Disease-syndrome combination modeling: metabolomic strategy for the pathogenesis of chronic kidney disease.
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ABSTRACT:
SUBMITTER: Li S
PROVIDER: S-EPMC5562836 | biostudies-other | 2017 Aug
REPOSITORIES: biostudies-other
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Project description:The term cardiorenal syndrome refers to the interaction between the heart and the kidney in disease and encompasses five distinct types according to the initial site affected and the acute or chronic nature of the injury. Type 4, or chronic renocardiac syndrome, involves the features of chronic renal disease (CKD) leading to cardiovascular injury. There is sufficient epidemiologic evidence linking CKD with increased cardiovascular morbidity and mortality. The underlying pathophysiology goes beyond the highly prevalent traditional cardiovascular risk burden affecting renal patients. It involves CKD-related factors, which lead to cardiac and vascular pathology, mainly left ventricular hypertrophy, myocardial fibrosis, and vascular calcification. Risk management should consider both traditional and CKD-related factors, while therapeutic interventions, apart from appearing underutilized, still await further confirmation from large trials.
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Project description:A critical issue in the management of chronic kidney disease (CKD) is to prevent patients from the progression to end-stage kidney disease (ESKD), however, there is only limited number of biomarkers for the discrimination of the high-risk CKD patients. We aimed to identify the metabolites which possess the ability to predict the earlier kidney deterioration. We performed capillary electrophoresis and liquid chromatography mass spectrometry (CE-MS)-based metabolic profiling in a prospective cohort, which consisted of referred 112 CKD patients with median follow-up period of 4.4 years. The association between the levels of candidate metabolites and the outcomes (progression to ESKD alone or in combination with death before ESKD) were assessed by multivariate Cox proportional hazard models after adjusting for the baseline covariates. A total of 218 metabolites were detected in the plasma of CKD patients. We identified 16 metabolites which have predictive values for the composite outcome: The risk for composite outcome was elevated from 2.0- to 8.0-fold in those with higher levels of 16 plasma metabolites. Our results suggest that the measurement of these metabolites may facilitate CKD management by predicting the risk of progression to ESKD.
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Project description:BackgroundGreater adherence to plant-based diets is associated with a lower risk of incident chronic kidney disease (CKD). Metabolomics can help identify blood biomarkers of plant-based diets and enhance understanding of underlying mechanisms.ObjectivesUsing untargeted metabolomics, we aimed to identify metabolites associated with 4 plant-based diet indices (PDIs) (overall PDI, provegetarian diet, healthful PDI, and unhealthful PDI) and incident CKD in 2 subgroups within the Atherosclerosis Risk in Communities study.MethodsWe calculated 4 PDIs based on participants' responses on an FFQ. We used multivariable linear regression to examine the association between 4 PDIs and 374 individual metabolites, adjusting for confounders. We used Cox proportional hazards regression to evaluate associations between PDI-related metabolites and incident CKD. Estimates were meta-analyzed across 2 subgroups (n1 = 1762; n2 = 1960). We calculated C-statistics to assess whether metabolites improved the prediction of those in the highest quintile compared to the lower 4 quintiles of PDIs, and whether PDI- and CKD-related metabolites predicted incident CKD beyond the CKD prediction model.ResultsWe identified 82 significant PDI-metabolite associations (overall PDI = 27; provegetarian = 17; healthful PDI = 20; unhealthful PDI = 18); 11 metabolites overlapped across the overall PDI, provegetarian diet, and healthful PDI. The addition of metabolites improved prediction of those in the highest quintile as opposed to the lower 4 quintiles of PDIs compared with participant characteristics alone (range of differences in C-statistics = 0.026-0.104; P value ≤ 0.001 for all tests). Six PDI-related metabolites (glycerate, 1,5-anhydroglucitol, γ-glutamylalanine, γ-glutamylglutamate, γ-glutamylleucine, γ-glutamylvaline), involved in glycolysis, gluconeogenesis, pyruvate metabolism, and γ-glutamyl peptide metabolism, were significantly associated with incident CKD and improved prediction of incident CKD beyond the CKD prediction model (difference in C-statistics for 6 metabolites = 0.005; P value = 0.006).ConclusionsIn a community-based study of US adults, we identified metabolites that were related to plant-based diets and predicted incident CKD. These metabolites highlight pathways through which plant-based diets are associated with incident CKD.
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Project description:While a complex network of cellular and molecular events is known to be involved in the pathophysiological mechanism of chronic kidney disease (CKD), the divergence point between reversal and progression and the event that triggers CKD progression are still unknown. To understand the different mechanisms between reversible and irreversible kidney disease and to search for urinary biomarkers that can predict prognosis, a metabolomic analysis was applied to compare acute and chronic experimental glomerulonephritis (GN) models. Four metabolites, namely, epoxyoctadecenoic acid (EpOME), epoxyeicosatetraenoic acid (EpETE), α-linolenic acid (ALA), and hydroxyretinoic acid, were identified as predictive markers after comparing the chronic nephritis model with acute nephritis and control groups (false discovery rate adjusted p-value (q-value) < 0.05). Renal mRNA expression of cytochrome P450 and epoxide hydrolase was also identified as being involved in the production of epoxide metabolites from these polyunsaturated fatty acids (p < 0.05). These results suggested that the progression of chronic kidney disease is associated with abnormally activated epoxide hydrolase, leading to an increase in EpOME and EpETE as pro-inflammatory eicosanoids.
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Project description:Severe obesity is a major risk for chronic kidney disease (CKD). Early detection and careful monitoring of renal function are critical for the prevention of CKD during obesity, since biopsies are not performed in patients with CKD and diagnosis is dependent on the assessment of clinical parameters. To explore whether distinct lipid and metabolic signatures in obesity may signify early stages of pathogenesis toward CKD, liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-high resolution accurate mass-mass spectrometry (GC-HRAM-MS) analyses were performed in the serum and the urine of severely obese patients with and without CKD. Moreover, the impact of bariatric surgery (BS) in lipid and metabolic signature was also studied, through LC-MS and GC-HRAM-MS analyses in the serum and urine of patients with severe obesity and CKD before and after undergoing BS. Regarding patients with severe obesity and CKD compared to severely obese patients without CKD, serum lipidome analysis revealed significant differences in lipid signature. Furthermore, serum metabolomics profile revealed significant changes in specific amino acids, with isoleucine and tyrosine, increased in CKD patients compared with patients without CKD. LC-MS and GC-HRAM-MS analysis in serum of patients with severe obesity and CKD after BS showed downregulation of levels of triglycerides (TGs) and diglycerides (DGs) as well as a decrease in branched-chain amino acid (BCAA), lysine, threonine, proline, and serine. In addition, BS removed most of the correlations in CKD patients against biochemical parameters related to kidney dysfunction. Concerning urine analysis, hippuric acid, valine and glutamine were significantly decreased in urine from CKD patients after surgery. Interestingly, bariatric surgery did not restore all the lipid species, some of them decreased, hence drawing attention to them as potential targets for early diagnosis or therapeutic intervention. Results obtained in this study would justify the use of comprehensive mass spectrometry-based lipidomics to measure other lipids aside from conventional lipid profiles and to validate possible early markers of risk of CKD in patients with severe obesity.
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