UNASSIGNED: Evidence for anorexia and bulimia in relation to the risk of ulcerative colitis (UC) is limited and inconsistent. The objective of this research was to utilize bi-directional, two-sample Mendelian randomization (MR) analysis to predict the causal association between anorexia nervosa and bulimia nervosa with UC.
UNASSIGNED: The genome-wide association studies (GWAS) provided data for anorexia and bulimia from the UK Biobank, utilizing single-nucleotide polymorphisms (SNP) as instrumental variables. Additionally, genetic associations with UC were collected from various sources including the FinnGen Biobank, the UK Biobank and the International Inflammatory Bowel Disease Genetics Consortium (IIBDGC). The main analytical approach utilized in this study was the inverse-variance-weighted (IVW) method. To evaluate horizontal pleiotropy, the researchers conducted MR-Egger regression and MR-PRESSO global test analyses. Additionally, heterogeneity was assessed using the Cochran\'s Q test.
UNASSIGNED: This study found a negative association between genetically predicted bulimia (OR = 0.943, 95% CI: 0.893-0.996; p = 0.034) and the risk of UC in the IIBDGC dataset, indicating that individuals with bulimia have approximately a 5.7% lower risk of developing UC. No association was observed in the other two datasets. Conversely, genetically predicted anorexia was not found to be causally associated with UC. In bi-directional Mendelian randomization, UC from the IIBDGC dataset was negatively associated with the risk of anorexia (OR = 0.877, 95% CI: 0.797-0.965; p = 0.007), suggesting that UC patients have approximately a 12.3% lower risk of developing anorexia, but not causally associated with bulimia.
UNASSIGNED: Genetically predicted bulimia may have a negative association with the onset of UC, while genetically predicted anorexia does not show a causal relationship with the development of UC. Conversely, genetically predicted UC may have a negative association with the development of anorexia.

Objective: To investigate the effects of eating and emotions on reproductive axis function in patients with functional hypothalamic amenorrhea (FHA).
Methods: A retrospective cohort study was conducted to summarize the clinical and endocrine characteristics of 58 patients with FHA at initial diagnosis and to follow up the recovery of ovulation and spontaneous menstruation in the patients to investigate these biochemical indicators and their effects on recovery outcomes.
Results: Among patients with FHA, 13.8% (8/58) and 15.5% (9/58) had above moderately severe depressive and severe anxiety symptoms respectively, and 25.9% (15/58) were at high risk for eating disorders. 34.5% (20/58) were included assessed as having recovered. The non-recovered group had higher scores on the Patient Health Questionnaire (PHQ-9) (p = .022) and higher scores on the Eating Attitude Test-26 (EAT-26) (p = .03) as well as bulimia and food preoccupation (p = .041). Follicle diameter >5 mm at initial diagnosis was an independent factor influencing recovery of reproductive axis function (odds ratio = 7.532; 95% confidence interval, 1.321-42.930; p = .023).
Conculsions: Mood disorders and a certain risk of eating disorders were present in FHA.These, together with weight loss, endocrine and follicle size, could influence the outcome.

Screening for eating disorders (EDs) is an essential part of the prevention and intervention of EDs. Traditional screening methods mostly rely on predefined cutoff scores which have limitations of generalizability and may produce biased results when the cutoff scores are used in populations where the instruments or cutoff scores have not been validated. Compared to the traditional cutoff score approach, the diagnostic classification modeling (DCM) approach can provide psychometric and classification information simultaneously and has been used for diagnosing mental disorders. In the present study, we introduce DCM as an innovative and alternative approach to screening individuals at risk of EDs. To illustrate the practical utility of DCM, we provide two examples: one involving the application of DCM to examine probable ED status from the 12-item Short form of the Eating Disorder Examination-Questionnaire (EDE-QS) to screen probable thinness-oriented EDs and the Muscularity-Oriented Eating Test (MOET) to screen probable muscularity-oriented EDs.

Anorexia nervosa (AN) is a serious psychiatric disease, but the neural mechanisms underlying its development are unclear. A subpopulation of amygdala neurons, marked by expression of protein kinase C-delta (PKC-δ), has previously been shown to regulate diverse anorexigenic signals. Here, we demonstrate that these neurons regulate development of activity-based anorexia (ABA), a common animal model for AN. PKC-δ neurons are located in two nuclei of the central extended amygdala (EAc): the central nucleus (CeA) and oval region of the bed nucleus of the stria terminalis (ovBNST). Simultaneous ablation of CeAPKC-δ and ovBNSTPKC-δ neurons prevents ABA, but ablating PKC-δ neurons in the CeA or ovBNST alone is not sufficient. Correspondingly, PKC-δ neurons in both nuclei show increased activity with ABA development. Our study shows how neurons in the amygdala regulate ABA by impacting both feeding and wheel activity behaviors and support a complex heterogeneous etiology of AN.

UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: Machine learning based on DTI could effectively distinguish between AN and BN, with MTG_L and STG_L potentially serving as neuroimaging biomarkers.

