Introduction
CKD has a high prevalence and represents a great burden for health care systems (1–234). CKD is associated with a higher risk for mortality and cardiovascular disease (5,6). Patients with CKD are at risk of progressing to kidney failure, i.e., of needing KRT. Kidney failure further increases health care costs and the risk for mortality and cardiovascular disease (3,4,7). Therefore, understanding the risk factors for mortality, cardiovascular disease, and kidney failure in patients with CKD is important.
Lower eGFRs and higher urinary albumin-creatinine ratio (ACR) are strongly associated with all-cause and cardiovascular mortality, progression of CKD, and kidney failure (8–910). However, both eGFR estimated from creatinine and ACR are influenced by parameters such as nutrition, muscle mass, and liver function. Furthermore, eGFR and ACR do not accurately represent the extent of tubular atrophy and interstitial fibrosis, but tubular atrophy is positively associated with CKD progression (11,12).
Uromodulin, also known as Tamm–Horsfall protein, is exclusively expressed in the cells of the ascending limb of the loop of Henle and secreted into the urine and, to a lower extent, into the circulation (13). Urinary uromodulin has been suggested as a proxy for tubular mass (14), whereas serum uromodulin is stronger correlated to eGFR (15,16). Although higher serum and urinary uromodulin have been associated with lower risk for mortality in elderly people and with lower risk for cardiovascular disease (16–1718), neither serum nor urinary uromodulin have been well studied in patients with CKD. A recent study performed in Chinese patients with CKD detected an association of high serum uromodulin with lower risk for kidney failure, but not mortality nor cardiovascular disease (19). Given these heterogeneous findings, we observationally tested whether higher serum uromodulin values are associated with lower risk for mortality, major adverse cardiovascular events (MACE), and kidney failure in the German CKD (GCKD) cohort over a follow-up period of 4 years.
Materials and Methods
Study Participants and Study Design
The GCKD cohort is a prospective, observational, nationwide cohort study. Main inclusion criteria were an eGFR between 30 and 60 ml/min per 1.73 m2 or overt proteinuria (urinary protein-creatinine ratio >500 m/g or equivalent) if eGFR was >60 ml/min per 1.73 m2 (20). Exclusion criteria were solid organ or bone marrow transplantation, malignancy, advanced heart failure, legal attendance, or inability to provide informed consent.
A total of 5217 participants were enrolled between 2010 and 2012. All participants gave written informed consent for participation, and local institutional review boards approved the study methods. Serum uromodulin measurements were available in 5134 participants, and these data form the basis for this analysis. A small group of 84 participants (approximately 2%) were lost to follow-up after 4 years. Any recorded event occurring before loss to follow-up was included in the analysis. If there was no event, these participants were censored at the time loss to follow-up occurred.
Exposure
Serum samples were obtained at the baseline study visit and stored at −80°C until they were thawed. Serum uromodulin measurements were performed in singlicate at the Laboratory for Clinical Chemistry and Laboratory Medicine, University Hospital Greifswald, Germany, using a commercial ELISA (Euroimmun, Medizinische Labordiagnostika AG, Lübeck, Germany) as described previously based on the manufacturer’s instructions (15). This assay is based on a colorimetric sandwich immunoassay using a polyclonal antibody against human uromodulin as the capture antibody and a biotinylated polyclonal antibody against human uromodulin as the detection antibody. Characteristics of the ELISA were as follows: intra-assay coefficient of variation, 1.8%–3.2%; interassay coefficient of variation, 6.6%–7.8%; mean linearity recovery, 97%; lower limit of detection, 2.0 ng/ml.
Outcomes
The outcomes analyzed were all-cause mortality; MACE as a composite of fatal cardiovascular event, nonfatal myocardial infarction, or nonfatal stroke and incident peripheral vascular disease; and incident kidney failure, a composite of incident dialysis or kidney transplantation. The GCKD cohort end point committee centrally adjudicated all outcomes.
Covariates
For the multivariable models, we used baseline data on sociodemographic variables (age, sex, body mass index), markers of kidney function (eGFR calculated using the CKD Epidemiology Collaboration equation (21), urinary ACR), cardiovascular risk factors (prevalent diabetes, defined by prescription of antidiabetic medication or hemoglobin A1c >6.5%), hypertension (defined by a mean of three BP measurements ≥140 mm Hg systolic or ≥90 mm Hg diastolic, or prescription of any antihypertensive drug), systolic BP, diastolic BP, HDL cholesterol, LDLcholesterol, C-reactive protein (CRP), serum phosphorus, pharmacologic therapy (lipid-lowering therapy, antihypertensive medication, diuretic therapy), and prevalent cardiovascular disease (defined by previous coronary, cerebrovascular, or peripheral vascular disease). All variables were selected based on their clinical relevance for the outcomes of interest.
Statistical Analyses
We describe the population overall and across serum uromodulin quartiles using means and SDs for continuous variables and number with percentages for binary and categoric variables. Multivariable Cox proportional hazard regression models were used to examine the association of serum uromodulin at baseline with all-cause mortality, MACE, and incident kidney failure during follow-up. We evaluated a series of nested models for all outcomes: (1) unadjusted, (2) adjusted for baseline demographic/clinical parameters (age, sex, body mass index; model 1), (3) model 1 with eGFR and ACR added (model 2), and (4) model 2 with diabetes, hypertension, prevalent cardiovascular disease, systolic BP, diastolic BP, HDL cholesterol, LDL cholesterol, CRP, serum phosphorus, and medication added (model 3). Restricted cubic splines were used to illustrate the individual hazard ratio (HR) of each participant in model 3. We further evaluated the influence of eGFR, ACR, and diabetes status on the associations of serum uromodulin with the outcomes mentioned above using interaction terms in the Cox regression models. In a secondary analysis, we examined the association of serum uromodulin with kidney failure and MACE in multivariable adjusted competing risk regression models using the Fine and Gray method (22), with mortality and/or kidney failure/MACE as the competing events. Estimates with P values <0.05 were considered statistically significant. The prevalence of missing data were very low in our cohort (<2% for all variables), and therefore no imputation methods were applied. To assess clinical utility in a predictive setting, we split the available data randomly in a training set and a test set (containing two thirds and one third of the patients, respectively) and fitted the aforementioned models to the training data. Discriminatory power was subsequently evaluated by estimating the concordance probability (C-index) from the test data. All analyses were conducted using R version 3.5.1 (R Core Team, Vienna, Austria). Estimates of the concordance probability were obtained using the methods implemented in the R add-on package pec.
