Uromodulin, which is exclusively expressed by epithelial cells of the thick ascending limb (TAL) of Henle loop, is the most abundant protein excreted in the normal urine. Rare mutations in the uromodulin (UMOD) gene coding for uromodulin cause a dominant form of tubulointerstitial kidney disease (ADTKD) (1–8), characterized by defective urinary concentrating ability, hyperuricemia, gout, and progression to ESRD (8–10). The mechanisms by which defective trafficking of mutant uromodulin leads to hyperuricemia are uncertain, but defective urine concentrating ability suggests a mechanism linked to volume contraction (5). This hypothesis is supported by a reduced TAL sodium reabsorption in uromodulin-deficient mice (11,12).
In addition to the rare mutations causing ADTKD, genome–wide association studies have evidenced robust associations between common variants near the UMOD gene and urinary uromodulin levels, hypertension, eGFR, and renal function decline over time (1,13–17). These common variants, which form a linkage disequilibrium block in the promoter of UMOD, are associated with increased renal expression and urinary levels of uromodulin (18,19). Uromodulin overexpression in transgenic mice leads to manifestations of kidney damage and an NaCl-sensitive hypertension mediated by the activation of sodium cotransporter NKCC2 in the TAL (18).
Recent findings have shed light on the physiologic role of uromodulin (20), including studies in knockout mice evidencing a link between uromodulin and regulation of sodium transport in the TAL (11,12) as well as previous data showing that uromodulin expression increases with salt intake in animal models (21).
To improve our understanding of the factors influencing uromodulin, we tested (in a random sample of the Canadian population ages 40–69 years old) the associations between uromodulin excretion and medical history, physical examination, medication, serum and urinary solutes, and established single-nucleotide polymorphisms (SNPs) in the UMOD locus.
Materials and Methods
Study Design and Participants
This is a cross-sectional analysis of individuals from the CARTaGENE (CaG) Study. Participants were obtained from the CaG population (http://www.cartagene.qc.ca/). A detailed description of the cohort and sampling method is provided elsewhere (22). Briefly, it includes 20,004 participants or 1% of the Quebec population ages 40–69 years old. The survey assessed past medical history, including kidney disease and medication usage (23), using a standardized questionnaire.
Given the strong link between ADTKD and gout, we recorded the use of drugs influencing renal handling of uric acid (Ua), specifically the use and type of diuretics (24). Nonsteroidal anti–inflammatory drugs were not considered, because they were mostly prescribed as needed and taken irregularly. We also noted uricosuric drugs, mainly fenofibrate and losartan (25). Although other medications enhance or reduce Ua excretion, they were not used in this cohort. Many were taking ASA at a dosage unlikely to alter Ua handling by the kidney (<100 mg/d) (26). Finally, we recorded the use of calcium, magnesium, and vitamin D supplements, because they influence urinary solutes.
At the time of the outpatient visit clinic, BP was measured three times every 2 minutes after an initial 10-minute rest period, and the mean was reported. Individuals had blood and urine samples taken at the time of the questionnaire. Of 20,004 individuals, we had access to 946 with genotyping and urinary samples.
Individuals within the CaG population with available genotyping were selected by the following criteria on the basis of an ongoing substudy on common variant associations in cardiovascular disease: top 150 and bottom 150 Framingham scores for both men and women (n=600) and top and bottom 100 patients on the basis of the vascular rigidity index for both men and women (n=400). Genotyping was performed using the Illumina HumanOmni2.5 BeadChip.
We tested candidate SNPs on the basis of a meta-analysis of 10,884 individuals of European descent to investigate the genetic association of urinary uromodulin levels in the general population (19). In this study, rs12917707 located near UMOD on chromosome 16 had the strongest association with urinary uromodulin levels. The rs4293393 variant located in the promoter region 550 bp upstream of UMOD is in perfect linkage disequilibrium with rs12917707 in the HapMap CEU, and its frequency within our cohort is identical to HapMap CEU (27); rs12446492 in the adjacent gene Protein Disulfide Isomerase–Like, Testis Expressed (PDILT) was also independently associated with uromodulin levels (19). These two variants, partly in linkage disequilibrium (D′=0.674) in our cohort, were included as genetic predictors of uromodulin excretion.
Blood and Urinary Measurements
Serum measurements included urea, creatinine, electrolytes (Na, K, Cl, Mg, Ca, and PO4), Ua, glucose, hemoglobin (Hb) A1c, TSH, total cholesterol, HDL, LDL, and triglycerides. Urinary measurements included glucose, electrolytes (Na, K, Cl, Mg, Ca, and PO4), Ua, creatinine, osmolality, and albuminuria.
Uromodulin was measured from urinary samples stored at −80°C in the same biochemical platform at the University of Zürich. Urinary creatinine levels were measured using the Beckman Coulter Synchron System Creatinine Assay (Unicell DxC Synchron Clinical System) following the manufacturer’s instructions. Urinary uromodulin concentration was measured by ELISA as described previously (28). We used human uromodulin (stock solution, 100 μg/ml; Millipore) to establish the standard curve. The uromodulin ELISA had a sensitivity of 2.8 ng/ml, a linearity of 1.0, an interassay variability of 3.3%, and an intra-assay variability of 5.5%. We estimated the eGFR using the Chronic Kidney Disease Epidemiology Collaboration formula.
Categorical variables are presented using proportions and compared using the chi-squared test. Normally distributed variables are presented as means±SDs and compared using t test, one-way ANOVA, or Pearson correlation as appropriate. Non–normally distributed variables are presented as medians with interquartile ranges (25th and 75th percentiles) and compared using the Spearman Rho correlation.
There are multiple ways to express urinary solutes: by volume, fractional excretion (FE; [ ]Soluteurine/[ ]Soluteserum divided by [ ]creatinineurine/[ ]creatinineserum), or ratio to creatinine (or ratio to osmolality). We chose to report uromodulin as a ratio to creatinine and Na, K, Cl, Mg, Ca, PO4, and Ua as FEs on the basis of previous publication preferences (19). However, we repeated our association analyses using different units (e.g., using values expressed by volume only) as in other studies (17) to assess consistency and avoid reporting associations falsely created by a common denominator.
FEs of solutes displayed a normal distribution. Solutes expressed by volume or as a ratio to creatinine or a ratio to osmolality were skewed but with a sufficiently normal distribution after transformed using the natural logarithm. In particular, all analyses using uromodulin to creatinine ratios were done using this transformation, and the corresponding figures use a log-transformed axis for uromodulin. Glycosuria, which was present in a minority of individuals, could not be normalized and was, therefore, dichotomized as present or absent on the basis of a 0.5-g/g creatinine cutoff (29).
To determine the independent predictors of uromodulin excretion, we performed multivariable linear regression using the log–transformed uromodulin to creatinine ratio as the dependent variable. Covariates showing P values <0.05 by univariate analyses were included in the model using a stepwise methodology. Different methods of variables entry (stepwise, forward, and backward) were tested to see if our findings were modified. Although not the main objective of this study, we also addressed using multiple linear regression if rs4293393 was an independent predictor of eGFR as shown in previous studies (1).
The statistical software of SPSS 19 (IBM Corporation, New York, NY) was used, and the significance level was set at 0.05.
Nine hundred forty-six individuals from the CaG cohort had available genotype and urine measurement. We excluded three with a known history of renal disease. The remaining 943 were 54±9 years old, with 51% women and a body mass index (BMI) of 26 (23–30) (Table 1); 83% were white, 5% had a parent from Africa, 1% had a parent from Latin America, 4% had a parent from the Middle East, 2% were Asian, and 5% were from another descent. Cardiovascular risk factors and history were prevalent: 28% reported hyperlipidemia, 17% reported hypertension, 11% reported diabetes, and 6.7% had a history of coronary artery disease or stroke. The mean eGFR was 90±14 ml/min per 1.73 m2. Similar findings existed within the rest of the cohort, except for a higher prevalence of self-declared hypertension in the nongenotyped population (25%). Table 1 details medications usage, whereas serum and urine parameters are shown in Table 2. Uromodulin excretion was 25 (11–42) mg/g creatinine, with a skewed distribution (Figure 1A) that was relatively normal after logarithmic transformation (Figure 1B).
Clinical Associations with Uromodulin Excretion
Multiple clinical and serum variables correlated with uromodulin excretion by univariate analysis, but these associations were weak (Supplemental Table 1). Uromodulin excretion paralleled the eGFR (Figure 2). Diabetes was associated with lower urinary levels along with HbA1c (Spearman Rho, −0.095; P=0.004). In addition, higher triglyceride and lower HDL cholesterol, more common in uncontrolled diabetes, also correlated inversely with uromodulin. A higher diastolic BP was also associated with a lower uromodulin excretion (Pearson correlation, −0.09; P<0.01). Uromodulin was inversely associated with serum Ua (Pearson correlation, −0.12; P<0.001). Men had significantly lower urinary levels compared with women when expressed in milligrams per gram creatinine, although this association was reversed when expressed in milligrams per liter (data not shown). Similarly, a greater BMI was associated with a lower excretion expressed in milligrams per gram creatinine, but this association disappeared when expressed in milligrams per liter. Age, ethnicity, smoking, history of coronary or cerebral vascular disease, and TSH levels were not associated with uromodulin excretion.
Genetic Determinants of Uromodulin Excretion
The SNPs mapping to the promoter region of UMOD and PDILT were predictive of uromodulin excretion, with the TT genotype of rs4293393 and the TT genotype of rs12446492 showing the highest levels of uromodulin (Figure 3).
Urinary Findings and Uromodulin Excretion
The uromodulin to creatinine ratio paralleled the Na, K, Cl, Ca, and Ua excretions (Supplemental Table 1). These associations were statistically stronger compared with clinical and serum findings, with r2 values of 10% and 16% for urinary Ua expressed as a FE or a ratio to creatinine, respectively. The FE-Na and FE-Cl showed the next strongest associations with uromodulin. Higher FE-Na and FE-Cl were also associated with higher BP (r=0.16 and 0.17, respectively, with the systolic BP; both P<0.001). Uromodulin excretion correlated inversely with the presence of glycosuria (Figure 4). The FE-PO4 and FE-Mg were not statistically associated with uromodulin. The findings were similar when we expressed solutes as a ratio to creatinine or osmolality or by volume. We found no association between uromodulin and albuminuria (data not shown).
Associations between Medication and Uromodulin Excretion
As expected, the use of diuretics (n=62) increased the FE-Na and lowered the FE-Ua (Figure 5). Individuals taking furosemide had levels of uromodulin of 13 (8–23) mg/g creatinine (n=6) compared with 23 (13–40) mg/g creatinine with other diuretics (n=52; P=0.20). The use of uricosuric drugs (losartan and fenofibrate) was associated with a greater FE-Ua and a significantly lower uromodulin excretion (Figure 4). The use of calcium and magnesium supplements increased their respective urinary levels but was not statistically associated with uromodulin excretion (data not shown). Vitamin D intake had no measurable effect. Other antihypertensive drugs (angiotensin–converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, and β-blockers) were not associated with uromodulin excretion.
Independent Factors Associated with Uromodulin Excretion
Given the possible confounding variables associated with the uromodulin to creatinine ratio, we performed a stepwise multivariate linear regression using the significant variables cited above (Table 3). We did not include in the model the serum Ua, because it is used to calculate the FE-Ua. We also excluded the FE-Cl given its strong correlation with FE-Na (r=0.92). The r2 for the model was 0.14. Uromodulin excretion was independently higher in parallel to eGFR, the genotype (TT variant of rs4293393 and TT variant of rs12446492), FE-Na, and FE-Ua. The FE-Ua explained most of the variability of the model (standardized-β, 0.29; P<0.001). The presence of glycosuria and the use of uricosuric drugs (fenofibrate or losartan) were independently associated with a lower excretion of uromodulin. When we repeated this model using urinary solutes expressed by volume, the r2 was 0.22, and urinary Ua still showed the strongest association with uromodulin (P<0.001).
Interrelations between Genetic Variants of UMOD, eGFR, Serum Ua, FE-Ua, and FE-Na
We also addressed whether rs4293393 was independently linked to eGFR, because this was an important finding of previous studies (1). Using a multivariate linear regression adjusting for age, sex, BMI, history of hypertension, diabetes, and cardiovascular disease, the presence of the TT alleles at rs4293393 was associated with a 2.3±0.9 ml/min per 1.73 m2 lower eGFR (P<0.01); rs4293393 showed no observable association with serum Ua, FE-Ua, or FE-Na, and rs12446492 was not associated with eGFR, serum Ua, FE-Ua, or FE-Na. Finally, a strong association existed between FE-Na and FE-Ua (Pearson correlation, 0.34; P<0.001).
This large study illustrates, for the first time, interrelations between uromodulin excretion and clinical, genetic, and urinary variables. Independent variables associated with greater uromodulin excretion in this cohort include eGFR, the TT genotype of rs4293393 (UMOD), the TT genotype of rs12446492 (PDILT), and the FEs of sodium and Ua. Furthermore, uromodulin excretion was lower in the presence of glycosuria and with the use of uricosuric drugs. The strongest independent association was that between FE-Ua and uromodulin. We also confirmed the influence of the rs4293393 variant of UMOD on eGFR.
The biologic role of uromodulin has been linked to cast formation, prevention of nephrolithiasis, and binding to proteins (e.g., Igs) and bacteria (30). More recently, uromodulin has been pointed to in ADTKD related to mutations in UMOD (4). Mutant proteins retained in the endoplasmic reticulum exert a dominant-negative effect on the normal uromodulin isoform (3), causing a significant decrease in the urinary excretion of uromodulin (5). Hyperuricemia, early gout, and progressive renal fibrosis leading to ESRD in adulthood are the clinical features of ADTKD (7). Noteworthy, hyperuricemia is caused by a decreased FE-Ua, which appears early and precedes the decline in renal function (6). It has been hypothesized that defective reabsorption of NaCl in the TAL may lead to volume contraction and a secondary increase in proximal tubule reabsorption of Ua (31). Of note, uromodulin knockout mice display a reduced NKCC2 cotransporter phosphorylation, whereas overexpression of uromodulin in vitro increases NKCC2 phosphorylation (11). Furthermore, uromodulin increases the activity of ROMK, which supports the view that it modulates NKCC2–dependent NaCl reabsorption in the TAL (12,32,33). In view of its exclusive distribution in the TAL, uromodulin is a priori unrelated to the proximal tubule, where urate is principally handled. However, it is not excluded that the TAL dysfunction caused by defective uromodulin may somehow influence the function of the neighboring proximal tubules (34,35).
Common variants in the promoter of the UMOD gene are linked to hypertension, CKD, and kidney stones (13–17). They also affect uromodulin excretion (19). The Global BPGen Consortium found that each copy of the minor G allele of rs13333226 in the 5′ region of the UMOD gene associated with a 0.49 mmHg lower systolic BP and a 0.30 mmHg lower diastolic BP (16). These findings are supported by a greater response to furosemide in lowering BP in patients who are hypertensive and homozygous for UMOD promoter risk variants (18). The reduced excretion of uromodulin with declining eGFR is perhaps a reflection of reduced renal mass and TAL activity.
Most of the clinical and serum associations with uromodulin excretion identified by univariate analysis were weak and disappeared after adjustments. Simple explanations support these confounding associations. For example, glycosuria is dependent on the presence of diabetes and more likely to exist at higher HbA1c levels. Individuals with uncontrolled diabetes also have higher triglycerides and lower HDL levels, both of which were significantly associated by univariate analysis but not by multivariate analysis. As expected, there was no link between uromodulin and albuminuria, primarily a marker of endothelial and glomerular disease.
