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Background: Probiotics have been used in livestock production for many years, but information on their benefits during the early life of calves is inconsistent. This study aimed to assess the effects of probiotics on the performance of pre-weaning dairy calves and identify the factors influencing their effect sizes. Results: Forty-nine studies were selected for meta-analysis based on the inclusion and exclusion criteria. The study qualities were evaluated using a predefined risk assessment tool following GRADE guidelines. Meta-analysis results showed that probiotics increased the growth performance (body weight by 1.988 kg and average daily gain by 40.689 g/d), decreased digestibility and feed efficiency (feed conversion rate by 0.073), altered rumen parameter (decreased acetate by 2.815 mmol/L and increased butyrate by 0.788 mmol/L), altered blood parameter (decreased AST by 4.188 U/L, increased BHBA by 0.029 mmol/L and IgG by 0.698 g/L), increased faecal parameter (faecal bacte- ria counts by 0.680 log CFU/g), based on the strict criteria (P < 0.05, I < 50%). Additionally, probiotics increased 10 SMD digestibility and feed efficiency (starter dry matter intake by 0.034 kg/d and total dry matter intake by 0.020 kg/d), altered blood parameter (increased IgA by 0.313 g/L, IgM by 0.262 g/L, and total antioxidant capacity by 0.441 U/mL, decreased MDA by 0.404 nmol/mL), decreased faecal parameter (faecal score by 0.052), based on the loose criteria (P < 0.05, I > 50%). SMD Regression and sub-group analyses showed that probiotic strains, supplementation dosage, and methods signifi- cantly affected the performance of calves. The probiotics supplied with more than 9.5 log CFU/d significantly increased IgA and IgM contents (P < 0.05). Additionally, the compound probiotics significantly increased TDMI, SMD IgA, and IgM (P ≤ 0.001). Furthermore, probiotics supplemented in liquid (whole milk or milk replacer) significantly SMD increased TDMI and decreased faecal score (P < 0.05), while in whole milk, they significantly increased body weight, SMD IgA, and IgM (P < 0.001). SMD Conclusions: Probiotics could improve the growth performance, feed intake and efficiency, rumen fermentation, immune and antioxidant capacity, and health of pre-weaning calves. However, the effect sizes were related to the dosage, composition, and supplementation methods of probiotics. Keywords: Calves, Growth, Health, Meta-analysis, Probiotics Introduction Most modern intensive rearing systems for dairy cows require that the calves are immediately separated from dams after birth and then artificially fed on whole milk Liyun Wang and Honghong Sun contributed equally to this work. or milk replacer. As a result, the newborn calves can- *Correspondence: firstname.lastname@example.org not rapidly acquire microflora from the saliva and feces of their mothers and other cows. This slows the forma - College of Animal Science and Technology, Northwest A&F University, No.22 Xinong Road, Yangling, Shaanxi 712100, P. R. China tion of microbial communities and can even cause an © The Author(s) 2023. 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The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 2 of 15 imbalanced microbial flora in the digestive tracts of the understanding of the effects of probiotics on pre-wean - calves . Further, it may negatively affect the growth ing dairy calves. The existence of heterogeneity and its rate and health status of calves and even production per- sources were also assessed. Therefore, this study will pro - formance if proper feeding and management strategies vide insights into establishing proper feeding and man- are not adopted during this critical life stage . Feed agement strategies for efficient application of probiotics additives have been extensively studied and commer- in calf rearing. cially explored in livestock production. Probiotics are one of the most popular feed additives due to their ben- Methods eficial effect on domestic animals . Probiotics are live Literature search strategy and selection criteria microorganisms that modulate the balance and activities This study was based on the Preferred Reporting Items of the gastrointestinal microbiota and thus can enhance for Systematic Reviews and Meta-analysis Statement the host’s health and growth if administered in adequate . Three researchers independently searched Pub - dosages . Med, ScienceDirect, Web of Science, and Google Scholar Numerous studies have examined the effect of probiot - (before Jan 10, 2022) using the MeSH terms “probiotics, ics on the production performance and health of dairy or any name of the species or strain of probiotics, in com- calves . However, the results are inconsistent and even bination with “calf ” or “calves” to identify eligible studies. contradictory. Some studies have shown that probiotics can promote total dry matter intake (TDMI), body weight Inclusion and exclusion criteria (BW), and growth rate of calves [5, 6]; others have con- Search results from the four databases were pooled in cluded that probiotics do not affect the growth perfor - EndNote (Version X9) and then duplicate publications mance or feed efficiency [7, 8]. Additionally, some studies were removed. Literature was rigorously screened based have found that probiotics can increase blood IgA, IgG, on the inclusion and exclusion criteria. Differences were and IgM concentrations and correspondingly improve resolved through discussions. The references from the the immunity of calves ; others observed no effect of search were included or excluded based on the following probiotics on the concentration of these three immune criteria: Inclusion criteria: (1) manuscripts published in globulins . Furthermore, some studies have identified English in peer-reviewed journals, (2) studies involving that probiotics can reduce faecal scores of calves [11, 12]; the use of probiotics in the diet of dairy calves; (3) stud- other reported no differences on faecal scores by adding ies including probiotics treatment and negative control probiotics . Meta-analysis has been applied in animal groups; (4) studies with continuous experiment rather science for systemic evaluation of the effect of probiotics than Latin square or change-over designs; (5) studies on calves. A meta-analysis reported that probiotics could providing adequate probiotics data, the number of cat- increase the average daily gains (ADG) by 83.14 g/d and tle, mean, standard deviation or standard error of at least decrease the feed conversion ratio (FCR) by 0.13 com- one of the traits corresponding to the probiotics group pared with calves fed on a control diet, but it did not suf- and control group. The exclusion criteria included: (1) ficiently show the effect of probiotics on calf health . studies without probiotics data or correlated traits data; Additionally, a meta-analysis included dairy calves (Hol- (2) studies with the data from non-dairy cattle; (3) stud- stein and Jersey), beef calves (Charolais and Red Angus), ies with post-weaning calves; (4) studies with probiotics other cross-bred cattle, local cattle