Unraveling the Intersection of Aging and Parkinson's Disease: A Collaborative Roadmap for Advancing Research Models
Bibliographic record
Abstract
Aging is the most significant risk factor for Parkinson’s disease (PD), yet its role in PD pathogenesis remains underexplored. The challenges linked to modeling PD in commonly used rodent models, coupled with the prolonged timelines required for aging studies, have hindered progress in this critical area. The International Network for Parkinson’s Disease Modelling and Aging (PD-AGE), funded by the Michael J. Fox Foundation, was established to address these challenges. Through collaborative efforts, PD-AGE developed a roadmap to reach consensus on experimental approaches, prioritize suitable models, and standardize protocols to investigate the intersection of aging and PD. This initiative advocates for prioritizing the crossing of mouse PD models with incomplete penetrance, including genetic (Pink1, Lrrk2, Gba) sporadic (α-synuclein pre-formed fibril) and environmental (paraquat) models, with well described accelerated aging models showing dopaminergic neuron vulnerability (Ercc1-/Δ, Nfkb1-/- ). We proposed that a tiered approach to experimental testing will enable systematic and rigorous characterization of these models, offering efficiency, economy, and further prioritization of model systems for testing specific hypotheses. By fostering collaboration and optimizing resource utilization, this roadmap provides a foundation for understanding the synergistic effects of aging and PD. It aims to accelerate mechanistic insights and refine preclinical models, ultimately supporting the development of interventions that address the aging-related dimensions of PD pathogenesis.
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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 | 0.114 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.008 | 0.023 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".