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The Effect of Probiotics and Synbiotics on Risk Factors Associated with Cardiometabolic Diseases in Healthy People—A Systematic Review and Meta-Analysis with Meta-Regression of Randomized Controlled Trials

Department of Human Nutrition and Metabolomics, Pomeranian Medical University in Szczecin, 71-460 Szczecin, Poland
Department of Surgical Oncology, Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdańsk, Poland
Department of Clinical and Molecular Biochemistry, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
Sanprobi sp. z o.o. sp. k. Kurza Stopka 5/c, 70-535 Szczecin, Poland
Department of Neonatal Diseases, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
Department of Pharmacology, Pomeranian Medical University in Szczecin, 70-111 Szczecin, Poland
Department of Gastroenterology, Pomeranian Medical University, 71-252 Szczecin, Poland
Authors to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(6), 1788;
Received: 3 May 2020 / Revised: 1 June 2020 / Accepted: 5 June 2020 / Published: 8 June 2020
(This article belongs to the Section Gastroenterology & Hepatopancreatobiliary Medicine)


We aimed to systematically review the effectiveness of probiotic/synbiotic formulations to counteract cardiometabolic risk (CMR) in healthy people not receiving adjunctive medication. The systematic search (PubMed/MEDLINE/Embase) until 1 August 2019 was performed for randomized controlled trials in >20 adult patients. Random-effect meta-analysis subgroup and meta-regression analysis of co-primary (haemoglobin A1c (HbA1C), glucose, insulin, body weight, waist circumference (WC), body mass index (BMI), cholesterol, low-density lipoproteins (LDL), high-density lipoproteins (HDL), triglycerides, and blood pressure) and secondary outcomes (uric acid, plasminogen activator inhibitor-1–PAI-1, fibrinogen, and any variable related to inflammation/endothelial dysfunction). We included 61 trials (5422 persons). The mean time of probiotic administration was 67.01 ± 38.72 days. Most of probiotic strains were of Lactobacillus and Bifidobacterium genera. The other strains were Streptococci, Enterococci, and Pediococci. The daily probiotic dose varied between 106 and 1010 colony-forming units (CFU)/gram. Probiotics/synbiotics counteracted CMR factors (endpoint data on BMI: standardized mean difference (SMD) = −0.156, p = 0.006 and difference in means (DM) = −0.45, p = 0.00 and on WC: SMD = −0.147, p = 0.05 and DM = −1.21, p = 0.02; change scores on WC: SMD = −0.166, p = 0.04 and DM = −1.35, p = 0.03) in healthy persons. Overweight/obese healthy people might additionally benefit from reducing total cholesterol concentration (change scores on WC in overweight/obese: SMD: −0.178, p = 0.049). Poor quality of probiotic-related trials make systematic reviews and meta-analyses difficult to conduct and draw definite conclusions. “Gold standard” methodology in probiotic studies awaits further development.

1. Introduction

Cardiovascular diseases (CVD) are the most prevalent noncommunicable disorders, with cardiometabolic risk factors (CMRF) including obesity [1], abnormal lipid profile and hypertension [2], insulin resistance, and aberrant glycaemia [3], playing a role in the pathogenesis. Increased consumption of unhealthy, high-calorie foods combined with a sedentary lifestyle further contribute to their poor outcomes [4,5]. In healthy persons, modestly skewed metabolic parameters may stand for the early onset CMRF [2].
Metabolic malfunctions of diverse nature, with epigenetic, hormonal, and infectious factors, are involved in the pathogenesis [6,7]. Intestinal microbiota actively participating in metabolism is an important factor regulating body metabolism [8]. Microorganisms, primarily bacteria, inhabiting our digestive tract actively participate in the digestion of nutrients and, through its metabolites, can regulate not only energy recovery from food but also lipogenesis or fat formation [9]. The mechanisms by which the gut microbiota can contribute to the pathogenesis of metabolic disorders include the short chain fatty acids (SCFAs) biosynthesis to triglycerides and glucose as well as the phenomenon of endotoxemia leading to increased blood levels of liposaccharide (LPS), which aggravates the process of systemic inflammation [10]. Both LPS and LPS-related inflammation have been linked to metabolic diseases, e.g., diabetes and insulin resistance (IR) [11].
The microbiota communicates with the host via toll-like receptors, nuclear factor-ĸB, and mitogen-activated protein kinase [12], which were shown to improve serum and glucose lipid concentration, to reduce insulin resistance [13,14], and to induce hypocholesterolemic effects [13]. Also, the products of the metabolic activity of the microbiota-predominant SCFAs were shown to regulate various metabolic processes [15]. These molecules after binding to G-protein-coupled receptors make the secretion of peptide YY, which lowers gut motility and augments nutrient absorption [16]. Also, butyrate serves as a source of energy for intestinal cells and improves tissue sensitivity to insulin, counteracting the development of type 2 diabetes. Together with propionic acid, it can stimulate the production of satiety hormones. Of note, butyrate can also stimulate the formation of fat cells and the storage of fat droplets in these cells, presumably through increased glucose uptake or participation in lipid formation. On the other hand, it may also inhibit lipolysis, which, together with stimulating glucose uptake and triglyceride synthesis, makes it a potential therapeutic agent in the fight against hyperglycemia and hyperlipidemia [17].
Considering these facts, metabolic impairment is at least a consequence of gut microbiota alteration. The use of probiotics and synbiotics to counteract metabolic disturbances has been reported. Probiotics are “live microorganisms that, when administered in adequate amounts, confer a health benefit on the host”, which has been confirmed in properly controlled studies [18]. Synbiotics are combinations of probiotics and prebiotics. Prebiotics are substrates that are selectively utilized by host microorganisms conferring a health benefit, which must be scientifically documented [19].
A few meta-analyses evaluating the efficacy of probiotics and synbiotics in persons diagnosed with diabetes or hypertension have been published [20,21,22]. However, early-onset CMRF have never been meta-analysed and reported in the literature. Therefore, we conducted the first systematic review and meta-analysis in healthy individuals. We hypothesized that probiotics/synbiotics would be superior to placebo yet would result in greater improvement of some metabolic indices—possibly via microbiota and/or inflammatory as well as gut barrier related pathways as assessed by biochemical parameter alterations—with very few adverse effects. We included studies in which clinically healthy people including those with excess body weight, those who are overweight, and those who are obese.

2. Methods

2.1. Search Strategy and Inclusion Criteria

Two independent authors (K.S.Z. and K.B.) searched PubMed/MEDLINE/Embase from database inception until 1 August 2019 for randomized controlled trials (RCTs) comparing probiotics and synbiotics with placebo/no-intervention/physical activity/diet to counteract cardiometabolic malfunctions in healthy people with normal weight or moderate/high-risk obesity (i.e., not exceeding 40 kg/m2).
The following search string was used in PubMed (probiotic* OR synbiotic* OR microbiota* OR lactobacillus OR bifidobacterium) AND (RCT OR random* OR placebo*) AND (“hemoglobin A1C” OR HbA1C OR glucose OR “fasting glucose” OR “glucose tolerance” OR hyperglycemia OR “oral glucose tolerance test” OR OGTT insulin OR hyperinsulinemia OR “insulin resistance” OR IR OR “insulin sensitivity” OR weight OR obesity OR obese OR overweight OR over-weight OR weight-gain OR “waist circumference” OR “body mass index” OR BMI OR cholesterol OR LDL OR HDL OR triglycerides OR dyslipidemia OR lipid OR “blood pressure” OR SBP OR DBP OR uric acid OR “Plasminogen activator inhibitor-1” OR PAI-1 OR PAI1 OR fibrinogen OR inflamma* OR C-reactive OR “C-reactive protein” OR CRP OR WBC OR leukocytes OR lymphoctes OR endothel* OR “endothelial dysfunction”). In the Embase database, the search string was (‘normal human’/exp OR ‘healthy adult’ OR ‘healthy human’ OR ‘healthy humans’ OR ‘healthy patient’ OR ‘healthy people’ OR ‘healthy person’ OR ‘healthy subject’ OR ‘healthy subjects’ OR ‘healthy volunteer’ OR ‘healthy volunteers’ OR ‘human, normal’ OR ‘normal human’ OR ‘normal humans’ OR ‘normal subject’ OR ‘normal subjects’ OR ‘normal volunteer’ OR ‘normal volunteers’) AND (‘probiotic agent’/exp OR ‘probiotic’ OR ‘probiotic agent’ OR ‘probiotics’ OR ‘synbiotic agent’/exp OR ‘synbiotic’ OR ‘synbiotic agent’ OR ‘synbiotics’ OR ‘microflora’/exp OR ‘microbial flora’ OR ‘microbiota’ OR ‘microflora’ OR ‘lactobacillus’/exp OR ‘bifidobacterium’/exp) AND (‘glycosylated hemoglobin’/exp OR ‘glycated haemoglobin’ OR ‘glycated hemoglobin’ OR ‘glycated hemoglobin a’ OR ‘glycohaemoglobin’ OR ‘glycohemoglobin’ OR ‘glycosyl haemoglobin’ OR ‘glycosyl hemoglobin’ OR ‘glycosylated haemoglobin’ OR ‘glycosylated hemoglobin’ OR ‘glycosylhaemoglobin’ OR ‘glycosylhemoglobin’ OR ‘glycosylised haemoglobin’ OR ‘glycosylized hemoglobin’ OR ‘haemoglobin a1’ OR ‘haemoglobin a 1’ OR ‘haemoglobin a, glycosylated’ OR ‘haemoglobin ai’ OR ‘haemoglobin alpha 1’ OR ‘haemoglobin glycoside’ OR ‘haemoglobin glycosylation’ OR ‘hemoglobin a, glycosylated’ OR ‘hemoglobin glycoside’ OR ‘glucose’/exp OR ‘glucose’ OR ‘fasting blood glucose’/exp OR ‘fasting plasma glucose’/exp OR ‘insulin’/exp OR ‘insulin’ OR ‘insuline’ OR ‘insulin resistance’/exp OR ‘insulin resistance’ OR ‘resistance, insuline’ OR ‘insulin sensitivity’/exp OR ‘insulin insensitivity’ OR ‘insulin sensitivity’ OR ‘insulin sensitivity test’ OR ‘insulin test’ OR ‘sensitivity, insulin’ OR ‘hyperglycemia’/exp OR ‘glucose blood level, elevated’ OR ‘glycemia, hyper’ OR ‘hyperglycaemia’ OR ‘hyperglycemia’ OR ‘hyperglycemic syndrome’ OR ‘glucose tolerance test’/exp OR ‘gtt’ OR ‘g.t.t.’ OR ‘glucogram’ OR ‘glucose load’ OR ‘glucose loading test’ OR ‘glucose test’ OR ‘glucose tolerance curve’ OR ‘glucose tolerance factor’ OR ‘glucose tolerance test’ OR ‘glucose toleration test’ OR ‘body weight’/exp OR ‘body weight’ OR ‘total body weight’ OR ‘weight, body’ OR ‘waist circumference’/exp OR ‘waist circumference’ OR ‘waist size’ OR ‘body mass’/exp OR ‘bmi (body mass index)’ OR ‘quetelet index’ OR ‘body ban mass’ OR ‘body mass’ OR ‘body mass index’ OR ‘cholesterol’/exp OR ‘cholesterol’ OR ‘low density lipoprotein cholesterol’/exp OR ‘ldl cholesterol’ OR ‘cholesterol, ldl’ OR ‘lipoproteins, ldl cholesterol’ OR ‘low density lipoprotein cholesterol’ OR ‘high density lipoprotein cholesterol’/exp OR ‘hdl cholesterol’ OR ‘cholesterol, hdl’ OR ‘high density lipoprotein cholesterol’ OR ‘lipoproteins, hdl cholesterol’ OR ‘triacylglycerol’/exp OR ‘triacylglycerol’ OR ‘triglyceride’ OR ‘triglycerides’ OR ‘tryglyceride’ OR ‘dyslipidemia’/exp OR ‘dyslipaemia’ OR ‘dyslipemia’ OR ‘dyslipidaemia’ OR ‘dyslipidaemias’ OR ‘dyslipidemia’ OR ‘dyslipidemias’ OR ‘blood pressure’/exp OR ‘blood pressure’ OR ‘blood tension’ OR ‘pressure, blood’ OR ‘vascular pressure’ OR ‘plasminogen activator’/exp OR ‘fibrinogen’/exp OR ‘factor 1’ OR ‘factor i’ OR ‘fibrinogen’ OR ‘human fibrinogen’ OR, OR ‘inflammation’/exp OR ‘acute inflammation’ OR ‘bacterial inflammation’ OR ‘inflammation’ OR ‘inflammation reaction’ OR ‘inflammation response’ OR ‘inflammatory condition’ OR ‘inflammatory lesion’ OR ‘inflammatory process’ OR ‘inflammatory reaction’ OR ‘inflammatory response’ OR ‘inflammatory syndrome’ OR ‘reaction, inflammation’ OR ‘response, inflammatory’ OR ‘serositis’ OR ‘sterile inflammation’) AND (‘randomized controlled trial’/exp OR ‘controlled trial, randomized’ OR ‘randomised controlled study’ OR ‘randomised controlled trial’ OR ‘randomized controlled study’ OR ‘randomized controlled trial’ OR ‘trial, randomized controlled’).
A manual review of reference lists from the most recent reviews followed the electronic search. Inclusion criteria were (i) full-text randomized controlled trial, (ii) populations containing >20 adult (>18 years old participants, excluding pregnant women), (iii) treatment with pro-/synbiotics for at least 4 weeks, (iv) randomization to probiotic/synbiotic vs. controls (placebo, no intervention, physical activity, and dietary elements, e.g., yoghurts and milk), and (v) available meta-analyzable change score/endpoint data on any of the following outcomes: HbA1C OR glucose OR OGTT OR insulin OR weight OR waist circumference OR BMI OR cholesterol OR LDL OR HDL OR triglycerides OR blood pressure OR SBP OR DBP OR uric acid OR Plasminogen activator inhibitor-1 OR fibrinogen OR any outcome related to inflammation/endothelial dysfunction. The exclusion criteria were as follows: (i) intervention with microbial agent and adjunctive medication aiming or known to prevent or counteract metabolic dysregulation, e.g., metformin, and (ii) disease, excluding morbid and super obese persons. Data from more than 2-arm studies were abstracted separately for particular comparators; however, placebos were preferentially selected, and regarding dietary comparators, products contained no lactic acid bacteria (e.g., milk vs. yoghurt).

