Evaluation of Salivary Proteomic and Genomic Biomarkers as Non-Invasive Diagnostic Tools for Early Detection of Alzheimer's and Parkinson's Diseases: A Schematic Assessment and Meta-Analysis
Bibliographic record
Abstract
Background: Salivary biomarkers are non-invasive molecules that indicate neurodegenerative illnesses, especially Alzheimer disease (AD) and Parkinson disease (PD).this study was conducted to determine the diagnostic precision of salivary proteomic and genomic biomarkers in terms of early AD and PD detection. Methods: A systematic literature search was conducted in PubMed, web of science and Google Scholar, and studies included from 2016 to 2025. Research that examined salivary biomarkers in AD and PD was eligible. The data were analyzed with a random-effects model and odds ratios (OR), standard mean differences (SMD), and 95% confidence interval (CI) was estimated. Also, subgroup and sensitivity analysis were performed. To assess the risk of bias, the Newcastle-Ottawa Scale (NOS) was applied for included observational studies. Results: A total of 11 eligible studies concerning proteomic biomarkers, including amyloid-β (Aβ42, Aβ40) and alpha-synuclein total (α-synTotal) and alpha-synuclein Oligomer (α-synOligo), and genomic biomarkers like different salivary microRNAs were included. Meta-analysis indicated that Aβ42 (OR=0.70; 95% CI: 0.41 to 1.1) and Aβ40 (OR=1.01; 95% CI: 0.97 to 1.06) had significant discriminatory potential in AD patients; but α-synOligo (SMD = 2.90; 95% CI: -0.59–6.39) and α-synTotal (SMD = 0.44; 95% CI: -3.14 to 4.02) was higher in PD patients as compared with controls. Genomic biomarkers demonstrated inconsistent findings (SMD = -0.18; 95% CI: -1.79–1.42) because of difference in microRNA types. Heterogeneity was high (I2 > 90%), which is caused by alterations in study design and in the methods to measure biomarkers. Discussion: Salivary biomarkers were found to be an insignificant yet exceptional method of early examination of AD and PD. Nonetheless, the inconsistency of different studies points to develop standardized protocols.
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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 | 0.001 |
| 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 | 0.000 |
| 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".