Abstract：
Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.
摘要：
背景针对各种临床适应症进行的CT具有预测心脏代谢疾病的潜力。然而,个别CT参数的预测能力仍未得到充分探索。目的评估自动CT衍生标志物预测糖尿病和相关心脏代谢合并症的能力。材料与方法这项回顾性研究包括2012年1月至2015年12月期间接受氟18氟脱氧葡萄糖PET/CT健康筛查的韩国成年人（年龄≥25岁）。全自动CT标记包括内脏和皮下脂肪,肌肉，骨密度,肝脏脂肪,全部归一化为高度(米的平方)，主动脉钙化.在横截面和生存分析中，用受试者工作特征曲线下面积（AUC）和HarrellC指数评估预测性能。分别。结果横断面和队列分析包括32166名(平均年龄，45年±6[SD]，28833名男性)和27298名成年人(平均年龄，44年±5[SD]，24820名男子），分别。基线时糖尿病患病率和发病率为6%，7.3年中位随访期间为9%。分别。内脏脂肪指数对普遍和偶然发生的糖尿病显示出最高的预测性能，男性的AUC为0.70（95％CI：0.68，0.71），女性为0.82（95％CI：0.78，0.85），男性的C指数为0.68（95％CI：0.67，0.69），女性为0.82（95％CI：0.77，0.86），分别。结合内脏脂肪，肌肉面积,肝脏脂肪分数，主动脉钙化改善了预测性能，男性的C指数为0.69（95％CI：0.68，0.71），女性为0.83（95％CI：0.78，0.87）。男性内脏脂肪指数的AUC为0.81（95%CI：0.80，0.81），女性为0.90（95%CI：0.88，0.91）。CT衍生的标记物也确定了美国诊断的脂肪肝，冠状动脉钙评分大于100，肌肉减少症，骨质疏松症，AUC范围从0.80到0.95。结论自动多器官CT分析确定了糖尿病和其他心脏代谢合并症的高风险个体。©RSNA,2024补充材料可用于本文。另请参阅本期Pickhardt的社论。