2.2. Data Abstraction

We used the standard data extraction sheet according to our previous studies [23,24,25]. Due to a high number of studies included into metaanalysis, the abstraction stage was done by 4 independent authors. The study list was divided into two parts, and each was abstracted by 2 authors (the 1st part by K.S.Z. and K.B. and the 2nd part by D.M. and J.Ś.-D.). We abstracted data on the study design, the persons enrolled, and the probiotic intervention characteristics in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). For evaluation of the risk of bias (ROB) [26], we incorporated The Cochrane Collaboration’s tool and reported the number of low-risk assessments [26]. This was done by one investigator (D.M.). If some data were missing or difficult to abstract (e.g., from figures) for the review, authors were contacted via email twice, one week apart. All inconsistencies were resolved by senior author (W.M. and I.Ł.) consensus. Data from figures was extracted by means of WebPlotDigitizer software (

2.3. Outcomes

Co-primary outcomes were the changes within glycosylated haemoglobin A1c (HbA1C), glucose, insulin, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), body weight, waist circumference (WC), body mass index (BMI), lipid profile (total cholesterol, low-density lipoproteins (LDL), high-density lipoproteins (HDL), and triglycerides), and blood pressure. Secondary outcomes included uric acid, plasminogen activator inhibitor-1, fibrinogen, and any outcome related to inflammation/endothelial dysfunction (e.g., C-reactive protein (CRP) and leukocyte count). Additionally, we abstracted all-cause and adverse-events discontinuation.

2.4. Data Synthesis and Statistical Analysis

We conducted a random-effects [27] meta-analysis of outcomes for which ≥3 studies contributed data, using Comprehensive Meta-Analysis V3 ( We explored study heterogeneity using the chi-square test of homogeneity, with p < 0.05 indicating significant heterogeneity. All analyses were two-tailed with alpha = 0.05.
Group differences in continuous outcomes were analysed as the pooled standardized mean difference (SMD) in either endpoint scores (preferred) or change scores from endpoint to baseline (if endpoint scores were not available) using observed cases (OC). For continuous metabolic outcomes, standardized mean difference (SMD) and, where applicable, differences in means (DM) were calculated. The additional analyses included studies with participants with proper BMI value (20–25 kg/m2) and trials including overweight and obese persons (BMI > 25 kg/m2, not exceeding 45 kg/m2)
To understand the relationship between effect sizes and various study-level predictors, we fit random-effect meta-regression (multiple) models without interaction term using DerSimonian–Laird estimator estimation of the amount of heterogeneity. The test statistics of the individual coefficient (and confidence intervals) for predictors were based on standard normal distribution (z), and the overall test was based on the chi-square distribution (Q statistics following the chi-square distribution with degrees of freedom representing the number of predictors). Meta-regression variables included (i) number of low ROB assessments, (ii) study duration, (iii) mono- vs. multi-strain probiotic intervention, (iv) sample size (analysed persons), and (v) age of participants (mean). Finally, we evaluated funnel plots and conducted Egger’s regression test [28] to detect whether publication bias could have influenced the results we obtained.

3. Results

3.1. Search Results

The initial search yielded 2813 hits. Almost 97% (n = 2727) of screened studies were excluded, being duplicates and/or after evaluation on the title/abstract level. Two (n = 2) additional articles were identified via hand search. After exclusion of duplicates between the initial search and hand search results, 88 (n = 88) full-text articles were reviewed. Of those, a total of 27 (n = 27) papers were excluded due to not fitting the inclusion criteria. The primary reasons for exclusion were wrong study aim (n = 10); non-healthy participants (n = 7); no probiotic treatment (n = 5); too few participants (n = 3); too short a study duration (n = 2); unavailability of full texts (n = 2); and another language other than English, German, and Polish (n = 1), yielding 61 (n = 61) studies that were included in the meta-analysis (Figure 1).

3.2. Study, Treatment, and Patient Characteristics

As demonstrated in Table 1, altogether, 61 studies (n = 61) were included [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89], comprising 84 interventions. The mean probiotic administration was 67.01 ± 38.72 days (range = 28–186 days). Probiotic, not synbiotic, interventions were predominantly conducted (n = 54) [29,30,31,32,33,34,35,36,39,40,41,42,43,44,46,47,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,66,67,68,69,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,88,89]. Probiotic powders were administered in 15 studies [31,36,39,40,42,44,48,49,50,51,60,72,79,80,81], and in the cases of 14 [30,38,41,53,54,61,63,66,69,74,76,83,88,89] and 9 trials [29,33,43,52,56,57,58,84,86], yoghurt and milk products served as probiotic carriers, respectively. Almost all but eight of probiotic strains utilized in the trails belonged to Lactobacillus and Bifidobacterium genera. The other strains ingested by study participants were from Streptococcus, Enterococcus, and Pediococcus genera. The daily doses varied between 106 and 1010 CFU (colony-forming units). The trials were financed by only industry budgets in 20 (n = 20) [29,30,33,34,39,41,42,46,47,48,55,64,67,69,75,76,78,79,83,84]. Studies were financed only by academic resources in 10 studies (n = 10) [31,32,35,37,40,43,44,52,58,60]. The sponsorships in other studies were partially academic/industrial/government.
All studies included healthy subjects (including overweight and obese but excluding morbidly obese persons), with a total of 6820 subjected to randomization and 5422 subjected to analysis. The overall mean age was 44.26 ± 12.87 (range: 21.43–71.9) years. The majority of studied persons were females (n = 2934, 57.22%). Baseline metabolic parameters of included persons are presented in Table S1, and the smoking status and diet along with physical activity are in Table S2. When analysing discontinuation events being consequences of adverse events, we found that the probiotic intervention was linked to very few adverse effects, the majority of which were of gastrointestinal origin. Apart from the most common bowel discomforts, i.e., nausea, diarrhea, constipation, and flatulence, there were also cardiac-related events, dental infections, chest tightness, sleep dysregulation, as well as hives. The details on are presented in Supplementary Table S3).

3.3. Risk of Bias Assessment

As evaluated by means of a ROB assessment tool, the mean number of low risks of bias assessment was 3 (median 2.5). The highest score, i.e., 7 low ROB assessments was detected in only one study [35] and 6 low ROB assessments were detected in two studies only [73,82]. Additionally, while analysing the papers, we detected a number of unclear risks of bias. The exact ROB evaluation in particular domains is in Table S4.

3.4. Effects on Metabolic Indices

Out of all the metabolic indices that we evaluated, BMI and waist circumference decrease were significantly lower with the probiotic compared to controls. For endpoint data, the results were as follows: BMI studies = 16, n = 1256, SMD = −0.156, 95%CI = −0.27 to −0.04, p = 0.006 and DM = −0.45, 95%CI = −0.69 to −0.21, p = 0.00 and WC studies = 8, n = 690, SMD = −0.147, 95%CI = −0.30 to 0.03, p = 0.05 and DM = −1.21, 95%CI = −2.27 to −0.16, p = 0.02. In the case of the meta-analysis using change scores, the following results were obtained: WC studies = 5, n = 711, SMD = −0.166, 95%CI = −0.32 to −0.005, p = 0.04 and DM = −1.35, 95%CI = −2.59 to −2.15, p = 0.03 (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7). In one case, Egger’s test did indicate publication bias (DM for BMI: t value = 2.37, p = 0.02). For complete results, see Figures S1–S6.
As for the other metabolic indices, we found that probiotic ingestion in clinically healthy subjects did not affect those (Table S5).

