Quantitative and qualitative changes in gene expression patterns characterize the activity of plaques in multiple sclerosis

Multiple sclerosis (MS) is a complex autoimmune disorder of the CNS with both genetic and environmental contributing factors. Clinical symptoms are broadly characterized by initial onset, and progressive debilitating neurological impairment. In this study, RNA from MS chronic active and MS acute lesions was extracted, and compared with patient matched normal white matter by fluorescent cDNA microarray hybridization analysis. This resulted in the identification of 139 genes that were differentially regulated in MS plaque tissue compared to normal tissue. Of these, 69 genes showed a common pattern of expression in the chronic active and acute plaque tissues investigated (Pvalue<0.0001, ρ=0.73, by Spearman's ρ analysis); while 70 transcripts were uniquely differentially expressed (≥1.5-fold) in either acute or chronic active tissues. These results included known markers of MS such as the myelin basic protein (MBP) and glutathione S-transferase (GST) M1, nerve growth factors, such as nerve injury-induced protein 1 (NINJ1), X-ray and excision DNA repair factors (XRCC9 and ERCC5) and X-linked genes such as the ribosomal protein, RPS4X. Primers were then designed for seven array-selected genes, including transferrin (TF), superoxide dismutase 1 (SOD1), glutathione peroxidase 1 (GPX1), GSTP1, crystallin, alpha-B (CRYAB), phosphomannomutase 1 (PMM1) and tubulin β-5 (TBB5), and real time quantitative (Q)-PCR analysis was performed. The results of comparative Q-PCR analysis correlated significantly with those obtained by array analysis (r=0.75, Pvalue<0.01, by Pearson's bivariate correlation). Both chronic active and acute plaques shared the majority of factors identified suggesting that quantitative, rather than gross qualitative differences in gene expression pattern may define the progression from acute to chronic active plaques in MS.

Utility of magnetic resonance imaging-based finite element analysis for the biomechanical stress analysis of hemorrhagic and non-hemorrhagic carotid plaques

Background: Biomechanical stress analysis has been used for plaque vulnerability assessment. The presence of plaque hemorrhage (PH) is a feature of plaque vulnerability and is associated with thromboembolic ischemic events. The purpose of the present study was to use finite element analysis (FEA) to compare the stress profiles of hemorrhagic and non-hemorrhagic profiles. Methods and Results: Forty-five consecutive patients who had suffered a cerebrovascular ischemic event with an underlying carotid artery disease underwent high-resolution magnetic resonance imaging (MRI) of their symptomatic carotid artery in a 1.5-T MRI system. Axial images were manually segmented for various plaque components and used for FEA. Maximum critical stress (M-CstressSL) for each slice was determined. Within a plaque, the maximum M-CstressSL for each slice of a plaque was selected to represent the maximum critical stress of that plaque (M-CstressPL) and used to compare hemorrhagic and non-hemorrhagic plaques. A total of 62% of plaques had hemorrhage. It was observed that plaques with hemorrhage had significantly higher stress (M-CstressPL) than plaques without PH (median [interquartile range]: 315 kPa [247-434] vs. 200 kPa [171-282], P=0.003). Conclusions: Hemorrhagic plaques have higher biomechanical stresses than non-hemorrhagic plaques. MRI-based FEA seems to have the potential to assess plaque vulnerability.

Curvedness study on atherosclerosis plaques and its implications to plaque stress

Atherosclerosis plaque rupture has been considered to be a mechanical failure of the thin fibrous cap, resulted from extreme plaque stress. Plaque stress was affected by many factors from morphological features to biological abnormalities. In this study, geometrical factors (curvedness, fibrous cap thickness) were studied on assessing plaque vulnerability in comparison with stress analysis results obtained by fluid structure interaction from 20 human carotid atherosclerosis plaques. The results show that plaque surface curvedness could contribute to extreme stress level, especially in plaque shoulder region. General plaque stress distribution could be predicted by fibrous cap thickness and curvedness with multi-regression model. With more features included in the regression model, plaque stress could be easily calculated and used to assess plaque vulnerability.

