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The effects of prebiotic, probiotic, and synbiotic supplementation on blood parameters of renal function: A systematic review and meta-analysis of clinical trials
Nutrition 51-52 (2018) 104–113
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Nutrition j o u r n a l h o m e p a g e : w w w. n u t r i t i o n j r n l . c o m
Review
The effects of prebiotic, probiotic, and synbiotic supplementation on blood parameters of renal function: A systematic review and meta-analysis of clinical trials Somayyeh Firouzi Ph.D. a, Fahimeh Haghighatdoost Ph.D. b,* a Department b Department
of Nutrition and Dietetics, University Putra Malaysia, Kuala Lumpur, Malaysia of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
A R T I C L E
I N F O
Article history: Received 16 January 2017 Received in revised form 16 December 2017 Accepted 9 January 2018 Keywords: Probiotic Prebiotic Creatinine Glomerular filtration rate Blood urea nitrogen
A B S T R A C T
Objectives: Recent studies have demonstrated promising results regarding possible improvements in renal function after prebiotic, probiotic, and synbiotic supplementation. The aim of this review was to demonstrate whether such supplementation will improve renal profile indexes including glomerular filtration rate (GFR), creatinine, blood urea nitrogen (BUN), uric acid (UA), and urea. Method: The meta-analysis included all studies that examined the effect of prebiotic, probiotic, and synbiotic supplements on one or more renal function parameters and had a control group. We searched July 1967 through to March 2016 MEDLINE, Scopus, and Google Scholar databases. Results: Of 437 studies, 13 were eligible for inclusion in the meta-analysis. GFR levels tended to be reduced; whereas creatinine levels increased in the intervention group compared with the placebo group, both in a non-significant manner. The pooled effect on BUN demonstrated a significant decline compared with the placebo group (MD, −1.72 mmol/L; 95% confidence interval [CI], −2.93 to −0.51; P = 0.005). Urea significantly decreased after intervention (−0.46 mmol/L; 95% CI, −0.60 to −0.32; P < 0.0001). The UA levels significantly increased in the intervention group compared with the placebo group (12.28 μmol/L; 95% CI, 0.85–23.71; P = 0.035). Conclusion: This study showed a significant increase in UA and a decrease in urea and BUN. The use of prebiotic, probiotic, and synbiotic supplements among those with compromised renal function or those at risk for renal failure should be limited until large-scale, well-designed randomized controlled trials prove the safety and efficacy of these supplements in improving renal function. © 2018 Elsevier Inc. All rights reserved.
Introduction Numerous studies have demonstrated that gut microbiome hosts billions of bacteria that interact with many physiological conditions [1]. The range of these conditions varies from gastrointestinal disturbances [2] to glucose homeostasis [3], obesity [4], metabolic endotoxemia [5,6] and bone density [7]. A connection between gut microbiome and kidney function has been suggested in the literature [8,9]. The proof for this claim lies in the fact that the composition of the gut microbiome interacts with levels of urea. It affects uremic retention and solute production, resulting in the generation of uremic toxins with a strong biological effect on progression toward kidney failure [10]. On the The authors have no conflicts of interest to declare. * Corresponding author. Tel.: +98 313 792 2719; fax: +98 313 668 2509. E-mail address: [email protected] (F. Haghighatdoost). https://doi.org/10.1016/j.nut.2018.01.007 0899-9007/© 2018 Elsevier Inc. All rights reserved.
other hand, the levels of uremia affect the composition of the gut microbiome through disturbances in the protective epithelial barrier of the intestine and the translocation of the intestinal microbiome in to the body [11]. Considering these facts, a question is raised as to whether manipulation of the gut microbiome with prebiotic, probiotic, or synbiotic supplements will improve kidney function. In recent years, several clinical trials investigated the effect of gut microbiome manipulation on generating uremic toxins among patients on hemodialysis. Results from the studies demonstrated that intervention with probiotics or prebiotics will beneficially reduce toxins that are generated from the kidney [12–14]. A meta-analysis of the randomized controlled trials (RCTs) also proved this positive effect [15]. Although the positive affect of gut microbiome manipulation on renal-generating toxins has been shown previously [12–15], fewer data are available in terms of its effect on renal
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
parameters such as urea, creatinine, uric acid (UA), blood urea nitrogen (BUN), and glomerular filtration rate (GFR). Firouzi et al. demonstrated that levels of urea were significantly reduced after probiotic supplementation among individuals with type 2 diabetes, whereas levels of GFR and creatinine remained unchanged [8]. In a randomized crossover study, BUN was significantly reduced among patients with stage III and IV chronic kidney disease (CKD) [16], whereas it did not change among hospitalized, enterally fed elderly patients [17]. In terms of changes in UA level, although probiotic supplementation improved UA levels in patients with stage III and IV CKD [16], it showed no effect on UA among healthy active adults [18], leading to uncertainty about the beneficial effect of probiotics on modulating UA levels. Overall, data regarding the effects of probiotic, prebiotic, and synbiotic supplementation on renal profiles remains limited and inconclusive. The present systematic review and meta-analysis aimed to discover whether the manipulation of gut microbiome with the aid of prebiotic, probiotic, and synbiotic supplementation will improve renal parameters including urea, creatinine, UA, GFR, and BUN. Material and methods Search strategy and study selection criteria The present meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [19]. A systematic literature search was developed to search the MEDLINE, Scopus, and Google scholar databases for RCTs from July 1967 to March 2016. Additionally, references to identified articles were manually searched to complement the database searching. The following MeSH search terms were used to identify relevant published articles: prebiotics, probiotics, synbiotics in combination with urinary tract, kidney, UA, urea, creatinine and glomerular filtration rate. No restrictions were made in terms of publication date. We independently performed the literature search, study selection, and data extraction, and disagreements were resolved by consensus. Study inclusion and exclusion criteria Published articles were included in the meta-analysis if they were in the English language and investigated the effects of prebiotic, probiotic or synbiotic supplements on renal outcomes. These data were extracted from studies that investigated the renal profile as main outcomes, or from other trials where renal data could be obtained from secondary outcomes. Studies that did not have a control group or only investigated renal toxins were excluded. The analysis was restricted to participants with age ≥18 y. Data extraction We independently extracted the following data from each article: first author’s last name, year of publication, number of participants in the studies, underlying condition of their participants, their age range and mean, sex, the study design, the type of supplement in the intervention and control groups, the dosage of supplements, study duration, and mean and SD of the renal profile tests before and after intervention. Contacts were made with authors of some papers to request additional data. The standardized mean difference and corresponding SEs were calculated by using postintervention data for all eligible reports. Quality assessment The quality of the studies was assessed using the Jadad Scale. The scores ranged from 0 to 5, with 5 indicating the best quality of research. The Jadad Scale scores studies based on randomization, blinding, and providing an account of all participants [20]. Statistical analysis This meta-analysis was conducted on the mean difference and SE for intervention and control groups. To calculate summary mean estimates with their corresponding 95% confidence interval (CI), a random-effects model was used [21]. Cochran’s Q test and I2 were used to examine between study statistical heterogeneity [22]. Subgroup analyses were performed to recognize the possible sources
105
of heterogeneity between studies. A fixed model was run to identify the betweensubgroup heterogeneity. Sensitivity analyses were run to examine the extent to which conclusions might rely on a particular study or studies. Visual inspections of funnel plots for asymmetry [23], Egger’s regression asymmetry and Begg’s adjusted rank correlation test [24] were carried out to evaluate publication bias. Statistical analyses were conducted in Stata, version 11.2 (Stata Corp., College Station, TX, USA). P < 0.05 was considered statistically significant.
Results Literature search Of 437 screened studies, 25 received full-text reviews. Of these studies, 13 were entered into the meta-analysis (Fig. 1). Cox et al.’s study was included as two reports that evaluated single- and multiple-strains of probiotic supplementation [18]. The fulltext of one of the eligible articles was not found despite contacting the corresponding author to obtain as much accurate information as possible; therefore, relevant data were extracted by using its abstract [25]. One study was excluded [16] because it was the pilot of another study, which was used in this meta-analysis [26]. One study was an open-label prospective study [27], one was an open-label RCT [25], three studies were crossover RCTs [26,28,29], and the rest were parallel-group double-blind RCTs [8,17,18,30–34]. The quality of studies was between 1 and 5 using Jadad scaling, with only five having a complete score of 5 [8,28,30,31,34] (Table 1). Characteristics of studies and participants Included in these studies, were 721 participants ranging in age from 24 to 84 y, including 326 men and 383 women (the number of each sex was not clear in the Pavan study [25]). In all, 185 participants had CKD, 155 had type 2 diabetes, 162 were hospitalized patients, and 192 were healthy individuals. Duration of intervention ranged from 1 to 24 wk. Three studies used singlestrain probiotics for intervention [17,27,33], five used multistrain probiotics [8,26,29,31,32], one used prebiotics [34], three used synbiotics [25,28,30], and one used single and double strains in each arm [18]. Eight studies examined the effects of probiotic supplementation on serum creatinine (N = 464) [8,17,26–28,31,33,34]. Six trials explored changes in GFR (N = 376) [8,25,27,28,30,34] and UA (N = 296) [18,26,29,31,32], three studies evaluated changes in BUN levels (N = 108) [17,26,31] and two studies (three effect sizes) examined changes in urea levels (N = 226) [8,18]. Findings from the meta-analysis Figure 2 presents the pooled effect of prebiotic, probiotic, and synbiotic supplementation on GFR. A non-significant reduction of GFR after consumption of prebiotics, probiotics, and synbiotics was noted (MD, −2.00 mL/min/1.73 m2; 95% CI, −5.15 to 1.16; P = 0.215). There was significant heterogeneity among studies (I2 = 88.9%; P < 0.0001). Subgroup analyses based on the duration of study, the strain of probiotic supplement, and presence of renal disease could not explain the heterogeneity between studies (Table 2). However, individuals who consumed a single strain of probiotics had significantly lower GFR compared with the control group (MD, −9.32 mL/min/1.73 m2; 95% CI, −16.92 to −1.73; P = 0.016) with no significant heterogeneity (I2 = 0.0; P = 0.56) (Table 3). Findings from sensitivity analysis indicated that excluding an individual study would not change the significance of the findings. No evidence of publication bias was observed (for Egger’s test, P = 0.305).
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Fig. 1. Screening flowchart.
Figure 3 indicates the pooled effect of prebiotic, probiotic, and synbiotic supplementation on serum creatinine. Results revealed a non-significant increment in serum creatinine levels by prebiotic and probiotic supplementation (MD, 2.20 μmol/L; 95%
CI, −2.14 to 6.54; P = 0.3.21). The heterogeneity level between studies was high (I2 = 90.6%; P < 0.0001). Subgroup analyses according to the study duration, probiotic strain, and renal disease status indicated significant subgroup effects on creatinine but
Table 1 Quality assessment of the included studies Code/Author (y)
1–Ranganathan et al. 2010 2–Cox et al. 2014 3–Guida et al. 2014 4–Akoglu et al. 2015* 5–Fukushima et al. 2007 6–Wang et al. 2015 7–Firouzi et al. 2016 8–Pavan 2014 9–Asemi et al. 2013 10–Wind et al. 2010 11–Rossi et al. 2016 12–Fabian et al. 2007 13–Farhangi et al. 2016
Randomization
Blinding
Account of all patients
Overall
– –
1 – 1
4 2 5
1 1 1
– – –
– 1 1
3 5 5
– – 1 – 1
– – – – –
1 1 1 – 1
3 3 5 1 5
Mentioned
Correct
Not correct
Mentioned
Correct
Not correct
1 1 1
– – 1
– – –
1 1 1
1 – 1
1 1 1
– 1 1
– – –
1 1 1
1 1 1 1 1
– – 1 – 1
– – – – –
1 1 1 – 1
* Paper was an open-label prospective study.
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Guida (2014)
-1.91 (-5.27, 1.45)
16.09
Pavan (2014)
-8.20 (-10.96, -5.44)
17.11
Akuglo without RF (2015)
-12.34 (-25.20, 0.52)
4.61
Akuglo with RF (2015)
-7.71 (-17.12, 1.70)
7.15
Firouzi (2016)
3.39 (1.48, 5.30)
18.37
Rossi (2016)
0.00 (-1.10, 1.10)
19.24
Farhangi (2016)
1.21 (-1.35, 3.77)
17.43
-25.2
0
25.2
Fig. 2. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on glomerular filtration rate. CI, confidence interval; RF, renal failure.
could not eliminate heterogeneity in the subgroups, except for studies that lasted for ≥12 wk (I2 = 55.4; P = 0.081; Table 2). None of the subgroups revealed significant change in the creatinine level by probiotic supplementation (Table 3). Findings from sensitivity analysis showed that the removal of a study did not significantly influence the overall effect. No evidence of publication bias was found (for Egger’s test, P = 0.482). The pooled effect of probiotic and synbiotic supplementation on BUN demonstrated a significant decline in comparison with the placebo group (MD, −1.72 mmol/L; 95% CI, −2.93 to −0.51; P = 0.005; Fig. 4). No significant heterogeneity was found between studies (I2 = 0.0%; P = 0.635). In the sensitivity analysis, excluding the study by Ranganathan et al. [26], eliminated the significance of the association (MD, −1.48 mmol/L; 95% CI, −3.05 to 0.09). Publication bias was not detected (for Egger’s test, P = 0.280). Meta-analysis on three effect sizes (two trials) that presented urea, indicated that pooled estimated difference was −0.46 mmol/L (95% CI, −0.60 to −0.32; P < 0.0001), with a moderate between-study heterogeneity (I2 = 48.4%; P = 0.144; Fig. 5). Sensitivity analysis showed that not excluding a study substantially altered this association. The Egger Regression test was not significant for bias (P = 0.58). The pooled effect size for all six effect sizes (derived from six studies) for UA was 12.28 μmol/L (95% CI, 0.85–23.71; P = 0.035), with a significant heterogeneity (I2 = 97.1%; P < 0.0001; Fig. 6). Subgroup analysis according to the probiotic strain could not eliminate heterogeneity (Table 3), but in studies that lasted for ≥12 wk, heterogeneity disappeared. Probiotics had a significant effect on the increment of UA in studies that lasted <12 wk (MD, 18.24 μmol/L; 95% CI, 2.02 to −34.47; P = 0.028; I2 = 98.7, P for heterogeneity < 0.0001), prescribed multiple strains of probiotics (MD, 13.82 μmol/L; 95% CI, 1.11–26.53; P = 0.033; I2 = 97.6; P for heterogeneity < 0.0001) and was conducted among individuals with renal diseases (MD, 9.56 μmol/L; 95% CI, 3.19–15.92; P = 0.003; I2 = 82.8; P for heterogeneity = 0.003) (Table 3). In the sensitivity analysis, withdrawal of Ranganathan et al.’s study [26] (MD, 12.02 μmol/L; 95% CI, −5.85 to 29.90; P = 0.187), Wang et al.’s study [31] (MD, 12.16 μmol/L; 95% CI, −0.30 to 24.61; P = 0.056), and Fabian and Elmadfa’s study [29] (MD; 13.71 μmol/L; 95% CI, −0.66 to 28.08; P = 0.061), eliminated significant increase in serum UA
after probiotic supplementation. The Egger Regression test was not significant for bias (P = 0.879). Discussion The aim of the present study was to investigate the effects of prebiotic, probiotic, and synbiotic consumption on parameters of kidney function. Findings from this meta-analysis did not reveal any statistically significant effects of prebiotic, probiotic, and synbiotic consumption on GFR and creatinine, whereas such consumption effectively reduced BUN and urea and increased UA. Although many studies have suggested that supplementation with prebiotics, probiotics, and synbiotics can delay the progression of kidney failure [16,25,26,28], this meta-analysis showed overall non-significant reduction in GFR. However, in subgroup analysis, GFR was significantly reduced in studies that used single-strain probiotics or that were conducted with renal patients. The reason remained unclear. Increase in protein intake might be a reason for decreasing GFR [35]. Although the studies included in this meta-analysis tried to homogenize the dietary intake of the subjects, only one [8] measured protein levels during and at the end of the study to ensure consistent levels consumption. The increase in the level of creatinine might be due to GFR reduction; nevertheless, this increase was not significant. Furthermore, some medications used by the subjects (e.g., angiotensin-converting enzymes) [36] and the protein content of the diet [35] might alter the creatinine levels; however, there was not enough information regarding these factors in the included studies in the current meta-analysis. Further studies may examine the effects of prebiotic, probiotic, and synbiotic supplementation over a long period of time with a strictly homogenized diet. In terms of the effects on renal function, the present study demonstrated a significant reduction in BUN levels after probiotic and synbiotic supplementation. One study showed that synbiotics can effectively reduce levels of non-dialyzable protein-bound uremic toxins like p-cresyl sulphate and indoxyl sulphate, which are responsible for deterioration of residual renal function [15]. Reduction of these toxins will lead to improvement of renal function and better excretion of BUN from the bloodstream. Decreases in metabolic endotoxemia [6,37] inflammatory biomarkers and
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Table 2 Characteristics of the included studies Author, y
Participants
Patients with CKD, stage III & IV
Cox et al., 2014
Healthy active adults
Guida et al., 2014
Patientswith CKD, stage III & IV
Akogluet al., 2015
Hospitalizedpatients withacutegastroenteritis
Fukushima et al., 2007 Wang et al., 2015
Elderly with dysphasia & dementia fed total enteral nutrition Peritoneal dialysis patients
Firouzi et al., 2016
Type 2 diabetic patients
Design
Age (mean)
Intervention type Intervention (name and composition)
Control (name and composition)
