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. 2025 Aug 15;104(33):e42492. doi: 10.1097/MD.0000000000042492

Gut microbiome and diabetic kidney disease: Insights from Mendelian randomization and genetic correlations

Kongming Yang a,b, Zhihan Zhao c, Yuanping Yin a,b,*, Yuhan Liu a,b, Xinyue Wu d
PMCID: PMC12366943  PMID: 40826771

Abstract

The causal effects of specific gut microbiota (GM) on the development of diabetic kidney disease (DKD) have not yet been revealed. We employed independent single nucleotide polymorphisms from 196 gut bacterial taxa (N = 18,340) as instrumental variables in a 2-sample Mendelian randomization framework. DKD summary statistics were sourced from publicly accessible genome-wide association study databases, the FinnGen Consortium R9 publications, and the Juvenile Diabetes Research Foundation-funded collaborative research program. The analysis was conducted on outcome data from various sources using GM data as exposure. DKD outcomes from different sources were meta-analyzed based on 196 GM classifications. Furthermore, we evaluated the genetic association between specific GM populations and DKD by using the linkage disequilibrium score regression approach. Results showed Coprococcus2 (odds ratios [OR] = 0.8297, 95% confidence interval [CI]: 0.7427–0.9268, P = .0009) and Defluviitaleaceae (OR = 0.8802, 95% CI: 0.8121–0.9539, P = .0019) offer protection, while Bacteroidetes (OR = 1.1869, 95% CI: 1.0261–1.3729, P = .0211), Lachnoclostridium (OR = 1.1602, 95% CI: 1.0267–1.3111, P = .0172), and Veillonellaceae (OR = 1.0998, 95% CI: 1.0183–1.1879, P = .0154) increase risk. This study establishes a causal relationship between GM and DKD and highlights potential protective and risk-related microbial taxa.

Keywords: causal inference, diabetic kidney disease, genetic correlations, intestinal flora, Mendelian randomization study

1. Introduction

Gut microbiota (GM) balance is crucial for metabolic functions. Diabetic kidney disease (DKD), a metabolic complication, is linked to glucose metabolism dysfunction. Mendelian randomization (MR) and genetic studies reveal their complex relationship. DKD, a chronic kidney disease and frequent diabetes complication, is the primary cause of end-stage renal disease.[1] Similar to diabetes, the prevalence of DKD is increasing, resulting in significant healthcare costs, making it a health and social issue.[2] DKD is expected to affect approximately 40% of patients with type 2 diabetes (T2DM) by 2045.[3] Therefore, DKD poses a major threat to human life and health, and its mechanisms are complex and multifactorial.

GM has emerged as a potential factor in DKD pathogenesis and progression. Host genetics can influence the abundance of many microbial groups.[4] The GM of healthy adults consists mainly of thick-walled bacteria Bacteroidetes, Aspergillus, and Actinobacteria, with thick-walled bacteria and Bacteroidetes constituting approximately 90% of the composition.[5] Under normal physiological conditions, GM maintains relatively stable species and numbers to maintain the integrity of the intestinal mucosal barrier.[6]

Imbalances in GM have been implicated in the pathogenesis of DKD based on available evidence.[7] Previous studies have indicated that certain bacteria such as Lactobacillus fermentum, Lactobacillus plantarum, Lactobacillus casei, Mucor Akerman, and Mimicobacterium fragilis may mitigate the risk of diabetes by lowering pro-inflammatory markers and preserving the integrity of the intestinal barrier.[8,9] These bacteria have also been shown to improve glucose metabolism and insulin sensitivity while inhibiting pro-inflammatory cytokines.[10] However, studies have shown increased proportions of Paramecium spp.,[11] Enterococcus spp.,[12] Enterobacteriaceae,[13] and Klebsiella spp.[12] in patients with chronic kidney disease compared with others. This suggests that an increase in GM in these 4 groups may have contributed to the exacerbation of DKD.

According to the gut–kidney axis theory, kidney disease typically results in GM dysbiosis.[14] However, the correlation between GM diversity and DKD remains controversial due to inconsistent results. GM abundance in patients with DKD was reported to be significantly lower than that in healthy controls (HCs), while GM diversity remains unchanged.[15] A previous study found that GM diversity in the DKD group was similar to that in the control group. However, GM abundance in the DKD group was significantly higher than that in the T2DM group.[16] Nevertheless, a systematic evaluation of 42 studies on the GM of patients with T2DM showed no significant correlation between GM diversity and T2DM.[17]

Confounding factors complicate causal inference and influence GM–DKD association. This study used MR to estimate the causal relationship between GM and DKD, finding a significant difference in GM diversity and implying a potential causality.

