Advertisement
American Journal of Kidney Diseases

Metabolomic Markers of Kidney Function Decline in Patients With Diabetes: Evidence From the Chronic Renal Insufficiency Cohort (CRIC) Study

      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.

      Index Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to American Journal of Kidney Diseases
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Bailey R.A.
        • Wang Y.
        • Zhu V.
        • Rupnow M.F.
        Chronic kidney disease in US adults with type 2 diabetes: an updated national estimate of prevalence based on Kidney Disease: Improving Global Outcomes (KDIGO) staging.
        BMC Res Notes. 2014; 7: 1-7
        • Koro C.E.
        • Lee B.H.
        • Bowlin S.J.
        Antidiabetic medication use and prevalence of chronic kidney disease among patients with type 2 diabetes mellitus in the United States.
        Clin Ther. 2009; 31: 2608-2617
        • Saran R.
        • Robinson B.
        • Abbott K.C.
        • et al.
        US Renal Data System 2018 Annual Data Report: epidemiology of kidney disease in the United States.
        Am J Kidney Dis. 2019; 73: A7-A8
        • Grams M.E.
        • Yang W.
        • Rebholz C.M.
        • et al.
        Risks of adverse events in advanced CKD: the Chronic Renal Insufficiency Cohort (CRIC) Study.
        Am J Kidney Dis. 2017; 70: 337-346
        • Waikar S.S.
        • Rebholz C.M.
        • Zheng Z.
        • et al.
        Biological variability of estimated GFR and albuminuria in CKD.
        Am J Kidney Dis. 2018; 72: 538-546
        • Krolewski A.S.
        • Niewczas M.A.
        • Skupien J.
        • et al.
        Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria.
        Diabetes Care. 2014; 37: 226-234
        • Pena M.J.
        • De Zeeuw D.
        • Mischak H.
        • et al.
        Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes.
        Nephrol Dial Transplant. 2015; 30: iv86-iv95
        • Abbiss H.
        • Maker G.
        • Trengove R.
        Metabolomics approaches for the diagnosis and understanding of kidney diseases.
        Metabolites. 2019; 9: 34
        • Colhoun H.M.
        • Marcovecchio M.L.
        Biomarkers of diabetic kidney disease.
        Diabetologia. 2018; 61: 996-1011
        • Hirayama A.
        • Nakashima E.
        • Sugimoto M.
        • et al.
        Metabolic profiling reveals new serum biomarkers for differentiating diabetic nephropathy.
        Anal Bioanal Chem. 2012; 404: 3101-3109
        • Kalim S.
        • Rhee E.P.
        An overview of renal metabolomics.
        Kidney Int. 2017; 91: 61-69
        • Zhang Y.
        • Zhang S.
        • Wang G.
        Metabolomic biomarkers in diabetic kidney diseases - a systematic review.
        J Diabetes Complications. 2015; 29: 1345-1351
        • Pena M.J.
        • Lambers Heerspink H.J.
        • Hellemons M.E.
        • et al.
        Urine and plasma metabolites predict the development of diabetic nephropathy in individuals with Type 2 diabetes mellitus.
        Diabet Med. 2014; 31: 1138-1147
        • Sharma K.
        • Karl B.
        • Mathew A.V.
        • et al.
        Metabolomics reveals signature of mitochondrial dysfunction in diabetic kidney disease.
        J Am Soc Nephrol. 2013; 24: 1901-1912
        • Lash J.P.
        • Go A.S.
        • Appel L.J.
        • et al.
        Chronic Renal Insufficiency Cohort (CRIC) Study: baseline characteristics and associations with kidney function.
        Clin J Am Soc Nephrol. 2009; 4: 1302-1311
        • Denker M.
        • Boyle S.
        • Anderson A.H.
        • et al.
        Chronic Renal Insufficiency Cohort Study (CRIC): overview and summary of selected findings.
        Clin J Am Soc Nephrol. 2015; 10: 2073-2083
        • Feldman H.I.
        • Appel L.J.
        • Chertow G.M.
        • et al.
        The Chronic Renal Insufficiency Cohort (CRIC) Study: design and methods.
        J Am Soc Nephrol. 2003; 14: 148S-153S
        • Fuhrer T.
        • Heer D.
        • Begemann B.
        • Zamboni N.
        High-throughput, accurate mass metabolome profiling of cellular extracts by flow injection-time-of-flight mass spectrometry.
        Anal Chem. 2011; 83: 7074-7080
        • Levey A.S.
        • Stevens L.A.
        • Schmid C.H.
        • et al.
        A new equation to estimate glomerular filtration rate.
