The Profile and Function of Gut Microbiota in Chronic Kidney Disease

doi:10.21203/rs.3.rs-2732036/v1

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The Profile and Function of Gut Microbiota in Chronic Kidney Disease

https://doi.org/10.21203/rs.3.rs-2732036/v1

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Objective:The gut microbiota was considered to be an important hidden "organ" of the human body, which was of great significance in maintaining the body's physiology and pathological regulation. Previous studies had found that the gut microbiota was closely related to various diseases, but there was no unified conclusion on the distribution characteristics of the gut microbiota in chronic kidney disease (CKD) and its relationship with the progression of CKD. In this study, we tried to investigate the profile and function of gut microbiota in CKD and its relationship with the progression of CKD.

Methods: A total of 80 people were enrolled in this study. Twenty were healthy people, and 60 were CKD patients. The CKD patients were divided into three stages including stage 3, 4, and 5. We conducted taxonomic analyses in different groups. The distributions of phyla, classes, orders, families and genera in different groups and samples were investigated. We also evaluated the correlations between clinical parameters and gut microbiota in 60 CKD patients.

Results:The gut microbiota in the healthy group and CKD group had 2351 operational taxonomic units (OTUs) in total. The healthy group had 1076 OTUs, and the CKD group had 2259 OTUs. The diversity of gut microbiota in the stage 3 CKD group was smaller than that in the other groups. Bacteroides was positively correlated with serum creatinine (Scr) and serum cholesterol, while was negatively correlated with albumin (ALB), haemoglobin, and estimated glomerular filtration rate (eGFR). Blautia was positively correlated with Scr, blood urea nitrogen (BUN), 24-hour urine protein (24-h UTP), and serum cholesterol, while was negatively correlated with haemoglobin and eGFR。Bifidobacterium was positively correlated with eGFR, while was negatively correlated with Scr and BUN. Prevotella was negatively correlated with BUN, while was positively correlated with haemoglobin. Megamonas was negatively correlated with BUN, while was positively correlated with haemoglobin and eGFR. Subdoligranulum was negatively correlated with UA. Parabacteroides and megasphaera were positively correlated with serum cholesterol. Klebsiella was negatively correlated with haemoglobin.

Conclusions:The gut microbiota might be one of the important pathological mechanisms underlying the development and progression of CKD. The changes of diversity in gut microbiota were associated with disease progression. Some kinds of gut microbiota including bacteroides, blautia, parabacteroides, megasphaera and klebsiella might be detrimental factors in CKD, while other kinds of gut microbiota including bifidobacterium, prevotella, megamonas and subdoligranulum might be beneficial factors in CKD.

According to the national epidemiological survey of chronic kidney disease (CKD) reported in 2012, the prevalence rate of CKD in China was 10.8%[1]. With the aging of the population and the changes of the disease spectrum, the prevalence of CKD was increasing year by year, and the rate of morbidity and mortality was high. CKD had become a worldwide health problem, which indicated that the early screening and intervention of CKD had become one of the main tasks of the country[2]. Risk factors for CKD included proteinuria, hypertension, and hyperuricemia[3–4]. There were some emerging biomarkers reported to be connected with the development of CKD, such as tumor necrosis factor-α receptor 2 and interleukin-6[5–6]. There were multiple mechanisms that promoted the progression of CKD to renal fibrosis, such as oxidative stress, inflammation and metabolic factors. With a greater understanding of the risk factors and the pathogenesis of CKD, it can be more possible to prevent CKD progressing into end stage kidney disease.

