Posttraumatic stress disorder (PTSD) is a prevalent anxiety disorder marked by behavioral, physiologic, and hormonal alterations. The etiology of PTSD is unknown, although exposure to a traumatic event constitutes a necessary, but not sufficient, factor. Serotonergic dysfunction has been implicated in PTSD. The present study examined the possible association between the serotonin-transporter-linked polymorphic region (SERTPR) and PTSD. The genotype and allele frequencies of the SERTPR were analyzed in 100 PTSD patients and 197 unrelated healthy controls using a case–control design. The frequency of the s/s genotype was significantly higher in PTSD patients than in normal controls. These findings suggest that the SERTPR s/s genotype is one of the genetic factors for the susceptibility to PTSD. Further investigations are required into the influence of gene polymorphisms on the biological mechanisms of PTSD, its clinical expression, and its response to treatment. Depression and Anxiety 00:1–5, 2005.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Virtually all organisms on Earth have their own circadian rhythm, and humans are no exception. Circadian rhythms are associated with various human states, especially mood disorders and disturbance of circadian rhythm is known to be very closely related. Attempts have also been made to derive clinical implications associated with mood disorders using the acquiring vast amounts of the digital log as digital technologies develop and using computational analysis techniques. Objective: The present study was conducted to evaluate the mood state/episode, activity, sleep, light exposure, and heart rate during a period of about two years by acquiring various digital log data through wearable devices and smartphone applications as well as conventional clinical assessments. We investigated a mood prediction algorithm developed with machine learning using passive data phenotypes based on circadian rhythms. Methods: We performed a prospective observational cohort study on fifty-five patients with mood disorders (major depressive disorder, bipolar disorder type 1 and 2; MDD, BD I, and BD II, respectively) for two years. A smartphone application for self-recording daily mood scores and detecting light exposure (using the installed sensor) were provided. From daily worn activity trackers, digital log data of activity, sleep, and heart rate were collected. Passive digital phenotypes were processed into 130 features based on circadian rhythms, and a mood prediction algorithm was developed by random forest. Results: The mood state prediction accuracies for next three days in all patients, MDD, BD I, and BD II were 65, 65, 64, and 65%, with 0.7, 0.69, 0.67, and 0.67 area under the curve (AUC) values, respectively. The accuracies of all patients for no episode (NE), depressive episode (DE), manic episode (ME) and hypomanic episode (HME) were 85.3, 87, 94, and 91.2%, with 0.87, 0.87, 0.958, and 0.912 of AUCs, respectively. The prediction accuracy in BD II patients was distinctively balanced high showing 82.6, 74.4, and 87.5% of the accuracies (with generally good sensitivity and specificity), with 0.919, 0.868, and 0.949 of the AUCs for NE, DE, and HME, respectively. Conclusions: Based on the theoretical basis of chronobiology, this study proposed a good model of future research by developing a mood prediction algorithm using machine learning by processing and reclassifying digital log data. In addition to academic value, it is expected that this study will be of practical help to improve the prognosis of patients with mood disorder by making it possible to apply actual clinical application due to the rapid expansion of digital technology. Clinical Trial: ClinicalTrials.gov: NCT03088657

Disturbances in circadian rhythms have been suggested as a possible cause of bipolar disorder (BD). Included in this study were 31 mood episodes of 26 BD patients, and 18 controls. Circadian rhythms of BD were evaluated at admission, at 2-week intervals during hospitalization, and at discharge. All participants wore wrist actigraphs during the studies. Saliva and buccal cells were obtained at 8:00, 11:00, 15:00, 19:00, and 23:00 for two consecutive days. Collected saliva and buccal cells were used for analysis of the cortisol and gene circadian rhythm, respectively. Circadian rhythms had different phases during acute mood episodes of BD compared to recovered states. In 23 acute manic episodes, circadian phases were ~7hour advanced (equivalent to ~17hour delayed). Phases of 21 out of these 23 cases returned to normal by ~7hour delay along with treatment, but two out of 23 cases returned to normal by ~17hour advance. In three cases of mixed manic episodes, the phases were ~6-7hour delayed. For five cases of depressive episodes, circadian rhythms phases were ~4-5hour delayed. After treatment, circadian phases resembled those of healthy controls. Circadian misalignment due to circadian rhythm phase shifts might be a pathophysiological mechanism of BD.

Obesity is a common disorder with many complications. Although chronodisruption plays a role in obesity, few epidemiological studies have investigated the association between artificial light at night (ALAN) and obesity. Since sleep health is related to both obesity and ALAN, we investigated the association between outdoor ALAN and obesity after adjusting for sleep health. We also investigated the association between outdoor ALAN and sleep health. This cross-sectional survey included 8526 adults, 39–70 years of age, who participated in the Korean Genome and Epidemiology Study. Outdoor ALAN data were obtained from satellite images provided by the US Defense Meteorological Satellite Program. We obtained individual data regarding outdoor ALAN; body mass index; depression; and sleep health including sleep duration, mid-sleep time, and insomnia; and other demographic data including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking status and consumption of caffeine or alcohol before sleep. A logistic regression model was used to investigate the association between outdoor ALAN and obesity. The prevalence of obesity differed significantly according to sex (women 47% versus men 39%, p < 0.001) and outdoor ALAN (high 55% versus low 40%, p < 0.001). Univariate logistic regression analysis revealed a significant association between high outdoor ALAN and obesity (odds ratio [OR] 1.24, 95% confidence interval [CI] 1.14–1.35, p < 0.001). Furthermore, multivariate logistic regression analyses showed that high outdoor ALAN was significantly associated with obesity after adjusting for age and sex (OR 1.25, 95% CI 1.14–1.37, p < 0.001) and even after controlling for various other confounding factors including age, sex, educational level, type of residential building, monthly household income, alcohol consumption, smoking, consumption of caffeine or alcohol before sleep, delayed sleep pattern, short sleep duration and habitual snoring (OR 1.20, 95% CI 1.06–1.36, p = 0.003). The findings of our study provide epidemiological evidence that outdoor ALAN is significantly related to obesity.