Front. Med.Frontiers in MedicineFront. Med.2296-858XFrontiers Media S.A.10.3389/fmed.2026.1731528Systematic ReviewEfficacy and safety of probiotic/synbiotic supplementation for osteoporosis: a meta-analysis of randomized controlled trialsWangXinyu1†ConceptualizationData curationFormal analysisMethodologyResourcesWriting – original draftWriting – review & editingZhouLei1†ConceptualizationData curationFormal analysisMethodologyResourcesWriting – original draftWriting – review & editingYuXingming1SoftwareSupervisionValidationVisualizationWriting – original draftHouQiang2Formal analysisInvestigationResourcesSoftwareWriting – original draftWangChenglong1Formal analysisSoftwareSupervisionValidationWriting – original draftCuiWei1Data curationFormal analysisInvestigationResourcesWriting – original draftHuYuheng1InvestigationSoftwareSupervisionValidationWriting – original draftWangXiumei3*ConceptualizationMethodologyProject administrationResourcesWriting – original draftWriting – review & editingZhuZhuangchen1*ConceptualizationMethodologyProject administrationResourcesWriting – original draftWriting – review & editingDepartment of Orthopaedic Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, ChinaDepartment of Burn and Plastic Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, ChinaDepartment of Pediatric Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, Shandong, ChinaCorrespondence: Xiumei Wang, 627265713@qq.comZhuangchen Zhu, Zhuzhuangchen1981@163.com
These authors have contributed equally to this work
To evaluate the efficacy and safety of probiotic/synbiotic supplementation for osteoporosis.
Methods
PubMed, Embase, Web of Science, and Cochrane databases were used to screen studies up to October 2025. Data pooling used standardized mean differences (SMD) or risk ratios (RR) with 95% confidence intervals (CI). Sensitivity analysis assessed result stability. Review Manager 5.4 and STATA 15.1 were used to analyze. Publication bias was assessed by Egger’s test and funnel plots. Evidence for each outcome was evaluated and graded according to GRADE.
Results
Ten randomized controlled trials (RCTs) with 732 patients were included. Significant improvements in lumbar spine bone mineral density (BMD) (SMD: 0.85; 95% CI: 0.01, 1.69; P = 0.049) and parathyroid hormone (SMD: −1.21; 95% CI: −2.19, −0.23; P = 0.02) existed in the probiotic/synbiotic group. No increase in adverse event risk was observed (RR: 1.03; 95% CI: 0.86, 1.23; P = 0.78). No significant effects were found on total hip BMD, osteocalcin, C-terminal telopeptide, alkaline phosphatase, or osteoprotegerin. No publication bias was detected.
Conclusion
Probiotic/synbiotic supplementation may be safe and effective as an adjunctive treatment for osteoporosis, improving bone density without increasing adverse reactions. Larger, multicenter RCTs are needed to confirm these findings.
meta-analysisosteoporosisprobioticsRCTssynbioticsThe author(s) declared that financial support was not received for this work and/or its publication.section-at-acceptancePrecision MedicineIntroduction
Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and a high risk of fractures (1, 2). Its incidence continues to rise with the aging population, becoming a serious public health challenge worldwide. According to the latest data from the International Osteoporosis Foundation, approximately 200 million people worldwide suffer from this disease, resulting in more than 8.9 million fractures annually–an osteoporotic fracture every 3 s on average (3, 4). Therefore, prevention and treatment strategies for osteoporosis must be proactive, focusing on early diagnosis and intervention to effectively prevent the first fracture. However, existing major treatments, including basic measures such as calcium and vitamin D supplementation, as well as anti-resorption drugs (such as bisphosphonates and RANKL inhibitors like denosumab) and bone-forming drugs (such as parathyroid hormone analogs like teriparatide), while reducing fracture risk to some extent, still face many limitations in their clinical application (5, 6). For example, long-term use of bisphosphonates may increase the risk of atypical femoral fractures and osteonecrosis of the mandible (7); while estrogen receptor modulators increase the risk of venous thromboembolic events (8). Furthermore, these drugs are often expensive and require long-term or even lifelong administration, posing significant challenges to patient adherence management (9). Many patients frequently discontinue treatment due to side effects, cost, or inconvenience of administration, resulting in a substantial reduction in efficacy (10). These treatment challenges highlight the urgent need to explore novel, safe, economical, and easily sustainable adjuvant osteoporosis treatment strategies. In recent years, gut-bone axis-targeting microecological interventions–especially the use of probiotics and synbiotics–have injected new vitality into this field (11).