MR has been widely used to explore the causal relationship between GM and various diseases including metabolic disorders,[18] autoimmune diseases,[19] rheumatoid arthritis,[20] and cancer.[21] This approach allowed us to analyze the effects of specific genetic variants on GM and to investigate how these variants modulate the risk of DKD. The strength of this approach lies in its ability to evaluate the direct impact of GM on disease risk while considering the genetic background.

A 2-sample MR analysis was conducted to determine the causal relationship between the GM and DKD using genome-wide association studies (GWASs) pooled statistics from the MiBioGen and FinnGen consortia. In recent years, statistical methods based on GWAS have been proposed to estimate correlations and causal relationships among traits. LD score regression (LDSC) can assess genetic correlations for GWAS-pooled statistics and is unaffected by sample overlap.[22] The present study evaluated the causal and genetic correlations between genetically predicted GM and DKD by using MR and LDSC.

2. Method

2.1. Study design

This study categorized each bacterial taxon in the GM as a separate exposure. Two-sample MR analysis was conducted using publicly available summary-level data from GWAS, FinnGen studies, and the International Consortium for Genomics to determine which bacterial taxa had a causal effect on DKD. Figure 1 provides an overview of the study’s design. The aggregated data selected in this study pertain solely to individuals of European descent, and do not encompass issues of population diversity. An MR study must satisfy 3 key assumptions: (1) genetic variation is linked to the exposure of interest, (2) genetic variation is independent of confounding factors, and (3) genetic variation affects the outcome only through the exposure of interest.[23] The study design followed the STROBE-MR guidelines,[24] which can be found in the supplementary material. Furthermore, according to the original annotation documents of the aggregated data, this study did not pertain to autoimmune diseases (see Files S1–S3, Supplemental Digital Content, http://links.lww.com/MD/P722; https://links.lww.com/MD/P650; https://links.lww.com/MD/P651, which details the specific information regarding the 3 DKD database sources). The corresponding review boards approved all studies included in the cited GWASs. Informed consent was obtained from all participants, and ethical clearance was not required for studies based on the summarized data (see Table 1, which details all the data sources used in this study).

Figure 1.

Figure 1.

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Overview of the research workflow. IVs = instrumental variables, LD = linkage disequilibrium, LDSC = LD score regression, MR = Mendelian randomization, SNPs = single nucleotide polymorphisms.

Table 1.

The cohorts included in this study.

Type Traits Year Number Decent N case N control PMID
Exposure Gut microbiota 2021 18,340 Mixed NA NA 33462485
Outcome
Diabetic kidney disease		 ebi-a-GCST90018832 2021 452,280 European 1032 451,248 34594039
finngen_R9_DM_NEPHROPATHY_EXMORE 2023 312,650 European 4111 308,539 36653562
finngen_R9_DM_SEVERAL_COMPLICATIONS 2023 288,137 European 16,320 271,817 36653562
finngen_R9_E4_DMNASNASCOMP 2023 309,319 European 1039 308,280 36653562
CMDKP 2021 19,406 European NA NA 31537649
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2.2. DKD data sources

DKD resources were obtained from the GWAS database, Common Metabolic Diseases Knowledge Portal (CMDKP) consortium study, and FinnGen consortium R9 release. The studied populations were of European ancestry, totaling 452,280 individuals (1032 cases and 451,248 controls) from the GWAS data.[25] To investigate the causal relationship between GM and DKD, we obtained separate GWAS data for type 1 diabetes mellitus (T1DM) and T2DM patients. The definition of DKD in this study adhered to the International Classification of Diseases, 10th Revision. According to the original annotation files of the aggregated data (see Files S1–S3, Supplemental Digital Content, http://links.lww.com/MD/P722; https://links.lww.com/MD/P650; https://links.lww.com/MD/P651, which details the specific information regarding the 3 DKD database sources), both “diabetic nephropathy” and “diabetic kidney disease” were included in the disease definition for this research. The FinnGen Consortium collects health and genetic data from the Finnish Health Registry. We obtained statistically summarized data on T2DM with renal complications from the FinnGen database’s R9 data release of the FinnGen database (see File S4, Supplemental Digital Content, https://links.lww.com/MD/P652, which details the specific information regarding the FinnGen R9).[26]

