ClinCirc identifies alterations of the circadian peripheral oscillator in critical care patients
Peter S. Cunningham, … , Andrew L. Hazel, John F. Blaikley
Peter S. Cunningham, … , Andrew L. Hazel, John F. Blaikley
Published December 20, 2022
Citation Information: J Clin Invest. 2023;133(4):e162775. https://doi.org/10.1172/JCI162775.
View: Text | PDF
Clinical Research and Public Health Inflammation

ClinCirc identifies alterations of the circadian peripheral oscillator in critical care patients

Abstract

Background Assessing circadian rhythmicity from infrequently sampled data is challenging; however, these types of data are often encountered when measuring circadian transcripts in hospitalized patients.Methods We present ClinCirc. This method combines 2 existing mathematical methods (Lomb-Scargle periodogram and cosinor) sequentially and is designed to measure circadian oscillations from infrequently sampled clinical data. The accuracy of this method was compared against 9 other methods using simulated and frequently sampled biological data. ClinCirc was then evaluated in 13 intensive care unit (ICU) patients as well as in a separate cohort of 29 kidney-transplant recipients. Finally, the consequences of circadian alterations were investigated in a retrospective cohort of 726 kidney-transplant recipients.Results ClinCirc had comparable performance to existing methods for analyzing simulated data or clock transcript expression of healthy volunteers. It had improved accuracy compared with the cosinor method in evaluating circadian parameters in PER2:luc cell lines. In ICU patients, it was the only method investigated to suggest that loss of circadian oscillations in the peripheral oscillator was associated with inflammation, a feature widely reported in animal models. Additionally, ClinCirc was able to detect other circadian alterations, including a phase shift following kidney transplantation that was associated with the administration of glucocorticoids. This phase shift could explain why a significant complication of kidney transplantation (delayed graft dysfunction) oscillates according to the time of day kidney transplantation is performed.Conclusion ClinCirc analysis of the peripheral oscillator reveals important clinical associations in hospitalized patients.Funding UK Research and Innovation (UKRI), National Institute of Health Research (NIHR), Engineering and Physical Sciences Research Council (EPSRC), National Institute on Academic Anaesthesia (NIAA), Asthma+Lung UK, Kidneys for Life.

Authors

Peter S. Cunningham, Gareth B. Kitchen, Callum Jackson, Stavros Papachristos, Thomas Springthorpe, David van Dellen, Julie Gibbs, Timothy W. Felton, Anthony J. Wilson, Jonathan Bannard-Smith, Martin K. Rutter, Thomas House, Paul Dark, Titus Augustine, Ozgur E. Akman, Andrew L. Hazel, John F. Blaikley

×

Figure 3

Kidney transplantation induces a phase shift that is associated with altered clinical outcomes.

Options: View larger image (or click on image) Download as PowerPoint
Kidney transplantation induces a phase shift that is associated with alt...
(A) The relative amplitude of each clock gene’s circadian oscillation 0 to 24 hours after transplantation (24 hours, n = 22) and 48 to 72 hours (72 hours, n = 7) after kidney transplantation was compared with the amplitude from healthy volunteers. Data are represented as mean ± SEM. **q < 0.01, 2-way ANOVA, post hoc Dunnett’s. Each circle indicates patient. (B) Acrophase plot of PER3’s circadian oscillation 0 to 24 hours and 48 to 72 hours after kidney transplantation was compared with that of healthy volunteers. Circles indicate individual patients. Data are represented as median ± IQR (color band). (C) PER3 transcript expression plotted against either time of day or time after allograft reperfusion for the first 24 hours following transplantation (n = 22). Circles indicate individual patients. Regression line is shown. (D) The acrophase of PER3’s circadian oscillation was compared against time of organ reperfusion immediately after kidney transplantation (24 hours). Circles indicate individual patients. r2 = 0.87 linear regression. (E) The prevalence of DGF after kidney transplantation from brain-dead donors was calculated in a 10-year retrospective cohort (n = 536). The probability density for DGF was then plotted against allograft reperfusion time. Data are represented as mean ± 95%CI. Gaussian smoothing with bootstrap. (F) The probability density of DGF was also plotted against allograft harvest time from the donor. Data are represented as mean ± 95%CI. Gaussian smoothing with bootstrap. For circadian rhythm analysis, data were only plotted if a circadian rhythm was detected. Black dotted lines show uniform lines.