DNA Methylation Reveals Senolytic Treatments Do Not Reverse Epigenetic Aging
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Trial Title
DNA Methylation Signatures of Cellular Senescence are Not Reversed by Senolytic Treatment
Collaborating Institutions
Yale University, Program in Computational Biology and Bioinformatics, Department of Pathology, Department of Psychiatry; Institute for Hormonal Balance
Summary Snapshot
Study Type: Computational meta-analysis with epigenetic predictor development
Sample Size: Multiple dataset cohorts, including cell lines from GSE227160, GSE197723; in vivo: 656 in GSE40279, 4,205 in FHS
Duration: Senolytic interventions ranged from 3 days in vitro to 6 months in vivo
Primary Endpoint: Response of senescence-enriched epigenetic clocks to senolytic treatment
The DNA Methylation Edge
The study aimed to identify a core subset of DNA methylation CpGs associated with cellular senescence, age, and mortality to develop senescence-enriched epigenetic clocks and test their response to senolytic treatments, under the hypothesis that conserved senescence signals would improve detection of senescence and treatment effects compared to traditional clocks. It addressed inconsistencies in prior studies wherein epigenetic clocks showed unreliable associations with senescence and no reversal with senolytics, which is significant because senescence drives age-related diseases, and reliable biomarkers could accelerate clinical trials for anti-aging interventions like senolytics to extend the healthspan.
Objective & Rationale
Study Goal and Scientific Context: The study aimed to identify a core subset of DNA methylation CpGs associated with cellular senescence, age, and mortality to develop senescence-enriched epigenetic clocks and test their response to senolytic treatments, under the hypothesis that conserved senescence signals would improve detection of senescence and treatment effects compared to traditional clocks. It addressed inconsistencies in prior studies wherein epigenetic clocks showed unreliable associations with senescence and no reversal with senolytics, which is significant because senescence drives age-related diseases, and reliable biomarkers could accelerate clinical trials for anti-aging interventions like senolytics to extend the healthspan.
Methodology
Design: Systematic meta-analysis of CpG associations with in vitro senescence, in vivo age, and mortality; elastic net regression to train senescence (binary), age (linear), and mortality (Cox) predictors; validation in independent datasets.
Population: In vitro: Primary human cell lines (dermal fibroblasts, mesenchymal stem cells, mammary epithelial cells) from young donors. In vivo: Whole blood from healthy adults (e.g., ages 19-101 in GSE40279, mean ~70 in FHS). Senolytic cohorts: Adults with age-related conditions (e.g., mean age ~60).
Intervention: Analysis of senolytic treatments including dasatinib + quercetin (DQ), DQ + fisetin (DQF), ABT-263 (navitoclax), Pep 14, and BI01; in vitro: Applied to senescent cells; in vivo: Oral administration over months.
Duration: In vitro senescence induction: Days to months (e.g., serial passaging for replicative); senolytic exposure: 2-3 days in vitro, 6 months in vivo.
Epigenetic Measurements: Infinium HumanMethylation450/EPIC BeadChips and Mammalian 320k array; Custom clocks: SenCultureAge (binary senescence predictor), SenChronoAge (age predictor), SenMortalityAge (mortality predictor), and mouse variants; trained on 9,363 (human) or 1,081 (mouse) selected CpGs.
Key Findings
Results: Only 9,363 CpGs (2.4% of analyzed) showed consistent directional changes with senescence, age, and mortality; three senescence-enriched clocks (SenCultureAge, SenChronoAge, SenMortalityAge) were trained and validated, showing acceleration in replicative/oncogenic senescence and correlations with age/mortality, but none decelerated post-senolytic treatment. Rather, they trended toward acceleration (e.g., SenCultureAge accelerated after 3 months DQ). CpG changes were inconsistent across senolytics, with many moving in the same direction as senescence; mouse models and sensitivity analyses confirmed no reversal, suggesting epigenetic aging and senescence may be distinct, and clocks may not capture senolytic benefits.
Standard Metrics: % change in biological age, direction of effect, significance. SenCultureAge: observed accelerated DNA damage senescence; no change or acceleration post-senolytic. SenChronoAge: Accelerated in replicative senescence; no significant deceleration post-senolytic. SenMortalityAge: Accelerated in replicative/oncogenic senescence; no deceleration post-senolytic. No % changes reported.
Academic Significance: In this case, even though the research team’s findings did not align with their original hypothesis, EPBs and biological clocks helped capture these unexpected senolytic outcomes and suggest potential explanations for these results, adding to overall knowledge in the field of cellular aging and hinting at distinct cellular pathway interactions that could require further research.
