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Decreased urinary uromodulin is potentially associated with acute kidney injury: a systematic review and meta-analysis



Urinary uromodulin (uUMOD) is one of the novel biomarkers for predicting AKI. However, currently available publications showed inconsistent results. We designed this meta-analysis to evaluate the potential association between uUMOD and AKI.


We searched research articles with no language restriction in Medline, Web of Science, Cochrane Library, Embase, and 3 Chinese datasets from inception to February 2021. We used random-effects models to estimate the standardized mean difference (SMD) between patients with AKI or not, while the leave-one-out method and random-effects meta-regression to evaluate the sensitivity and the impact of potential confounders such as age and surgery.


The meta-analysis comprising 3148 subjects from 11 studies showed that the uUMOD of the AKI group is significantly lower than the non-AKI group (SMD: − 0.71; 95% confidence interval (CI), − 1.00, − 0.42, P < 0. 001, I2 = 78.8%). Subgroup analysis revealed the difference is also significant in a different age, surgery condition, and assay time but not acute rejection (AR) group, especially in children (SMD: − 1.21, 95% CI: − 1.80, − 0.61; P < 0.001) and patients undergoing surgery (SMD: − 1.03, 95% CI: − 1.75, − 0.30; P < 0.001). Lower uromodulin is associated with higher odds for AKI incidence (odds ratio = 2.47, 95% CI: 1.12, 5.47; P < 0.001, I2 = 89%). Meta-reggression found that age was associated with the SMD of uUMOD. The study outcome was reliably confirmed by the sensitivity analysis.


The present study suggested a negative association between uUMOD and AKI especially in children and surgical patients.


Acute kidney injury (AKI) is a collection of syndromes characterized by a sudden decrease in glomerular filtration rate, induced by various causes, like dehydration, sepsis, glomerulonephritis, and acute intoxication. Early recognition can guide clinical management, provide preventive measures, and reduce mortality rates [1]. The most common method of diagnosing AKI is the increase of serum creatinine (Scr) according to various criteria [2]. However, Scr is a late biomarker reflecting the glomerular function. Moreover, Scr provides little reliable information on the prognosis of kidney injury [3]. The lack of sensitive biomarkers is an obstacle to the timely treatment of AKI in the clinical setting.

Many other biomarkers, such as neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (Kim-1), tissue inhibitor of metalloproteinase-2 (TIMP-2) × insulin-like growth factor-binding protein 7 (IGFBP7), and C–C Motif Chemokine Ligand 14 (CCL14) have been studied for diagnostic value in early AKI detection [4]. However, these biomarkers reflect the cell injury in the setting of AKI, other than the cell function, which can identify a patient’s predisposition and risk of developing AKI.

Uromodulin is the most abundant protein in human urine, and the daily secretion is about 50–150 mg [5]. It has many biological functions, such as protection against urinary tract infection, regulating water and salt metabolism, immunomodulation, and so on [6]. We could detect urinary uromodulin (uUMOD), circulation uromodulin, and anti-uromodulin antibodies [7]. Recently, uromodulin was reported as a biomarker of the renal tubular reserve function in Fabry nephropathy [8] and could predict tubulointerstitial inflammation in patients with active lupus nephritis [9]. Several studies have also explored the relationship between uromodulin and AKI [10,11,12]. However, the results among these studies are inconsistent due to their limited sample size. Therefore, we conducted this meta-analysis to evaluate the difference of uromodulin between AKI and non-AKI.


Data sources and searches

We searched the original articles in both the English language database (Pubmed-Medline, Web of Science, Embase and Cochrane library) and Chinese language database (China National Knowledge Infrastructure, Weipu Database, and China Biology Medicine) from inception to February 2021. The searching strategy combined keywords with synonyms in both English and Chinese datasets. The MeSH terms of the Pubmed are "uromodulin" and "acute kidney injury". The free-text terms are "Tamm-Horsfall Protein; Tamm Horsfall Protein; Tamm-Horsfall Glycoprotein; Tamm Horsfall Glycoprotein or Uromucoid" and the freedom combination of "acute, kidney, renal, injury, insufficiency, and failure". The syntax used for the English database is shown in Additional file 1: Tables S1–S4. There was no restriction on publication forms or language. Besides, we manually searched the reference lists to find relevant studies.

Study selection

We screened all original articles with the inclusion criteria: (1) studies included AKI patients and non-AKI group; (2) uUMOD was detected and its relationship with AKI was discussed. Exclusion criteria were as follows: (1) manuscripts in the format of notes, letters, editorials, conference posters, or reviews; (2) studies without specific data of the uUMOD.

Data extraction and quality assessment

Two independent investigators screened all retrieved records according to the titles and abstracts and resolved the discrepancies by discussion. For the manuscripts without specific data, we sent e-mails to authors requesting original data. The extracted study characteristics were countries of origin, publication years, study types, ages, inclusion objects, conditions in renal disease, the AKI definition, the measurement-related data (assay method, time, and measurement object). We collected the outcomes information, including AKI occurrence, AKI recovery, renal replacement therapy, mortality, the in-hospital time, and cost.

We evaluated the quality of the studies by the non-randomized studies of interventions (ROBINS-I) tool [13]. This tool includes seven confounding biases, namely bias due to confounding; bias in the selection of study participants; bias in exposure measurement; bias due to misclassification of exposure during follow-up; bias due to missing data; bias in the measurement of outcomes; and bias in the selection of reported results. The levels of all bias were stratified as "low", "moderate", "serious", and "critical". The results were visualized by the web-based application robvis ( [14].

Data synthesis and analysis

The statistical analyses were performed with STATA (Version 12; StataCorp, College Station, Texas) and R 4.0.2 software. Data presented as the median and interquartile range (IQR) or range, we converted data to mean and standard deviation (SD) according to the Wan formula [15]. Where data were presented in geometric mean and 95% confidence interval (CI), we transformed it to the arithmetic mean and SD according to the Higgins formula [16]. The differences in pooled standardized mean difference (SMD) of uUMOD between AKI and non-AKI in subgroups (divided by age and surgery condition) were analyzed. Followed the "patients (patients who are at the risk of developing AKI), intervention (concentration levels of uUMOD), comparison (non-AKI patients), and outcomes (the incidence of AKI) (PICO)" principles, we extracted the hazard ratio (HR) and odds ratio (OR) of included studies.

We synthesized the studies by fixed-effects models with low heterogeneity (I2 < 50%), otherwise a random-effects model will be performed. Subgroup difference analysis was conducted by Revman 5.3 version (The Cochrane Collaboration, Oxford, UK). The heterogeneity analysis was performed by Cochrane's Q test (significance level of P < 0.10) and I2 statistics (ranges from 0 to 100% with lower values representing less heterogeneity), while further analysis of the heterogeneous source by Galbraith radial plot, and the leave-one-out method for sensitivity analysis. Meta-regression was made in the restricted maximum likelihood (ReML) model to check if the age, surgery conditions, and assay time are the sources of the heterogeneity (P ≤ 0.05 was considered significant).


Study selection and characteristics

As shown in Fig. 1, there are 15 studies focused on the relationship between uUMOD and AKI, but 4 studies without specific uUMOD data [10, 17,18,19]. A total of 11 studies were included in the meta-analysis [20,21,22,23,24,25,26,27,28,29,30]. Except for one paper written in German, others were written in English. Three studies investigated the relationship between uUMOD and surgery-associated AKI, while two studies recruited participants undergoing renal transplant surgery. The patients in five studies are below 18 years old, with infants and newborns in three studies. Five studies were case–control studies, while six studies were cohort studies. The study "Ashwani (2017)" listed the causes of AKI as pre-renal AKI (n = 22), acute tubular necrosis (ATN) (n = 3), and hepatorenal syndrome (HRS) (n = 12). And it reported there was no difference in the uUMOD/creatinine concentration among the different causes. The causes for AKI in study "P. Jeremy (1993)" were acute rejection (AR) (n = 37) and ATN (n = 7). As for the outcome information, only "Ashwani (2017)" offered the concentration of uUMOD and renal function recovery after the liver transplant surgery and suggested there was no significant correlation. The main characteristics of the studies are shown in Table 1.

Fig. 1

Search plot diagram. Additional records include articles searching from the Chinese language database. sUMOD serum uromodulin

Table 1 Characteristics of the studies included in the meta-analysis

Quality assessment and publication bias

Based on the ROBINS-I tool, 2 studies were identified as "low risk", while 8 articles were assessed as "moderate risk" studies. One study was considered as "critical risk" The details are shown in Fig. 2. The Egger's test indicated no publication bias in this meta-analysis (P = 0.213) (Additional file 1: Fig. S1).

Fig. 2

Risk of bias for included studies

Quantitative data synthesis

In total, the sample size of this meta-analysis was 3148 subjects, with 468 AKI patients, and 2680 non-AKI patients. The detailed mean and SD of the uUMOD are in Additional file 1: Table S5. Because of the high heterogeneity of the included studies, the meta-analysis was carried out by the random-effects model. The SMD of uUMOD was significantly lower in the AKI group (− 0.71; 95% confidence interval (CI): − 1.00, − 0.42; P < 0.001) than the non-AKI counterparts.

We conducted subgroup analysis according to age, surgery conditions, and assay time. Subgroup analysis by age showed that the uUMOD was significantly lower in the AKI group in both age > 18 years old (SMD = − 0.41, 95% CI: − 0.62, − 0.19; P < 0.001) and age < 18 years old (SMD = − 1.21, 95% CI: − 1.80, − 0.61; P < 0.001) (Fig. 3A). When stratified by surgery conditions, the uUMOD was significantly lower in the AKI group in "surgery" (SMD = − 1.03, 95% CI: − 1.75, − 0.30; P = 0.005) and "no-surgery" groups (SMD = -0.54, 95% CI: − 0.72, − 0.36; P < 0.001), while no significant difference in the AR group (SMD = − 1.89, 95% CI: − 5.70, − 1.91; P = 0.329) (Fig. 3B). In the subgroup analysis of assay time, the concentration of uUMOD is significantly different between AKI and non-AKI in “before” (SMD = − 0.66, 95%CI: − 0.97, − 0.35; P < 0.001) and “during” subgroups (SMD = − 1.06, 95%CI: − 1.94, − 0.17; P = 0.019) (Fig. 3C). The difference of the uUMOD between AKI and non-AKI is more significant in younger age, as the subgroup difference analysis in age showed the P is 0.004, while surgery conditions is 0.36, and assay time is 0.44.