The gut microbiota coevolved with the human body, and was considered to be metabolically active endogenous "organs". When their qualitative and quantitative were stable, they could promote the function of host nutrition and immunity[7]. Influenced by both genetic and environmental factors, the profile and function of gut microbiota differed among different individuals[8]. In recent years, the association of gut microbiota and multiple diseases, such as obesity, colon cancer, diabetes and CKD have been reported[9–11]. Some types of gut microbiota were beneficial, while other types of gut microbiota were harmful to humans. In the beneficial aspect, the gut microbiota could drive the immune system to mature, protect against pathogens, and regulate metabolism, thus contributing to the health of host[12]. Short-chain fatty acids (SCFAs) were "beneficial" metabolites of gut microbiota[13]. By combining intestinal GPR 41/43 receptors, reducing the expression of inflammatory factors, such as TNF-αand IL-17[14], and reducing the uremic inflammation[15], which made it possible to inhibit the kidney sclerosis and fibrosis. In terms of harmful aspects, the gut microbiota might participate in the development of kidney disease by increasing intestinal permeability, causing LPS leakage, destroying the balance of intestinal metabolites, and promoting inflammation and oxidative damage[16–20]. Some kinds of gut microbiota disorders could produce uremic toxins, mainly including phenols and indoles.P-cresol could inhibit the expression of endothelial adhesion molecule and induce the increasing of permeability of endothelial[22]. It has been proved that the concentrations of p-cresol sulfate in serum were negatively correlated with the level of renal function[23], and their levels were predictors of kidney disease progression, which were also closely related to glomerulosclerosis[24].

Although previous studies have revealed the correlation between gut microbiota and various diseases, the distribution characteristics of gut microbiota in CKD and its relationship with the progress of CKD have not been clarified. In this study, we investigated the profile and function of gut microbiota in CKD. In addition, we also revealed the characteristics of gut microbiota in CKD patients at different stages, and investigated the correlation between clinical parameters and gut microbiota in patients with CKD. We hope to provide useful descriptions and pictures about the distribution characteristics of gut microbiota of CKD and preliminarily reveal the relationship between gut microbiota and the development of CKD.

Subjects and data

A total of 80 people were enrolled in this study. Of the 80 people, 20 were healthy subjects, 20 were CKD patients. The CKD patients were divided into three stages: stage 3, stage 4, and stage 5. The numbers of CKD 3, CKD 4, and CKD 5 patients were 22, 17 and 21 respectively. The criteria of CKD were as follows: stage 3, eGFR of 30–59 mL/min/1.73 m2; stage 4, eGFR of 15–29 mL/min/1.73 m2 and stage 5, eGFR < 15 mL/min/1.73 m2. The other clinical parameters of CKD patients included age, sex, body mass index (BMI), blood pressure, serum creatinine (Scr), estimated glomerular filtration rate (eGFR), blood urea nitrogen (BUN), uric acid (UA), albumin (ALB), 24-hour urine protein (24-hUTP), C-reactive protein (CRP), serum cholesterol, triglyceride, alanine transaminase (ALT), aspartate aminotransferase (AST), K, Ca, P, Na ,CL and haemoglobin. Patients who had severe diseases in other systems were excluded. The faecal samples were stored at − 80℃ before further experiments. All patients signed informed consent prior to participation in the study. This study was approved by the Ethics Committee of Dongzhimen Hospital, Beijing University of Chinese Medicine (2017-JYB-JS-076). The baseline characteristics of participants are shown in Table 1.

