A putative model of overeating and obesity based on brain-derived neurotrophic factor: Direct and indirect effects.
Bibliographic record
Abstract
Increased food intake is a major contributor to the obesity epidemic in all age groups. Elucidating brain systems that drive overeating and that might serve as targets for novel prevention and treatment interventions is thus a high priority for obesity research. The authors consider 2 major pathways by which decreased activity of brain-derived neurotrophic factor (BDNF) may confer vulnerability to overeating and weight gain in an obesogenic environment. The first "direct" pathway focuses on the specific role of BDNF as a mediator of food intake control at brain areas rich in BDNF receptors, including the hypothalamus and hindbrain. It is proposed that low BDNF activity limited to this direct pathway may best explain overeating and obesity outside the context of major neuropsychiatric disturbance. A second "indirect" pathway considers the broad neurotrophic effects of BDNF on key monoamine systems that mediate mood dysregulation, impulsivity, and executive dysfunction as well as feeding behavior per se. Disruption in this pathway may best explain overeating and obesity in the context of various neuropsychiatric disturbances including mood disorders, attention-deficit disorder, and/or binge eating disorders. An integrative model that considers these potential roles of BDNF in promoting obesity is presented. The implications of this model for the early prevention and treatment of obesity are also considered.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".