Biomarkers of Glucose Homeostasis and Inflammation with Risk of Prostate Cancer: A Case–Cohort Study
Bibliographic record
Abstract
BACKGROUND: Few prospective studies have examined biomarkers of glucose homeostasis or inflammation with prostate cancer risk by tumor stage or grade. METHODS: We conducted a case-cohort study to examine associations of prediagnosis hemoglobin A1c (HbA1c), C-peptide, and C-reactive protein (CRP) with prostate cancer risk overall and stratified by tumor stage and grade. The study included 390 nonaggressive (T1-2, N0, M0, and Gleason score <8) and 313 aggressive cases (T3-4, or N1, or M1, or Gleason score 8-10) diagnosed after blood draw (1998-2001) and up to 2013, and a random subcohort of 1,303 cancer-free men at blood draw in the Cancer Prevention Study-II Nutrition Cohort. Prentice-weighted Cox proportional hazards regression models were used to estimate HRs and 95% confidence intervals (CI). RESULTS: In the multivariable-adjusted model without body mass index, HbA1c was inversely associated with nonaggressive prostate cancer (HR per unit increase, 0.89; 95% CI, 0.80-1.00; P = 0.04). Analyses stratified by tumor stage and grade separately showed that HbA1c was inversely associated with low-grade prostate cancer (HR per unit increase, 0.89; 95% CI, 0.80-1.00) and positively associated with high-grade prostate cancer (HR per unit increase, 1.15; 95% CI, 1.01-1.30). C-peptide and CRP were not associated with prostate cancer overall or by stage or grade. CONCLUSIONS: The current study suggests that associations of hyperglycemia with prostate cancer may differ by tumor grade and stage. IMPACT: Future studies need to examine prostate cancer by tumor stage and grade, and to better understand the role of hyperglycemia in prostate cancer progression.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".