Visceral Obesity and Its Shared Role in Cancer and Cardiovascular Disease: A Scoping Review of the Pathophysiology and Pharmacological Treatments
Bibliographic record
Abstract
The association between obesity, cancer and cardiovascular disease (CVD) has been demonstrated in animal and epidemiological studies. However, the specific role of visceral obesity on cancer and CVD remains unclear. Visceral adipose tissue (VAT) is a complex and metabolically active tissue, that can produce different adipokines and hormones, responsible for endocrine-metabolic comorbidities. This review explores the potential mechanisms related to VAT that may also be involved in cancer and CVD. In addition, we discuss the shared pharmacological treatments which may reduce the risk of both diseases. This review highlights that chronic inflammation, molecular aspects, metabolic syndrome, secretion of hormones and adiponectin associated to VAT may have synergistic effects and should be further studied in relation to cancer and CVD. Reductions in abdominal and visceral adiposity improve insulin sensitivity, lipid profile and cytokines, which consequently reduce the risk of CVD and some cancers. Several medications have shown to reduce visceral and/or subcutaneous fat. Further research is needed to investigate the pathophysiological mechanisms by which visceral obesity may cause both cancer and CVD. The role of visceral fat in cancer and CVD is an important area to advance. Public health policies to increase public awareness about VAT's role and ways to manage or prevent it are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".