The combined effect of lifestyle factors and polygenic scores on age at onset in Parkinson’s disease
Bibliographic record
Abstract
Abstract Objective To investigate the association between a Parkinson’s disease (PD)-specific polygenic score (PGS) and protective lifestyle factors on age at onset (AAO) in PD. Methods We included data from 4375 patients with idiopathic PD, 167 patients with GBA1 -PD, and 3091 healthy controls of European ancestry from AMP-PD, PPMI, and Fox Insight cohorts. The PGS was calculated based on a previously proposed composition of 1805 variants. The association between PGS and lifestyle factors (i.e., coffee, tobacco, and aspirin) on AAO was assessed with linear and Cox proportional hazards models. Results The PGS showed a negative association with AAO (β=-1.07, p=6x10 -7 ). The use of one, two, or three of the protective lifestyle factors showed a reduction in the hazard ratio by 21% (p=0.0001), 45% (p<2x10 -16 ), and 55% (p<2x10 -16 ), respectively, compared to no use. An additive effect of aspirin (β=7.61, p=8x10 -7 ) and PGS (β=-1.63, p=0.0112) was found for AAO without an interaction (p=0.9789) in the linear regressions, and similar effects were seen for tobacco. Aspirin is shown to be a better predictor of AAO (R 2 =0.1740) compared to coffee and tobacco use (R 2 =0.0243, R 2 =0.0295) or the PGS (R 2 =0.0141). In contrast, no association between aspirin and AAO was found in GBA1 -PD (p>0.05). Interpretation In our cohort, coffee, tobacco, aspirin, and PGS are independent predictors of PD AAO. Additionally, lifestyle factors seem to have a greater influence on AAO than common genetic risk variants with aspirin presenting the largest effect. External validation of our findings is needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".