Alterations of gut microbiota in biopsy

Scientific Reports volume 13, Article number: 12150 (2023) Cite this article

307 Accesses

25 Altmetric

Metrics details

The gut microbiota is closely related to parenteral noncommunicable diseases through intestinal immunity and plays an important role in the occurrence of diabetes and diabetic nephropathy. The aim of the study was to understand the gut–kidney axis by an analysis of gut microbiota composition among patients with biopsy-proven diabetic nephropathy (DN), patients with type 2 diabetes for more than 10 years without kidney damage (DM), and healthy controls (NC). Thirty-five DN patients, 40 DM patients and 40 healthy subjects matched by age and sex were enrolled between January 2022 and December 2022. Baseline information and clinical parameters were collected. 16S rDNA sequencing was performed to characterize the gut microbiome and identify gut microbes that were differentially abundant between patients and healthy controls. The relationship between the relative abundance of specific bacterial taxa in the gut and clinical phenotype and pathological indicators was evaluated. Substantial differences were found in the richness of the gut microbiota and the variation in the bacterial population among DN patients, DM patients and healthy controls. DM patients could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Clostridium-XVIII (AUC = 0.929), and DN patients could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Gemmiger (AUC = 0.842). DN patients could be accurately distinguished from age- and sex-matched DM patients by variations in g_Flavonifractor or g_Eisenbergiella (AUC = 0.909 and 0.886, respectively). The gut microbiota was also closely related to clinical phenotypes and pathological indicators. The study of gut microbiota composition was explored to determine its relationship to the occurrence of DN and a long history of diabetes without kidney damage. The renal pathological progression of DN may be delayed by regulating changes in the gut microbiota.

With the increasing prevalence of diabetes, the number of patients with diabetic nephropathy (DN) has also increased significantly. The results of an epidemiological survey in China from 2015 to 2017 showed that the prevalence of diabetes in adults was 11.2%1. As a serious microvascular complication of diabetes, DN has become the main cause of end-stage renal disease (ESRD) worldwide2,3,4. There are about 500–1000 species of bacteria in the human gastrointestinal tract, with a number of 1014 CFU, which are vividly referred to as acquired "organs"5. They assist the host in maintaining normal physiological functions, including absorbing energy, producing vitamins and basic active molecules, biological antagonism, immune regulation. Its composition and number have maintained a dynamic balance with the host in evolution6. A large number of studies have shown that gut microbiota can participate in the occurrence and development of diseases by regulating host energy metabolism, systemic inflammatory response, and the secretion of intestinal hormones. Gut microbiota also play an important role in a variety of kidney diseases, and the endotoxins, proteins and some metabolites produced by them have certain effects on the kidney through the gut-kidney axis7. By observing the gut microbiota of patients with diabetic nephropathy confirmed by renal biopsy and patients with long-term diabetes without kidney damage, this study aimed to explore the imbalance pattern and functional changes in the two disease states and establish a classification algorithm model to distinguish the two by gut microbiota. We also explore the correlation between gut microbiota and clinical phenotypes and pathological indexes.

The retrospective study included 35 patients with diabetic nephropathy confirmed by renal biopsy, 40 patients with type 2 diabetes for more than 10 years without kidney damage, and 40 healthy subjects matched by age and sex. We enrolled patients and healthy controls in Shanxi Provincial People’s Hospital between January 2022 and December 2022. In the DN group, inclusion criteria were as follows:

18–65 y of age;

Diagnosed with type 2 diabetes;

Diabetic nephropathy diagnosed based on renal puncture pathological examination;

No evidence of primary renal disease;

Estimated glomerular filtration rate(eGFR) ≥ 60 ml/min/1.73 m2;

Informed consent can be signed.

In the T2DM group, inclusion criteria were as follows:

18–65 y of age;

The duration of type 2 diabetes was more than 10 years;

No diabetic microvascular complications, including diabetic retinopathy and renal damage (eGFR ≥ 60 ml/min/1.73 m2 and urine albumin creatinine ratio (UACR) < 30 mg/g);

Informed consent could be signed.