breeds (Qinchuan cat- combined with prebiotics or antibiotics. tle in China and Hanwoo in Korea), Bubalus bubalis, and Murrah buffalo, but dairy calves had a very different rear - Data extraction ing and management system from others, especially in The following variables: first author, year, country, the pre-weaning stage. Therefore, it could not accurately calf breed, age, sample size, probiotic composition, reflect the probiotics function on the pre-weaning dairy experiment duration, diet composition, supplementa- calves . In another meta-analysis including 15 trials, tion methods, mean, standard deviation or standard supplementation of probiotics reduced the relative risk error of all traits corresponding to probiotics and con- of diarrhea and feeding in the whole milk improved the trol groups, were extracted from each study. The main protective effect . However, it did not include other traits were in five categories: growth performance (BW, production performance and needs to update due that it ADG, withers height, heart girth, hip width, hip height, has been done 10 years ago. and body length); feed digestibility and efficiency In this study, we hypothesized that probiotics could (organic matter digestibility, dry matter digestibility, improve the growth rate, digestibility, immune, and ether extract digestibility, crude protein digestibil- health of dairy calves across the published studies. This ity, NDF digestibility, ADF digestibility, TDMI, SDMI, study aimed to critically review the studies and enhance and FCR (TDMI/ADG)); rumen parameter (rumen W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 3 of 15 pH, microbial protein, NH , total volatile fatty acids Heterogeneity assessment (VFA), acetate, propionate, butyrate, and valerate); Heterogeneity between-study variability was assessed using 2 2 2 haematology parameter (biochemical indexes, such as the I (I < 25% = no heterogeneity, 25% ≤ I < 50% = mod- alkaline phosphatase (ALP), albumin (ALB), alanine erate heterogeneity, 50% ≤ I < 75% = high heterogeneity, aminotransferase (ALT), aspartate aminotransferase 75% ≤ I < 100% = extreme heterogeneity) . Meta-regres- (AST), beta-hydroxybutyric acid (BHBA), blood urea sion or sub-group analysis was necessary to further deter- nitrogen (BUN), glucose, total protein, total choles- mine the sources of heterogeneity when the studies had a terol, triglyceride, and lactate dehydrogenase (LDH)); substantial heterogeneity (I > 50%) . immune indices (immunoglobulin A (IgA), immuno- globulin G (IgG), immunoglobulin M (IgM), insulin- Meta‑regression analysis like growth factor 1 (IGF1), and interferon-γ (IFNγ)); Meta-regression analyses were conducted using effect antioxidant indices (malondialdehyde (MDA), glu- sizes (RMD) for each outcome (P < 0.05, I > 25%, SMD tathione peroxidase (GSH-Px), superoxide dismutase n ≥ 10) as the dependent variable to examine heterogene- (SOD), and total antioxidant capacity (T-AOC)); faecal ity sources of meta-analysis. The covariates included the parameter (faecal score, count of faecal bacteria, coli- calf age (d), beginning BW (kg), probiotic composition form, Lactobacilli, and Streptococcus). (single strain/multiple strains), dosage (log CFU/d), The possible sources of variability, calf ages, BW at supplementation methods (milk, replacer, starter), and the beginning and end of the experiment, additive dos- experiment duration (d). Tests of the null hypothesis age, supplementation methods, and experiment duration for the covariate coefficients were obtained from the were also extracted from each study. modified Knapp-Hartung method. The adjusted R rep- resented the proportion of between-study variation explained by the covariates . The RMDs were meas - Study quality assessment ured via sub-group analysis if the P-value of covariates in Two researchers independently assessed the study qual- meta-regression was less than 0.05. The sub-groups were ity following the criteria in the Cochrane Collaboration’s divided based on the original categories and practical tool and the statement of Consolidated Standards of implications where necessary. Reporting Trials [16, 17]. The assessment items included random sequence generation (selection bias), allocation Publication bias concealment (selection bias), blinding of participants Begg’s and Egger’s tests were used to assess publication and personnel (performance bias), blinding of outcome bias. A P-value less than 0.05 was defined as significant assessment (detection bias), incomplete outcome data [24, 25]. Egger’s test was first adopted if the significant (attrition bias), selective reporting (reporting bias), and tests disagreed using both methods . other bias. The disagreements on assessment were settled by discussions with a third researcher. Results The process and results of the literature search and selec - tion are shown in Fig. S1. Initially, 7033 articles were Statistical analysis identified for screening from PubMed, ScienceDirect, Meta‑analysis Web of Science, Google Scholar, and other sources. A Meta-analysis was performed using the STATA/MP total of 657 articles remained after excluding duplication, 14.0 software (version 11.0, College Station, TX). The articles with non-dairy cattle, post-weaning calves, feed- random-effects model was used to estimate the effect ing probiotics mixed with other additives, published in size, 95% confidence interval (CI), and statistical signifi - non-English, or with no corresponding production traits. cance for each trait since it is more conservative than An additional 506 articles were excluded after full-text the fixed-effects model [18, 19]. The effect size of probi - review based on previous protocols. Finally, 49 articles otics was expressed as standard mean difference (SMD) were included in this meta-analysis. and raw mean difference (RMD). The SMD showed the The main characteristics of the 49 studies are shown effect size in standard deviation unit and was more gen - in Table S1. The bias risks for each study and overall are eralizable, while RMD expressed the effect size in the shown in Fig. S2 and S3. Selection bias (random sequence same unit as the original measurement and was more generation and allocation concealment), attrition bias, interpretable . SMD values of < 0.2, 0.2 < SMD < 0.7, and reporting bias were at low risk in over 75% of the and > 0.7 indicated small, moderate, and high effects, studies. Performance and detection biases were largely respectively [20, 21]. A P-value of SMD less than 0.05 unclear in over 90% of the studies due to insufficient was considered statistically significant. Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 4 of 15 information on the blinding of participants and report- Egger’s and Begg’s tests indicated some evidence of publi- ing. Descriptive statistics for the five categories of vari - cation bias in SDMI, TDMI, and FCR (P < 0.05) (Table 2). ables are shown in Table S2. Rumen parameter The summary of the meta-analysis on the effects of Growth performance traits probiotics on the rumen fermentation parameters of The summary of the meta-analysis on the effects of pro - pre-weaning calves is shown in Table 3. Probiotics did biotics on the growth performance of pre-weaning calves not significantly affect rumen pH, microbial protein, is shown in Table 1. Probiotics did not significantly affect NH , total VFA, propionate, and valerate (P > 0.05). withers height, heart girth, hip width, hip height, and 3 SMD However, probiotics significantly decreased acetate body length (P > 0.05). However, probiotics signifi - SMD (SMD = −0.453, P = 0.016, I = 36.2%) and increased cantly increased BW (SMD = 0.315, P < 0.001, I = 41.2%) butyrate (SMD = 0.722, P < 0.001, I = 49.0%), indicating and ADG (SMD = 0.374, P < 0.001, I = 39.0%), indicating moderate effect on acetate and high effect on butyrate. moderate effect (0.2 < SMD < 0.7). Correspondingly, the Correspondingly, the RMD analysis showed that probi- RMD analysis showed that probiotics increased BW and otics decreased acetate by 2.815 mmol/L and increased ADG by 1.988 kg and 40.689 g/d, respectively. butyrate by 0.788 mmol/L. Meta-regression analysis showed that per kilogram Meta-regression analysis showed that none of the six of beginning BW of calves increased the final BW by covariates was a significant source of heterogeneity for 0.312 kg (P < 0.001). The supplementation methods acetate. However, the supplementation dosage was the increased the final BW by 1.576 kg (P = 0.002) (Table 6). source of heterogeneity for butyrate (P = 0.039) (Table 6). Sub-group analysis indicated that probiotics supplied Sub-group analysis indicated that probiotics supplied at in whole milk significantly increased the final BW by the high dosage (> 10 log CFU/d) significantly increased 4.439 kg (P < 0.001, I = 31.0%) (Table 7). The Egger’s and butyrate by 0.463 mmol/L (P = 0.013, I = 47.8%), but Begg’s tests results indicated no evidence of publication those at the low dosage (< 9 log CFU/d) had high het- bias in the two growth traits (P > 0.05) (Table 1). erogeneity (I = 69.9%) (Table 7). The Egger’s and Begg’s tests indicated no evidence of publication bias in the two Feed digestibility and efficiency traits (P > 0.05) (Table 3). The summary of the meta-analysis on the effects of pro - biotics on the feed digestibility and efficiency of pre- Haematology parameter weaning calves is shown in Table 2. Probiotics did not The summary of the meta-analysis on the effects of probi - significantly affect the digestibility of organic matter, dry otics on the blood biochemistry, immunity, and antioxidant matter, ether extract, crude protein, neutral detergent indices of pre-weaning calves is shown in Table 4. Probiot- fiber, and acid detergent fiber (P > 0.05). However, SMD ics did not significantly affect some biochemical indexes probiotics significantly increased SDMI (SMD = 0.439, (ALP, ALB, ALT, BUN, Glucose, total protein, total choles- P < 0.001, I = 64.2%) and TDMI (SMD = 0.329, P = 0.004, terol, triglyceride), immune indices (IGF1 and IFNγ), and I = 69.5%), while they decreased FCR (SMD = −0.305, antioxidant indices (GSH-Px and SOD) (P > 0.05). How- P < 0.001, I = 32.1%), indicating moderate effect. Cor - SMD ever, probiotics significantly influenced some biochemical respondingly, the RMD analysis showed that probiotics indexes, including AST (P = 0.001, RMD = −4.188 U/L, increased SDMI and TDMI by 0.034 kg/d and 0.020 kg/d, SMD I = 44.6%), BHBA (P = 0.044, RMD = 0.029 mmol/L, respectively, while decreased FCR by 0.073. SMD I = 27.6%), and LDH (P < 0.001, RMD = −78.796 U/L, Meta-regression analysis showed that per kilogram SMD I = 52.7%), immune indices, including IgA (P < 0.001, of beginning BW of calves tended to increase TDMI SMD RMD = 0.313 g/L, I = 68.4%), IgG (P < 0.001, by 0.052 kg/d (P = 0.096). In contrast, probiotic strains SMD RMD = 0.698 g/L, I = 28.5%), IgM (P < 0.001, tended to decrease TDMI by 0.032 kg/d (P = 0.074). SMD RMD = 0.262 g/L, I = 68.7%), and antioxidant indices, Additionally, the supplementation methods significantly including MDA (P = 0.027, RMD = −0.404 nmol/ml, increased the TDMI by 0.019 kg/d (P = 0.045) (Table 6). SMD I = 80.5%) and T-AOC (P = 0.016, RMD = 0.441 U/mL, Sub-group analysis showed that although compound pro- SMD I = 65.1%). biotics significantly increased the TDMI by 0.028 kg/d Meta-regression analysis for blood traits (n > 10) showed (P = 0.001), heterogeneity was still high (I = 76.9%) that increasing probiotics by per log dosage increased IgA (Table 7). Furthermore, probiotics supplied in the starter by 0.545 g/L (P = 0.003) and IgM by 0.267 g/L (P = 0.018). did not affect TDMI (P > 0.05). However, probiotics Probiotic strains decreased IgA by 0.487 g/L (P = 0.004) supplied in whole milk and milk replacer significantly and tended to decrease IgM by 0.395 g/L (P = 0.074) increased TDMI by 0.033 kg/d (P = 0.018, I = 87.0%) (Table 6). Sub-group analysis showed that probiotics and 0.019 kg/d (P = 0.030, I = 64.3%), respectively. The W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 5 of 15 Table 1 The summary of the meta-analysis on the effects of probiotics on the growth performance of pre-weaning calves SMD Heterogeneity RMD Egger’s test Begg’s test n Random effect 95% CI P-value Chi-squared (Q) P-value I , % Control mean Random effect 95% CI P-value P-value P-value Withers height, cm 26 0.128 −0.169, 0.425 0.397 106.650 < 0.001 76.6 82.433 0.507 −0.538, 1.553 0.342 0.005 0.038 Heart girth, cm 25 0.095 −0.149, 0.339 0.447 63.110 < 0.001 62.0 92.334 0.270 −0.454, 0.994 0.464 0.005 0.072 Hip width, cm 7 0.244 −0.093, 0.581 0.156 9.860 0.131 39.2 20.950 0.377 −0.132, 0.885 0.146 0.049 0.133 Hip height, cm 14 −0.067 −0.486, 0.353 0.756 52.150 < 0.001 75.1 87.649 0.148 −0.956, 1.251 0.793 0.274 0.584 Body length, cm 11 −0.401 −0.821, 0.020 0.062 31.200 0.001 67.9 86.987 −1.149 −2.274, −0.024 0.045 0.078 0.350 BW, kg 62 0.315 0.188, 0.441 < 0.001 103.770 0.001 41.2 73.498 1.988 1.173, 2.803 < 0.001 0.090 < 0.001 ADG, g/d 79 0.374 0.275, 0.473 < 0.001 127.960 < 0.001 39.0 505.731 40.689 30.881, 50.496 < 0.001 0.052 0.002 Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 6 of 15 Table 2 The summary of the meta-analysis on the effects of probiotics on the feed digestibility and efficiency traits of pre-weaning calves SMD Heterogeneity RMD Egger’s test Begg’s test n Random effect 95% CI P-value Chi-squared (Q) P-value I , % Control mean Random effect 95% CI P-value P-value P-value Organic matter digest- 8 0.242 −0.138, 0.623 0.211 1.480 0.983 0 86.400 0.724 −0.924, 2.372 0.389 0.282 0.386 ibility, % Dry matter digest- 8 0.332 −0.049, 0.713 0.087 0.630 0.999 0 84.240 1.195 −0.368, 2.757 0.134 0.955 0.386 ibility, % Ether extract digest- 5 0.324 −0.149, 0.796 0.179 0.230 0.994 0 83.800 1.587 −1.509, 4.684 0.315 0.059 1.000 ibility, % Crude protein digest- 7 0.290 −0.113, 0.692 0.158 3.240 0.779 0 82.986 1.339 −0.613, 3.291 0.179 0.893 1.000 ibility, % NDF digestibility, % 2 3.828 −6.266, 13.921 0.457 8.550 0.003 88.3 54.900 8.808 −10.889, 28.506 0.381 1.000 ADF digestibility, % 3 0.134 −0.796, 1.065 0.777 3.940 0.140 49.2 57.200 0.711 −2.717, 4.138 0.684 0.518 1.000 SDMI, kg/d 42 0.439 0.234, 0.644 < 0.001 114.640 < 0.001 64.2 2.655 0.034 0.014, 0.053 0.001 0.030 0.050 TDMI, kg/d 47 0.329 0.105, 