Clinical trial NCT06877585: Association Between Fecal Microbiota Composition, Metabolite Concentrations, and Indoxyl Sulfate Levels
Official title: Association Between Fecal Microbiota Composition, Metabolite Concentrations, and Indoxyl Sulfate Levels in Hemodialysis Patients
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Dysbiosis
Keywords: gut microbiome
gut dysbiosis
hemodialysis patients
indoxyl sulfate
Lead sponsor: Tungs' Taichung Metroharbour Hospital
Sponsor class: OTHER
First posted: 2025-03-14
Enrollment: 60
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: hemodialysis patients

Brief summary: Gut dysbiosis is frequently characterized by decreased microbial diversity and alterations in the abundance of certain microbial species. In individuals with chronic kidney disease (CKD), dysbiosis and metabolic imbalances are prevalent, contributing to the buildup of gut-derived retention solutes and metabolites in the bloodstream. Research has consistently shown that CKD patients exhibit lower levels of beneficial gut bacteria. However, the specific functional changes in gut microbiota and their interactions with levels of uremic toxins in hemodialysis (HD) patients remain incompletely understood. This study seeks to explore the association of fecal metagenomics and targeted metabolomics in a cohort of 60 patients with different levels of to characterize the complex interplay between the gut microbiome and fecal and serum metabolites.

Detailed description: Patients and Study Design

Sixty HD patients will be enrolled, from dialysis unit, Renal Division of The Tungs' Taichung Metroharbour Hospital. The written informed consent to take part in the study is obtained from each participant after being informed on study design and aims. The experimental protocol will send to the Ethical Committees of IRB for approval. Inclusion criteria are age 18-80 years and diagnosed with CKD stage V (currently receiving hemodialysis treatment\>3 months). Exclusion criteria included pregnant or nursing women; patients with kidney transplant, severe infections, severe cardiac diseases and liver diseases, malignancy, autoimmune disorders, severe malnutrition; consumed any type of pre-or probiotics or had antibiotic therapy within 1 month of study commencement; diagnosed irritable bowel syndrome, Crohn's disease, or ulcerative colitis; receiving or have received bowel radiation or had large bowel resection The participants will receive dietary instructions and are encouraged to maintain stable dietary intake, which is quantified by nutritional questionnaires during patient interviews. The questionnaires gathered information about the weekly amount and frequency of food and beverage consumption. Data regarding the nutrient composition of the different foods are obtained using the tables of the Institute of Nutrition and Food Safety, National Taiwan University.

Sample Collection, Preparation and Analysis Blood samples are collected separately from each participant. Blood samples are collected after an 8 to 12-hour fasting period, using sodium fluoride tubes, and kept on ice until transfer to the research laboratory. Once there, the samples are centrifuged at 3,000×g for 15 minutes at 4°C. The serum is then transferred to sterile tubes and stored at -20°C for future analysis.

investigators measure serum levels of acetate, propionate, butyrate, and valerate, along with branched-chain SCFAs such as isobutyrate and isovalerate. These metabolite profiles are analyzed using ultraperformance liquid chromatography-mass spectrometry (UPLC-MS). In brief, 100 μL of each serum sample is mixed in 1.5-mL microtubes with 20 mg of NaCl, 10 mg of citric acid, 20 μL of 1 M HCl, and 100 μL of butanol. The mixture is vortexed for 2 minutes and then centrifuged at 18,000×g for 15 minutes. The resulting supernatant is transferred to fresh microtubes for analysis.

Measurement of uremic toxins Investigators measure total uremic toxin levels rather than free levels. The concentrations of total indoxyl sulfate, p-cresyl sulfate, indole-3-acetic acid (IAA), and hippuric acid are centrally quantified using a previously described method \[19\]. Briefly, for binding competition, 200μl serum to which investigators added 20μl 0.50mM 1-naphthalenesulfonic acid (internal standard) was vortex-mixed with 250μl 0.24M sodium octanoate (binding competitor).After incubation at room temperature for 5min, investigators added 2ml cold acetone to precipitate proteins. Following vortex-mixing and centrifuging at 4 ◦C, 1860×g for 20 min, the supernatant was transferred to 12mm×100mm, GL 14 glass test tubes and 2ml dichloromethane was added. After vortex-mixing and centrifuging at 4 ◦C, 1860×g for 10min, 200μl of the upper layer was transferred to glass autosampler vials, followed by addition of 20μl 1M HCl and 15μl was injected onto the HPLC. The HPLC analysis was performed on an Agilent 1100 series LC (Santa Clara, CA),and Agilent ChemStations software were used for the chromatographic analysis. The separation was carried out on a ZORBAX SB-C18 Solv Saver Plus HPLC column (5 μm, 3.0 mm×150 mm).at a flow rate of 0.6 ml/min. Mobile phase A is 0.2% trifluoroacetic acid in Milli-Q water and mobile phase B is 0.2% trifluoroacetic acid in acetonitrile. The analytical method consists of an isocratic run with 92% mobile phase A for 23 min.. Each analytical run was followed by a 1.3 min washout gradient to 100% B. Column temperature was 25 ◦C, and autosampler tray temperature was 6 ◦C. Investigators quantified the analytes by using the analyte to standard peak area ratio on a Agilent 1100 High Performance Fluorescence detector G1321A and Agilent 1100 Series UV-Visible detectors G1314A. Detector settings were λex 260 nm/λem288nm for p-cresyl sulfate and λex 280 nm/λem 390nm for indoxyl sulfate, indole-3-acetic acid and internal standard. Hippuric acid was monitored by UV-Vis detector at 254 nm. Quantitative results are obtained and calculated in terms of their concentrations (mg/L).

