Effects of probiotics and synbiotics supplementation on cardiovascular risk factors in adults with prediabetes: a GRADE-assessed systematic review and meta-analysis of RCTs

Abstract

Background and aim

Prediabetes is associated with metabolic disturbances that significantly increase the risk of developing cardiovascular disease (CVD). Probiotics and synbiotics may improve metabolic health, but their impact on CVD risk factors in prediabetic adults needs further evaluation. This meta-analysis evaluates the effects of probiotic and synbiotic supplementation on cardiovascular risk factors in individuals with prediabetes.

Methods

An extensive search of scientific databases was conducted from inception through February 2025 to identify relevant randomized controlled trials (RCTs). After screening, data were extracted. A random-effects model was used to compute pooled weighted mean differences (WMD) with 95% confidence intervals (CIs), accounting for study heterogeneity. All statistical analyses were performed using Stata.

Results

The pooled analysis of seven RCTs revealed that probiotics and synbiotics supplementation in prediabetic adults had no significant changes in fasting blood glucose (FBG) (WMD: -2.15 mg/dL, p = 0.135), fasting insulin (FI) (WMD: -0.43 µIU/mL, p = 0.526), Homeostasis model assessment of insulin resistance (HOMA-IR) (WMD: -0.31, p = 0.090), triglycerides (TG) (WMD: -5.53 mg/dL, p = 0.614), total cholesterol (TC) (WMD: -1.92 mg/dL, p = 0.730), low-density lipoprotein cholesterol (LDL-C) (WMD: 1.39 mg/dL, p = 0.542), systolic blood pressure (BP) (WMD: -2.24 mmHg, p = 0.480), diastolic BP (WMD: -1.10 mmHg, p = 0.260), body weight (WMD: 0.30 kg, p = 0.861), and body mass index (BMI) (WMD: -0.20 kg/m², p = 0.510). In contrast, a significant improvement was noted in hemoglobin A1c (HbA1c) levels (WMD: -0.16, p < 0.001) and high-density lipoprotein cholesterol (HDL-C) (WMD: 1.63 mg/dL, p = 0.020).

Conclusion

Probiotics and synbiotics show the potential to improve specific cardiovascular risk factors in prediabetic individuals, notably through reductions in HbA1c and an increase in HDL-C levels. However, their effects on other metabolic markers, such as FBG, insulin resistance, lipid profiles, and weight, remain inconclusive. Further high-quality trials are necessary to fully assess their impact on cardiovascular risks and diabetes progression in this population.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-025-01700-x.

Introduction

Prediabetes is a high-risk intermediate state between normal glucose homeostasis and diabetes, which is defined by impaired fasting glucose, impaired glucose tolerance, and/or elevated HbA1c levels [1]. The prevalence of prediabetes remains increased, with an estimated IFG and IGT of approximately 300 million people and 470 million people, respectively. Epidemiological reports estimate that the number could reach more than 600 million by 2045 [2]. It is shown that Prediabetes is linked to an increased risk of cardiovascular diseases (CVD), including coronary artery disease, stroke, and peripheral vascular diseases [3, 4]. Numerous studies have demonstrated strong correlations between prediabetes and the risk of CVD and mortality [5–8]. People with prediabetes were associated with a higher risk of dyslipidemia, obesity, and hypertension. Furthermore, the odds of CVD were increased in prediabetes [3, 4, 9]. These findings emphasize the importance of early diagnosis and management of prediabetes to reduce cardiovascular complications.

In recent years, many studies have explored that the composition of gut microbiota plays a role in the development of insulin resistance and type 2 diabetes mellitus (T2DM), and normal gut microbiota could have protective effects [10–12]. Probiotics are defined as live microorganisms that provide health benefits when administered in adequate amounts and are commonly derived from strains of Bifidobacterium and Lactobacillus that are found in various dietary supplements [13]. on the other hand, Prebiotics are indigestible food components, such as fibers or polysaccharides, that promote health by modulating gut microbiota [13, 14]. When prebiotics and probiotics are combined, they form a synbiotics, which may provide synergistic health benefits [15]. Nowadays, probiotics or synbiotics are used as non-invasive supplementary therapy tools to delay the progression of prediabetes and CVD [12, 16]. The modulating influence of probiotics or synbiotics acts via a variety of means: anti-inflammatory effects, improved lipid metabolism, enhanced insulin sensitivity, regulated systemic glucose homeostasis, modulation of the immune system, improved enzyme formation, and production of antimicrobial compounds [10, 13, 17, 18].

Previous meta-analyses have examined the effects of probiotics and synbiotics on cardiovascular risk factors in individuals with prediabetes and T2DM [19, 20], but they have several limitations, including a narrow focus on select risk factors, a lack of specific analysis for prediabetic populations, and insufficient consideration of effective dosages or dose-response relationships. Moreover, findings from individual studies remain inconsistent, likely due to variability in study designs, probiotic strains, dosages, and intervention durations. To address these gaps, the present study aims to conduct a systematic review and dose-response meta-analysis to evaluate the effects of probiotics and synbiotics supplementation on cardiovascular risk factors in adults with prediabetes, assess the quality of the current evidence, and provide insights into optimal supplementation strategies for clinical practice.

Methods

Study protocol and design

This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline [21]. The study was designed based on the PICOS (Population, Intervention, Comparison, Outcomes, and Study) framework [22], including:

• P (Population): adults with prediabetes.

• I (Intervention): probiotics and synbiotics supplementation.

• C (Comparison): Placebo, no supplementation, or control.

