Longitudinal Central Adiposity Accumulation is Associated with Cortical Alteration and Impaired Cognitive Function in Adolescents

Reviewer #1 (Public review):

Summary:

In this manuscript, the authors used the ABCD study to understand how changes in body fat composition affect brain development during the teenage years. They found that the key determinants of changes in the brain were changes in fat accumulation rather than just baseline measures. Less importantly, but still interesting, they reaffirmed that more complex measures, such as body roundness index, better captured the impact of obesity than body mass index.

Strengths:

This study uses an excellent open access dataset, asks an important set of questions, and is executed with diligence for proper methodology. The finding that trajectories in adiposity matter more than baseline differences is important both for understanding how the brain adapts to body composition changes and has potential impact for public health interventions.

Weaknesses:

While I am overall impressed with the study, there are a few weaknesses I would like to see the authors address:

(1) There is no reason to limit analyses to the cerebral cortex. Body composition changes are as likely to affect the subcortex or cerebellum as the neocortex. In some cases, like the hypothalamus, potentially even more likely.

(2) Confounders should be examined in more detail. How are adiposity changes mediated by (for example) socio-economic status?

(3) I'd like to see more rationale for excluding 483 kids with extreme adiposity indicators. Is it that they are untrustworthy entries? Otherwise, they could be particularly informative.

(4) Are there any blood measures of glucose/insulin available?

(5) Some of the figures could benefit from having raw data included rather than just showing the fitted trend lines.

(6) Are there any alternate explanations for baseline differences? Particular examples that came to mind are the influence that maternal adiposity has on offspring brain development.

Reviewer #2 (Public review):

This manuscript addresses an important public health question in adolescents using a large longitudinal ABCD cohort. It is generally well organized and presents a compelling story linking central adiposity, brain development, and cognition. The authors use baseline and 4-year follow-up data from the ABCD Study (N = 8,519 baseline; N = 1,873 longitudinal, the use of 4-year follow-up only should be justified) to examine how adiposity relates to cognitive performance and cortical structure in early adolescence. Major findings include: (1) central adiposity indices (BRI, WHtR) show stronger cross-sectional associations with cognition than BMI, partially mediated by frontotemporal cortical morphology; (2) the rate of adiposity accrual, rather than static adiposity at either timepoint, predicts follow-up cognition and altered (attenuated) cortical thinning; and (3) among overweight/obese adolescents, central fat reduction is accompanied by accelerated cortical thinning and catch-up in inhibitory control.

The use of large longitudinal data from ABCD is considered a strength as most prior neuroimaging work is cross-sectional. The focus on adiposity change velocity within adolescence is considered novel over single-timepoint designs, and the comparison of central-fat indices (BRI/WHtR) against BMI in a neurodevelopmental context is important given growing interest in these markers in cardiometabolic research. The subgroup observation that fat reduction may be accompanied by normalization of both cortical trajectories and inhibitory control is potentially clinically meaningful, suggesting reversibility rather than fixed deficit, and would be of broad interest if it withstands more rigorous analysis.

However, several major issues were identified. For example, the strength of the causal language is not supported by the design, the headline comparison between adiposity indices rests on coefficients that are described as standardized but evidently are not, and several analytic and reporting issues (attrition and selection, family clustering, regression to the mean in the subgroup analysis, internal inconsistencies in reported p values) must be resolved before the conclusions can be considered established.

Major Concerns

(1) ABCD provides more follow-ups and should be included. Also, there is a severe longitudinal attrition (8519 vs 1873) that needs to be addressed. No comparison of completers versus non-completers is provided. The cohort description of having 78.5% White seems to be wrong, suggesting either a coding error in the race variable or strong selection introduced by the exclusions. The exclusion of 483 participants for "extreme adiposity indicator values" is especially concerning in a study of obesity and should be justified with explicit criteria in the main text, with sensitivity analyses retaining these participants where possible.

(2) Adiposity is heavily influenced by socio-economic status, which itself contributes heavily to cognition and mental health as well. Many ABCD studies have reported this. Another important factor contributing to adiposity is sleep, which itself can contribute significantly to cognition and mental health. Many ABCD-based sleep studies have been published and can be reviewed.

