CCA-13488; No of Pages 2 Clinica Chimica Acta xxx (2014) xxx–xxx
Contents lists available at ScienceDirect
Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Letter to the Editor Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome
To the Editor Hong et al. reported that estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) showed better predictive ability for metabolic syndrome (MetS) than eGFR from the Modification of Diet in Renal Disease (MDRD) by their cross-sectional study [1]. They used receiveroperating characteristic (ROC) curve analysis and multivariate logistic regression analysis as statistical procedures, and they handled a large representative national data. Although the advantages of eGFR from CKD-EPI against eGFR from MDRD for the prediction of actual GFR have been reported [2,3], eGFR from CKD-EPI does not cover all population with wide range of GFR [3]. I have 2 concerns on their study outcome. First, Okada et al. reported that glomerular hyperfiltration had a risk of prediabetes and prehypertension [4]. Osawa et al. also presented a significant increase in mortality for subjects with increased eGFR from CKD-EPI and albuminuria [5]. Hong et al. set eGFR ≥ 105 ml/min/1.73 m2 as a control, and the risk of glomerular hyperfiltration could not be determined in their study. As an additional analysis, I recommend the authors presenting the odds ratio of highest eGFR for MetS by splitting their control into two groups. Second, Cirillo et al. reported that combination of increased albuminuria and lower GFR from MDRD showed an increase of cardiovascular risk [6]. In contrast, Osawa et al. reported the risk of mortality for subjects with increased eGFR from CKD-EPI and albuminuria [6]. The inconsistent results would be partly related to the adjusting variables for MetS, and Hong et al. also described the lack of information on albuminuria as a limitation on their study. Finally, Hong et al. used age, sex, body mass index, monthly income, current smoking status, physical activity, previous atherosclerotic cardiovascular disease, and two types of insulin-associated indicators as adjusting variables for predicting MetS. Among these variables, age is used for the calculation of eGFR from CKD-EPI and eGFR from MDRD. I suppose that over-adjustment to the independent variable cannot be excluded for their analysis.
Two equations for the eGFR estimation were constructed by the functions of age and serum creatinine on each sex, and threedimensional presentation would support easy understanding for the effects of age and serum creatinine on eGFR. I present information on 2 simulations for male subjects (Fig. 1). In addition, I recently reported as one of the co-authors that elevated serum cystatin C level could predict the risk of incident hypertension in a middle-aged male population with a maximum follow-up period of 4 years. In contrast, eGFR did not become a significant predictor for the incident hypertension [7]. Anyway, Hong et al. handled large representative data, and follow-up data would clarify the causality of eGFR on MetS. References [1] Hong N, Oh J, Lee YH, et al. Comparison of association of glomerular filtration rate with metabolic syndrome in a community-based population using the CKD-EPI and MDRD study equations. Clin Chim Acta 2014;429:157–62. [2] Madero M, Sarnak MJ. Creatinine-based formulae for estimating glomerular filtration rate: is it time to change to chronic kidney disease epidemiology collaboration equation? Curr Opin Nephrol Hypertens 2011;20:622–30. [3] Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med 2012;156:785–95. [4] Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Glomerular hyperfiltration in prediabetes and prehypertension. Nephrol Dial Transplant 2012;27:1821–5. [5] Ohsawa M, Fujioka T, Ogasawara K, et al. High risks of all-cause and cardiovascular deaths in apparently healthy middle-aged people with preserved glomerular filtration rate and albuminuria: a prospective cohort study. Int J Cardiol 2013;170:167–72. [6] Cirillo M, Lanti MP, Menotti A, et al. Definition of kidney dysfunction as a cardiovascular risk factor: use of urinary albumin excretion and estimated glomerular filtration rate. Arch Intern Med 2008;168:617–24. [7] Otsuka T, Kato K, Kachi Y, et al. Serum cystatin C, creatinine-based estimated glomerular filtration rate, and the risk of incident hypertension in middle-aged men. Am J Hypertens 2014;27:596–602.
Tomoyuki Kawada Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan Tel.: +81 3 3822 2131; fax: +81 3 5685 3065. E-mail address: [email protected]. 25 March 2014 Available online xxxx
http://dx.doi.org/10.1016/j.cca.2014.04.017 0009-8981/© 2014 Elsevier B.V. All rights reserved.
Please cite this article as: Kawada T, Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome, Clin Chim Acta (2014), http://dx.doi.org/10.1016/j.cca.2014.04.017
2
Letter to the Editor
140 120
CKD-EPI
100 80 60 40
75 64
20
53 42
Age
20 1.3
Serum creatinine
1.26
1.22
1.14
1.18
1.1
1.02
1.06
31 0.98
0.9
0.94
0.86
0.78
0.82
0.7
0.74
0
140 120
MDRD
100 80 60 40
75 64
20
53 42
Age
20 1.3
1.26
1.18
1.22
Serum creatinine
1.14
1.1
1.02
1.06
31 0.98
0.9
0.94
0.86
0.78
0.82
0.7
0.74
0
Fig. 1. Estimated glomerular filtration rates from the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) (upper) and from the Modification of Diet in Renal Disease equation (MDRD) (lower) for male subjects by using age and serum creatinine.
