Dataset Information

The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function.

ABSTRACT:

Background

The kidney transplant recipients (KTRs) were diagnosed with Chronic Kidney Disease after transplantation (CKD-T). CKD-T can be affected by the microbial composition and metabolites. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of CKD-T.

Methods

We collected 100 fecal samples of KTRs and divided them into two groups according to the stage progression of CKD-T. Among them, 55 samples were analyzed by Hiseq sequencing, and 100 samples were used for non-targeted metabolomics analysis. The gut microbiome and metabolomics of KTRs were comprehensively characterized.

Results

As well as significant differences in gut microbiome diversity between the CKD G1-2T group and CKD G3T group. Eight flora including Akkermansia were found to be enriched in CKD G3T group. As compared with CKD G1-2T group, the relative abundance of some amino acid metabolism, glycerophospholipid metabolism, amino acid biosynthesis, carbohydrate metabolism and purine metabolism in CKD G3T group were differential expressed significantly. In addition, fecal metabolome analysis indicated that CKD G3T group had a unique metabolite distribution characteristic. Two differentially expressed metabolites, N-acetylornithine and 5-deoxy-5'-(Methylthio) Adenosine, were highly correlated with serum creatinine, eGFR and cystatin C. The enrichment of gut microbial function in CKD-T is correlated with the expression of gut metabolites.

Conclusion

Gut microbiome and metabolites in the progression of CKD-T display some unique distribution and expression characteristics. The composition of the gut microbiome and their metabolites appears to be different between patients with CKD G3T and those with CKD G1-2T.

SUBMITTER: Lan Y

PROVIDER: S-EPMC10312025 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function.

Lan Yang Y Wang Duo D He Jiayang J Yang Hongji H Hou Yifu Y Di Wenjia W Wang Hailian H Luo Xiangwei X Wei Liang L

Renal failure 20231201 1

<h4>Background</h4>The kidney transplant recipients (KTRs) were diagnosed with Chronic Kidney Disease after transplantation (CKD-T). CKD-T can be affected by the microbial composition and metabolites. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of CKD-T.<h4>Methods</h4>We collected 100 fecal samples of KTRs and divided them into two groups according to the stage progression of CKD-T. Among them, 55 samples were analyzed by H ...[more]

PMID: 37381833

Similar Datasets

Project description:Background and objectivesProprotein convertase subtilisin/kexin type 9 (PCSK9) is a key regulator of lipid homeostasis. Studies investigating the association between PCSK9 and cardiovascular disease in large cohorts of patients with CKD are limited.Design, setting, participants, & measurementsThe association of PCSK9 concentrations with prevalent and incident cardiovascular disease was investigated in 5138 White participants of the German Chronic Kidney Disease study with a median follow-up of 6.5 years. Inclusion criteria were eGFR of 30-60 or >60 ml/min per 1.73 m2 in the presence of overt proteinuria (urine albumin-creatinine ratio >300 mg/g or equivalent). Prevalent cardiovascular disease was defined as a history of nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous transluminal coronary angioplasty, carotid arteries interventions, and stroke. Incident major adverse cardiovascular disease events included death from cardiovascular causes, acute nonfatal myocardial infarction, and nonfatal stroke.ResultsMedian PCSK9 concentration in the cohort was 285 ng/ml (interquartile range, 231-346 ng/ml). There was no association between PCSK9 concentrations and baseline eGFR and albuminuria. With each 100-ng/ml increment of PCSK9, the odds for prevalent cardiovascular disease (n=1284) were 1.22-fold (95% confidence interval, 1.12 to 1.34; P<0.001) higher in a model with extended adjustment for major confounders. This association was stronger in nonstatin than statin users (P value for interaction =0.009). During follow-up, 474 individuals experienced a major adverse cardiovascular disease event, and participants in PCSK9 quartiles 2-4 had a 32%-47% higher risk compared with those in quartile 1 (P<0.05). Subgroup analysis revealed that this association was restricted to those participants who already had cardiovascular disease at baseline (all hazard ratios >1.75; P=0.01). In addition, PCSK9 showed a valuable gain in classification accuracy for both prevalent cardiovascular disease (net reclassification index =0.27; 95% confidence interval, 0.20 to 0.33) and incident major adverse cardiovascular disease events during follow-up (net reclassification index =0.10; 95% confidence interval, 0.01 to 0.21) when added to an extended adjustment model.ConclusionsOur findings reveal no relation of PCSK9 with baseline eGFR and albuminuria but a significant association between higher PCSK9 concentrations and risk of cardiovascular disease independent of traditional risk factors, including LDL cholesterol levels.Clinical Trial registry name and registration number: German Chronic Kidney Disease Study (GCKD), DRKS 00003971.

Project description:BackgroundGraft loss is a major health concern for kidney transplant (KTx) recipients. It is of clinical interest to develop a prognostic model for both graft function, quantified by estimated glomerular filtration rate (eGFR), and the risk of graft failure. Additionally, the model should be dynamic in the sense that it adapts to accumulating longitudinal information, including time-varying at-risk population, predictor-outcome association, and clinical history. Finally, the model should also properly account for the competing risk by death with a functioning graft. A model with the features above is not yet available in the literature and is the focus of this research.MethodsWe built and internally validated a prediction model on 3,893 patients from the Wisconsin Allograft Recipient Database (WisARD) who had a functioning graft 6 months after kidney transplantation. The landmark analysis approach was used to build a proof-of-concept dynamic prediction model to address the aforementioned methodological issues: the prediction of graft failure, accounted for competing risk of death, as well as the future eGFR value, are updated at each post-transplant time. We used 21 predictors including recipient characteristics, donor characteristics, transplant-related and post-transplant factors, longitudinal eGFR, hospitalization, and rejection history. A sensitivity analysis explored a less conservative variable selection rule that resulted in a more parsimonious model with reduced predictors.ResultsFor prediction up to the next 1 to 5 years, the model achieved high accuracy in predicting graft failure, with the AUC between 0.80 and 0.95, and moderately high accuracy in predicting eGFR, with the root mean squared error between 10 and 18 mL/min/1.73m2 and 70%-90% of predicted eGFR falling within 30% of the observed eGFR. The model demonstrated substantial accuracy improvement compared to a conventional prediction model that used only baseline predictors.ConclusionThe model outperformed conventional prediction model that used only baseline predictors. It is a useful tool for patient counseling and clinical management of KTx and is currently available as a web app.

Dataset Information

The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function.

Background

Methods

Results

Conclusion

Publications

The gut microbiome and metabolome in kidney transplant recipients with normal and moderately decreased kidney function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets