Cardiovascular Risk Based on ASCVD and KDIGO Categories in Chinese Adults: A Nationwide, Population-Based, Prospective Cohort Study
Authors: Yu Xu, Mian Li, Guijun Qin, Jieli Lu, Li Yan, Min Xu, Tiange Wang, Zhiyun Zhao, Meng Dai, Di Zhang, Qin Wan, Yanan Huo, Lulu Chen, Lixin Shi, Ruying Hu, Xulei Tang, Qing Su, Xuefeng Yu, Yingfen Qin, Gang Chen, Zhengnan Gao, Guixia Wang, Feixia Shen, Zuojie Luo, Li Chen, Yuhong Chen, Yinfei Zhang, Chao Liu, Youmin Wang, Shengli Wu, Tao Yang, Qiang Li, Yufang Bi, Jiajun Zhao, Yiming Mu, Weiqing Wang, Guang Ning
Journal: Journal of the American Society of Nephrology (2021)

Significance Statement Cardiology professional organizations recommend considering individual risk-enhancing clinical factors in addition to the atherosclerotic cardiovascular disease (ASCVD) score when making decisions about preventive treatment. Using data from a prospective multicenter cohort of 115,366 Chinese community residents aged ≥40 years without a history of cardiovascular disease (CVD), the authors found that addition of nontraditional CVD risk factors, such as eGFR and urinary albumin-creatinine ratio (ACR), further improved stratification of future CVD risks beyond that of the ASCVD risk score calculated by traditional CVD risk factors. Adding ACR and eGFR to the ASCVD risk score also significantly improved prediction and reclassification of CVD risks. Clinical applications of the Kidney Disease Improving Global Outcomes (KDIGO) risk categories based on eGFR and ACR levels for CVD prevention should be evaluated by interventional studies and in other ethnic populations. Background 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. Methods 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. Results 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 P values for log-rank test and most P values for trend in Cox regression analysis &lt;0.01). Increases in c 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%). Conclusions 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.