Table 1

Baseline Characteristics of Participants
Factors	Health	CKD3	CKD4	CKD5
Patients (n)	20	22	17	21
Age, year	55.15 ± 13.77	58.27 ± 14.37	54.35 ± 15.09	52.62 ± 15.13
Female gender, n (%)	12(60)	11(50)	7 (41.2)	12 (57.1%)
BMI (kg/m²)	24.67 ± 3.31	26.44 ± 4.82	25.61 ± 2.61	23.77 ± 4.36
SBP (mm Hg)	128.10 ± 12.77	136.95 ± 23.60	139.29 ± 15.50	137.29 ± 23.89
DBP (mm Hg)	75.20 ± 5.85	83.41 ± 12.19	84.06 ± 17.30	81.57 ± 12.22
Scr (µmol/L)	57.09 ± 14.78	138.89 ± 33.26	259.15 ± 66.25	647.43 ± 279.31
eGFR (mL/min/ 1.73 m2)	125.81 ± 33.14	43.57 ± 8.66	21.65 ± 3.84	7.87 ± 3.08
BUN(mmol/L)	5.15 ± 1.52	9.38 ± 3.79	15.88 ± 3.36	26.17 ± 9.29
UA(µmol/L)	293.01 ± 91.21	439.98 ± 94.28	470.28 ± 73.81	399.84 ± 94.13
ALB(g/L)	38.72 ± 6.06	36.10 ± 6.49	35.92 ± 6.17	36.83 ± 5.52
24-h UTP(g/24h)		1.21 ± 3.22	2.45 ± 3.95	3.36 ± 3.09
CRP(mg/L)	1.40 ± 1.88	1.42 ± 3.86	1.00 ± 2.19	1.00 ± 2.30
Serum Cholesterol (mmol/L)	4.43 ± 1.01	4.31 ± 1.63	4.21 ± 1.09	4.41 ± 1.30
Triglyceride(mmol/L)	1.56 ± 0.98	2.35 ± 1.42	2.40 ± 0.96	1.91 ± 1.10
ALT(U/L)	24.38 ± 14.22	13.30 ± 6.85	12.10 ± 9.50	10.30 ± 5.40
AST(U/L)	22.93 ± 10.77	17.75 ± 6.38	14.90 ± 9.00	13.00 ± 7.25
K(mmol/L)	4.12 ± 0.42	4.22 ± 0.66	4.48 ± 0.52	4.63 ± 0.85
Ca(mmol/L)	2.29 ± 0.12	2.27 ± 0.23	2.16 ± 0.17	2.03 ± 0.27
P(mmol/L)	1.26 ± 0.14	1.28 ± 0.32	1.42 ± 0.29	2.04 ± 0.75
Na(mmol/L)	139.62 ± 2.39	139.35 ± 3.70	139.00 ± 3.95	137.97 ± 2.79
CL(mmol/L)	104.03 ± 2.75	107.08 ± 3.83	108.75 ± 3.21	105.33 ± 7.03
Haemoglobin(g/dL)	12.51 ± 2.13	12.33 ± 2.12	10.75 ± 1.47	9.15 ± 1.85

Experimental procedure

The experiment process included ①Extraction of gene DNA: after the extraction of genomic DNA was completed, the extracted genomic DNA was detected by 1% agarose gel electrophoresis. ②PCR amplification: synthesizing specific primers with barcode or fusion primers with misplaced bases according to the designated sequencing region. To ensure the accuracy and reliability of subsequent data analysis, two conditions shall be met:1)Using low cycle number amplification as far as possible; 2)Ensuring that the number of cycles amplified by each sample was consistent. Randomly selecting representative samples for pre experiment to ensure that most samples can amplify products with appropriate concentration in the minimum number of cycles. PCR products were detected by 1% agarose gel electrophoresis and purified with Agencourt AMPure XP nucleic acid purification kit. ③To build the Miseq library: 1) Connecting the "Y" joint; 2) Magnetic bead screening was used to remove the self connected segment of the joint; 3) PCR amplification was used to enrich the library template; 4) Sodium hydroxide denaturing to produce single stranded DNA fragments. ④Miseq computer sequencing: 1) One end of the DNA fragment was complementary to the primer base and fixed on the chip; 2) The other end was randomly complementary to another primer nearby, and was also fixed to form a "bridge"; 3) PCR amplification to produce DNA clusters; 4) The DNA amplicon was linearized into a single strand;5) The modified DNA polymerase and dNTP with four fluorescent tags were added, and only one base was synthesized per cycle; 6) Scanning the surface of reaction plate with laser, reading the type of nucleotide polymerized in the first round of reaction of each template sequence;7) Chemically cleaving the "fluorescent group" and the "termination group", restoring the viscosity of the 3 'end, and continuing to polymerize the second nucleotide; 8) Counting the fluorescence signal results collected in each round to obtain the sequence of template DNA fragments.

