Plasma Biomarkers and Incident CKD Among Individuals Without Diabetes

Abstract

Rationale & objective: Biomarkers of kidney disease progression have been identified in individuals with diabetes and underlying chronic kidney disease (CKD). Whether or not these markers are associated with the development of CKD in a general population without diabetes or CKD is not well established.

Study design: Prospective observational cohort.

Setting & participants: In the Atherosclerosis Risk in Communities) study, 948 participants were studied.

Exposures: The baseline plasma biomarkers of kidney injury molecule-1 (KIM-1), monocyte chemoattractant protein-1 (MCP-1), soluble urokinase plasminogen activator receptor (suPAR), tumor necrosis factor receptor 1 (TNFR-1), tumor necrosis factor receptor 2 (TNFR-2), and human cartilage glycoprotein-39 (YKL-40) measured in 1996-1998.

Outcome: Incident CKD after 15 years of follow-up defined as ≥40% estimated glomerular filtration rate decline to <60 mL/min/1.73 m² or dialysis dependence through United States Renal Data System linkage.

Analytical approach: Logistic regression and C statistics.

Results: There were 523 cases of incident CKD. Compared with a random sample of 425 controls, there were greater odds of incident CKD per 2-fold higher concentration of KIM-1 (OR, 1.49; 95% CI, 1.25-1.78), suPAR (OR, 2.57; 95% CI, 1.74-3.84), TNFR-1 (OR, 2.20; 95% CI, 1.58-3.09), TNFR-2 (OR, 2.03; 95% CI, 1.37-3.04). After adjustment for all biomarkers, KIM-1 (OR, 1.42; 95% CI, 1.19-1.71), and suPAR (OR, 1.86; 95% CI, 1.18-2.92) remained associated with incident CKD. Compared with traditional risk factors, the addition of all 6 biomarkers improved the C statistic from 0.695-0.731 (P < 0.01) and using the observed risk of 12% for incident CKD, the predicted risk gradient changed from 5%-40% (for the 1st-5th quintile) to 4%-44%.

Limitations: Biomarkers and creatinine were measured at one time point.

Conclusions: Higher levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with higher odds of incident CKD among individuals without diabetes.

Plain-language summary: For people with diabetes or kidney disease, several biomarkers have been shown to be associated with worsening kidney disease. Whether these biomarkers have prognostic significance in people without diabetes or kidney disease is less studied. Using the Atherosclerosis Risk in Communities study, we followed individuals without diabetes or kidney disease for an average of 15 years after biomarker measurement to see if these biomarkers were associated with the development of kidney disease. We found that elevated levels of KIM-1, suPAR, TNFR-1, and TNFR-2 were associated with the development of kidney disease. These biomarkers may help identify individuals who would benefit from interventions to prevent the development of kidney disease.

Keywords: Incident kidney disease; KIM-1; MCP-1; TNFR-1; TNFR-2; YKL-40; chronic kidney disease; plasma biomarkers; sUPAR.

Graphical abstract

Figure 1

Odds ratio for incident chronic kidney disease per 2-fold higher concentration of each individual plasma biomarker^a. KIM-1, kidney injury molecule-1; MCP-1, monocyte chemoattractant protein-1; suPAR, soluble urokinase plasminogen activator receptor; TNFR-1, tumor necrosis factor receptor 1; TNFR-2 tumor necrosis factor receptor 2; YKL-40, human cartilage glycoprotein-39. ^aLogistic regression models were adjusted for age, sex, race, study site, body mass index, systolic blood pressure, anti-hypertensive therapy, smoking status, history of cardiovascular disease, log-transformed urine albumin-to-creatinine ratio, and eGFR.

Figure 2

Association of quartiles of plasma biomarkers with incident chronic kidney disease in multivariable logistic regression models in ARIC^a. KIM-1, kidney injury molecule-1; MCP-1, monocyte chemoattractant protein-1; suPAR, soluble urokinase plasminogen activator receptor; TNFR-1, tumor necrosis factor receptor 1; TNFR-2 tumor necrosis factor receptor 2; YKL-40, human cartilage glycoprotein-39. ^aLogistic regression model adjusted for age, sex, race, study site, body mass index, systolic blood pressure, anti-hypertensive medication use, smoking status, history of cardiovascular disease, log-transformed urine albumin-to-creatinine ratio, and eGFR. P-value from model 2 of individual biomarkers.