Dietary composition modulate gut microbiota and related biomarkers in patients with chronic kidney disease

Abstract

Emerging evidence indicates gut microbiota is essential to chronic kidney disease (CKD) progression. This study investigated the association between gut microbiota profiles, plasma trimethylamine-N-oxide (TMAO), and circulating inflammatory markers in CKD patients according to dietary patterns, particularly low-protein, high-fiber (LP-HF) versus high-protein, low-fiber (HP-LF) diet. In this cross-sectional study, patients with non-dialysis CKD and healthy subjects were enrolled. Dietary patterns among participants were assessed using three-day diet records with detailed nutrient analysis. The 16 S ribosomal RNA sequencing was conducted to examine fecal gut microbiota composition. Plasma samples were analyzed for TMAO concentration and cytokine levels. A total of 135 CKD patients were recruited. A distinct shift in gut microbiota composition in CKD patients was observed compared to 19 healthy controls, particularly a significant reduction of short-chain fatty acid (SCFA)-producing bacteria. TMAO and several cytokine levels were significantly elevated in CKD patients compared to healthy subjects. Within CKD, patients with LP-HF diet displayed a greater abundance of SCFA-producing bacteria, such as the Lachnospiraceae NK4A136 group and Eubacterium ruminantium group, than those with the HP-LF diet. The HP-LF subgroup showed enriched proteolytic bacterial genera such as Klebsiella. The HP-LF subgroup also exhibited significantly higher plasma levels of TMAO, interleukin (IL)-18, and monocyte chemoattractant protein-1 (MCP-1). CKD patients displayed marked alterations in gut bacterial composition compared to healthy controls. Our results also highlighted the potential advantages of adopting a high fiber-rich and low-protein diet intake in reducing gut dysbiosis in CKD patients.

Introduction

Modification of dietary habits has long been recognized as a cornerstone in slowing the progression of chronic kidney disease (CKD)^1,2. Previous studies highlighted that high-protein diets exacerbate CKD by promoting glomerular hyperfiltration, interstitial fibrosis, and tubular atrophy^{3, 4–5}. In contrast, recent attention has shifted toward plant-based regimens—especially those rich in fruits and vegetables—which appear to preserve glomerular filtration rate (GFR) through several pathways, including alkali generation to neutralize dietary acid load, provision of bioactive phytochemicals, and antioxidant effects^1,2,6. Reflecting this evidence, the updated Kidney Disease Improving Global Outcomes (KDIGO) CKD guideline recommends that patients with CKD stages G3–G5 consume predominantly plant-derived foods rather than animal-based sources, while maintaining a total protein intake of approximately 0.8 g/kg/day.⁷

The influence of diet on CKD progression is significantly mediated by the gut-kidney axis, involving complex interactions between gut microbiota and their metabolites^8,9. In CKD, disruptions in gut microbial composition (dysbiosis) and increased intestinal permeability can lead to the accumulation of uremic toxins and systemic inflammation, thereby promoting progressive kidney damage¹⁰. This uremic state can, in turn, further exacerbate gut dysbiosis by fostering pathogenic bacterial overgrowth and diminishing beneficial microbes, which compromises gut barrierintegrity.^10,8,11 Furthermore, undigested proteins reaching the distal intestine under uremic conditions can stimulate the proliferation of proteolytic bacteria⁸. These bacteria generate specific protein-bound uremic toxins, including trimethylamine oxide (TMAO), p-cresyl sulfate, and indoxyl sulfate, which are implicated in cardiovascular complications and adverse outcomes among patients with CKD¹⁰. Previous studies have shown that a high-protein diet alters the gut microbiome in mice, leading to increased intestinal permeability and kidney injury¹². In humans, higher protein intake is associated with enrichment of proteolytic bacteria and elevated levels of p-cresyl sulfate and indoxyl sulfate, uremic toxins implicated in cardiovascular and kidney disease^{13, 14–15}.

Although low-protein, plant-based diets are postulated to attenuate CKD progression through favorable modulation of the gut–kidney axis, the specific gut microbiota profiles linked to different dietary patterns in CKD remain poorly defined. To address this gap, we compared the intestinal microbiota of non-dialysis CKD patients adhering to a low-protein, high-fiber diet versus those consuming a high-protein, low-fiber diet. We also measured circulating levels of TMAO and inflammatory cytokines to investigate how these biomarkers vary with diet. Our results may inform tailored dietary counseling that targets gut dysbiosis and associated metabolic markers in the clinical management of CKD.