Fecal microbiota profile Sample Collection Qualified stool samples are self-collected in sterile frozen tubes by subjects and were transported immediately to the laboratory where they were stored at - 80 ◦C for further testing.

DNA Extraction and Sequencing Genomic DNA from fecal samples is extracted using the QIAGEN DNA Stool Fast Kit (QIAGEN) according to the manufacturer's guidelines. The quality of the extracted metagenomic DNA is assessed through 1.0% agarose gel electrophoresis and spectrophotometric analysis (measuring the optical density at a 260/280 nm ratio). For the extracted DNA to be considered suitable, the concentration has to exceed 20 ng/μl, with a 260/280 nm ratio within the range of 1.8 to 2.0. The variable region 4 (V4) of the bacterial 16S rRNA gene is amplified through polymerase chain reaction (PCR) with bacterial/archaeal primers 515F/806R, including barcodes for sample identification. The resulting amplicons are purified using the GeneJET Gel Extraction Kit (Thermo Scientific) and quantified with a Qubit dsDNA HS Assay Kit on a Qubit 2.0 Fluorometer (Qubit). Sequencing libraries are prepared using the NEBNext® Ultra™ DNA Library Prep Kit for Illumina (NEB), following the manufacturer's protocol. The purified libraries are quantified, normalized, pooled, and then use for cluster generation and sequencing on an Illumina HiSeq 2500 platform to produce 250 bp paired-end reads.

Paired-end reads are merged using FLASH v1.2.7, and quality filtering is performed using the QIIME 1.7 pipeline with Python scripts. Chimeric sequences were removed by UCHIME. The processed reads (effective tags) are clustered into operational taxonomic units (OTUs) at 97% sequence identity using UPARSE, and taxonomic classification is assigned based on the SILVA database. To assess the phylogenetic relationships of different OTUs, multiple sequence alignments are performed using PyNAST v1.2 against the SILVA database, and a phylogenetic tree was constructed using FastTree.

Alpha diversity is estimated by species richness using the Chao1 index at the OTU level. A rarefaction curve is generated by randomly selecting a subset of sequencing data from each sample to represent the number of observed species, and a species accumulation curve is plotted to show the occurrence of new OTUs (species) with continuous sampling. For beta diversity, Bray-Curtis dissimilarities at the OTU level are calculated and analyzed using the vegan package. Principal coordinate analysis (PCoA) is conducted based on Bray-Curtis distances, and both weighted and unweighted UniFrac parameters are computed through the QIIME pipeline. Non-metric dimensional scaling (NMDS) is performed using the weighted correlation network analysis (WGCNA), stat, and ggplot2 packages in R by transforming a distance matrix of weighted and unweighted UniFrac parameters into a new set of orthogonal axes. All analyses are conducted using in-house R scripts, unless otherwise specified.

Functional composition of metagenomes is predicted from 16S rRNA data using the PICRUSt software with Python scripts. A precomputed table of gene copy numbers for each gene family from sequenced bacterial and archaeal genomes, based on the IMG database, along with a phylogenetic tree from the Greengenes database, is used for gene content prediction.

