Significance Statement
Measuring urine biomarkers provides an opportunity to assess kidney tubular health, tubular injury, and inflammation from the filtrate that passes through the tubular lumen. As such, urine biomarkers such as EGF, α-1 microglobulin, KIM-1, MCP-1, and YKL-40 might provide insights into CKD pathophysiology and improve risk prediction of CKD progression in children. In this prospective cohort study of 665 children with CKD, children in the lowest quartile of urine EGF (versus the highest quartile) or those in the highest quartile of urine KIM-1, MCP-1, or α-1 microglobulin concentrations (versus the respective lowest quartiles) were at a significantly higher risk of CKD progression, even after multivariable adjustment. The addition of the five urine biomarkers to a clinical model significantly improved discrimination and reclassification for CKD progression.
Keywords: chronic kidney disease, children, end-stage kidney disease, pediatric nephrology
Abstract
Background
Novel urine biomarkers may improve identification of children at greater risk of rapid kidney function decline, and elucidate the pathophysiology of CKD progression.
Methods
We investigated the relationship between urine biomarkers of kidney tubular health (EGF and α-1 microglobulin), tubular injury (kidney injury molecule-1; KIM-1), and inflammation (monocyte chemoattractant protein-1 [MCP-1] and YKL-40) and CKD progression. The prospective CKD in Children Study enrolled children aged 6 months to 16 years with an eGFR of 30–90ml/min per 1.73m2. Urine biomarkers were assayed a median of 5 months [IQR: 4–7] after study enrollment. We indexed the biomarker to urine creatinine by dividing the urine biomarker concentration by the urine creatinine concentration to account for the concentration of the urine. The primary outcome was CKD progression (a composite of a 50% decline in eGFR or kidney failure) during the follow-up period.
Results
Overall, 252 of 665 children (38%) reached the composite outcome over a median follow-up of 6.5 years. After adjustment for covariates, children with urine EGF concentrations in the lowest quartile were at a seven-fold higher risk of CKD progression versus those with concentrations in the highest quartile (fully adjusted hazard ratio [aHR], 7.1; 95% confidence interval [95% CI], 3.9 to 20.0). Children with urine KIM-1, MCP-1, and α-1 microglobulin concentrations in the highest quartile were also at significantly higher risk of CKD progression versus those with biomarker concentrations in the lowest quartile. Addition of the five biomarkers to a clinical model increased the discrimination and reclassification for CKD progression.
Conclusions
After multivariable adjustment, a lower urine EGF concentration and higher urine KIM-1, MCP-1, and α-1 microglobulin concentrations were each associated with CKD progression in children.
Progression of CKD in children can lead to kidney failure, which is associated with adverse cardiovascular outcomes and mortality rates significantly higher than those observed in the general pediatric population.1 Additionally, CKD in children is associated with impaired cognitive function and decreased quality of life.2,3 Recognition of the morbidity and mortality associated with pediatric CKD has led to efforts to conduct definitive trials to prevent progression, and to discover novel biomarkers that can maximize the efficiency of these clinical trials.4,5 Furthermore, novel biomarkers of CKD progression may allow for the early identification of children at the highest risk of CKD progression to optimally schedule follow-up, referral for transplant evaluation, and to provide better guidance to families.
The clinical biomarkers used to predict progression of CKD, urine albumin concentration and serum creatinine concentration, do not adequately explain the high variability in the rates of GFR decline in children with CKD.6 Biomarkers that indicate key pathways of CKD progression (such as markers of proximal tubular injury, inflammation, and tubular health) may improve the prediction of decline in GFR. We recently observed that biomarkers measured in the plasma reflecting tubular injury (kidney injury molecule-1 [KIM-1]) and inflammation (soluble TNF receptor 1 and 2) were each independently associated with CKD progression in the CKD in Children (CKiD) cohort.7 However, plasma biomarker concentrations are often strongly affected by GFR and may be altered substantially by systemic processes and multiorgan dysfunction.7–10 Measuring biomarkers in the urine provides an opportunity to directly evaluate processes of tubule health, repair, injury, and inflammation of the kidney that manifest in the filtrate that passes through the tubular lumen. Urine biomarkers also provide a noninvasive means by which to improve characterization and prognostication of CKD in children without requiring venipuncture.
Previous studies of children and adults with CKD have identified several candidate urine biomarkers that are associated with CKD progression.11–14 Most pediatric studies have evaluated urine biomarkers in single-center cross-sectional studies.14 In one prospective multicenter study, Azukaitis et al. studied urine EGF in 623 children (92% of children with nonglomerular types of CKD), and reported that a higher urinary EGF level was associated with a decreased risk of CKD progression after multivariable adjustment.12 However, candidate biomarkers such as urine KIM-1, monocyte chemoattractant protein-1 (MCP-1), YKL-40, and α-1 microglobulin have not been studied before in a longitudinal pediatric CKD cohort to examine their associations with GFR decline over time, nor to understand the inter-relation of the biomarkers with one another.
In the present investigation, we assayed urine biomarkers of kidney tubule health (EGF, α-1 microglobulin), tubular injury (KIM-1), and inflammation (MCP-1, YKL-40) to examine their associations with CKD progression in the CKiD multicenter cohort study. These five urine biomarkers were selected on the basis of a review of the published literature on CKD biomarkers and pilot studies within the CKD Biomarker consortium.9,15–19 We hypothesized that these urine biomarkers would be associated with and improve discrimination for CKD progression incrementally over conventional risk factors.
Materials and Methods
Study Participants
The CKiD study is a cohort study of children with CKD enrolled from 54 medical centers in the United States and Canada from 2006 through 2016.21,21 Children were prospectively enrolled in the CKiD study if they were between the ages of 6 months and 16 years. From 2006 to 2011 CKiD enrolled children with an eGFR of 30–75 ml/min per 1.73m2. The enrollment criteria were changed in 2011 to an eGFR between 45 and 90 ml/min per 1.73m2 to target enrollment of children with mild to moderate CKD. Children were excluded if they had a history of kidney, solid-organ, or bone marrow transplantation, dialysis within 3 years, or history of cancer. Written informed consent was obtained from all parents or legal guardians, along with assent, when appropriate, from children. The CKiD study was approved by the institutional review board of each participating institution. The CKiD study is registered at ClinicalTrials.gov with the identifier NCT00327860. For this investigation, we included children in the CKiD study if they had a sufficient volume of stored urine and had data on eGFR, blood pressure, CKD diagnosis, and body mass index (BMI) data at study entry and urine albumin-creatinine ratio at the same time point as the urine biomarker measurement (Supplemental Figure 1). We defined the baseline for analyses as the first CKiD study visit. Participants underwent annual study visits to assess height, weight, medication use, blood pressure, eGFR, and urine albumin.
Primary Exposures
The primary exposures were urine biomarker levels indexed to urine creatinine. The urine biomarker and urine creatinine concentration were measured from stored urine, collected a median of 5 months (interquartile range [IQR], 4–7) after the baseline visit. We calculated the indexed biomarker value by dividing the urine biomarker concentration by the urine creatinine concentration. The raw urine KIM-1, MCP-1, YKL-40, and EGF concentration are presented in units of pg/ml and urine α-1 microglobulin is presented as mg/L. For our primary analysis, the urine biomarkers KIM-1, MCP-1, YKL-40, and EGF are indexed to urine creatinine and presented in units of (pg/ml)/(mg/dl) whereas urine α-1 microglobulin is presented as (mg/L)/(mg/dl).