Furthermore, we acquired data from a novel international genomics consortium, CMDKP, owing to the limited availability of outcome GWAS resources. CMDKP is a Collaborative Diabetic kidney disease Research Program funded by the Juvenile Diabetes Research Foundation. The study describes 10 case–control definitions of DKD based on urinary albumin excretion rate and estimated glomerular filtration rate[27,28] (see File 3, Supplemental Digital Content, https://links.lww.com/MD/P651, which provides detailed phenotypic definition information and data regarding the duration of diabetes). The project aims to obtain the GWAS meta-analysis of the consortium, which includes association results from up to 19,406 patients with T1DM of European descent (see Files S5 and S6, Supplemental Digital Content, https://links.lww.com/MD/P653; https://links.lww.com/MD/P654, which details the CMDKP of cohorts contributing to analyses). Owing to constraints imposed by the study consent form and EU and national regulations, individual-level genotype data could not be shared for all cohorts.

2.3. Selection of the instrumental variables (IVs)

The genetic IVs for each GM taxon were obtained from the MiBioGen International Consortium. The consortium collected information from 24 population-based cohorts, with a total of 18,340 individuals. Each cohort investigated GM by 16S rRNA sequencing and genotyped 14,363 participants of European ancestry using a genome-wide single nucleotide polymorphism (SNP) array (see Table 2, which details the cohorts included in the gut microbiota meta-GWAS).

Table 2.

The cohorts included in gut microbiota meta-GWAS.

Acronym Study design Ethnicity Country N samples GWAS
BSPSPC Population-based European Germany 721
CARDIAb Population-based AfroAmerican USA 114
CARDIAw Population-based European USA 257
COPSAC Children European Denmark 380
DanFunD16 Population-based European Denmark 2396
FGFP Population-based European Belgian 2259
FOCUS Population-based European Germany 960
GEM_HCE_v12 Population-based European Canada 378
GEM_HCE_v24 Population-based Admixed Canada 203
GEM_ICHIP_HCE Population-based European Canada 662
GenR Population-based Multiethnic The Netherlands 1328
HCHS/SOL Population-based Hispanic USA 1097
KSCS Population-based East Asian South Korea 811
LLD Population-based European The Netherlands 875
METSIM Population-based European Finland 522
MIBS Population-based European The Netherlands 80
NGRC Population-based European USA 77
NTR MZ twins study European The Netherlands 279
PNP Population-based Middle-East Israel 481
POPCOL Population-based European Sweden 134
RS3 Population-based European The Netherlands 1220
SHIP Population-based European Germany 996
SHIP-TREND Population-based European Germany 905
TwinsUK Twins study European UK 1205
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GWAS = genome-wide association study.

After excluding 15 unknown bacterial taxa, the final GWAS data obtained in this study covered 196 taxa (sorted by taxonomy), including 9 phyla, 16 orders, 20 families, 32 genera, and 119 species (see Table S1, Supplemental Digital Content, https://links.lww.com/MD/P649, which specifies the 196 taxa enrolled in this study).[29] (1) This decision was based on previous studies, as the number of eligible IVs below the genome-wide significance threshold (P < 5 × 10−8) is minimal.[29–32] (2) An aggregation process was performed (R2 < 0.001, window size = 10,000 kb) to exclude variants with strong linkage disequilibrium (LD) and ensure the independence of each SNP. (3) SNPs with minor allele frequencies <0.01, ambiguous SNPs with incongruent alleles, or palindromic SNPs were excluded. To capture the potential genomic set variants that might be enriched for association and obtain more comprehensive results, a relatively less stringent threshold (P < 1 × 10−5) was chosen.[33–35] For reverse MR analysis, independent SNPs with genome-wide significance (P < 1 × 10−5, R2 < 0.001, window size = 10,000 kb) were selected as IVs for DKD.

