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Canonical representation of the human energy metabolism of lean mass, fat mass, and insulin resistance

机译:瘦肉,脂肪和胰岛素抵抗的人体能量代谢的典型表示

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This innovation involves a metabolic health monitor (MHM) app using self-adaptive individualized stochastic mathematical models to capture dynamic slow changes and trajectories of the metabolism with the goal of observing and controlling fat weight, lean body mass, and insulin resistance. Notably, a canonical representation of the metabolism was created expressed by the daily energy density of the lean body mass change, the daily energy density of the fat mass change, and the ratio of lean body mass change velocity and fat mass change velocity or R ratio. These three parameters can serve as metrics for dynamic changes such as trend prediction for fat mass, lean body mass, and insulin resistance changes, allow for intra- and interindividual comparisons, and make possible self-directed therapy and optional web based feedback by a healthy lifestyle team. The real time feedback of predicted dynamic changes of metabolic parameters allow for the user to make appropriate modifications of diet and physical activity realizing adaptive self-control of dietary and exercise regime for lifestyle change to fight obesity and insulin resistance. It is shown here that from control engineering point of view our canonical representation assures full observability and controllability. Proof of concept for clinical observability and controllability is provided with simulation studies using clinical trial data. For the first time the otherwise difficult to measure parameters of daily utilized macronutrient energy intake, macronutrient oxidation rate, daily changes of fat weight, lean body mass, and insulin resistance is realized with minimum error variance supported by the Kalman filter.
机译:这项创新涉及一个代谢健康监测器(MHM)应用程序,该应用程序使用自适应的个性化随机数学模型来捕获动态的缓慢变化和新陈代谢的轨迹,以观察和控制脂肪重量,瘦体重和胰岛素抵抗为目标。值得注意的是,通过瘦体重变化的每日能量密度,脂肪质量变化的每日能量密度以及瘦体重变化速度与脂肪质量变化速度之比或R比来表示代谢的典型表示。 。这三个参数可以用作动态变化的度量标准,例如脂肪量,瘦体重和胰岛素抵抗变化的趋势预测,可以进行个体内和个体间比较,并可以进行自我指导的治疗和健康人士可选的基于网络的反馈生活方式团队。预测的代谢参数动态变化的实时反馈允许用户对饮食和身体活动进行适当的修改,从而实现饮食和运动方式的自适应自我控制,以应对生活方式的改变,从而对抗肥胖和胰岛素抵抗。从控制工程的角度来看,我们的规范表示确保了完全的可观察性和可控制性。使用临床试验数据进行的模拟研究提供了临床可观察性和可控性的概念证明。第一次,利用卡尔曼滤波器支持的最小误差方差,实现了原本难以测量的每日利用的常量营养素能量摄入,常量营养素氧化率,脂肪重量,瘦体重和胰岛素抵抗的每日变化等参数。

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