Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, preclinical health effects assessments, pharmacological drug development and testing, and medical diagnostics. Within this framework, the intra-class correlation coefficient (ICC) serves as an important tool for gaining insight into human variability and responses and for developing risk-based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide data for clinical and public health efforts are continually evolving to expand our knowledge base of the many thousands of environmental and biomarker chemicals that define human systems biology. These chemicals range from the smallest molecules from energy metabolism (i.e., the metabolome), through larger molecules including enzymes, proteins, RNA, DNA, and adducts. In additiona, the human body contains exogenous environmental chemicals and contributions from the microbiome from gastrointestinal, pulmonary, urogenital, naso-pharyngeal, and skin sources. This complex mixture of biomarker chemicals from environmental, human, and microbiotic sources comprise the human exposome and generally accessed through sampling of blood, breath, and urine. One of the most difficult problems in biomarker assessment is assigning probative value to any given set of measurements as there are generally insufficient data to distinguish among sources of chemicals such as environmental, microbiotic, or human metabolism and also deciding which measurements are remarkable from those that are within normal human variability. The implementation of longitudinal (repeat) measurement strategies has provided new statistical approaches for interpreting such complexities, and use of descriptive statistics based upon intra-class correlation coefficients (ICC) has become a powerful tool in these efforts. This review has two parts; the first focuses on the history of repeat measures of human biomarkers starting with occupational toxicology of the early 1950s through modern applications in interpretation of the human exposome and metabolic adverse outcome pathways (AOPs). The second part reviews different methods for calculating the ICC and explores the strategies and applications in light of different data structures.

中文翻译：

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人类生物标志物的解释：类内相关系数（ICC）的重要性及其基于混合模型，方差分析和方差估计的计算

人体生物监测是环境毒理学，社区公共卫生评估，临床前健康影响评估，药理药物开发和测试以及医学诊断的基础。在此框架内，类内相关系数（ICC）可作为重要工具，以了解人的变异性和应对措施，并在面对稀疏或高度复杂的测量数据时进行基于风险的评估。为临床和公共卫生工作提供数据的分析程序正在不断发展，以扩展我们对定义人类系统生物学的数千种环境和生物标志物化学品的知识库。这些化学物质的范围从能量代谢的最小分子（即代谢组）到更大的分子，包括酶，蛋白质，RNA，DNA和加合物。此外，人体中还含有外源性环境化学物质，以及来自胃肠道，肺，泌尿生殖器，鼻咽和皮肤来源的微生物组的贡献。来自环境，人类和微生物来源的生物标志物化学物质的这种复杂混合物构成了人类的脂质体，通常可通过对血液，呼吸和尿液进行采样来获得。生物标志物评估中最困难的问题之一是为任何给定的测量值分配证明价值，因为通常没有足够的数据来区分环境，微生物或人类新陈代谢等化学物质的来源，并且还要确定哪些测量结果与那些测量结果显着不同。在正常的人类变异范围内。纵向（重复）测量策略的实施为解释这种复杂性提供了新的统计方法，基于类内相关系数（ICC）的描述性统计的使用已成为这些工作中的有力工具。这篇评论分为两个部分：第一部分着重介绍人类生物标志物重复测量的历史，从1950年代初期的职业毒理学开始，一直到现代用于解释人类暴露小体和代谢不良结局途径（AOP）的应用。第二部分回顾了计算ICC的不同方法，并根据不同的数据结构探讨了策略和应用。基于类内相关系数（ICC）的描述性统计数据的使用已经成为这些工作中的有力工具。这篇评论分为两个部分：第一部分着重介绍人类生物标志物重复测量的历史，从1950年代初期的职业毒理学开始，一直到现代用于解释人类暴露小体和代谢不良结局途径（AOP）的应用。第二部分回顾了计算ICC的不同方法，并根据不同的数据结构探讨了策略和应用。基于类内相关系数（ICC）的描述性统计数据的使用已经成为这些工作中的有力工具。这篇评论分为两个部分：第一部分着重介绍人类生物标志物重复测量的历史，从1950年代初期的职业毒理学开始，一直到现代用于解释人类暴露小体和代谢不良结局途径（AOP）的应用。第二部分回顾了计算ICC的不同方法，并根据不同的数据结构探讨了策略和应用。