Probiotics are defined as “live microorganisms that, when ingested in adequate amounts, produce beneficial effects on the health of the host,” (12) while synbiotics refer to a mixture of probiotics and prebiotics that probiotics can selectively utilize to promote the health of the host (13). Animal studies have provided strong support for the bone-protective effects of probiotics; for example, supplementing ovariectomized mice with strains of Lactobacillus and Bifidobacterium significantly alleviates bone loss (14). However, when the research focus shifts to human clinical trials, the evidence presents considerable heterogeneity and contradiction. Several newly published randomized controlled trials (RCTs) have reached inconsistent conclusions, with some studies showing no significant effect of probiotics/synbiotics on hip bone mineral density or specific bone turnover markers (15–17). These controversies leave the exact efficacy and safety of probiotics/synbiotics in osteoporosis management unresolved, hindering their translation into clinical practice. The inconsistencies in conclusions may stem from significant differences among studies in strain types, dosages, intervention durations, and baseline characteristics of study subjects, all of which constitute significant sources of clinical heterogeneity.
Therefore, this study aims to address the uncertainties in the current evidence framework through an updated and more comprehensive systematic review and meta-analysis. Compared to the work of Zeng et al. (18), the innovation and value of this study are reflected in the following aspects: first, the timeliness is updated. We have expanded the scope of the literature search to October 2025, maximizing the inclusion of the latest published clinical randomized controlled trial evidence to ensure that the conclusions reflect the latest research progress. Second, the scope is expanded. For the first time, we have included synbiotic intervention in the analysis and systematically evaluated the impact of probiotics/synbiotics on a wider range of outcome indicators, including lumbar and hip bone mineral density (BMD), a series of key bone turnover markers [osteocalcin, C-terminal telopeptide (CTX), alkaline phosphatase, osteoprotegerin, parathyroid hormone], and the incidence of adverse events, thereby providing a more comprehensive efficacy and safety profile. In summary, this study aims to clarify the key clinical question of whether probiotic/synbiotic supplements can serve as a safe and effective adjunctive therapy to improve bone mineral density, regulate bone metabolism, and avoid additional risks in patients with osteoporosis by integrating the latest high-quality evidence up to October 2025. The goal is to provide the highest level of evidence-based medicine for future clinical practice guidelines and research directions.
MethodsLiterature search
This study was carried out following the PRISMA 2020 guidelines (19). The registration ID is CRD42024540614 in PROSPERO. We systematically searched PubMed, Embase, Web of Science, and Cochrane up to October 2025 evaluating the efficacy and safety of probiotic/synbiotic supplementation for osteoporosis. Search terms included “Probiotics,” “Synbiotics,” and “Osteoporosis.” The detailed PubMed search strategy is as follows: (((“Probiotics”[Mesh]) OR (Probiotic)) OR ((“Synbiotics”[Mesh]) OR (Synbiotic))) AND ((“Osteoporosis”[Mesh]) OR ((((((((((Osteoporoses) OR (Post-Traumatic Osteoporoses)) OR (Post-Traumatic Osteoporosis)) OR (Senile Osteoporoses)) OR (Senile Osteoporosis)) OR (Age-Related Bone Loss)) OR (Age-Related Bone Losses)) OR (Age-Related Osteoporosis)) OR (Age Related Osteoporosis)) OR (Age-Related Osteoporoses))). We also screened the bibliographies of RCTs. Two researchers screened the titles and abstracts for retrieving eligible articles independently, and resolved discrepancies through discussion. The search strategy was depicted in Supplementary Table 1.
