Proteomic, circulating and functional biomarkers of cardiovascular disease

Cardiovascular disease is the leading cause of morbidity and mortality in the Western world, mainly through cerebrovascular and coronary artery related events. Cardiovascular disease is a chronic progressive disease with different stages. These stages can be assessed by a variety of biomarkers. Biomarker quantification can be used for different purposes: screening, prediction of disease recurrence, therapeutic monitoring, diagnosis and prognostication. Noninvasive, inexpensive diagnostic tests currently applied in clinical practice have a relative high rate of false positive and false negative results. Therefore further refinement of the diagnostic process could improve clinical care. Regarding prognostication the need for improvement also remains as current risk models only predict a small quantity of occurring cardiovascular events. The concept of the cardiovascular continuum postulates that cardiovascular disease consists of a chain of events, is initiated by numerous cardiovascular risk factors and subsequently progresses through pathophysiological processes, ultimately leading to end-stage heart failure. For that reason cardiovascular diseases are chronic progressive conditions and can be divided into different stages, such as early tissue dysfunction or subclinical atherosclerosis prior to development of clinically overt disease. Biomarkers suitable for prognostication and diagnosis can differ at each stage. The general aim of this thesis was therefore the investigation of a variety of biomarkers in diagnosis and prediction of cardiovascular disease at different stages of the cardiovascular continuum, as covered by three different study cohorts contributing to this thesis. This included several approaches: the comparison of central and peripheral pulse pressure in middle aged hypertensive patients in regards of their prognostic potential; the application of established circulating, functional and structural biomarkers to the diagnostic process of coronary artery disease in stable angina patients; the development/refinement of a urinary proteomic biomarker for coronary artery disease and the examination of its diagnostic potential in stable angina patients. Biomarkers successful in the diagnosis of coronary artery disease were included in multiple biomarker models. Aside from biomarker development for the general population, investigations of specific cohorts, such as patients with certain diseases and belonging to certain age groups or sharing specific biochemical features provided advances in the past. To estimate the potential of a biomarker in risk prediction association studies with surrogate biomarkers are applicable. We collected a cohort of middle-aged hypertensive patients to assess if central pulse pressure, derived from non-invasive assessment of arterial stiffness, could improve risk prediction. Central pulse pressure has been previously shown to have prognostic value in populations with end-stage renal failure, coronary artery disease and high prevalence of diabetes mellitus. Considering the prognostic information of peripheral pulse pressure in the elderly, the hypothesis that central pulse pressure could improve risk prediction is comprehensive and was investigated as part of this thesis. This was accomplished by comparing the strength of correlation between central or peripheral pulse pressure and these surrogate biomarkers. When compared to peripheral pulse pressure, central pulse pressure had stronger associations with aortic pulse wave velocity, carotid intima-media thickness, and left ventricular mass index, but equal association with the albumin:creatinine ratio. In contrast, after adjustment for age, mean arterial pressure, heart rate and hypertension status there was no significant difference between central and peripheral pulse pressure for prediction of listed surrogate biomarkers in multivariate analysis. These results suggested that central pulse pressure is unlikely to provide more prognostic information than peripheral pulse pressure in middle-aged hypertensive patients. The diagnosis of coronary artery disease is clinically relevant in symptomatic patients, either acute or stable. The diagnosis of stable flow limiting coronary artery disease is especially challenging as non-cardiac as well as other cardiac conditions can mimic symptoms. Non-invasive diagnostic tools have either moderate sensitivities or specificities, or are not widely available. Therefore new biomarkers for the diagnosis of flow limiting coronary artery disease have the potential to improve current diagnostic strategies. This could be accomplished adjacent to existing biomarkers or by replacement of such, due to cost effectiveness, better discriminatory etc. As part of this thesis, a biomarker identification and validation study was conducted into urinary proteomics of coronary artery disease. First we tried to replicate a study conducted by our research group in the past. Therein, an established coronary artery disease specific polypeptide pattern was unable to differentiate between patients with severe coronary artery disease and healthy controls despite strong cohort similarities to the original study. We therefore recalibrated the urinary polypeptide pattern using an enlarged biomarker discovery cohort and adjusted the pattern for lipid lowering and angiotensin converting enzyme inhibitor treatment effects. We calculated a score from the resulting polypeptide pattern, which identified coronary artery disease patients with a sensitivity of 79% and a specificity of 88% in a biomarker validation cohort. As the next step of biomarker development we performed a diagnostic validation study. The investigated clinical cohort consisted of stable angina patients with or without coronary artery disease. The new polypeptide pattern score was unable to differentiate between these two groups. The score however correlated strongly with coronary artery disease extent as measured by the Gensini score, implying that urinary proteomics in the diagnosis of coronary artery disease is promising, yet requires further effort before clinical employment. In addition to the urinary proteomic biomarker development second diagnostic approach was selected. As coronary artery disease is a complex chronic disease, the combination of different biomarkers should result in a better discrimination between stable angina patients with or without coronary artery disease. This approach attempts to position the individual as precisely as possible on the cardiovascular continuum including serologic, functional vascular and imaging biomarkers of subclinical atherosclerosis. Serologic markers thereby present a plasma proteomic approach covering pathophysiological processes with known correlation or causative for coronary artery disease. Functional and structural changes of the peripheral vasculature resemble the coronary artery system. We investigated circulating biomarkers and vascular biomarkers separately. A variety of circulating biomarkers differentiated patients with severe coronary artery disease from healthy control subjects. When patients with stable angina and with or without coronary artery disease as diagnosed by coronary angiography were investigated no statistically significant differences could be detected for circulating biomarkers. In the same study a microvascular biomarker, the reactive hyperaemia index, and a macrovascular biomarker, the carotide plaque score, were able to differentiated between cases and controls. Both markers either added separately or together improved the risk classification of exercise treadmill test results. This suggests that a multiple biomarker approach in the diagnosis of coronary artery disease in stable angina patients could be successful. Different aspects of the cardiovascular continuum can be applied to diagnosis and prognostication of cardiovascular disease. In this regard we were able to show, that early processes such as endothelial dysfunction or later processes such as plaque formation can support the diagnostic process. However, randomly collected circulating biomarkers might be unable to do this. Our finding that central pulse pressure is unlikely to have more prognostic value in middle aged hypertensive patients underlines that biomarkers can be useful in specific patient collectives but not necessarily in all cohorts. Instead of applying established biomarkers, also new biomarkers can be developed. Urine proteomics showed great promise in this regard, as specific polypeptide patterns reflect coronary artery disease and are strongly correlated to its extent

