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Updated: Apr 21, 2026

A Phenotyping Regimen for Genetically Modified Mice Used to Study Genes Implicated in Human Diseases of Aging
Published on: July 14, 2016
Systematic Druggable Genome-Wide Analysis Identifies Therapeutic Targets for Aging: A Mendelian Randomization Study
Yanfang Zhang1, Menglong Wang2,3, Hang Li1
1Department of Geriatrics Zhongnan Hospital of Wuhan University Wuhan Hubei China.
Background And Aims:
Currently, there are no effective drug treatments for aging. Mendelian randomization (MR) has been widely applied to repurpose existing drugs and identify new therapeutic targets. Our aim is to identify aging-related therapeutic targets and evaluate their potential adverse effects, underlying mechanisms, and actionable drugs.
Methods:
We integrated druggable genome data, cis-expression quantitative trait loci (eQTL) data from human blood, and genome-wide association study (GWAS) summary data on aging. Using two-sample MR, we analyzed the potential causal relationships between druggable genes and aging. To ensure the reliability of our results, we conducted sensitivity analyses, Bayesian colocalization analyses, and repeated validations using different blood cis-eQTL data sets. We assessed the side effects of identified drug targets using phenome-wide MR (Phe-MR). Additionally, we performed two-step mediation MR analyses to evaluate the role of plasma proteins in mediating the causal relationships between these genes and aging, exploring potential mechanisms. Finally, we investigated actionable drugs for the target genes using relevant databases.
Results:
MR analysis identified five potential druggable genes related to aging: ATP1B3, VKORC1, SLC5A11, HNRNPA1, and SMN2. Phe-MR revealed no significant adverse effects when targeting these genes. Mediation MR identified ten plasma proteins linking these genes to aging, offering mechanistic insights. Drug repurposing analysis supports that cardiac glycosides, Bisacodyl, Olsalazine, and Tegoprazan might be potential therapeutics for aging by inhibiting ATP1B3.
Conclusions:
Our research indicates that ATP1B3, VKORC1, SLC5A11, HNRNPA1, and SMN2 are potential targets for antiaging therapies. These findings could help prioritize the development of aging-related drugs.
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