urinary angiotensinogen level predicts aki in acute

CLINICAL RESEARCH www.jasn.org

Urinary Angiotensinogen Level Predicts AKI in AcuteDecompensated Heart Failure: A Prospective,Two-Stage Study

Xiaobing Yang,* Chunbo Chen,*† Jianwei Tian,* Yan Zha,‡ Yuqin Xiong,* Zhaolin Sun,‡

Pingyan Chen,* Jun Li,* Tiecheng Yang,§ Changsheng Ma,| Huafeng Liu,¶ Xiaobin Wang,**and Fan Fan Hou*

*National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, NanfangHospital, Southern Medical University, Guangzhou, China; †Guangdong General Hospital, Guangdong Academy ofMedical Sciences, Guangdong Cardiovascular Institute, Guangzhou, China; ‡Guizhou Provincial People’s Hospital,Guiyang Medical University, Guiyang, China; §Futian Hospital, Guangdong Medical College, Shenzhen, China;|Beijing An Zhen Hospital, Capital Medical University, Beijing Institute of Heart, Lung, and Blood Vessel Diseases,Beijing, China; ¶Institute of Nephrology, Guangdong Medical College, Zhanjiang, China; and **Center on Early LifeOrigins of Disease, Johns Hopkins University, Baltimore, Maryland

ABSTRACTA major challenge in prevention and early treatment of acute cardiorenal syndrome (CRS) is the lack of high-performance predictors. To test the hypothesis that urinary angiotensinogen (uAGT) is an early predictor foracute CRS and 1-year prognosis in patients with acute decompensated heart failure (ADHF), we performed aprospective, two-stage, multicenter cohort study in patients with ADHF. In stage I (test set), 317 patients wererecruited from four centers. In stage II (validation set), 119 patients were enrolled from two other centers. DailyuAGT levels were analyzed consecutively. AKI was defined according to KidneyDisease ImprovingGlobalOutcomes (KDIGO) Clinical Practice Guidelines. In stage I, 104 (32.8%) patients developed AKI duringhospitalization. Daily uAGT peaked on the first hospital day in patients who subsequently developed AKI.After multivariable adjustment, the highest quartile of uAGT on admission was associated with a 50-foldincreased risk of AKI compared with the lowest quartile. For predicting AKI, uAGT (area under the receiver-operating characteristic curve [AUC]=0.84) outperformed urinary neutrophil gelatinase-associated lipocalin(AUC=0.78), the urinary albumin/creatinine ratio (AUC=0.71), and the clinical model (AUC=0.77). Survivors instage Iwere followedprospectively for 1 year after hospital discharge. TheuAGT level independently predictedthe risk of 1-year mortality (adjusted odds ratio, 4.5; 95% confidence interval, 2.1 to 9.5) and rehospitalization(adjustedodds ratio, 3.6; 95%confidence interval, 1.6 to5.7). Theabilityof uAGT inpredictingAKIwas validatedin stage II (AUC=0.79). In conclusion, uAGT is a strong predictor for acute CRS and 1-year prognosis in ADHF.

J Am Soc Nephrol 26: 2032–2041, 2015. doi: 10.1681/ASN.2014040408

Acute heart failure is the leading cause of hospitaliza-tion worldwide and represents a significant economicburden.1 In patients with acute decompensated heartfailure (ADHF), the incidence of AKI is high, rangingfrom 25% to 51%.2–4 Coexistence of acute cardiacand renal dysfunction, known as acute (or type 1)cardiorenal syndrome (CRS),5 is a serious clinicalstate associated with significantly increased morbidityand mortality.6,7

Amajor barrier to improved clinical outcomes inpatients with ADHF is the lack of ability to identify

Received April 28, 2014. Accepted September 22, 2014.

X.Y. and C.C. contributed equally to this work.

Published online ahead of print. Publication date available atwww.jasn.org.

Correspondence:Dr. Fan Fan Hou, Division ofNephrology, NationalClinical Research Center for Kidney Disease, State Key Laboratory ofOrgan Failure Research Nanfang Hospital, SouthernMedical University,1838 North Guangzhou Avenue, Guangzhou 510515, China; orDr. XiaobinWang,Center onEarly LifeOrigins ofDisease, JohnsHopkinsUniversity Bloomberg School of Public Health and School of Medicine,Baltimore, MD 21205-2179. Email: [email protected] [email protected]

Copyright © 2015 by the American Society of Nephrology

2032 ISSN : 1046-6673/2608-2032 J Am Soc Nephrol 26: 2032–2041, 2015

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mailto:[email protected]

patients at high risk of AKI early enough to initiate beneficialinterventions. It is well recognized that serum creatinine is notan early marker of AKI in patients with ADH.8 In recent years,efforts have been directed toward the development of bio-markers for the early detection of AKI, and have yielded severalbiomarkers such as neutrophil gelatinase-associated lipocalin(NGAL), kidney injury molecule-1, and IL-18.9–12 To date,only a few small clinical studies have tested the utility of thesebiomarkers, mostly NGAL, in predicting acute CRS and haveyielded only modest performance in general.13–19 In addition,there is a particular lack of predictors for longer-term progno-sis in patients with acute CRS. In each of these circumstances,the early detection of patients at high risk of developing AKI,before a detectable change in serum creatinine, would affordclinicians a critical time window to halt or reverse the ongoingrenal injury.

