(1) Background: Schizophrenia is a severe mental disorder characterized by various symptom groups that tremendously affect health-related quality of life (HRQoL). We aimed to specify whether negative symptoms and cognitive deficits of schizophrenia correlate and can predict HRQoL. (2) Methods: Patients diagnosed with paranoid schizophrenia were invited to participate in the study. Participants were evaluated using the Montreal Cognitive Assessment (MoCA) and the Brief Psychiatric Rating Scale (BPRS) and were asked to fill out the Self-evaluation of Negative Symptoms scale (SNS) and the Medical Outcomes Short Form Survey (SF-36). Pearson’s and Spearman’s correlations were used to calculate the correlations between cognitive deficits and negative symptoms. We performed the receiver operating characteristic (ROC) analysis for the variables correlated with SF-36 scores. (3) Results: HRQoL correlated significantly with the negative symptoms; however, it did not correlate with cognitive deficits. ROC analysis showed that the abulia subscore of the SNS showed the most significant predictive potential of HRQoL. (4) Conclusions: Negative symptoms correlate more significantly with the HRQoL than cognitive symptoms. The SNS offers the possibility of predicting the HRQoL of patients with schizophrenia and is useful as a screening tool in clinical practice.

Background and Objectives: Prevalence data of different classes of negative symptoms in an inpatient setting are scarce. Most of the data are collected in clinical trials under strict inclusion criteria, using older psychometric scales with known content validity flaws. The objectives of this study were to report the prevalence of different classes of negative symptoms in a naturalistic inpatient setting using both older and newer psychometric scales and to determine whether second-generation psychometric scales were not inferior to older scales in detecting negative symptoms. Materials and Methods: Negative symptoms were evaluated using older-generation (Positive and Negative Symptoms Scale, PANSS) and second-generation (Brief Negative Symptoms Scale, BNSS, and Self-Evaluation of Negative Symptoms Scale, SNS) measures. Primary, prominent, core, and predominant negative symptoms were calculated according to various descriptions used in other studies. Results: A total of 323 participants were included in this study. The mean score of the PANSS negative symptoms subscores was 25.33 (SD = 6.86, 95% CI 24.57–26.09), the mean total BNSS score was 36.48 (SD = 15.93, 95% CI 34.74–38.23), and the mean SNS total score was 18,9 (SD = 8.99, 95% CI 17.91–19.89). The prevalence of primary negative symptoms was similar in all groups. PANSS and BNSS categorized 71% of the sample as having prominent negative symptoms. The average prevalence of predominant negative symptoms, as measured by PANSS and BNSS, was 9.6%, whereas SNS reported 10.5%. Conclusions: Negative symptoms were highly prevalent in our inpatient sample. Most of our participants had primary and prominent negative symptoms; however, only a small fraction met the criteria for predominant negative symptoms. Prevalence data were similar between PANSS, BNSS, and SNS, showing that BNSS and SNS are not inferior to PANSS.

Background: Diffusion-prepared pseudo-continuous arterial spin labelling (DP-pCASL) can quantify the cerebral blood flow (CBF) and the water exchange rate (kw) across the blood–brain barrier (BBB). Little is known about the BBB water exchange in major depressive disorder (MDD). Objective: We aimed to explore the associations between kw, CBF, peripheral inflammation, and MDD. Methods: Using DP-pCASL, we measured the global and selected regional kw and CBF together with blood plasma levels of lipopolysaccharide (LPS) and inflammatory cytokines in 85 patients with MDD and 51 controls. Results: The global CBF was significantly lower in MDD patients compared with controls (means of 51 and 57 mL/100 g/min, respectively; p = 0.006), with similar reductions found in the dorsolateral and ventromedial prefrontal, anterior, and posterior cingulate regions, while no differences were found in the amygdala and the isthmic cingulate. There were no differences in the kw between groups globally (means of 128 min⁻¹; p = 0.958) and in the studied regions. Among MDD patients, the kw was weakly correlated with the MADRS scores (r = 0.231, p = 0.034). There were no associations between kw, CBF, and inflammatory markers (LPS, IL-6, IL-10, TNF-α, IFN-γ). Logistic regression showed that a combination of the regional CBF < 59.22 mL/100 g/min together with LPS > 143.58 pg/mL and/or IL-10 > 0 pg/mL distinguished MDD patients from controls with a moderate accuracy of 83.1% (sensitivity = 94.1%, specificity = 64.7%, AUC = 0.876). Conclusions: DP-pCASL imaging confirmed previous findings of reduced CBF in MDD, which together with LPS and IL-10 concentrations were independent significant predictors of MDD. However, no changes in the BBB water exchange were found, suggesting that it may not be as significant as CBF in MDD pathophysiology.

