Science Inventory

Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates

Citation:

Pleil, J., A. Wallace, M. Stiegel, AND W. Funk. Human biomarker interpretation: the importance of intra-class correlation coefficients (ICC) and their calculations based on mixed models, ANOVA, and variance estimates. JOURNAL OF TOXICOLOGY AND ENVIRONMENTAL HEALTH - PART B: CRITICAL REVIEWS. Taylor & Francis, Inc., Philadelphia, PA, 21(3):161-180, (2018). https://doi.org/10.1080/10937404.2018.1490128

Impact/Purpose:

The human body is a repository of a myriad of chemical compounds, large and small, exogenous and endogenous, comprising what is referred to as the human exposome, which includes various “omics” classifications such as the metabolome, icrobiome, volatilome, and proteome etc. (Wild 2005, Rappaport and Smith 2010, Johnson and nzalez 2012, Tremaroli and Backhead 2012, Orchard et al. 2012, Amann et al. 2014). In theory, the patterns of the chemicals in biological media such as blood, breath and urine document past environmental exposures, describe current health state, and portend future clinical outcomes (Pleil et al. 2011a, Pleil et al. 2012, Pleil and Sheldon 2011, Kessler and Glasgow 2011, Ballard-‐Barbash 2012, Mehan et al. 2013, Wallace et al. 2016). The use of the “omics” suites of biomarkers is equally important in medical diagnostics, clinical practice, and public health applications; for the ensuing discussions here, the focus is on public and community health.

Description:

Human biomonitoring is the foundation of environmental toxicology, community public health evaluation, pre-‐clinical 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 response, and for developing risk-‐based assessments in the face of sparse or highly complex measurement data. The analytical procedures that provide the data for these 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 their adducts. Additionally, 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 is 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 (e.g., environmental, microbiotic, 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 the use of descriptive statistics based on intra-‐class correlation coefficients (ICC) has become a powerful tool in these efforts. This article has a dual focus; it presents methodology to create theoretical data with specific statistical character, including distribution, central tendency, spread, and ICC and it presents the fundamentals of ICC calculations using three approximation methods of increasing sophistication: calculated variance estimates, ANOVA, and mixed models, and assesses these methods using aforementioned theoretical data. The ultimate purpose is to allow researchers to choose an appropriate method to estimate ICC for their particular needs and data structures.