The exclusion criteria for both groups included:

Severe heart, lung, liver, kidney and other organ dysfunction.

Malignant tumor, autoimmune disease or gastrointestinal disease; Use of antibiotics, preparations of live bacteria, lactulose or immunosuppressants within nearly a month.

Pregnant or lactating women.

The healthy controls were from the Physical Examination Center of Shanxi Provincial People’s Hospital and were 18–65 y of age. The results indicated that they were healthy, had no gastrointestinal symptoms and had not taken any drug in the past one month.

All study procedures complied with the ethical guidelines of the

Declaration of Helsinki.The studies involving human participants were reviewed and approved by the Biomedical Ethics Committee of Shanxi Provincial People’s Hospital (No. 2022–222). The patients/participants provided their written informed consent to participate in this study.

Fresh fecal samples were collected from the selected individuals. The fecal samples were discharged into a sterile stool box and transferred to a − 80 °C refrigerator for storage within 2 h after sampling. DNA was extracted from the samples using the QIAamp PowerFecal DNA Kit. Agarose gel electrophoresis was used to analyze DNA integrity, NanoDrop was used to measure the purity, and DNA concentration was accurately quantified with Qubit.

High-fidelity DNA polymerase was used to amplify the V3-V4 variable region of DNA by two-step PCR amplification-specific amplification and the addition of tag and joint sequences. An FC Magnetic Beads Kit (Enlighten) was used to purify and recover the product. Qubit4.0 was used to quantify the purified library, Qsep100 was used to check whether the length of the library was as expected, and each sample was diluted to 4 nM. The hybrid library was prepared and denatured by DNA, and at least a 5% Phix library was added to balance the library polymorphism. Sequencing was carried out on an Illumina MiSeq sequencer using the PE300 strategy.

The quality of the original data was controlled by QIIME2, low-quality sequences and joint sequences were filtered by Trimmomatic software, and chimeric sequences were removed by USEARCH software. OTU clustering was carried out by UPARSE software, and the bacterial 16S region was compared to the RDP database.

Statistical analysis was performed using R software (version 4.0.2). Amplicon analysis included only microorganisms that existed in ≥ 12% of samples. Alpha diversity was used to measure the richness of microbial species in a single sample. Beta diversity was used to measure the similarity of microbiota composition among different samples. In this study, principal coordinate analysis (PCoA) was used to analyze the β diversity of the microbiota. Through PCoA, we observed whether there was an overall deviation of microbiota between the disease group and the healthy population. LEfSe, or linear discriminant analysis effect size analysis, can find biomarkers with statistically significant differences between groups. This method was used to compare the gut bacteria with statistically significant differences between each disease group and the normal control group. Random forest is an integrated machine learning method that can obtain the final result by establishing multiple decision trees and voting (average) on the prediction results of each decision tree, which has the same function as the decision tree to complete the classification and regression tasks. In this study, the random forest prediction model was established by omicStudio software. The classification efficiency of the model was evaluated by the area under the curve (AUC) of the working characteristic curve (ROC). The prediction performance of the model was analyzed. The discriminant value was low when AUC < 0.70, good when 0.7 ≤ AUC ≤ 0.9, and high when AUC > 0.9. All heatmaps were visualized by the package pheatmap (version 1.0.12). The clinical data of patients with DN and DM and healthy controls were expressed as the mean ± SD or median, and the numerical data were expressed as percentages. A t test or nonparametric test was used to compare continuous variables. Chi-square tests were used to compare categorical variables. The statistical analysis was performed using SPSS version 26.0.

We included 35 patients with DN confirmed by renal biopsy with a median age of 54.09 years and 40 DM patients with a median age of 58.83 years. The baseline characteristics of the DN, DM and healthy control (NC) groups are summarized in Table 1.