3.5. Effects on Metabolic Indices Regarding Obesity Status

When conducting analysis by BMI status, i.e., in persons with BMI within normal (BMI: 20–25 kg/m2) and abnormal (BMI: >25 kg/m2) range, we were able to demonstrate that probiotic intake significantly affected total cholesterol (endpoint analyses) in persons with normal BMI value (SMD: −0.974; 95% CI: −1.661 to −0.286, p = 0.006). However, Egger’s test did indicate publication bias (SMD for total cholesterol (endpoint data): t value = 5.38, p = 0.000006). On the other hand, the analyses on the same parameter but regarding change scores depicted that probiotics significantly lowered the parameter in persons with abnormal BMI only (SMD: −0.206, 95% CI: −0.395 to −0.018, p = 0.032). In this case, no publication bias was detected (SMD for total cholesterol (change sores): t value = 1.64, p = 0.137). At last, we evaluated that WC (change score) was significant also in persons with abnormal BMI (SMD: −0.178, 95% CI: −0.354 to −0.001, p = 0.049). In this case, no publication bias was detected (SMD for total cholesterol (change sores): t value = 1.29, p = 0.265).

3.6. Metaregression Analyses

For endpoint data (SMD) regarding diastolic blood pressure (DBP), p values for all predictors were significant (Q = 19.22, df = 7, p = 0.0075): ROB (−0.31, z = −2.18, p = 0.029) and age (0.03, z = 2.07, p = 0.038), indicating that the predicted effect size decreases with increasing risk of bias and increases with age. The model explained 96% of the heterogeneity; however, the permutation test did not confirm the significance of predictors (p = 0.103 and p = 0.093, for ROB and age, respectively). In the case of insulin, p values for all predictors were found to be significant (Q = 16.37, df = 6, p = 0.012): number of low ROB assessments (−0.59, z = −2.57, p = 0.010), number of persons analysed (−0.04, z = −2.43, p = 0.015), duration of probiotic intervention (0.02, z = 2.27, p = 0.023), and BMI of analysed subjects (0.37, z = 3.15, p = 0.0016), indicating that the predicted effect size tended to decrease with ROB and study sample size, whereas with increasing duration and BMI, the effect size tended to be greater. The model did not explain heterogeneity, and the permutation test was nonsignificant. When analysing the triglycerides level, p values for all predictors were also significant (Q = 19.76, df = 7, p = 0.0061): monostrain vs. multistrain probiotics (−0.28, z = −2.47, p = 0.014) and BMI (0.056, z = 3.00, p = 0.003), indicating that the predicted effect size tended to be smaller for multistrain formulas whereas the effect size increased with increasing BMI. The model explained 90% of heterogeneity, and permutation tests were significant (p = 0.022 and p = 0.005 for type of formula and BMI, respectively). Finally, for total cholesterol, p values for all predictor were significant (Q = 90.55, df = 7, p < 0.0001): number of low ROB assessments (−0.67, z = −4.67, p < 0.0001), number of persons analysed (−0.06, z = −7.01, p < 0.0001), duration of probiotic intervention (0.02, z = 3.69, p = 0.0002), age (0.04, z = 2.60, p = 0.009), and BMI of participants (0.19, z = 2.98, p = 0.003). For BMI, HOMA-IR, LDL, and systolic blood pressure (SBP), p values for all predictors and for the effect sizes calculated for change score data were found to be nonsignificant (Q).

3.7. Microbiota Parameters

In 18 studies [35,36,40,48,55,61,63,65,72,75,76,77,79,81,82,83,85,90], microbiota and gut-barrier-related outcomes were evaluated following probiotic intervention. Composition and/or metabolites and/or immunological and/or gut-barrier-related outcomes were evaluated following probiotic intervention. These were data on faecal microbiota composition (n = 13), bacterial metabolites analyses (n = 9), as well as gut barrier integrity markers (mostly LPS, CRP, and zonulin) (n = 13) and various blood immune markers (mostly cytokines) (n = 12). Table 2 presents major results on these parameters. In the analysed studies, particular genera abundance was reported. Only in four studies, microbiota by means of next generation sequencing (NGS) technique was evaluated. Other trials utilized the culture-dependent technique and quantitative polymerase chain reaction (qPCR). We analysed also the association between clinical outcome, microbiota changes, anti-inflammatory effects, and gut barrier markers caused by probiotics administration (Supplementary Table S6). Clinical outcome was associated, in six studies, with microbial changes; in two studies, with microbial metabolites changes; and in two studies, with anti-inflammatory or gut barrier markers alterations. In four studies, changes of microbiota were observed despite lack of clinical efficacy of probiotic treatment.

4. Discussion

In past years, many studies revealed that probiotics and synbiotics, through interactions with hosts, could affect nutrient metabolism and energy balance. Our current meta-analysis of 61 clinical trials and 5422 persons exclusively investigated the impact of probiotic and synbiotic interventions to reduce cardiovascular risk factors in otherwise healthy adults. The only factor we decided not to exclude was overweight and obesity, as their prevalence is worldwide and as they impact human’s health [91]. Morbidly obese persons (BMI ≥ 45 kg/m2) were excluded from the present analysis. We also decided to exclude studies with adjunct medications with reported efficacy against metabolic dysregulation (e.g., metformin [92]). Similarly, we excluded patients with diagnosed diseases, as meta-analyses in such patients have already been published [93,94,95] The results of the present meta-analysis indicated that probiotics may reduce the BMI by 0.5 unit (provide stats) and decrease waist circumference by more than 1.5 cm (stats). The effect sizes were/were not affected by meta-regression statistics. The up-to-date published data indicate that probiotics may reduce body weight, BMI, and other anthropometric indices, e.g., fat mass and waist circumference, via several mechanisms. While restoring the microecological ecosystem, probiotics diminish the inflammation responsible for insulin sensibility in the hypothalamus [96]. This in turn, together with increased concentration of glucagon-like peptide-1 (GLP-1) as well as peptide YY (PYY), improve satiety and suppress appetite by delaying gastric emptying. It should be emphasized that gut-derived GLP-1 is able to attenuate gut motility and to facilitate the aggregation of the constitutive flora to ferment more polysaccharides [97]. Furthermore, healthy microbiomes within the gut upregulate the expression of fasting-induced adipocyte factor (FIAF) and thus limits the degradation of lipoproteins and the deposition of free fatty acids in adipose tissue. Together with reduced food intake, the abovementioned healthy microbiome can promote reduction of body weight [96,98]. The systematic review by Crovesy et al. [96] indicated that strains of Lactobacillus gasseri and Lactobacillus amylovorus may promote decrease of body weight in the overweight population. The meta-analysis by John et al. [97] confirmed that probiotic therapy was associated with a significant reduction of BMI and, thus, body weight and fat mass. The study group consisted of overweight and obese persons. Notwithstanding, another systematic review and meta-analysis in a similar group of subjects showed that administration of probiotics was related to reduction of body weight in comparison to the placebo; however, the effect sizes were small (weighted mean difference (95% confidence interval); −0.60 (−1.19, −0.01) kg, I2 = 49%), BMI (−0.27 (−0.45, −0.08) kg m−2, I2 = 57%) and fat percentage (−0.60 (1.20, −0.01) %, I2 = 19%). Similarly to our findings, the effect of probiotics on fat mass was not significant (−0.42 (−1.08, 0.23) kg, I2 = 84%) [99]. Also, a study by Depommier et al. [100] demonstrated that supplementation with Akkermansia Muciniphila in overweight and obese human volunteers improved insulin sensitivity and total plasma cholesterol with a small reduction of body mass compared to controls. In contrast, in healthy, but overweight subjects, the administration of Lactobacillus amylovorus and Lactobacillus fermentum strains reduced this body fat mass [101].
The current meta-analysis did not confirm the efficacy of probiotics administration in reduction of other cardiovascular risk in healthy people. Of note, carbohydrate and lipid metabolism was not significantly affected by this type of intervention. In contrary to diabetic patients, we did not find any effect of probiotic therapy on carbohydrate metabolism. A study by Raygan et al. [102] which was conducted in patients with type 2 diabetes mellitus (T2DM) and coronary heart disease found that the intervention, during which the strains of Bifidobacterium bifidum, Lactobacillus casei, and Lactobacillus acidophilus were ingested for 12 weeks, significantly decreased the plasma glucose and insulin resistance. In a meta-analysis by Samah et al., [103] moderately hypoglicaemic properties (lower levels of fasting blood glucose) of microbial agents were confirmed. As in previously quoted studies, the meta-analysis cohort coincided with T2DM patients. Probiotics were demonstrated to affect glucose metabolism via several mechanisms, including antioxidant activity, and thus diminished gut-barrier integrity disruption, enhanced NK cells activity in the liver cells, and diminished insulin resistance by modulating the expression of proinflammatory cytokines and NF-kB-binding activity. Indeed, eubiosis within the gut may serve as a protective point for the preDM and DM onsets, diminishing low-grade inflammation which characterizes all metabolic diseases [104,105]. As concerns inflammation status, we did not find the relationship between common inflammatory markers (CRP and leukocytes count) as well as other indices associated with insulin resistance, including endothelial markers and uric acid. In T2DM patients, probiotics were found to lower the concentrations of hs-CRP, IL-6, and TNF-α [106]. Similar results, regarding hs-CRP, were demonstrated lately in a meta-analysis by Zheng et al. [107] and by Tabrizi et al. [108]. At last, the increase of the bioavailability of gliclazide regulating the intestinal absorption of glucose may also play a role [93].
In our study, we found that probiotics can decrease the total cholesterol level in persons with increased BMI, but other lipid parameters were not affected by probiotics and synbiotics administration. In Wang et al.’s meta-analysis including 32 randomized controlled trials (1971 participants with various metabolic entities), it was proved that probiotics significantly reduced serum total cholesterol (MD = −13.27, 95% CI (−16.74–9.80), p < 0.05) in comparison to controls [109]. Similar results were obtained in the meta-analyses by Chao et al. [110] and Shimizu et al. [111] (30 RCTs and 33 RCTs, respectively; hypocholesterolemic effects of probiotics–mean net change of total cholesterol: 7.8 mg/dL and 6.57 mg/dL, respectively, both in persons with mild lipid malfunctions). There are many hypotheses regarding mechanisms in which probiotics may lower the cholesterol level, such as binding of cholesterol to the probiotic cellular surface and incorporation of cholesterol molecules into the probiotic cellular membrane. However, the deconjugation of bile via bile salt hydrolase (BSH) activity seems to be the most profound mechanism in which probiotics reduce cholesterol level [112]. Bile salt hydrolase is the enzyme that catalyses the hydrolysis of glycine- and/or taurine-conjugated bile salts into amino acids residues and free bile acids. The most BSH-active probiotics belong to the genera of Lactobacillus, Lactococcus, and Bifidobacterium. These probiotics increase the production of bile salts from cholesterol in their colonized area and, as a consequence, contribute to reduced risk of coronary heart diseases [112].
The administration of probiotics improved blood pressure in humans, which was confirmed in Khalesi et al.’s meta-analysis including 9 randomized, controlled trials [113]. The consumption of probiotics significantly decreased systolic blood pressure by 3.56 mmHg and diastolic blood pressure by 2.38 mmHg in comparison to control groups (the duration of intervention is ≥8 weeks or daily dose > 1011 CFU). In contrast to our study, the authors included studies evaluating people with metabolic syndrome, hypertension, and hypercholesterolemia. As the menopause period is a strong contributor of CVD [114], we looked for metabolic effects on probiotic intake in this particular subgroup of participants. We were able to demonstrate that probiotic intake decreased the vascular stiffness in obese postmenopausal women [80]. Also, as reported by Lambert et al. [62], probiotics significantly diminished vasomotor symptoms of menopause. In a study by Szulińska et al. [81] was found that probiotics administration favorably affected the risk factors in a dose-dependent manner, showing beneficial effects on the cardiometabolic parameters and gut permeability of obese postmenopausal women. However, Brahe et al. [36] did not record that metabolic index was affected by microbial agent administration. Only these three studies reported on metabolic effects in the perimenopausal period; thus, we did not conduct a subgroup analysis. More studies are needed to clarify if and how probiotics can affect CVD risk in women at the menopause period.
Last but not least, we abstracted data related to the influence of probiotic administration on gut microbiota and immunological markers. The most frequently studied variables were (i) the effects of probiotic administration on the composition of the microbiota and (ii) colonization with probiotics. Among microbial metabolites, mostly faecal SCFAs were analyzed. The authors analyzed also markers of gut-barrier integrity—mostly LPS and different cytokines as well as inflammatory markers. CRP measured in few studies can be considered as an inflammatory marker as well as a gut integrity marker. Based on the results obtained, no definite association can be found between the use of probiotics, microbiota changes, modulation of the immune system, and either presence or lack of clinical effects (Table 2 and Table S6). Of note, the results cannot be subjected to meta-analysis due to very diverse methods used to analyze the microbiota. Therefore, the results are difficult to compare. For this reason, in order to fully assess the causal relationship between the microbiota and the function of the immune system and gut-integrity markers with relation to cardiovascular risk prevention, a multifactorial analysis should be performed, which was not performed in the works described in this systematic review. In only one study, the correlation between microbiota changes and cardiovascular risk factors was demonstrated [48]; however, in this study, no preventive outcome of probiotics administration was observed. In addition, the results of metabolomic studies did not contribute to elucidation of the mechanism of action of probiotics studied. Therefore, it cannot be determined whether the effect of probiotics in cardiovascular risk prevention is related to their effect on microbiota or the immune system or gut-barrier function. The relationship observed in some studies is rather based on association and not causation. We conclude that mechanistic studies should be an important point in analysis of probiotics/synbiotics efficacy.