Data Analysis

SPSS 23.0 software was used for statistical analysis. Frequency and composition ratio(%) were used for counting data. The measurement data was expressed as mean ± standard deviation or median ± interquartile interval. T-test of independent samples or Mann Whitney U test was used for comparison of measurement data between the two groups. For the correlation analysis of the two counting data, pearson correlation analysis or spearman correlation analysis was used. Vsearch (v2.7.1) software was used to cluster the OTUs, and the biological information of OTUs at 97% similarity level was statistically analyzed. Based on the OTU clustering results, we used mothur to do the rarefaction analysis, and used R language tools to make the rarefaction curve, rank abundance curve and analyze the species composition. Analyzing alpha diversity index with qiime version v.1.8.0 software, including Chao1, observed_ species, PD_ whole_ Tree and Shannon. Mothur was used to construct a heat map to analyze the correlation between gut microbiota and clinical parameters.

Baseline Characteristics of Patients

In our study, a total of 80 patients were enrolled: 20 were healthy subjects and 60 were CKD patients. The average age of 20 healthy control (12 females, 8 males) was 55.15±13.77 years. The average age of 60 CKD patients (30 females, 30 males) was 55.18±14.80 years. In the total 60 CKD patients, the average age of stage 3 CKD patients (n=22) was 58.27±14.37 years, the average age of stage 4 CKD patients (n=17) was 54.35±15.09 years and the average age of stage 5 CKD patients (n=21) was 52.62±15.13 years. The parameters of healthy subjects included age, BMI, blood pressure, Scr, eGFR, BUN, UA, ALB, CRP, serum cholesterol, triglyceride, ALT, AST, K, Ca, P, Na ,CL and haemoglobin. Detailed informations about healthy people were listed in Table 1. The 60 CKD patients were divided into three stages: stage 3, stage 4, and stage 5. The clinical parameters of CKD patients included age, BMI, blood pressure, Scr, eGFR, BUN, UA, ALB, 24-hUTP CRP, serum cholesterol, triglyceride, ALT, AST, K, Ca, P, Na ,CL and haemoglobin. The levels of Scr were significantly increased from CKD 3 to CKD 5 (138.89±33.26 ,259.15±66.25, and 647.43±279.31, P = 0.000). eGFR was significantly decreased from CKD 3 to CKD 5 (43.57±8.66, 21.65±3.84, and 7.87±3.08, P = 0.000). The levels of BUN were significantly increased from CKD 3 to CKD 5 (9.38±3.79, 15.88±3.36, and 26.17±9.29, P = 0.000). The levels of Ca significantly decreased from CKD 3 to CKD 5 (2.27±0.23, 2.16±0.17, and 2.03±0.27, P = 0.004). The levels of P were significantly increased from CKD 3 to CKD 5 (1.28±0.32, 1.42±0.29, and 2.04±0.75, P = 0.000). The levels of haemoglobin decreased from CKD 3 to CKD 5 (12.33±2.12, 10.75±1.47, and 9.15±1.85, P = 0.000).

Gut Microbiota Structure and Alpha Diversity Analysis

A total of 80 patient samples were enrolled to test gut microbiota. The Venn graph results indicated that the gut microbiota in the two groups (healthy subjects and CKD patients) had 2351 OTUs in total (Figure 1). Among the two groups, the healthy subjects group had 1076 OTUs and the CKD group had 2259 OTUs. The rarefaction curve tended to be flat, indicating that the sequencing data volume was reasonable (Figure 2). The rank abundance curve showed that the sample size was sufficient (Figure 3). Within a certain range, with the increase of the sample size, the curve rises sharply, indicating that the sample size is insufficient and needs to be increased; On the contrary, the flatit carve indicates that the sample size is sufficient for data analysis. More than 10 samples are meaningful.

Next, We evaluated the alpha diversity index of the healthy subjects group, CKD 3 group, CKD 4 group, and CKD 5 group. As shown in Figure 4, Shannon analyses performed that the diversity of gut microbiota in the CKD 3 group was lower than that in the other groups in our study. Therefore, decreased diversity of gut microbiota might be the characters of CKD 3. It might be of enormus significance for indicating the progression and development of CKD.

