Quadratic relations of BMI with depression and brain volume in children: Analysis of data from the ABCD study

Abstract

BackgroundWeight-related health conditions and depression peak during adolescence and show relations with brain structure. Understanding how these conditions relate to each other prior to adolescence may guide research on the co-development of unhealthy weight conditions (both underweight and overweight) and depression, with a potential brain-based link. This study examines the cross-sectional relations between body mass index (BMI), depressive symptoms, and brain volume (total and regional) to determine whether BMI has a linear or quadratic relation with depressive symptoms and brain volume and how depressive symptoms and brain volume are related. MethodsCross-sectional study using structural magnetic resonance imaging, height and weight to calculate BMI z-scores, and Child Behavior Checklist withdrawn depression scores. Data were from the Adolescent Brain Cognitive Development Study, collected at 21 sites across the United States from 11,875 9- and 10-year-old children recruited as a national sample. Mixed models were used to examine the linear and quadratic effects of BMI z-score on both brain volume (total and regional) and withdrawn depression scores, as well as the relations between brain volume and depression scores. Intracranial volume, age, sex, race, site, and family were included in the models as covariates. ResultsOverall, BMI z-scores showed a quadratic relation with brain volumes and depressive symptoms. When including intracranial volume as a covariate, regional volumes investigated did not follow the same global pattern of effects except for right hippocampus and left lateral orbitofrontal cortex. Total brain volume was negatively related to depressive symptoms. ConclusionsLinks between depressive symptoms and low or high weight could improve our understanding of brain structural differences in depression. These findings also emphasize the importance of including the full spectrum of BMI from underweight to overweight and testing for nonlinear effects in models.