Browsing by Subject "Circadian rhythms"

Now showing 1 - 4 of 4
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    Circadian rhythms in diabetic retinopathy: an overview of pathogenesis and investigational drugs
    (Taylor & Francis, 2020) Bhatwadekar, Ashay D.; Rameswara, Varun; Ophthalmology, School of Medicine
    Introduction: Circadian rhythm is a natural endogenous process occurring roughly every 24 hours. Circadian rhythm dysfunction is involved in diabetic retinopathy (DR) pathogenesis. Interestingly, there are investigational drugs that exhibit potential in the treatment of DR by targeting circadian rhythm dysfunction. Areas covered: We performed a literature search in June 2020 using PubMed's Medical Subject Heading (MeSH) terms 'circadian clock,' 'circadian rhythms,' and 'diabetic retinopathy.' This article offers an overview of the physiology of the biological clock and clock regulatory genes and presents an examination of the retinal clock. It discusses the pathogenic mechanisms of DR and emphasizes how circadian rhythm dysfunction at structural, physiological, metabolic and cellular levels, plays a critical role in the development of DR. The latter part of the paper sheds light on those investigational drugs (such as melatonin, tasimelteon and metformin) which exhibit potential in the treatment of DR by the targeting of circadian rhythm dysfunction. Expert opinion: An enhanced understanding of circadian rhythm and its role in DR could offer therapeutic potential by targeting of circadian rhythm dysfunction.
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    Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort
    (IEEE, 2021) Cakmak, Ayse S.; Perez Alday, Erick A.; Da Poian, Giulia; Rad, Ali Bahrami; Metzler, Thomas J.; Neylan, Thomas C.; House, Stacey L.; Beaudoin, Francesca L.; An, Xinming; Stevens, Jennifer S.; Zeng, Donglin; Linnstaedt, Sarah D.; Jovanovic, Tanja; Germine, Laura T.; Bollen, Kenneth A.; Rauch, Scott L.; Lewandowski, Christopher A.; Hendry, Phyllis L.; Sheikh, Sophia; Storrow, Alan B.; Musey, Paul I., Jr.; Haran, John P.; Jones, Christopher W.; Punches, Brittany E.; Swor, Robert A.; Gentile, Nina T.; McGrath, Meghan E.; Seamon, Mark J.; Mohiuddin, Kamran; Chang, Anna M.; Pearson, Claire; Domeier, Robert M.; Bruce, Steven E.; O’Neil, Brian J.; Rathlev, Niels K.; Sanchez, Leon D.; Pietrzak, Robert H.; Joormann, Jutta; Barch, Deanna M.; Pizzagalli, Diego A.; Harte, Steven E.; Elliott, James M.; Kessler, Ronald C.; Koenen, Karestan C.; Ressler, Kerry J.; Mclean, Samuel A.; Li, Qiao; Clifford, Gari D.; Emergency Medicine, School of Medicine
    Post-Traumatic Stress Disorder (PTSD) is a psychiatric condition resulting from threatening or horrifying events. We hypothesized that circadian rhythm changes, measured by a wrist-worn research watch are predictive of post-trauma outcomes. Approach: 1618 post-trauma patients were enrolled after admission to emergency departments (ED). Three standardized questionnaires were administered at week eight to measure post-trauma outcomes related to PTSD, sleep disturbance, and pain interference with daily life. Pulse activity and movement data were captured from a research watch for eight weeks. Standard and novel movement and cardiovascular metrics that reflect circadian rhythms were derived using this data. These features were used to train different classifiers to predict the three outcomes derived from week-eight surveys. Clinical surveys administered at ED were also used as features in the baseline models. Results: The highest cross-validated performance of research watch-based features was achieved for classifying participants with pain interference by a logistic regression model, with an area under the receiver operating characteristic curve (AUC) of 0.70. The ED survey-based model achieved an AUC of 0.77, and the fusion of research watch and ED survey metrics improved the AUC to 0.79. Significance: This work represents the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.
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    Implementation of Sleep and Circadian Science: Recommendations from the Sleep Research Society and National Institutes of Health Workshop
    (Oxford, 2016-12-01) Parthasarathy, Sairam; Carskadon, Mary A.; Jean-Louis, Girardin; Owens, Judith; Bramoweth, Adam; Combs, Daniel; Hale, Lauren; Harrison, Elizabeth; Hart, Chantelle N.; Hasler, Brant P.; Honaker, Sarah M.; Hertenstein, Elisabeth; Kuna, Samuel; Kushida, Clete; Levenson, Jessica C.; Murray, Caitlin; Pack, Allan I.; Pillai, Vivek; Pruiksma, Kristi; Seixas, Azizi; Strollo, Patrick; Thosar, Saurabh S.; Williams, Natasha; Buysse, Daniel; Pediatrics, School of Medicine
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    Peripheral immune circadian variation, synchronisation and possible dysrhythmia in established type 1 diabetes
    (Springer, 2021-08) Beam, Craig A.; Beli, Eleni; Wasserfall, Clive H.; Woerner, Stephanie E.; Legge, Megan T.; Evans-Molina, Carmella; McGrail, Kieran M.; Silk, Ryan; Grant, Maria B.; Atkinson, Mark A.; DiMeglio, Linda A.; Pediatrics, School of Medicine
    Aims/hypothesis: The circadian clock influences both diabetes and immunity. Our goal in this study was to characterise more thoroughly the circadian patterns of immune cell populations and cytokines that are particularly relevant to the immune pathology of type 1 diabetes and thus fill in a current gap in our understanding of this disease. Methods: Ten individuals with established type 1 diabetes (mean disease duration 11 years, age 18-40 years, six female) participated in a circadian sampling protocol, each providing six blood samples over a 24 h period. Results: Daily ranges of population frequencies were sometimes large and possibly clinically significant. Several immune populations, such as dendritic cells, CD4 and CD8 T cells and their effector memory subpopulations, CD4 regulatory T cells, B cells and cytokine IL-6, exhibited statistically significant circadian rhythmicity. In a comparison with historical healthy control individuals, but using shipped samples, we observed that participants with type 1 diabetes had statistically significant phase shifts occurring in the time of peak occurrence of B cells (+4.8 h), CD4 and CD8 T cells (~ +5 h) and their naive and effector memory subsets (~ +3.3 to +4.5 h), and regulatory T cells (+4.1 h). An independent streptozotocin murine experiment confirmed the phase shifting of CD8 T cells and suggests that circadian dysrhythmia in type 1 diabetes might be an effect and not a cause of the disease. Conclusions/interpretation: Future efforts investigating this newly described aspect of type 1 diabetes in human participants are warranted. Peripheral immune populations should be measured near the same time of day in order to reduce circadian-related variation.