2.4. Statistical analysis

This study used the inverse-variance weighted (IVW) method as the primary analytical tool to assess the potential causal effect of each GM variable on DKD risk. The study employed various methods to test the robustness of the results, including the weighted median method, MR-Egger regression, Mendelian randomized multivariate residuals and outliers (MR-PRESSO), and IVW.[36,37] Heterogeneity between the SNP estimates was assessed using the Cochrane Q test. MR-Egger intercepts were tested for estimation-level multivariate validity. Leave-one-out analyses were conducted to identify and eliminate potential outliers that may have independently influenced the observed causal relationships. The MR-PRESSO test was used to detect outliers and adjust for heterogeneity to estimate the significance. If heterogeneity was detected among the IVs, outliers were removed and MR analysis was performed. In this study, the GM IVs for forward MR analysis must meet the following criteria simultaneously: (1) The IVW method, as the primary analytical approach, must have P < .05; (2) the MR-Egger regression analysis must have P > .05; and (3) the MR-PRESSO method for detecting potential outliers must have P > .05. For the reverse analysis, P < .05 obtained for the IVW method is sufficient.

The strength of the IVs was assessed by calculating the F-statistic using F = R2(n − k − 1)/k(1 − R2), where R2 is the proportion of variance explained by the selected SNPs, n is the sample size, and k is the number of IVs. An F-statistic of >10 indicates no significant weak instrumental bias.[38,39] Statistical analyses were performed using R software (version 4.3.2), with the methods and settings consistent with forward MR. The studies used the 2-sample MR (version 0.5.7), MR-PRESSO (version 1.0), and q-value R software packages. This reporting adhered to the STROBE-MR statement.[24]

2.5. Genetic correlation analysis

In this study, genetic correlation (rg) analysis was performed using LDSC to assess the meta-merged GM that affect DKD. We filtered the GWAS summary statistics based on HapMap3 ref. Non-SNP variants and SNPs with ambiguous strands, duplicates, and minor allele frequency <0.01 were excluded. LDSC was used to examine the associations between the test statistic and LD to quantify contributions from true polygenic signals or biases.[40] This approach allows for the assessment of genetic correlations from GWAS summary statistics and is not biased by sample overlap.[22] The z-score for each variant of Trait 1 was multiplied by the z-score for each variant of Trait 2. Genetic covariates were estimated by regressing the product of the LD scores.[41] Genetic covariates normalized by SNP heritability represent genetic correlations. P < 2.5 × 10−4 (0.05/196 = 2.5 × 10−4, after strict Bonferroni correction) was considered statistically significant. Thus, P = 2.5 × 10−4–.05 indicated a potential genetic correlation. However, after applying the false discovery rate correction, the adjusted P value remained above .05. Statistical significance was considered at P < .05.

3. Results

3.1. Taxon IVs

The results were validated by screening for a genome-wide significance threshold (P < 1 × 10−5), LD, Harmonizing, MR-PRESSO, and F-statistic tests. Abnormal SNPs detected by MR-PRESSO (global test: P < .05) were removed, and only 1 SNP was retained for those recurring from different sources. A total of 501 SNPs were identified as IVs. The retained SNPs had F-statistics >10, indicating sufficient correlation with the corresponding bacterial taxa (see Table S2, Supplemental Digital Content, https://links.lww.com/MD/P649, which presents the final list of retained SNPs and their related statistics).

3.2. Causal effects of GM on DKD

During the exploratory phase, preliminary investigations were conducted using the IVW method. Cochran Q test did not reveal significant heterogeneity (see Table S3, Supplemental Digital Content, https://links.lww.com/MD/P649, which presents the results of the initial analyses of the relationship between genetic proxies for GM taxa and DKD risk from various sources). All estimates were expressed as odds ratios (OR) for each standard deviation of the increment in the corresponding exposure. We identified potential causal associations between the 31 taxa and DKD (see Table S4, Supplemental Digital Content, https://links.lww.com/MD/P649, which demonstrates that the meta-merged results of the IVW analyses for these 31 phenotypes reached nominal significance with a P value of <.05). Figure 2 shows a marginally substantial causal relationship between Bacteroidales and DKD risk, determined using the IVW approach in the 196 tested taxon phenotypes. This study found that a high abundance of Bacteroidales in the human gut, as predicted by genetics, was causally associated with an increased risk of DKD (IVW, OR = 1.2756; 95% confidence interval [CI]: 1.1263–1.4447; P = .0001). The IVW method was used to calculate the statistical power of causal inference, which was found to be 0.0001 with a type I error rate of 0.05.

Figure 2.

Figure 2.

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Forest plot (meta-analysis). MR analysis using IVW methods for the combined results of 196 GM on DKD. Statistical significance was set at P < .05. An odds ratio <1 indicates a protective factor, while an odds ratio >1 indicates a risk factor. CI = confidence interval, DKD = diabetic kidney disease, GM = gut microbiota, IVW = inverse-variance weighted, MR = Mendelian randomization, OR = odds ratios.

