Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Advanced glycation end products dietary restriction effects on bacterial gut microbiota in peritoneal dialysis patients; a randomized open label controlled trial

  • Rabi Yacoub ,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft

    rabiyaco@buffalo.edu

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Melinda Nugent,

    Roles Data curation, Methodology, Writing – review & editing

    Affiliation Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

    ⨯
  • Weijin Cai,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

    ⨯
  • Girish N. Nadkarni,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Writing – review & editing

    Affiliation Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

    ⨯
  • Lee D. Chaves,

    Roles Formal analysis, Methodology, Validation, Writing – original draft

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Sham Abyad,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Amanda M. Honan,

    Roles Formal analysis, Writing – review & editing

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Shruthi A. Thomas,

    Roles Data curation, Formal analysis, Writing – review & editing

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Wei Zheng,

    Roles Formal analysis, Methodology, Writing – original draft

    Affiliation Department of Computer Science and Engineering, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Sujith A. Valiyaparambil,

    Roles Formal analysis, Writing – original draft

    Affiliation Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Mark A. Bryniarski,

    Roles Formal analysis, Software, Validation, Writing – original draft

    Affiliation Department of Phamaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, Buffalo, New York, United States of America

    ⨯
  • Yijun Sun,

    Roles Formal analysis, Software, Supervision, Validation, Writing – review & editing

    Affiliation Department of Microbiology and Immunology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Michael Buck,

    Roles Conceptualization, Formal analysis, Software, Supervision, Writing – review & editing

    Affiliation Department of Biochemistry, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Robert J. Genco,

    Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – review & editing

    Affiliation Department of Oral Biology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • Richard J. Quigg,

    Roles Formal analysis, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

    Affiliation Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, United States of America

    ⨯
  • John C. He,

    Roles Conceptualization, Formal analysis, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

    Affiliation Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

    ⨯
  •  [ ... ],
  • Jaime Uribarri

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – original draft

    Affiliation Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America

    ⨯
  • [ view all ]
  • [ view less ]

Abstract

The modern Western diet is rich in advanced glycation end products (AGEs). We have previously shown an association between dietary AGEs and markers of inflammation and oxidative stress in a population of end stage renal disease (ESRD) patients undergoing peritoneal dialysis (PD). In the current pilot study we explored the effects of dietary AGEs on the gut bacterial microbiota composition in similar patients. AGEs play an important role in the development and progression of cardiovascular (CVD) disease. Plasma concentrations of different bacterial products have been shown to predict the risk of incident major adverse CVD events independently of traditional CVD risk factors, and experimental animal models indicates a possible role AGEs might have on the gut microbiota population. In this pilot randomized open label controlled trial, twenty PD patients habitually consuming a high AGE diet were recruited and randomized into either continuing the same diet (HAGE, n = 10) or a one-month dietary AGE restriction (LAGE, n = 10). Blood and stool samples were collected at baseline and after intervention. Variable regions V3-V4 of 16s rDNA were sequenced and taxa was identified on the phyla, genus, and species levels. Dietary AGE restriction resulted in a significant decrease in serum Nε-(carboxymethyl) lysine (CML) and methylglyoxal-derivatives (MG). At baseline, our total cohort exhibited a lower relative abundance of Bacteroides and Alistipes genus and a higher abundance of Prevotella genus when compared to the published data of healthy population. Dietary AGE restriction altered the bacterial gut microbiota with a significant reduction in Prevotella copri and Bifidobacterium animalis relative abundance and increased Alistipes indistinctus, Clostridium citroniae, Clostridium hathewayi, and Ruminococcus gauvreauii relative abundance. We show in this pilot study significant microbiota differences in peritoneal dialysis patients’ population, as well as the effects of dietary AGEs on gut microbiota, which might play a role in the increased cardiovascular events in this population and warrants further studies.

Citation: Yacoub R, Nugent M, Cai W, Nadkarni GN, Chaves LD, Abyad S, et al. (2017) Advanced glycation end products dietary restriction effects on bacterial gut microbiota in peritoneal dialysis patients; a randomized open label controlled trial. PLoS ONE 12(9): e0184789. https://doi.org/10.1371/journal.pone.0184789

Editor: Pasqual Barretti, Universidade Estadual Paulista Julio de Mesquita Filho, BRAZIL

Received: June 29, 2017; Accepted: August 28, 2017; Published: September 20, 2017

Copyright: © 2017 Yacoub et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1TR001412 to the University at Buffalo and by the NIH. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. This was also supported by University at Buffalo Genome, Environment and Microbiome (GEM), and department fund for RY and LC. Innovative Micro-Programs Accelerating Collaboration in Themes (IMPACT) for RY. GNK is supported by National Institute of Diabetes and Digestive and Kidney Diseases, K23DK107908-01A1. YS is supported by NIH RO1AI125982, RO1DE024523. JCH issupported by I01BX000345, R01DK078897.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Advanced glycation end products (AGEs) are a heterogeneous group of compounds that are formed by the non-enzymatic reaction of reducing α-carbonylic compounds with free amino groups in proteins, lipids and nucleic acids resulting in alteration in their function [1–3]. We know, however, that AGEs may form through many other biological pathways [4]. AGEs are constantly formed at a small rate endogenously and a significant amount is consumed as food. There is significant literature linking elevated AGE levels with increased cardiovascular risk [5, 6], diabetes [7, 8], aging [9, 10], and renal dysfunction [11–14]. AGE accumulation can be harmful through several mechanisms including affecting protein structure and function by directly cross-linking with them, or by activating cellular receptors [15–18].

