0962 Age, Sleep and Inflammation: A Correlational Study
Bibliographic record
Abstract
Abstract Introduction This correlational study explores the relationship between sleep and inflammation in aged individuals. Cytokines are involved in various central nervous system processes, including wakefulness, appetite, mood regulation, sexual behaviors, and thermoregulation. Sleep disorders commonly associated with aging, such as insomnia, circadian disruptions, sleep apnea, and periodic leg movements, might have significant biological impacts on the inflammatory system and immune functions. Aging is also associated with increased levels of some inflammatory markers. Methods 77 individuals (women: 51, men: 26) aged 60–87 years (mean age 69.8 ± 6.1 years) were recruited from the community. None were using psychoactive drugs. Participants underwent three nights of polysomnographic sleep recording in the laboratory: the first night served for adaptation and sleep disorder assessment, while nights two and three were for experimentation. A 19-channel EEG montage was used with additional sensors for eye movements, muscle activity, respiration, and leg movements. Fasting, resting venous blood was drawn in the morning upon awakening for further measurement of cytokine receptors IL-1α, IL-1β, IL-1RA, IL-6, IL-8, IL-10, TNFα and sTNFR1 in serum (Milliplex). Questionnaires assessed health (SF-36), sleep quality (PSQI), anxiety (BAI), and depressive symptoms (BDI, POMS). Results Periodic Leg Movement during Sleep (PLMS) was correlated to four inflammation markers (IL10 (.361), IL6 (.307), IL1B (.264) IL1A (.227)), the most of any other sleep variables, clearly indicating that PLMS is associated with a prolonged state of inflammation. sTNFR1 was the biomarker most associated with other variables (Micro-arousal (-.375), AHI (Apnea Hypopnea Index:.278), #Arousal (-.261), Stage1 (-.238), POMS (.401)). The first three variables are sleep disrupters and Stage1 augments when there is less of deeper sleep states, it seems counterintuitive that they would consistently negatively correlate with an anti-inflammatory marker. AHI was further correlated with IL1RA (.277). Conclusion Correlations are modest but many and significant, pointing toward a definite role of sleep in inflammatory processes. The signaling relevance of biomarkers, being pro, anti or both, depending on circumstances, remains ambiguous. Support (if any)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".