Several limitations of this meta-analysis need to be underlined. These include (i) a relatively small number of high-quality double-blinded studies comparing probiotic intervention to controls with a wide range within the number of participants preceded by no sample size calculations; (ii) heterogeneous study inclusion criteria (various age, profession of participants, and dietary and physical activity add-on interventions), and (iii) various type of strains and duration of probiotic intervention. In studies incorporated into the present meta-analysis, the association between the probiotic effect in relation to supplement dose and treatment duration was not analyzed. At last, most of the studies were financed by the industry and include products combined with different ingredients. These all are confounding factors for probiotic efficacy, which may have resulted in some publication bias as evaluated by Eagerr’s test and funnel plots [115]. Consequently, in order to draw some evidence-based conclusions and to give some guidelines regarding probiotic intake in healthy adults, strict inclusion criteria and homogenous intervention protocols are needed. Lastly, during meta-analysis, we did not use intent-to-treat data but adopted per-protocol evaluation as the majority of studies reported on that. We could have introduced potential bias during the review process and could have missed studies not clearly aimed at reducing cardiovascular risk but possibly reporting such outcomes.

5. Conclusions

Probiotics may counteract some CMRF (e.g., BMI and waist circumference) in clinically healthy participants. Overweight/obese persons might benefit from the reduction of total cholesterol serum concentration. Poor quality of probiotic-related trials make systematic reviews and meta-analyses difficult to conduct and draw exact conclusions. “Gold standard” methodology in probiotic studies awaits further development.

Supplementary Materials

The following are available online at, Table S1. Baseline metabolic parameters in studies persons, Table S2. Smoking status and physical activity, Table S3. Trial discontinuations and adverse effects, Table S4. Risk of bias, Table S5. Effect sizes regarding metabolic outcomes, Table S6. Summary of the preventive outcome and changes in microbial composition and metabolites as well as anti-inflammatory effects and gut-barrier markers associated with probiotics administration; Figure S1. Funnel plot for endpoint BMI (SMD) in the present meta-analysis, Figure S2. Funnel plot for endpoint BMI (DM) in the present meta-analysis, Figure S3. Funnel plot for endpoint WC (SMD) in the present meta-analysis, Figure S4. Funnel plot for endpoint WC (DM) in the present meta-analysis, Figure S5. Funnel plot for WC change scores (SMD) in the present meta-analysis, Figure S6. Funnel plot for WC change scores (DM) in the present meta-analysis.

Author Contributions

Conceptualization, I.Ł. and K.S.-Ż.; methodology, K.S.-Ż. and M.K.; software, K.S.-Ż. and M.K.; validation, K.S.-Ż., I.Ł., and W.M. ; formal analysis, K.S.-Ż., I.Ł., and W.M.; investigation, K.S.-Ż., K.J., J.Ś.-D., D.M. (Damian Malinowski), K.B., and D.M. (Dominika Maciejewska); resources, K.S.-Ż.; data curation, K.S.-Ż., K.K.-S., and M.K.; writing—preparation, K.S.-Ż., K.K.-S., E.S., and D.M. (Dominika Maciejewska); writing—review and editing, B.Ł., W.M., and I.Ł.; visualization, K.S.-Ż. and M.K.; supervision, W.M. and I.Ł.; project administration, K.S.Ż.; funding acquisition, W.M. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

Igor Łoniewski and Wojciech Marlicz are probiotic company shareholders. Joanna Śliwa-Dominiak is a probiotic company employee. Karolina Skonieczna-Żydecka and Mariusz Kaczmarczyk receive renumeration from a probiotic company. The other authors declare no conflict of interest.


