Effects of probiotics, prebiotics, and synbiotics on gut microbiota in older adults: a systematic review and meta-analysis of randomized controlled trials

Abstract

Background

There is a lack of evidence on microbial compositions and associated metabolic changes in probiotics, prebiotics, or synbiotics (PPS) in older adults.

Objective

This meta-analysis aims to evaluate the effects of PPS on gut microbiota composition, short-chain fatty acids (SCFAs), and inflammatory markers in older adults.

Methods

PubMed, Embase, Cochrane Library, and Scopus databases were searched for randomized controlled trials (RCTs) published up to May 2025. RCTs were included if they examined microbiome-related outcomes in individuals aged ≥ 60 years following PPS interventions. The Cochrane Risk of Bias Tool was adopted for Quality appraisal. Meta-analysis was performed in RevMan 5.3, with standardized mean difference (SMD) as effect measures.

Results

29 RCTs were included, involving 1,633 participants. PPS supplementation notably increased Bifidobacterium abundance (prebiotics: SMD = 1.09; probiotics: SMD = 0.40), whereas synbiotics showed no overall effect but enhanced the abundance of specific strains (B. angulatum, B. longum, B. breve). Probiotic supplementation enhanced microbial diversity (Shannon index: SMD = 0.76), while synbiotics increased Lactobacillus casei abundance (SMD = 0.75) and reduced Pseudomonas levels (SMD = -0.55). For inflammatory markers, prebiotics increased IL-10 levels (SMD = 0.61) and reduced IL-1β (SMD = -0.39), whereas synbiotics reduced TNF-α (SMD = -0.36). Synbiotic supplementation enhanced valeric acid (SMD = 0.50) and acetic acid levels (SMD = 0.62).

Conclusion

PPS interventions demonstrated potential benefits for older adults by increasing beneficial bacteria such as Bifidobacterium and Lactobacillus casei, reducing harmful genera like Pseudomonas, improving anti-inflammatory responses, and enhancing the production of SCFAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12937-025-01218-1.

This study followed the PRISMA statement and was preliminarily registered on PROSPERO (CRD42022357834).

Introduction

Over the past decades, population aging has accelerated significantly [1]. The aging process is frequently correlated with surgical trauma, tumors, cognitive dysfunction, insulin resistance, and chronic inflammation [2–6]. To address the socioeconomic challenges posed by an aging population, extensive research has been conducted to explore various strategies for managing healthcare demands and mitigating the broader socioeconomic impacts of aging [7].

During aging, the gastrointestinal microbiota plays a crucial role in various pathophysiological processes, and it may contribute to the age-related chronic, sterile, and low-grade inflammation, known as inflammaging [8]. Alpha diversity is commonly used to describe the richness and evenness of species within a sample, whereas beta diversity is often used to compare the similarity between two or more communities [9, 10]. Compared to young individuals, older adults appear to have enhanced alpha diversity and different beta diversity [11]. Increased abundance of potentially harmful bacteria such as Enterobacteriaceae, along with reduced levels of beneficial genera like Bifidobacterium, possibly reinforces the pro-inflammatory state caused by inflammaging [12].

Aging can also reduce the production of short-chain fatty acids (SCFAs) and exacerbate chronic inflammation [13]. SCFAs represent the primary end products of intestinal microbiota fermentation of indigestible carbohydrates [14]. In older adults, supplementation with probiotics, prebiotics, or synbiotics (PPS) may help enhance SCFA production, thus exerting anti-inflammatory effects and improving glucose and lipid metabolism [15, 16]. Given the complex etiology of older adults, the combined use of PPS often yields better therapeutic outcomes by improving gut microbiota homeostasis [17]. Moreover, PPS can greatly enhance SCFA production and relieve inflammatory responses in older adults [18–21].