Fig. 3

Subgroup analysis of the difference between urinary uromodulin in the patients of AKI and non-AKI according to age (A), surgery condition (B) and assay time (C). AKI acute kidney injury, AR acute rejection, CI confident interval, SMD standardized mean difference

We pooled the hazard ratio (HR) and odds ratio (OR) of three included studies [20, 24, 26]. We converted the reciprocal HR to take the high-level uromodulin as control [31]. Randomized meta-analysis showed the lower uromodulin is associated with the increased risk of developing AKI (pooled OR: 2.47, 95% CI: 1.12, 5.47; P < 0.001; I2 = 89.2%) (Fig. 4).

Fig. 4

A forest plot of incidence of acute kidney injury. CI confident interval, OR odds ratio

Heterogeneity analysis and sensitivity analysis

We detected moderate heterogeneity for all included studies (I2 = 79.5%, P < 0.001). For further analysis on finding the heterogeneous source, the Galbraith radial plot showed studies "Michael (2018)" and "Tara K (2010)" were fallen outside the 95% CI line, while studies "Pranav S (2017)" and "Ashwani (2017)" are on the 95% CI line (Additional file 1: Fig. S2). The I2 reduced to 38.3% and P = 0.113 when removed "Michael (2018)" and "TaraK (2010)" studies. Subgroup analysis of age showed that the I2 are 44.8% (> 18 years) and 79.7% ("< 18 years" group), while P = 0.107 (" > 18 years" group) and 0.001 (" < 18 years" group). Subgroup analysis of surgery showed the I2 = 18.9% ("no surgery" group), 90.2% ("surgery" group) and 94.5% ("AR" group), while P = 0.290 ("no surgery" group) and P < 0.001 in "surgery" and "AR" subgroups. The I2 are 75.5% and 86.7% in the "before" and "during" subgroups, respectively, and the P values are less than 0.001 in the two groups.

The leave-one-out method showed the result of the pooled SMD and OR is robust (Additional file 1: Figs. S3, S4). Meta-regression analysis showed that age was one source of the heterogeneity (P = 0.079) (Additional file 1: Fig. S5). Surgery conditions and assay time were not the sources of the heterogeneity (P > 0.1).


In this study, we first validated that uromodulin could diagnose and predict AKI by meta-analysis. We observed that uUMOD in the AKI group was significantly lower than the non-AKI group. Subgroup analysis showed that the difference of uromodulin between AKI and non-AKI was more significant in children than in adults, and could predict AKI before the operation.

AKI occurs in approximately 10–15% of in-hospital patients, while more than 50% in intensive care units [32]. It elevates mortality rates, extends hospitalization, and increases expenses [33]. In the past 50 years, the diagnosis of AKI was mainly based on Scr [34], which was influenced by muscle mass, diet, age, medication, hydration status, and sex. Besides, for most forms of AKI, the renal tubular epithelium is the primary locus of injury, not glomeruli reflected by Scr. Decreased eGFR based on the Scr is a late and insensitive indicator that cannot timely reveal the kidney injury within 25–50% of renal function lost, thus far from sufficient in clinical practice [35]. In the end, there is no optimal biomarker in the prediction or diagnosis of AKI, with a promising direction for machine learning and artificial intelligence in the construction of a clinical prediction model utilizing a set of potential biomarkers. Thus, the discovery of more biomarkers is meaningful.

Uromodulin is exclusively produced by the cells in the thick ascending limb (TAL) and the distal convoluted tubule (DCT) [36]. Though extensive biochemical studies have been conducted, the physiological roles of uromodulin remained elusive for decades. Genome-wide association studies (GWAS) identified that common UMOD variants were strongly associated with renal function and the chronic kidney disease (CKD) progress risk in the general population [37, 38]. Uromodulin has regained the researcher's attention. Studies showed that uromodulin was correlated with various diseases, including hypertension, CKD, diabetic nephropathy [39], and diabetic-related aortic stiffness [40].

Several studies have elucidated that uromodulin can protect against AKI in various ways. LaFavers KA reported that uromodulin could protect against AKI by suppressing the activity of transient receptor potential cation channel, subfamily M, member 2 (TRPM2), a multifunctional nonselective calcium ion channel, attenuating systemic oxidative stress via interfering with the RAC1/JNK/c-JUN pathway [41]. THP-deficient (THP−/−) mice have more tubular casts, increased inflammatory reaction and necrosis, and worse renal function. THP may exert protective function by raising the expression of TLR4 and its targeting to the apical membrane of proximal tubular S3 segments, where the interaction of TLR4 with pro-inflammatory interstitial ligands is released after ischemia [42]. Complement activation was a reported cause of renal ischemia–reperfusion injury [43]. Recently collectin-11 (CL-11) was considered to be involved in AKI, and uromodulin could bind to the CL-11 mediated by the glycan, dampening the lectin pathway [44]. After ischemic AKI, UMOD was redistributed in the TAL with the presence of interstitial increasing, which would decrease the transition of macrophages from a pro-inflammatory (M1) to a pro-healing (M2) phenotype [45]. This process could positively regulate mononuclear phagocyte numbers, plasticity, and phagocytic activity. Thus inhibit the persistent inflammatory response and enhancing kidney recovery after ischemia–reperfusion injury [46]. The detailed underlying mechanism of the relationship between AKI and UMOD needs further investigation.

