Efficacy of probiotic and synbiotic supplementation on metabolic and endocrine parameters in polycystic ovary syndrome: a meta-analysis of randomized controlled trials

Abstract

Background

This systematic review and meta-analysis aimed to evaluate the effects of probiotic and synbiotic supplementation on insulin resistance, reproductive endocrine function, lipid metabolism, inflammatory response, anthropometric parameters, and oxidative stress in patients with polycystic ovary syndrome (PCOS).

Methods

Databases including PubMed, Web of Science, the Cochrane Library and Embase were systematically searched from inception to March 19, 2026. The Cochrane Risk of Bias Assessment Tool (RoB 2.0) was used for study quality evaluation. Meta-analysis was conducted with RevMan 5.4 and Stata 17.0. The study was prospectively registered in PROSPERO (CRD420251125178) and reported following PRISMA guidelines.

Results

Eleven randomized controlled trials involving 780 participants were included. Probiotic and synbiotic supplementation significantly reduced fasting insulin (MD = − 1.41, 95% CI: −2.76 to − 0.05), total testosterone (MD = − 0.19, 95% CI: −0.25 to − 0.12), triglycerides (MD = − 12.28, 95% CI: −24.21 to − 0.35), low-density lipoprotein cholesterol (MD = − 6.19, 95% CI: −10.08 to − 2.30), BMI (MD = − 0.49, 95% CI: −0.88 to − 0.10), and body weight (MD = − 1.07, 95% CI: −2.08 to − 0.05), while increasing high-density lipoprotein cholesterol (MD = 1.73, 95% CI: 0.40 to 3.06) and total antioxidant capacity (MD = 124.02, 95% CI: 40.49 to 207.54). No significant effects were observed for fasting glucose, HOMA-IR, QUICKI, FSH, LH, SHBG, DHEAS, total cholesterol, C-reactive protein, waist circumference, or hip circumference. The single prebiotic trial contributed only to the meta-analyses of BMI, body weight, and waist circumference. According to the GRADE framework, the certainty of evidence was high for Testosterone, TG, LDL‑C, HDL‑C, BMI, and body weight, moderate for fasting insulin, and low for total antioxidant capacity.

Conclusion

Probiotic or synbiotic supplementation significantly improves multiple clinical parameters in women with PCOS, including reductions in fasting insulin, total testosterone, triglycerides, LDL cholesterol, BMI, and body weight, while increasing HDL cholesterol and total antioxidant capacity. However, the effects on fasting blood glucose, HOMA-IR, and certain sex hormones remain unclear and require further validation. The single available prebiotic trial showed benefits only for BMI and body weight, but its evidence is limited.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12902-026-02398-5.

Introduction

Polycystic ovary syndrome (PCOS) is one of the most prevalent endocrine and metabolic disorders affecting women of reproductive age, with an estimated global prevalence ranging from 6% to 20% [1]. The syndrome is characterized by a constellation of clinical features, including hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, frequently accompanied by insulin resistance (IR), dyslipidemia, obesity, and oxidative stress [2]. Among these, insulin resistance plays a pivotal role in PCOS pathophysiology, not only exacerbating hyperandrogenism and reproductive abnormalities but also substantially elevating the long-term risk of type 2 diabetes mellitus and cardiovascular disease [3].

In recent years, emerging evidence has highlighted the involvement of gut microbiota dysbiosis in PCOS pathogenesis [4, 5]. Alterations in gut microbiota composition and their metabolic products, such as short-chain fatty acids and lipopolysaccharides, have been shown to influence insulin sensitivity, sex hormone synthesis, and lipid metabolism, potentially via the gut–brain–ovary axis [6–8]. Probiotics, synbiotics, and prebiotics, as strategies for restoring intestinal microbial homeostasis, have demonstrated beneficial effects on insulin resistance and lipid profiles in metabolic disorders such as obesity and type 2 diabetes [9]. Guo et al. published a meta-analysis examining gut microbiota modulation in mixed populations; they confirmed glycemic and anti-inflammatory improvements but reported inconclusive effects on lipids and reproductive hormones [10]. However, the inclusion of diverse interventions (e.g., prebiotics, synbiotics, dietary supplements) and non-PCOS populations limits the applicability of their findings to PCOS-specific management.

More recently, Razmpoosh et al. conducted a meta-analysis focusing on prebiotics, alone or as part of synbiotics, for cardiometabolic parameters in PCOS women [11]. While that study provided valuable insights into the effects of prebiotic-based interventions, it primarily emphasized prebiotics and synbiotics, included whole-dietary pattern interventions (e.g., DASH diet), and did not separately analyze the efficacy of probiotics alone. Furthermore, the evidence certainty for several key endocrine and metabolic outcomes (e.g., total testosterone, fasting insulin, lipid fractions) was rated as low or very low according to GRADE, and oxidative stress markers such as total antioxidant capacity were not assessed.

