 Research
 Open Access
 Published:
Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index
BMC Nephrology volume 22, Article number: 197 (2021)
Abstract
Background
We aimed to investigate the accuracy of different equations in evaluating estimated glomerular filtration rate (eGFR) in a Chinese population with different BMI levels.
Methods
A total of 837 Chinese patients were enrolled, and the eGFRs were calculated by three Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) equations, three fullage spectrum (FAS) equations and two Modification of Diet in Renal Disease (MDRD) equations. Results of measured GFR (mGFR) by the 99Tcmdiathylenetriamine pentaacetic acid (99TcmDTPA) renal dynamic imaging method were the reference standards. According to BMI distribution, the patients were divided into three intervals: below 25th(BMI_{P25}), 25th to 75th(BMI_{P25–75}) and over 75th percentiles (BMI_{P75}).
Results
The medium BMI of the three BMI intervals were 20.9, 24.8 and 28.9 kg/m^{2}, respectively. All deviations from mGFR (eGFR) were correlated with BMI (p < 0.05). The percentage of cases in which eGFR was within mGFR ±30% (P30) was used to represent the accuracy of each equation. Overall, eGFR_{FAS_Cr_CysC} and eGFREPI_Cr_2009 performed similarly, showing the best agreement with mGFR among the eight equations in BlandAltman analysis (biases: 4.1 and − 4.2 mL/min/1.73m^{2}, respectively). In BMI_{P25} interval, eGFR_{FAS_Cr} got − 0.7 of the biases with 74.2% of P30, the kappa value was 0.422 in classification of CKD stages and the AUC_{60} was 0.928 in predicting renal insufficiency, and eGFREPI_Cr_2009 got 2.3 of the biases with 71.8% of P30, the kappa value was 0.418 in classification of CKD stages and the AUC_{60} was 0.920 in predicting renal insufficiency. In BMI_{P25–75} interval, the bias of eGFR_{FAS_Cr_CysC} was 4.0 with 85.0% of P30, the kappa value was 0.501 and the AUC_{60} was 0.941, and eGFR_{FAS_Cr_CysC} showed balanced recognition ability of each stage of CKD (62.3, 63.7, 68.0, 71.4 and 83.3% respectively). In BMI_{P75} interval, the bias of eGFR_{EPI_Cr_CysC_2012} was 3.8 with 78.9% of P30, the kappa value was 0.484 the AUC_{60} was 0.919, and eGFR_{EPI_Cr_CysC_2012} equation showed balanced and accurate recognition ability of each stage (60.5, 60.0, 71.4, 57.1 and 100% respectively). In BMI_{P75} interval, the bias of eGFR_{FAS_Cr_CysC} was − 1.8 with 78.5% of P30, the kappa value was 0.485, the AUC_{60} was 0.922. However, the recognition ability of each stage of eGFR_{FAS_Cr_CysC} eq. (71.1, 61.2, 70.0, 42.9 and 50.0% respectively) was not as good as GFR_{EPI_Cr_CysC_2012} equation.
Conclusion
For a Chinese population, we tend to recommend choosing eGFR_{FAS_Cr} and eGFR_{EPI_Cr_2009} when BMI was around 20.9, eGFR_{FAS_Cr_CysC} when BMI was near 24.8, and eGFR_{EPI_Cr_CysC_2012} when BMI was about 28.9.
Introduction
Chronic kidney disease (CKD) is defined as a reduced glomerular filtration rate (GFR), increased urinary albumin excretion, or both, and has been recognized as an increasing public health issue worldwide [1]. Rising prevalence, poor outcomes, and high costs of CKD have led to considerable social and economic burdens in both developed and developing countries. Prevalence of CKD is estimated to be 8–16% worldwide [2]. In 2017, there were 132.3 million [95% confidence interval (95% CI) 121.8 to 143.7] people were diagnosed as CKD in China [3]. Therefore, the early prevention and accurate detection of CKD are particularly important.
Ideally, GFR should be measured. Measured (m) GFR gives an accurate assessment of kidney function and avoids confounding by interactions with variables, such as age or weight. Tc99 m DTPA renal dynamic scintigraphy is a useful tool for clinicians in assessing renal function [4]. Because of the complicated process and nuclear pollution of above method, estimated GFR (eGFR) was considered as a convenient and noinvasive means which had been widely used in clinical diagnosis and treatment.
Many eGFR equations are based on the creatinine or/and cystatin C concentrations in serum. However, multiple factors such as muscle mass, weight, race, sex, gender and other individual differences affect the levels of serum creatinine [5]. Performance of Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKDEPI) equations remains suboptimal for estimating GFR in obese populations [6, 7]. Serum cystatin C also has the disadvantage in obesity population. Enlarged adipose tissues lead to elevation of serum cystatin C [8]. In fact, overweight and obesity account for a large proportion in CKD, while the muscle percentage is not synchronized with body weight. Therefore, the accuracy of eGFR assessments is affected by irregular fluctuation in creatinine and cystatin C.