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Figure 1. Study flow chart. Tx—treatment.
Figure 1. Study flow chart. Tx—treatment.
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Figure 2. Effect size and standardized mean difference for BMI in persons taking probiotics vs. controls (endpoint data). Q = 18.487, df (Q) = 21, p = 0.618, I-squared = 0.0. BMI—body mass index. [31,44,49,50,51,56,59,60,61,66,67,68,72,74,81,89].
Figure 2. Effect size and standardized mean difference for BMI in persons taking probiotics vs. controls (endpoint data). Q = 18.487, df (Q) = 21, p = 0.618, I-squared = 0.0. BMI—body mass index. [31,44,49,50,51,56,59,60,61,66,67,68,72,74,81,89].
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Figure 3. Effect size and difference in means for BMI in persons taking probiotics vs. controls (endpoint data). Q = 12.996, df (Q) = 21, p = 0.909, I-squared = 0.0. BMI—body mass index. [31,44,49,50,51,56,59,60,61,66,67,68,72,74,81,89].
Figure 3. Effect size and difference in means for BMI in persons taking probiotics vs. controls (endpoint data). Q = 12.996, df (Q) = 21, p = 0.909, I-squared = 0.0. BMI—body mass index. [31,44,49,50,51,56,59,60,61,66,67,68,72,74,81,89].
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Figure 4. Effect size and standardized mean difference for waist circumference (WC) in persons taking probiotics vs. controls (endpoint data). Q = 9.773, df (Q) = 12, p = 0.636, I-squared = 0.0. WC—waist circumference. [44,49,56,59,66,76,80,81].
Figure 4. Effect size and standardized mean difference for waist circumference (WC) in persons taking probiotics vs. controls (endpoint data). Q = 9.773, df (Q) = 12, p = 0.636, I-squared = 0.0. WC—waist circumference. [44,49,56,59,66,76,80,81].
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Figure 5. Effect size and difference in means for WC in persons taking probiotics vs. controls (endpoint data). Q = 8.698, df (Q) = 12, p = 0.729, I-squared = 0.0. WC—waist circumference. [44,49,56,59,66,76, 80,81].
Figure 5. Effect size and difference in means for WC in persons taking probiotics vs. controls (endpoint data). Q = 8.698, df (Q) = 12, p = 0.729, I-squared = 0.0. WC—waist circumference. [44,49,56,59,66,76, 80,81].
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Figure 6. Effect size and standardized mean difference for WC in persons taking probiotics vs. controls (change scores). Q = 1.539, df (Q) = 6, p = 0.959, I-squared = 0.0. WC—waist circumference. [38,49,56,57,81].
Figure 6. Effect size and standardized mean difference for WC in persons taking probiotics vs. controls (change scores). Q = 1.539, df (Q) = 6, p = 0.959, I-squared = 0.0. WC—waist circumference. [38,49,56,57,81].
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Figure 7. Effect size and difference in means for WC in persons taking probiotics vs. controls (change scores). Q = 1.102, df (Q) = 6 p = 0.981, I-squared = 0.0. WC—waist circumference. [38,49,56,57,81].
Figure 7. Effect size and difference in means for WC in persons taking probiotics vs. controls (change scores). Q = 1.102, df (Q) = 6 p = 0.981, I-squared = 0.0. WC—waist circumference. [38,49,56,57,81].
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Table 1. Study characteristics.
Table 1. Study characteristics.
No.Study DescriptionInterventionStudy Characteristisc
Reference/Year/Country/SponsorshipBlinding/Crossover (Y/N)/Multiarm > 2Focus onROBForm/Probiotic Strain/PrebioticProbiotic Dose CFUDuration of Probiotic Administration (Days)/ComparatorN Total Randomized and Allocated to Intervention/AnalyzedAge Years (Mean ± SD)Males (n/%)BMI Baseline (kg/m2): Probiotic Group (Mean ± SD)BMI Baseline (kg/m2): Control group (Mean ± SD)
1Agerbaek et al./1995/Denmark/Industry [29]DB/N/NLipoprotein levels2Naturally fermented milk/Enterococcus faecium (1 strain), Streptococcus termophilus (2 strains)Daily: Enterococcus faecium 4 × 1010 ; Streptococcus termophilus 1.4 × 101142/chemically fermented milk58/5744 (±ND)57/10024.3 (±2)24.1 (±1.7)
2aAgerholm-Larsen et al./2000/Denmark/Industry [30]DB/N/Y: 5 arms: 3 probiotic groups and 2 placebo groups
(3 probiotic and PBO tablet arms were analyzed)
Risk factors for cardiovascular disease2Yoghurt/2 strains of Streptococcus thermophilus and 2 strains of Lactobacillus acidophilus3 days/week: Streptococcus thermophilus 4.5 × 1010, Lactobacillus acidophilus 9 × 10956/placebo tablets262638.49 (±2.58)7/26.9230 (±2.8)29.9 (±3.48)
2bYoghurt/2 strains of Streptococcus thermophilus and 1 strain of Lactobacillus rhamnosus3 days/week: Streptococcus thermophiles 3.6 × 1011, Lactobacillus rhamnosus 9 × 101056/placebo tablets242438.07 (±2.77)7/29.1730.2 (±2.62)29.9 (±3.48)
2cYoghurt/1 strain of Enterococcus faecium and 2 strains of Streptococcus termophilus3 days/week: Enterococcus faecium 2.7 × 1010, Streptococcus thermophilus 4.5 × 101156/placebo tablets26/2637.99 (±2.54)7/26.9230.1 (±2.4)29.9 (±3.48)
3Ahn et al./2015/South Korea/Non-industry [31]DB/N/NTriglyceride level and fasting plasma metabolome2Powder/Lactobacillus curvatus HY7601, Lactobacillus plantarum KY1032Daily: Lactobacillus curvatus HY7601 5 × 109 and Lactobacillus plantarum KY1032 5 × 10984/placebo powder92/9253.4 (±8.38)30/32.6124.7 (±2.91)24.9 (±2.26)
4Ahn et al./2015a/South Korea/Non-industry [32]DB/N/NTriglyceride and apolipoprotein A-V levels2Powder/Lactobacillus curvatus HY7601, L. Plantarum KY1032Daily: Lactobacillus curvatus HY7601 0.5 × 1010 and Lactobacillus plantarum KY1032 0.5 × 101084/placebo powder128/12152.87 (±9.02)33/27.2724.9 (±3.2)24.8 (±2.62)
5aAndrade and Borges/2009/Portugal/industry [33]DB/Y/NPlasma lipids concentration0Fermented milk/L. Acidophilus 145 and Bifidobacterium longum BB536Daily: Lactobacillus acidophilus 145 5.25–7.88 × 1010 and Bifidobacterium longum BB536 1.01–3.75 × 101028-7 washout-28/regular yoghurt)41/3435.44 (±11.17)0/0Baseline: Group probiotic-placebo 24.6 (±3.5)
Group placebo-probiotic 24.9 (±3.40)
6Bjerg et al./2015/Denamark/industry [34]DB/N/NBlood lipids, fatty acids levels and stearoyl-coa desaturase−1 (SCD1) activity3Capsules/L. Casei W8Daily: 1 × 101028/placebo capsules contained rice flour70/64Range: 20–4534/48.5723.7 (±)23.7 (±)
7Boesmans et al./2018/Belgium/Non-industry [35]DB/Y/NBlood parameters, fecal microbiota composition and metabolites7Capsules/Butyricicoccus pullicaecorum 25-3TDaily: 1 × 10828-21 washout-28-21 washout/placebo capsules30/28Group probiotic-placebo
32 (26–45) Group placebo- probiotic 28 (25–33) 30(±ND)
Albo Range: 25–45
14/46.67Baseline: Group probiotic-placebo 23.6 (±2.1)
Group placebo-probiotic 22.1 (±1.9)
8Brahe et al./2015/Multicenter/Academic/industry [36]SB/N/Y
3 arms: 1 probiotic 1 prebiotic and 1 placebo group
(Probiotic and placebo groups were analyzed)
The gut microbiota composition, fecal SCFA concentration and metabolic risk markers in obesity3Powder/Lactobacillus paracasei F19Daily: 9.4 × 101042/placebo powder39/3559.92 (±6.09)0/034.2 (±3.1)34.3 (±3.8)
9Bukowska et al./1998/poland/Non-industry [37]DB/N/NMetabolic parmeteres2Drink/Lactobacillus plantarum 299 v/oat fibersDaily: Lactobacillus plantarum 299v 1 × 1010 and 160 mg oat fibers42/control drink (rose hip drink)30/3042.65 (±2.57)30/10026.6 (±3.7)25.9 (±2.6)
10Chang et al./2011/South Korea/Non-industry/industry [38]DB/N/NMetabolic parameters2The functional yogurt: starters: S. thermophilus, L. acidophilus, B. infantis; probiotics: Enterococcus faecalis FK-23, Bifidobacterium breve; fibersol-2 (resistant maltodextrin); pine needle extract; whey protein hydroxylate; Rice germ extract powder; Yucca schidigera and Quillaja saponaria extractND87/(control yoghurt with starters)103/10136.78 (±9.45)31/30.6922.63 (±3.26)22.13 (±2.8)
11aCox et al./2014/Multicenter/industry [39]DB/N/Y: 3 arms: 2 probiotic groups and 1 placebo groupRoutine haematology and clinical chemistry measures2Powder/Bifidobacterium animalis subsp. Lactis Bl-04,Daily: 2 × 109150/placebo powder87/8440.43 (±13.72)44/52.3824.6 (±3.2)24.1 (±3.1)
11bPowder/Lactobacillus acidophilus NCFM, Bifidobacterium animalis subsp. Lactis Bi-07Daily: total dose1 × 1010 (equal amount of each strain)91/9038.14 (±11.17)42/5024.4 (±3.8)24.1 (±3.1)
12De Roos et al./2017/netherlands/Non-industry [40]DB/N/NMigraine symptom reduction, an effect on intestinal permeability and inflammation markers4Powder/Bifidobacterium bifidum W23, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Lactobacillus brevis W63, Lactobacillus casei W56, Lactobacillus salivarius W24, Lactococcuslactis W19, Lactococcus lactis W58Daily: 5 × 10984/placebo powder63/6040.07, Range: 18–704/6.6724.2 (±NA)25.6 (±NA)
13Fabian et al./2006/Austria/industry [41]ND/N/NPlasma lipid profile0Yoghurt/starter cultures: Streptococcus thermophilus and Lactobacillus bulgaricus; probiotic: L. casei DN-114 001Daily: Days 1–14: 3.6 × 1010/Days 15–28: 7.2 × 101028/placebo regular yoghurt34/3324 ± 2.560/020.7 (±3)21 (±2.7)
14Gleeson et al./2012/United Kingdom/Industry [42]DB/N/NIncidence of upper respiratory tract infections (URTI) and mucosal immune markers1Powder/Lactobacillus salivariusDaily: 2 × 1010112/placebo powder66/5423.9 (±4.7)
On PRO age 25 ± 5 years PBO age 24 ± 4 years
28/42.42 (during randomization)24.2 (±3.4)23.2 (±2.7)
15Gohel et al./2016/india/Non-industry [43]DB/Y/NCalcium level and haematological parameters3Fermented milk/Lactobacillus helveticus MTCC 5463Daily: min. 2 × 101028-28 washout-28-28 washout/placebo milk76/5968.93 (±4.1)38/50NDND
16Gomes et al./2017/Brazil/Non-industry [44]DB/N/NBody composition, lipid profile, endotoxemia, inflammation, and antioxidant profile5Powder/Lactobacillusacidophilus LA-14, Lactobacillus casei LC-11, Lactococcus lactis LL-23, Bifidobacterium bifidum BB-06, and Bifidobacteriumlactis BL-4Daily: 2 × 1010 (equal amount of each strain)56/(placebo powder)60/43Range: 20–590/031.7 (±3.9)33.34 (±4.69)
17Greany et al./2008/USA/Non-industry/industry [45]SB/N/NPlasma lipids2Capsules/Lactobacillus acidophilus DDS-1, Bifidobacterium longum UABL-14/FOSDaily: L. acidophilus DDS-1: 3.75 × 109; B. longum UABL-14: 3.75 109 plus 30–45 mg FOS52/placebo capsules64/5526.77 (±5.07)22/4024.1 (±3.1)22.8 (±3.5)
18Guillemard et al.2010/Multicenter/Industry [46]DB/N/NIncidence of respiratory and gastrointestinal common infectious diseases (cids) and immune functions3Fermented dairy drink/Lactobacillus casei DN-114 001Daily: 2 × 101084/placebo diary drink)1000/96232.15 (±8.91)435/43.5 (during randomization)24 (±2.8)24.2 (±2.9)
19Hatakka et al./2008/Finland/Industry [47]DB/Y/NSerum cholesterol and triglyceride levels2Capsules/L. rhamnosus LC705 and P. freudenreichii JSDaily: L. rhamnosus LC70 5 2 × 1010; P. freudenreichii JS 2 × 101028-28/(placebo capsules)38/3842 (±7.28)38/100Baseline: Group probiotic-placebo
25.2 (±3.4)
Group placebo-probiotic 24.5 (±2.6)
20aHibberd et al./2019/Multicenter/industry [48]DB/N/Y: 4 arms: 1 probiotic group, 1 synbiotic group and 2 control groups
(Prebiotic and synbiotic and placebo arms were analyzed)
Body fat mass and obesity-related markers1Powder/Bifidobacterium animalis subsp. Lactis 420Daily: Bifidobacterium animalis subsp. Lactis 420, 1 × 1010168/placebo powder61/6148.63 (±10.09)17/27.8730.9 (±1.9)31 (±2.2)
20bPowder/Bifidobacterium animalis subsp. Lactis 420 and Litesse Ultra (refined polydextrose)Daily: Bifidobacterium animalis subsp. Lactis 420, 1 × 1010 + 12 g Litesse Ultra (refined polydextrose)73/7347.69 (±9.85)16/21.9231.2 (±2)31 (±2.2)
21aHigashikawa et al./2016/Japan/Non-industry [49]DB/N/Y: 3 arms: probiotic group, killed bacteria group and control groupBody fat and body weight6Powder/Pediococcus pentosaceus LP28 (live)Daily: LP28 1 × 101184/placebo powder41/4152.65 (±11.7)15/36.5826.84 (±1.15)27.37 (±1.43)
21bPowder/Pediococus pentosaceus LP28 (heat-killed)41/4154.18 (±10.89)27.1 (±1.24)