Urine proteomics in the diagnosis of stable angina.

BACKGROUND: We have previously described a panel of 238 urinary polypeptides specific for established severe coronary artery disease (CAD). Here we studied this polypeptide panel in patients with a wider range of CAD severity. METHODS: We recruited 60 patients who underwent elective coronary angiography for investigation of stable angina. Patients were selected for either having angiographic evidence of CAD or not (NCA) following coronary angiography (n = 30/30; age, 55 ± 6 vs. 56 ± 7 years, P = 0.539) to cover the extremes of the CAD spectrum. A further 66 patients with severe CAD (age, 64 ± 9 years) prior to surgical coronary revascularization were added for correlation studies. The Gensini score was calculated from coronary angiograms as a measure of CAD severity. Urinary proteomic analyses were performed using capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. The urinary polypeptide pattern was classified using a predefined algorithm and resulting in the CAD238 score, which expresses the pattern quantitatively. RESULTS: In the whole cohort of patients with CAD (Gensini score 60 [40; 98]) we found a close correlation between Gensini scores and CAD238 (ρ = 0.465, P < 0.001). After adjustment for age (β = 0.144; P = 0.135) the CAD238 score remained a significant predictor of the Gensini score (β =0.418; P < 0.001). In those with less severe CAD (Gensini score 40 [25; 61]), however, we could not detect a difference in CAD238 compared to patients with NCA (-0.487 ± 0.341 vs. -0.612 ± 0.269, P = 0.119). CONCLUSIONS: In conclusion the urinary polypeptide CAD238 score is associated with CAD burden and has potential as a new cardiovascular biomarker

Impaired renal function impacts negatively on vascular stiffness in patients with coronary artery disease.

