diabetes

Metabolic and genetic response to probiotics

Introduction

supplementation in patients with diabetic nephropathy: a randomized, double-blind, placebo-controlled trial†

This study was carried out to evaluate the effects of probiotics administration on the metabolic and genetic profiles in patients with diabetic nephropathy (DN). This was a randomized, placebo-controlled clinical trial with homeostasis model of assessment-estimated insulin resistance (HOMA-IR) as the primary and other metabolic profiles, and biomarkers of inflammation and oxidative stress as the secondary outcomes. This randomized, double-blind, placebo-controlled clinical trial was performed on 60 patients with DN. The patients were randomly assigned into two groups to receive either 8 × 109 CFU day−1 probiotic supplements or placebo (n = 30 in each group) for 12 weeks. Fasting blood was collected at the baseline and end of intervention to measure glycemic control, lipid profiles, biomarkers of inflammation and oxidative stress. Multiple linear regression models were used to assess the treatment effects on the outcomes adjusting for confounding variables. Probiotics supplementation, compared with the placebo, resulted in a significant reduction in fasting plasma glucose (P = 0.01), serum insulin concentrations (P = 0.01) and HOMA-IR (P = 0.007), and a significant increase in the quantitative insulin sensitivity check index (P = 0.04). Additionally, compared with the placebo, probiotic intake resulted in a significant reduction in triglycerides (P = 0.001) and total-/HDL-cholesterol ratio (P < 0.001), and a significant increase in HDL-cholesterol levels (P < 0.001). Supplementation with probiotics, compared with the placebo, was associated with a significant reduction in high-sensitivity C-reactive protein (P = 0.001), malondialdehyde (P < 0.001) and advanced glycation end products (P < 0.001), and a significant elevation in plasma total glutathione (P < 0.001). Diabetic nephropathy (DN) is the most important complication of diabetes which affects approximately 40% of type 1 and type 2 diabetic people,1 and it would result in end-stage renal failure, disability, and low quality of life worldwide.2 Alterations of endothelial cells and the vasculature due to insulin resistance and lipid abnormality play a central role in the pathogenesis of DN.3,4 In addition, protein, lipid, and glucose loads are correlated with a considerable production of A dysbiotic microbiota seems to represent a susceptibility factor for the progress of chronic kidney disease (CKD) following injury or in predisposed people.8 Furthermore, it is known that the progressive loss of kidney efficacy significantly contributes to worsen the intestinal dysbiosis found in CKD subjects.9 Different mechanisms are involved due to the derangement of the intestinal barrier and modifications of the microbiota composition.10 Several studies tried to modulate the

a Research Center for Biochemistry and Nutrition in Metabolic Diseases, Kashan University of Medical Sciences, Kashan, I.R. Iran.

E-mail: namazi-gh@kaums.ac.ir; Fax: +98-31-55540022; Tel: +98-31-55540022

b Department of Internal Medicine, Kashan University of Medical Sciences, Kashan,

Iran. E-mail: asemi_r@yahoo.com

†Electronic supplementary information (ESI) available. See DOI: 10.1039/ c8fo00888d

intestinal environment and microbiota by using probiotics11,12 or synbiotics.13,14 Few studies have reported the beneficial effects of probiotic intake on kidney function in patients with CKD. In one study on probiotics, the use of a mixture of bacteria for 6 months decreased blood urea nitrogen (BUN) and uric acid levels in stage 3–4 CKD patients.12 In addition, a 2-month treatment with a dairy product containing 16 × 109 CFU of Lactobacillus casei Shirota reduced BUN levels in CKD patients of stages 3 and 4.15 In another study by Miraghajani et al.,16 the consumption of probiotic soy milk had a beneficial influence on the renal function in patients with DN. We have previously demonstrated that probiotics supplementation for 12 weeks to diabetic hemodialysis people had favorable effects on glycemic control, and a few biomarkers of inflammation and oxidative stress, but did not affect lipid profiles.17 Few studies have proved the beneficial effects of probiotic intake on gene expression related to insulin and lipid metabolism, and inflammatory cytokines in patients without DN. Hsieh et al.18 showed that the reduced levels of gene expression of the peroxisomal proliferator activated receptor gamma (PPAR-γ) after high fructose treatment were significantly elevated by Lactobacillus reuteri intake in animal models. In addition, Steed et al.19 found that synbiotic supplementation for 6 months to people with active Crohn’s disease for 6 months decreased the gene expression of tumor necrosis factor alpha (TNF-α).

