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**|**Data Brief**|**v.14; 2017 October**|**PMC5596330

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Data Brief. 2017 October; 14: 763–772.

Published online 2017 September 1. doi: 10.1016/j.dib.2017.08.034

PMCID: PMC5596330

Hans Pottel,^{a,}^{} Laurence Dubourg,^{b} Elke Schaeffner,^{c} Bjørn Odvar Eriksen,^{d} Toralf Melsom,^{d} Edmund J. Lamb,^{e} Andrew D. Rule,^{f} Stephen T. Turner,^{f} Richard J. Glassock,^{g} Vandréa De Souza,^{h} Luciano Selistre,^{h,}^{i} Karolien Goffin,^{j} Steven Pauwels,^{k} Christophe Mariat,^{l} Martin Flamant,^{m} Sebastjan Bevc,^{n} Pierre Delanaye,^{o} and Natalie Ebert^{c}

Hans Pottel: eb.kaluk-nevueluk@lettop.snah

Received 2017 June 8; Revised 2017 August 17; Accepted 2017 August 25.

Copyright © 2017 The Authors

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

The data presented in this article are related to the research article entitled “The Diagnostic Value of Rescaled Renal Biomarkers Serum Creatinine and Serum Cystatin C and their Relation with Measured Glomerular Filtration Rate” (Pottel et al. (2017) [1]). Data are presented demonstrating the rationale for the normalization or rescaling of serum cystatin C, equivalent to the rescaling of serum creatinine. Rescaling biomarkers brings them to a notionally common scale with reference interval [0.67–1.33]. This article illustrates the correlation between rescaled biomarkers serum creatinine and serum cystatin C by plotting them in a 2-dimensional graph. The diagnostic value in terms of sensitivity and specificity with measured Glomerular Filtration Rate as the reference method is calculated per age-decade for both rescaled biomarkers. Finally, the interchangeability between detecting impaired kidney function from renal biomarkers and from the Full Age Spectrum FAS-estimating GFR-equation and measured GFR using a fixed and an age-dependent threshold is shown.

**Value of the data**

- • The data present the rationale for the choice of the rescaling factor for serum cystatin C.
- • Rescaling brings the biomarker to a notionally common scale making its interpretation easy with reference to the reference interval [0.67–1.33].
- • The upper limit of the reference interval (1.33) is used as a threshold to detect impaired kidney function and this is compared to the definition of impaired kidney function based on a fixed and age-dependent threshold for GFR.
- • These data give new insights into the relation between renal biomarkers and measured GFR.

Analogous to the normalization or rescaling of serum creatinine (Scr), the normalization or rescaling factor(s) for ScysC is defined as the mean (or median) of the ScysC-distribution(s) for healthy subjects. The rescaling factors have previously been defined as Q_{cysC} = 0.82 mg/L for subjects aged < 70 years and Q_{cysC} = 0.95 mg/L for subjects aged ≥ 70 years [2]. In this article, data and a new analysis are presented to further support these choices for the rescaling of ScysC.

Only ‘healthy’ subjects were selected, that is, a subgroup is selected from the total collection of 8584 subjects, obtained from the normal population and from nephrology clinics. First, it was required that Scr/Q_{crea} ≤ 1.33, or, only subjects with ‘normal’ Scr-values were selected. Q_{crea}-values for Scr have been reported for children and adolescents [3], [4]. For adults, Q_{crea} = 0.70 mg/dL is used for females and Q_{crea} = 0.90 mg/dL for males. This selection requirement reduces the total dataset from 8584 to 5352 patients. The additional requirement that mGFR ≥ 60 mL/min/1.73 m² further reduces the dataset from 5352 to 4907. Table 1 shows the numbers, mean, median, standard deviation (SD) and interquartile range (IQR) per age-decade for ScysC in this healthy subjects subgroup.

For each decade, a truncated cumulative Gaussian fit was performed to determine the mean and standard deviation of the sample (Fig. 1 and Table 1). The dotted line in Fig. 1 represents the linear increase in normalization factor beyond the age of 70 years. In the FAS-cystatin C article [2] it was shown that there was no added value to using this (dotted) straight line fit for the normalization factor beyond 70 years, therefore, to keep it simple, the value of 0.95 mg/L was chosen as the rescaling factor for ScysC for ages > 70 years.

The FAS-equation has been designed for Scr/Q_{crea} but it has recently been shown that it can also be used for ScysC/Q_{cysC} and for the combination of both normalized biomarkers [2], [5]. The fact that the same equation can be used to estimate mGFR from renal biomarkers also means that it is expected that Scr/Q_{crea} ≈ ScysC/Q_{cysC}.

