Developing a method to reliably discriminate physio-pathological dynamic adrenal hormone profiles


Lead AcademicCo-InvestigatorsCentre Fellow(s)Secondee
Thomas Upton

Hossein Mohammadi (CEMPS)

Stafford Lightman

Eder Zavala (CEMPS)

Diane Fraser

Hossein Mohammadi

Jamie Walker

 Joel Tabak


Lay summary:

The adrenal glands produce hormones that have important roles in the regulation of inflammation, metabolism, and mental health. Levels of hormones normally fluctuate during the day and respond rapidly to stressors (both physical and psychological). In all healthy individuals, fluctuations are organised rhythmically. However, patients with adrenal endocrine conditions (e.g., Cushing’s, Addison’s and primary aldosteronism) exhibit clear disruptions of this rhythmicity that deviate from “normal” variability in healthy subjects.

This is important since current diagnosis of these conditions is difficult with current clinical tools, which rely on single time point sampling from blood. Consequently, diagnosis is often delayed and may result in inadequate or inappropriate treatment. This results in further deterioration of the patient’s health and increased costs.

We propose to analyse data obtained from a novel, minimally invasive, high frequency sampling system called U-RHYTHM. This portable system obtains hormone samples during out-of-hospital normal activity, including during sleep, without the need for blood.

Using mathematical analysis we will develop a computational tool to distinguish normal vs abnormal sampling profiles with a specific degree of confidence. Such a tool would allow us to quantify the deviation of a given profile from normal, thus accelerating and increasing the confidence on the diagnosis.