These discrepancies are at least partially attributed to the variation in the population studied, the dosage of probiotics used, the duration of the intervention, and the underlying levels of metabolic profiles. Despite these findings, the data on the effects of probiotics supplementation on the metabolic and genetic profiles in DN patients are limited. Therefore, the aim of this study was to examine the effects of probiotics supplementation on the metabolic and genetic profiles in DN patients.

Subjects and methods

Participants

This randomized, double-blind, placebo-controlled trial was conducted by Kashan University of Medical Sciences (KAUMS) at an internal clinic in Kashan, Iran from May to October 2017 on patients with DN with proteinuria levels>0.3 g per 24 hours, aged 45–85 years. This clinical trial was registered by the Iranian Registry of Clinical Trials (http://www.irct.ir: # IRCT2017061134458N1). We defined DN as diabetic renal disease with proteinuria, with or without the elevation of serum

weeks. The treatment group received 8 × 109 CFU day−1 of probiotic supplements containing Lactobacillus acidophilus strain ZT-L1, Bifidobacterium bifidum strain ZT-B1, Lactobacillus reuteri strain ZT-Lre, and Lactobacillus fermentum strain ZT-L3 (each 2 × 109) for 12 weeks. It is well known that it would be more appropriate if the strains used in probiotic supplements for human consumption were derived from the human intestinal tract, well characterised, able to outlive the rigors of the digestive tract and possibly colonise it, biologically active against the target as well as stable and amenable for commercial production and distribution.21 Therefore, we combined Lactobacillus with Bifidobacterium as an intervention strategy. The placebo group received capsules in similar shape and packaging as the probiotic. The probiotic and placebo (starch) were produced by LactoCare®, Zisttakhmir Company (Tehran, Iran) and Barij Essence Pharmaceutical Company (Kashan, Iran), respectively. The adherence to supplements and placebo was determined by counting the tablet containers. Moreover, the participants received a daily reminder message on their cell phones to take their supplements. All participants completed 3-day dietary records (two weekdays and one weekend) at week 1, 5, 9 and 12 of the trial. To calculate participants’ nutrient intake, using 3-day food records, we applied the Nutritionist IV software (First Databank, San Bruno, CA) adopted for the Iranian food pattern.

Outcomes

The primary outcome variable was HOMA-IR. As insulin resistance is the most important variable in patients with DN, we considered markers of insulin resistance as the primary outcomes. The secondary outcome variables were other metabolic profiles, biomarkers of inflammation and oxidative stress, serum creatinine, and blood urea nitrogen (BUN) concentrations. At the baseline and end-of-treatment, 15 mL blood samples were obtained from each patient at the Kashan reference laboratory in the early morning after an overnight fast. Available commercial kits were used to determine fasting plasma glucose (FPG), lipid profiles, creatinine, BUN and urine protein concentrations (Pars Azmun, Tehran, Iran). All inter- and intra-assay coefficient variances (CVs) for FPG, lipid concentrations, creatinine and BUN were less than 5%. Serum insulin values were quantified by the use of an available ELISA kit (Monobind, California, USA) with inter- and intra-assay CVs of 3.3 to 4.8%, respectively. To determine the HOMA-IR and the quantitative insulin sensitivity check index (QUICKI), we used suggested formulas.22 Serum hs-CRP levels were quantified by using a commercial ELISA kit (LDN, Nordhorn, Germany) with inter- and intra-assay CVs lower than 7%. The plasma nitric oxide (NO) concentrations were measured by using the Griess method.23 Plasma total antioxidant capacity (TAC) concentrations were measured using the method of ferric reduction antioxidant power developed by Benzie and Strain.24 Total glutathione (GSH) and malondialdehyde (MDA) concentrations were also measured using the Beutler method25 and the thiobarbituric acid reactive substance spectrophotometric test, respectively.26 The CVs for plasma NO, TAC, GSH and MDA were less than 5%. Serum AGEs were determined by the fluorometric method. Renal function was estimated by using the Cockcroft–Gault (CG) formula in mL min−1 [140 − age (years)] ×