Noninvasive imaging of atheromatous carotid plaques

Atherothrombosis is a systemic disease of the arterial wall that affects the carotid, coronary, and peripheral vascular beds, and the aorta. This condition is associated with complications such as stroke, myocardial infarction, and peripheral vascular disease, which usually result from unstable atheromatous plaques. The study of atheromatous plaques can provide useful information about the natural history and progression of the disease, and aid in the selection of appropriate treatment. Plaque imaging can be crucial in achieving this goal. In this Review, we focus on the various noninvasive imaging techniques that are being used for morphological and functional assessment of carotid atheromatous plaques in the clinical setting.

Correlation of carotid atheromatous plaque inflammation using USPIO-enhanced MR imaging with degree of luminal Stenosis

BACKGROUND AND PURPOSE Inflammation is a recognized risk factor for the vulnerable atherosclerotic plaque. The study explores the relationship between the degree of Magnetic Resonance (MR)"defined inflammation using Ultra Small Super-Paramagnetic Iron Oxide (USPIO) particles and the severity of luminal stenosis in asymptomatic carotid plaques. METHODS Seventy-one patients with an asymptomatic carotid stenosis of ĝ‰¥40% underwent multi-sequence USPIO-enhanced MR imaging. Stenosis severity was measured according to the NASCET and ECST methods. RESULTS No demonstrable relationship between inflammation as measured by USPIO-enhanced signal change and the degree of luminal stenosis was found. CONCLUSIONS Inflammation and stenosis are likely to be independent risk factors, although this needs to be further validated.

The impact of wall shear stress and pressure drop on the stability of the atherosclerotic plaque

Rupture of vulnerable atheromatous plaque in the carotid and coronary arteries often leads to stroke and heart attack respectively. The mechanism of blood flow and plaque rupture in stenotic arteries is still not fully understood. A three dimensional rigid wall model was solved under steady state conditions and unsteady conditions by assuming a time-varying inlet velocity profile to investigate the relative importance of axial forces and pressure drops in arteries with asymmetric stenosis. Flow-structure interactions were investigated for the same geometry and the results were compared with those retrieved with the corresponding 2D cross-section structural models. The Navier-Stokes equations were used as the governing equations for the fluid. The tube wall was assumed hyperelastic, homogeneous, isotropic and incompressible. The analysis showed that the three dimensional behavior of velocity, pressure and wall shear stress is in general very different from that predicted by cross-section models. Pressure drop across the stenosis was found to be much higher than shear stress. Therefore, pressure may be the more important mechanical trigger for plaque rupture other than shear stress, although shear stress is closely related to plaque formation and progression.

Identifying vulnerable carotid plaques in vivo using high resolution magnetic resonance imaging-based finite element analysis

Object. Individuals with carotid atherosclerosis develop symptoms following rupture of vulnerable plaques. Biomechanical stresses within this plaque may increase vulnerability to rupture. In this report the authors describe the use of in vivo carotid plaque imaging and computational mechanics to document the magnitude and distribution of intrinsic plaque stresses. Methods. Ten (five symptomatic and five asymptomatic) individuals underwent plaque characterization magnetic resonance (MR) imaging. Plaque geometry and composition were determined by multisequence review. Intrinsic plaque stress profiles were generated from 3D meshes by using finite element computational analysis. Differences in principal (shear) stress between normal and diseased sections of the carotid artery and between symptomatic and asymptomatic plaques were noted. Results. There was a significant difference in peak principal stress between diseased and nondiseased segments of the artery (mean difference 537.65 kPa, p < 0.05). Symptomatic plaques had higher mean stresses than asymptomatic plaques (627.6 kPa compared with 370.2 kPa, p = 0.05), which were independent of luminal stenosis and plaque composition. Conclusions. Significant differences in plaque stress exist between plaques from symptomatic individuals and those from asymptomatic individuals. The MR imaging-based computational analysis may therefore be a useful aid to identification of vulnerable plaques in vivo.

