Polymorphisms in genes regulating androgen activity among prostate cancer low‐risk Inuit men and high‐risk Scandinavians
Bibliographic record
Abstract
In Greenland, with a male population of approximately 30 000 individuals, the incidence of prostate cancer is extremely low with only three cases described during the period 1988-1997. Polymorphisms related to high androgen metabolism and/or response in the 5alpha-reductase type 2 (SRD5A2) and the androgen receptor (AR) genes, respectively, have been linked to prostate cancer. Our objective was to analyse whether the distribution of these polymorphisms differed between the prostate cancer low-risk population from Greenland and the relatively high-risk Swedish male population. The SRD5A2 polymorphisms A49T, V89L and R227Q, and the CAG and GGN repeats in the AR gene were genotyped in leucocyte DNA from 196 Greenlanders and 305 Swedish military conscripts. All subjects had the wild-type R/R genotype of the R227Q marker. The high-activity variants A49T A/T and V89L V/V occurred less frequently (2% vs. 5%, p = 0.048 and 33% vs. 46%, p = 0.0027) in Greenland compared with Sweden, whereas the low-activity L/L genotype was more frequent in Greenland (24% vs. 13%, p = 0.0024). Greenlanders also had longer AR CAG repeats than the Swedish population (median 24 vs 22, p < 0.0005). Greenlanders also had a higher frequency of the GGN = 23 allele (85% vs. 54%, p < 0.0001). Our results suggest that Greenlanders are genetically predisposed to a lower activity in testosterone to 5alpha-dihydrotestosterone turnover and to lower AR activity, which, at least partly, could explain their low incidence of prostate cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".