TY - JOUR
T1 - Metabolomic Markers of Kidney Function Decline in Patients With Diabetes
T2 - Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study
AU - CRIC Study Investigators
AU - Kwan, Brian
AU - Fuhrer, Tobias
AU - Zhang, Jing
AU - Darshi, Manjula
AU - Van Espen, Benjamin
AU - Montemayor, Daniel
AU - de Boer, Ian H.
AU - Dobre, Mirela
AU - Hsu, Chi yuan
AU - Kelly, Tanika N.
AU - Raj, Dominic S.
AU - Rao, Panduranga S.
AU - Saraf, Santosh L.
AU - Scialla, Julia
AU - Waikar, Sushrut S.
AU - Sharma, Kumar
AU - Natarajan, Loki
AU - Appel, Lawrence J.
AU - Feldman, Harold I.
AU - Go, Alan S.
AU - He, Jiang
AU - Lash, James P.
AU - Rahman, Mahboob
AU - Townsend, Raymond R.
N1 - Funding Information:
Lawrence J. Appel, MD, MPH, Harold I. Feldman, MD, MSCE, Alan S. Go, MD, Jiang He, MD, PhD, James P. Lash, MD, Mahboob Rahman, MD, and Raymond R. Townsend, MD. Brian Kwan, BS, Tobias Fuhrer, PhD, Jing Zhang, MSc, Manjula Darshi, PhD, Benjamin Van Espen, MS, Daniel Montemayor, PhD, Ian H. de Boer, MD, MS, Mirela Dobre, MD, MPH, Chi-yuan Hsu, MD, MSc, Tanika N. Kelly, PhD, MPH, Dominic S. Raj, MD, Panduranga S. Rao, MD, Santosh L. Saraf, MD, Julia Scialla, MD, MHS, Sushrut S. Waikar, MD, MPH, Kumar Sharma, MD, and Loki Natarajan, PhD. Research idea and study design: KS, LN; data acquisition: TF, MDa, BvE; data analysis/interpretation: all authors; statistical analysis: BK; supervision or mentorship: KS, LN. Each author contributed important intellectual content during manuscript drafting or revision, accepts personal accountability for the author's own contributions, and agrees to ensure that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved. Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; U01DK060990, U01DK060984, U01DK061022, U01DK061021, U01DK061028, U01DK060980, U01DK060963, and U01DK060902). In addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award National Institutes of Health (NIH)/National Center for Advancing Translational Sciences (NCATS) UL1TR000003, Johns Hopkins University UL1 TR-000424, University of Maryland GCRC M01 RR-16500, Clinical and Translational Science Collaborative of Cleveland, UL1TR000439 from the NCATS component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433, University of Illinois at Chicago Clinical and Translational Science Awards UL1RR029879, Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036, Kaiser Permanente NIH/National Center for Research Resources UCSF-CTSI UL1 RR-024131. Mr Kwan, Dr Fuhrer, Ms Zhang, and Drs Darshi, Montemayor, Sharma, and Natarajan were partially supported by NIDDK 1R01DK110541-01A1. Mr Kwan was also partially supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1650112. Ms Zhang and Drs Darshi, Montemayor, Natarajan, and Sharma were also partially supported by DP3DK094352. Dr Dobre was supported by 5R01HL141846-02. Dr Hsu was supported by U01DK60902. Dr Kelly was partially supported by NIDDK R01DK101505-01A1. Dr Raj was supported by NIH grants R01 DK073665-01A1, 1U01DK099924-01, and 1U01DK099914-01. Dr Rao was supported by U01DK061028. Dr Saraf was supported in part by the National Heart, Lung, and Blood Institute (K23HL125984 and R03HL146788). Dr Scialla was supported in part by a pilot and feasibility award from the Diabetes Complications Consortium (U24DK115255). Dr Waikar was supported by U01DK085660, U01DK104308, R01DK103784, and UG3DK114915. The funders of this study had no role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication. Dr de Boer is consulting for Boehringer-Ingelheim, Ironwood, George Clinical, and Goldfinch Bio and has research equipment and supplies from Medtronic and Abbott. Dr Dobre had received consulting fees from Relypsa and Tricida. Dr Saraf is a consultant for Novartis and a speaker for Global Blood Therapeutics. Dr Scialla has modest research support for clinical event committee activities Sanofi and Glaxo Smith Kline and is part of the advisory board for Tricida. Dr Waikar reports personal fees from Harvard Clinical Research Institute, Cerus, Strataca, Venbio, Takeda, CVS, Janssen, Mass Medical International, and GSK; grants and personal fees from Allena; personal fees from Wolters Kluwer, outside the submitted work; and expert witness consultation for litigation related to Granuflo, Omniscan, statins, cisplatin nephrotoxicity, and mercury exposure. Dr Sharma was on advisory board for Janssen and served on DSMB for Sanofi in the past year. The other authors declare that they have no relevant financial interests. We thank Lisa E. Wesby, MS, for assistance as CRIC project manager in managing and facilitating the manuscript among all the authors. This material is based on work supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1650112. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Received October 8, 2019. Evaluated by 2 external peer reviewers and a statistician, with editorial input from an Acting Editor-in-Chief (Editorial Board Member Jerry Yee, MD). Accepted in revised form January 17, 2020. The involvement of an Acting Editor-in-Chief to handle the peer-review and decision-making processes was to comply with AJKD's procedures for potential conflicts of interest for editors, described in the Information for Authors & Journal Policies.
Funding Information:
Funding for the CRIC Study was obtained under a cooperative agreement from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK; U01DK060990 , U01DK060984 , U01DK061022 , U01DK061021 , U01DK061028 , U01DK060980 , U01DK060963 , and U01DK060902 ). In addition, this work was supported in part by the Perelman School of Medicine at the University of Pennsylvania Clinical and Translational Science Award National Institutes of Health (NIH)/ National Center for Advancing Translational Sciences (NCATS) UL1TR000003 , Johns Hopkins University UL1 TR-000424 , University of Maryland GCRC M01 RR-16500 , Clinical and Translational Science Collaborative of Cleveland , UL1TR000439 from the NCATS component of the NIH and NIH roadmap for Medical Research, Michigan Institute for Clinical and Health Research (MICHR) UL1TR000433 , University of Illinois at Chicago Clinical and Translational Science Awards UL1RR029879 , Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases P20 GM109036 , Kaiser Permanente NIH/ National Center for Research Resources UCSF-CTSI UL1 RR-024131 . Mr Kwan, Dr Fuhrer, Ms Zhang, and Drs Darshi, Montemayor, Sharma, and Natarajan were partially supported by NIDDK 1R01DK110541-01A1 . Mr Kwan was also partially supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1650112 . Ms Zhang and Drs Darshi, Montemayor, Natarajan, and Sharma were also partially supported by DP3DK094352. Dr Dobre was supported by 5R01HL141846-02. Dr Hsu was supported by U01DK60902. Dr Kelly was partially supported by NIDDK R01DK101505-01A1 . Dr Raj was supported by NIH grants R01 DK073665-01A1 , 1U01DK099924-01 , and 1U01DK099914-01 . Dr Rao was supported by U01DK061028. Dr Saraf was supported in part by the National Heart, Lung, and Blood Institute ( K23HL125984 and R03HL146788 ). Dr Scialla was supported in part by a pilot and feasibility award from the Diabetes Complications Consortium ( U24DK115255 ). Dr Waikar was supported by U01DK085660, U01DK104308, R01DK103784, and UG3DK114915. The funders of this study had no role in study design; collection, analysis, and interpretation of data; writing the report; and the decision to submit the report for publication.
Funding Information:
We thank Lisa E. Wesby, MS, for assistance as CRIC project manager in managing and facilitating the manuscript among all the authors. This material is based on work supported by the National Science Foundation Graduate Research Fellowship Program under grant no. DGE-1650112.
Publisher Copyright:
© 2020 National Kidney Foundation, Inc.
PY - 2020/10
Y1 - 2020/10
N2 - 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 70 mL/min/1.73 m2 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/1.73 m2 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.
AB - 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 70 mL/min/1.73 m2 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/1.73 m2 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.
KW - Biomarker
KW - Chronic Renal Insufficiency Cohort (CRIC)
KW - chronic kidney disease (CKD)
KW - diabetes
KW - end-stage renal disease (ESRD)
KW - estimated glomerular filtration rate (eGFR)
KW - incident kidney failure
KW - kidney disease progression
KW - kidney function decline
KW - longitudinal study
KW - metabolomics
KW - multivariate model
KW - prediction
KW - prognosis
KW - risk factor
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U2 - 10.1053/j.ajkd.2020.01.019
DO - 10.1053/j.ajkd.2020.01.019
M3 - Article
C2 - 32387023
AN - SCOPUS:85084235523
SN - 0272-6386
VL - 76
SP - 511
EP - 520
JO - American Journal of Kidney Diseases
JF - American Journal of Kidney Diseases
IS - 4
ER -