Our study further validated that uromodulin could be a potential biomarker to diagnose and predict AKI. As a candidate for building a diagnostic model and early risk stratification model of AKI, it could improve the efficacy. As for diagnosing AKI, the area under the curve (AUC) of the receiver operating characteristic curve (ROC) of the study "Michael 2018" is 0.9 [20], while in the study "Askenazi 2012" is 0.77 [23]. For predicting AKI, studies revealed that the lowest quartile of uUMOD is associated with an increased risk of developing AKI compared with the highest quartile. In 2019, Bullen demonstrated that lower uromodulin independently predicted subsequent AKI, adjusted by eGFR and albuminuria [24].

Subgroup analysis of different ages showed the uUMOD was significantly lower in the AKI group. Compared with the adults, Scr was not considered good enough to predict AKI in children at the growing stage, whose muscle volume kept increasing. The baseline Scr concentration in younger children is 0.2–0.3 mg/dl, which is close to the laboratory variability of 0.1–0.2 mg/dl. Therefore, an absolute increase in Scr might vary substantially in children and was not suitable for diagnosing AKI in younger ages [47]. The serum UMOD levels also increased significantly during growth [48], but with the elucidated subgroup difference in different age groups. Therefore, uUMOD is more valuable and helpful to predict AKI of children, as well as a more easy to accessible and non-invasive process than serum UMOD for children. Intensively, since children have fewer complications than adult patients, there are fewer confounding variables in the outcome, enhancing the potential diagnostic efficacy.

Another subgroup analysis revealed the consistent result of the uUMOD in AKI prediction of surgery groups. AKI is a common postoperative complication, with the incidence is about 8% in general surgery and about 30% in cardiac surgery [49]. Approximately 80% of AKI cases in surgery were due to ischemia that occurred during an operation or postoperative blooding [50], which caused fluid depletion and renal hypofusion, activating adaptive mechanisms to maintain normal function. GFR is initially decreased without renal injury and continue to cause organ damage when the hypoperfusion sustained, or the adaptive response was not adequate. It always happened because of the autoregulation mechanism disorder, the sympathetic nervous system and renin–angiotensin–aldosterone system (RAAS) therapy. If the kidney could not restore adequate perfusion, continuous organ damage and nutrient deficiency and ATP-depletion would lead to the necrosis or apoptosis of the epithelial cell, activation of the inflammatory response and parenchymal damage [51]. Early recognition of AKI caused by hypovolemia is quite important since it is relatively easier to handle compared with other AKI causes. However, currently, few biomarkers can specifically predict surgery-associated AKI. Pranav S reported that preoperative uUMOD could predict AKI developing after cardiac surgery, as well as liver transplantation in Ashwani's study [25, 26]. Our study provided further evidence for the promising application of uUMOD in predicting operation-associated AKI, which was mostly caused by circulatory volume reduction and could be preventable.

The assay time subgroup analysis shows that the difference of uUMOD between AKI and non-AKI is significant in both "before" and "during" subgroups, indicating that uUMOD can predict and detect AKI. The pooled OR suggested the lowest quantile uromodulin at baseline is 2.47 times of developing AKI than the highest quantiles. With the limited data of the included studies, we considered it is better to send UMOD when people are at the risk of developing AKI, such as septic or perioperative patients. What's more, sequential measurements of uUMOD are needed to help define the optimal timing of sampling and assessing its utility as a marker of detective, predictive, and recovery from AKI.

Uromodulin has some advantages in predicting and diagnosing AKI. The pathological analysis of AKI showed tubular injuries were in the outer renal medulla, the late proximal tubule (S3), and the TAL, the primary secretion site of uromodulin. UMOD could reflect the reserve of the kidney function. The other well-known AKI markers, such as NGAL, TIMP-2/IGFBP7, were widely used to predict the acute injury and recovery of the renal tubular for years. But NGAL is expressed mainly in the distal tubular and induced in injured epithelia [52], the TIMP-2 secreted in the distal tubule, while IGFBP7 is from the proximal tubule [53]. None of them was good enough to predict all AKI patients in critical patients or with low risk. Secondly, once injury has occurred, those well-known markers to forecast AKI are ineffective [32]. The uromodulin did quite well in predicting early AKI in children, in patients with septic or perioperative surgery. However, the development of a biomarker includes five phases, namely discovery, quantification/verification, validation, and implementation [54]. uUMOD is currently at the discovery phase, and future large-scale multicenter prospective studies are needed to precisely evaluate the diagnostic efficacy and cut-offs of uUMOD and to determine the range of its concentration in people with and without AKI. The comparison of sUMOD and uUMOD could find out which would be a better indicator [55].

