| Title: | Proteomic Analyses in Diverse Populations Improved Risk Prediction and Identified New Drug Targets for Type 2 Diabetes. |
| Journal: | Diabetes Care |
| Published: | 16 Apr 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38623619/ |
| DOI: | https://doi.org/10.2337/dc23-2145 |
| URL: | ~Ehttps://diabetesjournals.figshare.com/articles/figure/Proteomic_analyses_in_diverse_populations_improved_risk_prediction_and_identified_new_drug_targets_for_type_2_diabetes/25393255/1/files/44994253.pdf~ |
Publication 9896
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
OBJECTIVE: Integrated analyses of plasma proteomics and genetic data in prospective studies can help assess the causal relevance of proteins, improve risk prediction, and discover novel protein drug targets for type 2 diabetes (T2D).</p>
RESEARCH DESIGN AND METHODS: We measured plasma levels of 2,923 proteins using Olink Explore among ∼2,000 randomly selected participants from China Kadoorie Biobank (CKB) without prior diabetes at baseline. Cox regression assessed associations of individual protein with incident T2D (n = 92 cases). Proteomic-based risk models were developed with discrimination, calibration, reclassification assessed using area under the curve (AUC), calibration plots, and net reclassification index (NRI), respectively. Two-sample Mendelian randomization (MR) analyses using cis-protein quantitative trait loci identified in a genome-wide association study of CKB and UK Biobank for specific proteins were conducted to assess their causal relevance for T2D, along with colocalization analyses to examine shared causal variants between proteins and T2D.</p>
RESULTS: Overall, 33 proteins were significantly associated (false discovery rate < 0.05) with risk of incident T2D, including IGFBP1, GHR, and amylase. The addition of these 33 proteins to a conventional risk prediction model improved AUC from 0.77 (0.73-0.82) to 0.88 (0.85-0.91) and NRI by 38%, with predicted risks well calibrated with observed risks. MR analyses provided support for the causal relevance for T2D of ENTR1, LPL, and PON3, with replication of ENTR1 and LPL in Europeans using different genetic instruments. Moreover, colocalization analyses showed strong evidence (pH4 > 0.6) of shared genetic variants of LPL and PON3 with T2D.</p>
CONCLUSIONS: Proteomic analyses in Chinese adults identified novel associations of multiple proteins with T2D with strong genetic evidence supporting their causal relevance and potential as novel drug targets for prevention and treatment of T2D.</p>
28 Authors
- Pang Yao
- Andri Iona
- Alfred Pozarickij
- Saredo Said
- Neil Wright
- Kuang Lin
- Iona Millwood
- Hannah Fry
- Christiana Kartsonaki
- Mohsen Mazidi
- Yiping Chen
- Fiona Bragg
- Bowen Liu
- Ling Yang
- Junxi Liu
- Daniel Avery
- Dan Schmidt
- Dianjianyi Sun
- Pei Pei
- Jun Lv
- Canqing Yu
- Michael Hill
- Derrick Bennett
- Robin Walters
- Liming Li
- Robert Clarke
- Huaidong Du
- Zhengming Chen
1 Application
| Application ID | Title |
|---|---|
| 50474 | Multi-trait trans-ethnic genetic analyses |
Enabling scientific discoveries that improve human health