0.552 0.004 150.820 < 0.001 69.5 1.003 0.020 0.006, 0.033 0.005 0.025 0.137 FCR, kg/kg 35 −0.305 −0.466, −0.144 < 0.001 50.090 0.037 32.1 2.467 −0.073 −0.115, −0.031 0.001 0.012 0.005 W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 7 of 15 Table 3 The summary of the meta-analysis on the effects of probiotics on the rumen fermentation parameters of pre-weaning calves SMD Heterogeneity RMD Egger’s test Begg’s test n Random 95% CI P-value Chi-squared (Q) P-value I , % Control mean Random effect 95% CI P-value P-value P-value effect Rumen pH 20 0.412 −0.171, 0.995 0.166 137.78 < 0.001 86.9 5.958 0.154 0.128, 0.180 < 0.001 0.009 0.014 Microbial protein, mg/dL 2 2.173 −0.975, 5.321 0.176 15.29 < 0.001 93.5 163.620 13.184 10.457, 15.912 < 0.001 1.000 NH , mmol/L 17 −0.103 −0.482, 0.276 0.595 47.55 < 0.001 66.4 7.924 −0.113 −0.830, 0.604 0.757 0.228 0.138 Total VFA, mmol/L 13 0.053 −0.538, 0.643 0.861 63.37 < 0.001 81.1 71.916 −0.271 −3.238, 2.696 0.858 0.006 0.017 Acetate, mmol/L 10 −0.453 −0.822, −0.084 0.016 14.10 0.119 36.2 40.463 −2.815 −4.946, −0.684 0.010 0.477 0.788 Propionate, mmol/L 12 0.168 −0.740, 1.075 0.718 102.28 < 0.001 89.2 26.808 −0.44 −2.861, 1.981 0.722 0.094 0.216 Butyrate, mmol/L 15 0.722 0.356, 1.088 < 0.001 27.43 0.017 49.0 4.823 0.788 0.307, 1.270 0.001 0.570 0.234 Valerate, mmol/L 15 −0.174 −0.772, 0.423 0.568 97.21 < 0.001 85.6 1.739 −0.055 −0.127, 0.016 0.130 0.288 0.692 Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 8 of 15 Table 4 The summary of the meta-analysis on the effects of probiotics on the blood biochemistry, immunity, and antioxidant indices of pre-weaning calves SMD Heterogeneity RMD Egger’s test Begg’s test n Random effect 95% CI P-value Chi-squared (Q) P-value I , % Control mean Random effect 95% CI P-value P-value P-value Biochemical indexes ALP, U/L 8 0.554 −0.231, 1.339 0.166 36.490 < 0.001 80.8 134.004 4.368 −3.901, 12.637 0.301 0.037 0.266 ALB, g/L 9 0.751 −0.124, 1.627 0.093 50.100 < 0.001 84.0 29.402 0.473 −0.205, 1.151 0.172 0.001 0.251 ALT, U/L 7 0.476 −0.597, 1.549 0.385 46.600 < 0.001 87.1 13.556 0.237 −0.619, 1.093 0.587 0.235 0.548 AST, U/L 8 −0.792 −1.243, −0.342 0.001 12.650 0.081 44.6 51.416 −4.188 −6.741, −1.635 0.001 0.069 0.035 BHBA, mmol/L 7 0.419 0.011, 0.826 0.044 8.290 0.217 27.6 0.257 0.029 −0.003, 0.062 0.077 0.533 0.548 BUN, mmol/L 14 0.162 −0.998, 1.321 0.785 172.060 < 0.001 92.4 3.484 0.056 −0.245, 0.358 0.714 0.748 0.827 Glucose, mmol/L 20 −0.274 −0.828, 0.280 0.333 123.790 < 0.001 84.7 4.219 −0.055 −0.236, 0.125 0.548 0.081 0.074 Total protein, g/L 20 0.260 −0.110, 0.630 0.169 63.780 < 0.001 70.2 56.384 0.750 −0.506, 2.007 0.242 0.492 0.347 Total cholesterol, 12 −0.731 −1.834, 0.373 0.194 121.270 < 0.001 90.9 2.224 −0.060 −0.387, 0.268 0.720 0.803 0.304 mmol/L Triglyceride, mmol/L 5 −0.760 −2.270, 0.749 0.323 40.950 < 0.001 90.2 0.309 −0.018 −0.051, 0.016 0.298 0.534 1.000 LDH, U/L 6 −1.359 −1.990, −0.728 < 0.001 10.570 0.061 52.7 708.462 −78.796 −123.059, −34.533 < 0.001 0.159 0.060 Immune indices IgA, g/L 24 0.806 0.468, 1.144 < 0.001 72.700 < 0.001 68.4 2.847 0.313 0.209, 0.418 < 0.001 < 0.001 < 0.001 IgG, g/L 26 0.371 0.165, 0.578 < 0.001 34.970 0.089 28.5 17.123 0.698 0.300, 1.095 0.001 0.073 0.011 IgM, g/L 25 0.823 0.486, 1.160 < 0.001 76.600 < 0.001 68.7 2.010 0.262 0.162, 0.363 < 0.001 < 0.001 < 0.001 IGF1, ng/mL 7 0.872 −0.637, 2.381 0.258 89.830 < 0.001 93.3 59.386 4.338 −3.577, 12.253 0.283 0.001 0.035 IFN-γ, pg/mL 4 1.214 −0.262, 2.690 0.107 24.330 < 0.001 87.7 47.043 4.277 3.359, 5.195 < 0.001 0.379 0.089 Antioxidant indices MDA, nmol/mL 5 −1.238 −2.338, −0.138 0.027 20.550 < 0.001 80.5 4.796 −0.404 −0.786, −0.021 0.039 0.881 0.462 GSH-Px, U/mL 5 −0.076 −1.169, 1.016 0.891 23.690 < 0.001 83.1 145.008 −2.938 −15.692, 9.817 0.652 0.416 0.806 SOD, U/mL 9 0.199 −0.531, 0.929 0.592 50.610 < 0.001 84.2 124.680 1.825 −6.306, 9.956 0.660 0.574 0.466 T-AOC, U/mL 8 0.596 0.112, 1.079 0.016 20.030 0.005 65.1 8.009 0.441 0.208, 0.675 < 0.001 0.101 0.108 W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 9 of 15 supplied at the low dosage (< 9 log CFU/d) did not affect VFA, and vitamins to their hosts . About 90% of IgA and IgM (P > 0.05) (Table 7). However, the middle protein absorbed in the small intestine and 50% of the (9.5—9.9 log CFU/d) and high (> 10 log CFU/d) dos- energy requirement of the host are provided by rumi- 10 10 ages significantly increased IgA and IgM (P < 0.05, nal microbes . Therefore, calves should be fed on a SMD respectively). Additionally, single probiotics did not affect nutritional diet and reared in favorable conditions to IgA and IgM (P > 0.05) while compound strains increased promote rumen and ruminal microbe development and IgA (P < 0.001, RMD = 0.471 g/L, I = 62.9%) and IgM to maintain health and promote growth . Many rear- SMD (P < 0.001, RMD = 0.324 g/L, I = 73.2%). Further- ing and management strategies, such as supplementation SMD more, probiotics supplied in whole milk increased IgA with probiotics, have been introduced into dairy produc- (P < 0.001, RMD = 0.554 g/L, I = 42.2%) and IgM tion. But the effectiveness of probiotics on pre-weaning SMD (P < 0.001, RMD = 0.408 g/L, I = 59.5%), while probiot- dairy calves is inconsistent due to divergent experiment SMD ics in milk replacer did not affect IgA and IgM (P > 0.05) conditions. Therefore, this meta-analysis will systemati - SMD (Table 7). The Egger’s and Begg’s tests indicated some cally evaluate the effects of probiotics in improving the evidence of publication bias in IgA and IgM (P < 0.05) production and health of calves and identify the poten- (Table 4). tial variables modulating the effect sizes. The results will allow cattle ranchers optimize the application of probiot- ics and maximize the benefit of calf production. Faecal quality and intestinal flora parameter The summary of the meta-analysis on the effects of probiotics on the faecal quality and intestinal flora The increase in production performance of pre-weaning calves is shown in Table 5. Probiotics In this meta-analysis, the effects of probiotics on pre- did not significantly affect the counts (log CFU/g) of weaning calves (crucial stage for growth and production) coliform, Lactobacilli, and Streptococcus (P > 0.05). were systematically evaluated. The results showed that SMD However, probiotics significantly decreased faecal score additive probiotics increased the SDMI and TDMI. The (SMD = −0.383, P = 0.015, I = 78.4%) and increased diet components and fiber contents of starter significantly faecal bacteria counts (SMD = 0.361, P = 0.002, influence the development of rumen bacteria, specifically I = 36.9%). Correspondingly, the RMD analysis showed fibrolytic bacteria and the corresponding fermentation that probiotics decreased the faecal score by 0.052 and function of young calves . Additionally, the diet pro- increased faecal bacteria counts by 0.680 lo g CFU/g. vides the substrates available for rumen fermentation, Meta-regression analysis showed that per day of age and its fermenting products stimulate the development decreased faecal score by 0.009 (P = 0.012). Moreo- and function of rumen . For instance, increasing ver, each day of