Methods

Study design and population

This cross-sectional study was conducted at the outpatient clinic of King Chulalongkorn Memorial Hospital in Bangkok, Thailand. Adult patients (age > 18 years) with stages G3-G4 CKD (eGFR 15–59 mL/min/1.73 m²) were included, based on the Thai eGFR equation, which has been validated in the Thai population¹⁶. Patients were excluded if they received immunosuppressive medications or immunomodulating agents, such as monoclonal or polyclonal antibodies, which may affect the gut microbiota profiles^17,18. Additionally, patients receiving prebiotics, probiotics, synbiotics, phosphate binders, or potassium-binding agents were excluded from the study. The recruited patients consented to undergo fecal collection, as described in the following section, alongside routine laboratory investigations during their scheduled outpatient follow-up visit. Each participant was instructed to monitor a diet pattern using a three-day food record. Patients experiencing abnormal bowel movements, such as diarrhea or constipation, were excluded from the study. Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research.

This study received approval from the Ethical Review Board of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand (IRB No. 0436/66). It was conducted following international guidelines for human research protection, as outlined in the Declaration of Helsinki, The Belmont Report, the CIOMS Guideline, and the International Conference on Harmonization in Good Clinical Practice (ICH-GCP). Informed consent was obtained from all subjects and/or their legal guardian(s).

Data collection

Demographic information, including sex, age, and comorbidities, was collected by electronic medical record. Blood samples were collected following an overnight fast of 12 h and analyzed for a complete blood count and comprehensive metabolic panel, including serum creatinine, eGFR, blood urea nitrogen (BUN), lipid profile, uric acid, liver function tests, and electrolytes. Spot urine protein and albumin levels were measured and normalized to urine creatinine, resulting in the urine protein-creatinine ratio and urine albumin-creatinine ratio.

Each patient was assigned a three-day diet record, which included 2 weekdays and 1 weekend day as recommended in the Kidney Disease Outcomes Quality Initiative (KDOQI) guideline¹⁹. Patients were instructed to accurately record the exact amounts and types of food and drink consumed, with guidance from an experienced renal dietitian. All specimens (blood, urine, and fecal) were obtained within five days of completing the three-day diet record, on the same day the record was submitted to the investigator. The data were then analyzed using a national nutrition database program (INMUCAL^®) based on a food composition Table²⁰ The average nutritional information from the 3 days was calculated and used to determine total calorie intake and the composition of macro- and micronutrients for further analysis. In this study, CKD patients were classified into four subgroups according to dietary patterns as follows: high-protein and high-fiber (HP-HF), high-protein and low-fiber (HP-LF), low-protein and high-fiber (LP-HF), and low-protein and low-fiber (LP-LF). Protein intake was divided into two categories: individuals consuming ≥ 0.8 g/kg/day were classified as having high protein intake, while those consuming < 0.8 g/kg/day were classified as having low protein intake. Fiber intake was categorized based on the median intake of the study population, which was 8 g/day.

Fecal collection, DNA extraction and sequencing

Fecal samples were collected from all participants, each providing approximately 1 gram of stool in DNA/RNA Shield Fecal Collection Tubes (Zymo Research, USA). After collection, the samples were thoroughly mixed and stored at -80 °C until further processing. DNA extraction was performed using the ZymoBIOMICS DNA Miniprep Kit (Zymo Research, USA), following the manufacturer’s protocol. The purity and concentration of the extracted DNA were assessed using a DeNovix™ UV-Vis spectrophotometer, and the samples were preserved at -20 °C for subsequent analysis. For 16 S rRNA gene sequencing, the V4 hypervariable region was amplified using the primers 515 F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). Paired-end sequencing (2 × 300 bp) was performed using the Illumina MiSeq platform (San Diego, CA, USA) at ModGut Co., Ltd. (Bangkok, Thailand).

Data processing and analysis

The raw sequencing data from the Illumina platform were processed using the nfcore/ampliseq pipeline. Paired-end reads were merged to reconstruct the V4 region of the 16 S rRNA gene, with overlapping sequences aligned and chimeras removed via DADA2. Unique sequence features were then assigned using the ASV approach, resulting in a microbial feature table. Taxonomic classification was performed with the SILVA 138.1 database. Further analyses—including relative abundance, alpha and beta diversity—were conducted using the MicrobiomeAnalyst web platform, with the Mann-Whitney U and Kruskal-Wallis tests comparing the top 50 genera. Predictive functional profiling was performed using PICRUSt2 and the MetaCyc database.

Quantification of fecal BCoAT gene

The quantification of the butyryl-CoA: acetate CoA transferase (BCoAT) gene in fecal samples, which reflects the production of butyrate by gut microbiota, was investigated by qPCR using 4X CAPITAL™ qPCR Green Master Mix (Biotech Rabbit) with the degenerate primers: (forward primer) 5’-GCIGAICATTTCACITGGAAYWSITGGCAYATG-3’ and (reverse primer) 5’-CCTGCCTTTGCAATRTCIACRAANGC-3’ as described previously²¹. Briefly, the qPCR conditions were initiated by a DNA-denaturation step at 95 °C for 15 min, followed by 40 cycles of denaturation at 95 °C for 15 s, annealing at a primer-specific temperature for 20 s, and extension at 72 °C for 30 s. The quantification of the BCoAT gene in each fecal sample was measured and normalized using the V4 gene, representing the total bacteria.

Plasma biomarker analysis

Peripheral blood specimens were collected from the patients, handled within two hours to separate plasma, and then preserved at -80 °C. Plasma intestinal fatty acid binding protein (I-FABP), representing intestinal permeability,²² was measured using an ELISA kit (Hycult Biotech, Uden, The Netherlands) after being diluted at 1:2, following the manufacturer’s protocol.

Plasma TMAO levels were analyzed using ultra-high performance liquid chromatography-tandem spectrometry (UHPLC–MS/MS), as described previously²³. Plasma cytokine levels were assessed using the LEGENDplex™ Human Cytokine Panel 1 (BioLegend, San Diego, CA, USA)²⁴.

Statistical analysis

Statistical analysis of the variables was performed by using SPSS (version 22.0.0, SPSS Inc., Chicago, IL, USA), GraphPad Prism (version 9.5.0, Boston, MA, USA), and R program (version 4.2.0). Categorical data were assessed using the Chi-square test and one-way ANOVA. The Mann-Whitney Test was used to compare non-paired groups, while the Kruskal-Wallis Test was applied to compare medians across three or more independent groups. Spearman’s rank test was conducted to evaluate the correlations between parameters. A P-value of less than 0.05 was considered statistically significant.

Results

Baseline characteristics of participants

Nineteen healthy controls and 135 patients with CKD were recruited. The baseline clinical characteristics of the patients and healthy controls are shown in Table 1. Compared with healthy controls, the CKD group was older and had significantly higher body mass index (BMI), HbA1c, and serum triglyceride levels. CKD patients were in stages G3 and G4. The prevalence of CKD subgroups by dietary patterns was 36 (26.7%) HP-HF, 32 (23.7%) HP-LF, 17 (12.6%) LP-HF, and 50 (37.0%) LP-LF. Supplementary Table S1 demonstrates the clinical characteristics of CKD patients in the HP-LF versus LP-HF subgroups. There were no significant differences in clinical parameters between these subgroups, except for intake of dairy protein and fiber.

Table 1. Characteristics of healthy controls and all patients with chronic kidney disease.

Variable	All CKD patients (n = 135)	Healthy controls (n = 19)	p-value
Age, years	65.4 ± 12.3	40.3 ± 13.3	< 0.001*
Male	73 (54%)	6 (32%)	0.066
BMI, kg/m2	25.4 ± 4.8	21.4 ± 2.1	< 0.001*
Diabetes mellitus	82 (59%)	-	NA
Hypertension	71 (51%)	-	NA
HbA1c, %	6.2 ± 1.1	5.2 ± 0.4	< 0.001*
LDL, mg/dL	108.2 ± 74.8	142.4 ± 33.8	0.156
Triglyceride, mg/dL	123.8 ± 74.4	81.2 ± 20.3	0.015*
Albumin, mg/dL	4.2 ± 0.5	4.4 ± 0.3	0.216
Hemoglobin, g/dL	11.8 ± 2.1	12.9 ± 1.4	0.088
Creatinine, mg/dL	2.07 ± 1.08	0.75 ± 0.13	< 0.001*
eGFR, mL/min/1.73 m2	43.3 ± 12.7	104.9 ± 16.8	< 0.001*
Chronic kidney disease stage 3a 3b 4	66 (48%) 48 (36%) 22 (16%)	- - -	NA
UPCR, g/gCr	1.6 ± 2.8	-	NA

Data are as shown as mean ± SD or n (%), *p < 0.05, BMI; body mass index, eGFR; estimated glomerular filtration rate, LDL; low-density lipoprotein, UPCR; urine protein-creatinine ratio.

Table 2. The median relative abundance of key bacterial taxa (A) healthy controls vs. all patients with chronic kidney disease (B) the low protein-high fiber diet vs. the high protein-low fiber diet.

Bacteria	CKD	Healthy	p-value*
	median(IQR)	median(IQR)
(A)
Bifidobacterium	0.002 (0.000-0.008)	0.017 (0.006–0.038)	0.000
Ruminococcus	0.011 (0.002–0.027)	0.018 (0.008–0.024)	0.000
Lachnoclostridium	0.014 (0.009–0.020)	0.007 (0.004–0.011)	0.001
Coprococcus	0.005 (0.000-0.011)	0.011 (0.008–0.016)	0.003
Erysipelotrichaceae UCG003	0.000 (0.000-0.005)	0.005 (0.002–0.010)	0.004
Paraprevotella	0.000 (0.000-0.005)	0.005 (0.001–0.008)	0.008
Sutterella	0.006 (0.000-0.012)	0.013 (0.006–0.016)	0.008
Eubacterium ruminantium group	0.000 (0.000-0.001)	0.002 (0.000-0.010)	0.011
Subdoligranulum	0.006 (0.000-0.019)	0.014 (0.004–0.028)	0.023
Faecalibacterium	0.047 (0.011–0.076)	0.066 (0.050–0.092)	0.032

Bacteria	High protein-low fiber diet	Low protein-high fiber diet	p-value
	median(IQR)	median(IQR)
(B)
Romboutsia	0.006 (0.001–0.012)	0.000 (0.000-0.005)	0.017
Clostridium sensu stricto 1	0.006 (0.001–0.011)	0.001 (0.000-0.005)	0.031
Klebsiella	0.005 (0.001–0.018)	0.000 (0.000-0.005)	0.041
Lachnospiraceae NK4A136 group	0.003 (0.000-0.007)	0.008 (0.002–0.016)	0.043
Eubacterium ruminantium group	0.000 (0.000–0.000)	0.000 (0.000-0.009)	0.045

*Independent-Samples Mann-Whitney U Test.

Table 3. Comparison of plasma cytokine levels (A) healthy controls vs. all patients with chronic kidney disease (B) patients with a high protein-low fiber diet vs. low protein-high fiber diet.

Cytokine levels	All CKD patients (n = 135)	Healthy controls (n = 19)	p-value
(A)
IL-1β	12.3 ± 13.4	7.0 ± 5.5	0.091
IL-6	16.1 ± 29.1	4.9 ± 4.3	0.097
IL-8	51.8 ± 53.1	20.1 ± 12.1	< 0.001*
IL-10	20.5 ± 49.3	6.6 ± 3.9	0.222
IL-12p70	12.6 ± 25.0	4.8 ± 4.5	0.005*
IL-17 A	1.4 ± 5.7	0.4 ± 0.4	0.445
IL-18	749.7 ± 601.3	409.3 ± 186.8	< 0.001*
IL-23	27.2 ± 56.5	7.9 ± 6.3	0.145
IL-33	184.4 ± 374.2	133.9 ± 88.1	0.584
IFN-α2	6.1 ± 41.4	2.2 ± 2.3	0.679
IFN-γ	15.6 ± 23.9	12.1 ± 11.5	0.536
TNF-α	1.4 ± 5.7	0.4 ± 0.4	0.445
MCP-1	485.2 ± 261.1	284.9 ± 133.7	< 0.001*

Cytokine levels	High protein-low fiber diet (n = 32)	Low protein-high fiber diet (n = 17)	p-value
(B)
IL-1β	13.5 ± 14.4	11.0 ± 12.4	0.273
IL-6	9.1 ± 9.9	8.7 ± 7.3	0.881
IL-8	32.0 ± 33.6	23.4 ± 17.4	0.334
IL-10	7.3 ± 5.9	6.1 ± 5.2	0.500
IL-12p70	11.7 ± 29.2	6.1 ± 9.5	0.134
IL-17 A	1.5 ± 6.9	1.2 ± 3.9	0.805
IL-18	854.2 ± 809.7	408.5 ± 197.5	0.031*
IL-23	10.7 ± 12.7	8.1 ± 6.6	0.560
IL-33	106.0 ± 178.0	58.8 ± 77.1	0.370
IFN-α2	9.2 ± 57.8	3.0 ± 9.3	0.404
IFN-γ	7.8 ± 10.2	6.6 ± 6.6	0.659
TNF-α	1.2 ± 3.9	1.5 ± 6.9	0.805
MCP-1	416.2 ± 194.8	288.8 ± 187.9	0.032*

Data are shown as mean ± SD (ng/ml), *p < 0.05, IL; interleukin, IFN; interferon, TNF; tumor necrosis factor, MCP; monocyte chemotactic protein.

The alpha and beta diversities of gut microbiota

To identify alterations in alpha diversity between all CKD patients and healthy controls, the Chao1, Shannon, Simpson, and the observed features were used (Fig. 1). There were no significant differences between CKD patients and healthy controls in these indices. Moreover, differences in alpha diversity among the patient subgroups were not significant. These findings indicated that the community richness and evenness were not associated with the dietary patterns of the patients.

The beta diversity based on Bray-Curtis dissimilarity was further analyzed for similarities and differences among microbial communities. Our data showed that the CKD clusters differed significantly from healthy controls (PERMANOVA, p-value 0.003, Fig. 2A). However, there was no significant difference in the bacterial communities among patient subgroups (PERMANOVA, p-value 0.052, Fig. 2B), indicating no distinct clustering of gut microbiota composition based on dietary patterns.

Fig. 1 [Images not available. See PDF.]

Alpha-diversity differences between CKD patients and healthy controls. (A) Chao1 index, (B) Shannon index, (C) Simson index, (D) Observed feature.

Fig. 2 [Images not available. See PDF.]

Principal Coordinate Analysis (PCoA) beta diversity plots based on Bray Curtis’s distance. (A; left figure)) CKD vs. healthy control (B; right figure) subgroups of CKD vs. healthy control.

Differential abundance of microbial features

At the genus level, healthy individuals had a significantly higher abundance of SCFA-producing genera, including Bifidobacterium, Ruminococcus, Eubacterium, Coprococcus, Subdoligranulum, and Faecalibacterium, than CKD patients. In contrast, CKD patients showed enrichment of Lachnoclostridium, a genus linked to increased TMAO and atherosclerosis, compared with healthy subjects (Fig. 3).

Fig. 3 [Images not available. See PDF.]

Gut microbial composition between all CKD patients and healthy controls at the genus level.

We performed a differential abundance analysis of the top 50 genera using the independent-samples Mann-Whitney U test to identify significant differences among groups. The analysis revealed ten bacterial genera that differed between healthy individuals and patients with CKD, as shown by median relative abundances with interquartile ranges presented in Table 2A. SCFAs-producing bacteria, including Bifidobacterium, Ruminococcus, Coprococcus, ErysipelotrichaceaeUG003, Paraprevotella, Sutterella, Eubacterium ruminantium group, Subdoligranulum, and Feacilbacterium were significantly more abundant in the healthy group, whereas CKD patients had a higher abundance of Lachnoclostridium.

Further subgroup analysis divided CKD patients into two groups, LP-HF and HP-LF, to compare gut microbial composition (Fig. 4). The results revealed distinct differences in microbial abundance between the two dietary groups. Notably, the LP-HF group exhibited increased abundance of SCFA-producing bacteria, including the Lachnospiraceae NK4A136 group and the Eubacterium ruminantium group. In contrast, the HP-LF group showed enrichment of pathogenic bacteria such as Romboutsia, Clostridium sensu stricto 1, and Klebsiella, which have been associated with gut dysbiosis and inflammation. These findings highlight the potential impact of dietary macronutrient composition on the gut microbiota, which may influence CKD progression and metabolic health (Table 2B).

Fig. 4 [Images not available. See PDF.]

Gut microbial composition between subgroups of CKD patients and healthy controls at the genus level.

Predicted functional pathways based on differential abundances

Prediction of the most represented functions of the microbial communities between the HP-LF and LP-HF subgroups was performed. Figure 5 demonstrates 14 different pathways between the two subgroups based on the MetaCyc Metabolic Pathway Database. Compared with the LP-HF subgroup, several alcohol-related pathways predominated in the HP-LF subgroup, such as acetyl CoA fermentation to butanoate II, glycerol degradation to butanol (butyl alcohol), and the super-pathway of 2,3-butanediol synthesis.

Fig. 5 [Images not available. See PDF.]

Functional pathways performed with PICRUSt2 based on the MetaCyc Metabolic Pathway Database between the HP-LF and LP-HF diet.

Fecal BCoAT, plasma I-FABP, and TMAO levels

We further evaluated fecal BCoAT, a reliable semiquantitative assay for butyrate production by gut microbiota²¹. Compared with healthy controls, BCoAT expression was significantly lower in all CKD patients (0.033 ± 0.021 vs. 0.011 ± 0.009, p = 0.007). Among CKD subgroups, fecal BCoAT concentration were comparable (one-way ANOVA, p = 0.918) (Fig. 6A).

Plasma I-FABP levels, representing gut epithelial permeability, were also examined. Overall, plasma I-FABP levels differed significantly between all CKD patients and healthy controls (2,385.9 ± 1,751.0 vs. 432.8 ± 279.9 ng/ml, p < 0.001). Within CKD subgroups, there was no significant difference across dietary patterns (one-way ANOVA, p = 0.966) (Fig. 6B).

Fig. 6 [Images not available. See PDF.]

Levels of biomarkers between healthy controls vs. all CKD and among subgroups of CKD (A) fecal BCoAT, (B) plasma I-FABP, and (C) plasma TMAO.

