Glucose impairments and insulin resistance in prostate cancer: the role of obesity, nutrition and exercise
Bibliographic record
Abstract
BACKGROUND: Hyperinsulinemia, obesity and related metabolic diseases are associated with prostate cancer development. Prostate cancer patients undergoing androgen deprivation therapy (ADT) are at increased risk for metabolic syndrome, cardiovascular disease and diabetes, while pre-existing metabolic conditions may be exacerbated. PURPOSE: An integrative approach is used to describe the interactions between insulin, glucose metabolism, obesity and prostate cancer. The potential role of nutrition and exercise will also be examined. FINDINGS: Hyperinsulinemia is associated with prostate cancer development, progression and aggressiveness. Prostate cancer patients who undergo ADT are at risk of diabetes in survivorship. It is unclear whether this is a direct result of treatment or related to pre-existing metabolic features (e.g. hyperinsulinemia and obesity). Obesity and metabolic syndrome are also associated with prostate cancer development and poorer outcomes for cancer survivors, which may be driven by hyperinsulinemia, pro-inflammation, hyperleptinemia and/or hypoadiponectinemia. CONCLUSIONS: Independently evaluating changes in glucose metabolism near the time of prostate cancer diagnosis and during long-term ADT treatment is important to distinguish their unique contributions to the development of metabolic disturbances. Integrative approaches, including metabolic, clinical and body composition measures, are needed to understand the role of adiposity and insulin resistance in prostate cancer and to develop effective nutrition and exercise interventions to improve secondary diseases in survivorship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".