22aIbrahim et al./2018/Malaysia/Non-industry/industry [50]ND/N/Y: 4 arms: 2 sedentary groups: probiotic group, placebo group; 2 circuit training groups: probiotic, placeboMuscular strength and power and cytokine responses1Powder/Lacidophilusacidophilus BCMC 12130, L. casei BCMC 12313, L. lactis BCMC 12451, Bifidobacterium bifidum BCMC 02290, B. infantis BCMC 02129, and B. longum BCMC 02120)Sedentary groups
Daily: 6 × 1010
84/placebo powder24/2022.5 (±1.66)24/10021.8 (±3.4)21.1 (±2.8)
22bCircuit training groups Daily: 6 × 101084/placebo powder plus circuit training24/2121.43 (±2.53)22.1 ±3.421.1 ±2.7
23Inoue et al./2018/Japan/Non-industry [51]DB/N/NCognitive function, mental state, body composition, and bowel movement were measured4Powder/B. longum BB536, B. infantis M-63, B. breve M-16V and B. breve B-3Daily: 1.25 × 1010 of each strain84/placebo powder+39/3870.3 (±3.1)14/36.8224 (±2.8)23 (±2.7)
24Ito et al./2017/Japan/Non-industry [52]DB/N/NSerum lipids level5Fermented milk/Streptococcus thermophilus YIT 2001Daily: ≥1 × 101184/placebo non-fermented milk60/5947.35 (±8.25)30/50.8422.4 (±2.8)23.3 (±2.8)
25Ivey et al./2014/Australia/Non-industry/Industry [53]DB/N/Y: 4 arms: 2 probiotic yoghurt groups: probiotic capsules group, placebo group; 2 control milk: probiotic capsules, placebo (probiotic yoghurt, control milkBiomarkers of glycaemic control4Yoghurt and capsules/Lactobacillus acidophilus La5, Bifidobacterium animalis subsp lactis Bb12Daily: 3 × 109 (both yoghurt and capsules)42/probiotic yoghurt placebo capsules,77/7768.4 (±8.25)50/64.9330.6 (±3.8)30.2 (±4.3)
Capsules/Lactobacillus acidophilus La5, Bifidobacterium animalis subsp lactis Bb1242/control milk, placebo capsules79/7965.05 (±7.79)46/58.2330.8 (±3.5)30.8 (±3.5)
26Ivey et al./2015/Australia/Non-industry/industry [54]DB/N/Y: 4 arms: 2 probiotic yoghurt groups: probiotic capsules group, placebo group; 2 control milk groups: probiotic capsules, placeboBlood pressure and serum lipid profile4Yoghurt and capsules/Lactobacillus acidophilus La5, Bifidobacterium animalis subsp lactis Bb12Daily: 3 × 109 (both yoghurt and capsules)42/placebo capsules, control milkProbiotic yoghurt
68 (±8.34)50/64.9331 (±4)30 (±4)
Control milk
65 (±7.52)46/58.2331 (±4)31 (±4)
27Jones et al./2016/Canada/industry [55]DB/N/NBlood cholesterol concentration5Capsules/Lactobacillus. reuteri NCIMB 30242Daily: min. 4.0 × 10963/(placebo capsules)131/12749.09 (±13.57)55/43.3126.83 (±3.05)27.62 (±2.81)
28Kadooka et al./2010/Japan/ND [56]DB/NAbdominal adiposity, body weight and other body measures in adults with obese tendencies2Fermented milk/Lactobacillus gasseri SBT2055 (LG2055)Daily: 1 × 101184/placebo fermented milk87/8748.76 (±9.21)59/67.8227.5 (±1.67)27.2 (±1.69)
29aKadooka et al./2013/Japan/Non-industry [57]DB/N/Y: 3 arms: lower dose probiotic group, higher dose probiotic group, control groupAbdominal adiposity, anthropometric measures and body composition2Fermented milk/Lactobacillus gasseri SBT2055Daily: 2 × 10984/fermented placebo milk139/13947.15 (±7.21)69/49.6427.5 (±1.9)27.2 (±1.9)
29bDaily: 2 × 108141/14147.29 (±7.21)72/51.0627.2 (±1.8)27.2 (±1.9)
30Kawase et al./2000/Japan/Non-industry [58]SB/N/NSerum lipid level2Fermented milk/Lactobacillus casei subsp. casei TMC0409, Streptococcus thermophilus TMC1543Daily: L. casei TMC0409 2.44 × 1011, S. thermophilus TMC1543 1.04 × 101059/fermented placebo milk20/2040.1 (±ND)20/100NDND
31aKim et al./2018/South Korea/Non-industry/Non-industry [59]DB/N/Y: 3 arms: lower dose probiotic group, higher dose probiotic group, control groupAdiposity5Capsules/Lactobacillus gasseri BNR17Daily: 1 × 10984/placebo capsules60/6038.7 (±11.76)20/33.327.9 (±1.07)28.6 (±1.96)
31bDaily: 1 × 101060/6038 (±10.4)21/3528.8 (±2.24)28.6 (±1.96)
32Kim et al./2017/South Korea/Non-industry [60]DB/N/NAdiposity parameters and metabolomic profile5Powder/L. curvatus HY7601 and L. Plantarum KY1032Daily: Lactobacillus curvatus HY7601 2.5 × 109, Lactobacillus plantarum KY1032 2.5 × 10984/placebo powder120/6638.99 (±1.93)ND26.6 (±1.3)27.1 (±1.57)
33Klein et al./2008/Germany/Non-industry [61]DB/Y/NBlood lipids, faecal microbiota, and immunological parameters3Yoghurt/B. lactis DGCC420, L. acidophilus 74-2Daily:B. lactis 9 × 108; L. acidophilus 74-2 2.79 × 101135-35/placebo yoghurt26/2625 (±3)13/50Baseline: Group probiotic-placebo 21.3(±2.1)
Group placebo-probiotic21.5(±2.0)
34Lambert et al./2017/Denmark/industry/Non-industry [62]DB/N/NAnthropometric data, lipids level, and menopausal symptoms4Drink/Heterogeneous culture of probiotic lactic acid bacteria in red clover drinkND84/placebo water based drink62/5952.34 (±3.66)0/026.02 (±5.38)25.45 (±3.34)
35aLee et al./2017/USA/Non-industry [63]Partially SB/Y/Y: 4 arms: (1) placebo yogurt, (2) yogurt with probiotic added pre-fermentation, (3) yogurt with probiotic added post-fermentation, and (4) probiotic capsulesBlood lipids level and fecal excretion of scfas3Yoghurt, capsules/Bifidobacterium animalis subsp. Lactis BB-12®Daily: 3.16 × 109Each treatment period 28-washout 14/placebo capsules36/3028.2 (±6.4)11/36.67All crossover groups 24.2 (±2.6)
36Lin et al./1989/USA/industry [64]DB/Y/NSerum lipids level4Tablets/L. acidophilus (ATCC 4962) and L. Bulgaricus (ATCC 33409)Daily: 8 × 10642-21 washout-42/placebo tablets334/334NDNDND
37Macfarlane et al./2013/United Kingdom/Non-industry [65]DB/Y/NColonic microbiota composition, immune function and health status5Capsules/Bifidobacterium longum/Powder/Synergy I mixture of inulin and oligofructose (DP2-60)Daily: 4 × 1011 B. longum + 12 gof prebiotic)28-28 washout-28/placebo capsules and powder47/4371.9 (±5.4)21/48.83All crossover groups 26.9 (±4.2)
38Madjd et al./2016/Multicenter/Non-industry [66]SB/N/NBody weight and cardiometabolic risk factors3Yoghurt/Lactobacillus acidophilus LA5, Bifidobacterium lactis BB12ND84/low fat yoghurt89/8931.98 (±6.88)0/032.14 (±3.2)32.05 (±3.94)
39aMohammad Moradi et al./2015.Iran/non-industry [67]TB/N/Y: 3 arms: (1) probiotic cheese and extract of chicory root, (2) probiotic cheese, and (3) controlLipid profile5Cheese/Lactobacillus acidophilus LA5, Bifidobacterium lactis BB12and raw chicory rootND49/no intervention120/12037.55 (±15.97)60/5022.38 (±2.01)22.14 (±0.97)
39bCheese/Lactobacillus acidophilus LA5, Bifidobacterium lactis BB12120/12039.4 (±15.92)60/5021.94 (±2.19)22.14 (±0.97)
40Naruszewicz et al./2002/poland/Non-industry [68]DB/N/NLipid profiles, inflammatory markers, and monocyte function3Drink/Lactobacillus plantarum 299vDaily: 2 × 101042/placebo drink36/3642.3 (±3.9)18/5024.8 (±4.8)25.8 (±3.7)
41Nishiyama et al./2018/Japan/industry [69]DB/N/NImmunity and metabolic syndrome parameters2Yoghurt/L. lactis 11/19-B1, B. Lactis BB-12No data available56/placebo yoghurt79/7642.35 (±11.15)29/38.15NDND
42Nova et al./2011/Spain/Non-industry [70]DB/N/NSelf-perceived gastrointestinal well-being and immunoinflammatory status3Tablets/L. Acidophilus La5, B. animalis Ssp. Lactis Bb-12, Lactobacillus delbrueckii ssp. bulgaricus, Streptococcus thermophilus, and Lactobacillus paracasei ssp. paracasei and FOSDaily: 2.4 × 10942/placebo tablets with no probiotics)37/36Range 25–4516/44.423.74 (±2.19)23.06(±2.32)
43Ostan et al./2015/Multicenter/Non-industry [71]ND/N/Y: from 4 arms only probiotic and control arm were analysedInflammageing, oxidative stress, and gut microbiota composition3Capsules/Lactobacillus paracasei, L. plantarum, L. acidophilus, L. delbrueckii subsp bulgaricus, bifidobacterium longum, B. breve, B. infantis, Streptococcus thermophilusDaily: 2.24 × 101156/Ristomed diet alone69/5970.4 (±3.9)58/46.426.7(±3.8)26.9(±3.4)
44Osterberg et al./2015/USA/Non-industry [72]DB/N/NBody and fat mass, insulin sensitivity, and skeletal muscle substrate oxidation3Powder/Streptococcus thermophilus DSM24731, Lactobacillus acidophilus DSM24735, Lactobacillus delbrueckii ssp. Bulgaricus DSM24734, Lactobacillus paracasei DSM24733, Lactobacillus plantarum DSM24730, Bifidobacterium longum DSM24736, Bifidobacterium infantis DSM24737, and Bifidobacterium breve DSM24732Daily: 9 × 101128/placebo powder20/2022.6 (±3.59)20/10023.9 (±2.7)23.2 (±1.99)
45aRajkumar et al./2014/india/Non-industry [73]SB/N/Y: 4 arms: placebo, probiotic, omega-3 fatty acid, omega-3 fatty acid + probiotic (probiotics and placebo arms were analyzed)Insulin sensitivity, blood lipids, and inflammation6Capsules/Bifidobacterium longum, B. infantis, B. breve, Lactobacillus acidophilus, L. paracasei, L. delbrueckii subsp. bulgaricus, L. Plantarum, Streptococcus salivarius subsp. thermophilus;Daily: 1.13 × 101142/placebo capsules30/3049 (40-60)30/5028.79 (Range: 27–30)
45bCapsules/Bifidobacterium longum, B. infantis, B. breve, Lactobacillus acidophilus, L. paracasei, L. delbrueckii subsp. bulgaricus, L. plantarum, Streptococcus salivarius subsp. thermophilus, Omega 3 fatty acidsDaily: 1.13 × 1011 + Omega 3 (360 mg EPA and 240 mg DHA)
46Sadrzadeh-Yeganeh et al./2010/Multicenter/industry [74]TB/N/Y: 3 arms: placebo yoghurt, probiotic yoghurt, and no intervention (probiotic and placebo arms were analysed)Lipid profile2Yoghurt/Placebo: Lactobacillus bulgaricus and Streptococcus thermophilus. Probiotic: Placebo + Lactobacillus acidophilus La5, Bifidobacterium lactis Bb12ND42/placebo yoghurt,60/5934.06 (±5.74)NDPlacebo yoghurt
23 (±2.4)
Probiotic yoghurt
24 (±2.4)
47Sanchez et al./2014/Multicenter/industry [75]DB/N/NWeight loss and weight maintenance4Capsules/Lactobacillus rhamnosus CGMCC1.3724 and mix of oligofructose and inulin2 Daily: 3.24 × 108 and 600 mg of a mix of oligofructose and inulin (70:30, v/v)168/placebo capsules125/9336 (±79.06)48/38.433.8 (±25.98)33.3 (±25.39)
48aSavard et al./2011/Canada/industry [76]DB/N/Y: 3 arms: lower dose of probiotics and green tea extract, higher dose of probiotic and green tea extract, and placeboFecal bacterial counts of Lactobacillus acidophilus LA-5 and Bifidobacterium animalis subsp. Lactis BB-12 and lipid profile3Yoghurt/Starters: Lactobacillus delbrueckii subsp. Bulgaricus and Streptococcus thermophilus; Probiotics: Bifidobacterium animalis subsp. Lactis BB-12, Lactobacillus acidophilus LA-5 and green tea extract1 arm, daily: Bifidobacterium animalis subsp. Lactis BB-12 1 × 109, Lactobacillus acidophilus LA-5 1 × 109 and 40 mg of green tea extract28/placebo yoghurt containing no starter culture, no probiotic, and no green tea extract40/3832 (±11.9)12/3022.8 (±3.8)23.8 (±4.1)
48b2 arm, daily: Bifidobacterium animalis subsp. Lactis BB-12 1 × 1010, Lactobacillus acidophilus LA-5 1 × 109 and 40 mg of green tea extract38/3633.27 (±12.37)12/31.623.7 (±2.7)23.8 (±4.1)
49Simon et al./2015/Multicenter/Non-industry [77]DB/N/Y: 4 arms: lean group: 1) probiotics, 2) placebo; obese group: 1) probiotics, 2) placeboInsulin sensitivity4Capsules/Lactobacillus reuteri SD5865Daily: 2 × 101028/placebo capsulesLean: 11/11
50 (±7)Lean: 5/45
Lean: 23.6 (±6 1.7)
Obese: 35.5±4.9
50Simons et al./2006/Australia/Industry [78]DB/N/NLDL cholesterol and other lipid fractions level3Capsules/Lactobacillus fermentumDaily: 8 × 10970/placebo capsules46/4451.5 (±11.5)16/36.3627 (±5.7)24.4 (±3.7)
51aStenman et al./2016/Multicenter/industry [79]DB/N/Y: 4 arms: (1) placebo, (2) prebiotic, (3) probiotic (4) synbiotic (probiotic, synbiotic vs. Placebo arms were analyzed)Body fat mass and other obesity-related parameters4Powder/Probiotic: Bifidobacterium animalis ssp. Lactis 420 (B420); Prebiotic: polydextrose; Synbiotic: combination of aboveDaily: B420 1 × 1010;186/placebo powder,112/6148.67 (±10.23)17/27.8630.9 (±1.9)
31.2 (±2)
31 (±2.2)
31 (±2.2)
51bSynbiotic: B420 1 × 1010 +; polydextrose 12g113/7347.75 (±9.75)16/21.92
52aSzulińska et al./2018/Poland/Non-industry [80]DB
DB/N/Y: 3 arms: (1) lower dose probiotic, (2) higher dose probiotic, and (3) placebo
