Smoking Is Associated with Acute and Chronic Prostatic Inflammation: Results from the REDUCE Study
Bibliographic record
Abstract
Both anti- and proinflammatory effects of cigarette smoking have been described. As prostate inflammation is common, we hypothesized smoking could contribute to prostate inflammation. Thus, we evaluated the association of smoking status with acute and chronic inflammation within the prostate of men undergoing prostate biopsy. We retrospectively analyzed 8,190 men ages 50 to 75 years with PSA levels between 2.5 and 10 ng/mL enrolled in the Reduction by Dutasteride of Prostate Cancer Events study. Smoking status was self-defined as never, former, or current. Prostate inflammation was assessed by systematic central review blinded to smoking status. The association of smoking with inflammation in the baseline, 2-year, and 4-year biopsies was evaluated with univariable and multivariable logistic regressions. At study enrollment, 1,233 (15%), 3,203 (39%), and 3,754 (46%) men were current, former, and never smokers, respectively. Current smokers were significantly younger and had smaller prostates than former and never smokers (all P < 0.05). Former smokers were significantly heavier than current and never smokers (P < 0.001). Acute and chronic prostate inflammations were identified in 1,261 (15%) and 6,352 (78%) baseline biopsies, respectively. In univariable analysis, current smokers were more likely to have acute inflammation than former (OR, 1.35; P, 0.001) and never smokers (OR, 1.36; P, 0.001). The results were unchanged at 2- and 4-year biopsies. In contrast, current smoking was linked with chronic inflammation in the baseline biopsy, but not at 2- and 4-year biopsies. In conclusion, among men undergoing prostate biopsy, current smoking was independently associated with acute and possibly chronic prostate inflammations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".