Examining commonly used equations for estimating the glomerular filtration rate (GFR) in a healthy cohort of children and adolescents

Examining commonly used equations for estimating the glomerular filtration rate (GFR) in a healthy cohort of children and adolescents | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Examining commonly used equations for estimating the glomerular filtration rate (GFR) in a healthy cohort of children and adolescents Luise Trenkmann, Niels Ziegelasch, Katalin Dittrich, Anja Willenberg, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6744772/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In this study, we compared established equations for estimating the glomerular filtration rate (GFR) in a cohort of healthy children and adolescents and aimed to evaluate the equations’ validity. Methods Blood, urine, and anthropometric data from 4,776 healthy participants (0.25–21 years) were analyzed. The glomerular filtration rate was estimated (eGFR) using the revised Schwartz Bedside (2009), the Cystatin C- and Serum Creatinine- based Schwartz equation, the Chronic Kidney Disease in Children (CKiD) equation, the Chronic Kidney Disease in Children (CKiD) equation, the 3 versions of the U25 (U25cys, U25scr, U25ave), the Grubb equation, and the Cystatin C-Creatinine-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). The resulting eGFR distributions were compared, and the percentages of eGFR values within the expected physiological eGFR range were calculated stratified by age and sex. Subsequently, age ranges with implausible distributions were identified. Results CKiD, Schwartz, and U25ave yielded the highest proportion of eGFRs within the expected range (75–135 mL/min/1.73m²) and showed consistent values across age groups without large jumps. Bland-Altman analysis indicated that the average U25 and CKiD had the lowest bias compared with Schwartz (-2.93 and − 4.79). U25cys and U25scr also exhibited low bias, while Grubb, Bedside, and CKD-EPI had larger biases, overestimating eGFR. Males showed higher eGFRs than females. Conclusion We found that the CKiD, Schwartz, and combined U25 resulted in the most plausible eGFR distributions for a healthy pediatric cohort. Estimates from simpler equations such as Bedside and Grubb were less plausible. eGFR Kidney Cystatin C Creatinine Children Pediatric Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The glomerular filtration rate (GFR) is a key marker of renal function and is particularly significant in pediatrics. Accurate GFR estimation is essential not only for staging kidney function but also for mitigating nephrotoxicity risks associated with medications that rely on renal elimination. In neonates and children, kidney function and GFR undergo significant developmental changes. During the fetal period, the placenta primarily handles the elimination of metabolic waste. Nephrogenesis, which involves growth and proliferation, is completed by the 36th week of gestation. Postnatally, GFR averages approximately 20 mL/min/1.73m² in full-term infants[ 1 ]. Within the first 2 weeks of life, GFR increases rapidly, reaching adult levels by around 2 years of age[ 2 , 3 ]. According to KDIGO guidelines, the lower limit for a normal GFR is set at 90 mL/min/1.73m² for both adults and children older than 2 years of age[ 4 ]. Pottel et al. proposed a slightly lower threshold of 75 mL/min/1.73m²[ 5 ] as the lower limit for children older than 2 years, with an upper normal limit of 135 mL/min/1.73m²[ 6 ]. Inulin is an excellent marker since the polysaccharide undergoes strict renal elimination without any tubular secretion or non-renal excretion[ 7 ]. However, this method is impractical for pediatric patients due to the challenges of precisely timed urine collection, often requiring urinary catheterization, which carries a risk of infection[ 8 , 9 ]. Alternative methods, including iohexol disappearance, MRI imaging, radiolabeled isotopes, and estimation equations, are more commonly used in clinical practice. Estimation equations have become the most widely employed tools for assessing GFR, as more accurate methods such as inulin- or iohexol-based methods are not feasible in children. The equations rely on endogenous markers such as serum creatinine (SCr), Cystatin C (CysC), or blood urea nitrogen, combined with biometric factors such as height, age, or sex. However, many of these equations have been derived from cohorts with renal impairments, which can introduce biases when applied to healthy children. Studies have suggested that some equations may underestimate GFR in this population[ 10 , 11 ]. The Schwartz Bedside equation, which has been validated in non-CKD populations[ 12 ], has also faced scrutiny regarding its application in healthy children[ 13 ]. The reliability of the U25 equation is also still debated[ 13 ]. For example, the 2024 KDIGO Guidelines continue to endorse equations using SCr as validated tools for estimating GFR in individuals aged 1–25 years[ 4 ]. In this study, we aimed to compare the performances of various estimation equations in a healthy pediatric cohort and evaluate how well these estimates align with the expected normal GFR range. Methods Study population LIFE Child is a pediatric cohort study conducted in the city of Leipzig, Germany. LIFE Child examines children’s normal development and health from pregnancy to early adulthood. It also studies the etiology of lifestyle diseases, such as obesity. The ages of the recruited subjects range from 24 weeks of gestation up to 16 years[ 14 ]. Our study sample is a subset of the LIFE Child Study, including all participants between 0.25 and 21 years with the necessary laboratory assessment (Table 1 ). Participants with signs of infections, fever, diabetes, or chronic renal conditions were excluded. Table 1 Description of age (years), height (SDS), weight (SDS), cystatin C (CysC) median, Serum Creatinine (SCr) median LIFE Child cohort sorted in age interval Characteristic 0–2 2–5 5–10 10–18 18+ female 1 male 1 female 1 male 1 female 1 male 1 female 1 male 1 female 1 male 1 Age (years) 0.72 (0.51) 0.74 (0.53) 3.50 (0.90) 3.50 (0.90) 7.52 (1.45) 7.57 (1.46) 13.55 (2.17) 13.38 (2.12) 18.85 (0.68) 18.88 (0.67) Height group >10th Percentile 142 (7.7%) 121 (6.0%) 153 (13%) 126 (9.7%) 214 (9.6%) 190 (7.7%) 217 (7.0%) 216 (6.5%) 17 (8.9%) 20 (13%) 10th-90th Percentile 1,484 (81%) 1,614 (80%) 956 (81%) 1,081 (84%) 1,761 (79%) 1,987 (80%) 2,469 (79%) 2,604 (78%) 155 (82%) 121 (79%) >90th Percentile 210 (11%) 282 (14%) 68 (5.8%) 87 (6.7%) 245 (11%) 306 (12%) 424 (14%) 517 (15%) 18 (9.5%) 13 (8.4%) Height SDS 0.13 (0.99) 0.21 (0.98) -0.23 (0.93) -0.13 (0.94) 0.04 (1.01) 0.13 (0.98) 0.16 (0.99) 0.28 (1.00) -0.08 (0.98) -0.12 (1.08) Weight group UW 160 (8.7%) 214 (11%) 38 (3.2%) 70 (5.4%) 160 (7.2%) 200 (8.1%) 249 (8.0%) 300 (9.0%) 22 (12%) 18 (12%) NW 1,504 (82%) 1,623 (80%) 1,065 (90%) 1,127 (87%) 1,821 (82%) 2,037 (82%) 2,215 (71%) 2,399 (72%) 125 (66%) 112 (73%) OW 109 (5.9%) 123 (6.1%) 56 (4.8%) 73 (5.6%) 96 (4.3%) 100 (4.0%) 257 (8.3%) 274 (8.2%) 16 (8.4%) 17 (11%) OB 63 (3.4%) 57 (2.8%) 18 (1.5%) 24 (1.9%) 143 (6.4%) 146 (5.9%) 389 (13%) 364 (11%) 27 (14%) 7 (4.5%) BMI SDS 0.01 (0.98) 0.01 (1.03) 0.09 (0.82) 0.12 (0.84) -0.01 (1.04) -0.06 (1.00) 0.31 (1.24) 0.20 (1.18) 0.38 (1.41) 0.11 (1.25) Serum Creatinine (mg/dl) 0.25 (0.05) 0.25 (0.05) 0.32 (0.07) 0.33 (0.06) 0.48 (0.08) 0.48 (0.08) 0.65 (0.11) 0.69 (0.15) 0.76 (0.10) 0.94 (0.12) Cystatin C (mg/l) 0.95 (0.13) 0.95 (0.14) 0.84 (0.10) 0.86 (0.11) 0.88 (0.10) 0.87 (0.10) 0.87 (0.12) 0.94 (0.12) 0.81 (0.10) 0.90 (0.09) 1 Mean (SD); n (%); SDS (Standard deviation score), UW - underweight, NW - normal weight, OW - overweight, OB - obese There may be multiple observations of participants that belong to different age intervals. BMI groups: underweight 90th percentile, obese > 97th percentile, n number of observations, SD standard deviation, BMI body mass index Measures Height and weight were determined following standardized procedures by trained study personnel. BMI was calculated and subsequently transformed into age- and sex-adjusted standard deviation scores following the guidelines of the German Obesity Society and the German Society of Pediatrics and Adolescent Medicine (DGKJ). Using the same guidelines, weight groups were defined as underweight (BMI-SDS < -1.28), normal weight (-1.28 < BMI-SDS < 1.28), overweight (1.28 < BMI-SDS 1.881)[ 15 ]. Height groups were defined as < 10th Percentile (Height-SDS < -1.28), 10th Percentile to 90th Percentile (-1.28 < Height-SDS 90th Percentile (Height-SDS > 1.881)[ 15 ]. We categorized values as inside/outside the physiological range of eGFRs as 75–135 ml/min/1.73m 2 [ 5 ](used for scientific purposes) for all age groups except those under 2 years old because they are assumed to have a deviating physiological range. In addition, the analysis was repeated using a reference range of 90–135 ml/min/1.73m 2 because of its prevalent use in clinics [ 4 ]. Age groups were defined to account for increasing eGFR in infancy[ 2 , 3 ] and potential changes during puberty[ 16 ]: infants ( 5–10 years), adolescents (> 10–18), and adults (> 18 years). The venous blood samples, using serum monovettes (Sarstedt AG&Co, Nümbrecht, Germany), were analyzed by the Institute for Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM) at the University Hospital Leipzig on an automated laboratory analyzer Cobas 8000 (Roche Diagnostics, Mannheim Germany) according to the manufacturer’s protocol. Serum creatinine (SCr) was measured with an enzymatic assay (Roche Diagnostics). Blood Urea Nitrogen (BUN) was performed with a kinetic test