scholarly journals Diet, gut microbiome and indoxyl sulphate in chronic kidney disease patients

Nephrology ◽  
2018 ◽  
Vol 23 ◽  
pp. 16-20 ◽  
Author(s):  
Chih-Yu Yang ◽  
Der-Cherng Tarng
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Indoxyl Sulphate

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

2022 ◽  
pp. ASN.2021040538
Author(s):  
Arthur M. Lee ◽  
Jian Hu ◽  
Yunwen Xu ◽  
Alison G. Abraham ◽  
Rui Xiao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Support Vector ◽  
Pathway Enrichment Analysis ◽  
Disease Etiology ◽  
Content Type ◽  
Extreme Gradient Boosting ◽  
Pediatric Ckd

BackgroundUntargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).MethodsUntargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.ResultsML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome–derived histidine metabolites.ConclusionML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome–derived histidine metabolites are associated with OU.

scholarly journals Identification of Potential Biomarkers of Chronic Kidney Disease in Individuals with Diabetes: Protocol for a Cross-sectional Observational Study (Preprint)

2019 ◽  
Author(s):  
Ashani R Lecamwasam ◽  
Mohammadreza Mohebbi ◽  
Elif I Ekinci ◽  
Karen M Dwyer ◽  
Richard Saffery
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Data Collection ◽  
Gut Microbiome ◽  
Single Point ◽  
Therapeutic Interventions ◽  
Kidney Dysfunction ◽  
Early Diabetes ◽  
Cross Sectional ◽  
Potential Biomarkers

BACKGROUND The importance of identifying people with diabetes and progressive kidney dysfunction relates to the excess morbidity and mortality of this group. Rates of cardiovascular disease are much higher in people with both diabetes and kidney dysfunction than in those with only one of these conditions. By the time these people are identified in current clinical practice, proteinuria and renal dysfunction are already established, limiting the effectiveness of therapeutic interventions. The identification of an epigenetic or blood metabolite signature or gut microbiome profile may identify those with diabetes at risk of progressive chronic kidney disease, in turn providing targeted intervention to improve patient outcomes. OBJECTIVE This study aims to identify potential biomarkers in people with diabetes and chronic kidney disease (CKD) associated with progressive renal injury and to distinguish between stages of chronic kidney disease. Three sources of biomarkers will be explored, including DNA methylation profiles in blood lymphocytes, the metabolomic profile of blood-derived plasma and urine, and the gut microbiome. METHODS The cross-sectional study recruited 121 people with diabetes and varying stages (stages 1-5) of chronic kidney disease. Single-point data collection included blood, urine, and fecal samples in addition to clinical data such as anthropometric measurements and biochemical parameters. Additional information obtained from medical records included patient demographics, medical comorbidities, and medications. RESULTS Data collection commenced in January 2018 and was completed in June 2018. At the time of submission, 121 patients had been recruited, and 119 samples remained after quality control. There were 83 participants in the early diabetes-associated CKD group with a mean estimated glomerular filtration rate (eGFR) of 61.2 mL/min/1.73 m2 (early CKD group consisting of stage 1, 2, and 3a CKD), and 36 participants in the late diabetic CKD group with a mean eGFR of 23.9 mL/min/1.73 m2 (late CKD group, consisting of stage 3b, 4, and 5), <i>P</i><.001. We have successfully obtained DNA for methylation and microbiome analyses using the biospecimens collected via this protocol and are currently analyzing these results together with the metabolome of this cohort of individuals with diabetic CKD. CONCLUSIONS Recent advances have improved our understanding of the epigenome, metabolomics, and the influence of the gut microbiome on the incidence of diseases such as cancers, particularly those related to environmental exposures. However, there is a paucity of literature surrounding these influencers in renal disease. This study will provide insight into the fundamental understanding of the pathophysiology of CKD in individuals with diabetes, especially in novel areas such as epigenetics, metabolomics, and the kidney-gut axis. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/16277

scholarly journals Alterations of the Human Gut Microbiome in Chronic Kidney Disease

2020 ◽  
Vol 7 (20) ◽  
pp. 2001936 ◽  
Author(s):  
Zhigang Ren ◽  
Yajuan Fan ◽  
Ang Li ◽  
Quanquan Shen ◽  
Jian Wu ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Human Gut ◽  
Human Gut Microbiome

Gut Microbiome in Chronic Kidney Disease

2017 ◽  
Vol 19 (4) ◽  
Author(s):  
R. G. Armani ◽  
A. Ramezani ◽  
A. Yasir ◽  
S. Sharama ◽  
M. E. F. Canziani ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome

scholarly journals Gut Microbiome Profiling Uncovers a Lower Abundance of Butyricicoccus in Advanced Stages of Chronic Kidney Disease

2021 ◽  
Vol 11 (11) ◽  
pp. 1118
Author(s):  
Tessa Gryp ◽  
Karoline Faust ◽  
Wim Van Biesen ◽  
Geert R. B. Huys ◽  
Francis Verbeke ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Amplicon Sequencing ◽  
Uremic Toxins ◽  
Microbial Composition ◽  
Cross Sectional ◽  
Organ Systems ◽  
Significant Difference ◽  
Advanced Stages

Chronic kidney disease (CKD) is characterized by the accumulation of uremic toxins which exert deleterious effects on various organ systems. Several of these uremic toxins originate from the bacterial metabolization of aromatic amino acids in the colon. This study assessed whether the gut microbial composition varies among patients in different stages of CKD. Uremic metabolites were quantified by UPLC/fluorescence detection and microbial profiling by 16S rRNA amplicon sequencing. Gut microbial profiles of CKD patients were compared among stages 1–2, stage 3 and stages 4–5. Although a substantial inter-individual difference in abundance of the top 15 genera was observed, no significant difference was observed between groups. Bristol stool scale (BSS) correlated negatively with p-cresyl sulfate and hippuric acid levels, irrespective of the intake of laxatives. Butyricicoccus, a genus with butyrate-generating properties, was decreased in abundance in advanced stages of CKD compared to the earlier stages (p = 0.043). In conclusion, in this cross-sectional study no gradual differences in the gut microbial profile over the different stages of CKD were observed. However, the decrease in the abundance of Butyricicoccus genus with loss of kidney function stresses the need for more in-depth functional exploration of the gut microbiome in CKD patients not on dialysis.

scholarly journals Bacterial metabolites and cardiovascular risk in children with chronic kidney disease

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Julia Schlender ◽  
Felix Behrens ◽  
Victoria McParland ◽  
Dominik Müller ◽  
Nicola Wilck ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Cardiovascular Risk ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Short Chain Fatty Acids ◽  
Underlying Disease ◽  
Human Gut ◽  
Endogenous Factors ◽  
Human Gut Microbiome

AbstractCardiovascular complications are the major cause of the marked morbidity and mortality associated with chronic kidney disease (CKD). The classical cardiovascular risk factors such as diabetes and hypertension undoubtedly play a role in the development of cardiovascular disease (CVD) in adult CKD patients; however, CVD is just as prominent in children with CKD who do not have these risk factors. Hence, the CKD-specific pathophysiology of CVD remains incompletely understood. In light of this, studying children with CKD presents a unique opportunity to analyze CKD-associated mechanisms of CVD more specifically and could help to unveil novel therapeutic targets.Here, we comprehensively review the interaction of the human gut microbiome and the microbial metabolism of nutrients with host immunity and cardiovascular end-organ damage. The human gut microbiome is evolutionary conditioned and modified throughout life by endogenous factors as well as environmental factors. Chronic diseases, such as CKD, cause significant disruption to the composition and function of the gut microbiome and lead to disease-associated dysbiosis. This dysbiosis and the accompanying loss of biochemical homeostasis in the epithelial cells of the colon can be the result of poor diet (e.g., low-fiber intake), medications, and underlying disease. As a result of dysbiosis, bacteria promoting proteolytic fermentation increase and those for saccharolytic fermentation decrease and the integrity of the gut barrier is perturbed (leaky gut). These changes disrupt local metabolite homeostasis in the gut and decrease productions of the beneficial short-chain fatty acids (SCFAs). Moreover, the enhanced proteolytic fermentation generates unhealthy levels of microbially derived toxic metabolites, which further accumulate in the systemic circulation as a consequence of impaired kidney function. We describe possible mechanisms involved in the increased systemic inflammation in CKD that is associated with the combined effect of SCFA deficiency and accumulation of uremic toxins. In the future, a more comprehensive and mechanistic understanding of the gut–kidney–heart interaction, mediated largely by immune dysregulation and inflammation, might allow us to target the gut microbiome more specifically in order to attenuate CKD-associated comorbidities.