Eligibility criteria: Inclusion Criteria:

* age 18-80 years and
* diagnosed with CKD stage V
* currently receiving hemodialysis treatment\>3 months

Exclusion Criteria:

* pregnant or nursing women
* patients with kidney transplant
* severe infections
* severe cardiac diseases
* liver diseases
* malignancy
* autoimmune disorders
* severe malnutrition
* consumed any type of pre-or probiotics
* had antibiotic therapy within 1 month
* diagnosed irritable bowel syndrome
* Crohn's disease
* ulcerative colitis

Primary outcomes:
measure: Fecal microbiota profile
timeFrame: 1 years
Secondary outcomes:
measure: Measurement of serum uremic toxins
timeFrame: 1 years
measure: Evaluation of short-chain fatty acids(SCFAs) and branched-chain SCFAs
timeFrame: 1 years
measure: Measurement of serum uremic toxins
timeFrame: 1 years
measure: Measurement of serum uremic toxins
timeFrame: 1 years
measure: Measurement of serum uremic toxins
timeFrame: 1 years
Other outcomes:
Locations:
city: Taichung
country: Taiwan
facility: Tungs' Taichung Metroharbour Hospital
state: Wuqi District
Study officials:
affiliation: Tungs' Taichung Metroharbour Hospital
name: Paik Seong Lim, PhD
role: PRINCIPAL_INVESTIGATOR
References:
citation: DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D, Hu P, Andersen GL. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006 Jul;72(7):5069-72. doi: 10.1128/AEM.03006-05.
citation: Markowitz VM, Chen IM, Palaniappan K, Chu K, Szeto E, Grechkin Y, Ratner A, Jacob B, Huang J, Williams P, Huntemann M, Anderson I, Mavromatis K, Ivanova NN, Kyrpides NC. IMG: the Integrated Microbial Genomes database and comparative analysis system. Nucleic Acids Res. 2012 Jan;40(Database issue):D115-22. doi: 10.1093/nar/gkr1044.
citation: Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, Clemente JC, Burkepile DE, Vega Thurber RL, Knight R, Beiko RG, Huttenhower C. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013 Sep;31(9):814-21. doi: 10.1038/nbt.2676. Epub 2013 Aug 25.
citation: Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011 Feb;5(2):169-72. doi: 10.1038/ismej.2010.133. Epub 2010 Sep 9. No abstract available.
citation: Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009 Jul;26(7):1641-50. doi: 10.1093/molbev/msp077. Epub 2009 Apr 17.
citation: Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010 Jan 15;26(2):266-7. doi: 10.1093/bioinformatics/btp636. Epub 2009 Nov 13.
citation: Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013 Jan;41(Database issue):D590-6. doi: 10.1093/nar/gks1219. Epub 2012 Nov 28.
citation: Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013 Oct;10(10):996-8. doi: 10.1038/nmeth.2604. Epub 2013 Aug 18.
citation: Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011 Aug 15;27(16):2194-200. doi: 10.1093/bioinformatics/btr381. Epub 2011 Jun 23.
citation: Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Pena AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010 May;7(5):335-6. doi: 10.1038/nmeth.f.303. Epub 2010 Apr 11. No abstract available.
citation: Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011 Nov 1;27(21):2957-63. doi: 10.1093/bioinformatics/btr507. Epub 2011 Sep 7.
citation: Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012 Aug;6(8):1621-4. doi: 10.1038/ismej.2012.8. Epub 2012 Mar 8.
citation: de Loor H, Poesen R, De Leger W, Dehaen W, Augustijns P, Evenepoel P, Meijers B. A liquid chromatography - tandem mass spectrometry method to measure a selected panel of uremic retention solutes derived from endogenous and colonic microbial metabolism. Anal Chim Acta. 2016 Sep 14;936:149-56. doi: 10.1016/j.aca.2016.06.057. Epub 2016 Jul 2.
citation: Rukavina Mikusic NL, Kouyoumdzian NM, Choi MR. Gut microbiota and chronic kidney disease: evidences and mechanisms that mediate a new communication in the gastrointestinal-renal axis. Pflugers Arch. 2020 Mar;472(3):303-320. doi: 10.1007/s00424-020-02352-x. Epub 2020 Feb 17.
citation: Vaziri ND, Wong J, Pahl M, Piceno YM, Yuan J, DeSantis TZ, Ni Z, Nguyen TH, Andersen GL. Chronic kidney disease alters intestinal microbial flora. Kidney Int. 2013 Feb;83(2):308-15. doi: 10.1038/ki.2012.345. Epub 2012 Sep 19.