9 × 1010 CFUs containing Lactobacillus acidophilus KB27, Bifidobacterium longum KB31, and Streptococcus thermophiles KB19 SS: 2 × 109 B. animalis subsp. lactis Bl-04 DS: 1.0 × 1010 L. acidophilus NCFM & B. animalis subsp. lactis Bi-07 Probinul-neutro, 5 × 109 L. plantarum, 2 × 109m L.caseis subsp. rhamnosus, 2 × 109 L. gasseri, 1 × 109 B. infantis, 1 × 109 B. longum, 1 × 109 L. acidophilus, 1 × 109 L. salivarius, 1 × 109 L. sporogenes, 5 × 109 Streptococcus thermophilus, prebiotic inulin (2.2 g; VB Beneo Synergy 1) & 1.3 g tapioca resistant starch 1.3 × 1010 L. casei Shirota
46
RCTcrossover
55.4
125
Double-blind RCT
39.4
30
138
Double-blind RCT
59.5
An open-label prospective study
65.8
Duration (wk)
Outcome Parameter
Intervention mean ± SD
Wheat germ plus psyllium husks
12
Creatinine (μmol/L) UA (μmol/L) BUN (μmol/L)
KB-PL: −25.52 ± 143.55 KB-PL: 12.61 ± 69.70 KB-PL: −2.06 ± 6.59
Placebo
15
Urea (mmol/L)
SS: −0.1 ± 0.85 DS: −0.32 ± 0.87 SS: 19.4 ± 43.1 DS: 15.1 ± 37.4
0.3 ± 1.11 0.3 ± 1.11 15.2 ± 49.2 15.2 ± 49.2
UA (μmol/L)
Control mean ± SD
Placebo: tapiocaresistantstarch
4
eGFR (mL/min)
−2.1 ± 10.41
−0.19 ± 7.55
–
1
Creatinine (μmol/L) Acute RF Without acute RF GFR (mL/min) Acute RF Without acute RF BUN (mmol/L) Creatinine (μmol/L)
−45.9 ± 37.2 10.64 ± 72
−141.4 ± 328 −3.52 ± 38.7
12.04 ± 23.3 −11.65 ± 37.7
19.75 ± 41.7 0.69 ± 45.6
1 ± 31.95 0 ± 111.3
2.17 ± 33.25 84 ± 223.2
24
Double-blind controlled study
84.5
L. johnsonii L1 (LC1)
–
12
38
Double-blind RCT
52
Placebo
24
BUN (mmol/L) Creatinine (μmol/L) UA (μmol/L)
−0.24 ± 4.74 −2 ± 210.6 −8 ± 56.2
0.3 ± 5.3 15 ± 133.8 −21 ± 42.4
101
Double-blind RCT
53.5
109 CFU B. bifidum A218, 109 CFU B. catenulatum A302, 109 CFU B. longum A101, 109 CFU L. plantarum A87 L. acidophilus, L. casei, L. lactis, B. bifidum, B. longum, B. infantis with daily dose of 6 × 1010
Placebo
12
Urea (mmol/L) Creatinine (μmol/L) GFR (mL/min)
−0.28 ± 0.71 2.7 ± 12.3 −1.38 ± 11.34
−0.21 ± 0.77 4.4 ± 10.8 −4.77 ± 8.52
(continued on next page)
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Ranganathan et al., 2010
n
Table 2 (Continued) Code/Author,y
Participants
n
CKD patients
24
Asemi et al., 2013
Type 2 diabetic patients females)
54
Wind et al., 2010 Rossi et al., 2016
Healthy volunteers
34
Predialysis adult with CKD
34
Fabian et al., 2007
Young Healthy Women
Farhangi et al., 2016
Diabetic female patients
Open-labeled RCT Double-blind RCT
Age (mean)
51
Double-blind RCT Double-blind crossover RCT
42
33
Randomized, placebo– controlled, crossover trial
24.4
49
Randomized placebocontrolled trial
48.3
69
Intervention type
Duration (wk)
Outcome Parameter
Intervention mean ± SD
Control mean ± SD
Placebo
24
eGFR (mL/min/1.73 m2/y)
11.6 ± 8.6
3.4 ± 4.6
Placebo
8
UA (μmol/L)
48 ± 16
9 ± 12
Placebo
3
Creatinine (μmol/L)
−3 ± 1.9
2 ± 1.84
Maltodextrin powder and capsule
6
Creatinine (μmol/L) GFR (mL/min/1.73 m2)
7.0 ± 9.57 −1 ± 5.06
2 ± 7.8 −1 ± 5.06
Conventional yogurt: 3.9 × 107 cfu/g S. thermophilus 6.4 × 107 cfu/g L. bulgaricus 10 g Maltodextrin
4
UA (μmol/L)
14.9 ± 17.3
9.7 ± 17.2
8
Creatinine (μmol/L) GFR (mL/min/ 1.73 m2)
1.8 ± 5.9 −2.04 ± 8.71
3.3 ± 7.6 −3.25 ± 9.65
Intervention (name and composition)
Control (name and composition)
Low protein diet + probiotic +prebiotic L. acidophilus (2 × 109 CFU), L. casei (7 × 109 CFU), L. rhamnosus (1.5 × 109 CFU), L. bulgaricus (2 × 108 CFU), B. breve (2 × 1010 CFU), B. longum (7 × 109 CFU), Streptococcus thermophiles (1.5 × 109 CFU), 100 mg FOS 1 × 1011 CFUs L. rhamnosus PRSF-L477 High-molecular-weight inulin, FOS, GOS, 9 strains from Lactobacillus, Bifidobacteria, Streptococcus genera Probiotic yogurt: 2 × 108 cfu/g S. thermophiles 3.6 × 108 cfu/g L. paracasei 107 cfu/g L. bulgaricus subsp. Paracasei (L. casei DN-114 001) 10 g chicory inulin enriched with oligofructose
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
Pavan 2014
Design
CFU, colony forming unit; CKD, chronic kidney disease; DS, double strain; FOS, fructooligosaccharides; GFR, glomerular filtration rate; GOS, galactooligosaccharides; QOL, quality of life; RCT, randomized controlled trial; RF, renal failure; SS, single strain.
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Table 3 Results of subgroup-analysis
GFR Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Prebiotic Diseases Renal diseases Other diseases or healthy Creatinine Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Prebiotic Diseases Renal diseases Other diseases or healthy UA Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Diseases Renal diseases Other diseases or healthy
No. of effect sizes
References
Mean difference (95% CI)
I2 (%)
P within
5 2
[27,28,30,34] [8,25]
−0.76 (−2.91 to 1.38) −2.36 (−13.72 to 9.00)
50.9 97.8
0.087 <0.0001
2 4 1
[27] [8,25,28,30] [34]
−9.32 (−16.92 to −1.73) −1.56 (−5.62 to 2.51) 1.21 (−1.35 to 3.77)
0.0 93.6 –
0.569 <0.0001 –
4 3
[25,27,28,30] [8,27,34]
−3.92 (−8.68 to −0.85) 1.31 (−2.36 to 4.98)
90.4 71.1
<0.0001 0.032
5 4
[27,28,33,34] [8,17,26,31]
4.03 (−0.75 to 8.81) −15.07 (−43.74 to 13.61)
92.3 55.4
<0.0001 0.081
4 4 1
[17,27,33] [8,26,28,33] [34]
12.34 (−18.75 to 43.44) 1.11 (−5.24 to 7.46) −1.50 (−3.40 to 0.40)
82.8 78.6 –
0.001 0.003 –
4 5
[26–28,33] [8,17,27,33,34]
17.43 (−20.30 to 55.16) 0.92 (−4.28 to 6.11)
76.5 94.4
0.005 <0.0001
3 3
[26,29,32] [18,31]
18.24 (2.02–34.47) 3.59 (−2.66 to 9.84)
98.7 0.0
<0.0001 0.379
1 5
[18] [18,26,29,31,32]
4.20 (−5.73 to 14.13) 13.82 (1.11–26.53)
– 97.6
– <0.0001
3 3
[26,29,31] [18,29,32]
9.56 (3.19–15.92) 13.97 (−13.18 to 41.13)
82.8 97.4
0.003 <0.0001
P between 0.814
0.035
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.032
<0.0001
CI, confidence interval; GFR, glomerular filtration rate; UA, uric acid.
oxidative stress after prebiotic [38], probiotic, or synbiotic consumption [39] also might indirectly affect renal function. Less inflammation and oxidative stress are associated with less catabolism and protein degradation [40], which will lead to a decrease in BUN [17]. Moreover, lower oxidative stress could restore UA, which has a potential therapeutic role as an antioxidant agent [41]. The increase in level of UA might be related to the natural course of underlying diseases in participants. By taking into account the relevance of baseline values, findings regarding
Fukushima et al. (2007) Ranganathan et al. (2010) Wind et al. (2010) Akuglo et al. without RF (2015) Akuglo et al. with RF (2015) Wang et al. (2015) Firouzi et al. (2016) Rossi et al. (2016) Farhangi et al. (2016)
–155
0
BUN might not be generalizable to individuals with normal BUN levels as all three included studies in the current meta-analysis had high baseline values of BUN [16,17,31]. Both studies (three effects) that measured urea levels and were included in this meta-analysis were conducted among participants with healthy renal status [8,18]. As probiotics have a greater effect on those with higher baseline urea levels [8], it is assumed that probiotic supplementation might improve the urea levels among patients with CKD as well. Alatriste et al. [42]
–84.00 (–154.57, –13.43)
0.37
–25.52 (–67.01, 15.97)
1.05
5.00 (4.37, 5.63)
24.66
14.16 (–6.81, 35.13)
3.66
95.50 (41.01, 149.99)
0.62
13.00 (–44.41, 70.41)
0.56
–1.70 (–4.03, 0.63)
23.15
5.00 (2.06, 7.94)
22.26
–1.50 (–3.40, 0.40)
23.67
155
Fig. 3. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on creatinine. CI, confidence interval; RF, renal failure.
