Cardiac Imaging-Based Risk Assessment: Prediction of Atrial Fibrillation and Stroke Using Imaging Biomarkers
Atrial fibrillation (AF) is a prevalent cardiovascular condition associated with a significantly increased risk of stroke and other adverse outcomes. Current diagnostic and preventive strategies often fail to detect AF and assess stroke risk in a timely and personalized manner. This project aims to address this gap by developing a multidisciplinary, image-based workflow that leverages advanced cardiac imaging techniques, such as cardiac CT and transesophageal echocardiography. The workflow will focus on analyzing structural and functional parameters of the left atrium (LA) and left atrial appendage (LAA), which are central to AF progression and thrombus formation. The research involves five phases: imaging dataset acquisition, preprocessing through segmentation and registration, defining clinically relevant evaluation metrics, developing automated analysis algorithms, and validating the approach on diverse datasets. By incorporating advanced image processing and machine learning tools, the project seeks to create a robust,
non-invasive diagnostic tool for early detection of AF and stroke risk stratification.
This novel approach has the potential to enhance clinical decision-making, enabling more effective management strategies such as anticoagulation therapy and interventional procedures. By improving the accuracy and efficiency of AF diagnosis and stroke prevention, this research aims to contribute significantly to reducing the global healthcare burden of cardiovascular
diseases.
Sachal Hussain
Senior Researcher
Dr. Tuomas Kiviniemi
Associate Professor
Morphological and functional characterization of the pericoronary epicardial adipose tissue in atherosclerosis and its incremental diagnostic value
1.1 Short summary
The measurement of local changes in the radiodensity of the fat around the coronary arteries in computed tomography (CT) images offers a promising method to find individuals at high risk of having myocardial infarction, potentially benefitting the most from treatment, but the method needs to be standardized and reference values for healthy and diseased populations need to be established.
We have developed a fully automated AI-based model, capable of extracting the required parameters from CT images in 30,000 healthy individuals in the Swedish the SCAPIS study and 3,000 patients with suspected coronary artery disease examined at the Turku University Hospital (TYKS). Thereby, we can obtain world-unique results, both in terms of technical solutions and the size of the populations.
1.2 The epicardial adipose tissue
The epicardial adipose tissue (EAT) surrounding the coronary arteries has been linked to atherosclerosis and both increased total EAT volume (EATV) and EAT attenuation around the arteries (P-EATA) on computed tomography (CT) have been shown to be predictive of adverse cardiac events. Changes in P-EATA, thought to reflect inflammatory activity, is a radiomic feature influenced by pathophysiological processes in the vessel wall. However, there is no standard for measuring P-EATA, and age, sex, seasonal, metabolic and iodine-contrast related effects confound measurements
1.2 The epicardial adipose tissue
The epicardial adipose tissue (EAT) is in contact with the myocardium, envelops the coronary arteries and is enclosed by the pericardium. It is a special type of visceral adipose tissue, which provides warmth and metabolic support to the myocardium, being a reservoir of both energy and rich in brown adipose tissue. The discovery of increased inflammatory activity in the EAT in coronary artery disease (CAD) has suggested a possible immunological or endocrine function. Increased EAT volumes (EATV) on CT are associated with increased risk of cardiac events (MACE) and morphological signs of coronary atherosclerosis. The EAT attenuation (EATA), or its radiodensity, is strongly correlated to the EATV at a whole-heart level, while local increases of EATA in the immediate vicinity of the coronary arteries seem to be associated with high-risk plaques and could provide added value in risk estimation.
The concept of the vulnerable plaque provides a model, which can explain the presence of CAD in the absence of extensive calcification and the findings of a local increase in EATA around them could indicate inflammation, which is thought to extend into the adjoining EAT causing edema or infiltration of cells in the peri-coronary EAT.
No standardized methods for measuring P-EATA exist, although lately the fat attenuation index has been proposed, which is essentially an average measurement in a “cylinder” of EATA measured along the vessels with specified dimensions, not accounting for true local changes adjacent to, e.g., plaques. Also, importantly, the EATA is subject to influence from several factors: it seems to vary with sex and age, as well the relative amounts of brown adipose tissue, while the use of iodine contrast in CT angiography (CTA) can by itself affect the EATA, dependent on dose, timing, and perfusion of the EAT23. It is not yet defined how these factors interact, and standardized measurements of the P-EATA which accounts for confounding from non-CAD related factors should greatly improve the sensitivity and specificity of any predictive model using P-EATA data
1.4 Positron emission tomography (PET) in a combined model for risk stratification
PET using H215O as a tracer allows for the exact quantification of myocardial blood flow in the evaluation of CAD and is the current gold standard for its assessment. It has been shown to complement the morphological information gained from CTA, overbridging problems of discrepancy between stenosis severity and de facto ischemia. The effects of vessel obstruction in CAD on contrast flow to the EAT can be derived and defined using PET-data as a reference.
1.5 Clinical importance of improved risk-stratification
Pharmacotherapy is the mainstay of chronic CAD treatment. In selected patients with chronic CAD surgical or interventional revascularization is the method of choice, but the decision to perform PCI should be guided by functional measures of stenosis severity to improve outcome. Absolute myocardial perfusion, which can be obtained from H215O-PET can improve sensitivity for functionally important disease, e.g. in a balanced 3-vessel disease setting.
Increased accuracy in identifying patients in need of treatment is desirable in non-obstructive CAD, where the optimal use of preventive medication is not well understood. This is of considerable clinical importance, as nearly 40% of patients evaluated with CTA can have non-obstructive CAD29. P-EATA in the modelling of risk is very promising, but needs to be standardized and compared to both stenosis and functional data to better define its added value.
Key Words:
Epicardial adipose tissue, inflammation, coronary atherosclerosis, perfusion imaging, computed tomography
David Molnar
Senior Researcher
Juhani Knuuti
Professor, Director, Turku PET Centre
Proteomic evaluation of Stress Granules and Processing Bodies in pathophysiology of Atrial Fibrillation
Atrial fibrillation (AF) is the most common cardiac arrhythmia with lifetime risk increasing with advancing age, obesity, smoking, hypertension, and diabetes mellitus. AF is often a progressive condition, beginning with brief episodes that start and stop spontaneously (paroxysmal AF), progressing to longer episodes that do not terminate without either drug- or electrical shock induced cardioversion (persistent AF).
Stress granules (SG) and Processing bodies (PB) are two cytosolic membrane-less organelles involved in post-transcriptional regulation and translational control, which form via co-assembly or condensation of translationally inactive mRNAs associated with distinct RNAbinding proteins (RBPs), notably with translation initiation components (SGs) and mRNA degradation machinery components (PBs). SGs and PBs are increasingly implicated in human neurodegeneration, but their role in cardiomyocytes during AF is much less known.
Emerging evidence suggests oxidative stress, to be an inducer of SG formation in the pathologies of AF. So, to study the effect of these SGs and PBs in AF, we designed this unbiased, untargeted, discovery-based top-down study to identify novel proteins. Along with that, a cardiac cell model will also be prepared using HEK cells to study the genetic predisposition of the atrial fibrillation in several AF associated genes. The project will be carried out in Cardiology lab under the guidance of Dr. Tuomas Kiviniemi (CAREFIB) in collaboration with Associate Prof. Otto Kauko (Proteomics core) and Medicity Research Laboratories, University of Turku
Key words:
Atrial fibrillation, stress granules, processing bodies, proteomics, cardiovascular diseases
Aim:
The main objective of the project is to identify and isolate the proteins which are exclusive to SGs and PBs and their relation to the AF disease pathophysiology.
Material and method:
The study will be carried out in three groups AF, non-AF and patients who develop AF in a later stage. The LCMS based quantitative proteomic analysis will be carried out on the tissue samples from the three groups for discovery of differentially expressed proteins (DEPs) and deregulated pathways after isolating SGs and PBs from tissue samples. Statistical models like ANOVA, PCA, and ROC will be used to interpret data. This will be exclusive research to study the SBs and PBs in AF as there is not much literature available on the protein content of SGs and PBs impact in the disease outcome.