Background: Cardiovascular disease and chronic kidney disease share several common risk factors. The Framingham risk score is hypothesized to predict chronic kidney disease development. We determined if the Framingham risk scoring system can correctly predict incident chronic kidney disease in the general population. Methods: This study included 9,080 subjects who participated in the Korean Genome and Epidemiology Study between 2001 and 2014 and had normal renal function. The subjects were classified into low-(< 10%), intermediate-(10-20%), and high-(> 20%) risk groups based on baseline Framingham risk scores. The primary endpoint was de novo chronic kidney disease development (estimated glomerular filtration rate [eGFR], < 60 mL/min/1.73 m2). Results: During a mean follow-up duration of 8.9 ± 4.3 years, 312 (5.3%), 217 (10.8%), and 205 (16.9%) subjects developed chronic kidney disease in the low, intermediate, and high risk groups, respectively (P < 0.001). Multivariable analysis after adjustment for confounding factors showed the hazard ratios for the high-and intermediate risk groups were 2.674 (95% confidence interval [CI], 2.197-3.255) and 1.734 (95% CI, 1.447-2.078), respectively. This association was consistently observed irrespective of proteinuria, age, sex, obesity, or hypertension. The predictive power of this scoring system was lower than that of renal parameters, such as eGFR and proteinuria, but increased when both were included in the prediction model. Conclusion: The Framingham risk score predicted incident chronic kidney disease and enhanced risk stratification in conjunction with traditional renal parameters in the general population with normal renal function.
Bibliographical noteFunding Information:
Data used in this study were obtained from the Korean Genome and Epidemiology Study (KoGES; 4851-302), National Research Institute of Health, Centers for Disease Control and Prevention, Ministry for Health and Welfare, Republic of Korea. Therefore, we acknowledge and appreciate the labor of all staff members working on the KoGES.
All Science Journal Classification (ASJC) codes