The status and risk factors for anxiety/depression in patients with atrophic chronic gastritis: a cross-sectional study.

BACKGROUND: Atrophic chronic gastritis (ACG) is a preneoplastic condition of gastric carcinoma. Numerous studies have shown anxiety and depression can affect gastrointestinal function, which may promote gastrointestinal disorders development and progression. Thus, we hypothesized that anxiety and depression may enhance the development and progression of ACG. In this study, we aimed to analyse risk factors for anxiety and depression in ACG patients and integrate these risk factors to construct an effective clinical prediction model. METHODS: In total, 118 ACG patients were included from July 2021 to May 2022. Anxiety and depression were assessed utilizing the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Data were collected on demographic characteristics, lifestyle, and dietary habits. Risk factors for anxiety and depression were explored with univariate analysis and multivariate stepwise logistic regression, and risk prediction models were built. RESULTS: Among 118 ACG patients, 36.4% had anxiety, 25.4% had depression, and 21.2% had both anxiety and depression. Poor sleep quality [odd ratio (OR) 4.32, 95% confidence interval (CI): 1.60-11.65, P=0.004] was positively associated with risk of anxiety, while smoking (OR 0.15, 95% CI: 0.03-0.68, P=0.014) and weekly exercise time (OR 0.89, 95% CI: 0.79-0.99, P=0.037) were negatively associated with risk of anxiety. The area under the receiver operating characteristic (ROC) curve was 80.3%, 95% CI: [0.722-0.885]. The sensitivity was 72.1%, and the specificity was 78.7%. Poor sleep quality (P<0.001, OR 23.89, 95% CI: 4.05-141.05), high salt diet (P=0.004, OR 6.94, 95% CI: 1.86-25.96), family history of tumours (P=0.020, OR 6.10, 95% CI: 1.33-27.93), and abdominal pain (P=0.018, OR 4.44, 95% CI: 1.29-15.23) were positively associated with the risk of depression, with an area under the ROC curve of 77.3%, 95% CI: 0.687-0.860. The sensitivity was 83.3%, and the specificity was 62.5%. CONCLUSIONS: Potential anxiety and depression in ACG patients can be identified early by referring to risk factors and protective factors. The prediction model could be used to detect anxiety and depression in ACG patients at their earliest stage and provide meaningful suggestions for ACG patients.

Abnormal network homogeneity of default-mode network and its relationships with clinical symptoms in antipsychotic-naïve first-diagnosis schizophrenia.

Schizophrenia is a severe mental disorder affecting around 0.5-1% of the global population. A few studies have shown the functional disconnection in the default-mode network (DMN) of schizophrenia patients. However, the findings remain discrepant. In the current study, we compared the intrinsic network organization of DMN of 57 first-diagnosis drug-naïve schizophrenia patients with 50 healthy controls (HCs) using a homogeneity network (NH) and explored the relationships of DMN with clinical characteristics of schizophrenia patients. Receiver operating characteristic (ROC) curves analysis and support vector machine (SVM) analysis were applied to calculate the accuracy of distinguishing schizophrenia patients from HCs. Our results showed that the NH values of patients were significantly higher in the left superior medial frontal gyrus (SMFG) and right cerebellum Crus I/Crus II and significantly lower in the right inferior temporal gyrus (ITG) and bilateral posterior cingulate cortex (PCC) compared to those of HCs. Additionally, negative correlations were shown between aberrant NH values in the right cerebellum Crus I/Crus II and general psychopathology scores, between NH values in the left SMFG and negative symptom scores, and between the NH values in the right ITG and speed of processing. Also, patients' age and the NH values in the right cerebellum Crus I/Crus II and the right ITG were the predictors of performance in the social cognition test. ROC curves analysis and SVM analysis showed that a combination of NH values in the left SMFG, right ITG, and right cerebellum Crus I/Crus II could distinguish schizophrenia patients from HCs with high accuracy. The results emphasized the vital role of DMN in the neuropathological mechanisms underlying schizophrenia.

Association of Reduced Local Activities in the Default Mode and Sensorimotor Networks with Clinical Characteristics in First-diagnosed Episode of Schizophrenia.

The neurodevelopmental hypothesis states that schizophrenia is a brain disease. Exploring abnormal brain activities can improve understanding of the neural pathologic mechanism of clinical characteristics and determine subjective biomarkers to differentiate patients with schizophrenia from healthy controls. We collected clinical characteristics (i.e., demographics, positive and negative syndrome scale (PANSS) scores, and cognitive scores) and magnetic resonance imaging (MRI) data from 57 first-diagnosed drug-naïve patients with schizophrenia and 50 healthy controls. The fractional amplitude of low-frequency fluctuation (fALFF) was used to detect local activities. Partial correlation analysis was applied to estimate the relationship between abnormal regions and clinical characteristics. The support vector machine (SVM) analysis was used to calculate the accuracy of classification in abnormal regions. In our study, the fALFF values in the right postcentral gyrus, left precentral gyrus/postcentral gyrus, left postcentral gyrus/superior parietal lobule, bilateral supplementarymotor area, bilateral paracentral lobule, and bilateral precuneus were decreased in patients with schizophrenia and associated with clinical characteristics. However, the related patterns of cognition of patients were different from those of healthy controls. Additionally, the combination of fALFF values in the bilateral paracentral lobule and right postcentral gyrus might distinguish patients with schizophrenia from healthy controls with high accuracy (98.13%), specificity (98.00%), and sensitivity (98.25%). Our study suggests that reduced local activities in the default mode network and sensorimotor network may be regarded as neural underpinnings of clinical characteristics and may discriminate patients with schizophrenia from healthy controls.

Attractin Participates in Schizophrenia by Affecting Testosterone Levels.

Attractin (ATRN) is a widely expressed glycoprotein that is involved in energy homeostasis, neurodevelopment, and immune response. It is encoded by a gene spanning 180 kb on chromosome 20p13, a region previously implicated in schizophrenia by linkage studies. To address a possible role of ATRN in disorders of the central nervous system, we created an atrn knockout zebrafish line and performed behavioral tests. Adult atrn-/- zebrafish exhibited more pronounced attack behavior relative to wild-type control zebrafish in a tracking analysis. Biochemical analysis revealed elevated testosterone levels in atrn-/- zebrafish. At the gene expression level, we noted an upregulation of cyp51 and hsd17b7, key proteins in testosterone synthesis in the brains of both adult and larvae of atrn-/- zebrafish. In order to further elucidate the relationship between testosterone and behavioral syndromes, we then compared testosterone levels of 9,008 psychiatric patients and 247 healthy controls from the same catchment area. Of all subjects examined, male subjects with schizophrenia exhibited lower testosterone levels compared with controls. In contrast, female subjects with a diagnosis of schizophrenia or bipolar disorder featured higher testosterone levels than did same sex controls. Purposeful sampling of extreme groups showed reduced ATRN expression in a subset of these subjects. Finally, we identified 14 subjects with ATRN mutations. All of whom displayed abnormal testosterone levels. In summary, the interplay of ATRN and testosterone may help to explain sexual dimorphisms in selected behavioral phenotypes.