Additionally, plasma TMAO levels in all CKD patients were significantly higher than those in healthy controls (11.46 ± 13.00 vs. 2.43 ± 3.14 µM, p < 0.001). TMAO levels in the HP-HF and HP-LF subgroups were comparable (14.28 ± 13.14 vs. 15.64 ± 20.08, p = 0.741). Similarly, circulating TMAO concentrations did not differ significantly between the LP-HF and LP-LF diets (6.06 ± 3.16 vs. 8.60 ± 6.70, p = 0.140). However, TMAO levels were significantly lower in the LP-HF subgroup compared with the HP-HF and HP-LF subgroups (p = 0.001 and p = 0.013, respectively). TMAO levels in the LP-LF subgroup were significantly lower than those in the HP-HF subgroup (p = 0.021) and showed a non-significant trend toward lower levels than the HP-LF subgroup (p = 0.063) (Fig. 6C).

Plasma cytokine levels

CKD patients exhibited significantly higher plasma levels of IL-8, IL-12p70, IL-18, and MCP-1 than healthy controls (Table 3A). However, other cytokines did not differ between the CKD group and healthy individuals. Moreover, the HP-LF subgroup had significantly higher plasm IL-18 and MCP-1 levels than the LP-HF subgroup (854.2 ± 809.7 vs. 408.5 ± 197.5 ng/ml, p = 0.031, and 416.2 ± 194.8 vs. 288.8 ± 187.9 ng/ml, p = 0.032, respectively) (Table 3B).

Correlations between microbial genera and clinical parameters

Figure 7 illustrates associations between gut microbial genera and key clinical parameters. For example, the relative abundance of Bifidobacterium was negatively associated with TMAO and I-FABP levels but positively associated with eGFR. In contrast, the abundance of Romboutsia was negatively associated with eGFR. Moreover, the relative abundance of Feacilbacterium was positively correlated with Fecal BCoAT expression.

Discussion

Gut dysbiosis, defined as an imbalance in microbial diversity and composition, with decreased beneficial bacteria and overgrowth of potentially pathogenic bacteria, is well recognized in patients with CKD¹⁰. However, data on gut bacterial alterations relating to specific dietary patterns, particularly between the LP-HF and HP-LF subgroups, are limited. In this study, we aimed to compare the gut microbial profiles of patients with CKD stages G3-G4 and healthy subjects by analyzing fecal samples. Our results showed that CKD patients displayed distinct gut microbial compositions, including reductions in SCFA-producing bacteria and fecal BCoAT levels compared with healthy individuals. Additionally, CKD patients had significantly higher plasma I-FABP and TMAO levels. We also assessed gut composition between the LP-HF and HP-LF subgroups and revealed several diverse bacterial genera. These data provide a better understanding of bacterial community alterations and related biomarkers in the context of differing dietary patterns in CKD patients. Moreover, our findings support the KDIGO-recommended low-protein, plant-based diet for slowing CKD progression and underscore the need to further investigate gut microbiota modulation as a therapeutic strategy in CKD⁷.

Recently, the mechanistic role of gut microbiota alterations in CKD has received substantial attention. Indeed, gut dysbiosis is now recognized as playing an essential role in the development and progression of CKD, from early stages to ESKD^8,9. In this study, our results demonstrated remarkably compositional shifts in the gut microbiota of CKD patients compared with healthy individuals. Although alpha diversity was unchanged, beta diversity differed significantly between patients and healthy controls. These results are consistent with a systematic review indicating that changes in alpha diversity are not consistently observed in CKD patients, as only 40% of published data reported significant differences in the alpha indices²⁵. Moreover, changes in microbiota composition at the genus level were observed in our cohort.

At the genus level, 10 genera differed significantly in relative abundance between patients with CKD and healthy individuals. Compared with healthy subjects, the CKD group showed increased Lachnoclostridium, and decreased abundances of Bifidobacterium, Ruminococcus, Coprococcus, Subdoligranulum, and Faecalibacterium among other SCFAs-producing taxa. Moreover, the bacterial gene coding BCoAT, a reliable semiquantitative assay of fecal butyrate production, was significantly lower in CKD patients than in healthy controls. SCFAs regulate energy metabolism, oxidative stress, immune activation, and inflammatory response²⁶. A recent study showed that decreased propionate and butyrate levels were associated with progressive loss of renal function in CKD patients and that early administration of these SCFAs could prevent disease progression in a pre-clinical model²⁷. Likewise, several clinical studies have shown that probiotic-containing SCFAs-producing bacteria including Bifidobacterium genera reduce uremic toxins and may slow disease progression²⁸.

