Is visceral obesity a physiological adaptation to stress?
Bibliographic record
Abstract
Visceral obesity represents an important risk factor associated with hypertension, diabetes and cardiovascular diseases. Since this condition is associated with a disruption of the functioning of the HPA axis, stress-induced HPA axis activation has been identified to play an important role in this preferential body fat accumulation. HPA axis activation increases cortisol (corticosterone) production which has been shown to exert hyperphagic and antithermogenic effects. Since abdominal adipose tissue has more cells per mass units, higher blood flow and more glucocorticoid receptors, glucocorticoids affect abdominal fat to a greater extent than subcutaneous adipose tissue. Cushing's syndrome in humans is the best evidence showing a link between hypercortisolemia and accumulation of central fat. The Hervey's hypothesis which suggests that fat cells take up and catabolize glucocorticoids is one of the possible regulatory effect that supports the adaptive role of visceral fat in response to stress. This is also supported by other evidence showing that abdominal obesity is associated with an increased cortisol clearance. Hormonal and enzymatic changes have been implicated in this preferential body fat accumulation in response to stress. Specific genetic background may also accentuate this visceral fat accumulation in some individuals exposed to stress. Alternatively, obesity could also be a source of stress promoting the visceral fat accumulation since visceral fat is able to release cytokines which stimulate the HPA axis. Even if the available literature does not permit to establish clearly which comes first, it suggests that visceral obesity could represent a non optimal physiological adaptation to stress. In this context, visceral obesity treatment should focus on stress management and weight loss strategies in order to stop this vicious circle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".