The underlying causes of acute CRS are multifactorial, butare likely related to thederangement incardiorenal interactionsas well as renal hypoperfusion as the result of inappropriate useof diuretics or other strategies to alleviate congestion.20 Increas-ing evidence has revealed that the intrarenal renin-angiotensinsystem (RAS) plays a vital role in maintaining hemodynamicbalance and cardiorenal interaction, which are often disrupted inpatients with acute heart failure.21,22 In animal studies, activationof the intrarenal RAS is an initial response to hypoperfusion incardiac and renal injury, and is also an important contributor tothe progression of the disease.22,23 Intrarenal angiotensinogen(AGT), a principal substrate of the local RAS, is mainly formedin the proximal tubule cells and secreted into the tubule lumen.24

Urinary AGT (uAGT) level correlates with intrarenal AGT andangiotensin II, and has been shown to be an indicator of intra-renal RAS activity in both animal and clinical studies.25,26

We conducted a prospective, two-stage, multicenter observa-tional study in 436 patients with ADHFwhowere enrolled on thefirst dayof admission. In stage I (test set), 317patientswithADHFwere recruited from four centers. In stage II (validation set), 119patients were enrolled from two other centers. Our primaryobjectivewas to test and validate thehypothesis that dailymeasuresof uAGT during the first week of admission predict the de-velopment of AKI and 1-year prognosis in these patients. This isthe first study in patients with ADHF to demonstrate the utilityof uAGTas a predictive biomarker of AKI and 1-year mortality,rehospitalization, and recovery of renal function.

RESULTS

Cohort DescriptionStage I (Test Set)There were 423 patients with ADHF (all Chinese) enrolledfrom four centers who were screened for potential participa-tion. Of these patients, 106 were excluded according to theexclusion criteria. A total of 317 patients, 47.7% with newlydiagnosed ADHF, were included in the study (SupplementalFigure 1).

Stage II (Validation Set)There were 149 patients with ADHF from the other two centerswho were screened according to a protocol exactly the same asthat used in stage I. Of these patients, 30 were excluded. A totalof 119 patients, 42.9% with newly diagnosed ADHF, wereenrolled (Supplemental Figure 1).

uAGT as a Predictor for the Primary End PointIn the stage I study, AKI occurred in 104 (32.8%) patients, with90% (n=94) occurringwithin 5 days after admission.We groupedpatients into those with and without AKI. Compared with thosewho did not develop AKI, patients who developed AKI wereolder, withmore comorbidity andmore severe acute heart failure.AKI was more frequent in those with preexisting CKD (Table 1).However, no differences were found between the two groupswith respect to the use of renin-angiotensin-aldosterone systeminhibitors or diuretics (Supplemental Table 1).

Patients enrolled in the stage II study exhibited characteristicssimilar to those in stage I (Supplemental Table 2).

In the test set, uAGT values on admissionwere significantlyhigher in patients who subsequently developed AKI duringhospitalization compared with those who did not develop AKIand healthy volunteers (Figure 1A). Figure 1B displays serialmeasures of uAGTover the first 7 days of hospital admission.Compared with patients with ADHF who did not developAKI, those who developed AKI had a marked rise in uAGTat all-time points (P,0.001). The pattern of uAGTwas charac-terized by a peak on the first day of hospital admission; by con-trast, the peak of serum creatinine rise occurred on the fourthday of hospital admission. The increase in serum creatinine las-ted for$3 days in 81.7%of patients with AKI. The ELISA resultswere further confirmed by Western blot (Supplemental Figure2A). Elevationof uAGTwas noted in patients withAKI bothwithand without preexisting CKD, but the levels were significantlyhigher in patients with prior CKD (Figure 1C).

Unlike uAGT, plasmaAGT levels on admissionwere similarbetween patients who developed AKI and those who did notdevelopAKI.Therewasno significant change inplasmaAGTinpatients with ADHF over the first 7 days of hospital admission(Supplemental Figure 2, B and C).

uAGT levels were associated with incident AKI in patientswith and without preexisting CKD in the stage I cohort (Supple-mental Figure 3). This was further supported by the multivariateanalyses. After adjustment for centers and for clinical variablesincluding the use of RAS inhibitors or diuretics, uAGT was themost powerful predictor of AKI, which was true for patients withand without preexisting CKD. The highest quartile of uAGT onthe first day of admission was associated with increased risk forAKIby 50-fold comparedwith the lowest quartile (Table 2).WhenuAGT was analyzed as a continuous variable, higher uAGT wasalso associated with the development of AKI (odds ratio per SD,5.32; 95% confidence interval [95% CI], 3.32 to 8.52; P,0.001)in a multivariable model.

Furthermore, changes in uAGT (baseline levelsminus levelsat days 2–7), when used as time-dependent covariates, were

J Am Soc Nephrol 26: 2032–2041, 2015 Urinary AGT Predicts Acute CRS 2033

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significantly associated with increased risk of AKI after adjust-ment for other known risk factors (hazard ratio [HR], 1.26;95% CI, 1.13 to 1.41; P,0.001).

Performance of uAGT for Predicting AKI in SubgroupAnalysesFor predicting AKI, the area under the receiver-operatingcharacteristic (ROC) curve (AUC) of uAGTon admission forall participants in the test set was 0.84. A cutoff of 55 mg/gcreatinine yielded good sensitivity (0.80) and specificity (0.78)(Figure 2A). The AUCs of uAGT, in subgroups with and with-out preexisting CKD, were greater than those of urinaryNGAL, the urinary albumin to creatinine ratio (UACR), andthe clinical model, in particular, among patients with priorCKD (Figure 2, A–C). The best cutoff value of uAGT for pre-dicting AKI was significantly higher in patients with priorCKD (96.6 mg/g creatinine) compared with those with pre-served eGFR on admission (50.0 mg/g creatinine).

To further validate the predictive value of uAGT forAKI, theAUC for predicting AKI was analyzed in an independent

validation cohort recruited from the other centers (stage IIstudy). As shown in Figure 2D, uAGTwas validated in predict-ing AKI in the stage II cohort (AUC=0.79). Moreover, usingthe optimal cutoff of uAGTobtained from the test cohort (55mg/g), the predictive performance of uAGT in the validationset yielded similar sensitivity (0.72) and specificity (0.76)compared with those in the test set (0.80 and 0.78, respec-tively).

uAGT as a Predictor for Secondary End PointsOf the 317 participants in the stage I study, 55 (17.3%) diedwithin 1 year after admission. A level of uAGT$55 mg/g cre-atinine on admission was associated with a significantly in-creased probability of all-cause mortality (HR, 4.7; 95% CI,2.7 to 8.2) and rehospitalization (HR, 3.7; 95% CI, 2.2 to 6.6)over the 1-year follow-up period (Supplemental Figure 4, Aand B). Because of sample size constraints, we were unable toconduct subgroup analyses.

Figure 3A shows the mortality of participants as a functionof uAGT level and brain natriuretic peptide (NT-proBNP)

Table 1. Characteristics of patients on admission in the stage I cohort

Characteristic Total (N=317) No AKI (n=213) AKI (n=104) P Value

Demographic variablesAge, yr 63.8615.8 61.1616.2 69.4613.4 0.00Men 209 (65.9) 136 (63.8) 73 (70.2) 0.26

Preexisting clinical conditionsHypertension 157 (49.5) 94 (44.1) 63 (60.6) 0.01Diabetes 86 (27.2) 47 (22.1) 39 (38.1) 0.004CKDa 82 (25.8) 37 (17.4) 45 (43.2) 0.00Prior hospitalization for HF 166 (52.3) 109 (51.2) 57 (54.8) 0.54Atrial fibrillation 96 (30.3) 64 (30.0) 32 (30.7) 0.90

Primary causes of heart failureIschemic heart disease 162 (51.1) 104 (48.8) 58 (55.8) 0.25Hypertension 40 (12.6) 23 (10.8) 17 (16.3) 0.16Rheumatic heart disease 57 (18.0) 44 (20.7) 13 (12.5) 0.08Cardiomyopathy 39 (12.3) 28 (13.1) 11 (10.6) 0.51Other 19 (6.0) 14 (6.6) 5 (4.8) 0.53

Characteristics on admissionLVEF,45% 153 (48.3) 102 (47.9) 51 (49.0) 0.85NYHA (class IV) 136 (42.9) 76 (35.7) 60 (57.7) 0.00NT-proBNP, pg/ml 5500 (2748–9000) 5200 (2289–8825) 7163 (3859–9000) 0.00Systolic BP, mmHg 125.8625.3 125.0626.5 127.4623.2 0.43Diastolic BP, mmHg 75.2614.7 76.0615.6 73.8613.0 0.22Serum creatinine, mmol/L 100.5634.0 91.2627.5 113.3628.1 0.00Serum albumin, g/L 32.566.1 34.365.6 29.365.5 0.00Serum triglyceride, mmol/L 1.561.0 1.460.9 1.661.2 0.10Serum cholesterol, mmol/L 4.160.7 4.161.1 4.261.7 0.53Serum sodium, mmol/L 137.664.8 137.564.5 137.965.0 0.48Serum potassium, mmol/L 3.960.6 3.960.6 3.960.6 0.90Serum chlorine, mmol/L 104.164.5 104.064.7 104.365.0 0.60Hemoglobin, g/L 122.1623.2 125.1622.0 116.8624.3 0.00UACR, mg/g Cr 90.1 (22.2–252.6) 45.8 (15.2–154.7) 184.2 (64.7–535.0) 0.00uAGT, mg/g Cr 41.2 (13.5–146.0) 22.0 (8.9–54.4) 162.7 (66.6–362.3) 0.00

Continuous variables are expressed as the mean6SD or median (25th percentile to 75th percentile [interquartile range]). Categorical variables are expressed as n(%). HF, heart failure; LVEF, left ventricular ejection fraction; NYHA, New York Heart Association; Cr, creatinine.aDefined as preadmission eGFR,60 ml/min per 1.73 m2. Preadmission eGFR is the mean of at least three measurements over a 6-month period before admission.