Results
Population Characteristics
Mean age of the cohort (n=5143) was 60±12 years, 60% were male, serum uromodulin concentration was 98±60 ng/ml, eGFR was 49±18 ml/min per 1.73 m2, and 78% had an eGFR <60 ml/min per 1.73 m2 (Table 1). Individuals in the higher serum uromodulin quartile were more frequently female and had a nominally lower prevalence of diabetes and diabetic nephropathy, hypertension, coronary heart disease, and stroke (Table 1). They also less frequently received antihypertensive, diuretic, and lipid-lowering medication.
Table 1. -
Baseline characteristics of German CKD study participants, stratified by quartiles of serum
uromodulin (
n=5143)
| Characteristics |
Total Cohort (n=5143) |
Quartile 1 (n=1286) |
Quartile 2 (n=1287) |
Quartile 3 (n=1285) |
Quartile 4 (n=1285) |
Missing Data (n [%]) |
| Serum uromodulin range (ng/ml) |
0.0–490.4 |
≤55.6 |
>55.6–83.4 |
>83.4–125.3 |
>125.3 |
|
|
Demographics
|
|
|
|
|
|
|
| Age (yr) |
60±12 |
61±12 |
61±11 |
60±12 |
58±13 |
1 (0) |
| Male |
3088 (60) |
833 (65) |
824 (64) |
766 (60) |
665 (52) |
0 (0) |
|
Laboratory measures
|
|
|
|
|
|
|
| eGFR<60 ml/min per 1.73 m2
|
4007 (78) |
1185 (92) |
1116 (87) |
984 (77) |
722 (56) |
28 (0.5) |
| eGFR (ml/min per 1.73 m2) |
49±18 |
40±13 |
45±14 |
51±17 |
62±20 |
28 (0.5) |
| Albuminuria (ACR>30 mg/g Cr) |
2987 (58) |
867 (67) |
793 (62) |
720 (56) |
607 (47) |
83 (2) |
| ACR (mg/g Cr) |
51 (10–389) |
89 (15–603) |
61 (13–426) |
43 (9–350) |
24 (7–250) |
83 (2) |
| Hemoglobin (g/dl) |
13.6±1.7 |
13.1±1.7 |
13.5±1.7 |
13.8±1.6 |
14.0±1.5 |
96 (2) |
| CRP (mg/dl) |
2.3 (1.0–5.0) |
3.0 (1.3–6.6) |
2.5 (1–5.5) |
2.2 (1–4.9) |
1.7 (0.8–3.5) |
27 (0.5) |
| HbA1c (%) |
6.2±1.0 |
6.5±1.2 |
6.4±1.0 |
6.4±1.0 |
6.1±0.8 |
37 (0.7) |
| Total cholesterol (mg/dl) |
211±53 |
206±54 |
207±53 |
214±50 |
218±52 |
33 (0.6) |
| LDL cholesterol (mg/dl) |
118±43 |
112±44 |
115±44 |
121±42 |
125±44 |
38 (0.7) |
| HDL cholesterol (mg/dl) |
52±18 |
48±17 |
50±17 |
53±18 |
57±19 |
37 (0.7) |
| Triglycerides (mg/dl) |
113 (89–143) |
182 (126–268) |
177 (123–247) |
161 (115–233) |
155 (105–212) |
38 (0.7) |
| Phosphate (mg/dl) |
3.4±0.6 |
3.5±0.7 |
3.4±0.6 |
3.4±0.6 |
3.4±0.6 |
26 (0.5) |
| Uric acid (mg/dl) |
7.2±1.9 |
7.8±2.1 |
7.4±1.9 |
7.2±1.7 |
6.4±1.7 |
26 (0.5) |
|
Cardiovascular risk factors and prevalent cardiovascular disease
|
|
| Systolic BP (mm Hg) |
140±20 |
140±21 |
141±21 |
140±20 |
138±20 |
32 (0.6) |
| Diastolic BP (mm Hg) |
79±12 |
78±13 |
80±12 |
80±12 |
80±11 |
32 (0.6) |
| BMI (kg/m2) |
29.8±6.0 |
31.0±6.5 |
30.1±5.9 |
29.7±5.8 |
28.4±5.3 |
53 (1) |
| Diabetes |
1830 (36) |
575 (45) |
490 (38) |
465 (36) |
300 (23) |
0 (0) |
| Hypertension |
4952 (96) |
1268 (99) |
1271 (99) |
1239 (96) |
1174 (91) |
3 (0.1) |
| Coronary heart disease |
1022 (20) |
310 (24) |
284 (22) |
258 (20) |
170 (13) |
2 (0) |
| Stroke |
497 (10) |
151 (12) |
143 (11) |
118 (9) |
85 (7) |
2 (0) |
| Smoking history |
|
|
|
|
|
15 (0.3) |
|
Never
|
2101 (41) |
467 (36) |
494 (38) |
546 (43) |
594 (46) |
|
|
Former
|
2213 (43) |
585 (46) |
573 (45) |
537 (42) |
518 (40) |
|
|
Current
|
815 (16) |
228 (18) |
217 (17) |
199 (16) |
171 (13) |
|
|
Unknown
|
14 (0) |
6 (0) |
3 (0) |
3 (0) |
2 (0) |
|
|
Medication use
|
|
|
|
|
|
|
| ACE inhibitor |
2434 (47) |
639 (50) |
609 (47) |
624 (49) |
562 (44) |
0 (0) |
| Angiotensin-II receptor blocker |
2142 (42) |
583 (45) |
563 (44) |
533 (42) |
463 (36) |
0 (0) |
| Diuretic |
3133 (61) |
976 (76) |
870 (68) |
744 (58) |
543 (42) |
0 (0) |
| Lipid-lowering therapy |
2624 (51) |
734 (57) |
