Cyclo-glycylproline, a food-derived diketopiperazine, inhibits bacterial indole production: Implications for diabetic nephropathy prevention.

Neuregulin 4 (NRG4) has recently been introduced as a novel brown adipose tissue (BAT)-secreted adipokine with beneficial metabolic effects in mice. However, regulation of Nrg4 in end-stage kidney disease (ESKD) and type 2 diabetes mellitus (T2DM) has not been elucidated, so far. Serum NRG4 levels were quantified by ELISA in 60 subjects with ESKD on chronic hemodialysis as compared to 60 subjects with an estimated glomerular filtration rate >50 mL/min/1.73 m2 in a cross-sectional cohort. Within both groups, about half of the patients had a T2DM. Furthermore, mRNA expression of Nrg4 was determined in two mouse models of diabetic kidney disease (DKD) as compared to two different groups of non-diabetic control mice. Moreover, mRNA expression of Nrg4 was investigated in cultured, differentiated mouse brown and white adipocytes, as well as hepatocytes, after treatment with the uremic toxin indoxyl sulfate. Median serum NRG4 was significantly lower in patients with ESKD compared to controls and the adipokine was independently associated with a beneficial renal, glucose and lipid profile. In mice with DKD, Nrg4 mRNA expression was decreased in all adipose tissue depots compared to control mice. The uremic toxin indoxyl sulfate did not significantly alter Nrg4 mRNA expression in adipocytes and hepatocytes, in vitro. Circulating NRG4 is independently associated with a preserved renal function and mRNA expression of -Nrg4 is reduced in adipose tissue depots of mice with DKD. The BAT-secreted adipokine is further associated with a beneficial glucose and lipid profile supporting NRG4 as potential treatment target in metabolic and renal disease states.

Methanosphaera stadtmanae was the sole Methanosphaera representative to be cultured and detected by molecular methods in the human gut microbiota, further associated with digestive and respiratory diseases, leaving unknown the actual diversity of human-associated Methanosphaera species. Here, a novel Methanosphaera species, Candidatus Methanosphaera massiliense (Ca. M. massiliense) sp. nov. was isolated by culture using a hydrogen- and carbon dioxide-free medium from one human feces sample. Ca. M. massiliense is a non-motile, 850 nm Gram-positive coccus autofluorescent at 420 nm. Whole-genome sequencing yielded a 29.7% GC content, gapless 1,785,773 bp genome sequence with an 84.5% coding ratio, encoding for alcohol and aldehyde dehydrogenases promoting the growth of Ca. M. massiliense without hydrogen. Screening additional mammal and human feces using a specific genome sequence-derived DNA-polymerase RT-PCR system yielded a prevalence of 22% in pigs, 12% in red kangaroos, and no detection in 149 other human samples. This study, extending the diversity of Methanosphaera in human microbiota, questions the zoonotic sources of Ca. M. massiliense and possible transfer between hosts.IMPORTANCEMethanogens are constant inhabitants in the human gut microbiota in which Methanosphaera stadtmanae was the only cultivated Methanosphaera representative. We grew Candidatus Methanosphaera massiliense sp. nov. from one human feces sample in a novel culture medium under a nitrogen atmosphere. Systematic research for methanogens in human and animal fecal samples detected Ca. M. massiliense in pig and red kangaroo feces, raising the possibility of its zoonotic acquisition. Host specificity, source of acquisition, and adaptation of methanogens should be further investigated.

Intestinal microbiome diversity of diabetic and non-diabetic kidney disease: Current status and future perspective

This study aims to identify biomarkers for reliably predicting diabetic kidney disease (DKD), systematically characterize serum metabolites and gut microbiota in DKD patients, and investigate the correlation between differential serum metabolites and gut microbiota. From September 2021 to January 2023, 90 subjects were recruited: 30 with DKD, 30 with type 2 diabetes mellitus (T2DM), and 30 normal controls (NCs). Serum metabolites, including 180 different metabolites, were analyzed using untargeted metabolomics UPLC-MS/MS, and gut microbiota were assessed via 16S rRNA sequencing. Differential metabolites were identified through univariate (t-test or Mann–Whitney U-test, P < 0.05) and multivariate analyses (OPLS-DA model, VIP > 1, P < 0.05), followed by selection using the least absolute shrinkage and selection operator (LASSO). The selected overlapping serum metabolites, along with DKD-associated differential gut microbiota, were used to develop a logistic regression prediction model for DKD based on six markers. In the DKD group compared to the DM and NC groups, 39 and 60 differential serum metabolites were identified, respectively (VIP > 1, P < 0.01). Among these, 36 serum metabolites, including alpha-Hydroxyisobutyric acid, were significantly elevated in DKD patients compared to those with DM. Of these, 28 metabolites showed a negative correlation with estimated glomerular filtration rate (eGFR), while 29 showed a positive correlation with urine albumin creatinine ratio (UACR). Patients with DKD were further categorized into subgroups (DKD middle and DKD early) based on eGFR (eGFR < 90 ml/min/1.73m2, eGFR ≥ 90 ml/min/1.73m2), revealing 23 differential metabolites. Dysbiosis of the gut microbiota was evident in DKD patients, with lower relative abundances of g_Prevotella and g_Faecalibacterium compared to the DM and NC groups. Subgroup analysis indicated decreased relative abundances of g_Prevotella and g_Faecalibacterium in the DKD middle group, along with a decrease in g_Klebsiella compared to the DKD early group, which correlated positively with DKD patients’ eGFR. There were 11 common metabolites among the three groups of differential metabolites. Among these, three serum metabolites—imidazolepropionic acid, adipoylcarnitine, and 1-methylhistidine—were identified as predictive serum metabolic markers. Disease prediction models (logistic regression models) were constructed based on these three metabolites combined with three genera of bacteria. These models demonstrated strong discriminatory power for diagnosing patients with DKD compared to patients with DM (area under the receiver operating characteristic curve (AUROC) = 0.939 and precision-recall curve (AUPR) = 0.940). The models also effectively discriminated between patients with DKD and NCs (0.976, 0.973). This study revealed distinctive serum metabolites and gut microbiota in patients with DKD. It demonstrated the potential utility of three specific serum metabolites and three genera of bacteria in diagnosing patients with DKD and assessing their renal dysfunction.

7-Phenylheptanoic acid (7-PH) has been reported to slow the progression of chronic kidney disease (CKD) in mice. Although the mechanism behind this renal protection remains unclear, it is strongly linked to reduced production of indoxyl sulfate (IS), a uremic toxin derived from dietary L-tryptophan through a multistep enzymatic process. It is also known that there is a strong association between CKD progression and gut microbiome dysbiosis. This study aimed to determine the involvement of each enzymatic step and identify the primary target enzymes through which 7-PH suppresses IS production, as well as investigate whether the protective effects of 7-PH involve microbiome modulation. First, we evaluated the effects of 7-PH on tryptophan indole-lyase (TIL), cytochrome P450 2E1 (CYP2E1), and sulfotransferase enzymes using in vitro enzyme kinetic studies. We then analyzed the impact of 7-PH on gut microbiome homeostasis in adenine-induced CKD mice using high-throughput 16S rRNA gene sequencing of fecal samples. Our findings indicate that 7-PH primarily targets the conversion of L-tryptophan to indole by acting as a competitive inhibitor of TIL (Ki = 92 μM). It also exhibits weak noncompetitive inhibition of CYP2E1 but does not affect sulfotransferase activity. Microbiome analysis revealed that 7-PH attenuates CKD-associated gut microbiota dysbiosis by selectively preserving beneficial bacterial taxa, such as Muribaculaceae and Alloprevotella, while inhibiting dysbiotic and opportunistic pathogenic groups, including Staphylococcus and Oligella. Therefore, the renal protective effects of 7-PH involve at least 2 interconnected mechanisms: the suppression of IS production through TIL inhibition and the modulation of the gut microbiome. SIGNIFICANCE STATEMENT: The mechanisms behind the renal protective effects of 7-phenylheptanoic acid (7-PH) in chronic kidney disease mice have been clarified. 7-PH reduces indoxyl sulfate production by primarily inhibiting tryptophan indole-lyase activity. Additionally, 7-PH modulates the gut microbiome by preserving beneficial bacterial taxa while selectively suppressing opportunistic pathogens, thereby attenuating chronic kidney disease-associated microbiome dysbiosis.