How to choose an appropriate eGFR equation which can estimate renal function accurately? We used the body mass index (BMI) as the breakthrough point. There are many researches on the comparison of different eGFR equations, but still lack of researches on which special equation should be recommended in certain BMI range. In the present study, we assessed the accuracy of eight eGFR equations [CKDEPI cr_2009 (eGFR_{EPI_Cr_2009}) [9], CKDEPI cys_2012 (eGFR_{EPI_CysC_2012}) [1], CKDEPI cr_cys_2012 (eGFR_{EPI_Cr_CysC_2012}) [1, 10]], three full age spectrum (FAS) equations (eGFR_{FAS _Cr}, eGFR_{FAS _CysC}, and eGFR_{FAS _Cr_CysC}) [11], abbreviated_MDRD (eGFR_{a_MDRD}) [12], and Chinese_MDRD (eGFR_{c_MDRD}) [13] compared with GFR measurement using 99TcmDTPA scintigraphy. Our research aimed to identify which equation performed better at estimating GFR and ideally predicting the CKD stage in the corresponding BMI interval, and finally, provide credible eGFR in certain BMI intervals to the clinicians.
Methods
Participants
A total of 904 patients who underwent GFR measurement using 99Tcmdiathylenetriamine pentaacetic acid (99TcmDTPA) scintigraphy from January 2016 to September 2017 in Shanghai General Hospital, were observed. Exclusion criteria included amputation, pregnant women, obstructive nephropathy, solitary kidney or a single kidney, urinary tract infection, acute kidney injury, any history of malignancy or kidney surgery, hyperthyroidism, use of antibacterial agents within 2 weeks, and malignant hypertension. Finally, a total of 837 patients were enrolled in this study. General characteristics were included such as sex, age, body mass index (BMI), serum creatinine, serum cystatin C, measured GFR (mGFR) and the situation of basic diseases. BMI was calculated following the equation: BMI (Kg/m^{2}) = weight (kg) /height^{2} (m). Three intervals were divided based on BMI percentiles, percentile 25% (BMI_{P25}), percentile 25% ~ 75% (BMI_{P25–75}) and percentile 75% (BMI_{P75}). Research has been conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shanghai General Hospital. Written informed consent was obtained from all participants. All methods were carried out in accordance with the relevant guidelines and regulations.
Measurement of reference GFR (mGFR)
The mGFR was measured by gate’s method of radionuclide renal dynamic imaging. The instrument used Siemens Excel Evo SPECT which equipped with low energy and high resolution parallel hole collimator, energy peak 140 keV, window width ± 20%. 99TcmDTPA (radiochemical purity, > 95%; percentage of 99TcmDTPA bound to plasma protein, < 5%) was provided by Shanghai Atom Kexing Pharmaceutical Co., Ltd., China. Determined the mGFR by gate’s method.
Definition of renal insufficiency and CKD classification
The definition of renal insufficiency and CKD classification were referred to the 2012 KDIGO clinical practice guideline [1]. Renal insufficiency was defined as mGFR < 60 mL/min/1.73 m^{2}. CKD was classified into five stages based on the mGFR values as follows: stage 1, mGFR ≥90 mL/min/1.73 m^{2}; stage 2, 60 mL/min/1.73 m^{2} ≤ mGFR < 90 mL/min/1.73 m^{2}; stage 3, 30 mL/min/1.73 m^{2} ≤ mGFR < 60 mL/min/1.73 m^{2}; stage 4, 15 mL/min/1.73 m^{2} ≤ mGFR < 30 mL/min/1.73 m^{2}; stage 5, mGFR < 15 mL/min/1.73 m^{2}.
Measurement of serum creatinine (Scr) and cystatin C (CysC) levels and GFRestimating equations
Blood samples were obtained after the patients had fasted for 12 h. Both Scr and CysC were measured by an automatic biochemical autoanalyzer (Cobas 8000; Roche Products Ltd. Basel, Switzerland), used original matching assay kit (Roche Diagnostics, Mannheim, Germany). Based on the Scr, eGFR was calculated by CKDEPI Cr_2009 (eGFR_{EPI_Cr_2009}) [9], FAS Cr (eGFR_{FAS_Cr)} [11], abbreviated _MDRD (eGFR_{a_MDRD}) [12], and Chinese _MDRD (eGFR_{c_MDRD}) [13]. Based on the CysC, eGFRs was calculated by CKDEPI CysC_2012 (eGFR_{EPI_CysC_2012}) [1] and FAS CysC (eGFR_{FAS _CysC)} [11]. Based on both SCr and CysC, eGFR was calculated by CKDEPI Cr_CysC_2012 (eGFR_{EPI_Cr_CysC_2012}) [10] and FAS Cr_CysC (eGFR_{FAS _Cr_CysC}) [11].