• O (Outcomes): Changes in cardiovascular risk factors, including triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), fasting insulin (FI), glycated hemoglobin (HbA1c), homeostatic model assessment of insulin resistance (HOMA-IR), systolic blood pressure (SBP), diastolic blood pressure (DBP), body weight (BW), and body mass index (BMI).

• S (Study Design): Randomized controlled trials (RCTs).

Search strategy

A comprehensive literature search was conducted from inception until February 2025, across major scientific databases, including PubMed, ISI Web of Science, and Scopus, to identify RCTs assessing the effects of probiotics and synbiotics supplementation on cardiovascular risk factors in adults with prediabetes. The search strategy was developed using a combination of Medical Subject Headings (MeSH) terms and non-MeSH phrases, applied within the [Title/Abstract] field to enhance sensitivity and specificity. The following search terms were utilized: (“probiotics” OR “probiotic” OR “synbiotic” OR “synbiotics” OR “postbiotic” OR “postbiotics” OR “pro-biotic*” OR “post-biotic*” OR “syn-biotic*” OR “Lactobacillus” OR “bifidobacteria” OR “Bifidobacterium”) AND (“prediabetes” OR “pre-diabetic” OR “prediabetes” OR “prediabetic” OR “hyperglycemia” OR “hyperglycemic”) (Supplementary Tables 1 and 2). The search was restricted to peer-reviewed articles published in English. Additional relevant studies were identified through a manual search in Google Scholar to ensure a comprehensive evaluation of the existing literature. Furthermore, gray literature, including conference proceedings, dissertations, and preprints, was assessed to minimize publication bias and capture potentially relevant findings.

Eligibility criteria

Studies were eligible if they met the inclusion criteria: only RCTs were included. Participants consisted of adults diagnosed with prediabetes, with no restrictions on gender, ethnicity, or the presence of associated comorbidities. The intervention involved the consumption of probiotics or synbiotics for a minimum duration of one week. These interventions were compared against a placebo, a control group, or no treatment. There were no restrictions regarding the publication time frame or language to ensure comprehensive coverage of relevant studies.

Studies were excluded if they did not meet the inclusion criteria or fell into the following categories: non-randomized trials, case-control studies, cross-sectional studies, case series, case reports, cohort studies, systematic reviews, meta-analyses, abstracts, letters to the editor, in vitro studies, and animal studies. Additionally, studies with a follow-up duration of less than one week after the intervention were excluded. Research focusing on non-prediabetic individuals, studies assessing unrelated parameters, or those not providing full-text availability were also excluded from the analysis.

Data extraction

Two reviewers (O.A. and A.S.) performed data extraction independently, systematically assessing the included studies and extracting relevant information. Any discrepancies regarding the relevance or interpretation of the data were resolved through discussion, and if consensus was not reached, a third reviewer (M.K.) was consulted to mediate. The extracted data were systematically recorded in an Excel spreadsheet to ensure consistency and clarity.

The following information was collected from each eligible study: the first author’s name, year of publication, country where the trial was conducted, study design, sample size, mean age of participants, BMI, gender distribution, type of intervention, dosage, duration of supplementation, and the overall health status of participants. The primary outcomes of interest included changes in the cardiometabolic risk factors in CVD (TG, TC, LDL-C, HDL-C, FBG, FI, HbA1c, HOMA-IR, BP, BW, BMI). Any additional relevant findings related to cardiovascular risk factors were also noted to enhance the robustness of the analysis.

Quality assessment

The quality of the RCTs was independently assessed by two reviewers (O.A. & M.K.), following the Cochrane criteria for evaluating the risk of bias (RoB). The assessment was conducted using version 2 of the risk of bias tool (RoB-2) [23]. The quality of all qualified studies was evaluated across five domains (D): bias arising from the randomization process, deviations from the intended intervention, missing outcome data, accuracy in measuring outcomes, and selection of reported results. Each domain was classified regarding its risk of bias as either “Low”, “High”, or “Some concerns” in terms of assessed domains.

Certainty of evidence (GRADE Assessment)

The overall certainty of evidence across RCTs was evaluated separately by two reviewers (A.S. and O.A.) utilizing the Grading of Recommendations Assessment, Development, and Evaluation Working Group (GRADE) guidelines [24]. As a result, the quality of evidence was classified into four categories: high, moderate, low, and very low.

Statistical analysis

Statistical analyses were conducted using Stata version 11.1 (Stata Corp, College Station), with a significance threshold of p < 0.05 for all two-tailed tests. A random-effects model was employed to calculate pooled weighted mean differences (WMD), accounting for heterogeneity [25]. Mean differences (MD) in outcomes were determined between baseline and post-intervention for the intervention and control groups. The standard deviation (SD) of mean differences was derived using the formula: SD = √[(SD at baseline)² + (SD at the end)² − (2 × r × SD at baseline × SD at the end)] [26], with a correlation coefficient (r) of 0.8. For studies reporting standard errors (SE) instead of SD, the Hozo et al. [27] method was used to convert SE, 95% confidence intervals (CI), and interquartile ranges into SDs. Heterogeneity was evaluated using Cochrane’s Q test and the I² statistic, which was classified as follows: I² < 40% (unlikely to be important), 40–60% (moderate heterogeneity), 60–75% (substantial heterogeneity), and > 75% (considerable heterogeneity). To find the source of heterogeneity, a subgroup analyses were conducted based on the baseline level of outcomes, trial duration (< 12 weeks vs. ≥12 weeks), intervention type (probiotics vs. synbiotics), and baseline BMI (normal, overweight, obese). Publication bias was assessed via visual inspection of funnel plots and Egger’s and Begg’s tests, with sensitivity analyses performed using the leave-one-out method [28, 29]. Meta-regression and non-linear dose-response regression were employed to assess the effects of intervention dose and duration on glycemic outcomes and to synthesize correlated dose-response data across studies [30, 31]. The trim-and-fill method was applied to address publication bias [32].