Based on the Bonferroni correction threshold (0.05/196 = 2.5 × 10−4), we conclude that Bacteroidales has a causal effect on DKD. The strict Bonferroni correction supports this conclusion, with the consistent direction of the weighted median, MR-Egger, and MR-PRESSO results and the meta-analysis results meeting the Bonferroni correction threshold. Furthermore, the IVW method estimated the results with statistical power of 0.93. When one bacterial order was eliminated from the analysis, the P value approached the Bonferroni correction threshold. However, including as many taxa as possible provides a more comprehensive view of the overall impact of GM on DKD. Moreover, leave-one-out analysis revealed no significant association between the SNPs and the results. For the sensitivity analyses, MR-Egger regression analyses showed no evidence of directed pleiotropy (intercept, P > .05).

3.3. Causal effects of DKD on GM

In contrast, 487 SNPs associated with DKD met the IV use criteria. The inverse MR analysis results of the IVW analysis method showed that 11 microorganisms were potentially causally associated with DKD, including the Ruminococcaceae_UCG013 (IVW OR = 1.0454; 95% CI: 1.0070–1.0853; P = .0202) and Enterobacterales (IVW OR = 1.0520; 95% CI: 1.0077–1.0984; P = .0210). The abundance of these gut microbes may correspond to an increased risk of developing DKD. Additionally, Ruminococcaceae (IVW OR = 0.9663; 95% CI: 0.9356–0.9979; P = .0369), Bilophila (IVW OR = 0.9539; 95% CI: 0.9129–0.9968, P = .0354), and Christensenellaceae (IVW OR = 0.9251; 95% CI: 0.8564–0.9993; P = .0480) were significantly reduced compared to DKD (see Table S5, Supplemental Digital Content, https://links.lww.com/MD/P649, which elucidates the MR results of DKD on GM).

3.4. LDSC regression analysis

After meta-merging, we conducted LDSC regression analyses to evaluate genetic correlations between GM and DKD. Some species were excluded from the study because of limitations, such as low heritability and sample size. Ultimately, we obtained 23 genetic correlation estimates using DKD. Figure 3 shows that LDSC found suggestive correlations between Terrisporobacter and Veillonellaceae and DKD. Specifically, Terrisporobacter (rg = 0.5858; P = .0089) and Veillonellaceae (rg = 0.8346; P = .0007) were correlated (see Table S6, Supplemental Digital Content, https://links.lww.com/MD/P649, which presents the results of the genetic correlation analysis).

Figure 3.

Figure 3.

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Heatmap of LDSC regression analysis. rg_p < 0.05 is considered statistically significant. Figure illustrates the suggestive correlations identified by LDSC regression analysis between Terrisporobacter and Veillonellaceae and DKD. DKD = diabetic kidney disease, LDSC = LD score regression.

4. Discussion

This study utilized GWAS summary statistics to explore the potential causal relationships and genetic associations between GM and DKD. These findings suggest a genetic correlation between Terrisporobacter and Veillonellaceae and DKD. In addition, we conducted a 2-sample MR analysis, followed by meta-merging, to evaluate the causal relationship between GM composition and DKD risk. The present study observed that a high abundance of Bacteroidetes, Lachnoclostridium, Coprococcus3, and Veillonellaceae increased the risk of DKD. It is important to note that all evaluations were objective and free of bias. However, when enriched, some GM genera were protective against DKD, including Bacteroides, Clostridium sensu stricto 1e, Enterorhabdus, Clostridiaceae1, and Eubacterium coprostanoligenes. This finding suggests that GM significantly influences the occurrence and development of DKD.

