Association between salivary pH and metabolic syndrome in women: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The salivary flow rate is an important determinant of salivary pH. It is influenced by several metabolic syndrome (MetS) components as well as the menopausal status. The cluster of cardiometabolic risk factors that characterizes the MetS could be exacerbated following menopause. The objective of this study was therefore to document the association between salivary pH and MetS expression in women according to the menopausal status. METHODS: In this cross-sectional study, unstimulated saliva collection was performed on 198 Caucasian women of French-Canadian origin of which 55 were premenopausal women (PMW) and 143 menopausal women (MW). Student's t test, ANOVA and correlation analyses were used to assess the association between salivary pH and MetS components. RESULTS: The salivary pH level was significantly correlated with several MetS covariates, namely triglycerides (TG), apolipoprotein B (apo B) and plasma glucose concentrations as well as waist circumference and the number of MetS components present in the whole sample and PMW only. Mean pH levels decreased as the number of MetS components increased (p = 0.004). The correlations between salivary pH and variables associated with MetS components tended to be stronger in PMW. The proportion of the variance (R2) of salivary pH explained by MetS-related variables in PMW, MW and the whole sample was 23.6% (p = 0.041), 18.1% and 17.0% (p < 0.001) respectively. CONCLUSIONS: The increasing prevalence of obesity calls for the development of new technologies to more easily monitor health status without increasing the burden of healthcare costs. As such, the salivary pH could be an inexpensive screening tool. These exploratory data suggest that salivary pH may be a significant correlate of the expression of MetS components. However, other studies with different populations are needed to confirm these findings before our observations lead to practical use in clinical settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".