Effect of Neprilysin Inhibition on Various Natriuretic Peptide Assays.

BACKGROUND: With sacubitril/valsartan treatment, B-type natriuretic peptide (BNP) concentrations increase; it remains unclear whether change in BNP concentrations is similar across all assays for its measurement. Effects of sacubitril/valsartan on atrial natriuretic peptide (ANP) concentrations in patients are unknown. Lastly, the impact of neprilysin inhibition on mid-regional pro-ANP (MR-proANP), N-terminal pro-BNP (NT-proBNP), proBNP1-108, or C-type natriuretic peptide (CNP) is not well understood. OBJECTIVES: This study sought to examine the effects of sacubitril/valsartan on results from different natriuretic peptide assays. METHODS: Twenty-three consecutive stable patients with heart failure and reduced ejection fraction were initiated and titrated on sacubitril/valsartan. Change in ANP, MR-proANP, BNP (using 5 assays), NT-proBNP (3 assays), proBNP1-108, and CNP were measured over 3 visits. RESULTS: Average time to 3 follow-up visits was 22, 46, and 84 days. ANP rapidly and substantially increased with initiation and titration of sacubitril/valsartan, more than doubling by the first follow-up visit (+105.8%). Magnitude of ANP increase was greatest in those with concentrations above the median at baseline (+188%) compared with those with lower baseline concentrations (+44%); ANP increases were sustained. Treatment with sacubitril/valsartan led to inconsistent changes in BNP, which varied across methods assessed. Concentrations of MR-proANP, NT-proBNP, and proBNP1-108 variably declined after treatment; whereas CNP concentrations showed no consistent change. CONCLUSIONS: Initiation and titration of sacubitril/valsartan led to variable changes in concentrations of multiple natriuretic peptides. These results provide important insights into the effects of sacubitril/valsartan treatment on individual patient results, and further suggest the benefit of neprilysin inhibition may be partially mediated by increased ANP concentrations.

A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography.

BACKGROUND: Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). HYPOTHESIS: Clinical and biomarker data can predict AKI more accurately. METHODS: Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO). RESULTS: Forty-three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C-reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen-like and Factor VII) had a negative association with AKI risk. The final model had a cross-validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in-sample AUC of 0.82 (P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). CONCLUSIONS: We describe a clinical and proteomics-supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

Performance of a clinical/proteomic panel to predict obstructive peripheral artery disease in patients with and without diabetes mellitus.

Background: Patients with diabetes mellitus (DM) are at substantial risk of developing peripheral artery disease (PAD). We recently developed a clinical/proteomic panel to predict obstructive PAD. In this study, we compare the accuracy of this panel for the diagnosis of PAD in patients with and without DM. Methods and results: The HART PAD panel consists of one clinical variable (history of hypertension) and concentrations of six biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1 and eotaxin-1). In a prospective cohort of 354 patients undergoing peripheral and/or coronary angiography, performance of this diagnostic panel to detect ≥50% stenosis in at least one peripheral vessel was assessed in patients with (n=94) and without DM (n=260). The model had an area under the receiver operating characteristic curve (AUC) of 0.85 for obstructive PAD. At optimal cut-off, the model had 84% sensitivity, 75% specificity, positive predictive value (PPV) of 84% and negative predictive value (NPV) of 75% for detection of PAD among patients with DM, similar as in those without DM. In those with DM, partitioning the model into five levels resulted in a PPV of 95% and NPV of 100% in the highest and lowest levels, respectively. Abnormal scores were associated with a shorter time to revascularisation during 4.3 years of follow-up. Conclusion: A clinical/biomarker model can predict with high accuracy the presence of PAD among patients with DM. Trial registration number: NCT00842868.

Association of an HDL Apolipoproteomic Score With Coronary Atherosclerosis and Cardiovascular Death.

BACKGROUND: Concentrations of circulating apolipoproteins are strongly linked to risk for coronary artery disease (CAD). The relative importance of the additional knowledge of apolipoprotein concentrations within specific lipoprotein species for CAD risk prediction is limited. OBJECTIVES: This study sought to evaluate the performance of a high-density lipoprotein (HDL) apolipoproteomic score, based on targeted mass spectrometry of HDL-associated apolipoproteins, for the detection of angiographic CAD and outcomes. METHODS: HDL-associated apolipoprotein (apo) A-1, apoC-1, apoC-2, apoC-3, and apoC-4 were measured in 943 participants without prevalent myocardial infarction (MI) referred for coronary angiography in the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) study. A composite HDL apolipoproteomic score (pCAD) was associated with likelihood of obstructive CAD (≥70% lesion in ≥1 vessel) and with incident cardiovascular outcomes over 4-year follow-up. RESULTS: There were 587 (62.2%) patients with coronary stenosis. The pCAD score was associated with the presence of obstructive CAD (odds ratio: 1.39; 95% confidence interval [CI]: 1.14 to 1.69; p < 0.001), independently of conventional cardiovascular risk factors including circulating plasma apoA-1 and apoB. The C-index for pCAD was 0.63 (95% CI: 0.59 to 0.67) for the presence of obstructive CAD. Although pCAD was not associated with cardiovascular mortality among all individuals (hazard ratio: 1.24; 95% CI: 0.93 to 1.66; p = 0.15), there was evidence of association for individuals with obstructive CAD (hazard ratio: 1.48; 95% CI: 1.07 to 2.05; p = 0.019). CONCLUSIONS: An HDL apolipoproteomic score is associated with the presence of CAD, independent of circulating apoA-1 and apoB concentrations and other conventional cardiovascular risk factors. Among individuals with CAD, this score may be independently associated cardiovascular death. (The CASABLANCA Study: Catheter Sampled Blood Archive in Cardiovascular Diseases [CASABLANCA]; NCT00842868).

