Review of senolytic, senomorphic and senoreversion strategies for aging
This review abstract describes three strategies targeting senescent cells, plus metabolic interventions that extended lifespan and healthspan in preclinical models, and notes unresolved issues in target specificity, off-target effects and clinical translation. [1]
Automatic summary · not editorially reviewed
The key findings
The abstract states that strategies targeting aging hallmarks can delay aging and mitigate disease risk. [1]
Senolytics eliminate senescent cells, with dasatinib plus quercetin given as an example. [1]
Metabolic interventions showed lifespan extension and healthspan improvement in preclinical models. [1]
The source at a glance
- Study type / format
- Review abstract
- Population / context
- Aging biology and age-related diseases, as summarized in a review abstract
- Summary based on
- Study abstract
- Original publication
- June 1, 2026
Aging hallmarks as therapeutic targets
The abstract frames aging as a complex biological process with progressive functional decline that drives age-related diseases such as neurodegeneration, metabolic disorders and cardiovascular diseases. It states that strategies targeting aging hallmarks can delay aging and reduce disease risk, and names cellular senescence, metabolic dysfunction, epigenetic alterations and mitochondrial impairment among the core mechanisms being modulated.
Supporting source [1]
Senolytic, senomorphic and senoreversion approaches
The review highlights three strategies: senolytics, which eliminate senescent cells (example given: dasatinib plus quercetin); senomorphics, which inhibit the senescence-associated secretory phenotype (example given: rapamycin); and senoreversion, which rejuvenates senescent cells via epigenetic reprogramming. Separately, metabolic interventions such as caloric restriction mimetics (examples given: spermidine, α-ketoglutarate, ergothioneine) are said to enhance mitochondrial function, activate autophagy and reprogram energy metabolism.
Supporting source [1]
Preclinical lifespan results and translation gaps
The abstract reports that the metabolic interventions showed lifespan extension and healthspan improvement in preclinical models; it does not report human lifespan or healthspan outcomes. It also names remaining challenges: target specificity, off-target effects and clinical translation. Artificial intelligence is described as accelerating discovery by integrating multiomics data, predicting candidate compounds, identifying biomarkers and enabling personalized interventions.
Supporting source [1]
How to interpret this
This is an abstract of a review; the full text was not reviewed. The lifespan and healthspan results described are from preclinical models, not human outcomes. The abstract names target specificity, off-target effects and clinical translation as unresolved challenges.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Dong R, Wu Q, Kan J, Fu C, Sorrentino V, Chow A, Lei Y, Wu D, Xu Z, Du J, Huang D. · europepmc.org · June 1, 2026