Experimental Models of Type-2 Diabetic Nephropathy

Fresh potable water is an indispensable drink which humans consume daily in substantial amounts. Nonetheless, very little is known about the composition of the microbial community inhabiting drinking water or its impact on our gut microbiota. In the current study, an exhaustive shotgun metagenomics analysis of the tap water microbiome highlighted the occurrence of a highly genetic biodiversity of the microbial communities residing in fresh water and the existence of a conserved core tap water microbiota largely represented by novel microbial species, representing microbial dark matter. Furthermore, genome reconstruction of this microbial dark matter from water samples unveiled homologous sequences present in the faecal microbiome of humans from various geographical locations. Accordingly, investigation of the faecal microbiota content of a subject that daily consumed tap water for 3 years provides proof for horizontal transmission and colonization of water bacteria in the human gut.

Context Diabetic kidney disease (DKD) affects nearly 40% of diabetic patients, often leading to end-stage renal disease that requires renal replacement therapies, such as dialysis and transplantation. The gut microbiota, an integral aspect of human evolution, plays a crucial role in this condition. Traditional Chinese medicine (TCM) has shown promising outcomes in ameliorating DKD by addressing the gut microbiota. Objective This review elucidates the modifications in gut microbiota observed in DKD and explores the impact of TCM interventions on correcting microbial dysregulation. Methods We searched relevant articles from databases including Web of Science, PubMed, ScienceDirect, Wiley, and Springer Nature. The following keywords were used: diabetic kidney disease, diabetic nephropathy, gut microbiota, natural product, TCM, Chinese herbal medicine, and Chinese medicinal herbs. Rigorous criteria were applied to identify high-quality studies on TCM interventions against DKD. Results Dysregulation of the gut microbiota, including Lactobacillus, Streptococcus, and Clostridium, has been observed in individuals with DKD. Key indicators of microbial dysregulation include increased uremic solutes and decreased short-chain fatty acids. Various TCM therapies, such as formulas, tablets, granules, capsules, and decoctions, exhibit unique advantages in regulating the disordered microbiota to treat DKD. Conclusion This review highlights the importance of targeting the gut-kidney axis to regulate microbial disorders, their metabolites, and associated signaling pathways in DKD. The Qing-Re-Xiao-Zheng formula, the Shenyan Kangfu tablet, the Huangkui capsule, and the Bekhogainsam decoction are potential candidates to address the gut-kidney axis. TCM interventions offer a significant therapeutic approach by targeting microbial dysregulation in patients with DKD.

Evidence has demonstrated that either metabolites or intestinal microbiota are involved in the pathogenesis of type 2 diabetes (T2D) and diabetic kidney disease (DKD). To explore the interaction between plasma metabolomics and intestinal microbiome in the progress of T2D-DKD, in the current study, we analyzed metabolomics in the plasma of db/db mice with liquid chromatography–mass spectrometry and also examined intestinal prokaryotes and entire gut microbiome dysbiosis at the genus level with both 16S rDNA and metagenomic sequencing techniques. We found that Negativibacillus and Rikenella were upregulated, while Akkermansia, Candidatus, Erysipelatoclostridium and Ileibacterium were downregulated in the colon of db/db mice compared with non-diabetic controls. In parallel, a total of 91 metabolites were upregulated, while 23 were downregulated in the plasma of db/db mice. The top five upregulated metabolites included D-arabinose 5-phosphate, estrone 3-sulfate, L-theanine, 3′-aenylic acid and adenosine 5′-monophosphate, and the five most significantly downregulated metabolites were aurohyocholic acid sodium salt, calcium phosphorylcholine chloride, tauro-alpha-muricholic acid sodium salt, galactinol and phosphocholine. These plasma metabolites were interacted with intestinal microbiomes, which are mainly involved in the pathways related to the biosynthesis of unsaturated fatty acids, fatty acid elongation, steroid biosynthesis, and D-arginine and D-ornithine metabolism. In the differential metabolites, N-acetyl-L-ornithine, ornithine and L-kyn could be metabolized by the correspondingly differential ontology genes in the intestinal metagenome. The current study thereby provides evidence for a gut–metabolism–kidney axis in the metabolism of db/db mice, in which the gut microbiome and circulating metabolomics interact, and suggests that information from this axis may contribute to our understanding of T2D and DKD pathogenesis.