Currently, many studies have focused on the efficacy of microbiome products in enhancing health outcomes in older adults, but the results remain contradictory [22, 23]. Supplementation with the same probiotic in similar populations has yielded opposing results in microbial diversity, composition shifts, and disease outcomes [24, 25]. Furthermore, existing systematic reviews and meta-analyses regarding the impact of microbiome products on older adults primarily focus on host physiology rather than associated changes in the microbiome and SCFAs [26, 27]. Therefore, this research intends to evaluate the effects of microbiome-based interventions on gut microbiota, SCFAs, and inflammatory markers in older adults. In addition, we hope to further understand the impacts of microbial formulations on the gut microenvironment in older adults, uncover correlations and causality, and ultimately provide support for clinical interventions to improve and prevent age-related diseases.

Methods

This study followed the PRISMA statement and was preliminarily registered on PROSPERO (CRD42022357834).

Search strategy

PubMed, Embase, Cochrane, and Scopus databases were comprehensively searched up to May 2025. The major terms used in the search strategy included “elderly”, “old”, “microbiota”, “prebiotics”, “probiotics”, and “synbiotics”. The detailed search strategy is presented in Additional Table 1. No more limitations were set.

Literature screening

Two independent reviewers separately screened the literature and discussed the results to solve the disagreements. The inclusion and exclusion criteria were preliminarily set based on PICOS principles. Inclusion criteria covered: (1) Studies investigating patients who reported gut microbiota outcomes; (2) The control group consisted of individuals who either did not receive PPS supplementation or those who were administered a placebo; (3) Randomized controlled trials (RCTs); (4) Analysis of the link between the intake of prebiotics, probiotics, or synbiotics and the microbiome. (5) Participants aged ≥ 60 years. Exclusion criteria were: (1) Data missing or unreported; (2) reports of studies cannot be retrieved. Details are provided in the PICOS table (Table 1).

Table 1.

Inclusion criteria based on PICOS principles

Domain	Criteria
P(Participants)	Older adults aged ≥60 years
I(Interventions)	Supplementation with any type of probiotics, prebiotics, or synbiotics (PPS).
C(Comparators)	Control groups: Placebo or no PPS intervention.
O(Outcomes)	Gut microbiota-related outcomes (e.g., composition, diversity, and SCFA (include the rest))
S(Study design)	Randomized controlled trials

Quality appraisal and data extraction

The Cochrane Risk of Bias 2.0 (RoB 2) tool was used to assess the methodological quality of the included RCTs. The risk of bias was evaluated independently by two reviewers across five domains: (a) bias arising from the randomization process; (b) bias due to deviations from intended interventions; (c) bias due to missing outcome data; (d) bias in outcome measurement; and (e) bias in the selection of the reported result. Each domain was judged as having a low risk of bias, some concerns, or a high risk of bias. Any disagreements were resolved through discussion with a third reviewer to reach a consensus.

Statistical analysis

Data analyses were performed in RevMan 5.3. Standard mean difference (SMD) and 95% confidence interval (CI) were effect estimates for continuous variables. Differences in means and standard deviations were estimated based on the methods recommended in the Cochrane Handbook for Systematic Reviews of Interventions. When only the sample size, median, range, and/or interquartile range were reported, the online tool available at https://www.math.hkbu.edu.hk/~tongt/papers/median2mean.html was used to estimate the sample mean and standard deviation. For subgroup data that required pooling, we performed the combination using the calculator at https://www.statstodo.com/CombineMeansSDs.php. Data that could not be reliably estimated were excluded from the analysis. Heterogeneity was quantitatively determined by I². If little or no heterogeneity (P > 0.1, I² ≤ 50%) was found, a fixed-effect model was selected. In the presence of significant heterogeneity (P ≤ 0.1, I² > 50%), the source of heterogeneity was further tested, and the random-effect model was selected after excluding the source of heterogeneity. Sensitivity analysis was performed using the leave-one-out method to assess the sources of heterogeneity and their impact on the overall results. Publication bias was evaluated only when a single outcome included more than 10 studies, as assessments based on fewer studies may yield unreliable results.

Results

Literature retrieval

3174 records were retrieved from electronic databases. After eliminating duplications, we read the titles and abstracts of the remaining 2272 documents, and then we excluded 2061 reports. The remaining 211 were read in full texts, while 71 of them were excluded for not being RCTs. Additionally, 67 studies were excluded for focusing on non-old populations, 29 for not aligning with the related interventions, and 15 for not meeting the specified outcomes. Finally, 29 RCTs were enrolled. The screening process is displayed in Fig. 1.