In subjects with normal renal function, AGEs are excreted in urine. However, in patients with end stage renal disease (ESRD) requiring hemodialysis (HD) or peritoneal dialysis (PD), AGEs build up in the body due to impaired urinary elimination and limited clearance during dialysis [19–21].

Due to food preparation methods, modern western diets contain large amounts of AGEs [22, 23], and the estimated daily amount supplied ranges from 25 to 75 mg of mainly pyrraline and carboxymethyllysine (CML) [24]. It has been suggested that about 10–30% of the ingested load of dietary AGEs (dAGEs) gets absorbed into the body and becomes incorporated in the body AGE pool [25–27]. The remainder of the ingested AGEs travels to the colon where it could interact with the bacterial microbiota [28–30]. Methylglyoxal (MG) is a reactive dicarbonyl intermediate and an AGE precursor [31]. Its concentration is increased in diabetes and it is known to increase oxidative stress [31], worsen vascular damage, and promote atherosclerosis and cardiovascular events [32, 33]. MG can lead to increase AGE induction resulting in worsening oxidative stress [31], diabetic myopathy [7], chronic low-grade inflammation, and impaired extracellular matrix remodeling [34].

In vitro studies have indicated that Maillard reaction products (MRPs) may affect bacterial growth [35] and gut microbiota composition, and that micro-organisms can degrade AGEs [35, 36]. Anaerobic bacteria, particularly Bifidobacteria strains, have been shown to be able to use bread melanoidins (the final products of the Maillard reaction) as a carbon source [29]. This indicates that dAGEs might affect gut microbiota through negative selection (direct toxic effects), or positive selection (favoring bacterial species overgrowth that can utilize dAGEs as source of energy). In this pilot study, we aimed to evaluate the effect of restricting a habitually high dAGE consumption on gut microbiota in a group of ESRD patients on maintenance PD, and hypothesize that dAGE restriction affects the diversity of bacterial gut microbiota in patients with ESRD receiving maintenance PD.

Materials and methods

Study participants and enrollment

This is a randomized, open label trial. This pilot study was aimed at exploring the effects of dAGEs restriction on the bacterial gut microbiota composition in PD patients. Patients were recruited from the Icahn School of Medicine at Mount Sinai PD unit from June 2015 until December 2015 (ClinicalTrials.gov; NCT02467530). Screening and feasibility evaluation was initiated on January 2015, and study protocol (Study protocol) was submitted on June 8th 2015. The first patient was recruited after finalizing the study submission on ClinicalTrials.gov. The authors confirm that all ongoing and related trials for this intervention are registered. All patients signed informed consent; the trial was conducted in accordance with the Declaration of Helsinki and good clinical practice guidelines. All research involving human participants was approved by the Icahn School of Medicine at Mount Sinai Institutional Review Board (IRB, GCO 14–1961, first approved December, 2nd 2014 till December, 1st 2015), and all clinical investigation have been conducted according to the principles expressed in the declaration of Helsinki. Written informed consent was obtained before enrolling. Inclusion criteria for the study included patients with ESRD receiving PD for at least 2 months, 18 years of age or older who consumed a diet high in AGEs (>12 Eq/day) [23]. Patients with advanced liver disease, heart failure, autoimmune disease, and those receiving probiotics, immunosuppressant medications, steroids, chemotherapy, multivitamins, or oral iron supplements were excluded. Patients who received a course of antibiotics were eligible three months after the last dose. Patients with medical history of any cancer, abdominal surgery, or bowel obstruction and those who sustained lower gastrointestinal bleeding were excluded (Fig 1, Consort 2010 checklist). No changes to methods after trial commencement were implemented.

thumbnail
Download:
Fig 1. CONSORT flowchart.

After enrollment, patients were randomized to either continue consuming high AGE diet, or a one month dAGE restriction. Stool and blood samples were collected at baseline and after intervention. AGEs, Advanced glycation end products; GI, gastrointestinal; PO, per os.

https://doi.org/10.1371/journal.pone.0184789.g001

Randomization and intervention protocol

Pre-prepared opaque envelopes were used to randomize subjects. After signing the consent, patients were randomly given an envelope (by the principal investigator) with their allocation, which was opened immediately to reveal their assignment. Patients had an initial interview with the research dietitian to determine their habitual consumption of a high AGE diet and then randomized to either a high (HAGE) or low AGE (LAGE) diet. Participants randomized to continue the same high AGE (HAGE) diet were instructed to continue eating as usual for the next one month and simply record a three-day diet log just before each study visit, baseline, and one month. Those randomized to the low AGE (LAGE) were individually instructed on meal planning to meet study requirements while maintaining their usual peritoneal dialysis diet instructions. To vary the AGE content, foods, particularly meat, were exposed to different cooking methods. LAGE subjects were instructed to boil, poach, stew or steam, avoid fried entrees, and reheat food indirectly using steam in a double boiler. The subjects were followed closely by phone calls (1 to 2 times/week) to assure dietary compliance.

Medical treatment was not changed during this month; blood pressure medications, phosphate binders, and erythropoietin stimulating agents (ESA) doses were kept the same. No new antibiotics, probiotics, multivitamin, oral iron or antacid prescription was initiated during this period. All participants’ samples and data were labeled using new unique identifiers and procedures and measurements were all conducted blindly.

Stool sample collection and DNA extraction

Stool samples were collected by the participants at home on the morning of both visits, placed in sterile, DNase, RNase and pyrogens free 50 ml tubes, transported on ice, and stored in -80 C within four hours of defecating. DNA was extracted using PowerFecal® DNA isolation kit, according to the manufacturer protocol (MOBIO laboratory Inc. QIAGEN Company CA. USA). Two separate isolations from each sample were obtained from a grossly firm stool area absent of undigested food, and placed immediately in extraction tubes. DNA extraction was performed in duplicates on each sample and only samples with DNA concentration ≥10 ng/microliter were sequenced.

