a tendency when the p-value was between 0.05 and 0.10 (Figure (4, 5)).

Results

Our meta-analysis findings indicate that the addition of probiotics increased body weight gain (BWG) (p < 0.001) (Table 2). Feed conversion ratio and feed intake were not affected by addition of probiotics. With regard to blood parameters, probiotics decreased triglycerides (p < 0.001) and concomitantly decreased albumin (p < 0.01) in domesticated rabbits (Table 2) (Figure 3). Low Density Lipoprotein (LDL) = 19.85+ −6.64X + 0.72 × 2; n = 7; p-value = 0.011; Cholesterol (mg/dL) = 62.85 + −0.90X; n = 35; p-value = 0.03; Triglyceride (mg/dL) = 79.96 + −0.82X; n = 27; p-value = 0.057 (Figure 3). With regard to carcase quality parameters, probiotics did not affect lightness, redness and yellowness but carcase percentage significantly increased (p < 0.1), in line with the weight of heart increase (p < 0.05). Meanwhile, nutrient digestibility was not affected by probiotics (Figure 2). $\mathit{Organic} \mathit{matter} (OM;\mathit{\text{ \% of }}\mathit{dry} \mathit{matter} (DM))=53.76-0.14X^2+1.46X;$ n = 19; p-value = 0.030. Crude protein (CP; % of DM) = $55.88-0.15X^2+1.55X;$ n = 27; p-value = 0.026, Neutral detergent fibre (NDF % of DM) = $30.94-0.46X^2+4.45X;$ n = 13; p-value = 0.013 (Figure 2). The PCA biplot is presented in dimension one (Dim1; 11.4%) and dimension two (Dim2; 8.5%) in Figure 4. The PCA biplot presented Dim1 (38.4%) and Dim2 (30.2%) Figure 5. The PCA biplot presented Dim1 (8.5%) and Dim1 (11.4%) in Figure 6. The PCA biplot presented Dim1 (38.4%) and Dim1 (30.2%) in Figure 7.

Figure 2. Linear Regression of correlation between probiotic level and nutrient digestibility of rabbit. $OM\mathrm{ }(\mathrm{\%}\mathrm{ }of\mathrm{ }DM)=53.8-0.14X^2+1.46X;$ n = 19; p-value = 0.03 $CP (\mathit{\text{\% of DM}}) = 55.9 - 0.15X^2 + 1.55X;$ n = 27; p-value = 0.026

$\mathit{NDF} (\mathit{\text{\% of DM}}) = 30.9 - 0.46X^2 + 4.45X;$ n = 13; p-value = 0.013. OM: organic matter; CP: crude protein; NDF: neutral detergent fibre; DM: dry matter.

Figure 3. Linear Regression of Correlation between probiotic level (log 10 CFU/kg of feed) and blood profile (mg/dL) of rabbit.

$\mathit{LDL}=19.9+0.72X^2-6.64X;$ n = 7; p-value = 0.011 $\mathit{Cholesterol} (mg/dL)=62.85-0.9X;$ n = 35; p-value = 0.03 $\mathit{Triglyceride} (mg/dL)=79.96-0.82X;$ n = 27; p-value = 0.057. OM: organic matter; CP: crude protein; NDF: neutral detergent fibre; DM: dry matter.

Figure 4. Principal Component Analysis (PCA) Biplot of probiotic in liquid and powder form based on the performance, blood parameters, nutrient digestibility, and carcases quality of rabbit.

Figure 5. Individual of principal component analysis (PCA) Biplot of group form liquid and powder of probiotics level (log 10 CFU/kg of feed).

Figure 6. Principal component analysis (PCA) Biplot of groups of Lactic acid Bacteria (BAL), control, yeast, both lactic acid bacteria (BAL) and yeast on the performance, blood parameters, nutrient digestibility, and carcases quality of rabbit.

Figure 7. Principal Component Analysis (PCA) Biplot of probiotic in liquid and powder form based on the performance of rabbit.

Table 2. Regression linear model of effect of probiotic of probiotics on the performance, blood parameters, nutrient digestibility, and carcases quality of rabbit.

