The Association of Salivary Cytokines and Oral Health in patients with Metabolic Diseases: A Systematic Review
Bibliographic record
Abstract
(1) Backrgound: Saliva is a complex biofluid secreted by the salivary glands and contains many biomolecules, such as cells, DNA, RNA, large number of proteins, hormones, metabolites and microorganisms. This systematic review aimed to identify and assess the association of salivary cytokines and oral health in patients with metabolic diseases. (2) Methods: Four databases (Scopus, PubMed, ScienceDirect, and Embase) were searched for articles published from 2013 to 2023, and the search process was conducted on October 9th, 2023. The studies included were the original articles of observational medical studies published in English that reported the association of salivary cytokines in patients with oral diseases and systemic diseases focusing on metabolic syndrome (diabetes mellitus and obesity). Those excluded were in vivo and in vitro studies, and no abstract was presented. The risk of bias in selected studies was assessed using the Newcastle-Ottawa Scale. (3) Results: Eight original articles with 481 subjects were included in the systematic review, with IL-17 being the most studied. Some of the pro and anti-inflammatory cytokines like IL-17A, IL-4, IL-13, IL-12, IL-2, IL-10, G-CSF, GM-CSF, MCP-4, hs-CRP, vaspin, TNF-α, IL-7, MCP-4, hs-CRP, IL-6, and MIP-1β were higher among metabolic syndrome patients with oral diseases when compared to healthy controls. Moreover, the pro and anti-inflammatory cytokines like Del-1 and IFNγ were higher in healthy controls than in patients with metabolic syndrome and oral diseases. (4) Conclusion: The salivary cytokines of MCP-4, hs-CRP, IL-6, TNF-α, IL-8, IL-7, and MIP-1β were affected by metabolic syndrome and oral diseases such as periodontitis and caries, which shows that metabolic diseases change some of the salivary cytokine profiles related to oral diseases. Metabolic diseases impact the profile of salivary cytokines through a combination of immunological dysregulation, systemic inflammation, tissue-to-tissue communication, adipose tissue malfunction, and oral health factors.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".