According to the pathological grade of diabetic nephropathy, the severity of pathological changes was evaluated by glomerular volume (G), glomerular basement membrane (GBM), mesangial matrix (M), glomerulosclerosis (S) and interstitial lesions (I). G0 represents no significant increase in glomerular volume, and G1 represents an increase in glomerular volume. GBM1 represents mild thickening of the glomerular basement membrane, and GBM2 represents significant thickening of the glomerular basement membrane. M1 represents mild proliferation of the mesangial matrix, M2 represents significant widening of the mesangial matrix, and M3 represents the formation of one or more tuberous sclerosis (Kimmelstiel-Wilson nodules). S1 represents less than 50% glomerulosclerosis and S2 represents more than 50% glomerulosclerosis. I1 represents mild renal interstitial lesions, I2 represents moderate renal interstitial lesions, and I3 represents severe renal interstitial lesions. Among the 35 diabetic nephropathy patients, there were 5 cases of G0 and 30 cases of G1, 23 cases of GBM1 and 12 cases of GBM2, 4 cases of M1, 4 cases of M2 and 27 cases of M3, 30 cases of S1 and 5 cases of S2, 17 cases of I1, 16 cases of I2 and 2 cases of I3, as shown in Table 2.

A total of 844 OTUs were obtained at a 97% homology cut-off. The NC group showed the largest number of OTUs. As shown in the Venn diagram (Fig. 1a), the number of OTUs in common between the NC and DM groups was 354, while the DM and DN groups had 290 OTUs in common. The DM group had 113 specific OTUs, and the DN group had 102 specific OTUs that the other two groups did not share.

Gut microbiota compositions differed among DN, DM, NC groups. (a) Venn diagram of DN, DM, and NC; (b) Analysis of Alpha diversity in DN, DM, and NC; (c) Beta diversity analysis of DN, DM, and NC. DN diabetic nephropathy, DM type 2 diabetes mellitus for more than 10 years without kidney damage, NC healthy controls, OTUs operational taxonomic units.

The Chao index of the OTU profile was used to evaluate gut microbiota richness, and the Shannon index was used to estimate community diversity. The differences in community richness were significant in the three groups (DM vs. DN Chao P = 0.005, DM vs. NC Chao P < 0.001, DN vs. NC Chao P < 0.001), but no different diversity of OTUs was found in the three groups (Shannon P > 0.05) (Fig. 1b). Principal coordinates analysis (PCoA) based on Bray–Curtis dissimilarity at the OTU level showed that the microbiota compositions of NC, DM, and DN patients were significantly different, as shown in Fig. 1c (P = 2.569e−16). The fecal microbiota of patients with DM and DN and the NC subjects was clearly separated along PCoA2 and PCoA1, which explained 11.58% and 12.93% of the total variation, respectively. Together, these data suggest that the fecal microbial structure was significantly altered in patients with DM and DN compared to that in NC subjects in the presence of OTUs.

To determine different taxa from the phylum to genus level among the three groups, the linear discriminate analysis (LDA) effect size (LEfSe) algorithm was used (Fig. 2a). LEfSe showed that compared with the healthy control group, the number of gut microbiota in 2 classes, 3 orders and 5 families was significantly decreased in the DM and DN groups from the phylum to family level, while the number of Negativicutes and Selenomonadales was increased in the T2DM group. At the genus level, the number of Romboutsia, Faecalibacterium, Acidaminococcus, Megasphaera and Sutterella species in the DM group was significantly increased (all P < 0.05). The numbers of Christensenella, Clostridium-XIVa, Eisenbergiella, Flavonifractor and Clostridium-XVIII in the DN group were significantly increased (all P < 0.05), while the numbers of butyric-producing bacteria, Bacillus, Enterobacter, Trichospira and Rosacella were significantly decreased in the DM group and DN group (P < 0.05). At the genus level, Clostridium XVIII and Gemmiger were significantly decreased in the DM and DN groups (Fig. 2b).

