Berge Kristian, Janssen Sylvan L J E, Velthuis Birgitta K, Myhre Peder Langeland, Mosterd Arend, Omland Torbjørn, Eijsvogels Thijs M H, Aengevaeren Vincent L
Department of Medical BioSciences, Exercise Physiology Research Group, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Cardiology, Division of Medicine, Akershus University Hospital, Lørenskog, Norway.
Eur Heart J Cardiovasc Imaging. 2025 Mar 3;26(3):461-470. doi: 10.1093/ehjci/jeae317.
Exercise improves cardiovascular health, but high-volume high-intensity exercise is associated with increased coronary artery calcification (CAC). We aimed to identify predictors of CAC in athletes.
We assessed the association of traditional and non-traditional cardiovascular risk factors with CAC using linear and logistic regression. A total of 289 male athletes from the MARC-2 study were included, with a median age of 60 (Q1-3 56-66) years, lifelong weekly training load of 26 (17-35) metabolic equivalent of task hours, body mass index of 24.5 (22.9-26.6) kg/m2, systolic blood pressure of 139 ± 18 mmHg, and reported 0.0 (0.0-8.0) smoking pack years. Thirty-one per cent had a CAC score > 100 and 13% > 400. Among traditional cardiovascular risk factors, higher age, systolic blood pressure, smoking pack years, and family history of coronary artery disease independently predicted greater CAC scores, while body mass index, low-density lipoprotein cholesterol, and diabetes mellitus did not. Among non-traditional risk factors, higher training loads, serum phosphate, and lower adjusted energy intake and fat percentage of energy intake independently predicted greater CAC scores. The full model with all traditional and non-traditional risk factors had higher accuracy in predicting CAC > 100 [receiver operating characteristic area under the curve 0.76, 95% confidence interval (0.70-0.82)] and CAC > 400 [0.85 (0.77-0.92)] than traditional cardiovascular risk factors alone [0.72 (0.65-0.78), P = 0.012, and 0.81 (0.74-0.90), P = 0.038, respectively].
Non-traditional risk factors, including training load, dietary patterns, and serum phosphate, were independently associated with CAC in aging male athletes. Prediction accuracy for CAC increased when including these variables in a prediction model with traditional risk factors.
运动可改善心血管健康，但大量高强度运动与冠状动脉钙化（CAC）增加有关。我们旨在确定运动员中CAC的预测因素。
我们使用线性和逻辑回归评估传统和非传统心血管危险因素与CAC的关联。纳入了MARC - 2研究中的289名男性运动员，中位年龄为60岁（四分位间距56 - 66岁），终生每周训练负荷为26（17 - 35）代谢当量任务小时，体重指数为24.5（22.9 - 26.6）kg/m²，收缩压为139 ± 18 mmHg，报告吸烟量为0.0（0.0 - 8.0）包年。31%的人CAC评分>100，13%的人>400。在传统心血管危险因素中，年龄较大、收缩压较高、吸烟包年数以及冠状动脉疾病家族史独立预测更高的CAC评分，而体重指数、低密度脂蛋白胆固醇和糖尿病则不然。在非传统危险因素中，较高的训练负荷、血清磷酸盐以及较低的调整能量摄入量和能量摄入中的脂肪百分比独立预测更高的CAC评分。包含所有传统和非传统危险因素的完整模型在预测CAC>100[曲线下受试者工作特征面积0.76，95%置信区间（0.70 - 0.82）]和CAC>400[0.85（0.77 - 0.92）]方面比仅使用传统心血管危险因素的模型[分别为0.72（0.65 - 0.78），P = 0.012，和0.81（0.74 - 0.90），P = 0.038]具有更高的准确性。
非传统危险因素，包括训练负荷、饮食模式和血清磷酸盐，在老年男性运动员中与CAC独立相关。在包含传统危险因素的预测模型中纳入这些变量时，CAC的预测准确性增加。
