Renal Function InfluencesDiagnosticMarkers inSerumandUrine: A Study of Guanidinoacetate,Creatine, Human Epididymis Protein 4, andNeutrophil Gelatinase–Associated Lipocalin inChildren

Cathrin L. Salvador,1,2* Camilla Tøndel,3,4 Alexander D. Rowe,1 Anna Bjerre,5 Atle Brun,6,7

Damien Brackman,3 Nils Bolstad,1,2 and Lars Mørkrid1,2

Background: Impaired renal functionmayaffect the level of diagnostic diseasemarkers. The aimof the studywas toinvestigate the effect of measured glomerular filtration rate (GFR) on 4 diagnostic markers in blood and urine—guanidinoacetate (GAA), creatine (CRE), human epididymis protein 4 (HE4), and neutrophil gelatinase–associated li-pocalin (NGAL)—and how this could affect the decision and reference limits.Methods:We examined 96 children (median age 9.2 years, range 0.25–17.5) with different stages of chronic kidneydisease (CKD). GFR [median 65.9 mL ·min−1 · (1.73 m2)−1, range 6.3–153] was measured by iohexol clearance using 7venous blood samples after iohexol injection. Fasting serum and urinary GAA, CRE, HE4, NGAL, and creatinine (crn)were analyzed. After appropriate transformation of the markers, a multiple linear regression analysis examined theinfluence of age, sex, andmeasured GFR.Results: The level of GFR significantly affected S-GAA (P = 2 × 10−4) andU-GAA/crn (P = 5 ×10−11), leading to decreasedvalues in renal impairment. GFR did not correlate significantly with the level of CRE and to a minor degree did theU-CRE/crn ratio (P = 0.54 and 0.01, respectively). The level of GFR significantly affected S-HE4 (P = 4 × 10−31) andU-HE4/S-HE4 ratio (P = 2 × 10−21) with increased serum values and decreased U-HE4/S-HE4 ratio in renal impairment.S-NGAL increased with decreasing kidney function (P = 2 × 10−19).Conclusions: Diagnostic disease markers may be influenced by the renal function, and this must be taken intoaccount when interpreting test results. Decreased renal function could change the level of themarker above or belowdecision limits, leading to diagnostic misinterpretation.Clinical Trial Registration: ClinicalTrials.gov, Identifier NCT01092260, https://clinicaltrials.gov/ct2/show/NCT01092260?term=tondel&rank=2

IMPACT STATEMENTWe investigated the effect of measured GFR on 4 diagnostic markers in blood and urine and how this could

affect the decision limits. We examined 96 children with different stages of chronic kidney disease. GFR wasmeasured by iohexol clearance. Diagnostic disease markers were influenced by the renal function, and this mustbe taken intoaccountwhen interpreting test results.Decreased renal functioncouldchange the levelof themarkerabove or below decision limits leading to diagnostic misinterpretation. This study may also serve as a model toinvestigate other disease markers.

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Copyright 2017 by American Association for Clinical Chemistry.

Renal function is impaired in many different pa-tient groups, both as a consequence of a primarykidney disorder or secondary to treatment that in-fluences glomerular filtration or tubular metabo-lism and transport (e.g., chemotherapy) (1).Molecules in plasmamay be eliminated by glomer-ular filtration, tubular secretion, or both; hence,the total renal clearance will be of importancewhen evaluating different diagnostic markers inplasma or urine. Impaired renal function, prefera-bly expressed by glomerular filtration rate (GFR),8

may affect the level of these markers differentlyand therefore should be examined in eachsituation.Guanidinoacetate (GAA) and creatine (CRE) in

blood and urine are essential diagnostic markersfor creatine deficiency syndrome (2). Urinary GAA/creatinine (crn) and CRE/crn are measured asscreening in children with unexplained intellectualdisability and delayed language development. Inguanidinoacetate methyltransferase (GAMT) defi-ciency, the level of urinary GAA/crn ratio and bloodGAA is increased and CRE/GAA ratio decreased.The opposite is seen in arginine:glycine amidino-transferase (AGAT) deficiency. X-linked creatinetransporter (CRT) defect usually presents with in-creased CRE/crn ratio in urine, at least in males.Some studies have shown low serum concentra-tion of GAA in patients with chronic kidney disease(CKD), especially in the lowest estimating GFRrange (3, 4). To our knowledge, the consequencesof variations inmeasuredGFR (mGFR) on the levels

