1,299 research outputs found
Improving stroke risk prediction and individualised treatment in carotid atherosclerosis
Background: Unstable carotid atherosclerosis causes stroke, but methods to identify patients and
lesions at risk are lacking. Currently, this risk estimation is based on measurements of stenosis and
neurological symptoms, which determines the therapy of either medical treatment with or without
carotid endarterectomy. The efficacy of this therapy is low and higher accuracy of diagnosis and
therapy is warranted. Imaging of carotid plaque morphology using software for visualisation of
plaque components may improve assessment of plaque phenotype and stroke risk. These studies
aimed firstly to investigate if, and if yes, how, the carotid plaque morphology with image analysis of
CTA associated with on-going biology in the corresponding specimen. Secondly, if risk
stratification in clinical risk scores can be linked to the aforementioned associations. Finally, if the
on-going biological processes can be specifically predicted out of the CTA imaging analysis.
Methods: Plaque features were analysed in pre-operative CTA with dedicated software. In study
I and II, the plaques were stratified according to quantified high and low of each feature, profiled
with microarrays, followed by bioinformatic analyses. Immunohistochemistry was performed to
evaluate the findings in plaques. In study III, patient phenotype, according to clinical stroke risk
scores of CAR and ABCD2 stratified the cohorts of high vs low scores which were subsequently
profiled with microarrays, followed by bioinformatic analyses and correlation analyses of plaque
morphology in CTA. In study IV, the microarray transcriptomes were individually coupled to
morphological data from the CTA analysis, developing models with machine intelligence to predict
the gene expression from a CTA image. The models were then tested in unseen patients.
Results: In study I, stabilising markers and processes related to SMCs and ECM organisation were
associated with highly calcified plaques, while inflammatory and lipid related processes were
repressed. PRG4, a novel marker for atherosclerosis, was identified as the most up-regulated gene
in highly calcified plaques. Study II showed that carotid lesions with large lipid rich necrotic core,
intraplaque haemorrhage or plaque burden were characterized by molecular signatures coupled
with inflammation and extracellular matrix degradation, typically linked with instability.
Symptomatology associated with large lipid rich necrotic core and plaque burden. Cross-validated
prediction model for symptoms, showed that plaque morphology by CTA alone was superior to
stenosis degree. Study III revealed that a high clinical risk score in CAR and ABCD2, reflect a
plaque phenotype linked to immune response and coagulation, where the novel ABCB5, was one
of the most up-regulated genes. The high risk scores correlated with the plaque components matrix
and calcification but no positive association with stenosis degree. Study IV resulted in 414 robustly
predicted transcripts from the CTA image analysis, of which pathway analysis showed biological
processes associated with typical pathophysiology of atherosclerosis and plaque instability. The
model testing demonstrated a good correlation between predicted and observed transcript
expression levels and pathway analysis revealed a unique dominant mechanism for each individual.
Conclusions: Biological processes in carotid plaques associated to vulnerability, can be linked to
plaque morphology analysed with CTA image analysis. Patient phenotype classified with clinical
risk scores associates to plaque phenotype and morphology in CTA. The biological processes in
the atherosclerotic plaque can be predicted with plaque morphology CTA analysis in this small
pilot study, providing a possibility to precision medicine after validation in larger scale studie
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
CAD-CDN: Coronary Artery Disease Prediction Using Convolutional Neural Network with Modified Densenet
Atherosclerosis is a synonym for coronary artery disease (CAD), a non-communicable cardiovascular disease. Coronary artery disease, cancer, and tumour illness pose significant human risks. Predicting coronary artery disease (CAD) is a difficult and time-consuming task in the medical field. Early prediction is a virtuoso skill in the medical area, particularly in the cardiovascular sector. Prior research on developing early prediction models provided a grasp of modern strategies for detecting variance in medical imaging. Cardiovascular disease prevention may be accomplished with a diet plan established by the concerned physician after early diagnosis. We proposed a CAD-CDN framework for coronary artery disease prediction using a Convolutional neural network (CNN) with modified densenet. The datasets are collected from the Kaggle repository, and the data normalization has been done with Affinity propagation with an adaptive damping factor (APADF). The best features are selected using ACO with SA as the Hybrid method. Finally, the classification was done with CNN with modified Densenet. The experimental result has been done with various existing algorithms and proposed one. And the results have shown performance indicators including accuracy, precision, sensitivity, specificity, and measure value
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Radiomics and Machine Learning in the Prediction of Cardiovascular Disease
Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For
example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may
not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had
different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and
advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun
Prediction of coronary artery disease using urinary proteomics
Aims: Coronary artery disease (CAD) is multifactorial, caused by complex pathophysiology, and contributes to a high burden of mortality worldwide. Urinary proteomic analyses may help to identify predictive biomarkers and provide insights into the pathogenesis of CAD.
