Salivary Heme Oxygenase-1: A Potential Biomarker for Central Neurodegeneration
Bibliographic record
Abstract
BACKGROUND: Parkinson disease (PD) is the second most common neurodegenerative disease, affecting 2% of the population over 65 years of age. PD diagnosis is based on clinical examination and can only be confirmed during autopsy. In 2018, we reported that heme oxygenase-1 (HO-1), an inducible stress response protein important for heme catabolism and implicated in PD pathology, was higher in PD saliva relative to healthy controls, suggesting that salivary HO-1 may serve as a potential biomarker of PD. OBJECTIVES: To ascertain whether HO-1 protein levels are elevated in PD saliva relative to degenerative neurological, non-degenerative neurological and healthy controls. METHODOLOGY: The study included 307 participants comprising 75 participants with idiopathic PD and 3 control groups: 162 non-neurological, 37 non-PD degenerative neurological, and 33 non-degenerative neurological participants. Salivary HO-1 and total protein concentrations were measured using ELISA and BCA assay, respectively. Receiver operating characteristic (ROC) curves were used to estimate model discrimination. Analyses were adjusted by age, sex, total protein, and relevant comorbidities. RESULTS: Elevated HO-1 concentrations were observed in the PD group and other neurodegenerative conditions compared to subjects with no neurological or non-degenerative neurological conditions. ROC curves using HO-1 levels and covariates yielded areas under the curve above 85% in models for PD or neurodegenerative conditions versus controls. CONCLUSIONS: Salivary HO-1 concentrations in combination with covariates may provide a biomarker signature that distinguishes patients with neurodegenerative conditions from persons without. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that salivary HO-1 multivariable models can distinguish neurodegenerative conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".