8 research outputs found

    The network structure of mania symptoms differs between people with and without binge eating

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    Objectives: People with bipolar disorder who also report binge eating have increased psychopathology and greater impairment than those without binge eating. Whether this co‐occurrence is related to binge eating as a symptom or presents differently across full‐syndrome eating disorders with binge eating is unclear. Methods: We first compared networks of 13 lifetime mania symptoms in 34,226 participants from the United Kingdom's National Institute for Health and Care Research BioResource with (n = 12,104) and without (n = 22,122) lifetime binge eating. Second, in the subsample with binge eating, we compared networks of mania symptoms in participants with lifetime anorexia nervosa binge‐eating/purging (n = 825), bulimia nervosa (n = 3737), and binge‐eating disorder (n = 3648). Results: People with binge eating endorsed every mania symptom significantly more often than those without binge eating. Within the subsample, people with bulimia nervosa most often had the highest endorsement rate of each mania symptom. We found significant differences in network parameter statistics, including network structure (M = 0.25, p = 0.001) and global strength (S = 1.84, p = 0.002) when comparing the binge eating with no binge‐eating participants. However, network structure differences were sensitive to reductions in sample size and the greater density of the latter network was explained by the large proportion of participants (34%) without mania symptoms. The structure of the anorexia nervosa binge‐eating/purging network differed from the bulimia nervosa network (M = 0.66, p = 0.001), but the result was unstable. Conclusions: Our results suggest that the presence and structure of mania symptoms may be more associated with binge eating as a symptom rather than any specific binge‐type eating disorder. Further research with larger sample sizes is required to confirm our findings

    Risk and protective factors for new onset binge eating, low weight, and self-harm symptoms in over 25,000 individuals in the UK during the COVID-19 pandemic

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    Objective: The disruption caused by the COVID-19 pandemic has been associated with poor mental health, including increases in eating disorder and self-harm symptoms. We investigated risk and protective factors for new onset of these symptoms during the pandemic. Method: Data were from the COVID-19 Psychiatry and Neurological Genetics study and the Repeated Assessment of Mental health in Pandemics Study (n = 45,058). Exposures were socio-demographic characteristics, lifetime psychiatric disorder, and COVID-related variables, including SARS-CoV-2 infection/illness with COVID-19. We identified four sub-samples of participants without pre-pandemic experience of our outcomes: binge eating (n = 18,172), low weight (n = 19,148), suicidal and/or self-harm ideation (n = 12,650), and self-harm (n = 20,266). Participants reported on our outcomes at frequent intervals (fortnightly to monthly). We fitted four logistic regression models to identify factors associated with new onset of our outcomes. Results: Within each subsample, new onset was reported by: 16.9% for binge eating, 8.9% for low weight, 26.6% for suicidal and/or self-harm ideation, and 3.3.% for self-harm. Shared risk factors included having a lifetime psychiatric disorder, not being in paid employment, and higher pandemic worry scores. Conversely, infection with SARS-CoV-2/illness with COVID-19 was linked to lower odds of all outcomes. Other factors were associated with one outcome, such as pandemic-related loneliness with suicidal and/or self-harm ideation. Discussion: Overall, we detected shared risk factors that may drive the comorbidity between eating disorders and self-harm. Subgroups of individuals with these risk factors may require more frequent monitoring during future pandemics

    Sociodemographic, mental health, and physical health factors associated with participation within re-contactable mental health cohorts: an investigation of the GLAD Study

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    Abstract Background The Genetic Links to Anxiety and Depression (GLAD) Study is a large cohort of individuals with lifetime anxiety and/or depression, designed to facilitate re-contact of participants for mental health research. At the start of the pandemic, participants from three cohorts, including the GLAD Study, were invited to join the COVID-19 Psychiatry and Neurological Genetics (COPING) study to monitor mental and neurological health. However, previous research suggests that participation in longitudinal studies follows a systematic, rather than random, process, which can ultimately bias results. Therefore, this study assessed participation biases following the re-contact of GLAD Study participants. Methods In April 2020, all current GLAD Study participants (N = 36,770) were invited to the COPING study. Using logistic regression, we investigated whether sociodemographic, mental, and physical health characteristics were associated with participation in the COPING baseline survey (aim one). Subsequently, we used a zero-inflated negative binomial regression to examine whether these factors were also related to participation in the COPING follow-up surveys (aim two). Results For aim one, older age, female gender identity, non-binary or self-defined gender identities, having one or more physical health disorders, and providing a saliva kit for the GLAD Study were associated with an increased odds of completing the COPING baseline survey. In contrast, lower educational attainment, Asian or Asian British ethnic identity, Black or Black British ethnic identity, higher alcohol consumption at the GLAD sign-up survey, and current or ex-smoking were associated with a reduced odds. For aim two, older age, female gender, and saliva kit provision were associated with greater COPING follow-up survey completion. Lower educational attainment, higher alcohol consumption at the GLAD Study sign-up, ex-smoking, and self-reported attention deficit hyperactivity disorder had negative relationships. Conclusions Participation biases surrounding sociodemographic and physical health characteristics were particularly evident when re-contacting the GLAD Study volunteers. Factors associated with participation may vary depending on study design. Researchers should examine the barriers and mechanisms underlying participation bias in order to combat these issues and address recruitment biases in future studies
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