Preventing Cardiovascular Disease. Complementary precision medicine.

AbstractBackground: Non-communicable diseases are the number one killer worldwide and the leading one,cardiovascular disease (CVD), is responsible for more than 30% of all deaths. CVD is a progressive disease whichalso makes it economical, easy and effective to prevent. There are many stages of CVD that ultimately can lead tocoronary atherosclerosis, measurable as coronary artery calcification (CAC) score by cardiac computed tomography(CT). Before coronary atherosclerosis develops there are many stages of the process: inflammation, reducedendothelial function, hypertension and impaired microcirculation. Precision medicine is a popular novelty inmedicine that combines well-established results and medical history with computer science and novel biomedicalinformation.Aims: The aims of the study were: (I) to evaluate whether aged garlic extract (AGE) can influence CAC and topredict the individual effect of AGE; (II) to assess the effect of long-term treatment with AGE on cutaneous tissueperfusion; (III) to evaluate whether a daily supplement of AGE could reduce inflammation in females with low risk ofcardiovascular disease; (IV) to assess the effect of long-term treatment with AGE on peripheral tissue perfusion inpatients with confirmed atherosclerosis; and (V) to validate a prediction model to explore whether an individualpatient will have a positive effect of AGE on their CAC score and blood pressure.Methods: Studies I-IV were single-centre parallel randomised controlled studies. Patients were randomised, in adouble-blind manner, through a computer-generated randomisation chart to an intake of placebo or AGE (2400 mgdaily) for 12 months. In Study I a prediction model was developed using a cross-industry standard process for datamining and in Study V this method for developing prediction models was validated in a new cohort.The cohort usedwas pooled from previously published studies in the USA.Results: There was a significant change in CAC progression (OR: 2.95 [1.05–8.27]), in favour of the AGE group.The developed algorithm could predict with 79% precision which patient would have a more favourable effect ofAGE on CAC score. Cutaneous microcirculation was measured at 0 and 12 months and the mean post-occlusivereactive hyperaemia (PORH) differed significantly between time points. The mean percent was 102, 64 (174, 15)%change for AGE and 78, 62 (107, 92)% change for the placebo group (F[1, 120] = 5. 95, p < 0.016). Femalestreated with AGE showed lower levels of inflammatory biomarker interleukin-6 (IL-6) after 12 months of AGEtreatment. After 12 months of AGE, an increase of 21.6% (95% CI 3.2%-40.0%, p < 0.05) was seen in the relativechange of PORH. The same response was seen for CVC and acetylcholine with an increase of 21.4% (95% CI3.4%-39.4%, p < 0.05) in the AGE group. Study V demonstrated that it is possible to develop predictive models.Theconstructed algorithm was able to predict with 64% precision which patient would have a significant reduction ofCAC.Conclusion: AGE inhibits CAC progression, lowers IL-6, glucose levels and blood pressure and increases themicrocirculation in patients at increased risk of cardiovascular events. It is also possible to predict which patient willhave a more favourable effect of AGE. AGE lowers IL-6 in females with a low risk of CVD. AGE regeneratedperipheral tissue perfusion and increased microcirculation in patients with arteriosclerosis.For many patients it is essential to know if they will have an effect of a treatment before changing their daily lives.The developed algorithm shows that it is feasible to develop predictive models for answering this question

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/non-COVID-19 Frameworks using Artificial Intelligence Paradigm: A Narrative Review

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for lowincome countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, lowcost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

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

Coronary Angiography

In the intervening 10 years tremendous advances in the field of cardiac computed tomography have occurred. We now can legitimately claim that computed tomography angiography (CTA) of the coronary arteries is available. In the evaluation of patients with suspected coronary artery disease (CAD), many guidelines today consider CTA an alternative to stress testing. The use of CTA in primary prevention patients is more controversial in considering diagnostic test interpretation in populations with a low prevalence to disease. However the nuclear technique most frequently used by cardiologists is myocardial perfusion imaging (MPI). The combination of a nuclear camera with CTA allows for the attainment of coronary anatomic, cardiac function and MPI from one piece of equipment. PET/SPECT cameras can now assess perfusion, function, and metabolism. Assessing cardiac viability is now fairly routine with these enhancements to cardiac imaging. This issue is full of important information that every cardiologist needs to now