Taxonomic Analysis

We investigated the distribution of phylum, class, order, family, and genus indifferent groups with taxonomic analysis. The phyla in different groups included bacteroidota, firmicutes, proteobacteria, actinobacteriota, fusobacteriota, verrucomicrobiota, desulfobacterota and cyanobacteria(Figure 5(A)). The classes in different groups included bacteroidia, clostridia, gammaproteobacteria, negativicutes, actinobacteria, fusobacteriia, bacilli, verrucomicrobiae, coriobacteriia, alphaproteobacteria and desulfovibrionia(Figure 5(B)). The orders in different groups included bacteroidales, lachnospirales, lachnospirales, enterobacterales, bifidobacteriales, selenomonadales, fusobacteriales, lactobacillales, verrucomicrobiales, christensenellales, vurkholderiales, pasteurellales, acidaminococcales, erysipelotrichales, coriobacteriales, rhodospirillales, tissierellales, clostridiales and monoglobales(Figure 5(C)). The families in different groups included bacteroidaceae, lachnospiraceae, prevotellaceae, ruminococcaceae, enterobacteriaceae, bifidobacteriaceae, fusobacteriaceae, veillonellaceae, tannerellaceae, selenomonadaceae, lactobacillaceae, akkermansiaceae, christensenellaceae, sutterellaceae, oscillospiraceae, pasteurellaceae, rikenellaceae, acidaminococcaceae and streptococcaceae(Figure 5(D)). The genera in different groups included bacteroides, faecalibacterium, prevotella, blautia, roseburia, megamonas, klebsiella, shigella, agathobacter, bifidobacterium, ruminococcus, parabacteroides, subdoligranulum, dorea, alistipes, phascolarctobacterium, lachnospira, lachnoclostridium, fusicatenibacter, fusobacterium and megasphaera(Figure 5(E)). Furthermore, taxonomic analysis in different samples were conducted. The phyla included firmicutes, bacteroidota proteobacteria and actinobacteriota(Figure 6(A)). The classes included clostridia, bacteroidia, negativicutes, gammaproteobacteria, bacilli, actinobacteria and coriobacteriia(Figure 6(B)). The orders included lachnospirales, bacteroidales, selenomonadales, tissierellales, lactobacillales and enterobacterales(Figure 6(C)). The families in different groups included lachnospiraceae, bacteroidaceae, veillonellaceae, peptostreptococcaceae and enterobacteriaceae(Figure 6(D)). The genera in different groups included bacteroides, blautia, roseburia, veillonella, lachnoclostridium, klebsiella, clostridioides, lactobacillus and parasutterella(Figure 6(E)).

Figure 6 Taxonomic analyses in all samples. (A) The phyla in all samples. (B) The classes in all samples. (C) The orders in all samples. (D) The families in all samples. (E) The genera in all samples.The figure showed the species information with relative abundance above 1%.

Diversity of gut microbiota among different CKD patients

We divided 60 CKD patients into the male group (n=30) and the female group (n=30). There were 1741 OTUs in the male group and 1778 OTUs in the female group, and 1440 OTUs were shared(Figure7). Then, by evaluating the α diversity index of gut microbiota between the male and female groups, it was found that the species and diversity of gut microflora in the male group were lower than that in the female group(Figure8).

Secondly, we divided CKD patients into three groups according to 24hUTP, and evaluated α diversity index(Figure 9). The results showed that the diversity index of gut microflora was highest when the 24hUTP was less than 1g/d, and was higher than the 24hUTP 1g/d-3.5g/d group and the 24hUTP＞3.5g/d group.