2034 Journal of the American Society of Nephrology J Am Soc Nephrol 26: 2032–2041, 2015




concentration on admission. Patients with ADHF with auAGT level $55 mg/g creatinine and an NT-proBNP levelabove the median had the highest overall mortality. Patientswith an elevatedNT-proBNP but without an increase in uAGThad a prognosis comparable to those with lower NT-proBNP.For predicting 1-year mortality, the AUC of uAGT was 0.77,which was greater than that of NT-proBNP (0.63) and of theclinical model alone (0.75) (Figure 3B).

Compared with those with uAGT,55 mg/g creatinine,patients with AKI with uAGT$55 mg/g creatinine on admis-sion exhibited a significantly higher rate of failure in renalrecovery at discharge (26.5% versus 70.6%). Among patientswho developed AKI in the absence of preexisting CKD, ahigher incidence of progression to CKD (48.0%) was foundin those with uAGT$55 mg/g creatinine compared with pa-tients with uAGT,55 mg/g creatinine (16.0%). After multi-variate adjustment, uAGT$55 mg/g creatinine was the mostpowerful predictor for 1-year mortality and rehospitalization(Table 3), as well as failure in renal recovery (Table 4).

In addition to uAGT levels on admission, uAGT concen-trations at discharge also predicted postdischarge mortality(AUC, 0.71, 95% CI, 0.62 to 0.81) and failure in renal recovery(AUC, 0.64; 95% CI, 0.51 to 0.77).

Effect of uAGT on Risk Reclassification of AKI and 1-Year MortalityTo determine whether uAGTmaterially improved risk reclas-sification, we used the net reclassification index (NRI) and theintegrated discrimination improvement (IDI) in the stage Istudy. As shown in Table 5, the addition of uAGTsignificantlyimproved the risk reclassification of AKI over urinary NGAL,the clinical model, and the combination of urinary NGAL andthe clinical model. The addition of uAGT also significantlyimproved the prognostic prediction over NT-proBNP andthe clinical model alone.

DISCUSSION

A major challenge in prevention and early treatment of acuteCRS has been a lack of high-performance predictive bio-markers. The current use of serum creatinine as an indicator ofCRS often leads to diagnostic delays and potential misclassi-fication of actual injury status, and produces little informationregarding the underlying cause.27

In a stage I study of 317 patients with ADHF, we found thatuAGT measured on the first day of hospital admission is apowerful predictor for AKI. Elevation of uAGTpredicted acuteCRS 2–4 days before rises in serum creatinine. The perfor-mance of uAGT is superior to previously reported biomarkerssuch as NGAL and UACR, as well as the clinical predictivemodel. The value of uAGT for predicting acute CRS was furtherdemonstrated in a validation cohort of 119patients in the stage IIstudy. These data suggest that uAGTmight be a novel and strongbiomarker for identifying patients at high risk of CRS in thesetting of ADHF. Supporting our finding, previous reportsidentified an association between elevated uAGT levels andthe deterioration of renal function in patients with CKD.26

The question of whether the uAGT level specifically predictsCRS or whether it also serves as a biomarker for AKI fromothercauses remains to be addressed.Active investigations are underway.

To date, only small-scale clinical studies have been con-ducted to examine the utility of the biomarkers, mostly NGAL,

Figure 1. Analyses of uAGT in the stage I cohort. (A) Median ofuAGT concentration on hospital admission in patients with ADHFand in healthy volunteers. (B) Mean uAGT and serum creatinineconcentration at various time points after admission. (C) MeanuAGT level at various time points in patients stratified by AKI orprior CKD. AGT levels are measured by ELISA in all panels. Errorbars are the SEM. Cr, creatinine; SCr, serum creatinine.



in predicting AKI in ADHF, which only yielded modestperformance. NGAL is a well established biomarker for renalinjury and is themost testedmarker in patients with ADHF.13–19

Consistent with previous reports,19,28 the performance of

urinary NGAL in this cohort had an AUC of 0.78. uAGT im-proved the AUC to 0.84. The predictive performance of uAGTwas also better than UACR (0.84 versus 0.71), a marker that isassociated with postoperative AKI in patients undergoing car-

diac surgery.29 Although uAGT level was re-ported to be correlated with proteinuria inpatients with renal impairment,30 elevationof uAGT was an independent predictor ofCRS even after adjusting UACR. The risk re-classification, as measured by NRI and IDI,was significantly improved through the ad-dition of uAGT to the clinical model and tothe combination of urinary NGAL and theclinical model, supporting the concept that asingle biomarker is not sufficient for theevaluation of a complex clinical settingsuch as acute CRS. A multimarker approachis therefore more likely to be of greater use.