Multiple biomarker panel to screen for severe aortic stenosis: results from the CASABLANCA study.

Objective: Severe aortic valve stenosis (AS) develops via insidious processes and can be challenging to correctly diagnose. We sought to develop a circulating biomarker panel to identify patients with severe AS. Methods: We enrolled study participants undergoing coronary or peripheral angiography for a variety of cardiovascular diseases at a single academic medical centre. A panel of 109 proteins were measured in blood obtained at the time of the procedure. Statistical learning methods were used to identify biomarkers and clinical parameters that associate with severe AS. A diagnostic model incorporating clinical and biomarker results was developed and evaluated using Monte Carlo cross-validation. Results: Of 1244 subjects (age 66.4±11.5 years, 28.7% female), 80 (6.4%) had severe AS (defined as aortic valve area (AVA) <1.0 cm2). A final model included age, N-terminal pro-B-type natriuretic peptide, von Willebrand factor and fetuin-A. The model had good discrimination for severe AS (OR=5.9, 95% CI 3.5 to 10.1, p<0.001) with an area under the curve of 0.76 insample and 0.74 with cross-validation. A diagnostic score was generated. Higher prevalence of severe AS was noted in those with higher scores, such that 1.6% of those with a score of 1 had severe AS compared with 15.3% with a score of 5 (p<0.001), and score values were inversely correlated with AVA (r=-0.35; p<0.001). At optimal model cut-off, we found 76% sensitivity, 65% specificity, 13% positive predictive value and 98% negative predictive value. Conclusions: We describe a novel, multiple biomarker approach for diagnostic evaluation of severe AS. Trial registration number: NCT00842868.

A clinical and proteomics approach to predict the presence of obstructive peripheral arterial disease: From the Catheter Sampled Blood Archive in Cardiovascular Diseases (CASABLANCA) Study.

BACKGROUND: Peripheral arterial disease (PAD) is a global health problem that is frequently underdiagnosed and undertreated. Noninvasive tools to predict the presence and severity of PAD have limitations including inaccuracy, cost, or need for intravenous contrast and ionizing radiation. HYPOTHESIS: A clinical/biomarker score may offer an attractive alternative diagnostic method for PAD. METHODS: In a prospective cohort of 354 patients referred for diagnostic peripheral and/or coronary angiography, predictors of ≥50% stenosis in ≥1 peripheral vessel (carotid/subclavian, renal, or lower extremity arteries) were identified from >50 clinical variables and 109 biomarkers. Machine learning identified variables predictive of obstructive PAD; a score derived from the final model was developed. RESULTS: The score consisted of 1 clinical variable (history of hypertension) and 6 biomarkers (midkine, kidney injury molecule-1, interleukin-23, follicle-stimulating hormone, angiopoietin-1, and eotaxin-1). The model had an in-sample area under the receiver operating characteristic curve of 0.85 for obstructive PAD and a cross-validated area under the curve of 0.84; higher scores were associated with greater severity of angiographic stenosis. At optimal cutoff, the score had 65% sensitivity, 88% specificity, 76% positive predictive value (PPV), and 81% negative predictive value (NPV) for obstructive PAD and performed consistently across vascular territories. Partitioning the score into 5 levels resulted in a PPV of 86% and NPV of 98% in the highest and lowest levels, respectively. Elevated score was associated with shorter time to revascularization during 4.3 years of follow-up. CONCLUSIONS: A clinical/biomarker score demonstrates high accuracy for predicting the presence of PAD.

Single-Molecule Counting of High-Sensitivity Troponin I in Patients Referred for Diagnostic Angiography: Results From the CASABLANCA (Catheter Sampled Blood Archive in Cardiovascular Diseases) Study.

BACKGROUND: The meaning of high-sensitivity troponin I (hsTnI) concentrations in patients without acute myocardial infarction (MI) requires clarity. We hypothesized that among patients referred for diagnostic coronary angiography without acute MI, hsTnI concentrations would correlate with prevalent coronary artery disease (CAD) and predict incident cardiovascular events and mortality. METHODS AND RESULTS: We measured hsTnI using a single-molecule counting assay (99th percentile, 6 ng/L) in samples from 991 patients obtained at the time of angiography. Concentrations of hsTnI were assessed relative to the severity of CAD and prognosis during mean follow-up of 3.7 years. Median hsTnI concentration was 4.19 ng/L; 38% of patients had hsTnI concentrations ≥99th percentile. Across increasing hsTnI quartiles, patients had higher prevalence of angiographic CAD; in multivariate models, hsTnI ≥99th percentile independently predicted obstructive CAD (odds ratio: 2.57; P<0.001) and incident MI (hazard ratio [HR]: 2.68; P<0.001), cardiovascular death (HR: 2.29; P=0.001), and all-cause death (HR: 1.84; P=0.004). In those with >70% coronary stenosis, hsTnI ≥99th percentile independently predicted incident MI (HR: 1.87; P=0.01), cardiovascular mortality (HR: 2.74; P=0.001), and the composite end point of MI and all-cause death (HR: 2.06; P<0.001). In participants with coronary stenosis <70%, hsTnI ≥99th percentile even more strongly predicted incident MI (HR: 8.41; P<0.001), cardiovascular mortality (HR: 3.60; P=0.03), and the composite end point of MI and all-cause death (HR: 3.62; P<0.001). CONCLUSIONS: In a large prospective cohort of patients who were free of prevalent MI and undergoing diagnostic coronary angiography, hsTnI concentrations were associated with higher prevalence of CAD and predicted incident MI, cardiovascular death, and all-cause death. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00842868.