Misaligned hormonal rhythmicity: Mechanisms of origin and their clinical significance

Abstract

Rhythmic hormonal secretion is key for sustaining health. While a central pacemaker in the hypothalamus is the main driver of circadian periodicity, many hormones oscillate with different frequencies and amplitudes. These rhythms carry information about healthy physiological functions, while at the same time they must be able to respond to external cues and maintain their robustness against severe perturbations.Since endocrine disruptions can lead to hormonal misalignment and disease, understanding the clinical significance of these rhythms can help support diagnosis and disease management. While the misalignment of dynamic hormone profiles can be quantitatively analysed though statistical and computational techniques, mathematical modelling can provide fundamental understanding about the mechanisms underpinning endocrine rhythms, particularly around the question of what makes them robust to some perturbations but fragile to others. In this study, I will review the key challenges of understanding hormonal rhythm misalignment from a mathematical perspective, including their causes and clinical significance. By reviewing modelling examples of coupled endocrine axes, I will address the question of how perturbations in one endocrine axis propagate to another, leading to the more complex issue of disentangling the contribution of each endocrine system to a robust dynamic environment.

High-resolution daily profiles of tissue adrenal steroids by portable automated collection

“…Our dMs allow us to shift from a qualitative to a quantitative description of variability. Because they are mathematically defined, dMs constitute an unambiguous method to describe healthy hormonal rhythms and facilitate further analysis through nonstationary statistics and machine learning techniques ( 39 ). In our analysis, it is interesting that we found only a few factors likely to influence healthy hormone concentrations, and none were profound.…”

“…Our dMs may allow stratification of normality across subpopulations (for example, by sex, age, or BMI), and the same approach could also be generalized to quantify hormonal misalignment arising from behavioral and environmental factors (such as sleep, light exposure, meal content and timing, smoking habits, etc.) ( 39 ). The combination of high–time resolution continuous hormone sampling with noninvasive wearable devices and computational analysis techniques has the potential to support clinical diagnosis and disease management according to individual physiotypes ( 40 ).…”

Electrochemical Detection of Hormones Using Nanostructured Electrodes

“…The hypothalamus's intrinsic pacemaker functions as the principal regulator of circadian periodicity, whereas several hormones exhibit oscillatory patterns with varying frequencies and amplitudes. The aforementioned rhythms are required to exhibit responsiveness to external stimuli, retain their resilience when confronted with significant disruptions, and convey pertinent data regarding the state of sound physiological operation [20]. Regular hormone release is crucial for sustaining optimal health.…”

“…Estriol stimuli, retain their resilience when confronted with significant disruptions, and convey pertinent data regarding the state of sound physiological operation [20].…”

Analysis of wearable time series data in endocrine and metabolic research

“…Combining experimental physiology with mathematical and computational techniques contributes to our understanding of the mechanisms underpinning rhythmic hormonal secretion, how rhythms are decoded by target tissues, and their responses to perturbations [ 1 ]. This allows us to understand how regulatory mechanisms ensure that hormone fluctuations achieve dynamic equilibration, and how disruptions of these mechanisms lead to rhythm misalignment and disease [ 2 , 3 ]. Phenomenologically, these techniques allow more precise descriptions of the endocrine systems over time, and are foundational to the development of digital health tools, as in diagnostic and screening algorithms [ 4 ].…”