Plasma calprotectin and risk of cardiovascular disease: Findings from the PREVEND prospective cohort study.

BACKGROUND AND AIMS: We aimed to assess the association of circulating calprotectin, an inflammation-associated protein, with cardiovascular disease (CVD) risk and determine whether it improves risk prediction. METHODS: Plasma calprotectin measurements were made at baseline in 5290 participants in the PREVEND prospective study. Hazard ratios (95% confidence intervals [CI]) for CVD were calculated. RESULTS: After a median follow-up of 8.3 years, 339 first CVD events were recorded. Calprotectin concentration was correlated with several conventional risk factors as well as with high-sensitivity C-reactive protein (hsCRP) (r = 0.42). Calprotectin was log-linearly associated with CVD risk. The risk for CVD adjusted for conventional cardiovascular risk factors was 1.26 (95% CI, 1.13-1.41) per 1 standard deviation higher baseline loge calprotectin, and was 1.24 (95% CI, 1.11-1.39) following further adjustment for triglycerides, body mass index, and other potential confounders. The association remained present after further adjustment for hsCRP 1.15 (95% CI, 1.02-1.30). Comparing extreme quartiles of plasma calprotectin levels, the corresponding adjusted HRs for CVD were 1.96 (1.37-2.82), 1.89 (1.31-2.72), and 1.56 (1.07-2.29). The association of calprotectin with CVD risk did not vary importantly in several relevant clinical subgroups. Adding calprotectin to the Framingham CVD Risk Score was associated with a C-index change (0.0016; p=0.42) difference in -2 log likelihood (p=0.038), IDI (0.0080; p < 0.001), and NRI (4.03%; p=0.024). CONCLUSIONS: There is a log-linear association of calprotectin concentration with risk of CVD, which may be partly dependent on hsCRP. Adding calprotectin to conventional risk factors improves CVD risk assessment using measures of reclassification and -2 log likelihood.

Urinary proteomics for prediction of mortality in patients with type 2 diabetes and microalbuminuria.

BACKGROUND: The urinary proteomic classifier CKD273 has shown promise for prediction of progressive diabetic nephropathy (DN). Whether it is also a determinant of mortality and cardiovascular disease in patients with microalbuminuria (MA) is unknown. METHODS: Urine samples were obtained from 155 patients with type 2 diabetes and confirmed microalbuminuria. Proteomic analysis was undertaken using capillary electrophoresis coupled to mass spectrometry to determine the CKD273 classifier score. A previously defined CKD273 threshold of 0.343 for identification of DN was used to categorise the cohort in Kaplan-Meier and Cox regression models with all-cause mortality as the primary endpoint. Outcomes were traced through national health registers after 6 years. RESULTS: CKD273 correlated with urine albumin excretion rate (UAER) (r = 0.481, p = <0.001), age (r = 0.238, p = 0.003), coronary artery calcium (CAC) score (r = 0.236, p = 0.003), N-terminal pro-brain natriuretic peptide (NT-proBNP) (r = 0.190, p = 0.018) and estimated glomerular filtration rate (eGFR) (r = 0.265, p = 0.001). On multivariate analysis only UAER (ß = 0.402, p < 0.001) and eGFR (ß = - 0.184, p = 0.039) were statistically significant determinants of CKD273. Twenty participants died during follow-up. CKD273 was a determinant of mortality (log rank [Mantel-Cox] p = 0.004), and retained significance (p = 0.048) after adjustment for age, sex, blood pressure, NT-proBNP and CAC score in a Cox regression model. CONCLUSION: A multidimensional biomarker can provide information on outcomes associated with its primary diagnostic purpose. Here we demonstrate that the urinary proteomic classifier CKD273 is associated with mortality in individuals with type 2 diabetes and MA even when adjusted for other established cardiovascular and renal biomarkers.