To address these gaps and extend the evidence base, we conducted a focused meta-analysis of randomized controlled trials (RCTs) evaluating probiotic, synbiotic, or prebiotic supplementation specifically in PCOS patients. Our study represents the first meta-analysis to simultaneously incorporate probiotics, synbiotics, and prebiotics (including resistant dextrin) in this population, with a comprehensive assessment of outcomes spanning insulin resistance, reproductive endocrine function, lipid metabolism, inflammatory status, anthropometric parameters, and oxidative stress markers (total antioxidant capacity). Moreover, we applied rigorous GRADE assessment and present high-certainty evidence for multiple clinically important outcomes. Therefore, the primary objective of this meta-analysis is to evaluate the efficacy of probiotic and synbiotic supplementation on key metabolic (insulin resistance, lipid profile), endocrine (total testosterone, SHBG, FSH, LH, DHEAS), and anthropometric outcomes (BMI, body weight, waist/hip circumference) in women with PCOS. The secondary objective is to assess the effects on inflammatory (CRP) and oxidative stress (TAC) markers, and to determine the certainty of evidence using the GRADE framework. By doing so, we aim to provide a holistic, updated evidence base to inform nutritional strategies for the clinical management of PCOS.

Materials and methods

Study protocol

This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [12]. The study protocol has been registered on the PROSPERO International Prospective Systematic Review Registration Platform (ID: CRD420251125178).The full protocol is available at: https://www.crd.york.ac.uk/prospero/display_record.php? ID=CRD420251125178.

Search strategy

Searches were conducted in the PubMed, Web of Science, Embase, and Cochrane Library databases, with English as the search language. The search was performed between March 18 and March 19, 2026, covering the period from database inception to March 19, 2026. The search was carried out using a combination of free terms and subject terms, and it was adapted to the characteristics of each database. The search terms included: “Polycystic Ovary Syndrome”, “PCOS”, “Probiotics”, “Synbiotic”,“Prebiotic Supplementation”,“Lactobacillus”, “Bifidobacterium”, “Randomized Controlled Trial”, “RCT”.

Inclusion and exclusion criteria

Inclusion criteria

Studies were included if they met the following criteria

1. Participants were women clinically diagnosed with polycystic ovary syndrome (PCOS) in accordance with the Rotterdam criteria or National Institutes of Health (NIH) criteria, with complete documentation of outcome measures and relevant medical history data.

2. The intervention group received probiotic monotherapy, synbiotic combinations (probiotics + prebiotics such as inulin or fructooligosaccharides), or prebiotic (resistant dextrin) supplementation, with no restrictions on probiotic strains, doses (ranging from 1 × 10⁹~1 × 10¹⁰ CFU/day), or formulations (capsules, powdered preparations).

3. The control group received placebo (with consistent appearance and administration route as the intervention) or conventional treatment without probiotic/synbiotic/prebiotic components.

4. Primary and secondary outcomes were defined a priori in the study protocol registered with PROSPERO (CRD420251125178). Primary outcomes included three core domains: insulin resistance and glucose homeostasis parameters (fasting insulin, homeostatic model assessment of insulin resistance [HOMA-IR], fasting plasma glucose [FPG], quantitative insulin sensitivity check index [QUICKI]), reproductive endocrine parameters (follicle - stimulating hormone [FSH], luteinizing hormone [LH], total testosterone, sex hormone - binding globulin [SHBG], dehydroepiandrosterone sulfate [DHEAS]), and lipid metabolism parameters (triglycerides [TG], total cholesterol [TC], low - density lipoprotein cholesterol [LDL-C], high - density lipoprotein cholesterol [HDL-C]).

5. Secondary outcomes included anthropometric parameters (body mass index [BMI], body weight, waist circumference [WC], hip circumference [HC]) and inflammatory/oxidative stress markers (C - reactive protein [CRP], total antioxidant capacity [TAC]).

6. The single prebiotic trial contributed only to the meta-analyses of anthropometric outcomes (BMI, body weight, waist circumference). It was not pooled with probiotic or synbiotic studies for any other outcome (e.g., insulin, testosterone, lipid profile, CRP, TAC).

7. The study design was a randomized controlled trial (RCT).

8. Full - text articles were available for data extraction, with intervention durations ranging from 8 to 24 weeks, and clear reporting of baseline characteristics and outcome measurement methods for both intervention and control groups.

Exclusion criteria

Studies were excluded if they met any of the following criteria

1. Non-RCT designs, including systematic reviews, meta - analyses, case reports, commentaries, conference abstracts, or animal experiments.

2. Participants did not meet the specified PCOS diagnostic criteria.

3. Key outcome data (including primary/secondary outcomes specified in inclusion criteria) were insufficient, unclear, or unavailable even after attempting to contact the authors for supplementation.

4. Baseline characteristics or outcome measurement methods were not reported in detail, leading to an inability to assess the comparability between groups or the reliability of results.

Data extraction and quality assessment

Two researchers (ZZS and RZ) independently conducted the two-stage literature screening. In the first stage, studies that clearly did not meet the inclusion criteria were preliminarily excluded by reviewing titles and abstracts. In the second stage, the full texts of the remaining studies were obtained, and the researchers independently screened the full texts based on predefined inclusion and exclusion criteria. During the screening process in both phases, any disagreements were first resolved through discussion between the two researchers. If consensus could not be reached, the matter was referred to a third senior researcher (TL) for adjudication. Independent screening results prior to consensus were not recorded, so Cohen’s kappa could not be calculated retrospectively. Disagreements were resolved through discussion or by a third author, consistent with PRISMA 2020 guidance [12]. Future updates will prospectively record and report kappa statistics.The specific reasons for full-text exclusion were recorded and presented in the PRISMA flowchart (Fig. 1).

Fig. 1.

PRISMA flow diagram for the identification and selection of studies included in the meta-analysis

The methodological quality of the included RCTs was independently assessed by two researchers using the Cochrane Risk of Bias Assessment Tool (RoB 2.0) [13]. This tool covers seven key domains: random sequence generation, allocation concealment, blinding of participants/personnel, blinding of outcome assessors, incomplete outcome data, selective reporting, and other potential biases. Each domain was rated as “low risk”, “unclear risk”, or “high risk”. Discrepancies were resolved through discussion to ensure the reliability of the assessment.

Statistical analysis

Meta-analyses were performed using RevMan 5.4 and Stata 17.0 software. All continuous outcomes were reported in consistent units across the included studies; therefore, mean difference (MD) with 95% confidence intervals (CI) was uniformly used as the effect measure. For studies reporting data as mean ± standard error (SE), these were converted to mean ± standard deviation (SD) using standard formulas prior to meta-analysis.

Heterogeneity was evaluated using the I² statistic and the Q test, with a significance threshold of P < 0.10. Substantial heterogeneity was defined as I² > 50% and P < 0.10, in which case a random‑effects model was applied; otherwise, a fixed‑effects model was used. The certainty of evidence for key outcomes was evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework. Statistical significance was set at α = 0.05.

Results

Study selection

A systematic search of four electronic databases (PubMed, Embase, Cochrane Library, and Web of Science) yielded a total of 197 records. After removing 78 records prior to screening—including 47 duplicates and 31 ineligible records (21 reviews and 10 conference abstracts) identified via automated tools—119 unique records remained for title and abstract screening. Of these, 73 records were excluded based on preliminary eligibility criteria, leaving 46 records for full-text evaluation. During full-text assessment, 35 articles were excluded: 6 lacked specific available data, 4 were animal experiments, 20 did not meet the predefined inclusion criteria, and 5 had been retracted. Ultimately, 11 studies were included in both the qualitative synthesis and meta-analysis (Fig. 1).

Basic characteristics of included studies

The 11 included studies, with a total of 780 participants (396 in the intervention groups and 384 in the control groups). These comprised four probiotic, six synbiotic, and one prebiotic trials. All interventions were Lactobacillus-based (1 × 10⁹~1 × 10¹¹ CFU/day), and most were combined with prebiotic components including inulin, fructooligosaccharides, and resistant dextrin. The intervention duration ranged from 8 to 24 weeks, with eight of the 11 trials lasting 12 weeks. The primary outcome measures assessed included insulin resistance and glucose homeostasis parameters, reproductive endocrine parameters, and lipid metabolism parameters; Secondary outcomes included anthropometric parameters and inflammatory/oxidative stress markers. (see Table 1).

Table 1.

Basic characteristics of studies included in the review

Author, Year	Intervention Type	Intervention Details (Strains/Dosage)	Sample size (Inter/Con)	Duration (week)	Outcomes
Shirani M 2025 [14]	Probiotic	Lactobacillus helveticus R0052 + Bifidobacterium longum R0175, 3 × 10⁹ CFU/day, 1 capsule/day (before meals)	45/45	After 8 Weeks	Total testosterone; SHBG; CRP; TAC
Chudzicka-Strugała I 2025 [15]	Synbiotic	7 probiotic strains (including Lactobacillus acidophilus) + Inulin 42 mg, 2 capsules/day (1.15 × 10¹⁰ CFU per capsule)	19/14	After 24 Weeks	Weight; BMI; WC; HC; Total testosterone; DHEAS; SHBG; LH; FSH; Fasting insulin; Fasting glucose; Total cholesterol; LDL cholesterol; HDL cholesterol; Triglycerides
Hariri Z 2024 [16]	Synbiotic	Bacillus coagulans GBI-30 (10¹¹ spores) + Lactobacillus rhamnosus + Lactobacillus helveticus (10¹⁰ CFU each) + Fructooligosaccharides 500 mg, 2 g/day (orange flavor)	28/24	After 12 Weeks	Weight; BMI; WC; HC
Arab A 2022 [17]	Synbiotic	7 probiotic strains (including Lactobacillus acidophilus) + Inulin 42 mg, 2 capsules/day (1.15 × 10¹⁰ CFU per capsule)	45/43	After 12 Weeks	FSH; LH; Total testosterone; SHBG; DHEAS
Kaur I 2022 [18]	Probiotic	7 strains (including Lactobacillus acidophilus) + Fructooligosaccharides 100 mg, 1 capsule/day for first 2 months, 2 capsules/ day for subsequent 4 months (1 billion CFU per capsule)	52/52	After 24 Weeks	Total testosterone; DHEAS; Fasting insulin; Weight; WC; HC; BMI
Chudzicka-Strugala I 2021 [19]	Synbiotic	7 probiotic strains + Fructooligosaccharides + Inulin, 4 capsules/day	20/19	After 12 Weeks	Weight; BMI; WC; HC; Total testosterone; DHEAS; SHBG; LH; FSH; Fasting insulin; Fasting glucose; Total cholesterol; LDL cholesterol; HDL cholesterol; Triglycerides
Karimi E 2020 [20]	Synbiotic	7 probiotic strains (including Lactobacillus acidophilus) + Inulin, 2 capsules/day	50/49	After 12 Weeks	Weight; BMI; WC; HC; Total cholesterol; LDL cholesterol; HDL cholesterol; Triglycerides
Karimi E 2018 [21]	Synbiotic	7 probiotic strains (including Lactobacillus acidophilus) + Inulin, 2 capsules/day (1000 mg/day)	44/44	After 12 Weeks	Fasting plasma glucose; Fasting insulin; CRP; HOMA-IR; QUICKI
Gholizadeh Shamasbi S 2018 [22]	Prebiotic	Resistant Dextrin 20 g/day (dissolved in water)	31/31	After 12 Weeks	BMI; Weight; WC
Karamali M 2018 [23]	Probiotic	3 probiotic strains (including Lactobacillus acidophilus, 2 × 10⁹ CFU/g each), 1 capsule/day	30/30	After 12 Weeks	Total testosterone; SHBG; DHEAS; TAC
Shoaei T 2015 [24]	Probiotic	Familact capsules (7 strains, including Lactobacillus casei 7 × 10⁹ CFU/g), 500 mg/day	32/33	After 8 Weeks	Fasting insulin; HOMA-IR; QUICKI; CRP

Note: CFU, colony-forming units; SHBG, sex hormone-binding globulin; CRP, C-reactive protein; TAC, total antioxidant capacity; BMI, body mass index; WC, waist circumference; HC, hip circumference; DHEAS, dehydroepiandrosterone sulfate; LH, luteinizing hormone; FSH, follicle-stimulating hormone; HOMA-IR, homeostatic model assessment of insulin resistance; QUICKI, quantitative insulin sensitivity check index; Inter, intervention group; Con, control group

Risk of bias and evidence quality in the included studies

Risk of bias was assessed according to Cochrane criteria. The summary graph (Fig. 2a) demonstrates that five key bias domains (random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, and selective reporting) were all rated as low risk across all included studies, reflecting robust study design. Incomplete outcome data was the only domain with unclear or high risk, attributed to high attrition rates in a small number of trials. Additionally, other bias was uniformly judged as unclear risk for all studies, indicating limited reporting on potential biases such as funding sources or baseline imbalance.

Fig. 2.

Risk of bias summary and graph for included studies

The study-level assessment (Fig. 2b) confirms these findings: all studies were rated as low risk for random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, and selective reporting. For incomplete outcome data, 9 studies were low risk, 1 was high risk (40% attrition rate), and 1 was unclear risk (49% attrition rate). All studies were unclear for other bias due to insufficient reporting of methodological details beyond core Cochrane domains. as summarized in Fig. 2.

Meta-analysis results

All pre‑specified primary and secondary outcomes from the PROSPERO protocol (including HOMA‑IR, QUICKI, FPG, FSH, LH, SHBG, DHEAS, TC, CRP, waist circumference, and hip circumference) are reported below, regardless of statistical significance.

Insulin resistance–related indices

The results of the meta-analyses evaluating the effects of probiotic supplementation on insulin resistance and glucose homeostasis parameters in women with polycystic ovary syndrome (PCOS) are presented in Fig. 3. Pooled analysis of five studies showed that probiotic supplementation significantly reduced fasting insulin levels compared with placebo (MD = − 1.41, 95% CI − 2.76 to − 0.05, P = 0.04), with no evidence of between-study heterogeneity (I² = 0%, P = 0.54). For fasting plasma glucose, three studies were included in the analysis, revealing no significant difference between groups (MD = 0.13, 95% CI − 1.23 to 2.49, P = 0.91), with low heterogeneity (I² = 26%, P = 0.26). Similarly, two studies assessed the effect on homeostasis model assessment of insulin resistance, showing no significant impact (MD = − 0.11, 95% CI − 0.54 to 0.32, P = 0.61, I² = 0%), and two studies evaluated the quantitative insulin sensitivity check index, also indicating no significant effect (MD = 0.00, 95% CI − 0.02 to 0.02, P = 0.90, I² = 0%). These findings suggest that probiotic supplementation may improve fasting insulin levels in women with PCOS, but does not significantly affect fasting plasma glucose, HOMA-IR, or QUICKI.

Fig. 3.

Forest Plots of Meta-Analysis: Effects of Probiotic Intervention on Insulin Resistance and Glucose Homeostasis-Related Parameters in Patients with Polycystic Ovary Syndrome (PCOS). (a) Fasting insulin; (b) Fasting plasma glucose (FPG); (c) Homeostasis model assessment of insulin resistance (HOMA-IR); (d) Quantitative insulin sensitivity check index (QUICKI)

Reproductive endocrine marker results

The results of the meta-analyses evaluating the effects of probiotic or synbiotic supplementation on reproductive hormones in women with polycystic ovary syndrome (PCOS) are presented in Fig. 4. Pooled analysis of six studies showed that probiotic or synbiotic intervention significantly reduced testosterone levels compared with placebo (MD = − 0.19, 95% CI: − 0.25 to − 0.12, P < 0.00001), with moderate heterogeneity (I² = 46%). For follicle-stimulating hormone, three studies were included in the analysis, revealing no significant difference between groups (MD = 1.08, 95% CI: − 0.05 to 2.21, P = 0.06), with no evidence of heterogeneity (I² = 0%). Three randomized controlled trials reported the effect of synbiotic supplementation on LH levels in women with PCOS [14–16]. All three individual trials found no significant change in LH levels compared with placebo. Substantial heterogeneity was observed across the studies (I² = 65%), but due to the small number of studies, the source of heterogeneity could not be reliably identified. Therefore, a pooled estimate is not presented. Overall, the available evidence does not suggest that probiotic or synbiotic supplementation significantly affects LH levels in women with PCOS. (Supplementary Table S3).