using urease and glutamate dehydrogenase (GLDH) (Roche Diagnostics). Cystatin C (CysC) was determined using the turbidimetric immunoassay (PETIA) Tina-quant® Cystatin C (Roche Diagnostics, measurement range: 0.4–8.0 mg/l), standardized against a Roche in-house reference preparation of recombinant human CysC. Since 2015, the second-generation Tina-quant® Cystatin C-assay(Roche Diagnostics, primary measurement range: 0.4–6.8 mg/l) is now standardized against the international reference material ERM-DA471/IFCC[ 17 ]. Both methods showed good conformity and no relevant bias[ 16 ]. Statistics eGFR was calculated by applying the different equations. We calculated eGFRs within the 90–135 ml/min/1.73m 2 range as well as within the 75–135 ml/min/1.73m 2 range for all age groups. Associations between each of the eGFR (Schwartz, CKID, U25ave) measures as outcomes and sex, weight group, or height group as predictors were estimated using hierarchical linear models, and we adjusted for multiple measurements per child by adding the subject to the model as a random effect. Furthermore, we used the Bland-Altman analyses to compare the equations, using the Schwartz (2009) equation as a reference. All statistical analyses and visualizations were implemented with the R software (R 4.3.2, R Core Team). Equations We included the most commonly used eGFR equations (Table 2 ). The revised Schwartz Bedside (2009) is one of the simpler equations, including only the variables height and serum creatinine[ 18 ]. We also included the full CysC- and SCr-based Schwartz equation, as well as the CKiD equation[ 18 , 19 ]. The three variations of U25 uses CysC (U25cys) or SCr (U25scr), and the averaged value (U25ave)[ 20 ]. Table 2 Overview of used eGFR equations Equation Age Sex Height Serum Creatinine BUN Cystatin C Age range Schwartz Bedside (2009)[ 18 ] x x 1–18 Schwartz (2009)[ 18 ] x x x x x 1–19 CKiD (2012)[ 19 ] x x x x x 1–18 Grubb (2014)[ 21 ] x x x > 1 U25cys (2021)[ 20 ] x x x 1–25 U25scr (2021)[ 20 ] x x x x 1–25 U25ave (2021)[ 20 ] x x x x 1–25 CKD-EPI (2021)[ 22 ] x x x x > 18 This table shows which laboratory or anthropometric measures are used to calculate the GFR for each equation, respectively, as well as the intended age range To contrast the SCr-based Schwartz Bedside calculation, we added the Grubb equation, which is also a simpler calculation based only on CysC and age[ 21 ]. Since our cohort also included adolescents and a few young adults, we included the CysC-SCr-based CKD-EPI[ 22 ]. The complete equations are listed in the Online Resource 1. Results For CKID, Schwartz, U25ave, and U25scr, the interval defined by mean ± 1 SD eGFR fell entirely within the expected range of 75 to 135 ml/min/1.73m 2 for all age groups. For U25cys, the 0-to-2-year-old children showed lower values, with the first quartile lower than 75ml/min/1.73m 2 . On the other hand, both Grubb and Bedside showed higher values for 0-to-5-year-old children, with the third quartile above the upper reference limit of 135 ml/min/1.73m 2 . Even higher values were reached when we used CKD-EPI with the median well above and the first quartile near the upper reference limit for 0-to-5-year-old children and the median near the upper reference limit for 5-to-10-year-old children (Table 3 ). Table 3 Distribution of eGFR for each equation grouped by age (in years) This table shows the mean and standard deviation (SD) of the eGFRs for each equation. For visual clarity, eGFR values are color-coded as follows: green (90–120 mL/min/1.73m², within the normal range), yellow (±15 mL/min/1.73m² deviation from the expected range), and red (≥15 mL/min/1.73m² deviation), indicating normal, mildly altered, and severely altered values, respectively. There may be multiple observations of participants that belong to different age intervals. For CKiD, Schwartz, and U25ave, more than 97% of the eGFR fell within the reference range across all age groups (Table 4 ). Besides, they showed similar eGFR values without large jumps between adjacent age groups (Fig. 1). Table 4 Percentage of estimated GFR between 75-135mL/min/1.73m² (N) grouped by age (years) The table shows the percentage of results within the normal eGFR-range, the number of measurements is stated in parentheses. For visual clarity, percentage values are color-coded as follows: green (≥95%), yellow (≥90%), and red ( 86%). The remaining 3 equations showed implausible age jumps as well as very low percentages within the reference range for several age groups (Table 4 ). The results when the clinical cutoffs were applied are shown in Online Resource 2. Bland-Altman Analysis We found the eGFR estimated by the U25ave to be closest to those estimated by the Schwarz equation as a reference (bias= -2.92, proportional bias= -0.02). The proportional bias was also low for CKiD (-0.12), U25cys (0.18), and U25scr (0.26), which also showed low biases (-4.79, -6.20, 0.35). By contrast, Grubb, Bedside, and CKDEPI had larger (positive) biases (13.1, 9.2, 27.5) and proportional biases, indicating considerably higher eGFR than Schwartz, especially for the higher eGFRs (Fig. 2 ). Associations with sex, weight, and height group For the three investigated equations (Schwartz, CKID, U25ave), eGFRs were higher in males than females across the entire study population as well as within all weight groups (Table 3 , all p < 0.001). For the Schwartz equation and the CKiD, we found higher eGFRs for children with overweight and obesity than for normal weight children. For Schwartz, these differences reached statistical significance only for girls with obesity. For CKiD, the comparisons yielded similar results but showed only weak effects that were not statistically significant. No consistent patterns were found for U25ave (Fig. 3 ). For Schwartz and CKiD, we found higher eGFRs in taller children for girls as well as for boys. For the U25ave, estimated GRFs were similar across the height groups (Fig. 4 ). Discussion Chronic kidney failure is a global health problem and a leading cause of morbidity and mortality for millions of people. Chronic kidney insufficiency often progresses silently, and by the time individuals develop significant clinical symptoms, kidney function has already been severely impaired. To implement a preventive approach, methods for assessing kidney function are relevant for all age groups[ 4 ]. Accurate eGFR estimation is crucial for assessing kidney health, adjusting medication dosages, and mitigating nephrotoxic risks associated with various pharmaceuticals. The reliable estimation of GFR is particularly important in children, as absolute values of kidney retention parameters such as serum creatinine (SCr) or cystatin C (CysC) undergo changes during phases of growth and puberty, making it difficult to assess overall kidney function accurately. It is undisputed that there are differences in kidney size, nephron count, and, ultimately, overall kidney function to the disadvantage of the female sex. However, this difference seems to be compensated by women’s smaller body size and muscle mass. Almost all methods for estimating GFR take sex into account and use "modifiers" when reporting eGFR. Sex-based differences in eGFR estimation remain a topic of debate, particularly in prepubescent and early adolescent populations. Variability in muscle mass, body composition, and hormonal influences on endogenous markers contribute to sex-based discrepancies in eGFR calculations[ 23 – 25 ]. Most equations tend to estimate higher GFR values in males due to these physiological differences[ 26 ]. For example, markers such as Cystatin C, while less influenced by muscle mass, still show associations with age, sex, and pubertal stage[ 16 ]. In our cohort, sex differences were evident across all equations incorporating sex as a factor, with females generally showing lower eGFR values. These findings are consistent with studies in adult populations, where women are often reported to have lower baseline kidney function [ 27 , 28 ]. Considering the KDIGO guideline threshold over 90 ml/min/1.73m² for normal kidney function, a greater proportion of female participants in our cohort fell below this limit. However, we would like to emphasize that an eGFR between 75–90 ml/min/1.73 m², in the absence of albuminuria or hematuria, still represents a low risk for the development of end-stage kidney failure[ 4 ]. Therapeutic interventions such as adjustments of medication dosages or avoiding contrast agents are typically considered only for eGFR values below 60 ml/min/1.73 m² [ 29 ]. Thus, the methods that are commonly used to estimate GFR can be applied for both sexes in these decisions. The ongoing pandemic of childhood obesity is expected to contribute to an increased risk of chronic kidney disease (CKD) in later life, as early-life adiposity has been linked to long-term CKD development[ 30 , 31 ]. Van Dam et al. found correlations between elevated BMI and below-average values for creatinine-based eGFRs, depending on which equation they used[ 32 ]. In our cohort, we found no evidence of impaired GFR at higher weight or overweight. This result may have occurred because the overweight patients are still in the "hyperfiltration" phase, with eGFR values above 135 ml/min/1.73m². This constellation may lead to nephrosclerosis and a significant loss of eGFR in the medium term. Adolescents and young adults represent a unique subgroup that requires special consideration. The transition from pediatric to adult care often involves a shift in the equations used for eGFR estimation, which can lead to variability in reported kidney function[ 33 ]. This transition period highlights the need for standardized approaches that can account for the physiological and developmental changes that occur during this stage of life. The CKD-EPI equation is designed for individuals 18 years of age and older; prior research has shown its limited reliability not only in children but also in young adults[ 34 – 36 ]. Our results align with these observations, showing substantial differences in eGFR estimates across age groups and unusually high mean eGFR values in younger participants. However, the CKD-EPI equation provided more results within the expected eGFR range in participants over the age of 10, suggesting some applicability in older pediatric populations. More importantly, further investigation is needed to determine whether, when, and to which equations the transition should be applied to accurately reflect an individual’s GFR. In our study, we compared eGFR values from a large, healthy cohort of children and adolescents using several estimation equations. Since we did not have measured filtration rates for direct comparison, our research should not be viewed as a definitive statement about their accuracy. While our research cannot pinpoint one formula for estimating kidney function as the most accurate, we were able to show that some formulas led to implausible results for a pediatric non-CKD cohort. Notably, we observed that simpler equations, such as Schwartz Bedside and Grubb, are more prone to producing extreme outliers and implausible discontinuities between age groups. This tendency suggests that these formulas may be less suitable for estimating kidney function in healthy paediatric, non-CKD populations. However, we consider any pediatric equation to be suitable for assessing changes in overall kidney function in individual patients. These changes can reflect both therapeutic outcomes and the risk of progression to end-stage renal disease. Among the three versions of the U25 equation, the combined formula, which averages estimates from single-marker equations (Cystatin C and serum creatinine), demonstrated slightly better consistency compared with either marker individually. These findings align with the performance of other equations, such as Schwartz and CKiD, which also incorporate both Cystatin C and serum creatinine. This alignment suggests that equations leveraging multiple endogenous markers provide more plausible and reliable eGFR results for healthy children and adolescents. In summary, our results indicate that equations incorporating multiple endogenous markers—particularly those combining Cystatin C and serum creatinine—yield more probable eGFR estimates in healthy pediatric populations. Declarations Acknowledgments The authors would like to express their sincere gratitude to the participants and their guardians for their contributions to the LIFE Child study. We would also like to thank the LIFE Child study team. The authors also thank Jane Zagorski for her excellent language editing and editorial support (as always)! Funding The Leipzig Research Center for Civilization Diseases at the University of Leipzig is funded by the European Union through the European Regional Development Fund (ERDF) and the Free State of Saxony, as part of the excellence initiative of the Saxonian Ministry of Science and Arts (SMWK). NCT Trial Number: 02550236 (NIH) Ethics declarations Ethics approval All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards and was been approved by the Ethics Committee of the University of Leipzig (Reg. No. 477-19-09102020). Consent to participate Informed consent was obtained from all individual participants, or their legal guardians, included in the study. Conflict of Interest The authors have no relevant financial or non-financial interests to disclose. References Sulemanji M, Vakili K (2013) Neonatal renal physiology. Semin Pediatr Surg 22:195–198. https://doi.org/10.1053/j.sempedsurg.2013.10.008 Rubin MI, Bruck E, Rapoport M, et al (1949) MATURATION OF RENAL FUNCTION IN CHILDHOOD: CLEARANCE STUDIES12. J Clin Invest 28:1144–1162. https://doi.org/10.1172/JCI102149 Filler G, Bhayana V, Schott C, Díaz‐González De Ferris ME (2021) How should we assess renal function in neonates and infants? Acta Paediatr 110:773–780. https://doi.org/10.1111/apa.15557 Stevens PE, Ahmed SB, Carrero JJ, et al (2024) KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int 105:S117–S314. https://doi.org/10.1016/j.kint.2023.10.018 Pottel H, Hoste L, Delanaye P (2015) Abnormal glomerular filtration rate in children, adolescents and young adults starts below 75 mL/min/1.73 m2. Pediatr Nephrol 30:821–828. https://doi.org/10.1007/s00467-014-3002-5 Pottel H, Adebayo OC, Nkoy AB, Delanaye P (2023) Glomerular hyperfiltration: part 1 — defining the threshold — is the sky the limit? Pediatr Nephrol 38:2523–2527. https://doi.org/10.1007/s00467-022-05827-4 Roberfroid MB (2005) Introducing inulin-type fructans. Br J Nutr 93:S13–S25. https://doi.org/10.1079/BJN20041350 Filler G, Yasin A, Medeiros M (2014) Methods of assessing renal function. Pediatr Nephrol 29:183–192. https://doi.org/10.1007/s00467-013-2426-7 Soveri I, Berg UB, Björk J, et al (2014) Measuring GFR: A Systematic Review. Am J Kidney Dis 64:411–424. https://doi.org/10.1053/j.ajkd.2014.04.010 Björk J, Nyman U, Larsson A, et al (2021) Estimation of the glomerular filtration rate in children and young adults by means of the CKD-EPI equation with age-adjusted creatinine values. Kidney Int 99:940–947. https://doi.org/10.1016/j.kint.2020.10.017 Schwaderer AL, Maier P, Greenbaum LA, et al (2023) Application of GFR estimating equations to children with normal, near-normal, or discordant GFR. Pediatr Nephrol 38:4051–4059. https://doi.org/10.1007/s00467-023-06045-2 Staples A, LeBlond R, Watkins S, et al (2010) Validation of the revised Schwartz estimating equation in a predominantly non-CKD population. Pediatr Nephrol 25:2321–2326. https://doi.org/10.1007/s00467-010-1598-7 Pottel H, Björk J, Delanaye P, Nyman U (2022) Evaluation of the creatinine-based chronic kidney disease in children (under 25 years) equation in healthy children and adolescents. Pediatr Nephrol 37:2213–2216. https://doi.org/10.1007/s00467-022-05429-0 Poulain T, Baber R, et al (2017) The LIFE Child study: a population-based perinatal and pediatric cohort in Germany. Eur J Epidemiol 32:145–158. https://doi.org/10.1007/s10654-016-0216-9 Moß A, Kunze D, Wabitsch M (2011) Evidenzbasierte Leitlinie der Arbeitsgemeinschaft Adipositas im Kindes- und Jugendalter zur Therapie der Adipositas im Kindes- und Jugendalter. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 54:584–590. https://doi.org/10.1007/s00103-011-1269-2 Ziegelasch N, Vogel M, Müller E, et al (2019) Cystatin C serum levels in healthy children are related to age, gender, and pubertal stage. Pediatr Nephrol 34:449–457. https://doi.org/10.1007/s00467-018-4087-z Quante M, Hesse M, Döhnert M, et al (2012) The LIFE child study: a life course approach to disease and health. BMC Public Health 12:1021. https://doi.org/10.1186/1471-2458-12-1021 Schwartz GJ, Mun[Combining Tilde]oz A, Schneider MF, et al (2009) New Equations to Estimate GFR in Children with CKD. J Am Soc Nephrol 20:629–637. https://doi.org/10.1681/ASN.2008030287 Schwartz GJ, Schneider MF, Maier PS, et al (2012) Improved equations estimating GFR in children with chronic kidney disease using an immunonephelometric determination of cystatin C. Kidney Int 82:445–453. https://doi.org/10.1038/ki.2012.169 Pierce CB, Muñoz A, Ng DK, et al (2021) Age- and sex-dependent clinical equations to estimate glomerular filtration rates in children and young adults with chronic kidney disease. Kidney Int 99:948–956. https://doi.org/10.1016/j.kint.2020.10.047 Grubb A, Horio M, Hansson L-O, et al (2014) Generation of a New Cystatin C–Based Estimating Equation for Glomerular Filtration Rate by Use of 7 Assays Standardized to the International Calibrator. Clin Chem 60:974–986. https://doi.org/10.1373/clinchem.2013.220707 Inker LA, Eneanya ND, Coresh J, et al (2021) New Creatinine- and Cystatin C–Based Equations to Estimate GFR without Race. N Engl J Med 385:1737–1749. https://doi.org/10.1056/NEJMoa2102953 Neugarten J, Acharya A, Silbiger SR (2000) Effect of Gender on the Progression of Nondiabetic Renal Disease: A Meta-Analysis. J Am Soc Nephrol 11:319–329. https://doi.org/10.1681/ASN.V112319 Jafar TH (2003) The rate of progression of renal disease may not be slower in women compared with men: a patient-level meta-analysis. Nephrol Dial Transplant 18:2047–2053. https://doi.org/10.1093/ndt/gfg317 Nitsch D, Grams M, Sang Y, et al (2013) Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: a meta-analysis. BMJ 346:f324–f324. https://doi.org/10.1136/bmj.f324 Pottel H, Hoste L, Dubourg L, et al (2016) An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant 31:798–806. https://doi.org/10.1093/ndt/gfv454 Melsom T, Norvik JV, Enoksen IT, et al (2022) Sex Differences in Age-Related Loss of Kidney Function. J Am Soc Nephrol 33:1891–1902. https://doi.org/10.1681/ASN.2022030323 Wetzels JFM, Kiemeney LALM, Swinkels DW, et al (2007) Age- and gender-specific reference values of estimated GFR in Caucasians: The Nijmegen Biomedical Study. Kidney Int 72:632–637. https://doi.org/10.1038/sj.ki.5002374 Stefani M, Singer RF, Roberts DM (2019) How to adjust drug doses in chronic kidney disease. Aust Prescr 42:163. https://doi.org/10.18773/austprescr.2019.054 Jadresic L, Silverwood RJ, Kinra S, Nitsch D (2019) Can childhood obesity influence later chronic kidney disease? Pediatr Nephrol 34:2457–2477. https://doi.org/10.1007/s00467-018-4108-y Stern-Zimmer M, Calderon-Margalit R, Skorecki K, Vivante A (2021) Childhood risk factors for adulthood chronic kidney disease. Pediatr Nephrol 36:1387–1396. https://doi.org/10.1007/s00467-020-04611-6 Van Dam MJCM, Pottel H, Vreugdenhil ACE (2023) Relation between obesity-related comorbidities and kidney function estimation in children. Pediatr Nephrol 38:1867–1876. https://doi.org/10.1007/s00467-022-05810-z Webster-Clark M, Jaeger B, Zhong Y, et al (2018) Low agreement between modified-Schwartz and CKD-EPI eGFR in young adults: a retrospective longitudinal cohort study. BMC Nephrol 19:194. https://doi.org/10.1186/s12882-018-0995-1 Selistre L, Rabilloud M, Cochat P, et al (2016) Comparison of the Schwartz and CKD-EPI Equations for Estimating Glomerular Filtration Rate in Children, Adolescents, and Adults: A Retrospective Cross-Sectional Study. PLOS Med 13:e1001979. https://doi.org/10.1371/journal.pmed.1001979 Björk J, Nyman U, Courbebaisse M, et al (2020) Prospects for improved glomerular filtration rate estimation based on creatinine—results from a transnational multicentre study. Clin Kidney J 13:674–683. https://doi.org/10.1093/ckj/sfaa039 Nyman U, Grubb A, Larsson A, et al (2014) The revised Lund-Malmö GFR estimating equation outperforms MDRD and CKD-EPI across GFR, age and BMI intervals in a large Swedish population. Clin Chem Lab Med CCLM 52:. https://doi.org/10.1515/cclm-2013-0741 Supplementary Files ESM1.pdf PedNephGraphicalAbstractTrenkmannetal.pptx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6744772","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463054523,"identity":"bb95c6fb-755b-44c0-8ee3-ba46345b55c2","order_by":0,"name":"Luise Trenkmann","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBACPgbGBgaGChCTjUgtbGAtZwxI0gIEjG0kaZFIbv7wcd6ffH7pttQNP3fcYeCXSCCkJbFNcuY2A8uZc44du9l75hmD5AwitDDzbjMwMLiR3naDt+0wg8ENwlqaP/+dY2BgD9Ry8y9Qiz0RWhqkGRuAtkikHbsNtoWgX3getkn2HDM2kLiRlnZbtu0wj8SZB/i18LOnP/7wo0bOgH9GmtnNt22H5fjbCdiCAXhIVD8KRsEoGAWjABsAAJSBQf8WaQYJAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-8680-1358","institution":"Universitat Leipzig","correspondingAuthor":true,"prefix":"","firstName":"Luise","middleName":"","lastName":"Trenkmann","suffix":""},{"id":463054524,"identity":"0a82b962-b25e-436a-bd46-00702a254217","order_by":1,"name":"Niels Ziegelasch","email":"","orcid":"","institution":"Universitat Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Niels","middleName":"","lastName":"Ziegelasch","suffix":""},{"id":463054525,"identity":"f1c8dfb4-9fce-4ac8-8a78-c50e3d21cce0","order_by":2,"name":"Katalin Dittrich","email":"","orcid":"","institution":"Universitätsklinikum Leipzig: Universitatsklinikum Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Katalin","middleName":"","lastName":"Dittrich","suffix":""},{"id":463054526,"identity":"58c4f550-7786-4579-b152-d6619534490b","order_by":3,"name":"Anja Willenberg","email":"","orcid":"","institution":"Universitat Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"","lastName":"Willenberg","suffix":""},{"id":463054527,"identity":"256706c3-b71e-4329-82e3-90fd7b028e68","order_by":4,"name":"Wieland Kiess","email":"","orcid":"","institution":"Universitat Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Wieland","middleName":"","lastName":"Kiess","suffix":""},{"id":463054528,"identity":"d772c934-198f-4ffb-890f-fb91d40326f1","order_by":5,"name":"Mandy Vogel","email":"","orcid":"","institution":"Universitat Leipzig","correspondingAuthor":false,"prefix":"","firstName":"Mandy","middleName":"","lastName":"Vogel","suffix":""}],"badges":[],"createdAt":"2025-05-25 16:17:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6744772/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6744772/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83893596,"identity":"c14dff9d-cf6c-4929-bcec-b495e89d365f","added_by":"auto","created_at":"2025-06-04 08:27:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130884,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplot of eGFRs grouped by age and equation\u003c/p\u003e\n\u003cp\u003eThe dotted line represents the upper and lower cutoffs for the GFR in healthy pediatric older than 2 years of age (75-135 ml/min/1.73m²)\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/72a9ab0e535fe93818753728.png"},{"id":83893616,"identity":"3a711ebc-75eb-4dbb-99f3-edbbbe4d76e9","added_by":"auto","created_at":"2025-06-04 08:28:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":415039,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman-Plots plotting the difference between the equations and Schwartz against their averages\u003cbr\u003e\nThe plots show varying consistency between the different eGFR and the Schwartz equation as reference, with the lowest bias (black solid line) for the U25ave and the U25scr. The U25ave also reached the slope nearest to 0, and therefore, reached the most similar eGFR (Schwartz)\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/3a5ba9207fbb6edc03179845.png"},{"id":83893597,"identity":"b3fb62b3-af35-447b-a092-a166feae51a2","added_by":"auto","created_at":"2025-06-04 08:27:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58441,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the eGFR for weight, grouped by sex\u003c/p\u003e\n\u003cp\u003eThe dotted line represents the 75-135 ml/min/1.73m² range\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/31429f5137efc9e5d929fa7b.png"},{"id":83893600,"identity":"f8350559-22d2-4559-a6b9-cb9df686e4fa","added_by":"auto","created_at":"2025-06-04 08:27:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":111682,"visible":true,"origin":"","legend":"\u003cp\u003eBoxplots showing the eGFR for height, grouped by sex\u003c/p\u003e\n\u003cp\u003eThe dotted line represents the 75-135 ml/min/1.73m² range\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/8cb9646e31fc669279bd63b3.png"},{"id":84892915,"identity":"e595340a-610e-4bb8-9e36-00bce28effd6","added_by":"auto","created_at":"2025-06-18 13:12:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1438941,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/bcdaefab-6f5f-4ada-9607-d3a57b679844.pdf"},{"id":83895535,"identity":"0143f8c1-b1df-4981-8a3e-e8b9e8a857e0","added_by":"auto","created_at":"2025-06-04 08:43:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":221778,"visible":true,"origin":"","legend":"","description":"","filename":"ESM1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/5c7c72cfab31dbf1dc6d3b56.pdf"},{"id":83894015,"identity":"6d9f6056-2520-4c32-a2af-0d65b41212c9","added_by":"auto","created_at":"2025-06-04 08:35:59","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":157412,"visible":true,"origin":"","legend":"","description":"","filename":"PedNephGraphicalAbstractTrenkmannetal.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6744772/v1/153150f3cdb5f1e10e948317.pptx"}],"financialInterests":"","formattedTitle":"Examining commonly used equations for estimating the glomerular filtration rate (GFR) in a healthy cohort of children and adolescents","fulltext":[{"header":"Background","content":"\u003cp\u003eThe glomerular filtration rate (GFR) is a key marker of renal function and is particularly significant in pediatrics. Accurate GFR estimation is essential not only for staging kidney function but also for mitigating nephrotoxicity risks associated with medications that rely on renal elimination.\u003c/p\u003e \u003cp\u003eIn neonates and children, kidney function and GFR undergo significant developmental changes. During the fetal period, the placenta primarily handles the elimination of metabolic waste. Nephrogenesis, which involves growth and proliferation, is completed by the 36th week of gestation. Postnatally, GFR averages approximately 20 mL/min/1.73m\u0026sup2; in full-term infants[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Within the first 2 weeks of life, GFR increases rapidly, reaching adult levels by around 2 years of age[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to KDIGO guidelines, the lower limit for a normal GFR is set at 90 mL/min/1.73m\u0026sup2; for both adults and children older than 2 years of age[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Pottel et al. proposed a slightly lower threshold of 75 mL/min/1.73m\u0026sup2;[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] as the lower limit for children older than 2 years, with an upper normal limit of 135 mL/min/1.73m\u0026sup2;[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInulin is an excellent marker since the polysaccharide undergoes strict renal elimination without any tubular secretion or non-renal excretion[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, this method is impractical for pediatric patients due to the challenges of precisely timed urine collection, often requiring urinary catheterization, which carries a risk of infection[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Alternative methods, including iohexol disappearance, MRI imaging, radiolabeled isotopes, and estimation equations, are more commonly used in clinical practice.