        Ann Intern Med. 2009; 150: 604-612
        • Anderson A.H.
        • Yang W.
        • Hsu C.Y.
        • et al.
        Estimating GFR among participants in the Chronic Renal Insufficiency Cohort (CRIC) Study.
        Am J Kidney Dis. 2012; 60: 250-261
        • Fitzmaurice G.M.
        • Laird N.M.
        • Ware J.H.
        Applied Longitudinal Analysis.
        John Wiley & Sons, Inc, Hoboken, NJ2011
        • Fischer M.J.
        • Lora C.M.
        • Ricardo A.C.
        • et al.
        CKD progression and mortality among Hispanics and non-Hispanics.
        J Am Soc Nephrol. 2016; 27: 3488-3497
        • Hsu C.Y.
        • Lin F.
        • Vittinghoff E.
        • Shlipak M.G.
        Racial differences in the progression from chronic renal insufficiency to end-stage renal disease in the United States.
        J Am Soc Nephrol. 2003; 14: 2902-2907
        • Hastie T.
        • Tibshirani R.
        • Friedman J.
        The Elements of Statistical Learning.
        Springer New York, New York, NY2009
        • Pepe M.S.
        • Kerr K.F.
        • Longton G.
        • Wang Z.
        Testing for improvement in prediction model performance.
        Stat Med. 2013; 32: 1467-1482
        • R Core Team
        R: A Language and Environment for Statistical Computing. 2019.
        (Accessed September 13, 2019)
        • Hallan S.
        • Afkarian M.
        • Zelnick L.R.
        • et al.
        Metabolomics and gene expression analysis reveal down-regulation of the citric acid (TCA) cycle in non-diabetic CKD patients.
        EBioMedicine. 2017; 26: 68-77
        • Jang C.
        • Oh S.F.
        • Wada S.
        • et al.
        A branched-chain amino acid metabolite drives vascular fatty acid transport and causes insulin resistance.
        Nat Med. 2016; 22: 421-426
        • Mardinoglu A.
        • Gogg S.
        • Lotta L.A.
        • et al.
        Elevated plasma levels of 3-hydroxyisobutyric acid are associated with incident type 2 diabetes.
        EBioMedicine. 2018; 27: 151-155
        • Newgard C.B.
        • An J.
        • Bain J.R.
        • et al.
        A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance.
        Cell Metab. 2009; 9: 311-326
        • Neinast M.D.
        • Jang C.
        • Hui S.
        • et al.
        Quantitative analysis of the whole-body metabolic fate of branched-chain amino acids.
        Cell Metab. 2019; 29: 417-429.e4
        • Gall W.E.
        • Beebe K.
        • Lawton K.A.
        • et al.
        Α-Hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population.
        PLoS One. 2010; 5e10883
        • Kamerling J.P.
        • Gerwig G.J.
        • Duran M.
        • Ketting D.
        • Wadman S.K.
        The absolute configuration of urinary 2-hydroxybutyric acid in patients with ketosis and lactic acidosis.
        Clin Chim Acta. 1978; 88: 183-188
        • Landaas S.
        • Pettersen J.E.
        Clinical conditions associated with urinary excretion of 2-hydroxybutyric acid.
        Scand J Clin Lab Invest. 1975; 35: 259-266
        • Breit M.
        • Weinberger K.M.
        Metabolic biomarkers for chronic kidney disease.
        Arch Biochem Biophys. 2016; 589: 62-80
        • Niewczas M.A.
        • Sirich T.L.
        • Mathew A.V.
        • et al.
        Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study.
        Kidney Int. 2014; 85: 1214-1224
        • Pencina M.J.
        • Parikh C.R.
        • Kimmel P.L.
        • et al.
        Statistical methods for building better biomarkers of chronic kidney disease.
        Stat Med. 2019; 38: 1903-1917
        • Sas K.M.
        • Karnovsky A.
        • Michailidis G.
        • Pennathur S.
        Metabolomics and diabetes: analytical and computational approaches.
        Diabetes. 2015; 64: 718-732
        • Harhay M.N.
        • Xie D.
        • Zhang X.
        • et al.
        Cognitive impairment in non–dialysis-dependent CKD and the transition to dialysis: findings from the Chronic Renal Insufficiency Cohort (CRIC) Study.
        Am J Kidney Dis. 2018; 72: 499-508
        • Koye D.N.
        • Magliano D.J.
        • Reid C.M.
        • et al.
        Risk of progression of nonalbuminuric CKD to end-stage kidney disease in people with diabetes: the CRIC (Chronic Renal Insufficiency Cohort) Study.
        Am J Kidney Dis. 2018; 72: 653-661