Simulation of the interaction between blood flow and atherosclerotic plaque

It has been well accepted that over 50% of cerebral ischemic events are the result of rupture of vulnerable carotid atheroma and subsequent thrombosis. Such strokes are potentially preventable by carotid interventions. Selection of patients for intervention is currently based on the severity of carotid luminal stenosis. It has been, however, widely accepted that luminal stenosis alone may not be an adequate predictor of risk. To evaluate the effects of degree of luminal stenosis and plaque morphology on plaque stability, we used a coupled nonlinear time-dependent model with flow-plaque interaction simulation to perform flow and stress/strain analysis for stenotic artery with a plaque. The Navier-Stokes equations in the Arbitrary Lagrangian-Eulerian (ALE) formulation were used as the governing equations for the fluid. The Ogden strain energy function was used for both the fibrous cap and the lipid pool. The plaque Principal stresses and flow conditions were calculated for every case when varying the fibrous cap thickness from 0.1 to 2mm and the degree of luminal stenosis from 10% to 90%. Severe stenosis led to high flow velocities and high shear stresses, but a low or even negative pressure at the throat of the stenosis. Higher degree of stenosis and thinner fibrous cap led to larger plaque stresses, and a 50% decrease of fibrous cap thickness resulted in a 200% increase of maximum stress. This model suggests that fibrous cap thickness is critically related to plaque vulnerability and that, even within presence of moderate stenosis, may play an important role in the future risk stratification of those patients when identified in vivo using high resolution MR imaging.

Non-invasive MR imaging of inflammation in a patient with both asymptomatic carotid atheroma and an abdominal aortic aneurysm: A case report

Inflammation is a recognized risk factor for the vulnerable atherosclerotic plaque. USPIO-enhanced MRI imaging is a promising non-i nvasive method to identify high-risk atheromatous plaque inflammation in vivo in humans, in which areas of focal signal loss on MR images have been shown to correspond to the location of activated macrophages, typically at the shoulder regions of the plaque. This is the first report in humans describing simultaneous USPIO uptake within atheroma in two different arterial territories and again emphasises that atherosclerosis is a truly systemic disease. With further work, USPIO-enhanced MR imaging may be useful in identifying inflamed vulnerable atheromatous plaques in vivo, so refining patient selection for intervention and allowing appropriate early aggressive pharmacotherapy to prevent plaque rupture.

Assessment of inflammatory burden contralateral to the symptomatic carotid stenosis using high-resolution ultrasmall, superparamagnetic iron oxide-enhanced MRI

BACKGROUND AND PURPOSE It is well known that the vulnerable atheromatous plaque has a thin, fibrous cap and large lipid core with associated inflammation. This inflammation can be detected on MRI with use of a contrast medium, Sinerem, an ultrasmall superparamagnetic iron oxide (USPIO). Although the incidence of macrophage activity in asymptomatic disease appears low, we aimed to explore the incidence of MRI-defined inflammation in asymptomatic plaques in patients with known contralateral symptomatic disease. METHODS Twenty symptomatic patients underwent multisequence MRI before and 36 hours after USPIO infusion. Images were manually segmented into quadrants, and the signal change in each quadrant was calculated after USPIO administration. A mixed mathematical model was developed to compare the mean signal change across all quadrants in the 2 groups. Patients had a mean symptomatic stenosis of 77% compared with 46% on their asymptomatic side, as measured by conventional angiography. RESULTS There were 11 (55%) men, and the median age was 72 years (range, 53 to 84 years). All patients had risk factors consistent with severe atherosclerotic disease. All symptomatic carotid stenoses had inflammation, as evaluated by USPIO-enhanced imaging. On the contralateral sides, inflammatory activity was found in 19 (95%) patients. Contralaterally, there were 163 quadrants (57%) with a signal loss after USPIO when compared with 217 quadrants (71%) on the symptomatic side (P=0.007). CONCLUSIONS - This study adds weight to the argument that atherosclerosis is a truly systemic disease. It suggests that investigation of the contralateral side in patients with symptomatic carotid stenosis can demonstrate inflammation in 95% of plaques, despite a mean stenosis of only 46%. Thus, inflammatory activity may be a significant risk factor in asymptomatic disease in patients who have known contralateral symptomatic disease. Patients with symptomatic carotid disease should have their contralateral carotid artery followed up.

