A mesocorticolimbic insulin receptor gene co-expression network moderates the association between early life adversity and food approach eating behaviour style in childhood
Bibliographic record
Abstract
Insulin receptors, located in brain regions associated with reward sensitivity and decision-making, facilitate insulin action in the brain, modulating intracellular signaling cascades, gene expression, and neural activity. Here, we tested if variations in the expression of the insulin receptor gene network in the prefrontal cortex (PFC) and striatum (STR) moderate the association between early life adversity and eating behaviour in childhood and if this moderation is sex-specific. Participants from the Maternal Adversity, Vulnerability and Neurodevelopment (MAVAN) and Basal Influences on the Baby's Development (BIBO) were included as two independent cohorts. A biologically-informed polygenic score reflecting functional variation of the mesocorticolimbic insulin receptor gene network was created by using insulin receptor co-expression data from the PFC and STR in mice, and validated in humans through filtering by homologous expression in PFC using well-known databases. Early life adversity exposure was measured as a composite score. Eating behaviour was characterized using the Child Eating Behaviour Questionnaire administered to mothers of children aged 4 and 6 years in MAVAN, and 6 years in BIBO. We found that only in those with high expression of the mesocorticolimbic insulin receptor gene network a higher early adversity score associated with a higher desire to drink in 4-year boys and 6-year girls, as well as a higher food approach score and food approach/food avoidance ratio in 4-year girls. Also, a higher early life adversity was associated with higher food responsiveness, food approach score and food approach/food avoidance ratio at 6 years in the MAVAN full sample. The moderation observed on desire to drink was partially replicated in BIBO children aged 6 years. Identifying individual differences in response to early adversity may help to prioritize individuals at high risk for long-term disease and design suitable interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".