\u003c/p\u003e \u003cp\u003eEstimation equations have become the most widely employed tools for assessing GFR, as more accurate methods such as inulin- or iohexol-based methods are not feasible in children. The equations rely on endogenous markers such as serum creatinine (SCr), Cystatin C (CysC), or blood urea nitrogen, combined with biometric factors such as height, age, or sex. However, many of these equations have been derived from cohorts with renal impairments, which can introduce biases when applied to healthy children. Studies have suggested that some equations may underestimate GFR in this population[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The Schwartz Bedside equation, which has been validated in non-CKD populations[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], has also faced scrutiny regarding its application in healthy children[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The reliability of the U25 equation is also still debated[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For example, the 2024 KDIGO Guidelines continue to endorse equations using SCr as validated tools for estimating GFR in individuals aged 1\u0026ndash;25 years[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, we aimed to compare the performances of various estimation equations in a healthy pediatric cohort and evaluate how well these estimates align with the expected normal GFR range.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eLIFE Child is a pediatric cohort study conducted in the city of Leipzig, Germany. LIFE Child examines children\u0026rsquo;s normal development and health from pregnancy to early adulthood. It also studies the etiology of lifestyle diseases, such as obesity. The ages of the recruited subjects range from 24 weeks of gestation up to 16 years[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study sample is a subset of the LIFE Child Study, including all participants between 0.25 and 21 years with the necessary laboratory assessment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants with signs of infections, fever, diabetes, or chronic renal conditions were excluded.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of age (years), height (SDS), weight (SDS), cystatin C (CysC) median, Serum Creatinine (SCr) median LIFE Child cohort sorted in age interval \u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0\u0026ndash;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2\u0026ndash;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e10\u0026ndash;18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e18+\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003efemale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003emale\u003c/em\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.72 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.74 (0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.50 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50 (0.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.52 (1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.57 (1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.55 (2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13.38 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.85 (0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.88 (0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;10th Percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e142 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e153 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e126 (9.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e214 (9.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e190 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e217 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e216 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e20 (13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10th-90th Percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,484 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,614 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e956 (81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,081 (84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,761 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,987 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,469 (79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,604 (78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e155 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e121 (79%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;90th Percentile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e282 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e68 (5.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e245 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e306 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e424 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e517 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.23 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.04 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.13 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.16 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.08 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.12 (1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (3.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e160 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e200 (8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e249 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e300 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,504 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,623 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,065 (90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,127 (87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,821 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,037 (82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2,215 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,399 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e125 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e112 (73%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e257 (8.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e274 (8.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16 (8.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e17 (11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e146 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e389 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e364 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI SDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01 (1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.12 (0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01 (1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31 (1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.20 (1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.38 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.11 (1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum Creatinine (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25 (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.33 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.48 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.69 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.76 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.94 (0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C (mg/l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.95 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.84 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.87 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.94 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.81 (0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.90 (0.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Mean (SD); n (%); SDS (Standard deviation score), UW - underweight, NW - normal weight, OW - overweight, OB - obese\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eThere may be multiple observations of participants that belong to different age intervals. BMI groups: underweight\u0026thinsp;\u0026lt;\u0026thinsp;10th percentile, overweight\u0026thinsp;\u0026gt;\u0026thinsp;90th percentile, obese\u0026thinsp;\u0026gt;\u0026thinsp;97th percentile, \u003cem\u003en\u003c/em\u003e number of observations, \u003cem\u003eSD\u003c/em\u003e standard deviation, \u003cem\u003eBMI\u003c/em\u003e body mass index\u003c/p\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003cp\u003eHeight and weight were determined following standardized procedures by trained study personnel. BMI was calculated and subsequently transformed into age- and sex-adjusted standard deviation scores following the guidelines of the German Obesity Society and the German Society of Pediatrics and Adolescent Medicine (DGKJ). Using the same guidelines, weight groups were defined as underweight (BMI-SDS \u0026lt; -1.28), normal weight (-1.28\u0026thinsp;\u0026lt;\u0026thinsp;BMI-SDS\u0026thinsp;\u0026lt;\u0026thinsp;1.28), overweight (1.28\u0026thinsp;\u0026lt;\u0026thinsp;BMI-SDS\u0026thinsp;\u0026lt;\u0026thinsp;1.88), and obese (BMI-SDS\u0026thinsp;\u0026gt;\u0026thinsp;1.881)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Height groups were defined as \u0026lt;\u0026thinsp;10th Percentile (Height-SDS \u0026lt; -1.28), 10th Percentile to 90th Percentile (-1.28\u0026thinsp;\u0026lt;\u0026thinsp;Height-SDS\u0026thinsp;\u0026lt;\u0026thinsp;1.28), and \u0026gt;\u0026thinsp;90th Percentile (Height-SDS\u0026thinsp;\u0026gt;\u0026thinsp;1.881)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe categorized values as inside/outside the physiological range of eGFRs as 75\u0026ndash;135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e](used for scientific purposes) for all age groups except those under 2 years old because they are assumed to have a deviating physiological range. In addition, the analysis was repeated using a reference range of 90\u0026ndash;135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e because of its prevalent use in clinics [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge groups were defined to account for increasing eGFR in infancy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and potential changes during puberty[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]: infants (\u0026lt;\u0026thinsp;2 years), preschool children (2\u0026ndash;5 years), primary school children (\u0026gt;\u0026thinsp;5\u0026ndash;10 years), adolescents (\u0026gt;\u0026thinsp;10\u0026ndash;18), and adults (\u0026gt;\u0026thinsp;18 years).