Aims: According to the gut-kidney axis theory, gut microbiota (GM) has bidirectional crosstalk with the development of diabetic kidney disease (DKD). However, empirical results have been inconsistent, and the causal associations remain unclear. This study was aimed at exploring the causal relationship between GM and DKD as well as the glomerular filtration rate (GFR) and urinary albumin-to-creatinine ratio (UACR). Materials and Methods: Two-sample Mendelian randomisation (MR) analysis was performed with inverse-variance weighting as the primary method, together with four additional modes (MR-Egger regression, simple mode, weighted mode, and weighted median). We utilised summary-level genome-wide association study statistics from public databases for this MR analysis. Genetic associations with DKD were downloaded from the IEU Open GWAS project or CKDGen consortium, and associations with GM (196 taxa from five levels) were downloaded from the MiBioGen repository. Results: In forward MR analysis, we identified 13 taxa associated with DKD, most of which were duplicated in Type 2 diabetes with renal complications but not in Type 1 diabetes. We observed a causal association between genetic signature contributing to the relative abundance of Erysipelotrichaceae UCG003 and that for both DKD and GFR. Similarly, host genetic signature defining the abundance of Ruminococcaceae UCG014 was found to be simultaneously associated with DKD and UACR. In reverse MR analysis, the abundance of 14 other GM taxa was affected by DKD, including the phylum Proteobacteria, which remained significant after false discovery rate correction. Sensitivity analyses revealed no evidence of outliers, heterogeneity, or horizontal pleiotropy. Conclusion: Our findings provide compelling causal genetic evidence for the bidirectional crosstalk between specific GM taxa and DKD development, contributing valuable insights for a comprehensive understanding of the pathological mechanisms of DKD and highlighting the possibility of prevention and management of DKD by targeting GM.

Objective: Diabetic kidney disease (DKD) is one of the most prevalent complications of diabetes mellitus (DM) and is highly associated with devastating outcomes. Hypoxia-inducible factor (HIF), the main transcription factor that regulates cellular responses to hypoxia, plays an important role in regulating erythropoietin (EPO) synthesis. FG-4592 is the HIF stabilizer that is widely used in patients with renal anemia. We investigated the effect of FG-4592 on DKD phenotypes and the pharmacologic mechanism from the perspective of gut microbiota and systemic metabolism.Design: We collected the clinical data of 73 participants, including 40 DKD patients with combined renal anemia treated with FG-4592, and 33 clinical index-matched DKD patients without FG-4592 treatment from The First Affiliated Hospital of Zhengzhou University at the beginning and after a 3–6-month follow-up period. We established DKD mouse models treated by FG-4592 and performed fecal microbiota transplantation from FG-4592-treated DKD mice to investigate the effects of FG-4592 on DKD and to understand this mechanism from a microbial perspective. Untargeted metabolome–microbiome combined analysis was implemented to globally delineate the mechanism of FG-4592 from both microbial and metabolomic aspects.Result: DKD phenotypes significantly improved after 3–6 months of FG-4592 treatment in DKD patients combined with renal anemia, including a decreased level of systolic blood pressure, serum creatinine, and increased estimated glomerular infiltration rate. Such effects were also achieved in the DKD mouse model treated with FG-4592 and can be also induced by FG-4592-influenced gut microbiota. Untargeted plasma metabolomics-gut microbiota analysis showed that FG-4592 dramatically altered both the microbial and metabolic profiles of DKD mice and relieved DKD phenotypes via upregulating beneficial gut microbiota-associated metabolites.Conclusion: FG-4592 can globally relieve the symptoms of DKD patients combined with renal anemia. In the animal experiment, FG-4592 can reconstruct the intestinal microbial profiles of DKD to further upregulate the production of gut-associated beneficial metabolites, subsequently improving DKD phenotypes.