Methods and results: Urinary proteome was analysed in 965 participants using capillary electrophoresis coupled with mass spectrometry. A proteomic classifier was developed in a discovery cohort with 36 individuals with CAD and 36 matched controls using the support vector machine. The classifier was tested in a validation cohort with 115 individuals who progressed to CAD and 778 controls and compared with two previously developed CAD-associated classifiers, CAD238 and ACSP75. The Framingham and SCORE2 risk scores were available in 737 participants. Bioinformatic analysis was performed based on the CAD-associated peptides. The novel proteomic classifier was comprised of 160 urinary peptides, mainly related to collagen turnover, lipid metabolism, and inflammation. In the validation cohort, the classifier provided an area under the receiver operating characteristic curve (AUC) of 0.82 [95% confidence interval (CI): 0.78–0.87] for the CAD prediction in 8 years, superior to CAD238 (AUC: 0.71, 95% CI: 0.66–0.77) and ACSP75 (AUC: 0.53 and 95% CI: 0.47–0.60). On top of CAD238 and ACSP75, the addition of the novel classifier improved the AUC to 0.84 (95% CI: 0.80–0.89). In a multivariable Cox model, a 1-SD increment in the novel classifier was associated with a higher risk of CAD (HR: 1.54, 95% CI: 1.26–1.89, P \u3c 0.0001). The new classifier further improved the risk reclassification of CAD on top of the Framingham or SCORE2 risk scores (net reclassification index: 0.61, 95% CI: 0.25–0.95, P = 0.001; 0.64, 95% CI: 0.28–0.98, P = 0.001, correspondingly).
Conclusion: A novel urinary proteomic classifier related to collagen metabolism, lipids, and inflammation showed potential for the risk prediction of CAD. Urinary proteome provides an alternative approach to personalized prevention
Chronic kidney disease : a clinical model of premature vascular aging
Patients with chronic kidney disease (CKD) are prone to develop an accelerated vascular aging
phenotype characterized by vascular calcification (VC), a major culprit of cardiovascular
complications and premature death. While VC has been recognized as an active
pathophysiologic process with involvement of specific mediators and effectors, the coexistence
of traditional risk factors (i.e., high age, diabetes, hypertension, dyslipidemia),
inflammaging stimuli and pharmacological interventions (e.g., phosphate binders, warfarin and
statin therapy) adds to the complexity of the course and consequences of different types of VC
(e.g., intima and media VC, micro- and macrocalcification) in the context of CKD. This work
attempts to further explore the prognostic value, predictive markers as well as collateral
therapeutic consequence of VC in uremic milieu.
Study I explores the associations of the composites of coronary artery calcium (CAC) score,
i.e., CAC density and CAC volume, with mortality risk in patients with CKD stage 5 (CKD
G5). We found that while mortality risk increases with higher CAC score and CAC volume,
CAC density shows an inverse-J shaped pattern, with the crude mortality rate being highest in
the middle tertile of CAC density.
Study II evaluates the overlapping presence of aortic valve calcium (AVC) and CAC and the
prognostic value of AVC in CKD5 patients. We found a more common overlap of AVC and
CAC in CKD G5 than that observed in general population. High AVC score is associated with
increased all-cause mortality independent of presence of CAC, traditional risk factors and
inflammation.
Study III investigates phenotypic factors associated with the presence of biopsy-verified media
VC in CKD G5 patients using the relaxed linear separability feature selection model. We
identified through a mapping and ranking process, 17 features including novel biomarkers and
traditional risk factors that can differentiate patients with media VC from those without. These
results, if confirmed, may inform future investigations on media VC without the need of arterial
biopsies.
Study IV assesses the association of commonly prescribed phosphate binder sevelamer with
gut microbial metabolites in CKD G5 patients. We found that sevelamer therapy associates
with increased gut-derived uremic toxins and poor vitamin K status, suggesting potential tradeoffs
of sevelamer therapy in CKD.
Study V explores the plausible association between plasma dephosphorylated-uncarboxylated
matrix Gla-protein (dp-ucMGP, a circulating marker of functional vitamin K deficiency), VC
and mortality in CKD G5 patients. We found an independent association between high dpucMGP
levels and increased mortality risk that is not modified by presence of CAC and AVC
in CKD G5
Pathways linking atherosclerosis to aortic stenosis
Cardiovascular disease is the most common cause of death world-wide where atherosclerosis
is the main culprit and aortic valve disease accounts about two percent of all CVD deaths.
Atherosclerosis is a lipid and inflammation driven disease that share many features with
aortic valve stenosis (AVS). Globally, the prevalence of AVS has been estimated to over 10
million patients and the incidence to over 12 500 new cases annually which is likely
increasing due to increased longevity, yet no medical treatment is available. A link between
atherosclerosis and AVS has previously been established by overlapping prevalence and
common pathobiological hallmarks including lipid infiltration, inflammation, and
calcification. Recent genetic studies have demonstrated several loci in which single
nucleotide polymorphisms are associated with both diseases. However, there is also evidence
pointing to separate etiologies including disease specific genetic risk factors,
histopathological differences, and isolated clinical presentation.
