Metabolic Syndrome (MetS) is a serious condition that can be an early warning sign of heart disease and Type 2 diabetes. MetS is characterized by having elevated levels of blood pressure, cholesterol, waist circumference, and fasting glucose. There are many articles in the literature exploring the relationship between physical activity and MetS, but most do not consider the measurement error in the physical activity measurements nor the correlations among the MetS risk factors. Furthermore, previous work has generally treated MetS as binary, rather than directly modeling the risk factors on their measured, continuous space. Using data from the National Health and Nutrition Examination Survey (NHANES), we explore the relationship between minutes of moderate to vigorous physical activity (MVPA) and MetS risk factors. We construct a measurement error model for the accelerometry data, and then model its relationship between MetS risk factors with nonlinear seemingly unrelated regressions, incorporating dependence among MetS risk factors. The novel features of this model give the medical research community a new way to understand relationships between MVPA and MetS. The results of this approach present the field with a different modeling perspective than previously taken and suggest future avenues of scientific discovery.

翻译：代谢综合征（MetS）是一种严重的疾病状态，可能成为心脏病和2型糖尿病的早期预警信号。该综合征的特征表现为血压、胆固醇、腰围和空腹血糖水平升高。文献中已有大量文章探讨体力活动与代谢综合征之间的关系，但多数研究未考虑体力活动测量中的测量误差，也未顾及代谢综合征各风险因素间的相关性。此外，既往研究通常将代谢综合征处理为二分类变量，而非直接基于其连续测量空间对风险因素进行建模。本研究利用国家健康与营养调查（NHANES）数据，探讨中高强度体力活动（MVPA）分钟数与代谢综合征风险因素之间的关系。我们构建了加速度计数据的测量误差模型，进而通过非线性似不相关回归对其与代谢综合征风险因素的关系进行建模，并纳入风险因素间的相关性。该模型的新颖特征为医学研究界提供了理解MVPA与代谢综合征关系的新途径。本研究结果展示了不同于以往研究视角的建模方式，并指明了未来科学探索的方向。