The neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, and neutrophil-to-high-density-lipoprotein ratio are correlated with the severity of Parkinson’s disease
Bibliographic record
Abstract
Background: Inflammation plays a pivotal role in the pathogenesis of Parkinson's disease (PD). However, the correlation between peripheral inflammatory markers and the severity of PD remains unclear. Methods: = 303) were assessed for the neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-high-density-lipoprotein ratio (NHR) in plasma, and neuropsychological assessments were performed for all patients with PD. Spearman rank or Pearson correlation was used to evaluate the correlation between the NLR, the LMR and the NHR and the severity of PD. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of the NLR, LMR and NHR for PD. Results: The plasma NLR and NHR were substantially higher in patients with PD than in HCs, while the plasma LMR was substantially lower. The plasma NLR was positively correlated with Hoehn and Yahr staging scale (H&Y), Unified Parkinson's Disease Rating Scale (UPDRS), UPDRS-I, UPDRS-II, and UPDRS-III scores. Conversely, it exhibited a negative relationship with Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. Furthermore, the plasma NHR was positively correlated with H&Y, UPDRS, UPDRS-I, UPDRS-II and UPDRS-III scores. Moreover, negative associations were established between the plasma LMR and H&Y, UPDRS, UPDRS-I, UPDRS-II, and UPDRS-III scores. Finally, based on the ROC curve analysis, the NLR, LMR and NHR exhibited respectable PD discriminating power. Conclusion: Our research indicates that a higher NLR and NHR and a lower LMR may be relevant for assessing the severity of PD and appear to be promising disease-state biomarker candidates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".