SUMMARY
Researchers have studied probiotics and prebiotics as potential dietary therapies to address this condition. This study aims to assess the efficacy of prebiotics and probiotics in reducing Salmonella in broiler chickens’ faeces and cloacae. On 15 January 2024, we followed PRISMA procedures and searched five databases – PubMed, Web of Science, Scopus, Agricola, and VHL – for relevant papers. The inclusion criteria centred on studies that employed prebiotics, probiotics, or both as treatments for broiler chickens infected with Salmonella. The authors extracted the data and evaluated the risk of bias using the SYRCLE program. We conducted meta-analyses using a random-effects model, reporting the results as relative risk (RR) and standardised mean difference (SMD), accompanied by 95% confidence intervals. We examined twenty-nine studies, each with an average sample size of 201 broiler chicks. It took a lot longer for Salmonella to grow and shed when prebiotics, especially mannan-oligosaccharides (MOS) (SMD −1.43; 95% CI [−2.30, −0.56]; I2 = 99%), were present. Probiotics helped a lot (SMD −1.93; 95% CI [−3.13, −0.46]; I2 = 99%), but their overall effectiveness was affected by strain specificity and dose differences. The combination of prebiotics and probiotics tended to benefit but did not reach statistical significance (SMD −2.09; 95% confidence interval [−4.22, 0.04]). Salmonella prevalence decreased significantly in the experimental groups (RR 0.59; 95% CI [0.45, 0.79]; I2 = 83%). Both prebiotics and probiotics reduce Salmonella colonisation and shedding in broiler hens’ faeces and cloacae, but prebiotics, especially MOS, have slightly stronger effects. Future research should try to standardise the intervention methods and how results are measured, focusing on faecal and cloacal colonisation.
KEYWORDS:
- Salmonella spp
- broiler chickens
- prebiotics
- probiotics
- colonisation
- shedding
- farm animals
- nutrition
Introduction
Foodborne illnesses contribute significantly to the global disease burden (Prevention CfDCa Citation2022). Salmonella spp. are significant pathogens in poultry, posing considerable health risks to birds and humans (Enan et al. Citation2022). These bacteria can lead to salmonellosis, a zoonotic disease that causes severe gastrointestinal illness in humans when they consume contaminated poultry products (Scharff Citation2020). Salmonella germs are the leading cause of foodborne illness worldwide, endangering human health (Bajpai et al. Citation2012). Chicken Salmonella rates vary by country; in some wealthy countries, 1% of chickens have Salmonella. This zoonotic disease affects human health and results in significant economic losses within the global poultry industry, influencing trade, production efficiency, and public confidence in food safety (USDA Citation2021). Due to epidemiological challenges, resource-poor countries may have 10%. Salmonella threatens public health; the United States Department of Agriculture (USDA) has prioritised poultry Salmonella control (USDA Citation2021; Kulshreshtha et al. Citation2017; Saleh et al. Citation2019).
Salmonella is spread in many ways, but faecal and cloacal shedding in poultry is one of the most important ones because it is the main way that the disease gets into the environment and then into people through chicken meat and eggs (USDA Citation2021; Bajpai et al. Citation2012; Kulshreshtha et al. Citation2017). Salmonella faecal and cloacal shedding varies greatly by area, with resource-poor countries reporting greater rates (up to 10%) than affluent ones (around 1%) (Kulshreshtha et al. Citation2017). Addressing this problem is critical for decreasing Salmonella transmission, improving poultry health, and increasing food safety (Saleh et al. Citation2019).
Many management and treatment approaches are utilised to reduce the amount of Salmonella that is shed in faeces and cloaca (Alghoribi et al. Citation2019; Saleh et al. Citation2019). These approaches aim to reduce chicken meat and egg contamination by limiting pathogen colonisation in poultry using various intervention strategies (Campos et al. Citation2013; Pietsch et al. Citation2021). These methods include vaccination, gut flora alteration to exclude Salmonella from chickens, and probiotics and prebiotics (Aljuwayd et al. Citation2024a, Citation2024b; Bierer Citation1963). Research indicates that the use of prebiotics and probiotics can enhance the resilience of poultry against Salmonella infections. For example, in experiments, chickens that were given Bacillus subtilis probiotics had much higher survival rates against highly contagious strains of Salmonella Enteritidis than chickens without any treatment (Enan et al. Citation2022, Nam et al. Citation2022). Additionally, prebiotic additives have shown potential in reducing the prevalence of Salmonella in cloacal samples, particularly during the early stages post-infection (Aboelhadid et al. Citation2021; Enan et al. Citation2022).
