Chronic kidney disease (CKD) is a worldwide healthcare burden featured by lasting abnormalities in renal function or structure for at least 3 months, often due to primary, secondary, or hereditary kidney diseases (Webster et al., 2017; Kalantar-Zadeh et al., 2021). In recent years, the global prevalence of CKD has been steadily increased, affecting approximately 13.4% of the world’s population (Lv and LX, 2019). The high incidence has led to significant medical and economic burdens (Liyanage et al., 2022). CKD typically develops slowly without noticeable symptoms in its early stages. However, as the disease progresses, life-sustaining treatments such as dialysis and kidney transplantation become necessary (Levey et al., 2009). Early detection and effective early interventions of CKD patients can minimize the risk of kidney failure and major cardiovascular events (James et al., 2010). Therefore, there is an urgent need to develop novel biomarkers and innovative strategies that facilitate early detection and prevention of CKD.

The potential role of the gut microbiome is pivotal in maintaining human physiological homeostasis and health (Clemente et al., 2012). The emergence of notions such as “intestinal-renal syndrome” (Ritz, 2011) and “gut-kidney axis” (Meijers and Evenepoel, 2011) have provided new avenues for investigating the relationship between gut microbiota and kidney disease. Recent research has demonstrated that a significant alteration in the composition of gut microbes in CKD patients (Vaziri et al., 2013; Wu et al., 2021), indicating that the gut microbiota can serve as innovative targets for interventions. Research findings have indicated that patients with end-stage renal disease (ESRD) exhibit a significant reduction in the overall microbial quantity, and these alterations have been linked to the inflammatory state of the disease and renal function (Jiang et al., 2017; Li et al., 2019). Indoxylsulfate (IS), p-cresylsulfate (pCS) and phenylacetylglutamine (PAG) can serve as early indicators of renal dysfunction, with detectable changes in the gut microbiota associated with these metabolites in early kidney disease (Remuzzi et al., 2015). These findings provide promising insights into potential strategies for kidney disease management through the manipulation of the gut microbiome.

Several factors during CKD, such as inadequate elimination of uremic toxins, medications and antibiotics use, and dietary restrictions, can cause a uremic environment that can further impact the gut microbiota and disrupt the integrity of the intestinal epithelial barrier (Vaziri et al., 2013; Hobby et al., 2019; Steenbeke et al., 2021; Noce et al., 2022). Furthermore, the gut microbiota itself accelerates the deterioration of CKD by producing precursor toxins via the fermentation of dietary aromatic amino acids (Gryp et al., 2020). Taking these findings together, it appears that gut microbiota and kidney function are bidirectional. Even so, functional alterations in gut microbiota and their interaction with metabolic changes associated with CKD remain unclear. Ren et al. found that Blautia decreased in CKD (Ren et al., 2020), while another study found this genus increased in patient cohort (Liu et al., 2021). The particular alterations in the microbial community of CKD have long been a subject of controversy. Factors such as study projects, geographic location, sequencing platform, sequencing region, and analytical method influenced the intergroup differences. In comparison to individual studies, datasets from multiple studies are capable of comprehensively detecting alterations in the composition of microbiota related to CKD through the augmentation of sample size and the elimination of confounding factors. Therefore, conducting integrative analysis on multicenter across different nations with standardized methods can help address these research questions. In this study, we systematically integrated studies concerning the gut microbiota of CKD patients and collected existing 16S rRNA sequencing data from PubMed, NCBI and GMrepo databases. Through rigorous bioinformatics analysis, we examined the variations in bacterial diversity, relative abundance of microbial taxa, and the major metabolic functions within the gut microbiota of CKD patients compared to HC. The results of this study are important for the identification of microbial taxa that may contribute to the development of CKD and serve as future targets that can be targeted for prevention and treatment.