We evaluated the association between clinical parameters and gut microbiota in 60 patients with CKD. The top 20 genera in 60 CKD patients were calculated by taxonomic analysis. The top 20 genera included bacteroides, faecalibacterium, prevotella, blautia, escherichia-Shigella, bifidobacterium, roseburia, fusobacterium, parabacteroides, agathobacter, akkermansia, megamonas, klebsiella, uncultered, ruminococcus_torques_group, ruminococcus, subdoligranulum, megasphaera, lachnoclostridium, alistipes(Figure10). The results performed that Scr was positively correlated with bacteroides(r=0.271, P=0.036) and blautia(r=0.435, P=0.0005), while was negatively correlated with bifidobacterium(r=-0.325, P=0.011). BUN was positively correlated with blautia(r=0.461, P=0.0002), while was negatively correlated with prevotella(r=-0.271, P=0.036), bifidobacterium(r=-0.349, P=0.006) and megamonas(r=-0.368, P=0.004). UA was negatively correlated with subdoligranulum(r=-0.286, P=0.027). ALB was negatively correlated with bacteroides(r=-0.308, P=0.017). 24hUTP was positively correlated with blautia(r=0.337, P=0.009). Serum Cholesterol was positively correlated with bacteroides(r=0.307, P=0.021), blautia(r=0.284, P=0.034), parabacteroides(r=0.296, P=0.027) and megasphaera(r=0.294, P=0.028). Haemoglobin was positively correlated with prevotella(r=0.326, P=0.011) and megamonas(r=0.369, P=0.0037), while was negatively correlated with bacteroides(r=-0.297, P=0.021), blautia(r=-0.331, P=0.0098) and klebsiella(r=-0.270, P=0.037). eGFR was negatively correlated with bacteroides(r=-0.258, P=0.046) and blautia(r=-0.45, P=0.0003), while was positively correlated with bifidobacterium(r=0.351, P=0.006), megamonas(r=0.279, P=0.0308). Some kinds of gut microbiota including bacteroides, blautia, parabacteroides, megasphaera and klebsiella might be detrimental factors in CKD, while other kinds of gut microbiota including bifidobacterium, prevotella, megamonas and subdoligranulum might be beneficial factors in CKD.

Gut microbiota coevolved with the human organism and were considered to be metabolically active endogenous "organs"[7]. Changes in gut microbiota can promote CKD progress by changing immune response, regulating blood pressure and metabolism[25]. The internal environment in patients with CKD may lead to the loss of "key flora", thus changing the biological community structure, resulting in intestinal ecological imbalance, which can produce a variety of uremic toxins to drive the progress of CKD. Unbound proteins in the internal environment of CKD were transferred to colon, which promoted the preferential proliferation of urease, uricase and bacteria involved in phenol metabolism. Protein binding uremic toxins (PBUTs) were the most destructive toxins and one of the difficulties in clinical treatment of CKD[26], PBUTs were mainly produced by intestinal bacteria metabolizing aromatic amino acids[27].At the same time, the reduction of beneficial flora lead to the reduction of production of short chain fatty acid[28]. Short chain fatty acids were important substances for maintaining energy homeostasis, regulating blood pressure and regulating immunity. Fecal microbiota transplantation could change amino acid metabolism by changing the structure and function of gut microbiota, thus delaying the malignant development of CKD and providing a new potential way for clinical treatment of CKD[29]. A study involving 15 studies and 1640 subjects showed that compared with healthy people, the diversity of gut microbiota in patients with diabetes nephropathy was significantly reduced[30].The expansion of proteus and the reduction of spirochete laspehensis might be the key characteristics of gut microbiota changes in patients with idiopathic membranous nephropathy[31].Some reports have shown that intestinal microecology disorder participated in the occurrence and development of kidney diseases by mediating the inflammatory state of the body[32–33].

In this study, we investigated the profile and function of gut microbiota in patients with CKD stage 3–5 and its relationship with the progress of CKD. We found that the diversity of gut microbiota in CKD stage 3 was the lowest, and was lower than that in normal people, CKD stage 4 patients and CKD stage 5 patients. In CKD stage 3, there might be no clinical symptoms, or only had mild discomfort such as fatigue, lumbago and increased nocturia. A few patients had anorexia, mild metabolic acidosis and mild anemia in CKD stage 3. CKD stage 3 was reversible, which was a critical stage for treatment and delay of CKD progress. However, it was not easy to be found because that the clinical symptoms were mild. This study found that the diversity of gut microbiota in CKD stage 3 was significantly reduced, so it was inferred that the reduction of gut microbiota diversity was an important pathological mechanism and potential marker of CKDstage 3, which was of enormous significance for clinical diagnosis and treatment.