Our study has some strengths. To our knowledge, itis the first study that pooled the existed studies focusing on the UMOD and AKI. We conducted the subgroup analysis on age, surgery condition, and assay time. Meta-regression analysis focused on the age, surgery condition, and assay time screened that age might be a confounder. We found that the uUMOD played an important role in predicting and diagnosing the AKI, especially in children and patients before surgery. We combined the OR and find that the lower baseline uromodulin is inversely associated with the AKI occurrence. The outcome of the meta-analysis is robust: The heterogeneity of the studies was mainly sourced from the studies "Michael (2018)" and "Tara K (2010)", with the I2 reduced to 46% (P = 0.07). It was partially attributed to the confounding factors and has been balanced by subgroup analysis.

There are several limitations to the present study. First, the included studies did not serve the sensitivity or specificity of uUMOD, we could not do the diagnostic meta-analysis and get the synthetic cut-off value, sensitivity, or specificity of uUMOD for predicting AKI. Second, the vast majority of studies originated from Western countries; thus, extrapolation of these results to Eastern populations is questionable. Third, we completed the analysis before pre-registration without PROSPERO registration number, which should be avoided in our future meta-analysis. Fourth, several studies did not offer the causes of AKI, so we could not make the subgroup analysis of different causes of the AKI. Fifth, only one animal study detected the concentration of the uUMOD without specific data [56]. Two studies offered the fold-change of the UMOD mRNA levels in mice kidney tissue [57, 58]. So we did not combine the SMD of the uUMOD. Last, we pooled the HR and OR together which may cause some bias.

In summary, this study is the first systematic review and meta-analysis combining current manuscripts in the field on the utility of uromodulin in the prediction of AKI. We observed that decreased uUMOD might be a potential novel biomarker for AKI prediction, especially in children and patients before surgery.

Availability of data and materials

All data generated or analyzed during this study are included in this published article. Corresponding author can be contacted for more information.



Acute kidney injury


Acute rejection


Acute tubular necrosis


Area under the curve


C–C motif chemokine ligand 14


Confidence interval


Chronic kidney disease




Distal convoluted tubule


Glomerular filtration rate


Genome-wide association study


Insulin-like growth factor-binding protein 7


Interquartile range


Kidney injury molecule-1


Neutrophil gelatinase-associated lipocalin


Odds ratio




Restricted maximum likelihood


Receiver operating characteristic curve


Standard deviation


Standardized mean difference


Thick ascending limb


Tamm-Horsfall protein


Tissue inhibitor of metalloproteinase-2


Transient receptor potential cation channel, subfamily M, member 2




Wild type


  1. 1.

    Meersch M, Schmidt C, Zarbock A. Perioperative acute kidney injury. Anesth Analg. 2017;125(4):1223–32.

    PubMed  Google Scholar 

  2. 2.

    Roy AK, Mc Gorrian C, Treacy C, Kavanaugh E, Brennan A, Mahon NG, Murray PT. A comparison of traditional and novel definitions (RIFLE, AKIN, and KDIGO) of acute kidney injury for the prediction of outcomes in acute decompensated heart failure. Cardiorenal Med. 2013;3(1):26–37.

    PubMed  PubMed Central  Google Scholar 

  3. 3.

    Parikh CR, Mishra J, Thiessen-Philbrook H, Dursun B, Ma Q, Kelly C, Dent C, Devarajan P, Edelstein CL. Urinary IL-18 is an early predictive biomarker of acute kidney injury after cardiac surgery. Kidney Int. 2006;70(1):199–203.

    CAS  PubMed  Google Scholar 

  4. 4.

    Kulvichit W, Kellum JA, Srisawat N. Biomarkers in acute kidney injury. Crit Care Clin. 2021;37(2):385–98.

    PubMed  Google Scholar 

  5. 5.

    Kraus D, Wanner C. Uromodulin in the bloodstream: old wine in a new wineskin. J Am Soc Nephrol. 2017;28(7):1955–7.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Bleyer AJ, Kmoch S. Tamm Horsfall glycoprotein and uromodulin: it is all about the tubules! Clin J Am Soc Nephrol. 2016;11(1):6–8.

    CAS  PubMed  Google Scholar 

  7. 7.

    Devuyst O, Olinger E, Rampoldi L. Uromodulin: from physiology to rare and complex kidney disorders. Nat Rev Nephrol. 2017;13(9):525–44.

    CAS  PubMed  Google Scholar 

  8. 8.

    Matafora V, Cuccurullo M, Beneduci A, Petrazzuolo O, Simeone A, Anastasio P, Mignani R, Feriozzi S, Pisani A, Comotti C, et al. Early markers of Fabry disease revealed by proteomics. Mol Biosyst. 2015;11(6):1543–51.

    CAS  PubMed  Google Scholar 

  9. 9.

    Tsai CY, Wu TH, Yu CL, Lu JY, Tsai YY. Increased excretions of beta2-microglobulin, IL-6, and IL-8 and decreased excretion of Tamm-Horsfall glycoprotein in urine of patients with active lupus nephritis. Nephron. 2000;85(3):207–14.

    CAS  PubMed  Google Scholar 

  10. 10.