Inclusion and exclusion criteria
Participants in this study were patients with osteoporosis. The intervention was probiotic/synbiotic supplementation, while the control group consisted of placebo or standard treatment. Outcome measures of interest included lumbar spine and total hip bone mineral density, bone turnover markers, and adverse events. The study type was limited to randomized controlled trials. Exclusion criteria included: study protocols, unpublished studies, non-original studies, non-RCT studies, studies with insufficient data, and review articles.
Data abstraction
Two authors conducted the data extraction independently, with discrepancies resolved by another investigator. First author, publication year, study region, design, registration number, intervention, control, sample size, age, BMI, intervention duration, lumbar spine BMD, total hip BMD, alkaline phosphatase, osteocalcin, CTX, osteoprotegerin, parathyroid hormone, and adverse events were all extracted. Complete dataset would be retrieved from corresponding authors for addressing the insufficient problem.
Quality evaluation
Randomized controlled trials’ quality was assessed using the Cochrane Handbook for Systematic Reviews of Interventions 5.1.0 via seven domains: sequence generation randomization, allocation concealment, blinding of participants and personnel, outcome assessment blinding, incomplete outcome data, selective outcome reporting, and other potential biases (20). Each domain was rated as low risk, high risk, or unclear risk. Studies with more “low risk” ratings were considered of higher quality. Two authors evaluated the studies’ quality, resolving disagreements through discussion.
Statistical analysis
Review Manager 5.4.1 was used to analyze. Standardized mean differences (SMD) with 95% confidence intervals (CI) were used for continuous outcomes. Risk ratios (RR) with 95% CI were applied for dichotomous data. Heterogeneity was evaluated with the chi-squared (χ2) test (Cochran’s Q) and the inconsistency index (21). Substantial heterogeneity was defined by a χ2P-value < 0.1 or an I2 > 50%. The overall SMD or RR was calculated using a random-effects model. For results with more than two studies, a sensitivity analysis was conducted. Funnel plots and Egger’s regression tests were used for evaluating publication bias (22) in Stata 15.1 (Stata Corp., College Station, Texas, USA), with a P-value < 0.05 indicating statistically. When the number of included studies is ≥10, Egger’s regression test is used to assess publication bias; if the number of included studies is <10, visual inspection is performed only using a funnel plot. Additionally, according to GRADE, each outcome’s evidence was evaluated and graded as “high,” “moderate,” “low,” or “very low” quality (23).
ResultsLiterature retrieval, study characteristics, and baseline
Figure 1 showed the literature screening process. A total of 1,085 articles from PubMed (n = 243), Embase (n = 577), Web of Science (n = 224), and Cochrane (n = 41) were identified. After removing duplicates, 709 titles and abstracts were reviewed, and 10 RCTs (15–17, 24–30) involving 732 patients were included. Table 1 lists the characteristics of studies, and Figure 2 presents the quality evaluation results. These studies, published between 2013 and 2023, were conducted in multiple locations worldwide, including Sweden (2 studies), China (1 study), Thailand (1 study), Iran (1 study), South Korea (1 study), Indonesia (1 study), Japan (1 study), Spain (1 study), and Denmark (1 study). All studies employed a randomized controlled design, with 7 studies prospectively registered on international clinical trial registries such as ClinicalTrials.gov.
Flowchart of the systematic search and selection process.
Flowchart depicting the selection process for a meta-analysis. Initially, 1,085 records are identified through database searching, with none from other sources. After removing duplicates, 709 records are screened. Of these, 697 are excluded for reasons such as not being related to the topic and other criteria. Twelve full-text articles are assessed for eligibility, with ten included in the qualitative and quantitative synthesis. Two articles are excluded due to insufficient data.
Characteristics of included studies.
