Multimodal neuroimaging assessments were utilized to identify generalizable brain correlates of current body mass index (BMI) and predictors of pathological weight gain (i.e., beyond normative development) one year later. Multimodal data from children enrolled in the Adolescent Brain Cognitive Development Study® at 9-to-10-years-old, consisted of structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), resting state (rs), and three task-based functional (f) MRI scans assessing reward processing, inhibitory control, and working memory. Cross-validated elastic-net regression revealed widespread structural associations with BMI (e.g., cortical thickness, surface area, subcortical volume, and DTI), which explained 35% of the variance in the training set and generalized well to the test set (R2 = 0.27). Widespread rsfMRI inter- and intra-network correlations were related to BMI (R2train = 0.21; R2test = 0.14), as were regional activations on the working memory task (R2train = 0.20; (R2test = 0.16). However, reward and inhibitory control tasks were unrelated to BMI. Further, pathological weight gain was predicted by structural features (Area Under the Curve (AUC)train = 0.83; AUCtest = 0.83, p < 0.001), but not by fMRI nor rsfMRI. These results establish generalizable brain correlates of current weight and future pathological weight gain. These results also suggest that sMRI may have particular value for identifying children at risk for pathological weight gain.

Although pediatric growth curves provide clinical utility, using these metrics for within‐person change over time can be misleading. As research is focused on understanding cardiometabolic consequences of weight gain, it is important to use precise metrics to analyze these longitudinal research questions. Despite several foundational recommendations to limit the use of reference pediatric growth curves (e.g., BMI z scores) for within‐person longitudinal research, it has evolved into the “gold standard” for using growth curves for pediatric weight gain analyses. Therefore, the objective of this paper is to discuss (A) the methodology used to create reference growth curves; (B) the appropriate use of reference pediatric BMI growth curves within the context of cross‐sectional and longitudinal analyses in research; and (C) how to select metrics based on desired evaluations. Careful consideration using standardized references scores is essential when assessing obesity‐related questions and comorbid risk over time in pediatric populations.

Objective: The aim of this study was to investigate the mediating role of child brain structure in the relationship between prenatal gestational diabetes mellitus (GDM) exposure and child adiposity.Methods: This was a cross‐sectional study of 9– to 10‐year‐old participants and siblings across the US. Data were obtained from the baseline assessment of the Adolescent Brain Cognitive Development (ABCD) Study®. Brain structure was evaluated by magnetic resonance imaging. GDM exposure was self‐reported, and discordance for GDM exposure within biological siblings was identified. Mixed effects and mediation models were used to examine associations among prenatal GDM exposure, brain structure, and adiposity markers with sociodemographic covariates.Results: The sample included 8521 children (7% GDM‐exposed), among whom there were 28 sibling pairs discordant for GDM exposure. Across the entire study sample, prenatal exposure to GDM was associated with lower global and regional cortical gray matter volume (GMV) in the bilateral rostral middle frontal gyrus and superior temporal gyrus. GDM‐exposed siblings also demonstrated lower global cortical GMV than unexposed siblings. Global cortical GMV partially mediated the associations between prenatal GDM exposure and child adiposity markers.Conclusions: The results identify brain markers of prenatal GDM exposure and suggest that low cortical GMV may explain increased obesity risk for offspring prenatally exposed to GDM. image

Objective: The study aim was to determine whether (A) differences in executive function (EF) and cognition precede weight gain or (B) weight gain causes changes to EF and cognition.Methods: Data were gathered from the Adolescent Brain Cognitive Development (ABCD) Study (release 4.0; ages 9–12 years old [N = 2794]; 100% had healthy weight at baseline [i.e., 9/10 years old], 12.4% had unhealthy weight by ages 11/12 years). EF and cognition were assessed across several domains (e.g., impulsivity, inhibitory control, processing speed, memory); BMI was calculated from height and weight. Nested random‐effects mixed models examined (A) BMI ~ EF × Time (i.e., variation in EF/cognition precedes weight gain) and (B) EF ~ BMI × Time (weight gain causes changes to EF/cognition) and controlled for sex, puberty, and caregiver education; random effects were site and subject.Results: Variation in impulsivity, memory, learning, and processing speed was associated with greater increases in BMI trajectories from 9 to 12 years old. Weight gain was associated with a decrease in inhibitory control, but no other associations were observed.Conclusions: Underlying variation in EF and cognition may be important for weight gain, but 2 years of weight gain may not be enough to have clinical implications for EF and cognition beyond inhibitory control. These findings suggest that more attention should be paid to the inclusion of EF programs in obesity prevention efforts.