Background: Inflammatory mechanisms contribute to major depressive disorder (MDD) in a subset of patients. Lipopolysaccharide (LPS), a potent inflammatory endotoxin, can induce depressive-like symptoms, but its relationship with specific depressive symptom profiles remains unclear. Methods: In total, 95 adults with MDD (mean age was 44 ± 14 years) were recruited from psychiatric inpatient and outpatient services. Depressive symptom severity was assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS). Serum concentrations of LPS, IL-10, TNF-α, IL-6, and IFN-γ were measured by ELISA. Associations between inflammatory markers and depressive symptoms were examined using group comparisons, correlation analyses, multivariable linear regression, quantile regression, and mediation analyses adjusted for demographic and clinical covariates. Results: Patients with severe depression exhibited significantly lower circulating LPS levels than those with mild-to-moderate depression (95.6 vs. 165.2 pg/mL, p = 0.007), despite no significant differences in cytokine concentrations. LPS levels were inversely associated with depression severity, anhedonia factor score, and MADRS item 8 (“inability to feel”). Lower LPS concentrations were independently predicted by higher inability to feel, reduced appetite and combined pharmacotherapy use. Quantile regression demonstrated heterogeneity across the LPS distribution, with symptom–LPS associations evident among patients with higher LPS concentrations. Higher LPS levels were associated with greater apparent sadness, concentration difficulties, and reduced sleep, but lower levels of anhedonia, less reduced appetite, less lassitude, lower inner tension, and lower suicidality. Mediation analyses indicated that anhedonia mediated the association between LPS levels and depression severity. Conclusions: Higher circulating LPS may characterize a biologically distinct subgroup of MDD patients with lower symptom severity and less anhedonia, supporting biological heterogeneity within MDD.

Background/Objectives: Identifying the factor most strongly associated with patients’ quality of life (QoL) is crucial for establishing treatment goals focused on improved recovery. This study aimed to determine whether sociodemographic factors, negative, or depressive symptoms have the strongest association with QoL. Methods: Inpatients diagnosed with schizophrenia were recruited. We collected data on sociodemographic factors, asked patients to rate their well-being on a subjective well-being scale, and evaluated their psychopathology using observer-rated psychometric scales (Positive and Negative Symptoms Scale (PANSS), Brief Negative Symptoms Scale (BNSS), and Calgary Depression Scale for Schizophrenia (CDSS) as well as self-rated scales (Self-evaluation Negative Symptoms Scale (SNS). QoL was evaluated using Short-Form 36 (SF-36). Patients were also divided into primary, prominent, and predominant negative symptom groups. We conducted correlation and linear regression analyses to identify which factors were most strongly associated with QoL. Results: In this study, 323 participants were included. The CDSS total score showed the strongest correlation with QoL scores, followed by negative symptoms assessed with the SNS. Positive and negative symptoms, assessed using either the PANSS or the BNSS, showed weak or insignificant correlations with QoL. Among sociodemographic factors, the subjective well-being score, previous history of hospitalization, or suicide attempts had the strongest correlation with QoL. CDSS scores were the variable with the strongest independent association with QoL in regression analysis. Conclusions: Depressive symptom severity showed the strongest and most consistent association with QoL across both correlation and multivariable analyses. These findings are hypothesis-generating and require longitudinal confirmation.

Background and Objectives: Research from recent decades provides strong evidence of an association between childhood adversity and later mental illness in adulthood. The current study analyzed the frequency and intensity of exposure to adverse childhood experiences (ACEs) in a clinical sample of patients with mood disorders. Effect sizes for each ACE were also evaluated for the onset of depressive disorders (DD) and bipolar disorder (BD). Materials and Methods: A study enrolled total of 147 patients admitted to the Psychiatry Department with diagnoses of DD or BD between October 2018 and December 2019. The control group comprised 156 healthy volunteers. Psychiatric diagnoses were assessed using the Mini International Neuropsychiatric Interview (MINI), version 6.0. Adverse childhood experiences were assessed using a self-administered Adverse Childhood Experiences - International Questionnaire (ACE-IQ). Results: Results indicated that patients with mood disorders had significantly more adverse childhood experiences (ACEs) compared to controls. The occurrence of high ACEs exposure (≥4 ACEs) was also notably greater among these patients. Higher cumulative ACEs scores correlated with increased likelihoods of depressive disorders (DD) and bipolar disorder (BD). Factors such as emotional and physical neglect, sexual abuse, household mental illness, having an incarcerated household member, collective violence, parental separation or death significantly raised the odds of DD. Similarly, household mental illness, emotional neglect, parental separation or death, and household substance abuse, collective violence significantly elevated the odds for BD. Multivariate binary logistic regression identified emotional neglect and household mental illness as independent predictors of DD and BD. Conclusions: Our results highlight a significant association between adverse childhood experiences and mood disorders. Early recognition of adversity in childhood and adolescence, and appropriate interventions, must play an important role in the prevention of mood disorders.