the effect of dapagliflozin on albuminuria in declare-timi 58

University of Groningen

The Effect of Dapagliflozin on Albuminuria in DECLARE-TIMI 58Mosenzon, Ofri; Wiviott, Stephen D.; Heerspink, Hiddo J. L.; Dwyer, Jamie P.; Cahn, Avivit;Goodrich, Erica L.; Rozenberg, Aliza; Schechter, Meir; Yanuv, Ilan; Murphy, Sabina A.Published in:Diabetes Care

DOI:10.2337/dc21-0076

IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.

Document VersionPublisher's PDF, also known as Version of record

Publication date:2021

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):Mosenzon, O., Wiviott, S. D., Heerspink, H. J. L., Dwyer, J. P., Cahn, A., Goodrich, E. L., Rozenberg, A.,Schechter, M., Yanuv, I., Murphy, S. A., Zelniker, T. A., Gause-Nilsson, I. A. M., Langkilde, A. M.,Fredriksson, M., Johansson, P. A., Bhatt, D. L., Leiter, L. A., McGuire, D. K., Wilding, J. P. H., ... Raz, I.(2021). The Effect of Dapagliflozin on Albuminuria in DECLARE-TIMI 58. Diabetes Care, 44(8), 1805-1815.https://doi.org/10.2337/dc21-0076

CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).

The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.

Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.

Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.

Download date: 26-09-2022

https://doi.org/10.2337/dc21-0076

https://research.rug.nl/en/publications/224f9049-a872-483b-8b9e-7a6f23016570

https://doi.org/10.2337/dc21-0076

The Effect of Dapagliflozinon Albuminuria inDECLARE-TIMI 58Diabetes Care 2021;44:1805–1815 | https://doi.org/10.2337/dc21-0076

Ofri Mosenzon,1,2 Stephen D. Wiviott,3

Hiddo J.L. Heerspink,4 Jamie P. Dwyer,5

Avivit Cahn,1,2 Erica L. Goodrich,3

Aliza Rozenberg,1,2 Meir Schechter,1,2

Ilan Yanuv,1,2 Sabina A. Murphy,3

Thomas A. Zelniker,3,6

Ingrid A.M. Gause-Nilsson,7

Anna Maria Langkilde,7

Martin Fredriksson,7 Peter A. Johansson,7

Deepak L. Bhatt,3 Lawrence A. Leiter,8

Darren K. McGuire,9,10

John P.H. Wilding,11

Marc S. Sabatine,3 and Itamar Raz1,2

OBJECTIVE

Sodium–glucose cotransporter 2 inhibitors (SGLT2i) improve albuminuria inpatients with high cardiorenal risk. We report albuminuria change in the Dapagli-flozin Effect on Cardiovascular Events (DECLARE-TIMI 58) cardiovascular outcometrial, which included populations with lower cardiorenal risk.

RESEARCH DESIGN AND METHODS

DECLARE-TIMI 58 randomized 17,160 patients with type 2 diabetes, creatinineclearance >60 mL/min, and either atherosclerotic cardiovascular disease (CVD;40.6%) or risk-factors for CVD (59.4%) to dapagliflozin or placebo. Urinary albu-min-to-creatinine ratio (UACR) was tested at baseline, 6 months, 12 months, andyearly thereafter. The change in UACR over time was measured as a continuousand categorical variable (#15, >15 to <30, $30 to #300, and >300 mg/g) bytreatment arm. The composite cardiorenal outcome was a $40% sustaineddecline in the estimated glomerular filtration rate (eGFR) to <60 mL/min/1.73m2, end-stage kidney disease, and cardiovascular or renal death; specific renaloutcome included all except cardiovascular death.