BACKGROUND: Chronic kidney disease (CKD) and coronary artery disease (CAD) are independently associated with increased vascular stiffness. We examined whether renal function contributes to vascular stiffness independently of CAD status. METHODS: We studied 160 patients with CAD and 169 subjects without CAD. The 4-variable MDRD formula was used to estimate glomerular filtration rate (eGFR); impaired renal function was defined as eGFR <60 mL/min. Carotid-femoral pulse wave velocity (PWV) was measured with the SphygmoCor® device. Circulating biomarkers were assessed in plasma using xMAP® multiplexing technology. RESULTS: Patients with CAD and impaired renal function had greater PWV compared to those with CAD and normal renal function (10.2 [9.1;11.2] vs 7.3 [6.9;7.7] m/s; P < 0.001). In all patients, PWV was a function of eGFR (β = -0.293; P < 0.001) even after adjustment for age, sex, systolic blood pressure, body mass index and presence or absence of CAD. Patients with CAD and impaired renal function had higher levels of adhesion and inflammatory molecules including E-selectin and osteopontin (all P < 0.05) compared to those with CAD alone, but had similar levels of markers of oxidative stress. CONCLUSIONS: Renal function is a determinant of vascular stiffness even in patients with severe atherosclerotic disease. This was paralleled by differences in markers of cell adhesion and inflammation. Increased vascular stiffness may therefore be linked to inflammatory remodeling of the vasculature in people with impaired renal function, irrespective of concomitant atherosclerotic disease

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS

Characterization of interstitial diffuse fibrosis patterns using texture analysis of myocardial native T1 mapping.

BACKGROUND:The pattern of myocardial fibrosis differs significantly between different cardiomyopathies. Fibrosis in hypertrophic cardiomyopathy (HCM) is characteristically as patchy and regional but in dilated cardiomyopathy (DCM) as diffuse and global. We sought to investigate if texture analyses on myocardial native T1 mapping can differentiate between fibrosis patterns in patients with HCM and DCM. METHODS:We prospectively acquired native myocardial T1 mapping images for 321 subjects (55±15 years, 70% male): 65 control, 116 HCM, and 140 DCM patients. To quantify different fibrosis patterns, four sets of texture descriptors were used to extract 152 texture features from native T1 maps. Seven features were sequentially selected to identify HCM- and DCM-specific patterns in 70% of data (training dataset). Pattern reproducibility and generalizability were tested on the rest of data (testing dataset) using support vector machines (SVM) and regression models. RESULTS:Pattern-derived texture features were capable to identify subjects in HCM, DCM, and controls cohorts with 202/237(85.2%) accuracy of all subjects in the training dataset using 10-fold cross-validation on SVM (AUC = 0.93, 0.93, and 0.93 for controls, HCM and DCM, respectively), while pattern-independent global native T1 mapping was poorly capable to identify those subjects with 121/237(51.1%) accuracy (AUC = 0.78, 0.51, and 0.74) (P<0.001 for all). The pattern-derived features were reproducible with excellent intra- and inter-observer reliability and generalizable on the testing dataset with 75/84(89.3%) accuracy. CONCLUSION:Texture analysis of myocardial native T1 mapping can characterize fibrosis patterns in HCM and DCM patients and provides additional information beyond average native T1 values

Urine proteomics in the diagnosis of stable angina.

BACKGROUND: We have previously described a panel of 238 urinary polypeptides specific for established severe coronary artery disease (CAD). Here we studied this polypeptide panel in patients with a wider range of CAD severity. METHODS: We recruited 60 patients who underwent elective coronary angiography for investigation of stable angina. Patients were selected for either having angiographic evidence of CAD or not (NCA) following coronary angiography (n = 30/30; age, 55 ± 6 vs. 56 ± 7 years, P = 0.539) to cover the extremes of the CAD spectrum. A further 66 patients with severe CAD (age, 64 ± 9 years) prior to surgical coronary revascularization were added for correlation studies. The Gensini score was calculated from coronary angiograms as a measure of CAD severity. Urinary proteomic analyses were performed using capillary electrophoresis coupled online to micro time-of-flight mass spectrometry. The urinary polypeptide pattern was classified using a predefined algorithm and resulting in the CAD238 score, which expresses the pattern quantitatively. RESULTS: In the whole cohort of patients with CAD (Gensini score 60 [40; 98]) we found a close correlation between Gensini scores and CAD238 (ρ = 0.465, P < 0.001). After adjustment for age (β = 0.144; P = 0.135) the CAD238 score remained a significant predictor of the Gensini score (β =0.418; P < 0.001). In those with less severe CAD (Gensini score 40 [25; 61]), however, we could not detect a difference in CAD238 compared to patients with NCA (-0.487 ± 0.341 vs. -0.612 ± 0.269, P = 0.119). CONCLUSIONS: In conclusion the urinary polypeptide CAD238 score is associated with CAD burden and has potential as a new cardiovascular biomarker

