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Background: Rumination, a tendency to focus on negative self-related thoughts, is a central symptom of depression. Studying the self-related aspect of such symptoms is challenging because of the need to distinguish self effects from the emotional content of task stimuli. This study employed an emotionally neutral self-related paradigm to investigate possible altered self- processing in depression and its link to rumination. Methods: People with major depressive disorder (n = 25) and controls (n = 25) underwent task-based electroencephalogram recording. We studied late event-related potentials, along with low-frequency oscillatory power. We compared electroencephalogram metrics between groups and correlated them with depressive symptoms and reported rumination. Results: Participants with major depressive disorder displayed a difference in late positive potentials across frontocentral electrodes between self-related and non-self-related conditions. We found no such difference in controls. The magnitude of this difference was positively correlated with depressive symptoms and reported rumination. Participants with major depressive disorder also had elevated theta oscillation power at central electrodes in self-related conditions, a finding that we did not see in controls. Limitations: Patients with major depressive disorder were medicated at the time of the study. The group studied was primarily female, so the observed effects may have been sex-specific. Conclusion: Rumination appears to be linked to altered self-related processing in depression, independent of stimuli-related emotional confounds. This connection between self-related processing and depression may point to a self disorder as a core component of depression., Introduction Rumination involves repetitive thoughts about feelings and problems. (1) It is a common symptom in major depressive disorder (MDD), in which the thoughts tend to focus on negative emotions [...]

Insomnia is a common sleep complaint among patients who had a stroke and has been recognized as an independent risk factor for cognitive impairment. However, the relationship between poststroke insomnia and cognitive impairment over time is under-researched. Therefore, we examined the association between poststroke insomnia and the risk of cognitive impairment. Stroke participants who had a stroke and were 20 years and older This multicenter hospital-based retrospective cohort study with a 13-year follow-up period (2004–2017). The diagnosis of stroke, insomnia, and cognitive impairment was based on the International Classification of Diseases. The study participants who experienced a stroke were divided into two cohorts: those who also had insomnia and those who did not have insomnia. A Cox proportional-hazards regression model was used. A total of 1,775 patients with a mean age of 67.6 years were included. Of these patients, 146 and 75 patients were diagnosed with insomnia and cognitive impairment during the follow-up period, respectively. The cumulative incidence of cognitive impairment in the stroke with insomnia cohort was significantly lower than that in the stroke without insomnia cohort (log-rank test, P <.001). The adjusted hazard ratio and 95% confidence interval (CI) of the stroke with insomnia cohort indicated a higher risk of cognitive impairment compared with the stroke without insomnia cohort (adjusted hazard ratio: 2.38; 95% CI: 1.41–4.03). Patients who had a stroke and were diagnosed with insomnia exhibited a substantial increased risk of cognitive impairment over time. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) with a low arousal threshold (low-ArTH) phenotype can cause minor respiratory events that exacerbate sleep fragmentation. Although anthropometric features may affect the risk of low-ArTH OSA, the associations and underlying mechanisms require further investigation. This study investigated the relationships of body fat and water distribution with polysomnography parameters by using data from a sleep center database. The derived data were classified as those for low-ArTH in accordance with criteria that considered oximetry and the frequency and type fraction of respiratory events and analyzed using mean comparison and regression approaches. The low-ArTH group members (n = 1850) were significantly older and had a higher visceral fat level, body fat percentage, trunk-to-limb fat ratio, and extracellular-to-intracellular (E–I) water ratio compared with the non-OSA group members (n = 368). Significant associations of body fat percentage (odds ratio [OR]: 1.58, 95% confident interval [CI]: 1.08 to 2.3, p < 0.05), trunk-to-limb fat ratio (OR: 1.22, 95% CI: 1.04 to 1.43, p < 0.05), and E–I water ratio (OR: 1.32, 95% CI: 1.08 to 1.62, p < 0.01) with the risk of low-ArTH OSA were noted after adjustments for sex, age, and body mass index. These observations suggest that increased truncal adiposity and extracellular water are associated with a higher risk of low-ArTH OSA. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a risk factor for neurodegenerative diseases. This study determined whether continuous positive airway pressure (CPAP), which can alleviate OSA symptoms, can reduce neurochemical biomarker levels. Thirty patients with OSA and normal cognitive function were recruited and divided into the control (n = 10) and CPAP (n = 20) groups. Next, we examined their in-lab sleep data (polysomnography and CPAP titration), sleep-related questionnaire outcomes, and neurochemical biomarker levels at baseline and the 3-month follow-up. The paired t-test and Wilcoxon signed-rank test were used to examine changes. Analysis of covariance (ANCOVA) was performed to increase the robustness of outcomes. The Epworth Sleepiness Scale and Pittsburgh Sleep Quality Index scores were significantly decreased in the CPAP group. The mean levels of total tau (T-Tau), amyloid-beta-42 (Aβ42), and the product of the two (Aβ42 × T-Tau) increased considerably in the control group (ΔT-Tau: 2.31 pg/mL; ΔAβ42: 0.58 pg/mL; ΔAβ42 × T-Tau: 48.73 pg2/mL2), whereas the mean levels of T-Tau and the product of T-Tau and Aβ42 decreased considerably in the CPAP group (ΔT-Tau: −2.22 pg/mL; ΔAβ42 × T-Tau: −44.35 pg2/mL2). The results of ANCOVA with adjustment for age, sex, body mass index, baseline measurements, and apnea–hypopnea index demonstrated significant differences in neurochemical biomarker levels between the CPAP and control groups. The findings indicate that CPAP may reduce neurochemical biomarker levels by alleviating OSA symptoms. [ABSTRACT FROM AUTHOR]