OBJECTIVE: The objective of this study was to determine the association between cyberbullying and eating disorder symptoms in a national sample of 10-14-year-old early adolescents.
METHODS: We analyzed cross-sectional data from the Adolescent Brain Cognitive Development (ABCD) Study (Year 2, 2018-2020, N = 10,258/11,875, 49% female, 46% non-White). Data were collected using multi-stage probability sampling. Modified Poisson regression analyses examined the association between cyberbullying and self-reported eating disorder symptoms based on the Kiddie Schedule for Affective Disorders and Schizophrenia (KSADS-5).
RESULTS: Cyberbullying victimization was associated with worry about weight gain (prevalence ratio [PR] 2.41, 95% confidence interval [CI] 1.48-3.91), self-worth tied to weight (PR 2.08, 95% CI 1.33-3.26), inappropriate compensatory behavior to prevent weight gain (PR 1.95, 95% CI 1.57-2.42), binge eating (PR 1.95, 95% CI 1.59-2.39), and distress with binge eating (PR 2.64, 95% CI 1.94-3.59), in models adjusting for potential confounders. Cyberbullying perpetration was associated with worry about weight gain (PR 3.52, 95% CI 1.19-10.37), self-worth tied to weight (PR 5.59, 95% CI 2.56-12.20), binge eating (PR 2.36, 95% CI 1.44-3.87), and distress with binge eating (PR 2.84, 95% CI 1.47-5.49).
CONCLUSIONS: Cyberbullying victimization and perpetration in early adolescence are associated with eating disorder symptoms. Clinicians may consider assessing for cyberbullying and eating disorder symptoms in early adolescence and provide anticipatory guidance.
UNASSIGNED: Eating disorders often onset in adolescence and have among the highest mortality rates of any psychiatric disorder. In addition, cyberbullying has increased in prevalence among adolescents and significantly impacts mental health. In a national study of early adolescents, we found that cyberbullying victimization and perpetration are associated with eating disorder symptoms. Screening for and providing anticipatory guidance on cyberbullying and eating disorder symptoms in early adolescents may be warranted.

UNASSIGNED: Eating disorders (EDs) and depression are common in university students, especially during the COVID-19 pandemic. The aim of this study was to elucidate characteristics of EDs and depression symptoms networks among Chinese university students in the later stage of the COVID-19 pandemic in China.
UNASSIGNED: A total of 929 university students completed the SCOFF questionnaire measuring EDs and Patient Health Questionnaire with 9 items (PHQ-9) measuring depression in Guangzhou, China. The network model was applied to identify central symptoms, bridge symptoms, and important connections between SCOFF and PHQ-9 using R studio. The subgroup analyses of both genders in medical and non-medical students were further explored.
UNASSIGNED: In the networks of the whole sample, central symptoms included \"Loss of control over eating\" (EDs) and \"Appetite changes\" (depression). The bridge connections were between \"Loss of control over eating\" (EDs) and \"Appetite changes\" (depression), between \"Deliberate vomiting\" (EDs) and \"Thoughts of death\" (depression). \"Appetite changes\" (depression) and \"Feeling of worthlessness\" (depression) were central symptoms in both subgroups of medical and non-medical students. \"Fatigue\" (depression) was the central symptom in the female and medical students group. The edge between \"Loss of control over eating\" (EDs) and \"Appetite changes\" (depression) acted as a bridge in all subgroups.
UNASSIGNED: Social network approaches offered promising ways of further understanding the association between EDs and depression among university students during the pandemic of COVID-19 in China. Investigations targeting central and bridge symptoms would help to develop effective treatments for both EDs and depression for this population.

This study assessed picky eating in pregnant women by exploring whether picky eating is associated with pregnant women\'s well-being, including life satisfaction, psychological distress, and psychosocial impairment.
Data collected were from 345 Chinese pregnant women ( M age = 29.95 years, SD = 5.58). Pearson correlation analyses were conducted to examine zero-order correlations between picky eating and well-being variables (i.e., life satisfaction, psychological distress, and psychosocial impairment). Hierarchical multiple regressions were conducted to examine the unique associations of picky eating with well-being variables, adjusting for demographic and pregnancy-related characteristics and thinness-oriented disordered eating.
Picky eating was significantly and negatively correlated with life satisfaction (r = -.24, p < .001) and positively correlated with psychological distress (r = .37, p < .001) and psychosocial impairment (r = .50, p < .001). When adjusting for covariates and thinness-oriented disordered eating, picky eating was still significantly associated with lower life satisfaction, higher psychological distress, and higher psychosocial impairment.
The findings suggest that picky eating may be a significant correlate of pregnant women\'s poorer well-being. Future research with longitudinal designs is warranted to further examine the temporal associations between picky eating and pregnant women\'s well-being.
Picky eating behaviors are poorly understood in pregnant women. Our results revealed that higher picky eating behaviors were associated with lower life satisfaction and higher psychological distress and psychosocial impairment in Chinese pregnant women. Researchers and clinicians may consider picky eating in the assessment and treatment of mental health and disordered eating in pregnant women.

Feeding requires sophisticated orchestration of neural processes to satiate appetite in natural, capricious settings. However, the complementary roles of discrete neural populations in orchestrating distinct behaviors and motivations throughout the feeding process are largely unknown. Here, we delineate the behavioral repertoire of mice by developing a machine-learning-assisted behavior tracking system and show that feeding is fragmented and divergent motivations for food consumption or environment exploration compete throughout the feeding process. An iterative activation sequence of agouti-related peptide (AgRP)-expressing neurons in arcuate (ARC) nucleus, GABAergic neurons in the lateral hypothalamus (LH), and in dorsal raphe (DR) orchestrate the preparation, initiation, and maintenance of feeding segments, respectively, via the resolution of motivational conflicts. The iterative neural processing sequence underlying the competition of divergent motivations further suggests a general rule for optimizing goal-directed behaviors.

Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.