Taxonomic differences in the fecal microbiota exhibited by patients with diabetic nephropathy (DN), type 2 diabetes mellitus for more than 10 years without kidney damage (DM) and healthy control (NC) subjects. (a) A Linear discriminant analysis (LDA; (log10) > 3) and effect size (LEfSe) analysis revealed significant differences (P < 0.05) in the fecal microbiota exhibited by the DM (reg score),DN(green score) and NC (blue score) groups. (b) This analyse revealed the most differentially abundant taxa at the genus level of bacterial between DM (yellow),DN (blue) and NC (red) groups. *P + 0.05 + **P + 0.01.

To illustrate the microbial signature of DM and DN and further explore the potential of the gut microbiome in DN identification, receiver operating characteristic (ROC) curves for classifying DM from NC, DN from NC and DN from DM were developed. We could detect DM individuals accurately based on only one genus (g_Clostridium-XVIII), as indicated by an area under the receiver operating curve (AUC) of up to 0.929 (Fig. 3a). Similarly, the richness of one genus (g_Gemmiger) was effective in distinguishing DN individuals from healthy controls, showing an AUC of 0.842 (Fig. 3b). The richness of two genera (g_Flavonifractor and g_Eisenbergiella) was effective in classifying DN subjects from DM subjects, showing AUCs of 0.909 and 0.886, respectively (Fig. 3c).

Receiver operating characteristic curve (ROC) analysis of the sensitivity and specificity of the differentially abundant genera as diagnostic factors for type 2 diabetes mellitus for more than 10 years without kidney damage(DM) and diabetic nephropathy(DN).(a) ROC curve classifying DM from healthy controls(NC), based on g_Clostridium-XVIII; (b) ROC curve classifying DN from NC, based on g_Gemmiger; (c) ROC curve classifying DN from DM, based on g_Flavonifractor and g_Eisenbergiella.

Spearman correlation analysis was used to further evaluate the correlation between gut microbiota differences and clinical characteristics. At the genus level, Gemmiger, Clostridium XlVb, Lachnospiracea incertae sedis and Vallitalea showed strongly negative correlations with glycosylated haemoglobin (HbA1c) and fasting blood glucose (FBG) levels, while Megasphaera had a positive correlation with HbA1c levels. Pseudomonas, Eisenbergiella and Flavonifractor showed markedly positive correlations with the urine albumin/creatinine ratio (UACR). Enterococcus, Clostridium XlVa and Eisenbergiella exhibited a negative correlation with the estimated glomerular filtration rate (eGFR) (Fig. 4).

Heatmaps showing correlations between differentially abundant microbiota genera and clinical parameters. SCr—serum creatinine; FBG—fasting blood glucose; HbA1c—glycosylated 16 hemoglobin; UACR—urine albumin creatinine ratioe; GFR—estimated glomerular filtration rate; Spearman test, *P < 0.05, +P < 0.01.

Correlations between differences in gut microbiota and pathological types were analyzed using spearman correlation analysis. At the genus level, Ruminococcus callidus and Clostridium viride showed strongly negative correlations with glomerular volume and positive correlations with glomerulosclerosis. Thickening of the glomerular basement membrane was positively correlated with Citrobacter werkmanii, Eisenbergiella tay, Klebsiella pneumoniae subsp. ozaenae, Ruminococcus callidus, Megamonas funiformis and Barnesiella intestinihominis. Flavonifractor plautii and Hungatella hatheway were positively correlated with mesangial matrix hyperplasia. Megasphaera micronuciformis showed a positive correlation with renal interstitial lesions (Fig. 5). It is suggested that the changes in the above microbiota lead to glomerular volume reduction, sclerosis, glomerular basement membrane thickening, mesangial matrix hyperplasia and renal interstitial fibrosis, thus promoting the progression of DN. It is suggested that the renal pathological progression of DN may be delayed by regulating changes in the gut microbiota.

Heatmap showing correlations between genera and 18 pathological classification of diabetic nephropathy. Spearman test, *P < 0.05, +P < 0.01.

