Hormones are essential for maintaining good health. For example, cortisol is a vital hormone that mediates the body’s stress response, modulates inflammation, cardiometabolic function, and cognitive performance. In basal, non-stressed conditions, cortisol displays circadian (~24 hrs) and ultradian (<24 hrs) rhythms governed by feedback loops within the Hypothalamic-Pituitary-Adrenal (HPA) axis. Disruption of these hormonal rhythms can occur due to external stimuli (e.g., stressors), or in slow-progressing stages of disease. From a mathematical perspective, the HPA axis can be thought of as a dynamical system adapted to respond to a wide range of stimuli. Despite misaligned hormonal rhythms being associated with morbidity, a quantitative understanding of their variability, mechanistic origin and pathogenicity is missing. Also unknown is what makes these rhythms robust to some perturbations but fragile to others, especially in diseased states. Addressing these challenges is a critical step toward the development of digital tools to support clinical decision-making.
This talk will explore how these challenges are being addressed by combining novel biosampling techniques with mathematical and computational analysis methods. I will showcase digital biomarkers that help quantify variability of high-resolution daily profiles of HPA axis rhythms, define normative ranges and signal endocrine dysfunction. We will discuss how mathematical models can help us understand endocrine responses to perturbations, and how non-invasive wearable device data could constitute surrogates of hormonal rhythm misalignment. By shifting from a qualitative to a quantitative description of endocrine function, these insights will take us a step closer to personalised clinical interventions for which timing is key.