Composite Outcome
The primary outcome was the time to the composite event of a 50% decline in eGFR or kidney failure (dialysis or transplantation). Participants not experiencing the event were censored at their last visit at which they had active data collection or January 15, 2020, whichever came first.
Clinical and Laboratory Variables
Hypertension was defined as a systolic or diastolic blood pressure ≥95th percentile for age, sex, and height or ≥130/80 mmHg, whichever blood pressure threshold was lower.22 BMI was standardized for age and sex. Kidney disease subtypes were classified according to etiology as glomerular (focal segmental glomerulosclerosis, hemolytic uremic syndrome, systemic immunologic disease, chronic GN, familial nephritis, IgA nephropathy, membranoproliferative GN, Henoch Schonlein Nephritis, other) or nonglomerular (obstructive uropathy, aplastic kidney, dysplastic kidney, reflux nephropathy, autosomal recessive polycystic kidney disease, renal infarct, other).20 We determined the eGFR using published equations derived from the CKiD population on the basis of serum creatinine, cystatin C, and BUN concentrations.6 Serum creatinine and eGFR were measured annually. Serum creatinine measurements were performed in the same CKiD central laboratory at the University of Rochester. The total number of serum creatinine measurements was 3813. There was median of 6 (IQR, 4–8) serum creatinine measurements performed on each participant included in the analysis.
Biomarker Measurements
Stored biospecimens were centrifuged and urine supernatant was aliquoted. Barcoded aliquots of urine were stored at −80°C until biomarker measurement. All assays were performed in a central laboratory (Brigham and Women’s Hospital Central Biomarker Consortium Laboratory) blinded to clinical outcomes. Urine EGF, KIM-1, MCP-1, and YKL-40 were measured in duplicate using a Luminex multiplex assay (Luminex Corporation, Austin, TX) and the mean values for each biomarker were used in the analyses. Urine α-1 microglobulin was measured using a Siemen’s nephelometer (Siemens, Munich, Germany). Biomarker measurements were repeated on participants’ urine samples if two or more analytes had intra-assay coefficients of variation >15%. The intra- and interassay coefficients of variation were all <10% for the final sample (Supplemental Table 1). Values below the limits of detection (KIM-1: 27.43 pg/ml, MCP-1: 5.49 pg/ml, YKL-40: 65.84 pg/ml, EGF: 6.86 pg/ml, α-1 microglobulin: 5.64 mg/L, and urine albumin: 5.11 mg/L) for the urine biomarkers and urine albumin were imputed on the basis of a lognormal distribution. This imputation method, truncated lognormal imputation, uses information on the distribution of observed data to recreate the lower tail of the distribution, then randomly draws a value to replace the missing information below the limit of detection. High proportions of nondetectable values were observed in α-1 microglobulin and YKL-40, 34% and 31%, respectively, limiting the degree to which inferences could be made regarding values in the lower tail of the distribution. Indexed biomarker values were considered in analyses both as continuous and categorical (i.e., quartile) variables. Plasma KIM-1, MCP-1, and YKL-40 concentrations were previously measured using a Meso Scale Discovery multiplex assay to study the correlation between plasma and urine biomarkers as previously described.7
Statistical Analysis
The Spearman correlations between indexed biomarkers, age, eGFR, and urine albumin-creatinine ratio were estimated in the total sample, adjusting for the expected correlation due to the indexing variable.23 We adjusted for the expected correlation as the indexed biomarkers are likely to have an induced correlation as a result of indexing to urine creatinine. As such, we used formulas described by Kim et al. to correct for the expected spurious correlations.23 We also estimated the Spearman correlations between the biomarkers that were measured in both urine and plasma (KIM-1, MCP-1, and YKL-40). For the primary analysis, we estimated the unadjusted incidence rate of CKD progression events by quartile of urine biomarker concentration for each biomarker. We used Cox proportional hazards regression models to assess the association between quartiles of each indexed urine biomarker and CKD progression in separate models, after adjustment for baseline values of age, sex, BMI, glomerular diagnosis, hypertension status, and eGFR at study entry and the urine albumin-creatinine ratio at the same time point of the urine biomarker measurement. The first quartile was used as the referent group for all biomarkers, except for EGF where the fourth quartile was used as the referent group. As 31% of YKL-40 measurements and 34% of α-1 microglobulin measurements were below the limit of detection (>25%), for YKL-40 and α-1 microglobulin we combined the lower two quartiles for the referent group. Global Wald tests (Type III) were used to assess the statistical significance across the biomarker quartiles. The analyses were bootstrap resampled 500 times and the median of the adjusted coefficient estimates along with the bootstrap 95% confidence interval (95% CI) were presented. Bootstrapping of the samples helps provide some measure of the empirical variability associated with the particular sample and provides an internal validation by simulating repeating the study 500 times. Although all five tested urine biomarkers were selected on the basis of literature reviews, we used a conservative Bonferroni α of 0.01 (the pooled original α was 0.05, which we divided by five or the number of biomarkers studied) as a threshold for statistical significance.
Because biomarker levels could variably predict progression according to kidney disease etiology and by level of baseline GFR, secondary analyses examine the associations stratified by both etiology (glomerular/nonglomerular) and by eGFR level (above versus below 60 ml/min per 1.73m2). Interactions by etiology or baseline eGFR were tested by incorporating statistical interaction terms with biomarker quartiles in fully adjusted models and using a Global Wald test. Specifically, the interaction terms between indicators of the indexed biomarker quartile and either glomerular diagnosis or eGFR at study entry were added to the linear regression model, including the same set of covariates.
To examine the predictive value of the biomarkers, we used several metrics to determine the ability of elevated biomarker concentrations (defined as the highest quartile versus the lower three quartiles, except in the case of EGF), to discriminate between participants with and without subsequent development of the composite outcome. We used Uno’s concordance statistic for the survival context, continuous net reclassification improvement (NRI), and integrated discrimination improvement.24,25 The Uno’s concordance statistic ensures consistent estimation of the concordance by modeling the censoring distribution and using it to weight the uncensored observations. The continuous NRI reflects whether the urine biomarkers increase the predicted risks of those participants who go on to develop CKD progression, while decreasing the predicted risks of those who do not develop CKD progression. The integrated discrimination improvement is the difference in mean predicted probabilities of CKD progression between children with and without CKD progression. We estimated the performances of a clinical model (incorporating age, sex, glomerular diagnosis, BMI z-score, urine albumin-creatinine ratio, hypertension, eGFR) and compared it with the added discrimination of the clinical model plus each individual biomarker for CKD progression. Calibration of the model was evaluated by comparing the predicted and empirical survival curves. We also tested discrimination of biomarkers in combination. Analyses were performed using SAS 9.4 for Windows (SAS Institute Inc, NC) and R (R Core Team Version 3.5.1).
