A statistical model of diurnal variation in human growth hormone
A statistical model of diurnal variation in human growth hormone
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DOI：
10.1152/ajpendo.00562.2002
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发表时间：
2003-11-01
影响因子：
5.1
通讯作者：
Brown, EN
中科院分区：
文献类型：
--
作者：
Klerman, EB;Adler, GK;Brown, EN
生长激素（GH）血清水平的昼夜模式取决于GH分泌事件的频率和幅度、GH输注到循环中和从循环中清除的动力学以及GH对其分泌的反馈。基于这些生理学原理，我们提出了一个二维线性微分方程模型来描述GH的日变化规律。该模型的特征的分泌事件，分泌事件的振幅，以及输液，清除和反馈GH的半衰期的开始时间。我们用最大似然法来说明该模型，以适应生长激素测量收集在12个正常，健康的妇女在8小时的预定睡眠和16小时的昼夜节律恒定的常规协议。我们评估的重要性，模型组件使用参数的标准误估计和赤池的信息准则。在睡眠期间，输注和清除半衰期的中位数估计值均为13.8 min，分泌事件的中位数为2。在常规给药期间，中位输注半衰期估计值为12.6 min，中位清除半衰期估计值为11.7 min，中位分泌事件数为5。输注和清除半衰期估计值以及分泌事件数量与当前发表的报告一致。我们的模型对每个GH数据系列都有很好的拟合。我们的分析范式提出了一种方法来分解生长激素的昼夜模式，可用于表征这种激素在正常和病理条件下的生理特性。
The diurnal pattern of growth hormone (GH) serum levels depends on the frequency and amplitude of GH secretory events, the kinetics of GH infusion into and clearance from the circulation, and the feedback of GH on its secretion. We present a two-dimensional linear differential equation model based on these physiological principles to describe GH diurnal patterns. The model characterizes the onset times of the secretory events, the secretory event amplitudes, as well as the infusion, clearance, and feedback half-lives of GH. We illustrate the model by using maximum likelihood methods to fit it to GH measurements collected in 12 normal, healthy women during 8 h of scheduled sleep and a 16-h circadian constant-routine protocol. We assess the importance of the model components by using parameter standard error estimates and Akaike's Information Criterion. During sleep, both the median infusion and clearance half-life estimates were 13.8 min, and the median number of secretory events was 2. During the constant routine, the median infusion half-life estimate was 12.6 min, the median clearance half-life estimate was 11.7 min, and the median number of secretory events was 5. The infusion and clearance half-life estimates and the number of secretory events are consistent with current published reports. Our model gave an excellent fit to each GH data series. Our analysis paradigm suggests an approach to decomposing GH diurnal patterns that can be used to characterize the physiological properties of this hormone under normal and pathological conditions.