Kidney Week

Abstract: TH-PO919

Urinary Glycolic Acid Predicts Kidney Function Decline in Type 1 Diabetic Subjects

Session Information

• Diabetic Kidney Disease: Biomarkers, Pathogenesis
  November 07, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
  Abstract Time: 10:00 AM - 12:00 PM

Category: Diabetic Kidney Disease

• 602 Diabetic Kidney Disease: Clinical

Authors

• Darshi, Manjula, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Manjula Darshi,

• Kim, Jiwan John, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Jiwan John Kim,

• Ahluwalia, Tarunveer S., Steno Diabetes Center Copenhagen, Gentofte, Denmark

Tarunveer S. Ahluwalia,

• Rossing, Peter, Steno Diabetes Center Copenhagen, Gentofte, Denmark

Peter Rossing,

• Groop, Per-Henrik, University of Helsinki and Helsinki University Hospital, Helsinki, Finland

Per-Henrik Groop,

• Sharma, Kumar, University of Texas Health Science Center at San Antonio, San Antonio, Texas, United States

Kumar Sharma,

Group or Team Name

• Center for Renal Precision Medicine

Background

Diabetes is the common cause of chronic kidney disease (CKD) and end-stage renal failure. Albuminuria and eGFR are widely approved biomarkers to identify disease progression. However, due to considerable heterogeneity not all subjects progress at the same rate. Here we evaluated a set of urine metabolites toward prediction of rapid progression of CKD in patients with type 1 diabetes.

Methods

We used a nested case-control study in four diverse cohorts (CACTI, EDC, FinnDiane, and Steno) with long-standing type 1 diabetes and normal kidney function. Subjects were classified into slow decliners/controls with eGFR decline ≤1ml/min/1.73m²/yr or rapid progressors/cases with eGFR decline of ≥3ml/min/1.73m²/yr. In training phase 34 urine metabolites were measured from 595 subjects. The most prognostic biomarkers were validated on additional 200 subjects from the same 4 cohorts. Logistic regression model was used to predict rapid eGFR decline. Area under the curve (AUC) were used to assess model performance.

Results

Baseline mean eGFR and median ACR in training phase controls (n=340) and cases (n=212) were 91.97 (sd 18.68) and 9.44 (IQR 30), and 98.37 (sd 25.44) and 33.49 (IQR 283.01), respectively. Analysis with clinical variables revealed age, baseline ACR, and baseline eGFR to be significant predictors of rapid decline. Three metabolites 3-methylcrotonylglycine, glycolic acid and citric acid had a univariate association to rapid progression (FDR ≤ 0.05). When these 3 metabolites were added to the model with the clinical variables, 3-methylcrotonylglycine and glycolic acid remained significant (p < 0.005) but the predictive performance of the model did not improve. In a stratified analysis of eGFR≥60 ml/min/1.73m² and micro- or macroalbuminuria (MA+) group, 3 methylcrotonylglycine and glycolic acid significantly improved the AUC from 0.69 (0.45-0.84) to 0.76 (0.61-0.89). The metabolites were further validated in 95 controls and 95 cases with eGFR≥60 ml/min/1.73m² and micro- or macroalbuminuria (MA+). Glycolic acid was univariately associated with rapid decline (P<0.01) and remained significant when added to clinical model (P:0.04).

Conclusion

In subjects with albuminuria and normal eGFR (≥60 3ml/min/1.73m²) urine glycolic acid may be useful as a prognostic biomarker along with clinical variables for loss of renal function.

Funding

• NIDDK Support