Saliva insulin tracks plasma insulin across the day following high-carbohydrate and low-carbohydrate meals
Bibliographic record
Abstract
Non-invasive monitoring of insulin could hold promise to identify those with, or at risk for developing, insulin resistance. We aimed to examine saliva insulin responses across the day following high- and low-carbohydrate meals and evaluate whether changes in saliva insulin might accurately reflect changes in plasma insulin. In two randomized crossover studies, young normal weight men (NW; n = 8; Study 1) and adults with overweight/obesity (OO; n = 8; Study 2) completed two 9-h experimental trials in which the participants consumed isocaloric mixed high-carbohydrate (HC) or low-carbohydrate (LC) meals at 0, 3, and 6 h. Plasma and saliva samples were collected at fasted baseline and every 30 min for a total of 19 samples across 9 h. Overall, findings revealed a similar trend for postprandial saliva and plasma insulin responses regardless of the time of the day with a ∼30–45 min lag between saliva and plasma insulin responses. In both NW and OO groups, saliva and plasma insulin area under the curve (AUC) and incremental AUC were significantly higher in HC condition as compared to LC condition (all P ≤ 0.002). Nine-hour plasma and saliva insulin total AUCs were strongly and very strongly correlated in both HC ( r = 0.68; P = 0.007) and LC ( r = 0.84, P < 0.001) conditions, respectively. Saliva insulin is proportionate to and appears to reasonably track plasma insulin across the day with a ∼30–45 min delay. Saliva insulin shows promise as a non-invasive method to discern between low and high plasma insulin and may have utility in predicting the degree of insulin resistance (NCT03374436).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".