Diabetic nephropathy is a common microvascular complication in patients with diabetes. With the sharp increase in the number of diabetic patients8, diabetic nephropathy has become the main cause of chronic kidney disease9, which constitutes a major public health problem worldwide10. A large number of studies have shown that the gut microbiota is closely related to parenteral noncommunicable diseases through gut immunity11,12,13. Qin et al.14 and other studies15 found a significant decrease in butyrate production in diabetic patients. Wu et al.16 found that compared with healthy controls, the relative abundance of Streptococcus and Enterococcus in patients with IgAN was higher, while that of Bacteroides was lower. The changes in the gut microbiota of IgAN affected the metabolism and absorption of polyunsaturated fatty acids and activated the metabolic pathway of arachidonic acid, thus constructing the metabolic system of IgAN. Li et al.17 found that the genera Lactobacillus, Clostridium IV, Paraprevotella, Clostridium sensu stricto, Desulfovibrio, and Alloprevotella were enriched in the fecal samples of patients with CKD, while Akkermansia and Parasutterella were enriched in those of NC subjects.

This study found that the α diversity index of patients in the DM and DN groups was higher than that in healthy people, which was consistent with the report of Larsen et al.18. As the imbalance pattern of gut microbiota in patients with metabolic diseases is greatly affected by regional factors, there are differences in the imbalance pattern in different regions19. Some studies have also found that the α diversity of the gut microbiota in patients with DM decreased20. The β diversity analysis showed that there were significant differences among the DM, DN and healthy control groups. Importantly, DM could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Clostridium-XVIII (AUC = 0.929), and DN could be accurately distinguished from age- and sex-matched healthy controls by variations in g_Gemmiger (AUC = 84.2%). DN could be accurately distinguished from age- and sex-matched DM patients by variations in g_Flavonifractor or g_Eisenbergiella (AUC = 0.909 and 0.886, respectively). These results show that the powerful function and practicability of data mining technology are reflected in the diagnosis and prediction of disease markers through a large number of multidimensional genome-related databases. Therefore, future research should continue to look for specific microbial markers for the diagnosis of DN.

Our study found that in healthy people, the content of Clostridium-XVIII was significantly higher than that in the diabetes group. Clostridium-XVIII has the ability to ferment and produce short-chain fatty acids (SCFAs) in the human intestinal tract, and SCFAs are generally considered to play a variety of important roles in maintaining human health. For example, they act as a special nutrition and energy component of the intestinal epithelium, protect the intestinal mucosal barrier, reduce the level of inflammation and enhance gastrointestinal motility21. Our findings demonstrated that healthy controls had more Gemmiger in the gut microbiota than DN patients. The correlation analysis between Gemmiger and biochemical indices also found that there was a negative correlation between Gemmiger and blood glucose level. Gemmiger are typically producing butyrate bacteria22, which are involved in the adjustment of the body’s reaction to inflammation. This study also found that Eisenbergiella was enriched in gut microorganisms of DN patients, and Eisenberg was positively correlated with urinary microalbumin. The correlation analysis between Eisenbergiella and renal pathological classification of DN showed that Eisenberg was positively correlated with glomerulosclerosis and glomerular basement membrane thickening. This bacterium was highly abundant in the intestinal tract of mice with colitis and might participate in intestinal inflammation23. Flavonifractor is a conditional pathogen that was more enriched in DN patients than in the NC and DM groups and synthesizes lipopolysaccharide (LPS). When LPS binds to the receptor complex in macrophages, it activates the signal cascade, resulting in the production of a large number of inflammatory cytokines, such as IL-1β, IL-6, TNF α, and IL-1224.

Studies have shown that there is an interaction between intestinal microorganisms and the kidney, called the gut‐kidney axis, and the balance between intestinal symbiotic bacteria and pathogenic bacteria ensures the integrity of the intestinal barrier and normal renal function. Healthy kidneys communicate with the intestinal microbiota through cellular and molecular signals to ensure normal intestinal microbiota homeostasis. The imbalance of DN or gut microbiota will lead to the destruction of this balance, loss of the integrity of the intestinal barrier, activation of immune cells and secretion of cytokines, thus further worsening renal function and symbiotic relationships25.

