Bayesian network analysis of panomic biological big data identifies the importance of triglyceride-rich LDL in atherosclerosis development.

Introduction: We sought to explore biomarkers of coronary atherosclerosis in an unbiased fashion.

Methods: We analyzed 665 patients (mean ± SD age, 56 ± 11 years; 47% male) from the GLOBAL clinical study (NCT01738828). Cases were defined by the presence of any discernable atherosclerotic plaque based on comprehensive cardiac computed tomography (CT).

Precision phenotyping, panomics, and system-level bioinformatics to delineate complex biologies of atherosclerosis: rationale and design of the "Genetic Loci and the Burden of Atherosclerotic Lesions" study.

Background: Complex biological networks of atherosclerosis are largely unknown.

Objective: The main objective of the Genetic Loci and the Burden of Atherosclerotic Lesions study is to assemble comprehensive biological networks of atherosclerosis using advanced cardiovascular imaging for phenotyping, a panomic approach to identify underlying genomic, proteomic, metabolomic, and lipidomic underpinnings, analyzed by systems biology-driven bioinformatics.

Methods: By design, this is a hypothesis-free unbiased discovery study collecting a large number of biologically related factors to examine biological associations between genomic, proteomic, metabolomic, lipidomic, and phenotypic factors of atherosclerosis.

The M235T single nucleotide polymorphism in the angiotensinogen gene is associated with coronary artery calcium in patients with a family history of coronary artery disease.

Little is known about the contribution of genetics and lipoprotein subclasses to the development of coronary artery calcification (CAC) in asymptomatic, first-degree relatives of patients with CAD. We evaluated 100 asymptomatic, non-smoking, lipid-lowering therapy-naïve, first-degree relatives of patients with obstructive CAD through testing for 27 biomarkers, 15 single nucleotide polymorphisms in 12 candidate genes, and CAC and compared them to matched controls without family history. We compared prevalence of CAC in those with and without family history and biomarkers between those with and without CAC.

Demonstration of the Glagov phenomenon in vivo by CT coronary angiography in subjects with elevated Framingham risk.

Computed tomography coronary angiography (CTA) is a novel, non-invasive method for coronary plaque detection and quantification. We hypothesized that CTA can detect early vessel wall thickening with preserved luminal size in patients without known coronary artery disease and intermediate/high Framingham Risk Score (FRS) compared to those with low FRS. Vessel-wall and plaque geometrical and compositional parameters were measured on CTA in 375 coronary segments with a highly standardized method.

A lesion-specific coronary artery calcium quantification framework for the prediction of cardiac events.

CT-based coronary artery calcium (CAC) scanning has been introduced as a noninvasive, low-radiation imaging technique for the assessment of the overall coronary arterial atherosclerotic burden. A 3-D CAC volume contains significant clinically relevant information, which is unused by conventional whole-heart CAC quantification methods. In this paper, we have developed a novel distance-weighted lesion-specific CAC quantification framework that predicts cardiac events better than the conventional whole-heart CAC measures.

Lesion- and vessel-specific coronary artery calcium scores are superior to whole-heart Agatston and volume scores in the diagnosis of obstructive coronary artery disease.

Background: The whole-heart coronary artery calcium (CAC) score has poor predictive value for obstructive coronary artery disease (CAD). We hypothesized that vessel- and lesion-specific CAC scores are more accurate.

Objectives: To evaluate the usefulness of vessel- and lesion-specific CAC in predicting obstructive CAD and to assess the incremental value added by the vessel- and lesion-specific CAC to the conventional whole-heart CAC approach.

Lesion-specific coronary artery calcium quantification for predicting cardiac event with multiple instance support vector machines.

Conventional whole-heart CAC quantification has been demonstrated to be insufficient in predicting coronary events, especially in accurately predicting near-term coronary events in high-risk adults. In this paper, we propose a lesion-specific CAC quantification framework to improve CAC's near-term predictive value in intermediate to high-risk populations with a novel multiple instance support vector machines (MISVM) approach. Our method works on data sets acquired with clinical imaging protocols on conventional CT scanners without modifying the CT hardware or updating the imaging protocol.