The mechanisms by which prebiotics and probiotics function to reduce Salmonella colonisation and shedding in chickens are multifaceted. Bacillus subtilis is believed to enhance the gut microbiota, thereby outcompeting pathogenic bacteria for resources and binding sites within the intestinal tract (Padgett Citation2021; Rajput et al. Citation2020; Rychlik et al. Citation2014). Prebiotics, conversely, are believed to serve as non-digestible components that enhance the growth of beneficial bacteria in the gut (Murate et al. Citation2015). These findings underscore the importance of incorporating alternative strategies, such as probiotics and prebiotics, in poultry management programmes to mitigate the risks associated with Salmonella spp. and improve overall animal health and food safety (Chinivasagam et al. Citation2012; Dunn et al. Citation2022; Ngogang et al. Citation2020). However, little data supports their efficiency in reducing the spread of human illnesses such as salmonellosis in chickens (Acevedo-Villanueva et al. Citation2021; Clavijo and Florez Citation2018; Rajput et al. Citation2020). Thus, it is necessary to review the literature for the past decade.
Even though these methods have shown promise, more research is needed to find out how well they work at specifically targeting faecal and cloacal Salmonella shedding. Existing research and reviews often offer a broad overview of Salmonella colonisation but lack specificity for faecal and cloacal locales. This systematic study seeks to fill this gap by examining the efficacy of prebiotics and probiotics in decreasing Salmonella colonisation and shedding in broiler chickens. The ultimate goal is to give practical information for improving chicken management techniques and reducing Salmonella risks.
Materials and methods
Registration
The protocol of the study was prospectively registered and reviewed by PROSPERO, and necessary changes were addressed. The resulting document can be found with the identifier CRD42024529410.
Literature search strategy and eligibility criteria
A systemic review process adapted from Hwang et al. (Citation2020) addressed the question: Can prebiotics and probiotics supplemented diet reduce the colonization and shedding of Salmonella Enteritidis in broiler chickens compared to regular diet? Initial inquiries were conducted using certain terms to evaluate the practicality of conducting a comprehensive topic analysis. A sufficient study of the issue was found to support the attempt. On 15 January 2024, we systematically searched five databases simultaneously: PubMed, Web of Science, Scopus, Cochrane Library, and OVID to identify relevant articles. The following search strategy was carried out for collecting potentially relevant publications: PubMed: (Chick* OR broiler* OR poultry* OR gallus* AND Salmonell* AND ((‘probiotics’[Mesh Terms] OR ‘probiotics’[All Fields]) OR (‘prebiotics’[Mesh Terms] OR ‘prebiotics’[All Fields] OR ‘Dietary Carbohydrates’[All Fields] OR ‘Dietary Fiber’[All Fields] OR ‘fructo-oligosaccharides’[All Fields] OR ‘galacto-oligosaccharides’[All Fields] OR ‘yeast cell wall’[All Fields])). Web of Science: (Chicken or broiler OR poultry OR gallus) AND (Salmonella OR salmonellosis) AND (probiotic) OR (prebiotic) OR (Dietary Fiber) OR (fructo-oligosaccharide) OR (galacto-oligosaccharide) OR (yeast cell wall). SCOPUS: TITLE-ABS-KEY (Chicken*) AND (salmonell*) AND (probiotic*) OR (prebiotic*). National Agricultural Library (Agricola): (Chick AND salmonel AND probiotic OR prebiotic). Virtual Health Library (VHL): (Chicken) AND (Salmonella) AND (probiotic) OR (prebiotic) OR (Dietary Fiber) OR (oligosaccharide) OR (yeast cell wall). Variable search terms were necessary to accommodate differences in database structures, search algorithms, and indexing. Each database has unique characteristics that sometimes require adjusting syntax or keywords to retrieve the most relevant results. Detailed search strategy for five database searches can be seen in Supplementary Table S1.
PRISMA guidelines
This study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, http://www.prisma-statement.org., for screening, data extraction, and reporting systematic reviews (Page et al. Citation2021). PRISMA guidelines are summarised in the PRISMA Checklist, and the PRISMA Abstracts Checklist can be found in Supplementary Tables S2 and S3.
Selection criteria
Studies in English were included without any constraints or filters based on publication date. No citation searching or supplemental data retrieval via author contact was conducted. The research group assessed all articles against a predetermined set of exclusion criteria. The study was excluded based on the following criteria: 1- If the research encompassed both an in vitro or human study, in silico studies (simulations), and an in vivo non-chicken investigation; 2- if it wasn’t relevant, like studies that focused on non-commercial chickens and broiler; 3- if it was published as a conference abstract instead of a research paper (review); 4- if the poultry samples were imported; 5- if the study evaluating the effects of dietary fibres in chicken intestinal cell lines and non-research studies, such as reviews, editorials, book chapters, or opinions, as well as non-peer 6- if there wasn’t enough information on the concentration of Salmonella on chicken and low-quality studies.
Outcome measures
The primary outcome of this study is to evaluate the efficacy of probiotics and prebiotics in reducing Salmonella in broiler chickens. Thus, to make our review more manageable, we streamlined the systematic review outcomes to focus on Salmonella shedding and colonisation based on administering probiotics and prebiotics by oral gavage (feed, water), followed by the Salmonella Challenge. For colonisation, bacterial counts were detected by culture or molecular techniques in faeces or cloacal swabs of intestinal contents.
Data extraction
We used the Rayyan program to automatically detect duplicates, which we then eliminated after a reviewer personally checked the possible duplication (Ouzzani et al. Citation2016). After eliminating duplicate entries, each researcher independently evaluated the titles and abstracts, and follow-up meetings were conducted to dispute any disagreement and facilitate consensus and the discrepancies. After the abstract screening, the study’s full text was screened. Researchers carefully review the full text of each study to determine whether it meets the predetermined inclusion criteria. Full-text screening gives a full assessment of the research’s methods. After reaching an agreement, the essential information was filtered, and the following information was extracted: first author, year, sample size, age, intervention, study length, dosage and outcome indicators. We finally loaded the retrieved data into Excel. If continuous, we uniformly represented the retrieved outcome index data as means and standard deviations (SD). We reported the binary result index data as the number of occurrence instances and samples per group. If not, the data underwent conversion. When two researchers gathered conflicting information, they consulted a third researcher (Malli) for clarification.
Quality assessment
We assessed the risk of bias in the included papers using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool (Hooijmans et al. Citation2014). In animal intervention studies, this modified version of the Cochrane risk-of-bias test is used to look for possible biases or confusion in how trials are planned, carried out, and measured. We used six types of bias to determine how relevant the study was to other types and areas of animal research. These were selection, execution, measurement, loss of follow-up, reporting, and other variables. The danger of bias in each area was assessed as either low (+), uncertain (?), or high risk (−). Two researchers (Awadh and Shakori) independently evaluated the bias risk in the included papers. We contacted a third researcher (Malli) to resolve any disputes.
Regarding the microbiome, it is important to recognise the potential bias that may arise from microbiome variances caused by living circumstances or exposure to pathogens. Specific pathogen-free (SPF) was utilised to address selection bias. We assessed a low risk of bias when we indicated the animals’ SPF status. If the animals did not have specific pathogen-free status, they were considered to have a high risk of bias. We used a delivery technique where we housed only one animal in each cage and closely monitored their water consumption to mitigate bias.
Statistical analysis
The meta-analysis and forest plot production of this review were conducted using the RevMan Web and Comprehensive Meta-Analysis (Version 4.0) software (Borenstein et al. Citation2024; Collaboration Citation2024). We categorised the data based on the type and concentration of probiotics and prebiotics to conduct a more detailed subgroup analysis. The statistical relative risk (RR) measure was used for data that could be categorised into two groups. At the same time, the standardised mean difference was applied to continuous data. The impact estimates were computed using 95% confidence intervals (CIs). Firstly, the heterogeneity test, Cochran’s Q test, and the I-squared index (I2) were performed to address the expected heterogeneity using the I2 statistic in a meta-analysis. This is crucial due to the differences in sampling procedures and experimental methodology across the original studies. Thus, we combined and analysed all relevant data in our investigation using a random-effects model (J. P. Higgins et al. Citation2003). The statistical significance for heterogeneity was determined using Cochran’s Q test with a threshold of p < 0.10 (S. E. Higgins et al. Citation2008). To explore the causes of variability, statistical methods like meta-regression, subgroup analysis, and sensitivity analyses were utilised. Funnel plots were created to investigate publication bias, following the Cochrane Handbook’s guideline of using a minimum of 10 relevant literature sources. The publication bias evaluation included visually examining the funnel plot’s asymmetry (Borenstein et al. Citation2024; Collaboration Citation2024).
Results
Studies selection and characteristics
A total of 1,617 studies were initially included in the screening phase, with 172 studies having been removed as identified as duplicates. The distribution of articles from each database is detailed in the PRISMA flowchart, . 1,142 remaining studies were excluded after screening, and three additional articles were not retrieved and excluded due to a high risk of bias, as the number of animals per research group was unclear. Of the 300 articles left, 29 were included (Adhikari et al. Citation2018; Alkhulaifi Aha et al. Citation2022; Corrier et al. Citation1990; Faber et al. Citation2012; Fernandez et al. Citation2000, Citation2002; Gurram et al. Citation2021; Gurram et al. Citation2022; S. E. Higgins et al. Citation2008; Hinton et al. Citation1990; Islam and Yang Citation2017; Jazi et al. Citation2018; Khan and Chousalkar Citation2020; Kulshreshtha et al. Citation2017; Nam et al. Citation2022; Nakphaichit et al. Citation2019; Pascual et al. Citation1999; Pourabedin et al. Citation2017; Prado-Rebolledo et al. Citation2017; Price et al. Citation2020; Salim et al. Citation2013; Shao et al. Citation2022; Shivaramaiah et al. Citation2011; Spring et al. Citation2000; Teirlynck et al. Citation2009; Vandeplas et al. Citation2009; Zhen et al. Citation2023; Ziprin and Deloach Citation1993; Ziprin et al. Citation1990) after eligibility evaluation. Following a comprehensive quality check of the full texts, 26 sets of Salmonella prevalence data in poultry meat were obtained. 12 studies focusing on probiotics and 14 focusing solely on prebiotics met the selection criteria, as they were linked with the interventions. The average sample size of animal experiments (extracted from 29 SRs) was 19. summarises the baseline characteristics for the included studies.
Figure 1. PRISMA flowchart of literature searching and collecting.
Table 1. Baseline characteristics of included studies.
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Quality assessment and Risk-of-Bias
In this study, the SYRCLE’s risk of bias tool provides a systematic approach to evaluate the methodological quality and potential biases within preclinical animal research, ensuring the findings are robust and reliable (Hooijmans et al. Citation2014). Robvis traffic light plots were utilised to illustrate visually the potential for bias in the meta-analysis (McGuinness and Higgins Citation2021). The first column contains details about the study, such as author and year of publication. The second and subsequent columns contain the judgements in each domain of the assessment tool. The number of columns containing domain-level assessments will vary by tool used. The column (named ‘Overall’) contains the overall risk-of-bias judgements for each study. The traffic light plots show many domains as green flags, indicating a low bias risk in the included studies.