Interestingly, a study revealed that Bacteroidetes are associated with an increased risk of DKD.[42] Moreover, this phylum significantly contributes to renal inflammation through the LPS–toll-like receptor 2/4 (TLR2/4) signaling pathway.[43] Clinical studies have shown that Bacteroidetes are the most abundant gut microorganisms in healthy individuals and patients with DKD, accounting for 41.76% and 40.23% of the total valid reads, respectively.[12] Bacteroidetes are linked to the production of short-chain fatty acids, such as acetate, propionate, and butyrate, which are produced through the fermentation of indigestible carbohydrates.[44] Bacteroidetes primarily produces acetate and propionate, whereas butyrate is mainly produced by Firmicutes.[45] A study on animals investigated the ratio of Firmicutes to Bacteroidetes,[46] potential microbiota biomarkers, and metabolic profiles of bile acids in diabetic nephropathic mice. Bacteroidetes were significantly increased compared with those in the controls.[47] The ratio of Firmicutes to Bacteroidetes was also analyzed to determine diabetic blood glucose levels. Bacteroidetes have been suggested as a potential indicator of blood glucose levels.[10,48–50] Some studies have indicated that elevated or reduced F/B ratios are regarded as ecological disorders, with the former typically associated with obesity and the latter with inflammatory bowel disease.[51,52] According to a previous study,[53] Bacteroidetes, specifically Heterobacterium and anaerobic Bacillus, may contribute to a reduced glomerular filtration rate, while Blautia spp. may have a protective effect against DKD.

GM has been reported to improve insulin response and promote immunomodulation through butyrate production. However, abnormalities in propionate can increase T2DM risk.[33] Additionally, T2DM patients have been reported to have a lower abundance of Bacteroidetes.[54] Coprococcus3 is capable of producing butyrate.[55] A clinical study reported that butyrate levels were reduced in all patients with end-stage renal disease, indicating a decrease in short-chain fatty acid-producing bacteria, specifically butyrate-producing bacteria. As microbial research has evolved, current studies have focused on the linkages between GM, hosts, and pathogens,[56] and these studies have revealed how GM composition can provide resistance or assistance to invasive disease-causing/pathogenic species. In GM, certain commensal species such as Eubacterium cannot synthesize certain amino acids and must be obtained from the contents of the host intestinal lumen or habitat.[57,58]

The present study also found that Lachnospiraceae UCG–001, Lachnospiraceae ND3007, Bacteroidales, Veillonellaceae, Lachnoclostridium, and Terrisporobacter may be associated with an increased risk of DKD. A previous clinical study demonstrated that Lachnoclostridium was enriched in both patients with diabetes and DKD.[15] Information on the effects of the Lachnospiraceae UCG–001 and Lachnospiraceae ND3007 on DKD is unavailable, although both are part of the Lachnospiraceae family. A review and analysis revealed that tyrosine fermentation within this family can result in the conversion of p-cresol, which binds to sulfate or glucuronic acid to form toluene thioglucoside or cresol sulfate.[59] Cresol sulfate affects the viability of HK-2 cells, resulting in cell death, production of reactive oxygen species, and release of inflammatory cytokines.[60,61] Toluene thioglucoside causes phenotypic changes in proximal tubular cells.[62] Protein-bound neurotoxins serve as markers and risk factors for DKD pathogenesis. However, the molecular mechanisms underlying DKD remain unknown and require further investigation. Thus, this study provides a potential avenue for this mechanism.

The study found that several bacterial genera and families, including Bacteroides, Clostridium sensu stricto 1e, Enterorhabdus, Clostridiaceae1, Eubacterium brachy, and E coprostanoligenes, were associated with a reduced risk of DKD. Bacteroides is a genus that plays an important role in the renal protection of the intestine. This was mainly due to the systemic induction of Th1 responses, as demonstrated in previous studies.[63,64] Additionally, a human peripheral blood in vitro culture assay suggested that Bacteroides can increase their ability to colonize and induce FOXP3+ regulatory T cells through the secretion of octa capsular polysaccharide, which contributes to the regulation of biased Th/Treg levels by increasing IL-10.[65]

Prior research has established that TLRs aid in the identification of microbial entities to eliminate pathogens. In contrast, the intestinal microorganism Bacteroides can stimulate TLR pathways on T lymphocytes to promote host–microbe symbiosis. The lack of TLR2 on CD4+ T cells predisposes to an antimicrobial immune response, diminishing the colonization of distinct mucosal ecological niches by Bacteroides during homeostasis.

