Evaluation of Polygenic Risk Scores for a Possible Genetic Basis of the Inverse Association Between Cancer and Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: Accumulating evidence suggests that the incidence of cancer and dementia are inversely associated. Bias does not appear to fully account for the relationship, but causal explanations have not been adequately investigated. We thus considered a possible inverse shared genetic basis. METHODS: We constructed polygenic risk scores for cancer (PRS cancer ) and Alzheimer disease (PRS AD ) in European ancestry UK Biobank (UKB) and Health and Retirement Study (HRS) participants aged 60 years or older. Linear mixed-effects models evaluated associations of PRS cancer with cognition, and logistic regression evaluated associations of PRS AD with cancer. RESULTS: In UKB, PRS cancer was nominally associated with improved fluid intelligence ( β : 0.12, 95% CI: 0.01-0.22). Twelve variants in PRS cancer , including 7 in the human leukocyte antigen (HLA) complex, were positively associated with fluid intelligence, and 7 were inversely associated ( P <5.8×10 -5 ). PRS cancer and its contributing variants were not associated with cognitive outcomes in HRS. PRS AD was not associated with cancer risk in either study cohort. DISCUSSION: Though not conclusive, the direction of the association between PRS cancer and fluid intelligence was consistent with our a priori hypothesis that cancer risk variants would decrease cognitive decline. The association pattern with HLA-related variants suggests potential relevance of immune surveillance for the inverse association between dementia and 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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.005 | 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".