High circulating concentrations of TMAO in CKD patients are influenced by decreased renal excretion and increased production of its precursor due to uremic-associated gut dysbiosis and impaired intestinal barrier²⁹. Interestingly, our study identified a significant negative correlation between TMAO levels and the relative abundance of Bifidobacterium. Moreover, recent evidence indicates that gut dysbiosis is closely linked to low-grade systemic inflammation in CKD patients³⁰. TMAO has been associated with heightened systemic inflammation—reflected by elevated IL-18, IL-1β, IL-6, and high-sensitivity C-reactive protein—and is an independent predictor of mortality in patients with CKD stages G3–G5.^31–33 In this regard, our data showed that several circulating inflammatory cytokines were significantly elevated in CKD patients and associated with microbial abundance. Notably, the enrichment of Bifidobacterium was negatively correlated with circulating levels of MCP-1, IL-8, and IL-18. Similarly, the abundance of Akkermansia, a promising next-generation probiotic,³⁴ was negatively correlated with plasma TNF-α, IL-6, IL-10, and IL-12p70 levels. I-FABP is a marker of enterocyte injury and has been linked to increased gut permeability^35,36. However, because I-FABP is primarily cleared by the kidneys, concentrations are elevated in CKD and ESKD. Accordingly, I-FABP levels in CKD should be interpreted with caution, accounting for reduced kidney function rather than attributing elevations solely to gut permeability³⁷. Consistent with this, our study showed significantly higher plasma I-FABP in CKD than in healthy controls, regardless of dietary pattern.

Fig. 7 [Images not available. See PDF.]

Spearman’s correlation analysis between microbial genera and clinical parameters (significant correlations are shown in yellow border).

Fig. 8 [Images not available. See PDF.]

Summary of the association between gut microbiota composition, dietary pattern, and circulating biomarkers.

Dietary patterns have essential impacts on promoting or counteracting CKD progression^2,3. Moreover, the heterogeneity of gut dysbiosis may be modulated positively or negatively by diverse dietary patterns³⁸. Thus, complex interactions between diet and gut microbial composition can affect human health and disease, including CKD. Currently, data linking dietary patterns with gut microbiota in patients with stage G3-G4 CKD are still lacking. Previous data in an animal model of CKD showed that the LP-HF diet was associated with increased saccharolytic bacteria, including SCFA-producing genera. On the other hand, the HP-LF diet was linked to increased relative abundance of predominantly proteolytic bacterial strains, leading to accumulation of gut-derived uremic toxins and systemic inflammation³⁹. In our study, patients consuming a LP-HF diet displayed greater abundance of butyrate-producing bacteria than those on a HP-LF diet. In contrast, the HP-LF subgroup showed enrichment of Romboutsia, Clostridium sensu stricto1, and Klebsiella. Clostridium sensu stricto1 has been correlated with inflammatory factors and poor nutritional status in CKD patients^40,41. Indeed, the members of the Enterobacteriaceae family (e.g., Enterobacter, Klebsiella, and Escherichia) are commonly enriched in CKD patients, indicating that these gram-negative proteolytic bacteria constitute uremic flora responsible for producing protein-bound uremic toxins, including TMAO, p-cresyl sulfate, and indoxyl sulfate^42,43. Among CKD patients in this study, diabetes mellitus was present in 66% of the HP–LF subgroup and 53% of the LP–HF subgroup (Supplementary Table S1). Patients with diabetes exhibited lower relative abundances of SCFA–producing taxa (e.g., Bifidobacterium, Faecalibacterium) and higher abundances of pathobionts (e.g., Lachnoclostridium, Klebsiella)^44,45. Although dietary pattern was associated with distinct microbiota profiles, interactions between diet and comorbidities beyond CKD—particularly diabetes—could not be fully delineated due to the limited sample size and warrant investigation in future, adequately powered studies.

Another novelty in this study is that the HP-LF subgroup had significantly higher TMAO levels compared to the LP-HF diet group. TMAO is a crucial gut microbe-derived metabolite produced from multiple nutrients, such as L-carnitine, betaine, and choline, which are found primarily in animal-based products²⁹. Importantly, gut dysbiosis with enriched proteolytic bacteria can convert these dietary nutrients into trimethylamine (TMA), which is subsequently oxidizing to TMAO. In animal models, mice fed with choline or TMAO exhibited progressive reduced renal function and developed renal fibrosis, indicating that TMAO is not only a biomarker of renal insufficiency but also an essential contributor to disease progression⁴⁶. A meta-analysis has also shown that increased circulating TMAO concentrations are positively correlated with lower GFR and increased risks of all-cause mortality and cardiovascular outcomes in a dose-dependent manner among non-dialysis CKD patients⁴⁷. In this respect, a mice model demonstrated that inhibition of microbiota-dependent TMAO production reduced disease progression, suggesting that targeting TMAO could be a novel therapeutic approach in CKD⁴⁸. Alternatively, in the absence of pharmacological therapies, nutritional interventions such as higher fiber consumption with reduced protein intake and the use of probiotics, prebiotics, or synbiotics, may also modify gut microbial profiles and related metabolites, including TMAO. ⁴⁹