Fig. 2 is a scatterplot of ScysC/Q_{cysC} against Scr/Q_{crea}, using the corresponding age/sex dependent Q_{crea}-values and Q_{cysC}-values, for all 8584 subjects. The diagonal line is the identity line, representing equal rescaled biomarkers. The scatter around the identity line indicates the amount to which the rescaled biomarkers deviate from each other. The overall Pearson correlation coefficient (r) between the rescaled biomarkers is 0.87 (p < 0.0001, n = 8584) and Lin's Concordance Correlation Coefficient is 0.857 with 95%CI [0.852–0.863]. Lin's CCC evaluates the degree to which pairs of observations fall on the diagonal or identity line through the origin. For children, r = 0.85, Lin's CCC = 0.828 (n = 767); for adults, r = 0.87 and Lin's CCC = 0.861 (n = 6068) and for older adults r = 0.88, Lin's CCC = 0.852 (n = 1749).

The diagnostic value of the single renal biomarkers is presented in the Table 2a, Table 3a. The fixed threshold for mGFR of 60 mL/min/1.73 m² is compared to the age-dependent threshold CO_{AD} = 107.3/1.33 [ × 0.988^{(Age-40)} if Age > 40 years] [1], [6].

Frequency of patients with rescaled Serum creatinine ≤ and > 1.33 in the subgroups defined by mGFR (fixed and age-dependent threshold CO_{AD}).

2×2 frequency table comparing measured GFR (with fixed threshold of 60 mL/min/1.73 m²) with the average of the biomarkers (with threshold 1.33).

Sensitivity (S) and Specificity (Sp) in Fig. 3a-b are calculated as follows:

- a)in case a true positive test result is defined as Scr/Q
_{crea}> 1.33 in the mGFR < 60 subgroup, and a true negative test result is defined as Scr/Q_{crea}≤ 1.33 in the mGFR ≥ 60 subgroup. E.g. in the age-group 2–10 years, S = 28 / (28 + 0) = 100% and Sp = 170 / (170 + 48) = 78.0%; in the age-group 80–90 years, S = 180 / (180 + 96) = 65.2% and Sp = 147 / (147 + 10) = 93.6%. Reversing the role of Scr/Q_{crea}and mGFR, we find for the 2–10 year age-group: S = 28/76 = 36.8% and Sp = 170/170 = 100%; in the age-group 80–90 years, we have S = 180/190 = 94.7% and Sp = 147/243 = 60.5%. - b)in case a true positive test result is defined as Scr/Q
_{crea}> 1.33 in the mGFR < CO_{AD}subgroup, and a true negative test result is defined as Scr/Q_{crea}≤ 1.33 in the mGFR ≥ CO_{AD}subgroup. E.g. in the age-group 2–10 years, S = 61 / (61 + 20) = 75.3% and Sp = 220 / (220 + 37) = 85.6%; in the age-group 80–90 years, S = 180 / (180 + 96) = 65.2% and Sp = 147 / (147 + 10) = 93.6%. Reversing the role of Scr/Q_{crea}and mGFR, we find for the 2–10 year age-group: S = 61/76 = 80.3% and Sp = 150/170 = 88.2%; in the age-group 80–90 years, we have S = 153/190 = 80.5% and Sp = 220/243 = 90.5%.

Sensitivity (S) and Specificity (Sp) are calculated as follows:

- a)in case a true positive test result is defined as ScysC/Q
_{cysC}> 1.33 in the mGFR < 60 subgroup, and a true negative test result is defined as ScysC/Q_{cysC}≤ 1.33 in the mGFR ≥ 60 subgroup. E.g. in the age-group 2–10 years, S = 27 / (27 + 1) = 96.4% and Sp = 157 / (157 + 61) = 72.0%; in the age-group 80–90 years, S = 182 / (182 + 94) = 65.9% and Sp = 152 / (152 + 5) = 96.8%. Reversing the role of ScysC/Q_{cysC}and mGFR, we find for the 2–10 year age-group: S = 27/88 = 30.7% and Sp = 285/290 = 98.3%; in the age-group 80–90 years, we have S = 182/187 = 97.3% and Sp = 152/246 = 61.8%. - b)in case a true positive test result is defined as ScysC/Q
_{cysC}> 1.33 in the mGFR < CO_{AD}subgroup, and a true negative test result is defined as ScysC/Q_{cysC}≤ 1.33 in the mGFR ≥ CO_{AD}subgroup. E.g. in the age-group 2–10 years, S = 61 / (61 + 20) = 75.3% and Sp = 138 / (138 + 27) = 83.6%; in the age-group 80–90 years, S = 154 / (154 + 22) = 87.5% and Sp = 224 / (224 + 33) = 87.2%. Reversing the role of ScysC/Q_{cysC}and mGFR, we find for the 2–10 year age-group: S = 61/88 = 69.3% and Sp = 138/158 = 87.3%; in the age-group 80–90 years, we have S = 154/187 = 82.4% and Sp = 224/246 = 91.1% (Fig. 4).

Comparing (Scr/Q_{crea}+ScysC/Q_{cysC})/2 using the threshold of 1.33 with mGFR using the fixed threshold of 60 mL/min/1.73 m², for the complete n = 8584 dataset, to detect renal impairment, we have (Table 3a):

Exact McNemar's test: p < 0.0001. % agreement = (5067 + 2488) / 8584 = 88.0%.

Comparing (Scr/Q_{crea}+ScysC/Q_{cysC})/2 using the threshold of 1.33 with mGFR using an age-dependent threshold, for the complete n = 8584 dataset, to detect renal impairment, we have (Table 3b):

2×2 frequency table comparing measured GFR (with age-dependent threshold) with the average of the biomarkers (with threshold 1.33).

Exact McNemar's test: p = 0.1027. % agreement = (5043 + 2711) / 8584 = 90.3%.

Using the FAS_{combi} equation to calculate eGFR from both Scr/Q_{crea} and ScysC/Q_{cysC}, the following table is obtained when comparing FAS-eGFR using the age-dependent threshold with the combined biomarker value (Scr/Q_{crea}+ScysC/Q_{cysC})/2 using the threshold of 1.33 (Table 4):

2×2 frequency table comparing (FAS) estimated GFR (with age-dependent threshold) with the average of the biomarkers (with threshold 1.33).

In Fig. 5a-b, the raw mGFR-values are plotted against age, for the subgroups defined by (Scr/Q_{crea}+ScysC/Q_{cysC})/2 below and above the threshold of 1.33, together with the fixed threshold for mGFR = 60 mL/min/1.73 m² and the age-dependent threshold obtained from the FAS-equation with (Scr/Q_{crea}+ScysC/Q_{cysC})/2 = 1.33. These figures correspond to the Table 3a, Table 3b.

This is a retrospective study, where the data presented here were collected from 12 previously published cohorts (grand total of 8584 patients) and centralized for pooled data-analysis. Assay data for Scr and ScysC, together with measured GFR, age, sex were centralized for the data-analysis. The total number of patients was subdivided into subgroups corresponding with age-decades with the aim to perform a data-analysis of the diagnostic value (in terms of sensitivity and specificity) of the biomarkers per age-decade. Sensitivity and specificity were calculated with reference to measured GFR (fixed and age-dependent threshold), and with reference to the rescaled biomarker threshold of 1.33.

Scr was traceable to the gold standard Isotope Dilution Mass Spectrometry method, ScysC was obtained from assays calibrated to the international standard or ScysC was recalculated against the calibrator and measured GFR was obtained from accepted reference methods, as described in the main article [1].

The Chronic Renal Insufficiency Cohort Study (CRIC) was conducted by the CRIC Investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The data from the CRIC Study reported here were supplied by the NIDDK Central Repositories. This manuscript was not prepared in collaboration with Investigators of the CRIC study and does not necessarily reflect the opinions or views of the CRIC study, the NIDDK Central Repositories, or the NIDDK.

^{Transparency document}Supplementary data associated with this article can be found in the online version at doi:10.1016/j.dib.2017.08.034.

Supplementary material

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1. Pottel H., Dubourg L., Schaeffner E., Eriksen B.O., Melsom T., Lamb E.J., Rule A.D., Turner S.T., Glassock R.J., De Souza V., Selistre L., Goffin K., Pauwels S., Mariat Ch, Flamant M., Bevc S., Delanaye P., Ebert N. The diagnostic value of rescaled renal biomarkers serum creatinine and serum cystatin C and their relation with measured glomerular filtration rate. Clin. Chim. Acta. 2017;471:164–170. [PubMed]

2. Pottel H., Delanaye P., Schaeffner E., Dubourg L., Eriksen B.O., Melsom T., Lamb E.J., Rule A.D., Turner S.T., Glassock R.J., De Souza V., Selistre L., Goffin K., Pauwels S., Mariat Ch, Flamant M., Ebert N. Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C. Nephrol. Dial. Transpl. 2017;32:497–507. [PubMed]

3. Hoste L., Dubourg L., Selistre L., De Souza V.C., Ranchin B., Hadj-Aïssa A., Cochat P., Martens F., Pottel H. A new equation to estimate the glomerular filtration rate in children, adolescents and young adults. Nephrol. Dial. Transplant. 2014;29:1082–1091. [PubMed]

4. Pottel H. Measuring and estimating glomerular filtration rate in children. Pediatr. Nephrol. 2016:1–15.

5. Pottel H., Hoste L., Dubourg L., Ebert N., Schaeffner E., Eriksen B.O., Melsom T., Lamb E.J., Rule A.D., Turner S.T., Glassock R.J., De Souza V., Selistre L., Mariat Ch, Martens F., Delanaye P. An estimating glomerular filtration rate equation for the full age spectrum. Nephrol. Dial. Transplant. 2016;31:798–806. [PubMed]

6. Pottel H., Delanaye P., Weekers L., Selistre L., Goffin K., Gheysens O., Dubourg L. Age-dependent reference intervals for estimated and measured glomerular filtration rate. Clin. Kidney J. 2017;10:545–551. [PubMed]

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