| Title: | Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference |
| Journal: | Journal of Pharmaceutical Analysis |
| Published: | 14 Mar 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40979545/ |
| DOI: | https://doi.org/10.1016/j.jpha.2025.101265 |
Publication 15673
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Abstract
Diabetic kidney disease (DKD) with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression. Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials. In this study, we integrated large-scale plasma proteomics, genetic-driven causal inference, and experimental validation to identify prioritized targets for DKD using the UK Biobank (UKB) and FinnGen cohorts. Among 2844 diabetic patients (528 with DKD), we identified 37 targets significantly associated with incident DKD, supported by both observational and causal evidence. Of these, 22% (8/37) of the potential targets are currently under investigation for DKD or other diseases. Our prospective study confirmed that higher levels of three prioritized targets-insulin-like growth factor binding protein 4 (IGFBP4), family with sequence similarity 3 member C (FAM3C), and prostaglandin D2 synthase (PTGDS)-were associated with a 4.35, 3.51, and 3.57-fold increased likelihood of developing DKD, respectively. In addition, population-level protein-altering variants (PAVs) analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD, through the classic NLR family pyrin domain containing 3 (NLRP3)-caspase-1-gasdermin D (GSDMD) apoptotic axis. Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets.</p>
10 Authors
- Junyu Zhang
- Jie Peng
- Chaolun Yu
- Yu Ning
- Wenhui Lin
- Mingxing Ni
- Qiang Xie
- Chuan Yang
- Huiying Liang
- Miao Lin
1 Application
| Application ID | Title |
|---|---|
| 33952 | Risk profiles for cancers; polygenic risk scores with and without non-genetic risk factors |
Enabling scientific discoveries that improve human health