Detection of Salivary Insulin Following Low versus High Carbohydrate Meals in Humans
Bibliographic record
Abstract
Developing non-invasive alternatives to monitor insulin levels in humans holds potential practical value for identifying individuals with, or at risk of developing, insulin resistance. The aims of this study were: (1) to determine if saliva insulin can be used to delineate between low and high postprandial insulin levels following the ingestion of mixed breakfast meals; and (2) to determine if expected differences in postprandial hyperinsulinemia between young lean and young overweight/obese participants could be detected in saliva. Sixteen individuals (n = 8 classified as normal weight (NW); BMI 20.0–24.9 kg/m2, and n = 8 classified as overweight/obese (OO); BMI ≥ 28.0 kg/m2) completed two isocaloric mixed-meal tolerance tests following an overnight fast, consisting of a low-carbohydrate (LC) breakfast or a high-carbohydrate (HC) breakfast. Blood and saliva samples were collected at regular intervals for two hours postprandially. In both groups, plasma and saliva insulin total area under the curve (AUC) and incremental AUC (iAUC) were significantly higher after the HC as compared to the LC meal (all p ≤ 0.005). Insulin AUC and iAUC in both plasma and saliva were higher in OO than in NW after the HC meal (all p ≤ 0.02) but only plasma and saliva total AUC were higher in OO after the LC meal (both p ≤ 0.01). Plasma insulin AUC was significantly correlated with salivary insulin AUC in LC (r = 0.821; p < 0.001) and HC (r = 0.882; p < 0.001). These findings indicate that saliva could potentially be used to delineate between low and high insulin levels following mixed breakfast meals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".