BackgroundComputed tomography (CT) body compositions reflect age‐related metabolic derangements. We aimed to develop a multi‐outcome deep learning model using CT multi‐level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long‐term mortality.MethodsThe derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age‐ and sex‐stratified random sampling from two community‐based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi‐automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi‐layer perceptron (MLP)‐based multi‐label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary‐level institution (n = 10 141).ResultsThe mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi‐level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT‐parameter‐based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow‐up 4.9 years), a total of 907 individuals (8.9%) died during follow‐up. Among model‐predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.ConclusionsA CT body composition‐based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community‐dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long‐term mortality, independent of covariates.

中文翻译：

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利用计算机断层扫描深度学习对代谢综合征、骨质疏松症和肌肉减少症进行代谢表型分析可预测成人死亡率

背景计算机断层扫描（CT）身体成分反映了与年龄相关的代谢紊乱。我们的目标是开发一种多结果深度学习模型，使用 CT 多级身体成分参数，通过同时识别代谢簇来检测代谢综合征 (MS)、骨质疏松症和肌肉减少症。我们还研究了 CT 模型代谢表型分析对长期死亡率的预后价值。方法推导集（n= 516； 75% 的训练集，25% 的内部测试集）是使用来自两个社区队列的年龄和性别分层随机抽样构建的。来自个人健康评估计划参与者的数据（n= 380）用作外部测试集1。对腹部CT扫描的多个级别的身体成分进行半自动量化，以训练基于多层感知器（MLP）的多标签分类模型。用于测试死亡率模型输出的预后价值的外部测试集 2 是使用在三级机构接受腹部 CT 的个人的数据构建的（n= 10 141）。结果推导组和外部组的平均年龄分别为 62.8 岁和 59.7 岁，性别分布（女性 50%）或体重指数（BMI；23.9 kg/m）没有差异2）。骨骼肌密度（SMD）和骨密度（BD）随着年龄的增长，比骨骼肌面积呈现出更线性的衰减。另外，男性和女性的内脏脂肪面积（VFA）均有所增加。基于多级 CT 身体成分参数的分层聚类揭示了三个独特的表型簇：正常、MS 和骨肌减少症簇。基于 L3 CT 参数的模型，无论有或没有临床变量（年龄、性别和 BMI），所有结果均优于临床模型预测（受试者工作特征曲线下面积：MS，0.76 与 0.55；骨质疏松症，0.90 与 0.55）。 0.79；肌肉减少症，外部测试集 1 中为 0.85 与 0.81；磷全部 < 0.05）。 VFA 对 MS 预测贡献最大，而 SMD、BD 和皮下脂肪面积对于检测骨质疏松症和肌肉减少症非常重要。在外部测试集 2 中（平均年龄 63.5 岁，女性 79%；中位随访时间 4.9 年），随访期间共有 907 人 (8.9%) 死亡。在模型预测的代谢表型中，单独的肌肉减少症（调整后的风险比 [aHR] 1.55）、MS + 肌肉减少症（aHR 1.65）、骨质疏松 + 肌肉减少症（aHR 1.83）以及所有三种组合（aHR 1.87）在调整后仍然是死亡率的稳健预测因子。结论基于 CT 身体成分的 MLP 模型同时检测到社区居住和住院成人的多发性硬化症、骨质疏松症和肌肉减少症。 CT MLP 模型预测的代谢表型与长期死亡率相关，与协变量无关。