Assessing a multivariate model of brain-mediated genetic influences on disordered eating in the ABCD cohort

Abstract

Eating disorders (EDs) are complex psychiatric conditions that often emerge during adolescence, and affected individuals frequently demonstrate high rates of psychiatric comorbidity, particularly with depressive and anxiety disorders. Although risk for EDs reflects both genetic and neurobiological factors, knowledge of how genetic risk for EDs relates to neurobiology and psychiatric symptoms during critical developmental periods remains limited. We therefore implemented a novel multivariate framework, which sought to advance knowledge of the etiology of EDs by simultaneously estimating associations between genetic risk, brain structure and ED-related psychopathology symptoms in over 4,500 adolescents of European ancestry from the Adolescent Brain and Cognitive Development study (M(SD)age=119.29(7.49) months). Polygenic scores for anorexia nervosa (AN PGS) and body mass index (BMI PGS) were generated and related to three morphometric brain features-cortical thickness, surface area and subcortical grey matter volume-and to latent psychopathology factors using structural equation modeling. We identified a three-factor structure of ED-related psychopathology symptoms: eating, distress and fear factors. Increased BMI PGS were uniquely associated with greater eating factor scores, whereas AN PGS were unrelated to psychopathology factors. Moreover, genetic risk for high BMI and for AN had distinct neural correlates, where greater BMI PGS predicted widespread increases in cortical thickness and reductions in surface area while AN PGS were nominally related to reduced caudate volume. Altered default mode and visual network thickness was associated with greater eating factor scores, whereas distress and fear factor scores reflected a shared reduction in somatomotor network thickness. Our novel findings indicate that greater genetic risk for high BMI and altered cortical thickness of canonical brain networks underpin ED symptomatology in early adolescence. As neurobiological factors appear to shape disordered eating earlier in the life course than previously thought, these results underscore the need for early detection and intervention efforts for EDs.

A common neural signature between genetic and environmental risk

“…Furthermore, regional connectivity with the fronto-parietal and visual networks showed the strongest relationship to genetic liability, suggesting that these circuits may be particularly sensitive to genetic influences. 45,46 This lends support to the idea that pleotropic risk genes increase susceptibility to a variety of clinically-distinct psychiatric conditions through non-specific changes in cortico-limbic connections. 47 Together, this study demonstrates that the neural correlates of adversity largely mirror those of genetic liability for mental health, with some notable regional differences.…”

Disordered eating in adolescents with type 1 diabetes: risk factors and screening recommendations

“…Adolescence is a vulnerable period for the emergence of DEBs, particularly restrictive eating, body image disturbance, binge eating, and compensatory behaviors [7,9,15]. While the heritability of eating disorders is between 35 and 70% [15,17,18], there are multiple interacting factors that increase risk for the emergence of DEBs during adolescence in general. These risks include biological (e.g., female sex, obsessive-compulsive traits, higher BMI), psychological (e.g., perfectionism, strong ability to delay reward, body image disturbance, low self-esteem), psychosocial (e.g., peer stress, trauma, cultural messaging around thin idealism and value in controlling shape/weight, parental eating problems), and behavioral factors (e.g., past dieting and weight loss attempts, social isolation, and impaired quality of life) [15].…”

Two human brain systems micro-structurally associated with obesity

“…Together, these studies provide robust evidence for an association between obesity and macro-structural features of brain anatomy such as grey matter volume and cortical thickness. However, changes in grey matter volume and cortical thickness can be driven by multiple different underlying processes and our understanding of the microstructural features that underpin this relationship remain largely unknown ( Westwater et al, 2022 ). For example, it is currently not known whether obesity-associated differences in grey matter volume relate to changes in the size, shape or number of neurons e.g.…”