European journal of preventive cardiology最新文献

Recent trends indicate a concerning increase in early-onset atherosclerotic cardiovascular disease (ASCVD) among younger individuals (men aged <55 years women aged <65 years). These findings highlight the pathobiology of ASCVD as a disease process that begins early in life and underscores the need for more tailored screening methods and preventive strategies. Increasing attention has been placed on the growing burden of traditional cardiometabolic risk factors in young individuals while also recognizing unique factors that mediate risk of pre-mature atherosclerosis in this demographic such as substance use, socioeconomic disparities, adverse pregnancy outcomes, and chronic inflammatory states that contribute to the increasing incidence of early ASCVD. Additionally, mounting evidence has pointed out significant disparities in the diagnosis and management of early ASCVD and cardiovascular outcomes based on sex and race. Moving towards a more personalized approach, emerging data and technological developments using diverse tools such as polygenic risk scores and coronary artery calcium scans have shown potential in earlier detection of ASCVD risk. Thus, we review current evidence on causal risk factors that drive the increase in early ASCVD and highlight emerging tools to improve ASCVD risk assessment in young individuals.

Aims: Elevated lipoprotein(a) [Lp(a)] is a causal risk factor for atherosclerotic cardiovascular disease, but the mechanisms of risk are debated. Studies have found inconsistent associations between Lp(a) and measurements of atherosclerosis. We aimed to assess the relationship between Lp(a), low-density lipoprotein cholesterol (LDL-C), and coronary artery plaque severity.

Methods and results: The study population consisted of participants of the Million Veteran Program who have undergone an invasive angiogram. The primary exposure was genetically predicted Lp(a) estimated by a polygenic score. Genetically predicted LDL-C was also assessed for comparison. The primary outcome was coronary artery plaque severity categorized as normal, non-obstructive disease, one-vessel disease, two-vessel disease, and three-vessel or left main disease. Among 18 927 adults of genetically inferred European ancestry and 4039 adults of genetically inferred African ancestry, we observed consistent associations between genetically predicted Lp(a) and obstructive coronary plaque, with effect sizes trending upward for increasingly severe categories of disease. Associations were independent of risk factors, clinically measured LDL-C and genetically predicted LDL-C. However, we did not find strong or consistent evidence for an association between genetically predicted Lp(a) and risk for non-obstructive plaque.

Conclusion: Genetically predicted Lp(a) is positively associated with coronary plaque severity independent of LDL-C, consistent with Lp(a) promoting atherogenesis. However, the effects of Lp(a) may be greater for progression of plaque to obstructive disease than for the initial development of non-obstructive plaque. A limitation of this study is that Lp(a) was estimated using genetic markers and could not be directly assayed nor could apo(a) isoform size.

Aims: To investigate if adding ECG abnormalities as a predictor improves the performance of incident CVD-risk prediction models for people with type 2 diabetes (T2D).

Methods: We evaluated the four major prediction models that are recommended by the guidelines of the American College of Cardiology/American Heart Association and European Society of Cardiology, in 11,224 people with T2D without CVD (coronary heart disease, heart failure, stroke, thrombosis) from the Hoorn Diabetes Care System cohort (1998-2018). Baseline measurements included CVD-risk factors and ECG recordings coded according to the Minnesota Classification as no, minor or major abnormalities. After confirming good reference model fit, model performance was assessed before and after addition of ECG abnormalities and compared using c-statistics, net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

Results: C-statistics (95%CI) of reference models (ASCVD, AD-ON, ADVANCE and SCORE2-Diabetes) were 0.67 (0.65-0.70), 0.73 (0.71-0.76), 0.71 (0.68-0.74) and 0.67 (0.65-0.69), respectively. Adding ECG abnormalities as predictor improved c-statistics with +0.02 (0.01-0.03), +0.01 (0.00-0.01), +0.02 (0.01-0.03), and +0.02 (0.01-0.02), respectively. Reclassification indicators also showed improvement: categorical NRI (+6%, +3%, +8%, and +5%), continuous NRI (95%CI) 0.25 (0.08-0.37), 0.32 (0.23-0.42), 0.54 (0.34-0.69) and 0.28 (0.09-0.33)), and IDI (95%CI) 0.005 (0.001-0.010), 0.002 (-0.001-0.007), 0.006 (0.001-0.007) and 0.004 (0.000-0.006)). Sensitivity analyses yielded similar results.

Conclusion: The addition of ECG abnormalities to incident CVD-risk prediction models moderately but consistently improves the ability of models to correctly classify people with T2D in the appropriate CVD-risk category with up to 8%, which is approximately equivalent to many established predictors and (bio)markers.