Salivary biomarkers and cardiovascular disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: The purpose of this systematic review is to summarize the literature examining associations between salivary biomarkers and cardiovascular disease (CVD) status. CONTENTS: An advanced search was conducted using MeSH terms related to salivary biomarkers and CVD, and entered into the PubMed, Web of Science, and Google Scholar search databases. Four hundred and thirty-three records were narrowed to 22 accepted articles. Included titles were assessed for quality using the Newcastle-Ottawa scale, and ranked into categories of low, moderate, or high. SUMMARY: A total of 40 salivary biomarkers were analyzed among accepted articles. The most studied markers were salivary creatine kinase isoform MB, C-reactive protein (CRP), matrix metalloproteinase-9, troponin I, myeloperoxidase, myoglobin, and brain natriuretic peptide. Salivary CRP provided the most consistent trends. Statistically significant increases of salivary CRP were present with CVD in every study that analyzed it. The remaining six markers demonstrated varying patterns. OUTLOOK: Existing studies provide insufficient data to draw definitive conclusions. Current research shows that there is an association between some salivary biomarkers and CVD, but the details of existing studies are conflicting. Despite inconclusive results, the diagnostic potential of saliva shows promise as a non-invasive means of cardiovascular risk assessment.
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 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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".