Modification effect of polygenic risk scores on the risk of the most common multimorbidity associated with aging among Canadian adults: An analysis of the CLSA data
Bibliographic record
Abstract
OBJECTIVE: We investigated the modification effects of polygenic risk score (PRS) on the risk of the most common multimorbidity (MCM) in Canadian adults associated with aging. METHODS: were used to compute the PRS for each individual, which was further divided into three PRS groups (high, median, and low) by the PRS terciles. Multivariate logistic regression models were used to examine the PRS-by-age interaction, adjusting for potential confounders and population structure. RESULTS: Multivariate analysis showed that increasing age was significantly associated with a higher risk of MCM, regardless of the PRS group. However, individuals in the top one-third of the PRS tercile (i.e., the high PRS group) were at the highest risk of MCM. For a one-year increase in age, participants in the high PRS group were 1.13 times more likely to have MCM, whereas for a one-year increase in age, it was associated with 1.09- and 1.10-times increased risk of MCM among individuals in the median and low PRS groups, respectively. CONCLUSION: PRS is an important tool for identifying individuals at a higher risk of MCM associated with aging and improves our understanding of the potential biological mechanisms of aging-related diseases.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".