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Distinct Patterns of Weight Gain, Age, and Subcortical Microstructure in Early Adolescence

2025· article· en· W4412602088 on OpenAlexaff
Shana Adise, Zhaolong Li, Jonatan Ottino‐González, Filip Morys, Peter A. Chiarelli, Tamara Hershey

Bibliographic record

VenueJAMA Network Open · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsWeight gainMedicineBody mass indexCohortOverweightDemographyPercentileCohort studyLongitudinal studyObesitySynaptic pruningWeight changeGerontologyInternal medicinePsychologyWeight lossBody weightPathology

Abstract

fetched live from OpenAlex

Importance: Associations between childhood obesity and brain microstructural differences have been observed. It remains unknown whether these associations are driven by sex-specific excessive weight gain. Restriction spectrum imaging characterizes brain tissue microstructural health via water diffusion, where the restricted normalized isotropic (RNI) compartment assesses neuronal and glial cellularity, which may reflect neuroinflammation, synaptic pruning, or both. Objective: To identify associations among RNI scaling factor values, normal neurodevelopment, and weight gain during early adolescence. Design, Setting, and Participants: This cohort study used data from the Adolescent Brain Cognitive Development (ABCD) Study, a 10-year, ongoing, multisite longitudinal cohort study conducted among youths aged 9 to 20 years. The analyses focused on data from baseline (collected in 2016-2018) and the 2-year follow-up (collected in 2018-2020). Participants who initially had healthy weight (body mass index [BMI] percentile <85th) at age 9 to 10 years were eligible for this study. Data analysis was performed between March 2024 and March 2025. Main Outcomes and Measures: Linear mixed-effects models were used to examine bidirectional associations among RNI, age, and BMI (a proxy for weight gain) across 16 appetite-controlling brain regions. First, analysis was performed among youths with healthy-weight, weight-stable (HW-WS) status, stratified by sex. Second, an evaluation was conducted to determine how these associations changed among all youths (eg, conversion to healthy-weight, non-weight-stable [HW-NS] status). Results: At baseline, data were available for 3110 youths (mean [SD] baseline age, 119.2 [7.5] months); at year 2, only 1855 youths had complete data. Of these 1855 youths, 1072 (592 males [55.2%]) had HW-WS and 773 (445 females [57.2%]) had HW-NS. Among youths with HW-WS, RNI values were associated with age but not BMI. Among youths with varying weight gain, RNI values had bidirectional associations with BMI across many subcortical regions independent of age. In females, but not in males, higher RNI values had robust associations with greater increases in BMI over time. Conclusions and Relevance: In this cohort study, RNI values were associated with age and BMI, and greater RNI values beyond normal developmental processes may suggest neuroinflammation. Thus, higher RNI values may signal neuroinflammatory processes associated with unhealthy weight gain, suggesting potential for the RNI scaling factor as an early indicator of obesity-related neurodevelopmental changes in adolescence. Future mechanistic studies are needed to determine the specific cellular changes underlying these associations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.339
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2025
Admission routes1
Has abstractyes

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