Food for Thought: Hormonal, Experiential, and Neural Influences on Feeding and Obesity
Bibliographic record
Abstract
Obesity is a growing public health problem. Although convenient, the notion that obesity is simply a problem of will power is increasingly antiquated. It is becoming clear that complex interactions of environment, neurohormonal systems, and transgenerational effects directly contribute to obesity. This review highlights data presented at the Society for Neuroscience Annual Meeting in San Diego, California in 2013; and although not meant as an exhaustive review of the area, this reivew will explore seemingly disparate areas of research that, when taken as a whole, illuminate the complex topography of the causes and consequences of obesity. We discuss how disruption of the biological clock, a consequence of modern society, can lead to changes in the brain and periphery that lead to obesity. We explore how obesity can actually cause pathological changes within the hypothalamus of the brain (a key regulator of food intake and metabolic homeostasis). How reward circuitry, particularly the ventral tegmental area, responds to insulin and how these effects modulate feeding and the salience of feeding cues are mechanistically described. We also investigate how nutrition may cross generational boundaries to affect the development and function of offspring, underscoring the long reach of metabolic effects. Finally, the role of the endocannabinoid system is emphasized as a critical node in the transduction of many of these effects. Together, this review should provide perspective into the neural causes and consequences of obesity, and hopefully lead to new areas of interdisciplinary research to tackle this important public health epidemic.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".