[weight (kg)]/72 × (serum creatinine) × 0.85 if female].27

Isolation of lymphocyte, RNA extraction and cDNA synthesis

Blood samples were collected in anti-coagulant EDTA tubes. Lymphocytes were isolated using a 50% percoll solution (Sigma-Aldrich, Dorset, UK) gradient by centrifugation for 20 min at 3000 rpm at 4 °C.28 Total RNA was extracted based on the acid guanidinium–phenol–chloroform procedure using the RNX™-plus reagent (Cinnacolon, Tehran, Iran) according to the manufacturer’s instructions. RNAs were treated with DNAase I (Fermentas, Lithuania) for the elimination of any genomic DNA contamination. 3 μg of total RNA was used for cDNA synthesis with a random hexamer and oligo (dT) 18 primers through RevertAid™ Reverse Transcriptase (Fermantase, Canada) in a total of 20 μl reaction mixture.28

Real-time PCR analysis

Appropriate primers for PPAR-γ, low-density lipoprotein receptor (LDLR), interleukin-1 (IL-1), TNF-α and transforming growth factor beta (TGF-β), and glyceraldehyde-3 phosphate dehydrogenase – as an internal control – were designed (Table 1). Quantitative real-time PCR was performed with LightCycler® 96 sequence detection systems (Roche Diagnostics, Rotkreuz, Switzerland) using 4 μl of 5× EVA GREEN I master mix (Salise Biodyne, Japan), 10 ng cDNA, 200 nM of each forward and reverse primer in a final volume of 20 μl. All experiments were performed at least in triplicate. In the current study, the levels of the expressed genes were measured by relative quantitative RT-PCR. Relative quantification determines the changes in the steady-state mRNA levels of a gene across multiple samples and expresses it relative to the levels of an internal control RNA.29,30 The relative transcription values were calculated by the Pffafi method.29,30 The reference gene in this method is often a housekeeping gene. The intra-assay and inter-assay coefficients of variance were

0.3–0.1% and 1.1–4.0%, respectively.

Statistical methods

The Kolmogrov–Smirnov test was performed to determine if the data were normally distributed. The analyses were done based on the intention-to-treat approach (ITT). The missing values were treated based on the Last-Observation-Carried-

Table 1 Specific primers used for real-time quantitative PCR

Forward (LOCF) method. LOCF ignores whether the participant’s condition was improving or deteriorating at the time of dropout but instead freezes outcomes at the value observed before dropout (i.e., last observation). An independent sample t-test was used to compare the differences in baseline characteristics, daily dietary macro- and micro-nutrient intake and gene expression related to insulin, lipid and inflammation between the two treatment groups. Pearson’s chi-square test was used for the comparison of categorical variables. To determine the effects of probiotics supplementation on metabolic profiles, we used repeated measures analysis of variance (RM ANOVA). P values less than 0.05 were considered as significant. All statistical analyses were performed using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, Illinois, USA).

Results

Among the patients in the probiotic group, 3 patients [withdrawn due to personal reasons (n = 3)], and in the placebo group, 3 persons [withdrawn due to personal reasons (n = 3)] were excluded (Fig. 1). Finally, 54 participants [probiotic (n = 27) and placebo (n = 27)] completed the trial. However, as the analysis was done based on the ITT principle, all 60 people (30 in each group) were included in the final analysis.

Mean age, baseline and end-of-trial weight and BMI of the study participants were not statistically different between the two groups (Table 2).

Based on the three-day dietary records obtained throughout the trial, we found no significant difference in dietary macroand micro-nutrient intake (ESI 1†).

After 12 weeks of intervention, probiotics supplementation, compared with the placebo, resulted in a significant reduction in FPG (−23.4 ± 40.4 vs. +1.3 ± 31.7 mg dL−1, P = 0.01), serum insulin concentrations (−3.8 ± 5.1 vs. −0.9 ± 3.9 µIU mL−1, P =

Discussion

In this study, we evaluated the beneficial effects of probiotics supplementation on glycemic control, markers of cardio-metabolic risk, and gene expression related to metabolic status among patients with DN. We found that probiotics supplementation for 12 weeks to DN patients had beneficial effects on glycemic control, a few markers of cardio-metabolic risk, and gene expression related to metabolic status. To our knowledge, this investigation is the first report of the effects of probiotic intake on glycemic control, markers of cardio-metabolic risk, and gene expression related to metabolic status among patients with DN.

