The association between insulin resistance and depressive symptoms - A national representative cross-sectional study.

OBJECTIVE: Increasing evidence suggests a positive association between insulin resistance (IR) and depression. However, whether sex-or body mass index-specific differences exist remains controversial, and only few studies have analyzed specific symptom domains. Thus, the present study aimed to analyze the association between IR and depressive symptom domains and to clarify the effects of sex and body mass index. METHODS: The study sample comprised 4007 participants, aged 19-79, from the Korea National Health and Nutrition Examination Study 2020. Participants completed health interviews and examinations, providing data on circulating insulin and glucose levels, the Patient Health Questionnaire-9 (PHQ-9), and related covariates. IR was calculated using the homeostasis model assessment of insulin resistance. Associations between IR and PHQ-9 were analyzed using negative binomial regression with adjustments for the complex survey design. RESULTS: The association between log-transformed IR and PHQ-9 total scores was statistically significant (incidence rate ratio [IRR] = 1.17, 95% confidence interval [CI] = 1.07-1.29, p = 0.001). Only body mass index specific differences were statistically significant, as the association was only significant in those without obesity (IRR = 1.21, 95% CI = 1.06-1.38, p = 0.005). IR was associated with cognitive/affective (IRR = 1.23, 95% CI = 1.08-1.41, p = 0.002) and somatic (IRR = 1.14, 95% CI = 1.04-1.25, p = 0.005) depressive symptom domains. Sensitivity analyses revealed similar results. CONCLUSIONS: IR was positively associated with cognitive/affective and somatic depressive symptoms in non-obese individuals.

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality.

BACKGROUND: Assessing a patient's suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. OBJECTIVE: This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. METHODS: We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. RESULTS: A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. CONCLUSIONS: Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

The Aftermath: Post-pandemic Psychiatric Implications of the COVID-19 Pandemic, a South Korean Perspective.

The coronavirus disease 2019 (COVID-19) pandemic has disrupted our everyday life. Along with the fear of getting infected or of having loved ones infected, the lifestyle changes and the socioeconomic consequences of the pandemic have profound impact on mental health of the general population. While numerous studies on immediate psychological responses to COVID-19 are being published, there is a lack of discussion on its possible long-term sequelae. In this study, we systematically reviewed and meta-analyzed longitudinal studies that examined mental health of the general population prior to and during the pandemic. Furthermore, we explored the long-term psychiatric implications of the pandemic with data from South Korea. Our analysis showed that the number of suicidal deaths during the pandemic was lower than the previous years in many countries, which is in contrast with the increased depression, anxiety, and psychological distress in the general population in South Korea as well as in other countries. To explain this phenomenon, we propose a possibility of delayed impacts. The post-traumatic stress, long-term consequences of social restrictions, and maladaptive response to the "new normal" are discussed in the paper. COVID-19 being an unprecedented global crisis, more research and international collaboration are needed to understand, to treat, and to prevent its long-term effects on our mental health.

Predictors of treatment response to pharmacotherapy in patients with persistent postural-perceptual dizziness.

The study aimed to identify the predictors of response to selective serotonin reuptake inhibitors (SSRIs) for 12 weeks in patients with persistent postural-perceptual dizziness (PPPD). Short-term treatment responses were studied in 197 outpatients [127 (64.5%) women, mean age ± SD = 51.7 ± 15.9] diagnosed with PPPD. Clinical and self-rated assessments were analyzed at the baseline and at the end of pharmacotherapy for 12 weeks. Multivariable logistic regression analysis was used to determine the following variables as the potential predictors of treatment response as measured by scoring in clinical global impression-improvement scale (CGI-I) scores: age, sex, comorbidity, baseline CGI-S score, Beck Depression Inventory-II score, State-Trait Anxiety Inventory score, Dizziness Handicap Inventory score at the baseline, and prescribed doses of antidepressants or benzodiazepines. The overall response rate to pharmacotherapy was 65.0% (128/197). Female sex and greater disease severity at the baseline (higher CGI-S score) were associated with a better response to the pharmacotherapy. Subgroup analyses by sex identified younger age and lower anxiety as the indicators for better outcomes in men, and absence of comorbidities in women. During the initial assessment, the severity of PPPD was associated with depressive symptoms and subjective functional handicap due to dizziness. The response to pharmacotherapy is favorable in PPPD. Sex, age and initial disease severity are the predictors of the response to SSRIs in patients with PPPD.