    Koyner JL, Davison DL, Brasha-Mitchell E, Chalikonda DM, Arthur JM, Shaw AD, Tumlin JA, Trevino SA, Bennett MR, Kimmel PL, et al. Furosemide stress test and biomarkers for the prediction of AKI severity. J Am Soc Nephrol. 2015;26(8):2023–31.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Park JH, Jang HR, Huh W, Kim DJ, Kim Y-G, Oh HY, Lee JE. MP264A predictive model for successful conversion of continuous renal replacement therapy to intermittent hemodialysis in critically ill patients. Nephrol Dial Transplant. 2017;32(suppl_3):iii524.

    Google Scholar 

  12. 12.

    Mansour SG, Hall I, Reese P, Ashkar T, Jia Y, Thiessen-Philbrook H, Doshi M, Parikh CR. Urine uromodulin associates with deceased donor acute kidney injury and allograft failure. Am J Transplant. 2019;19:654.

    Google Scholar 

  13. 13.

    Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, Henry D, Altman DG, Ansari MT, Boutron I, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): an R package and Shiny web app for visualizing risk-of-bias assessments. Res Syn Meth. 2020;12(1):55–61.

    Google Scholar 

  15. 15.

    Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range.1471–2288 (Electronic). BMC Med Res Methodol. 2014;14(135):135.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Higgins JPT, White IR, Anzures-Cabrera J. Meta-analysis of skewed data: combining results reported on log-transformed or raw scales. Stat Med. 2008;27(29):6072–92.

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Hanna M, Brophy PD, Giannone PJ, Joshi MS, Bauer JA, RamachandraRao S. Early urinary biomarkers of acute kidney injury in preterm infants. Pediatr Res. 2016;80(2):218–23.

    CAS  PubMed  Google Scholar 

  18. 18.

    Aregger F, Pilop C, Uehlinger DE, Brunisholz R, Carrel TP, Frey FJ, Frey BM. Urinary proteomics before and after extracorporeal circulation in patients with and without acute kidney injury. J Thorac Cardiovasc Surg. 2010;139(3):692–700.

    CAS  PubMed  Google Scholar 

  19. 19.

    Kokot M, Duława J, Nowicki M, Kokot F, Machowska J. Urinary excretion of Tamm-Horsfall protein by patients with acute renal failure. Pol Arch Med Wewn. 1992;88(4):225–9.

    CAS  PubMed  Google Scholar 

  20. 20.

    Bennett MR, Pyles O, Ma Q, Devarajan P. Preoperative levels of urinary uromodulin predict acute kidney injury after pediatric cardiopulmonary bypass surgery. Pediatr Nephrol. 2018;33(3):521–6.

    PubMed  Google Scholar 

  21. 21.

    Sweetman DU, Onwuneme C, Watson WR, O’Neill A, Murphy JFA, Molloy EJ. Renal function and novel urinary biomarkers in infants with neonatal encephalopathy. Acta Paediatr Int J Paediatr. 2016;105(11):e513–9.

    CAS  Google Scholar 

  22. 22.

    Askenazi DJ, Koralkar R, Patil N, Halloran B, Ambalavanan N, Griffin R. Acute kidney injury urine biomarkers in very low-birth-weight infants. Clin J Am Soc Nephrol. 2016;11(9):1527–35.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Askenazi DJ, Koralkar R, Hundley HE, Montesanti A, Parwar P, Sonjara S, Ambalavanan N. Urine biomarkers predict acute kidney injury in newborns. J Pediatr. 2012;161(2):270-275.e271.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Bullen AL, Katz R, Lee AK, Anderson CAM, Cheung AK, Garimella PS, Jotwani V, Haley WE, Ishani A, Lash JP, et al. The SPRINT trial suggests that markers of tubule cell function in the urine associate with risk of subsequent acute kidney injury while injury markers elevate after the injury. Kidney Int. 2019;96(2):470–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Singal AK, Jackson B, Pereira GB, Russ KB, Fitzmorris PS, Arora S, Kakati D, Seay T, Kuo Y-F, Singh KP, et al. Biomarkers of renal injury in patients with cirrhosis: association with acute kidney injury and recovery after liver transplantation. Gastroenterology. 2017;152(5):S1132–S1132.

    Google Scholar 

  26. 26.

    Garimella PS, Jaber BL, Tighiouart H, Liangos O, Bennett MR, Devarajan P, El-Achkar TM, Sarnak MJ. Association of preoperative urinary uromodulin with AKI after cardiac surgery. Clin J Am Soc Nephrol. 2017;12(1):10–8.

    PubMed  Google Scholar 

  27. 27.

    Sigdel TK, Kaushal A, Gritsenko M, Norbeck AD, Qian WJ, Xiao W, Camp DGN, Smith RD, Sarwal MM. Shotgun proteomics identifies proteins specific for acute renal transplant rejection. Proteomics Clin Appl. 2010;4(1):32–47.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Romero MC, Zanaro N, Gonzalez L, Trigo P, Imventarza O, Nesse A. Tamm-Horsfall protein excretion to predict the onset of renal insufficiency. Clin Biochem. 2002;35(1):65–8.

    CAS  PubMed  Google Scholar 

  29. 29.