In patients who developed acute CRS,those with preexisting CKD had a higherrisk of mortality and morbidity than thosewithout.4 However, prediction of AKI inpatients with ADHF with preexistingCKD has not been well investigated in pre-vious biomarker studies. In this study, werecruited patients with ADHF whose se-rum creatinine records over the 6-monthperiod before admission were available.This design allowed us to determine thepredictive performance of uAGT in pa-tients with and without prior CKD. Ourresults showed that elevation of uAGT onadmission was more prominent in patientswith preexisting CKD compared with thosewithout, and that the best cutoff value was

Table 2. Multivariate logistic regression analyses of uAGT as a predictor for AKI in the stage I cohort

uAGT (mg/g Cr) on Admission Unadjusted OR Adjusted ORa 95% CI P Value

All study participants (N=317; 79–80 per quartile)Quartile 1 (,10) 1.0 (referent) 1.0 (referent)Quartile 2 (10–35) 3.67 2.23 1.05 to 4.38 0.04Quartile 3 (36–148) 22.92 11.31 4.58 to 27.99 0.00Quartile 4 (.148) 51.64 50.01 12.32 to 203.23 0.00

Patients without preexisting CKD (n=235; 58–59 per quartile)Quartile 1 (,9) 1.0 (referent) 1.0 (referent)Quartile 2 (9–30) 8.27 1.44 0.60 to 3.48 0.42Quartile 3 (31–120) 42.52 13.68 3.90 to 47.92 0.00Quartile 4 (.120) 71.61 73.27 6.96 to 777.12 0.00

Patients with preexisting CKD (n=82; 20–21 per quartile)Quartile 1 (,12) 1.0 (referent) 1.0 (referent)Quartile 2 (12–71) 1.50 2.50 0.42 to 14.82 0.31Quartile 3 (72–221) 12.50 10.67 1.94 to 58.69 0.01Quartile 4 (.221) 65.00 68.00 8.50 to 541.80 0.00

Cr, creatinine.aAdjusted for age, NT-proBNP, serum creatinine, serum albumin, hypertension, diabetes, UACR, hemoglobin, centers, treatment with diuretics, and treatmentwithRAS inhibitors. Cr, creatinine; OR, odds ratio.

Figure 2. ROC analyses for predicting AKI. (A–C) uAGT, uNGAL, UACR, and clinicalmodel for predicting AKI in all participants (A), in patients without preexisting CKD (B),and in patients with preexisting CKD (C). (D) ROC analysis for the test and validationsets. Cr, creatinine.



higher in patients with prior CKD. In contrast with urinaryNGAL, which best identifies AKI in patients with an eGFR inthe range of 90 to 120ml/min,31 the predictive performance ofuAGT was better in individuals with eGFR,60 ml/min (pa-tients with preexisting CKD). This unique characteristic ofuAGTmakes it a useful predictor for AKI, in particular, amongthose with preexisting CKD. This may have considerable im-plications for patients with ADHF, 30%–45% of whom presentwith preexisting CKD.4,6 Early prediction of AKI in these pa-tients remains a challenge and previously reported biomarkershave shown only modest performance in this situation.14,16

Our data also suggest that uAGT could be used at the time ofhospital admission to assess 1-year prognosis of ADHF. ADHFis often associated with poor in-hospital and postdischarge

outcomes, with a 25%–35% mortality rate at 12 months.32

Identification of those at higher risk of poor prognosisremains a challenge in clinical practice. Results of the studyshowed that a level of uAGT$55 mg/g creatinine indepen-dently predicted 1-year mortality and rehospitalization in pa-tients with ADHF. Elevation of uAGTalso predicted an increasedrisk for progression toCKD inacuteCRS, supporting thefindingthat a marked increase in uAGT is associated with severe AKI.33

Importantly, we compared the prognostic performance of uAGTwith a recently established clinical model34 and NT-proBNP, avalidated marker for prognosis of acute heart failure.35,36 uAGTperformed substantially better than NT-proBNP in predictingmortality (AUC=0.77 versus AUC=0.63). Furthermore, the ad-dition of uAGT significantly improved risk reclassification ofmortality over the clinicalmodel andNT-proBNPalone asmea-sured by NRI and IDI.

