The Association Between Serum Prostate‐Specific Antigen and Glycemic Index, Glycemic Load, and Metformin in Individuals with Diabetes: a Cross‐sectional Analysis
Bibliographic record
Abstract
Background Improving glycemic control with Metformin therapy, glycemic index (GI), and glycemic load (GL) have been protectively associated with prostate cancer risk. No studies have yet assessed the relationship between GI, GL, and Metformin on serum prostate‐specific antigen (PSA), a controversial‐yet‐widely used marker of prostate health. Methods A cross‐section analysis of baseline PSA data from men with type 2 diabetes mellitus (T2DM) who previously completed a trial at this research center was conducted. Multivariate linear regression quantified associations between PSA and log‐PSA with GI, GL, and Metformin dose. Results Data were available from 317 men, with a mean PSA of 1.41±0.09ng/mL. Medians exposures (ranges) were 80 (56 – 96) for GI, 164 (62 – 386) for GL, and 1500mg (0 ‐ 3000 mg) for Metformin; 90% of men were under Metformin therapy. Neither PSA nor log‐PSA were significantly related to GI or GL. A negative trend was seen with PSA and metformin dose (p=0.09) in multivariate models. PSA was 24% lower among individuals taking 蠅2000mg of Metformin (n=125) compared to individuals taking <1000mg (n=62) (p=0.06). Log‐PSA significantly decreased by 0.15±0.09 ng/mL for every 1000mg increase in Metformin dose (r=‐0.38, p=0.02). Conclusions Metformin, but not GI or GL, inversely related with log‐PSA in a sample of men with T2DM. Modification of PSA‐test sensitivity as a marker of prostate cancer with diet and Metformin warrants attention. Funding CIHR, Barilla, Loblaw Brands ltd
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".