Results
Study Participants
Overall, 665 children were included in this study cohort and they had a median age of 12 years (IQR, 8–15). Overall, 38% were female, 21% were Black, and 14% were Hispanic, 206 (31%) had a glomerular cause of CKD, and 459 (69%) had a nonglomerular cause of CKD (Table 1).20 At study entry, the median eGFR was 53 ml/min per 1.73m2 (IQR, 39–66) and participants had a diagnosis of CKD for a median of 8 years (IQR, 4–13). The median urine albumin-creatinine ratio was 57.8 mg/g (IQR, 8.8–312) at a median of 5 months (IQR, 4–7) after study entry when the urine biomarkers were assayed.
Table 1.
Baseline characteristics of the overall sample
| Characteristic | Overall (n=665) |
|---|---|
| Age, yrs | 12 (8, 15) |
| Sex (female) | 256 (38%) |
| Black race | 142 (21%) |
| Hispanic ethnicity | 94 (14%) |
| Premature birth (<36 weeks) | 74 (11%) |
| BMI, kg/m2 | 19 (16, 22) |
| eGFR, ml/min per 1.73m2 | 53 (39, 66) |
| Glomerular disease | 206 (31%) |
| Time with CKD, (years) | 8 (4, 13) |
| Hypertension | 165 (25%) |
| BUN, mg/dl | 24 (18, 32) |
| Hemoglobin level, g/dl | 12.7 (11.7, 13.7) |
| Urine albumin-creatininea (mg/g) | 57.8 (8.8, 312.0) |
| Treatment with RAAS inhibitor | 373 (56%) |
All characteristics were assessed at study entry except for urine albumin-creatinine ratio, which was measured at the same time point as the urine biomarker measurement.
CKD Progression Events
The median follow-up time was 6.5 years (IQR, 3.6–8.5). During the follow-up time, the composite outcome was reached by 252 (38%) children; 148 (22%) with incident kidney failure and 104 (16%) with a 50% decline in eGFR. Of the 665 individuals entering the study at baseline, 211 were censored due to loss of follow-up during the observation period for a censoring rate of 32%.
Correlations between Urine Biomarker Levels, eGFR, and Albuminuria
The raw and log-transformed biomarker levels at baseline were included in Supplemental Table 2. The baseline characteristics are displayed by quartile of urine EGF concentration in Supplemental Table 3. A description of Spearman correlations between indexed baseline biomarker levels, urine albumin-creatinine ratio, and eGFR is provided in Supplemental Table 4. Urine EGF was highly correlated with eGFR (ρ=0.71), and inversely correlated with all other urine biomarkers. Urine KIM-1 and MCP-1 were highly correlated with each other (ρ=0.70). Both KIM-1 and MCP-1 were moderately correlated with urine albumin (KIM-1: ρ=0.48, MCP-1: ρ=0.42). The Spearman correlations comparing the same biomarker measured in both urine and plasma for KIM-1, MCP-1, and YKL-40 concentrations were 0.43, −0.11, and 0.27, respectively.
Biomarkers Associated with CKD Progression
We examined the incidence rate of CKD progression events according to quartiles of the biomarker concentrations (Figure 1). The highest rate of CKD progression events was observed in the lowest quartile of urine EGF and the highest quartile for all of the other biomarkers. The incidence rate of the first quartile was nine times higher for urine EGF with 14.7 events per 100-person years, versus 1.6 events per 100-person years in quartile four. For urine KIM-1 and MCP-1 there were 12.3 and 11.6 events, respectively, per 100-person years in quartile four versus 3.0 and 3.1 events, respectively, per 100-person years in quartile one.
Figure 1.
The incidence rate of CKD progression events according to quartile of urine biomarker concentration. All urine biomarkers are indexed to urine creatinine. The asterisk (*) shows for quartile analyses of YKL-40 and α-1 microglobulin (a1m), the referent quartile was the combination of both quartile 1 and 2 as 31% of YKL-40 measurements and 34% of a1m measurements were below the limit of detection.
Urine EGF was significantly lower in participants with CKD progression compared with those without CKD progression (P<0.001 for all). After adjustment for covariates, children with urine EGF concentrations in the lowest quartile were at an approximately seven times higher risk of CKD progression compared with those with biomarker concentrations in the highest quartile from bootstrap results (EGF adjusted hazard ratio [aHR]; 7.1, 95% CI, 3.9 to 20.0), which also was below the Bonferroni-corrected statistical significance threshold (P<0.01) (Table 2). Urine KIM-1, MCP-1, YKL-40, and α-1 microglobulin concentrations were significantly higher in patients with CKD progression compared with those without CKD progression (P<0.001 for all). Children with urine KIM-1, MCP-1, and α-1 microglobulin concentrations in the highest quartile were at a significantly higher risk of CKD progression compared with those with biomarker concentrations in the lowest quartiles from bootstrap results: KIM-1 aHR, 3.0; 95% CI, 1.9 to 4.8, MCP-1 aHR, 2.7; 95% CI, 1.8 to 4.2, α-1 microglobulin aHR, 1.7; 95% CI, 1.0– to 2.6 (Table 2). The significance level for urine KIM-1 and MCP-1 was lower than the Bonferroni threshold of P=0.01, although α-1 microglobulin was not. The bootstrap median and 95% CI of the adjusted HR were similar to the observed ones. To evaluate whether including urine creatinine as an adjustment variable (instead of indexing the biomarker to urine creatinine as was done in the primary analysis) affected our results, we conducted an additional analysis and adjusted for urine creatinine in our Cox proportional hazards regression model. We found that either approach of adjusting for or indexing to urine creatinine did not change our inferences (Supplemental Table 5).
Table 2.
