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Record W4414972413 · doi:10.3389/fnagi.2025.1600782

Parkinson’s disease beyond the brain: implications for treatments

2025· review· en· W4414972413 on OpenAlexafffund
Setareh Malekian Naeini, Marc Danzell Lopez, Paul E. Fraser, Anurag Tandon

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2025
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsOccupational Cancer Research CentreOntario Institute for Cancer ResearchUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDiseaseNeurodegenerationEnteric nervous systemImmune systemMicrobiomeInflammationNervous systemSystemic inflammation

Abstract

fetched live from OpenAlex

The presence of α-synuclein pathology in peripheral nervous system neurons is linked to early prodromal, non-motor symptoms in a segment of Parkinson’s disease (PD) patients referred to as “body-first.” These features of the disease suggest a convergence of environmental, genetic, immune and age-related factors within the enteric nervous system as initiating triggers of disease. This review explores the changes in the gut microbiome and intestinal permeability that may drive systemic inflammation and precede neurodegeneration in PD. The pathways leading to the formation of α-synuclein aggregates are explored as well as their role in transneuronal propagation and the spreading of pathology within the brain. Lastly, advances in systemic gene therapy that could be used to target multiple PD-affected organs following systemic injection are highlighted. By integrating insights from molecular biology and clinical research, it may be possible to shed light on the multifactorial etiology of PD and the interconnectedness of the gut and brain, which could lead to novel diagnostic and therapeutic strategies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.042
GPT teacher head0.346
Teacher spread0.304 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Aging NeuroscienceSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207