We also analyzed the differences in the characteristics of gut microbiota in patients with CKD stage 3–5 based on gender and 24-hour urine protein quantification.We found that compared with male patients, female patients had more species and higher diversity of gut microbiota. At the same time, it was performed that the diversity index of gut microbiota was the highest when the urine protein quantification was less than 1g/d, which was higher than that of CKD patients with urine protein quantification between 1g/d-3.5g/d and CKD patients with urine protein quantification more than 3.5g/d. For CKD patients, the disease progression speed of male patients and patients with urine protein quantification > 1g/d was faster than that of female patients and patients with urine protein quantification < 1g/d. Therefore, we inferred that the decrease of species and diversity of gut microbiota was a symbol of disease progression and poor prognosis in CKD patients.

Next, we assessed taxonomic analyses in different groups. The distributions of phyla, classes, orders, families and genera in different groups and samples were investigated. Finally, we conducted the association between clinical parameters and gut microbiota in 60 CKD patients. The results performed that some types of bacteria in the gut microbiota were positively correlated with clinical parameters, while other types were negatively correlated with clinical parameters. Bacteroides was positively associated with Scr and cholesterol, and negatively correlated with ALB, haemoglobin and eGFR. Blautia was positively associated with Scr, BUN, 24hUTP and cholesterol, and negatively correlated with haemoglobin and eGFR. Bifidobacterium was negatively correlated with Scr and BUN, and was positively associated with eGFR. Prevotella was negatively correlated with BUN, and was positively associated with haemoglobin. Megamonas was negatively correlated with BUN, and was positively associated with HGB and eGFR. Subdoligranulum was negatively correlated with UA. Parabacteroides and Megasphaera were positively associated with cholesterol. Klebsiella was negatively correlated with haemoglobin.

Scr, BUN, UA and eGFR were traditional indicators for evaluating the condition of kidney disease. When kidney was damaged, eGFR decreased and uremic toxins accumulated in the body. The change trend of bacteroides and blautia was consistent with that of Scr or (and) BUN, while was negatively correlated with eGFR. Bifidobacterium, prevotella, egamonas and subdoligranulum were negatively correlated with Scr or (and) BUN or (and) UA. Bifidobacterium and megamonas were positively correlated with eGFR. Bacteroides could transfer virulence genes. These factors might contribute to the pathogenesis of extraintestinal organs. At the same time, Bacteroides could translocate to normal sterile tissues through intestinal mucosa, leading to inflammatory diseases[34–35]. It was speculated that the pathogenesis of CKD was related to bacteroides. Blautia, as a symbiotic obligate anaerobe, played an important role in maintaining intestinal environmental balance and preventing inflammation by upregulating intestinal regulatory T cells and producing SCFAs. However, compared with healthy individuals, higher blautia abundance was found in the fecal microbiota of patients with inflammatory diseases. These contradictory conclusions might be based on the beneficial or adverse effects of blautia of different species on human health[36–38].Bifidobacterium has been proved to be able to regulate the intestinal microecological balance, rebuild the integrity of intestinal barrier, and improve human immune function[39–42]. Although there was controversy about the usefulness of bifidobacterium alone in the treatment of CKD, previous studies have shown that bifidobacterium combined with traditional Chinese medicine could play a renal protective role in CKD by inhibiting the inflammatory reaction of macrophages in the kidney and intestine[43].In addition, the association of probiotics with low protein diet might play a beneficial role in the control and regulation of microbiota derived and atherogenic toxins in CKD patients[44].Prevotella was generally considered to be symbiotic bacteria because they were widespread in healthy people and rarely participated in infection. It was reported that only a few strains caused opportunistic endogenous infections, including chronic infections, abscesses and anaerobic pneumonia[45–47].Megamons was involved in the metabolism of carbohydrate fermentation to SCFA[48], so we assumed that megamons might antagonize CKD by affecting the diversity of SCFA. Previous studies have shown that the level of subdoliganulum in patients with CKD could be significantly changed through synbiotic treatment, thereby reducing the level of serum indole sulfate and improving eGFR[49].Louis et al. proposed that subdoligranulum was associated with lower inflammatory parameters of the body[50].Van Hul et al. hypothesized in their metagenome research that subdoligranulum was a sign of higher microbial diversity[51].This study showed that cholesterol was positively correlated with bacteroides, blautia, parabolides and megasphaera. ALB or (and) haemoglobin was negatively correlated with bacteroides, blautia and klebsiella, while was positively correlated with prevotella and megamonas.Cholesterol metabolism has been identified as an important way to promote the development of CKD. Its cell accumulation could lead to lipotoxicity, and ultimately caused renal dysfunction and failure[52].The main function of ALB in human body was to maintain plasma colloid osmotic pressure and transport various substances in blood, such as bilirubin, fatty acids, etc.Hypoalbuminemia was not only related to the formation of hyperlipidemia and edema in nephrosis, but also related to the pathological mechanisms of endocrine dysfunction and coagulation dysfunction. Experimental research showed that hypoalbuminemia was also one of the reasons for inducing infection and increasing the mortality of infection[53]. Haemoglobin played an important role in health-related quality of life in CKD patients. Anemia, deterioration of renal function and cardiac insufficiency in CKD population were cause and effect for each other. Therefore, it was speculated that parabacteroides, megasphaera and klebsiella might be harmful bacteria of CKD.

