TY - JOUR
T1 - Cardiovascular Risk Based on ASCVD and KDIGO Categories in Chinese Adults
T2 - A Nationwide, Population-Based, Prospective Cohort Study
AU - Xu, Yu
AU - Li, Mian
AU - Qin, Guijun
AU - Lu, Jieli
AU - Yan, Li
AU - Xu, Min
AU - Wang, Tiange
AU - Zhao, Zhiyun
AU - Dai, Meng
AU - Zhang, Di
AU - Wan, Qin
AU - Huo, Yanan
AU - Chen, Lulu
AU - Shi, Lixin
AU - Hu, Ruying
AU - Tang, Xulei
AU - Su, Qing
AU - Yu, Xuefeng
AU - Qin, Yingfen
AU - Chen, Gang
AU - Gao, Zhengnan
AU - Wang, Guixia
AU - Shen, Feixia
AU - Luo, Zuojie
AU - Chen, Li
AU - Chen, Yuhong
AU - Zhang, Yinfei
AU - Liu, Chao
AU - Wang, Youmin
AU - Wu, Shengli
AU - Yang, Tao
AU - Li, Qiang
AU - Bi, Yufang
AU - Zhao, Jiajun
AU - Mu, Yiming
AU - Wang, Weiqing
AU - Ning, Guang
N1 - Publisher Copyright:
Copyright © 2021 by the American Society of Nephrology.
PY - 2021/4
Y1 - 2021/4
N2 - 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,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.
AB - 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,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.
UR - https://www.scopus.com/pages/publications/85117631157
U2 - 10.1681/ASN.2020060856
DO - 10.1681/ASN.2020060856
M3 - Article
C2 - 33788701
AN - SCOPUS:85117631157
SN - 1046-6673
VL - 32
SP - 927
EP - 937
JO - Journal of the American Society of Nephrology
JF - Journal of the American Society of Nephrology
IS - 4
ER -