{
"METABOLOMICS WORKBENCH":{"STUDY_ID":"ST002820","ANALYSIS_ID":"AN004596","VERSION":"1","CREATED_ON":"August 18, 2023, 8:44 am"},
"PROJECT":{"PROJECT_TITLE":"Improving kidney function assessment in health and disease","PROJECT_SUMMARY":"Use results from a global metabolomics platform with existing measured GFR assessments to identify a set of markers from which to develop a highly accurate targeted mass spectrometry multiplex assay","INSTITUTE":"Tufts Medical Center","DEPARTMENT":"Nephrology","LABORATORY":"Metabolon; University of Minnesota","LAST_NAME":"Inker","FIRST_NAME":"Lesley","ADDRESS":"800 Washington Street, Boston, Massachusetts, 02111, USA","EMAIL":"Lesley.Inker@tuftsmedicine.org","PHONE":"6176368783","FUNDING_SOURCE":"NIDDK"},
"STUDY":{"STUDY_TITLE":"Evaluation of Novel Candidate Filtration Markers from a Global Metabolomics Discovery for Glomerular Filtration Rate Estimation (AASKG1)","STUDY_SUMMARY":"Background: 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 <