Distinct Dimensions of Kidney Health and Risk of Cardiovascular Disease, Heart Failure, and Mortality

Author:

Lee Alexandra K.1,Katz Ronit2,Jotwani Vasantha1,Garimella Pranav S.3,Ambrosius Walter T.4,Cheung Alfred K.56,Gren Lisa H.7,Neyra Javier A.89,Punzi Henry10,Raphael Kalani L.5,Shlipak Michael G.1,Ix Joachim H.3

Affiliation:

1. From the Department of Medicine, University of California San Francisco (A.K.L., V.J., M.G.S.)

2. Division of Nephrology, University of Washington, Seattle (R.K.)

3. Division of Nephrology-Hypertension, University of California San Diego (P.S.G., J.H.I)

4. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC (W.T.A.)

5. Division of Nephrology (A.K.C., K.L.R.), University of Utah, Salt Lake City

6. Medical Service, Veterans Affairs Salt Lake City Healthcare System, UT (A.K.C.)

7. Division of Public Health (L.H.G.), University of Utah, Salt Lake City

8. Division of Nephrology, University of Texas Southwestern, Dallas (J.A.N.)

9. Division of Nephrology, Bone, and Mineral Metabolism, University of Kentucky, Lexington (J.A.N.)

10. Punzi Medical Center Carrollton, TX (H.P.).

Abstract

Chronic kidney disease is a strong risk factor for cardiovascular disease (CVD), but clinical kidney measures (estimated glomerular filtration rate and albuminuria) do not fully reflect the multiple aspects of kidney tubules influencing cardiovascular health. Applied methods are needed to integrate numerous tubule biomarkers into useful prognostic scores. In SPRINT (Systolic Blood Pressure Intervention Trial) participants with chronic kidney disease at baseline (estimated glomerular filtration rate cr&cys <60 mL/minute per 1.73 m 2 ), we measured 8 biomarkers from urine (α1M [α1M microglobulin], β2M [β2M microglobulin], umod [uromodulin], KIM-1 [kidney injury molecule-1], MCP-1 [monocyte chemoattractant protein-1], YKL-40 [chitinase-3-like protein-1], NGAL [neutrophil gelatinase-associated lipocalin], and IL-18 [interleukin 18]) and 2 biomarkers from serum (intact parathyroid hormone, iFGF-23 [intact fibroblast growth factor-23]). We used an unsupervised method, exploratory factor analysis, to create summary scores of tubule health dimensions. Adjusted Cox models evaluated each tubule score with CVD events, heart failure, and all-cause mortality. We examined CVD discrimination using Harrell C-statistic. Factor analysis of 10 biomarkers from 2376 SPRINT-chronic kidney disease participants identified 4 unique dimensions of tubular health: tubule injury/repair (NGAL, IL-18, YKL-40), tubule injury/fibrosis (KIM-1, MCP-1), tubule reabsorption (α1M, β2M), and tubular reserve/mineral metabolism (umod, intact parathyroid hormone, iFGF-23). After adjustment for CVD risk factors, estimated glomerular filtration rate, and albumin-to-creatinine ratio, 2 of the 4 tubule scores were associated with CVD (hazard ratio per SD; reabsorption, 1.21 [1.06–1.38]; reserve, 1.24 (1.08–1.38]), 1 with heart failure (reserve, 1.41 [1.13–1.74]), and none with mortality. Compared with a base model (C-statistic=0.674), adding estimated glomerular filtration rate and albumin-to-creatinine ratio improved the C-statistic (C=0.704; P =0.001); further adding tubule scores additionally improved the C-statistic (C=0.719; P =0.009). In the setting of chronic kidney disease, dimensions of tubule health quantified using factor analysis improved CVD discrimination beyond contemporary kidney measures. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01206062.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Internal Medicine

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