16s sequencing and annotation

Metagenomic DNA was amplified using the 16S V3 (341F) forward and V4 (805R) reverse primer pairs with Illumina adapter overhang. Reactions were purified using Agencourt AMPure XP beads (Beckman Coulter). Illumina sequencing adapters and dual Indexes were added to the amplified fragments using an 8 cycle PCR reaction, further purified as above, and quantified using Quant-iT PicoGreen assay (Invitrogen). Quality control, quantification and average size distribution of the libraries was assessed with the Advance Analytical Fragment Analyzer. Libraries were normalized and pooled to 4 ηM, further quantified using NEBNext NGS library quantification kit (NEB) prior to being denatured and diluted to a final concentration of 10 pM with a 20% PhiX spike-in (Illumina) control. Sequencing was performed using MiSeq Reagent V3 Kit (illumina) using 2 × 300 bp paired-end sequencing.

For the OTU picking, we merged Illumina paired-end reads using Pear (version 0.9.6)[37] with default parameters applied. To improve taxonomic accuracy, we applied strict sequence quality filtering. A sequence read with 90% of base quality scores higher than or equal to Q30 was considered as good quality read. After trimming the forward and reverse primer, we used UPARSE (version 8.0)[38] to perform de novo OTU picking at different distance levels ranging from 0.03 to 0.20 with step size 0.01. We removed chimera sequences by searching against the gold UCHIME reference (http://drive5.com/uchime/gold.fa). By using QIIME (version 1.9.1)[39] command line, the species level OTUs defined at the 0.03 distance level were annotated by searching the representative sequence of each OTU against the Greengenes database (version 13.5) [40].

Measurements

Dietary intake.

Assessment of daily dAGE content was based on 3-day food records and estimated from a database of ∼560 foods that lists AGE values expressed as AGE equivalents per day (1 AGE equivalent = 1000 kilo units). Nutrient intakes were estimated from food records using a nutrient software program (Food Processor, version 10.1; ESHA Research, Salem, OR, USA).

AGE determination.

AGEs (CML and MG) in serum were determined by well-validated, competitive ELISAs based on monoclonal antibodies for protein-bound CML (4G9) and protein-bound MG derivatives, i.e. arginine-MG-H1, characterized by HPLC [4, 23, 25–27]. We initially aimed to determine AGE concentration using skin fluorescent AGE measurements. Since not all AGEs exhibit fluorescent properties, we decided to use more precise ELISA-based test [41–43].

Statistical analysis

Descriptive analyses summarized continuous variables at baseline through their mean (SD) and median (first and third quartiles). Categorical variables were summarized using percentages. AGE levels were reported as time point values and the difference between post intervention and baseline concentrations. This is a pilot study to evaluate the effects of dAGE restriction on gut microbiota in PD patients; no previous similar study was conducted to aid in evaluating and assessing sample size needed and power analysis. We believe that this current study will provide this information for future studies.

Results

Patient characteristics

Patients were recruited until we reached the goal of 10 participants in each group. Of the 103 ESRD patients initially screened, five were excluded because of a low AGE diet at baseline. There was no significant difference between the two groups in regards to age, gender or race (Table 1). All participants were receiving PD at Mount Sinai Hospital for more than 2 months, with similar dialysis vintage between the two groups, and no statistically significant difference in regards to use of phosphate binders or erythropoietin. Laboratory data at baseline including baseline AGE levels CML and MG were similar, except for lower ferritin levels in the HAGE group (Tables 1 and 2). As expected, the low AGE diet group showed a significant decrease in serum AGE levels post intervention (Table 2).

thumbnail
Download:
Table 1. Patient basic characteristics.

https://doi.org/10.1371/journal.pone.0184789.t001

thumbnail
Download:
Table 2. AGE levels before and after intervention.

https://doi.org/10.1371/journal.pone.0184789.t002

Baseline gut microbiota profile in ESRD patients on PD

To better understand the compositions of bacterial gut microbiota in ESRD patients on PD, we first performed a descriptive analysis to evaluate the relative abundance at phyla, genus, and species levels. In our cohort, Bacteroidetes and Firmicutes accounted for approximately 56% and 32%, respectively of the phyla (S1A Fig, S1 Table). At the genus level, we found that Bacteroides and Prevotella both accounted for 50%; Faecalibacterium, Blautia, Ruminococcus, Akkermansia, Parabacteroides, Roseburia, Escherichia, and Clostridium accounted for 40%; and the rest of 66 genus accounted for less than 10% of the gut bacterial relative abundance (S1B Fig, S2 Table). Bacterial species that showed high relative abundance (>5%) in our cohort at baseline were: Prevotella copri, Faecalibacterium prausnitzii, Bacteroides ovatus, Bacteroides fragilis, and Bacteroides uniformis (S3 Table). Our data show high variability across the subjects within each group in regards to species identified, suggesting multiple environmental/co-morbid conditions beyond dietary AGE consumption.

