0942 The Association of Insomnia with Circulating Biomarkers of Accelerated Aging: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Insomnia is a prevalent sleep disorder defined by difficulty initiating or maintaining sleep or waking up earlier than intended. Studies suggest that insomnia may contribute to premature biological aging and increase susceptibility to age-related health complications. Various circulating biomarkers of biological aging have been used to assess the aging processes; increased biological age compared to chronologic age indicates premature biological aging. This study aims to systematically review and summarize the literature with respect to the relationship between insomnia and circulating biomarkers of premature aging. Methods Following the 2020 PRISMA guidelines, five electronic databases (MEDLINE, Embase, CINAHL, Cochrane, PsycINFO) were searched from inception to October 2024. Eligibility criteria included full manuscript English studies on adult humans with insomnia that evaluated a circulating marker of aging. We only included studies that assessed insomnia symptoms and severity with validated sleep surveys or by the Diagnostic and Statistical Manual of Mental Disorders. Quality assessments were conducted using standard methods. Results Twenty-two studies (17422 participants) were included in our analysis: biomarkers included telomere length (TL, n=17 studies), epigenetic age acceleration (EAA, n=3), mitochondrial DNA (MtDNA, n=1) and klotho (n=1), a protein involved in the aging process. Several studies (N=13) found insomnia was associated with reduced TL, with greater reductions in more severe insomnia, as indicated by higher survey scores. Additionally, there was a positive correlation between the number of insomnia symptoms and the degree of EAA. Single studies on MtDNA copy number and klotho showed lower values in insomnia. One study found longer TL in insomnia that was not significant. However, despite promising associations, the findings are undermined by the variety of surveys (five surveys) used and subsequent definitions of the control and exposure group. Conclusion Insomnia is associated with increased biomarkers of premature aging, such as reduced TL and EAA. Although the data is suggestive, the inconsistency and breadth of methodological approaches limits the conclusions that can be made. This underscores the need for further research in this area, including the study of additional markers not included in our review (e.g. sirtuin levels, autophagic activity), and the association of these markers with robust age-related clinical outcomes. Support (if any)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".