Saliva insulin concentration following ingestion of a standardized mixed meal tolerance test: influence of obesity status
Bibliographic record
Abstract
Early detection of hyperinsulinemia may help identify and prevent metabolic diseases, but accurate insulin measurement is challenging, costly, and requires blood samples. This study aimed to characterize saliva insulin responses to a standardized meal tolerance test in people with different body mass index (BMI) classes to help develop potential saliva insulin thresholds based on varying levels of insulin resistance. A total of 94 healthy normoglycemic adults (aged 18–69 years, fasting blood glucose 5.2 ± 0.5 mmol/L) were recruited, categorized into groups with normal weight (NW, n = 41), overweight (OW, n = 23), and obesity (OB, n = 30). Participants fasted for ≥4 h and then consumed a standardized liquid meal (350 kcal; 45 g carbohydrate, 20 g protein, 11 g fat). Saliva samples and finger prick blood glucose were collected at fasting, 60 min, and 90 min post-meal. Saliva insulin levels at all time points were significantly higher in the group with OB compared to OW (all P ≤ 0.02) and NW (all P ≤ 0.001). The OW group also had higher insulin levels compared to NW (all P ≤ 0.02). No significant differences in fasting and post-meal glucose levels were found among groups (all P ≥ 0.12). Strong positive correlations were observed between obesity markers (waist circumference, BMI) and saliva insulin levels. Preliminary cut-off values for fasting (∼16 pmol/L), 60 min (∼97 pmol/L), and 90 min (∼115 pmol/L) saliva insulin may delineate between normal and hyperinsulinemic responses. Saliva insulin can effectively differentiate hyperinsulinemic responses among normoglycemic individuals with varying body weights and waist circumference, suggesting its potential as a non-invasive screening tool for metabolic disease risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".