Systematic Druggable Genome‐Wide Analysis Identifies Therapeutic Targets for Aging: A Mendelian Randomization Study
Year: 2026
Type: article
Study design: Observational Study
Abstract: ABSTRACT 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.
Source: Health Science Reports
DOI: 10.1002/hsr2.72044
Authors: Yanfang Zhang, Menglong Wang, Hang Li, Ting Jin, Shanshan Peng, Yi Dong, Chaowei Deng, Hang Li, Xinyi Li, Juanjuan Qin, Xujun Ye
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,280
Awards: G5455814501, G7144345351
OpenAlex ID: W7142773688