Nevertheless, several studies show red flags in ‘blinding of performance bias’ and ‘blinding of detection bias’, indicating possible biases that may impact the findings. In this plot, we detected several unclear risks and yellow flags, suggesting that although we adequately addressed most biased concerns, some information was not mentioned. Many included studies did not have enough information to do a risk of bias analysis or report on randomisation or blinding. This is because most systematic reviews of animal studies have not yet done a risk of bias analysis, and many of these studies do not report randomisation or blinding. Thus, the inadequate reporting of critical details in these investigations hindered our evaluation of adequate detection randomisation in animal experiments. In addition, the standards used to decide if these things are sufficient for randomisation or blinding in animal research are very different from those used in cohort studies, as shown in .
Figure 2. Risk of bias assessment summary of included studies.
Outcome assessment
The anticipated prevalence of Salmonella in broilers was 48%, 89% among the control and 49% among experimental groups, accompanied by a 95% confidence interval (CI) ranging from 50% to 100% in untreated and 2% to 87% in treated groups. shows a substantial heterogeneity of 83%. Broiler samples, comprising caeca from live birds at farms or processing facilities, were incorporated into the analysis. As a result, the samples were categorised into two subgroups according to the treatment options: prebiotics and probiotics. A sampling of broiler cercal revealed a prevalence of 48%, with a 95% confidence interval of 2% to 87%, suggesting that approximately half of the broilers harbour Salmonella after the intervention of probiotics or prebiotics exposure. presents the risk of bias assessment summary.
Figure 3. Forest plot of the efficacy of prebiotics and probiotics in reducing Salmonella colonisation and shedding in broiler chickens.
Table 2. Risk of bias assessment summary.
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Meta-analysis of total Salmonella shedding and colonization in broiler chickens
The meta-analysis results on the shedding colonisation and heterogeneity of Salmonella in broiler chickens are presented in . The forest plot presents a meta-analysis comparing the effects of experimental treatments (prebiotics, probiotics or a combination) on Salmonella colonisation and shedding in broiler chickens, focusing on reducing Salmonella. The total effect across all studies and subgroups shows a mean difference of −1.60 (95% CI: [−2.19, −1.00]), favouring the experimental treatments (prebiotics, probiotics, or their combination) over control. The I2 value of 99% suggests substantial heterogeneity across the studies, meaning that the results vary widely.
Subgroup analysis of Salmonella shedding and colonization in broiler chickens
The combination section assesses the impact of concurrent prebiotic and probiotic interventions. The analysis reveals a significant level of heterogeneity (I2 = 99%), suggesting considerable variation across the included studies. The total effect size is −2.09 (95% CI: [−4.22, 0.04]), indicating a tendency towards the combination being beneficial in decreasing Salmonella colonisation and shedding. However, as the confidence interval encompasses 0, this impact lacks statistical significance, suggesting a degree of doubt about the efficacy of the combination therapy.
The prebiotic therapies show a high level of heterogeneity (I2 = 99%), indicating significant variation across the research. The meta-analysis yielded a pooled effect size of −1.43 (95% CI: [−2.30, −0.56]) for prebiotics, indicating that prebiotics will likely decrease Salmonella colonisation and shedding in broiler chickens. The confidence interval does not intersect with 0, showing that this outcome is statistically significant. The prebiotic therapies demonstrate significant heterogeneity (I2 = 99%), consistent with prebiotics, indicating substantial variation in decreasing Salmonella colonisation and shedding. The total impact size is −1.93 (95% confidence interval: [−3.13, −0.46]), showing a statistically significant decrease in Salmonella colonisation and shedding with probiotic medication. The confidence interval does not intersect with 0, indicating that the impact of probiotics is statistically significant, therefore validating their potential effectiveness.
Salmonella prevalence in broiler chickens
The meta-analysis results on the prevalence and heterogeneity of Salmonella in broiler chickens are presented in . The plot presents the results of a meta-analysis assessing the effects of different interventions (combinations, prebiotics and probiotics) on Salmonella prevalence in broiler chickens. The total pooled effect across all interventions is a risk ratio 0.59 (95% CI: [0.45, 0.79]). This suggests a statistically significant reduction in the outcome risk when using experimental treatments (prebiotics, probiotics or their combination) compared to control. The I2 value of 83% indicates substantial heterogeneity across the studies, meaning there is variability in the results among the included studies.