Functional (primary endpoint) and biochemical parameters (secondary endpoint) of endothelial dysfunction5Powder/Bifidobacterium bifidum W23, Bifidobacterium lactis W51, B. Lactis W52, Lactobacillus acidophilus W37, Lactobacillus brevis W63, Lactobacillus casei W56, Lactobacillus salivarius W24, Lactococcus lactis W19, Lactococcus lactis W58Daily: 1 × 10984/placebo powder54/4857.55 (±7)0/036 (±5.2)36.1 (±4.37)
52bDaily: 2.5 × 101054/4756.94 (±7.28)36.57 (±5.9536.1 (±4.37)
53aSzulińska et al./2018a/Poland/Non-industry [81]DB/N/Y: 3 arms: (1) lower dose probiotic, (2) higher dose probiotic, (3) placeboCardiometabolic biochemical parameters, and lipopolysaccharide levels5Powder/Bifidobacterium bifidum W23, Bifidobacterium lactis W51, Bifidobacterium lactis W52, Lactobacillus acidophilus W37, Lactobacillus brevis W63, Lactobacillus casei W56, Lactobacillus salivarius W24, Lactococcus lactis W19, and Lactococcus lactis W58.Daily: 1 × 10984/placebo powder54/4857.55 (±7)0/036 (±5.2)36.1 (±4.37)
53bDaily: 2.5 × 101054/4756.94 (±7.28)36.57 (±5.95)36.1 (±4.37)
54aTenore et al./2019/Italy/Non-industry [82]DB/N/Y: 3 arms: lactofermented Annurca apple puree, probiotic, unfermented apple pureeLipid profile and oxidative metabolites level6Capsules/Lactobacillus rhamnosus LRH11, Lactobacillus plantarum SGL071 arm, daily: 3 × 108112/Annurca apple puree with no probiotics54/4246.65 (±10.36)30/71.43≤30
54bAnnurca apple puree fermeted with Lactobacillus rhamnosus LRH11, Lactobacillus plantarum SGL07Daily: probiotics 3.0 × 108 + Annurca apple puree 125 2 arm, Daily: probiotics 3.0 × 108 + Annurca apple puree 125 g53/4145.64 (±10.51)31/75.61
55Trautvetter et al./2012/Germany/industry [83]DB/Y/NIntestinal colonisation of L. Paracasei and blood cholesterol level3Yoghurt/Lactobacillus paracasei LPC37 and bread containing pentacalcium hydroxy-triphosphateDaily: probiotic 1 × 1012 and calcium 1 g28-28 washout-28/placebo yoghurt and bread)32/3225 (±5)ND22 (±3)
56aUsinger et al./2010/Denmark/Industry [84]DB/N/Y:3 arms: 150mL probiotic milk, 300 mL of probiotic milk, chemically acidifies milkBlood pressure5Milk/Lactobacillus helveticus Cardi-04Daily 150 mL of milk fermented with probiotic strain (dose not available) and contains 1.25 mg Val–Pro–Pro (VPP) and 0.55 mg Ile–Pro–Pro (IPP)56/chemically acidified milk47/4553.3 (±7.41)36/6026 (±4)26 (±4)
56bDaily 300 mL of milk fermentem with probiotic strain (dose not available) and contains 2.5 mg Val–Pro–Pro (VPP) and 1.1 mg Ile–Pro–Pro (IPP)47/4428/46.6727 (±4)26 (±4)
57Valentini et al./2015/Multicenter/Non-industry [85]SB/N/NBiomarkers of inflammation, nutrition, oxidative stress and intestinal microbiota4Capsules/Bifidobacterium infantis DSM24737, Bifidobacterium longum DSM24736, Bifidobacterium breve DSM24732, Lactobacillus acidophilus DSM24735, Lactobacillus delbruckii ssp.bulgaricus DSM 27734, Lacctobacillus paracasei DSM 24733, Lactobacillus plantarum DSM24730, Streptococcus thermophilus DSM 24731Daily: 2.24 × 101156/Ristomed diet69/6270.1(±3.9)29/46.7726.8 (±3.59)
58Välimäki et al./2012/finland/Non-industry [86]DB/N/NOxidized LDL lipids, serum antioxidant potential (s-TRAP) and serum antioxidants (s-α-tocopherol, s-γ-tocopherol, s-retinol, s-β-carotene, and s-ubiquinone-10)4Milk drink or capsules/Lactobacillus rhamnosus GGDaily: drink 4x × 1010 or capsules 1 × 101084/placebo drink or capsules141/11940 (23–69)105/88.222 (Range: 18–26)
59Venkataraman et al./2018/India/ND [87]SB/N/NBlood glycemic markers concentration2Capsules/Lactobacillus salivarius UBLS22, Lactobacillus casei UBLC 42, Lactobacillus plantarum UBLP 40, Lactobacillus acidophilus UBLA 34, Bifidobacteriu breve UBBR 01, Bacillus coagulans Unique-IS2/FOS3.0 × 108 cfu/30 × 109 CFU/capsule84/placebo capsule80/80NDNDNDND
60Xiao et al./2003/Japan/industry/Non-industry [88]SB/N/NBlood lipids level2Yoghurt/Bifidobacterium longum BL1Daily: 3 × 101028/placebo yoghurt32/3243.85 (±8.05)32/100NDND
61aZarrati et al./2014/Iran/Non-industry [89]DB/N/Y: 3 arms: probiotic yoghurt with low calorie diet (LCD), probiotic yoghurt without LCD, regular yoghurt with LCDBody fat percentage, blood proinflammatory markers and cytokines content3Yoghurt/Lactobacillus acidophilus LA5, Lactobacillus casei DN001, Bifidobacterium lactis BB12 without LCDDaily: 2 × 101056/regular yoghurt with LCD50/5035.5 (±9.27)24/3232 (±3.62)33.9 (±6.73)
61bYoghurt/Lactobacillus acidophilus LA5, Lactobacillus casei DN001, Bifidobacterium lactis BB12 with lcd50/5036 (±9.07)33.8 (±6.35)33.9 (±6.73)
DB—double blind, SB—single blind, TB—triple blind, N—no, Y—yes, NA—not applicable, CFU—colony-forming units, ROB—risk of bias, SD—standard deviation, URTI—upper respiratory tract infection, CIDs—common infectious diseases, LDL—low-density lipoprotein, TRAP—total reactive antioxidant potential, EPA—eicosapentaenoic acid, DHA—decosahexaenoic acid, ND—not determined, PBO—placebo, PRO—probiotic, FOS—fructooligosaccharides.
Table 2. Microbiota and gut-barrier-related outcomes.
Table 2. Microbiota and gut-barrier-related outcomes.
No.Reference/Year/Country/SponsorshipMicrobiotaMicrobiota Related (Metabolites)Gut Barrier and Inflammatory MarkersMethods
1Boesmans et al./2018/Belgium/Non-industry [35]No impact on the microbiota richness and single genera abundances, only transcient gut colonization by probiotic strain used in the studyNo influence on microbiota metabolic activity as well as on saccharolytic and proteolytic fermentation processes markers (SCFAs and dimethyl sulfide, p-cresol, indole, and the branched-chain fatty acids)No influence on faecal calprotectin concentrationsNGS, GC-MS
2Brahe et al./2015/Multicenter/Academic/Industry [36]Probiotc group: alterations in faecal abundance of 2493 bacterial genes ≥ ↑ Eubacterium rectale and ↑Ruminococcus torques.
Placebo group: altered faecal abundance of 7436 genes ≥ ↑Roseburia hominis, ↑two Clostridiales, ↑ one unknown species, ↓ Eubacterium ventriosum, and ↓ one unknown species.
No impact on faecal total SCFAs and butyric acidNo impact on lipopolysaccharide-(LPS)-binding protein and inflammatory markers (plasma high-sensitivity C-reactive protein (CRP), serum tumor necrosis factor-α (TNFα), and plasma interleukin (IL)-6metagenomics, ethyl chloroformate NEFA method and GC
3de Roos et al./2017/Netherlands/Non-industry [40] No impact on zonulin concentration and intestinal permeability measured by means of lactulose-mannitol test; no changes of IL -6, IL-10, TNFα, and CRPELISA, GC
4Hibberd et al./2019/Multicenter/Industry [48]Probiotic group
Akkermansia muciniphila, ↑Lactobacillus, ↑ Bifidobacterium OTU, ↑Akkermansia, ↑ Streptococcus,
↑ S24-7, ↑Methanobrevibacter, ↑Clostridiaceae spp., ↑Clostridium, ↑Phascolarctobacterium, ↑Dialister; ↓Bacteroides, ↓ Erysipelotrichaceae spp.,
Enterobacteriaceae spp., ↓ RF39 spp.
Bifidobacterium was positively correlated to lean body mass (total, arms, legs, trunk, and android).
Paraprevotella negatively correlated with fat mass.
Synbiotic group
The most pronounced changes of microbiota alterations in clustering analysis (long-term effect).
Phylum Bacteroidetes:
↑ taxa: S24-7, Barnesiellaceae spp., Parabacteroides, and Rickenellaceae spp.;
↓ taxa: Paraprevotella.
Phylum Firmicutes:
↑ taxa: Christensenellaceae, Ruminococcaceae spp., Oscillospira, Phascolarctobacterium, Erysipelotrichaceae spp.
↓ taxa: Lactobacillus, Lactococcus, Turicibacter
Streptococcus, Clostridiales spp., Lachnospira
Phylum Actinobacteria:
↓ taxa: Adlercreutzia, Collinsella, Eggerthella
Methanobrevibacter, ↑ Akkermansia;
Enterobacteriaceae spp., and ↓ RF39 spp.
Christensenellaceae spp. abundance was correlated negatively to WHR and energy intake at baseline, and waist-area body fat and cholesterol markers were correlated to android fat, trunk fat, and lipid parameters
Christensenellaceae spp. was positively correlated to the faecal branched-chain fatty acids (BCFAs), isobutyric acid, isovaleric acid, 2-methyl-butyric acid, and 3-methyl-2-oxovalerate, and to the plasma bile acids.
Probiotic group
↓ propionate
Synbiotic group
↓ primary conjugated plasma bile acid glycocholic acid (GCA)
↓ secondary conjugated bile acids glycoursodeoxycholic
acid (GUDCA) and taurohyodeoxycholic acid and tauroursodeoxycholic acid (THDCA + TUDCA).
↑ carbohydrates/polysaccharides
PICRUSt: “Cellular Processes” and “Metabolism”-KEGG pathways differentially abundant from placebo group
No significant changes in short-chained fatty acids (SCFA) or amino acids for any treatment group
5Jones et al./2016/Canada/Industry [55] Highly sensitive (hs) CRP was unchanged.LC-MS, GC-MS
6Klein et al./2008/Germany/Non-industry [61]L. acidophilus and B. lactis elevationNo impact on SCFAs↑ phagocytic activity as a marker for the unspecific cellular immune responseEUB-positive, DAPI-staining, FISH-based quantification, GC
7Lee et al./2017/USA/Non-industry [63] ↑fecal acetate in control yoghurt group and in probiotic added before fermentation group; other SCFAs were unchanged.CRP level was unchanged.GC-MS
8Macfarlane et al./2013/United Kingdom/Non-industry [65]↑Actinobacteria, ↑ some species of Firmicutes,↑ total bifidobacterial population, ↑ B. angulatum, ↑ B. longum, ↑ B. adolescentis, ↑ B. bifidum. ↓Proteobacteria.↑ Firmicutes/Bacteroidetes ratio.↑ butyrate, succinate, total acetate, propionate.↓TNF-αFISH, GC
9Osterberg et al./2015/USA/Non-industry [72]Streptococcus thermophiles, ↑ Lactobacillus acidophilus No changes of LPS Binding Protein (LBP), LBP/sCD14, IL6, TNFα, hsCRPqPCR
10Rajkumar et al./2014/India/Non-industry [73]Probiotic group and probiotic + omega-3 group
↑ total aerobes, ↑total anaerobes, ↑lactobacillus, ↑ bifidobacteria, ↑ streptococcus in the.
Probiotic + omega-3 group significant effect on↑ Bacteroides, ↓coliforms, and ↓E. coli.
↓hsCRP culture-dependent
11Sanchez et al./2014/Multicenter/Industry [75]Males: No changes in gut microbiota
Females: ↓, Lachnospiraceae family↓ Subdoligranulum genus.
No change of β-hydroxybutyrate level.No change of CRP and LPS levelNGS, ELISA
12Savard et al./2011/Canada/Industry [76]B. animalis subsp. Lactis, ↑L. acidophilus LA-5, ↑Bifidobacteria, ↑ Lactobacilli, ↓Enterococci qPCR
13Simon et al./2015/Multicenter/Non-industry [77]No impact on microbiota, only ↑L. reuterii-probiotic bacteria used in the study No changes of LPS and cytokinesNGS
14Stenman et al./2016/Multicenter/Industry [79] Probiotic: ↑propionic acid, butyric acid, and valeric acid,Synbiotic: changes in zonulin and hsCRP were statistically significantly correlated with changes in trunk fat mass; ↑ LPS level, but no effect on inflammatory markers.Limulus Amebocyte Lysate assay
15Szulińska et al./2018a/Poland/Non-industry [81] ↓LPS levelkinetic assay
16Tenore et al./2019/Italy/Non-industry [82]Bifidobacterium and ↑Lactobacillus population, and ↓ Bacteroides and ↓Enterococcus genera but predominantly in the control group followed by probiotic one and lactofermented control meal↓TMAO blood level culture-dependent
17Trautvetter et al./2012/Germany/Industry [83]L. paracasei and ↑ Lactobacilli qPCR
18Valentini et al./2015/Multicenter/Non-industry [85]No significan influence on gut microbiota. qPCR
qPCR—quantitative polymerase chain reaction, NGS—next generation sequencing, ELISA—enzyme-linked immunosorbent assays, FISH—fluorescent in situ hybridization, GC-MS—gas chromatography-mass spectrometry, LC-MS—liquid chromatography-mass spectrometry, DAPI—4′,6-diamidino-2-phenylindole, NMR—nuclear magnetic resonance, Tx—treatment, NEFA—non-estrified fatty acid, GC—gas chromatography, SCFAs—short chain fatty acids, EUB-positive—Eubacteria positive, HD—high dose, LPS—lipopolysaccharide, TMAO—trimethylamine-N-oxide, OTU—operational taxonomic unit, PICRUSt—Phylogenetic Investigation of Communities by Reconstruction of Unobserved States, KEGG—Kyoto Encyclopedia of Genes and Genomes, WHR—waist to hip ratio, hsCRP—high sensitivity C-reactive protein, ↑—elevated, ↓—lowered.

