Could there be a fine-tuning role for brain-derived adipokines in the regulation of bodyweight and prevention of obesity?
Bibliographic record
Abstract
Obesity is one of the most prevalent medical conditions, often associated with several negative stereotypes. Although it is true that weight gain occurs when food intake exceeds energy expenditure, it is important to note that even a 1% mismatch between the two can lead to a substantial weight gain after only a few years. Further, the body appears to balance energy metabolism via an endogenous lipostatic loop in which adipose stores send hormonal signals (e.g. adipokines such as leptin) to the hypothalamus in order to reduce appetite and increase energy expenditure. However, the brain is also a novel site of expression of many of these adipokine genes. This led to the hypothesis that hypothalamic-derived adipokines might also be involved in bodyweight regulation by exerting some effect on the control of appetite or hypothalamic function. When RNA interference (RNAi) was used to specifically silence adipokine gene expression in various in vitro models, this led to increases in cell death, modification of the expression of key signaling genes (i.e. suppressor of cytokine signaling-3; SOCS-3), and modulation of the activation of cellular energy sensors (i.e. adenosine monophosphate-activated protein kinase; AMPK). Subsequently, when RNAi was used to inhibit the expression of brain-derived leptin in adult rats this resulted in minor increases in weight gain in addition to modifying the expression of other adipokine genes (eg. resistin). In summary, although adipokines secreted by adipose tissue appear to the main regulator of lipostatic loop, this review shows that the fine tuning that is required to maintain a stable bodyweight by this system might be accomplished by hypothalamic-derived adipokines. Perturbations in this central adipokine system could lead to alterations in normal hypothalamic function which leads to unintended weight gain.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".