The aim of this thesis was to establish the interplay between atherosclerosis and AVS. A
physiologic part was covered in Article I, specific mechanisms in Article II-IV and
molecular epidemiology in Article IV.
In Article I, arterial stiffness was determined in a cohort with ascending aortic dilatation
and/or aortic valve disease before and after cardiac surgery. Arterial stiffness correlates with
atherosclerotic cardiovascular disease and aggravates the increased left ventricular stress in
AVS. Cardio-ankle vascular index (CAVI) measures arterial stiffness from the heart to the
ankle and was lower in subjects with AVS compared with aortic regurgitation and ascending
aortic dilation, before surgery, despite being older. In contrast, aortic stiffness assessed by
carotid femoral pulse wave velocity (cfPWV) was not different between the groups. After
surgery, CAVI but not cfPWV increased in patients with AVS but remained unchanged in
patients undergoing aortic surgery. Age, diabetes, lower body mass index, decreased ejection
time and lower preoperative CAVI was associated with an increased CAVI after surgery. The
results suggest that AVS may mask an increased arterial stiffness if peripheral arteries are
included in the measurement. Also, ejection time emerged as an important variable to account
for when measuring arterial stiffness in aortic valve disease patients. Future work should aim
to establish if arterial stiffness may be used to risk-stratify AVS patients.
In Article II, the impact of a single nucleotide polymorphism (SNP) within FADS1 on aortic
valve gene expression and fatty acid composition was identified. Fatty acid desaturase
(FADS)1 and FADS2 encode rate limiting enzymes in the metabolism of omega-3 and
omega-6 polyunsaturated fatty acids (PUFAs) and the SNP within this locus is associated
with lower risk of both AVS and CAD. The SNP rs17547 was associated with FADS2
mRNA expression in calcified aortic valve tissue and the enzymatic activity of both FADS1
and FADS2. In addition, the aortic valve omega-3 PUFA docosahexaenoic acid proportion
was higher in non-calcified compared with calcified tissue and positively correlated with the
SNP. The results indicate that the protective effects of the SNP might be mediated via an
increased DHA proportion in the aortic valve and/or possibly via downstream mediators from
DHA such as specialized pro-resolving mediators which have been shown to dampen
inflammation.
Further pathophysiological evidence of shared pathways between CAD and AVS was
obtained in Article III. The presence of antiphospholipid antibodies (aPL) in the general
population is higher in patients with a recent myocardial infarction. Positivity for antibodies
against β2-glycoprotein I and/or cardiolipin of IgG isotype was identified to be 8-fold higher
in AVS patients compared with matched controls. In aortic valve tissue, aPL positivity was
associated with downregulated interferon pathways and upregulated pathways related to
mechanosensory signaling. Importantly, the differentially expressed genes could predict
resilient (healthy), thickened (fibrotic) and calcified aortic valve tissue with high accuracy
using supervised machine learning models suggesting a tight relationship between aPL
related genes and local disease progression. The overall results imply that aPL IgG in the
general population (without rheumatic disease) could be a risk factor for AVS and may
potentially be used guide AVS precision medicine.
In Article IV, CAD associated gene expression in aortic valve tissue was identified. First, the
prevalence of CAD in a contemporary surgical tricuspid AVS cohort was established at 49%
and was associated with claudication, smoking, male sex, and diabetes. An exploratory
analysis of aortic valve transcriptomic data from 74 patients revealed that severe CAD,
affecting 2 or 3 vessel territories, was associated with the most prominent difference in gene
expression. The differentially expressed genes were primarily found in non-calcified tissue
and were enriched in pathways related to oxidative stress, inflammation, and lipids.
Furthermore, a supervised machine learning model could predict if aortic valve tissue
stemmed from patients with severe CAD, at high accuracy. The most important gene
predictors of severe CAD could further be used to predict atherosclerotic or macroscopically
normal carotid artery tissue. The results suggest that AVS patients with concomitant severe
CAD exhibit more atherosclerosis related mechanisms in non-calcified tissue, ultimately
leading to a common end-stage disease with severe AVS.
In summary, the results in this thesis demonstrate that AVS may be a cause of masked
systemic arterial stiffness. Furthermore, pathways related to fatty acid metabolism and aPL
are implicated in the pathophysiology of AVS and patients with severe CAD exhibit
upregulated pathways related to atherosclerosis in the aortic valve. Collectively, pathways
linking and differentiating aortic valve and vascular atherosclerotic disease were unraveled
which open up for novel precision treatment regiments to halt AVS
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