709 (55) |
632 (49) |
549 (43) |
0 (0) |
|
β-Blocker |
2822 (55) |
828 (64) |
773 (60) |
678 (53) |
543 (42) |
0 (0) |
| Calcium-channel blocker |
2036 (40) |
605 (47) |
565 (44) |
488 (38) |
378 (29) |
0 (0) |
|
Lifestyle factors
|
|
|
|
|
|
|
| Drinking |
|
|
|
|
|
31 (0.6) |
|
Light
|
4142 (81) |
1003 (78) |
1036 (81) |
1062 (83) |
1041 (81) |
|
|
Heavy
|
971 (19) |
272 (21) |
244 (19) |
217 (17) |
238 (19) |
|
|
Unknown
|
30 (0) |
11 (0) |
7 (0) |
6 (0) |
6 (0) |
|
|
Underlying kidney disease
|
|
|
|
|
|
0 (0) |
| Diabetes |
1382 (27) |
454 (35) |
376 (29) |
341 (27) |
211 (16) |
|
| Hypertension |
2121 (41) |
576 (45) |
558 (43) |
528 (41) |
459 (36) |
|
| Systemic disease |
606 (12) |
140 (11) |
142 (11) |
164 (13) |
160 (13) |
|
| GN |
1171 (23) |
288 (22) |
278 (22) |
274 (21) |
331 (26) |
|
| Interstitial nephropathy |
437 (9) |
94 (7) |
111 (9) |
115 (9) |
117 (9) |
|
| Hereditary |
232 (5) |
59 (5) |
58 (5) |
72 (6) |
43 (3) |
|
| Obstructive |
374 (7) |
79 (6) |
103 (8) |
88 (7) |
104 (8) |
|
| AKI |
242 (5) |
75 (6) |
65 (5) |
62 (5) |
40 (3) |
|
| Solitary kidney |
324 (8) |
92 (7) |
69 (5) |
76 (6) |
87 (7) |
|
| Miscellaneous |
236 (5) |
74 (6) |
53 (4) |
42 (3) |
67 (5) |
|
| Undetermined |
319 (6) |
71 (6) |
72 (6) |
82 (6) |
94 (7) |
|
Continuous variables are presented as mean and SD or median and interquartile range, and categoric variables are presented in percentage of referring population, unless otherwise specified. ACR, albumin-creatinine ratio; CRP, C-reactive protein; HbA1c, hemoglobin A1c; BMI, body mass index; ACE, angiotensin-converting enzyme.
Serum Uromodulin and Outcomes
A total of 335 participants died during the median follow-up period of 4 years, with the lowest cumulative incidence rates in the highest serum uromodulin quartile (n=41, cumulative incidence per person year 0.6%; 95% CI, 0.4 to 0.9; Table 2). In univariable Cox regression analysis, each SD (i.e., 60 ng/ml) higher serum uromodulin was associated with a 39% lower hazard (HR, 0.61; 95% CI, 0.52 to 0.71) of all-cause mortality (Table 3). Compared with the lowest serum uromodulin quartile, all other quartiles had a significantly lower HR for this end point. In multivariable analysis, each SD higher serum uromodulin was associated with a 20% lower hazard of mortality (HR, 0.80; 95% CI, 0.66 to 0.96); the two highest serum uromodulin quartiles had a significantly lower HR compared with the lowest quartile.
Table 2. -
Cumulative incidence rates for mortality, kidney failure, and major adverse cardiovascular events in the total cohort and by serum
uromodulin quartiles
| Event |
Total Cohort (0.0–490.4 ng/ml) |
Quartile 1 (≤55.6 ng/ml) |
Quartile 2 (>55.6–83.4 ng/ml) |
Quartile 3 (>83.4–125.3 ng/ml) |
Quartile 4 (>125.3 ng/ml) |
|
All-cause mortality
|
|
|
|
|
|
| Events, n
|
335 |
145 |
86 |
63 |
41 |
| Incidence per person year, % (95% CI) |
1.1 (0.8 to 1.4) |
1.7 (1.2 to 2.3) |
0.9 (0.6 to 1.4) |
1.0 (0.7 to 1.4) |
0.6 (0.4 to 0.9) |
|
Kidney failure
|
|
|
|
|
|
| Events, n
|
229 |
122 |
64 |
35 |
8 |
| Incidence per person year, % (95% CI) |
0.4 (0.3 to 0.6) |
0.9 (0.6 to 1.4) |
0.4 (0.3 to 0.7) |
0.2 (0.1 to 0.4) |
0.1 (0.0 to 0.1) |
|
MACE
|
|
|
|
|
|
| Events, n
|
417 |
152 |
115 |
91 |
59 |
| Incidence per person year, % (95% CI) |
4.8 (4.3 to 5.5) |
6.7 (5.8 to 7.8) |
5.5 (4.7 to 6.4) |
4.0 (0.3 to 4.8) |
3.2 (2.7 to 3.9) |
MACE, major cardiovascular event—defined as a composite of fatal cardiovascular event, nonfatal myocardial infarction, nonfatal stroke, or incident peripheral vascular disease.