Figure 4. Forest plot of the efficacy of prebiotics and probiotics in reducing Salmonella prevalence in broiler chickens.
Subgroup analysis Salmonella prevalence in broiler chickens
Analysis of the combination of prebiotic and probiotic treatments shows moderate heterogeneity (I2 = 78%), indicating some variability across the included studies. The pooled risk ratio for this combination is 0.85 (95% CI: [0.48, 1.49]), which is not statistically significant as the confidence interval crosses 1. This suggests uncertainty regarding the effectiveness of the combination treatment, implying that the evidence does not conclusively support its benefit in reducing the prevalence of Salmonella in broiler chickens. In contrast, prebiotics solely reveal high heterogeneity (I2 = 87%), reflecting considerable variability among the studies.
Despite this, the overall effect size for prebiotics is more favourable, with a pooled risk ratio of 0.60 (95% CI: [0.39, 0.93]). This indicates a statistically significant reduction in Salmonella prevalence, as the confidence interval does not cross 1. The results suggest that prebiotics alone effectively provide stronger evidence for their use than the combination treatment. Similarly, the evaluation of probiotics alone also shows high heterogeneity (I2 = 84%), consistent with the other analyses. The pooled risk ratio for probiotics is 0.63 (95% CI: [0.39, 1.03]), with the confidence interval slightly crossing 1. While this suggests that the effect of probiotics alone might not be statistically significant, it tends towards being effective. Therefore, while probiotics appear promising, the evidence is less conclusive than that for prebiotics, warranting further investigation to confirm their efficacy in reducing Salmonella prevalence.
Discussion
Researchers have extensively studied prebiotics and probiotics as potential antibiotic substitutes to enhance gut health and reduce pathogen levels in chickens, particularly Salmonella, a significant foodborne pathogen (Micciche et al. Citation2018; Ruvalcaba-Gómez et al. Citation2022). By providing fermentation substrates, prebiotics help beneficial bacteria grow, and probiotics bring live beneficial microorganisms directly to the digestive tract, which improves the balance of gut microbiota (Micciche et al. Citation2018; Yaqoob et al. Citation2022). Researchers have extensively studied mannan-oligosaccharides (MOS), a prebiotic. They help beneficial bacteria grow in the gut and also play a big part in controlling Salmonella colonisation by stopping it from sticking to intestinal cells. MOS inhibits harmful bacteria’s adhesion to the gut lining, limiting colonisation and encouraging competitive exclusion (Fernandez et al. Citation2002; Spring et al. Citation2000). These therapies are believed to reduce pathogen colonisation by competitive exclusion, boost immune responses, and improve gut barrier function (Ruvalcaba-Gómez et al. Citation2022; Shehata et al. Citation2022). This method has garnered interest in broiler chickens because it reduces Salmonella shedding, mitigating the risk of contamination in poultry products (Micciche et al. Citation2018; Ruvalcaba-Gómez et al. Citation2022). Thus, this systematic review evaluated the effectiveness of prebiotics and probiotics in reducing Salmonella colonisation and shedding in broiler chickens.
The results of this systematic review and meta-analysis confirm what was reported by Shao et al. (Citation2022) the effectiveness of prebiotics and probiotics in reducing Salmonella colonisation and shedding in broiler chickens (Shao et al. Citation2022). Probiotics, including Lactobacillus and Bacillus subtilis strains, have shown efficacy by creating unfavourable conditions for pathogenic bacteria through competitive exclusion, production of short-chain fatty acids, and reducing gut pH levels (Shao et al. Citation2022). Also, similarly to what was reported by Micciche et al. (Citation2018) prebiotics such as mannan-oligosaccharides (MOS) and fructo-oligosaccharides (FOS) were found to enhance the proliferation of beneficial microbiota and improve immune responses by increasing mucosal IgA production, which blocks Salmonella adhesion to intestinal cells. Studies like Spring et al. (Citation2000) and Fernandez et al. (Citation2002) show that adding MOS stops Salmonella from colonising the caeca through competitive exclusion and the gut microbiota. These data highlight the significance of MOS as a targeted intervention in chicken feed for reducing Salmonella shedding.
