Background: Bulimia nervosa (BN) is a psychiatric disorder with unclear pathophysiology. Several studies have associated BN with structural and functional changes in the brain, but findings have been inconsistent. Here we explored this potential association in a small group of Chinese women with BN. Methods: This retrospective study examined 34 women with BN and 34 age-matched healthy controls, all of whom underwent T1-weighted magnetic resonance imaging (MRI). Voxel-based morphometry was carried out to explore alterations in regional grey matter volume (GMV) that may be associated with BN. Results: The BN group showed smaller GMV in the left medial superior frontal gyrus (SFGmed.L), right superior temporal gyrus (STG.R), right median cingulate and paracingulate gyri (DCG.R), left median cingulate and paracingulate gyri (DCG.L) and left dorsolateral superior frontal gyrus (SFGdor.L). No regions showing GMV increases in BN were identified. The GMV reduction did not correlate with body mass index, duration of illness, or patients’ self-esteem or overall self-evaluation. GMV reduction correlated negatively with age in the SFGmed. L (r = − 0.516, P < 0.005), DCG. R (r = − 0.556, P < 0.005), DCG. L (r = − 0.576, P < 0.05) and SFGdor. L (r = − 0.576, P < 0.005). Conclusions: Women with BN show reduced GMV in several brain regions, but it is difficult to know whether these changes are the result of BN pathology or of binge-eating and compensatory behavior. These changes may be associated with impaired inhibitory control, body dissatisfaction and emotion dysregulation.

The Kessler Psychological Distress Scale (K10) is a 10-item questionnaire designed to measure psychological distress. The present study aimed to validate the factor structure of the K10 and its reliability and validity using a large sample from healthcare professionals in China mainland. K10 data were collected in September 19 to October 20, 2023 via anonymous online survey (total sample, N = 8602). The internal consistency reliability of the K10 was examined. Exploratory factor analysis (N = 4301) and confirmatory factor analysis(N = 4301) were conducted to validate the structural validity. Exploratory factor analysis revealed two factors, depression (DEP) and anxiety (ANX), which together accounted for 75.91 % of the total variance. Confirmatory factor analysis further supported the two-factor oblique model as the best fit, outperforming the one-factor model. The internal consistency of the K10 was α = 0.952, while the internal consistency coefficients for ANX and DEP were 0.883 and 0.940, respectively. K10 is a validated and concise tool that serves a dual purpose of screening psychological distress and assessing depressive and anxiety symptoms among healthcare professionals in China.

Background: Anorexia nervosa (AN) and bulimia nervosa (BN), two subtypes of eating disorders, often present diagnostic challenges due to their overlapping symptoms. Machine learning has proven its capacity to improve group classification without requiring researchers to specify variables. The study aimed to distinguish between AN and BN using machine learning models based on diffusion tensor images (DTI).Methods: This is a cross-sectional study, drug-naive females diagnosed with anorexia nervosa (AN) and bulimia nervosa (BN) were included. Demographic data and DTI were collected for all patients. Features for machine learning included Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Support vector machine was constructed by LIBSVM, MATLAB2013b, and FSL5.0.9 software.Results: A total of 58 female patients (24 AN, 34 BN) were included in this study. Statistical analysis revealed no significant differences in age, years of education, or course of illness between the two groups. AN patients had significantly lower BMI than BN patients. The AD model exhibited an area under the curve was 0.793 (accuracy: 75.86%, sensitivity: 66.67%, specificity: 88.23%), highlighting the left middle temporal gyrus (MTG_L) and the left superior temporal gyrus (STG_L) as differentiating brain regions. AN patients exhibited lower AD features in the STG_L and MTG_L than BN. Machine learning analysis indicated no significant differences in FA, MD, and RD values between AN and BN groups (p > 0.001).Conclusion: Machine learning based on DTI could effectively distinguish between AN and BN, with MTG_L and STG_L potentially serving as neuroimaging biomarkers.

Bodily distress syndrome (BDS) is a new, empirical-based diagnosis of functional somatic symptoms. This study aimed to explore the prevalence of BDS and its association with psychosocial variables in a Chinese clinical population. A multicentre cross-sectional study of 1269 patients was conducted in 9 different Chinese tertiary outpatient hospitals. The BDS was identified by trained interviewers face-to face, based on a brief version of the Schedules for Assessment in Neuropsychiatry (RIFD) and the BDS Checklist-25. Sociodemographic data and further information were characterised from psychometric questionnaires (The Patient Health Questionnaire-15, the Patient Health Questionnaire-9, the General Anxiety Disorder-7, the Whiteley scale-8) . Complete data were available for 697 patients. The prevalence of BDS was 26.8% (95% confidence interval (CI): 23.5–30.1). Among the participants, 5.8% (95% CI: 4.1–7.6) fulfilled the criteria for single-organ BDS, while 20.9% (95%CI: 17.9–24.0) had multi-organ BDS. Comparison of the PHQ-15, PHQ-9, GAD-7, and WI-8 scores revealed higher scores on all dimensions for patients with BDS. In a binary logistic regression analysis, BDS was significantly associated with increased health-related anxiety (WI-8) and depression (PHQ-9). The explained variance was Nagelkerke’s R2 = 0.42. In China, the BDS is a common clinical condition in tertiary outpatient hospital settings with high prevalence, and is associated with health anxiety and depressive symptoms. In this clinical population, the severe multi-organ subtype of BDS was the most frequent.

Altered volumes in the hippocampus and amygdala have been linked to anorexia nervosa (AN). This study aimed to investigate amygdala and hippocampal subfields volume abnormalities in AN patients, and their associations with parental rearing practices and clinical psychological characteristics. This study included twenty-nine drug-naive females with AN from West China Hospital of Sichuan University, China, and fifty-nine age- and gender-matched healthy controls (HCs) recruited through advertisement. All participants underwent T1-weighted imaging. Amygdala and hippocampal subfields volume was calculated using FreeSurfer 7.0. The Core Self-Evaluation Scale (CSES) and Rosenberg Self-Esteem Scale (RSES) were used to assess the psychological characteristics of AN patients. The Egna Minnen av Barndoms Uppfostran (EMBU) was employed to evaluate parental rearing practices. Group differences in brain volumes were analyzed with covariates like age and total intracranial volume (TIV). Partial correlation analysis explored the correlations between brain region volumes and clinical psychological characteristics. AN patients exhibited lower RSES and CSES scores, and more adverse parental rearing style than healthy norms. After adjusting for covariates, AN patients showed decreased gray matter volume (GMV) in the left medial (Me) and cortical (Co) nucleus, as well as in the right hippocampal-amygdala transition area (HATA). GMV in the left Me was correlated with years of education among HCs but not among AN patients. GMV in the right HATA was positively correlated with paternal penalty and severity, as well as maternal overinterference. This study supports structure abnormalities in amygdala and hippocampus in AN patients and suggests that parental rearing practices may be associated with hippocampal abnormalities, potentially contributing to the pathophysiology of AN. Addressing appropriate parental rearing styles may offer a positive impact on AN.