3. Results

Study selection

A comprehensive search was conducted in online databases, including PubMed (n = 387), ISI Web of Science (n = 568), Scopus (n = 1046), and a manual search in Google Scholar. A total of 2001 records were identified. In the initial phase, 463 studies were removed due to duplication. Subsequently, 1538 studies underwent screening by evaluating their titles and abstracts. This screening process resulted in excluding 1526 records deemed irrelevant to the subject. A total of 12 articles were assessed in full text for eligibility, excluding five studies that did not report the desired data for our analysis. Ultimately, seven eligible RCTs were included in this systematic review and meta-analysis (Fig. 1).

Fig. 1.

PRISMA Flow chart of study selection for inclusion trials in the systematic review

Study characteristics

This meta-analysis includes seven RCTs with nine effect sizes, as detailed in the characteristics shown in Table 1. All study designs employ a parallel, randomized, double-blind design. Five studies consist of a single arm [27, 33–36], whereas two studies are designed with two arms [10, 37]. This meta-analysis included 435 prediabetic participants, 250 cases, and 185 controls. Studies were conducted in Iran [10, 33, 36, 37], New Zealand [34, 35], and Korea [27] between 2014 and 2023. The participants were both male and female. The intervention group’s mean age ranged from 44 to 60.4 years, while their BMI varied between 25.03 and 34.7 (kg/m2). The trial lasted from 8 to 24 weeks.

Table 1.

Characteristics of the included studies in the systematic review and meta-analysis

Study	Country	Study design	Participant	Gender	Sample size (IG/CG)	Trial duration (week)	Means Age		Means BMI		Intervention	Dose	CG
							IG	CG	IG	CG
Mahboobi et al. (2014)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (NR)	55 (28/27)	8	51.03 ± 1.37	50.36 ± 1.32	28.87 ± 0.8	29.7 ± 0.8	Probiotics (multi-strain)	500 mg/day	Placebo
Kassaian et al. (2018) (A)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (23, 21)	44 (30/14)	24	52.97 ± 6.8	52.97 ± 5.9	29.1 ± 2.9	30.4 ± 3.2	Synbiotics	6 g (1 × 109 CFU for each) + inulin-based prebiotic	Placebo
Kassaian et al. (2018) (B)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (18, 23)	41 (27/14)	24	52.9 ± 6.3	52.97 ± 5.9	29.6 ± 3.5	30.4 ± 3.2	Probiotics (multi-strain) (Bifidobacterium spp.)	6 g/day (1 × 109 CFU for each)	Placebo
Kassaian et al. (2019) (A)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (18, 23)	41 (27/14)	24	52.9 ± 6.3	52.97 ± 5.9	29.6 ± 3.5	30.4 ± 3.2	Probiotics (multi-strain) (Bifidobacterium spp.)	6 g/day (1 × 109 CFU for each)	Placebo
Kassaian et al. (2019) (B)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (23, 21)	44 (30/14)	24	52.97 ± 6.8	52.97 ± 5.9	29.1 ± 2.9	30.4 ± 3.2	Synbiotics	6 g (1 × 109 CFU for each) + inulin-based prebiotic	Placebo
Tay et al. (2020)	New Zealand	RCT, PC, parallel, DB	Prediabetic	M & F (8, 18)	26 (15/11)	12	52.9 ± 8.7	54.1 ± 6.4	34.7 ± 4.9	33.6 ± 3.7	Probiotic (L. Rhamnosus)	6 × 109 CFU	Placebo
Oh et al. (2021)	Korea	RCT, PC, parallel, DB	Prediabetic	M & F (29, 8)	37 (20/17)	8	53.55 ± 10.18	56.4 ± 11.57	25.03 ± 1.92	25.25 ± 3.14	Probiotic (L. Rhamnosus)	Once per day (4 × 109 CFU/capsule)	Placebo
Barthow et al. (2022)	New Zealand	RCT, PC, parallel, DB	Prediabetic	M & F (36, 41)	77 (38/39)	24	60.4 ± 10.66	59.9 ± 8.66	30.9 ± 5.3	29.9 ± 6.6	Probiotics	6 × 109 (CFU/day)	Placebo
AkbariRad et al. (2023)	Iran	RCT, PC, parallel, DB	Prediabetic	M & F (20, 50)	70 (35/35)	12	44 ± 8.66	43.06 ± 8.53	30.74 ± 3.56	31.41 ± 3.97	Synbiotics	500 mg/day	Placebo

IG intervention group, CG control group, RCT randomized clinical trial, PC placebo control, SB single-blind, DB double-blind, M male, F female, CFU Colony-forming unit

Meta-analysis on glycemic markers

The meta-analysis of six intervention arms [10, 27, 34–36] assessed the impact of probiotics and synbiotics supplementation on glycemic markers in prediabetic adults. The pooled results showed a slight but non-significant reduction in FBG (WMD: −2.15 mg/dL; 95% CI: [−4.97, 0.68]; p = 0.135), accompanied by substantial heterogeneity (I² = 67.8%, p = 0.008) (Fig. 2A). In contrast, HbA1c levels demonstrated a significant reduction (WMD: −0.16%; 95% CI: [−0.25, −0.07]; p < 0.001), with no heterogeneity across studies (I² = 0.0%, p = 0.896) (Fig. 2B). No significant change was found for FI (WMD: −0.43 µIU/mL; 95% CI: [−1.76, 0.90]; p = 0.526) (Fig. 2C), or HOMA-IR (WMD: −0.31; 95% CI: [−0.68, 0.05]; p = 0.090), both of which also showed low heterogeneity (I² = 4.1% and 3.6%, respectively) (Fig. 2D).

