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.

中文翻译：

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大规模蛋白质组学提高了糖尿病患者慢性肾脏病的预测


目的 开发和验证用于预测糖尿病患者慢性肾脏病 (CKD) 的蛋白质风险评分，并将其预测性能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。研究设计和方法 这项队列研究包括 2,094 名糖尿病患者，这些患者有蛋白质组学和遗传信息，并且基线时没有 CKD 病史，这些患者来自英国生物银行制药蛋白质组学项目。基于近 3,000 个血浆蛋白，在训练集中（包括 1,047 名参与者；117 个 CKD 事件）构建了包括 11 种蛋白质的 CKD 蛋白风险评分。结果 中位随访时间为 12.1 年。在测试集中（包括 1,047 名参与者；112 例 CKD 事件），CKD 蛋白风险评分与 CKD 事件呈正相关（每 SD 增量；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蛋白风险评分（C指数增加0.122；95% CI 0.071，0.177），CKD- PC风险因素（C指数增加0.175；95% CI 0.126，0.217）显着改善了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）没有显着改善改进。将CKD蛋白风险评分加入CKD-PC危险因素中，最大C指数为0.825（C指数从0.802到0.825；差异0.023；95% CI 0.006，0.044），并且显着改善了连续10年净值重新分类（0.199；95% CI 0.059，0.299）和 10 年综合歧视指数（0.041；95% CI 0.007，0.083）。 结论 将 CKD 蛋白风险评分添加到经过验证的临床风险模型中，可显着改善糖尿病患者 CKD 风险的辨别和重新分类。