Increased Neuroinflammation in Asymptomatic LRRK2 Mutation Carriers: A Pilot PET Imaging Study (P4.321)
Bibliographic record
Abstract
Objective: To investigate if subjects that carry LRRK2 mutations, a known risk factor for Parkinson’s disease (PD), exhibit increased neuroinflammation compared to age matched healthy controls. Background: LRRK2 is known to play a role in the regulation of the innate immune system and response to inflammation. Similarly, neuroinflammation is hypothesized to be a likely contributor to PD origin and its progression. Thus, we investigated if increased neuroinflammation could be a mechanism by which LRRK2 mutations increase the risk of PD. Methods: In this pilot study we imaged 4 Healthy Controls (HC) (age 44 ± 16, mean, std; range 24-63) and 3 age matched unaffected LRRK2 G2019S mutation carriers (UC)(age 51 ± 12, mean, std; range 37-61) with 11C-PBR28, a second generation TSPO binding PET ligand. All subjects were mixed affinity binders (MAB). 38 MRI guided regions of interest were placed on the PET images. The primary outcome measures were standard uptake values (SUV), previously cross-validated with the total distribution values in a subset of the data. Mean SUV values across ROI were compared between groups using a t-test. Results: Mean SUV values were highly significantly elevated in the LRRK2 mutation carriers (SUVHC = 0.52 ±0.04, SUVUC = 0.83 ±0.13, p< 0.0001). Values were elevated in every region and no age dependence in the values was observed. Conclusions: These data suggest increased neuroinflammation is present in asymptomatic LRRK2 mutation carriers well before the expected age of disease onset and may indeed contribute to increase susceptibility to PD. While this was a very limited sample, the separation in the SUV values between groups was statistically highly significant. Extension to a larger sample including LRRK2-associated and sporadic PD will provide further information on the role of neuroinflammation in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".