Halberg Chronobiology Center, University of Minnesota, Mayo Mail Code 8609, 420 Delaware St. S.E., Minneapolis, MN, 55455, USA.
J Pharmacokinet Pharmacodyn. 2021 Jun;48(3):339-359. doi: 10.1007/s10928-021-09748-x. Epub 2021 Mar 23.
Study design and data analysis are two important aspects relevant to chronopharmacometrics. Blunders can be avoided by recognizing that most physiological variables are circadian periodic. Both ill health and treatment can affect the amplitude, phase, and/or period of circadian (and other) rhythms, in addition to their mean. The involvement of clock genes in molecular pathways related to important physiological systems underlies the bidirectional relationship often seen between circadian rhythm disruption and disease risk. Circadian rhythm characteristics of marker rhythms interpreted in the light of chronobiologic reference values represent important diagnostic tools. A set of cosinor-related programs is presented. They include the least squares fit of multiple-frequency cosine functions to model the time structure of individual records; a cosinor-based spectral analysis to detect periodic signals; the population-mean cosinor to generalize inferences; the chronobiologic serial section to follow the time course of changing rhythm parameters over time; and parameter tests to assess differences among populations. Relative merits of other available cosinor and non-parametric algorithms are reviewed. Parameter tests to compare individual records and a self-starting cumulative sum (CUSUM) make personalized chronotherapy possible, where the treatment of each patient relies on an N-of-1 design. Methods are illustrated in a few examples relevant to endocrinology, cancer and cardiology. New sensing technology yielding large personal data sets is likely to change the healthcare system. Chronobiologic concepts and methods should become an integral part of these evolving systems.
研究设计和数据分析是与时间药物动力学相关的两个重要方面。通过认识到大多数生理变量是昼夜周期性的，可以避免错误。除了平均值外，疾病和治疗都会影响昼夜（和其他）节律的幅度、相位和/或周期。时钟基因参与与重要生理系统相关的分子途径，这是昼夜节律紊乱与疾病风险之间经常存在的双向关系的基础。根据生物钟参考值解释的标记节律的昼夜节律特征代表了重要的诊断工具。本文提出了一组与余弦相关的程序。它们包括：将多频余弦函数的最小二乘拟合应用于个体记录的时间结构模型；基于余弦的频谱分析以检测周期性信号；群体平均余弦以推广推断；生物钟序列切片以随时间跟踪节律参数的变化过程；以及参数测试以评估群体之间的差异。本文还回顾了其他可用的余弦和非参数算法的相对优点。参数测试可用于比较个体记录和自启动累积和（CUSUM），从而实现个体化时间治疗，其中每位患者的治疗都依赖于 N-of-1 设计。本文通过一些与内分泌学、癌症和心脏病学相关的示例说明了这些方法。产生大量个人数据集的新传感技术可能会改变医疗保健系统。生物钟概念和方法应该成为这些不断发展的系统的一个组成部分。