We simultaneously measured both urine and plasma AGTin this study. Unlike uAGT, plasma AGT did not significantlychange in patients with ADHF with or without AKI. uAGT isan indicator reflecting the activity of intrarenal RAS, a hormonalcascade that has pleiotropic effects in the kidney, including theregulation of hemodynamics, sodium reabsorption, cellularproliferation, and apoptosis.21 It is now apparent that intrarenalRAS is regulated independently with circulating RAS. Renal AGTproduced in proximal tubular cells seems to be secreted directlyinto the tubular lumen.37 In rats infused with human AGT, cir-culating AGTwas not detectable in the urine, suggesting limitedglomerular permeability and/or tubular degradation.38 Thesefindings support the concept that uAGT originates from thatsecreted by renal tubule cells. In keeping with our findings, aprevious study reports activation of intrarenal RAS in a renalischemic model in the absence of change in circulating RAS.39

Our study has the following strengths. First, we measuredbiomarkers from admission for 7 consecutive days, and com-pared thepredictiveperformanceofuAGTtopreviously reportedbiomarkers or clinical models in the setting of ADHF. Wedemonstrated that uAGTis apowerful predictorofAKI inADHFand outperforms NGAL, UACR, NT-proBNP, and clinicalmodels. Using a two-stage design, we showed consistent resultsin both the test and validation cohorts. The study participantswere recruited from six centers, representing patients withADHF in various clinical settings. In addition, the stage I studyprospectively followed the patients with ADHF for 1 year afterdischarge, which allowed us to assess the utility of uAGT as apredictor of 1-year prognosis. Another strength of this studywas that serum creatinine before admission was available forthe study patients, which allowed us to determine the predictiveperformanceofuAGTinsubgroupswithandwithoutpriorCKD.Finally, the level of uAGT was measured and validated by anELISA kit40 and by Western blot in a central laboratory, whichensured high-quality uAGTmeasurements.

The study also had limitations. First, uAGT, but not bloodAGT, was predictive of AKI in patients with ADHF. Althoughurinary markers have several advantages, including the non-invasive nature of sample collection and few interfering

Figure 3. Predictive performance of uAGT for mortality in thestage I cohort. (A) Cumulative probability of all-cause mortality asa function of uAGT level and NT-proBNP concentration on ad-mission. (B) ROC curve analyses: uAGT, NT-proBNP, and clinicalmodel for predicting mortality in all participants. Cr, creatinine.



proteins, some disadvantages also exist, including difficulty incollecting samples from patients with severe oliguria andpotential changes in urinary biomarker levels induced by fluidstatus and diuretic therapy. Second, the diagnosis of AKI wasbased on an increase in serum creatinine, whichmay introducethe conundrumof using a flawed outcome variable to analyze theperformance of novel biomarkers.31 Evidence of AKI on renalbiopsy would be the gold standard but was not feasible in ourlarge cohort. As is true in the case of most AKI studies, we werenot able to use urine output for AKI diagnosis because an in-dwelling urinary catheter was not present inmost of the patients.

In summary, this study showed that uAGT can serve as anearly predictor for the development of AKI and 1-yearprognosis in the setting of ADHF. If further confirmed, useof uAGT as a predictive biomarker may improve clinicians’ability to assess ADHF patients’ risk of developing acuteCRS and prognosis, which in turn would help clinicians to

plan and initiate the most appropriate management strategiesduring admission and after discharge for patients with ADHF.

CONCISE METHODS

PatientsThis is a prospective, two-stage,multicenter cohort study approved by

the Institutional Review Board of the National Clinical Research

Center of Kidney Disease. All of the study participants provided

written informed consent. The stage I study (test set)was conducted in

four academic medical centers in three cities (Guangzhou, Shenzhen,

and Guiyang) between September 2011 and September 2013. Sample

collection for the stage II study (validation set) was performed in two

centers in Beijing and Zhanjiang from February 2013 to February

2014. The patients in two stage study were enrolled according to the

same inclusion and exclusion criteria described below.

Table 3. Multivariate logistic regression analyses: Predictors of mortality (n=317) and rehospitalization in the stage I cohort(n=286)

Variables on Admission Unadjusted OR Adjusted ORa 95% CI P Value

Mortality from admission to 1-yr follow-upAge (per 10-yr increase) 1.83 1.71 1.25 to 2.35 0.004Treatment with diuretics (yes versus no) 2.53 2.45 1.11 to 5.41 0.03NT-proBNP $ median (yes versus no) 2.92 2.59 1.22 to 5.52 0.02uAGT$55 mg/g Cr (yes versus no) 5.19 4.47 2.10 to 9.54 0.001

Rehospitalization from discharge to 1-yr follow-upDiabetes (yes versus no) 2.20 1.94 1.00 to 3.80 0.05Preexisting CKD (yes versus no) 2.86 2.41 1.04 to 5.60 0.02uAGT$55 mg/g Cr (yes versus no) 3.98 3.61 1.63 to 5.74 0.00

OR, odds ratio; Cr, creatinine.aAdjusted for age, hypertension, diabetes, preexisting CKD, serum albumin, NT-proBNP, hemoglobin, UACR, treatment with an angiotensin-converting enzymeinhibitor/angiotensin receptor blocker, treatment with diuretics, and centers. Hosmer–Lemeshow goodness-of-fit test: for mortality, chi-squared value=6.035(P=0.64); and for rehospitalization, chi-squared value=1.837 (P=0.87).

Table 4. Multivariate logistic regression analyses: Predictors of failure in renal recovery in the stage I cohort (n=82)

Variables on Admission Unadjusted OR Adjusted ORa 95% CI P Value

Model 1uAGT$55 mg/g Cr (yes versus no) 6.96 6.59 2.14 to 20.30 0.001Age (per 10-yr increase) 1.20 1.11 0.78 to 1.57 0.58Preexisting CKD (yes versus no) 1.81 2.81 1.05 to 7.47 0.04Diabetes (yes versus no) 1.94 1.76 0.68 to 4.55 0.25Hypertension (yes versus no) 1.14 1.30 0.05 to 3.33 0.60

Model 2uAGT$55mg/g Cr (yes versus no) 6.96 4.35 1.20 to 15.70 0.03Serum albumin,35 g/L (yes versus no) 4.16 2.63 0.59 to 11.75 0.20NT-proBNP $ median (yes versus no) 1.45 1.37 0.49 to 3.55 0.59Hemoglobin,110 g/L (yes versus no) 1.64 1.31 0.25 to 7.58 0.71UACR (mg/g Cr) 1.01 1.01 1.01 to 1.03 0.12

Model 3uAGT$55 mg/g Cr (yes versus no) 6.96 8.30 2.34 to 29.41 0.001Treatment with diuretics (yes versus no) 4.41 4.72 1.58 to 14.14 0.01Treatment with ACEI/ARB (yes versus no) 0.80 1.40 0.47 to 4.18 0.55

OR, odds ratio; Cr, creatinine; ACEI, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.aHosmer–Lemeshow goodness-of-fit test: model 1, chi-squared value=6.524 (P=0.59); model 2, chi-squared value=6.610 (P=0.58); and model 3, chi-squaredvalue=2.356 (P=0.67).



Eligible participants were patients with ADHF (1) aged 18–80 years

whowere admitted to the four participating hospitals, and (2) who had

at least threemeasurements of serumcreatinine over a 6-month period

before admission. Exclusion criteria included exposure to nephrotoxin

within 4 weeks before admission or during their hospital stay, preex-

isting advanced CKD (CKD, chronic dialysis, or preadmission

eGFR,30ml/min per 1.73m2), urinary tract infection or obstruction,

cancer, a concurrent diagnosis of an acute coronary syndrome, car-

diogenic shock or need for inotropes, a history of cardiac transplan-

tation and/or ventricular assist devices, and heart failure after cardiac

surgery. To test the predictive ability of the biomarkers for incident

AKI, patients who had AKI on admission (i.e., those with a 50% in-

crease in serum creatinine from preadmission level on the day of hos-

pitalization) were also excluded.

ProceduresThis was an observational study in which all of the study patients

received the standard of care for ADHF.41 Sample collection in two

stages was as follows.

Spot urine and blood samples were collected immediately after

admission before any in-hospital treatment and every 24 hours for the

first 7 days during hospitalization. The remaining urine and blood

samples were obtained at the time of routinemorning sample collection

forclinical carepurposesuntil hospital discharge.Wealso tookurine and

bloodsamples fromage-andsex-matchedhealthyvolunteers toestablish

normal AGT values. The urine samples were centrifuged at 30003g for

10 minutes and the supernatants were stored at 280°C. Serum creati-

nine wasmeasured on admission and at least twice a day during the first

3 days and daily thereafter. All survivors in stage Iwere followed-up after

discharge every 3 months in a designated outpatient clinic or by phone

for up to 12 months. Serum creatinine was measured to determine

changes in renal function. Death status and date of death were obtained

through initiating phone calls to patients’ homes, searching the local

Death Index, and reviewing the hospital records.

Laboratory MeasurementsUrine and blood samples collected from the participating hospitals

were shipped by commercial cold chain transportation. All of the

biomarkers were measured in a central laboratory using a standard

protocol, and all of the sampleswere labeled using study identification

numbers without personal identifiers or clinical conditions.

ELISA for AGT QuantificationWeused a sandwich ELISA kit (Immuno-Biological Laboratories Co.,

Ltd., Fujioka, Japan) for quantifying AGT in urine and plasma in both

stage Iandstage IIstudiesaccording to themanufacturer’sprotocol.Values

for intra- and inter-assay variability were 3.0% and 7.0%, respectively.

Western Blot Analyses for uAGTThe samples were boiled for 10 minutes in denaturing buffer and

subjected to standard Western blot analyses with an affinity-purified

goat polyclonal antibody against humanAGT (R&D Systems). Simul-

taneous blots under identical conditions with known quantities of

recombinant human AGT (Immuno-Biological Laboratories) were

included as standards for quantification of uAGT.

Other Biomarker MeasurementsTo compare the predictive performance of uAGT and reported renal

injury markers, urinary NGAL was measured with an ELISA kit

(Antibody Shop,Denmark).We chose urinary, but not plasma,NGAL

as a reference because the urine level more likely reflects renal tubular

injury in theADHF setting.17Urinary albuminwasmeasured using an

automatic analyzer (BNPro Spec; Siemens, Germany). All of the uri-

nary biomarkers were normalized for urinary creatinine and ex-

pressed as micrograms per gram of creatinine.

Serum N-terminal prohormone of NT-proBNP was quantified

with an immunoassay analyzer (Elecsys proBNP; Roche Diagnostics,

IN). Creatinine levels weremeasured using an automatic biochemical

analyzer (AU 480;Olympus, Japan). The eGFRwas determined by the

Chronic Kidney Disease Epidemiology Collaboration equation.42

Outcome DefinitionsThe primary outcome was the development of AKI defined as an

increase in serum creatinine by 26.5 mmol/L (0.3 mg/dl) within

48 hours of admission or a 50% increase in serum creatinine from the

preadmission level within 7 days of admission (mean of at least three

Table 5. NRI and IDI analyses for risk reclassification of AKI and 1-year mortality in the stage I cohort

Outcome

AUC IDI NRIa

BiomarkerBiomarker+Clinical

ModelClinical Modelb P Valuec

Value(SEM)

P ValueValue(SEM)

P Value

AKIuAGT 0.84 0.86 0.77 0.01 0.06 (0.02) 0.003 0.13 (0.06) 0.03uNGAL 0.78 0.84 0.04 0.05 (0.02) 0.03 0.08 (0.04) 0.05uAGT+uNGAL 0.86 0.90 ,0.001 0.09 (0.03) ,0.001 0.19 (0.09) 0.01

MortalityuAGT 0.77 0.81 0.75 0.03 0.06 (0.02) 0.01 0.16 (0.07) 0.02NT-proBNP 0.63 0.77 0.22 0.02 (0.01) 0.08 0.07 (0.03) 0.10uAGT+NT-proBNP 0.79 0.82 0.02 0.09 (0.03) ,0.001 0.16 (0.07) 0.01

LVEF, left ventricular ejection fraction.aThe NRI is calculated through two-way category by using the event rate of AKI and mortality in the stage I cohort as thresholds.bThe clinical model for predicting AKI is composed of age, hypertension, diabetes, preadmission eGFR, LVEF, NT-proBNP, hemoglobin, and UACR. The clinicalmodel for predicting mortality is composed of age, sex, hypertension, preadmission eGFR, LVEF, serum Na, and hemoglobin.cBiomarker+clinical model versus clinical model.



measurements over a 6-month period before admission) according to

the Kidney Disease Improving Global Outcomes Clinical Practice

Guidelines for Acute Kidney Injury.43 We did not use urine output

criteria (,0.5 ml/kg per hour for .6 hours) for AKI diagnosis be-

cause of limited sensitivity when diuretics are administered, reduced

specificity in the presence of dehydration, and lack of practicality in

measurement when an indwelling urinary catheter is not present.27

Secondary outcomes included the following: (1) all-cause mortal-

ity (number of deaths per 100 patients with ADHF) from admission

to 1-year follow-up; (2) rehospitalization (number of patients who

were discharged from the hospital and readmitted to any acute care

hospital within 1 year divided by the total number of patients who

were discharged alive from the hospital) after discharge during the

1-year follow-up; (3) failure in renal recovery defined as serum cre-

atinine at the time of hospital discharge that did not return to the level

on the day of admission; and (4) progression of AKI to CKD defined

as an eGFR,60ml/min per 1.73m2 for.3months after AKI in those

without preexisting CKD.

Statistical AnalysesSPSS 13.0 software was used for all analyses. To compare continuous

variables, we used a two-sample t test or a Mann–Whitney rank sum

test. To compare categorical variables, we used the Pearson chi-

squared test. To measure the sensitivity and specificity of uAGT on

admission at different cutoff values, a conventional ROC curve was

generated and the AUC of uAGT was compared with those of other

biomarkers and the clinical models. Optimal cutoffs were determined

by selecting the data point that minimized the geometric distance

from 100% sensitivity and 100% specificity on the ROC curve. To

evaluate the utility of biomarkers on risk classification, we deter-

mined the NRI and the IDI, as previously described.44,45 We deter-

mined the adjusted odd ratios for AKI and prognosis with multiple

logistic regression analysis. The selection of covariates was based on

known risk factors of AKI in the ADHF setting, including age, clinical

risk factors (hypertension, diabetes, serum creatinine, serum albumin,

UACR, hemoglobin, and NT-proBNP), factors that may affect uAGT

level (treatment with diuretics or RAS inhibitors), and study site. In

these analyses, uAGTwas modeled both as a categorical variable (cate-

gorized into quartiles) and a continuous variable (log-transformed).

ACKNOWLEDGMENTS

This work was supported by grants from theMajor State Basic Research

Development Program of China (973 Program) (2012CB517703 and

2011CB504005 to F.F.H.), the National Key Technologies R&D

Program (2011BAI10B02 to F.F.H.), the Major Scientific and Tech-

nological Planning Project of Guangzhou (15020010 to F.F.H.),

Science and Technology Planning Project of Guangdong Province

(2013B022000073 to X.Y.), and the National Natural Science Founda-

tion of China (Key Program) (81430016 to F.F.H.).

DISCLOSURESNone.

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