Because diabetic nephropathy is mostly clinically diagnosed, many patients with diabetes complicated with chronic kidney disease are easily misdiagnosed as diabetic nephropathy, resulting in phenotypic heterogeneity. For the accuracy of phenotype, diabetic nephropathy patients confirmed by renal biopsy were selected for the DN group, and eGFR ≥ 60 ml/min/1.73 m2 to rule out the effect of end-stage renal disease on gut microbiota26. In the DM group, we selected patients with diabetes course of more than 10 years without renal damage, and the difference between the two groups can better reflect the difference of gut microbiota in diabetes and diabetic nephropathy. A number of studies have found that pathological classification of diabetic nephropathy has an important guiding value for judging renal prognosis27,28. Therefore, we analyzed the correlation between gut microbiota and pathological indicators of diabetic nephropathy, and found that various gut microbiota play an important role in the pathological progression of diabetic nephropathy. It is suggested that regulating the changes of gut microbiota may delay the progression of renal pathology in diabetic nephropathy.

However, this study is a cross-sectional study of a single-centre and small-scale population, and its results and conclusions have some limitations, so it is necessary to verify them in a follow-up multicentre population cohort. In addition, most of the patients in this study took different types of hypoglycaemic or antihypertensive drugs, so the effect of drugs on microbiota variation also needs to be considered.

Our study explored the imbalance pattern and functional changes of gut microbiota in the two disease states, identified the dangerous strains that promote the progression of diabetic nephropathy in diabetic patients, and established a classification algorithm model to distinguish the two by gut microbiota, providing a certain basis for the diagnosis and prediction of diabetic nephropathy through gut microbiota. The follow-up research can try to restore microbial homeostasis by interfering with gut microbiota, and then protect the kidney by regulating the gut-kidney axis, bringing a new method for the treatment of diabetic nephropathy.

The datasets analysed during the current study are available in the Sequence Read Archive (SRA) repository, the BioProject ID is PRJNA943281.

Li, Y. et al. Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes association: National cross sectional study. BMJ 369, m997 (2020).

Article PubMed PubMed Central Google Scholar

Zhang, L. et al. Trends in chronic kidney disease in China. N. Engl. J. Med. 375(9), 905–906 (2016).

Article PubMed Google Scholar

Umanath, K. & Lewis, J. B. Update on diabetic nephropathy: Core curriculum 2018. Am. J. Kidney Dis. 71(6), 884–895 (2018).

Article PubMed Google Scholar

Cheng, H. T. et al. Worldwide epidemiology of diabetes-related end-stage renal disease, 2000–2015. Diabetes Care 44(1), 89–97 (2021).

Article PubMed Google Scholar

Afkarian, M. et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988–2014. JAMA 316(6), 602–610 (2016).

Article PubMed PubMed Central Google Scholar

Turner, R. & Holman, R. Tight blood pressure control and risk of macrovascular and microvascular complications. BMJ Brit. Med. J. Int. Ed. 317(7160), 703–713 (1998).

Article Google Scholar

Patel, A. et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358(24), 2560–2572 (2008).

Article CAS PubMed Google Scholar

Nathan, D. M. et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 329(14), 977–986 (1993).

Article CAS PubMed Google Scholar

Tao, S. et al. Understanding the gut-kidney axis among biopsy-proven diabetic nephropathy, type 2 diabetes mellitus and healthy controls: An analysis of the gut microbiota composition. Acta Diabetol. 56(5), 581–592 (2019).

Article PubMed Google Scholar

Karlsson, F. H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498(7452), 99–103 (2013).

Article ADS CAS PubMed Google Scholar

Xu, Y. et al. Prevalence and control of diabetes in Chinese adults. JAMA 310(9), 948–959 (2013).

Article CAS PubMed Google Scholar

Afkarian, M. et al. Kidney disease and increased mortality risk in type 2 diabetes. J. Am. Soc. Nephrol. 24(2), 302–308 (2013).

Article CAS PubMed PubMed Central Google Scholar

Jiao, F. et al. Annual direct medical costs associated with diabetes-related complications in the event year and in subsequent years in Hong Kong. Diabet. Med. 34(9), 1276–1283 (2017).