Disturbances in the normal gut microbiome have been associated with the development of various chronic diseases, including cerebrovascular diseases (Liu et al., 2023), obesity (Liu et al., 2017) and diabetic nephropathy (Zhang et al., 2022). Regulating or reshaping gut microbiota is crucial for host health. Li et al. found that polysaccharides and glycosides from Aralia echinocaulis can protect arthritic rats by modulating gut microbiota composition (Li et al., 2021). Wang et al. discovered that Schisandrin improves ulcerative colitis in mice by inhibiting the SGK1/NLRP3 signaling pathway and reshaping gut microbiota (Wang et al., 2023b). We previously reviewed the changes in gut microbiota composition and functionality in CKD patients, explored the inflammatory and immune-related mechanisms by which gut microbiota influence CKD progression, and elucidated the potential application of targeting gut microbiota as a therapeutic approach for CKD (Zhang et al., 2023). There were differences in the composition and diversity of microbial communities in feces from patients with CKD and HC in this study. In patients with CKD, Bacteroidetes and Proteobacteria richness exceeded that of the control individuals dramatically, while Firmicutes richness declined. The main role of Firmicutes is facilitating host energy absorption from the diet (Tabibian et al., 2015), and they are capable of converting polysaccharides into short-chain fatty acids (SCFA) and monosaccharides, which leads to increased energy absorption, and SCFAs can inhibit inflammation and reduce kidney injury (Marzocco et al., 2018). Bacteroidetes can also produce butyric acid, propionic acid, acetic acid, and other SCFAs by fermentation (Hobby et al., 2019). In this study, alterations in the ratio of Firmicutes to Bacteroidetes in the CKD group may indicate the presence of dysbiosis in the gut microbiome and disrupted energy regulation. Moreover, Proteobacteria can induce inflammation by weakening the gut barrier function and synthesizing pro-hypertensive neurotransmitters such as norepinephrine and serotonin. Elevated levels of Proteobacteria may contribute to the development of proteinuric nephropathy and the elevation of blood pressure (Kanbay et al., 2018). Microbes of the genus Blautia, Faecalibacterium, Roseburia, Coprococcus, Lachnospira and Bifidobacterium linked to the generation of SCFAs, particularly butyrate (Louis and Flint, 2009; Jiang et al., 2016). The number of these species was reduced in CKD patients and a reduction in SCFA-producing bacteria is a prominent feature. In this study, fifteen dominant microbial species in CKD and HC groups were identified by LEfSe. Studies have indicated these species such as Blautia producta (Yang et al., 2022), Ruminococcus obeum (Barathikannan et al., 2019), Coprococcus eutactus (Yang et al., 2023), Bacteroides plebeius (Pei et al., 2022) and Prevotella copri (Jiang et al., 2022), which are found to be in reduced abundance in the CKD group, may have potential effects in alleviating host diseases such as hyperlipidemia and colitis. Among them, as a major producer of butyrate, F. prausnitzii had the most significant decrease in relative abundance among CKD patients. As a strict anaerobe, Faecalibacterium is a core commensal microbe in the human gut, with an abundance of up to 10%. Among them, F. prausnitzii is highly important for the human gut and health. A reduction in this bacterium can lead to a weakening of intestinal anti-inflammatory and immune regulatory capabilities, earning it the title of a “key species” in the gut and being recognized as a next-generation probiotic with therapeutic potential (Lopez-Siles et al., 2017; Khan et al., 2023). Jiang et al. identified a negative correlation between Roseburia spp., F. prausnitzii and C-reactive protein levels and renal function, suggesting that a decrease in butyrate-producing bacteria may contribute to CKD-associated inflammation and the progression of disease (Jiang et al., 2016). As a result of these findings, the microbial marker-based classifiers are effective in determining whether a patient has CKD or not. Interestingly, it was found that F. prausnitzii made the greatest contribution in discriminating CKD from HC. This highlights the potential importance of F. prausnitzii for monitoring the progression of CKD. Previous studies have has shown that F. prausnitzii exerts its anti-inflammatory effects and enhances renal function through its butyrate metabolite and the renal GPR-43 receptor (Li et al., 2022). Additionally, it has been reported that amylose resistant starch supplementation (HAM-RS2) increased Faecalibacterium levels and reduced systemic inflammation in CKD patients (Laffin et al., 2019). Besides, a number of recent studies have reported that the depletion of F. prausnitzii in various gastrointestinal disorders, drawing interest in this bacterium as a potential probiotic in the future (Pied et al., 2011; Kabeerdoss et al., 2013; Karlsson et al., 2013; Machiels et al., 2014).

Functionally, the KEGG pathway prediction analysis was categorized into level 1, level 2 and level 3. Level 1 was found to be the most closely associated with metabolism, and level 3 indicated that multiple compounds were being degraded. As a metabolic organ, the kidney can eliminate exogenous substances, including geraniol (Pluym et al., 2022), nitrotoluene (Shen et al., 2023), glycosaminoglycan (Afolayan et al., 2019), and ketone bodies (Halperin and Cheema-Dhadli, 1989). And level 3 related to the biosynthesis of substances, such as ketone bodies, glycosphingolipid (D'Angelo et al., 2013), siderophore group nonribosomal peptides (Swayambhu et al., 2021), lipopolysaccharide (Chen et al., 2015) and ubiquinone and other terpenoid-quinone biosynthesis (Wang et al., 2023a). Remarkably, Wang et al. first reported on the significance of ubiquinone and other terpenoid-quinone biosynthesis pathways in the progression of CKD (Wang et al., 2023a). Chen et al. discovered that microbial genes associated with lipopolysaccharide biosynthesis were more prevalent in advanced stage cases of CKD. Specifically, the serum levels of lipopolysaccharide increased with the severity of renal dysfunction and reached its peak in the advanced stage of CKD (Chen et al., 2015). Additionally, most of the other KEGG pathways are related to metabolism. Phosphate is a key uremic toxin that is linked to unfavorable results. With the progression of CKD, the kidney’s ability to eliminate excessive dietary phosphate diminishes. This reduction in excretion prompts compensatory endocrine responses, leading to the development of CKD-mineral and bone disorder (CKD-MBD) (Favero et al., 2021). Ketteler conducted a review that suggests the need to assess the effectiveness of phosphate-lowering strategies for patients in CKD stages 3-5 in order to slow down the progression of CKD-MBD (Ketteler, 2011). There are several instances of medications metabolized predominantly by cytochrome P450 (CYP) that exhibit modified pharmacokinetics in CKD. The altered CYP metabolism can potentially contribute to variations in drug responses and the occurrence of adverse drug events among CKD patients, necessitating potential dosing adjustments (Ladda and Goralski, 2016). Previous studies have confirmed a negative correlation between phenylalanine levels and diminished renal function, suggesting its potential as a valuable predictor of CKD incidence and prevalence (Lee et al., 2020). Glutathione, a crucial intracellular antioxidant found in nearly all tissues, including the kidney. Dong et al. discovered that rewiring the metabolism of glutathione shields renal tubule cells from apoptosis and ferroptosis induced by cisplatin (Dong et al., 2023).

