Effect of subjective sleep quality on learning and memory in drug-free patients with schizophrenia

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

Targeted metabolomics-based understanding of the sleep disturbances in drug-naïve patients with schizophrenia

BACKGROUND

Sleep disturbances are a common occurrence in patients with schizophrenia, yet the underlying pathogenesis remain poorly understood. Here, we performed a targeted metabolomics-based approach to explore the potential biological mechanisms contributing to sleep disturbances in schizophrenia.

METHODS

Plasma samples from 59 drug-naïve patients with schizophrenia and 36 healthy controls were subjected to liquid chromatography-mass spectrometry (LC-MS) targeted metabolomics analysis, allowing for the quantification and profiling of 271 metabolites. Sleep quality and clinical symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Positive and Negative Symptom Scale (PANSS), respectively. Partial correlation analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to identify metabolites specifically associated with sleep disturbances in drug-naïve schizophrenia.

RESULTS

16 characteristic metabolites were observed significantly associated with sleep disturbances in drug-naïve patients with schizophrenia. Furthermore, the glycerophospholipid metabolism (Impact: 0.138, p<0.001), the butanoate metabolism (Impact: 0.032, p=0.008), and the sphingolipid metabolism (Impact: 0.270, p=0.104) were identified as metabolic pathways associated with sleep disturbances in drug-naïve patients with schizophrenia.

CONCLUSIONS

Our study identified 16 characteristic metabolites (mainly lipids) and 3 metabolic pathways related to sleep disturbances in drug-naïve schizophrenia. The detection of these distinct metabolites provide valuable insights into the underlying biological mechanisms associated with sleep disturbances in schizophrenia.

Poor sleep quality in schizophrenia patients: A systematic review and meta-analyses of epidemiological and case-control studies

OBJECTIVE

Poor sleep quality is common in patients with schizophrenia but estimated prevalence rates in this population have been mixed. This systematic review and meta-analysis examined the prevalence of poor sleep quality in schizophrenia samples and moderators of prevalence from epidemiological studies as well as the risk of poor sleep quality in schizophrenia patients based on case-control studies.

METHODS

Both international (PubMed, Web of Science, PsycINFO, EMBASE) and Chinese databases [Chinese Nation knowledge Infrastructure (CNKI) and WANFANG] were systematically searched. Studies that estimated the prevalence of poor sleep quality in schizophrenia were analyzed using a random effects model. Funnel plots and Egger's tests were used to assess publication bias. Statistical analyses were performed using R software.

RESULTS

In total, 23 epidemiological studies and nine case-control studies were included. Based on the epidemiological studies, the pooled overall prevalence of poor sleep quality was 63.4 % [95 % confidence interval (CI): 57.0 %-69.9 %]. Additionally, based on the nine case-control studies, schizophrenia patients had a significantly higher risk for poor sleep quality compared to healthy controls [odd ratio (OR) = 4.5; 95%CI: 2.4-8.3; P < 0.0001].

CONCLUSION

Poor sleep quality is common among schizophrenia patients. Considering negative outcomes caused by poor sleep quality, regular screening on poor sleep quality should be conducted and effective interventions should be provided to those in need.

Sleep-dependent memory consolidation in schizophrenia: A systematic review and meta-analysis

Sleep disturbances and cognitive impairment are both persistent and common features of schizophrenia. Accumulating evidence indicates that sleep-dependent memory consolidation might be impaired in patients with schizophrenia compared to healthy controls. The current systematic review was performed in accordance with PRISMA guidelines. A random-effects model was used to calculate effect sizes (Hedge's g). In the quantitative review, three separate meta-analyses were conducted for procedural memory in healthy controls, schizophrenia, and comparison between healthy controls and schizophrenia. Additionally, separate meta-analyses were conducted for the studies using finger tapping motor sequence task, as it is the most commonly used task. The current systematic review included 14 studies including 304 patients with schizophrenia and 209 healthy controls. The random-effects model analyses for sleep-dependent procedural memory consolidation resulted in a small effect size in schizophrenia (g = 0.26), a large effect size in healthy controls (g = 0.98), a moderate effect size in healthy controls vs schizophrenia (g = 0.64). For the studies using finger tapping motor sequence task, meta-analyses resulted in a small effect size in schizophrenia (g = 0.19), a large effect size in healthy controls (g = 1.07), a moderate effect size in healthy controls vs schizophrenia (g = 0.70). In the qualitative review, there was also impaired sleep-dependent declarative memory consolidation in schizophrenia compared to healthy controls. Current findings support that sleep improves memory consolidation in healthy adults, but there is a deficit in sleep-dependent memory consolidation in people with schizophrenia. Future studies investigating sleep-dependent consolidation of different memory subtypes with polysomnography in different stages of psychotic disorders are needed.