Dietary AGE modification effects on gut microbiota

The gut microbiota composition was studied separately at both baseline and post intervention. At baseline, dimension reduction analysis showed that the three leading principal components (PC) accounted for 17.44%, 10.09%, and 9% of variances, respectively, and there was no difference in the gut microbiota relative abundance between the two groups (Fig 2A). After intervention, dimension reduction resulted in three leading PCs explaining 13.19%, 11.84%, and 9.14% of variances, respectively. The two groups started showing small distinction in their projection onto the three principal components (Fig 2B). We found that the distinction between the two groups is mostly due to their projection onto the third principal component (P = 0.047), while subjects’ projection onto the first and second principal component were not statistically significant (P = 0.345, and P = 0.308 respectively). Analysis of the bacterial species that have contributed significantly in the third principal component (and all components) is shown in S4 Table. It is important to note that in our analysis we have ignored small coefficients (absolute factor coefficient <0.3) to make sure that the bacterial species identified are the ones with the most effects. From the table we notice that most of the bacterial species have positively or negatively influenced only one of the principal components. This indicates that these species increase/decrease in relative abundance in a dependent fashion in our patient population. This is of certain importance as when each species relative abundance was tested and compared between the two groups, a limited set of bacteria have shown a significant relative abundance differences after changing AGE dietary habits. (Table 3, Fig 3A, S2 Fig). Comparing the bacterial species that changed in relative abundance from baseline after intervention (delta OTUs in Table 3) to their participation in each PC, reveals that Prevotella copri, Clostridium citroniae, and Clostridium hathewayi contributed solely to PC3, while Ruminococcus gauvreauii contributed to only PC2, and Bifidobacterium animalis and Alistipes indistinctus have not contributed to more than 0.3 absolute coefficient to any of the components. Phylogenic analysis (Fig 3B) indicates an increase in Firmicutes abundance and a decrease in Verrucomicrobia in the dietary intervention group. Genus analysis shown in Fig 3C illustrates the changes observed between the groups, which did not reach statistical significance due to the small samples size. We then performed Shannon-Wiener diversity index test (Fig 4) on the unannotated operational taxonomic units (OTUs) at different similarity distances (80%, 90%, 95% and 97% that corresponds to phyla, class, genus, and species levels). No significant difference between all groups was found in diversity across different similarity distances.

thumbnail
Download:
Fig 2. One month dAGE restriction resulted in changes in gut bacterial microbiota.

Operational taxonomic units were annotated and analyzed at species levels, and shows no difference in microbiota projection onto the principal components at baseline (A), followed by changes after dietary intervention (B). HAGE, high advanced glycation end products group; LAGE, low advanced glycation end products group.

https://doi.org/10.1371/journal.pone.0184789.g002

thumbnail
Download:
Fig 3. Species (A), genus (B), and Phyla (C) differences between groups at baseline and after intervention.

HAGE (bl), high advanced glycation end products group at baseline; LAGE (bl), low advanced glycation end products group at baseline; HAGE (Int), high advanced glycation end products group after intervention; LAGE (Int), low advanced glycation end products group after intervention. The top ten species in relative abundance (74%) are shown in Fig 3A, full species are shown in S2 Fig.

https://doi.org/10.1371/journal.pone.0184789.g003

thumbnail
Download:
Fig 4. Shannon-Wiener diversity index indicating no differences among groups before and after intervention (bars and columns represent diversity index mean ± standard deviation).

HAGE (bl), high advanced glycation end products group at baseline; LAGE (bl), low advanced glycation end products group at baseline; HAGE (Int), high advanced glycation end products group after intervention; LAGE (Int), low advanced glycation end products group after intervention.

https://doi.org/10.1371/journal.pone.0184789.g004

thumbnail
Download:
Table 3. Species with significant differences between the groups.

https://doi.org/10.1371/journal.pone.0184789.t003

Discussion

This pilot study shows that a one-month intervention with dietary AGE restriction in a population of ESRD patients on maintenance PD resulted in changes in the composition of their gut microbiota. One of the biggest changes occurred in regards to Prevotella copri relative abundance, which significantly decreased following the low AGE diet. We confirmed previous work showing significant reduction in serum AGEs in response to a low AGE diet, which further indicates good adherence to the dietary instructions [44]. Moreover, compared to healthy participants from the human microbiome project (HMP) [45], our peritoneal dialysis patients appear to have unique different genera at baseline.

There are several lines of evidence to suggest gut microbiota is likely to be altered in patients with CKD [46]. In general, gut microbiota dysbiosis affects and increases the risk of multiple metabolic conditions including diabetes mellitus type 2, dyslipidemia, and obesity [47–54]. Numerous studies have shown the direct effects of alteration of gut bacterial compositions on traditional cardiac risk factors [55], and growing evidence in clinical studies suggests that obese individuals with insulin resistance are characterized by an altered composition of gut microbiota, particularly an elevated Firmicutes/Bacteroidetes ratio as compared with healthy people [56, 57].

Peritoneal dialysis solutions depend mostly on variable concentrations of dextrose [58, 59], and the high molecular weight glucose polymer icodextran to increase intraperitoneal tonicity and achieve adequate ultrafiltration [60]. Altered renal function, high prevalence of diabetes mellitus type 2, and the use of glucose based solutions all appear to play a role in the microbiota dysbiosis of ESRD PD patients. Our intervention and control groups were similar in almost all basic characteristics with the exception of baseline concentrations of serum ferritin. It is important to note that 50% of LAGE group patients were using icodextran compared to 20% of HAGE group. Though this could have affected the baseline bacterial composition between the two groups, no change in dialysis prescription was implemented during the study duration and OTUs changes from baseline in each patient were calculated to ensure normalization of these confounding factors.

We have performed a detailed descriptive analysis evaluating the composition of the gut microbiota in our cohort (S2 Table). Ding and Scholes divided the HMP participants based on their gut microbiota into four communities. Most of the four communities had a higher relative abundance of Bacteroides than our cohort [45]. The four healthy communities identified also showed much lower Prevotella abundance compared to our cohort. Interestingly, Alistipes accounted for less than 1.5% of the relative abundance in our cohort and was the 11th genus in abundance compared to third in the HMP study [45], indicating that ESRD, the dialysis procedure, and/or the co-morbidities associated with it directly affect Alistipes abundance.