The Bacteroides symbiotic factor directly activates TLR2 in FOXP3+ Treg cells through a novel process that generates mucosal tolerance. In a retrospective study of 35 patients with renal biopsy-proven DKD, the number of Bacteroides was significantly reduced in the diabetes mellitus and DKD groups compared to that in HCs.[66] In addition to DKD, other kidney diseases showed a similar trend, with a higher relative abundance of Streptococcus and Enterococcus and a lower relative abundance of Bacteroides in patients with IgAN. However, it was recently discovered that phages of Escherichia/Shigella and Bacteroides can activate interferon gamma (IFN-γ)-mediated immune responses through a TLR9-dependent pathway and exacerbate colitis. This could be because Escherichia/Shigella and Bacteroides stimulate IFN-γ through the nucleotide-sensing receptor TLR9, and the resulting immune response is both phage- and bacterial-specific. Additionally, increased phage levels exacerbate colitis via TLR9 and IFN-γ.[67]

This study primarily confirms prior research indicating that a sustained high-glucose environment in patients with DKD elevates the expression of immune-related localization factors. A higher abundance of Clostridium sensu stricto 1e was associated with a lower risk of increased DKD. This is supported by a previous study that reported Clostridium sensu stricto 1e can stimulate regulatory T cells in the colon, favor the maturation of mucosa, and the formation of natural killer T cells and lymphocyte structures.[68] These findings provided a clear basis for the amelioration of DKD. Finally, high abundance of Eubacterium spp. may be associated with a lower risk of DKD. A previous study found that E brachy, Eubacterium eligens, and E coprostanoligenes belong to the same Eubacterium spp. Several studies have found a positive correlation between Eubacterium spp., butyric acid producers, and insulin sensitivity. The present study investigated the potential correlation between GM and T2DM pathophysiology by comparing independent studies of the macrogenomes of healthy individuals and individuals with T2DM.[69,70] Research conducted in Chinese and European populations has indicated substantial decreases in butyrate-producing bacteria, including Eubacterium spp., among individuals with T2DM.[71,72]

This suggests that Eubacterium spp. may play a role in the metabolism of short-chain fatty acids as a component of the intestinal–renal axis, thereby protecting the kidneys. The genetic correlation results of this study suggest a correlation between a higher abundance of Terrisporobacter and Veillonellaceae and an increased risk of DKD, which is consistent with the results of the 2-sample MR after meta-merging.

A previous report on Egyptian patients demonstrated that the abundances of Terrisporobacter and Turicibacter were significantly higher in the control group than in the T1DM or T2DM groups.[73] According to a previous meta-analysis, Veillonellaceae showed an opposite trend to those reported in earlier studies. This study included 16 studies involving 578 patients with DKD and 444 HCs. Compared with HCs, patients with DKD had significantly lower bacterial abundance and a nonsignificant decrease in the diversity index. In addition, there was a significant difference in β-diversity and enrichment in the relative abundance of GM, including Veillonellaceae.[74] The causal effects of Terrisporobacter and Veillonellaceae on DKD were not indicated. Further validation and exploration of the influence of genetic effects are essential, as many other factors limit them.

Currently, treatment of DKD majorly involves lifestyle interventions to control blood pressure, lipids, glucose, and proteinuria.[75] Clinical trials of widely utilized sodium-glucose cotransporter-2 inhibitors have conclusively demonstrated that sodium-glucose cotransporter-2 inhibition is an effective adjunctive strategy to standard treatment for decelerating DKD progression and mitigating certain associated complications.[76] However, managing DKD remains a challenge, with significant residual risk, even with optimal drug therapy.[77]

GM plays a crucial role in the development and progression of DKD. This study aimed to provide new insights into and potential treatment targets for DKD. Targeted interventions, such as fecal microbiota transplants,[78] probiotics,[79] and prebiotics,[80] may be a promising strategy for preventing and treating DKD. Traditional Chinese medicine (TCM) has been extensively investigated for its therapeutic potential against DKD in animal models.[81–86] These studies revealed multiple ameliorative effects of DKD through TCM. The TCM preparation Shen-Qi-Jiang-Tang granules may have the ability to alleviate the hyperglycemic state and improve renal function, renal pathological changes, oxidative stress, and inflammatory responses in patients with DKD. The mechanism of action of DKD may be related to the improvement in GM.[84] Panax notoginseng, a TCM containing Ginsenoside Rb1, effectively reduced foot cell damage in DKD compared with epinastat.[86] Additionally, studies have demonstrated the benefits of combining acupuncture with conventional medications in the treatment of DKD.[87]

This study has some limitations. Demographic factors, diet, and medications affect GM, resulting in variability and low heritability, thereby reducing statistical robustness. GWAS meta-analysis mainly involved Europeans, risking bias, and limited generalizability. This study focused solely on DKD, without addressing other diabetes-related complications. Genetic IVs below the conventional significance threshold were also employed to increase the risk of false positives. Further population-based observational studies and animal experiments are necessary to elucidate the role of GM, including Bacteroides, Clostridium sensu stricto 1e, Enterorhabdus, Clostridiaceae1, and others, in DKD and their potential protective mechanisms against it. The protective effects of Acidaminococcaceae and Victivallaceae on the risk of DKD have also not been reported in clinical studies. Further randomized controlled trials are required to elucidate the protective effects of GM against DKD and their specific protective mechanisms.

