CCA-13483; 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 Authors reply: Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome
The authors reply Kawada raises the concern of glomerular hyperfiltration in the highest estimated glomerular filtration rate (eGFR) category in our study. Additional analyses were performed by splitting the highest eGFR category into two groups. Comparison of baseline characteristics of two groups was presented in Table 1. When compared to reference eGFR range (105–119 ml/min/1.73 m2), however, neither the highest eGFR category (≥ 120 ml/min/1.73 m 2 ) by the CKD Epidemiology Collaboration equation (eGFRCKD-EPI, Odds ratio [OR] 0.76, 95% CI 0.53–1.08, P = 0.129) nor by the Modification of Diet in Renal Disease study equation (eGFRMDRD , OR 1.17, 95% CI 0.85– 1.61, P =0.325) was associated with increased odds of metabolic syndrome in multivariate logistic analysis. Matsushita et al. suggested that paradoxically increased mortality risk at highest eGFR might be due to low muscle mass or muscle wasting by critical illness, an inherent limitation of serum creatinine based GFR estimating equations [1]. In our study, those in the highest eGFR category were more likely to be young, female, and to have lower body
Table 1 Comparison of baseline characteristics of participants in the highest eGFR category by splitting into 2 groups (eGFR ≥ 120 or 105–119 ml/min/1.73 m2).
mass index, indicating the possibility of confounding by low serum creatinine level rather than true glomerular hyperfiltration. Although acceptable definition for glomerular hyperfiltration is still on debating, further studies may be needed to discriminate glomerular hyperfiltration from muscle-wasting status using muscleindependent filtration markers such as Cystatin C [2]. We fully agree that concurrently measured albuminuria for defining chronic kidney disease may aid accurate prognostication when combined with eGFR [3]. Unfortunately, our data lacked albuminuria data, which was already described as one of limitations of this study. Kawada also noted the possibility of over-adjustment by incorporating both eGFR and age in same multivariate model. Indeed, Steeper decline of eGFRCKD-EPI was shown toward increasing age in all range compared with eGFRMDRD (Fig. 1). Mean value of eGFRCKD-EPI was higher than that of eGFRMDRD at b60 y. However, in age group ≥ 60 y, mean eGFRCKD-EPI was similar or lower than mean eGFRMDRD. However, variation inflation factor (VIF) with age was 2.08 for eGFR CKD-EPI and 1.24 for eGFR MDRD , suggesting low possibility of multi-collinearity given the common rule that VIF excess 5 or 10 is regarded as sign of multi-collinearity [4]. Also, the analysis after removal of age from multivariate model did not significantly alter our findings, either (Data not shown). Our findings suggest that the CKD-EPI equation may provide better identification of the burden of metabolic syndrome according to kidney function, particularly in near-normal GFR range, mainly by reclassifying lower risk population to higher eGFR categories. Further investigations on Cystatin C-based equations and concurrent use of albuminuria in identifying metabolic syndrome in community population are of interest.
CKD-EPI eGFR (ml/min/1.73 m2)
No. (row %) eGFR (CKD-EPI) eGFR (MDRD) Age, y Male, no. (%) Income (/month) Quartile 4 Quartile 1 Smoking, no. (%) Exercise Previous ASCVD BMI, kg/m2 Waist circumference, cm Systolic BP, mm Hg Diastolic BP, mm Hg Fasting glucose, mg/dl HDL cholesterol, mg/dl Triglyceride, mg/dl Fasting insulin, μU/ml HOMA-IR score Serum creatinine, mg/dl
Total
≥120
105–119
12700 (100) 96.8 ± 17.2 90.3 ± 17.5 48.8 ± 16.2 5520 (43.5)
980 (7.7) 125.1 ± 3.9 119.6 ± 13.4 26.2 ± 5.3 198 (20.2)
3230 (25.4) 111.7 ± 4.3 102.2 ± 9.6 37.5 ± 8.2 1153 (35.7)
3311 (26.4) 3060 (24.4) 4024 (31.7) 3765 (29.7) 496 (3.91) 23.6 ± 3.4 80.9 ± 9.9 117.9 ± 17.5 75.0 ± 10.5 97.3 ± 22.4 48.0 ± 10.9 132.8 ± 106.3 8.9 (7.1–11.6) 2.1 (1.6–2.8) 0.82 ± 0.22
279 (28.9) 120 (12.4) 238 (24.3) 260 (26.5) 2 (0.2) 22.1 ± 3.9 74.8 ± 10.7 105.4 ± 10.3 68.6 ± 8.9 88.3 ± 14.4 52.4 ± 10.7 97.8 ± 87.0 9.3 (7.6–11.8) 2.0 (1.6–2.6) 0.65 ± 0.11
1010 (31.6) 337 (10.6) 1007 (31.2) 950 (29.4) 21 (0.7) 23.2 ± 3.5 78.4 ± 9.8 111.1 ± 14.3 73.3 ± 10.4 93.7 ± 20.8 49.1 ± 10.7 119.2 ± 104.4 8.8 (7.1–11.3) 2.0 (1.6–2.6) 0.73 ± 0.12
120
Mean eGFR (CKD-EPI) 95% CI (CKD-EPI) Mean eGFR (MDRD) 95% CI (MDRD)
110
100 90
80
70 19-
30-
40-
50-
60-
70-
80-
Age (year) Fig. 1. The mean value of eGFRCKD-EPI and eGFRMDRD by age groups.