scholarly journals Potential for Novel Biomarkers in Diabetes-Associated Chronic Kidney Disease: Epigenome, Metabolome, and Gut Microbiome

Biomedicines ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 341
Author(s):  
Ashani Lecamwasam ◽  
Elif I. Ekinci ◽  
Richard Saffery ◽  
Karen M. Dwyer
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Chronic Condition ◽  
Clinical Validation ◽  
Slow Disease Progression ◽  
Susceptible Population ◽  
Novel Biomarkers ◽  
Global Increase ◽  
Number Of Individuals

Diabetes-associated chronic kidney disease is a pandemic issue. Despite the global increase in the number of individuals with this chronic condition together with increasing morbidity and mortality, there are currently only limited therapeutic options to slow disease progression. One of the reasons for this is that the current-day “gold standard” biomarkers lack adequate sensitivity and specificity to detect early diabetic chronic kidney disease (CKD). This review focuses on the rapidly evolving areas of epigenetics, metabolomics, and the gut microbiome as potential sources of novel biomarkers in diabetes-associated CKD and discusses their relevance to clinical practice. However, it also highlights the problems associated with many studies within these three areas—namely, the lack of adequately powered longitudinal studies, and the lack of reproducibility of results which impede biomarker development and clinical validation in this complex and susceptible population.

Metagenomic profiling of gut microbiome in early chronic kidney disease

2020 ◽  
Author(s):  
Noriaki Sato ◽  
Masanori Kakuta ◽  
Takanori Hasegawa ◽  
Rui Yamaguchi ◽  
Eiichiro Uchino ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Gut Microbiome ◽  
Short Chain Fatty Acids ◽  
Toxin Production ◽  
Metagenomic Sequencing ◽  
Stool Samples ◽  
The Mean ◽  
Estimated Glomerular Filtration Rates ◽  
The Relationship

Abstract Background The relationship between chronic kidney disease (CKD) and the gut microbiome, which interact through chronic inflammation, uraemic toxin production and immune response regulation, has gained interest in the development of CKD therapies. However, reports using shotgun metagenomic analysis of the gut microbiome are scarce, especially for early CKD. Here we characterized gut microbiome differences between non-CKD participants and ones with early CKD using metagenomic sequencing. Methods In total, 74 non-CKD participants and 37 participants with early CKD were included based on propensity score matching, controlling for various factors including dietary intake. Stool samples were collected from participants and subjected to shotgun sequencing. Bacterial and pathway abundances were profiled at the species level with MetaPhlAn2 and HUMAnN2, respectively, and overall microbiome differences were determined using Bray–Curtis dissimilarities. Diabetic and non-diabetic populations were analysed separately. Results For diabetic and non-diabetic participants, the mean estimated glomerular filtration rates of the CKD group were 53.71 [standard deviation (SD) 3.87] and 53.72 (SD 4.44), whereas those of the non-CKD group were 72.63 (SD 7.72) and 76.10 (SD 9.84), respectively. Alpha and beta diversities were not significantly different between groups. Based on taxonomic analysis, butyrate-producing species Roseburia inulinivorans, Ruminococcus torques and Ruminococcus lactaris were more abundant in the non-CKD group, whereas Bacteroides caccae and Bacteroides coprocora were more abundant in the non-diabetic CKD group. Conclusions Although gut microbiome changes in individuals with early CKD were subtle, the results suggest that changes related to producing short-chain fatty acids can already be observed in early CKD.

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