Fig. 2.

Forest plot detailing weighted mean difference (WMD) with 95% confidence intervals (CIs) for the effect of probiotic and synbiotic supplementation on glycemic markers, including: A) fasting blood glucose (FBG), B) Hemoglobin A1c (HbA1c), C) fasting insulin (FI), D) Homeostasis model assessment of insulin resistance (HOMA-IR)

Subgroup analyses indicated that the hypoglycemic effect on FBG was more pronounced in overweight individuals (WMD = − 4.31 mg/dL; 95% CI: −7.02, − 1.59; p = 0.002) but not in prediabetic individuals with obesity. Longer intervention durations (≥ 12 weeks) and probiotic-only formulations showed larger but non-significant reductions in FBG. Both probiotics and synbiotics significantly decreased HbA1c and HOMA-IR, particularly in overweight individuals, while the effect remained non-significant in obese participants (Table 2).

Table 2.

Meta-analysis findings for the effect of probiotics and synbiotics supplementation on cardiovascular risk factors in adults with prediabetes

	No. of studies	WMD (95%CI)	P-value	Heterogeneity
				P heterogeneity	I2
Fasting blood glucose (FBG) (mg/dL)
Overall effect	6	-2.15 (-4.98, 0.67)	0.135	0.008	67.8%
Trial duration (week)
< 12	1	-1.37 (-5.09, 2.35)	0.471	-	-
≥ 12	5	-2.30 (-5.79, 1.18)	0.196	0.004	73.9%
Intervention type
Probiotic	4	-2.46 (-5.32, 0.40)	0.092	0.175	39.4%
Synbiotic	2	-2.02 (-9.07, 5.01)	0.573	0.002	89.8%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-4.31 (-7.02, -1.59)	0.002*	0.168	43.9%
Obese (> 30)	3	0.89 (-1.45, 3.23)	0.457	0.642	0.0%
Glycated hemoglobin (HbA1c) (%)
Overall effect	5	-0.16 (-0.25, -0.07)	< 0.001*	0.896	0.0%
Trial duration (week)
< 12	1	-0.13 (-0.27, 0.01)	0.079	-	-
≥ 12	4	-0.17 (-0.29, -0.06)	0.002*	0.842	0.0%
Intervention type
Probiotic	3	-0.15 (-0.26, -0.03)	0.008*	0.852	0.0%
Synbiotic	2	-0.17 (-0.31, -0.02)	0.019*	0.396	0.0%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-0.17 (-0.26, -0.07)	< 0.001*	0.730	0.0%
Obese (> 30)	2	-0.08 (-0.32, 0.14)	0.469	0.883	0.0%
Fasting insulin (FI) (mIU/mL)
Overall effect	6	-0.43 (-1.76, 0.90)	0.526	0.390	4.1%
Trial duration (week)
< 12	1	-0.00 (-2.11, 2.11)	1.000	-	-
≥ 12	5	-0.54 (-2.45, 1.36)	0.574	0.293	19.2%
Intervention type
Probiotic	4	-0.02 (-1.74, 1.68)	0.974	0.314	15.5%
Synbiotic	2	-1.47 (-3.94, 1.00)	0.243	0.388	0.0%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-0.79 (-2.20, 0.61)	0.271	0.565	0.0%
Obese (> 30)	3	1.25 (-2.49, 4.99)	0.513	0.267	24.3%
Homeostatic model assessment of insulin resistance (HOMA-IR),
Overall effect	5	-0.32 (-0.68, 0.05)	0.090	0.386	3.6%
Trial duration (week)
< 12	1	-0.13 (-0.27, 0.01)	0.079	-	-
≥ 12	4	-0.17 (-0.29, -0.06)	0.002*	0.842	0.0%
Intervention type
Probiotic	3	-0.15 (-0.26, -0.03)	0.008*	0.852	0.0%
Synbiotic	2	-0.17 (-0.31, -0.02)	0.019*	0.396	0.0%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-0.17 (-0.26, -0.07)	< 0.001*	0.730	0.0%
Obese (> 30)	2	-0.08 (-0.32, 0.14)	0.469	0.883	0.0%
Trigeliceryds (TG) (mg/dL)
Overall effect	6	-5.53 (-27.05, 15.97)	0.614	< 0.001	90.2%
Baseline TG (mg/dL)
< 150	2	-5.47 (-64.97, 54.02)	0.857	< 0.001	95.3%
≥ 150	4	-6.42 (-24.87, 12.03)	0.495	0.023	68.6%
Trial duration (week)
< 12	1	23.60 (18.27, 28.92)	< 0.001*	-	-
≥ 12	5	12.11 (-30.59, 6.37)	0.199	0.005	73.1%
Intervention type
Probiotic	4	-3.66 (-29.40, 22.06)	0.780	< 0.001	90.6%
Synbiotic	2	-9.34 (-55.00, 36.32)	0.688	0.003	88.7%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-14.31 (-60.66, 32.04)	0.545	< 0.001	95.3%
Obese (> 30)	3	2.06 (-9.24, 13.36)	0.721	0.370	0.0%
Total cholesterol (TC) (mg/dL)
Overall effect	5	-1.92 (-12.85, 9.01)	0.730	0.002*	76.5%
Baseline TC (mg/dL)
< 200	2	5.50 (2.22, 8.79)	0.001	0.808	0.0%
≥ 200	3	-7.89 (-24.16, 8.37)	0.342	0.052	66.3%
Trial duration (week)
< 12	1	5.61 (2.22, 8.99)	0.001*	-	-
≥ 12	4	-4.75 (-18.20, 8.70)	0.489	0.029	66.8%
Intervention type
Probiotic	4	1.36 (-9.32, 12.06)	0.802	0.023	68.6%
Synbiotic	1	-15.30 (-29.21, -1.38)	0.031	-	-
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-7.58 (-24.64, 9.47)	0.384	< 0.001	87.6%
Obese (> 30)	2	6.78 (-4.87, 18.44)	0.254	0.419	0.0%
Low-density lipoprotein cholesterol (LDL-C) (mg/dL)
Overall effect	6	1.40 (-3.10, 5.90)	0.542	0.149	38.6%
Trial duration (week)
< 12	1	3.76 (0.92, 6.59)	0.009*	-	-
≥ 12	5	-0.05 (-6.01, 5.91)	0.987	0.222	30.0%
Intervention type
Probiotic	4	3.60 (0.98, 6.22)	0.007	0.514	0.0%
Synbiotic	2	-3.52 (-12.46, 5.40)	0.439	0.189	41.9%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	-0.92 (-8.09, 6.25)	0.802	0.035	70.2%
Obese (> 30)	3	5.08 (-2.40, 12.57)	0.183	0.617	0.0%
High-density lipoprotein cholesterol (HDL-C) (mg/dL)
Overall effect	6	1.63 (0.26, 3.01)	0.020*	0.084	48.5%
Baseline HDL (mg/dL)
< 50	5	1.92 (0.51, 3.33)	0.008*	0.127	44.2%
≥ 50	1	0.00 (-2.92, 2.92)	1.000	-	-
Trial duration (week)
< 12	1	2.69 (1.99, 3.38)	< 0.001*	-	-
≥ 12	5	0.98 (-0.47, 2.44)	0.186	0.335	12.4%
Intervention type
Probiotic	4	2.42 (1.27, 3.57)	< 0.001*	0.295	19.1%
Synbiotic	2	0.11 (-1.75, 1.98)	0.903	0.940	0.0%
Baseline BMI (kg/m2)
Overweight (25-29.9)	3	2.48 (1.53, 3.43)	< 0.001*	0.343	6.5%
Obese (> 30)	3	1.13 (-1.10, 3.37)	0.322	0.139	49.3%
Systolic blood pressure (SBP) (mmHg)
Overall effect	3	-2.24 (-8.47, 3.99)	0.480	< 0.001	93.0%
Diastolic blood pressure (DBP) (mmHg)
Overall effect	3	-1.11 (-3.04, 0.82)	0.260	0.077	60.9%
Body weight (BW) (kg)
Overall effect	3	0.30 (-3.09, 3.69)	0.861	0.966	0.0%
Body mass index (BMI) (kg/m2)
Overall effect	6	-0.20 (-0.79, 0.39)	0.510	0.993	0.0%
Trial duration (week)
< 12	1	0.00 (-2.17, 2.17)	1.000	-	-
≥ 12	5	-0.21 (-0.83, 0.40)	0.493	0.980	0.0%
Intervention type
Probiotic	4	-0.12 (-0.91, 0.66)	0.759	0.945	0.0%
Synbiotic	2	-0.30 (-1.20, 0.60)	0.516	1.000	0.0%
Baseline BMI (kg/m2)
Overweight (25-29.9)	4	-0.29 (-0.96, 0.36)	0.380	1.000	0.0%
Obese (> 30)	2	0.18 (-1.13, 1.51)	0.780	0.830	0.0%

ES effect size, CI Confidence interval, WMD weighted mean differences, TG Triglycerides, TX total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C, high-density lipoprotein cholesterol, FBG fasting blood glucose, FI fasting insulin, HbA1c glycated hemoglobin, HOMA-IR homeostatic model assessment of insulin resistance, SBP systolic blood pressure, DBP diastolic blood pressure, BW body weight, BMI and body mass index

Meta-analysis on lipid profile

A meta-analysis of five studies [33–37] evaluated the effects of probiotic and synbiotic supplementation on lipid profiles in prediabetic adults. The pooled results indicated a slight but non-significant reduction in TG (WMD: −5.54 mg/dL; 95% CI: [−27.05 to 15.97]; p = 0.614), with substantial heterogeneity observed among studies (I² = 90.2%, p < 0.001) (Fig. 3A). Similarly, TC showed a slight, non-significant decrease (WMD: −1.92 mg/dL; 95% CI: [−12.85 to 9.01; p = 0.730), accompanied by considerable heterogeneity (I² = 76.5%, p = 0.002) (Fig. 3B).

Fig. 3.

Forest plot detailing weighted mean difference (WMD) with 95% confidence intervals (CIs) for the effect of probiotic and synbiotic supplementation on serum lipid profile, including: A) triglycerides (TG), B) total cholesterol, C) low-density lipoproteins cholesterol (LDL-C), and D) high-density lipoproteins cholesterol (HDL-C)

LDL-C levels showed a non-significant increase (WMD: 1.40 mg/dL; 95% CI: [−3.10 to 5.90; p = 0.542), with low heterogeneity among studies (I² = 38.6%, p < 0.001) (Fig. 3C). In contrast, HDL-C levels significantly increased (WMD: 1.63 mg/dL; 95% CI: [0.25 to 3.01]; p = 0.002), with low heterogeneity (I² = 48.5%, p = 0.084) (Fig. 3D).

Subgroup analyses showed that participants with baseline HDL < 50 mg/dL had greater improvements (WMD = 1.92 mg/dL; 95% CI: 0.51, 3.33; p = 0.008). Shorter trials (< 12 weeks) demonstrated a more pronounced increase in HDL-C compared to longer interventions. Probiotic interventions were more effective than synbiotics in raising HDL-C (WMD = 2.42 mg/dL vs. 0.11 mg/dL). Improvements were more notable in overweight participants compared to obese individuals (Table 2).

Meta-analysis on blood pressure (BP)

The overall pooled results from three RCTs [33, 35, 36] assessed the effects of probiotics and synbiotics supplementation on BP in individuals with prediabetes (Table 2). For systolic BP, a non-significant reduction was observed (WMD: −2.24 mmHg; 95% CI: [−8.47 to 3.99]; p = 0.480), with substantial heterogeneity across studies (I² = 93%, p < 0.001; Fig. 4A). Similarly, diastolic BP showed a non-significant decrease (WMD: −1.11 mmHg; 95% CI: [−3.04 to 0.82]; p = 0.260), accompanied by moderate, non-significant heterogeneity (I² = 60.9%, p = 0.77; Fig. 4B).

Fig. 4.

Forest plot detailing weighted mean difference (WMD) with 95% confidence intervals (CIs) for the effect of probiotic and synbiotic supplementation on A) systolic blood pressure (SBP), B) diastolic blood pressure (DBP), C) body weight (BW), and D) body mass index (BMI)

Meta-Analysis on body weight and BMI

A random-effects pooled analysis of three RCTs [34–36] showed that probiotics and synbiotics supplementation had no significant effect on BW (WMD: 0.30 kg; 95% CI: [−3.09, 3.69]; p = 0.861), with no heterogeneity among studies (I² = 0%, p = 0.966) (Fig. 4C).

A pooled analysis of four RCTs with six intervention arms [10, 34, 35, 37] found no significant change in BMI (WMD: −0.20 kg/m²; 95% CI: [−0.79, 0.39]; p = 0.510), also with no observed heterogeneity (I² = 0%, p = 0.993) (Fig. 4D).

Subgroup analyses similarly revealed no significant differences based on trial duration (< 12 vs. ≥12 weeks), intervention type (probiotic vs. synbiotic), or baseline BMI category (overweight vs. obese), indicating a consistent lack of effect across study subpopulations (Table 2).

Publication bias

The findings from Egger’s and Begg’s tests showed signs of publication bias for TG (Egger’s test: p = 0.018; Begg’s test: p = 0.260) and DBP (Egger’s test: p = 0.019; Begg’s test: p = 1.000). However, there was no indication of publication bias for TC, LDL-C, HDL-C, FBG, FI, HbA1c, HOMA-IR, SBP, BW, and BMI (Fig. 5, Supplementary Table 3).

Fig. 5.

Funnel plot of publication bias of fasting blood glucose (FBG), Hemoglobin A1c (HbA1c), fasting insulin (FI), Homeostasis model assessment of insulin resistance (HOMA-IR), triglycerides (TG), total cholesterol, low-density lipoproteins (LDL), high-density lipoproteins (HDL), systolic blood pressure (SBP), diastolic blood pressure (DBP), body weight (BW), and body mass index (BMI)

Sensitivity analysis

The sensitivity analysis revealed that none of the individual studies had a significant effect on the overall results concerning TG, TC, FI, HbA1c, SBP, DBP, BW, and BMI. Nonetheless, the study conducted by Kassaian et al. (2019) [37] revealed a significant impact within the synbiotics intervention arm (WMD: 3.48, 95% CI: [0.94, 6.02]), highlighting its impact on the overall findings for LDL-C. Additionally, a study by Mahboobi et al. (2014) [33] (WMD: 0.98, 95% CI: −0.47, 2.44), Kassian et al. (2019) [37] (Probiotics intervention arm) (WMD: 1.52, 95% CI: − 0.043, 3.09), and Barthow et al. (2022) [35] (WMD: 1.27, 95% CI: −0.28, 2.83) suggest significant influences on the overall results for HDL-C. Moreover, research conducted by AkbariRad et al. (2023) [36] found a WMD of −3.26 (95% CI: −5.74, −0.78), indicating its influence on the overall results for FBG. Lastly, the study by Oh et al. (2021) [27] showed a noteworthy effect on the overall result for HOMA-IR, reporting a WMD of −0.54 (95% CI: −1.00, −0.07) (Supplementary Table 3).

Risk of bias (RoB) assessment

The Risk of Bias assessment using the Cochrane RoB-2 tool for the included randomized clinical trials shows that most studies demonstrate a low risk of bias across key domains. Specifically, all studies have a low risk of bias arising from the randomization process (D1) and bias due to deviations from intended interventions (D2). Some studies, such as Tay et al. [2020] [34] and Oh et al. [2021] [27], exhibit “some concerns” for bias due to missing outcome data (D3) and bias in the measurement of the outcome (D4). Additionally, all studies present “some concerns” for bias in the selection of the reported result (D5), which also contributes to an overall judgment of “some concerns” for these studies. Overall, while the majority of domains indicate low risk, specific areas, particularly D5, warrant further attention (Fig. 6).

Fig. 6.

Cochrane risk of bias assessment (RoB-2) assessment in included randomized clinical trials

GRADE assessment

According to the GRADE assessment, the quality of evidence for the effects of probiotics and synbiotics supplementation on cardiovascular risk factors in prediabetic adults varied across outcomes. Very high-quality evidence was found for improvements in HbA1c and HDL-C, indicating strong confidence in the observed effects. High-quality evidence supported the findings for TC, LDL-C, FBG, FI, HOMA-IR, SBP, BW, and BMI, though these outcomes were limited by imprecision due to non-significant effects. Moderate-quality evidence was assigned to TG and DBP, downgraded due to both imprecision and the presence of publication bias. No serious limitations in risk of bias, inconsistency, or indirectness were identified for any outcome, strengthening the overall credibility of the findings despite some methodological constraints (Table 3).

Table 3.

GRADE profile for the effects of probiotics and synbiotics supplementation on cardiovascular risk factors in adults with prediabetes

Outcomes	Risk of bias	Inconsistency	Indirectness	Imprecision	Publication Bias	Quality of evidence
TG	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	Serious limitation 2	⊕⊕◯◯ Moderate
TC	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
LDL-C	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
HDL-C	No serious limitation	No serious limitation	No serious limitation	No serious limitation	No serious limitation	⊕⊕⊕⊕ Very High
FBG	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
FI	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
HbA1c	No serious limitation	No serious limitation	No serious limitation	No serious limitation	No serious limitation	⊕⊕⊕⊕ Very High
HOMA-IR	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
SBP	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
DBP	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	Serious limitation 2	⊕⊕◯◯ Moderate
BW	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High
BMI	No serious limitation	No serious limitation	No serious limitation	Serious limitation 1	No serious limitation	⊕⊕⊕◯ High

TG Triglycerides, TC total cholesterol, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, FBG fasting blood glucose, FI fasting insulin, HbA1c glycated hemoglobin, HOMA-IR homeostatic model assessment of insulin resistance, SBP systolic blood pressure, DBP diastolic blood pressure, BW body weight, BMI, and body mass index

1. There is no significant effect of probiotics and symbiotics

2. There is a significant publication bias

Discussion

Aim and main findings

This meta-analysis is the first to be specifically conducted to evaluate the effects of probiotics and synbiotics supplementation on cardiovascular risk factors in individuals with prediabetes. Significant improvements in HbA1c and HDL-C levels were observed, indicating potential benefits in glycemic control and lipid metabolism. However, no significant effects were detected for other key cardiometabolic markers, including blood glucose, insulin resistance indices, lipid fractions (TG, TC, LDL-C), BP, and BW. These mixed outcomes suggest that while probiotics and synbiotics may confer modest benefits on specific cardiovascular risk parameters, their overall impact appears to be limited. Likely, variability in strains, dosages, intervention durations, and study quality contributed to the inconsistent results. Further, well-designed randomized controlled trials are warranted to better clarify their therapeutic potential in prediabetic populations.

In contrast to the findings of the current meta-analysis, Chao Sun et al. [38] found probiotics can reduce HOMA-IR in prediabetes by promoting the secretion of Glucagon-like peptide-1 (GLP-1), which is an important mechanism for reducing glycosylated hemoglobin [38]. Another study by Ya Li et al. showed that using probiotics in prediabetes could significantly decrease the levels of TC, TG, and LDL-C [39]. This controversy could result from variations in sample size, dosage, duration interval, wide BMI range of the included patients, strain, and type of bioactive agent. One of the main reasons for dosage variations could be that some strains are more resistant to storage, and the dosage can also vary depending on how it is administered [40]. The lack of a standardized dietary structure among patients can affect the results of gut microbiota changes.

Basic underlying mechanism

Gut microbiota plays a role in various human physiological and metabolic functions. Recent evidences indicate a strong correlation between prediabetes and gut dysbiosis [16, 41]. Probiotics and synbiotics supplementation have demonstrated health benefits across various conditions, including T2DM [42, 43], kidney disease [44], obesity [45, 46], polycystic ovary syndrome [47], CVD [48], gastrointestinal diseases [49, 50], etc. The underlying mechanism of this phenomenon may originate from the following sources: First, the fermentation of dietary fibers by gut microbiota produces short-chain fatty acids, which regulate energy metabolism, appetite, inflammation, and influence glucose homeostasis [44, 51–54]. Second, Probiotics and synbiotics can promote GLP-1 secretion by endocrine cells in the intestine, regulate blood glucose homeostasis, and repair pancreatic islet β-cell function [38, 54, 55]. Certain strains, such as Lactobacillus and Bifidobacterium, have been shown to promote GLP-1 production indirectly [38, 39]. Third, probiotics produce bile acid hydrolases, which deconjugate bile acids and reduce their reabsorption, promoting cholesterol conversion into bile salts and influencing lipid metabolism [56, 57]. They also inhibit HMG-CoA reductase, reduce endogenous cholesterol synthesis, and increase fecal cholesterol excretion [58].

Glycemic markers

Aligning with our investigation, the study by Chao Sun et al. observed significant reductions in HbA1c and improvements in HDL-C, though no significant changes were noted in FBG, insulin, LDL-C, TC, TG, or BMI [38]. Similarly, Ya Li et al. found probiotics were able to significantly decrease the levels of HbA1c, TC, TG, and LDL-C in patients with prediabetes [39]. A meta-analysis of Xiaoyan Cai et al. highlighted the association between prediabetes and increased risks of all-cause mortality and cardiovascular events, emphasizing the importance of HbA1c (5.7–6.4%) as a cardiovascular risk factor [5]. Similarly, Yunzhen Lei et al. suggested adding probiotics or synbiotics to conventional medications for coronary artery disease, which can significantly reduce the risk of coronary artery lesions [59]. Our study further supports that adding probiotics and synbiotics may help reduce CVD risk and delay the progression of diabetes, particularly by improving HbA1c, a critical marker for cardiovascular risk in prediabetes, and HDL-C levels.