of GAA and CRE in blood and urine have previouslynot been investigated in children.Human epididymis protein 4 (HE4) is a tumor

marker for ovarian cancer, used either in combina-tion with CA125 or alone. HE4 is not amarker com-monly used in pediatric patients, but previousstudies have demonstrated higher HE4 levels inblood in patients with high crn levels (5) and inpatients with decreased renal function based ondifferent GFR-estimating formulas (6, 7); hence,this is an example to illustrate this mechanism. Asfar as we know, the relationship between mGFRand HE4 has previously not been examined.Neutrophil gelatinase–associated lipocalin (NGAL) is

a protein that may be used for early detection ofacute kidney injury, and the plasma and urinarylevels of NGAL are thought to increase beforeplasma crn increases (8–10). NGAL iswidely studiedin both adults and children after cardiac surgery, butalso in critically ill patients and after contrast agents(8, 11, 12). One study examined the relationship be-tween serumNGAL and ioversol clearance in pediat-ric patients with CKD stages 2–4 and showed thatserumNGALoutperformsestimatedGFR (eGFR) andcystatin C at lower levels of mGFR (13). To our knowl-edge, there are no publications that describe the re-lationship between mGFR (based on iohexolclearance) and NGAL in both urine and blood in chil-dren with CKD stages 1–5.The purpose of our study was to investigate the

influence of kidney function, represented by mea-sured GFR on the following 4 diagnostic disease

1Department ofMedical Biochemistry, OsloUniversityHospital, Oslo, Norway; 2Faculty ofMedicine, Institute of ClinicalMedicine, University ofOslo,Oslo, Norway; 3Department of Pediatrics, Haukeland University Hospital, Bergen, Norway; 4Department of Clinical Medicine, University of Bergen,Bergen, Norway; 5Department of Pediatrics, Oslo University Hospital, Oslo, Norway; 6Laboratory for Clinical Biochemistry, Haukeland UniversityHospital, Bergen, Norway; 7Department of Clinical Science, University of Bergen, Bergen, Norway.*Address correspondence to this author at: Department of Medical Biochemistry, Oslo University Hospital, PB 4950 Nydalen, 0424 Oslo,Norway. Fax 47-23071080; e-mail catsal@ous-hf.no.This study was previously presented as a poster at the SSIEM (Society for the Study of Inborn Errors of Metabolism) Congress in Rome, Italy,September 2016.DOI: 10.1373/jalm.2016.022145© 2017 American Association for Clinical Chemistry8Nonstandard abbreviations: GFR, glomerular filtration rate; GAA, guanidinoacetate; CRE, creatine; crn, creatinine; GAMT, guanidinoacetatemethyltransferase; AGAT, arginine:glycine amidinotransferase; CRT, creatine transporter; CKD, chronic kidney disease; mGFR, measured GFR;HE4, human epididymis protein 4; NGAL, neutrophil gelatinase–associated lipocalin; eGFR, estimated GFR; I, iohexol; IDMS, isotope dilution massspectrometry.

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markers in blood and urine, (a) GAA, (b) CRE, (c) HE4,and (d) NGAL, and how this could affect the decisionand reference limits in a cohort of children withCKD.

MATERIALS AND METHODS

Study population

Children (n = 96) at Haukeland University Hospi-tal and Oslo University Hospital with CKD were re-cruited (ClinicalTrial.gov Identifier NCT01092260).The patients included had stable kidney functionand a variety of kidney disorders, had no edema,and represented different GFR stages: 28, 27,23, and 18 patients in CKD stage 1, 2, 3, and 4–5,respectively.Themedian age was 9.2 years (range 3months to

17.5 years), and median mGFR was 65.9 mL ·min−1 · (1.73 m2)−1 (range 6.3–153) (Table 1). Exclu-sion criteria were allergy to iohexol and x-ray con-trast agents given less than 5 days before the survey.The studywas approvedby theRegional Ethics Com-mittee of Western Norway, and written informed

consent was obtained in all cases before inclusion inthe study. The procedures followed were in accor-dance with the Helsinki Declaration and GoodClinical Practice.

