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Kidney Week

Abstract: TH-PO1108

Creating Axes of Kidney Tubule Health: A Factor Analysis of Biomarkers in SPRINT

Session Information

Category: CKD (Non-Dialysis)

  • 1902 CKD (Non-Dialysis): Clinical, Outcomes, and Trials

Authors

  • Lee, Alexandra K., Kidney Health Research Collaborative, UCSF & VA, San Francisco, California, United States
  • Katz, Ronit, University of Washington, Seattle, Washington, United States
  • Jotwani, Vasantha, Kidney Health Research Collaborative, UCSF & VA, San Francisco, California, United States
  • Garimella, Pranav S., UCSD, San Diego, California, United States
  • Ix, Joachim H., UCSD, San Diego, California, United States
  • Shlipak, Michael, San Francisco VA Medical Center, San Francisco, California, United States
Background

Several biomarkers of kidney tubule function and injury associate with cardiovascular disease (CVD) and mortality risks, but associations are weakened by their physiological overlap and inter-correlation. Factor analysis is an agnostic method to condense information by analyzing covariance of measurements. We hypothesized that factor scores would represent underlying physiology and be associated with outcomes independently of eGFR and albuminuria.

Methods

Among 2,376 SPRINT participants with eGFR <60, we measured urine levels of kidney tubule injury (IL-18, NGAL, KIM-1, MCP-1, YKL-40) and function (α-1 microglobulin, β-2 microglobulin, uromodulin), and serum levels of PTH and FGF23. Factor analysis utilized principal-component factor estimation and promax rotation. We used Cox models to evaluate associations of factor scores with composite CVD, heart failure (HF), and all-cause death.

Results

Mean age was 73±9, 40% female, and mean eGFR 46±11. Factor scores reflected biology: there were two injury factors, Factor 1 comprised by IL-18, NGAL, and YKL-40; Factor 2 by MCP-1 and KIM-1; one factor of proximal tubule reabsorption, Factor 3, α-1 and β-2; and one factor of renal reserve: Factor 4, uromodulin, PTH, and FGF23. After adjusting for eGFR, albuminuria, and CVD risk factors, Factors 1, 3, and 4 were associated with CVD risk (Table), and Factor 1 was associated with HF. Factors 1 and 4 were associated with higher mortality risk. In participants without prior CVD or HF, Factor 3 was more strongly associated with CVD (HR 1.28, 95%CI 1.08-1.52) and HF (1.36, 1.02-1.81) (p-for-interaction=0.03, 0.04).

Conclusion

Combining biomarkers into factors may be an efficient method to create biologically plausible axes of kidney tubule health that have important associations with CVD risk, independently of glomerular markers.

Adjusted Associations of Factor Scores (per SD) with CVD, HF, and Death in SPRINT-CKD Participants
 CVD Composite
(events = 306)
Adjusted HR (95%CI)
Heart Failure
(events=123)
Adjusted HR (95%CI)
All-Cause Death
(events=233)
Adjusted HR (95%CI)
Factor 1: IL-18, YKL-40, NGAL1.14 (1.01 - 1.28)1.17 (1.02 - 1.33)1.21 (1.02 - 1.44)
Factor 2: MCP-1, KIM-11.07 (0.94 - 1.22)1.14 (0.97 - 1.33)1.17 (0.94 - 1.46)
Factor 3: α-1, β-21.18 (1.04 - 1.34)1.01 (0.88 - 1.16)1.13 (0.93 - 1.37)
Factor 4: uromodulin, PTH, FGF231.21 (1.07 - 1.38)1.10 (0.95 - 1.28)1.36 (1.13 - 1.63)

Adjusted for age, sex, race, randomization arm, eGFR, ACR, history of CVD/HF, number of hypertensive agents, SBP, DBP, HDL, total cholesterol, triglycerides, statin use, smoking.

Funding

  • NIDDK Support