Brian Kwan1, Tobias Fuhrer2, Jing Zhang3, Manjula Darshi4, Benjamin Van Espen5, Daniel Montemayor4, Ian H de Boer6, Mirela Dobre7, Chi-Yuan Hsu8, Tanika N Kelly9, Dominic S Raj10, Panduranga S Rao11, Santosh L Saraf12, Julia Scialla13, Sushrut S Waikar14, Kumar Sharma15, Loki Natarajan16. 1. Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA; Moores Cancer Center, University of California, San Diego, La Jolla, CA. 2. Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. 3. Moores Cancer Center, University of California, San Diego, La Jolla, CA. 4. Center of Renal Precision Medicine, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX. 5. Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA. 6. Department of Medicine, University of Washington, Seattle, WA. 7. Division of Nephrology and Hypertension, University Hospitals Cleveland Medical Center, Case Western Reserve University, Cleveland, OH. 8. Department of Medicine, University of California, San Francisco, San Francisco, CA. 9. Department of Epidemiology, Tulane University, New Orleans, LA. 10. Division of Kidney Disease and Hypertension, George Washington University, Washington, DC. 11. Department of Medicine, University of Michigan, Ann Arbor, Ann Arbor, MI. 12. Department of Medicine, University of Illinois at Chicago, Chicago, IL. 13. Department of Medicine and Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC; Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA; Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA. 14. Renal Division, Brigham and Women's Hospital, Boston, MA; Renal Section, Boston University Medical Center, Boston, MA. 15. Center of Renal Precision Medicine, Department of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX. Electronic address: sharmak3@uthscsa.edu. 16. Department of Family Medicine and Public Health, University of California, San Diego, La Jolla, CA; Moores Cancer Center, University of California, San Diego, La Jolla, CA. Electronic address: lnatarajan@ucsd.edu.
Abstract
RATIONALE & OBJECTIVE: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years. PREDICTORS: 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. OUTCOMES: Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). ANALYTICAL APPROACH: Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT. RESULTS: During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]). LIMITATIONS: Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. CONCLUSIONS: Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.
RATIONALE & OBJECTIVE: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified 13 urine metabolites that had levels reduced in DKD compared with healthy controls. We evaluated associations of these 13 metabolites with future DKD progression. STUDY DESIGN: Prospective cohort. SETTING & PARTICIPANTS: 1,001 Chronic Renal Insufficiency Cohort (CRIC) participants with diabetes with estimated glomerular filtration rates (eGFRs) between 20 and 70mL/min/1.73m2 were followed up prospectively for a median of 8 (range, 2-10) years. PREDICTORS: 13 urine metabolites, age, race, sex, smoked more than 100 cigarettes in lifetime, body mass index, hemoglobin A1c level, blood pressure, urinary albumin, and eGFR. OUTCOMES: Annual eGFR slope and time to incident kidney failure with replacement therapy (KFRT; ie, initiation of dialysis or receipt of transplant). ANALYTICAL APPROACH: Several clinical metabolite models were developed for eGFR slope as the outcome using stepwise selection and penalized regression, and further tested on the time-to-KFRT outcome. A best cross-validated (final) prognostic model was selected based on high prediction accuracy for eGFR slope and high concordance statistic for incident KFRT. RESULTS: During follow-up, mean eGFR slope was-1.83±1.92 (SD) mL/min/1.73m2 per year; 359 (36%) participants experienced KFRT. Median time to KFRT was 7.45 years from the time of entry to the CRIC Study. In our final model, after adjusting for clinical variables, levels of metabolites 3-hydroxyisobutyrate (3-HIBA) and 3-methylcrotonyglycine had a significant negative association with eGFR slope, whereas citric and aconitic acid were positively associated. Further, 3-HIBA and aconitic acid levels were associated with higher and lower risk for KFRT, respectively (HRs of 2.34 [95% CI, 1.51-3.62] and 0.70 [95% CI, 0.51-0.95]). LIMITATIONS: Subgroups for whom metabolite signatures may not be optimal, nontargeted metabolomics by flow-injection analysis, and 2-stage modeling approaches. CONCLUSIONS: Urine metabolites may offer insights into DKD progression. If replicated in future studies, aconitic acid and 3-HIBA could identify individuals with diabetes at high risk for GFR decline, potentially leading to improved clinical care and targeted therapies.
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