References
Year
Country
Study design
Registration number
Population
Intervention
Control
Jansson et al. (17)
2019
Sweden
RCT
NCT02722980
Postmenopausal women with osteoporosis
Lactobacillus paracasei, Lactobacillus plantarum and Lactobacillus plantarum
Placebo
Nilsson et al. (25)
2015
Sweden
RCT
NCT02422082
Women from the population who were 75 to 80 years old and had low BMD
Dairy products rich in bioactive nutrients (L-leucine and the probiotic Lactobacillus plantarum)
Eat one serving of the same product, but not fortified
Lambert et al. (27)
2013
Denmark
RCT
NCT02174666
Postmenopausal women with osteoporosis
Isoflavones combined with probiotics
90 l of water and 250 g of brown food coloring
Treatment time
Sample size
Age (years)
BMI (kg/m2)
Outcomes
Intervention
Control
Intervention
Control
Intervention
Control
6 months
116
118
59.1 ± 3.8
58.1 ± 4.3
24.2 ± 2.7
23.9 ± 2.6
F1; F8
12 months
34
36
76.4 ± 1.0
76.3 ± 1.1
25.5 ± 3.5
25.3 ± 3.3
F1; F2; F8
3 months
15
12
62.77 ± 6.00
61.63 ± 7.86
23.13 ± 2.19
23.37 ± 2.53
F3; F4; F7
6 months
20
20
62 ± 5.07
64.05 ± 3.58
23.35 ± 3.77
24.20 ± 2.78
F5; F7
6 months
20
21
58.85 ± 0.68
57.29 ± 0.72
24.86 ± 0.41
23.82 ± 0.38
F2; F4; F5; F6; F7
6 months
27
26
58.4 ± 3.4
59.5 ± 3.4
24.3 ± 2.6
23.0 ± 2.0
F4; F5
3 months
35
20
55.88 ± 4.53
65.16 ± 8.25
27.44 ± 4.19
25.51 ± 6.19
F4
6 months
34
35
57.5 ± 4.3
57.8 ± 5.4
22.2 ± 3.3
22.1 ± 2.7
F1; F2
6 months
33
32
NA
NA
F3; F5; F7
12 months
38
40
60.84 ± 1.07
62.85 ± 0.99
24.84 ± 0.62
26.65 ± 0.81
F4; F5; F6
F1, change of lumbar spine; F2, change of total hip; F3, alkaline phosphatase; F4, Osteocalcin (OC); F5, C-terminal telopeptide (CTX); F6, OPG; F7, parathyroid hormone; F8, any adverse event.
Details of the quality evaluation for included RCTs.
Risk of bias summary table features several studies with ratings for criteria D1 through D5. Ratings are color-coded as green for low risk, yellow for some concerns, and red for high risk. D1 to D5 represent different bias aspects like randomisation and outcome measurement. The overall risk combines individual criteria ratings.
The patient populations included in the studies were highly homogeneous, all with a confirmed diagnosis of osteoporosis. Nine studies specifically targeted postmenopausal women. Baseline patient characteristics showed that the mean age of the overall sample ranged from 50 to 76 years, with most studies showing a mean age of 57–62 years, reflecting the high prevalence of osteoporosis in middle-aged and older women. One study from Sweden [Nilsson et al. (25)] focused on an even older population (mean age approximately 76 years). Patient body mass index (BMI) data were fully reported in all studies, with baseline BMI values mostly between 22 and 27 kg/m2, indicating that the overall weight of the included population was within the normal range, avoiding potential bias in bone metabolism caused by obesity or thinness. None of the studies reported detailed information on patient ethnicity. Regarding the severity of osteoporosis, most studies used a bone mineral density (BMD) T-score ≤ −2.5 SD as the primary diagnostic criterion, but there were differences among studies in specific descriptions of disease course, fracture history, etc. The duration of interventions, i.e., follow-up time, varied considerably, ranging from a minimum of 3 months [Zhao et al. (29)] to a maximum of 12 months [Nilsson et al. (25), Lambert et al. (27)]. The remaining studies typically had intervention durations of 6 months (4 studies) or 12 weeks (90 days, 1 study). This variation is one of the important reasons for the heterogeneity in subsequent meta-analyses.
