Association of lipids and inflammatory markers with left ventricular wall thickness in patients with bipolar disorder.

BACKGROUND: Individuals with bipolar disorder (BD) face a high risk of heart failure and left ventricular (LV) dysfunction. Despite strong evidence that high LV relative wall thickness (RWT) is a risk marker for heart failure, few studies have evaluated LV RWT and aggravating factors in individuals with BD. METHODS: We recruited 104 participants (52 patients with BD and 52 age- and sex-matched mentally healthy controls) to undergo echocardiographic imaging and biochemistry, high-sensitivity C-reactive protein (hs-CRP), and blood cell count measurements. LV RWT was estimated using the following equation: (2 × LV posterior wall end-diastolic thickness)/LV end-diastolic diameter. Clinical data were obtained through interviews and chart reviews. RESULTS: The BD group exhibited a significantly greater LV RWT (Cohen's d = 0.53, p = 0.003) and a less favorable mitral valve E/A ratio (Cohen's d = 0.54, p = 0.023) and LV global longitudinal strain (Cohen's d = 0.57, p = 0.047) than did the control group. Multiple linear regression revealed that in the BD group, serum triglyceride levels (ß = 0.466, p = 0.001), platelet-to-lymphocyte ratios (ß = 0.324, p = 0.022), and hs-CRP levels (ß = 0.289, p = 0.043) were all significantly and positively associated with LV RWT. LIMITATIONS: This study applied a cross-sectional design, meaning that the direction of causation could not be inferred. CONCLUSIONS: Patients with BD are at a risk of heart failure, as indicated by their relatively high LV RWT. Lipid levels and systemic inflammation may explain this unfavorable association.

Determining cut-off values and predictors for the Snaith-Hamilton Pleasure Scale: comparison between clinical and school settings.

BACKGROUND: Few previous studies have established Snaith-Hamilton Pleasure Scale (SHAPS) cut-off values using receiver operating characteristic curve analysis and applied these values to compare predictors of anhedonia between clinical and nonclinical groups. AIMS: To determine the optimal cut-off values for the SHAPS and use them to identify predictors of anhedonia in clinical and nonclinical groups in Taiwan. METHOD: This cross-sectional and correlational study used convenience sampling to recruit 160 patients from three hospitals and 412 students from two universities in northern Taiwan. Data analysis included receiver operating characteristic curve, univariate and multivariate analyses. RESULTS: The optimal SHAPS cut-off values were 29.5 and 23.5 for the clinical and nonclinical groups, respectively. Moreover, two-stage analysis revealed that participants in the clinical group who perceived themselves as nondepressed, and participants in the nonclinical group who did not skip classes and whose fathers exhibited higher levels of care and protection were less likely to attain the cut-off values. Conversely, participants in the nonclinical group who reported lower academic satisfaction and were unwilling to seek help from family or friends were more likely to attain the cut-off values. CONCLUSIONS: Our findings highlight the importance of optimal cut-off values in screening for depression risk within clinical and nonclinical groups. Accordingly, the development of comprehensive, individualised programmes to monitor variation trends in SHAPS scores and relevant predictors of anhedonia across different target populations is crucial.

Depression assessment using integrated multi-featured EEG bands deep neural network models: Leveraging ensemble learning techniques.

Mental Status Assessment (MSA) holds significant importance in psychiatry. In recent years, several studies have leveraged Electroencephalogram (EEG) technology to gauge an individual's mental state or level of depression. This study introduces a novel multi-tier ensemble learning approach to integrate multiple EEG bands for conducting mental state or depression assessments. Initially, the EEG signal is divided into eight sub-bands, and then a Long Short-Term Memory (LSTM)-based Deep Neural Network (DNN) model is trained for each band. Subsequently, the integration of multi-band EEG frequency models and the evaluation of mental state or depression level are facilitated through a two-tier ensemble learning approach based on Multiple Linear Regression (MLR). The authors conducted numerous experiments to validate the performance of the proposed method under different evaluation metrics. For clarity and conciseness, the research employs the simplest commercialized one-channel EEG sensor, positioned at FP1, to collect data from 57 subjects (49 depressed and 18 healthy subjects). The obtained results, including an accuracy of 0.897, F1-score of 0.921, precision of 0.935, negative predictive value of 0.829, recall of 0.908, specificity of 0.875, and AUC of 0.8917, provide evidence of the superior performance of the proposed method compared to other ensemble learning techniques. This method not only proves effective but also holds the potential to significantly enhance the accuracy of depression assessment.

Antidepressant sertraline increases thioflavin-S and Congo red deposition in APPswe/PSEN1dE9 transgenic mice.

Introduction: Depression is strongly associated with Alzheimer's disease (AD). Antidepressants are commonly used in patients before and after their diagnosis of AD. To date, the relationship between antidepressants and AD remains unclear. Methods: In our study, we administered sertraline or paroxetine to wild type (WT) and APPswe/PSEN1dE9 (APP/PSEN1) transgenic mouse models for up to 12 months. We quantified the drug concentrations using LC-MS/MS analysis and measured serum serotonin level using an ELISA assay. Additionally, we evaluated the amyloid burdens through thioflavin-S and Congo red stainings, and recognition memory using the novel object recognition test. Results: Our findings revealed that mice treated with paroxetine exhibited a significantly higher level of weight gain compared to the control group and increased mortality in APP/PSEN1 mice. After 12 months of antidepressant treatment, the sertraline level was measured at 289.8 ng/g for cerebellum, while the paroxetine level was 792.9 ng/g for cerebellum. Sertraline significantly increased thioflavin-S and Congo red depositions, along with gliosis, in both isocortex and hippocampus of APP/PSEN1 mice compared to the control group. Both antidepressants also led to a decreased recognition index in APP/PSEN1 mice. Conclusion: These findings suggest a potential role of sertraline in AD pathogenesis, emphasizing the need to reassess the use of these antidepressants in patients with AD.

Higher body mass index associated with smaller frontal cortical volumes in older adult patients with bipolar disorder

Background and objectives: Patients with bipolar disorder (BD) tend to have accelerated decline in executive function during the aging. Thus far, only few studies have examined the effect of obesity on frontal cortical volumes in young patients with BD. Herein we aimed to ascertain the association between body mass index (BMI) and frontal cortical volumes in older adult patients with BD.MethodsWe recruited outpatients who were diagnosed as bipolar I disorder (BD-I) and aged over 50 years to undergo volumetric magnetic resonance imaging and anthropometric measurement. Clinical data were obtained through interview and chart review.ResultsA total of 42 patients (mean age, 59.5 ± 7.9 years) with BD-I were recruited in this study. Compared with normal BMI group, overweight/obese patients (59.5%, n = 25) had significantly smaller volumes of the bilateral prefrontal cortex and right orbitofrontal cortex. After adjusting cardiometabolic variables, higher BMI and age were significantly associated with smaller volumes of the left prefrontal cortex and bilateral orbitofrontal cortex, accounting for 29.8% (left prefrontal cortex), 33.1% (left orbitofrontal cortex), and 42.0% (right orbitofrontal cortex) of the variance. BMI alone was negatively associated with the volumes of the right prefrontal and right medial frontal cortexes, accounting for 25.7% and 14.7% of the variance, respectively.ConclusionsHigher BMI was associated with smaller cortical volumes across individual frontal regions in older patients with BD independent of cardiometabolic morbidity. Future research is necessary to elucidate the mechanisms underlying the association between BMI and frontal cortical volumes in older patients with BD. (AU)