Exploring Salivary Biomarkers in Pediatric Obesity: A Scoping Review
Bibliographic record
Abstract
Childhood obesity and overweight are linked to subclinical inflammatory conditions. The present manuscript aimed to undertake a scoping review exploring the relationship between childhood obesity and salivary biomarkers to answer the following question: “Are salivary biomarkers trustful factors/indicators for childhood obesity?” The main search terms used were: “obesity and salivary biomarkers and children” (Pubmed, Scielo, Scopus, Embase databases: 1999–2025). Assessed articles were carefully classified according to a predetermined criterion (Newcastle–Ottawa Scale), and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were considered. Papers involving children >13 years, duplicates/triplicates, literature reviews, and non-related to the question addressed were excluded. More than 30 salivary biomarkers were assessed in the thirteen studies appraised. Three studies were rated as having a high level of evidence, two as moderate, and eight as having a low level. Fourteen biomarkers were found to be significantly increased in childhood obesity/overweight (p < 0.05): leptin, insulin, α-amylase, tumor necrosis factor α, interleukin 6, vascular endothelial growth factor-A, C-reactive protein, monocyte chemotactic protein-1, resistin, phosphate, nitric oxide, interleukin 1β, uric acid and fetuin-A; and three were found to be significantly decreased (p < 0.05): adiponectin, secretory immunoglobulin A, and interleukin-12p70. In conclusion, the present review supported the idea that saliva might be a promising diagnostic tool in early life and that it is a significant source of obesity biomarkers in children.
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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 | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".