Regarding interventions, all studies evaluated the effects of probiotics or synbiotic supplements, but the strains, dosages, and combinations used showed high diversity. The probiotic strains encompass multiple species from the genera Lactobacillus and Bifidobacterium, such as L. paracasei, L. reuteri, L. casei, L. plantarum, L. acidophilus, L. rhamnosus, and B. animalis. The strain dosage ranges from 10ˆ8 to 10ˆ10 CFU/day. Studies such as Jansson et al. (17) and Vanichanont et al. (15) employed multi-strain formulations, while studies such as Zhao et al. (29) and Han et al. (16) used single strains or combinations with calcium and vitamin D. Notably, studies such as Desfita et al. (30) and Morato-Martinez et al. (26) employed synbiotic interventions, which involved adding prebiotics (such as inulin) or various bone-health-related micronutrients (such as calcium, vitamin D, K, C, zinc, and magnesium) to the probiotics. In terms of control settings, eight studies used an inert placebo as a control, while two other studies [Desfita et al. (30), Morato-Martinez et al. (26)] used an equivalent basal nutritional supplement without probiotics as a control to assess the incremental benefits of probiotics themselves. All studies measured a wide range of outcome measures, including bone mineral density (lumbar spine, total hip), biochemical markers of bone turnover (osteocalcin, CTX, alkaline phosphatase, osteoprotegerin), parathyroid hormone, and adverse events, providing a rich data foundation for this meta-analysis to comprehensively assess the impact of probiotics/synbiotics on bone metabolism.
Change in BMD of lumbar spine
Results on lumbar spine BMD change were synthesized from 3 RCTs involving 393 patients. Meta-analysis showed a significantly greater increase in lumbar spine BMD in the probiotic/synbiotic group (SMD: 0.85; 95% CI: 0.01, 1.69; P = 0.049), with considerable heterogeneity (I2 = 92%, P < 0.00001) (Figure 3a).
Forest plots of panel (a) change in BMD of lumbar spine, (b) change in BMD of total hip, (c) change in osteocalcin, (d) change in CTX.
A forest plot with four panels labeled a, b, c, and d compares the effects of probiotics versus control groups across various studies. Each panel presents the study name, probiotic and control group statistics, and standardized mean difference with confidence intervals. Green squares represent individual study effects, and black diamonds indicate pooled effects. Heterogeneity statistics and overall effect tests are included for each panel. Panels differ in heterogeneity and effect values, with some favoring probiotics and others showing no significant difference.Change in BMD of total hip
The change in total hip BMD was synthesized from three RCTs involving 200 patients. No significant difference between the groups (SMD: 1.02; 95% CI: −0.61, 2.65; P = 0.22), with high heterogeneity (I2 = 96%, P < 0.00001) (Figure 3b).
Change in osteocalcin
The change in osteocalcin was synthesized from six RCTs involving 287 patients. No significant difference found between two groups (SMD: 0.24; 95% CI: −0.33, 0.80; P = 0.42), with high heterogeneity (I2 = 82%, P < 0.0001) (Figure 3c).
Change in CTX
The change in CTX was synthesized from five RCTs involving 279 patients. No significant difference existed between two groups (SMD: −0.36; 95% CI: −0.74, 0.02; P = 0.06), with moderate heterogeneity (I2 = 59%, P = 0.05) (Figure 3d).
Change in parathyroid hormone
The change in parathyroid hormone was synthesized from three RCTs involving 146 patients. Meta-analysis showed a significantly greater reduction in parathyroid hormone levels in the probiotic/synbiotic group (SMD: −1.21; 95% CI: −2.19, −0.23; P = 0.02), with high heterogeneity (I2 = 86%, P = 0.0009) (Figure 4a).
Forest plots of panel (a) change in parathyroid hormone, (b) change in alkaline phosphatase, (c) change in osteoprotegerin, (d) any adverse event.
