BACKGROUND: The purpose of this study was to compare three different equations to calculate estimated glomerular filtration rate (eGFR) based on serum creatinine (SCr) and to estimate the prevalence of chronic kidney disease (CKD) in the Icelandic population. METHODS: This was a cross-sectional study using data from the Reykjavik Heart Study. GFR was estimated with three equations: Equation I was based on 1/SCr; Equation II based on the Cockcroft-Gault equation; and Equation III was the modified MDRD equation. The eGFR calculated with Equation III and proteinuria were used to estimate the prevalence of CKD. The prevalence was age-standardized to the truncated world population. We used chi-square and ANCOVA to compare the group with low eGFR to age-matched controls. RESULTS: The subjects consisted of 9229 males and 10,027 females, aged 33-85 years. The equations performed very differently. Equation I showed women with higher eGFR than men and little change with age. Equation II showed men with higher eGFR than women and marked decline in eGFR with age. Equation III was similar to Equation II but the decline in eGFR with age was not as great. Regardless of the equation used, most subjects (63.7-80.7%) had an eGFR in the range of 60-89 ml/min/1.73 m2. Using Equation III, age-standardized prevalence of low eGFR for the population aged 35-80+ years was estimated to be 4.7 and 11.6% for men and women, respectively. The proportion of subjects with eGFR <60 ml/min/1.73 m2 increased with advancing age. An additional 2.39% of men and 0.89% of women had proteinuria. The prevalence of renal and cardiovascular risk factors including proteinuria, hypertension, lipid abnormalities and markers of inflammation was higher among those with low eGFR than age-matched controls. CONCLUSIONS: GFR estimates and the prevalence of CKD are dependent on the equation used to calculate eGFR. Unexpectedly, a low proportion of the Icelandic population had normal kidney function according to the eGFR regardless of the equation used. These equations may not be useful in epidemiological research.
BACKGROUND: The purpose of this study was to compare three different equations to calculate estimated glomerular filtration rate (eGFR) based on serum creatinine (SCr) and to estimate the prevalence of chronic kidney disease (CKD) in the Icelandic population. METHODS: This was a cross-sectional study using data from the Reykjavik Heart Study. GFR was estimated with three equations: Equation I was based on 1/SCr; Equation II based on the Cockcroft-Gault equation; and Equation III was the modified MDRD equation. The eGFR calculated with Equation III and proteinuria were used to estimate the prevalence of CKD. The prevalence was age-standardized to the truncated world population. We used chi-square and ANCOVA to compare the group with low eGFR to age-matched controls. RESULTS: The subjects consisted of 9229 males and 10,027 females, aged 33-85 years. The equations performed very differently. Equation I showed women with higher eGFR than men and little change with age. Equation II showed men with higher eGFR than women and marked decline in eGFR with age. Equation III was similar to Equation II but the decline in eGFR with age was not as great. Regardless of the equation used, most subjects (63.7-80.7%) had an eGFR in the range of 60-89 ml/min/1.73 m2. Using Equation III, age-standardized prevalence of low eGFR for the population aged 35-80+ years was estimated to be 4.7 and 11.6% for men and women, respectively. The proportion of subjects with eGFR <60 ml/min/1.73 m2 increased with advancing age. An additional 2.39% of men and 0.89% of women had proteinuria. The prevalence of renal and cardiovascular risk factors including proteinuria, hypertension, lipid abnormalities and markers of inflammation was higher among those with low eGFR than age-matched controls. CONCLUSIONS: GFR estimates and the prevalence of CKD are dependent on the equation used to calculate eGFR. Unexpectedly, a low proportion of the Icelandic population had normal kidney function according to the eGFR regardless of the equation used. These equations may not be useful in epidemiological research.
Authors: Jan C van Blijderveen; Sabine M Straus; Robert Zietse; Bruno H Stricker; Miriam C Sturkenboom; Katia M Verhamme Journal: Int Urol Nephrol Date: 2013-09-27 Impact factor: 2.370
Authors: Daniel F Gudbjartsson; Hilma Holm; Olafur S Indridason; Gudmar Thorleifsson; Vidar Edvardsson; Patrick Sulem; Femmie de Vegt; Frank C H d'Ancona; Martin den Heijer; Jack F M Wetzels; Leifur Franzson; Thorunn Rafnar; Kristleifur Kristjansson; Unnur S Bjornsdottir; Gudmundur I Eyjolfsson; Lambertus A Kiemeney; Augustine Kong; Runolfur Palsson; Unnur Thorsteinsdottir; Kari Stefansson Journal: PLoS Genet Date: 2010-07-29 Impact factor: 5.917
Authors: Suhnggwon Kim; Chun Soo Lim; Dong Cheol Han; Gyo Sun Kim; Ho Jun Chin; Seung-Jung Kim; Won Yong Cho; Yeong Hoon Kim; Yon-Su Kim Journal: J Korean Med Sci Date: 2009-01-28 Impact factor: 2.153
Authors: John B Eastwood; Sally M Kerry; Jacob Plange-Rhule; Frank B Micah; Sampson Antwi; Frances G Boa; Debasish Banerjee; Lynsey Emmett; Michelle A Miller; Francesco P Cappuccio Journal: Nephrol Dial Transplant Date: 2010-01-25 Impact factor: 5.992