citation: Wang X, Yang S, Li S, Zhao L, Hao Y, Qin J, Zhang L, Zhang C, Bian W, Zuo L, Gao X, Zhu B, Lei XG, Gu Z, Cui W, Xu X, Li Z, Zhu B, Li Y, Chen S, Guo H, Zhang H, Sun J, Zhang M, Hui Y, Zhang X, Liu X, Sun B, Wang L, Qiu Q, Zhang Y, Li X, Liu W, Xue R, Wu H, Shao D, Li J, Zhou Y, Li S, Yang R, Pedersen OB, Yu Z, Ehrlich SD, Ren F. Aberrant gut microbiota alters host metabolome and impacts renal failure in humans and rodents. Gut. 2020 Dec;69(12):2131-2142. doi: 10.1136/gutjnl-2019-319766. Epub 2020 Apr 2.
citation: Chen YY, Chen DQ, Chen L, Liu JR, Vaziri ND, Guo Y, Zhao YY. Microbiome-metabolome reveals the contribution of gut-kidney axis on kidney disease. J Transl Med. 2019 Jan 3;17(1):5. doi: 10.1186/s12967-018-1756-4.
citation: Aronov PA, Luo FJ, Plummer NS, Quan Z, Holmes S, Hostetter TH, Meyer TW. Colonic contribution to uremic solutes. J Am Soc Nephrol. 2011 Sep;22(9):1769-76. doi: 10.1681/ASN.2010121220. Epub 2011 Jul 22.
citation: Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA, Peters EC, Siuzdak G. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. Proc Natl Acad Sci U S A. 2009 Mar 10;106(10):3698-703. doi: 10.1073/pnas.0812874106. Epub 2009 Feb 20.
citation: Einheber A, Carter D. The role of the microbial flora in uremia. I. Survival times of germfree, limited-flora, and conventionalized rats after bilateral nephrectomy and fasting. J Exp Med. 1966 Feb 1;123(2):239-50. doi: 10.1084/jem.123.2.239.
citation: Barreto FC, Barreto DV, Liabeuf S, Meert N, Glorieux G, Temmar M, Choukroun G, Vanholder R, Massy ZA; European Uremic Toxin Work Group (EUTox). Serum indoxyl sulfate is associated with vascular disease and mortality in chronic kidney disease patients. Clin J Am Soc Nephrol. 2009 Oct;4(10):1551-8. doi: 10.2215/CJN.03980609. Epub 2009 Aug 20.
citation: Fan PC, Chang JC, Lin CN, Lee CC, Chen YT, Chu PH, Kou G, Lu YA, Yang CW, Chen YC. Serum indoxyl sulfate predicts adverse cardiovascular events in patients with chronic kidney disease. J Formos Med Assoc. 2019 Jul;118(7):1099-1106. doi: 10.1016/j.jfma.2019.03.005. Epub 2019 Mar 28.
citation: Hung SC, Kuo KL, Wu CC, Tarng DC. Indoxyl Sulfate: A Novel Cardiovascular Risk Factor in Chronic Kidney Disease. J Am Heart Assoc. 2017 Feb 7;6(2):e005022. doi: 10.1161/JAHA.116.005022. No abstract available.
citation: Li Q, Zhang S, Wu QJ, Xiao J, Wang ZH, Mu XW, Zhang Y, Wang XN, You LL, Wang SN, Song JN, Zhao XN, Wang ZZ, Yan XY, Jin YX, Jiang BW, Liu SX. Serum total indoxyl sulfate levels and all-cause and cardiovascular mortality in maintenance hemodialysis patients: a prospective cohort study. BMC Nephrol. 2022 Jun 28;23(1):231. doi: 10.1186/s12882-022-02862-z.
citation: Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Dore J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J; MetaHIT Consortium; Bork P, Ehrlich SD, Wang J. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010 Mar 4;464(7285):59-65. doi: 10.1038/nature08821.
citation: Meijers B, Glorieux G, Poesen R, Bakker SJ. Nonextracorporeal methods for decreasing uremic solute concentration: a future way to go? Semin Nephrol. 2014 Mar;34(2):228-43. doi: 10.1016/j.semnephrol.2014.02.012.
citation: Lau WL, Savoj J, Nakata MB, Vaziri ND. Altered microbiome in chronic kidney disease: systemic effects of gut-derived uremic toxins. Clin Sci (Lond). 2018 Mar 9;132(5):509-522. doi: 10.1042/CS20171107. Print 2018 Mar 15.
citation: Vanholder R, De Smet R, Glorieux G, Argiles A, Baurmeister U, Brunet P, Clark W, Cohen G, De Deyn PP, Deppisch R, Descamps-Latscha B, Henle T, Jorres A, Lemke HD, Massy ZA, Passlick-Deetjen J, Rodriguez M, Stegmayr B, Stenvinkel P, Tetta C, Wanner C, Zidek W; European Uremic Toxin Work Group (EUTox). Review on uremic toxins: classification, concentration, and interindividual variability. Kidney Int. 2003 May;63(5):1934-43. doi: 10.1046/j.1523-1755.2003.00924.x.
citation: Meyer TW, Hostetter TH. Uremia. N Engl J Med. 2007 Sep 27;357(13):1316-25. doi: 10.1056/NEJMra071313. No abstract available.