Application of data mining techniques in the prediction of coronary artery disease : use of anaesthesia time-series and patient risk factor data

The high morbidity and mortality associated with atherosclerotic coronary vascular disease (CVD) and its complications are being lessened by the increased knowledge of risk factors, effective preventative measures and proven therapeutic interventions. However, significant CVD morbidity remains and sudden cardiac death continues to be a presenting feature for some subsequently diagnosed with CVD. Coronary vascular disease is also the leading cause of anaesthesia related complications. Stress electrocardiography/exercise testing is predictive of 10 year risk of CVD events and the cardiovascular variables used to score this test are monitored peri-operatively. Similar physiological time-series datasets are being subjected to data mining methods for the prediction of medical diagnoses and outcomes. This study aims to find predictors of CVD using anaesthesia time-series data and patient risk factor data. Several pre-processing and predictive data mining methods are applied to this data. Physiological time-series data related to anaesthetic procedures are subjected to pre-processing methods for removal of outliers, calculation of moving averages as well as data summarisation and data abstraction methods. Feature selection methods of both wrapper and filter types are applied to derived physiological time-series variable sets alone and to the same variables combined with risk factor variables. The ability of these methods to identify subsets of highly correlated but non-redundant variables is assessed. The major dataset is derived from the entire anaesthesia population and subsets of this population are considered to be at increased anaesthesia risk based on their need for more intensive monitoring (invasive haemodynamic monitoring and additional ECG leads). Because of the unbalanced class distribution in the data, majority class under-sampling and Kappa statistic together with misclassification rate and area under the ROC curve (AUC) are used for evaluation of models generated using different prediction algorithms. The performance based on models derived from feature reduced datasets reveal the filter method, Cfs subset evaluation, to be most consistently effective although Consistency derived subsets tended to slightly increased accuracy but markedly increased complexity. The use of misclassification rate (MR) for model performance evaluation is influenced by class distribution. This could be eliminated by consideration of the AUC or Kappa statistic as well by evaluation of subsets with under-sampled majority class. The noise and outlier removal pre-processing methods produced models with MR ranging from 10.69 to 12.62 with the lowest value being for data from which both outliers and noise were removed (MR 10.69). For the raw time-series dataset, MR is 12.34. Feature selection results in reduction in MR to 9.8 to 10.16 with time segmented summary data (dataset F) MR being 9.8 and raw time-series summary data (dataset A) being 9.92. However, for all time-series only based datasets, the complexity is high. For most pre-processing methods, Cfs could identify a subset of correlated and non-redundant variables from the time-series alone datasets but models derived from these subsets are of one leaf only. MR values are consistent with class distribution in the subset folds evaluated in the n-cross validation method. For models based on Cfs selected time-series derived and risk factor (RF) variables, the MR ranges from 8.83 to 10.36 with dataset RF_A (raw time-series data and RF) being 8.85 and dataset RF_F (time segmented time-series variables and RF) being 9.09. The models based on counts of outliers and counts of data points outside normal range (Dataset RF_E) and derived variables based on time series transformed using Symbolic Aggregate Approximation (SAX) with associated time-series pattern cluster membership (Dataset RF_ G) perform the least well with MR of 10.25 and 10.36 respectively. For coronary vascular disease prediction, nearest neighbour (NNge) and the support vector machine based method, SMO, have the highest MR of 10.1 and 10.28 while logistic regression (LR) and the decision tree (DT) method, J48, have MR of 8.85 and 9.0 respectively. DT rules are most comprehensible and clinically relevant. The predictive accuracy increase achieved by addition of risk factor variables to time-series variable based models is significant. The addition of time-series derived variables to models based on risk factor variables alone is associated with a trend to improved performance. Data mining of feature reduced, anaesthesia time-series variables together with risk factor variables can produce compact and moderately accurate models able to predict coronary vascular disease. Decision tree analysis of time-series data combined with risk factor variables yields rules which are more accurate than models based on time-series data alone. The limited additional value provided by electrocardiographic variables when compared to use of risk factors alone is similar to recent suggestions that exercise electrocardiography (exECG) under standardised conditions has limited additional diagnostic value over risk factor analysis and symptom pattern. The effect of the pre-processing used in this study had limited effect when time-series variables and risk factor variables are used as model input. In the absence of risk factor input, the use of time-series variables after outlier removal and time series variables based on physiological variable values’ being outside the accepted normal range is associated with some improvement in model performance.

Cholesteryl ester transfer protein genotypes associated with venous thrombosis and dyslipoproteinemia in males

Although numerous genetic and acquired factors are appreciated as risk factors for venous thromboembolism (VTE) [1,2], only recently have male gender [3,4], dyslipoproteinemia [5], and silent atherosclerotic vascular disease [6] been linked to VTE. We recently found that high-density lipoprotein (HDL) deficiency is a key feature of a pattern of dyslipoproteinemia that is associated with VTE in males, and we found that the common TaqI B1 variation in the cholesteryl ester transfer protein (CETP) gene is significantly linked to VTE [5]. However, the TaqI B1/B2 single nucleotide polymorphism (SNP) itself is unlikely to affect directly CETP activity, but it is linked to nonsynonymous CETP SNPs Ala373Pro and Arg451Gln [7–9]. Here, we demonstrate that these two CETP variations are associated with VTE and low plasma HDL levels in males.

Is adiponectin implicated in venous thromboembolism?

In summary, these results imply that the relationship of adiponectin with lipoproteins is more complex than previously predicted using other methods of lipoprotein fractionation. Higher correlation of adiponectin was shown with large lipoprotein particle size, independent of the apolipoprotein content. Given the small population studied, we could not assess the influence of mild risk factors for venous thrombosis, such as obesity, on the analysis of the results. Thus, we can only state that adiponectin levels appear not to be a strong risk factor for VTE. It is possible that adiponectin deficiency may contribute indirectly to the etiology of VTE by enhancing the inflammatory state. © 2006 International Society on Thrombosis and Haemostasis.

Plasma metabolomics reveals biomarkers of the atherosclerosis

Atherosclerotic cardiovascular disease remains the leading cause of morbidity and mortality in industrialized societies. The lack of metabolite biomarkers has impeded the clinical diagnosis of atherosclerosis so far. In this study, stable atherosclerosis patients (n=16) and age- and sex-matched non-atherosclerosis healthy subjects (n=28) were recruited from the local community (Harbin, P. R. China). The plasma was collected from each study subject and was subjected to metabolomics analysis by GC/MS. Pattern recognition analyses (principal components analysis, orthogonal partial least-squares discriminate analysis, and hierarchical clustering analysis) commonly demonstrated plasma metabolome, which was significantly different from atherosclerotic and non-atherosclerotic subjects. The development of atherosclerosis-induced metabolic perturbations of fatty acids, such as palmitate, stearate, and 1-monolinoleoylglycerol, was confirmed consistent with previous publication, showing that palmitate significantly contributes to atherosclerosis development via targeting apoptosis and inflammation pathways. Altogether, this study demonstrated that the development of atherosclerosis directly perturbed fatty acid metabolism, especially that of palmitate, which was confirmed as a phenotypic biomarker for clinical diagnosis of atherosclerosis.

Gene expression studies in multiple sclerosis

Multiple sclerosis (MS) is a serious neurological disorder affecting young Caucasian individuals, usually with an age of onset at 18 to 40 years old. Females account for approximately 60x of MS cases and the manifestation and course of the disease is highly variable from patient to patient. The disorder is characterised by the development of plaques within the central nervous system (CNS). Many gene expression studies have been undertaken to look at the specific patterns of gene transcript levels in MS. Human tissues and experimental mice were used in these gene-profiling studies and a very valuable and interesting set of data has resulted from these various expression studies. In general, genes showing variable expression include mainly immunological and inflammatory genes, stress and antioxidant genes, as well as metabolic and central nervous system markers. Of particular interest are a number of genes localised to susceptible loci previously shown to be in linkage with MS. However due to the clinical complexity of the disease, the heterogeneity of the tissues used in expression studies, as well as the variable DNA chips/membranes used for the gene profiling, it is difficult to interpret the available information. Although this information is essential for the understanding of the pathogenesis of MS, it is difficult to decipher and define the gene pathways involved in the disorder. Experiments in gene expression profiling in MS have been numerous and lists of candidates are now available for analysis. Researchers have investigated gene expression in peripheral mononuclear white blood cells (PBMCs), in MS animal models Experimental Allergic Encephalomyelitis (EAE) and post mortem MS brain tissues. This review will focus on the results of these studies.