Lipid profile

Dyslipidemia is considered a risk factor for the development of CVD, especially in T2DM patients [60, 61]. Previous studies have shown that lowering HDL-C and increasing LDL-C are important indicators of CVD [62, 63]. While our study found no significant effects of probiotics and synbiotics on TC, TG, or LDL-C levels, HDL-C levels were significantly increased. This aligns with a meta-analysis by Li et al., which demonstrated that probiotics could significantly increase HDL-C levels but had no significant effect on TC, TG, and LDL-C [64]. Synbiotics supplementation has also been shown to increase HDL-C without affecting other lipid markers in T2DM patients [65]. Conversely, meta-analysis by Chen Wang on the effect of probiotics on dyslipidemia in T2DM found that probiotics intake could significantly reduce TC and TG levels, but did not regulate LDL-C or HDL-C concentrations [66]. These variations may be attributed to differences in probiotic strains, dosages, and intervention durations.

Blood pressure (BP)

Hypertension plays a role as a major risk factor in cardiovascular and cerebrovascular disease, and limited studies have suggested a link between gut dysbiosis and BP regulation [60, 61, 67]. While our study did not show significant changes in probiotics and synbiotics supplementation on SBP or DBP. This contrasts with the findings of Hadi et al., who showed that synbiotics interventions that lasted longer than 12 weeks significantly reduced SBP without changing DBP levels [68]. A meta-analysis by Nasiri et al. found that probiotics may slightly improve BP, but synbiotics did not influence BP in patients with prediabetes or T2DM [19]. Further research is needed to clarify the potential effects of probiotics and synbiotics on BP, particularly in individuals with prediabetes.

BW and BMI

Weight loss is critical for glycemic control in prediabetes patients, and obesity or overweight in individuals with prediabetes is highly prevalent, at risk of developing T2DM and cardiovascular complications [42, 69–71]. Many studies emphasize that modest reductions in weight (1.6 kg) and HbA1c (2 mmol/mol) can significantly reduce the progression to T2DM by 26% compared to usual care [72]. our findings indicated no significant effects on these parameters. This is consistent with previous meta-analyses [19]. and Yanyan Tian et al. [42], which also found no significant effects on BMI or weight despite improvements in other cardiovascular risk factors. Probiotics and synbiotics might affect BMI or BW through various mechanisms, such as modulation of gut microbiota composition and the production of short-chain fatty acids, which regulate metabolism, appetite, and gut-brain communication [44, 73, 74]. However, the effects on BW may vary depending on the duration of supplementation, dosage, and the specific strains used.

Clinical implications

While probiotics and synbiotics may not significantly impact most cardiometabolic markers in prediabetic individuals, their observed benefits in reducing HbA1c and increasing HDL-C suggest a potential role in improving glycemic control and lipid profiles. These findings support their cautious use as adjunctive interventions in prediabetes management, though more robust clinical trials are needed to confirm their broader cardiometabolic effects and long-term benefits.

Strengths and limitations

This meta-analysis is the first to specifically assess the effects of probiotics and synbiotics on cardiovascular risk factors in adults with prediabetes, using a rigorous PRISMA-compliant methodology and GRADE assessment. The study applied robust statistical techniques, including meta-regression and sensitivity analyses, enhancing the reliability of the findings. A comprehensive literature search and inclusion of only RCTs further strengthen its validity. However, limitations include a relatively small number of studies and participants, heterogeneity in intervention protocols (e.g., strain type, dose, duration), and the presence of some publication bias. Additionally, the lack of standardization across included trials limits generalizability, and the modest effect sizes suggest a need for larger, high-quality studies to confirm clinical relevance.

Future suggestions

Future research should focus on standardizing probiotic strains, dosages, and study designs to improve the comparability and reproducibility of findings. Additionally, studies should aim to establish uniform intervention protocols, investigate the long-term effects of supplementation, and elucidate the mechanisms of action of specific probiotic strains and synbiotic formulations. These efforts are essential to strengthen the evidence base and enhance the clinical relevance and applicability of probiotic and synbiotic interventions.

Conclusion

In conclusion, this study highlights the potential effects of probiotics and synbiotics to improve specific cardiovascular risk factors in individuals with prediabetes, particularly through significant reductions in HbA1c and increases in HDL-C levels. However, the evidence remains inconclusive regarding other metabolic markers, glycemic markers, serum lipid profile, BP, and body mass. Future high-quality RCTs with standardized protocols and longer durations are needed to fully elucidate the therapeutic potential of these interventions in preventing cardiovascular events and diabetes progression in prediabetic populations [75].

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

FBG

Fasting Blood Glucose

HbA1c

Hemoglobin A1c

FI

Fasting insulin

HOMA-IR

Homeostasis Model Assessment of Insulin Resistance

TG

Triglycerides

TC

Total Cholesterol

LDL-C

Low-Density Lipoprotein Cholesterol

HDL-C

High-Density Lipoprotein Cholesterol

BP

Blood Pressure

SBP

Systolic Blood Pressure

DBP

Diastolic Blood Pressure

BMI

Body Mass Index

CVD

Cardiovascular Diseases

T2DM

Type 2 Diabetes Mellitus

Author contributions

“O.A. and M.K. conceived the study and designed the research methodology. A.S and O.A. conducted the systematic review and data extraction. O.A. and M.K. performed the statistical analysis and interpreted the results. M.K., H.H., M.CH., and S.H.D. contributed to manuscript drafting. M.K. and B.L. reviewed, conceptualized, and supervised the project. All authors critically reviewed and approved the final manuscript.”

Funding

None.

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.

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare no conflicts of interest.

Clinical trial number

Not applicable.