Novel biomarkers in the saliva of healthy young males and females in a randomized crossover study on sedentary time: An exploratory analysis
Bibliographic record
Abstract
Several known biomarkers have been used to understand the physiological responses of humans to various short and long-term interventions such as exercise or dietary interventions. However, little exploratory work has been conducted to identify novel biomarkers in human saliva that could enable non-invasive physiological research to understand acute responses to interventions such as reducing sedentary time. The purpose of this study was to identify novel biomarkers in the saliva (cytokines, growth factors and vascular factors) that respond to prolonged (4 hours) and interrupted sitting (4 hours of sitting interrupted by 3 minutes of walking at 60% of maximal heart rate every 27 minutes) in young, healthy males and females. We also sought to determine whether responsive biomarkers would differ by sex. Participants (n = 24, 21.2 ± 2.2 years, 50% female) completed a prolonged sitting (PS) session and an interrupted sitting (IS) session in random order. Individual saliva samples were pooled into a male sample and a female sample to identify responsive biomarkers using a human cytokine antibody membrane array (42 targets). Several novel biomarkers were responsive in both sexes (e.g., IL-8, Angiogenin, VEGF, and EGF), in females only (e.g., TNF-α and IL-13), and in males only (e.g., IL-3, RANTES, and IL-12p40/p70). Importantly, several biomarkers appear to be responsive to the 4-hour prolonged and interrupted sitting sessions (e.g., TNF-α, IL-8, IL-3, RANTES, EGF, Angiogenin, and VEGF). This work highlights new directions for researchers aiming to investigate the effect of short-term or acute interventions on different physiological pathways using non-invasive methods. Our work clearly indicates that human saliva samples can provide a wealth of insight into physiological responses, and that a number of biomarkers can be used to understand changes induced by acute interventions such as interrupting prolonged sitting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".