Objective: Seasonal variations in mood and behavior and diurnal preference are associated with several genes that regulate circadian rhythms. In this study, we investigated the association of the <i>NPAS2</i> rs6725296 polymorphism with seasonality and diurnal preference.Methods: A total of 510 healthy subjects were genotyped for the <i>NPAS2</i> rs6725296 polymorphism and completed self-report questionnaires on seasonality and diurnal preferences. Seasonality was evaluated using the seasonal pattern assessment questionnaire, and diurnal preference was evaluated using the Composite Scale of Morningness (CSM). We assessed the association of genotype and allele carrier status with the total and subscale scores of global seasonality scores (GSS) for seasonality and the total and subscale scores of CSM for diurnal preference.Results: No significant associations were found between the <i>NPAS2</i> rs6725296 genotype and the allele carrier status and the GSS and CSM scores.Conclusion: The results of this study suggest that the <i>NPAS2</i> rs6725296 polymorphism may not contribute to seasonality and diurnal preference in healthy Koreans. More studies with a larger number of subjects, inclusion of different ethnic groups, and complementation with objective measurements of seasonality and diurnal preference are necessary to evaluate the influence of genetics on seasonality and diurnal preference.
Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation–maximization clustering analysis produced two groups: A (<i>n</i>=9) and B (<i>n</i>=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.
Abstract Background Insomnia is diagnosed through patients' voluntary reports. This feature leads to ambiguity in the diagnosis and subtyping of insomnia.(Ferini-Strambi et al., 2019) Digital phenotyping means to regard behavior patterns that appear on digital devices or online as a kind of phenotype and is attracting attention as a means of future medical care, but there are not many cases applied to insomnia.(Kim et al., 2023, Lee et al., 2023) Aims & Objectives Therefore, our study performs data-driven subtyping of insomnia to confirm the relationship between clinical characteristics and digital phenotypes, and this will enable a new approach to the disease. Method Participant recruitment is done by Korea University Anam Hospital. In this sample clustering, 73 subjects were included. All subjects are adults between 19 and 70 with an ISI insomnia severity index score of 15 or higher. Digital phenotypes related to the quality of sleep, life patterns, and environment of the subject are recorded on the application (for 4 weeks). Using 16 variables (working pattern, life pattern, number of steps/exercise time/movement distance, average heart rate, average total sleep time), K-means clustering of sci-kit learn was done to find the best grouping model. Then, a between-group analysis was conducted using data from the initial evaluation questionnaire of subjects. ANOVA and T-test or Chi-square test were conducted depending on the shape of the variable. Results We have applied the K-means clustering algorithm, and the optimal cluster number (k) was determined using the elbow curve method. According to the curve, the elbow point is at K=3. Therefore, 3 subgroups were divided (cluster0; n=32, cluster1; n=31, cluster2; n=10). Among 16 axes, steps, exercise time, and movement distance for each 4 weeks illustrated the best data point distributions to form clusters. Average heart rate, average total sleep time, working pattern, and life pattern had lower correlation coefficients with other variables, and cluster boundaries were unclear. After 10-fold cross-validation, the accuracy score was 80%, and the SHAP value plot was visualized. According to the plot, movement distance, exercise time, and steps were important features. Then demographic and clinical characteristics were compared of each cluster using ANOVA and Chi-square test. Among 28 features (age, lifestyle, BMI, and case report form of 25 self-rating questionnaires including sleep-related scale, mood-related scale, and health-related scale), only the DBAS score showed a significant difference between groups (p-value=0.04). A higher DBAS score indicates more dysfunctional beliefs and attitudes about sleep. The box plot shows the DBAS scale difference between clusters. A student’ s t-test was performed to show C0=C1, C1=C2, C0 References CHO, C.-H. 2021. Clinical application of digital therapeutics for insomnia. Sleep Medicine and Psychophysiology, 28, 6-12.FERINI-STRAMBI, L., FOSSATI, A., SFORZA, M. &GALBIATI, A. 2019. Subtyping insomnia disorder. The Lancet Psychiatry, 6, 284. KIM, W.-P., KIM, H.-J., PACK, S. P., LIM, J.-H., CHO, C.-H. &LEE, H.-J. 2023. Machine Learning– Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children. JAMA Network Open, 6, e233502-e233502. LEE, H.-J., CHO, C.-H., LEE, T., JEONG, J., YEOM, J. W., KIM, S., JEON, S., SEO, J. Y., MOON, E. &BAEK, J. H. 2023. Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study. Psychological Medicine, 53, 5636-5644.
This study investigated the utilization of digital phenotypes and machine learning algorithms to predict impending panic symptoms in patients with mood and anxiety disorders. A cohort of 43 patients was monitored over a two-year period, with data collected from smartphone applications and wearable devices. This research aimed to differentiate between the day before panic (DBP) and stable days without symptoms. With RandomForest, GradientBoost, and XGBoost classifiers, the study analyzed 3,969 data points, including 254 DBP events. The XGBoost model demonstrated performance with a ROC-AUC score of 0.905, while a simplified model using only the top 10 variables maintained an ROC-AUC of 0.903. Key predictors of panic events included evaluated Childhood Trauma Questionnaire scores, increased step counts, and higher anxiety levels. These findings indicate the potential of machine learning algorithms leveraging digital phenotypes to predict panic symptoms, thereby supporting the development of proactive and personalized digital therapies and providing insights into real-life indicators that may exacerbate panic symptoms in this population.