The equations used in the study population (with no correction for race and ethnicity) were the following (SCr indicates serum creatinine):

(1)
CKDEPI Cr_2009 equation:
$$ \mathrm{Female},\mathrm{SCr}\le 61.88\upmu \mathrm{mol}/\mathrm{L}:\mathrm{eGFR}=144\times {\left(\mathrm{SCr}/61.88\right)}^{0.329}\times {0.993}^{\mathrm{age}}\times \left(1.159\mathrm{ifblack}\right)\mathrm{Female},\mathrm{SCr}>61.88\upmu \mathrm{mol}/\mathrm{L}:\mathrm{eGFR}=144\times {\left(\mathrm{SCr}/61.88\right)}^{1.209}\times {0.993}^{\mathrm{age}}\times \left(1.159\mathrm{ifblack}\right)\mathrm{Male},\mathrm{SCr}\le 79.56\upmu \mathrm{mol}/\mathrm{L}:\mathrm{eGFR}=141\times {\left(\mathrm{SCr}/79.56\right)}^{0.411}\times {0.993}^{\mathrm{age}}\times \left(1.159\mathrm{ifblack}\right)\mathrm{Male},\mathrm{SCr}>79.56\upmu \mathrm{mol}/\mathrm{L}:\mathrm{eGFR}=141\times {\left(\mathrm{SCr}/79.56\right]}^{1.209}\times {0.993}^{\mathrm{age}}\times \left(1.159\mathrm{ifblack}\right) $$ 
(2)
CKDEPI CysC_2012 equation:
$$ \mathrm{Female},\mathrm{SCr}\le 61.88\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}\le 0.8\mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=130\times {\left(\mathrm{Scr}/61.88\right)}^{0.248}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.375}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Female},\mathrm{SCr}\le 61.88\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}>0.8\ \mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=130\times {\left(\mathrm{Scr}/61.88\right)}^{0.248}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.711}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Female},\mathrm{SCr}>61.88\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}\le 0.8\ \mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=130\times {\left(\mathrm{Scr}/61.88\right)}^{0.601}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.375}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Female},\mathrm{SCr}>61.88\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}\ge 0.8\mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=130\times {\left(\mathrm{Scr}/61.88\right)}^{0.601}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.711}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Male},\mathrm{SCr}\le 79.56\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}\le 0.8\ \mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=135\times {\left(\mathrm{Scr}/79.56\right)}^{0.207}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.375}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Male},\mathrm{SCr}\le 79.56\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}>0.8\mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=135\times {\left(\mathrm{Scr}/79.56\right)}^{0.207}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.711}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Male},\mathrm{SCr}>79.56\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}\le 0.8\ \mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=135\times {\left(\mathrm{Scr}/79.56\right)}^{0.601}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.375}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right)\mathrm{Male},\mathrm{SCr}>79.56\upmu \mathrm{mol}/\mathrm{L}\ \mathrm{and}\ \mathrm{SCys}>0.8\ \mathrm{mg}/\mathrm{dL}:\mathrm{eGFR}=135\times {\left(\mathrm{Scr}/79.56\right)}^{0.601}\times {\left(\mathrm{Scyst}/0.8\right)}^{0.711}\times {0.995}^{\mathrm{age}}\times \left(1.08\ \mathrm{if}\ \mathrm{black}\right) $$ 
(3)
CKDEPI CysC_2012 equation:
$$ \mathrm{SCys}\le 0.8\mathrm{mg}/\mathrm{L}:\mathrm{eGFR}=133\times {\left(\mathrm{Scys}\mathrm{t}/0.8\right)}^{0.499}\times {0.996}^{\mathrm{age}}\left(\times 0.932\ \mathrm{if}\ \mathrm{female}\right)\mathrm{SCys}>0.8\mathrm{mg}/\mathrm{L}:\mathrm{eGFR}=133\times {\left(\mathrm{Scys}/0.8\right)}^{1.328}\times {0.996}^{\mathrm{age}}\left(\times 0.932\ \mathrm{if}\ \mathrm{female}\right) $$ 
(4)
FAS Cr equation:
$$ \mathrm{eGFR}=107.3/\left(\mathrm{SCr}/\mathrm{QCys}\right)\times \left[{0.988}^{\left(\mathrm{age}40\right)},\mathrm{when}\ \mathrm{age}>40\ \mathrm{years}\right]\left(\mathrm{female}:\mathrm{QScr}=0.70\ \mathrm{mg}/\mathrm{dl};\mathrm{male}:\mathrm{QScr}=0.90\ \mathrm{mg}/\mathrm{dl}\right); $$ 
(5)
FAS CysC equation:
$$ \mathrm{eGFR}=107.3/\left(\mathrm{SCys}/\mathrm{QCys}\right]\times \left[{0.988}^{\left(\mathrm{age}40\right)}\ \mathrm{when}\ \mathrm{age}>40\ \mathrm{years}\right]\left(\mathrm{age}<70\ \mathrm{years}\ \mathrm{old}:\mathrm{QCys}=0.82\ \mathrm{mg}/\mathrm{l};\mathrm{age}\ge 70\ \mathrm{years}\ \mathrm{old}:\mathrm{QCys}=0.95\ \mathrm{mg}/\mathrm{l}\right) $$ 
(6)
FAS CrCysC equation:
$$ \mathrm{eGFR}=107.3/\left[\upalpha \times \left(\mathrm{SCr}/\mathrm{QScr}\right)+\left(1\upalpha \right)\times \left(\mathrm{SCys}/\mathrm{QCys}\right)\right]\times \left[{0.988}^{\left(\mathrm{age}40\right)}\ \mathrm{when}\ \mathrm{age}>40\ \mathrm{years}\right]\left(\mathrm{female}:\mathrm{QScr}=0.70\ \mathrm{mg}/\mathrm{dl};\mathrm{male}:\mathrm{QScr}=0.90\ \mathrm{mg}/\mathrm{dl};\mathrm{age}<70\ \mathrm{years}\ \mathrm{old}:\mathrm{QCys}=0.82\ \mathrm{mg}/\mathrm{l};\mathrm{age}\ge 70\ \mathrm{years}\ \mathrm{old}:\mathrm{QCys}=0.95\ \mathrm{mg}/\mathrm{l};\upalpha =0.5\right) $$ 
(7)
abbreviated_MDRD equation:
$$ \mathrm{eGFR}=175\times \mathrm{SCr}\ {\left(\upmu \mathrm{mol}/\mathrm{L}\times 0.0011312\right)}^{1.154}\times \mathrm{age}\ {\left(\mathrm{years}\right)}^{0.203}\times \left(0.742,\mathrm{if}\ \mathrm{female}\right)\times \left(1.212,\mathrm{if}\ \mathrm{black}\right) $$ 
(8)
Chinese _MDRD equation:
$$ \mathrm{eGFR}=175\times \mathrm{SCr}\ {\left(\upmu \mathrm{mol}/\mathrm{L}\times 0.0011312\right)}^{1.234}\times \mathrm{age}\ {\left(\mathrm{years}\right)}^{0.179}\times \left(0.79,\mathrm{if}\ \mathrm{female}\right) $$
Statistical analysis
Statistical analyses were performed using SPSS version 22.0 for Windows (SPSS Inc., Chicago, USA) and Medcalc 11.4 for windows. KolmogorovSmirnov test (KS) was used to test the normality of variables [14]. Continuous variables were presented as the means ± standard deviation and were analyzed using unpaired Student’s ttests. Nonnormally distributed variables were presented as medians with corresponding 25th and 75th percentiles (interquartile ranges) and compared using the Mann–Whitney U test [15]. Wilcoxon test was used to compare the differences of the deviation from mGFR (△eGFR, which is mGFR minus eGFR) by these eight eGFRs when in different BMI interval. Plotting scatter diagrams were used to observe the trend of each △eGFR when in different BMI state. Partial correlation analysis was used to evaluate correlations between △eGFR and BMI. BlandAltman analysis [16] was used to determine the agreement between the mGFR and eGFR values, similar to the study by Chi et al [17], which were calculated by different equations. The percentage of cases in which eGFR was within mGFR ±30% (P30) was used to represent the accuracy of each equation. Kappa statistics were used to evaluate the agreement between stage classification from the mGFR values and from the eGFR values calculated by different equations, with the following interpretations: slight agreement (0–0.20), fair agreement (0.21–0.40), moderate agreement (0.41–0.60), substantial agreement (0.61–0.80), and almost perfect or perfect agreement (0.81–1.0) [18]. The receiver operating characteristic (ROC) curve was used to determine the diagnostic power at predicting the renal insufficiency (ROC_{60}) by the eight different equations, with the results reported as the areas under the ROC curve (AUC_{60}), sensitivity, and specificity [19]. Differences with P < 0.05 were considered statistically significant.
Results
Overview of the entire study population
The demographic and clinical features of the participants included in the analysis are listed in Table 1. The medium BMI was 24.8 Kg/m^{2} which 25.1 Kg/m^{2} for male, and 24.2 for female. According to the percentile of BMI, it was divided into three intervals, < 25% (BMI_{P25}), 25% ~ 75% (BMI_{P25–75}) and > 75% (BMI_{P75}), respectively. The average BMI was 20.9 Kg/m^{2} in BMI_{P25,} 24.8 Kg/m^{2} in BMI_{P25–75,} and 28.9 Kg/m^{2} in BMI_{P75} respectively. Among all the patients, 66.8% had diabetes and 64.5% had hypertension, and 71.6% had atherosclerosis which was the most common diagnosis. The average mGFR was 71.4 ± 28.1 mL/min/1.73 m^{2}, while the average eGFR varied according to different calculation formulas, ranging from 60.2(44.9, 74.9)mL/min/1.73 m^{2} to 88.5 ± 49.1 mL/min/1.73 m^{2}.
Relation between BMI and △eGFR based on different formulas
The correlation between △eGFR based on different formulas and BMI was shown by plotting scatter diagrams of △eGFR based on different formulas with the increase of BMI (Fig. 1). With the increase of BMI, trends of △eGFR differed with diverse formulas. Partial correlation coefficient was shown in Table 2, which was statistically significant (p = 0.012 for △eGFR_{c_MDRD} while the rest p < 0.001).
The comparison of △eGFR among different BMI groups was shown in Table 3. Delta eGFR_{EPI_Cr_2009}, △eGFR_{EPI_CysC_2012}, △eGFR_{EPI_Cr_CysC_2012}, △eGFR_{FAS}__{Cr}, △eGFR_{FAS_CysC} and △eGFR_{FAS_Cr_CysC} showed significant differences in different BMI intervals (p = 0.030, 0.010, 0.000, 0.0029, 0.000 and 0.001 respectively). While △eGFR_{a_MDRD} and △eGFR_{c_MDRD} had no significant difference in different BMI intervals (p = 0.234 and 0.522, respectively).
Consistency of eGFRs compared with mGFR
The consistency between the eGFR based on different formulas and the mGFR was analyzed by BlandAltman plots (Fig. 2, Table 4). The accuracy of each equation was represented by the percentage of cases in which eGFR was within the range of mGFR ±30% (P30). Compared with mGFR, biases of eGFR_{FAS_Cr_CysC} and eGFR_{EPI_Cr_2009} (4.1 and − 4.2, respectively) were much less than those of eGFR_{FAS_Cr} eGFR_{EPI_CysC_2012}, eGFR_{EPI_Cr_CysC_2012}, eGFR_{FAS_CysC}, eGFR_{a_MDRD} and eGFR_{c_MDRD} (− 6.9, 8.4, 9.6, 10.8, − 8.4 and − 20.8, respectively). The accuracy of each eGFR was as follows: 81.5% for eGFR_{FAS_Cr_CysC}, 74.1% for eGFR_{EPI_Cr_CysC_2012}, 74.1% for eGFR_{EPI_CysC_2012}, 73.4% for eGFR_{FAS_CysC}, 70.1% for eGFR_{EPI_Cr_2009}, 69.3% for eGFR_{FAS_Cr}, 63.0%for eGFR_{a_MDRD} and 47.0% for eGFR_{c_MDRD}.
However, the accuracy of different formulas varied in different BMI intervals. In the BMI_{P25} interval, the bias of eGFR_{FAS_Cr} was improved to − 0.7 mL/min/1.73 m^{2}(P = 0.679) with 74.2% of P30. And the bias of eGFR_{EPI_Cr_2009} was 2.3 mL/min/1.73 m^{2} (P = 0.061) with 71.8% of P30. In the range of BMI_{P25–75}, the bias of eGFR_{FAS_Cr_CysC} was 4.0 mL/min/1.73 m^{2} with 85.0% of P30, and the bias of eGFR_{EPI_Cr_2009} was 4.0 mL/min/1.73 m^{2} with 74.7% of P30, which were most consistent with mGFR. In the BMI_{P75} interval, the bias of eGFR_{EPI_Cr_CysC_2012} was 3.8 mL/min/1.73 m^{2} (P < 0.01) with 78.9% of P30 and the bias of eGFR_{EPI_CysC_2012} was 3.3 mL/min/1.73 m^{2} (P < 0.05) with 76.6% of P30. The bias of eGFR_{FAS_Cr_CysC} was − 1.8 mL/min/1.73 m^{2}, but there was no statistical significance (P = 0.095). It was suggested that the consistency of eGFR compared with the mGFR was the best when eGFR calculated by eGFR_{EPI_CysC_2012} and eGFR_{FAS_Cr_CysC} formulas.
Accuracy of eGFR in CKD staging in different BMI intervals
The kappa values of eGFR_{EPI_Cr_2009}, eGFR_{FAS_Cr} and eGFR_{a_MDRD} were similar(0.418, 0.422 and 0.412 respectively), which were higher than that of other formulas when in BMI_{P25} interval (Supplemental Table 1). They showed high accuracy (84.4, 76.6 and 88.3%, respectively) in the identification of stage 1 CKD and moderate accuracy in the identification of stage 2 and 3 CKD. In BMI_{P25–75} interval, eGFR_{FAS_Cr_CysC} had highest kappa value (0.504), which was higher than eGFR_{EPI_CysC_2012} (0.431), eGFR_{FAS_Cr}(0.415) and eGFR_{EPI_Cr_CysC_2012} (0.415). The eGFR_{FAS_Cr_CysC} showed a better accuracy in the identification of stage 2 and 3 CKD (63.7 and 68.0% respectively) (Supplemental Table 2). In the BMI_{P75} interval, eGFREPI_Cr_CysC_2012 was found to be the best, with a kappa value of 0.484, showing balanced and accurate recognition ability of each stage (60.5, 60.0, 71.4, 57.1 and 100% respectively) (Supplemental Table 3). However, the recognition ability of each CKD stage of FAS_Cr_CysC eq. (71.1, 61.2, 70.0, 42.9 and 50.0% respectively) was not as good as EPI_Cr_CysC_2012 equation.
Diagnostic performance of each eGFR equation for predicting renal insufficiency in different BMI intervals
The diagnostic performance for predicting renal insufficiency based on each eGFR equation in three BMI intervals was summarized and showed in Supplemental Table 4, 5 and 6 and Fig.3. In the BMI_{P25} interval, with a sensitivity of 89.7% and a specificity of 84.1%, at a cutoff point of 67.1 mL/min/1.73 m^{2}, eGFREPI_Cr_2009 got an AUC_{60} of 0.920 which had no significant difference compared with other equations (p > 0.05), suggesting appropriate diagnostic ability for predicting renal insufficiency. In BMI_{P25} interval, eGFR_{FAS_Cr} had similar performance with a sensitivity of 79.3%, specificity of 88.1%, and an AUC_{60} of 0.928 at a cutoff point of 56.6 mL/min/1.73 m^{2}.
In BMI_{P25–75} interval, the cutoff point of eGFR_{FAS_Cr_CysC} was 62.9 mL/min/1.73 m^{2}, with an AUC_{60} of 0.941 which had no significant difference compared with other equations (except eGFR_{FAS_CysC}, p = 0.021). When the cutoff values of eGFREPI_CysC_2012 and eGFREPI_Cr_CysC_2012 were revised to 60.1 and 60.2 mL/min/ 1.73 m^{2} respectively, the sensitivity was increased to 90.3 and 91.8% respectively, but the specificity was decreased to 78.9 and 77.2% respectively. Looking back at eGFR_{FAS_Cr_CysC}, when cutoff point of eGFR_{FAS_Cr_CysC} was 62.9 mL/min/1.73 m^{2}, the sensitivity was 92.5% and the specificity was 78.6%, while after revising cutoff value to 60.0 mL/min/1.73 m^{2}, the sensitivity was 87.3% and the specificity was 82.0%, indicating that the diagnostic performance for predicting renal insufficiency was relatively stable. In BMI_{P75} interval, the optimal cutoff point of eGFR_{EPI_Cr_CysC_2012} for predicting renal insufficiency was 60.5 mL/min/1.73 m^{2}, with an ideal sensitivity of 90.7%, a specificity of 80.5%, and an AUC_{60} of 0.919 (P < 0.05 vs. eGFR_{EPI_CysC_2012}), highlighted itself. The optimal cutoff point of eGFR_{FAS_Cr_CysC} for predicting renal insufficiency was 61.5 mL/min/1.73 m^{2}, with a sensitivity of 87.2%, a specificity of 84.6%, and an AUC_{60} of 0.922 (P < 0.05 vs. eGFR_{FAS_CysC}). It suggested that eGFR_{EPI_Cr_CysC_2012} and eGFR_{FAS_Cr_CysC} had the strongest ability to predict renal insufficiency in BMI_{P75} interval.