(3) The manuscript repeatedly uses causal language that the observational design cannot support (e.g., "longitudinal fat accumulation ... drives cortical alteration," "fat reduction activated adaptive neural change," "neuroprotective management"). The cited literature (Likhitweerawong et al., 2022) is explicitly bidirectional: poor inhibitory control may promote weight gain rather than the reverse. The baseline mediation analysis is particularly problematic because exposure, mediator, and outcome were measured concurrently, so the temporal ordering required for mediation is assumed rather than established. Causal claims should be tempered throughout. Similarly, mediation analyses should be interpreted cautiously as statistical rather than causal.

(4) zBMI, WC, BRI, and WHtR are highly intercorrelated, as are follow-up adiposity and delta-adiposity (Table 3). Entering them simultaneously invites unstable estimates and sign flips. Please report pairwise correlations and variance inflation factors, and demonstrate that the key conclusions are robust.

(5) Subgroup analysis: regression to the mean, group labels, and internal inconsistencies. (a) Stratifying on delta-BRI >= 0 enriches the "decreasing" group for high baseline values, so the observed cognitive "catch-up" and accelerated thinning may partly reflect regression to the mean; the percentile-based sensitivity analysis mitigates but does not resolve this. Group comparisons should adjust for baseline BRI and baseline cognition. (b) zBMI >= 1 corresponds to overweight by WHO criteria, not obesity; labelling this group "obese adolescents" throughout (including the Abstract) is inaccurate. (c) The Abstract claims a Flanker slope difference of p < 0.05, but the Results report p = 0.058 versus controls (described, incorrectly, as significant) and p = 0.021 only versus the increasing-BRI group; these statements must be reconciled. (d) It is unclear whether subgroup p values were FDR-corrected and how t-tests/Wilcoxon tests produced "adjusted" p values; if covariate adjustment was intended, regression models are required.

Tables 2-4 mention that beta values are standardized, yet the magnitudes are clearly scale-dependent (e.g., WHtR beta = -7.5 vs. zBMI beta = -0.42 in Table 2; WHtR delta-beta = -780 in Table 3). This undermines the central claim that BRI/WHtR "outperform" BMI, since predictors cannot be compared on unstandardized coefficients. Please either truly standardize all predictors and outcomes, or compare indices formally (e.g., delta-R2, AIC/BIC, or non-nested model comparison tests), and report 95% confidence intervals for all estimates.

(6) Underspecified mixed models and family clustering. The random-effects structure of the LME models is never stated. ABCD includes twins and siblings, so family nesting must be modelled (and the treatment of site - fixed covariate versus random effect - clarified). As written, the analyses may be anti-conservative. Please provide full model specifications and confidence intervals.

(7) Interpretation of accelerated cortical thinning is overly strong. The manuscript equates faster thinning with better maturation and slower thinning with delay. This reading is contested: apparent cortical thinning also reflects myelination-related signal changes, and thickness-cognition associations are age- and region-dependent. Moreover, the present null finding for baseline adiposity contradicts Kaltenhauser et al. (2023), who reported that baseline adiposity attenuates thinning in ABCD; the discrepancy is cited but never reconciled and deserves direct discussion (differences in sample, covariates, or modelling) Unclear units and implausible mediation estimates. Table 1 gives delta-WC = 0.264, interpretable only if delta is per month (age in months), yet the text describes "annualized" change. Units for all delta variables must be stated explicitly and used consistently. Mediation effects reported as wide ranges (e.g., ACME = -29.95 to -0.18; "ACME = -0.1.17" is a typographical error) are uninterpretable and, at face value, implausibly large relative to the cognitive score scale; please report per-indicator ACMEs with confidence intervals and proportion mediated. Not sure how the ranges such as "beta = -0.29 to -0.00" cited as significant effects are informative.

(8) Pubertal confounding. Only baseline pubertal category is adjusted for, but pubertal tempo over the follow-up window confounds both adiposity change and cortical development. This should be adjusted for change in pubertal stage or acknowledged as a substantive limitation. Puberty interactions and sex-specific analysis should be performed too.

(9) Effect sizes should be discussed.

(10) Inclusion of both baseline adiposity and adiposity change in longitudinal models should be justified.

(11) Would there be nonlinear age/puberty effects?