Article CAS PubMed Google Scholar

Chow, J., Tang, H. & Mazmanian, S. K. Pathobionts of the gastrointestinal microbiota and inflammatory disease. Curr. Opin. Immunol. 23(4), 473–480 (2011).

Article CAS PubMed PubMed Central Google Scholar

Hooper, L. V., Littman, D. R. & Macpherson, A. J. Interactions between the microbiota and the immune system. Science 336(6086), 1268–1273 (2012).

Article ADS CAS PubMed PubMed Central Google Scholar

Sekirov, I. et al. Gut microbiota in health and disease. Physiol. Rev. 90(3), 859–904 (2010).

Article CAS PubMed Google Scholar

Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490(7418), 55–60 (2012).

Article ADS CAS PubMed Google Scholar

Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528(7581), 262–266 (2015).

Article CAS PubMed PubMed Central Google Scholar

Wu, H. et al. Identification of a novel interplay between intestinal bacteria and metabolites in Chinese patients with IgA nephropathy via integrated microbiome and metabolome approaches. Ann. Transl. Med. 9(1), 32 (2021).

Article CAS PubMed PubMed Central Google Scholar

Li, F., Wang, M., Wang, J., Li, R. & Zhang, Y. Alterations to the Gut Microbiota and their correlation with inflammatory factors in chronic kidney disease. Front. Cell. Infect. Microbiol. 9, 206 (2019).

Article CAS PubMed PubMed Central Google Scholar

Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults[J]. PLoS ONE 5(2), e9085 (2010).

Article ADS PubMed PubMed Central Google Scholar

He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24(10), 1532–1535 (2018).

Article CAS PubMed Google Scholar

Tanti, J. F. et al. Implication of inflammatory signaling pathways in obesity-induced insulin resistance. Front. Endocrinol. Lausanne 3, 181 (2012).

PubMed Google Scholar

Kayama, H., Okumura, R. & Takeda, K. Interaction between the microbiota, epithelia, and immune cells in the intestine. Annu. Rev. Immunol. 38, 23–48 (2020).

Article CAS PubMed Google Scholar

Wang, Z. et al. Gut microbiota remodeling: A promising therapeutic strategy to confront hyperuricemia and gout. Front. Cell Infect. Microbiol. 12, 935723 (2022).

Article CAS PubMed PubMed Central Google Scholar

Kameyama, K. & Itoh, K. Intestinal colonization by a Lachnospiraceae bacterium contributes to the development of diabetes in obese mice. Microbes Environ. 29(4), 427–430 (2014).

Article PubMed PubMed Central Google Scholar

Yang, K. et al. Low cellular NAD(+) compromises lipopolysaccharide-induced inflammatory responses via inhibiting TLR4 signal transduction in human monocytes. J. Immunol. 203(6), 1598–1608 (2019).

Article CAS PubMed Google Scholar

Meijers, B. K. & Evenepoel, P. The gut-kidney axis: indoxyl sulfate, p-cresyl sulfate and CKD progression. Nephrol. Dial. Transplant. 26(3), 759–761 (2011).

Article CAS PubMed Google Scholar

Download references

The funding was provided by 136 Special Fund for Medical Development Project.

Department of Nephrology, Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan, China

Xiao Lu & Rongshan Li

Department of Thoracic Surgery, Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan, China

Junjun Ma

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

You can also search for this author in PubMed Google Scholar

XL: clinical analyses and manuscript writing. JM: sequencing analyses and statistical analyses. RL: study design, project supervision, and manuscript revision.

Correspondence to Rongshan Li.

The authors declare no competing interests.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

Lu, X., Ma, J. & Li, R. Alterations of gut microbiota in biopsy-proven diabetic nephropathy and a long history of diabetes without kidney damage. Sci Rep 13, 12150 (2023). https://doi.org/10.1038/s41598-023-39444-4

Download citation

Received: 24 February 2023

Accepted: 25 July 2023

Published: 27 July 2023

DOI: https://doi.org/10.1038/s41598-023-39444-4

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.