In general, Prevotella is considered a “good bacteria”. It uses xylan, xylose, and carboxymethylcellulose to produce high levels of short chain fatty acids (SCFAs) [61], while Alistipes spp. are pathogenic causing colitis and site specific tumors in animal studies [62]. The increased Prevotella and decreased Alistipes abundance in our cohort does not have to be very protective. Prevotella copri that accounted for almost 23% of all species relative abundances in our cohort has been found recently to be associated with autoimmune diseases, mainly rheumatoid arthritis [63, 64]. Detailed analysis of the baseline bacterial composition also reveals a very low percentage of Bifidobacterium animalis (B. animalis).

In healthy individuals, B. animalis accounts for almost 16% of the Bifidobacterial population [65], while in our cohort it accounted for less than 2%. Commercially, B. animalis is included in most of the probiotic treatments available as it is considered to confer beneficial effects.[65, 66] This decreased percentage of B. animalis compared to all Bifidobacterium may be of clinical importance and warrants further investigation. In our study, B. animalis is decreased in LAGE group and increased in HAGE, compatible with the previous in vitro studies [29, 35]. Bifidobacterium and Lactobacillus are also major lactate producing and pH regulating bacteria with the consumption of hexose sugars [67], thus altering intestinal sugar contents that would result in decreased available nutrients and affects their relative abundance.

Alpha diversity (the mean species diversity in each individual) did not seem to differ among the groups before and after intervention as shown in the Shannon-Weiner index test. However, small changes in the bacterial gut microbiota composition that might not be independently statistically significant on its own can add up to a significant shift in the overall bacterial composition between the groups when combined. As shown in Table 3 and S4 Table, significant bacterial taxas have contributed to the same principal component indicating a possible interaction among these taxas and the influence of the dietary AGEs on this interaction. The gut microbiota is dynamic and bacterial species interact closely either by competition (competing for the same nutrients) [68, 69], producing antibacterial substances to regulate other species, or by producing nutrients that can be used by other bacteria.

As shown in this evaluation, numerous subtle changes combined may result in a significantly different bacterial composition after intervention. Thus, it is of crucial importance to identify the correlation between all bacterial species residing in human gut. This interaction could of course be affected by diseases state (ESRD in this cohort). Understanding the interaction between these species will enable us to develop better and novel therapeutic strategies aimed at favoring the growth of the “good bacteria” at the expense of the “bad bacteria”. The correlation matrix in S5 Table explains the interaction between all species identified in the stool of our PD cohort.

In this report, we show in detail the bacterial gut microbiota changes associated with dAGE restriction. As shown before, AGE restriction plays a favorable role in cardio-protection and there is a strong association between certain bacterial metabolites/abundance and cardiovascular disease risk [11, 55]. Dietary AGE restriction may thus result in gut microbiota changes that could play a mechanistic role in the previously observed cardio-protection properties. This study represents a step towards addressing this and further experimental and population-based studies are warranted.

Limitations and conclusion

Even though this study identified multiple bacterial species with altered relative abundance after dietary intervention, these findings should be read with caution. The human gut harbors numerous bacterial species, and this study is not powered to accurately identify the small changes in all species relative abundance changes. Due to the high variability and the small sample size, the effects of dialysis procedure, dialysate content or the renal failure itself on certain bacterial species needs to be further explored. Another issue is the comparison with published literature and the lack of normal control subjects-not on dialysis for direct comparison. The main goal of this study was to evaluate the effects of dietary AGE restriction on gut microbiota, and the unique microbiota found in total cohort compared with published normal subjects should be interpreted with caution. Though dAGEs decreased significantly in the intervention group it is hard to dissect the contribution of dAGE consumption effects on changes observed from the effects of modification in diet preparation. A potential limitation of the study is that despite measuring AGE serum levels at both baseline and after intervention, the initial assessment and inclusion procedures were based on estimated dietary intakes centered on dietary log and recall and these procedures are subjected to recall bias. However, we believe that the dietary assessment was reliable because dAGEs estimates were consistently high at both enrollment and first visit. Lastly, it is still possible that incomplete dietary adherence could have affected our results. To minimize lack of adherence, frequent nutritionist follow up phone calls were made to ensure strict adherence to assigned low/high AGE diets during the intervention. Moreover, results of serum AGE measurements before and after intervention suggest dietary compliance.

In summary, our findings again confirm that even short-term restriction of dAGE intake can significantly decrease circulating AGE levels in renal failure patients on maintenance peritoneal dialysis and further suggest that this dietary intervention may have an effect in the gut microbiota. Larger studies are needed to confirm the effect of dietary AGEs on the microbiota.

Supporting information

S1 Fig. Total cohort phylogenic and genus relative abundance.