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111
Fukushima et al. (2007)
–7.00 (–20.07, 6.07)
0.86
Ranganathan et al. (2010)
–2.07 (–3.97, –0.16)
40.76
Wang et al. (2015)
–1.40 (–2.99, 0.19)
58.38
–20.1
0
20.1
Fig. 4. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on blood urea nitrogen. CI, confidence interval.
should attempt to demonstrate the effect of probiotic supplementation on urea levels among patients with CKD. The possible mechanism behind the urea-lowering effect of probiotics is that their supplementation increases the amount of lactic acid
demonstrated that 8-wk probiotic supplementation with dosage of 16 × 109 improved urea levels by 11% in patients with chronic renal failure. However, the study was excluded from the present analysis because it did not include a control group. Further RCTs
Cox et al. (2014) Cox et al. (2014) Firouzi et al. (2016)
0
–0.816
–0.40 (–0.60, –0.20)
29.30
–0.62 (–0.82, –0.42)
29.30
–0.39 (–0.53, –0.25)
41.40
0.816
Fig. 5. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on urea. CI, confidence interval.
Fabian et al. (2007) Ranganathan et al. (2010) Asemi et al. (2013) Cox et al. (2014) Cox et al. (2014) Wang et al. (2015)
-40.8
0
5.20 (1.48, 8.92)
17.97
12.61 (10.50, 14.72)
18.20
37.00 (33.17, 40.83)
17.95
4.20 (-5.73, 14.13)
16.09
-0.10 (-9.39, 9.19)
16.34
13.00 (-3.06, 29.06)
13.45
40.8
Fig. 6. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on uric acid. CI, confidence interval.
112
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bacteria such as Lactobacillus and Bifidobacterium [42,43]. As such, these bacteria prevent proliferation of aerobic bacteria in the gut and promote a balanced microbiome in the gut, which leads to modulating urea levels [24]. Furthermore, the urease activity of special probiotic species, such as Bacteroides, might improve urea degradation [44] and decrease urea levels [8,18]. Subgroup analysis revealed that duration of study, supplement strains and health status were sources for heterogeneity, and that they could not be eliminated in any category. The effects of prebiotics, probiotics, and synbiotics on GFR were statistically significant in patients with renal diseases and in those who consumed single-strains of probiotics. Also, UA increased significantly in studies that lasted <12 wk, in patients with renal diseases, and in those who consumed multistrain probiotics. However, the reason that the increase in UA disappeared after ≥12 wk, and findings regarding GFR in subgroup analysis remain unclear and warrant further investigation. To provide a comprehensive assessment of the effects of prebiotics, probiotics, and synbiotics on renal function, the urine parameters of renal function should be considered. To our knowledge very few studies have examined the effects of probiotic or synbiotic supplementation on proteinuria and albuminuria [15]. In data from the National Health and Nutrition Examination Survey, an inverse link was observed between frequent probiotic and/or yogurt consumption (as a natural source of probiotics) and the odds of proteinuric kidney disease [45]. The significant increase in albuminuria after synbiotic supplementation was observed in one clinical trial, although it was not associated with a significant increase in proteinuria [28]. Conversely, a doubleblind clinical trial revealed that consumption of probiotic soymilk decreased albuminuria in diabetic patients with nephropathy compared with conventional soymilk [46]. Data on these parameters are limited and thus do not allow for a conclusion. Further exploration is warranted. This meta-analysis benefits from a set of data that covers the most important indexes of kidney function. Subanalysis according to being single strain and multistrain, type of disease, and duration of supplementation presents a broad view of the parameters that might affect the results. This study, however, had several limitations. Despite the broad scope of the present review, very few trials contributed data to each renal function parameter; hence, the number of participants for some parameters was small. The studies included in the present meta-analysis were not homogenous in terms of dosage and type of supplementation used, nor in terms of underlying diseases in participants. The quality of the majority of the studies was questionable, with only a handful having a complete Jadad score. Meta-regression analysis was not conducted due to the limited number of studies in each parameter. Additionally, the baseline value and changes in dietary intake were not specified in most of the studies. This is relevant as it has been suggested that some dietary components such as protein intake could affect levels of renal parameters [35]. Larger well-designed RCTs, controlled for baseline values of renal parameters and dietary intake, are necessary to assess the effects of modulation of gut microbiome by prebiotics, probiotics, and synbiotics on renal profile. Optimal probiotic strains, dosing, and duration of therapy must be determined. Conclusion This study showed non-significant decline in GFR and nonsignificant increase in creatinine. Additionally, results from the meta-analysis demonstrated that prebiotic, probiotic, and synbiotic
supplementation will result in a significant decrease of urea and BUN, but a significant increase in UA. As such, until large-scale, well-designed RCTs prove the safety and efficacy of prebiotics, probiotics, and synbiotics in improving renal function, the use of probiotics among those with compromised renal function or those at risk for renal failure should be limited. Acknowledgment The authors acknowledge Abbas Shafiee and Aidin Sadeghilar for their valuable comments. References [1] Turnbaugh PJ, Ley RE, Hamady M, Fraser Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature 2007;449:804–10. [2] Jayasimhan S, Yap NY, Roest Y, Rajandram R, Chin KF. Efficacy of microbial cell preparation in improving chronic constipation: a randomized, doubleblind, placebo-controlled trial. Clin Nutr 2013;32:928–34. [3] Firouzi S, Barakatun-Nisak MY, Ismail A, Majid HA, Nor Azmi K. Role of probiotics in modulating glucose homeostasis: evidence from animal and human studies. Int J Food Sci Nutr 2013;64:780–6. [4] Karimi G, Jamaluddin R, Parvaneh K. The effects of probiotics on body weight and biomarkers of animal. Pakistan J Nutr 2013;8:793–9. [5] Dehghan P, Farhangi MA, Tavakoli F, Aliasgarzadeh A, Akbari AM. Impact of prebiotic supplementation on T-cell subsets and their related cytokines, anthropometric features and blood pressure in patients with type 2 diabetes mellitus: a randomized placebo-controlled Trial. Complement Ther Med 2016;24:96–102. [6] Karimi P, Farhangi MA, Sarmadi B, Gargari BP, Javid AZ Pouraghaei M. The therapeutic potential of resistant starch in modulation of insulin resistance, endotoxemia, oxidative stress and antioxidant biomarkers in women with type 2 diabetes: a randomized controlled clinical trial. Ann Nutr Metab 2016;68:85–93. [7] Parvaneh K, Jamaluddin R, Karimi G, Erfani R. Effect of probiotic supplementation on bone mineral content and bone mass density: a review. Sci World J 2013;2014:595962. [8] Firouzi S, Mohd-Yusof B-N, Majid H-A, Ismail A, Kamaruddin N-A. Effect of microbial cell preparation on renal profile and liver function among type 2 diabetics: a randomized controlled trial. BMC Complement Altern Med 2015;15:433. [9] Vaziri ND. CKD impairs barrier function and alters microbial flora of the intestine: a major link to inflammation and uremic toxicity. Curr Opin Nephrol Hypertens 2012;21:587–92. [10] Massy ZA, Barreto D V, Barreto FC, Vanholder R. Uraemic toxins for consideration by the cardiologist—beyond traditional and non-traditional cardiovascular risk factors. Atherosclerosis 2010;211:381–3. [11] Vanholder R, Glorieux G. The intestine and the kidneys: a bad marriage can be hazardous. Clin Kidney J 2015;8:168–79. [12] Sirich TL, Plummer NS, Gardner CD, Hostetter TH, Meyer TW. Effect of increasing dietary fiber on plasma levels of colon-derived solutes in hemodialysis patients. Clin J Am Soc Nephrol 2014;9:1603–10. [13] Meijers BKI, De Preter V, Verbeke K, Vanrenterghem Y, Evenepoel P. p-Cresyl sulfate serum concentrations in haemodialysis patients are reduced by the prebiotic oligofructose-enriched inulin. Nephrol Dial Transplant 2010;25: 219–24. [14] Hida M, Aiba Y, Sawamura S, Suzuki N, Satoh T, Koga Y. Inhibition of the accumulation of uremic toxins in the blood and their precursors in the feces after oral administration of Lebenin, a lactic acid bacteria preparation, to uremic patients undergoing hemodialysis. Nephron 1996;74:349–55. [15] Rossi M, Klein K, Johnson DW, Campbell KL. Pre-, pro-, and synbiotics: do they have a role in reducing uremic toxins? A systematic review and metaanalysis. Int J Nephrol 2012;2012:673631. [16] Ranganathan N, Ranganathan P, Friedman EA, Joseph A, Delano B, Goldfarb DS, et al. Pilot study of probiotic dietary supplementation for promoting healthy kidney function in patients with chronic kidney disease. Adv Ther 2010;27:634–47. [17] Fukushima Y, Miyaguchi S, Yamano T, Kaburagi T, Iino H, Ushida K, et al. Improvement of nutritional status and incidence of infection in hospitalised, enterally fed elderly by feeding of fermented milk containing probiotic Lactobacillus johnsonii La1 (NCC533). Br J Nutr 2007;98:969–77. [18] Cox AJ, West NP, Horn PL, Lehtinen MJ, Koerbin G, Pyne DB, et al. Effects of probiotic supplementation over 5 months on routine haematology and clinical chemistry measures in healthy active adults. Eur J Clin Nutr 2014;68: 1255–7. [19] Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 2015;162:777–84.