Unadjusted and adjusted HRs for the risk of CKD progression according to baseline urine biomarker levels
| Biomarkers | No. of Events No. of Individuals |
Biomarker Alone | Adjusted Model (Plus Age, Sex, Glomerular Diagnosis, BMI z-Score, Hypertension Status) | Adjusted Model (Plus Log2 Urine Albuminuria) | Bootstrap Results of Full Adjusted Model (plus eGFR) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | HR | 95% CI | HR | 95% CI | HR | 95% CI | |||
| EGF | ||||||||||
| Per halvinga | 2.08 | (1.85 to 2.27) | 2.17 | (1.92 to 2.44) | 2.27 | 2.0 to 2.56 | 1.89 | (1.59 to 2.27) | ||
| Quartile 1 | 105/166 | 11.11 | (6.67 to 16.67)a | 16.67 | (9.09 to 25.0)a | 14.29 | (8.33 to 25.0)a | 7.14 | (3.45 to 20.0) | |
| Quartile 2 | 78/166 | 4.55 | (3.13 to 6.25)a | 5.26 | (3.57 to 7.69)a | 4.55 | (3.13 to 6.67)a | 2.78 | (1.67 to 4.76) | |
| Quartile 3 | 48/167 | 2.08 | (1.56 to 2.78)a | 2.27 | (1.67 to 3.03)a | 2.44 | (1.82 to 3.33)a | 1.72 | (1.15 to 2.56) | |
| Quartile 4 | 21/166 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Type 3 P value | <0.001 | <0.001 | <0.001 | <0.001 | ||||||
| KIM-1 | ||||||||||
| Per doublinga | 1.53 | (1.42 to 1.66) | 1.55 | (1.43 to 1.69) | 1.38 | (1.26 to 1.51) | 1.42 | (1.27 to 1.63) | ||
| Quartile 1 | 40/166 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 2 | 54/166 | 1.44 | (0.94 to 2.20) | 1.33 | (0.87 to 2.03) | 0.99 | (0.65 to 1.52) | 1.16 | (0.75 to 1.89) | |
| Quartile 3 | 61/167 | 2.17 | (1.45 to 3.25)a | 2.16 | (1.44 to 3.24)a | 1.29 | (0.86 to 1.96) | 1.43 | (0.96 to 2.29) | |
| Quartile 4 | 97/166 | 4.43 | (3.03 to 6.48)a | 4.41 | (2.99 to 6.50)a | 2.84 | (1.89 to 4.26)a | 3.03 | (1.92 to 4.76) | |
| Type 3 P value | <0.001 | <0.001 | <0.001 | <0.001 | ||||||
| MCP-1 | ||||||||||
| Per doublinga | 1.33 | (1.24 to 1.42) | 1.34 | (1.25 to 1.44) | 1.29 | (1.20 to 1.39) | 1.24 | (1.12 to 1.39) | ||
| Quartile 1 | 40/166 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 2 | 54/166 | 1.53 | (1.02 to 2.31) | 1.62 | (1.07 to 2.44) | 1.17 | (0.77 to 1.77) | 1.18 | (0.78 to 1.79) | |
| Quartile 3 | 61/167 | 2.14 | (1.43 to 3.19)a | 2.18 | (1.45 to 3.27)a | 1.45 | (0.96 to 2.20) | 1.52 | (1.04 to 2.25) | |
| Quartile 4 | 97/166 | 4.05 | (2.80 to 5.86)a | 4.10 | (2.80 to 5.98)a | 2.90 | (1.95 to 4.32)a | 2.72 | (1.75 to 4.18) | |
| Type 3 P value | <0.001 | <0.001 | <0.001 | <0.001 | ||||||
| YKL-40b | ||||||||||
| Per doublinga | 1.02 | (1.00 to 1.04) | 1.04 | (1.02 to 1.06) | 1.01 | (0.99 to 1.03) | 1.01 | (0.99 to 1.02) | ||
| Quartile 1 and 2 | 105/332 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 3 | 61/167 | 1.23 | (0.9 to 1.69) | 1.39 | (1.01 to 1.91) | 1.09 | (0.79 to 1.51) | 1.17 | (0.83 to 1.63) | |
| Quartile 4 | 86/166 | 1.88 | (1.41 to 2.5)a | 2.93 | (2.13 to 4.04)a | 1.81 | (1.30 to 2.50)a | 1.36 | (0.94 to 1.89) | |
| Type 3 P value | <0.001 | <0.001 | 0.001 | 0.198 | ||||||
| α-1 microglobulinb | ||||||||||
| Per doubling | 1.24 | (1.17 to 1.30) | 1.34 | (1.26 to 1.43) | 1.20 | (1.13 to 1.28) | 1.02 | (0.95 to 1.12) | ||
| Quartile 1 and 2 | 55/332 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 3 | 75/166 | 2.30 | (1.67 to 3.16)a | 2.77 | (1.99 to 3.86)a | 1.82 | (1.29 to 2.57)a | 1.36 | (0.89 to 2.02) | |
| Quartile 4 | 102/166 | 3.98 | (2.95 to 5.37)a | 6.16 | (4.47 to 8.48)a | 3.64 | (2.58 to 5.14)a | 1.66 | (1.02 to 2.56) | |
| Type 3 P value | <0.001 | <0.001 | 0.001 | 0.043 | ||||||
All urine biomarkers are indexed to urine creatinine. Bold signifies P value < 0.05 and the 95% CI does not include 1.0, the null value. a1m, alpha-1 microglobulin.
Observed Wald P value crossed Bonferonni threshold of 0.01. Fully adjusted model adjusts for age, sex, glomerular diagnosis, BMI z-score, urine albumin-creatinine ratio, hypertension status, baseline eGFR.
As 31% of YKL-40 measurements and 34% of a1m measurements were below the limit of detection, for quartile analyses of YKL-40 and a1m, the referent quartile was the combination of both quartile 1 and 2. 0.14% of KIM-1 and 0.42% of MCP-1 measurements were below the limit of detection. The primary outcome of CKD progression is defined as a composite of 50% decline in eGFR or kidney failure.
Association of Biomarkers with CKD Progression Stratified by Baseline eGFR and Kidney Disease Subtype
We assessed whether baseline eGFR or kidney disease subtypes modified the relationship between each urine biomarker and CKD progression (Table 3 and Table 4) (Supplemental Table 6). We found that for urine EGF, KIM-1, MCP-1, and YKL-40, there was no significant differences in the effect estimates in children with a baseline eGFR ≥60 ml/min per 1.73m2 versus <60 ml/min per 1.73m2. However, for urine α-1 microglobulin, we observed a significant effect when analyses were stratified by baseline eGFR (Table 3). Among those with a baseline eGFR ≥60 ml/min per 1.73m2, children with the highest quartile of urine α-1 microglobulin concentrations were at ten times higher risk of CKD progression compared with the reference group, whereas among those with a baseline eGFR <60 ml/min per 1.73m2 the risk was essentially the same in the two groups (urine α-1 microglobulin, interaction P value <0.001).
Table 3.
Unadjusted and adjusted HRs for the risk of CKD progression according to baseline urine biomarker levels, stratified by baseline GFR ≥60 versus <60 ml/min per 1.73m2
| Biomarkers | No. of Events /No. of Individuals |
Biomarker Alone | Adjusted Model (Plus Age, Sex, Glomerular Diagnosis, BMI z-score, Hypertension Status) | Adjusted Model (Plus Log2 Urine Albuminuria) | Bootstrap Results of Full Adjusted Model (plus eGFR) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | HR | 95% CI | HR | 95% CI | HR | 95% CI | |||
| α-1 microglobulin (GFR ≥60) | ||||||||||
| Per doubling | 1.23 | (1.06 to 1.43) | 1.34 | (1.12 to 1.6) | 1.12 | (0.95 to 1.33) | 1.10 | (0.91 to 1.49) | ||
| Quartile 1 and 2 | 26/182 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 3 | 9/39 | 1.64 | (0.77 to 3.51) | 2.58 | (1.15 to 5.81) | 1.12 | (0.46 to 2.74) | 1.16 | (0.35 to 3.42) | |
| Quartile 4 | 5/10 | 7.30 | (2.76 to 19.32) | 26.09 | (8.1 to 84.01) | 9.43 | (2.89 to 30.72) | 10.59 | (2.65 to 76.17) | |
| Type 3 P value | 0.0003 | <0.0001 | 0.0007 | 0.0007 | ||||||
| α-1 microglobulin (GFR <60) | ||||||||||
| Per doubling | 1.16 | (1.09 to 1.23) | 1.25 | (1.17 to 1.34) | 1.14 | (1.05 to 1.22) | 0.99 | (0.92 to 1.1) | ||
| Quartile 1 and 2 | 49/150 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. | |
| Quartile 3 | 66/128 | 1.94 | (1.34 to 2.81) | 2.17 | (1.48 to 3.16) | 1.55 | (1.05 to 2.28) | 1.29 | (0.81 to 1.98) | |
| Quartile 4 | 97/156 | 2.82 | (2.00 to 3.98) | 3.90 | (2.72 to 5.60) | 2.53 | (1.73 to 3.69) | 1.39 | (0.82 to 2.26) | |
| Type 3 P value | <0.0001 | <0.0001 | <0.0001 | 0.2933 | ||||||
All urine biomarkers are indexed to urine creatinine. Interaction P value for quartiles of a1m=0.0007. Interaction P value for all other biomarkers >0.05 when stratified by baseline eGFR.
Table 4.
Unadjusted and adjusted HRs for the risk of CKD progression according to baseline urine biomarker levels, stratified by glomerular versus nonglomerular CKD diagnosis
| Biomarkers | No. of Events/ No. of Individuals | Biomarker Alone | Adjusted Model (plus age, sex, glomerular diagnosis, BMI z-score, Hypertension Status) | Adjusted Model (Plus Log2 Urine Albuminuria) | Bootstrap Results of Full Adjusted Model (plus eGFR) | ||||
|---|---|---|---|---|---|---|---|---|---|
| HR | 95% CI | HR | 95% CI | HR | 95% CI | HR | 95% CI | ||
| Glomerular diagnosis | |||||||||
| YKL-40 | |||||||||
| Per doubling | 1.09 | (1.04 to 1.14) | 1.10 | (1.05 to 1.16) | 1.04 | (1.0 to 1.09) | 1.03 | (1.00 to 1.08) | |
| Quartile 1 and 2 | 38/122 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. |
| Quartile 3 | 24/63 | 1.28 | (0.77 to 2.14) | 1.51 | (0.89 to 2.54) | 0.88 | (0.5 to 1.56) | 0.90 | (0.49 to 1.68) |
| Quartile 4 | 20/21 | 11.37 | (6.38 to 20.23) | 15.40 | (8.31 to 28.54) | 5.19 | (2.48 to 10.84) | 3.31 | (1.4 to 8.21) |
| Type 3 P value | <0.0001 | <0.0001 | <0.0001 | 0.0177 | |||||
| α-1 microglobulin | |||||||||
| Per doubling | 1.56 | (1.4 to 1.75) | 1.56 | (1.39 to 1.75) | 1.42 | (1.24 to 1.63) | 1.19 | (0.97 to 1.53) | |
| Quartile 1 and 2 | 29/138 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. |
| Quartile 3 | 26/36 | 5.41 | (3.15 to 9.29) | 5.29 | (3.02 to 9.26) | 2.78 | (1.5 to 5.14) | 2.17 | (1.04 to 4.21) |
| Quartile 4 | 27/32 | 10.71 | (6.24 to 18.39) | 10.73 | (6.13 to 18.78) | 6.29 | (3.39 to 11.7) | 2.95 | (1.08 to 8.91) |
| Type 3 P value | <0.0001 | <0.0001 | <0.0001 | 0.0030 | |||||
| Nonglomerular diagnosis | |||||||||
| YKL-40 | |||||||||
| Per Doubling | 1.01 | (0.99 to 1.03) | 1.02 | (1.0 to 1.04) | 1.00 | (0.98 to 1.02) | 1.00 | (0.98 to 1.02) | |
| Quartile 1 and 2 | 67/210 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. |
| Quartile 3 | 37/104 | 1.20 | (0.8 to 1.8) | 1.34 | (0.89 to 2.01) | 1.17 | (0.78 to 1.76) | 1.30 | (0.85 to 1.95) |
| Quartile 4 | 66/145 | 1.62 | (1.15 to 2.28) | 2.03 | (1.42 to 2.89) | 1.40 | (0.97 to 2.01) | 1.05 | (0.69 to 1.54) |
| Type 3 P value | 0.0202 | 0.0004 | 0.1953 | 0.4894 | |||||
| α-1 microglobulin | |||||||||
| Per Doubling | 1.20 | (1.13 to 1.28) | 1.28 | (1.19 to 1.38) | 1.14 | (1.05 to 1.23) | 0.96 | (0.89 to 1.05) | |
| Quartile 1 and 2 | 46/194 | 1 | Ref. | 1 | Ref. | 1 | Ref. | 1 | Ref. |
| Quartile 3 | 49/131 | 1.85 | (1.23 to 2.76) | 1.96 | (1.31 to 2.95) | 1.31 | (0.86 to 1.99) | 0.88 | (0.54 to 1.37) |
| Quartile 4 | 75/134 | 3.43 | (2.38 to 4.96) | 4.90 | (3.31 to 7.25) | 2.80 | (1.85 to 4.25) | 1.08 | (0.62 to 1.74) |
| Type 3 P value | <0.0001 | <0.0001 | <0.0001 | 0.6613 | |||||
All urine biomarkers are indexed to urine creatinine. Interaction P value for quartiles of YKL-40 <0.0001, a1m=0.0039. Interaction P value for all other biomarkers >0.05 when stratified by glomerular versus nonglomerular CKD diagnosis.
We found that for urine EGF, KIM-1, MCP-1, and α-1 microglobulin, there was no significantly different magnitude of effect when analyses were stratified by glomerular versus nonglomerular etiology (Table 4). However, for urine YKL-40 and α-1 microglobulin, among those with glomerular types of kidney disease, children with the highest quartile of urine YKL-40 concentrations or urine α-1 microglobulin experienced a three-fold higher risk of CKD progression compared to the reference group. Among those with nonglomerular disease, the risk was essentially the same in the two groups (urine α-1 microglobulin, interaction P value = 0.004) (urine YKL-40, interaction P value <0.001). Effect estimates did not differ significantly for all other biomarkers when stratified by baseline eGFR or kidney disease subtype (interaction P value >0.05).
Risk Discrimination and Reclassification
The c-statistic for the clinical model alone (age, sex, BMI, glomerular diagnosis, hypertension status, albuminuria, and eGFR) for CKD progression was 0.80 (95% CI, 0.76 to 0.84) (Table 5). Addition of each individual urine biomarker, EGF, KIM-1, and MCP-1, to the clinical model increased the c-statistic to 0.82 (95% CI, 0.79 to 0.86), 0.83 (95% CI, 0.79 to 0.86), and 0.81 (95% CI, 0.78 to 0.85), respectively. Addition of all five urine biomarkers increased the c-statistic to 0.85 (95% CI, 0.82 to 0.88). Urine EGF and KIM-1 provided significant reclassification as indicated by a net reclassification index of 0.32 (95% CI, 0.08 to 0.42) and 0.31 (95% CI, 0.13 to 0.41), respectively. Addition of all five urine biomarkers yielded a net reclassification index of 0.40 (95% CI, 0.24 to 0.48). The integrated discrimination improvement, which is the difference in mean predicted probabilities of event between children with and without event, was 0.08 (95% CI, 0.05 to 0.12) for the clinical model plus the urine biomarkers. The calibration curve for the probability of CKD progression over the full follow-up time displayed only a modest fit between the actual observed risk of CKD progression and the risk of CKD progression predicted by the clinical model and all urine biomarkers (Supplemental Figure 2).