As a commensal bacterium, F. prausnitzii possesses anti-inflammatory properties and has been reported to mitigate the extent of acute (Sokol et al., 2008) and chronic (Martín et al., 2014) inflammation in murine models. These beneficial effects are attributed, at least in part, to the secretion of metabolites that can impede NF-κB activation and restrain IL-8 production (Sokol et al., 2008), while simultaneously promoting upregulation of regulatory T cell generation (Qiu et al., 2013). On the basis of the functional prediction analysis, six pathways linked to the abundance of F. prausnitzii were identified, and three of these pathways exhibited a negative correlation. Lipoic acid metabolism (Solmonson and DeBerardinis, 2018) and ubiquinone and other terpenoid quinone biosynthesis (Abby et al., 2020) pathways are both closely associated with oxidative stress. Additionally, the proximal tubule bicarbonate reclamation plays a vital role in secreting acid into the renal tubules, facilitating the reabsorption of approximately 80% of bicarbonate (HCO₃ ^-) to regulate the acid-base balance in systemic blood. According to the above findings, a new hypothesis for the mechanism of CKD is presented. F. prausnitzii may contribute to regulate acid-base disturbance and pro-oxidant metabolism in the gut and host, thereby ameliorating the progression of CKD. Possible mechanisms for the impact of CKD by F. prausnitzii were summarized in KO genes and KEGG pathways ( Figure 6 ). Using gut microbiome as a therapeutic target for CKD, this finding is of great significance and might result in the development of new therapies.

Admittedly, one of the main limitations of this study is that we did not differ the clinical staging, the etiology of CKD, and treatment received among the CKD groups, which is due to the uncompleted documentation of the public databases. The disease status, particularly conditions like hypertension (Yan et al., 2020) and diabetes (Mosterd et al., 2021), was the primary factor shaping the microbial profiles associated with CKD. Additionally, differences in treatment methods within the CKD group were not taken into analysis. It has been reported that different hemodialysis techniques can impact the gut microbiome in patients with uremia (He and Xie, 2020). Therefore, it is crucial for future basic and clinical research to assess the influence of these factors on the microbial profile of CKD patients.

Our study offers a detailed comparison of the variances in the fecal microbiota between CKD and healthy people. Specifically, the influence of F. prausnitzii on CKD suggests a crucial connection between metabolic processes and the gut microbiota in the pathogenesis of CKD. Meanwhile, our study identified certain gut bacteria that are potentially associated with CKD as biomarkers. This study offers a novel perspective on how gut microbiota can affect kidney disease in a vast population, and it may benefit future early detection and monitoring of CKD.

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

Ethical approval was not required for the studies involving humans because the data used in this study are 16s rRNA sequencing data from public databases. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements because the data used in this study are 16s rRNA sequencing data from public databases. Written informed consent was not obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article because the data used in this study are 16s rRNA sequencing data from public databases.

YZ: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis. WCZ: Writing – original draft, Visualization, Investigation, Formal analysis. WL: Writing – review & editing, Methodology, Visualization, Formal analysis. XW: Writing – review & editing, Resources, Methodology, Investigation. GL: Writing – original draft, Methodology, Data curation. JL: Writing – original draft, Project administration, Data curation. JF: Writing – original draft, Visualization, Investigation. XM: Writing – review & editing, Resources, Conceptualization. SJ: Writing – review & editing, Visualization, Investigation. JH: Writing – review & editing, Writing – original draft, Project administration, Methodology, Conceptualization. WJZ: Writing – original draft, Writing – review & editing, Supervision, Project administration, Funding acquisition, Conceptualization. ZZ: Writing – original draft, Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization.