Introduction
Cardiovascular disease (CVD) is highly prevalent in children with CKD. As CKD progresses and kidney function declines in children, the risk of CVD increases. Young adults with a history of childhood onset ESKD have as much as a 10–100-fold increased risk of coronary artery calcification, carotid arteriopathy, and left ventricular hypertrophy (LVH), compared with the general population.1 LVH, an increase in left ventricular mass (LVM), is an adaptive response to a chronically increased workload.1 LVH is considered an early or intermediate marker of CVD because it is strongly associated with an increased risk of future cardiovascular morbidity and mortality.2 As kidney function declines, the occurrence of LVH in children increases.3 In multiple studies, the prevalence of LVH in children with CKD was approximately 10%–35% of the overall study population.2,4–7
We have hypothesized that many of the biomarkers that are associated with CKD progression may also be associated with LVH development because CKD is strongly associated with LVH. Biomarkers of tubular injury, tubular dysfunction, and inflammation could potentially provide additional insight beyond traditional biomarkers, such as serum creatinine and proteinuria, as we strive to understand the pathophysiology of LVH in children with CKD. Kidney biomarkers may be used to characterize tubular health which plays a key role in the excretion of salt and water. Impaired salt and water excretion can lead to volume expansion, an increase in cardiac output, hypertension (HTN), and LVH.8 Furthermore, the reduced nephron mass and poor tubular health of CKD can activate the renin angiotensin system, adrenergic system, sympathetic nervous system, and cause endothelial dysfunction, leading to an increased risk of HTN and LVH.9 Currently, there is a paucity of research on biomarkers of LVH in children with CKD. In a study of children with CKD by Mitsnefes et al., plasma fibroblast growth factor 23 (FGF23) was associated with a two-fold greater risk of LVH.10 Because CVD affects the long-term morbidity and mortality of children with CKD, a better understanding of LVH is critical to elucidate its pathophysiology and discover effective treatments.3
In prospective cohort studies of children with CKD, plasma biomarkers of tubular injury (kidney injury molecule-1 [KIM-1]), inflammation (tumor necrosis factor receptor [TNFR]-1, TNFR-2, soluble urokinase-type plasminogen activator receptor [suPAR]), and urine biomarkers of tubular injury (KIM-1), tubular dysfunction (alpha-1-microglobulin [alpha-1m]), tubular health (epidermal growth factor [EGF]), and inflammation (monocyte chemoattractant protein-1 [MCP-1], YKL-40) were found to be associated with CKD progression.11,12 In this present investigation, we analyzed these plasma and urine biomarkers in the CKD in children (CKiD) multicenter cohort study to determine their associations with LVH.
Methods
Study Participants
Participants were from the CKiD cohort, a study of children with CKD from 54 medical centers in the United States and Canada from 2006 through 2016.13 The children had a baseline eGFR of 30–90 ml/min per 1.73 m2 and were between the ages of 6 months and 16 years at study entry. Children who had a history of kidney, solid organ, or bone marrow transplantation; dialysis within 3 years of diagnosis; or a history of cancer were excluded from enrollment. Assent and written informed consent were provided by children and their parents or legal guardians, respectively. The CKiD study received approval by each participating organization's Institutional Review Board and has been registered with the identifier NCT00327860 on ClinicalTrials.gov. For our study, we included CKiD participants if they had sufficient plasma and urine volume stored and data on eGFR, urine protein-to-creatinine ratio (urine Pr/Cr), BP, glomerular diagnosis (GD), and body mass index (BMI) at study entry, as well as an echocardiographic assessment performed 1 year after study enrollment.12
Plasma and Urine Biomarker Measurements
The exposures included plasma biomarker and urine biomarker levels indexed to urine creatinine. Plasma and urine samples were collected from participants a median of 5 months after CKiD study enrollment. All urine specimens used for biomarker measurements were from spot urine collections. Stored plasma and urine biospecimens were centrifuged and supernatents were aliquoted. The aliquots were labeled with a barcode and were stored at -80°C until the biomarker assays were measured in a central, clinical outcome-blind laboratory. All biomarkers were measured in a batch, and all measurements were performed in the Brigham and Women's Hospital Central Biomarker Consortium Laboratory.
Eight biomarkers were selected based on a literature review of CKD biomarkers and previous studies within the CKD Biomarker Consortium with consideration for mechanisms of CKD progression. A Meso Scale Discover assay was used to measure plasma KIM-1, TNFR-1, TNFR-2, and suPAR (Meso Scale Discovery, Gaithersburg, MD). A Luminex multiplex assay (Luminex Corporation, Austin, TX) was used to measure urine EGF, KIM-1, MCP-1, and YKL-40. All assays were conducted in duplicate, and the mean values for each biomarker were used in the analysis. A Siemen's nephelometer (Siemens, Munich, Germany) was used to measure urine alpha-1m. All intra-assay and inter-assay coefficients of variation were <10% (Supplemental Table 1).
Values below the limits of detection for urine biomarkers (KIM-1: 27.43 pg/ml, MCP-1: 5.49 pg/ml, YKL-40: 65.84 pg/ml, EGF: 6.86 pg/ml, alpha-1m: 5.64 mg/ml) and plasma biomarkers (KIM-1: 1.98 pg/ml, TNFR-1: 0.667 pg/ml, TNFR-2: 0.168 pg/ml, suPAR: 53 pg/ml) were imputed based on a lognormal distribution. This imputation method, truncated lognormal imputation, uses the distribution of observed data to recreate the lower tail of the distribution and then randomly draws a value to replace the missing information below the limit of detection.12 Nondetectable values were observed in 171, 156, 2, and 1 participant(s) for urine alpha-1m, urine YKL-40, urine MCP-1, and urine KIM-1, respectively. Plasma biomarkers and indexed urine biomarker values were considered continuous variables after transforming to log2 in analyses.
Cardiovascular Outcomes from Echocardiograms
The outcome was LVH determined based on echocardiographic assessments conducted 1 year after enrollment in the CKiD cohort. M-mode and Doppler echocardiography were conducted at individual participating centers. Reading and analysis were performed by the Cardiovascular Core Imaging Research Laboratory at Cincinnati Children's Hospital Medical Center. LVH was defined as a LVM index (LVMI) ≥95th percentile for healthy children, and LVM was measured by two-dimensional directed M-mode echocardiography at rest according to the American Society of Echocardiography.2,14,15 LVM was indexed to height (mass [g]/height [m2.7]) to account for body size, but because LVMI does not adequately account for changes due to growth, we also expressed LVMI as Z-scores on the basis of age and sex.14,16 To have standardization and uniformity among the echocardiographic images across the centers, the qualifying recordings were sent to the Cardiovascular Core Imaging Research Laboratory.2
Clinical and Laboratory Covariates
BMI was standardized for age and sex. HTN was defined as a systolic or diastolic BP ≥95th percentile for height, age, and sex. Glomerular and nonglomerular distinction was made for classification of kidney disease.17 We calculated eGFR on the basis of serum creatinine, cystatin C, and blood urea nitrogen concentrations from the modified Schwartz equation which is derived from the CKiD population.18 All serum creatinine measurements were performed in the CKiD central laboratory at the University of Rochester.
Statistical Analysis
The primary analysis, a cross-sectional association of log2-transformed plasma and urine biomarker levels with prevalence of LVH, was estimated using a Poisson regression, with adjustment for demographics (age, sex, and race) and clinical variables (BMI, HTN, GD [yes/no], urine Pr/Cr, and eGFR at study entry). Two nested models were used to assess the influence of key clinical CKD factors on biomarker relationships: one partially adjusted model included age, sex, race, BMI, HTN, and GD and a second fully adjusted model that additionally included eGFR and urine Pr/Cr measured at study enrollment. In a secondary analysis, we assessed the cross-sectional association between log2-transformed plasma and urine biomarker levels and LVMI Z-score. Generalized estimating equations were used for a robust estimation of the error variance to account for potential overdispersion. Subgroup analysis by glomerular versus nonglomerular CKD status evaluated the potential heterogeneity of biomarker relationships by diagnosis. The presence of effect modification was tested by adding an interaction term of biomarker and diagnosis of glomerular CKD to models. Significant associations were recognized with P values <0.05. 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
Of the 619 children with available biomarker measurements, 115 were excluded due to missing echocardiographic assessments, leaving 504 in the final analyses (Figure 1). The median age was 12 years (interquartile range [IQR], 8–15), 311 (62%) were male, 94 (19%) were Black, 67 (13%) were Hispanic, 156 (31%) had a glomerular cause of CKD, 85 (17%) had HTN, median baseline eGFR was 54 (IQR, 41–68) ml/min per 1.73 m2, and median urine Pr/Cr was 0.32 (IQR, 0.11–0.94) g/g (Table 1). Spearman correlations between baseline plasma and urine biomarker levels and characteristics of the patient population are presented in Supplemental Table 2. The median LVMI Z-score was 0.15 (IQR, −0.74 to 1.04), and the overall prevalence of LVH was 12% (n=59) (Figure 1).
;)
Figure 1.: Echocardiogram data were taken during the second visit, 6 months after their first visit. Box plots show mean, upper and lower quartiles, and range of LVMI (A) for all enrolled participants, children without LVH, and children with LVH. LVH, left ventricular hypertrophy; LVMI, left ventricular mass index.
Table 1. -
Baseline characteristics by type of CKD
Characteristic |
Overall (N=504) |
Nonglomerular (n=348) |
Glomerular (n=156) |
| Age, yr |
12 (8–15) |
10 (7–14) |
14 (11–16) |
| Male, % (n) |
62 (311) |
64 (223) |
56 (88) |
| Black, % (n) |
19 (94) |
15 (51) |
28 (43) |
| Hispanic, % (n) |
13 (67) |
13 (46) |
14 (21) |
| CKD time, yr |
8 (4–12) |
9 (6–13) |
3 (1–6) |
| Height Z-score |
−0.5 (−1.3 to 0.3) |
−0.6 (−1.4 to 0.1) |
−0.1 (−1 to 0.7) |
| Weight Z-score |
0.1 (−0.7 to 1.0) |
−0.1 (−0.9 to 0.7) |
0.7 (−0.2 to 1.6) |
| BMI Z-score |
0.4 (−0.3 to 1.3) |
0.2 (−0.4 to 1.1) |
0.9 (0.2–1.6) |
| eGFR, ml/min/1.73 m2
|
54 (41–68) |
51 (39–63) |
63 (48–79) |
|
CKD stage, % (n)
|
|
|
|
| 1 (≥60 ml/min per 1.73 m2) |
5 (24) |
2 (7) |
11 (17) |
| 2 (≥60 ml/min per 1.73 m2) |
33 (164) |
27 (94) |
45 (70) |
| 3a (45–60 ml/min per 1.73 m2) |
30 (153) |
34 (117) |
23 (36) |
| 3b (30 to <45 ml/min per 1.73 m2) |
25 (125) |
29 (101) |
15 (24) |
| 4–5 (<30 ml/min per 1.73 m2) |
8 (38) |
8 (29) |
6 (9) |
| Blood urea nitrogen, mg/dl |
24 (17–31) |
24 (17.5–32) |
22 (16–30) |
| HTN, % (n) |
17 (85) |
19 (65) |
13 (20) |
| Hemoglobin, g/dl |
12.7 (11.7–13.7) |
12.8 (11.8–13.7) |
12.4 (11.6–13.6) |
| Urine protein-to-creatinine, g/g |
0.32 (0.11–0.94) |
0.24 (0.1–0.67) |
0.62 (0.21–1.62) |
| RAAS inhibitor, % (n) |
57 (285) |
45 (155) |
83 (130) |
| Anti-HTN drugs, % (n) |
10 (52) |
10 (36) |
10 (16) |
| Premature birth (<36 wk), % (n) |
11 (52) |
11 (38) |
9 (14) |
BMI, body mass index; HTN, hypertension; RAAS, renin–angiotensin–aldosterone system.