Fig. 4.

Forest plots of the effects of probiotic/synbiotic intervention on reproductive endocrine parameters in patients with PCOS. (a) Testosterone; (b) FSH; (c) SHBG; (d) DHEAS

For sex hormone-binding globulin (SHBG), five studies were included. The pooled analysis showed no statistically significant effect (MD = 10.73, 95% CI :–3.84 to 25.30, P = 0.15), but with substantial heterogeneity (I² = 89%, P < 0.00001). To explore the source of heterogeneity, we performed a leave‑one‑out sensitivity analysis. Excluding the study by Chudzicka‑Strugała 2025 [17], the pooled MD changed to 15.91 (95% CI: − 1.96 to 33.79, P = 0.08), and heterogeneity remained high (I² = 89%, P < 0.00001) (Supplementary Table S4). Excluding any other single study did not materially alter the results. Thus, although the outlier study contributed to the large effect, the sensitivity analysis confirms that probiotic/synbiotic supplementation does not significantly alter SHBG levels. For DHEAS, five studies were included, showing no significant effect (SMD = 0.11, 95% CI: − 0.11 to 0.32, P = 0.34), with no evidence of heterogeneity (I² = 0%). These findings indicate that probiotic or synbiotic supplementation lowers testosterone levels but does not significantly affect other reproductive hormones in women with PCOS.

Lipid profile indicator results

The results of the meta-analyses evaluating the effects of probiotic supplementation on blood lipid profiles in women with polycystic ovary syndrome (PCOS) are presented in Fig. 5. For triglycerides, three studies were included in the analysis, showing that probiotic intervention significantly reduced triglyceride levels compared with placebo (MD = − 12.28, 95% CI: − 24.21 to − 0.35, P = 0.04), with no evidence of heterogeneity (I² = 0%). For low-density lipoprotein cholesterol, three studies were included, revealing a significant reduction (MD = − 6.19, 95% CI: − 10.08 to − 2.30, P = 0.002), with low heterogeneity (I² = 31%). For high-density lipoprotein cholesterol, three studies were included, showing a significant increase (MD = 1.73, 95% CI: 0.40 to 3.06, P = 0.01), with no evidence of heterogeneity (I² = 0%).

Fig. 5.

Forest plots of the meta‑analysis on the effects of probiotic intervention on blood lipid parameters in patients with PCOS. (a) TG; (b) LDL‑C; (c) HDL‑C

Three randomized controlled trials have reported on the effects of Synbiotic supplements on total cholesterol (TC) levels in women with polycystic ovary syndrome (PCOS) [17–19]. All three individual trials found that, compared with placebo, there were no significant changes in TC levels. There was significant heterogeneity among the studies (I² = 82%); however, due to the small number of studies, the source of heterogeneity could not be reliably determined. Therefore, no pooled estimates were provided. Overall, the available evidence does not support the notion that probiotic or synbiotic supplements significantly affect TC levels in women with polycystic ovary syndrome (PCOS). (Supplementary Table S3).These findings suggest that probiotic supplementation improves lipid profiles in women with PCOS by reducing triglycerides and low-density lipoprotein cholesterol, and increasing high-density lipoprotein cholesterol.

Inflammatory and oxidative stress markers

The results of the meta-analyses evaluating the effects of probiotic or synbiotic supplementation on oxidative stress and inflammatory parameters in women with polycystic ovary syndrome (PCOS) are presented in Fig. 6. For total antioxidant capacity, two studies were included in the analysis, showing that probiotic or synbiotic intervention significantly increased total antioxidant capacity levels compared with placebo (MD = 124.02, 95% CI: 40.49 to 207.54, P = 0.004), with no evidence of heterogeneity (I² = 0%). Three studies assessed the effect of probiotic or synbiotic supplementation on CRP levels [14, 20, 21]. Two trials reported no significant change [20, 21], and one trial reported a significant reduction [14]. Substantial heterogeneity was present across the three studies (I² = 72%). Leave‑one‑out sensitivity analysis did not identify a single study as the source of heterogeneity, and due to the small number of studies, the heterogeneity could not be reliably explained. Therefore, a pooled estimate is not presented. Overall, the current evidence is insufficient to support an effect of probiotic or synbiotic supplementation on CRP in women with PCOS (Supplementary Table S3). These findings should be interpreted with caution given the limited number of studies included for each outcome.

Fig. 6.

Forest plot of the effect of probiotic/synbiotic intervention on total antioxidant capacity in patients with PCOS

Anthropometric indicators

The results of the meta-analyses evaluating the effects of probiotic or synbiotic supplementation on anthropometric parameters in women with polycystic ovary syndrome (PCOS) are presented in Fig. 7. For body mass index, six studies were included in the analysis, showing that probiotic or synbiotic intervention significantly reduced body mass index compared with placebo (MD = − 0.49, 95% CI: − 0.88 to − 0.10, P = 0.01), with no evidence of heterogeneity (I² = 0%). For body weight, six studies were included, revealing a significant reduction (MD = − 1.07, 95% CI: − 2.08 to − 0.05, P = 0.04), with low heterogeneity (I² = 8%, P = 0.37). For waist circumference, six studies were included, indicating no significant difference between groups (MD = − 0.05, 95% CI: − 0.16 to 0.07, P = 0.42), with moderate heterogeneity (I² = 41%, P = 0.13). For hip circumference, five studies were included, also showing no significant effect (MD = 0.16, 95% CI: − 1.03 to 1.35, P = 0.79), with no evidence of heterogeneity (I² = 0%, P = 0.70). These findings suggest that probiotic or synbiotic supplementation improves body mass index and body weight in women with PCOS, but does not significantly affect waist circumference or hip circumference.This analysis includes one prebiotic trial (resistant dextrin) alongside probiotic and synbiotic trials. Separate subgroup analysis by intervention type (probiotic vs. synbiotic vs. prebiotic) is provided in Supplementary Table S1.

Fig. 7.

Forest plots of the effects of probiotic/synbiotic intervention on anthropometric parameters in patients with PCOS. (a) Body Mass Index (BMI); (b) Body Weight; (c) Waist Circumference (WC); (d) Hip Circumference (HC)

Sensitivity analysis and publication bias

Leave-one-out sensitivity analysis was performed for outcomes with I² > 50%.

For SHBG, excluding the study by Chudzicka‑Strugała 2025 changed the pooled mean difference (MD) from 10.73 (95% CI: − 3.84 to 25.30) to 15.91 (95% CI: − 1.96 to 33.79), and heterogeneity remained high (I² = 89%). Excluding any other single study did not materially alter the results, confirming that no single study solely drove the heterogeneity, although the outlier contributed to the wide confidence interval.

For total cholesterol (TC), excluding the study by Chudzicka‑Strugała 2025 reduced the I² value from 82% to 21%, and the pooled mean difference (MD) became 0.44 (95% CI: − 7.71 to 8.58), indicating that this trial was the primary source of heterogeneity.

For C-reactive protein (CRP), no single study could be identified as the unique source of heterogeneity. I² remained between 68% and 85% after omission of any one study.

For luteinizing hormone (LH), due to the limited number of studies (n = 3), leave-one-out analysis suggested that the observed heterogeneity (I² = 65%) was largely driven by the study by Chudzicka‑Strugała 2021. After its exclusion, I² dropped to 0%, and the effect remained non‑significant (MD = -3.40, 95% CI: − 7.08 to 0.29).These findings are now fully detailed in Supplementary Table S3.

For outcomes with at least 6 studies (Testosterone, BMI, Body weight, WC), we conducted Egger’s regression test to evaluate publication bias. The Egger’s test: testosterone: P = 0.120 (>0.05); BMI: P = 0.205 (>0.05); Body weight: P = 0.368 (>0.05); WC: P = 0.094 (>0.05), indicating no statistically significant publication bias (See Fig. 8 ). For outcomes with ≤ 5 studies (fasting insulin, FPG, HOMA-IR, QUICKI, FSH, SHBG, DHEAS, TG, triglycerides, LDL‑C, HDL‑C, TAC), no funnel plots or publication bias tests were conducted. Therefore, the potential impact of publication bias on the pooled estimates cannot be ruled out.

Fig. 8.

Funnel plots for assessment of publication bias in primary outcomes among patients with polycystic ovary syndrome (PCOS). (a) Testosterone; (b) Body mass index (BMI); (c) Body weight; (d) WC

GRADE evidence level

Using the GRADE framework, evidence certainty for each outcome was assessed across five domains: risk of bias, inconsistency, indirectness, imprecision, and publication bias. For core outcomes (Testosterone, TG, LDL‑C, HDL‑C, BMI, and body weight), evidence certainty was rated as high. For fasting insulin, evidence was downgraded from high to moderate due to imprecision: the 95% CI (− 2.76 to − 0.05) was close to the null (upper bound − 0.05), and the total sample size was below the optimal information size (OIS) [22]. For TAC, evidence certainty was rated as low due to a very high RoB and the limited number of included trials. (See Fig. 9 and Supplementary Table S5)

Fig. 9.

GRADE evidence summary diagram of meta-analysis results

Discussion

This meta-analysis of 11 randomized controlled trials (RCTs) systematically investigated the efficacy of probiotic, synbiotic, and prebiotic supplementation on metabolic, endocrine, anthropometric, and redox-inflammatory parameters in women with polycystic ovary syndrome (PCOS). Our core findings confirm that such microbial interventions significantly reduce fasting insulin, total testosterone, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), body mass index (BMI), and body weight, while elevating high-density lipoprotein cholesterol (HDL-C) and total antioxidant capacity (TAC). No significant effects were observed for fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), reproductive hormones (FSH, LH, SHBG, DHEAS), waist/hip circumference, or C-reactive protein (CRP). These results reinforce the gut microbiota as a modifiable therapeutic target for mitigating key phenotypic abnormalities in PCOS [4, 7, 8], and extend the findings of prior microbiota-focused meta-analyses [10] by focusing exclusively on PCOS cohorts and incorporating prebiotics (e.g., resistant dextrin) alongside probiotics and synbiotics. In contrast to a recent meta‑analysis by Razmpoosh et al. that focused on prebiotics/synbiotics and reported low‑certainty evidence for key endocrine outcomes, our study provides high‑certainty GRADE evidence across multiple metabolic and endocrine parameters, including novel findings for total antioxidant capacity [11].

The significant reduction in fasting insulin aligns with the established role of gut dysbiosis in the pathogenesis of PCOS-related insulin resistance [5, 7, 23]. This finding is further supported by an umbrella meta‑analysis of 48 meta‑analyses by Zarezadeh et al., which demonstrated that probiotic supplementation significantly reduces fasting plasma glucose, HbA1c, HOMA‑IR, and insulin levels across diverse populations, with particularly robust effects in patients with type 2 diabetes and PCOS [24]. Mechanistically, Lactobacillus-based interventions, the predominant strain in our included studies, may enhance intestinal barrier integrity [25], reduce metabolic endotoxemia, and promote the production of short-chain fatty acids (SCFAs); SCFAs modulate hepatic insulin signaling and peripheral glucose uptake, thereby improving insulin sensitivity [6, 26]. The lack of significant changes in HOMA-IR and QUICKI is likely attributable to the limited number of studies reporting these indices (n = 2 each), which constrains statistical power to detect subtle but clinically relevant differences in insulin resistance. This finding is consistent with earlier PCOS RCTs [20, 21], which also reported modest improvements in fasting insulin without concomitant changes in composite insulin resistance scores, likely due to limited statistical power from small sample sizes.

Our observation of reduced total testosterone levels corroborates recent clinical evidence linking microbial modulation to suppressed ovarian androgen synthesis [7, 14, 27]. The gut–liver–gonadal axis is a key mediator of this effect, probiotics and synbiotics alter bile acid metabolism [8], reduce systemic low-grade inflammation [4], and inhibit the cytochrome P450 enzyme system responsible for ovarian and adrenal androgen production [27]. The absence of significant effects on FSH, LH, SHBG, and DHEAS may reflect heterogeneity in baseline hormonal profiles across included studies, as well as the relatively short intervention durations (8–24 weeks); long-term microbial modulation may be required to alter the hypothalamic-pituitary-ovarian axis and sex hormone-binding protein dynamics [17, 18].

Our findings extend and refine those of the recent meta-analysis by Guo et al. [10], which reported beneficial effects of gut microbiota modulation on glycemic and inflammatory parameters in a mixed population but yielded inconclusive results for lipids and reproductive hormones. By focusing exclusively on PCOS cohorts and employing a more granular outcome classification, our study not only confirmed improvements in insulin resistance but also demonstrated significant benefits in hyperandrogenism (total testosterone) and lipid profiles (TG, LDL-C, HDL-C). This suggests that the effects of microbial interventions may be particularly relevant to the distinct metabolic and endocrine phenotype of PCOS.

The favorable effects on lipid metabolism (reduced TG and LDL-C, elevated HDL-C) are consistent with prior RCTs in PCOS [19, 20] and mechanistic studies of probiotic action [28]. A recent umbrella meta‑analysis by Musazadeh et al. confirmed that synbiotic supplementation significantly reduces total cholesterol, LDL‑C, and triglycerides in various populations, supporting the lipid‑lowering potential of combined probiotic‑prebiotic interventions [29]. Lactobacillus strains exert lipid-lowering effects by inhibiting intestinal cholesterol absorption, enhancing bile acid excretion via the enterohepatic circulation, and suppressing hepatic lipogenesis [19]. The high heterogeneity (I²=82%) in total cholesterol (TC) analyses is likely due to small sample sizes in individual trials and variable baseline lipid profiles, and explains the lack of a significant pooled effect for TC despite improvements in other lipid parameters. These findings extend the metabolic benefits of microbial interventions in PCOS beyond glucose homeostasis, highlighting potential utility in reducing cardiovascular disease risk— a key long-term complication of PCOS [3].

For anthropometric parameters, the significant reductions in BMI and body weight without concomitant changes in waist or hip circumference suggest that probiotic/synbiotic supplementation primarily modulates general adiposity rather than central abdominal fat. This observation is supported by Hariri et al. [15] and Gholizadeh Shamasbi et al. [30], who also reported modest reductions in overall body mass but no changes in visceral adiposity markers in PCOS patients. Mechanistically, probiotics may alter energy harvest from the diet [31] and reduce adipocyte inflammation [4], but may not target the visceral fat depots that drive central obesity in PCOS. The low heterogeneity (I²=0% for BMI, 8% for body weight) in these analyses confirms the consistency of this effect across different intervention regimens and study populations.

