The Departments of Radiology and Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, SE1 7EH, UK.
Sci Rep. 2024 Sep 19;14(1):21875. doi: 10.1038/s41598-024-72702-7.
Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c. For example, in the unenhanced female cohort, mean changes between normal and poorly-controlled diabetes showed: 53% increase in visceral adipose tissue area, 22% increase in kidney volume, 24% increase in liver volume, 6% decrease in liver density (hepatic steatosis), 16% increase in skeletal muscle area, and 21% decrease in skeletal muscle density (myosteatosis) (all p < 0.001). The multisystem changes of metabolic syndrome can be objectively and retrospectively measured using automated CT biomarkers, with implications for diabetes, metabolic syndrome, and GLP-1 agonists.
糖尿病和代谢综合征与内脏体成分密切相关，但临床评估仅限于外部测量和包括糖化血红蛋白 (HbA1c) 在内的实验室值。现代深度学习和人工智能算法允许从常规计算机断层扫描 (CT) 检查中自动提取器官大小、密度和体成分的生物标志物。比较不同血糖控制水平的内脏 CT 生物标志物显示，随着 HbA1c 的增加，CT 生物标志物发生了显著的、渐进的变化。例如，在未增强的女性队列中，正常和控制不佳的糖尿病之间的平均变化显示：内脏脂肪组织面积增加 53%，肾脏体积增加 22%，肝脏体积增加 24%，肝密度降低（脂肪肝）6%，骨骼肌面积增加 16%，骨骼肌密度降低（肌脂病）21%（均 p < 0.001）。使用自动 CT 生物标志物可以客观和回顾性地测量代谢综合征的多系统变化，这对糖尿病、代谢综合征和 GLP-1 激动剂具有重要意义。
