The value of uromodulin as a new serum marker to predict decline in renal function

Leiherer, Andreas; Muendlein, Axel; Saely, Christoph H.; Brandtner, Eva-Maria; Geiger, Kathrin; Fraunberger, Peter

doi:10.1097/hjh.0000000000001527

Cited by 55 publications

(60 citation statements)

References 39 publications

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“…Finally, in the serial analysis, we found a direct correlation between better GFR and plasma Tamm‐Horsfall protein levels (also known as uromodulin). This fits well with previous studies showing uromodulin being protective and inversely associated with CKD …”

Section: Discussionsupporting

confidence: 93%

Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation

Levitsky¹,

Asrani

Klintmalm

et al. 2020

Hepatology

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BaCKgRoUND aND aIMS:A high proportion of patients develop chronic kidney disease (CKD) after liver transplantation (LT). We aimed to develop clinical/protein models to predict future glomerular filtration rate (GFR) deterioration in this population. appRoaCH aND ReSUltS: In independent multicenter discovery (CTOT14) and single-center validation (BUMC) cohorts, we analyzed kidney injury proteins in serum/plasma samples at month 3 after LT in recipients with preserved GFR who demonstrated subsequent GFR deterioration versus preservation by year 1 and year 5 in the BUMC cohort. In CTOT14, we also examined correlations between serial protein levels and GFR over the first year. A month 3 predictive model was constructed from clinical and protein level variables using the CTOT14 cohort (n = 60). Levels of β-2 microglobulin and CD40 antigen and presence of hepatitis C virus (HCV) infection predicted early (year 1) GFR deterioration (area under the curve [AUC], 0.814). We observed excellent validation of this model (AUC, 0.801) in the BUMC cohort (n = 50) who had both early and late (year 5) GFR deterioration. At an optimal threshold, the model had the following performance characteristics in CTOT14 and BUMC, respectively: accuracy (0.75, 0.8), sensitivity (0.71, 0.67), specificity (0.78, 0.88), positive predictive value (0.74, 0.75), and negative predictive value (0.76, 0.82). In the serial CTOT14 analysis, several proteins, including β-2 microglobulin and CD40, correlated with GFR changes over the first year. CoNClUSIoNS:We have validated a clinical/protein model (PRESERVE) that early after LT can predict future renal deterioration versus preservation with high accuracy. This model may help select recipients at higher risk for subsequent CKD for early, proactive renal sparing strategies. (Hepatology

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Section: Discussionsupporting

confidence: 93%

Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation

Levitsky¹,

Asrani

Klintmalm

et al. 2020

Hepatology

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“…Although its antiinflammatory and renal protective properties in urine have long been known, the systemic function of UMOD remains elusive (8,9). Previous studies (10)(11)(12) have shown that lower serum UMOD (SUMOD) level is associated with a decline in kidney function in adults without diabetes. Further, lower SUMOD level has been linked with cardiovascular disease mortality in coronary patients (13,14).…”

mentioning

confidence: 99%

Serum Uromodulin Predicts Less Coronary Artery Calcification and Diabetic Kidney Disease Over 12 Years in Adults With Type 1 Diabetes: The CACTI Study

et al. 2018

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Novel biomarkers are needed to better predict coronary artery calcification (CAC), a marker of subclinical atherosclerosis, and diabetic kidney disease (DKD) in type 1 diabetes. We evaluated the associations between serum uromodulin (SUMOD [a biomarker associated with anti-inflammatory and renal protective properties]), CAC progression, and DKD development over 12 years. RESEARCH DESIGN AND METHODS Participants (n = 527, 53% females) in the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study were examined during 2002-2004, at a mean age of 39.6 6 9.0 years and a median duration of diabetes of 24.8 years. Urine albumin-tocreatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) determined by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) creatinine equation were measured at baseline and after a mean follow-up period of 12.1 6 1.5 years. Elevated albumin excretion was defined as ACR ‡30 mg/g, rapid GFR decline (>3 mL/min/1.73 m 2 /year), and impaired GFR as eGFR <60 mL/min/1.73 m 2. SUMOD was measured on stored baseline plasma samples (Meso Scale Discovery). CAC was measured using electron beam computed tomography. CAC progression was defined as a change in the square root-transformed CAC volume of ‡2.5. RESULTS Higher baseline SUMOD level conferred lower odds of CAC progression (odds ratio 0.68; 95% CI 0.48-0.97), incident elevated albumin excretion (0.37; 0.16-0.86), rapid GFR decline (0.56; 0.35-0.91), and impaired GFR (0.44; 0.24-0.83) per 1 SD increase in SUMOD (68.44 ng/mL) after adjustment for baseline age, sex, systolic blood pressure, LDL cholesterol, and albuminuria/GFR. The addition of SUMOD to models with traditional risk factors also significantly improved the prediction performance for CAC progression and incident DKD. CONCLUSIONS Higher baseline SUMOD level predicted lower odds of both CAC progression and incident DKD over 12 years in adults with type 1 diabetes.

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“…In summary, as several different UMOD SNPs have been linked to both CKD and eGFR, UMOD may prove to be useful as a diagnostic and prognostic genomic biomarker of CKD. Indeed, levels of serum uromodulin have recently been shown to useful in disease detection as CKD patients were shown to have a significantly lower serum uromodulin concentration (p < 0.001) and measurement of serum UMOD was shown to significantly enhance performance of a CKD prediction model (p = 0.049) [ 46 ]. UMOD often exhibits one of the strongest associations with CKD [ 22 , 26 ], suggesting that UMOD would likely be suitable as a diagnostic genomic biomarker of CKD.…”

Section: Genetic Biomarkersmentioning

confidence: 99%

Genomic approaches in the search for molecular biomarkers in chronic kidney disease

et al. 2018

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BackgroundChronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD.Main bodyConventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management.ConclusionCKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases.

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The value of uromodulin as a new serum marker to predict decline in renal function

Abstract: The current study is the first evaluating the value of uromodulin in serum as a novel predictive biomarker for renal function and for the incidence of CKD.

Cited by 55 publications

References 39 publications

Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation

Discovery and Validation of a Biomarker Model (PRESERVE) Predictive of Renal Outcomes After Liver Transplantation

Serum Uromodulin Predicts Less Coronary Artery Calcification and Diabetic Kidney Disease Over 12 Years in Adults With Type 1 Diabetes: The CACTI Study

Genomic approaches in the search for molecular biomarkers in chronic kidney disease

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