Identification of DNA methylation signature of frailty in postmenopausal women and extracellular vesicle mediated epigenetic age reversal in skeletal myoblasts
Bibliographic record
Abstract
As the Canadian population continues to age, healthcare systems are expected to be burdened with an increasing incidence of age-related illnesses. Current strategies are aimed at promoting healthy living and aging in place, but the underlying biology of frailty is not yet understood at the depth required for therapeutic intervention. DNA methylation (DNAm) studies implicate epigenetic maintenance systems as some of the molecular contributors leading to cellular dysfunction. By leveraging changes at particular DNAm loci, biological clocks have been created to predict an i4ndividual’s apparent age. Parabiosis experiments with extracellular content from young donors have successfully reduced the severity of age-related diseases in treated old organisms, indicating that extracellular signaling plays a crucial role in aging. How epigenetic dysregulation and extracellular communication interact in the context of frailty is still not understood, and even less so in the often under-studied population of older women. We sought to better understand the DNA methylation signature of frailty and transmission of epigenetic age through extracellular cargo in the woman-centered WARMHearts Study (Clinical Trial #NCT02863211). In this study, we: 1) isolated and profiled genome-wide DNA methylation from frail and robust women between 55-79 years old (Md = 64, IQR [59-68]) to identify frailty-linked DNAm loci, and 2) co-cultured robust/slow-aging extracellular vesicles (EVs), and frail/fast-aging EVs with chronologically young and old primary human skeletal myoblasts to explore the effect of circulating EVs from individuals of similar chronological age. Our results demonstrate that epigenetic clocks based on biomarkers of health and inflammation are better at predicting frailty than those trained only on chronological age data. We also found 9 cytosine-guanine dimers (CpG) that were differentially methylated (p < 1 x 10-6, |Δβ|> 0.01, N = 56), 8 CpGs that were variably methylated (p < 1 x 10-6, N = 56), and 43 regions with multiple CpGs within 1 kb that were differentially methylated (FDR < 0.05, N = 56) with frailty. The myoblast co-culture experiments demonstrated epigenetic age acceleration only in young myoblasts treated with robust plasma (p = 0.024, GrimAgeAccel = 1.79), although our collaborators in the Saleem Lab found significant phenotypic alterations in cell viability and senescence with robust EVs. Our data demonstrates that inflammation, cancer, and cardiometabolic disease drive frailty-associated alterations in DNAm in postmenopausal women. We also found that our EV treatment does not show significant alterations in epigenetic aging rates, likely from the amount of baseline drift in cultured cells from variable passaging and plating density. This thesis demonstrates that epigenetic aging and gene-regulation contribute to frailty in postmenopausal women, even after controlling for tobacco smoking, income, and chronological age.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".