Fig. 1.

PRISMA search flowchart

Risk of bias and demographic characteristics of included trials

Detailed information regarding the risk of bias is displayed in Fig. 2. All RCTs specified their methods of random allocation. Only 3 RCTs were rated as high risk for missing outcome data [28–30]. In addition, 3 RCTs were judged as high risk for the randomization process [30–32]. Each item was manifested as a percentage, which implied the proportion of risk levels (Fig. 2). To ensure comprehensive data coverage and minimize the influence of subjective selection bias, we included the above-mentioned studies in this systematic review and meta-analysis although they were considered to have a high risk of bias.

Fig. 2.

Risk of bias details

1,633 old individuals were included, with 864 in the intervention group and 769 in the control group. In the probiotic group, one study each was conducted in Denmark, New Zealand, UK, Finland, USA, and Italy. Additionally, two RCTs were carried out in China and three RCTs were carried out in Japan. A multicenter study included patients from Italy, France, and Germany. In the prebiotic group, one study each was conducted in Japan, China, the Netherlands, and Germany, while two RCTs each were from the UK and USA. In the synbiotic group, two RCTs each were in Finland, Japan, Brazil, and UK. Among the probiotic studies, three RCTs (Rubin, 2022; Ouwehand, 2008; Kwak, 2020) reported changes in relative abundance of gut microbiota rather than absolute abundance [28, 33, 34]. Similarly, one prebiotic study (Konstanti, 2022) focused on relative abundance [35]. Consequently, these RCTs were not considered when assessing the extent of microbiota changes. Detailed study characteristics are listed in Table 2.

Table 2.

Characteristics of the included RCTs

Study	Trial registration number	Country	Group	Sample size		Age (years)	Intervention	Dosage	Disease characteristics	Duration
Probiotics
Akatsu 2012	NA	Japan	Control		22	81.0 ± 9.7	Dextrin	2 g/day	Older adult - patients receiving enteral feeding	12 Weeks
			Trial		23	82.5 ± 7.9	BB536	5*10^10 CFU, b.i.d.
Rubin 2022	NCT03560700	Denmark	Control		27	74 (64.5–82.5)	Placebo	2 capsules/day	VREfm-positive patients	4 Weeks
			Trial		21	76 (71–82)	LGG	3*10^10 CFU, b.i.d.
Asaoka 2022	UMIN000031507	Japan	Control		60	78.9 ± 4.3	Maize starch	1 sachet/day	Older adults with mild cognitive impairment	8 Weeks
			Trial		55	77.2 ± 5.8	B. breve MCC1274	2*10^10 CFU/day
Kondo 2013	NA	Japan	Control		64	83.3 ± 8.5119	Dextrin	2 g/day	Long-term tube-fed older adults	16 Weeks
			Trial		104	84.8577 ± 8.0847	BB536	2.5*10^10 − 5*10^10 CFU/day
Ahmed 2007	NA	New Zealand	Control		20	Mean: 69	Reconstituted skim milk	250 mL/day (no CFU)	Healthy older adults	4 Weeks
			Trial		60	Mean: 69	Reconstituted skim milk + B.lactis HN019	250 mL, 6.5*10^7 − 5*10^9 CFU/day
Nyangale 2015	NA	UK	Control		36	65–80	Microcrystalline cellulose	1 capsule/day	Healthy older adults	4 Weeks
			Trial		36		BC30	1*10^9 CFU/day
Ouwehand 2008	NA	Finland	Control		18	84.3 ± 0.98	Product without any added probiotic	NA	Healthy older adults	12 Weeks
			Trial		18		Product contain B. animalis ssp.lactis BB-12	1*10^9 CFU/day
Kwak 2020	NCT02299570	USA	Control		21	Mean: 63	Normal saline and formulation solution	NA	Patients with recurrent clostridioides difficile infection	4 Weeks
			Trial		23	Mean: 68	RBX2660	≥ 1*10^7 − 2*10^7 live organisms/day
Valentini 2015	NCT01069445-NCT01179789	Italy, France, Germany	Control		30	70.1 ± 3.9	RISTOMED diet alone	NA	Healthy older adults	8 Weeks
			Trial		30		Food combined with VSL#3 bacterial blend	2.24*10^11 CFU/day
Shen 2022	NA	China	Control		80	64.95 ± 4.347	Traditional intestinal preparation	NA	Patients with colorectal cancer	1 Weeks
			Trial		80	66.85 ± 4.05	Traditional intestinal preparation + bifidobacteria triplex viable capsules	2.0 g per dose, t.i.d.
Salvesi 2022	NA	Italy	Control		38	81.5 ± 8.9	Maltodextrin	NA	Healthy older adults	24 Weeks
			Trial		59	81.3 ± 10.1	Synbio®	5*10^9 CFU/day
Wang 2025	NA	China	Control		52	77.61 ± 15.32	Placebo	1.5 g per dose, t.i.d.	Older adults with depression	4 Weeks
			Trial		53	75.72 ± 12.65	L. plantarum HEAL9 + L. paracasei 8702 + P. pentosaceus HH-LP56 + B. longum R0175	1.5 g per dose, t.i.d.
Toda 2005	NA	Japan	Control		9	67.1 ± 4.8	Placebo	NA	Healthy older adults	8 Weeks
			Trial		9		L. lactis subsp. cremoris FC	1.5*10^9 CFU/day
Prebiotics
Akatsu 2015	NA	Japan	Control		11	84.5 ± 7.5	Meibalance	NA	Stroke bed-ridden patients	10 Weeks
			Trial		12	77.8 ± 9.6	Fibren YH + GOS + BGS	NA/4 g/0.4 g/day
Chung 2007	NA	China	Control		9	79.8 ± 6.6	sucrose	4 g/day	Healthy older adults	3 Weeks
			Trial		13	77.5 ± 6.7	XOS	4 g/day
Vulevic 2008	NA	USA	Control		41	69.3 ± 4.0	Maltodextrin	5.5 g/day	Healthy older adults	10 Weeks
			Trial		41		B-GOS	5.5 g/day
Kleessen 1997	NA	germany	Control		15	76.4 (68–89)	lactose	40 g/day	Female constipated patients	19 Days
			Trial		10		Inulin	40 g/day
Majid 2014	ISRCTN06446184	UK	Control		12	71.2 ± 10.6	Maltodextrin	7 g/day	Gastrointestinal patients	2 Weeks
			Trial		10	70.6 ± 8.9	Synergy-1	7 g/day
Konstanti 2022	NCT03026244	Netherlands	Control		13	74 (69–85)	Maltodextrin	NA	Healthy older adults	6 Weeks
			Trial		12	74 (70–84)	BLF + GOS	1 g/2.64 g/day
Vulevic 2015	NCT01303484	UK	Control		20	70.4 ± 3.8	Maltodextrin	5.5 g/day	Healthy older adults	10 Weeks
			Trial		20		B-GOS	5.5 g/day
Wilms 2021	NCT03077529	USA	Control		10	74.3 ± 3.7	Maltodextrin	21.6 g/day	Healthy older adults	4 Weeks
			Trial		10		B-GOS	21.6 g/day
Synbiotics
Bartosch 2005	NA	UK	Control		9	71 (63–85)	MOS + gelatin capsule	6 g/day	Healthy older adults	8 Weeks
			Trial		9	73 (68–90)	Raftilose Synergy1 + B. bifidum strain BB-02 + B. lactis BL-01	6 g/3.5*10^10 CFU/day
Arthur 2008	NA	Finland	Control		23	71.7 ± 6.2	Sucrose	5 g, b.i.d.	Older NSAID users	4 Weeks
			Trial		24	70.3 ± 7.2	Lactitol + L.acidophilus NCFM	Total: 2*10^9 CFU/g; 5–5.5 g, b.i.d.
Shimizu 2018	UMINR000007633	Japan	Control		37	74 (64–81)	No-Synbiotics	NA	Septic older adults	2 Weeks
			Trial		35	74 (64–82)	Yakult BL Seichoyaku + GOS	3 g/10 g/day
Manzoni 2017	NA	Brazil	Control		15	Mean: 71	Saussurea + soy extracts supplemented	150 mL/day	Healthy older adults	4 Weeks
			Trial		14	Mean: 67	Saussurea + soy extracts supplemented + BB-12	1.5*10^10 CFU, 150 mL/day
Björklund 2010	NA	Finland	Control		24	70.3 ± 7.2	Saccharose	10 g/day	Healthy older adults	2 Weeks
			Trial		23	71.7 ± 6.2	Lactitol + L.acidophilus NCFM	10 g/2*10^10 cells/day
Mitsuyoshi 2012	NA	Japan	Control		23	78 (70–92)	No-Synbiotics	NA	Older adults with gastrointestinal and hepatobiliary cancers	2 Weeks
			Trial		25	79 (70–87)	Biolactis powder + BBG-01 + GOS	1 g/1 g/15 g/day
Macfarlane 2013	NCT01226212	UK	Control		20	71.9 ± 5.4	Potato starch + maltodextrose	6 g, b.i.d.	Healthy older adults	4 Weeks
			Trial		23		Synergy I + B. longum	6 g/2*10^11 CFU, b.i.d.
João 2019	RBR-6qr9xx	Brazil	Control		10	77.60 ± 7.22	Maltodextrin	6 g, b.i.d.	Frail older adults	24 Weeks
			Trial		12	75.33 ± 6.85	FOS + L.paracasei LPC-31 + L.rhamnosus HN001 + L.acidophilus NCFM + B. lactis HN019	6 g/1*10^8 − 1*10^9 CFU, b.i.d.