Four forest plots labeled a, b, c, and d compare probiotics with control groups. Each section includes studies with their mean, standard deviation, total, and weight values. The plots show standard mean differences or risk ratios, heterogeneity statistics, and overall effect. Plots display squares for individual studies and diamonds for overall effects, with a vertical line indicating no effect.Change in alkaline phosphatase
The change in alkaline phosphatase was synthesized from two RCTs involving 105 patients. No significant difference between the groups (SMD: 0.58; 95% CI: −0.60, 1.77; P = 0.33), with high heterogeneity (I2 = 88%, P = 0.004) (Figure 4b).
Change in osteoprotegerin
The change in osteoprotegerin was synthesized from two RCTs involving 119 patients. No significant difference (SMD: −1.03; 95% CI: −3.14, 1.09; P = 0.34), with very high heterogeneity (I2 = 95%, P < 0.00001) (Figure 4c).
Any adverse event
The risk of any adverse event was synthesized from three RCTs involving 375 patients. Meta-analysis showed no significant difference between the groups (RR: 1.03; 95% CI: 0.86, 1.23; P = 0.78), with moderate heterogeneity (I2 = 44%, P = 0.17) (Figure 4d).
Sensitivity analysis and publication bias
We conducted sensitivity analysis for the change in lumbar spine BMD, total hip BMD, osteocalcin, CTX, parathyroid hormone, and adverse events to assess the impact of each RCT on the overall SMD or RR by sequentially excluding individual RCTs. The analysis showed that the metrics remained stable after removing each RCT for total hip BMD (Supplementary Figure 1a), osteocalcin (Supplementary Figure 1b), and adverse events (Supplementary Figure 1c). For lumbar spine BMD (Supplementary Figure 1d), Jansson et al. (17) and Nilsson et al. (25) were the main sources of instability. Removing these two studies significantly altered the direction of the combined effect size, indicating that their smaller effect sizes significantly influenced the overall results. For CTX (Supplementary Figure 1e), removing either the Han et al. (16) study or the Lambert et al. (27) study led to significant changes in the effect size and its significance. For parathyroid hormone (Supplementary Figure 1f), removing either the Morato-Martínez et al. (26) study or the Zhao et al. (29) study significantly altered the size and confidence interval of the combined effect size. No significant asymmetry was observed in the funnel plots for lumbar spine BMD (Supplementary Figure 2a), total hip BMD (Supplementary Figure 2b), osteocalcin (Supplementary Figure 2c), CTX (Supplementary Figure 2d), parathyroid hormone (Supplementary Figure 2e), and adverse events (Supplementary Figure 2f).
GRADE rating
Based on the GRADE ratings, this study assessed the quality of evidence for multiple outcome measures. The results showed that the GRADE ratings for changes in lumbar spine bone mineral density (BMD), total hip bone mineral density, osteocalcin, CTX, parathyroid hormone, alkaline phosphatase, and osteoprotegerin were all low. This was primarily due to significant inconsistencies and imprecision in these measures, although the risk of bias and indirectness were not severe, and publication bias was neither detected nor assessed. However, for any adverse event, the GRADE rating was moderate because its inconsistencies were not severe, but other aspects, such as imprecision, still presented limitations. Overall, the quality of evidence was generally low, and the findings should be interpreted with caution. Detailed GRADE classification results are shown in Table 2.
GRADE rating of each outcome.