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Skonieczna-Żydecka, K.; Kaźmierczak-Siedlecka, K.; Kaczmarczyk, M.; Śliwa-Dominiak, J.; Maciejewska, D.; Janda, K.; Stachowska, E.; Łoniewska, B.; Malinowski, D.; Borecki, K.; et al. The Effect of Probiotics and Synbiotics on Risk Factors Associated with Cardiometabolic Diseases in Healthy People—A Systematic Review and Meta-Analysis with Meta-Regression of Randomized Controlled Trials. J. Clin. Med. 2020, 9, 1788.

AMA Style

Skonieczna-Żydecka K, Kaźmierczak-Siedlecka K, Kaczmarczyk M, Śliwa-Dominiak J, Maciejewska D, Janda K, Stachowska E, Łoniewska B, Malinowski D, Borecki K, et al. The Effect of Probiotics and Synbiotics on Risk Factors Associated with Cardiometabolic Diseases in Healthy People—A Systematic Review and Meta-Analysis with Meta-Regression of Randomized Controlled Trials. Journal of Clinical Medicine. 2020; 9(6):1788.

Chicago/Turabian Style

Skonieczna-Żydecka, Karolina, Karolina Kaźmierczak-Siedlecka, Mariusz Kaczmarczyk, Joanna Śliwa-Dominiak, Dominika Maciejewska, Katarzyna Janda, Ewa Stachowska, Beata Łoniewska, Damian Malinowski, Krzysztof Borecki, and et al. 2020. "The Effect of Probiotics and Synbiotics on Risk Factors Associated with Cardiometabolic Diseases in Healthy People—A Systematic Review and Meta-Analysis with Meta-Regression of Randomized Controlled Trials" Journal of Clinical Medicine 9, no. 6: 1788.

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