Meta-analysis

Primary outcomes

Twenty-nine RCTs reported various interventions involving PPS and their impacts on the gut microbiota of older adults. A notable increase was found in the abundance of Bifidobacterium in the prebiotics (SMD = 1.09, 95% CI, 0.31 to 1.86, p< 0.001) (Fig. 3 and Additional Fig. 1) and probiotics (SMD = 0.40, 95% CI, 0.06 to 0.75, p < 0.05) groups (Fig. 3 and Additional Fig. 2). In addition, the abundance of Bifidobacterium longum subsp. longum also increased significantly following probiotic supplementation (Fig. 3 and Additional Fig. 3). Specific strains of Bifidobacterium, such as Bifidobacterium angulatum (SMD = 1.69, 95% CI, 1.01 to 2.38, p< 0.001) (Fig. 3 and Additional Fig. 4), Bifidobacterium longum (SMD = 0.99, 95% CI, 0.39 to 1.59, p< 0.01) (Fig. 3 and Additional Fig. 5), and Bifidobacterium breve strain Ya (SMD = 0.95, 95% CI, 0.47 to 1.44, p< 0.001) (Fig. 3 and Additional Fig. 6) were substantially enhanced after synbiotic supplementation. However, the increase in Bifidobacterium abundance was not statistically significant (P= 0.17). Furthermore, old individuals in the probiotics groups exhibited a significant increase in the gut Shannon index (SMD = 0.76, 95% CI, 0.47 to 1.05, p< 0.001) (Fig. 3 and Additional Fig. 7), suggesting enhanced gut microbial diversity. In the synbiotics group, total microbial abundance (SMD = 0.44, 95% CI: 0.19 to 0.69, p < 0.001) was greatly elevated (Fig. 3 and Additional Fig. 8), particularly in Lactobacillus casei strain Shirota (SMD = 0.75, 95% CI: 0.27 to 1.23, p< 0.01) (Fig. 3 and Additional Fig. 9). Furthermore, synbiotic supplementation considerably reduced the abundance of the opportunistic pathogen Pseudomonas (SMD = -0.55, 95% CI: -0.91 to -0.18, p < 0.01) (Fig. 3 and Additional Fig. 10).

Fig. 3.