5. Conclusion

This study established a causal relationship between GM and DKD, suggesting that physicians should monitor renal function in patients with GM disorders when there is a fluctuation in the abundance of specific bacteria. This study identified specific bacterial taxa linked to DKD that may function as novel biomarkers for future conditions. Additional research on these bacterial taxa may aid in preventing and treating DKD, thereby offering theoretical support for investigating the gut–kidney axis.

Acknowledgments

We would like to extend our sincere gratitude to all the genome-wide association studies (GWASs) consortia that have made the pooled data publicly accessible. Their commitment to open science was instrumental in advancing our research. We also deeply appreciative the numerous investigators and participants who contributed their time, expertise, and resources to these studies.

Author contributions

Conceptualization: Kongming Yang, Zhihan Zhao, Yuanping Yin.

Data curation: Kongming Yang, Yuhan Liu.

Formal analysis: Kongming Yang, Zhihan Zhao, Yuhan Liu.

Funding acquisition: Zhihan Zhao.

Investigation: Kongming Yang, Xinyue Wu.

Methodology: Kongming Yang, Yuanping Yin, Yuhan Liu.

Project administration: Kongming Yang, Yuanping Yin.

Software: Kongming Yang.

Supervision: Yuanping Yin.

Validation: Zhihan Zhao, Yuanping Yin, Yuhan Liu, Xinyue Wu.

Writing – original draft: Kongming Yang, Zhihan Zhao.

Writing – review & editing: Yuanping Yin, Yuhan Liu, Xinyue Wu.

Supplementary Material

medi-104-e42492-s001.xlsx (10.5KB, xlsx)
medi-104-e42492-s002.pdf (712.2KB, pdf)
medi-104-e42492-s003.pdf (57.2KB, pdf)
medi-104-e42492-s004.xlsx (10.5KB, xlsx)
medi-104-e42492-s005.xlsx (24.2KB, xlsx)
medi-104-e42492-s007.xlsx (363.8KB, xlsx)

Abbreviations:

CI
confidence interval
CMDKP
Common Metabolic Diseases Knowledge Portal
DKD
diabetic kidney disease
GM
gut microbiota
GWAS
genome-wide association study
HCs
healthy controls
IFN-γ
interferon gamma
IVs
instrumental variables
IVW
inverse-variance weighted
LD
linkage disequilibrium
LDSC
LD score regression
MR
Mendelian randomization
OR
odds ratios
SNPs
single nucleotide polymorphisms
T1DM
type 1 diabetes mellitus
T2DM
type 2 diabetes mellitus
TCM
traditional Chinese medicine
TLR
toll-like receptor

This study is approved by Liaoning Agricultural Vocational and Technical College 2023 Young Teachers’ Scientific Research Ability Promotion Project (Dean Scientific Research Fund Project) (Project number: Lnzqn202306). This work was supported by the “Xingliao Talent Program” Project of Liaoning Province (Project Number: XLYC2211088).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Yang K, Zhao Z, Yin Y, Liu Y, Wu X. Gut microbiome and diabetic kidney disease: Insights from Mendelian randomization and genetic correlations. Medicine 2025;104:33(e42492).

KY and ZZ contributed to this article equally.

Contributor Information

Kongming Yang, Email: ykm20232023@163.com.

Zhihan Zhao, Email: 2020110536@sdutcm.edu.cn.

Yuhan Liu, Email: 2462760384@qq.com.

Xinyue Wu, Email: wuxinyue111666@163.com.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

medi-104-e42492-s001.xlsx (10.5KB, xlsx)
medi-104-e42492-s002.pdf (712.2KB, pdf)
medi-104-e42492-s003.pdf (57.2KB, pdf)
medi-104-e42492-s004.xlsx (10.5KB, xlsx)
medi-104-e42492-s005.xlsx (24.2KB, xlsx)
medi-104-e42492-s006.txt (762B, txt)
medi-104-e42492-s007.xlsx (363.8KB, xlsx)

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