The significant elevation in TAC, a key marker of antioxidant capacity, adds to the growing evidence of probiotic-mediated improvements in PCOS-related oxidative stress [14, 32]. Probiotic strains such as Lactobacillus helveticus and Bifidobacterium longum scavenge reactive oxygen species and upregulate endogenous antioxidant enzymes [14, 32], mitigating oxidative damage to ovarian and metabolic tissues. Consistently, an umbrella meta‑analysis by Musazadeh et al. pooling data from 15 meta‑analyses confirmed that probiotics significantly increase total antioxidant capacity and glutathione while reducing malondialdehyde, highlighting the systemic antioxidant effects of microbial interventions [33].The low certainty of evidence for TAC is due to the small number of included studies (n = 2), and future large-scale RCTs are needed to validate this effect. In contrast, the null finding for CRP is consistent with prior meta-analyses in PCOS populations that reported no significant changes in inflammatory markers following probiotic supplementation, and may reflect the mild, chronic inflammatory state in PCOS [3], as well as high heterogeneity (I²=72%) in CRP analyses and small sample sizes. Probiotics may modulate local intestinal inflammation without significantly altering systemic CRP levels [34], or longer intervention durations may be required to detect anti-inflammatory effects.

Sensitivity analysis confirmed the robustness of our pooled estimates, with no single study driving the observed effects for any primary outcome. Egger’s regression tests for the two outcomes with 6 studies (testosterone and BMI) showed no evidence of publication bias. For all other outcomes (≤ 5 studies), formal publication bias assessment was not performed due to insufficient statistical power. Thus, the potential influence of publication bias on those pooled estimates cannot be fully excluded. GRADE evaluation assigned high certainty of evidence to most core outcomes (testosterone, TG, LDL‑C, HDL‑C, BMI, body weight) and moderate certainty to fasting insulin due to imprecision. This high-quality evidence supports the clinical relevance of our findings and distinguishes this analysis from prior work [10] that included heterogeneous interventions (e.g., prebiotics, synbiotics, dietary supplements) and non-PCOS populations.

To our knowledge, this is the first meta-analysis to comprehensively evaluate the effects of probiotics, synbiotics, and prebiotics (including resistant dextrin) in a PCOS-specific population. Key strengths include the exclusive focus on PCOS patients diagnosed by Rotterdam or NIH criteria, the inclusion of only RCTs with robust methodological quality as assessed by RoB 2.0, and the GRADE assessment which confirmed high certainty of evidence for the majority of clinically important outcomes.

This meta-analysis has several limitations. (1) This meta-analysis only searched four English-language databases (PubMed, Web of Science, Cochrane Library, and Embase) and did not include other databases. (2) The evidence for prebiotic monotherapy in PCOS is derived from a single trial using resistant dextrin, which limits generalizability. (3) Furthermore, for most outcomes, the number of included studies was too small (≤ 5) to allow a reliable assessment of publication bias using funnel plots or Egger’s regression. Therefore, the potential influence of publication bias on the pooled estimates for those outcomes cannot be fully excluded. (4) Most interventions were short-term (8–24 weeks), leaving long-term effects on clinical outcomes such as ovulatory function, fertility, and metabolic risk unknown. (5)The independent results of the two reviewers prior to consensus were not recorded during the screening process, preventing a retrospective calculation of Cohen’s kappa and thereby limiting the quantitative reporting of screening reproducibility. Additionally, the efficacy of prebiotic monotherapy and the role of specific probiotic strains or consortia warrant further investigation to enable personalized nutritional interventions for PCOS.

Conclusion

In conclusion, this meta-analysis provides high-certainty evidence that probiotic and synbiotic supplementation significantly improves key metabolic parameters (insulin resistance and dyslipidemia), endocrine abnormalities (hyperandrogenism), and anthropometric measures (BMI and body weight) in women with PCOS, alongside enhancing antioxidant capacity. These microbial interventions represent a promising, evidence-based adjunctive strategy to lifestyle modification for the clinical management of PCOS, addressing the unmet need for targeted nutritional approaches that modulate gut microbiota in this population.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

BMI

Body mass index

CI

Confidence interval

DHEAS

Dehydroepiandrosterone sulfate

FPG

Fasting plasma glucose

FSH

Follicle-stimulating hormone

GLP-1

Glucagon-like peptide-1

GRADE

Grading of Recommendations Assessment, Development and Evaluation

HC

Hip circumference

HDL-C

High-density lipoprotein cholesterol

HOMA-IR

Homeostasis model assessment of insulin resistance

IR

Insulin resistance

LDL-C

Low-density lipoprotein cholesterol

LH

Luteinizing hormone

MD

Mean difference

NIH

National Institutes of Health

PCOS

Polycystic ovary syndrome

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PROSPERO

International Prospective Register of Systematic Reviews

RCT

Randomized controlled trial

RoB

Risk of bias

SHBG

Sex hormone-binding globulin

TC

Total cholesterol

TG

Triglycerides

TAC

Total antioxidant capacity

WC

Waist circumference

CRP

C-reactive protein

Author contributions

ZZS contributed to data collection, the analysis plan, and manuscript preparation. RZ and XT provided advice and support on data statistics and writing. TL and ZKL prepared and reviewed the manuscript. All authors read and approved the final version submitted for publication.

Funding

This work was supported by a grant from the Science and Technology Development Plan Project of the Jilin Province Science and Technology Department (20260204086YY).

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.