1
|
Witt RM, Byars KC, Decker K, Dye TJ, Riley JM, Simmons D, Smith DF. Current Considerations in the Diagnosis and Treatment of Circadian Rhythm Sleep-Wake Disorders in Children. Semin Pediatr Neurol 2023; 48:101091. [PMID: 38065634 PMCID: PMC10710539 DOI: 10.1016/j.spen.2023.101091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 09/29/2023] [Indexed: 12/18/2023]
Abstract
Circadian Rhythm Sleep-Wake Disorders (CRSWDs) are important sleep disorders whose unifying feature is a mismatch between the preferred or required times for sleep and wakefulness and the endogenous circadian drives for these. Their etiology, presentation, and treatment can be different in pediatric patients as compared to adults. Evaluation of these disorders must be performed while viewed through the lens of a patient's comorbid conditions. Newer methods of assessment promise to provide greater diagnostic clarity and critical insights into how circadian physiology affects overall health and disease states. Effective clinical management of CRSWDs is multimodal, requiring an integrated approach across disciplines. Therapeutic success depends upon appropriately timed nonpharmacologic and pharmacologic interventions. A better understanding of the genetic predispositions for and causes of CRSWDs has led to novel clinical opportunities for diagnosis and improved therapeutics.
Collapse
Affiliation(s)
- Rochelle M Witt
- Division of Child Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Kelly C Byars
- Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Kristina Decker
- Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Thomas J Dye
- Division of Child Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Jessica M Riley
- Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Danielle Simmons
- Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH; Division of Behavioral Medicine and Clinical Psychology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - David F Smith
- Division of Pulmonary Medicine and the Sleep Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Center for Circadian Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Division of Pediatric Otolaryngology-Head and Neck Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH; Department of Otolaryngology- Head and Neck Surgery, University of Cincinnati College of Medicine, Cincinnati, OH.
| |
Collapse
|
2
|
Abstract
The timing, duration, and consolidation of sleep result from the interaction of the circadian timing system with a sleep-wake homeostatic process. When aligned and functioning optimally, this allows for wakefulness throughout the day and a long consolidated sleep episode at night. Changes to either the sleep regulatory process or how they interact can result in an inability to fall asleep at the desired time, difficulty remaining asleep, waking too early, and/or difficulty remaining awake throughout the day. This mismatch between the desired timing of sleep and the ability to fall asleep and remain asleep is a hallmark of a class of sleep disorders called the circadian rhythm sleep-wake disorders. In this updated article, we discuss typical changes in the circadian regulation of sleep with aging; how age influences the prevalence, diagnosis, and treatment of circadian rhythm sleep disorders; and how neurologic diseases in older patient impact circadian rhythms and sleep.
Collapse
Affiliation(s)
- Jee Hyun Kim
- Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Gonghangdae-ro 260, Gangseo-gu, Seoul, Republic of Korea
| | - Alexandria R Elkhadem
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue BLI438, Boston, MA 02115, USA
| | - Jeanne F Duffy
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
3
|
St. Hilaire MA. Modeling (circadian). PROGRESS IN BRAIN RESEARCH 2022; 273:181-198. [DOI: 10.1016/bs.pbr.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
4
|
Brown LS, Hilaire MAS, McHill AW, Phillips AJK, Barger LK, Sano A, Czeisler CA, Doyle FJ, Klerman EB. A classification approach to estimating human circadian phase under circadian alignment from actigraphy and photometry data. J Pineal Res 2021; 71:e12745. [PMID: 34050968 PMCID: PMC8474125 DOI: 10.1111/jpi.12745] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 11/30/2022]
Abstract
The time of dim light melatonin onset (DLMO) is the gold standard for circadian phase assessment in humans, but collection of samples for DLMO is time and resource-intensive. Numerous studies have attempted to estimate circadian phase from actigraphy data, but most of these studies have involved individuals on controlled and stable sleep-wake schedules, with mean errors reported between 0.5 and 1 hour. We found that such algorithms are less successful in estimating DLMO in a population of college students with more irregular schedules: Mean errors in estimating the time of DLMO are approximately 1.5-1.6 hours. We reframed the problem as a classification problem and estimated whether an individual's current phase was before or after DLMO. Using a neural network, we found high classification accuracy of about 90%, which decreased the mean error in DLMO estimation-identifying the time at which the switch in classification occurs-to approximately 1.3 hours. To test whether this classification approach was valid when activity and circadian rhythms are decoupled, we applied the same neural network to data from inpatient forced desynchrony studies in which participants are scheduled to sleep and wake at all circadian phases (rather than their habitual schedules). In participants on forced desynchrony protocols, overall classification accuracy dropped to 55%-65% with a range of 20%-80% for a given day; this accuracy was highly dependent upon the phase angle (ie, time) between DLMO and sleep onset, with the highest accuracy at phase angles associated with nighttime sleep. Circadian patterns in activity, therefore, should be included when developing and testing actigraphy-based approaches to circadian phase estimation. Our novel algorithm may be a promising approach for estimating the onset of melatonin in some conditions and could be generalized to other hormones.