Table 5.
Discrimination of CKD progression from values in the lowest quartile of EGF and highest quartile of KIM-1, MCP-1, YKL-40, and a1m
| Models | Uno C-statistic (95% CI) | Difference in C-statistic (95% CI) | Integrated Discrimination Improvement (95% CI) | Continuous NRI (95% CI) |
|---|---|---|---|---|
| Clinical model (as reference) | 0.80 (0.76, 0.84) | |||
| Clinical model plus biomarker | ||||
| KIM-1 | 0.83 (0.79, 0.86) | 0.03 (0.01 to 0.04) | 0.05 (0.02 to 0.07) | 0.31 (0.13 to 0.41) |
| MCP-1 | 0.81 (0.78, 0.85) | 0.01 (0.01 to 0.02) | 0.01 (0.00 to 0.03) | 0.06 (-0.01 to 0.21) |
| EGF | 0.82 (0.79, 0.86) | 0.02 (0.01 to 0.04) | 0.03 (0.01 to 0.06) | 0.32 (0.08 to 0.42) |
| YKL-40 | 0.80 (0.76, 0.84) | 0.00 (0.00 to 0.01) | 0.01 (0.00 to 0.02) | 0.05 (-0.12 to 0.16) |
| a1M | 0.80 (0.77, 0.84) | 0.00 (0.00 to 0.01) | 0.01 (0.00 to 0.02) | 0.06 (-0.21 to 0.17) |
| Clinical model plus biomarker combinations | ||||
| KIM-1 and EGF | 0.84 (0.81, 0.87) | 0.04 (0.02 to 0.06) | 0.07 (0.04 to 0.11) | 0.29 (0.18 to 0.41) |
| plus MCP-1 | 0.84 (0.81, 0.87) | 0.04 (0.02 to 0.06) | 0.07 (0.05 to 0.11) | 0.34 (0.22 to 0.46) |
| plus YKL-40 | 0.84 (0.81, 0.87) | 0.04 (0.02 to 0.06) | 0.07 (0.04 to 0.11) | 0.25 (0.16 to 0.42) |
| plus a1m | 0.84 (0.81, 0.87) | 0.04 (0.02 to 0.06) | 0.07 (0.05 to 0.10) | 0.28 (0.19 to 0.41) |
| Clinical with all biomarkers | 0.85 (0.82, 0.88) | 0.05 (0.02 to 0.07) | 0.08 (0.05 to 0.12) | 0.40 (0.24 to 0.48) |
All urine biomarkers are indexed to urine creatinine. Clinical model includes age, sex, glomerular diagnosis, BMI z-score, hypertension status, log2 urine albuminuria, and eGFR.
Discussion
We observed that children in the lowest quartile of urine EGF or the highest quartile of urine KIM-1, MCP-1, or α-1 microglobulin, measured a median of 5 months after study enrollment, were at increased risk of experiencing CKD progression during follow-up even after adjusting for established risk factors including age, sex, glomerular diagnosis, BMI, hypertension, albuminuria, and eGFR. After multivariable adjustment, children with urine EGF in the lowest quartile were at a seven-fold higher risk of CKD progression versus those in the highest quartile. Additionally, those children with KIM-1, MCP-1, or α-1 microglobulin concentrations in the highest quartile were at a 3.0, 2.7, and 1.7-fold higher risk, respectively, of progressing to the composite endpoint compared with those in the lowest quartile. In stratified analyses, we observed that the high quartiles of α-1 microglobulin and YKL-40 were independently associated with CKD progression in children with glomerular causes of CKD, but not in children with nonglomerular causes of CKD. We also observed that α-1 microglobulin was independently associated with CKD progression in children with a baseline GFR ≥60 ml/min per 1.73m2 but not in children with a baseline GFR <60 ml/min per 1.73m2. The addition of all five urine biomarkers significantly increased the c-statistic of the clinical model from 0.80 (95% CI, 0.76 to 0.84) to 0.85 (95% CI, 0.82 to 0.88).
We observed that low levels of urine EGF were very strongly and independently associated with an increased risk of CKD progression. EGF binds to the EGF receptor, which plays essential roles in cell signaling pathways that regulate cell proliferation and differentiation.26 In the kidney, EGF acts as a proproliferative protein that mediates kidney tubular cell differentiation, repair, and regeneration.27 Animal models have shown that after kidney ischemia, exogenous EGF can regulate tubule cell recovery and accelerate repair.28 EGF also appears to be a marker of functional tubular cell mass and in multiple studies, including in this study, urine EGF concentrations have a strong direct relationship with eGFR.29 Urine EGF levels have previously been shown to have an inverse association with interstitial fibrosis and tubular atrophy.11 In the 4C cohort, which enrolled 623 children with mostly nonglomerular types of kidney disease, lower urine EGF was independently associated with eGFR decline.12 In a study of 117 children with Alport’s syndrome, lower levels of urine EGF were found to be strongly and independently associated with CKD progression.13 Additionally, in three independent cohorts of adults with glomerular diseases, lower urine EGF concentration was shown to be strongly correlated with tubular atrophy and interstitial fibrosis and was associated with eGFR decline.11 In our study, the strong association between EGF and CKD progression was not attenuated despite adjustment for eGFR and albuminuria, suggesting a role in key pathways of CKD progression not indicated by these conventional markers.
We also observed that higher urine KIM-1 is independently associated with CKD progression. KIM-1 is a protein found on the apical membrane of proximal tubular cells and is highly expressed with any form of proximal tubule injury.16,30 The ectodomain of KIM-1 is cleaved and released into the tubular lumen.16,31 In our previous research, we observed a significant and strong unadjusted and adjusted association between plasma KIM-1 and CKD progression, consistent with our present findings with urine KIM-1 in the CKiD cohort.7 Urine KIM-1 may better reflect the variability with changes in tubular injury, whereas the steady state plasma KIM-1 levels may capture ongoing chronic injury. Prior studies have documented that urine KIM-1 is independently associated with CKD progression.16,32 In a study of 145 renal transplant patients, the highest tertile of urinary KIM-1 was associated with eGFR decline over 5 years of follow-up with an adjusted HR of 5.1 (95% CI, 1.5 to 17.8), when compared with the first tertile.33 The prognostic role of urine KIM-1 in both pediatric and adult cohorts suggest it could be a promising biomarker of CKD progression. Furthermore, urine KIM-1 is Food and Drug Administration qualified as a safety biomarker to detect tubular injury and our results suggest urine KIM-1 may have utility in clinical trials enrolling children.34
We found that high urine MCP-1 was also associated with an increased risk of CKD progression. MCP-1 is a member of the CC chemokine family, which recruits monocytes and promotes their transformation into macrophages.35 In patients with diabetic nephropathy, urine MCP-1 levels correlate with kidney macrophage accumulation and fibrosis.19 Urine MCP-1 levels also associated with eGFR slope in adults with diabetic and nondiabetic CKD.36 In one study of 380 adults with diabetic kidney disease, the highest quartile of urine MCP-1 was associated with a five-fold increased risk of CKD progression in adjusted analyses (odds ratio, 5.27; 95% CI, 2.19 to 12.71) although notably high levels of urine KIM-1 were not associated with CKD progression. In our study, we observed that MCP-1 and KIM-1 concentrations were highly correlated (ρ=0.70), which suggests a close relationship and that similar information is conveyed by urine concentrations of these two proteins in this pediatric CKD cohort. Our urine MCP-1 results were also notable in the context of our previous plasma MCP-1 results in the same pediatric population, in which there was no difference in plasma MCP-1 levels in children with and without CKD progression.7 Validation of our urine MCP-1 results are needed in other cohorts of pediatric kidney disease.