Table 3. -
Associations of serum
uromodulin with mortality, kidney failure, and major adverse cardiovascular events
| Outcome |
Events |
Hazard Ratio (95% CI) |
| Univariable |
Model 1
a
|
Model 2
b
|
Model 3
c
|
|
All-cause mortality
|
|
|
|
|
|
| HR per SD higher serum uromodulin |
335/5143 |
0.61 (0.52 to 0.71) |
0.67 (0.57 to 0.78) |
0.75 (0.63 to 0.89) |
0.80 (0.66 to 0.96) |
| Quartile 1 (≤55.6 ng/ml) |
|
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
| Quartile 2 (>55.6–83.4 ng/ml) |
|
0.63 (0.48 to 0.82) |
0.69 (0.52 to 0.90) |
0.76 (0.57 to 1.01) |
0.80 (0.60 to 1.07) |
| Quartile 3 (>83.4–125.3 ng/ml) |
|
0.48 (0.36 to 0.65) |
0.53 (0.39 to 0.71) |
0.62 (0.45 to 0.86) |
0.70 (0.50 to 0.97) |
| Quartile 4 (>125.3 ng/ml) |
|
0.31 (0.22 to 0.44) |
0.40 (0.28 to 0.57) |
0.50 (0.34 to 0.75) |
0.57 (0.38 to 0.87) |
|
Kidney failure
|
|
|
|
|
|
| HR per SD higher serum uromodulin |
229/5143 |
0.30 (0.24 to 0.39) |
0.31 (0.25 to 0.40) |
0.61 (0.46 to 0.80) |
0.61 (0.46 to 0.81) |
| Quartile 1 (≤55.6 ng/ml) |
|
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
| Quartile 2 (>55.6–83.4 ng/ml) |
|
0.50 (0.37 to 0.68) |
0.50 (0.37 to 0.68) |
0.73 (0.53 to 0.99) |
0.73 (0.52 to 1.01) |
| Quartile 3 (>83.4–125.3 ng/ml) |
|
0.27 (0.19 to 0.40) |
0.28 (0.19 to 0.42) |
0.64 (0.43 to 0.96) |
0.65 (0.43 to 0.99) |
| Quartile 4 (>125.3 ng/ml) |
|
0.07 (0.03 to 0.14) |
0.06 (0.03 to 0.13) |
0.27 (0.12 to 0.59) |
0.24 (0.10 to 0.55) |
|
MACE
|
|
|
|
|
|
| HR per SD higher serum uromodulin |
417/5143 |
0.68 (0.60 to 0.77) |
0.74 (0.65 to 0.84) |
0.80 (0.70 to 0.92) |
0.87 (0.77 to 1.02) |
| Quartile 1 (≤55.6 ng/ml) |
|
1 (reference) |
1 (reference) |
1 (reference) |
1 (reference) |
| Quartile 2 (>55.6–83.4 ng/ml) |
|
0.73 (0.57 to 0.93) |
0.75 (0.59 to 0.96) |
0.76 (0.59 to 0.99) |
0.85 (0.66 to 1.11) |
| Quartile 3 (>83.4–125.3 ng/ml) |
|
0.61 (0.47 to 0.79) |
0.64 (0.49 to 0.83) |
0.70 (0.53 to 0.93) |
0.78 (0.58 to 1.03) |
| Quartile 4 (>125.3 ng/ml) |
|
0.35 (0.26 to 0.48) |
0.43 (0.32 to 0.59) |
0.49 (0.35 to 0.69) |
0.63 (0.45 to 0.90) |
MACE, major cardiovascular event—defined as a composite of fatal cardiovascular event, nonfatal myocardial infarction, nonfatal stroke, or incident peripheral vascular disease; HR, hazard ratio.
aAdjusted for age, sex, and body mass index.
bModel 1 plus eGFR and urinary albumin-creatinine ratio.
cModel 2 plus prevalent cardiovascular disease, diabetes and hypertension at baseline, systolic BP, diastolic BP, serum high and LDL concentration, serum C-reactive protein and phosphorus concentration, prescription of diuretics, and lipid- and BP-lowering medication.
A total of 229 participants developed kidney failure, with the lowest cumulative incidence rates in the highest serum uromodulin quartile (n=8, cumulative incidence per person year 0.1%; 95% CI, 0.0 to 0.1; Table 2). In univariable analysis, each SD higher serum uromodulin was associated with a 70% lower hazard (HR, 0.30; 95% CI, 0.24 to 0.39) of kidney failure (Table 3). All quartiles had significantly lower HR compared with the lowest quartile. In multivariable analysis, each SD higher serum uromodulin was associated with a 39% lower hazard of kidney failure (HR, 0.61; 95% CI, 0.46 to 0.81); the two highest serum uromodulin quartiles had a significantly lower HR.
A total of 417 participants experienced MACE, with the lowest cumulative incidence rates in the highest serum uromodulin quartile (n=59, cumulative incidence per person year 3.2%; 95% CI, 2.7 to 3.9; Table 2). In univariable analysis, each SD higher serum uromodulin was associated with a 32% lower hazard of MACE (HR, 0.68; 95% CI, 0.60 to 0.77; Table 3). All quartiles had lower HR compared with the lowest quartile. In multivariable analysis, each SD higher serum uromodulin was borderline associated with MACE, having a 13% lower hazard of MACE (HR, 0.87; 95% CI, 0.77 to 1.02); the highest serum uromodulin quartile had a significantly lower HR compared with the lowest. In all multivariable analyses there was a linear association between the HR for the respective outcome and serum uromodulin, with lower HR at higher serum uromodulin concentrations (Figure 1).
;)
Figure 1.: Penalized splines demonstrating the adjusted association of serum uromodulin with all-cause mortality, kidney failure, and MACE (major adverse cardiovascular events, a composite of cardiovascular mortality, non-fatal myocardial infarction or stroke, or incident peripheral vascular disease) . Serum uromodulin in ng/ml is shown on the x axis and the partial adjusted hazard ratio (HR) on the y axis. The solid black line indicates the partial HR; the two dotted lines around the black line indicate its 95% confidence interval. Ticks on the x axis represent individual participants.
When performing competing risk analyses with mortality as the competing risk, the quartile with the highest serum uromodulin concentrations still had a significantly lower hazard of kidney failure (subdistribution HR [SHR], 0.28; 95% CI, 0.13 to 0.60; Supplemental Table 1). For MACE, the group with the second highest serum uromodulin concentrations had 21% lower hazard (SHR, 0.79; 95% CI, 0.65 to 0.97), whereas the difference between the highest and the lowest quartile was only borderline significant (SHR for the highest quartile, 0.80; 95% CI, 0.64 to 1.01).
We further evaluated a potential interaction between serum uromodulin and eGFR, ACR, and diabetes status on the associations of serum uromodulin and mortality, kidney failure, and MACE. No significant interaction term was identified (P>0.05 for all outcomes, the lowest P=0.21 for all-cause mortality and the interaction with diabetes).
The predictive ability of the models showed a C-index of 0.744 for the univariable serum uromodulin model in regards to kidney failure within the first year; the C-index was lower in the adjusted model (0.706; Table 4). After 4 years of follow-up, the C-index for serum uromodulin in the univariable model for kidney failure was 0.575, lower than in the adjusted model (0.740). The C-index for the univariable serum uromodulin model was 0.675 and 0.573 for all-cause mortality after 1 and 4 years of follow-up, respectively. Adjustment resulted in nominally lower C-indices (0.646 and 0.555, respectively). The univariable serum uromodulin model showed the nominally lowest C-indices for MACE after 1 and 4 years of follow-up (0.567 and 0.528, respectively); multivariable adjustment led to a nominal increase (0.659 and 0.575, respectively).
Table 4. -
Concordance probability of different multivariable models including serum
uromodulin to predict all-cause mortality, kidney failure, and major cardiovascular events
| Outcome |
Events |
Univariable |
Model 1
a
|
Model 2
b
|
Model 3
c
|
|
All-cause mortality
|
335/5143 |
|
|
|
|
| Concordance probability for event within first year |
|
0.674 |
0.622 |
0.605 |
0.646 |
| Concordance probability for event within 4 years |
|
0.573 |
0.589 |
0.582 |
0.555 |
|
Kidney failure
|
229/5143 |
|
|
|
|
| Concordance probability for event within first year |
|
0.744 |
0.796 |
0.699 |
0.706 |
| Concordance probability for event within 4 years |
|
0.575 |
0.667 |
0.769 |
0.740 |
|
MACE
|
417/5143 |
|
|
|
|
| Concordance probability for event within first year |
|
0.567 |
0.677 |
0.647 |
0.659 |
| Concordance probability for event within 4 years |
|
0.528 |
0.551 |
0.547 |
0.575 |
Concordance probability indicates the discriminatory power and predictive ability of statistical models for respective outcomes. It estimates the probability of concordance between predicted and observed outcomes. MACE, major cardiovascular event—defined as a composite of fatal cardiovascular event, nonfatal myocardial infarction, nonfatal stroke, or incident peripheral vascular disease.
aAdjusted for age, sex, and body mass index.
bModel 1 plus eGFR and urinary albumin-creatinine ratio.
cModel 2 plus prevalent cardiovascular disease, diabetes and hypertension at baseline, systolic BP, diastolic BP, serum high and LDL concentration, serum C-reactive protein and phosphorus concentration, prescription of diuretics, and lipid- and BP-lowering medication.
Discussion
Our results show that higher serum uromodulin is associated with lower hazard for mortality, MACE, and incident kidney failure in a large European CKD cohort, independently from eGFR, ACR, and cardiovascular and CKD risk factors.
An association of serum uromodulin with all-cause and cardiovascular mortality, as well as cardiovascular events, has previously been reported in two studies of participants at high risk for cardiovascular disease (17,18). However, both studies only categorized serum uromodulin, which can be statistically problematic for various reasons (23). In addition, the results of both studies might be limited due to unmeasured confounding because analyses were not adjusted for ACR. In a more recent analysis, higher serum uromodulin was associated with lower risk for mortality in the Cardiovascular Health Study—an older, community-based cohort—independent of demographics, eGFR, ACR, and cardiovascular risk factors (24). Due to nonlinear association between serum uromodulin and the outcomes at both ends of the serum uromodulin range, the analysis was restricted to 95% of cohort participants. Our results show for the first time that the association between serum uromodulin and death is consistent over the whole range of serum uromodulin concentrations, also including serum uromodulin concentrations not previously covered in the Cardiovascular Health Study. However, comparison of the results should be done cautiously due to different cohort characteristics.
Another recent study evaluated the association of serum uromodulin with kidney failure in a large cohort of Chinese patients with CKD (19). This study did not find a significant association of serum uromodulin with mortality and cardiovascular disease, possibly due to limited statistical power. However, higher serum uromodulin was significantly associated with lower hazard for kidney failure in a multivariable model, a finding that we could reproduce in our white cohort.
There are several possible explanations for the association of serum uromodulin with mortality and cardiovascular disease. Serum uromodulin has been shown to be associated with markers of systemic inflammation such as CRP, fibrinogen, and galectin-3 (17,25,26). Also, uromodulin appears to regulate systemic granulopoiesis with uromodulin deficiency leading to systemic neutrophilia (27). Furthermore, circulating uromodulin ameliorates systemic oxidative damage (28). Therefore, reduced circulatory serum uromodulin concentrations might promote a proinflammatory milieu, which has been shown to be associated with mortality and cardiovascular disease (29,30). A recent study also demonstrated that higher serum uromodulin was associated with lower risk for progression of coronary artery calcification, suggesting that higher serum uromodulin might modulate arterial calcification (31).
For kidney failure, it has been shown that 24-hour urine uromodulin excretion correlates with tubular mass (14). Lower serum uromodulin could therefore also reflect tubular atrophy, which in turn has been shown to be associated with kidney failure and kidney function decline (12,32). In addition, basic research supports that serum uromodulin counteracts inflammatory processes in the kidney interstitium: serum uromodulin influences tubular crosstalk by inhibiting proinflammatory signaling from proximal tubular cells, which can improve the recovery from AKI (33). Uromodulin knock-out mice showed more inflammation and tubular necrosis in a model of ischemia-reperfusion injury and incomplete recovery (34). This might also be explained by different proinflammatory toll-like receptor 4 expression based on local uromodulin concentrations (34). Furthermore, uromodulin modulates the IL-23/-17 axis and mononuclear phagocytic activity in the kidney parenchyma (27,35). In addition, a cytokine imbalance was described in uromodulin-deficient mice (36). Low serum uromodulin might therefore reflect a proinflammatory state in the kidney, which could facilitate progressive fibrosis and loss of kidney function. Also, AKI might lead to more severe, irreversible damage in the state of serum uromodulin deficiency. Further work is warranted to decipher the mechanism responsible for the protective effect of serum uromodulin, which could potentially result in strategies to improve outcomes.
Major strengths of this study are the large sample size, covering all CKD stages in a well characterized, ethnically homogenous cohort with standardized assessment of independent variables and outcomes. We were able to adjust for a large number of risk factors for cardiovascular disease and CKD progression, among them ACR, which enabled us to reduce the risk of potential unmeasured confounding. A concerning limitation in this study is that serum uromodulin was only measured once at baseline. Future studies need to evaluate whether changes in serum uromodulin concentrations are also associated with adverse outcomes. Furthermore, we did not assess biomarkers of tubular damage such as neutrophil gelatinase–associated lipocalin and compare its associations with serum uromodulin. However, we believe that there are currently no other serum markers of tubular function that have the same evidence base in terms of associations with the outcomes evaluated in this study. Last, we did not validate our findings in a second, independent cohort.
The findings of our study may have clinical implications. Given the fact that serum uromodulin is exclusively secreted in the tubular system and is independently associated with adverse outcomes, assessing tubular function by measuring serum uromodulin may guide medical treatment that affects the tubular system such as exposure to potentially tubulotoxic agents. In addition, low serum uromodulin concentrations may help to identify patients deserving more intensive care and optimization of cardiovascular risk factors.
In conclusion, we demonstrate that higher serum uromodulin is associated with lower risk for all-cause mortality and kidney failure, as well as borderline associated with lower risk for cardiovascular disease in a large white CKD cohort, independent from eGFR, ACR, and other cardiovascular and CKD risk factors. Therefore, serum uromodulin might be a promising complementary circulatory marker to assess the mortality and kidney risk, along with markers of glomerular function, in patients with CKD.
Disclosures
Dr. Eckardt reports grants from AstraZeneca, Bayer, Fresenius, and Vifor, during the conduct of the study; grants from Amgen, Genzyme, and Shire, outside the submitted work; and personal fees from Akebia, Bayer, Fresenius, and Vifor, outside the submitted work. Dr. Krane reports grants from Government and KfH Foundation for Preventive Medicine, during the conduct of the study. Dr. Meiselbach reports grants from the German Ministry of Education and Research, KfH Foundation of Preventive Medicine, and Innovative Medicines Initiative 2 Joint Undertaking (Biomarker Enterprise to Attack Diabetic Kidney Disease), during the conduct of the study. Dr. Voelkl reports grants from Else Kröner-Fresenius-Stiftung and Renal Research Institute, outside the submitted work. Dr. Voelkl also has a patent PCT/EP2018/054839 pending to Charité Berlin, coinventor Dr. Voelkl. All remaining authors have nothing to disclose.
Funding
The GCKD study is funded by Bundesministerium für Bildung und Forschung (German Ministry of Education and Research; www.gesundheitsforschung-bmbf.de) grants 01ER 0804, 01ER 0818, 01ER 0819, 01ER 0820, 01ER 0821, and 01ER 0122 and the KfH Foundation for Preventive Medicine (http://www.kfh-stiftung-praeventivmedizin.de) (to Dr. Eckardt). Furthermore, this project was partially funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement 115974 (Biomarker Enterprise to Attack Diabetic Kidney Disease). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme, European Federation of Pharmaceutical Industries and Associations, and Juvenile Diabetes Research Foundation United Kingdom.
Acknowledgments
Current GCKD investigators are as follows: K.-U.E., H.M., M.P.S., Mario Schiffer, Thomas Dienemann, Hans-Ulrich Prokosch, Barbara Bärthlein, Andreas Beck, Detlef Kraska, André Reis, Arif B. Ekici, Susanne Becker, Dinah Becker-Grosspitsch, Ulrike Alberth-Schmidt, Birgit Hausknecht, Anke Weigel (University of Erlangen-Nürnberg); Gerd Walz, A.K., Ulla T. Schultheiß, F.K., Simone Meder, Erna Mitsch, Ursula Reinhard (University of Freiburg); Jürgen Floege, Georg Schlieper, T.S. (RWTH Aachen University); Elke Schaeffner, S.B.-A., Kerstin Theisen (Charité, University Medicine Berlin); Hermann Haller, Jan Menne (Hannover Medical School); Martin Zeier, C.S., Rebecca Woitke (University of Heidelberg); Gunter Wolf, Martin Busch, Rainer Paul (University of Jena); Thomas Sitter (Ludwig-Maximilians University of München); Christoph Wanner, V.K., Antje Börner-Klein, Britta Bauer (University of Würzburg); Florian Kronenberg, Julia Raschenberger, Barbara Kollerits, Lukas Forer, Sebastian Schönherr, Hansi Weissensteiner (Medical University of Innsbruck, Division of Genetic Epidemiology); Peter Oefner, Wolfram Gronwald, Helena Zacharias (University of Regensburg, Institute of Functional Genomics); M.C.S., J.N. (Department of Medical Biometry, Informatics and Epidemiology [IMBIE], University Hospital of Bonn).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Supplemental Material
This article contains the following supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.11780919/-/DCSupplemental.
Supplemental Table 1. Associations of serum uromodulin with kidney failure and major adverse cardiovascular events (MACE) with mortality and/or kidney failure/MACE as the competing risks.
References
1. Brück K, Stel VS, Gambaro G, Hallan S, Völzke H, Ärnlöv J, Kastarinen M, Guessous I, Vinhas J, Stengel B, Brenner H, Chudek J, Romundstad S, Tomson C, Gonzalez AO, Bello AK, Ferrieres J, Palmieri L, Browne G, Capuano V, Van Biesen W, Zoccali C, Gansevoort R, Navis G, Rothenbacher D, Ferraro PM, Nitsch D, Wanner C, Jager KJ; European CKD Burden Consortium: CKD prevalence varies across the European general population. J Am Soc Nephrol 27: 2135–2147, 2016 26701975
2. Murphy D, McCulloch CE, Lin F, Banerjee T, Bragg-Gresham JL, Eberhardt MS, Morgenstern H, Pavkov ME, Saran R, Powe NR, Hsu CY; Centers for Disease Control and Prevention
Chronic Kidney Disease Surveillance Team: Trends in prevalence of
chronic kidney disease in the United States. Ann Intern Med 165: 473–481, 2016 27479614
3. Eriksson JK, Neovius M, Jacobson SH, Elinder CG, Hylander B: Healthcare costs in
chronic kidney disease and renal replacement therapy: A population-based cohort study in Sweden. BMJ Open 6: e012062, 2016 27855091
4. Golestaneh L, Alvarez PJ, Reaven NL, Funk SE, McGaughey KJ, Romero A, Brenner MS, Onuigbo M: All-cause costs increase exponentially with increased
chronic kidney disease stage. Am J Manag Care 23[Suppl]: S163–S172, 2017 28978205
5. Astor BC, Matsushita K, Gansevoort RT, van der Velde M, Woodward M, Levey AS, Jong PE, Coresh J, Astor BC, Matsushita K, Gansevoort RT, van der Velde M, Woodward M, Levey AS, de Jong PE, Coresh J, El-Nahas M, Eckardt KU, Kasiske BL, Wright J, Appel L, Greene T, Levin A, Djurdjev O, Wheeler DC, Landray MJ, Townend JN, Emberson J, Clark LE, Macleod A, Marks A, Ali T, Fluck N, Prescott G, Smith DH, Weinstein JR, Johnson ES, Thorp ML, Wetzels JF, Blankestijn PJ, van Zuilen AD, Menon V, Sarnak M, Beck G, Kronenberg F, Kollerits B, Froissart M, Stengel B, Metzger M, Remuzzi G, Ruggenenti P, Perna A, Heerspink HJ, Brenner B, de Zeeuw D, Rossing P, Parving HH, Auguste P, Veldhuis K, Wang Y, Camarata L, Thomas B, Manley T;
Chronic Kidney Disease Prognosis Consortium: Lower estimated
glomerular filtration rate and higher
albuminuria are associated with mortality and
end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts. Kidney Int 79: 1331–1340, 2011 21289598
6. van der Velde M, Matsushita K, Coresh J, Astor BC, Woodward M, Levey A, de Jong P, Gansevoort RT, van der Velde M, Matsushita K, Coresh J, Astor BC, Woodward M, Levey AS, de Jong PE, Gansevoort RT, Levey A, El-Nahas M, Eckardt KU, Kasiske BL, Ninomiya T, Chalmers J, Macmahon S, Tonelli M, Hemmelgarn B, Sacks F, Curhan G, Collins AJ, Li S, Chen SC, Hawaii Cohort KP, Lee BJ, Ishani A, Neaton J, Svendsen K, Mann JF, Yusuf S, Teo KK, Gao P, Nelson RG, Knowler WC, Bilo HJ, Joosten H, Kleefstra N, Groenier KH, Auguste P, Veldhuis K, Wang Y, Camarata L, Thomas B, Manley T;
Chronic Kidney Disease Prognosis Consortium: Lower estimated
glomerular filtration rate and higher
albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int 79: 1341–1352, 2011 21307840
7. Foley RN, Parfrey PS, Sarnak MJ: Clinical epidemiology of cardiovascular disease in
chronic renal disease. Am J Kidney Dis 32[Suppl 3]: S112–S119, 1998 9820470
8. Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, Coresh J, Gansevoort RT;
Chronic Kidney Disease Prognosis Consortium: Association of estimated
glomerular filtration rate and
albuminuria with all-cause and cardiovascular mortality in general population cohorts: A collaborative meta-analysis. Lancet 375: 2073–2081, 2010 20483451
9. Fox CS, Matsushita K, Woodward M, Bilo HJ, Chalmers J, Heerspink HJ, Lee BJ, Perkins RM, Rossing P, Sairenchi T, Tonelli M, Vassalotti JA, Yamagishi K, Coresh J, de Jong PE, Wen CP, Nelson RG;
Chronic Kidney Disease Prognosis Consortium: Associations of kidney disease measures with mortality and
end-stage renal disease in individuals with and without diabetes: A meta-analysis [published correction appears in
Lancet 381: 374, 2013]. Lancet 380: 1662–1673, 2012 23013602
10. Amin AP, Whaley-Connell AT, Li S, Chen SC, McCullough PA, Kosiborod MN; KEEP Investigators: The synergistic relationship between estimated GFR and microalbuminuria in predicting long-term progression to ESRD or death in patients with diabetes: Results from the Kidney Early Evaluation Program (KEEP). Am J Kidney Dis 61[Suppl 2]: S12–S23, 2013 23507266
11. Rosenbaum JL, Mikail M, Wiedmann F: Further correlation of renal function with kidney biopsy in
chronic renal disease. Am J Med Sci 254: 156–160, 1967 4951793
12. Yamanouchi M, Hoshino J, Ubara Y, Takaichi K, Kinowaki K, Fujii T, Ohashi K, Mise K, Toyama T, Hara A, Shimizu M, Furuichi K, Wada T: Clinicopathological predictors for progression of
chronic kidney disease in nephrosclerosis: A biopsy-based cohort study. Nephrol Dial Transplant 34: 1182–1188, 2019 29788462
13. Devuyst O, Olinger E, Rampoldi L:
Uromodulin: From physiology to rare and complex kidney disorders. Nat Rev Nephrol 13: 525–544, 2017 28781372
14. Pivin E, Ponte B, de Seigneux S, Ackermann D, Guessous I, Ehret G, Pechère-Bertschi A, Olinger E, Mohaupt M, Vogt B, Martin PY, Burnier M, Bochud M, Devuyst O, Pruijm M:
Uromodulin and nephron mass. Clin J Am Soc Nephrol 13: 1556–1557, 2018 30054352
15. Steubl D, Block M, Herbst V, Nockher WA, Schlumberger W, Satanovskij R, Angermann S, Hasenau AL, Stecher L, Heemann U, Renders L, Scherberich J: Plasma
