Obesity, Adipokines, and Prostate Cancer in a Prospective Population-Based Study
Bibliographic record
Abstract
BACKGROUND: The purpose of this investigation was to examine the association of obesity and the adipokines leptin, adiponectin, and interleukin-6 (IL-6) with prostate cancer risk and aggressiveness. METHODS: One hundred twenty-five incident prostate cancer cases and 125 age-matched controls were sampled from among participants in the original San Antonio Center for Biomarkers of Risk of Prostate Cancer cohort study. The odds ratios (OR) of prostate cancer and high-grade disease (Gleason sum >7) associated with the WHO categories of body mass index (kg/m(2)) and with tertiles of serum concentrations of adiponectin, leptin, and IL-6 were estimated using multivariable conditional logistic regression models. RESULTS: Body mass index was not associated with either incident prostate cancer [obese versus normal; OR, 0.75; 95% confidence interval (95% CI), 0.38-1.48; P(trend) = 0.27] or high-grade versus low-grade disease (OR, 1.17; 95% CI, 0.39-3.52; P(trend) = 0.62). Moreover, none of the three adipokines was statistically significant associated with prostate cancer risk or high-grade disease, respectively: leptin (highest versus lowest tertile; OR, 0.77; 95% CI, 0.28-1.37; P(trend) = 0.57; OR, 1.20; 95% CI, 0.48-3.01; P(trend) = 0.85); adiponectin (OR, 0.87; 95% CI, 0.46-1.65; P(trend) = 0.24; OR, 1.93; 95% CI, 0.74-5.10; P(trend) = 0.85); IL-6 (OR, 0.84; 95% CI, 0.46-1.53; P(trend) = 0.98; OR, 0.84; 95% CI, 0.30-2.33; P(trend) = 0.17). CONCLUSIONS: Findings from this nested case-control study of men routinely screened for prostate cancer and who had a high prevalence of overweight and obesity do not provide evidence to support that prediagnostic obesity or factors elaborated by fat cells strongly influence prostate cancer risk or aggressiveness. However, due to the small sample population, a small or modest effect of obesity and adipokines on these outcomes cannot be excluded.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".