Common Polymorphisms in the Adiponectin and Its Receptor Genes, Adiponectin Levels and the Risk of Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Adiponectin, an insulin-sensitizing adipokine, is inversely associated with adiposity and prostate cancer risk and progression. However, the role of genetic variation in the adiponectin (ADIPOQ) and receptor genes (ADIPOR1/R2) in prostate cancer is largely unknown. METHODS: In a nested case-control study of 1,286 cases and 1,267 controls within the Physicians' Health Study, we evaluated 29 common single-nucleotide polymorphisms (SNP) in ADIPOQ (n = 13), ADIPOR1 (n = 5), and ADIPOR2 (n = 11) in relation to the risk of prostate cancer. In subgroups, we also evaluated the association of genotype and circulating adiponectin levels (n = 951) and prostate tumor expression of insulin receptor (IR) and insulin-like growth factor 1 (IGF-IR) receptor (n = 181). RESULTS: Among the 12 tagging polymorphisms in ADIPOQ, four (rs266729, rs182052, rs822391, and rs2082940) were significantly associated (P < 0.05) with overall prostate cancer risk, with no significant difference by tumor grade or clinical stage. Two of the risk SNPs (rs266729 and rs182052) plus four other SNPs (rs16861209, rs17366568, rs3774261, and rs7639352) were also associated with plasma adiponectin levels, and three of these (rs1686109, rs17366568, and rs3774261) were also significantly associated with IR expression in prostate tumor tissue. One additional SNP was associated with IGFI-R tumor tissue expression (rs16861205). None of the 16 variants in ADIPOR1/R2 were related to cancer risk or circulating adiponectin levels. CONCLUSIONS: Common variants in the adiponectin gene were associated with prostate cancer risk, plasma adiponectin levels, and IR or IGF-IR expression in the prostate tumor. IMPACT: These genotype-phenotype associations support the biological relevance of adiponectin for prostate carcinogenesis, particularly in earlier stages of development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".