    McLaughlin PJ, Aikawa A, Davies HM, Ward RG, Bakran A, Sells RA, Johnson PM. Uromodulin levels are decreased in urine during acute tubular necrosis but not during immune rejection after renal transplantation. Clin Sci (Lond). 1993;84(2):243–6.

    CAS  Google Scholar 

  30. 30.

    Dehne MG, Sablotzki A, Muhling J, Papke G, Kuntzsch U, Hempelmann G. Acute kidney failure. Non-invasive diagnosis of acute kidney failure in operative intensive care patients. Anaesthesist. 1998;47(3):193–201.

    CAS  PubMed  Google Scholar 

  31. 31.

    Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16.

    PubMed  PubMed Central  Google Scholar 

  32. 32.

    Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet. 2019;394(10212):1949–64.

    CAS  PubMed  Google Scholar 

  33. 33.

    Haase M, Bellomo R, Devarajan P, Schlattmann P, Haase-Fielitz A. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: a systematic review and meta-analysis. Am J Kidney Dis. 2009;54(6):1012–24.

    CAS  PubMed  Google Scholar 

  34. 34.

    Stevens PE, Levin A. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013;158(11):825–30.

    PubMed  Google Scholar 

  35. 35.

    Star RA. Treatment of acute renal failure. Kidney Int. 1998;54(6):1817–31.

    CAS  PubMed  Google Scholar 

  36. 36.

    Tokonami N, Takata T, Beyeler J, Ehrbar I, Yoshifuji A, Christensen EI, Loffing J, Devuyst O, Olinger EG. Uromodulin is expressed in the distal convoluted tubule, where it is critical for regulation of the sodium chloride cotransporter NCC. Kidney Int. 2018;94(4):701–15.

    CAS  PubMed  Google Scholar 

  37. 37.

    Pattaro C, Teumer A, Gorski M, Chu AY, Li M, Mijatovic V, Garnaas M, Tin A, Sorice R, Li Y, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Köttgen A, Glazer NL, Dehghan A, Hwang SJ, Katz R, Li M, Yang Q, Gudnason V, Launer LJ, Harris TB, et al. Multiple loci associated with indices of renal function and chronic kidney disease. Nat Genet. 2009;41(6):712–7.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Torffvit O, Agardh CD, Kjellsson B, Wieslander J. Tubular secretion of Tamm-Horsfall protein in type 1 (insulin-dependent) diabetes mellitus using a simplified enzyme linked immunoassay. Clin Chim Acta. 1992;205(1–2):31.

    CAS  PubMed  Google Scholar 

  40. 40.

    Then C, Then H, Lechner A, Huth C, Meisinger C, Heier M, Peters A, Koenig W, Rathmann W, Herder C, et al. Serum uromodulin is inversely associated with the metabolic syndrome in the KORA F4 study. Endocr Connect. 2019;8(10):1363–71.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    LaFavers KA, Macedo E, Garimella PS, Lima C, Khan S, Myslinski J, McClintick J, Witzmann FA, Winfree S, Phillips CL, et al. Circulating uromodulin inhibits systemic oxidative stress by inactivating the TRPM2 channel. Sci Transl Med. 2019;11(512):3639.

    Google Scholar 

  42. 42.

    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. 2008;295(2):F534-544.

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    de Vries B, Walter SJ, Peutz-Kootstra CJ, Wolfs TGAM, van Heurn LWE, Buurman WA. The mannose-binding lectin-pathway is involved in complement activation in the course of renal ischemia-reperfusion injury. Am J Pathol. 2004;165(5):1677–88.

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Gong K, Xia M, Wang Y, Bai L, Ying W, Zhu F, Chen Y. Importance of glycosylation in the interaction of Tamm-Horsfall protein with collectin-11 and acute kidney injury. J Cell Mol Med. 2020;24(6):3572–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Micanovic R, Khan S, Janosevic D, Lee ME, Hato T, Srour EF, Winfree S, Ghosh J, Tong Y, Rice SE, et al. Tamm-Horsfall protein regulates mononuclear phagocytes in the kidney. J Am Soc Nephrol. 2018;29(3):841–56.

    CAS  PubMed  Google Scholar 

  46. 46.

    El-Achkar TM, McCracken R, Liu Y, Heitmeier MR, Bourgeois S, Ryerse J, Wu XR. Tamm-Horsfall protein translocates to the basolateral domain of thick ascending limbs, interstitium, and circulation during recovery from acute kidney injury. Am J Physiol Renal Physiol. 2013;304(8):1066–75.

    Google Scholar 

  47. 47.

    Greenberg JH, Parikh CR. Biomarkers for diagnosis and prognosis of AKI in children: one size does not fit all. Clin J Am Soc Nephrol. 2017;12(9):1551–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Alfaham M, Peters TJ, Meyrick S, Avis P, Verrier Jones K. Serum Tamm-Horsfall protein levels in childhood: relationship with age and glomerular filtration rate. Nephron. 1989;52(3):216–21.

    CAS  PubMed  Google Scholar 

  49. 49.

    Fonseca Ruiz NJ, Castro DP, Guerra AM, Saldarriaga FM, Hernandez JD. Renal injury study in critical ill patients in accordance with the new definition given by the acute kidney injury network. J Crit Care. 2011;26(2):206–12.

