Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA. We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10s frame into a high-dimensional feature vector. A transformer model then mapped each day's feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.

翻译：内脏脂肪组织（VAT）是代谢健康与习惯性体力活动（PA）的关键标志物。过量VAT与2型糖尿病及胰岛素抵抗高度相关，其病理生理学机制涉及脂肪酸对肝脏的过度负荷。VAT同时是高度不稳定的脂肪储存库，运动期间儿茶酚胺会刺激其加速代谢周转。VAT可通过精密影像技术测量，亦可直接通过PA推算。我们利用2011-2014年美国国家健康与营养调查（NHANES）数据验证该关联，研究对象为20-60岁且拥有7天加速度计数据的个体（男性2,456人；女性2,427人）[1]。研究采用两种基于活动估算VAT的方法：其一通过步态与睡眠阶段的运动特征工程提取特征，再运用岭回归将这些特征的统计量映射为VAT估计值；其二采用基于24小时连续加速度数据的深度神经网络——基础模型先将每10秒帧转换为高维特征向量，再由Transformer模型将每日特征向量时间序列映射为单日VAT估计值，最终通过多日平均获得结果。两种方法在加入人口统计学与身体测量协变量后均获得最优估算精度。融合双方法的最佳方案使VAT估计相关系数达到r=0.86。这些发现证实了PA与VAT之间存在强关联，进而揭示了PA与代谢健康风险的内在联系。