Gut Microbial Product Predicts Cardiovascular Risk in Chronic Kidney Disease Patients.

BACKGROUND: The gut microbiota is altered in patients with chronic kidney disease (CKD), and cardiovascular risk increases with progressive CKD. This study examined the potential link between short chain fatty acids (SCFAs), which are produced by the gut microbiota, and cardiovascular outcomes in patients with CKD. METHODS: SCFAs were measured using a targeted liquid chromatography-mass spectrometry platform in baseline plasma samples from 214 patients with CKD enrolled in the Clinical Phenotyping Resource and Biobank Core; 81 patients with coronary artery disease (CAD) and 133 without CAD were randomly assigned to training and validation subsets. The primary outcome was a history of CAD and the secondary outcome was a composite history of cardiovascular disease (CVD) at enrollment. RESULTS: We found significantly higher levels of the SCFA valerate among patients with CAD as compared with patients without CAD in the training set (p < 0.001). The valerate concentrations were also significantly higher among subjects with composite outcomes of CVD compared to those without CVD (p = 0.006). These results were subsequently replicated in the validation set. Logistic regression analysis revealed a strong independent association between plasma valerate levels and CVD in both training and validation sets. When valerate was added to the base clinical model comprising of diabetes, hypertension, urinary protein-creatinine ratio, and estimated glomerular filtration rate, it increased the c-statistics for predicting CVD from 0.68 to 0.79 (p = 0.02) in the training set, an observation which was confirmed in the validation set. -Conclusion: This study provides evidence for alterations in gut-microbiota-derived SCFAs with advancing CKD, demonstrates the association of higher plasma valerate levels with pre-existing CVD, and reveals areas for future exploration of cardiovascular risk in patients with CKD.

Plasma lipidomic profiling identifies a novel complex lipid signature associated with ischemic stroke in chronic kidney disease.

RATIONALE AND OBJECTIVE: Despite contribution of dyslipidemia to ischemic stroke, plasma lipidomic correlates of stroke in CKD is not studied. This study is aimed to identify plasma lipid alterations associated with stroke. STUDY DESIGN: Cross sectional. SETTING AND POPULATION: 214 participants of Clinical Phenotyping and Resource Biobank Core (CPROBE). Clinical data and plasma samples at the time of recruitment were obtained and used to generate lipidomic data by liquid chromatography/mass-spectrometry-based untargeted platform. PREDICTORS: Various levels of free fatty acids, acylcarnitines and complex lipids. OUTCOME: Stroke. ANALYTIC APPROACH: includes compound by compound comparison of lipids using t-test adjusted by false discovery rate in patients with and without stroke, and application of logistic regression analysis to identify independent lipid predictors of stroke and to estimate the odds associated with their various levels. RESULTS: Overall, we identified 330 compounds. Enrichment analysis revealed overrepresentation of differentially regulated phosphatidylcholines (PC)s and phosphatidylethanolamines (PE)s were overrepresented in stroke (P<0.001). Abundance of PC38:4, PE36:4, PC34:0, and palmitate were significantly higher, but those of plasmenyl-PE (pPE)38:2, and PE 32:2 was significantly lower in patients with stroke (p≤0.0014). After adjusting, each 1-SD increase in palmitate and PC38:4 was independently associated with 1.84 fold (95% CI: 1.06-3.20, p=0.031) and 1.84 fold (1.11-3.05, p=0.018) higher risk of stroke, respectively. We observed a significant trend toward higher abundance of PCs, PEs, pPEs, and sphingomyelins in stroke (p≤0.046). LIMITATIONS: Small sample size; unclear, if similar changes in the same or opposite direction preceded stroke, as the cross-sectional nature of the observation does not allow determining the effect of time course on lipid alterations. CONCLUSION: Differential regulation of palmitate, PCs, and PEs in patients with CKD and a history of stroke may represent a previously unrecognized risk factor and might be a target of risk stratification and modification.

Lipidomics and Biomarker Discovery in Kidney Disease.

Technological advances in mass spectrometry-based lipidomic platforms have provided the opportunity for comprehensive profiling of lipids in biological samples and shown alterations in the lipidome that occur in metabolic disorders. A lipidomic approach serves as a powerful tool for biomarker discovery and gaining insight to molecular mechanisms of disease, especially when integrated with other -omics platforms (ie, transcriptomics, proteomics, and metabolomics) in the context of systems biology. In this review, we describe the workflow commonly applied to the conduct of lipidomic studies including important aspects of study design, sample preparation, biomarker identification and quantification, and data processing and analysis, as well as crucial considerations in clinical applications. We also review some recent studies of the application of lipidomic platforms that highlight the potential of lipid biomarkers and add to our understanding of the molecular basis of kidney disease.