장혁재 교수

BACKGROUND: Data on the relationship between the triglyceride glucose (TyG) index and coronary artery calcification (CAC) progression is limited. This longitudinal study evaluated the association of TyG index with CAC progression in asymptomatic adults. METHODS: We enrolled 12,326 asymptomatic Korean adults who had at least two CAC evaluations. The TyG index was determined using ln (fasting triglycerides [mg/dL] × fasting glucose [mg/dL]/2). CAC progression was defined as a difference ≥ 2.5 betw

BACKGROUND: Insulin resistance (IR) is an important risk factor for subclinical atherosclerosis. This study evaluated the relationship between the triglyceride glucose (TyG) index, which is a simple and reliable surrogate marker for IR, and arterial stiffness. METHODS: This study included 2560 Korean subjects without a previous history of coronary artery disease, stroke, and malignancies who participated in a community-based cohort study. Arterial stiffness was measured using the brachial-ankle

AIMS: High calcium (Ca), phosphate (P), and Ca-P product (CPP) are associated with cardiovascular disease in patients with chronic kidney disease. Whether this relationship persists in individuals with normal kidney function is not yet elucidated. We explored the relationship of serum Ca, P, and CPP to coronary atherosclerosis assessed by cardiac computed tomography angiography (cCTA) in participants with normal kidney function. METHODS AND RESULTS: This study included 7553 participants (52 ± 10

AIMS: Coronary artery calcium score (CACS) is a strong predictor of major adverse cardiac events (MACE). Conversely, statins, which markedly reduce MACE risk, increase CACS. We explored whether CACS progression represents compositional plaque volume (PV) progression differently according to statin use. METHODS AND RESULTS: From a prospective multinational registry of consecutive patients (n = 2252) who underwent serial coronary computed tomography angiography (CCTA) at a ≥ 2-year interval, 654 p

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography–determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angio