Evaluation of the systemic micro- and macrovasculature in stable angina: A case-control study

Aims The diagnosis of stable angina involves the use of probability estimates based on clinical presentation, age, gender and cardiovascular risk factors. In view of the link between the cardiac and systemic vasculature we tested whether non-invasive measures of systemic micro- and macrovascular structure and function differentiate between individuals with flow-limiting coronary artery disease (CAD) and those with normal coronary arteries (NCA). Methods and results We recruited 84 patients undergoing elective coronary angiography for investigation of symptoms of stable angina. Patients were selected for either having significant CAD or NCA (n = 43/41; age, 56±7 vs 57±7 years, P = 0.309). Only microvascular endothelial function, measured using the Endo-PAT2000 device to determine reactive hyperaemia index (CAD vs. NCA; 1.9 [1.5; 2.3] vs. 2.1 [1.8; 2.4], P = 0.03) and sonographic carotid plaque score (CAD vs. NCA; 3.0 [1.5; 4.5] vs. 1.2 [0; 2.55], P<0.001) were significantly different between patients with CAD and NCA. No significant differences were detected in reflection magnitude (CAD vs. NCA; 1.7 [1.5; 1.8] % vs 1.7 [1.5; 1.9] %, P = 0.342), pulse wave velocity (CAD vs. NCA; 7.8±1.4 m/sec vs. 8.3±1.5 m/sec, P = 0.186), carotid intima-media thickness (CAD vs. NCA; 0.73±0.10 mm vs. 0.75±0.10 mm, P = 0.518) or carotid distensibility (CAD vs. NCA; 3.8±1.2 10-3/kPa vs. 3.4±0.9 10-3/kPa, P = 0.092). Also, the c-statistic of the pre-test probability based on history and traditional risk factors (c = 0.665; 95% CI, 0.540–0.789) was improved by the addition of the inverse RHI (c = 0.720; 95% CI, 0.605–0.836), carotid plaque score (c = 0.770, 95% CI, 0.659–0.881), and of both markers in combination (c = 0.801; 95% CI, 0.701–0.900). Conclusion: There are distinct differences in the systemic vasculature between patients with CAD and NCA that may have the potential to guide diagnostic and therapeutic decisions. Carotid artery plaque burden and microvascular function appear to be most promising in this context

Aortic regurgitation assessment by cardiovascular magnetic resonance imaging and transthoracic echocardiography: intermodality disagreement impacting on prediction of post-surgical left ventricular remodeling

Transthoracic echocardiography (TTE) is the primary clinical imaging modality for the assessment of patients with isolated aortic regurgitation (AR) in whom TTE’s linear left ventricular (LV) dimension is used to assess disease severity to guide aortic valve replacement (AVR), yet TTE is relatively limited with regards to its integrated semi-quantitative/qualitative approach. We therefore compared TTE and cardiovascular magnetic resonance (CMR) assessment of isolated AR and investigated each modality’s ability to predict LV remodeling after AVR. AR severity grading by CMR and TTE were compared in 101 consecutive patients referred for CMR assessment of chronic AR. LV end-diastolic diameter and end-systolic diameter measurements by both modalities were compared. Twenty-four patients subsequently had isolated AVR. The pre-AVR estimates of regurgitation severity by CMR and TTE were correlated with favorable post-AVR LV remodeling. AR severity grade agreement between CMR and TTE was moderate (ρ = 0.317, P = 0.001). TTE underestimated CMR LV end-diastolic and LV end-systolic diameter by 6.6 mm (P < 0.001, CI 5.8–7.7) and 5.9 mm (P < 0.001, CI 4.1–7.6), respectively. The correlation of post-AVR LV remodeling with CMR AR grade (ρ = 0.578, P = 0.004) and AR volumes (R = 0.664, P < 0.001) was stronger in comparison to TTE (ρ = 0.511, P = 0.011; R = 0.318, P = 0.2). In chronic AR, CMR provides more prognostic relevant information than TTE in assessing AR severity. CMR should be considered in the management of chronic AR patients being considered for AVR

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS