Obesity-Linked Cancers: Current Knowledge, Challenges and Limitations in Mechanistic Studies and Rodent Models
Bibliographic record
Abstract
The worldwide prevalence of obesity has doubled during the last 50 years, and according to the World Obesity Federation, one third of the people on Earth will be obese by the year 2025. Obesity is described as a chronic, relapsing and multifactorial disease that causes metabolic, biomechanical, and psychosocial health consequences. Growing evidence suggests that obesity is a risk factor for multiple cancer types and rivals smoking as the leading preventable cause for cancer incidence and mortality. The epidemic of obesity will likely generate a new wave of obesity-related cancers with high aggressiveness and shortened latency. Observational studies have shown that from cancer risk to disease prognosis, an individual with obesity is consistently ranked worse compared to their lean counterpart. Mechanistic studies identified similar sets of abnormalities under obesity that may lead to cancer development, including ectopic fat storage, altered adipokine profiles, hormone fluctuations and meta-inflammation, but could not explain how these common mechanisms produce over 13 different cancer types. A major hurdle in the mechanistic underpinning of obesity-related cancer is the lack of suitable pre-clinical models that spontaneously develop obesity-linked cancers like humans. Current approaches and animal models fall short when discerning the confounders that often coexist in obesity. In this mini-review, we will briefly survey advances in the different obesity-linked cancers and discuss the challenges and limitations in the rodent models employed to study their relationship. We will also provide our perspectives on the future of obesity-linked cancer research.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".