No. of studies
Outcomes
Risk of bias
Inconsistency
Indirectness
Imprecision
Publication bias
Plausible confounding
Magnitude of effect
Dose-response gradient
GRADE
3
Change in BMD of lumbar spine
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Low
3
Change in BMD of total hip
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Low
6
Change in osteocalcin
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Low
5
Change in CTX
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Low
3
Change in parathyroid hormone
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Low
2
Change in alkaline phosphatase
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
NA
Would not reduce effect
No
No
Low
2
Change in osteoprotegerin
No serious risk
Serious inconsistency
No serious indirectness
Serious imprecision
NA
Would not reduce effect
No
No
Low
3
Any adverse event
No serious risk
No serious inconsistency
No serious indirectness
Serious imprecision
Undetected
Would not reduce effect
No
No
Moderate
Discussion
This study summarized data from 10 recent RCTs, finding that probiotic/synbiotic intervention significantly improves lumbar spine BMD in osteoporosis patients but has no effect on total hip BMD, consistent with Zeng et al.’s meta-analysis (18). However, sensitivity analysis showed that the improvement in lumbar spine BMD was unstable and largely influenced by one study. This indicates that current results do not strongly support probiotics as a significant treatment for osteoporosis. Larger RCTs with bigger sample sizes are needed to confirm these findings. Furthermore, the high heterogeneity observed in this study was primarily due to significant differences among the included studies, including but not limited to the types of probiotic/synbiotic strains used, their combinations, doses, duration of intervention, baseline characteristics of the study population (e.g., age, sex distribution, and osteoporosis severity), and subtle differences in measurement methods. Given the limited number of studies evaluating each specific outcome measure (e.g., only three studies for lumbar spine BMD), meaningful subgroup analyses to fully explore the sources of heterogeneity were not feasible at this stage, which is indeed a significant limitation. Additionally, based on Zeng et al.’s meta-analysis (18), this study found that probiotic/synbiotic intervention significantly reduces parathyroid hormone levels, which may help explain their potential role in protecting against bone loss. However, due to the small sample size, further research is needed to confirm this conclusion.
Probiotics have been reported to cause adverse reactions, such as dry stools and abdominal distension, with long-term and large-scale use potentially leading to constipation (31, 32). In this study, the duration of probiotic treatment varied, with the longest lasting 12 months. While it is unclear whether the adverse reactions reported in the literature are directly caused by probiotics, the drug safety study found no significant difference in overall adverse reactions. None of the studies reported serious adverse reactions related to probiotic treatment. Furthermore, the number of participants who withdrew due to intolerance to probiotic side effects was similar in both groups, indicating good tolerance and safety of oral probiotics. Probiotic metabolites, especially short-chain fatty acids (SCFAs), are considered potential effectors of probiotics on bones, but SCFAs are also associated with the pathogenesis of hepatic encephalopathy (33). Long-term probiotic treatment may increase SCFAs and worsen hepatic coma in patients with liver disease. Some reports suggest that SCFAs could also contribute to experimental hepatic encephalopathy in individuals without liver disease (34). These findings suggest that probiotic safety should be interpreted with caution, especially regarding the use of oral probiotics in patients with low immunity or other diseases and complications, requiring further observation and evaluation.
It has long been recognized that the gastrointestinal system plays a key role in bone homeostasis by regulating calcium absorption. Recent studies, however, have highlighted the primary role of the gut microbiota in regulating bone remodeling (35). Research has clearly shown that the gut influences bone health, particularly through the regulation of mineral absorption, including calcium, phosphorus, and magnesium, which are essential for healthy bones (36). In addition, gut-derived factors, such as incretin and serotonin, also influence bone turnover to varying degrees (37). Research has demonstrated the gut microbiome’s role in regulating bone physiology through studies on the effects of probiotics in germ-free mice (38). In these studies, germ-free mice, conventional mice, and germ-free mice colonized with normal microbiota were used to investigate the role of the gut microbiota in bone. The results showed that germ-free mice had significantly higher bone mass, fewer osteoclasts per bone surface, and a lower number of CD4+ T cells and osteoclast precursors in the bone marrow compared to conventional mice. However, the exact role of the microbiota in bone development remains inconclusive, as some studies have shown no significant difference in bone density between conventional and germ-free mice (39). This suggests that the impact of the microbiota on bone health is complex and requires long-term validation.
Studies have found that Lactobacillus reuteri reduces the expression of the inflammatory cytokine TNF-α in the jejunum and ileum, leading to a significant increase in femoral and vertebral trabecular density and trabecular number compared to the untreated control group (40). No difference was observed in serum tartrate-resistant acid phosphatase levels. This study further supports that Lactobacillus reuteri benefits bone density. In a study on the mechanism by which intestinal microbiota affects bone, a mouse model showed that yogurt containing Lactobacillus casei, Lactobacillus reuteri, and Lactobacillus gasseri increased calcium absorption in rats, with significantly higher bone mineral content in the experimental group (41). However, further research is needed to identify the specific microbiota that benefit bone health. Modifying the intestinal microbiota through probiotics may, therefore, be a feasible therapeutic strategy to regulate bone remodeling in conditions leading to bone loss and osteoporosis, effectively aiding in the treatment of osteoporosis.
We acknowledge several limitations of this meta-analysis. First, some of the 10 included RCTs did not report detailed blinding procedures, which may affect the reliability of the evidence. Second, the intervention measures in the included RCTs varied (e.g., different probiotic/synbiotic types, doses, and intervention durations), which may explain the significant heterogeneity and instability observed. Third, due to the limited number of studies, this meta-analysis could not gather sufficient data to analyze the effects of probiotic/synbiotic interventions on motor function, quality of life, and other outcomes in osteoporosis patients, which warrants further research. Additionally, due to data limitations, subgroup analysis based on factors such as probiotic types, doses, medication frequency, duration, age, and race were not possible, leaving the potential influence of these factors unclear. Fourth, most of the included studies are from Europe and Asia, with a lack of data from the Americas, Africa, and other regions. Therefore, it is uncertain whether the findings of this study are generalizable worldwide. Finally, although the improvement in lumbar spine BMD reached statistical significance (P = 0.049), the lower limit of the 95% confidence interval for the effect estimate was very close to the no effect line, indicating considerable uncertainty in the results. This reflects the instability revealed by sensitivity analyses and the significant heterogeneity. Unfortunately, due to differences in reporting among the original studies, the limited number of studies, and high heterogeneity, we were unable to reliably estimate the mean absolute change in lumbar spine BMD and its potential significance in reducing fracture risk. Therefore, the current results do not strongly support the use of probiotics as a primary treatment for osteoporosis, and larger studies are needed to validate this finding and assess its clinical relevance. Despite these limitations, this meta-analysis updates the analysis of high-quality published RCTs and further validates the efficacy and safety of probiotics and their products in patients with osteoporosis, building on previous studies.
Conclusion
This study found that probiotics/synbiotics supplementation may be a safe and effective adjunctive treatment for osteoporosis, improving bone density without increasing adverse reactions. However, due to the limitations of this study, including the small number of studies, significant heterogeneity, and potential instability, larger, multicenter RCTs are needed to further confirm the therapeutic effect and safety of probiotics and their products in osteoporosis patients.
Data availability statement
The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding authors.
Author contributions
XyW: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Writing – original draft, Writing – review & editing. LZ: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Writing – original draft, Writing – review & editing. XY: Software, Supervision, Validation, Visualization, Writing – original draft. QH: Formal analysis, Investigation, Resources, Software, Writing – original draft. CW: Formal analysis, Software, Supervision, Validation, Writing – original draft. WC: Data curation, Formal analysis, Investigation, Resources, Writing – original draft. YH: Investigation, Software, Supervision, Validation, Writing – original draft. XW: Conceptualization, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing. ZZ: Conceptualization, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Supplementary material
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Sensitivity analysis of panel (a) change in BMD of total hip, (b) change in osteocalcin, (c) and any adverse event, (d) change in BMD of lumbar spine, (e) change in CTX, and (f) change in parathyroid hormone.
Funnel plots of panel (a) change in BMD of lumbar spine, (b) change in BMD of total hip, (c) change in osteocalcin, (d) change in CTX, (e) change in parathyroid hormone, and (f) any adverse event.
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Edited by: Kiyan Heybati, Mayo Clinic, United States
Reviewed by: Narges Lashkarbolouk, Tehran University of Medical Sciences, Iran
Mahdi Mazandarani, Golestan University of Medical Sciences, Iran