Inflammatory profile discriminates clinical subtypes in <i>LRRK2</i>‐associated Parkinson's disease
Bibliographic record
Abstract
Background and purpose The presentation of Parkinson's disease patients with mutations in the LRRK2 gene (PDLRRK2) is highly variable, suggesting a strong influence of modifying factors. In this context, inflammation is a potential candidate inducing clinical subtypes. Methods An extensive battery of peripheral inflammatory markers was measured in human serum in a multicentre cohort of 142 PDLRRK2 patients from the MJFF LRRK2 Consortium, stratified by three different subtypes as recently proposed for idiopathic Parkinson's disease: diffuse/malignant, intermediate and mainly pure motor. Results Patients classified as diffuse/malignant presented with the highest levels of the pro‐inflammatory proteins interleukin 8 (IL‐8), monocyte chemotactic protein 1 (MCP‐1) and macrophage inflammatory protein 1‐β (MIP‐1‐β) paralleled by high levels of the neurotrophic protein brain‐derived neurotrophic factor (BDNF). It was also possible to distinguish the clinical subtypes based on their inflammatory profile by using discriminant and area under the receiver operating characteristic curve analysis. Conclusions Inflammation seems to be associated with the presence of a specific clinical subtype in PDLRRK2 that is characterized by a broad and more severely affected spectrum of motor and non‐motor symptoms. The pro‐inflammatory metabolites IL‐8, MCP‐1 and MIP‐1‐β as well as BDNF are interesting candidates to be included in biomarker panels that aim to differentiate subtypes in PDLRRK2 and predict progression.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".