We also observed that higher urine α-1 microglobulin and YKL-40 concentrations are independently associated with CKD progression, but only in the subgroup of children with glomerular causes of CKD. Additionally, we observed that higher urine α-1 microglobulin was only associated with CKD progression in children with a baseline eGFR ≥60 ml/min per 1.73m2. α-1 microglobulin is normally filtered at the glomerulus and then reabsorbed by proximal tubular cells. As such, α-1 microglobulin detected in the urine is an early biomarker of proximal tubular dysfunction. YKL-40 is upregulated in kidney macrophages after ischemia-reperfusion injury and contributes to the repair of tubular epithelium.18 Our findings highlight the opportunity to study urine α-1 microglobulin and YKL-40 in children with glomerular disease, because children with glomerular disease can progress rapidly to kidney failure. Additionally, novel biomarkers are needed in those with a baseline eGFR ≥60 because eGFR is a less reliable predictor of CKD progression in this group.37
Our study has several limitations. We acknowledge that multiple statistical testing in our study increases the risk of false positive results; however, we also evaluated significance using a conservative Bonferroni P value of 0.01. The use of continuous NRI to evaluate clinical risk prediction is also a limitation as reclassification of risk, although significant, may be small and not clinically meaningful. As an example, a urine biomarker may change a child’s predicted risk of CKD progression to be reclassified from 98%–99%, which may be a correct reclassification but clinically insignificant. We chose to use the continuous NRI because category-based version of the NRI is less meaningful when there are no clinically relevant risk thresholds.38 We also observed a disparity between the predicted and observed risks on the calibration curve as our model underestimated the observed risk. The disparity between predicted and observed risks may be due to including children with both glomerular and nonglomerular etiologies of kidney disease who have significantly different patterns and rates of CKD progression. Additionally, we used Cox models to be consistent with other research on biomarkers of CKD progression, but have found that CKD progression data in children may be better modeled using a lognormal fit. Because there are >30 primary diagnoses included in the study, we are underpowered to study specific diagnoses, such as focal segmental glomerulosclerosis, hemolytic uremic syndrome, obstructive uropathy, or kidney dysplasia. However, the wide range of primary diagnoses, prospective multicenter design, and minimal loss to follow-up are study strengths. Another strength of our study was that we conducted extensive validation testing of the urine biomarker assay and have verified several assay attributes including spike-in recovery, limit of detection, effect of interfering substances, calibration, and freeze-thaw stability.
In conclusion, we demonstrated that lower urine EGF and higher urine KIM-1, MCP-1, and α-1 microglobulin concentrations are independently associated with CKD progression in children. Notably, the strength of association was particularly strong for urine EGF, which maintained a robust association with CKD progression that was independent of baseline albuminuria and eGFR, and was similar across subtypes of kidney disease and levels of baseline eGFR. This suggests that urine EGF may be functioning in critical pathways of tubule regeneration and kidney health that are independent of conventional predictors and etiology of CKD. Although urine KIM-1, MCP-1, and α-1 microglobulin are putative biomarkers of CKD progression, urine EGF may be protective and may identify a specific mechanistic pathway that can be therapeutically targeted to limit CKD progression. Consideration of measured urine biomarkers that reflect kidney tubule health, proximal tubular injury, and inflammation increased the ability to discriminate between those children who did and who not experience progressive CKD. If validated in further studies, these urine biomarkers may potentially be used to more broadly characterize kidney disease and improve clinical monitoring of CKD progression in children. Serial monitoring of patients using this urine biomarker panel may lead to the early identification and prompt treatment of those at the highest risk of CKD progression. Moreover, urine biomarkers could be used to identify children at high risk of CKD progression for the purpose of enrolling them into therapeutic trials. These high-risk individuals would be the most likely to develop kidney failure and may benefit the most from a novel intervention. This prognostic enrichment approach would enable early-phase clinical trials to be conducted in a higher risk population with fewer patients.
Disclosures
A. Abraham reports receiving honoraria from the National Institutes of Health (NIH) for service on a data monitoring board; and reports being a scientific advisor or member of the Rare Kidney Stone disease study data monitoring board, and an Associate Editor of American Journal of Epidemiology. B. Warady reports consultancy agreements with Amgen, Akebia, Bayer, Glaxosmithkline, Reata, Relypsa, and UpToDate; reports receiving research funding from Baxter Healthcare; reports receiving honoraria from Akebia, GlaxoSmithKline, Relypsa, Reata, and UpToDate; and reports being a scientific advisor or member with North American Pediatric Renal Trials and Collaborative Studies, National Kidney Foundation, and Nephrologists Transforming Dialysis Safety Board of Directors. C. Parikh reports consultancy agreements with Genfit Biopharmaceutical Company; reports having an ownership interest in RenaltixAI; reports receiving research funding from National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Heart, Lung and Blood Institute; and reports being a scientific advisor or member of Genfit Biopharmaceutical Company and Renalytix. H. Feldman reports having consultancy agreements with Kyowa Hakko Kirin (ongoing), National Kidney Foundation (ongoing), InMed, Inc., and Dibb Lupton Alsop Piper; reports receiving honoraria from the Rogosin Institute (invited speaker); reports being a scientific advisor or member via the American Journal of Kidney Disease Editor-in-Chief, National Kidney Foundation (Member of Advisory Board), and the Steering Committee for Chronic Renal Insufficiency Cohort Study. J. Ix reports consultancy agreements with Ardelyx, AstraZeneca, Bayer, Jnana, and Sanifit; reports receiving research funding from Baxter International; and reports being a scientific advisor or membership with AlphaYoung. J. Schelling reports receiving honoraria from Gerson Lehrman and Spherix; and reports being a scientific advisor or member via Board of Directors of the Kidney Foundation of Ohio, and Editorial Board of Kidney360. J.V. Bonventre declares KIM-1 Patents assigned to Partners Healthcare: Intellectual Property (patent rights, royalty payments); reports having ownership interests (stock or stock options; excluding diversified mutual funds) in Goldfinch and Medibeacon; reports consultancy agreements with Aditum, Citrine, Janssen, MediBeacon, Praxis, Renalytix, and Sarepta; reports having an ownership interest in Autonomous Medical Devices, Coegin, Dicerna, Goldfinch, Goldilocks, Medibeacon, Pacific Biosciences, Rubius, Sentien, Theravance, and