(a) Objective: Obstructive sleep apnea syndrome (OSAS) is typically diagnosed through polysomnography (PSG). However, PSG incurs high medical costs. This study developed new models for screening the risk of moderate-to-severe OSAS (apnea-hypopnea index, AHI ≥15) and severe OSAS (AHI ≥30) in various age groups and sexes by using anthropometric features in the Taiwan population. (b) Participants: Data were derived from 10,391 northern Taiwan patients who underwent PSG. (c) Methods: Patients' characteristics – namely age, sex, body mass index (BMI), neck circumference, and waist circumference – was obtained. To develop an age- and sex-independent model, various approaches – namely logistic regression, k-nearest neighbor, naive Bayes, random forest (RF), and support vector machine – were trained for four groups based on sex and age (men or women; aged <50 or ≥50 years). Dataset was separated independently (training:70%; validation: 10%; testing: 20%) and Cross-validated grid search was applied for model optimization. Models demonstrating the highest overall accuracy in validation outcomes for the four groups were used to predict the testing dataset. (d) Results: The RF models showed the highest overall accuracy. BMI was the most influential parameter in both types of OSAS severity screening models. (e) Conclusion: The established models can be applied to screen OSAS risk in the Taiwan population and those with similar craniofacial features. [ABSTRACT FROM AUTHOR]

Background: This study aims to determine whether periodontitis is a risk factor for transient ischemic attack (TIA) in young adults. Methods: The National Health Insurance (NHI) Research Database in Taiwan was the source of the data used in this retrospective cohort study. Individuals aged 20 to 53 years with periodontitis in 2001 and 2002 (n = 792,426) and an age‐ and sex‐matched control group (n = 792,426) were selected. All participants were followed up until TIA diagnosis, 55 years of age, removal from the NHI program, death, or December 31, 2016. The incidence density and hazard ratio (HR) of new‐onset TIA were compared between individuals with periodontitis and controls. Periodontitis was defined by dentists according to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) codes 523.3–5 with concurrent antibiotic prescription or periodontal treatment excluding scaling performed by certified dentists. TIA was defined according to the ICD‐9‐CM code 435.x at hospital discharge. Results: After adjustment for confounding factors, the risk of developing TIA/minor ischemic stroke was calculated to be higher in participants with periodontitis (HR, 1.24; 95% confidence interval, 1.15‒1.32; P <0.001) than in those without. The HR was slightly higher among people aged 20 to 40 years than among those aged 40 to 53 years. Conclusion: Periodontitis is associated with an increased risk of developing TIA/minor ischemic stroke. Periodontitis might be a modifiable risk factor for stroke in young adults. Clinicians must devote greater attention to this potential association to develop new preventive and therapeutic strategies for stroke in young adults. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a global health concern and is typically diagnosed using in-laboratory polysomnography (PSG). However, PSG is highly time-consuming and labor-intensive. We, therefore, developed machine learning models based on easily accessed anthropometric features to screen for the risk of moderate to severe and severe OSA. We enrolled 3503 patients from Taiwan and determined their PSG parameters and anthropometric features. Subsequently, we compared the mean values among patients with different OSA severity and considered correlations among all participants. We developed models based on the following machine learning approaches: logistic regression, k-nearest neighbors, naïve Bayes, random forest (RF), support vector machine, and XGBoost. Collected data were first independently split into two data sets (training and validation: 80%; testing: 20%). Thereafter, we adopted the model with the highest accuracy in the training and validation stage to predict the testing set. We explored the importance of each feature in the OSA risk screening by calculating the Shapley values of each input variable. The RF model achieved the highest accuracy for moderate to severe (84.74%) and severe (72.61%) OSA. The level of visceral fat was found to be a predominant feature in the risk screening models of OSA with the aforementioned levels of severity. Our machine learning models can be employed to screen for OSA risk in the populations in Taiwan and in those with similar craniofacial structures. [ABSTRACT FROM AUTHOR]