Peripheral leptin receptor gene network modulates the impact of childhood adversity on mental health disorders
Bibliographic record
Abstract
Psychiatric disorders are highly prevalent and often co-morbid with metabolic syndrome. Exposure to adversity in early life is a risk factor for both metabolic and behavioral problems, modifying leptin metabolism and signaling. Leptin is not only an energy-balance regulator, being also associated with the development of affective disorders. Our objective was to investigate if individual variations in peripheral leptin receptor (LepR) gene network function moderate the effect of childhood adversity on psychopathology. We created expression-based polygenic scores (ePRS) reflecting genetic variations that affect expression of the liver LepR gene network. We investigated the interaction between the LepR-ePRS and early adversity on mental health outcomes, namely anxiety and depression, in childhood (MAVAN) and adolescence (ALSPAC). In both cohorts, there were interaction effects between early adversity exposure and the liver-based LepR-ePRS, in which adversity was associated with depression only in individuals from the high ePRS group. Our findings suggest that exposure to early adversity is associated with mental health problems in children and adolescents. The liver leptin receptor gene network is an important moderator of these effects, and this may be due to individual differences within metabolic and inflammatory pathways represented by this gene network.
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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 | 0.000 |
| 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".