Calcification prevalence in different vascular zones and its association with demographics, risk factors, and morphometry.

Abstract

Vascular calcification is associated with a higher incidence of cardiovascular events, but its prevalence in different vascular zones and the influence of demographics, risk factors, and morphometry remain insufficiently understood. Computerized tomography angiography scans from 211 subjects 5-93 yr old (mean age 47 ± 24 yr, 127 M/84 F) were used to build 3D vascular reconstructions and measure arterial diameters, tortuosity, and calcification volumes in six vascular zones spanning from the ascending thoracic aorta to the pelvic arteries. A machine learning random forest algorithm was used to determine the associations between calcification in each zone with demographics, risk factors, and vascular morphometry. Calcification appeared during the fourth decade of life and was present in all subjects after 65 yr. The abdominal aorta and the iliofemoral segment were the first to develop calcification, whereas the ascending thoracic aorta was the last. Demographics and risk factors explained 33-59% of the variation in calcification. Age, creatinine level, body mass index, coronary artery disease, and hypertension were the strongest contributors, whereas the effects of sex, race, tobacco use, diabetes, dyslipidemia, and alcohol and substance use disorders on calcification were small. Vascular morphometry did not directly and independently affect calcium burden. Vascular zones develop calcification asynchronously, with distal segments calcifying first. Understanding the influence of demographics and risk factors on calcium prevalence can help better understand the disease pathophysiology and may help with the early identification of patients that are at higher risk of cardiovascular events.NEW & NOTEWORTHY We investigated the prevalence of vascular calcification in different zones of the aorta and pelvic arteries using computerized tomography angiography reconstructions and have applied machine learning to determine how calcification is affected by demographics, risk factors, and morphometry. The presented data can help identify patients at higher risk of developing vascular calcification that may lead to cardiovascular events.