Abstract
Metabolism and physiology frequently follow non-linear rhythmic patterns which are reflected in concepts of homeostasis and circadian rhythms, yet few biomarkers are studied as dynamical systems. For instance, healthy human development depends on the assimilation and metabolism of essential elements, often accompanied by exposures to non-essential elements which may be toxic. In this study, we applied laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to reconstruct longitudinal exposure profiles of essential and non-essential elements throughout prenatal and early post-natal development. We applied cross-recurrence quantification analysis (CRQA) to characterize dynamics involved in elemental integration, and to construct a graph-theory based analysis of elemental metabolism. Our findings show how exposure to lead, a well-characterized toxicant, perturbs the metabolism of essential elements. In particular, our findings indicate that high levels of lead exposure dysregulate global aspects of metabolic network connectivity. For example, the magnitude of each element’s degree was increased in children exposed to high lead levels. Similarly, high lead exposure yielded discrete effects on specific essential elements, particularly zinc and magnesium, which showed reduced network metrics compared to other elements. In sum, this approach presents a new, systems-based perspective on the dynamics involved in elemental metabolism during critical periods of human development.
Highlights
There are myriad examples in biology of rhythmic patterns of physiology
Our initial analysis focused on the characterization of networks defined by recurrence rates in cross-recurrence quantification analysis (CRQA), a metric which essentially captures non-linear dependencies between elements
A unique network was constructed such that each discrete element in this analysis served as a node in the network, and edges were defined by the CRQA recurrence rates; from each network, six graph metrics were derived to characterize connectivity between elements in these networks
Summary
There are myriad examples in biology of rhythmic patterns of physiology. Sleep occurs on a 24 h cycle. Disruptions in these cyclic biological processes clearly affect health but are difficult to quantify using linear or even non-linear regression analysis. Children are persistently exposed to chemical elements throughout their pre- and post-natal development, many of which are essential to the emergence of healthy development and metabolic function [1]. Post-natal levels are mediated by parental influences, and, by children’s diet and environment [4,5]. These factors, and concomitant exposures to non-essential and toxic elements, are typically studied by the assessment of exposure biomarkers in blood or urine, which can capture a momentary “snapshot” of a child’s level of exposure [6]. Recent innovations in exposure assessment, primarily focusing on the analysis of shed deciduous (“baby”) teeth, allow for the reconstruction of longitudinal biomarker profiles which capture the time-varying concentration of exposure biomarkers throughout prenatal and postnatal development at a time scale that allows for rhythmic patterns to be quantified [6,7]
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