Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference.
10.1016/j.jpha.2025.101265
- Author:
Junyu ZHANG
1
;
Jie PENG
2
;
Chaolun YU
1
;
Yu NING
3
;
Wenhui LIN
4
;
Mingxing NI
5
;
Qiang XIE
6
;
Chuan YANG
2
;
Huiying LIANG
5
;
Miao LIN
1
Author Information
1. Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
2. Department of Endocrinology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
3. Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, China.
4. Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Southern Medical University, Guangzhou, 510080, China.
5. Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
6. Department of Cardiology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, 361003, China.
- Publication Type:Journal Article
- Keywords:
Causal inference;
Diabetic kidney disease;
Drug targets;
Proteomics
- From:
Journal of Pharmaceutical Analysis
2025;15(8):101265-101265
- CountryChina
- Language:English
-
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.