Figure A in S1: phyla level. Figure B in S1: genus level.

https://doi.org/10.1371/journal.pone.0184789.s001

(TIFF)

S2 Fig. Species differences between groups at baseline and after intervention.

https://doi.org/10.1371/journal.pone.0184789.s002

(TIF)

S1 Table. Relative abundance of bacterial phyla at baseline.

https://doi.org/10.1371/journal.pone.0184789.s003

(DOCX)

S2 Table. Relative abundance of bacterial genus at baseline.

https://doi.org/10.1371/journal.pone.0184789.s004

(DOCX)

S3 Table. Relative abundance of bacterial species at baseline.

https://doi.org/10.1371/journal.pone.0184789.s005

(DOCX)

S4 Table. Bacterial species principle components coefficients.

https://doi.org/10.1371/journal.pone.0184789.s006

(DOCX)

S5 Table. Interaction between all species identified.

https://doi.org/10.1371/journal.pone.0184789.s007

(PDF)

S1 File. Consort 2010 checklist: CONSORT 2010 checklist of information to include when reporting a randomized trial.

https://doi.org/10.1371/journal.pone.0184789.s008

(DOC)

S2 File. Study protocol: Study protocol submitted for IRB approval.

https://doi.org/10.1371/journal.pone.0184789.s009

(DOC)

