Obesity, Diabetes, and Risk of Prostate Cancer: Results from the Prostate Cancer Prevention Trial
Bibliographic record
Abstract
Studies on the relationship between obesity and prostate cancer incidence are inconsistent. In part, this inconsistency may be due to a differential effect of obesity on low-grade and high-grade cancer or confounding of the association of obesity with prostate cancer risk by diabetes. We investigated the associations of obesity and diabetes with low-grade and high-grade prostate cancer risk. Data were from 10,258 participants (1,936 prostate cancers) in the Prostate Cancer Prevention Trial who all had cancer presence or absence determined by prostate biopsy. Multiple logistic regression was used to model the risk of total prostate cancer, and polytomous logistic regression was used to model the risk of low-grade and high-grade prostate cancer. Compared with men with body mass index < 25, obese men (body mass index > or =30) had an 18% [odds ratio (OR), 0.82; 95% confidence interval (95% CI), 0.69-0.98] decreased risk of low-grade prostate cancer (Gleason <7) and a 29% (OR, 1.29; 95% CI, 1.01-1.67) increased risk of high-grade prostate cancer (Gleason > or =7) or, alternatively, a 78% (OR, 1.78; 95% CI, 1.10-2.87) increased risk defining high-grade cancer as Gleason sum 8 to 10. Diabetes was associated with a 47% (OR, 0.53; 95% CI, 0.34-0.83) reduced risk of low-grade prostate cancer and a 28% (OR, 0.72; 95% CI, 0.55-0.94) reduced risk of high-grade prostate cancer. Associations of obesity or diabetes with cancer risk were not substantially changed by mutually statistical controlling for each other. Obesity increases the risk of high-grade but decreases the risk of low-grade prostate cancer, and this relationship is independent of the lower risk for prostate cancer among men with diabetes.
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.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".