Verinano; and reports patents and inventions for Kim-1 patents assigned to Mass General Brigham, Kidney organoid patents assigned to Mass General Brigham; and reports being a scientific advisor or member as Editor for Seminars in Nephrology, and the Advisory Board of Northwest Kidney Center, Angion, C-Path AKI Biomarker Initiative, Kidney Health Initiative (KHI) AKI Biomarker Initiative, and Wearable Artificial Organs. M. Denburg reports consultancy agreements with Trisalus Life Sciences (spouse); reports an ownership interest in In-Bore (spouse) and Precision Guided Interventions (spouse); reports receiving research funding from Mallinckrodt; reports being a scientific advisor or member of the National Kidney Foundation Delaware Valley Medical Advisory Board and Trisalus Life Sciences Scientific Advisory Board (spouse); and reports other interests/relationships with the American Society of Pediatric Nephrology Research and Program Committees, and National Kidney Foundation Pediatric Education Planning Committee. M. Shlipak reports consultancy agreements with Cricket Health, Intercept Pharmaceuticals, University of Washington Cardiovascular Health Study, University of North Carolina at Chapel Hill, and Veterans Medical Research Foundation; reports having an ownership interest in Transplant and Immunology Diagnostics; reports receiving research funding from Bayer Pharmaceuticals; reports receiving honoraria from University of California at Irvine; reports being a scientific advisor or member of the American Journal of Kidney Disease, Circulation, Journal of the American Society of Nephrology, TAI Diagnostics; and reports other interests/relationships as a Board Member of Northern California Institute for Research and Education. P. Kimmel reports other interests/relationships as Co-Editor of Chronic Renal Disease Academic Press, Co-Editor of Psychosocial Aspects of Chronic Kidney Disease; and reports receiving Academic Press Royalties. S. Coca reports consultancy agreements with Akebia, Bayer, Boehringer Ingelheim, Congestive Heart Failure Solutions, Quark, RenalytixAI, Relypsa, and Takeda; reports having an ownership interest in pulseData and RenalytixAI; reports receiving research funding from inRegen and RenalytixAI; reports patents and inventions with RenalytixAI; reports being a scientific advisor or member of RenalytixAI; and reports other interests/relationships as Associate Editor for Kidney360, Editorial Board CJASN, JASN, and Kidney International. S. Waikar reports consultancy agreements with Allena, BioMarin, Consumer Value Stores, GlaxoSmithKline, Johnson & Johnson, Mallinckrodt, Mass Medical International, Metro Biotechnology, Oxidien, Pfizer, Regeneron, Roth Capital Partners, Sironax, Strataca/3ive, Venbio, and Wolters Kluwer; reports receiving research funding from Vertex; reports being a scientific advisor or member with Kantum (scientific advisory board); and other interests/relationships as an expert witness for litigation related to the General Electric product Omniscan, expert witness for litigation related to Fresenius product Granuflo, expert witness for litigation involving cisplatin toxicity, expert witness for litigation related to Gilead product tenofovir, and expert witness for litigation related to DaVita lab testing. V. Ramachandran reports consultancy agreements with NIDDK. V. Sabbisetti reports patents and inventions via patents on plasma Kim-1. All remaining authors have nothing to disclose.
Funding
This research was supported by NIH career development grant K08DK110536 (to J. Greenberg). This research was also supported by the CKD Biomarkers Consortium (NIDDK grants U01 DK085689, U01 DK102730, U01 DK103225, U01 DK085660) to J. Bonventre, S. Coca, H. Feldman, S. Furth, J. Ix, M. Jogalekar, C. Parikh, V. Sabbisetti, J. Schelling, M. Shlipak, R. Vasan, S. Waikar, and Y. Xu. C. Parikh is supported by NIH grants K24DK090203, R01HL085757, U01DK082185, and P30DK079310-07 O'Brien Center Grant. J. Bonventre is supported by NIH grants R37DK039773 and R01DKD072381. S. Furth is supported by the NIH K24DK078737 and U01DK66174. The CKiD study is funded by the NIDDK, with additional funding from the National Institute of Child Health and Human Development, and the National Heart, Lung, and Blood Institute (U01-DK-66143, U01-DK-66174, U01DK-082194, U01-DK-66116).
Supplementary Material
Acknowledgments
Data in this manuscript were collected by the CKiD prospective cohort study with clinical coordinating centers (Principal Investigators) at Children’s Mercy Hospital and the University of Missouri– Kansas City (B. Warady) and Children’s Hospital of Philadelphia (S. Furth), Central Biochemistry Laboratory (G. Schwartz) at the University of Rochester Medical Center, and data coordinating center (A. Muñoz) at the Johns Hopkins Bloomberg School of Public Health. The CKiD website is located at http://www.statepi.jhsph.edu/ckid. The biomarker analyses were performed in the CKD Biomarker consortium Central Laboratory (J. Bonventre, V. Sabbisetti). The views presented in this paper are those of the authors, and do not necessarily reflect those of the NIDDK, the NIH, the Department of Health and Human Services, or the government of the United States of America. J. Bonventre, S. Coca, S. Furth, J. Greenberg, M. Jogalekar, J. Schelling, H. Feldman, J. Ix, M. Jogalekar, P. Kimmel, C. Parikh, M. Shlipak, S. Waikar, R. Vasan, and Y. Xu designed the study. J. Bonventre and V. Sabbisetti carried out experiments and acquired data. S. Furth, J. Greenberg, and Y. Xu analyzed the data. J. Greenberg and Y. Xu made the figures. J. Bonventre, S. Coca, H. Feldman, J. Greenberg, S. Furth, J. Ix, M. Jogalekar, P. Kimmel, C. Parikh, V. Sabbisetti, J. Schelling, M. Shlipak, R. Vasan, S. Waikar, B. Warady, Y. Xu, drafted and revised the paper. All authors approved the final version of the manuscript.
Footnotes
Published online ahead of print. Publication date available at www.jasn.org.
Contributor Information
Collaborators: CKD Biomarker consortium, Vasan S. Ramachandran, Jeffrey Schelling, Michelle Denburg, Susan Furth, Bradley Warady, Joseph Bonventre, Sushrut Waikar, Venkata Sabbisetti, Josef Coresh, Morgan Grams, Casey Rebholz, Alison Abraham, Chirag Parikh, Steven Coca, Eugene Rhee, Paul L. Kimmel, John W. Kusek, Brad Rovin, Michael G. Shlipak, Mark Sarnak, Orlando M. Gutiérrez, Joachim Ix, Ruth Dubin, Tom Hostetter, Rajat Deo, Harold I. Feldman, Dawei Xie, Haochang Shou, Shawn Ballard, Krista Whitehead, Heather Collins, Jason H. Greenberg, and Peter Ganz
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021010094/-/DCSupplemental.
Supplemental Table 1. Biomarker intra- and interassay coefficients of variation.
Supplemental Table 2. Indexed, raw and log-transformed biomarker concentrations.
Supplemental Table 3. Baseline characteristics by quartile of urine EGF concentration.
Supplemental Table 4. Spearman correlations of biomarker concentrations and baseline participant characteristics.
Supplemental Table 5. Adjusted HRs for the risk of CKD progression using methods of indexing to urine creatinine versus adjusting for urine creatinine.
Supplemental Table 6. P value for interactions in continuous and quartile biomarker models.
Supplemental Figure 1. Study population flow chart.
Supplemental Figure 2, Calibration curve displaying the observed risk of CKD progression and predicted risk of CKD progression.
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