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Abstract: PO2361

Plasma Biomarkers and Incident CKD in Individuals Without Diabetes

Session Information

Category: CKD (Non-Dialysis)

  • 2101 CKD (Non-Dialysis): Epidemiology, Risk Factors, and Prevention

Authors

  • Sarnak, Mark J., Tufts Medical Center, Boston, Massachusetts, United States
  • Katz, Ronit, University of Washington, Seattle, Washington, United States
  • Coca, Steven G., Icahn School of Medicine at Mount Sinai, New York, New York, United States
  • Greenberg, Jason Henry, Yale University School of Medicine, New Haven, Connecticut, United States
  • Kimmel, Paul L., National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, United States
  • Sabbisetti, Venkata, Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Parikh, Chirag R., Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Rebholz, Casey, Johns Hopkins Medicine, Baltimore, Maryland, United States
  • Waikar, Sushrut S., Boston University School of Medicine, Boston, Massachusetts, United States
  • Schelling, Jeffrey R., Case Western Reserve University, Cleveland, Ohio, United States
  • Zheng, Zihe, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States
  • Cushman, Mary, University of Vermont College of Medicine, Burlington, Vermont, United States
  • Ramachandran, Vasan S., Boston University School of Medicine, Boston, Massachusetts, United States
  • Bonventre, Joseph V., Brigham and Women's Hospital, Boston, Massachusetts, United States
  • Shlipak, Michael, University of California San Francisco, San Francisco, California, United States
  • Ix, Joachim H., University of California San Diego, La Jolla, California, United States
  • Gutierrez, Orlando M., The University of Alabama at Birmingham, Birmingham, Alabama, United States
Background

Earlier prediction of CKD may facilitate risk factor mitigation prior to advanced disease. Albuminuria and reduced GFR are relatively insensitive markers of early CKD. We examined the association of several novel plasma biomarkers with incident CKD.

Methods

We used a case cohort design in participants without diabetes in the Multiethnic Study of Atherosclerosis (MESA) and the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohorts with a baseline eGFR ≥60 ml/min/1.73 m2. Incident CKD was defined as the development of an eGFR <60 ml/min/1.73 m2 and >40% decline in eGFR from baseline. We measured plasma markers of inflammation/fibrosis—soluble tumor necrosis factor receptors 1 and 2 (TNF-R1 and TNF-R2), monocyte chemotactic protein-1 (MCP-1) and soluble urokinase-type plasminogen activator receptor (suPAR)—and tubular injury (kidney injury marker 1, KIM-1) and repair [chitinase 3-like protein 1 (YKL-40)]. Cox regression models weighted for the case cohort design were used to estimate hazard ratios.

Results

In MESA (median follow-up 9.2 years), there were 497 individuals in the subcohort and 163 cases of incident CKD. In REGARDS (median follow-up 9.4 years), there were 497 individuals in the subcohort and 497 cases of incident CKD. Plasma KIM 1, suPAR, TNF-R1, TNF-R2 and YKL-40 concentrations were all independently associated with incident CKD in MESA. In REGARDS, TNF-R1 and TNF-R2 were independently associated with incident CKD (Table).

Conclusion

Plasma concentrations of soluble TNF-R1 and TNF-R2 are consistently associated with incident CKD in non-diabetic community-living individuals, independent of eGFR, UACR, and other CKD risk factors.

Association of individual plasma biomarkers (HR (95% CI) per two fold higher) with incident CKD
 MESAREGARDS
 UnadjustedAdjusted*UnadjustedAdjusted*
KIM-11.79 (1.46, 2.20)1.38 (1.05, 1.81)1.14 (0.97, 1.33)1.11 (0.94, 1.31)
MCP-11.59 (1.14, 2.22)1.17 (0.76, 1.79)1.32 (1.05, 1.66)1.25 (0.98, 1.59)
suPAR3.04 (2.08, 4.45)1.96 (1.10, 3.49)1.33 (1.02, 1.74)1.28 (0.95, 1.72)
TNF-R12.53 (1.80, 3.55)1.65 (1.04, 2.62)1.57 (1.19, 2.06)1.99 (1.43, 2.76)
TNF-R23.39 (2.25, 5.12)2.02 (1.21, 3.38)1.52 (1.11, 2.07)1.76 (1.22, 2.54)
YKL-401.90 (1.57, 2.30)1.38 (1.09, 1.75)1.08 (0.95, 1.24)1.07 (0.92, 1.24)

*adjusted for age, sex, race/ethnicity, education, BMI, SBP, HTN meds, smoking, UACR, and eGFR

Funding

  • NIDDK Support