Objective The objective of this study was to determine whether the circadian rhythm of heart rate or step count using wearable devices was related to that of the salivary cortisol levels and to test the possibility that the data from wearable devices could be used as an indicator of circadian rhythm misalignment, which is emerging as a cause of insomnia and mood disorders. Methods The heart rate and step count were continuously measured in 12 healthy young adults using wearable wrist devices for 5 days, and saliva was sampled every 4 hours, excluding sleeping time, for a total of 48 hours to measure the circadian rhythm of salivary cortisol concentration. Cortisol concentrations were assessed using the enzyme-linked immunosorbent assay. The cosinor analysis for the three measurements, salivary cortisol concentrations, heart rate, and step count, was used to estimate the circadian rhythm. Results The mean values of the acrophase of the cosine-fitted curve of cortisol, heart rate, and step count were 9.06, 15.84, and 19.09, respectively, while those of the amplitude were 7.70, 12.60, and 10.68, respectively. In addition, the mean values of the mesor of the cosine-fitted curve for cortisol, heart rate, and step count were 17.19, 73.55, and 45.45, respectively, and those of robustness were 0.82, 0.56, and 0.18, respectively. There was a possible positive correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of heart rate (r=0.55, p=0.064). However, there was no correlation between the acrophase of the cosine-fitted curve of salivary cortisol and that of step count (r=-0.2, p=0.533). Conclusion The findings suggest that the heart rate measured using the wearable activity tracker was a relatively reliable biomarker of circadian rhythm. Keywords: Circadian rhythm; Cortisol; Heart rate; Wearable activity tracker
Background Many mood disorder patients experience seasonal changes in varying degrees. Studies on seasonality have shown that bipolar disorder has a higher prevalence rate in such patients; however, there is limited research on seasonality in early-onset mood disorder patients. This study estimated the prevalence of seasonality in early-onset mood disorder patients, and examined the association between seasonality and mood disorders. Methods Early-onset mood disorder patients (n = 378; 138 major depressive disorder; 101 bipolar I disorder; 139 bipolar II disorder) of the Mood Disorder Cohort Research Consortium and healthy control subjects (n = 235) were assessed for seasonality with Seasonality Pattern Assessment Questionnaire (SPAQ). Results A higher global seasonality score, an overall seasonal impairment score, and the prevalence of seasonal affective disorder (SAD) and subsyndromal SAD showed that mood disorder subjects had higher seasonality than the healthy subjects. The former subject group had a significantly higher mean overall seasonal impairment score than the healthy subjects (p < .001); in particular, bipolar II disorder subjects had the highest prevalence of SAD, and the diagnosis of bipolar II disorder had significantly higher odds ratios for SAD when compared to major depression and bipolar I disorder (p < .05). Conclusions Early-onset mood disorders, especially bipolar II disorder, were associated with high seasonality. A thorough assessment of seasonality in early-onset mood disorders may be warranted for more personalized treatment and proactive prevention of mood episodes.
We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.
Objective: Benzodiazepines are a widely used class of medications for anxiety, depression, and insomnia. Despite their common use, concerns remain about memory problems with benzodiazepines. Despite the growing financial and social burden of dementia, inconsistent results persist regarding the association between benzodiazepines and dementia. Therefore, we aimed to evaluate the association between benzodiazepines and dementia in Korea. Methods: Diagnostic and prescription information from the Health Insurance Review and Assessment (HIRA) database in South Korea between 2009 and 2014 was utilized. The dementia group included people who were diagnosed with a dementia code and received one or more prescriptions for dementia. A total of 68,241 participants with dementia and 341,205 control participants were matched. Possible confounders, such as major medical and psychiatric disorders, were adjusted, and multivariate logistic regression was conducted to assess the association between benzodiazepines and dementia. Results: The highest odds ratio (OR) for dementia was noted for clonazepam (OR=2.86, 95% confidence interval [CI]=2.77–2.95) followed by those for diazepam (OR=2.60, 95% CI=2.53–2.66), lorazepam (OR=1.34, 95% CI=1.30–1.37), and triazolam (OR=1.26, 95% CI=1.21–1.32). Conclusion: Overall, relatively long-acting benzodiazepines, such as clonazepam, diazepam, and lorazepam, were associated with the incidence of dementia. Triazolam, which is approved for insomnia, was also significantly associated with dementia. Individuals who are prescribed with benzodiazepines should be cautious regarding memory loss and dementia. Further studies are needed to confirm the temporal and biological causality between benzodiazepines and dementia.
Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones.The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy.Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively.We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.
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