Forest plot of significant indicators in older adults taking PPS

Secondary outcomes

Old individuals exhibited significantly increased interleukin-10 (IL-10) levels (SMD = 0.61, 95% CI, 0.30 to 0.93, p < 0.001) (Fig. 3 and Additional Fig. 11) after probiotic supplementation, while IL-1β levels were greatly decreased (SMD = −0.39, 95% CI, −0.7 to 0.08, p < 0.05) (Fig. 3 and Additional Fig. 12). TNF-α was also lowered after synbiotic supplementation (SMD= −0.36, 95% CI, −0.69 to −0.02, p < 0.05) (Fig. 3 and Additional Fig. 13). Additionally, valeric acids (SMD = 0.5, 95% CI, 0.16 to 0.84, p < 0.01) (Fig. 3 and Additional Fig. 14) and acetic acids (SMD = 0.62, 95% CI, 0.22 to 1.03) (Fig. 3 and Additional Fig. 15) were greatly elevated after synbiotic supplementation.

Sensitivity analysis

Sensitivity analyses were conducted for outcomes with substantial heterogeneity (I² >50%) and where more than three studies were included. Notable heterogeneity was observed in several outcomes, such as Bifidobacterium abundance (prebiotics) and acetic acid abundance (synbiotics). When any single study was excluded, the overall effect size and statistical significance remained largely unchanged, indicating that the results were stable and not unduly influenced by any individual study.

However, in the analysis of Bifidobacterium abundance (probiotics), the pooled results were not statistically significant when individual studies such as Akatsu 2012, Salvesi 2022, Toda 2005, or Valentini 2015 were excluded [29, 32, 36, 37]. Similarly, for Bifidobacterium longum subsp. longum abundance (probiotics), the pooled estimates were also not statistically significant when the study by Akatsu 2012 or Wang 2025 was excluded. Nevertheless, the overall direction of the effect remained consistent, suggesting the moderate robustness of the findings (Additional Table 2).

In this study, five RCTs were rated as high risk of bias, including Ouwehand 2008, Valentini 2015, Toda 2005, Mitsuyoshi 2012, and Akatsu 2015 [28–32]. After excluding these studies in a sensitivity analysis, the previously significant effects on Bifidobacterium abundance (probiotics) and valeric acid levels (synbiotics) became non-significant (Additional Table 3). This change indicates that the above outcomes are highly sensitive to specific studies, suggesting limited robustness of the pooled results. Nevertheless, to preserve data completeness and minimize subjective judgment, all eligible RCTs were included in this meta-analysis regardless of their risk of bias. However, these findings should be interpreted with caution, and further validation is warranted through rigorously designed RCTs with a low risk of bias.

Subgroup analysis

To explore the potential sources of heterogeneity, we conducted subgroup analyses for PPS based on participants’ health status and intervention duration. When stratified by health status (healthy vs. unhealthy older adults), no statistically significant differences were observed in the abundance of Bifidobacterium following probiotic (Fig. 3, Additional Fig. 16) or prebiotic (Fig. 3, Additional Fig. 17) supplementation, nor in total microbial abundance following synbiotic supplementation (Fig. 3, Additional Fig. 18).

Similarly, when stratified by intervention duration, no significant subgroup differences were found. For probiotics (Fig. 3, Additional Figs. 19) and prebiotics (Fig. 3, Additional Figs. 20), Bifidobacterium abundance did not differ significantly between interventions lasting < 8 weeks and those ≥ 8 weeks. For synbiotics, total microbial abundance showed no significant difference between interventions < 4 weeks and those ≥ 4 weeks (Fig. 3, Additional Fig. 21).

When stratified by the type of probiotic supplemented (Bifido-based vs. Non-Bifido), no statistically significant difference was observed in Bifidobacterium abundance (Fig. 3, Supplementary Fig. 22), suggesting that the observed increase in Bifidobacterium abundance may not be solely attributable to the direct supplementation of Bifidobacterium strains. Similarly, for prebiotics, subgroup analysis of GOS-based vs. Non-GOS interventions revealed no significant difference in Bifidobacterium abundance (Fig. 3, Supplementary Fig. 23). For synbiotics, a subgroup analysis of Lacto-NCFM-based formulations vs. non-Lacto-NCFM ones also showed no statistically significant difference in total microbial abundance (Fig. 3, Supplementary Fig. 24). For probiotics (Fig. 3, Supplementary Fig. 25) and prebiotics (Fig. 3, Supplementary Fig. 26), the pooled estimates demonstrated no significant regional differences in Bifidobacterium abundance between studies conducted in East Asia and those conducted elsewhere. Similarly, for synbiotics, no significant differences in total microbial abundance were observed between studies conducted in East Asia and Europe (Fig. 3, Supplementary Fig. 27).