uromodulin correlates with kidney function and identifies early stages in
chronic kidney disease patients. Medicine (Baltimore) 95: e3011, 2016 26962815
16. Garimella PS, Biggs ML, Katz R, Ix JH, Bennett MR, Devarajan P, Kestenbaum BR, Siscovick DS, Jensen MK, Shlipak MG, Chaves PH, Sarnak MJ: Urinary
uromodulin, kidney function, and cardiovascular disease in elderly adults. Kidney Int 88: 1126–1134, 2015 26154925
17. Delgado GE, Kleber ME, Scharnagl H, Krämer BK, März W, Scherberich JE: Serum
uromodulin and mortality risk in patients undergoing
coronary angiography. J Am Soc Nephrol 28: 2201–2210, 2017 28242751
18. Leiherer A, Muendlein A, Saely CH, Ebner J, Brandtner EM, Fraunberger P, Drexel H: Serum
uromodulin is a predictive biomarker for cardiovascular events and overall mortality in coronary patients. Int J Cardiol 231: 6–12, 2017 28089453
19. Lv L, Wang J, Gao B, Wu L, Wang F, Cui Z, He K, Zhang L, Chen M, Zhao MH: Serum
uromodulin and progression of kidney disease in patients with
chronic kidney disease. J Transl Med 16: 316, 2018 30454063
20. Eckardt KU, Bärthlein B, Baid-Agrawal S, Beck A, Busch M, Eitner F, Ekici AB, Floege J, Gefeller O, Haller H, Hilge R, Hilgers KF, Kielstein JT, Krane V, Köttgen A, Kronenberg F, Oefner P, Prokosch HU, Reis A, Schmid M, Schaeffner E, Schultheiss UT, Seuchter SA, Sitter T, Sommerer C, Walz G, Wanner C, Wolf G, Zeier M, Titze S: The German
Chronic Kidney Disease (GCKD) study: Design and methods. Nephrol Dial Transplant 27: 1454–1460, 2012 21862458
21. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T, Coresh J; CKD-EPI (
Chronic Kidney Disease Epidemiology Collaboration): A new equation to estimate
glomerular filtration rate [published correction appears in
Ann Intern Med 155: 408, 2011]. Ann Intern Med 150: 604–612, 2009 19414839
22. Fine JP, Gray RJ: A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 94: 496–509, 1999
23. Bennette C, Vickers A: Against quantiles: Categorization of continuous variables in epidemiologic research, and its discontents. BMC Med Res Methodol 12: 21, 2012 22375553
24. Steubl D, Buzkova P, Garimella PS, Ix JH, Devarajan P, Bennett MR, Chaves PHM, Shlipak MG, Bansal N, Sarnak MJ: Association of serum
uromodulin with mortality and cardiovascular disease in the elderly-the Cardiovascular Health Study [published online ahead of print Mar 21, 2019]. Nephrol Dial Transplant doi:
10.1093/ndt/gfz008
25. Leiherer A, Muendlein A, Saely CH, Brandtner EM, Geiger K, Fraunberger P, Drexel H: The value of
uromodulin as a new serum marker to predict decline in renal function. J Hypertens 36: 110–118, 2018 28858977
26. Scherberich JE, Gruber R, Nockher WA, Christensen EI, Schmitt H, Herbst V, Block M, Kaden J, Schlumberger W: Serum
uromodulin-a marker of kidney function and renal parenchymal integrity. Nephrol Dial Transplant 33: 284–295, 2018 28206617
27. Micanovic R, Chitteti BR, Dagher PC, Srour EF, Khan S, Hato T, Lyle A, Tong Y, Wu XR, El-Achkar TM: Tamm-horsfall protein regulates granulopoiesis and systemic neutrophil homeostasis. J Am Soc Nephrol 26: 2172–2182, 2015 25556169
28. LaFavers KA, Macedo E, Garimella PS, Lima C, Khan S, Myslinski J, McClintick J, Witzmann FA, Winfree S, Phillips CL, Hato T, Dagher PC, Wu XR, El-Achkar TM, Micanovic R: Circulating
uromodulin inhibits systemic oxidative stress by inactivating the TRPM2 channel. Sci Transl Med 11: eaaw3639, 2019 31578243
29. Proctor MJ, McMillan DC, Horgan PG, Fletcher CD, Talwar D, Morrison DS: Systemic inflammation predicts all-cause mortality: A glasgow inflammation outcome study. PLoS One 10: e0116206, 2015 25730322
30. Sung KC, Ryu S, Chang Y, Byrne CD, Kim SH: C-reactive protein and risk of cardiovascular and all-cause mortality in 268 803 East Asians. Eur Heart J 35: 1809–1816, 2014 24569028
31. Bjornstad P, Wiromrat P, Johnson RJ, Sippl R, Cherney DZ, Wong R, Rewers MJ, Snell-Bergeon JK: Serum
uromodulin predicts less coronary artery calcification and diabetic kidney disease over 12 years in adults with type 1 diabetes: The CACTI Study. Diabetes Care 42: 297–302, 2019 30482755
32. Zhu X, Li H, Liu Y, You J, Qu Z, Yuan S, Peng Y, Liu F, Liu H: Tubular atrophy/interstitial fibrosis scores of Oxford classification combinded with proteinuria level at biopsy provides earlier risk prediction in lgA nephropathy. Sci Rep 7: 1100, 2017 28439112
33. El-Achkar TM, McCracken R, Rauchman M, Heitmeier MR, Al-Aly Z, Dagher PC, Wu XR: Tamm-Horsfall protein-deficient thick ascending limbs promote injury to neighboring S3 segments in an MIP-2-dependent mechanism. Am J Physiol Renal Physiol 300: F999–F1007, 2011 21228114
34. El-Achkar TM, Wu XR, Rauchman M, McCracken R, Kiefer S, Dagher PC: Tamm-Horsfall protein protects the kidney from ischemic injury by decreasing inflammation and altering TLR4 expression. Am J Physiol Renal Physiol 295: F534–F544, 2008 18495803
35. Micanovic R, Khan S, Janosevic D, Lee ME, Hato T, Srour EF, Winfree S, Ghosh J, Tong Y, Rice SE, Dagher PC, Wu XR, El-Achkar TM: Tamm-horsfall protein regulates mononuclear phagocytes in the kidney. J Am Soc Nephrol 29: 841–856, 2018 29180395
36. Liu Y, Goldfarb DS, El-Achkar TM, Lieske JC, Wu XR: Tamm-Horsfall protein/
uromodulin deficiency elicits tubular compensatory responses leading to
hypertension and hyperuricemia. Am J Physiol Renal Physiol 314: F1062–F1076, 2018 29357410