No.	Response	Unit	Model	N	Intercept	SE Intercept	Slope	SE Slope	P-Value	RMSE	AIC	Trend	P X L	LAB X Y
1.	IBW	G	L	100	977	133	−0.92	1.37	0.5	2.85	1284	–	0.21	0.34
2.	FBW	G	L	98	2277	134	9.51	2.71	0.0008	1.65	1349	+	0.33	0.21
3.	BWG	G	L	77	743	128	3.97	2.22	0.01	1.68	1012	+	0.25	0.35
4.	FCR	g feed / g weight	L	85	3.79	0.3	0.01	0.01	0.43	2.44	234	+	0.34	0.45
5.	ADG	G	L	71	36	5.81	0.27	0.21	0.2	2.03	573	+	0.33	0.32
6.	Mortality	%	L	46	6.13	2.22	0.02	0.1	0.81	1.8	284	+	1.23	0.54
7.	ADFI	G	L	77	121	22.4	0.5	0.4	0.2	2.55	750	+	0.67	0.21
8.	RBC	cell/μL	L	22	53.1	10.7	0.46	0.31	0.1	1.41	175	+	0.56	0.21
9.	WBC	cell/μL	L	26	19	9.6	0.15	0.11	0.18	1.16	173	+	0.64	0.12
10.	Lymphocyte	%	L	17	62	6.47	0.23	0.73	0.75	1.24	141	+	0.32	0.33
11.	Hemoglobin	mg/dL	L	23	12	1.15	0.023	0.03	0.47	1.08	87.6	+	0.21	0.21
12.	Total protein	mg/dL	L	44	33.1	7.78	0.1	0.04	0.05	1.40	253	+	0.33	0.11
13.	Glucose	mg/dL	L	26.	61.9	21.9	0.03	0.43	0.93	1.55	221	+	0.32	0.32
14.	Cholesterol	mg/dL	L	35	62.9	12.7	−0.9	0.4	0.03	1.1	302	–	0.21	0.23
15.	Triglyceride	mg/dL	L	27	80	19.4	−0.82	0.4	0.05	1.3	231	–	0.44	0.11
16.	Albumin	mg/dL	L	34	6.11	3.72	0.39	0.22	0.01	2	243	+	0.47	0.32
17.	HDL	mmol/L	L	7	32.4	5.75	−0.47	0.63	0.49	0.82	48.7	–	0.32	0.33
18.	LDL	mmol/L	L	7	18.7	3.07	−0.22	0.41	0.61	0.87	43.8	–	0.21	0.54
19.	IgG	no unit	L	10	20.4	17.4	0.24	0.14	0.14	0.93	59.1	+	0.35	0.32
21.	IgY	no unit	L	7	8.02	8.22	0.03	0.02	0.26	0.8	23.2	+	0.76	0.21
23.	Carcase	%	L	23	55.4	2.55	0.1	0.08	0.03	1.09	125	+	0.23	0.33
24.	SW	G	L	32	1939	252	6.55	6.03	0.28	1.57	441	+	0.12	0.33
25.	Kidney	g/Kg	L	23	0.5	0.11	0.0002	0.0008	0.76	1.3	−51.3	+	0.34	0.56
26.	Liver	g/Kg	L	23	10	6.06	0.032	0.02	0.22	1.08	97.3	+	0.54	0.57
27.	Heart	g/Kg	L	23	2.6	1.06	0.05	0.019	0.01	1.36	69.2	+	0.34	0.21
28.	Lightness	No unit	L	12	52.3	1.87	0.12	0.15	0.45	0.88	63	+	0.23	0.32
29.	Redness	No unit	L	12	2.84	1.47	−0.02	0.03	0.6	0.87	42.1	–	0.23	0.72
30.	Yellowness	No unit	L	12	5.6	1.11	0.02	0.04	0.63	0.91	43.6	+	0.53	0.33
31.	DM	% of fresh	L	30	60.2	5.73	−0.03	0.11	0.73	1.43	186	–	0.53	0.34
32.	OM	% of DM	L	19	54.5	7.45	0.11	0.08	0.18	1.25	107	+	0.44	0.34
33.	CP	% of DM	L	27	56.6	8.35	0.033	0.12	0.78	1.1	173	+	0.33	0.21
34.	EE	% of DM	L	27	56.1	12.55	0.08	0.06	0.20	1.16	157	+	0.23	0.32
35.	NDF	% of DM	L	13	31.9	4.95	0.2	0.28	0.48	1.02	86.5	+	0.23	0.45
36.	ADF	% of DM	L	9	19	5.12	−0.2	0.2	0.34	0.73	54.9	–	0.11	0.32

IBW: initial body weight; FBW: final body weight; BWG: body weight gain; FCR: feed conversion ratio; ADG: average daily gain; ADFI: average daily feed intake; RBC: red blood cell; WBC: white blood cell; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; DM: Dry matter; OM: organic matter; CP: crude protein; EE: ether extract; NDF: neutral detergent fibre; ADF: Acid detergent fibre; M: model; N: number of data; SE – standard error; RMSE – root mean square errors; AIC – akaike information criterion; L – linear; P: powder; L: liquid; LAB: lactic acid bacteria; Y: yeast; +: positive; -: negative

Discussion

Recently, a number of published reports have provided strong evidence explaining the role of probiotics in non-ruminant animals including rabbits. For example, a study from Phuoc and Jamikorn (2017) confirms the application of probiotics to help increase (BWG) of rabbits. In 42 days to 70 days of New Zealand rabbits there was a significant increase in BWG (p < 0.05) of approximately 5% using lactic acid bacteria as a probiotic supplement compared to a control. More detailed results indicate that a combination of yeast and lactic acid bacteria leads to greater growth and concomitantly decreased feed conversion ratio (FCR) than in rabbits receiving no probiotic supplement. Studies with other rabbit strains such as Spanish, Hyplus, Pannon White, Rex and Chincilla were also demonstrated to generate similar improvements in rabbit productive traits (Simonová et al. 2015; Belhassen et al. 2016; Bhatt et al. 2017; Fathi et al. 2017; Wang et al. 2017). The positive result for productive traits might correlate with the role of many biological pathways. Using microbiological tools, it is demonstrated that bacteria are the main constituents of probiotics, working closely with microorganisms in the rabbit caecum (Rotolo et al. 2014). The bacteria enhance the morphology of intestinal barrier-cell systems in rabbits and penetrate into the bloodstream to attract epithelial cells (Rotolo et al. 2014).

First, the probiotics’ role is not as a main nutrient in terms of a specific dose response until particular requirements are met. Because of the complexity of the intestines, individual variations of rabbits to probiotics might be the rule and not the exception. Second, microorganisms in probiotics can produce a wide variety of substances that are inhibitory to both gram-positive and gram-negative bacteria. These substances help to reduce not only viable cells but also affect cycles or even toxin production (Sjofjan et al. 2021b) For example research from Rotolo et al. (2014) using Saccharomyces cerevisiae var. boulardii protects rabbits against Clostridium strain intestinal disease through degradation of the toxin receptors of specific intestinal cells. Specific intestinal cells have functions as follows: a) nutrient transportation; b) absorption and secretion; c) maintenance of barrier and defense systems against bacterial infections; and d) adhesion by pathogenic and non-pathogenic bacteria (Rotolo et al. 2014).

Our meta-analysis results indicate that the use of probiotics helps to reduce cholesterol traits but that the effect is statistically insignificant. Reported from Fathi et al. (2017) thus probiotics can reduce cholesterol when given at the level of 400 g/ton of feed compared to untreated rabbits. Moreover, the decrease of cholesterol level is correlated with hydrolase activity. The bile salts inhibit enzymes involved in the biological synthesis of cholesterol. The lowering level of blood cholesterol is caused by microorganisms such as Lactobacillus and Bifidobacterium, which synergic for the development of short-chain fatty acids Helal et al. (2021). In addition, the elevation trends of total protein and albumin are good indicators of immunoglobulins and enhanced immune systems in domesticated rabbits. For instance, probiotics are involved in mechanisms lowering lipids in blood transportation followed by a reduction in triglyceride levels (Sjofjan et al. 2021a). Moreover, lowering levels of triglyceride are correlated with the decrease of remnant lipoprotein and the redistribution of lipids from plasma to target organs, in this case the liver (Fathi et al. 2017). Three factors were found to improve carcase quality: first, adding raw materials such as maize, meat-bone meal or fish meal; second, favouring the production of functional components during processing and enzymatic hydrolysis; and last, incorporating functional probiotics and lactic acid bacteria (Bhatt et al. 2017). Moreover, reported by Oso et al. (2013) the nutrient digestibility in rabbits was not affected by the composition of the probiotics but by the raw material given since the probiotics only stimulate gut function. An irrespective of dietary energy level may be correlated with the conclusive effect on nutrient digestibility traits of rabbits. Rabbits have a unique physiology of their digestive tract, and as representative of monogastric species and herbivorous animals, rabbits usually present a fragile balance in their intestinal function. Optimising feeding management is the key to meat production. Since rabbits are herbivorous animals, sometimes they are also called pseudo-ruminants. Rabbit also has a unique behaviour that is called caecotrophy. Approximately, at 3–8 h after feeding time rabbit produces a soft coated sticky faecal pellet that is eaten whole without processing (De Blas and Wiseman 2020). That was the result of the separation between digesta and particle size that was eaten on the rabbit. The feed remains in the caecum, where the feed is broken down into an absorbable nutrient by microorganisms. Without this caecotrophy, many of the nutrients from the feed would be lost and passed through the colon. It requires a high dietary fiber content compared with other non-ruminant animals such as horses, porcine, or poultry (De Blas and Wiseman 2020).

Conclusion

Because of the unique physiology of the domesticated rabbit digestive tract, and as representative of monogastric species and herbivorous animals, rabbits usually present a fragile balance in domesticated rabbit intestinal function. This meta-analysis demonstrates that probiotics work well to increase the production performance and blood traits of domesticated rabbits.

Ethical approval

Ethical Approval for the study was not needed, since there is no laboratory or animal subject in this research. This research is based on the literature review.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The authors are grateful to Universitas Brawijaya