Plasma and Urine Biomarkers Associated with LVH Risk and LVMI Z-Score
We examined the associations of plasma and urine biomarker concentrations with prevalent LVH. In the unadjusted model, the following plasma and urine biomarker levels were associated with LVH: plasma KIM-1 (prevalence ratio [PR] per log2: 1.37, 95% confidence interval [CI], 1.17 to 1.60), plasma suPAR (PR: 1.61, 95% CI, 1.09 to 2.38), urine KIM-1 (PR: 1.28, 95% CI, 1.11 to 1.48), urine MCP-1 (PR: 1.29, 95% CI, 1.15 to 1.46), and urine EGF (PR: 0.78, 95% CI, 0.65 to 0.94). In the fully adjusted model accounting for demographics and all clinical characteristics, including eGFR and urine Pr/Cr, only two-fold higher levels of plasma KIM-1 (PR: 1.27, 95% CI, 1.02 to 1.58), urine KIM-1 (PR: 1.21, 95% CI, 1.02 to 1.44), and urine MCP-1 (PR: 1.18, 95% CI, 1.06 to 2.92) were positively associated with LVH prevalence. Two-fold higher levels of urine alpha-1m (PR: 0.90, 95% CI, 0.82 to 0.99) were associated with a lower prevalence of LVH (Table 2).
Table 2. -
Plasma and urine biomarker prevalence ratio from first visit for prevalence of left ventricular hypertrophy in CKD in children cohort
Biomarker, Per Doubling |
Unadjusted |
Adjusted Model (Plus Age, Gender, Race, BMI, HTN Status, GD) |
Full Adjusted Model (Plus eGFR, Urine Pr/Cr) |
|
PR |
95% CI |
P Value |
PR |
95% CI |
P Value |
PR |
95% CI |
P Value |
|
Plasma
|
|
|
|
|
|
|
|
|
|
| KIM-1 |
1.37a
|
(1.17 to 1.60)a
|
<0.001a
|
1.39a
|
(1.17 to 1.65)a
|
<0.001a
|
1.27a
|
(1.02 to 1.58)a
|
0.031a
|
| TNFR-1 |
1.24 |
(0.97 to 1.58) |
0.085 |
1.36a
|
(1.09 to 1.71)a
|
0.007a
|
1.01 |
(0.66 to 1.54) |
0.99 |
| TNFR-2 |
1.43 |
(0.91 to 2.27) |
0.123 |
1.53a
|
(1.01 to 2.30)a
|
0.045a
|
1.06 |
(0.62 to 1.82) |
0.83 |
| suPAR |
1.61a
|
(1.09 to 2.38)a
|
0.016a
|
1.48a
|
(1.04 to 2.12)a
|
0.030a
|
1.05 |
(0.67 to 1.66) |
0.83 |
|
Urine
|
|
|
|
|
|
|
|
|
|
| KIM-1 |
1.28a
|
(1.11 to 1.48)a
|
0.001a
|
1.29a
|
(1.10 to 1.50)a
|
0.002a
|
1.21a
|
(1.02 to 1.44)a
|
0.030a
|
| MCP-1 |
1.29a
|
(1.15 to 1.46)a
|
<0.001a
|
1.25a
|
(1.10 to 1.41)a
|
<0.001a
|
1.18a
|
(1.04 to 1.34)a
|
0.012a
|
| YKL-40 |
1.00 |
(0.97 to 1.04) |
0.80 |
1.00 |
(0.97 to 1.04) |
0.86 |
0.99 |
(0.96 to 1.02) |
0.60 |
| alpha-1m |
1.01 |
(0.92 to 1.09) |
0.91 |
1.03 |
(0.94 to 1.13) |
0.55 |
0.90a
|
(0.82 to 0.99)a
|
0.026a
|
| EGF |
0.78a
|
(0.65 to 0.94)a
|
0.010a
|
0.71a
|
(0.57 to 0.88)a
|
0.002a
|
0.82 |
(0.59 to 1.15) |
0.26 |
aRepresents statistical significance.
Left ventricular hypertrophy is defined as left ventricular mass index ≥95th percentile for healthy children and adolescents. Left ventricular mass index is an index of left ventricular mass to height (mass [g]/height [m2.7]) to account for body size. Full model is adjusted for age, sex, race, glomerular diagnosis, body mass index, hypertension, urine protein-to-creatinine ratio, and baseline eGFR. Per doubling prevalence ratios are for a continuous log2 change in biomarker levels. BMI, body mass index; HTN, hypertension; GD, glomerular diagnosis; urine Pr/Cr, urine protein-to-creatinine ratio; PR, prevalence ratio; CI, confidence interval; KIM-1, kidney injury molecule-1; TNFR, tumor necrosis factor receptor; suPAR, soluble urokinase-type plasminogen activator receptor; MCP-1, monocyte chemoattractant protein-1; alpha-1m, alpha-1-microglobulin; EGF, epidermal growth factor.
Only urine YKL-40 demonstrated evidence of a significant difference in the effect estimates by glomerular disease etiology (Supplemental Table 3). Among those with nonglomerular disease, urine YKL-40 was associated with LVH in the unadjusted model (PR: 1.15, 95% CI, 1.03 to 1.28, interaction P value: 0.007), but not in those with glomerular disease (PR: 0.98, 95% CI, 0.95 to 1.01). However, after multivariable adjustment, effect estimates for urine YKL-40 in those with nonglomerular disease were attenuated and not significant (PR: 1.06, 95% CI, 0.98 to 1.13).
We also studied whether plasma and urine biomarkers were associated with LVMI Z-score. In the fully adjusted model, only plasma KIM-1 (β: 0.13, 95% CI, 0.03 to 0.22) was found to be significantly associated with LVMI Z-score (Table 3). There were no significant differences in the effect estimates for all the biomarkers and LVMI Z-score when stratified by glomerular versus nonglomerular etiology of CKD (interaction P value >0.05) (Supplemental Table 4).