DN is associated with multiple metabolic disturbances including insulin resistance and increased markers of cardiometabolic risk.31 The current study proved that probiotics supplementation for 12 weeks to patients with DN resulted in a significant reduction in FPG, serum insulin, HOMA-IR, serum triglycerides, VLDL- and total-/HDL-cholesterol ratio and gene expression of LDLR, and a significant elevation in QUICKI and HDL-cholesterol levels compared with the placebo, but did not influence other lipid profiles and gene expression of PPAR-γ. In a meta-analysis study, probiotics supplementation to women with gestational diabetes mellitus (GDM) was found to significantly reduce HOMA-IR and fasting insulin concentrations, but had no considerable influence on fasting glucose.32 In addition, in another meta-analysis study, we have previously reported that synbiotic supplementation to patients with diabetes resulted in an improvement in fasting glucose, insulin, HOMA-IR, QUICKI, triglycerides and total cholesterol levels, but did not affect other lipid fractions.33 In a meta-analysis study conducted by Yao et al.,34 it was observed that probiotics supplementation was correlated with a significant improvement in HbA1c, fasting insulin and HOMA-IR in people with T2DM, but did not influence fasting glucose and lipid profiles. Hyperinsulinemia, insulin resistance and dyslipidemia are a major risk factor for T2DM and cardiovascular disease (CVD). Accumulating studies indicate that the restoration of the impaired function of the diabetic macro- and microvasculature may ameliorate a range of CVD states and diabetes-associated complications.35 The production of short chain fatty acids (SCFA) by probiotics has been found to regulate the production of hormones effecting energy intake and expenditure including leptin and ghrelin.36 The binding of SCFA to G protein-coupled receptors GPR41 and GPR43 causes the intestinal expression of peptide YY and glucagon-like peptide-1 (GLP-1) hormones which act to decrease appetite by slowing intestinal transit time, elevating insulin sensitivity and improving lipid profiles.37 In addition, SCFA through decreasing gastrointestinal permeability by upregulating the transcription of tight junction proteins and increasing the production of GLP-2, and reducing inflammation in colonic epithelial

We found that the intake of probiotic supplements for 12 weeks by patients with DN was associated with a significant reduction in serum hs-CRP, plasma MDA, AGEs, BUN, creatinine, CG and gene expression of IL-1, and a significant increase in plasma GSH concentrations compared with the placebo, but did not influence other markers of inflammation and oxidative stress, and gene expression of TNF-α and TGF-β. In a meta-analysis study, probiotics supplementation to patients with colorectal cancer could decrease CRP levels.39 We have previously indicated that probiotics supplementation for 6 weeks to women with GDM was beneficial in improving hsCRP, TAC, MDA levels and the oxidative stress index, but did not influence other markers of inflammation and oxidative genesis of DN and cardiomyopathy. With inflammatory markers and signaling pathways as key mediators, targeting inflammation may be a useful approach to a new avenue for

treating diabetic events.44 In addition, oxidative damage has been proposed to play an important role in the pathogenesis of DN.45 Thus, multi-target agents are urgently need for the clinical treatment of DN. Probiotic intake may benefit in reducing inflammatory factors by producing SCFA in the gut46 and decreasing the production of hydrogen peroxide radicals.47 Moreover, the favorable effects of probiotics on oxidative damage might be mediated by the production of butyrate in the colon48 and reducing lipid peroxidation.49

This investigation had a few limitations. In the current study, due to funding limitations, we did not evaluate the microbiota and thus cannot establish whether probiotics administration over 12 weeks changed its composition. We did not evaluate any direct dynamic test such as hyperinsulinemic clamp in the current study. Therefore, these should be taken into account in the interpretation of our findings.

Fig. 3 Effect of 12-week supplementation with probiotics or placebo on the expression ratio of the IL-1, TNF-α and TGF-β gene in the PBMC of patients with DN. IL-1, interleukin-1; LDLR, low-density lipoprotein receptor; DN, diabetic nephropathy; PPAR-γ, peroxisome proliferator-activated receptor gamma; PBMC, peripheral blood mononuclear cells; TNF-α, tumor necrosis factor alpha; TGF-β, transforming growth factor beta.

Overall, our study indicated that probiotics supplementation for 12 weeks among DN patients had beneficial effects on glycemic control and markers of cardio-metabolic risk. This suggests that probiotics supplementation may confer advantageous therapeutic potential for patients with DN. Further research is needed in other participants and for longer periods to determine the beneficial effects of probiotics supplementation. Moreover, further studies should evaluate the gene expression levels related to other inflammatory markers such as nuclear factor-κB, and oxidative stress to explore the plausible mechanism and confirm our findings.