Salivary interleukins are associated with cognitive function
Bibliographic record
Abstract
Introduction: There is a need to accurately identify mild cognitive impairment (MCI) and diagnose dementia-causing diseases such as Alzheimer’s at an early stage. Saliva is an accessible and non-invasive biomarker. Studies have linked inflammatory salivary markers such as cortisol and cytokines to cognition in the context of neurodegenerative diseases. However, there are discrepancies in the literature, differences in methodology and study sample sizes are small. Aim: We aimed to test the feasibility of a simple saliva collection procedure amongst an older population and determine whether salivary cortisol, interleukin-6 (IL-6) and interleukin-1-beta (IL-1β) are associated with cognitive function. Method: The passive-drool method was used to collect saliva from 50 participants (age = 73 (70,77), which was analysed with cortisol, IL-6 and IL-1β ELISA kits (Salimetrics, State College, PA). The cognitive function of participants was assessed using the Montreal Cognitive Assessment (MoCA), adjusted for years in education. The data was analysed using the Mann-Whitney U test and multiple regression. Results: The median MoCA score was 73 (70,77). Median salivary levels were: cortisol 0.4 (0.3,0.6) µg/dL, IL-6 8.6 (3.9,23.1) pg/mL and IL-1β 997.4 (376.9,1929.8) pg/mL. There was no significant difference between participants classified as cognitively normal (n = 30) and MCI (n = 20) in levels of salivary cortisol (U = 299, p = 0.922), IL-6 (U = 261, p = 0.919) or IL-1β (U = 261, p = 0.634). However, multiple regression with all salivary biomarkers and age as covariates showed that adjusted MoCA scores had a significant negative association with salivary IL-6 levels (b = -0.009, t = -2.630, p = 0.019). Conclusion: The results from this feasibility and pilot study indicate that salivary IL-6 levels increase with decreasing cognitive function. This finding needs to be confirmed using a larger sample, but this may help to develop alternative diagnostic and therapeutic pathways for dementia-causing diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".