Large-Scale Proteomics Improve Prediction of Chronic Kidney Disease in People with Diabetes

Objective

To develop and validate a protein risk score for predicting chronic kidney disease (CKD) in patients with diabetes and compare its predictive performance with a validated clinical risk model (CKD Prediction Consortium [CKD-PC]) and CKD polygenic risk score.

Research Design and Methods

This cohort study included 2,094 patients with diabetes who had proteomics and genetic information and no history of CKD at baseline from the UK Biobank Pharma Proteomics Project. Based on nearly 3000 plasma proteins, a CKD protein risk score including 11 proteins was constructed in the training set (including 1,047 participants; 117 CKD events).

Results

The median follow-up duration was 12.1 years. In the test set (including 1,047 participants; 112 CKD events), CKD protein risk score was positively associated with incident CKD (per SD increment, HR, 1.78; 95%CI: 1.44,2.20). Compared with the basic model (age+ sex+ race, C-index,0.627; 95%CI: 0.578,0.675), CKD protein risk score(C-index increase, 0.122, 95%CI: 0.071, 0.177) and CKD-PC risk factors(C-index increase, 0.175, 95%CI: 0.126, 0.217) significantly improved the prediction performance of incident CKD, but CKD polygenic risk score(C-index increase,0.007, 95%CI: -0.016,0.025) had no significant improvement. Adding CKD protein risk score into CKD-PC risk factors had the largest C-index of 0.825 (C-index from 0.802 to 0.825; difference, 0.023; 95%CI: 0.006,0.044), and significantly improved the continuous 10-year net reclassification (0.199; 95%CI: 0.059,0.299) and 10-year integrated discrimination index (0.041; 95%CI: 0.007,0.083).

Conclusions

Adding the CKD protein risk score to a validated clinical risk model significantly improved the discrimination and reclassification of CKD risk in patients with diabetes.