BackgroundEarlier studies have implicated a crucial link between diabetic nephropathy (DN) and the gut microbiota (GM) by considering the gut-kidney axis; however, the specific cause-and-effect connections between these processes remain unclear.MethodsTo compare changes in the GM between DN patients and control subjects, a review of observational studies was performed. The examination focused on the phylum, family, genus, and species/genus categories. To delve deeper into the cause–effect relationship, instrumental variables for 211 GM taxa (9 phyla, 16 classes, 20 orders, 35 families, and 131 genera), which were eligible for the mbQTL (microbial quantitative trait locus) mapping analysis, were collected from the Genome Wide Association Study (GWAS). A Mendelian randomization investigation was then conducted to gauge their impact on DN susceptibility using data from the European Bioinformatics Institute (EBI) and the FinnGen consortium. The European Bioinformatics Institute data included 1032 DN patients and 451,248 controls, while the FinnGen consortium data consisted of 3283 DN patients and 210,463 controls. Two-sample Mendelian randomization (TSMR) was utilized to determine the link between the GM and DN. The primary method for analysis was the inverse variance weighted (IVW) approach. Moreover, a reverse Mendelian randomization analysis was carried out, and the findings were validated through sensitivity assessments.ResultsThis review examined 11 observational studies that satisfied the inclusion and exclusion criteria. There was a significant difference in the abundance of 144 GM taxa between DN patients and controls. By employing the MR technique, 13 bacteria were pinpointed as having a causal link to DN (including 3 unknown GM taxa). Even after Bonferroni correction, the protective impact of the phylum Proteobacteria and genus Dialister (Sequeira et al. Nat Microbiol. 5:304-313, 2020; Liu et al. EBioMedicine. 90:104527, 2023) and the harmful impact of the genus Akkermansia, family Verrucomicrobiaceae, order Verrucomicrobia and class Verrucomicrobiae on DN remained significant. No noticeable heterogeneity or horizontal pleiotropy was detected in the instrumental variables (IVs). However, reverse MR investigations have failed to reveal any substantial causal relationship between DN and the GM.ConclusionDifferences in the GM among DN patients and healthy controls are explored in observational studies. We verified the possible connection between certain genetically modified genera and DN, thereby emphasizing the connection between the “gut-kidney” axis and new insights into the GM’s role in DN pathogenesis underlying DN. Investigations into this association are necessary, and novel biomarkers for the development of targeted preventive strategies against DN are needed.

Introduction and Objective: Recently, gut microbiota emerged as an important factor for success of immunity-based cancer treatments. However, its steady-state interaction and contribution to developing tumors is largely unexplored in non-intestinal cancers. Our objective was to investigate the connection between prostate tumor and the gut microbiota independently of cancer therapies. Methods: Human fecal samples were obtained from men participating into a phase IIb double-blind randomized controlled trial testing 3g/day of monoglyceride-eicosapentaenoic acid (MAG-EPA) versus placebo for a 4-10 week period before their radical prostatectomy (NCT02333435). A second set of samples were from men taking the same intervention of MAG-EPA or placebo after a PSA increase following their radical prostatectomy (NCT03753334). Short chain fatty acids (SCFA) analysis of patient stool samples between baseline and surgery was performed by gas chromatography coupled with flame ionization detection. 16srRNA libraries were amplified by targeting a fragment of the V3-V4 hypervariable region of the bacterial 16S rRNA gene. High-throughput sequencing of the bar-coded amplicons was performed on a MiSeq apparatus and the bioinformatics analysis was conducted using Mothur pipeline. In addition to human fecal samples, fully immunocompetent C57BL/6 mice were injected subcutaneously with TRAMP-C2 or PTEN−/− or PTEN−/− RB1−/− mouse prostate cancer cells to measure changes in the gut microbiota during tumor growth. We also recapitulated the MAG-EPA intervention in our TRAMP-C2 mice model and fed by gavage four different fatty acids (omega-9 (high oleic sunflower oil), omega-6 (MAG-arachidonic acid) and two omega-3 (MAG-docosahexaenoic and MAG-EPA). Results: In human fecal samples from prostate cancer patients, we observed a reduced gut microbiota diversity correlating with tumor stage. We also found that tumor growth was sufficient to modulate the microbiota in three independent prostate cancer syngeneic mouse models. We showed that transplanted human gut flora was sufficient to modulate ectopic prostate tumor growth, supporting the causal impact of gut microbiota for prostate cancer. The analysis of SCFA in patient stool samples between baseline and surgery showed that MAG-EPA prebiotic intervention was associated with a decrease of fecal butyric acid levels in prostate cancer patients with downgrade at surgery. We finally investigated this gut-tumor connection using purified polyunsaturated fatty acids prebiotics in patients and mice. We observed a reduction in the levels of Ruminococcaceae following dietary omega-3 supplementation that correlated with prostate cancer downgrade in patients and reduced tumor growth in mice. Conclusion: Overall our findings suggest that diet-actionable components of the gut microbiome can regulate prostate cancer growth. Citation Format: Jalal Laaraj, Gabriel Lachance, Nikunj Gevariya, Thibaut Varin, Andrei Feldiorean, Fanny Gaignier, Isabelle Boudreau Julien, Hui Wen Xu, Tarek Hallal, Jean-François Pelletier, Sidki Bouslama, Nadia Boufaied, Nicolas Derome, Yves Fradet, Leigh Ellis, Ciriaco A. Piccirillo, Frédéric Raymond, David P. Labbé, Alain Bergeron, André Marette, Karine Robitaille, Vincent Fradet. The gut microbiome-prostate tumor crosstalk is modulated by dietary polyunsaturated fatty acids [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2021 Oct 5-6. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(1 Suppl):Abstract nr P001.

