Abstract
The hormone cortisol, released as the end-product of the hypothalamic-pituitary-adrenal (HPA) axis, has a well-characterized circadian rhythm that enables an allostatic response to external stressors. When the pattern of secretion is disrupted, cortisol levels are chronically elevated, contributing to diseases such as heart attacks, strokes, mental health disorders, and diabetes. The diagnosis of chronic stress and stress related disorders depends upon accurate measurement of cortisol levels; currently, it is quantified using mass spectroscopy or immunoassay, in specialized laboratories with trained personnel. However, these methods are time-consuming, expensive and are unable to capture the dynamic biorhythm of the hormone. This critical review traces the path of cortisol detection from traditional laboratory-based methods to decentralised cortisol monitoring biosensors. A complete picture of cortisol biology and pathophysiology is provided, and the importance of precision medicine style monitoring of cortisol is highlighted. Antibody-based immunoassays still dominate the pipeline of development of point-of-care biosensors; new capture molecules such as aptamers and molecularly imprinted polymers (MIPs) combined with technologies such as microfluidics, wearable electronics, and quantum dots offer improvements to limit of detection (LoD), specificity, and a shift toward rapid or continuous measurements. While a variety of different sensors and devices have been proposed, there still exists a need to produce quantitative tests for cortisol ─ using either rapid or continuous monitoring devices that can enable a personalized medicine approach to stress management. This can be addressed by synergistic combinations of technologies that can leverage low sample volumes, relevant limit of detection and rapid testing time, to better account for cortisol's shifting biorhythm. Trends in cortisol diagnostics toward rapid and continuous monitoring of hormones are highlighted, along with insights into choice of sample matrix.