Discussion
There is high disease burden of CKD in China [2]. The global increase in this disease is mainly driven by the increase in the prevalence of diabetes mellitus, hypertension, obesity, and aging. To make matters worse, the risk of death gradually increases with the deterioration of CKD [20]. Therefore, screening, diagnosis, and staging CKD early as well as accurately are more and more important. Estimating GFR accurately is crucial for clinical practice, research, and public health. Although Tc99 m DTPA renal dynamic scintigraphy is a useful tool for clinicians in assessing renal function, this method cannot be regularly used in clinical practice. On the contrary, GFR estimated from equations is a convenient approach to assess patients’ renal function. Due to the convenience of testing, it can be used as a method for largescale cases screening.
Each eGFR equation is established by statistically processing of certain population data, so it always performs less well outside the cohort in which they were developed [21]. All methods for the estimation of GFR have limitations, so no equation can perform best in all populations. Obesity is associated with a risk of CKD and is highly prevalent among patients with CKD [22, 23]. In our study, the average BMI of the cases was 24.8, of which 25.1 for males and 24.2 for females. A large number of patients were overweight or obese. Therefore, it inspired us to consider the influence of BMI, which can partly reflect the difference of body. If we properly handled this influence, can we make the best use of each eGFR equation? There are few studies on the applicability of different eGFR equations in different BMI intervals. In this study, we evaluated the value of different eGFR formulas in different BMI intervals.
After being analyzed by BlandAltman plots, biases of eGFR_{FAS_Cr_CysC} and eGFR_{EPI_Cr_2009} were much less than that of others on the whole, showing the best agreement with mGFR. In BMI_{P25} interval, eGFR_{FAS_Cr} and eGFR_{EPI_Cr_2009} formulas had optimal accuracy, excellent ability to classify CKD stages, and best diagnostic performance for predicting renal insufficiency. In BMI_{P25–75} interval, eGFR_{FAS_Cr_CysC} was the best one, with optimal accuracy and excellent ability in staging CKD2 and CKD3. In BMI_{75} interval, eGFR_{EPI_Cr_CysC_2012} equation showed excellent accuracy, stable identification power for CKD stages and the strongest ability to predict renal insufficiency. In BMI_{75} interval, the accuracy and ability to predict renal insufficiency of eGFR_{FAS_Cr_CysC} was similar to that of eGFR_{EPI_Cr_CysC_2012}. However, eGFR_{FAS_Cr_CysC} was not as good as eGFRE_{PI_Cr_CysC_2012} equation in identifying CKD stages. We found ScrcysCbased eGFR equations had superiority in evaluating eGFR compared to the Scrbased formulas in overweight or obese people.
It’s well known that SCr has limitations including its insensitivity to underlying changes in kidney function and the numerous nonkidney factors that are incompletely accounted for in equations to eGFR [24]. Although as an endogenous biomarker, concentration of cysC also can be affected by other nonrenal determinants, such as obesity, thyroid disorders, diabetes, and inflammation, however, compared to SCr, cysC appears to be less affected by age, race, sex, muscle mass, or dietary intake [25, 26]. It is increasingly accepted to use the use equations based on cystatin C or combined creatinine and cystatin C [27]. In fact, kidney function assessment in obese patients is challenging. Nephron number does not change with weight gain, and the increase of GFR observed in obese patients reflects compensatory hyperfiltration of nephrons. This hyperfiltration in obese patients can become maladaptive and is largely unaccounted for in existing eGFR equations [28]. According to our research, it may be acceptable to choose an eGFR formula based on combined creatinine and cystatin C before a better formula appears. It is worth mentioning that, our reseach proves that, the novel FAS equations [11] are suitable for Chinese population, and even have superiority compared to other formulas in many cases, especially the eGFR_{FAS_Cr_CysC} equation. In a multicenter study of 1184 patients in China, the performance of the eGFR_{FAS_Cr_CysC} equation was better than that of the eGFR_{EPI_Cr_CysC_2012} equation, particularly in the elderly [29]. It may be necessary to further modify the FAS equation from a largerscale study to make it more suitable for the Chinese population.
Exactly, each eGFR formula shows different clinical value in different BMI intervals. Therefore, the BMI of patients with CKD is an aspect worthy considering when choosing the appropriate eGFR equation. Which is the best choice? In our study, in normal or lowweight population, the formula based on serum creatinine is preferred, and in overweight or obese population, the formula based on serum creatinine and cystatin C may be more suitable. The reason may be that the combination of both biomarkers can cancel out the nonGFRrelated factors influencing creatinine and cystatin C in different directions compared with mGFR. Steubl et al. suggest that combining metabolites or proteins in equations to minimize the influence of nonkidneyrelated parameters appears to be a promising approach which is consistent with our view [30].
Our research had some strengths such as on ethnic factors that all were from Chinese population, concentrative age range, common highrisk diseases for CKD. These favoured us to identify the appropriate eGFR equation for Chinese population while considering the impact of BMI. However, it needs to be verified and confirmed by different types of studies based on a larger population. More comparative studies on different types of samples are needed to further illuminate which biomarkers are better tools for diagnosis and prognosis of CKD.
Nevertheless, our study has some limitations. Firstly, we did not obtain specific data such as appendicular lean mass index (ALMI) and total body fat percentage (TBF%) measured by dualenergy xray absorptiometry (DXA) of body composition [31, 32], so we cannot refine the population in the BMI intervals. Secondly, we didn’t have enough data of proteinuria to define renal dysfunction because we only got one urine protein test result for each patient’s first morning urine. Thirdly, as it was a crosssectional analysis, and a retrospective, singlecenter study, the results of this study should be carefully applied in practical clinical practice. Finally, although we assessed eight eGFR equations that were commonly used, there are also some other equations which were well praised were not included in our study.
In conclusion, after comprehensive analysis of factors that included consistency, accuracy, classification ability and diagnostic performance, we tend to suggest that choosing eGFR_{EPI_Cr_2009} or eGFR_{FAS_Cr} equation to estimate GFR of patients when BMI is around 20.9 kg/m^{2}, eGFR_{FAS_Cr_CysC} for overweight patients (BMI around 24.8 kg/m^{2}), and eGFR_{EPI_Cr_CysC_2012} for obese patients (BMI is about 28.9 kg/m^{2}).
Availability of data and materials
All data analysed during this study are included in Supplementary Information Files and also available from the corresponding author on reasonable request.
References
 1.
Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 2013;Suppl. 3(5):1–150. https://doi.org/10.1053/j.ajkd.2014.01.416.
 2.
Jha V, GarciaGarcia G, Iseki K, Li Z, Naicker S, Plattner B, et al. Chronic kidney disease: global dimension and perspectives. Lancet. 2013;382:260–72. https://doi.org/10.1016/S01406736(13)60687X.
 3.
GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 19902017: a systematic analysis for the global burden of disease study 2017. Lancet. 2020;395(10225):709–33. https://doi.org/10.1016/S01406736(20)300453.
 4.
Trimarchi H, Muryan A, Martino D, Toscano A, Iriarte R, CampoloGirard V, et al. Creatinine vs. cystatin Cbased equations compared with 99mTcDTPA scintigraphy to assess glomerular filtration rate in chronic kidney disease. J Nephrol. 2012;25(6):1003–15. https://doi.org/10.5301/jn.5000083.
 5.
Vinge E, Lindergard B, NilssonEhle P, Grubb A. Relationships among serum cystatin C, serum creatinine, lean tissue mass and glomerular filtration rate in healthy adults. Scand J Clin Lab Invest. 1999;59(8):587–92. https://doi.org/10.1080/00365519950185076.
 6.
Bouquegneau A, et al. Modification of Diet in Renal Disease versus Chronic Kidney Disease Epidemiology Collaboration equation to estimate glomerular filtration rate in obese patients. Nephrol Dial Transplant. 2013:28, iv122–iv130. https://doi.org/10.1093/ndt/gft329.
 7.
Lemoine S, GuebreEgziabher F, Sens F, NguyenTu MS, Juillard L, Dubourg L, et al. Accuracy of GFR estimation in obese patients. Clin J Am Soc Nephrol. 2014;9(4):720–7. https://doi.org/10.2215/CJN.03610413.
 8.
Wasén E, Isoaho R, Mattila K, Vahlberg T, Kivelä SL, Irjala K. Serum cystatin C in the aged: relationships with health status. Am J Kidney Dis. 2003;42(1):36–43. https://doi.org/10.1016/s02726386(03)004062.
 9.
Levey AS, Stevens LA, Schmid CH, Zhang Y(L), Castro AF III, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604–12. https://doi.org/10.7326/00034819150920090505000006.
 10.
Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367(1):20–9. https://doi.org/10.1056/NEJMoa1114248.
 11.
Pottel H, Delanaye P, Schaeffner E, Dubourg L, Eriksen BO, Melsom T, et al. Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C. Nephrol Dial Transplant. 2017;32(3):497–507. https://doi.org/10.1093/ndt/gfw425.
 12.
McKillop DJ, Cairns B, Duly E, Van Drimmelen M, Ryan M. The effect of serum creatinine method choice on estimated glomerular filtration rate determined by the abbreviated MDRD formula. Ann Clin Biochem. 2006;43(Pt 3):220–2. https://doi.org/10.1258/000456306776865098.
 13.
Ma YC, Zuo L, Chen JH, Luo Q, Yu XQ, Li Y, et al. Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol. 2006;17(10):2937–44. https://doi.org/10.1681/ASN.2006040368.
 14.
Smirnov N. Table for estimating the goodness of fit of empirical distributions. Ann Math Stat. 1948;19(2):279–81. https://doi.org/10.1214/aoms/1177730256.
 15.
Hart A. MannWhitney test is not just a test of medians: differences in spread can be important. BMJ. 2001;323(7309):391–3. https://doi.org/10.1136/bmj.323.7309.391.
 16.
Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10.
 17.
Chi XH, Li GP, Wang QS, Qi YS, Huang K, Zhang Q, et al. CKDEPI creatininecystatin C glomerular filtration rate estimation equation seems more suitable for Chinese patients with chronic kidney disease than other equations. BMC Nephrol. 2017;18(1):226. https://doi.org/10.1186/s128820170637z.
 18.
Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159–74. https://doi.org/10.2307/2529310.
 19.
DeLong ER, DeLong DM, ClarkePearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–45. https://doi.org/10.2307/2531595.
 20.
Sarnak MJ, Amann K, et al. Chronic Kidney Disease and Coronary Artery Disease: JACC StateoftheArt Review. J Am Coll Cardiol. 2019;74(14):1823–38. https://doi.org/10.1016/j.jacc.2019.08.1017.
 21.
Lamb EJ, Tomson CR, Roderick PJ, clinical sciences reviews Committee of the Association for clinical biochemistry. Estimating kidney function in adults using formulae. Ann Clin Biochem. 2005;42(Pt 5):321–45. https://doi.org/10.1258/0004563054889936 PMID: 16168188.
 22.
Stenvinkel P, Zoccali C, Ikizler TA. Obesity in CKDwhat should nephrologists know? J Am Soc Nephrol. 2013;24(11):1727–36. https://doi.org/10.1681/ASN.2013040330.
 23.
Wang Y, Chen X, Song Y, Caballero B, Cheskin LJ. Association between obesity and kidney disease: a systematic review and metaanalysis. Kidney Int. 2008;73(1):19–33. https://doi.org/10.1038/sj.ki.5002586.
 24.
Levey AS, Inker LA. Assessment of glomerular filtration rate in health and disease: a state of the art review. Clin Pharmacol Ther. 2017;102(3):405–19. https://doi.org/10.1002/cpt.729 Epub 2017 Jun 5. PMID: 28474735.
 25.
Stevens LA, Schmid CH, Greene T, Li L, Beck GJ, Joffe MM, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009;75(6):652–60. https://doi.org/10.1038/ki.2008.638 Epub 2008 Dec 31. PMID: 19119287; PMCID: PMC4557800.
 26.
Teaford HR, Barreto JN, Vollmer KJ, Rule AD, Barreto EF, Cystatin C. A Primer for Pharmacists. Pharmacy (Basel). 2020;8(1):35. https://doi.org/10.3390/pharmacy8010035 PMID: 32182861; PMCID: PMC7151673.
 27.
Yang M, Xu G, Ling L, Niu J, Lu T, Du X, et al. Performance of the creatinine and cystatin Cbased equations for estimation of GFR in Chinese patients with chronic kidney disease. Clin Exp Nephrol. 2017;21(2):236–46. https://doi.org/10.1007/s1015701612739 Epub 2016 Apr 28. PMID: 27125433.
 28.
Chang AR, Zafar W, Grams ME. Kidney Function in ObesityChallenges in Indexing and Estimation. Adv Chronic Kidney Dis. 2018;25(1):31–40. https://doi.org/10.1053/j.ackd.2017.10.007 PMID: 29499884; PMCID: PMC5836495.
 29.
Yong Z, Li F, Pei X, Liu X, Song D, Zhang X, et al. A comparison between 2017 FAS and 2012 CKDEPI equations: a multicenter validation study in Chinese adult population. Int Urol Nephrol. 2019;51(1):139–46. https://doi.org/10.1007/s1125501819974 Epub 2018 Oct 24. PMID: 30357600.
 30.
Steubl D, Inker LA. How best to estimate glomerular filtration rate? Novel filtration markers and their application. Curr Opin Nephrol Hypertens. 2018;27(6):398–405. https://doi.org/10.1097/MNH.0000000000000444 PMID: 30063487.
 31.
Thibault R, Pichard C. The evaluation of body composition: a useful tool for clinical practice. Ann Nutr Metab. 2012;60(1):6–16. https://doi.org/10.1159/000334879.
 32.
Marinangeli CP, Kassis AN. Use of dual Xray absorptiometry to measure body mass during short to mediumterm trials of nutrition and exercise interventions. Nutr Rev. 2013;71(6):332–42. https://doi.org/10.1111/nure.12025.
Acknowledgments
The research team acknowledge the support of the National Natural Science Foundation of China.
Funding
This study was supported in part by grants from Young Scientists Fund of the National Natural Science Foundation of China (No. 81900671).
Author information
Affiliations
Contributions
Jiayong Li and Xiang Xu designed the study. Jialing Luo, Wenjing Chen and Man Yang collected data. Ling Wang and Nan Zhu analyzed and explained the data. Weijie Yuan critically reviewed the content of the article. Lijie Gu statistically analyzed and wrote the article. All authors reviewed and approved the manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Research has been conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Shanghai General Hospital. Written informed consent was obtained from all participants. All methods were carried out in accordance with the relevant guidelines and regulations.
Consent for publication
Written informed consent for publication was obtained from all participants.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Additional file 1: Supplemental Table 1.
CKD stage classification based on eGFRs in BMI_{P25} interval. Supplemental Table 2. CKD stage classification based on eGFRs in BMI_{P25–75} interval. Supplemental Table 3. CKD stage classification based on eGFRs in BMI_{P75} interval. Supplemental Table 4. Diagnostic performance in BMI_{P25} interval of eGFRs for predicting renal insufficiency (mL/min/1.73 m2). Supplemental Table 5. Diagnostic performance in BMI_{P25–75} interval of eGFRs for predicting renal insufficiency (mL/min/1.73 m2). Supplemental Table 6. Diagnostic performance in BMI_{P75} interval of eGFRs for predicting renal insufficiency (mL/min/1.73 m2).
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Li, J., Xu, X., Luo, J. et al. Choosing an appropriate glomerular filtration rate estimating equation: role of body mass index. BMC Nephrol 22, 197 (2021). https://doi.org/10.1186/s1288202102395x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1288202102395x
Keywords
 Body mass index
 Estimated glomerular filtration rate
 Chronic kidney disease