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Contents lists available at ScienceDirect
Nutrition j o u r n a l h o m e p a g e : w w w. n u t r i t i o n j r n l . c o m
Review
The effects of prebiotic, probiotic, and synbiotic supplementation on blood parameters of renal function: A systematic review and meta-analysis of clinical trials Somayyeh Firouzi Ph.D. a, Fahimeh Haghighatdoost Ph.D. b,* a Department b Department
of Nutrition and Dietetics, University Putra Malaysia, Kuala Lumpur, Malaysia of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran
A R T I C L E
I N F O
Article history: Received 16 January 2017 Received in revised form 16 December 2017 Accepted 9 January 2018 Keywords: Probiotic Prebiotic Creatinine Glomerular filtration rate Blood urea nitrogen
A B S T R A C T
Objectives: Recent studies have demonstrated promising results regarding possible improvements in renal function after prebiotic, probiotic, and synbiotic supplementation. The aim of this review was to demonstrate whether such supplementation will improve renal profile indexes including glomerular filtration rate (GFR), creatinine, blood urea nitrogen (BUN), uric acid (UA), and urea. Method: The meta-analysis included all studies that examined the effect of prebiotic, probiotic, and synbiotic supplements on one or more renal function parameters and had a control group. We searched July 1967 through to March 2016 MEDLINE, Scopus, and Google Scholar databases. Results: Of 437 studies, 13 were eligible for inclusion in the meta-analysis. GFR levels tended to be reduced; whereas creatinine levels increased in the intervention group compared with the placebo group, both in a non-significant manner. The pooled effect on BUN demonstrated a significant decline compared with the placebo group (MD, −1.72 mmol/L; 95% confidence interval [CI], −2.93 to −0.51; P = 0.005). Urea significantly decreased after intervention (−0.46 mmol/L; 95% CI, −0.60 to −0.32; P < 0.0001). The UA levels significantly increased in the intervention group compared with the placebo group (12.28 μmol/L; 95% CI, 0.85–23.71; P = 0.035). Conclusion: This study showed a significant increase in UA and a decrease in urea and BUN. The use of prebiotic, probiotic, and synbiotic supplements among those with compromised renal function or those at risk for renal failure should be limited until large-scale, well-designed randomized controlled trials prove the safety and efficacy of these supplements in improving renal function. © 2018 Elsevier Inc. All rights reserved.
Introduction Numerous studies have demonstrated that gut microbiome hosts billions of bacteria that interact with many physiological conditions [1]. The range of these conditions varies from gastrointestinal disturbances [2] to glucose homeostasis [3], obesity [4], metabolic endotoxemia [5,6] and bone density [7]. A connection between gut microbiome and kidney function has been suggested in the literature [8,9]. The proof for this claim lies in the fact that the composition of the gut microbiome interacts with levels of urea. It affects uremic retention and solute production, resulting in the generation of uremic toxins with a strong biological effect on progression toward kidney failure [10]. On the The authors have no conflicts of interest to declare. * Corresponding author. Tel.: +98 313 792 2719; fax: +98 313 668 2509. E-mail address: [email protected] (F. Haghighatdoost). https://doi.org/10.1016/j.nut.2018.01.007 0899-9007/© 2018 Elsevier Inc. All rights reserved.
other hand, the levels of uremia affect the composition of the gut microbiome through disturbances in the protective epithelial barrier of the intestine and the translocation of the intestinal microbiome in to the body [11]. Considering these facts, a question is raised as to whether manipulation of the gut microbiome with prebiotic, probiotic, or synbiotic supplements will improve kidney function. In recent years, several clinical trials investigated the effect of gut microbiome manipulation on generating uremic toxins among patients on hemodialysis. Results from the studies demonstrated that intervention with probiotics or prebiotics will beneficially reduce toxins that are generated from the kidney [12–14]. A meta-analysis of the randomized controlled trials (RCTs) also proved this positive effect [15]. Although the positive affect of gut microbiome manipulation on renal-generating toxins has been shown previously [12–15], fewer data are available in terms of its effect on renal
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
parameters such as urea, creatinine, uric acid (UA), blood urea nitrogen (BUN), and glomerular filtration rate (GFR). Firouzi et al. demonstrated that levels of urea were significantly reduced after probiotic supplementation among individuals with type 2 diabetes, whereas levels of GFR and creatinine remained unchanged [8]. In a randomized crossover study, BUN was significantly reduced among patients with stage III and IV chronic kidney disease (CKD) [16], whereas it did not change among hospitalized, enterally fed elderly patients [17]. In terms of changes in UA level, although probiotic supplementation improved UA levels in patients with stage III and IV CKD [16], it showed no effect on UA among healthy active adults [18], leading to uncertainty about the beneficial effect of probiotics on modulating UA levels. Overall, data regarding the effects of probiotic, prebiotic, and synbiotic supplementation on renal profiles remains limited and inconclusive. The present systematic review and meta-analysis aimed to discover whether the manipulation of gut microbiome with the aid of prebiotic, probiotic, and synbiotic supplementation will improve renal parameters including urea, creatinine, UA, GFR, and BUN. Material and methods Search strategy and study selection criteria The present meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement [19]. A systematic literature search was developed to search the MEDLINE, Scopus, and Google scholar databases for RCTs from July 1967 to March 2016. Additionally, references to identified articles were manually searched to complement the database searching. The following MeSH search terms were used to identify relevant published articles: prebiotics, probiotics, synbiotics in combination with urinary tract, kidney, UA, urea, creatinine and glomerular filtration rate. No restrictions were made in terms of publication date. We independently performed the literature search, study selection, and data extraction, and disagreements were resolved by consensus. Study inclusion and exclusion criteria Published articles were included in the meta-analysis if they were in the English language and investigated the effects of prebiotic, probiotic or synbiotic supplements on renal outcomes. These data were extracted from studies that investigated the renal profile as main outcomes, or from other trials where renal data could be obtained from secondary outcomes. Studies that did not have a control group or only investigated renal toxins were excluded. The analysis was restricted to participants with age ≥18 y. Data extraction We independently extracted the following data from each article: first author’s last name, year of publication, number of participants in the studies, underlying condition of their participants, their age range and mean, sex, the study design, the type of supplement in the intervention and control groups, the dosage of supplements, study duration, and mean and SD of the renal profile tests before and after intervention. Contacts were made with authors of some papers to request additional data. The standardized mean difference and corresponding SEs were calculated by using postintervention data for all eligible reports. Quality assessment The quality of the studies was assessed using the Jadad Scale. The scores ranged from 0 to 5, with 5 indicating the best quality of research. The Jadad Scale scores studies based on randomization, blinding, and providing an account of all participants [20]. Statistical analysis This meta-analysis was conducted on the mean difference and SE for intervention and control groups. To calculate summary mean estimates with their corresponding 95% confidence interval (CI), a random-effects model was used [21]. Cochran’s Q test and I2 were used to examine between study statistical heterogeneity [22]. Subgroup analyses were performed to recognize the possible sources
105
of heterogeneity between studies. A fixed model was run to identify the betweensubgroup heterogeneity. Sensitivity analyses were run to examine the extent to which conclusions might rely on a particular study or studies. Visual inspections of funnel plots for asymmetry [23], Egger’s regression asymmetry and Begg’s adjusted rank correlation test [24] were carried out to evaluate publication bias. Statistical analyses were conducted in Stata, version 11.2 (Stata Corp., College Station, TX, USA). P < 0.05 was considered statistically significant.
Results Literature search Of 437 screened studies, 25 received full-text reviews. Of these studies, 13 were entered into the meta-analysis (Fig. 1). Cox et al.’s study was included as two reports that evaluated single- and multiple-strains of probiotic supplementation [18]. The fulltext of one of the eligible articles was not found despite contacting the corresponding author to obtain as much accurate information as possible; therefore, relevant data were extracted by using its abstract [25]. One study was excluded [16] because it was the pilot of another study, which was used in this meta-analysis [26]. One study was an open-label prospective study [27], one was an open-label RCT [25], three studies were crossover RCTs [26,28,29], and the rest were parallel-group double-blind RCTs [8,17,18,30–34]. The quality of studies was between 1 and 5 using Jadad scaling, with only five having a complete score of 5 [8,28,30,31,34] (Table 1). Characteristics of studies and participants Included in these studies, were 721 participants ranging in age from 24 to 84 y, including 326 men and 383 women (the number of each sex was not clear in the Pavan study [25]). In all, 185 participants had CKD, 155 had type 2 diabetes, 162 were hospitalized patients, and 192 were healthy individuals. Duration of intervention ranged from 1 to 24 wk. Three studies used singlestrain probiotics for intervention [17,27,33], five used multistrain probiotics [8,26,29,31,32], one used prebiotics [34], three used synbiotics [25,28,30], and one used single and double strains in each arm [18]. Eight studies examined the effects of probiotic supplementation on serum creatinine (N = 464) [8,17,26–28,31,33,34]. Six trials explored changes in GFR (N = 376) [8,25,27,28,30,34] and UA (N = 296) [18,26,29,31,32], three studies evaluated changes in BUN levels (N = 108) [17,26,31] and two studies (three effect sizes) examined changes in urea levels (N = 226) [8,18]. Findings from the meta-analysis Figure 2 presents the pooled effect of prebiotic, probiotic, and synbiotic supplementation on GFR. A non-significant reduction of GFR after consumption of prebiotics, probiotics, and synbiotics was noted (MD, −2.00 mL/min/1.73 m2; 95% CI, −5.15 to 1.16; P = 0.215). There was significant heterogeneity among studies (I2 = 88.9%; P < 0.0001). Subgroup analyses based on the duration of study, the strain of probiotic supplement, and presence of renal disease could not explain the heterogeneity between studies (Table 2). However, individuals who consumed a single strain of probiotics had significantly lower GFR compared with the control group (MD, −9.32 mL/min/1.73 m2; 95% CI, −16.92 to −1.73; P = 0.016) with no significant heterogeneity (I2 = 0.0; P = 0.56) (Table 3). Findings from sensitivity analysis indicated that excluding an individual study would not change the significance of the findings. No evidence of publication bias was observed (for Egger’s test, P = 0.305).