Clinical Significance: Senescent cells are apparently following a biological strategy to limit the spread of negative effects and the proliferation of damaged cells, but are also linked to increased inflammation and increased cellular aging in certain circumstances.
DNA Methylation Reveals Senolytic Treatments Do Not Reverse Epigenetic Aging
Trial Title
Exploring the Effects of Dasatinib, Quercetin, and Fisetin on DNA Methylation Clocks: A Longitudinal Study on Senolytic Interventions
Collaborating Institutions
Institute for Hormonal Balance; Yale University School of Medicine; Yale University Department of Psychiatry; Yale School of Public Health; Yale Center for Genomic Analysis; Yale Center for Genome Analysis; Yale University Department of Genetics; Yale University Department of Biostatistics; Yale University Department of Chronic Disease Epidemiology
Summary Snapshot
Study Type: Prospective non-randomized longitudinal pilot studies
Sample Size: 19 participants (DQ study); 19 participants (DQF study, including 10 continuing from DQ)
Duration: 6 months per treatment arm
Primary Endpoint: Changes in epigenetic age acceleration (EAA) across various DNAm clocks
The DNA Methylation Edge
DNA methylation allowed this study to track senolytic effects longitudinally across multiple aging clocks and biological systems in humans. This approach revealed system-specific and sometimes paradoxical responses that single-endpoint or symptom-based measures would miss.
Objective & Rationale
Study Goal and Scientific Context: The study aimed to evaluate the impact of senolytic treatments (Dasatinib + Quercetin [DQ], and DQ + Fisetin [DQF]) on epigenetic aging via DNA methylation clocks, telomere length, mitotic clocks, immune cell subsets, and proteomic surrogates in adults over 6 months, using a pilot longitudinal design. It addressed the lack of human data on senolytics' effects on molecular aging biomarkers, given senescence's role in inflammation and age-related decline. Findings show senolytics could extend healthspan by clearing senescent cells, and epigenetic clocks provide measurable proxies for biological aging, potentially guiding clinical trials amid rising chronic disease burdens.
Methodology
Design: Prospective non-randomized longitudinal pilot studies; Phase I for DQ (baseline, 3-month, 6-month sampling); follow-up for DQF (baseline, 6-month sampling); epigenome-wide association studies (EWAS) and immune deconvolution
Population: Adults ≥40 years (mean 59.6-60.9, range 43-88; 42-58% male); exclusions for cancer, immune disorders, BMI >40.
Intervention: DQ: 50 mg Dasatinib + 500 mg Quercetin orally 3 consecutive days/month; DQF: Same + 500 mg FisetinDuration: 6 months per arm; 1-year untreated washout for continuing participants
Epigenetic Measurements: Infinium HumanMethylationEPIC BeadChip for DNAm; preprocessing with Minfi; Clocks: First-generation (PC Horvath pan tissue, Horvath skin/blood, PC Hannum); second-generation (PC DNAmPhenoAge, GrimAge); third-generation (DunedinPACE); DNAmTL (telomere length); mitotic clocks (epiTOC, epiTOC2, MiAge); EpiDISH for immune deconvolution; EWAS via limma; methylation risk scores for proteomic surrogates (e.g., inflammation markers).
Key Findings
Results: DQ treatment increased epigenetic age acceleration (EAA) in first-generation clocks and PC DNAmPhenoAge; it also shortened telomeres (DNAmTL), altered mitotic clocks, and shifted immune subsets but made no changes according to GrimAge or DunedinPACE. DQF showed no significant EAA changes but decreased B Naive cells and enriched senescence pathways in EWAS. No strong correlations between EAA and immune/proteomic shifts post-adjustment; EWAS revealed senescence-related probes. Findings suggest senolytics may accelerate some epigenetic clocks short-term, possibly due to transient inflammation, with limited reversal of biological aging markers.
Standard Metrics: % change in biological age, direction of effect, significance PC Horvath pan tissue EAA: +2.74 years; PC Hannum EAA: +1.32 years; PC DNAmPhenoAge EAA: +1.21 years, +0.97 years; DNAmTL EAA: -0.04 years; epiTOC: -0.004 divisions, +0.005 divisions; no significant changes for DQF.
Academic Significance: EPBs helped distinguish cellular impacts between the first and second regimens tested and underscored performance differences between first and second or third-generation age clocks. Essentially, the study showed the value of understanding what various generation age clocks are capturing (CpG location responsiveness vs. underlying biological impact, for instance), which helps identify potentially useful avenues for further study.
Clinical significance: Age clocks helped demonstrate the difference in clinical outcomes obtained by administering different small molecules, which could assist with personalized medicine and with targeting therapies to achieve desired health outcomes by system. The study also yielded insights into shifting inflammation patterns and inflammation mediation over time.
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