Cytokine and chemokine map of peripheral specific immune cell subsets in Parkinson’s disease
Bibliographic record
Abstract
Abstract Peripheral immune cells play a vital role in the development of Parkinson’s disease (PD). However, their cytokine and chemokine secretion functions remain unclear. Therefore, we aimed to explore the cytokine and chemokine secretion functions of specific immune cell subtypes in drug-naïve patients with PD at different ages of onset. We included 10 early-onset and 10 late-onset patients with PD and age-matched healthy controls (HCs). We used mass cytometry to select specific immune cell subsets and evaluate intracellular cytokine and chemokine expression. Statistical tests included t -tests, analysis of variance, bivariate correlation analysis, and linear regression analysis. Compared with HCs, patients with PD exhibited significantly decreased intracellular pro-inflammatory cytokines and chemokines in selected clusters (e.g., tumor necrosis factor (TNF)-α, interleukin (IL)−8, IL-1β, and CC-chemokine ligand (CCL)17). Specific cytokines and cell clusters were associated with clinical symptoms. TNF-α played an important role in cognitive impairment. Intracellular TNF-α levels in the naïve CD8 + T-cell cluster C16 (CD57 − naïve CD8 + T) and natural killer (NK) cell cluster C32 (CD57 − CD28 − NK) were negatively correlated with Montreal Cognitive Assessment scores. The C16 cluster affected cognitive function and motor symptoms. Increased TNF-α and decreased interferon-γ expression in C16 correlated with increased Unified Parkinson’s Disease Rating Scale III scores in patients with PD. In summary, we developed a more detailed cytokine and chemokine map of peripheral specific CD8 + T cell and NK cell subsets, which revealed disrupted secretory function in patients with PD and provided unique clues for further mechanistic exploration.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".