Non-linear Age-related Change in Human Interleukin-11 and the receptor subunit alpha DNA Methylation
Bibliographic record
Abstract
Abstract Introduction Interleukin-11 (IL-11) is a cytokine involved in inflammatory processes and a previous study showed that blocking or knocking down IL11 in mice prolongs a healthy lifespan. This study investigates DNA methylation (DNAm) changes in the IL11 and IL-11 receptor subunit alpha (IL-11RA) gene across ages to reveal how aging might influence IL-11 production and sensitivity. Methods A genome-wide DNAm database focusing on Cytosine-phosphate-Guanine (CpG) sites within the IL11 and IL11RA was analyzed. Hierarchical regression analyses examined the relationship between DNAm, age, and the squared age term for quadratic associations. Results The database comprised 10,297 samples (5,156 males and 5,141 females) with a mean age of 53.9 years (SD = 14.1 years). The majority of IL11 and IL11RA CpG sites in the TSS1500 and 3’UTR regions exhibited significant inverse U-shaped associations with age. DNAm levels were low during youth, increased in middle age (40s-50s), and decreased again in older age. Conclusion The observed inverse U-shaped DNAm patterns in the IL11 and IL11RA suggest n non-linear, age-related regulation of IL-11 expression and sensitivity. These findings indicate that IL-11 may have different roles across life stages and suggest that therapeutic interventions targeting IL-11 should consider age-specific effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".