In addition to the distinct microbial compositions between CKD subgroups, our results also showed that the LP-HF diet had significantly decreased circulating MCP-1 and IL-18 levels than the HP-LF subgroup, indicating reduced low-grade systemic inflammation. As previously mentioned, greater abundance of Bifidobacterium was negatively associated with these circulating cytokines. MCP-1, also known as C-C chemokine ligand 2 (CCL2), is expressed by endothelial cells and macrophages, and functions as a chemoattractant in innate immune response and tissue injury⁵⁰. MCP-1 is a critical inflammatory chemokine in the development of renal fibrosis, predicting severity and disease progression across various nephropathy^50,51. In a well-characterized cohort with mild to moderate kidney disease, higher plasma MCP-1 levels were independently associated with increased risk of progressive CKD during long-term follow-up, particularly among diabetic individuals with eGFR < 45 ml/min per 1.73 m² at baseline⁵². Interestingly, an animal model demonstrated that long-term high protein feeding led to renal injury, elevated plasma MCP-1 level, and impaired intestinal permeability associated with altered gut microbial composition and function¹².

An animal model showed that IL-18 plays an essential role in disease advancement; as IL-18 deficiency can limit progression from AKI to CKD⁵³. Moreover, in a randomized controlled trial, serum IL-18 levels were reduced by n-3 polyunsaturated fatty acid supplementation and a fiber-rich Mediterranean diet, including whole grains, fruits, and vegetables⁵⁴. In a small, short duration study, a low-protein diet with or without prebiotic supplementation was associated with gut microbial modification, with increased SCFA-producing bacteria, decreased harmful genera, and improved inflammatory parameters⁵⁵. Together, previous data and our results indicate that dietary patterns can alter the abundance of bacterial taxa and inflammatory biomarkers in patients with stages G3-G4 CKD. Notably, a fiber-rich, low-protein diet might lead to more favorable alterations in gut microbial profiles and decrease inflammatory mediators and uremic toxins, particularly TMAO, with potential to slow CKD progression and reduce cardiovascular risk. Figure 8 summarizes the study’s key findings.

Despite the valuable insights from our study, some limitations should be mentioned. First, the sample size of patients in each subgroup of CKD is relatively small, which could limit the generalizability of our findings. Second, this cross-sectional study mainly proved the associations between gut microbiota and other clinical and laboratory variables, which might not be able to verify a causal relationship in CKD patients. Third, healthy control subjects did not complete food records, limiting direct comparisons between controls and the CKD dietary subgroups. Finally, our analysis was based on 16 S rRNA sequencing, which was limited at the strain level and lacked comprehensive functional information regarding the gut microbiota. Thus, whole genome or shotgun metagenomics would provide more detailed insight into microbial functional potential and strain-level differences.

In conclusion, our study provides significant evidence of gut microbial alterations in CKD patients and highlights the benefits of a fiber-rich, low-protein diet in reducing harmful dysbiosis and related biomarkers. These findings enhance understanding of the relationship between diet and gut microbiota, with potential implications for personalized dietary counseling to improve CKD outcomes. Future clinical trials could leverage gut microbiota profiles to design targeted interventions—such as prescribing a fiber-rich diet for patients with low abundances of the Lachnospiraceae NK4A136 and Eubacterium ruminantium groups—and compare these strategies to standard dietary counseling by monitoring kidney function and plasma TMAO levels.

Author contributions

S.U. wrote the first manuscript draft. S.U. and N.C. performed the analyses and provided figures. S.U., K.M., and P.K. recruited the participants. T.D. reviewed the manuscript and edited. P.S. performed microbiological analyses. P.K. and P.T. reviewed the manuscript and edited. P.T. supervised the whole project and received the funding.

Funding

This work was supported by the NSRF via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation (PMU-B, grant number B36G660010) and the Center of Excellence in Hepatitis and Liver Cancer, Chulalongkorn University.

Data availability

All data relevant to this study has been included in the manuscript. The data code supporting the findings of this study are available from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical consideration

This study received approval from the Ethical Review Board of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand (IRB No. 0436/66). It was conducted following international guidelines for human research protection, as outlined in the Declaration of Helsinki, The Belmont Report, the CIOMS Guideline, and the International Conference on Harmonization in Good Clinical Practice (ICH-GCP). Informed consent was obtained from all subjects and/or their legal guardian(s).

Publisher’s note

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

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