Xu Yu, Li Mian, Qin Guijun, Lu Jieli, Yan Li, Xu Min, Wang Tiange, Zhao Zhiyun, Dai Meng, Zhang Di, Wan Qin, Huo Yanan, Chen Lulu, Shi Lixin, Hu Ruying, Tang Xulei, Su Qing, Yu Xuefeng, Qin Yingfen, Chen Gang, Gao Zhengnan, Wang Guixia, Shen Feixia, Luo Zuojie, Chen Li, Chen Yuhong, Zhang Yinfei, Liu Chao, Wang Youmin, Wu Shengli, Yang Tao, Li Qiang, Bi Yufang, Zhao Jiajun, Mu Yiming, Wang Weiqing, Ning Guang
Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Endocrine and Metabolic Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
J Am Soc Nephrol. 2021 Apr;32(4):927-937. doi: 10.1681/ASN.2020060856. Epub 2021 Mar 4.
The Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline used eGFR and urinary albumin-creatinine ratio (ACR) to categorize risks for CKD prognosis. The utility of KDIGO's stratification of major CVD risks and predictive ability beyond traditional CVD risk prediction scores are unknown.
To evaluate CVD risks on the basis of ACR and eGFR (individually, together, and in combination using the KDIGO risk categories) and with the atherosclerotic cardiovascular disease (ASCVD) score, we studied 115,366 participants in the China Cardiometabolic Disease and Cancer Cohort study. Participants (aged ≥40 years and without a history of cardiovascular disease) were examined prospectively for major CVD events, including nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death.
During 415,111 person-years of follow-up, 2866 major CVD events occurred. Incidence rates and multivariable-adjusted hazard ratios of CVD events increased significantly across the KDIGO risk categories in ASCVD risk strata (all values for log-rank test and most values for trend in Cox regression analysis <0.01). Increases in statistic for CVD risk prediction were 0.01 (0.01 to 0.02) in the overall study population and 0.03 (0.01 to 0.04) in participants with diabetes, after adding eGFR and log(ACR) to a model including the ASCVD risk score. In addition, adding eGFR and log(ACR) to a model with the ASCVD score resulted in significantly improved reclassification of CVD risks (net reclassification improvements, 4.78%; 95% confidence interval, 3.03% to 6.41%).
Urinary ACR and eGFR (individually, together, and in combination using KDIGO risk categories) may be important nontraditional risk factors in stratifying and predicting major CVD events in the Chinese population.
改善全球肾脏病预后（KDIGO）临床实践指南使用估算肾小球滤过率（eGFR）和尿白蛋白肌酐比值（ACR）对慢性肾脏病（CKD）预后风险进行分类。KDIGO对主要心血管疾病（CVD）风险的分层效用以及超越传统CVD风险预测评分的预测能力尚不清楚。
为了基于ACR和eGFR（单独、联合以及使用KDIGO风险类别进行组合）以及动脉粥样硬化性心血管疾病（ASCVD）评分评估CVD风险，我们在中国心血管代谢疾病和癌症队列研究中对115366名参与者进行了研究。参与者（年龄≥40岁且无心血管疾病病史）接受了主要CVD事件的前瞻性检查，包括非致命性心肌梗死、非致命性中风和心血管死亡。
在415111人年的随访期间，发生了2866例主要CVD事件。在ASCVD风险分层中，CVD事件的发病率和多变量调整风险比在KDIGO风险类别中显著增加（对数秩检验的所有P值以及Cox回归分析趋势的大多数P值<0.01）。在将eGFR和log（ACR）添加到包含ASCVD风险评分的模型后，总体研究人群中CVD风险预测的C统计量增加了0.01（0.01至0.02），糖尿病参与者中增加了0.03（0.01至0.04）。此外，将eGFR和log（ACR）添加到具有ASCVD评分的模型中导致CVD风险的重新分类显著改善（净重新分类改善，4.78%；95%置信区间，3.03%至6.41%）。
尿ACR和eGFR（单独、联合以及使用KDIGO风险类别进行组合）可能是中国人群中分层和预测主要CVD事件的重要非传统风险因素。
