Palmas Fiorella, Ciudin Andreea, Melian Jose, Guerra Raul, Zabalegui Alba, Cárdenas Guillermo, Mucarzel Fernanda, Rodriguez Aitor, Roson Nuria, Burgos Rosa, Hernández Cristina, Simó Rafael
Endocrinology and Nutrition Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Diabetes and Metabolism Research Unit, Vall d'Hebron Institut De Recerca (VHIR), Barcelona, Spain.
Endocrinology and Nutrition Department, Hospital Universitari Vall d'Hebron, Barcelona, Spain; Diabetes and Metabolism Research Unit, Vall d'Hebron Institut De Recerca (VHIR), Barcelona, Spain; Department of Medicine, Universitat Autònoma De Barcelona, Barcelona, Spain; Centro De Investigación Biomédica En Red De Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto De Salud Carlos III (ISCIII), Madrid, Spain.
Clin Nutr ESPEN. 2025 Aug;68:494-500. doi: 10.1016/j.clnesp.2025.05.031. Epub 2025 May 26.
BACKGROUND & AIMS: The assessment of resting energy expenditure (REE) is a challenging task with the current existing methods. The reference method, indirect calorimetry (IC), is not widely available, and other surrogates, such as equations and bioimpedance (BIA) show poor agreement with IC. Body composition (BC), in particular muscle mass, plays an important role in REE. In recent years, computed tomography (CT) has emerged as a reliable tool for BC assessment, but its usefulness for the REE evaluation has not been examined. In the present study we have explored the usefulness of CT-scan imaging to assess the REE using AI machine-learning models.
Single-centre observational cross-sectional pilot study from January to June 2022, including 90 fasting, clinically stable adults (≥18 years) with no contraindications for indirect calorimetry (IC), bioimpedance (BIA), or abdominal CT-scan. REE was measured using classical predictive equations, IC, BIA and skeletal CT-scan. The proposed model was based on a second-order linear regression with different input parameters, and the output corresponds to the estimated REE. The model was trained and tested using a cross-validation one-vs-all strategy including subjects with different characteristics.
Data from 90 subjects were included in the final analysis. Bland-Altman plots showed that the CT-based estimation model had a mean bias of 0 kcal/day (LoA: -508.4 to 508.4) compared with IC, indicating better agreement than most predictive equations and similar agreement to BIA (bias 53.4 kcal/day, LoA: -475.7 to 582.4). Surprisingly, gender and BMI, ones of the mains variables included in all the BIA algorithms and mathematical equations were not relevant variables for REE calculated by means of AI coupled to skeletal CT scan. These findings were consistent with the results of other performance metrics, including mean absolute error (MAE), root mean square error (RMSE), and Lin's concordance correlation coefficient (CCC), which also favored the CT-based method over conventional equations.
Our results suggest that the analysis of a CT-scan image by means of machine learning model is a reliable tool for the REE estimation. These findings have the potential to significantly change the paradigm and guidelines for nutritional assessment.
使用现有方法评估静息能量消耗（REE）是一项具有挑战性的任务。参考方法间接测热法（IC）并不广泛适用，而其他替代方法，如公式和生物电阻抗（BIA）与IC的一致性较差。身体成分（BC），特别是肌肉质量，在REE中起重要作用。近年来，计算机断层扫描（CT）已成为评估BC的可靠工具，但其对REE评估的实用性尚未得到检验。在本研究中，我们探索了使用人工智能机器学习模型通过CT扫描成像评估REE的实用性。
2022年1月至6月进行的单中心观察性横断面试点研究，纳入90名禁食、临床稳定的成年人（≥18岁），他们没有间接测热法（IC）、生物电阻抗（BIA）或腹部CT扫描的禁忌证。使用经典预测公式、IC、BIA和骨骼CT扫描测量REE。所提出的模型基于具有不同输入参数的二阶线性回归，输出对应于估计的REE。使用包括具有不同特征受试者的交叉验证一对一策略对模型进行训练和测试。
90名受试者的数据纳入最终分析。Bland-Altman图显示，与IC相比，基于CT的估计模型平均偏差为0千卡/天（一致性界限：-508.4至508.4），表明其一致性优于大多数预测公式，与BIA相似（偏差53.4千卡/天，一致性界限：-475.7至582.4）。令人惊讶的是，性别和BMI是所有BIA算法和数学公式中包含的主要变量，对于通过人工智能结合骨骼CT扫描计算的REE而言并非相关变量。这些发现与其他性能指标的结果一致，包括平均绝对误差（MAE）、均方根误差（RMSE）和林氏一致性相关系数（CCC），这些指标也更支持基于CT的方法而非传统公式。
我们的结果表明，通过机器学习模型分析CT扫描图像是评估REE的可靠工具。这些发现有可能显著改变营养评估的范式和指南。