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Fig. 1. Screening flowchart.
Figure 3 indicates the pooled effect of prebiotic, probiotic, and synbiotic supplementation on serum creatinine. Results revealed a non-significant increment in serum creatinine levels by prebiotic and probiotic supplementation (MD, 2.20 μmol/L; 95%
CI, −2.14 to 6.54; P = 0.3.21). The heterogeneity level between studies was high (I2 = 90.6%; P < 0.0001). Subgroup analyses according to the study duration, probiotic strain, and renal disease status indicated significant subgroup effects on creatinine but
Table 1 Quality assessment of the included studies Code/Author (y)
1–Ranganathan et al. 2010 2–Cox et al. 2014 3–Guida et al. 2014 4–Akoglu et al. 2015* 5–Fukushima et al. 2007 6–Wang et al. 2015 7–Firouzi et al. 2016 8–Pavan 2014 9–Asemi et al. 2013 10–Wind et al. 2010 11–Rossi et al. 2016 12–Fabian et al. 2007 13–Farhangi et al. 2016
Randomization
Blinding
Account of all patients
Overall
– –
1 – 1
4 2 5
1 1 1
– – –
– 1 1
3 5 5
– – 1 – 1
– – – – –
1 1 1 – 1
3 3 5 1 5
Mentioned
Correct
Not correct
Mentioned
Correct
Not correct
1 1 1
– – 1
– – –
1 1 1
1 – 1
1 1 1
– 1 1
– – –
1 1 1
1 1 1 1 1
– – 1 – 1
– – – – –
1 1 1 – 1
* Paper was an open-label prospective study.
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107
Guida (2014)
-1.91 (-5.27, 1.45)
16.09
Pavan (2014)
-8.20 (-10.96, -5.44)
17.11
Akuglo without RF (2015)
-12.34 (-25.20, 0.52)
4.61
Akuglo with RF (2015)
-7.71 (-17.12, 1.70)
7.15
Firouzi (2016)
3.39 (1.48, 5.30)
18.37
Rossi (2016)
0.00 (-1.10, 1.10)
19.24
Farhangi (2016)
1.21 (-1.35, 3.77)
17.43
-25.2
0
25.2
Fig. 2. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on glomerular filtration rate. CI, confidence interval; RF, renal failure.
could not eliminate heterogeneity in the subgroups, except for studies that lasted for ≥12 wk (I2 = 55.4; P = 0.081; Table 2). None of the subgroups revealed significant change in the creatinine level by probiotic supplementation (Table 3). Findings from sensitivity analysis showed that the removal of a study did not significantly influence the overall effect. No evidence of publication bias was found (for Egger’s test, P = 0.482). The pooled effect of probiotic and synbiotic supplementation on BUN demonstrated a significant decline in comparison with the placebo group (MD, −1.72 mmol/L; 95% CI, −2.93 to −0.51; P = 0.005; Fig. 4). No significant heterogeneity was found between studies (I2 = 0.0%; P = 0.635). In the sensitivity analysis, excluding the study by Ranganathan et al. [26], eliminated the significance of the association (MD, −1.48 mmol/L; 95% CI, −3.05 to 0.09). Publication bias was not detected (for Egger’s test, P = 0.280). Meta-analysis on three effect sizes (two trials) that presented urea, indicated that pooled estimated difference was −0.46 mmol/L (95% CI, −0.60 to −0.32; P < 0.0001), with a moderate between-study heterogeneity (I2 = 48.4%; P = 0.144; Fig. 5). Sensitivity analysis showed that not excluding a study substantially altered this association. The Egger Regression test was not significant for bias (P = 0.58). The pooled effect size for all six effect sizes (derived from six studies) for UA was 12.28 μmol/L (95% CI, 0.85–23.71; P = 0.035), with a significant heterogeneity (I2 = 97.1%; P < 0.0001; Fig. 6). Subgroup analysis according to the probiotic strain could not eliminate heterogeneity (Table 3), but in studies that lasted for ≥12 wk, heterogeneity disappeared. Probiotics had a significant effect on the increment of UA in studies that lasted <12 wk (MD, 18.24 μmol/L; 95% CI, 2.02 to −34.47; P = 0.028; I2 = 98.7, P for heterogeneity < 0.0001), prescribed multiple strains of probiotics (MD, 13.82 μmol/L; 95% CI, 1.11–26.53; P = 0.033; I2 = 97.6; P for heterogeneity < 0.0001) and was conducted among individuals with renal diseases (MD, 9.56 μmol/L; 95% CI, 3.19–15.92; P = 0.003; I2 = 82.8; P for heterogeneity = 0.003) (Table 3). In the sensitivity analysis, withdrawal of Ranganathan et al.’s study [26] (MD, 12.02 μmol/L; 95% CI, −5.85 to 29.90; P = 0.187), Wang et al.’s study [31] (MD, 12.16 μmol/L; 95% CI, −0.30 to 24.61; P = 0.056), and Fabian and Elmadfa’s study [29] (MD; 13.71 μmol/L; 95% CI, −0.66 to 28.08; P = 0.061), eliminated significant increase in serum UA
after probiotic supplementation. The Egger Regression test was not significant for bias (P = 0.879). Discussion The aim of the present study was to investigate the effects of prebiotic, probiotic, and synbiotic consumption on parameters of kidney function. Findings from this meta-analysis did not reveal any statistically significant effects of prebiotic, probiotic, and synbiotic consumption on GFR and creatinine, whereas such consumption effectively reduced BUN and urea and increased UA. Although many studies have suggested that supplementation with prebiotics, probiotics, and synbiotics can delay the progression of kidney failure [16,25,26,28], this meta-analysis showed overall non-significant reduction in GFR. However, in subgroup analysis, GFR was significantly reduced in studies that used single-strain probiotics or that were conducted with renal patients. The reason remained unclear. Increase in protein intake might be a reason for decreasing GFR [35]. Although the studies included in this meta-analysis tried to homogenize the dietary intake of the subjects, only one [8] measured protein levels during and at the end of the study to ensure consistent levels consumption. The increase in the level of creatinine might be due to GFR reduction; nevertheless, this increase was not significant. Furthermore, some medications used by the subjects (e.g., angiotensin-converting enzymes) [36] and the protein content of the diet [35] might alter the creatinine levels; however, there was not enough information regarding these factors in the included studies in the current meta-analysis. Further studies may examine the effects of prebiotic, probiotic, and synbiotic supplementation over a long period of time with a strictly homogenized diet. In terms of the effects on renal function, the present study demonstrated a significant reduction in BUN levels after probiotic and synbiotic supplementation. One study showed that synbiotics can effectively reduce levels of non-dialyzable protein-bound uremic toxins like p-cresyl sulphate and indoxyl sulphate, which are responsible for deterioration of residual renal function [15]. Reduction of these toxins will lead to improvement of renal function and better excretion of BUN from the bloodstream. Decreases in metabolic endotoxemia [6,37] inflammatory biomarkers and
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Table 2 Characteristics of the included studies Author, y
Participants
Patients with CKD, stage III & IV
Cox et al., 2014
Healthy active adults
Guida et al., 2014
Patientswith CKD, stage III & IV
Akogluet al., 2015
Hospitalizedpatients withacutegastroenteritis
Fukushima et al., 2007 Wang et al., 2015
Elderly with dysphasia & dementia fed total enteral nutrition Peritoneal dialysis patients
Firouzi et al., 2016
Type 2 diabetic patients
Design
Age (mean)
Intervention type Intervention (name and composition)
Control (name and composition)
9 × 1010 CFUs containing Lactobacillus acidophilus KB27, Bifidobacterium longum KB31, and Streptococcus thermophiles KB19 SS: 2 × 109 B. animalis subsp. lactis Bl-04 DS: 1.0 × 1010 L. acidophilus NCFM & B. animalis subsp. lactis Bi-07 Probinul-neutro, 5 × 109 L. plantarum, 2 × 109m L.caseis subsp. rhamnosus, 2 × 109 L. gasseri, 1 × 109 B. infantis, 1 × 109 B. longum, 1 × 109 L. acidophilus, 1 × 109 L. salivarius, 1 × 109 L. sporogenes, 5 × 109 Streptococcus thermophilus, prebiotic inulin (2.2 g; VB Beneo Synergy 1) & 1.3 g tapioca resistant starch 1.3 × 1010 L. casei Shirota
46
RCTcrossover
55.4
125
Double-blind RCT
39.4
30
138
Double-blind RCT
59.5
An open-label prospective study
65.8
Duration (wk)
Outcome Parameter
Intervention mean ± SD
Wheat germ plus psyllium husks
12
Creatinine (μmol/L) UA (μmol/L) BUN (μmol/L)
KB-PL: −25.52 ± 143.55 KB-PL: 12.61 ± 69.70 KB-PL: −2.06 ± 6.59
Placebo
15
Urea (mmol/L)
SS: −0.1 ± 0.85 DS: −0.32 ± 0.87 SS: 19.4 ± 43.1 DS: 15.1 ± 37.4
0.3 ± 1.11 0.3 ± 1.11 15.2 ± 49.2 15.2 ± 49.2
UA (μmol/L)
Control mean ± SD
Placebo: tapiocaresistantstarch
4
eGFR (mL/min)
−2.1 ± 10.41
−0.19 ± 7.55
–
1
Creatinine (μmol/L) Acute RF Without acute RF GFR (mL/min) Acute RF Without acute RF BUN (mmol/L) Creatinine (μmol/L)
−45.9 ± 37.2 10.64 ± 72
−141.4 ± 328 −3.52 ± 38.7
12.04 ± 23.3 −11.65 ± 37.7
19.75 ± 41.7 0.69 ± 45.6
1 ± 31.95 0 ± 111.3
2.17 ± 33.25 84 ± 223.2
24
Double-blind controlled study
84.5
L. johnsonii L1 (LC1)
–
12
38
Double-blind RCT
52
Placebo
24
BUN (mmol/L) Creatinine (μmol/L) UA (μmol/L)
−0.24 ± 4.74 −2 ± 210.6 −8 ± 56.2
0.3 ± 5.3 15 ± 133.8 −21 ± 42.4
101
Double-blind RCT
53.5
109 CFU B. bifidum A218, 109 CFU B. catenulatum A302, 109 CFU B. longum A101, 109 CFU L. plantarum A87 L. acidophilus, L. casei, L. lactis, B. bifidum, B. longum, B. infantis with daily dose of 6 × 1010
Placebo
12
Urea (mmol/L) Creatinine (μmol/L) GFR (mL/min)
−0.28 ± 0.71 2.7 ± 12.3 −1.38 ± 11.34
−0.21 ± 0.77 4.4 ± 10.8 −4.77 ± 8.52
(continued on next page)
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
Ranganathan et al., 2010
n
Table 2 (Continued) Code/Author,y
Participants
n
CKD patients
24
Asemi et al., 2013
Type 2 diabetic patients females)
54
Wind et al., 2010 Rossi et al., 2016
Healthy volunteers
34
Predialysis adult with CKD
34
Fabian et al., 2007
Young Healthy Women
Farhangi et al., 2016
Diabetic female patients
Open-labeled RCT Double-blind RCT
Age (mean)
51
Double-blind RCT Double-blind crossover RCT
42
33
Randomized, placebo– controlled, crossover trial
24.4
49
Randomized placebocontrolled trial
48.3
69
Intervention type
Duration (wk)
Outcome Parameter
Intervention mean ± SD
Control mean ± SD
Placebo
24
eGFR (mL/min/1.73 m2/y)
11.6 ± 8.6
3.4 ± 4.6
Placebo
8
UA (μmol/L)
48 ± 16
9 ± 12
Placebo
3
Creatinine (μmol/L)
−3 ± 1.9
2 ± 1.84
Maltodextrin powder and capsule
6
Creatinine (μmol/L) GFR (mL/min/1.73 m2)
7.0 ± 9.57 −1 ± 5.06
2 ± 7.8 −1 ± 5.06
Conventional yogurt: 3.9 × 107 cfu/g S. thermophilus 6.4 × 107 cfu/g L. bulgaricus 10 g Maltodextrin
4
UA (μmol/L)
14.9 ± 17.3
9.7 ± 17.2
8
Creatinine (μmol/L) GFR (mL/min/ 1.73 m2)
1.8 ± 5.9 −2.04 ± 8.71
3.3 ± 7.6 −3.25 ± 9.65
Intervention (name and composition)
Control (name and composition)
Low protein diet + probiotic +prebiotic L. acidophilus (2 × 109 CFU), L. casei (7 × 109 CFU), L. rhamnosus (1.5 × 109 CFU), L. bulgaricus (2 × 108 CFU), B. breve (2 × 1010 CFU), B. longum (7 × 109 CFU), Streptococcus thermophiles (1.5 × 109 CFU), 100 mg FOS 1 × 1011 CFUs L. rhamnosus PRSF-L477 High-molecular-weight inulin, FOS, GOS, 9 strains from Lactobacillus, Bifidobacteria, Streptococcus genera Probiotic yogurt: 2 × 108 cfu/g S. thermophiles 3.6 × 108 cfu/g L. paracasei 107 cfu/g L. bulgaricus subsp. Paracasei (L. casei DN-114 001) 10 g chicory inulin enriched with oligofructose
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
Pavan 2014
Design
CFU, colony forming unit; CKD, chronic kidney disease; DS, double strain; FOS, fructooligosaccharides; GFR, glomerular filtration rate; GOS, galactooligosaccharides; QOL, quality of life; RCT, randomized controlled trial; RF, renal failure; SS, single strain.
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110
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Table 3 Results of subgroup-analysis
GFR Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Prebiotic Diseases Renal diseases Other diseases or healthy Creatinine Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Prebiotic Diseases Renal diseases Other diseases or healthy UA Duration of study <12 wk ≥12 wk Probiotic strain/Prebiotic Single-strain Multistrain Diseases Renal diseases Other diseases or healthy
No. of effect sizes
References
Mean difference (95% CI)
I2 (%)
P within
5 2
[27,28,30,34] [8,25]
−0.76 (−2.91 to 1.38) −2.36 (−13.72 to 9.00)
50.9 97.8
0.087 <0.0001
2 4 1
[27] [8,25,28,30] [34]
−9.32 (−16.92 to −1.73) −1.56 (−5.62 to 2.51) 1.21 (−1.35 to 3.77)
0.0 93.6 –
0.569 <0.0001 –
4 3
[25,27,28,30] [8,27,34]
−3.92 (−8.68 to −0.85) 1.31 (−2.36 to 4.98)
90.4 71.1
<0.0001 0.032
5 4
[27,28,33,34] [8,17,26,31]
4.03 (−0.75 to 8.81) −15.07 (−43.74 to 13.61)
92.3 55.4
<0.0001 0.081
4 4 1
[17,27,33] [8,26,28,33] [34]
12.34 (−18.75 to 43.44) 1.11 (−5.24 to 7.46) −1.50 (−3.40 to 0.40)
82.8 78.6 –
0.001 0.003 –
4 5
[26–28,33] [8,17,27,33,34]
17.43 (−20.30 to 55.16) 0.92 (−4.28 to 6.11)
76.5 94.4
0.005 <0.0001
3 3
[26,29,32] [18,31]
18.24 (2.02–34.47) 3.59 (−2.66 to 9.84)
98.7 0.0
<0.0001 0.379
1 5
[18] [18,26,29,31,32]
4.20 (−5.73 to 14.13) 13.82 (1.11–26.53)
– 97.6
– <0.0001
3 3
[26,29,31] [18,29,32]
9.56 (3.19–15.92) 13.97 (−13.18 to 41.13)
82.8 97.4
0.003 <0.0001
P between 0.814
0.035
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
0.032
<0.0001
CI, confidence interval; GFR, glomerular filtration rate; UA, uric acid.
oxidative stress after prebiotic [38], probiotic, or synbiotic consumption [39] also might indirectly affect renal function. Less inflammation and oxidative stress are associated with less catabolism and protein degradation [40], which will lead to a decrease in BUN [17]. Moreover, lower oxidative stress could restore UA, which has a potential therapeutic role as an antioxidant agent [41]. The increase in level of UA might be related to the natural course of underlying diseases in participants. By taking into account the relevance of baseline values, findings regarding
Fukushima et al. (2007) Ranganathan et al. (2010) Wind et al. (2010) Akuglo et al. without RF (2015) Akuglo et al. with RF (2015) Wang et al. (2015) Firouzi et al. (2016) Rossi et al. (2016) Farhangi et al. (2016)
–155
0
BUN might not be generalizable to individuals with normal BUN levels as all three included studies in the current meta-analysis had high baseline values of BUN [16,17,31]. Both studies (three effects) that measured urea levels and were included in this meta-analysis were conducted among participants with healthy renal status [8,18]. As probiotics have a greater effect on those with higher baseline urea levels [8], it is assumed that probiotic supplementation might improve the urea levels among patients with CKD as well. Alatriste et al. [42]
–84.00 (–154.57, –13.43)
0.37
–25.52 (–67.01, 15.97)
1.05
5.00 (4.37, 5.63)
24.66
14.16 (–6.81, 35.13)
3.66
95.50 (41.01, 149.99)
0.62
13.00 (–44.41, 70.41)
0.56
–1.70 (–4.03, 0.63)
23.15
5.00 (2.06, 7.94)
22.26
–1.50 (–3.40, 0.40)
23.67
155
Fig. 3. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on creatinine. CI, confidence interval; RF, renal failure.