Objective: Diabetic kidney disease (DKD) has become the major cause of end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study.Methods: Fecal and serum samples obtained from predialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometry, respectively. Forty-one predialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR ≥ 15 ml/min/1.73 m2) (n = 22), and the DKD ESRD group (eGFR < 15 ml/min/1.73 m2) (n = 19). The metabolic pathways related to differential serum metabolites were obtained by the KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap.Results: Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the 11 different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway [hippuric acid (HA), L-(−)-3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid] and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase in renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase in renal function indicators and serum metabolites [L-(−)-3-phenyllactic acid and IAA].Conclusions: This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in predialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways, which correlated with the progression of DKD.

Objective Diabetic kidney disease (DKD) has become the major cause of the end-stage renal disease (ESRD) associated with the progression of renal fibrosis. As gut microbiota dysbiosis is closely related to renal damage and fibrosis, we investigated the role of gut microbiota and microbiota-related serum metabolites in DKD progression in this study. Methods Fecal and serum samples obtained from pre-dialysis DKD patients from January 2017 to December 2019 were detected using 16S rRNA gene sequencing and liquid chromatography-mass spectrometer respectively. Forty-one pre-dialysis patients were divided into two groups according to their estimated glomerular filtration rate (eGFR): the DKD non-ESRD group (eGFR≥15ml/min/1.73m2) (n=22), and the DKD ESRD group (eGFR<15ml/min/1.73m2) (n=19). The metabolic pathways related to differential serum metabolites were obtained by KEGG pathway analysis. Differences between the two groups relative to gut microbiota profiles and serum metabolites were investigated, and associations between gut microbiota and metabolite concentrations were assessed. Correlations between clinical indicators and both microbiota-related metabolites and gut microbiota were calculated by Spearman rank correlation coefficient and visualized by heatmap. Results Eleven different intestinal floras and 239 different serum metabolites were identified between the two groups. Of 239 serum metabolites, 192 related to the eleven different intestinal flora were mainly enriched in six metabolic pathways, among which, phenylalanine and tryptophan metabolic pathways were most associated with DKD progression. Four microbiota-related metabolites in the phenylalanine metabolic pathway (hippuric acid (HA), L- (-)- 3-phenylactic acid, trans-3-hydroxy-cinnamate, and dihydro-3-coumaric acid), and indole-3 acetic acid (IAA) in the tryptophan metabolic pathway positively correlated with DKD progression, whereas L-tryptophan in the tryptophan metabolic pathway had a negative correlation. Intestinal flora g_Abiotrophia and g_norank_f_Peptococcaceae were positively correlated with the increase of renal function indicators and serum metabolite HA. G_Lachnospiraceae_NC2004_Group was negatively correlated with the increase of renal function indicators, and serum metabolites (L-(-)-3-Phenyllactic acid and IAA). Conclusions This study highlights the interaction among gut microbiota, serum metabolites, and clinical indicators in pre-dialysis DKD patients, and provides new insights into the role of gut microbiota and microbiota-related serum metabolites that were enriched in the phenylalanine and tryptophan metabolic pathways which correlated with the progression of DKD.