Summary of Study ST002819

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench,, where it has been assigned Project ID PR001762. The data can be accessed directly via it's Project DOI: 10.21228/M8ZB1D This work is supported by NIH grant, U2C- DK119886.


This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

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Study IDST002819
Study TitleEvaluation of Novel Candidate Filtration Markers from a Global Metabolomics Discovery for Glomerular Filtration Rate Estimation (MDRD)
Study SummaryBackground: Creatinine and cystatin-C are recommended for estimating glomerular filtration rate (eGFR) but accuracy is suboptimal. Using untargeted metabolomics data, we sought to identify candidate filtration markers using a novel approach based on their maximal joint association with measured GFR (mGFR) with flexibility to consider their biological and chemical properties later. Methods: We analyzed metabolites measured in seven diverse studies of 2,851 participants on the Metabolon H4 platform that had Pearson correlations with log mGFR <-0.5. We used a stepwise approach to develop models to estimate mGFR including two to 15 metabolites with and without inclusion of creatinine and demographics. We then selected candidate filtration markers from those metabolites found >20% in models that did not demonstrate substantial overfitting in cross-validation and with small (<0.1 in absolute value) coefficients for demographics. Results: In total, 456 named metabolites were present in all studies, and 36 had correlations <-0.5 with mGFR. We developed 2,225 models including these metabolites; all had lower RMSEs and smaller coefficients for demographic variables compared to estimates using untargeted creatinine. Cross-validated RMSEs (0.187-0.213) were similar to original RMSEs for models with ≤ 10 metabolites. Our criteria identified 17 metabolites, including 12 new candidate filtration markers. Conclusion: We identified candidate metabolites with maximal joint association with mGFR and minimal association with demographic variables across varied clinical settings. Future analyses will assess metabolite biological and chemical characteristics in the path towards development of a panel eGFR that is more accurate and less reliant on demographic variables than current eGFR. ACRONYMS AASKG1: African American Study of Kidney (patient data at G1 visit). ALTOLD: Assessing Long Term Outcomes in Living Kidney Donors study. MDRD: The Modification of Diet in Renal Disease study.
Tufts Medical Center
Last NameInker
First NameLesley
Address800 Washington Street
Submit Date2023-08-17
Analysis Type DetailOther
Release Date2023-09-06
Release Version1
Lesley Inker Lesley Inker application/zip

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Subject ID:SU002928
Subject Type:Human
Subject Species:Homo sapiens
Taxonomy ID:9606
Age Or Age Range:18 and older
Gender:Male and female