Kim Yoo Hyung, Yoon Ji Won, Lee Bon Hyang, Yoon Jeong Hee, Choe Hun Jee, Oh Tae Jung, Lee Jeong Min, Cho Young Min
Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea.
Department of Internal Medicine, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea.
J Diabetes Investig. 2025 Feb;16(2):272-284. doi: 10.1111/jdi.14365. Epub 2024 Nov 22.
AIM/INTRODUCTION: We assess the efficacy of artificial intelligence (AI)-based, fully automated, volumetric body composition metrics in predicting the risk of diabetes.
This was a cross-sectional and 10-year retrospective longitudinal study. The cross-sectional analysis included health check-up data of 15,330 subjects with abdominal computed tomography (CT) images between January 1, 2011, and September 30, 2012. Of these, 10,570 subjects with available follow-up data were included in the longitudinal analyses. The volume of each body segment included in the abdominal CT images was measured using AI-based image analysis software.
Visceral fat (VF) proportion and VF/subcutaneous fat (SF) ratio increased with age, and both strongly predicted the presence and risk of developing diabetes. Optimal cut-offs for VF proportion were 24% for men and 16% for women, while VF/SF ratio values were 1.2 for men and 0.5 for women. The subjects with higher VF/SF ratio and VF proportion were associated with a greater risk of having diabetes (adjusted OR 2.0 [95% CI 1.7-2.4] in men; 2.9 [2.2-3.9] in women). In subjects with normal glucose tolerance, higher VF proportion and VF/SF ratio were associated with higher risk of developing prediabetes or diabetes (adjusted HR 1.3 [95% CI 1.1-1.4] in men; 1.4 [1.2-1.7] in women). These trends were consistently observed across each specified cut-off value.
AI-based volumetric analysis of abdominal CT images can be useful in obtaining body composition data and predicting the risk of diabetes.
目的/引言：我们评估基于人工智能（AI）的全自动体成分容积测量指标在预测糖尿病风险方面的有效性。
这是一项横断面和为期10年的回顾性纵向研究。横断面分析纳入了2011年1月1日至2012年9月30日期间15330例接受腹部计算机断层扫描（CT）检查的受试者的健康体检数据。其中，10570例有可用随访数据的受试者被纳入纵向分析。使用基于AI的图像分析软件测量腹部CT图像中每个身体部位的容积。
内脏脂肪（VF）比例和VF/皮下脂肪（SF）比值随年龄增长而增加，二者均强烈预测糖尿病的发生及风险。男性VF比例的最佳临界值为24%，女性为16%；男性VF/SF比值为1.2，女性为0.5。VF/SF比值和VF比例较高的受试者患糖尿病的风险更大（男性调整后的比值比[OR]为2.0[95%置信区间（CI）为1.7 - 2.4]；女性为2.9[2.2 - 3.9]）。在糖耐量正常的受试者中，较高的VF比例和VF/SF比值与发生糖尿病前期或糖尿病的较高风险相关（男性调整后的风险比[HR]为1.3[95%CI为1.1 - 1.4]；女性为1.4[1.2 - 1.7]）。在每个指定的临界值中均一致观察到这些趋势。
基于AI的腹部CT图像容积分析有助于获取体成分数据并预测糖尿病风险。