supplementation duration tended to SDMI can modify the rumen fermentation pattern, alter decrease faecal score by 0.002 (P = 0.064) and supple- the proportion of VFA, and increase the concentration of mentation methods significantly decreased faecal score butyrate . Butyrate is more effectively produced from by 0.09 (P = 0.036) (Table 6). Sub-group analysis showed the fermentation of concentrate than from roughage, and that probiotics did not affect faecal score of young calves it plays pivotal roles in stimulating the development of (age ≤ 5 d) (P > 0.05) but significantly decreased fae - rumen mucosa . Butyrate is absorbed and converted cal score of older groups (age ≥ 10 d) (P = 0.010, into BHBA after the starter is fed, fermented in rumen, SMD RMD = −0.061) (Table 7). Additionally, supplementation and then released into the blood circulation . There - in short term (≤ 28 d) did not affect faecal score (P > 0.05) fore, the BHBA concentration in serum may indicate the while supplementation in long term (≥ 42 d) significantly concentrate intake and rumen development . So, this decreased faecal score (P = 0.002, RMD = −0.070). meta-analysis showed that probiotics increased butyrate SMD Furthermore, supplementation in starter significantly in rumen fluid and BHBA in the serum of calves, prob - increased faecal score (P = 0.017, RMD = 0.126, ably due to the increased SDMI and TDMI. SMD I = 0) while supplementation in liquid feed (whole milk Probiotics can promote the production and function or milk replacer) significantly decreased faecal score of digestive enzymes, such as cellulase, amylase, pro- (P < 0.05) but heterogeneities were still high (I = 66.6% tease, and others , balance and stabilize beneficial and 70.1%, respectively) (Table 7). microbial ecosystem in the gastrointestinal tract [34, 35], and restore the gut microflora . Supplementation of Discussion Lactobacillus rhamnosus in the pre-weaning period can The implications of this meta-analysis increase the microbial diversity and alter the order of Ruminants, especially cattle, have rumen and ruminal dominant bacteria and relative abundance of bacterial microbes that can produce and supply energy, protein, families in calf rumen, thus increasing VFA production Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 10 of 15 Table 5 The summary of the meta-analysis on the effects of probiotics on the faecal quality and intestinal flora of pre-weaning calves SMD Heterogeneity RMD Egger’s test Begg’s test n Random effect 95% CI P-value Chi-squared (Q) P-value I , % Control mean Random effect 95% CI P-value P-value P-value Faecal score 25 −0.383 −0.690, −0.076 0.015 111.050 < 0.001 78.4 1.455 −0.052 −0.124, 0.020 0.160 < 0.001 < 0.001 Faecal bacteria count, 12 0.361 0.130, 0.593 0.002 17.430 0.096 36.9 11.532 0.680 0.227, 1.133 0.003 0.051 0.304 log CFU/g coliform, log CFU/g 16 0.089 −0.117, 0.295 0.399 25.750 0.041 41.7 6.838 0.164 −0.125, 0.452 0.267 0.727 0.444 Lactobacilli, log CFU/g 23 0.180 −0.025, 0.384 0.085 45.300 0.002 51.4 6.212 0.161 −0.018, 0.340 0.077 < 0.001 0.006 Streptococcus, log 6 −0.058 −0.264, 0.149 0.583 1.180 0.947 0 11.707 −0.130 −0.372, 0.112 0.292 0.256 0.024 CFU/g W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 11 of 15 Table 6 The summary of the meta-regression analysis on the effects of probiotics on pre-weaning dairy calves Dependent Meta-regression parameter (P-value) variable Intercept Duration, d Age, d Beginning Dosage, log Probiotic strains Methodsadj R No. of (Y, RMD) 10 BW, kg CFU/d BW, kg −15.251 −0.003 (0.887) −0.120 (0.244) 0.312 (< 0.001) 0.331 (0.387) −0.581 (0.626) 1.576 (0.002) 5.38 55 TDMI, kg/d −0.114 0.000 (0.776) 0.001 (0.213) 0.052 (0.096) −0.009 (0.230) −0.032 (0.074) 0.019 (0.045) 38.98 39 Butyrate, mmol/L 38.493 −0.005 (0.826) 0.052 (0.710) −.084 (0.485) −3.057 (0.039) −0.651 (0.640) −2.036 (0.238) 33.98 13 IgA, g/L −1.450 −0.003 (0.371) −0.008 (0.200) −.049 (0.073) 0.545 (0.003) −0.487 (0.004) −0.317 (0.189) 91.99 20 IgM, g/L −1.876 −0.000 (0.871) −0.003 (0.603) 0.005 (0.800) 0.267 (0.018) −0.395 (0.074) −0.056 (0.755) 95.59 21 Faecal Score 0.523 −0.002 (0.064) −0.009 (0.012) −0.007 (0.464) −0.019 (0.455) 0.129 (0.110) −0.090 (0.036) < 0 24 . Supplementary yeast culture decreases Prevotella reduces faecal scores in pre-weaning calves, indicat- and increases Butyrivibrio in rumen fluid, thus increasing ing that probiotics have an antibiotic effect and can butyrate production, length of papilla , and rumen reduce or even exclude adherence of pathogens due to weight . Probiotics can also enhance mineral bio- the adhesive properties [45, 46]. In this meta-analysis, availability, digestive capacity, and nutrient absorption probiotics increased the immunoglobulin (IgA, IgG, [38, 39]. Therefore, the combination of above factors can and IgM) and antioxidant capacity (T-AOC), decreased increase ADG and final BW and decrease FCR in this AST, LDH, MDA, and synergistically lowered the faecal meta-analysis. score of calves, indicating decreased rate of diarrhea and improved health of calves. The improvement of calf health Blood parameters were measured to evaluate the nutri- Regression analysis tion level, metabolic capacity, pathological change, Regression analysis showed that six of the outcomes were immune status, antioxidant traits, health condition, etc. affected by at least one of the six covariates (P < 0.05) . AST, as a key enzyme in amino acid metabolism, is (Table 6). These covariates explained up to 38.98% and a specific indicator of liver inflammation. Decreased AST 33.98% of heterogeneity in TDMI and butyrate, respec- levels indicate an improved liver function in calves . tively. Furthermore, they explained most of the hetero- LDH plays a crucial role in carbohydrate metabolism. geneity in IgA (91.99%) and IgM (95.59%), but they did Elevated serum LDH levels occur during tissue damage not explain the heterogeneity in BW (5.38%), faecal score or liver disease. Therefore, elevated LDH can indicate (adj R < 0), and other outcomes (P > 0.05), indicating that common injuries and diseases . MDA is produced other unknown dietary and management associated fac- through lipid peroxidation of polyunsaturated fatty acids. tors may influence the effect of probiotics on pre-wean - Therefore, MDA levels can be used as a marker of lipid ing dairy calves. peroxidation and free radicals to assess the damage to tis- sues and cells . In this meta-analysis, probiotics sig- Supplementation dosage nificantly decreased the levels of AST, LDH, and MDA in Probiotics were supplied at the recommended dosages serum, implying that probiotics relieve dysfunction of the (7.570–10.397 log CFU/d) in the studies included in liver in this crucial stage and improve calf health . this meta-analysis, which are supposed to have a ben- Additionally, probiotics stimulate immunity by eficial effect on calves. Although probiotics significantly increasing immunoglobulin and macrophagic activity affected 17 outcomes, heterogeneity of some outcomes [34, 39], competitively excluding enteric pathogens by was high. For instance, IgA, and IgG contents were posi- colonizing several probiotics in the intestine , thus tively associated with the dosage of probiotics in this inducing an antioxidant effect and reducing inflamma - regression analysis. Research found that high dosages of tion . Probiotics increase SOD concentration in calf probiotics may improve the immune systems of calves blood and improve antioxidant capacity by terminating . Herein, our sub-group analysis further confirmed the chain reactions of free radicals or scavenging reac- that a lower dosage of probiotics (< 9 log CFU/d) did tive oxygen species . Additionally, faecal score can not affect IgA and IgG contents (Table 7), while a higher predict diarrhea in young animals . The higher fae - dosage (> 9.5 log CFU/d) significantly increased IgA cal score, the softer the feces, and the higher the rate of and IgG contents (P < 0.05). This may be due to the spe - diarrhea. Supplementation of Lactobacillus rhamnosus cial gastrointestinal environment of newborn calves Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 12 of 15 Table 7 The summary of the sub-group analysis on the effects of probiotics on pre-weaning dairy calves RMD Heterogeneity SMD n Random effect 95% CI P-value I , % P-value BW All trials 55 1.885 1.009, 2.761 < 0.001 45.5 < 0.001 Methods Milk replacer 30 0.847 −0.127, 1.822 0.088 50.2 0.151 starter 2 1.663 −0.378, 3.704 0.110 0.0 0.094 Whole milk 23 4.439 2.581, 6.297 < 0.001 31.0 < 0.001 TDMI All trials 39 0.023 0.009, 0.037 0.001 73.5 0.001 Strains Compound 24 0.028 0.011, 0.046 0.002 76.9 0.001 Single 15 0.013 −0.013, 0.040 0.332 66.7 0.329 Methods Milk replacer 20 0.019 0.001, 0.037 0.044 64.3 0.030 starter 8 0.002 −0.021, 0.025 0.873 0.0 0.379 Whole milk 11 0.033 0.007, 0.058 0.014 87.0 0.018 Butyrate All trials 13 0.828 0.312, 1.345 0.002 55.5 < 0.001 Dosage(log) low (< 9) 5 2.762 0.161, 5.363 0.037 69.9 0.022 high (> 10) 8 0.463 0.037, 0.889 0.033 47.8 0.013 IgA All trials 20 0.296 0.191, 0.400 < 0.001 73.8 < 0.001 Dosage(log) low (< 9) 3 0.040 −0.083, 0.163 0.523 0.0 0.533 middle (9.5–9.9) 11 0.219 0.093, 0.345 0.001 68.3 0.015 high (> 10) 6 0.618 0.295, 0.942 < 0.001 74.3 < 0.001 Strains Compound 15 0.471 0.283, 0.659 < 0.001 62.9 < 0.001 Single 5 −0.000 −0.058, 0.057 0.987 64.0 0.925 Methods Milk replacer 7 −0.017 −0.047, 0.013 0.268 0.0 0.441 Whole milk 13 0.554 0.367, 0.741 < 0.001 42.2 < 0.001 IgM All trials 21 0.255 0.152, 0.358 < 0.001 72.9 < 0.001 Dosage(log) low (< 9) 3 −0.037 −0.126, 0.052 0.420 0.0 0.424 middle (9.5–9.9) 11 0.266 0.130, 0.402 < 0.001 69.1 < 0.001 high (> 10) 7 0.400 0.218, 0.582 < 0.001 73.4 0.001 Strains Compound 15 0.324 0.192, 0.457 < 0.001 73.2 < 0.001 Single 6 0.255 0.152, 0.358 0.848 0.0 0.354 Methods Milk replacer 7 −0.006 −0.031, 0.019 0.653 0.0 0.892 Whole milk 14 0.408 0.317, 0.499 < 0.001 59.5 < 0.001 Faecal score All trials 24 −0.047 −0.083, −0.011 0.011 68.8 0.031 Age young(≤ 5) 17 −0.030 −0.084, 0.024 0.275 70.3 0.266 old(≥ 10) 7 −0.061 −0.105, −0.018 0.006 52.7 0.010 Duration short (≤ 28) 12 0.012 −0.059, 0.082 0.747 36.6 0.626 long (≥ 42) 12 −0.070 −0.113, −0.026 0.002 73.5 0.002 Methods Milk replacer 14 −0.051 −0.089, 0.014 0.007 70.1 0.018 starter 6 0.126 0.025, 0.227 0.014 0.0 0.017 Whole milk 4 −0.158 −0.273, −0.043 0.007 66.6 0.029 , and the actual contents, or the quality of probiotics and superior quality of probiotics should be supplied for was insufficient to show performance responses as the the desired effects in domestic production, especially in instruction described . Therefore, sufficient dosages calf diets. W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 13 of 15 Probiotics composition analysis. For experiment, 47 studies were included for Our regression analysis showed that probiotic strains sig- TDMI in mate-analysis but only 39 had detail informa- nificantly affected IgA, and tended to influence TDMI and tion on composition and supplementation methods for IgM. Sub-group analysis showed that the compound of further sub-group analysis. Thirdly, we identified some multiple strain probiotics significantly increased TDMI, sources of heterogeneity across studies but the het- IgA, and IgM (P < 0.001), while a single strain did not affect erogeneities were still large in some sub-group analy- these three traits (Table 7). This is because probiotics sis. This may be due to the complication of feeding might be species and strain-specific, and multiple strains experiments and variations within and among studies of probiotics may enhance the advantages compared with were not totally eliminated. Our sub-group analysis a single strain due to their synergetic effects . showed that other unknown factors may influence the effect sizes of probiotics apart from these six ones . Therefore, further experiment details are needed for Supplementation methods less heterogeneity, consistent results, and more accu- Meta-analysis showed that probiotics significantly rate and reliable conclusions. increased SDMI, TDMI, BW, IgA, and IgM, while decreasing faecal score. Sub-group analysis showed that probiotics supplementation in starters did not have a Conclusions beneficial effect on TDMI and faecal score. In contrast, This meta-analysis demonstrated that probiotics sup - supplementation in liquid (whole milk or milk replacer) plementation could improve growth performance and significantly and positively influenced TDMI and faecal feed efficiency, as indicated by the increased BW and score (P < 0.05) (Table 7). Additionally, probiotics sup- ADG, and the decreased FCR due to increased TDMI plied in whole milk, not in milk replacer, significantly and SDMI. Probiotics modified the ruminal fermen - affected BW, IgA, and IgM (P < 0.001) (Table 7). The tation, as indicated by the decreased acetate and supplementation methods significantly influenced the increased faecal bacteria counts, butyrate, and corre- six outcomes probably due to difference in pathogen sponding BHBA. Probiotic supplementation improved load between milk replacer and whole milk  or route the health of calves, as indicated by decreased AST, of delivery (solid or liquid) . Solids enter the rumen LDH, MDA, and faecal score and increased IgA, IgG, while liquid circumvents the rumen into the abomasum IgM, and T-AOC. The probiotics supplied with more in the early life of calves . The different effect could than 9.5 log CFU/d distinctly improved IgA and IgM also be due to the divergent protein and fiber contents contents. Compound probiotics significantly affected of the starter . Furthermore, the diet of calves grad- TDMI, IgA, and IgM. Additionally, the supplementa- ually transits from predominant liquids (milk or milk tion methods significantly influenced SDMI, TDMI, replacer) to a solid diet (starter) during weaning with BW, IgA, IgM, and faecal scores. These results fur - increasing fermentable carbohydrates . This transi - ther confirmed that probiotics supplementation could tion can change the components of ruminal microbi- improve the growth, feed efficiency, and health of pre- omes, such as increasing Proteobacteria and Firmicutes, weaning dairy calves. However, the effect sizes were and decreasing Bacteroidetes phylum . related to the dosage, composition of strains, and sup- plementation methods. This meta-analysis addresses The limitation of this meta-analysis the controversy regarding the effect of probiotics on This meta-analysis has some limitations. Firstly, we the pre-weaning of dairy calves and provides the fun- only included peer reviewed publications in English damentals for the efficient use of probiotics in cattle and excluded unpublished data, conference proceed- production. ings, non-English studies. But a meta-analysis showed that the effect of yeast on milk yield of lactating dairy cows had less heterogeneity compared peer-reviewed Abbreviations studies to non-peer-reviewed reports and the effect ADG: Average daily gain; ALB: Albumin; ALP: Alkaline phosphatase; ALT: Ala- nine aminotransferase; AST: Aspartate aminotransferase; BHBA: Beta-hydroxy- sizes had no significant difference between these two butyric acid; BUN: Blood urea nitrogen; BW: Body weight; CI: Confidence groups . Additionally, exclusion of non-English interval; FCR: Feed conversion rate; GSH-Px: Glutathione peroxidase; IFNγ: publications for meta-analysis did not change over- Interferon-γ; IgA: Immunoglobulin A; IGF1: Insulin-like growth factor 1; IgG: Immunoglobulin G; IgM: Immunoglobulin M; LDH: Lactate dehydrogenase; all conclusions . Secondly, the experiment details MDA: Malondialdehyde; RMD: Raw mean difference; SDMI: Starter dry matter were not fully reported in many included studies, intake; SMD: Standardized mean difference; SOD: Superoxide dismutase; which resulted that the number of studies for some T-AOC: Total antioxidant capacity; TDMI: Total dry matter intake; VFA: Volatile fatty acids. performances were small in regression and sup-group Wang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 14 of 15 8. Wang H, Yu Z, Gao Z, Li Q, Qiu X, Wu F, et al. Eec ff ts of compound probiot - Supplementary Information ics on growth performance, rumen fermentation, blood parameters, and The online version contains supplementary material available at https:// doi. health status of neonatal Holstein calves. J Dairy Sci. 2022;105:2190–200. org/ 10. 1186/ s40104- 022- 00806-z. 9. Wu Y, Wang L, Luo R, Chen H, Nie C, Niu J, et al. Eec ff t of a multispecies probiotic mixture on the growth and incidence of diarrhea, immune Additional file 1: Table S1. Characteristics of the included studies in the function, and fecal microbiota of pre-weaning dairy calves. Front Micro- meta-analysis. Table S2. Descriptive statistics of growth performance, biol. 2021;12:681014. digestibility and feed efficiency, rumen parameter, blood parameter, and 10. Khaziakhmetov F, Khabirov A, Tagirov K, Avzalov R, Tsapalova G, Basharov faecal parameter of pre-weaning calves supplied with probiotics. A. The influence of “Stimix Zoostim” and “Normosil” probiotics on fecal microflora, hematologic indicators, nutrient digestibility, and growth of Additional file 2: Fig. S1. The flowchart of the search strategy and selec- mother-bonded calves. Vet World. 2020;13:1091–7. tion of eligible studies for meta-analysis of the effects of probiotics on 11. Kong L, Yang C, Dong L, Diao Q, Si B, Ma J, et al. Rumen fermentation pre-weaning dairy calves. characteristics in pre-and post-weaning calves upon feeding with mul- Additional file 3: Fig. S2. Risk of bias graph depicting review authors’ berry leaf flavonoids and Candida tropicalis individually or in combination judgements about each risk of bias item presented as percentages across as a supplement. Animals (Basel). 2019;9:990. all included studies. 12. Zhang L, Jiang X, Liu X, Zhao X, Liu S, Li Y, et al. Growth, health, rumen fermentation, and bacterial community of Holstein calves fed Lac- Additional file 4: Fig. S3. Risk of bias summary depicting authors’ judge - tobacillus rhamnosus GG during the preweaning stage. J Anim Sci. ments about each risk of bias item for each included study. 2019;97:2598–608. 13. Fomenky BE, Chiquette J, Bissonnette N, Talbot G, Chouinard PY, Ibeagha- Awemu EM, et al. Impact of Saccharomyces cerevisiae boulardii CNCMI- Authors’ contributions 1079 and Lactobacillus acidophilus BT1386 on total lactobacilli population LW: Methodology, Data collection and analysis, Writing—original draft. HS: in the gastrointestinal tract and colon histomorphology of Holstein dairy Methodology, Data collection and analysis. HG: Methodology, Data collection. YX: calves. J Anim Feed Sci. 2017;234:151–61. Methodology, Data collection. LZ: Funding acquisition, Resources, Supervision. 14. Signorini M, Soto L, Zbrun M, Sequeira G, Rosmini M, Frizzo L. Impact of CZ: Conceptualization, Design, Funding acquisition, Resources, Supervision, Writ- probiotic administration on the health and fecal microbiota of young ing—review & editing. The author(s) read and approved the final manuscript. calves: a meta-analysis of randomized controlled trials of lactic acid bacteria. Res Vet Sci. 2012;93:250–8. Funding 15. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting The work was supported by Shaanxi Scientific and Technological Innovation items for systematic reviews and meta-analyses: the PRISMA statement. Plan Program (NYKJ-2022-YL(XN)03), China Postdoctoral Science Foundation BMJ. 2009;339:b2535. (2016M590977), Technology Innovation and Achievement Transformation for 16. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. the Demonstration Station of Northwest A&F University ( TGZX2019-22). The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928. Declarations 17. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis proto- Ethics approval and consent to participate cols (PRISMA-P) 2015 statement. System Rev. 2015;4:1–9. Not applicable. 18. Riley R, Higgins J, Deeks J. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549. Consent for publication 19. Fleiss J. The statistical basis of meta-analysis. Stat Methods Med Res. Not applicable. 1993;2:121–45. 20. Takeshima N, Sozu T, Tajika A, Ogawa Y, Hayasaka Y, Furukawa TA. Which is Competing interests more generalizable, powerful and interpretable in meta-analyses, mean The authors declare that they have no competing interests. difference or standardized mean difference? BMC Med Res Methodol. 2014;14:30. Received: 6 June 2022 Accepted: 23 November 2022 21 Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New York: Routledge; 1988. 22. Higgins J, Thompson S, Deeks J, Altman D. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–60. 23. Harbord RM, Higgins JP. Meta-regression in Stata. Stata J. References 2008;8:493–519. 1. Lopes RB, Bernal-Córdoba C, Fausak E, Silva-del-Río NJPO. Eec ff t of prebi- 24. Tyson M, Chang S. Enhanced recovery pathways versus standard care otics on growth and health of dairy calves: a protocol for a systematic after cystectomy: a meta-analysis of the effect on perioperative out - review and meta-analysis. PLoS ONE. 2021;16:e0253379. comes. Eur Urol. 2016;70:995–1003. 2. Krpálková L, Cabrera VE, Kvapilík J, Burdych J, Crump P. Associations 25. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis between age at first calving, rearing average daily weight gain, herd milk detected by a simple, graphical test. BMJ. 1997;315:629. yield and dairy herd production, reproduction, and profitability. J Dairy 26. Li Z, Xu B, Lu Z, Wang Y. Eec ff ts of long-chain fatty acid supplementation Sci. 2014;97:6573–82. on the growth performance of grower and finisher pigs: a meta-analysis. 3. Retta KS. Role of probiotics in rumen fermentation and animal perfor- J Anim Sci Biotechnol. 2019;10:65. mance: a review. Int J Livest Prod. 2016;7:24–32. 27. Pinloche E, McEwan N, Marden J-P, Bayourthe C, Auclair E, Newbold CJ. 4. Markowiak P, Śliżewska K. The role of probiotics, prebiotics and synbiotics The effects of a probiotic yeast on the bacterial diversity and population in animal nutrition. Gut Pathog. 2018;10:21. structure in the rumen of cattle. PLoS ONE. 2013;8:e67824. 5. Cangiano L, Yohe T, Steele M, Renaud D. Invited review: strategic use of 28. Russell JB. Rumen microbiology and its role in ruminant nutrition: Depart- microbial-based probiotics and prebiotics in dairy calf rearing. Appl Anim ment of Microbiology, Cornell University. 2002. Sci. 2020;36:630–51. 29. Callaway ES, Martin SA. Eec ff ts of a Saccharomyces cerevisiae culture 6. Abu-Tarboush HM, Al-Saiady MY, Keir El-Din AH. Evaluation of diet on ruminal bacteria that utilize lactate and digest cellulose. J Dairy Sci. containing Lactobacilli on performance, fecal coliform, and Lactobacilli of 1997;80:2035–44. young dairy calves. Anim Feed Sci Technol. 1996;57:39–49. 30. Henderson G, Cox F, Ganesh S, Jonker A, Young W, Janssen PH. Rumen 7. Alawneh JI, Barreto MO, Moore RJ, Soust M, Al-Harbi H, James AS, et al. microbial community composition varies with diet and host, but a Systematic review of an intervention: the use of probiotics to improve core microbiome is found across a wide geographical range. Sci Rep. health and productivity of calves. Prev Vet Med. 2020;183:105147. 2015;5:14567. W ang et al. Journal of Animal Science and Biotechnology (2023) 14:3 Page 15 of 15 31. Bayatkouhsar J, Tahmasebi A, Naserian AA, Mokarram R, Valizadeh R. Eec ff ts of supplementation of lactic acid bacteria on growth perfor - mance, blood metabolites and fecal coliform and Lactobacilli of young dairy calves. Anim Feed Sci Technol. 2013;186:1–11. 32. Mentschel J, Leiser R, Mülling C, Pfarrer C, Claus R. Butyric acid stimulates rumen mucosa development in the calf mainly by a reduction of apopto- sis. Arch Tierernahr. 2001;55:85–102. 33. Quigley Iii J, Caldwell L, Sinks G, Heitmann R. Changes in blood glucose, nonesterified fatty acids, and ketones in response to weaning and feed intake in young calves. J Dairy Sci. 1991;74:250–7. 34. Uyeno Y, Shigemori S, Shimosato T. Eec ff t of probiotics/prebiotics on cat - tle health and productivity. Microbes Environ. 2015;30:126–32. 35. Musa HH, Wu S, Zhu C, Seri H, Zhu G. The potential benefits of probiotics in animal production and health. J Anim Vet Adv. 2009;8:313–21. 36. Xiao JX, Alugongo GM, Chung R, Dong SZ, Li SL, Yoon I, et al. Eec ff ts of Saccharomyces cerevisiae fermentation products on dairy calves: ruminal fermentation, gastrointestinal morphology, and microbial community. J Dairy Sci. 2016;99:5401–12. 37. Harris TL, Liang Y, Sharon KP, Sellers MD, Yoon I, Scott MF, et al. Influence of Saccharomyces cerevisiae fermentation products, SmartCare in milk replacer and Original XPC in calf starter, on the performance and health of preweaned Holstein calves challenged with Salmonella enterica sero- type Typhimurium. J Dairy Sci. 2017;100:7154–64. 38. Oyetayo V, Oyetayo F. Potential of probiotics as biotherapeutic agents targeting the innate immune system. Afr J Biotechnol. 2005;4:123–7. 39. Timmerman HM, Mulder L, Everts H, Van Espen D, Van Der Wal E, Klaas- sen G, et al. Health and growth of veal calves fed milk replacers with or without probiotics. J Dairy Sci. 2005;88:2154–65. 40. Guo Y, Li Z, Deng M, Li Y, Liu G, Liu D, et al. Eec ff ts of a multi-strain probi- otic on growth, health, and fecal bacterial flora of neonatal dairy calves. Anim Biosci. 2022;35:204–16. 41. Mukai H, Noguchi T, Kamimura K, Nishioka K, Nishiyama S. Significance of elevated serum LDH (lactate dehydrogenase) activity in atopic dermatitis. J Dermatol. 1990;17:477–81. 42. Castillo C, Hernández J, Valverde I, Pereira V, Sotillo J, Alonso ML, et al. Plasma malonaldehyde (MDA) and total antioxidant status ( TAS) during lactation in dairy cows. Res Vet Sci. 2006;80:133–9. 43. Casas IA, Dobrogosz WJ. Validation of the probiotic concept: Lactobacillus reuteri confers broad-spectrum protection against disease in humans and animals. Microb Ecol Health Dis. 2000;12:247–85. 44. Renaud DL, Buss L, Wilms JN, Steele MA. Technical note: is fecal consist- ency scoring an accurate measure of fecal dry matter in dairy calves? J Dairy Sci. 2020;103:10709–14. 45. Gorbach SL. Probiotics and gastrointestinal health. Am J Gastroenterol. 2000;95:S2–4. 46. Km H, Sj K, Yc H, Ks J, Kj Y, Hj K. Eec ff ts of hydrolyzed yeast supplementa- tion in calf starter on immune responses to vaccine challenge in neonatal calves. Animal. 2011;5:953–60. 47. Malmuthuge N, Griebel PJ, Guan LL. The gut microbiome and its potential role in the development and function of newborn calf gastrointestinal tract. Front Vet Sci. 2015;2:36. 48. Collado MC, Meriluoto J, Salminen S. Measurement of aggregation prop- erties between probiotics and pathogens: in vitro evaluation of different methods. J Microbiol Methods. 2007;71:71–4. 49. Hill SR, Hopkins BA, Davidson S, Bolt SM, Diaz DE, Brownie C, et al. The addition of cottonseed hulls to the starter and supplementation of live yeast or man- nanoligosaccharide in the milk for young calves. J Dairy Sci. 2009;92:790–8. Re Read ady y to to submit y submit your our re researc search h ? Choose BMC and benefit fr ? Choose BMC and benefit from om: : 50. Vi RB, McLeod K, Klotz J, Heitmann R. Rumen development, intestinal growth and hepatic metabolism in the pre-and postweaning ruminant. J fast, convenient online submission Dairy Sci. 2004;87:E55–65. thorough peer review by experienced researchers in your ﬁeld 51. Meale SJ, Li SC, Azevedo P, Derakhshani H, DeVries TJ, Plaizier JC, et al. Weaning age influences the severity of gastrointestinal microbiome shifts rapid publication on acceptance in dairy calves. Sci Rep. 2017;7:198. support for research data, including large and complex data types 52. Poppy GD, Rabiee AR, Lean IJ, Sanchez WK, Dorton KL, Morley PS. A meta- • gold Open Access which fosters wider collaboration and increased citations analysis of the effects of feeding yeast culture produced by anaerobic fermentation of Saccharomyces cerevisiae on milk production of lactating maximum visibility for your research: over 100M website views per year dairy cows. J Dairy Sci. 2012;95:6027–41. 53. Nussbaumer-Streit B, Klerings I, Dobrescu AI, Persad E, Stevens A, Garritty At BMC, research is always in progress. C, et al. Excluding non-English publications from evidence-syntheses did Learn more biomedcentral.com/submissions not change conclusions: a meta-epidemiological study. J Clin Epidemiol. 2020;118:42–54.
Journal of Animal Science and Biotechnology – Springer Journals
Published: Jan 4, 2023
Keywords: Calves; Growth; Health; Meta-analysis; Probiotics
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