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Abstract: FR-PO192

Urinary Biomarkers of Tubular Dysfunction and Risk of CKD Progression Among SPRINT Participants

Session Information

Category: CKD (Non-Dialysis)

  • 1901 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention


  • Jotwani, Vasantha, University of California, San Francisco, San Francisco, California, United States
  • Katz, Ronit, University of Washington, Seattle, Washington, United States
  • Garimella, Pranav S., UCSD, San Diego, California, United States
  • Malhotra, Rakesh, UCSD, San Diego, California, United States
  • Cheung, Alfred K., University of Utah, Salt Lake City, Utah, United States
  • Chonchol, Michel, University of Colorado , Aurora, Colorado, United States
  • Drawz, Paul E., University of Minnesota , Minneapolis, Minnesota, United States
  • Freedman, Barry I., Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
  • Haley, William E., Mayo Clinic, Jacksonville, Florida, United States
  • Killeen, Anthony Alexander, University of Minnesota , Minneapolis, Minnesota, United States
  • Punzi, Henry A., Punzi Medical Center, Carrollton, Texas, United States
  • Sarnak, Mark J., Tufts Medical Center, Boston, Massachusetts, United States
  • Segal, Mark S., University of Florida, Gainesville, Florida, United States
  • Ix, Joachim H., UCSD, San Diego, California, United States
  • Shlipak, Michael, San Francisco VA Medical Center, San Francisco, California, United States

Tubular atrophy on biopsy is a strong predictor of kidney disease progression, but tubular health is poorly quantified by traditional measures including estimated glomerular filtration rate (eGFR) and albuminuria. We hypothesized that urinary biomarkers of tubule dysfunction would be associated with faster kidney function decline in persons with chronic kidney disease (CKD).


We measured baseline urine concentrations of α1-microglobulin (α1m), β2-microglobulin (β2m), and uromodulin among 2,428 participants of the Systolic Blood Pressure Intervention Trial who had an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2. We used linear mixed models to evaluate biomarker associations with annualized relative change in eGFR.


At baseline, the mean age was 72±9 years and eGFR was 48±11 ml/min/1.73m2. Over a median follow-up of 3.8 years, higher concentrations of urine α1m and β2m and lower concentrations of urine uromodulin were independently associated with faster annualized eGFR decline (Table). There were no statistically significant interactions by intervention arm (p>0.3 for all biomarkers).


Among hypertensive, nondiabetic patients with CKD, urinary biomarkers of tubular dysfunction were independently associated with subsequent declines in kidney function.

Associations of urinary biomarkers with annualized relative change in eGFR among SPRINT participants with CKD at baseline (N = 2,428)
β (95% CI)
Multivariable- adjusted
β (95% CI)
α1-microglobulin-0.57 (-0.69, -0.44)-0.57 (-0.70, -0.45)
β2-microglobulin-0.21 (-0.27, -0.15)-0.20 (-0.26, -0.15)
Uromodulin0.48 (0.33, 0.62)0.48 (0.33, 0.62)

β coefficient represents % eGFR change in ml/min/1.73m2/year per doubling of the biomarker. Demographic-adjusted model includes age, sex, race, intervention arm and urine creatinine. Multivariable-adjusted model additionally includes baseline eGFR, urine albumin, smoking status, history of cardiovascular disease, number of antihypertensive medications, systolic blood pressure, diastolic blood pressure, body mass index, high-density lipoprotein, and total cholesterol.


  • NIDDK Support