Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trial

Abstract

Background: Glucocorticoids (GC) are potent entrainers of the circadian clock. However, their effects on biological rhythms in chronic human exposure have yet to be studied. Endogenous hypercortisolism (Cushing's Syndrome, CS) is a rare condition in which circadian disruption is sustained by a tumorous source of GC excess, offering the unique opportunity to investigate GC's chronic effects in vivo.

Methods: In a 12-month prospective case-control multicentre trial, the daily fluctuations in the number of circulating peripheral blood mononuclear cells (PBMCs) and the time-specific expression of clock-related genes were analysed in a cohort of 68 subjects, 34 affected by CS and 34 matched controls. Cosinor mixed effects model, rhythmicity algorithms and machine learning techniques were applied to the multi-level dataset.

Findings: Multiple, 5-point daily sampling revealed profound changes in the levels, amplitude, and rhythmicity of several PBMC populations during active CS, only partially restored after remission. Clock gene analyses in isolated PBMCs showed a significant flattening of circadian oscillation of CLOCK, PER1, PER2, PER3, and TIMELESS expression. In active CS, all methods confirmed a loss of rhythmicity of those genes which were circadian in the PBMCs of controls. Most, but not all, genes regained physiological oscillation after remission. Machine learning revealed that while combined time-course sets of clock genes were highly effective in separating patients from controls, immune profiling was efficient even as single time points.

Interpretation: In conclusion, the oscillation of circulating immune cells is profoundly altered in patients with CS, representing a convergence point of circadian rhythm disruption and metabolic and steroid hormone imbalances. Machine learning techniques proved the superiority of immune profiling over parameters such as cortisol, anthropometric and metabolic variables, and circadian gene expression analysis to identify CS activity.

Funding: The research leading to these results has received funding from the European Union in the context of the National Recovery and Resilience Plan, Investment PE8 - Project Age-It: "Ageing Well in an Ageing Society". This resource was co-financed by the Next Generation EU [DM 1557 11.10.2022], the PRecisiOn Medicine to Target Frailty of Endocrine-metabolic Origin (PROMETEO) project (NET-2018-12365454) by the Italian Ministry of Health, and through internal funding to Sapienza University of Rome.

Keywords: Circadian rhythm; Cushing's syndrome; Glucocorticoids; Hypercortisolism; Immune system.

Fig. 1

Study flow chart: study flow chart for the clinical trial.

Fig. 2

Cosinor-based rhythmometry analysis of circadian genes. Results are presented as relative expression compared to housekeeping genes. Figures include patients with active CS (red, n = 34) in remission (green, n = 10) against controls (grey, n = 34). Dots are single measurements for each time point. Curves are obtained from the pooled data. Genes included in the figure are: PER family (a, b, c), CLOCK (d), ARNTL (e), PRF1 (f) and TIMELESS (g). p values for comparisons of cosinor-based rhythmometry parameters are available in Table 2.

Fig. 3

Cosinor-based rhythmometry analysis of peripheral blood mononuclear cells on the study population (n = 68). Results are presented as percentage of immune cells. The panels include patients with active CS (red, n = 34) in remission (green, n = 10) compared to controls (grey, n = 34). Dots are single measurements for each time point. Curves are obtained from the pooled data. Results are presented as percentages of immune subsets over whole PBMCs (a, b), lymphocytes (c, g, h), monocytes (d, e, f) and NK cells (i). p values for comparisons of cosinor-based rhythmometry parameters are available in Table 3.

Fig. 4

Two-dimensional visualisation of time-course variables. Analyses of controls (orange, n = 34) and patients with active CS (blue, n = 34) for time-course variables immune profiling (a) and circadian genes (b) at each time point and considering all time points (last square for each panel). 2D visualisation is provided by t-SNE.