The variability in efficacy observed among studies may be attributed to several factors, including the specific Salmonella strain targeted, variations in the dosage and delivery method of the prebiotic or probiotic, and the rearing conditions and management practices employed. Environmental stressors and differences in bird age or health status might also influence the effectiveness of these treatments (Nair et al. Citation2018; Shehata et al. Citation2022). Our findings highlight the importance of further research to optimise these interventions by refining their formulations, delivery methods, and synergistic potential to achieve consistent and reliable outcomes in poultry production settings. Future studies should look into the effects of MOS and different dietary changes, find the best doses, and see how they affect the chickens’ health over time and how well they reduce Salmonella.
While the figures present a valuable meta-analysis, we must address several inherent limitations of the synthesis methods and groupings. High heterogeneity was a consistent finding across the subgroups analysed, with I2 values ranging from 78% to 99%. This suggests significant variability among the included studies, likely stemming from differences in study designs, intervention protocols, and outcome measures. Such variability challenges the validity of the combined effect estimates, as it remains unclear whether the observed effects are due to the interventions or underlying differences among the studies. Therefore, the high heterogeneity restricts the findings’ generalisability, necessitating cautious interpretation of the pooled effect estimates.
Moreover, the broad grouping of studies into categories such as combination treatments, prebiotics, and probiotics may have oversimplified the complexity of the interventions and their effects. Within each category, there are likely substantial differences in the specific strains of probiotics used, the types of prebiotics administered and dosages and treatment durations. The aggregated results may not fully capture these variations, which could lead to different outcomes (Halder et al. Citation2024). For example, the probiotic subgroup’s borderline statistical significance could be due to a mix of strains that work well and strains that don’t work as well, which could hide the real effect of some interventions. This aggregation could result in either underestimating or overestimating the interventions’ true efficacy. The risk of bias across the included studies further complicates the interpretation of the results.
Although most studies showed a low risk of bias in most domains, certain areas, such as the blinding of chicken populations and grouping and the blinding of outcome assessments, were highlighted as having a high risk of bias. These biases are particularly concerning in subjective outcome measures, where knowledge of the intervention could influence the analyses. Despite efforts to reduce bias, high-risk domains in certain studies suggest that performance and detection biases could affect the synthesised results. So, even though the review gives us important information about the possible benefits of prebiotics and probiotics, we need to be careful about how we interpret the results because of the problems with the synthesis methods, groupings, and possible biases. Additionally, it should include more detailed subgroup analyses that consider unique aspects of the therapies. In addition, it will be necessary to eliminate bias, especially in blinding processes, to enhance the trustworthiness of future results. By addressing these constraints, we may get more definitive and strong information about the efficacy of prebiotics and probiotics in decreasing Salmonella colonisation and shedding in broiler chickens. This will eventually lead to enhanced poultry health and better food safety.
Conclusions
In conclusion, this systematic review and meta-analysis examined how well prebiotics and probiotics work to lower Salmonella colonisation and shedding, focusing on faeces and cloacal colonisation in broiler chickens. The data suggest that prebiotics, notably MOS, reduced Salmonella growth and shedding in faeces and cloacal samples. MOS alters gut microbiota, boosts immunity, and prevents Salmonella from attaching to intestinal cells. This reduces its faeces and cloacal discharge presence. Combining prebiotics and probiotics also helped manage Salmonella colonisation in various body areas. Probiotics alone have inconclusive evidence of benefit. Probiotics like Lactobacillus and Bacillus subtilis may reduce Salmonella growth in faeces and cloacas. This might happen via competitive exclusion and short-chain fatty acid synthesis. Not all findings were statistically significant. Strain specificity, dose, and environment may affect them. Study discrepancies in intervention techniques, Salmonella strains and outcome assessments may have obscured significant information. This may have miscalculated or overstated treatment efficacy. To improve findings, future studies should reduce variance using consistent intervention techniques and outcome evaluations focusing on Salmonella colonisation in faeces and cloacas.
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Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/00439339.2025.2492562.
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All data are available within the manuscript and its Supplementary Materials files.