Collapse
Affiliation(s)
- Lindsey S. Brown
- Harvard John A. Paulson School of Engineering and Applied Sciences, Allston, MA 02134
- Corresponding author: 150 Western Avenue, Allston, MA 02134, ,
| | - Melissa A. St. Hilaire
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA 02115
| | - Andrew W. McHill
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA 02115
- Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland OR 97239
| | - Andrew J. K. Phillips
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Clayton VIC 3168, Australia
| | - Laura K. Barger
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA 02115
| | - Akane Sano
- Affective Computing Group, Media Lab, Massachusetts Institute of Technology, Cambridge, MA 02139 (Akane Sano’s current address: Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77098)
| | - Charles A. Czeisler
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA 02115
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Allston, MA 02134
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
| | - Elizabeth B. Klerman
- Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115
- Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology, Brigham and Women’s Hospital, Boston, MA 02115
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114
- Corresponding author: 150 Western Avenue, Allston, MA 02134, ,
| |
Collapse
|
5
|
Huang Y, Mayer C, Cheng P, Siddula A, Burgess HJ, Drake C, Goldstein C, Walch O, Forger DB. Predicting circadian phase across populations: a comparison of mathematical models and wearable devices. Sleep 2021; 44:6278480. [PMID: 34013347 PMCID: PMC8503830 DOI: 10.1093/sleep/zsab126] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 03/22/2021] [Indexed: 12/17/2022] Open
Abstract
From smart work scheduling to optimal drug timing, there is enormous potential in translating circadian rhythms research results for precision medicine in the real world. However, the pursuit of such effort requires the ability to accurately estimate circadian phase outside of the laboratory. One approach is to predict circadian phase non-invasively using light and activity measurements and mathematical models of the human circadian clock. Most mathematical models take light as an input and predict the effect of light on the human circadian system. However, consumer-grade wearables that are already owned by millions of individuals record activity instead of light, which prompts an evaluation of the accuracy of predicting circadian phase using motion alone. Here, we evaluate the ability of four different models of the human circadian clock to estimate circadian phase from data acquired by wrist-worn wearable devices. Multiple datasets across populations with varying degrees of circadian disruption were used for generalizability. Though the models we test yield similar predictions, analysis of data from 27 shift workers with high levels of circadian disruption shows that activity, which is recorded in almost every wearable device, is better at predicting circadian phase than measured light levels from wrist-worn devices when processed by mathematical models. In those living under normal living conditions, circadian phase can typically be predicted to within 1 hour, even with data from a widely available commercial device (the Apple Watch). These results show that circadian phase can be predicted using existing data passively collected by millions of individuals with comparable accuracy to much more invasive and expensive methods.
Collapse
Affiliation(s)
- Yitong Huang
- Department of Mathematics, Dartmouth College, Hanover, NH, USA
| | - Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA
| | | | - Alankrita Siddula
- Department of Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Helen J Burgess
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | | | - Cathy Goldstein
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Olivia Walch
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Daniel B Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.,Michigan Institute for Data Science, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
6
|
Duffy JF, Abbott SM, Burgess HJ, Crowley SJ, Emens JS, Epstein LJ, Gamble KL, Hasler BP, Kristo DA, Malkani RG, Rahman SA, Thomas SJ, Wyatt JK, Zee PC, Klerman EB. Workshop report. Circadian rhythm sleep-wake disorders: gaps and opportunities. Sleep 2021; 44:zsaa281. [PMID: 33582815 PMCID: PMC8120340 DOI: 10.1093/sleep/zsaa281] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 10/02/2020] [Indexed: 01/09/2023] Open
Abstract
This White Paper presents the results from a workshop cosponsored by the Sleep Research Society (SRS) and the Society for Research on Biological Rhythms (SRBR) whose goals were to bring together sleep clinicians and sleep and circadian rhythm researchers to identify existing gaps in diagnosis and treatment and areas of high-priority research in circadian rhythm sleep-wake disorders (CRSWD). CRSWD are a distinct class of sleep disorders caused by alterations of the circadian time-keeping system, its entrainment mechanisms, or a misalignment of the endogenous circadian rhythm and the external environment. In these disorders, the timing of the primary sleep episode is either earlier or later than desired, irregular from day-to-day, and/or sleep occurs at the wrong circadian time. While there are incomplete and insufficient prevalence data, CRSWD likely affect at least 800,000 and perhaps as many as 3 million individuals in the United States, and if Shift Work Disorder and Jet Lag are included, then many millions more are impacted. The SRS Advocacy Taskforce has identified CRSWD as a class of sleep disorders for which additional high-quality research could have a significant impact to improve patient care. Participants were selected for their expertise and were assigned to one of three working groups: Phase Disorders, Entrainment Disorders, and Other. Each working group presented a summary of the current state of the science for their specific CRSWD area, followed by discussion from all participants. The outcome of those presentations and discussions are presented here.
Collapse
Affiliation(s)
- Jeanne F Duffy
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Sabra M Abbott
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Helen J Burgess
- Department of Psychiatry, University of Michigan, Ann Arbor, MI
| | - Stephanie J Crowley
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL
| | - Jonathan S Emens
- Department of Psychiatry, Oregon Health & Science University, Portland, OR
| | - Lawrence J Epstein
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Karen L Gamble
- Department of Psychiatry University of Alabama at Birmingham, Birmingham, AL
| | - Brant P Hasler
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - David A Kristo
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Roneil G Malkani
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Shadab A Rahman
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - S Justin Thomas
- Department of Psychiatry University of Alabama at Birmingham, Birmingham, AL
| | - James K Wyatt
- Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL
| | - Phyllis C Zee
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Elizabeth B Klerman
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| |
Collapse
|