The risk of eating disorders comorbid with attention‐deficit/hyperactivity disorder: A systematic review and meta‐analysis

Nazar, Bruno Palazzo; Bernardes, Camila; Peachey, Gemma; Sergeant, Joseph A.; Mattos, Paulo; Treasure, Janet

doi:10.1002/eat.22643

Cited by 112 publications

(126 citation statements)

References 85 publications

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“…There is a growing literature that humans are at risk for comorbid conditions, so that estimated PODs may be similar (e.g., within an order of magnitude) across a set of analyses. For example, neurodevelopment may impact impulsive behavior, which may lead to over-eating and to obesity (Nazar et al, 2016). The analysis of impulsive behavior may provide useful HBM values also to measures of obesity.…”

Section: Discussionmentioning

confidence: 99%

Incorporating regulatory guideline values in analysis of epidemiology data

Gennings

Shu

Rudén

et al. 2018

Environment International

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Fundamental to regulatory guidelines is to identify chemicals that are implicated with adverse human health effects and inform public health risk assessors about “acceptable ranges” of such environmental exposures (e.g., from consumer products and pesticides). The process is made more difficult when accounting for complex human exposures to multiple environmental chemicals. Herein we propose a new class of nonlinear statistical models for human data that incorporate and evaluate regulatory guideline values into analyses of health effects of exposure to chemical mixtures using so-called ‘desirability functions’ (DFs). The DFs are incorporated into nonlinear regression models to allow for the simultaneous estimation of points of departure for risk assessment of combinations of individual substances that are parts of chemical mixtures detected in humans. These are, in contrast to published so-called biomonitoring equivalent (BE) values and human biomonitoring (HBM) values that link regulatory guideline values from in vivo studies of single chemicals to internal concentrations monitored in humans. We illustrate the strategy through the analysis of prenatal concentrations of mixtures of 11 chemicals with suspected endocrine disrupting properties and two health effects: birth weight and language delay at 2.5 years. The strategy allows for the creation of a Mixture Desirability Function i.e., MDF, which is a uni-dimensional construct of the set of single chemical DFs; thus, it focuses the resulting inference to a single dimension for a more powerful one degree-of-freedom test of significance. Based on the application of this new method we conclude that the guideline values need to be lower than those for single chemicals when the chemicals are observed in combination to achieve a similar level of protection as was aimed for the individual chemicals. The proposed modeling may thus suggest data-driven uncertainty factors for single chemical risk assessment that takes environmental mixtures into account.

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Section: Discussionmentioning

confidence: 99%

Incorporating regulatory guideline values in analysis of epidemiology data

Gennings

Shu

Rudén

et al. 2018

Environment International

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“…To summarize shortly, the complex relationships between ADHD and eating disorder not just rely on single factors (e.g., impulsivity, genetic abnormalities, depression), but the interactions among these factors might interpret the associations. It has been suggested that not only ADHD neuropsychological functioning could alter information processing for food-related decision making, but also changes in nutritional or emotional state promoted by eating disorder could make ADHD individuals more prone to disinhibition 16 .…”

Section: Discussionmentioning

confidence: 99%

“…An updated meta-analysis study indicated that people with ADHD are 3.8 times more likely to present with any eating disorder as a comorbid diagnosis, and the risk increased to 5.7 times for Bulimia Nervosa 16 . Bulimia Nervosa is characterized by recurrent episodes of binge eating, and recurrent inappropriate compensatory behavior in order to prevent weight gain 17 .…”

Section: Introductionmentioning

confidence: 99%

Associations among ADHD, Abnormal Eating and Overweight in a non-clinical sample of Asian children

Tong

Shi

2017

Sci Rep

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Attention-deficit/hyperactivity disorder (ADHD) has been found to be comorbid with obesity in adults, but the association in children is uncertain. Because the underlying mechanism of comorbidity in children has not been researched sufficiently, this study aims to explore the associations among ADHD, abnormal eating, and body mass index (BMI), as well as the mediating effect of depression in children. We conducted a cross-sectional study of 785 primary students in China. The parent-report version of ADHD Rating Scale-IV (ADHDRS-IV), the Child Eating Behaviour Questionnaire (CEBQ) and the Children’s Eating Attitude Test (ChEAT) were used to identify ADHD symptoms and abnormal eating. The Child Behavior Checklist (CBCL) was applied to assess depression. Structural Equation Modeling was carried out to clarify the associations between ADHD symptoms, depression, abnormal eating, and overweight of students. We found that ADHD positively contributed to emotional eating and Bulimia Nervosa symptoms. However, neither emotional eating nor Bulimia Nervosa symptoms was related to BMI in children. We also found that ADHD significantly contributed to depression, and depression directly predicted emotional eating. In conclusion, ADHD increased the risk of abnormal eating in children, while no significant relationship existed between ADHD and BMI. Comorbid depression raised the risk of emotional eating, rather than Bulimia Nervosa symptoms.

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“…Neurodevelopmental disorders have been associated with eating disorders (ED), with attention-deficit/hyperactivity disorder linked to bulimia nervosa (BN) and binge-eating disorder (Nazar et al, 2016), and autism spectrum disorders (ASDs) to anorexia nervosa (AN) ( Baron-Cohen et al, 2013;Tchanturia et al, 2013). There has been interest in the impact these might have upon treatment and prognosis (Westwood & Tchanturia, 2017).…”

Section: Introductionmentioning

confidence: 99%