    PubMed  Google Scholar 

  50. 50.

    Wang HE, Muntner P, Chertow GM, Warnock DG. Acute kidney injury and mortality in hospitalized patients. Am J Nephrol. 2012;35(4):349–55.

    PubMed  PubMed Central  Google Scholar 

  51. 51.

    Makris K, Spanou L. Acute kidney injury: definition, pathophysiology and clinical phenotypes. Clin Biochem Rev. 2016;37(2):85–98.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Mishra J, Dent C, Tarabishi R, Mitsnefes MM, Ma Q, Kelly C, Ruff SM, Zahedi K, Shao M, Bean J, et al. Neutrophil gelatinase-associated lipocalin (NGAL) as a biomarker for acute renal injury after cardiac surgery. Lancet. 2005;365(9466):1231–8.

    CAS  PubMed  Google Scholar 

  53. 53.

    Ortega LM, Heung M. The use of cell cycle arrest biomarkers in the early detection of acute kidney injury. Is this the new renal troponin? Nefrologia (Engl Ed). 2018;38(4):361–7.

    Google Scholar 

  54. 54.

    Srisawat N, Kellum JA. The role of biomarkers in acute kidney injury. Crit Care Clin. 2020;36(1):125–40.

    PubMed  Google Scholar 

  55. 55.

    Bellos I, Fitrou G, Daskalakis G, Perrea DN, Pergialiotis V. Neutrophil gelatinase-associated lipocalin as predictor of acute kidney injury in neonates with perinatal asphyxia: a systematic review and meta-analysis. Eur J Pediatr. 2018;177(10):1425–34.

    CAS  PubMed  Google Scholar 

  56. 56.

    Skowron B, Baranowska A, Dobrek L, Ciesielczyk K, Kaszuba-Zwoinska J, Wiecek G, Malska-Wozniak A, Strus M, Gil K. Urinary neutrophil gelatinaseassociated lipocalin, kidney injury molecule-1, uromodulin, and cystatin C concentrations in an experimental rat model of ascending acute kidney injury induced by pyelonephritis. J Physiol Pharmacol. 2018;69(4):625–37.

    CAS  Google Scholar 

  57. 57.

    El-Achkar TM, McCracken R, Liu Y, Heitmeier MR, Bourgeois S, Ryerse J, Wu XR. Tamm-Horsfall protein translocates to the basolateral domain of thick ascending limbs, interstitium, and circulation during recovery from acute kidney injury. Am J Physiol Renal Physiol. 2013;304(8):F1066-1075.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Heitmeier M, McCracken R, Micanovic R, Khan S, El-Achkar TM. The role of tumor necrosis factor alpha in regulating the expression of Tamm-Horsfall Protein (uromodulin) in thick ascending limbs during kidney injury. Am J Nephrol. 2014;40(5):458–67.

    CAS  PubMed  Google Scholar 

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This work was partially supported by grants from the National Natural Scientific Foundation of China (81970607, 81470937 to C.L); the Key Research and Development Program of Ningxia Hui Autonomous Region (2018BFG02010 to C.L.); Capital’s Funds for Health Improvement and Research (CFH 2020-2-4018 to C.L.); Beijing Natural Science Foundation (L202035 to C.L.); CAMS Innovation Fund for Medical Sciences (CIFMS 2020-I2M-C&T-A-001 to C.L.); Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS 2016-I2M-2-004 to C.L.); and National Key-point Research Program Precision Medicine Grant (2016YFC0901500 to C.L.). The National Postdoctoral Research Fellowship, China (2018M631397 to Xu L). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 

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RY conceived and designed the study, performed the study, analyzed the data, wrote the paper, prepared figures and/or tables. HZ performed the meta-analysis, analyzed the data, prepared figures and/or tables. LX, TTM, GC prepared figures and/or tables. PX, XHF, PJ performed the experiments, analyzed the data, contributed materials/analysis tools. LW analyzed the data, contributed materials/analysis tools. LC conceived and designed the study, wrote the paper, reviewed drafts of the paper. All authors read and approved the final manuscript.

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Correspondence to Limeng Chen.

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Additional file 1:

Table S1. Search strategy for PubMed. Table S2. Search strategy for Embase. Table S3. Search strategy for Cochrane Library. Table S4. Search strategy for Web of Science. Table S5. Mean and standard deviations of the uUMOD included in the meta-analysis. Fig. S1. Egger plot shows detailing publication bias in the included studies. Fig. S2. Galbraith radial plot of Heterogeneity analysis. Fig. S3. Sensitivity analysis to test the robustness of the pooled Standardized Mean Difference by leave-one-out method. Fig. S4. Sensitivity analysis to test the robustness of the pooled Odds Ratio by leave-one-out method. Fig. S5. Meta-regression plot of the association between SMD of the uUMOD with age. MOOSE checklist.

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You, R., Zheng, H., Xu, L. et al. Decreased urinary uromodulin is potentially associated with acute kidney injury: a systematic review and meta-analysis. j intensive care 9, 70 (2021).

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  • Uromodulin
  • Acute kidney injury
  • Biomarkers