大规模蛋白质组学提高了对糖尿病患者慢性肾脏病的预测能力。

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

机构信息

Division of Nephrology, Nanfang Hospital, Southern Medical University; National Clinical Research Center for Kidney Disease; State Key Laboratory of Organ Failure Research; Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangdong Provincial Clinical Research Center for Kidney Disease, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China.

出版信息

Diabetes Care. 2024 Oct 1;47(10):1757-1763. doi: 10.2337/dc24-0290.

DOI:10.2337/dc24-0290

PMID:39042512

Abstract

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 3,000 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), the CKD protein risk score was positively associated with incident CKD (per SD increment; hazard ratio 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), the CKD protein risk score (C-index increase 0.122; 95% CI 0.071, 0.177), and the CKD-PC risk factors (C-index increase 0.175; 95% CI 0.126, 0.217) significantly improved the prediction performance of incident CKD, but the CKD polygenic risk score (C-index increase 0.007; 95% CI -0.016, 0.025) had no significant improvement. Adding the CKD protein risk score into the 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.

摘要

目的

开发并验证一种预测糖尿病患者慢性肾脏病（CKD）的蛋白质风险评分，并与经过验证的临床风险模型（CKD 预测联盟 [CKD-PC]）和 CKD 多基因风险评分进行比较，以评估其预测性能。

研究设计和方法

本队列研究纳入了来自英国生物库制药蛋白质组学项目的 2094 名基线时无 CKD 病史且具有蛋白质组学和遗传信息的糖尿病患者。基于近 3000 种血浆蛋白，在训练集中构建了包含 11 种蛋白质的 CKD 蛋白质风险评分（包含 1047 名参与者；117 例 CKD 事件）。

结果

中位随访时间为 12.1 年。在测试集中（包含 1047 名参与者；112 例 CKD 事件），CKD 蛋白质风险评分与 CKD 事件呈正相关（每 SD 增加，风险比 1.78；95%CI 1.44，2.20）。与基本模型（年龄+性别+种族，C 指数 0.627；95%CI 0.578，0.675）相比，CKD 蛋白质风险评分（C 指数增加 0.122；95%CI 0.071，0.177）和 CKD-PC 风险因素（C 指数增加 0.175；95%CI 0.126，0.217）显著提高了 CKD 事件的预测性能，但 CKD 多基因风险评分（C 指数增加 0.007；95%CI -0.016，0.025）无显著改善。将 CKD 蛋白质风险评分加入 CKD-PC 风险因素中可获得最大的 C 指数 0.825（C 指数从 0.802 增加至 0.825；差值 0.023；95%CI 0.006，0.044），并显著改善了连续 10 年的净重新分类（0.199；95%CI 0.059，0.299）和 10 年综合鉴别指数（0.041；95%CI 0.007，0.083）。