\u003c/p\u003e \u003cp\u003eThe venous blood samples, using serum monovettes (Sarstedt AG\u0026amp;Co, N\u0026uuml;mbrecht, Germany), were analyzed by the Institute for Laboratory Medicine, Clinical Chemistry and Molecular Diagnostics (ILM) at the University Hospital Leipzig on an automated laboratory analyzer Cobas 8000 (Roche Diagnostics, Mannheim Germany) according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003cp\u003eSerum creatinine (SCr) was measured with an enzymatic assay (Roche Diagnostics). Blood Urea Nitrogen (BUN) was performed with a kinetic test using urease and glutamate dehydrogenase (GLDH) (Roche Diagnostics). Cystatin C (CysC) was determined using the turbidimetric immunoassay (PETIA) Tina-quant\u0026reg; Cystatin C (Roche Diagnostics, measurement range: 0.4\u0026ndash;8.0 mg/l), standardized against a Roche in-house reference preparation of recombinant human CysC. Since 2015, the second-generation Tina-quant\u0026reg; Cystatin C-assay(Roche Diagnostics, primary measurement range: 0.4\u0026ndash;6.8 mg/l) is now standardized against the international reference material ERM-DA471/IFCC[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Both methods showed good conformity and no relevant bias[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003cp\u003eeGFR was calculated by applying the different equations. We calculated eGFRs within the 90\u0026ndash;135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e range as well as within the 75\u0026ndash;135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e range for all age groups. Associations between each of the eGFR (Schwartz, CKID, U25ave) measures as outcomes and sex, weight group, or height group as predictors were estimated using hierarchical linear models, and we adjusted for multiple measurements per child by adding the subject to the model as a random effect. Furthermore, we used the Bland-Altman analyses to compare the equations, using the Schwartz (2009) equation as a reference. All statistical analyses and visualizations were implemented with the R software (R 4.3.2, R Core Team).\u003c/p\u003e \u003cp\u003eEquations\u003c/p\u003e \u003cp\u003eWe included the most commonly used eGFR equations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The revised Schwartz Bedside (2009) is one of the simpler equations, including only the variables height and serum creatinine[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We also included the full CysC- and SCr-based Schwartz equation, as well as the CKiD equation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The three variations of U25 uses CysC (U25cys) or SCr (U25scr), and the averaged value (U25ave)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of used eGFR equations \u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSerum Creatinine\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBUN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCystatin C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAge range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwartz Bedside (2009)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSchwartz (2009)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKiD (2012)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrubb (2014)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU25cys (2021)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU25scr\u003c/p\u003e \u003cp\u003e(2021)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU25ave\u003c/p\u003e \u003cp\u003e(2021)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCKD-EPI (2021)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eThis table shows which laboratory or anthropometric measures are used to calculate the GFR for each equation, respectively, as well as the intended age range\u003c/p\u003e \u003cp\u003eTo contrast the SCr-based Schwartz Bedside calculation, we added the Grubb equation, which is also a simpler calculation based only on CysC and age[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Since our cohort also included adolescents and a few young adults, we included the CysC-SCr-based CKD-EPI[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The complete equations are listed in the Online Resource 1.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor CKID, Schwartz, U25ave, and U25scr, the interval defined by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;1 SD eGFR fell entirely within the expected range of 75 to 135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e for all age groups. For U25cys, the 0-to-2-year-old children showed lower values, with the first quartile lower than 75ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. On the other hand, both Grubb and Bedside showed higher values for 0-to-5-year-old children, with the third quartile above the upper reference limit of 135 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e. Even higher values were reached when we used CKD-EPI with the median well above and the first quartile near the upper reference limit for 0-to-5-year-old children and the median near the upper reference limit for 5-to-10-year-old children (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Distribution of eGFR for each equation grouped by age (in years)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"694\" height=\"280\"\u003e\u003c/p\u003e\n\u003cp\u003eThis table shows the mean and standard deviation (SD) of the eGFRs for each equation. For visual clarity, eGFR values are color-coded as follows: green (90\u0026ndash;120 mL/min/1.73m\u0026sup2;, within the normal range), yellow (\u0026plusmn;15 mL/min/1.73m\u0026sup2; deviation from the expected range), and red (\u0026ge;15 mL/min/1.73m\u0026sup2; deviation), indicating normal, mildly altered, and severely altered values, respectively. There may be multiple observations of participants that belong to different age intervals.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFor CKiD, Schwartz, and U25ave, more than 97% of the eGFR fell within the reference range across all age groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Besides, they showed similar eGFR values without large jumps between adjacent age groups (Fig.\u0026nbsp;1).\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Percentage of estimated GFR between 75-135mL/min/1.73m\u0026sup2; (N) grouped by age (years)\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"685\" height=\"336\"\u003e\u003c/p\u003e\n\u003cp\u003eThe table shows the percentage of results within the normal eGFR-range, the number of measurements is stated in parentheses. For visual clarity, percentage values are color-coded as follows: green (\u0026ge;95%), yellow (\u0026ge;90%), and red (\u0026lt;90%)\u003c/p\u003e\u003cp\u003eAlthough to a somewhat smaller extent, U25cys and U25scr also reached high percentages within the reference range (all \u0026gt;\u0026thinsp;86%). The remaining 3 equations showed implausible age jumps as well as very low percentages within the reference range for several age groups (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results when the clinical cutoffs were applied are shown in Online Resource 2.\u003c/p\u003e \u003cp\u003eBland-Altman Analysis\u003c/p\u003e \u003cp\u003eWe found the eGFR estimated by the U25ave to be closest to those estimated by the Schwarz equation as a reference (bias= -2.92, proportional bias= -0.02). The proportional bias was also low for CKiD (-0.12), U25cys (0.18), and U25scr (0.26), which also showed low biases (-4.79, -6.20, 0.35). By contrast, Grubb, Bedside, and CKDEPI had larger (positive) biases (13.1, 9.2, 27.5) and proportional biases, indicating considerably higher eGFR than Schwartz, especially for the higher eGFRs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAssociations with sex, weight, and height group\u003c/p\u003e \u003cp\u003eFor the three investigated equations (Schwartz, CKID, U25ave), eGFRs were higher in males than females across the entire study population as well as within all weight groups (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor the Schwartz equation and the CKiD, we found higher eGFRs for children with overweight and obesity than for normal weight children. For Schwartz, these differences reached statistical significance only for girls with obesity. For CKiD, the comparisons yielded similar results but showed only weak effects that were not statistically significant. No consistent patterns were found for U25ave (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor Schwartz and CKiD, we found higher eGFRs in taller children for girls as well as for boys. For the U25ave, estimated GRFs were similar across the height groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eChronic kidney failure is a global health problem and a leading cause of morbidity and mortality for millions of people. Chronic kidney insufficiency often progresses silently, and by the time individuals develop significant clinical symptoms, kidney function has already been severely impaired. To implement a preventive approach, methods for assessing kidney function are relevant for all age groups[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccurate eGFR estimation is crucial for assessing kidney health, adjusting medication dosages, and mitigating nephrotoxic risks associated with various pharmaceuticals. The reliable estimation of GFR is particularly important in children, as absolute values of kidney retention parameters such as serum creatinine (SCr) or cystatin C (CysC) undergo changes during phases of growth and puberty, making it difficult to assess overall kidney function accurately.