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.
Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trial / Hasenmajer, V.; Sbardella, E.; Sciarra, F.; Simeoli, C.; Pivonello, C.; Ceccato, F.; Pofi, R.; Minnetti, M.; Rizzo, F.; Ferrari, D.; Bonaventura, I.; Barbagallo, F.; Giannetta, E.; Alunni Fegatelli, D.; Conia, S.; Navigli, R.; Arnaldi, G.; Scaroni, C.; Pivonello, R.; Gianfrilli, D.; Venneri, M. A.; Isidori, A. M.. - In: EBIOMEDICINE. - ISSN 2352-3964. - 110:(2024). [10.1016/j.ebiom.2024.105462]
Circadian clock disruption impairs immune oscillation in chronic endogenous hypercortisolism: a multi-level analysis from a multicentre clinical trial
Hasenmajer V.;Sbardella E.;Sciarra F.;Pivonello C.;Pofi R.;Minnetti M.;Rizzo F.;Ferrari D.;Bonaventura I.;Giannetta E.;Alunni Fegatelli D.;Conia S.;Navigli R.;Arnaldi G.;Pivonello R.;Gianfrilli D.;Venneri M. A.;Isidori A. M.
2024
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