S. Firouzi, F. Haghighatdoost / Nutrition 51-52 (2018) 104–113
111
Fukushima et al. (2007)
–7.00 (–20.07, 6.07)
0.86
Ranganathan et al. (2010)
–2.07 (–3.97, –0.16)
40.76
Wang et al. (2015)
–1.40 (–2.99, 0.19)
58.38
–20.1
0
20.1
Fig. 4. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on blood urea nitrogen. CI, confidence interval.
should attempt to demonstrate the effect of probiotic supplementation on urea levels among patients with CKD. The possible mechanism behind the urea-lowering effect of probiotics is that their supplementation increases the amount of lactic acid
demonstrated that 8-wk probiotic supplementation with dosage of 16 × 109 improved urea levels by 11% in patients with chronic renal failure. However, the study was excluded from the present analysis because it did not include a control group. Further RCTs
Cox et al. (2014) Cox et al. (2014) Firouzi et al. (2016)
0
–0.816
–0.40 (–0.60, –0.20)
29.30
–0.62 (–0.82, –0.42)
29.30
–0.39 (–0.53, –0.25)
41.40
0.816
Fig. 5. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on urea. CI, confidence interval.
Fabian et al. (2007) Ranganathan et al. (2010) Asemi et al. (2013) Cox et al. (2014) Cox et al. (2014) Wang et al. (2015)
-40.8
0
5.20 (1.48, 8.92)
17.97
12.61 (10.50, 14.72)
18.20
37.00 (33.17, 40.83)
17.95
4.20 (-5.73, 14.13)
16.09
-0.10 (-9.39, 9.19)
16.34
13.00 (-3.06, 29.06)
13.45
40.8
Fig. 6. Effect size (ES) of prebiotic, probiotic, or synbiotic supplementation on uric acid. CI, confidence interval.
112
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bacteria such as Lactobacillus and Bifidobacterium [42,43]. As such, these bacteria prevent proliferation of aerobic bacteria in the gut and promote a balanced microbiome in the gut, which leads to modulating urea levels [24]. Furthermore, the urease activity of special probiotic species, such as Bacteroides, might improve urea degradation [44] and decrease urea levels [8,18]. Subgroup analysis revealed that duration of study, supplement strains and health status were sources for heterogeneity, and that they could not be eliminated in any category. The effects of prebiotics, probiotics, and synbiotics on GFR were statistically significant in patients with renal diseases and in those who consumed single-strains of probiotics. Also, UA increased significantly in studies that lasted <12 wk, in patients with renal diseases, and in those who consumed multistrain probiotics. However, the reason that the increase in UA disappeared after ≥12 wk, and findings regarding GFR in subgroup analysis remain unclear and warrant further investigation. To provide a comprehensive assessment of the effects of prebiotics, probiotics, and synbiotics on renal function, the urine parameters of renal function should be considered. To our knowledge very few studies have examined the effects of probiotic or synbiotic supplementation on proteinuria and albuminuria [15]. In data from the National Health and Nutrition Examination Survey, an inverse link was observed between frequent probiotic and/or yogurt consumption (as a natural source of probiotics) and the odds of proteinuric kidney disease [45]. The significant increase in albuminuria after synbiotic supplementation was observed in one clinical trial, although it was not associated with a significant increase in proteinuria [28]. Conversely, a doubleblind clinical trial revealed that consumption of probiotic soymilk decreased albuminuria in diabetic patients with nephropathy compared with conventional soymilk [46]. Data on these parameters are limited and thus do not allow for a conclusion. Further exploration is warranted. This meta-analysis benefits from a set of data that covers the most important indexes of kidney function. Subanalysis according to being single strain and multistrain, type of disease, and duration of supplementation presents a broad view of the parameters that might affect the results. This study, however, had several limitations. Despite the broad scope of the present review, very few trials contributed data to each renal function parameter; hence, the number of participants for some parameters was small. The studies included in the present meta-analysis were not homogenous in terms of dosage and type of supplementation used, nor in terms of underlying diseases in participants. The quality of the majority of the studies was questionable, with only a handful having a complete Jadad score. Meta-regression analysis was not conducted due to the limited number of studies in each parameter. Additionally, the baseline value and changes in dietary intake were not specified in most of the studies. This is relevant as it has been suggested that some dietary components such as protein intake could affect levels of renal parameters [35]. Larger well-designed RCTs, controlled for baseline values of renal parameters and dietary intake, are necessary to assess the effects of modulation of gut microbiome by prebiotics, probiotics, and synbiotics on renal profile. Optimal probiotic strains, dosing, and duration of therapy must be determined. Conclusion This study showed non-significant decline in GFR and nonsignificant increase in creatinine. Additionally, results from the meta-analysis demonstrated that prebiotic, probiotic, and synbiotic
supplementation will result in a significant decrease of urea and BUN, but a significant increase in UA. As such, until large-scale, well-designed RCTs prove the safety and efficacy of prebiotics, probiotics, and synbiotics in improving renal function, the use of probiotics among those with compromised renal function or those at risk for renal failure should be limited. Acknowledgment The authors acknowledge Abbas Shafiee and Aidin Sadeghilar for their valuable comments. References [1] Turnbaugh PJ, Ley RE, Hamady M, Fraser Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature 2007;449:804–10. [2] Jayasimhan S, Yap NY, Roest Y, Rajandram R, Chin KF. Efficacy of microbial cell preparation in improving chronic constipation: a randomized, doubleblind, placebo-controlled trial. Clin Nutr 2013;32:928–34. [3] Firouzi S, Barakatun-Nisak MY, Ismail A, Majid HA, Nor Azmi K. Role of probiotics in modulating glucose homeostasis: evidence from animal and human studies. Int J Food Sci Nutr 2013;64:780–6. [4] Karimi G, Jamaluddin R, Parvaneh K. The effects of probiotics on body weight and biomarkers of animal. Pakistan J Nutr 2013;8:793–9. [5] Dehghan P, Farhangi MA, Tavakoli F, Aliasgarzadeh A, Akbari AM. Impact of prebiotic supplementation on T-cell subsets and their related cytokines, anthropometric features and blood pressure in patients with type 2 diabetes mellitus: a randomized placebo-controlled Trial. Complement Ther Med 2016;24:96–102. [6] Karimi P, Farhangi MA, Sarmadi B, Gargari BP, Javid AZ Pouraghaei M. The therapeutic potential of resistant starch in modulation of insulin resistance, endotoxemia, oxidative stress and antioxidant biomarkers in women with type 2 diabetes: a randomized controlled clinical trial. Ann Nutr Metab 2016;68:85–93. [7] Parvaneh K, Jamaluddin R, Karimi G, Erfani R. Effect of probiotic supplementation on bone mineral content and bone mass density: a review. Sci World J 2013;2014:595962. [8] Firouzi S, Mohd-Yusof B-N, Majid H-A, Ismail A, Kamaruddin N-A. Effect of microbial cell preparation on renal profile and liver function among type 2 diabetics: a randomized controlled trial. BMC Complement Altern Med 2015;15:433. [9] Vaziri ND. CKD impairs barrier function and alters microbial flora of the intestine: a major link to inflammation and uremic toxicity. Curr Opin Nephrol Hypertens 2012;21:587–92. [10] Massy ZA, Barreto D V, Barreto FC, Vanholder R. Uraemic toxins for consideration by the cardiologist—beyond traditional and non-traditional cardiovascular risk factors. Atherosclerosis 2010;211:381–3. [11] Vanholder R, Glorieux G. The intestine and the kidneys: a bad marriage can be hazardous. Clin Kidney J 2015;8:168–79. [12] Sirich TL, Plummer NS, Gardner CD, Hostetter TH, Meyer TW. Effect of increasing dietary fiber on plasma levels of colon-derived solutes in hemodialysis patients. Clin J Am Soc Nephrol 2014;9:1603–10. [13] Meijers BKI, De Preter V, Verbeke K, Vanrenterghem Y, Evenepoel P. p-Cresyl sulfate serum concentrations in haemodialysis patients are reduced by the prebiotic oligofructose-enriched inulin. Nephrol Dial Transplant 2010;25: 219–24. [14] Hida M, Aiba Y, Sawamura S, Suzuki N, Satoh T, Koga Y. Inhibition of the accumulation of uremic toxins in the blood and their precursors in the feces after oral administration of Lebenin, a lactic acid bacteria preparation, to uremic patients undergoing hemodialysis. Nephron 1996;74:349–55. [15] Rossi M, Klein K, Johnson DW, Campbell KL. Pre-, pro-, and synbiotics: do they have a role in reducing uremic toxins? A systematic review and metaanalysis. Int J Nephrol 2012;2012:673631. [16] Ranganathan N, Ranganathan P, Friedman EA, Joseph A, Delano B, Goldfarb DS, et al. Pilot study of probiotic dietary supplementation for promoting healthy kidney function in patients with chronic kidney disease. Adv Ther 2010;27:634–47. [17] Fukushima Y, Miyaguchi S, Yamano T, Kaburagi T, Iino H, Ushida K, et al. Improvement of nutritional status and incidence of infection in hospitalised, enterally fed elderly by feeding of fermented milk containing probiotic Lactobacillus johnsonii La1 (NCC533). Br J Nutr 2007;98:969–77. [18] Cox AJ, West NP, Horn PL, Lehtinen MJ, Koerbin G, Pyne DB, et al. Effects of probiotic supplementation over 5 months on routine haematology and clinical chemistry measures in healthy active adults. Eur J Clin Nutr 2014;68: 1255–7. [19] Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med 2015;162:777–84.
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[34]
[35] [36]
[37]
[38]
[39]
[40]
[41] [42]
[43]
[44] [45]
[46]
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