Association between diabetes, obesity, aging, and cancer: review of recent literature
Bibliographic record
Abstract
Rates of obesity and diabetes have risen significantly in recent years and are projected to increase even further in the coming decades. Obesity and diabetes are associated with increased risk of certain tumours, with the strongest relationships demonstrated for colorectal, post-menopausal breast, and endometrial cancer. Another important risk factor for cancer development is aging. Aging is characterized by chronic inflammation and immunosenescence, and accelerated by obesity, which may further stimulate the development of cancer. In this review, we summarize recent literature on the complex interactions between obesity, diabetes, aging, and cancer risk and mortality. We will also provide an overview of both epidemiological as well as pathophysiologic data and their clinical implications. In the context of an aging population and anticipated rise in rates of obesity and diabetes, a better understanding of how these factors interact and impact on cancer risk and prognosis will be important in helping to guide therapeutic interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".