In summary, some kinds of gut microbiota including bacteroides, blautia, parabacteroides, megasphaera and klebsiella might be detrimental factors in CKD, while other kinds of gut microbiota including bifidobacterium, prevotella, megamonas and subdoligranulum might be beneficial factors in CKD.

In this study, we investigated the profile and function of the gut microbiota in CKD patients and its relationship with the progression of CKD. The diversity of gut microbiota in CKD stage 3 patients was lower than that in other groups. Therefore, it was concluded that the decrease of gut microbiota diversity was an important pathological mechanism and potential marker of CKD stage 3.Next, we conducted alpha diversity analyses and taxonomic analyses in different groups. The distributions of phyla, classes, orders, families and genera in different groups and samples were investigated. We also evaluated the correlation between clinical parameters and gut microbiota in 60 CKD patients. We found that the genera bacteroides, blautia, parabacteroides, megasphaera and klebsiella might be detrimental factors in CKD, while other kinds of gut microbiota including bifidobacterium, prevotella, megamonas and subdoligranulum might be beneficial factors in CKD. In the future, a more detailed mechanism of the correlation between gut microbiota and DN needs to be investigated. In the future, more detailed mechanisms of gut microbiota's role in CKD need to be studied.

Disclosure/conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval and consent to participate

This study was approved by ethical committee in Dongzhimen Hospital, The First Affiliated Hospital of Beijing University of Chinese Medicine (2017-JYB-JS-076). All experiments were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments as revised in 2008. Informed consent was obtained from all subjects and/or their legal guardian(s).

Consent for publication

Not Applicable

Availability of data and material

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

Competing interest

All the authors declare that they have no conflict of interest.

Funding

This project was not funded.

Authors' contributions

Weihong Chen conceived the study and participated in its design and coordination. Shuwu Wei analyzed the data and drafted the manuscript. Yue Shen, Wei Jiang, Wenna Wang and Fubin Teng recruited patients, collected data, and participated in patient visits. Hongfang Liu and Sun Weiwei participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript.

Acknowledgments

The authors thank all the participants in the study and colleagues in the nursing group in their department for blood sampling.

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No competing interests reported.

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The Profile and Function of Gut Microbiota in Chronic Kidney Disease

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