Methods

We obtained a serum sample (3 mL) at baselinebefore the injection of iohexol (Omnipaque®, 300mg iodine/mL, GE Healthcare; i.e., 647 mg iohexol/mL) for measurements of different biomarkersand to exclude the presence of potential sub-stances interfering with the measurements of io-hexol by HPLC. Omnipaque was administered viaan intravenous cannula in doses according to thepatients weight: <10 kg; 1 mL, 10–20 kg; 2 mL,20–30 kg; 3 mL, 30–40 kg; 4 mL, ≥40 kg; 5 mL. Thesyringe with iohexol was weighed before and afterinjection. The dose of iohexol (in mg) was calcu-lated from the difference in syringe weight multi-plied by the concentration of iohexol (647 mg/mL)and then divided by its density at room tempera-ture (1.345 g/mL). The cannula was flushed with 15mL physiologic saline. Serum samples (0.5 mL)were collected from the vein of the contralateralarm of the iohexol injection at 10, 30, 120, 180,210, 240, and 300 min after injection of iohexolgiven the 7-time point reference GFR. In 29 of the96 individuals, the second serum sample wasdrawn after 60 min instead of 30 min. Exact timingfor each blood sampling was recorded. Serumconcentrations of iohexol were analyzed by aHPLC system accredited by the Norwegian Accred-itation and complies with the requirements ofNS-EN ISO I5189, described elsewhere (14). HE4 inserum (S) and urine (U) was measured using theElecsys HE4 assay on Cobas e601 (Roche Diagnos-tics) and NGAL (S and U) using Roche Modular PNGAL immunoassay (reagents from Gentian®,Moss, Norway; www.gentian.no). Quantification ofCRE and GAA (S and U) was performed accordingto Bodamer et al. (15). In short, isotopically la-beled analogs of the analytes were added to aprotein precipitation solution. After centrifuga-

Table 1. Basic characteristics of the population.

Variable

Value given asmedian (range)or number

Total number, F/M 96 (41/55)Age, years 9.2 (0.3–17.5)Body weight, kg 28.3 (6.6–84.6)Height, cm 134 (59–177)mGFR, 7-point iohexol clearance,mL · min−1 · (1.73 m2)−1 65.9 (6.3–153)

S-GAA, μmol/L 1.37 (0.45–3.74)S-CRE, μmol/L 56.9 (10.8–260)U-GAA/crn, μmol/mmol 30.1 (2.2–218)U-CRE/crn, μmol/mmol 56.5 (6.5–2319)S-HE4, pmol/L 114 (36–1812)U-HE4/crn, pmol/mmol 937 (173–21212)U-HE4/S-HE4 37 (2.6–277)S-NGAL, μg/L 114 (40–570)U-NGAL/crn, μg/mmol 6.1 (0.05–2968)

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tion, reagents for derivitizing the analytes to bu-tyl esters was added to the samples, followed byLC-MS/MS.Creatinine (S and U) was measured with an enzy-

matic calorimetric method (reagents from RocheDiagnostics), isotope dilution mass spectrometry(IDMS) traceable. The laboratories participate in sev-eral external quality programs: LabQuality (crn, HE4),Equalis (iohexol clearance), and European ResearchNetwork for evaluation and improvement of screen-ing, Diagnosis, and treatment of Inherited Metabolicdisorders (ERNDIM) (CRE and GAA).

Calculation of GFR

The reference method for GFR in this study wasbased on the 7–time point (10, 30/60, 120, 180,

210, 240, 300 min) iohexol clearance and calcu-lated by themethod of Sapirstein (16, 17). GFR wasnormalized to body surface area calculated by themethod of Haycock et al. (18).

Statistical analysis

All variables were Box-Cox–transformed to re-move skewness. Optimal λ value for mGFR was 1.Optimal λ values for CRE, GAA, HE4, and NGAL inurine and serum are presented in Table 2 andwere based on amaximal correlation coefficient inthe central 95% variation interval of the z-scoreplot between the transformed variable (abscissa)and its corresponding z-score (ordinate). A z-scorewas calculated from the rank of sorted values (k)via its corresponding percentile, p = (k − 3/8)/(n +

Table 2. Estimated marker changes when mGFR decreases from 90 to 30 mL ·min−1 · (1.73 m2)−1 orage increases from 2 to 17 years.a

Diagnostic marker

Change of markervalue when mGFRdecreases from 90to 30 mL ·min−1 ·

(1.73 m2)−1

ExceedingGowans'criterion,yes/no

Change of markervalue when ageincreases from 2

to 17 years

ExceedingGowans'criterion,yes/no Optimal λ

Reference intervalfrom the literature

(reference)

S-GAA, μmol/L 1.8¡ 1.5 Yes 1.8¡ 3.0 Yes 0.363 <15 years: 0.35–1.8;>15 years: 1–3.5 (33)

S-CRE, μmol/L 109¡ 115 No 109¡ 46 Yes 0.175 <10 years: 17–109>10 years: 6–50 (33)

U-GAA/crn, μmol/mmol 200¡ 119 Yes 200¡ 180 No 0.303 Continuous referencelimits (21)

U-CRE/crn, μmol/mmol 2000¡ 2356 Yes 2000¡ 1043 Yes 0.303 Continuous referencelimits (21)

S-HE4, pmol/L 69.7¡ 198 Yes 69.7¡ 87.2 Yes −0.426 2–10 years: 27.3–69.710–19 years: 22.5–61.818–59 years: <60 (5, 34)

U-HE4/S-HE4 40¡ 7 Yes 40¡ 54 Yes 0.101 Not known

S-NGAL, μg/L 100¡ 300 Yes 100¡ 144 Yes −0.599 Cutoff >100–150 μg/L(12, 35, 36)

U-NGAL/crn, μg/mmol 26.2¡ 121 Yes 26.2¡ 3.3 Yes −0.014 Age-specific normativerange:0.2–6 years: 33.96–10 years: 26.210–14 years: 20.314–18 years: 15.7Cutoff >100–150 μg/L(12, 36, 37)

a Baseline values, to the left in columns 2 and 4, are age-adjusted upper-reference limits. The newmarker values, to the right, result from regressionanalyses. Optimal Box-Cox λ values, and whether a change exceeds Gowans' criterion or not, are also presented.

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1/4) in the standard normal distribution (19). Nosubstantial residual kurtosis was observed in theindividual z-score plots. An optimal λ value for thecovariate age was determined to give its distribu-tion a maximal SD/range ratio because this mini-mizes the effect of high-leverage points. Thebiological significance of a statistically significanteffect size was evaluated against Gowans' criterion(effect size <0.25 × total biological variation) (Table2) (20). Multiple forward and backward linear re-gression analysis was then performed betweenthe Box-Cox–transformed diagnostic marker (de-pendent variable) and the following covariates:age, sex, and mGFR. P value for inclusion and ex-clusion of variables was ≤0.05. Residuals were con-verted to z-scores and examined for normaldistribution. To evaluate the influence of renalfunction on the diagnostic markers, the differencein Box-Cox–transformed values for the differencein mGFR values was calculated and finally retrans-formed. The Pearson correlation coefficient wasused. Microsoft Excel (version 2010) and IBM SPSSStatistics Package (version 21) were used for statis-tical analysis.

RESULTS

Characteristics of the participants (n = 96) arepresented in Table 1. Table 2 shows the effect of achange (Δ) in mGFR and age on the level of a givenvalue of the diagnostic disease marker.GFR had a significant affect on S-GAA (P = 2 ×

10−4) and U-GAA/crn (P = 5 × 10−11), leading todecreased values in renal impairment (Fig. 1, A andC). In contrast, S-CRE did not correlate significantlywith GFR and only to a minor degree did the U-CRE/crn ratio (P = 0.54 and 0.01, respectively) (Fig.1, B andD). A change inGFR from, for example, 100to 10 mL · min−1 · (1.73 m2)−1, reduced the ex-pected S-GAA from 2 to 1.5 μmol/L and thus amodest change could alter the value well belowthe lower reference limit. The effect of mGFR onU-GAA/crn was even more pronounced, and the

U-GAA/crn level could easily be shifted below thediagnostic limit for GAMTdeficiency screening and,in addition, could result in false-positive results forAGAT deficiency (Table 2 and Fig. 2A). The sameeffect was not observed for screening of a CRTdeficiency (Fig. 2B). Age strongly influenced thelevel of S-GAA, S-CRE, and U-CRE/crn (P < 0.001),but not U-GAA/crn in this patient group (P = 0.50)[Table 2 and Supplemental Figs. 1–4 (see the DataSupplement that accompanies the online versionof this article at http://www.clinchem.org/content/vol2/issue2)]. Statistically, the U-GAA/crn ratioalso differed with respect to sex, with lower val-ues in males (P = 0.003) (see Fig. 3 in the onlineData Supplement). The difference was alsoshown to be biologically relevant (exceeding theGowans' criterion). Sex did not influence S-GAA(P = 0.42), S-CRE (P = 0.34), or U-CRE/crn (P =0.17) to the same extent (see Figs. 1, 2, and 4 inthe online Data Supplement).The level of GFR significantly affected the S-HE4

(P = 4 × 10−31) and U-HE4/S-HE4 ratio (P = 2 ×10−21), such that patients with low GFR showedincreased serum values and a decreasedU-HE4/S-HE4 ratio (Table 2, Fig. 3, A and B). Even amoderatedecrease in GFR could increase the level of theS-HE4 above the reference limits regardless of age.Age and sex did not influence the U-HE4/S-HE4ratio significantly (P = 0.15, just above Gowens' cri-terion), and there was no significant sex differencefor S-HE4 (P = 0.08), but a minor age effect (P =0.035) was observed (see Figs. 5 and 6 in the onlineData Supplement).NGAL in serum and urine was also affected by

GFR. We found a considerable increase in S-NGALlevels with decreasing kidney function (P = 2 ×10−19) and to a smaller extent an increase in U-NGAL/crn (P = 0.045) (Table 2, Figs. 4A and 4B). Ageinfluenced themarker in both serumandurine (P =0.007 and 0.012, respectively), but there was nosignificant sex difference (P = 0.11 and 0.10, re-spectively) (see Figs. 7 and 8 in the online DataSupplement).

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DISCUSSION

The present study showed that the majority ofthe diagnostic disease markers in blood and urineinvestigated in this cohort were influenced by thekidney function. U-GAA/crn level could easily beshifted below the diagnostic limit for screening ofGAMT deficiency. A small change in GFR could in-crease the level of the S-HE4 above the referencelimit regardless of age. A considerable increase inS-NGAL levels was observed with decreasing kid-ney function.

The ratio U-CRE/crn is strongly dependent onage andU-GAA/crn on sex, with higher urinemark-ers seen in females; however, Gowans' criterionsuggests that combined reference limits were ad-equate for both sexes in children (21). In our co-hort, the U-GAA/crn ratio was higher in femalesthan males, but sex did not influence the U-CRE/crn ratio. We found no age effect on theU-GAA/crnratio in this cohort. Studies on guanidine com-pounds in rats (22, 23) have demonstrated lowerserum GAA levels in renal injury. This result mightbe explained by reduced AGAT activity, the enzyme

Fig. 1. Scatter plots of S-GAA, S-CRE, U-GAA/crn, U-CRE/crn, and mGFR in children.(A), Correlation between untransformed S-GAA values and mGFR in children with CKD (n = 96); the gray area is the CIaround the regression line; correlation coefficient, r = 0.32. (B), Correlation between untransformed S-CRE values andmGFR in children with CKD (n = 96); the gray area is the CI around the regression line; correlation coefficient, r = −0.14.(C), Correlation between z-score U-GAA/crn and mGFR in children with CKD (n = 96) (quadratic line fit used in the figure);the gray area is the CI around the regression line; correlation coefficient, r = 0.55. (D), Correlation between z-scoreU-CRE/crn and mGFR in children with CKD (n = 96); the gray area is the CI around the regression line; correlationcoefficient, r = 0.15.

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that catalyzes conversion of glycine and arginine toornithine and GAA (24). The reduction of AGAT ac-tivity in the renal tubules has also been confirmedin rabbits with chronic renal failure (CRF) (25), andsome adult patients with CRF showed low serumand urine GAA (3, 4). This is in agreement with ourstudy. GFR did not influence creatine in the sameway, and one may speculate that this may be dueto a compensatory elevation of GAMT activity inother organs, e.g., pancreas and liver (26).Several biological and lifestyle factors affect HE4

concentration (27). One study found that high se-rumHE4 concentrationswere associatedwith highserum crn, but it was uncertain whether the reduc-tion in GFR with increasing age could explain theincreased HE4 in the elderly patients (5). Otherstudies have demonstrated a significant inverserelation between HE4 concentration and esti-mated GFR (6, 7), and therefore HE4 results shouldbe interpreted with caution in both patients withrenal disorders and in individuals with decliningGFR as a consequence of treatment with chemo-

therapy or in the elderly (1). Kappelmayer et al.(28) proposed an algorithm for predicting ovar-ian cancer in patients with kidney disorders us-ing serum HE4 and CA125 levels corrected foreGFR. If HE4 was >147 pmol/L and eGFR was >48mL · min−1 · (1.73 m2)−1, the chance of a tumor wasestimated to be 95%, but in postmenopausalwomenwith HE4 levels >210 pmol/L and eGFR <28mL · min−1 · (1.73 m2)−1, the chance of a tumor wasestimated at 30%. Our study demonstrated that amoderate change in mGFR may increase the se-rum HE4 levels above the reference limit. This re-sult is not a marker commonly used in pediatricpatients, but the purpose was to investigate theeffect of mGFR on the marker and illustrate howthis could affect the general interpretation of themarker.There are several studies on NGAL in different

patient populations (8, 12). Although NGAL con-centrations are known to increasewith a reductionin GFR, it is also a marker of tubular damage, andthus it is not only dependent on the GFR (29, 30).

Fig. 2. Percentile charts for U-GAA/crn and U-CRE/crn.(A), Percentile charts for U-GAA/crn [Morkrid et al. (21)]. Dotted lines indicate, frombottom to top, 2.5th and 97.5th percentile.Dashed line indicates the median. Square symbols represent the patients with mGFR <30 (n = 18), triangular symbols mGFR30–59 (n = 23), diamond symbols mGFR 60–89 (n = 27), and circular symbols mGFR ≥90 mL ·min−1 · (1.73 m2)−1 (n = 28). (B),Percentile charts for U-CRE/crn [Morkrid et al. (21)]. Dotted lines indicate, from bottom to top, 2.5th and 97.5th percentile.Dashed line indicates the median. Square symbols represent the patients with mGFR <30 (n = 18), triangular symbols mGFR30–59 (n = 23), diamond symbols mGFR 60–89 (n = 27), and circular symbols mGFR ≥90 mL ·min−1 · (1.73 m2)−1 (n = 28).

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Fig. 3. Scatter plots of S-HE4 and U-HE4/S-HE4 ratio and mGFR in children.(A), Correlation between untransformed S-HE4 values and mGFR in children with CKD (n = 96) (quadratic line fit used in thefigure); the gray area is the CI around the regression line; correlation coefficient, r = −0.57. (B), Correlation between untrans-formed U-HE4/S-HE4 ratio and mGFR in children with CKD (n = 96) (quadratic line fit used in the figure); the gray area is theCI around the regression line; correlation coefficient, r = 0.73.

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Fig. 4. Scatter plots of S-NGAL and U-NGAL/crn and mGFR in children.(A), Correlation between untransformed S-NGAL values andmGFR in children with CKD (n = 96) (quadratic line fit used in thefigure); the gray area is the CI around the regression line; correlation coefficient, r = −0.75. (B), Correlation between untrans-formed U-NGAL/crn values and mGFR in children with CKD (n = 96); the gray area is the CI around the regression line;correlation coefficient, r = −0.18.

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Serum NGAL investigated in children with CKDstages 2–4 has shown that with GFR <30mL · min−1 · (1.73m2)−1, serumNGAL correlated in-versely with mGFR (ioversol clearance) (r = 0.62).This study, where ioversol injection with 3 sam-pling times and an in-house ELISA was used to as-sess GFR, indicated that serum NGAL may be anadditional measure of kidney dysfunction in CKD(12). Another study showed that plasma NGAL in-creased progressively with GFR reduction in adultpatients with stage 2 CKD, generating a high num-ber of false-positive diagnoses of acute kidney in-jury in stable CKD patients. The impairment of GFRaffected plasma NGAL to a greater extent than uri-nary NGAL (30).Some authors have reported that urinary NGAL

level is a better biomarker for chronic renal dis-ease in children than serum NGAL, but in thesestudies, GFR was estimated and not measured ac-curately. Urinary NGAL level was increased in chil-dren with CKD with normal GFR when there wastubular dysfunction, as well as in patients with lowGFR (31). Our study demonstrated that GFR influ-enced the serum NGAL level significantly more

than urinary NGAL/crn ratio, and it could be diffi-cult to interpret the NGAL values for acute kidneyinjury diagnosis in patients with CKD.The strength of our study was use of a high-

quality, 7-point iohexol clearance procedure forassessment of measured GFR. This methodologyenabled use of values closer to the “true GFR” in-stead of less accurate GFR estimates based on crnand eGFR formulas. Multipoint iohexol clearance isconsidered to be in good agreement with the goldstandard inulin clearance (32).A limitation to our study is the number of pa-

tients (n = 96). On the other hand, few other stud-ies had multipoint mGFR data in such a largecohort of children with CKD. Also, we had only 1patient younger than 1 year, whereas 9 patientswere <2 years.In conclusion, our results indicate that several

diagnostic diseasemarkers are influenced by renalfunction and that this must be taken into accountwhen interpreting test results. Decreased renalfunction could change the level markers above orbelow decision limits and lead to incorrect diag-nostic interpretation.

Author Contributions: All authors confirmed they have contributed to the intellectual content of this paper and have met the following 4requirements: (a) significant contributions to the conceptionanddesign, acquisition of data, or analysis and interpretationof data; (b) draftingor revising the article for intellectual content; (c) final approval of the published article; and (d) agreement to be accountable for all aspects ofthearticle thus ensuring thatquestions related to theaccuracyor integrity of anypart of thearticle areappropriately investigatedand resolved.

Authors’ Disclosures or Potential Conflicts of Interest:Uponmanuscript submission, all authors completed the author disclosureform. Employment or Leadership: None declared. Consultant or Advisory Role: None declared. Stock Ownership: Nonedeclared.Honoraria:None declared. Research Funding: C.L. Salvador, Health Trust ofWesternNorway, TheNorwegian Societyof Nephrology, Haukeland, and Oslo University Hospital. The laboratory at Oslo University Hospital was supported with NGALreagents from Gentian, Moss, Norway. Expert Testimony: None declared. Patents: None declared.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review andinterpretation of data, or preparation or approval of manuscript.

Acknowledgments: The authors express gratitude to the pediatric study nurses Mai Britt Lynum, Hildur Grindheim, andRenathe Håpoldøy for technical assistance with the sample collection; to the laboratory engineers Kjersti Carstensen (iohexolanalyses), Linda Langdahl (HE4 analyses), Astrid Steiro, and Hildur Tannum (NGAL analyses); and the National Unit of LaboratoryDiagnostic for Inborn Errors of Metabolism (CRE and GAA analyses).

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