Table 3. -
Plasma and urine biomarkers from first visit and left ventricular mass index Z-score from second visit
Biomarker, Per Doubling |
Biomarker Alone |
Adjusted Model (Plus Age, Gender, Race, BMI, HTN Status, GD) |
Full Adjusted Model (Plus eGFR, Urine Pr/Cr) |
|
β
|
95% CI |
P Value |
β
|
95% CI |
P Value |
β |
95% CI |
P Value |
|
Plasma
|
|
|
|
|
|
|
|
|
|
| KIM-1 |
0.18a
|
(0.09 to 0.26)a
|
<0.001a
|
0.19a
|
(0.11 to 0.28)a
|
<0.001a
|
0.13a
|
(0.03 to 0.22)a
|
0.008a
|
| TNFR-1 |
0.15a
|
(0.04 to 0.26)a
|
0.007a
|
0.21a
|
(0.09 to 0.32)a
|
<0.001a
|
−0.04 |
(−0.20 to 0.12) |
0.64 |
| TNFR-2 |
0.26a
|
(0.10 to 0.42)a
|
0.002a
|
0.29a
|
(0.13 to 0.45)a
|
<0.001a
|
0.02 |
(−0.18 to 0.23) |
0.83 |
| suPAR |
0.30a
|
(0.13 to 0.47)a
|
0.001a
|
0.28a
|
(0.11 to 0.45)a
|
0.001a
|
0.01 |
(−0.19 to 0.22) |
0.90 |
|
Urine
|
|
|
|
|
|
|
|
|
|
| KIM-1 |
0.09a
|
(0.02 to 0.17)a
|
0.016a
|
0.09a
|
(0.02 to 0.17)a
|
0.015a
|
0.07 |
(−0.02 to 0.15) |
0.12 |
| MCP-1 |
0.11a
|
(0.04 to 0.17)a
|
0.001a
|
0.09a
|
(0.02 to 0.15)a
|
0.010a
|
0.05 |
(−0.02 to 0.12) |
0.14 |
| YKL-40 |
−0.004 |
(−0.02 to 0.01) |
0.57 |
−0.004 |
(−0.02 to 0.01) |
0.52 |
−0.01 |
(−0.02 to 0.004) |
0.15 |
| alpha-1m |
0.02 |
(−0.01 to 0.06) |
0.23 |
0.04a
|
(0.001 to 0.07)a
|
0.047a
|
−0.03 |
(−0.08 to 0.02) |
0.19 |
| EGF |
−0.15a
|
(−0.24 to −0.06)a
|
0.002a
|
−0.238a
|
(−0.34 to −0.14)a
|
<0.001a
|
−0.08 |
(−0.22 to 0.06) |
0.27 |
aRepresents statistical significance.
Left ventricular hypertrophy is defined as left ventricular mass index ≥95th percentile for healthy children and adolescents. Left ventricular mass index is an index of left ventricular mass to height (mass [g]/height [m2.7]) to account for body size. Full model is adjusted for age, sex, race, glomerular diagnosis, body mass index, hypertension, urine protein to creatinine ratio (Pr/Cr), and baseline eGFR. Per doubling prevalence ratios are for a continuous log2 change in biomarker levels. BMI, body mass index; HTN, hypertension; GD, glomerular diagnosis; urine Pr/Cr, urine protein-to-creatinine ratio; CI, confidence interval; KIM-1, kidney injury molecule-1; TNFR, tumor necrosis factor receptor; suPAR, soluble urokinase-type plasminogen activator receptor; MCP-1, monocyte chemoattractant protein-1; alpha-1m, alpha-1-microglobulin; EGF, epidermal growth factor.
Discussion
In this study, we observed that higher concentrations of plasma and urine KIM-1, urine MCP-1, and lower concentrations of urine alpha-1m were independently associated with an increased prevalence of LVH, even after adjustment for eGFR and other known factors associated with LVH. Our cross-sectional analysis shows that biologically plausible biomarkers of kidney tubular injury, dysfunction, and inflammation may indicate the severity of kidney pathology and are associated with LVH in children with CKD.
An increase of tubular injury and inflammation in the kidney may affect the health of distant organs, such as the lungs and heart.19 Kidney tubular injury and inflammation can lead to activation of the renin angiotensin system, adrenergic system, and sympathetic nervous system and cause endothelial dysfunction, all of which may affect blood pressure regulation.9 As such, the tubular injury and inflammation may be contributing to the severity of kidney disease and HTN, both risk factors for LVH.
We found that higher plasma and urine KIM-1 levels were independently associated with higher LVH prevalence. KIM-1 is a membrane-bound protein which is expressed in the proximal tubule and plays a role in mediating phagocytosis for apoptotic and necrotic epithelial cells during kidney injury.20 Higher concentrations of urine KIM-1 have been associated with a decline of kidney function, progression of CKD, and progression of CVD.21–23 In a retrospective cohort study of older adults by Driver et al., participants with the highest quartile for urine KIM-1 concentration were at higher risk for developing heart failure (Hazard Ratio, 1.32; 95% CI, 1.02 to 1.70), when compared with the lowest quartile.24 In another study by Ibrahim et al., plasma KIM-1 concentrations were higher in those with coronary artery stenosis (0.04 ng/ml, IQR, 0.01–0.1) compared with those without (0.03 ng/ml, IQR, 0.01–0.05) (P < 0.001), and plasma KIM-1 levels also improved risk model discrimination for predicting coronary artery stenoses.25 Furthermore, in a case–control study, Kaddourah et al. observed that children with dilated cardiomyopathy had a higher concentration of urine KIM-1 (386 pg/mg, IQR, 248.7–596.6) as compared with age and sex-matched healthy controls (307.3 pg/mg, IQR, 182.8–432.5) (P = 0.02).26
We also observed that urine MCP-1 concentrations were associated with LVH prevalence. MCP-1 is a CC chemokine that is found in monocytes and tubular epithelial cells and plays a role in immunoregulation, inflammation, and fibrosis.27,28 During injury, MCP-1 enhances the inflammatory response by directing monocytes and macrophages to affected areas. In human and animal studies, urinary MCP-1 concentrations were found to be correlated with kidney damage.29–32 Elevated levels of MCP-1 have also been associated with CVD. In a study conducted by de Lemos et al. that included 2270 patients experiencing acute coronary syndromes, elevated levels of plasma MCP-1 were associated with an increased risk of myocardial infarction.33 In a study by Viedt et al., it was demonstrated that MCP-1 promotes the proliferation of vascular smooth muscle cells, and this atherogenic process may be influential in CVD development.