The FE-Ua was the most important factor independently associated with uromodulin excretion. The normal handling of Ua by the kidney includes filtration, reabsorption, and secretion taking place in the proximal tubule, although urate transporters exist farther down the nephron (36). Molecular mechanisms involved in the proximal tubule include the organic anion transporters URAT1 and OAT4, with the exact contribution of other transporters remaining uncertain (37). One hypothesis for the relation between uromodulin and FE-Ua would be variations in the extracellular volume (ECV): greater uromodulin is associated with an enhanced TAL activity and consequently, a greater ECV, leading to reduced proximal tubule Ua reabsorption, lower serum Ua, higher Ua excretion, and consequently, higher FE-Ua (18,38) as observed here. The relationship between FE-Na and uromodulin in individuals in steady state could be explained by a higher sodium intake leading to ECV expansion, a reduced proximal tubule sodium reabsorption, greater sodium delivery to the TAL, and greater TAL activity and uromodulin excretion. Of interest, uromodulin knockout mice failed to increase BP after 2% NaCl infusion, contrary to wild-type mice, indicating a role for uromodulin in regulating sodium uptake in the TAL (39,40). The influence of common variants in KCNJ1, CAB39, and SORL1, which all regulate sodium reabsorption in the TAL, on uromodulin excretion levels in the general population (19) as well as the greater response to furosemide in patients who are hypertensive with UMOD variants (18) also support the latter hypothesis.
The recent observation of a dose-dependent increase in FE-Ua in healthy participants taking an SGLT2 inhibitor (41) is not caused by a direct effect of the drug on the renal handling of Ua but rather, the effects of glycosuria on GLUT9 isoform 2 (exchanging Ua for glucose reabsorption at the apical membrane of proximal tubule cells). This mechanism would explain the reduction in the serum Ua observed in patients with type 2 diabetes and glycosuria in our cohort (42,43). Glycosuria may also inhibit the activity of the TAL and consequently, lower uromodulin excretion (44).
The uricosuric drugs losartan and fenofibrate both inhibit URAT1 (24,45). Because the FE-Ua showed the strongest association of uromodulin excretion in our cohort, we could have expected a positive association with uromodulin excretion. However, the contrary was observed (Table 3). Previous studies have shown that, per se, uromodulin does not seem to play a significant role in urate homeostasis. If we assume that uromodulin excretion is a marker of TAL activity, it is uncertain how the FE-Ua or uricosuric drugs may affect that segment. Additional experimental studies addressing such drugs and modulating sodium intake and ECV will be necessary to explore the pathways linking Ua, TAL function, and uromodulin excretion.
Our study has the advantage of using a detailed large population. Measurements were performed in standardized conditions using a validated method (28). Limitations of this cross-sectional study include the lack of ethnic diversity and varying levels of eGFR. The effect of simple interventions (e.g., modifying sodium intake) could not be determined.
In conclusion, this study shows that clinical, genetic, and renal tubular function parameters correlate with the excretion of uromodulin. The strong associations between Ua, NaCl handling, and uromodulin excretion suggest a link between uromodulin excretion, the activity of the TAL, and the ECV.
We thank the dedicated team at CARTaGENE for their diligent help.
This study was funded by the Kidney Foundation of Canada and the Fonds de la Recherche du Québec Santé. Other funding sources for this study include European Commission Seventh Framework Programme FP7/2007-2013 Grant 246539 of the Marie Curie Actions Programme and Grant 305608 of the EURenOmics project, Fonds de la Recherche Scientifique and Fonds de la Recherche Scientifique Medicale, Gebert Rüf Stiftung Project GRS-038/12, the Swiss National Science Foundation Grants 310030-146490 and 32003B-149309, and the Swiss National Science Foundation NCCR Kidney.CH Program.
The results were presented at the 2014 American Society of Nephrology Annual Meeting in Philadelphia, PA (November 11–16, 2014).
Published online ahead of print. Publication date available at www.cjasn.org.
See related editorial, “Tamm Horsfall Glycoprotein and Uromodulin: It Is All about the Tubules!,” on pages 6–8.
This article contains supplemental material online at http://cjasn.asnjournals.org/lookup/suppl/doi:10.2215/CJN.04770415/-/DCSupplemental.
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