Please cite this article as: Kawada T, Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome, Clin Chim Acta (2014), http://dx.doi.org/10.1016/j.cca.2014.04.017
Contents lists available at ScienceDirect
Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Letter to the Editor Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome
To the Editor Hong et al. reported that estimated glomerular filtration rate (eGFR) from the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) showed better predictive ability for metabolic syndrome (MetS) than eGFR from the Modification of Diet in Renal Disease (MDRD) by their cross-sectional study [1]. They used receiveroperating characteristic (ROC) curve analysis and multivariate logistic regression analysis as statistical procedures, and they handled a large representative national data. Although the advantages of eGFR from CKD-EPI against eGFR from MDRD for the prediction of actual GFR have been reported [2,3], eGFR from CKD-EPI does not cover all population with wide range of GFR [3]. I have 2 concerns on their study outcome. First, Okada et al. reported that glomerular hyperfiltration had a risk of prediabetes and prehypertension [4]. Osawa et al. also presented a significant increase in mortality for subjects with increased eGFR from CKD-EPI and albuminuria [5]. Hong et al. set eGFR ≥ 105 ml/min/1.73 m2 as a control, and the risk of glomerular hyperfiltration could not be determined in their study. As an additional analysis, I recommend the authors presenting the odds ratio of highest eGFR for MetS by splitting their control into two groups. Second, Cirillo et al. reported that combination of increased albuminuria and lower GFR from MDRD showed an increase of cardiovascular risk [6]. In contrast, Osawa et al. reported the risk of mortality for subjects with increased eGFR from CKD-EPI and albuminuria [6]. The inconsistent results would be partly related to the adjusting variables for MetS, and Hong et al. also described the lack of information on albuminuria as a limitation on their study. Finally, Hong et al. used age, sex, body mass index, monthly income, current smoking status, physical activity, previous atherosclerotic cardiovascular disease, and two types of insulin-associated indicators as adjusting variables for predicting MetS. Among these variables, age is used for the calculation of eGFR from CKD-EPI and eGFR from MDRD. I suppose that over-adjustment to the independent variable cannot be excluded for their analysis.
Two equations for the eGFR estimation were constructed by the functions of age and serum creatinine on each sex, and threedimensional presentation would support easy understanding for the effects of age and serum creatinine on eGFR. I present information on 2 simulations for male subjects (Fig. 1). In addition, I recently reported as one of the co-authors that elevated serum cystatin C level could predict the risk of incident hypertension in a middle-aged male population with a maximum follow-up period of 4 years. In contrast, eGFR did not become a significant predictor for the incident hypertension [7]. Anyway, Hong et al. handled large representative data, and follow-up data would clarify the causality of eGFR on MetS. References [1] Hong N, Oh J, Lee YH, et al. Comparison of association of glomerular filtration rate with metabolic syndrome in a community-based population using the CKD-EPI and MDRD study equations. Clin Chim Acta 2014;429:157–62. [2] Madero M, Sarnak MJ. Creatinine-based formulae for estimating glomerular filtration rate: is it time to change to chronic kidney disease epidemiology collaboration equation? Curr Opin Nephrol Hypertens 2011;20:622–30. [3] Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med 2012;156:785–95. [4] Okada R, Yasuda Y, Tsushita K, Wakai K, Hamajima N, Matsuo S. Glomerular hyperfiltration in prediabetes and prehypertension. Nephrol Dial Transplant 2012;27:1821–5. [5] Ohsawa M, Fujioka T, Ogasawara K, et al. High risks of all-cause and cardiovascular deaths in apparently healthy middle-aged people with preserved glomerular filtration rate and albuminuria: a prospective cohort study. Int J Cardiol 2013;170:167–72. [6] Cirillo M, Lanti MP, Menotti A, et al. Definition of kidney dysfunction as a cardiovascular risk factor: use of urinary albumin excretion and estimated glomerular filtration rate. Arch Intern Med 2008;168:617–24. [7] Otsuka T, Kato K, Kachi Y, et al. Serum cystatin C, creatinine-based estimated glomerular filtration rate, and the risk of incident hypertension in middle-aged men. Am J Hypertens 2014;27:596–602.
Tomoyuki Kawada Department of Hygiene and Public Health, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-Ku, Tokyo 113-8602, Japan Tel.: +81 3 3822 2131; fax: +81 3 5685 3065. E-mail address: [email protected]. 25 March 2014 Available online xxxx
http://dx.doi.org/10.1016/j.cca.2014.04.017 0009-8981/© 2014 Elsevier B.V. All rights reserved.
Please cite this article as: Kawada T, Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome, Clin Chim Acta (2014), http://dx.doi.org/10.1016/j.cca.2014.04.017
2
Letter to the Editor
140 120
CKD-EPI
100 80 60 40
75 64
20
53 42
Age
20 1.3
Serum creatinine
1.26
1.22
1.14
1.18
1.1
1.02
1.06
31 0.98
0.9
0.94
0.86
0.78
0.82
0.7
0.74
0
140 120
MDRD
100 80 60 40
75 64
20
53 42
Age
20 1.3
1.26
1.18
1.22
Serum creatinine
1.14
1.1
1.02
1.06
31 0.98
0.9
0.94
0.86
0.78
0.82
0.7
0.74
0
Fig. 1. Estimated glomerular filtration rates from the Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI) (upper) and from the Modification of Diet in Renal Disease equation (MDRD) (lower) for male subjects by using age and serum creatinine.
Please cite this article as: Kawada T, Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome, Clin Chim Acta (2014), http://dx.doi.org/10.1016/j.cca.2014.04.017