http://dx.doi.org/10.1016/j.cca.2014.04.012 0009-8981/© 2014 Published by Elsevier B.V.
Please cite this article as: Kang S-M, et al, Authors reply: 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.2014.04.012
2
Letter to the Editor
References [1] Matsushita K, Mahmoodi BK, Woodward M, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA 2012;307:1941–51. [2] Shlipak MG, Matsushita K, Arnlov J, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med 2013;369:932–43. [3] Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–30. [4] O'brien R. A caution regarding rules of thumb for variance inflation factors. Qual Quant 2007;41:673–90.
Seok-Min Kang⁎ Corresponding author. Tel.: +82 222288450; fax: +82 23932041. E-mail address: [email protected] (S.-M. Kang). Namki Hong1 Jaewon Oh1 Cardiology, Internal Medicine, Yonsei University College of Medicine, 134 Shinchon-dong, Seodaemun-gu, Seoul 120752, Republic of Korea 15 April 2014 Available online xxxx
1
Tel.: +82 222288450; fax: +82 23932041.
Please cite this article as: Kang S-M, et al, Authors reply: 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.2014.04.012
Contents lists available at ScienceDirect
Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim
Letter to the Editor Authors reply: Estimated glomerular filtration rate by two equations and their relationship with metabolic syndrome
The authors reply Kawada raises the concern of glomerular hyperfiltration in the highest estimated glomerular filtration rate (eGFR) category in our study. Additional analyses were performed by splitting the highest eGFR category into two groups. Comparison of baseline characteristics of two groups was presented in Table 1. When compared to reference eGFR range (105–119 ml/min/1.73 m2), however, neither the highest eGFR category (≥ 120 ml/min/1.73 m 2 ) by the CKD Epidemiology Collaboration equation (eGFRCKD-EPI, Odds ratio [OR] 0.76, 95% CI 0.53–1.08, P = 0.129) nor by the Modification of Diet in Renal Disease study equation (eGFRMDRD , OR 1.17, 95% CI 0.85– 1.61, P =0.325) was associated with increased odds of metabolic syndrome in multivariate logistic analysis. Matsushita et al. suggested that paradoxically increased mortality risk at highest eGFR might be due to low muscle mass or muscle wasting by critical illness, an inherent limitation of serum creatinine based GFR estimating equations [1]. In our study, those in the highest eGFR category were more likely to be young, female, and to have lower body
Table 1 Comparison of baseline characteristics of participants in the highest eGFR category by splitting into 2 groups (eGFR ≥ 120 or 105–119 ml/min/1.73 m2).
mass index, indicating the possibility of confounding by low serum creatinine level rather than true glomerular hyperfiltration. Although acceptable definition for glomerular hyperfiltration is still on debating, further studies may be needed to discriminate glomerular hyperfiltration from muscle-wasting status using muscleindependent filtration markers such as Cystatin C [2]. We fully agree that concurrently measured albuminuria for defining chronic kidney disease may aid accurate prognostication when combined with eGFR [3]. Unfortunately, our data lacked albuminuria data, which was already described as one of limitations of this study. Kawada also noted the possibility of over-adjustment by incorporating both eGFR and age in same multivariate model. Indeed, Steeper decline of eGFRCKD-EPI was shown toward increasing age in all range compared with eGFRMDRD (Fig. 1). Mean value of eGFRCKD-EPI was higher than that of eGFRMDRD at b60 y. However, in age group ≥ 60 y, mean eGFRCKD-EPI was similar or lower than mean eGFRMDRD. However, variation inflation factor (VIF) with age was 2.08 for eGFR CKD-EPI and 1.24 for eGFR MDRD , suggesting low possibility of multi-collinearity given the common rule that VIF excess 5 or 10 is regarded as sign of multi-collinearity [4]. Also, the analysis after removal of age from multivariate model did not significantly alter our findings, either (Data not shown). Our findings suggest that the CKD-EPI equation may provide better identification of the burden of metabolic syndrome according to kidney function, particularly in near-normal GFR range, mainly by reclassifying lower risk population to higher eGFR categories. Further investigations on Cystatin C-based equations and concurrent use of albuminuria in identifying metabolic syndrome in community population are of interest.