\u003c/p\u003e \u003cp\u003eIt is undisputed that there are differences in kidney size, nephron count, and, ultimately, overall kidney function to the disadvantage of the female sex. However, this difference seems to be compensated by women\u0026rsquo;s smaller body size and muscle mass. Almost all methods for estimating GFR take sex into account and use \"modifiers\" when reporting eGFR.\u003c/p\u003e \u003cp\u003eSex-based differences in eGFR estimation remain a topic of debate, particularly in prepubescent and early adolescent populations. Variability in muscle mass, body composition, and hormonal influences on endogenous markers contribute to sex-based discrepancies in eGFR calculations[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Most equations tend to estimate higher GFR values in males due to these physiological differences[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For example, markers such as Cystatin C, while less influenced by muscle mass, still show associations with age, sex, and pubertal stage[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our cohort, sex differences were evident across all equations incorporating sex as a factor, with females generally showing lower eGFR values. These findings are consistent with studies in adult populations, where women are often reported to have lower baseline kidney function [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e Considering the KDIGO guideline threshold over 90 ml/min/1.73m\u0026sup2; for normal kidney function, a greater proportion of female participants in our cohort fell below this limit. However, we would like to emphasize that an eGFR between 75\u0026ndash;90 ml/min/1.73 m\u0026sup2;, in the absence of albuminuria or hematuria, still represents a low risk for the development of end-stage kidney failure[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therapeutic interventions such as adjustments of medication dosages or avoiding contrast agents are typically considered only for eGFR values below 60 ml/min/1.73 m\u0026sup2; [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Thus, the methods that are commonly used to estimate GFR can be applied for both sexes in these decisions.\u003c/p\u003e \u003cp\u003eThe ongoing pandemic of childhood obesity is expected to contribute to an increased risk of chronic kidney disease (CKD) in later life, as early-life adiposity has been linked to long-term CKD development[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Van Dam et al. found correlations between elevated BMI and below-average values for creatinine-based eGFRs, depending on which equation they used[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our cohort, we found no evidence of impaired GFR at higher weight or overweight. This result may have occurred because the overweight patients are still in the \"hyperfiltration\" phase, with eGFR values above 135 ml/min/1.73m\u0026sup2;. This constellation may lead to nephrosclerosis and a significant loss of eGFR in the medium term.\u003c/p\u003e \u003cp\u003eAdolescents and young adults represent a unique subgroup that requires special consideration. The transition from pediatric to adult care often involves a shift in the equations used for eGFR estimation, which can lead to variability in reported kidney function[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This transition period highlights the need for standardized approaches that can account for the physiological and developmental changes that occur during this stage of life. The CKD-EPI equation is designed for individuals 18 years of age and older; prior research has shown its limited reliability not only in children but also in young adults[\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Our results align with these observations, showing substantial differences in eGFR estimates across age groups and unusually high mean eGFR values in younger participants. However, the CKD-EPI equation provided more results within the expected eGFR range in participants over the age of 10, suggesting some applicability in older pediatric populations. More importantly, further investigation is needed to determine whether, when, and to which equations the transition should be applied to accurately reflect an individual\u0026rsquo;s GFR.\u003c/p\u003e \u003cp\u003eIn our study, we compared eGFR values from a large, healthy cohort of children and adolescents using several estimation equations. Since we did not have measured filtration rates for direct comparison, our research should not be viewed as a definitive statement about their accuracy. While our research cannot pinpoint one formula for estimating kidney function as the most accurate, we were able to show that some formulas led to implausible results for a pediatric non-CKD cohort.\u003c/p\u003e \u003cp\u003eNotably, we observed that simpler equations, such as Schwartz Bedside and Grubb, are more prone to producing extreme outliers and implausible discontinuities between age groups. This tendency suggests that these formulas may be less suitable for estimating kidney function in healthy paediatric, non-CKD populations.\u003c/p\u003e \u003cp\u003eHowever, we consider any pediatric equation to be suitable for assessing changes in overall kidney function in individual patients. These changes can reflect both therapeutic outcomes and the risk of progression to end-stage renal disease.\u003c/p\u003e \u003cp\u003eAmong the three versions of the U25 equation, the combined formula, which averages estimates from single-marker equations (Cystatin C and serum creatinine), demonstrated slightly better consistency compared with either marker individually. These findings align with the performance of other equations, such as Schwartz and CKiD, which also incorporate both Cystatin C and serum creatinine. This alignment suggests that equations leveraging multiple endogenous markers provide more plausible and reliable eGFR results for healthy children and adolescents.\u003c/p\u003e \u003cp\u003eIn summary, our results indicate that equations incorporating multiple endogenous markers\u0026mdash;particularly those combining Cystatin C and serum creatinine\u0026mdash;yield more probable eGFR estimates in healthy pediatric populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to the participants and their guardians for their contributions to the LIFE Child study. We would also like to thank the LIFE Child study team.\u0026nbsp;\u003cbr\u003e\u0026nbsp;The authors also thank Jane Zagorski for her excellent language editing and editorial support (as always)!\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Leipzig Research Center for Civilization Diseases at the University of Leipzig is funded by the European Union through the European Regional Development Fund (ERDF) and the Free State of Saxony, as part of the excellence initiative of the Saxonian Ministry of Science and Arts (SMWK).\u003cbr\u003e\u0026nbsp;NCT Trial Number: 02550236 (NIH)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards and was been approved by the Ethics Committee of the University of Leipzig (Reg. No. 477-19-09102020).\u003c/p\u003e\n\u003cp\u003eConsent to participate\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants, or their legal guardians, included in the study.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSulemanji M, Vakili K (2013) Neonatal renal physiology. Semin Pediatr Surg 22:195\u0026ndash;198. https://doi.org/10.1053/j.sempedsurg.2013.10.008\u003c/li\u003e\n\u003cli\u003eRubin MI, Bruck E, Rapoport M, et al (1949) MATURATION OF RENAL FUNCTION IN CHILDHOOD: CLEARANCE STUDIES12. J Clin Invest 28:1144\u0026ndash;1162. https://doi.org/10.1172/JCI102149\u003c/li\u003e\n\u003cli\u003eFiller G, Bhayana V, Schott C, D\u0026iacute;az‐Gonz\u0026aacute;lez De Ferris ME (2021) How should we assess renal function in neonates and infants? Acta Paediatr 110:773\u0026ndash;780. https://doi.org/10.1111/apa.15557\u003c/li\u003e\n\u003cli\u003eStevens PE, Ahmed SB, Carrero JJ, et al (2024) KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int 105:S117\u0026ndash;S314. https://doi.org/10.1016/j.kint.2023.10.018\u003c/li\u003e\n\u003cli\u003ePottel H, Hoste L, Delanaye P (2015) Abnormal glomerular filtration rate in children, adolescents and young adults starts below 75 mL/min/1.73 m2. Pediatr Nephrol 30:821\u0026ndash;828. https://doi.org/10.1007/s00467-014-3002-5\u003c/li\u003e\n\u003cli\u003ePottel H, Adebayo OC, Nkoy AB, Delanaye P (2023) Glomerular hyperfiltration: part 1 \u0026mdash; defining the threshold \u0026mdash; is the sky the limit? Pediatr Nephrol 38:2523\u0026ndash;2527. https://doi.org/10.1007/s00467-022-05827-4\u003c/li\u003e\n\u003cli\u003eRoberfroid MB (2005) Introducing inulin-type fructans. Br J Nutr 93:S13\u0026ndash;S25. https://doi.org/10.1079/BJN20041350\u003c/li\u003e\n\u003cli\u003eFiller G, Yasin A, Medeiros M (2014) Methods of assessing renal function. Pediatr Nephrol 29:183\u0026ndash;192. https://doi.org/10.1007/s00467-013-2426-7\u003c/li\u003e\n\u003cli\u003eSoveri I, Berg UB, Bj\u0026ouml;rk J, et al (2014) Measuring GFR: A Systematic Review. Am J Kidney Dis 64:411\u0026ndash;424. https://doi.org/10.1053/j.ajkd.2014.04.010\u003c/li\u003e\n\u003cli\u003eBj\u0026ouml;rk J, Nyman U, Larsson A, et al (2021) Estimation of the glomerular filtration rate in children and young adults by means of the CKD-EPI equation with age-adjusted creatinine values. Kidney Int 99:940\u0026ndash;947. https://doi.org/10.1016/j.kint.2020.10.017\u003c/li\u003e\n\u003cli\u003eSchwaderer AL, Maier P, Greenbaum LA, et al (2023) Application of GFR estimating equations to children with normal, near-normal, or discordant GFR. Pediatr Nephrol 38:4051\u0026ndash;4059. https://doi.org/10.1007/s00467-023-06045-2\u003c/li\u003e\n\u003cli\u003eStaples A, LeBlond R, Watkins S, et al (2010) Validation of the revised Schwartz estimating equation in a predominantly non-CKD population. Pediatr Nephrol 25:2321\u0026ndash;2326. https://doi.org/10.1007/s00467-010-1598-7\u003c/li\u003e\n\u003cli\u003ePottel H, Bj\u0026ouml;rk J, Delanaye P, Nyman U (2022) Evaluation of the creatinine-based chronic kidney disease in children (under 25 years) equation in healthy children and adolescents. Pediatr Nephrol 37:2213\u0026ndash;2216. https://doi.org/10.1007/s00467-022-05429-0\u003c/li\u003e\n\u003cli\u003ePoulain T, Baber R, et al (2017) The LIFE Child study: a population-based perinatal and pediatric cohort in Germany. Eur J Epidemiol 32:145\u0026ndash;158. https://doi.org/10.1007/s10654-016-0216-9\u003c/li\u003e\n\u003cli\u003eMo\u0026szlig; A, Kunze D, Wabitsch M (2011) Evidenzbasierte Leitlinie der Arbeitsgemeinschaft Adipositas im Kindes- und Jugendalter zur Therapie der Adipositas im Kindes- und Jugendalter. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz 54:584\u0026ndash;590. https://doi.org/10.1007/s00103-011-1269-2\u003c/li\u003e\n\u003cli\u003eZiegelasch N, Vogel M, M\u0026uuml;ller E, et al (2019) Cystatin C serum levels in healthy children are related to age, gender, and pubertal stage. Pediatr Nephrol 34:449\u0026ndash;457. https://doi.org/10.1007/s00467-018-4087-z\u003c/li\u003e\n\u003cli\u003eQuante M, Hesse M, D\u0026ouml;hnert M, et al (2012) The LIFE child study: a life course approach to disease and health. BMC Public Health 12:1021. https://doi.org/10.1186/1471-2458-12-1021\u003c/li\u003e\n\u003cli\u003eSchwartz GJ, Mun[Combining Tilde]oz A, Schneider MF, et al (2009) New Equations to Estimate GFR in Children with CKD. J Am Soc Nephrol 20:629\u0026ndash;637. https://doi.org/10.1681/ASN.2008030287\u003c/li\u003e\n\u003cli\u003eSchwartz GJ, Schneider MF, Maier PS, et al (2012) Improved equations estimating GFR in children with chronic kidney disease using an immunonephelometric determination of cystatin C. Kidney Int 82:445\u0026ndash;453. https://doi.org/10.1038/ki.2012.169\u003c/li\u003e\n\u003cli\u003ePierce CB, Mu\u0026ntilde;oz A, Ng DK, et al (2021) Age- and sex-dependent clinical equations to estimate glomerular filtration rates in children and young adults with chronic kidney disease. Kidney Int 99:948\u0026ndash;956. https://doi.org/10.1016/j.kint.2020.10.047\u003c/li\u003e\n\u003cli\u003eGrubb A, Horio M, Hansson L-O, et al (2014) Generation of a New Cystatin C\u0026ndash;Based Estimating Equation for Glomerular Filtration Rate by Use of 7 Assays Standardized to the International Calibrator. Clin Chem 60:974\u0026ndash;986. https://doi.org/10.1373/clinchem.2013.220707\u003c/li\u003e\n\u003cli\u003eInker LA, Eneanya ND, Coresh J, et al (2021) New Creatinine- and Cystatin C\u0026ndash;Based Equations to Estimate GFR without Race. N Engl J Med 385:1737\u0026ndash;1749. https://doi.org/10.1056/NEJMoa2102953\u003c/li\u003e\n\u003cli\u003eNeugarten J, Acharya A, Silbiger SR (2000) Effect of Gender on the Progression of Nondiabetic Renal Disease: A Meta-Analysis. J Am Soc Nephrol 11:319\u0026ndash;329. https://doi.org/10.1681/ASN.V112319\u003c/li\u003e\n\u003cli\u003eJafar TH (2003) The rate of progression of renal disease may not be slower in women compared with men: a patient-level meta-analysis. Nephrol Dial Transplant 18:2047\u0026ndash;2053. https://doi.org/10.1093/ndt/gfg317\u003c/li\u003e\n\u003cli\u003eNitsch D, Grams M, Sang Y, et al (2013) Associations of estimated glomerular filtration rate and albuminuria with mortality and renal failure by sex: a meta-analysis. BMJ 346:f324\u0026ndash;f324. https://doi.org/10.1136/bmj.f324\u003c/li\u003e\n\u003cli\u003ePottel H, Hoste L, Dubourg L, et al (2016) An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant 31:798\u0026ndash;806. https://doi.org/10.1093/ndt/gfv454\u003c/li\u003e\n\u003cli\u003eMelsom T, Norvik JV, Enoksen IT, et al (2022) Sex Differences in Age-Related Loss of Kidney Function. J Am Soc Nephrol 33:1891\u0026ndash;1902. https://doi.org/10.1681/ASN.2022030323\u003c/li\u003e\n\u003cli\u003eWetzels JFM, Kiemeney LALM, Swinkels DW, et al (2007) Age- and gender-specific reference values of estimated GFR in Caucasians: The Nijmegen Biomedical Study. Kidney Int 72:632\u0026ndash;637. https://doi.org/10.1038/sj.ki.5002374\u003c/li\u003e\n\u003cli\u003eStefani M, Singer RF, Roberts DM (2019) How to adjust drug doses in chronic kidney disease. Aust Prescr 42:163. https://doi.org/10.18773/austprescr.2019.054\u003c/li\u003e\n\u003cli\u003eJadresic L, Silverwood RJ, Kinra S, Nitsch D (2019) Can childhood obesity influence later chronic kidney disease? Pediatr Nephrol 34:2457\u0026ndash;2477. https://doi.org/10.1007/s00467-018-4108-y\u003c/li\u003e\n\u003cli\u003eStern-Zimmer M, Calderon-Margalit R, Skorecki K, Vivante A (2021) Childhood risk factors for adulthood chronic kidney disease. Pediatr Nephrol 36:1387\u0026ndash;1396. https://doi.org/10.1007/s00467-020-04611-6\u003c/li\u003e\n\u003cli\u003eVan Dam MJCM, Pottel H, Vreugdenhil ACE (2023) Relation between obesity-related comorbidities and kidney function estimation in children. Pediatr Nephrol 38:1867\u0026ndash;1876. https://doi.org/10.1007/s00467-022-05810-z\u003c/li\u003e\n\u003cli\u003eWebster-Clark M, Jaeger B, Zhong Y, et al (2018) Low agreement between modified-Schwartz and CKD-EPI eGFR in young adults: a retrospective longitudinal cohort study. BMC Nephrol 19:194. https://doi.org/10.1186/s12882-018-0995-1\u003c/li\u003e\n\u003cli\u003eSelistre L, Rabilloud M, Cochat P, et al (2016) Comparison of the Schwartz and CKD-EPI Equations for Estimating Glomerular Filtration Rate in Children, Adolescents, and Adults: A Retrospective Cross-Sectional Study. PLOS Med 13:e1001979. https://doi.org/10.1371/journal.pmed.1001979\u003c/li\u003e\n\u003cli\u003eBj\u0026ouml;rk J, Nyman U, Courbebaisse M, et al (2020) Prospects for improved glomerular filtration rate estimation based on creatinine\u0026mdash;results from a transnational multicentre study. Clin Kidney J 13:674\u0026ndash;683. https://doi.org/10.1093/ckj/sfaa039\u003c/li\u003e\n\u003cli\u003eNyman U, Grubb A, Larsson A, et al (2014) The revised Lund-Malm\u0026ouml; GFR estimating equation outperforms MDRD and CKD-EPI across GFR, age and BMI intervals in a large Swedish population. Clin Chem Lab Med CCLM 52:. https://doi.org/10.1515/cclm-2013-0741\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eGFR, Kidney, Cystatin C, Creatinine, Children, Pediatric","lastPublishedDoi":"10.21203/rs.3.rs-6744772/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6744772/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn this study, we compared established equations for estimating the glomerular filtration rate (GFR) in a cohort of healthy children and adolescents and aimed to evaluate the equations\u0026rsquo; validity.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBlood, urine, and anthropometric data from 4,776 healthy participants (0.25\u0026ndash;21 years) were analyzed. The glomerular filtration rate was estimated (eGFR) using the revised Schwartz Bedside (2009), the Cystatin C- and Serum Creatinine- based Schwartz equation, the Chronic Kidney Disease in Children (CKiD) equation, the Chronic Kidney Disease in Children (CKiD) equation, the 3 versions of the U25 (U25cys, U25scr, U25ave), the Grubb equation, and the Cystatin C-Creatinine-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI). The resulting eGFR distributions were compared, and the percentages of eGFR values within the expected physiological eGFR range were calculated stratified by age and sex. Subsequently, age ranges with implausible distributions were identified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCKiD, Schwartz, and U25ave yielded the highest proportion of eGFRs within the expected range (75\u0026ndash;135 mL/min/1.73m\u0026sup2;) and showed consistent values across age groups without large jumps. Bland-Altman analysis indicated that the average U25 and CKiD had the lowest bias compared with Schwartz (-2.93 and \u0026minus;\u0026thinsp;4.79). U25cys and U25scr also exhibited low bias, while Grubb, Bedside, and CKD-EPI had larger biases, overestimating eGFR. Males showed higher eGFRs than females.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe found that the CKiD, Schwartz, and combined U25 resulted in the most plausible eGFR distributions for a healthy pediatric cohort. Estimates from simpler equations such as Bedside and Grubb were less plausible.\u003c/p\u003e","manuscriptTitle":"Examining commonly used equations for estimating the glomerular filtration rate (GFR) in a healthy cohort of children and adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-04 08:27:54","doi":"10.21203/rs.3.rs-6744772/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"296ee072-28c6-490e-b5b4-7989536c11a2","owner":[],"postedDate":"June 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-18T13:04:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-04 08:27:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6744772","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6744772","identity":"rs-6744772","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}