Review: Diabetes, Obesity, and Cancer—Pathophysiology and Clinical Implications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Obesity and diabetes have both been associated with an increased risk of cancer. In the face of increasing obesity and diabetes rates worldwide, this is a worrying trend for cancer rates. Factors such as hyperinsulinemia, chronic inflammation, antihyperglycemic medications, and shared risk factors have all been identified as potential mechanisms underlying the relationship. The most common obesity- and diabetes-related cancers are endometrial, colorectal, and postmenopausal breast cancers. In this review, we summarize the existing evidence that describes the complex relationship between obesity, diabetes, and cancer, focusing on epidemiological and pathophysiological evidence, and also reviewing the role of antihyperglycemic agents, novel research approaches such as Mendelian Randomization, and the methodological limitations of existing research. In addition, we also describe the bidirectional relationship between diabetes and cancer with a review of the evidence summarizing the risk of diabetes following cancer treatment. We conclude this review by providing clinical implications that are relevant for caring for patients with obesity, diabetes, and cancer and provide recommendations for improving both clinical care and research for patients with these conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it