CKD-EPI eGFR (ml/min/1.73 m2)
No. (row %) eGFR (CKD-EPI) eGFR (MDRD) Age, y Male, no. (%) Income (/month) Quartile 4 Quartile 1 Smoking, no. (%) Exercise Previous ASCVD BMI, kg/m2 Waist circumference, cm Systolic BP, mm Hg Diastolic BP, mm Hg Fasting glucose, mg/dl HDL cholesterol, mg/dl Triglyceride, mg/dl Fasting insulin, μU/ml HOMA-IR score Serum creatinine, mg/dl
Total
≥120
105–119
12700 (100) 96.8 ± 17.2 90.3 ± 17.5 48.8 ± 16.2 5520 (43.5)
980 (7.7) 125.1 ± 3.9 119.6 ± 13.4 26.2 ± 5.3 198 (20.2)
3230 (25.4) 111.7 ± 4.3 102.2 ± 9.6 37.5 ± 8.2 1153 (35.7)
3311 (26.4) 3060 (24.4) 4024 (31.7) 3765 (29.7) 496 (3.91) 23.6 ± 3.4 80.9 ± 9.9 117.9 ± 17.5 75.0 ± 10.5 97.3 ± 22.4 48.0 ± 10.9 132.8 ± 106.3 8.9 (7.1–11.6) 2.1 (1.6–2.8) 0.82 ± 0.22
279 (28.9) 120 (12.4) 238 (24.3) 260 (26.5) 2 (0.2) 22.1 ± 3.9 74.8 ± 10.7 105.4 ± 10.3 68.6 ± 8.9 88.3 ± 14.4 52.4 ± 10.7 97.8 ± 87.0 9.3 (7.6–11.8) 2.0 (1.6–2.6) 0.65 ± 0.11
1010 (31.6) 337 (10.6) 1007 (31.2) 950 (29.4) 21 (0.7) 23.2 ± 3.5 78.4 ± 9.8 111.1 ± 14.3 73.3 ± 10.4 93.7 ± 20.8 49.1 ± 10.7 119.2 ± 104.4 8.8 (7.1–11.3) 2.0 (1.6–2.6) 0.73 ± 0.12
120
Mean eGFR (CKD-EPI) 95% CI (CKD-EPI) Mean eGFR (MDRD) 95% CI (MDRD)
110
100 90
80
70 19-
30-
40-
50-
60-
70-
80-
Age (year) Fig. 1. The mean value of eGFRCKD-EPI and eGFRMDRD by age groups.
http://dx.doi.org/10.1016/j.cca.2014.04.012 0009-8981/© 2014 Published by Elsevier B.V.
Please cite this article as: Kang S-M, et al, Authors reply: 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.2014.04.012
2
Letter to the Editor
References [1] Matsushita K, Mahmoodi BK, Woodward M, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA 2012;307:1941–51. [2] Shlipak MG, Matsushita K, Arnlov J, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med 2013;369:932–43. [3] Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med 2013;158:825–30. [4] O'brien R. A caution regarding rules of thumb for variance inflation factors. Qual Quant 2007;41:673–90.
Seok-Min Kang⁎ Corresponding author. Tel.: +82 222288450; fax: +82 23932041. E-mail address: [email protected] (S.-M. Kang). Namki Hong1 Jaewon Oh1 Cardiology, Internal Medicine, Yonsei University College of Medicine, 134 Shinchon-dong, Seodaemun-gu, Seoul 120752, Republic of Korea 15 April 2014 Available online xxxx
1
Tel.: +82 222288450; fax: +82 23932041.
Please cite this article as: Kang S-M, et al, Authors reply: 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.2014.04.012