In addition, we observed that higher urine alpha-1m is associated with lower LVH prevalence. Alpha-1m is a small protein which is primarily synthesized in the liver, filtered at the glomerulus, and is completely reabsorbed by the healthy proximal tubule. Urine alpha-1m is thus an indicator of tubular dysfunction, and increased levels of urine alpha-1m have been associated with incident CKD and progression of CKD.34 Urine alpha-1m has been examined in adult studies where higher levels have been shown to be associated with coronary artery disease, heart failure, and CVD incidence.34–36 In a retrospective observational study of adults with acute heart failure, Ishiwata et al. found that higher urine alpha-1m levels were associated with a higher left ventricular ejection fraction (P = 0.002).36 This previous research does not explain why higher alpha-1m concentrations were associated with a lower prevalence of LVH in our study. This was the opposite directionality observed with the other plasma and urine biomarkers and was not consistent with our original hypothesis since alpha-1m is a biomarker of proximal tubular dysfunction. The unexpected directionality suggests a mechanism not yet understood or could represent a chance finding. In addition, previous research of proximal tubular dysfunction and cardiac remodeling was completed mostly in adult populations, and age may modify this relationship. If confirmed, these findings would suggest that in a specific patient population and context, urine alpha-1m may identify a mechanistic pathway that could be targeted to limit LVH.
The strengths of our study include the use of validated biomarker assays in a centralized laboratory, standardized echocardiogram studies, and the number of participants in this pediatric CKD cohort. However, the results of our study should be interpreted in the context of several limitations. Owing to fewer echocardiograms being performed after the 1-year study visit, this research was limited to a cross-sectional analysis as opposed to the evaluation of longitudinal echocardiograms. In addition, our study does not measure biomarkers that may better capture the pathophysiology of myocardial injury, hypertrophy, and cardiac fibrosis. Finally, to develop more robust biomarkers of LVH with larger point estimates, proteins specific to cardiac pathophysiology will need to be evaluated.
Despite its limitations, our study is the largest prospective pediatric cohort study to assess specific biomarkers of tubular injury and inflammation and their relationship to LVH, and as such, we were able to evaluate LVH in a heterogenous pediatric cohort with multiple diagnoses. Previous studies have established associations between kidney biomarkers, such as serum creatinine and plasma FGF23, and LVH in children experiencing CKD. However, serum creatinine alone does not adequately estimate kidney function in children, and FGF23 is not an excellent predictor of LVH in children with advanced CKD.10,18 Given the frequency of LVH in the CKD population and the effect of LVH on cardiovascular health, it is in turn crucial to discover additional biomarkers of LVH so we can better inform our understanding of the relationship between CKD and LVH and the pathophysiology of LVH. Biomarkers may not only be used to routinely and noninvasively identify those children at highest risk of LVH, but they ultimately may serve as therapeutic targets as well.
In conclusion, we demonstrated that plasma KIM-1, urine KIM-1, MCP-1, and alpha-1m were independently associated with prevalent LVH in children with CKD. Notably, the strength of association between these biomarkers and LVH was preserved despite adjustment for eGFR and urine Pr/Cr. Because previous research has established the association between KIM-1, MCP-1, and alpha-1m with CKD progression, our results suggest the presence of shared mechanisms leading to the progression of CKD and the development of LVH. Future research is needed to better understand how tubular health, injury, dysfunction, and inflammation contribute to LVH and cardiovascular health.
Disclosures
A. Abraham reports the following: Consultancy: Implementation Group, Inc; Research Funding: NIH; Honoraria: Elsevier; and Advisory or Leadership Role: Associate Editor of American Journal of Epidemiology and Population Health Metrics. J.V. Bonventre reports the following: Consultancy: Aditum, Caladrius, Citrine, Janssen, MediBeacon, Mongram, Praxis, and Sarepta; Ownership Interest: Autonomous Medical Devices, Cascade Medical Technologies, Coegin, DxNow, Medibeacon, Oisin, Pacific Biosciences, Sentien, Theravance, and Verinano; Research Funding: Kantrum; Patents or Royalties: Kidney organoid patents assigned to Mass General Brigham and Kim-1 patents assigned to Mass General Brigham; and Advisory or Leadership Role: Advisory Board of Northwest Kidney Center, Angion, Editor Seminars in Nephrology, C-Path AKI Biomarker Initiative, KHI AKI Biomarker Initiative, National Kidney Foundation, and Wearable Artificial Organs. S.G. Coca reports the following: Employer: Consultancy: 3ive, Axon, Bayer, Boehringer-Ingelheim, Nuwellis, Renalytix, Reprieve Cardiovascular, Takeda, and Vifor; Ownership Interest: pulseData and Renalytix; Research Funding: ProKidney, Renal Research Institute, Renalytix, and XORTX; Patents or Royalties: Renalytix; and Other Interests or Relationships: Associate Editor for Kidney360, Editorial Boards of CJASN, JASN, and Kidney International. M.R. Denburg reports the following: Ownership Interest: In-Bore LLC (spouse) and Precision Guided Interventions LLC (spouse); Consultancy: Trisalus Life Sciences (spouse); Research Funding: Instylla (spouse) and Mallinckrodt; Patents or Royalties: InBore LLC; Advisory or Leadership Role: Editorial Board of Kidney International Reports; KDIGO Executive Committee; Trisalus Life Sciences Scientific Advisory Board (spouse). H.I. Feldman reports the following: Consultancy: Kyowa Hakko Kirin Co, Ltd. and National Kidney Foundation; Honoraria: InMed, Inc.; Advisory or Leadership Role: American Journal of Kidney Disease (AJKD), Editor in Chief, Chronic Renal Insufficiency Cohort Study, and Member of Advisory Board of the National Kidney Foundation, Steering Committee Chair; Speakers Bereau: InMed, Inc.S. Furth reports the following: Consultancy: Genentech. O.M. Gutierrez reports the following: Consultancy: QED; Research Funding: Amgen and GlaxoSmithKline; and Honoraria: Akebia, Amgen; Ardelyx, AstraZeneca, and Reata. J.H. Ix reports the following: Consultancy: Akebia, AstraZeneca, and Sanifit; Research Funding: Baxter International; and Advisory or Leadership Role: AlphaYoung. P.L. Kimmel reports the following: Ownership Interest: As a Federal Employee at NIDDK, my holdings are reviewed each year for potential conflict of interest. At this time my only stock holding related in any fashion to health care is CVS and GE Healthcare.; Patents or Royalties: Elsevier: Royalties for co-editing Chronic Renal Disease and Psychosocial Aspects of Chronic Kidney Disease; Advisory or Leadership Role: Unpaid member of Board of Directors of Academy of Medicine of Washington, DC; Other Interests or Relationships: Co-Editor, Chronic Renal Disease Academic Press; Co-Editor, Psychosocial Aspects of Chronic Kidney Disease. Academic Press; Royalties. M.M. Mitsnefes reports the following: Consultancy: KBP BIOSCIENCES. C.R. Parikh reports the following: Consultancy: Genfit Biopharmaceutical Company; Ownership Interest: Renaltix AI; Research Funding: National Heart, Lung and Blood Institute (NHLBI) and National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); and Advisory or Leadership Role: Genfit Biopharmaceutical Company and Renalytix. J.R. Schelling reports the following: Honoraria: Spherix; and Advisory or Leadership Role: Board of Directors—Kidney Foundation of Ohio; Editorial Board—Kidney360. M.G. Shlipak reports the following: Consultancy: Cricket Health and Intercept Pharmaceuticals; Ownership Interest: TAI Diagnostics; Research Funding: Bayer Pharmaceuticals; Honoraria: AstraZeneca, Bayer, and Boeringer Ingelheim; and Advisory or Leadership Role: American Journal of Kidney Disease, Board Member, Northern California Institute for Research and Education, Circulation, and JASN. R.S. Vasan reports the following: Consultancy: NIDDK. S.S. Waikar reports the following: Consultancy: Bain, BioMarin, CANbridge, Google, Goldfinch, GSK, Ikena, NovoNordisk, PepGen, Sironax, Strataca, and Wolters Kluewer; Research Funding: JNJ, Natera, Pfizer, and Vertex; Other Interests or Relationships: expert witness for litigation related to dialysis lab testing (Davita), PPIs (Pfizer), PFAO exposure (Dechert), voclosporin patent (Aurinia). B.A. Warady reports the following: Consultancy: Amgen, Bayer, GlaxoSmithKline, Lightline Medical, Reata, Relypsa, and UpToDate; Research Funding: Baxter Healthcare; Honoraria: Amgen, Bayer, Reata, Relypsa, and UpToDate; and Advisory or Leadership Role: Midwest Transplant Network Governing Board; National Kidney Foundation Board of Directors; NTDS Board of Directors; and Vice President, North American Pediatric Renal Trials and Collaborative Studies. N. Wettersten reports the following: Consultancy: Guidepoint; Ownership Interest: GE, JPMorgan Chase, Morgan Stanley, Nissan, and United Airlines; Research Funding: My spouse has received grant funding from Alpha Beta Therapeutics. Funding given to University of California, San Diego; and Patents or Royalties: Spouse—patent pending with Alpha Beta Therapeutics. All the remaining authors have nothing to disclose.
Funding
CKD Biomarkers Consortium: U01 DK085660, U01 DK085689, U01 DK102730, U01 DK103225; HHS |NIH| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK): U01-DK-66116, U01-DK-66143, U01-DK-66174, U01DK-082194; HHS|National Institutes of Health (NIH): K23DK118198-01A1, K24DK078737, K24DK090203, R01DKD072381, R01HL085757, R37DK039773, U01DK082185, U01DK66174; NIH Career Development Grant: K08DK110536; O'Brien Center Grant: P30DK079310-07.
Author Contributions
Conceptualization: Alison Abraham, Joseph V. Bonventre, Steven G. Coca, Michelle R. Denburg, Harold I. Feldman, Susan Furth, Jason H. Greenberg, Joachim H. Ix, Kuan Jiang, Paul L. Kimmel, Chirag R. Parikh, Jeffrey R. Schelling, Michael G. Shlipak, Ramachandran S. Vasan, Sushrut S. Waikar, Yunwen Xu.
Data curation: Yunwen Xu.
Formal analysis: Alison Abraham, Susan Furth, Jason H. Greenberg, Kuan Jiang, Sushrut S. Waikar, Yunwen Xu.
Funding acquisition: Alison Abraham, Joseph V. Bonventre, Steven G. Coca, Harold I. Feldman, Jason H. Greenberg, Joachim H. Ix, Chirag R. Parikh, Jeffrey R. Schelling, Sarah J. Schrauben, Michael G. Shlipak, Ramachandran S. Vasan, Yunwen Xu.
Investigation: Joseph V. Bonventre, Susan Furth, Bradley A. Warady.
Methodology: Alison Abraham, Joseph V. Bonventre, Steven G. Coca, Michelle R. Denburg, Harold I. Feldman, Susan Furth, Jason H. Greenberg, Joachim H. Ix, Paul L. Kimmel, Chirag R. Parikh, Jeffrey R. Schelling, Michael G. Shlipak, Ramachandran S. Vasan, Sushrut S. Waikar, Yunwen Xu.
Project administration: Jason H. Greenberg, Kuan Jiang.
Resources: Joseph V. Bonventre, Susan Furth, Bradley A. Warady.
Software: Alison Abraham, Yunwen Xu.
Supervision: Jason H. Greenberg.
Validation: Alison Abraham, Jason H. Greenberg, Yunwen Xu.
Visualization: Alison Abraham, Jason H. Greenberg, Kuan Jiang, Yunwen Xu.
Writing – original draft: Alison Abraham, Jason H. Greenberg, Kuan Jiang, Yunwen Xu.
Writing – review & editing: Alison Abraham, Steven G. Coca, Jason H. Greenberg, Orlando M. Gutierrez, Joachim H. Ix, Kuan Jiang, Mark M. Mitsnefes, Jeffrey R. Schelling, Sarah J. Schrauben, Michael G. Shlipak, Ramachandran S. Vasan, Sushrut S. Waikar, Bradley A. Warady, Nicholas Wettersten, Yunwen Xu
Data Sharing Statement
Data in this manuscript were collected by the Chronic Kidney Disease in children prospective cohort study (CKiD) with clinical coordinating centers (Principal Investigators) at Children's Mercy Hospital and the University of Missouri–Kansas City (Bradley Warady, MD) and Children's Hospital of Philadelphia (Susan Furth, MD, PhD), Central Biochemistry Laboratory (George Schwartz, MD) at the University of Rochester Medical Center, and data coordinating center (Alvaro Muñoz, PhD) at the Johns Hopkins Bloomberg School of Public Health. The CKiD study is funded by the National Institute of Diabetes and Digestive and Kidney Diseases, 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). The CKiD website is located at http://www.statepi.jhsph.edu/ckid. The biomarker analyses were performed in the CKD Biomarker consortium Central Laboratory (Joseph Bonventre, MD, PhD).
Supplemental Material
This article contains the following supplemental material online at https://links.lww.com/KN9/A366.
Supplemental Table 1. Biomarker intra-assay and interassay coefficients of variation.
Supplemental Table 2. Spearman correlations of plasma and urine biomarkers and baseline characteristics.
Supplemental Table 3. Biomarker concentrations from first visit and prevalence of LVH by glomerular versus nonglomerular diagnosis.
Supplemental Table 4. Biomarkers and LVMI Z-score stratified by glomerular versus nonglomerular diagnosis.
References
1. Mitsnefes MM. Cardiovascular complications of pediatric chronic kidney disease. Pediatr Nephrol. 2008;23(1):27–39. doi:
10.1007/s00467-006-0359-0
2. Mitsnefes M, Flynn J, Cohn S, et al. Masked hypertension associates with left ventricular hypertrophy in children with CKD. J Am Soc Nephrol. 2010;21(1):137–144. doi:
10.1681/ASN.2009060609
3. Schaefer F, Doyon A, Azukaitis K, et al. Cardiovascular phenotypes in children with CKD: the 4C study. Clin J Am Soc Nephrol. 2017;12(1):19–28. doi:
10.2215/CJN.01090216
4. Johnstone LM, Jones CL, Grigg LE, Wilkinson JL, Walker RG, Powell HR. Left ventricular abnormalities in children, adolescents and young adults with renal disease. Kidney Int. 1996;50(3):998–1006. doi:
10.1038/ki.1996.401
5. Mitsnefes MM, Kimball TR, Witt SA, Glascock BJ, Khoury PR, Daniels SR. Left ventricular mass and systolic performance in pediatric patients with chronic renal failure. Circulation. 2003;107(6):864–868. doi:
10.1161/01.cir.0000049744.23613.69
6. Matteucci MC, Wühl E, Picca S, et al. Left ventricular geometry in children with mild to moderate chronic renal insufficiency. J Am Soc Nephrol. 2006;17(1):218–226. doi:
10.1681/ASN.2005030276
7. Mitsnefes MM, Kimball TR, Kartal J, et al. Progression of left ventricular hypertrophy in children with early chronic kidney disease: 2-year follow-up study. J Pediatr. 2006;149(5):671–675. doi:
10.1016/j.jpeds.2006.08.017
8. VanDeVoorde RG, Mitsnefes MM. Hypertension and CKD. Adv Chronic Kidney Dis. 2011;18(5):355–361. doi:
10.1053/j.ackd.2011.03.003
9. Sparks MA, Crowley SD, Gurley SB, Mirotsou M, Coffman TM. Classical renin-angiotensin system in kidney physiology. Compr Physiol. 2014;4(3):1201–1228. doi:
10.1002/cphy.c130040
10. Mitsnefes MM, Betoko A, Schneider MF, et al. FGF23 and left ventricular hypertrophy in children with CKD. Clin J Am Soc Nephrol. 2018;13(1):45–52. doi:
10.2215/CJN.02110217
11. Greenberg JH, Abraham AG, Xu Y, et al. Plasma biomarkers of tubular injury and inflammation are associated with CKD progression in children. J Am Soc Nephrol. 2020;31(5):1067–1077. doi:
10.1681/ASN.2019070723
12. Greenberg JH, Abraham AG, Xu Y, et al. Urine biomarkers of kidney tubule health, injury, and inflammation are associated with progression of CKD in children. J Am Soc Nephrol. 2021;32(10):2664–2677. doi:
10.1681/ASN.2021010094
13. Furth SL, Cole SR, Moxey-Mims M, et al. Design and methods of the chronic kidney disease in children (CKiD) prospective cohort study. Clin J Am Soc Nephrol. 2006;1(5):1006–1015. doi:
10.2215/CJN.01941205
14. Khoury PR, Mitsnefes M, Daniels SR, Kimball TR. Age-specific reference intervals for indexed left ventricular mass in children. J Am Soc Echocardiogr. 2009;22(6):709–714. doi:
10.1016/j.echo.2009.03.003
15. Devereux RB, Reichek N. Echocardiographic determination of left ventricular mass in man. Anatomic validation of the method. Circulation. 1977;55(4):613–618. doi:
10.1161/01.cir.55.4.613
16. de Simone G, Daniels SR, Devereux RB, et al. Left ventricular mass and body size in normotensive children and adults: assessment of allometric relations and impact of overweight. J Am Coll Cardiol. 1992;20(5):1251–1260. doi:
10.1016/0735-1097(92)90385-z
17. Warady BA, Abraham AG, Schwartz GJ, et al. Predictors of rapid progression of glomerular and nonglomerular kidney disease in children and adolescents: the chronic kidney disease in children (CKiD) cohort. Am J Kidney Dis. 2015;65(6):878–888. doi:
10.1053/j.ajkd.2015.01.008
18. Schwartz GJ, Mun[Combining Tilde]oz A, Schneider MF, et al. New equations to estimate GFR in children with CKD. J Am Soc Nephrol. 2009;20(3):629–637. doi:
10.1681/ASN.2008030287
19. Sarnak MJ, Katz R, Newman A, et al. Association of urinary injury biomarkers with mortality and cardiovascular events. J Am Soc Nephrol. 2014;25(7):1545–1553. doi:
10.1681/ASN.2013070713
20. Yin C, Wang N. Kidney injury molecule-1 in kidney disease. Ren Fail. 2016;38(10):1567–1573. doi:
10.1080/0886022x.2016.1193816
21. Carlsson AC, Larsson A, Helmersson-Karlqvist J, et al. Urinary kidney injury molecule-1 and the risk of cardiovascular mortality in elderly men. Clin J Am Soc Nephrol. 2014;9(8):1393–1401. doi:
10.2215/CJN.11901113
22. Nauta FL, Bakker SJ, van Oeveren W, et al. Albuminuria, proteinuria, and novel urine biomarkers as predictors of long-term allograft outcomes in kidney transplant recipients. Am J Kidney Dis. 2011;57(5):733–743. doi:
10.1053/j.ajkd.2010.12.022
23. Vaidya VS, Niewczas MA, Ficociello LH, et al. Regression of microalbuminuria in type 1 diabetes is associated with lower levels of urinary tubular injury biomarkers, kidney injury molecule-1, and N-acetyl-β-D-glucosaminidase. Kidney Int. 2011;79(4):464–470. doi:
10.1038/ki.2010.404
24. Driver TH, Katz R, Ix JH, et al. Urinary kidney injury molecule 1 (KIM-1) and interleukin 18 (IL-18) as risk markers for heart failure in older adults: the Health, Aging, and Body Composition (Health ABC) Study. Am J Kidney Dis. 2014;64(1):49–56. doi:
10.1053/j.ajkd.2014.01.432
25. Ibrahim NE, Januzzi JL Jr., Magaret CA, et al. A clinical and biomarker scoring system to predict the presence of obstructive coronary artery disease. J Am Coll Cardiol. 2017;69(9):1147–1156. doi:
10.1016/j.jacc.2016.12.021
26. Kaddourah A, Goldstein SL, Basu R, et al. Novel urinary tubular injury markers reveal an evidence of underlying kidney injury in children with reduced left ventricular systolic function: a pilot study. Pediatr Nephrol. 2016;31(10):1637–1645. doi:
10.1007/s00467-016-3360-2
27. Viedt C, Dechend R, Fei J, Hänsch GM, Kreuzer J, Orth SR. MCP-1 induces inflammatory activation of human tubular epithelial cells: involvement of the transcription factors, nuclear factor-kappaB and activating protein-1. J Am Soc Nephrol. 2002;13(6):1534–1547. doi:
10.1097/01.ASN.0000015609.31253.7f
28. Nadkarni GN, Rao V, Ismail-Beigi F, et al. Association of urinary biomarkers of inflammation, injury, and fibrosis with renal function decline: the ACCORD trial. Clin J Am Soc Nephrol. 2016;11(8):1343–1352. doi:
10.2215/CJN.12051115
29. Rovin BH, Doe N, Tan LC. Monocyte chemoattractant protein-1 levels in patients with glomerular disease. Am J Kidney Dis. 1996;27(5):640–646. doi:
10.1016/s0272-6386(96)90097-9
30. Wada T, Yokoyama H, Su SB, et al. Monitoring urinary levels of monocyte chemotactic and activating factor reflects disease activity of lupus nephritis. Kidney Int. 1996;49(3):761–767. doi:
10.1038/ki.1996.105
31. Banba N, Nakamura T, Matsumura M, Kuroda H, Hattori Y, Kasai K. Possible relationship of monocyte chemoattractant protein-1 with diabetic nephropathy. Kidney Int. 2000;58(2):684–690. doi:
10.1046/j.1523-1755.2000.00214.x
32. Stephan M, Conrad S, Eggert T, Heuer R, Fernandez S, Huland H. Urinary concentration and tissue messenger RNA expression of monocyte chemoattractant protein-1 as an indicator of the degree of hydronephrotic atrophy in partial ureteral obstruction. J Urol. 2002;167(3):1497–1502. doi:
10.1016/s0022-5347(05)65352-9
33. de Lemos JA, Morrow DA, Sabatine MS, et al. Association between plasma levels of monocyte chemoattractant protein-1 and long-term clinical outcomes in patients with acute coronary syndromes. Circulation. 2003;107(5):690–695. doi:
10.1161/01.cir.0000049742.68848.99
34. Sandokji I, Greenberg JH. Plasma and urine biomarkers of CKD: a review of findings in the CKiD study. Semin Nephrol. 2021;41(5):416–426. doi:
10.1016/j.semnephrol.2021.09.003
35. Jotwani V, Katz R, Ix JH, et al. Urinary biomarkers of kidney tubular damage and risk of cardiovascular disease and mortality in elders. Am J Kidney Dis. 2018;72(2):205–213. doi:
10.1053/j.ajkd.2017.12.013
36. Ishiwata S, Matsue Y, Nakamura Y, et al. Clinical and prognostic values of urinary alpha1-microglobulin as a tubular marker in acute heart failure. Int J Cardiol. 2021;338:115–120. doi:
10.1016/j.ijcard.2021.06.041
