Brain insulin receptor gene network shapes risk for metabolic disease after early-life stress in women
Bibliographic record
Abstract
Stress happening during critical periods of development shapes individual physiology and increases the risk for obesity, inflammatory, and metabolic disturbances throughout life. However, there are individual differences and not everyone exposed to stress or adversity early during development develops chronic adult disease. Insulin regulates peripheral glucose metabolism, acts as a neuromodulator in the brain, and is possibly implicated in individual differences in response to early adversity. Expression-based polygenic scores (ePRS) reflect variations in the expression of a tissue-specific gene co-expression network. We have previously shown that brain-based insulin receptor ePRS (ePRS-IR) can identify risk for metabolic and frailty outcomes in older adults. Here, we show that the mesocorticolimbic ePRS-IR moderates the association between early adversity and increased visceral adipose tissue as well as metabolic syndrome in a large sample of adult women (UK Biobank). These findings suggest that variations in the function of the brain insulin receptor network influence the susceptibility to the long-term effects of adversity, highlighting a target system for prevention and novel treatments. How the brain responds to early-life stress may shape future health: individual differences in the expression of an insulin receptor gene network in the prefrontal cortex and striatum influence the risk of visceral fat accumulation and metabolic syndrome, especially in women exposed to early-life stress.
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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".