The metabolomic resetting effect of FG4592 in AKI to CKD transition (Part 2)
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus Musculus
TISSUE(S): Kidney
DISEASE(S): Kidney Disease
PROVIDER: ST001856 | MetabolomicsWorkbench |
REPOSITORIES: MetabolomicsWorkbench
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| ST001855 | MetabolomicsWorkbench
Project description:Background and objectivesCauses of CKD differ in prognosis and treatment. Metabolomic indicators of CKD cause may provide clues regarding the different physiologic processes underlying CKD development and progression.Design, setting, participants & measurementsMetabolites were quantified from serum samples of participants in the Modification of Diet in Renal Disease (MDRD) Study, a randomized controlled trial of dietary protein restriction and BP control, using untargeted reverse phase ultraperformance liquid chromatography tandem mass spectrometry quantification. Known, nondrug metabolites (n=687) were log-transformed and analyzed to discover associations with CKD cause (polycystic kidney disease, glomerular disease, and other cause). Discovery was performed in Study B, a substudy of MDRD with low GFR (n=166), and replication was performed in Study A, a substudy of MDRD with higher GFR (n=423).ResultsOverall in MDRD, average participant age was 51 years and 61% were men. In the discovery study (Study B), 29% of participants had polycystic kidney disease, 28% had glomerular disease, and 43% had CKD of another cause; in the replication study (Study A), the percentages were 28%, 24%, and 48%, respectively. In the discovery analysis, adjusted for demographics, randomization group, body mass index, hypertensive medications, measured GFR, log-transformed proteinuria, and estimated protein intake, seven metabolites (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, hippurate, homocitrulline, and 1,5-anhydroglucitol) were associated with CKD cause after correction for multiple comparisons (P<0.0008). Five of these metabolite associations (16-hydroxypalmitate, kynurenate, homovanillate sulfate, N2,N2-dimethylguanosine, and hippurate) were replicated in Study A (P<0.007), with all replicated metabolites exhibiting higher levels in polycystic kidney disease and lower levels in glomerular disease compared with CKD of other causes.ConclusionsMetabolomic profiling identified several metabolites strongly associated with cause of CKD.
| S-EPMC5672969 | biostudies-literature
Project description:Background and objectivesNontargeted metabolomics can measure thousands of low-molecular-weight biochemicals, but important gaps limit its utility for biomarker discovery in CKD. These include the need to characterize technical and intraperson analyte variation, to pool data across platforms, and to outline analyte relationships with eGFR.Design, setting, participants, & measurementsPlasma samples from 49 individuals with CKD (eGFR<60 ml/min per 1.73 m2 and/or ≥1 g proteinuria) were examined from two study visits; 20 samples were repeated as blind replicates. To enable comparison across two nontargeted platforms, samples were profiled at Metabolon and the Broad Institute.ResultsThe Metabolon platform reported 837 known metabolites and 483 unnamed compounds (selected from 44,953 unknown ion features). The Broad Institute platform reported 594 known metabolites and 26,106 unknown ion features. Median coefficients of variation (CVs) across blind replicates were 14.6% (Metabolon) and 6.3% (Broad Institute) for known metabolites, and 18.9% for (Metabolon) unnamed compounds and 24.5% for (Broad Institute) unknown ion features. Median CVs for day-to-day variability were 29.0% (Metabolon) and 24.9% (Broad Institute) for known metabolites, and 41.8% for (Metabolon) unnamed compounds and 40.9% for (Broad Institute) unknown ion features. A total of 381 known metabolites were shared across platforms (median correlation 0.89). Many metabolites were negatively correlated with eGFR at P<0.05, including 35.7% (Metabolon) and 18.9% (Broad Institute) of known metabolites.ConclusionsNontargeted metabolomics quantifies >1000 analytes with low technical CVs, and agreement for overlapping metabolites across two leading platforms is excellent. Many metabolites demonstrate substantial intraperson variation and correlation with eGFR.
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Project description:Background and objectivesCKD is a common public health problem. Identifying biomarkers adds prognostic/diagnostic value by contributing to an understanding of CKD at the molecular level and possibly defining new drug targets. Metabolomics provides a snapshot of biochemical events at a particular time in the progression of CKD. This cross-sectional metabolomics study ascertained whether plasma metabolite profiles are significantly different in CKD stages 2, 3, and 4.Design, setting, participants, & measurementsAn analysis of plasma metabolites, using gas and liquid chromatography coupled to mass spectrometry, was conducted on 30 nondiabetic men ages 40-52 years, with 10 participants each in CKD stages 2, 3, and 4 based on their estimated GFR (calculated by the Modified Diet in Renal Disease formula). Participants were recruited in late 2008, and plasma samples were tested at Metabolon Inc and analyzed in 2012.ResultsComparison of stage 3/stage 2 identified 62 metabolites that differed (P ≤ 0.05), with 39 higher and 23 lower in stage 3 compared with stage 2; comparisons of stage 4/stage 2 identified 111 metabolites, with 66 higher and 45 lower; and comparisons of stage 4/stage 3 identified 11 metabolites, with 7 higher and 4 lower. Major differences in metabolite profiles with increasing stage of CKD were observed, including altered arginine metabolism, elevated coagulation/inflammation, impaired carboxylate anion transport, and decreased adrenal steroid hormone production.ConclusionsGlobal metabolite profiling of plasma uncovered potential biomarkers of stages of CKD. Moreover, these biomarkers provide insight into possible pathophysiologic processes that may contribute to progression of CKD.
| S-EPMC3586968 | biostudies-literature
Project description:Background and objectivesData are scarce on blood metabolite associations with proteinuria, a strong risk factor for adverse kidney outcomes. We sought to investigate associations of proteinuria with serum metabolites identified using untargeted profiling in populations with CKD.Design, setting, participants, & measurementsUsing stored serum samples from the African American Study of Kidney Disease and Hypertension (AASK; n=962) and the Modification of Diet in Renal Disease (MDRD) study (n=620), two rigorously conducted clinical trials with per-protocol measures of 24-hour proteinuria and GFR, we evaluated cross-sectional associations between urine protein-to-creatinine ratio and 637 known, nondrug metabolites, adjusting for key clinical covariables. Metabolites significantly associated with proteinuria were tested for associations with CKD progression.ResultsIn the AASK and the MDRD study, respectively, the median urine protein-to-creatinine ratio was 80 (interquartile range [IQR], 28-359) and 188 (IQR, 54-894) mg/g, mean age was 56 and 52 years, 39% and 38% were women, 100% and 7% were black, and median measured GFR was 48 (IQR, 35-57) and 28 (IQR, 18-39) ml/min per 1.73 m2. Linear regression identified 66 serum metabolites associated with proteinuria in one or both studies after Bonferroni correction (P<7.8×10-5), 58 of which were statistically significant in a meta-analysis (P<7.8×10-4). The metabolites with the lowest P values (P<10-27) were 4-hydroxychlorthalonil and 1,5-anhydroglucitol; all six quantified metabolites in the phosphatidylethanolamine pathway were also significant. Of the 58 metabolites associated with proteinuria, four were associated with ESKD in both the AASK and the MDRD study.ConclusionsWe identified 58 serum metabolites with cross-sectional associations with proteinuria, some of which were also associated with CKD progression.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_02_07_CJASNPodcast_19_03_.mp3.
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Project description:Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry-based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m(2). Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P?0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non-GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77-0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR.
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