Discussion

PPS is pivotal in reshaping the gut microenvironment and alleviating intestinal inflammation [38]. This meta-analysis evaluated the effects of PPS supplementation on gut microbiota, related metabolic products, and inflammatory markers in older adults. The pooled results indicated that PPS increased the abundance of beneficial gut bacteria in this population. Additionally, synbiotic supplementation enhanced the production of SCFAs, such as acetic acid and valeric acid, and reduced the abundance of harmful bacteria, including Pseudomonas, whereas these effects were not observed with probiotics or prebiotics alone. Furthermore, prebiotic and synbiotic interventions exerted anti-inflammatory effects, which were not evident in the probiotic-only group.

This study showed that PPS supplementation upregulated Bifidobacterium abundance, consistent with findings reported in the literature [24]. Bifidobacterium abundance gradually declines with aging [39], and a high abundance of Bifidobacterium is considered a hallmark of longevity and extreme longevity [40]. Furthermore, Bifidobacterium not only improves chronic constipation and cognitive function in older adults but also alleviates colitis and maintains microbial homeostasis [41–43]. As a well-known beneficial bacterium, Bifidobacterium maintains gut barrier integrity and mitigates age-related cognitive decline [44, 45]. Lactobacillus casei strain Shirota is widely recognized for its anti-inflammatory and antioxidant properties [46]. In this study, we also found that synbiotic supplementation increased the abundance of this bacteria.

Additionally, the opportunistic pathogen Pseudomonas was reduced following synbiotic intervention in two RCTs of patients with critical diseases (sepsis and cancer) [31, 47]. This finding suggests that synbiotic or probiotic supplementation may help reduce pathogenic bacteria in older individuals with underlying diseases, potentially alleviating disease progression.

After prebiotic supplementation, IL-1β was lowered and IL-10 was elevated. In contrast, following synbiotic supplementation, TNF-α levels were reduced. Excessive production of inflammatory cytokines can lead to gut barrier dysfunction, characterized by downregulated tight junction proteins and increased intestinal permeability [48]. This compromised intestinal integrity allows the translocation of microbes and lipopolysaccharide, further contributing to systemic damage in older individuals [49]. Beneficial bacteria, including Lactobacillus and Bifidobacterium, can reduce Gram-negative bacteria and gut-derived lipopolysaccharide-induced inflammatory factors, such as TNF-α and IL-1β [50, 51]. IL-10 is crucial in suppressing excessive inflammation and is increased in response to beneficial bacteria like Lactobacillus and Bifidobacterium [52]. Therefore, supplementation with prebiotics and synbiotics may help improve gut function and alleviate chronic inflammation in older adults, which is significant for preventing age-related diseases. Ouwehand et al. found no significant changes in inflammatory cytokine levels following probiotic intervention [28]. Consistently, we did not observe significant effects on inflammatory cytokines in the probiotic group.

Gut microbiota influences the production of gastrointestinal metabolites, especially SCFAs, which are generated through the fermentation of complex polysaccharides. A reduction in SCFAs-producing species has been reported in older adults [53]. SCFAs cover butyric acids, propionic acids, acetic acids, and valeric acids. Acetic acids and valeric acids are energy sources for gut-resident microbiota that protect the intestinal mucosal barrier and alleviate inflammation [54]. However, this study observed a more pronounced effect of synbiotics in improving SCFA production, whereas such effects were not seen in probiotic or prebiotic interventions alone. By combining probiotics and prebiotics, synbiotics may provide a more favorable environment for SCFAs-producing microbiota, thereby promoting the efficient conversion of prebiotics into SCFAs.

The RCTs included in this study involved older adults with different baseline health conditions, with more than 50% of participants diagnosed with chronic diseases. Subgroup analyses were performed for outcomes reported in more than five RCTs, and the results indicated that neither intervention duration nor participants’ health status significantly influenced Bifidobacterium abundance (probiotics and prebiotics) or total microbial abundance (synbiotics). Similarly, no significant subgroup differences were observed based on the type of PPS used or the geographic region. These findings suggest that the above factors may not substantially affect the impact of PPS on gut microbiota in older adults. Although we intended to explore the impact of different dosages on outcomes, the substantial variability and a lack of standardization in the doses used prevented us from conducting any relevant subgroup analyses or meta-regression. Such variability in dosage may represent one of the major sources of heterogeneity. In addition, individual-level factors, such as sex, underlying disease types, and medication history, may also confound the intervention effects. These confounding factors were difficult to control in this study and may have affected the accuracy and interpretability of the results. Moreover, the wide variability in disease types may influence gut microecology through different mechanisms, contributing to substantial inter-individual differences in response to PPS interventions. Future trials should consider stratified designs based on participants’ health status to elucidate the differential efficacy of PPS in specific disease conditions or health states.

In the present meta-analysis, Bifidobacterium levels consistently increased following PPS interventions. Bifidobacterium interventions can improve various common conditions among older adults, including hypertension, coronary heart disease, diabetes, and cognitive impairment [45, 55, 56]. Therefore, future research should focus on evaluating the effects of Bifidobacterium at different doses, in various formulations, and in combination with other microbial strains in older populations, to optimize the benefits of Bifidobacterium-based interventions. The limited number of studies included and small sample sizes for certain outcomes, such as the Lactobacillus casei strain Shirota, may compromise the reliability and generalizability of the findings. Additional well-designed, large-scale trials are warranted to validate these preliminary observations.

During the literature screening process, studies that reported only relative abundance were excluded due to difficulties in data standardization and extraction. Most of these studies presented microbial composition in stacked bar chart without providing specific numerical values. Such Qualitative data are not suitable for meta-analysis, which requires standardized Quantitative measures. Nevertheless, we acknowledge the potential value of such studies in providing broader contextual insights. As the number of included studies for each outcome was fewer than 10, key reliability assessments—such as Egger’s test for publication bias—could not be performed. Although funnel plots appeared generally symmetrical (Additional Figs. 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 and 42), it may not be sufficient to rule out publication bias, especially when the number of studies is small. Therefore, the possibility of publication bias cannot be excluded. Under such circumstances, all findings should be considered exploratory and interpreted with caution.

Moreover, the effects of different probiotic strains and prebiotic types vary, and the optimal combination of probiotics and prebiotics remains unclear. Future research should prioritize large-scale, rigorously designed RCTs to ascertain the long-term effects of PPS on gut microbiota composition and metabolism in older adults. Given the variability in individual health status and gut microbiota characteristics, future studies should also explore personalized probiotic, prebiotic, and synbiotic interventions to optimize their effectiveness in the older population.

Conclusion

PPS may serve as an effective dietary intervention to improve gut microbiota homeostasis and regulate chronic inflammation in older adults. Our findings provide preliminary evidence to support the use of PPS to modulate gut microecology in older adults, which could offer a theoretical and practical foundation for future nutritional or clinical interventions targeting age-related microbial dysbiosis and inflammation. Specific supplementation of PPS may promote the production of beneficial metabolites, such as SCFAs, further enhancing the intestinal environment and potentially improving health outcomes. However, due to limitations such as small sample sizes and heterogeneity in intervention protocols, further well-designed, large-scale RCTs are necessary. Future research should standardize outcome measures and more comprehensively assess gut microbial and metabolic markers to confirm the clinical benefits of PPS in aging populations.

Authors’ contributions

KZ designed research; KZ, HL, MG conducted research; KZ, HL, MG analyzed data; and KZ, HL, SC, MG and YW wrote the paper. KZ, HL, SC and MG had primary responsibility for final content. All authors read and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (82174244).

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.