Inflammatory profile discriminates clinical subtypes in <i>LRRK2</i>‐associated Parkinson's disease
Why this work is in the frame
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Bibliographic record
Abstract
Background and purpose The presentation of Parkinson's disease patients with mutations in the LRRK 2 gene ( PD LRRK 2 ) 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 PD LRRK 2 patients from the MJFF LRRK 2 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 PD LRRK 2 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 PD LRRK 2 and predict progression.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it