References

  1. 1. Stirban A, Gawlowski T, Roden M. Vascular effects of advanced glycation endproducts: Clinical effects and molecular mechanisms. Mol Metab. 2014;3(2):94–108. Epub 2014/03/19. pmid:24634815
  2. 2. Gkogkolou P, Bohm M. Advanced glycation end products: Key players in skin aging? Dermatoendocrinol. 2012;4(3):259–70. Epub 2013/03/08. pmid:23467327
  3. 3. Ahmed N. Advanced glycation endproducts—role in pathology of diabetic complications. Diabetes Res Clin Pract. 2005;67(1):3–21. Epub 2004/12/29. pmid:15620429.
  4. 4. Uribarri J, Woodruff S, Goodman S, Cai W, Chen X, Pyzik R, et al. Advanced glycation end products in foods and a practical guide to their reduction in the diet. J Am Diet Assoc. 2010;110(6):911–16 e12. pmid:20497781
  5. 5. Hegab Z, Gibbons S, Neyses L, Mamas MA. Role of advanced glycation end products in cardiovascular disease. World J Cardiol. 2012;4(4):90–102. Epub 2012/05/05. pmid:22558488
  6. 6. Peppa M, Raptis SA. Advanced glycation end products and cardiovascular disease. Curr Diabetes Rev. 2008;4(2):92–100. Epub 2008/05/14. pmid:18473756.
  7. 7. Singh VP, Bali A, Singh N, Jaggi AS. Advanced glycation end products and diabetic complications. Korean J Physiol Pharmacol. 2014;18(1):1–14. Epub 2014/03/19. pmid:24634591
  8. 8. Prasad A, Bekker P, Tsimikas S. Advanced glycation end products and diabetic cardiovascular disease. Cardiol Rev. 2012;20(4):177–83. Epub 2012/02/09. pmid:22314141.
  9. 9. Semba RD, Nicklett EJ, Ferrucci L. Does accumulation of advanced glycation end products contribute to the aging phenotype? J Gerontol A Biol Sci Med Sci. 2010;65(9):963–75. Epub 2010/05/19. pmid:20478906
  10. 10. Luevano-Contreras C, Chapman-Novakofski K. Dietary advanced glycation end products and aging. Nutrients. 2010;2(12):1247–65. Epub 2012/01/19. pmid:22254007
  11. 11. Ross R. The pathogenesis of atherosclerosis: a perspective for the 1990s. Nature. 1993;362(6423):801–9. pmid:8479518.
  12. 12. Bohlender JM, Franke S, Stein G, Wolf G. Advanced glycation end products and the kidney. Am J Physiol Renal Physiol. 2005;289(4):F645–59. Epub 2005/09/15. pmid:16159899.
  13. 13. Noordzij MJ, Lefrandt JD, Smit AJ. Advanced glycation end products in renal failure: an overview. J Ren Care. 2008;34(4):207–12. Epub 2008/12/19. pmid:19090900.
  14. 14. Busch M, Franke S, Ruster C, Wolf G. Advanced glycation end-products and the kidney. Eur J Clin Invest. 2010;40(8):742–55. Epub 2010/07/24. pmid:20649640.
  15. 15. Chen Y, Wu Y, Gan X, Liu K, Xing LV, Shen H, et al. Iridoid glycoside from Cornus officinalis ameliorated diabetes mellitus-induced testicular damage in male rats: Involvement of suppression of the AGEs/RAGE/p38 MAPK signaling pathway. J Ethnopharmacol. 2016. pmid:27989876.
  16. 16. Shekhtman A, Ramasamy R, Schmidt AM. Glycation & the RAGE axis: targeting signal transduction through DIAPH1. Expert Rev Proteomics. 2016:1–10. pmid:27967251.
  17. 17. Yu T, Zheng Y, Wang Y, Xiong W, Lin L. Advanced glycation end products interfere with gastric smooth muscle contractile marker expression via the AGE/RAGE/NF-kappaB pathway. Exp Mol Pathol. 2016;102(1):7–14. pmid:27939576.
  18. 18. Aikawa E, Fujita R, Asai M, Kaneda Y, Tamai K. Receptor for Advanced Glycation End Products-Mediated Signaling Impairs the Maintenance of Bone Marrow Mesenchymal Stromal Cells in Diabetic Model Mice. Stem Cells Dev. 2016;25(22):1721–32. pmid:27539289.
  19. 19. Korbet SM, Makita Z, Firanek CA, Vlassara H. Advanced glycosylation end products in continuous ambulatory peritoneal dialysis patients. Am J Kidney Dis. 1993;22(4):588–91. pmid:8213800.
  20. 20. Makita Z, Radoff S, Rayfield EJ, Yang Z, Skolnik E, Delaney V, et al. Advanced glycosylation end products in patients with diabetic nephropathy. N Engl J Med. 1991;325(12):836–42. pmid:1875967.
  21. 21. Makita Z, Bucala R, Rayfield EJ, Friedman EA, Kaufman AM, Korbet SM, et al. Reactive glycosylation endproducts in diabetic uraemia and treatment of renal failure. Lancet. 1994;343(8912):1519–22. pmid:7911868.
  22. 22. Vlassara H, Uribarri J. Glycoxidation and diabetic complications: modern lessons and a warning? Rev Endocr Metab Disord. 2004;5(3):181–8. pmid:15211089.
  23. 23. Goldberg T, Cai W, Peppa M, Dardaine V, Baliga BS, Uribarri J, et al. Advanced glycoxidation end products in commonly consumed foods. J Am Diet Assoc. 2004;104(8):1287–91. pmid:15281050.
  24. 24. Henle T. AGEs in foods: do they play a role in uremia? Kidney Int Suppl. 2003;(84):S145–7. pmid:12694332.
  25. 25. Koschinsky T, He CJ, Mitsuhashi T, Bucala R, Liu C, Buenting C, et al. Orally absorbed reactive glycation products (glycotoxins): an environmental risk factor in diabetic nephropathy. Proc Natl Acad Sci U S A. 1997;94(12):6474–9. pmid:9177242
  26. 26. Uribarri J, Cai W, Peppa M, Goodman S, Ferrucci L, Striker G, et al. Circulating glycotoxins and dietary advanced glycation endproducts: two links to inflammatory response, oxidative stress, and aging. J Gerontol A Biol Sci Med Sci. 2007;62(4):427–33. pmid:17452738
  27. 27. He C, Sabol J, Mitsuhashi T, Vlassara H. Dietary glycotoxins: inhibition of reactive products by aminoguanidine facilitates renal clearance and reduces tissue sequestration. Diabetes. 1999;48(6):1308–15. pmid:10342821.
  28. 28. Ottum MS, Mistry AM. Advanced glycation end-products: modifiable environmental factors profoundly mediate insulin resistance. J Clin Biochem Nutr. 2015;57(1):1–12. pmid:26236094
  29. 29. Borrelli RC, Fogliano V. Bread crust melanoidins as potential prebiotic ingredients. Mol Nutr Food Res. 2005;49(7):673–8. pmid:15986388.
  30. 30. Kellow NJ, Coughlan MT, Savige GS, Reid CM. Effect of dietary prebiotic supplementation on advanced glycation, insulin resistance and inflammatory biomarkers in adults with pre-diabetes: a study protocol for a double-blind placebo-controlled randomised crossover clinical trial. BMC Endocr Disord. 2014;14:55. pmid:25011647
  31. 31. Baig MH, Jan AT, Rabbani G, Ahmad K, Ashraf JM, Kim T, et al. Methylglyoxal and Advanced Glycation End products: Insight of the regulatory machinery affecting the myogenic program and of its modulation by natural compounds. Sci Rep. 2017;7(1):5916. Epub 2017/07/21. pmid:28725008
  32. 32. Schmidt AM, Yan SD, Wautier JL, Stern D. Activation of receptor for advanced glycation end products: a mechanism for chronic vascular dysfunction in diabetic vasculopathy and atherosclerosis. Circ Res. 1999;84(5):489–97. Epub 1999/03/19. pmid:10082470.
  33. 33. Raj DS, Choudhury D, Welbourne TC, Levi M. Advanced glycation end products: a Nephrologist's perspective. Am J Kidney Dis. 2000;35(3):365–80. Epub 2000/02/29. pmid:10692262.
  34. 34. Ahmad S, Khan MS, Akhter F, Khan MS, Khan A, Ashraf JM, et al. Glycoxidation of biological macromolecules: a critical approach to halt the menace of glycation. Glycobiology. 2014;24(11):979–90. Epub 2014/06/21. pmid:24946787.
  35. 35. Ames JM, Wynne A, Hofmann A, Plos S, Gibson GR. The effect of a model melanoidin mixture on faecal bacterial populations in vitro. Br J Nutr. 1999;82(6):489–95. pmid:10690164.
  36. 36. O'Brien J, Morrissey PA. Nutritional and toxicological aspects of the Maillard browning reaction in foods. Crit Rev Food Sci Nutr. 1989;28(3):211–48. pmid:2669832.
  37. 37. Zhang J, Kobert K, Flouri T, Stamatakis A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics. 2014;30(5):614–20. Epub 2013/10/22. pmid:24142950
  38. 38. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8. pmid:23955772.
  39. 39. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6(3):610–8. pmid:22134646
  40. 40. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6. pmid:20383131
  41. 41. Meerwaldt R, Links TP, Graaff R, Hoogenberg K, Lefrandt JD, Baynes JW, et al. Increased accumulation of skin advanced glycation end-products precedes and correlates with clinical manifestation of diabetic neuropathy. Diabetologia. 2005;48(8):1637–44. Epub 2005/07/16. pmid:16021416.
  42. 42. Beisswenger PJ, Makita Z, Curphey TJ, Moore LL, Jean S, Brinck-Johnsen T, et al. Formation of immunochemical advanced glycosylation end products precedes and correlates with early manifestations of renal and retinal disease in diabetes. Diabetes. 1995;44(7):824–9. Epub 1995/07/01. pmid:7789650.
  43. 43. Hartog JW, de Vries AP, Lutgers HL, Meerwaldt R, Huisman RM, van Son WJ, et al. Accumulation of advanced glycation end products, measured as skin autofluorescence, in renal disease. Ann N Y Acad Sci. 2005;1043:299–307. Epub 2005/07/23. pmid:16037252.
  44. 44. Uribarri J, Peppa M, Cai W, Goldberg T, Lu M, He C, et al. Restriction of dietary glycotoxins reduces excessive advanced glycation end products in renal failure patients. J Am Soc Nephrol. 2003;14(3):728–31. pmid:12595509.
  45. 45. Ding T, Schloss PD. Dynamics and associations of microbial community types across the human body. Nature. 2014;509(7500):357–60. pmid:24739969
  46. 46. Vaziri ND, Wong J, Pahl M, Piceno YM, Yuan J, DeSantis TZ, et al. Chronic kidney disease alters intestinal microbial flora. Kidney Int. 2013;83(2):308–15. pmid:22992469.
  47. 47. Musso G, Gambino R, Cassader M. Obesity, diabetes, and gut microbiota: the hygiene hypothesis expanded? Diabetes Care. 2010;33(10):2277–84. Epub 2010/09/30. pmid:20876708
  48. 48. Knip M, Siljander H. The role of the intestinal microbiota in type 1 diabetes mellitus. Nat Rev Endocrinol. 2016;12(3):154–67. Epub 2016/01/06. pmid:26729037.
  49. 49. Hara N, Alkanani AK, Ir D, Robertson CE, Wagner BD, Frank DN, et al. The role of the intestinal microbiota in type 1 diabetes. Clin Immunol. 2013;146(2):112–9. Epub 2013/01/15. pmid:23314185.
  50. 50. Baothman OA, Zamzami MA, Taher I, Abubaker J, Abu-Farha M. The role of Gut Microbiota in the development of obesity and Diabetes. Lipids Health Dis. 2016;15:108. Epub 2016/06/19. pmid:27317359
  51. 51. Boulange CL, Neves AL, Chilloux J, Nicholson JK, Dumas ME. Impact of the gut microbiota on inflammation, obesity, and metabolic disease. Genome Med. 2016;8(1):42. Epub 2016/04/22. pmid:27098727
  52. 52. Ghazalpour A, Cespedes I, Bennett BJ, Allayee H. Expanding role of gut microbiota in lipid metabolism. Curr Opin Lipidol. 2016;27(2):141–7. Epub 2016/02/09. pmid:26855231
  53. 53. Eid HM, Wright ML, Anil Kumar NV, Qawasmeh A, Hassan STS, Mocan A, et al. Significance of Microbiota in Obesity and Metabolic Diseases and the Modulatory Potential by Medicinal Plant and Food Ingredients. Front Pharmacol. 2017;8:387. Epub 2017/07/18. pmid:28713266
  54. 54. Woting A, Blaut M. The Intestinal Microbiota in Metabolic Disease. Nutrients. 2016;8(4):202. Epub 2016/04/09. pmid:27058556
  55. 55. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. 2013;341(6150):1241214. Epub 2013/09/07. pmid:24009397
  56. 56. Tilg H, Kaser A. Gut microbiome, obesity, and metabolic dysfunction. The Journal of clinical investigation. 2011;121(6):2126–32. pmid:21633181
  57. 57. Tremaroli V, Backhed F. Functional interactions between the gut microbiota and host metabolism. Nature. 2012;489(7415):242–9. pmid:22972297.
  58. 58. Cullis B, Abdelraheem M, Abrahams G, Balbi A, Cruz DN, Frishberg Y, et al. Peritoneal dialysis for acute kidney injury. Perit Dial Int. 2014;34(5):494–517. pmid:25074995
  59. 59. Lan PG, Johnson DW, McDonald SP, Boudville N, Borlace M, Badve SV, et al. The association between peritoneal dialysis modality and peritonitis. Clin J Am Soc Nephrol. 2014;9(6):1091–7. pmid:24626434
  60. 60. Cho Y, Johnson DW, Badve S, Craig JC, Strippoli GF, Wiggins KJ. Impact of icodextrin on clinical outcomes in peritoneal dialysis: a systematic review of randomized controlled trials. Nephrol Dial Transplant. 2013;28(7):1899–907. pmid:23493329.
  61. 61. Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6(2):121–31. pmid:18180751.
  62. 62. Moschen AR, Gerner RR, Wang J, Klepsch V, Adolph TE, Reider SJ, et al. Lipocalin 2 Protects from Inflammation and Tumorigenesis Associated with Gut Microbiota Alterations. Cell Host Microbe. 2016;19(4):455–69. pmid:27078067.
  63. 63. Rosenbaum JT, Asquith MJ. The Microbiome: a Revolution in Treatment for Rheumatic Diseases? Curr Rheumatol Rep. 2016;18(10):62. pmid:27641915.
  64. 64. Kim D, Kim WU. Editorial: Can Prevotella copri Be a Causative Pathobiont in Rheumatoid Arthritis? Arthritis Rheumatol. 2016;68(11):2565–7. pmid:27390139.
  65. 65. Turroni F, Foroni E, Pizzetti P, Giubellini V, Ribbera A, Merusi P, et al. Exploring the diversity of the bifidobacterial population in the human intestinal tract. Appl Environ Microbiol. 2009;75(6):1534–45. pmid:19168652
  66. 66. Sanders ME. Summary of probiotic activities of Bifidobacterium lactis HN019. J Clin Gastroenterol. 2006;40(9):776–83. pmid:17016131.
  67. 67. Louis P, Flint HJ. Diversity, metabolism and microbial ecology of butyrate-producing bacteria from the human large intestine. FEMS Microbiol Lett. 2009;294(1):1–8. pmid:19222573.
  68. 68. Coyte KZ, Schluter J, Foster KR. The ecology of the microbiome: Networks, competition, and stability. Science. 2015;350(6261):663–6. pmid:26542567.
  69. 69. McNally L, Brown SP. Microbiome: Ecology of stable gut communities. Nat Microbiol. 2016;1:15016. pmid:27571760.