RESULTS

Baseline UACR was available for 16,843 (98.15%) participants: 9,067 (53.83%)with #15 mg/g, 2,577 (15.30%) with >15 to <30 mg/g, 4,030 (23.93%) with30–300 mg/g, and 1,169 (6.94%) with>300 mg/g. Measured as a continuous vari-able, UACR improved from baseline to 4.0 years with dapagliflozin, comparedwith placebo, across all UACR and eGFR categories (all P < 0.0001). Sustainedconfirmed$1 category improvement in UACR was more common in dapagliflozinversus placebo (hazard ratio 1.45 [95% CI 1.35–1.56], P < 0.0001). Cardiorenaloutcome was reduced with dapagliflozin for subgroups of UACR $30 mg/g (P <

0.0125, Pinteraction = 0.033), and the renal-specific outcome was reduced for allUACR subgroups (P< 0.05, Pinteraction = 0.480).

CONCLUSIONS

In DECLARE-TIMI 58, dapagliflozin demonstrated a favorable effect on UACR andrenal-specific outcome across baseline UACR categories, including patients withnormal albumin excretion. The results suggest a role for SGLT2i also in the pri-mary prevention of diabetic kidney disease.

Sodium–glucose cotransporter 2 inhibitors (SGLT2i) reduce the risk for adverse renaloutcomes in people with type 2 diabetes, including a reduction in deterioration of the

1Diabetes Unit, Department of Endocrinologyand Metabolism, Hadassah Medical Center,Jerusalem, Israel2Faculty of Medicine, Hebrew University ofJerusalem, Jerusalem, Israel3TIMI Study Group, Cardiovascular Division,Brigham and Women's Hospital and HarvardMedical School, Boston, MA4University of Groningen, University MedicalCenter Groningen, Groningen, the Netherlands5Vanderbilt University Medical Center, Nashville, TN6Division of Cardiology, Medical University ofVienna, Vienna, Austria7BioPharmaceuticals R&D, AstraZeneca, Gothenburg,Sweden8Li Ka Shing Knowledge Institute, St. Michael'sHospital, University of Toronto, Toronto, Ontario,Canada9Division of Cardiology, University of TexasSouthwestern Medical Center, Dallas, TX10Parkland Health and Hospital System, Dallas,TX11Department of Cardiovascular and MetabolicMedicine, University of Liverpool, Liverpool,U.K.

Corresponding author: Ofri Mosenzon, [email protected]

Received 12 January 2021 and accepted 14May 2021

Clinical trial reg. no. NCT01730534, clinicaltrials.gov

This article contains supplementary material onlineat https://doi.org/10.2337/figshare.14597772.

© 2021 by the American Diabetes Association.Readers may use this article as long as thework is properly cited, the use is educationaland not for profit, and the work is not altered.More information is available at https://www.diabetesjournals.org/content/license.

EMER

GINGTH

ERAPIES:

DRUGSANDREG

IMEN

S

Diabetes Care Volume 44, August 2021 1805

Dow

nloaded from http://diabetesjournals.org/care/article-pdf/44/8/1805/633170/dc210076.pdf by BIBLIO

THEEK D

ER R

IJKSUN

IV user on 24 January 2022

mailto:[email protected]

mailto:[email protected]

https://doi.org/10.2337/figshare.14597772

https://www.diabetesjournals.org/content/license

https://www.diabetesjournals.org/content/license

http://crossmark.crossref.org/dialog/?doi=10.2337/dc21-0076&domain=pdf&date_stamp=2021-08-11

estimated glomerular filtration rate(eGFR) and progression to end-stage kid-ney disease (ESKD) (1–7). This has beendemonstrated as secondary/exploratoryoutcomes in cardiovascular (CV) out-comes trials (CVOTs) (1–4,7) and con-firmed as a primary outcome in patientswith proteinuric chronic kidney disease(CKD), with or without type 2 diabetes(5,6).

Albuminuria is frequently a compo-nent of diabetic kidney disease (8,9). Thepresence of albuminuria in patients withor without diabetes has been associatedwith an increased risk for adverse renaland CV outcomes (10,11), while a reduc-tion in albuminuria has been associatedwith lower rates of adverse renal and CVoutcomes, both in observational studies(12) and clinical trials (13,14). The Ameri-can Diabetes Association Standards ofMedical Care in Diabetes recommendstesting urinary albumin excretion annu-ally (15) as part of the laboratory screen-ing in patients with type 2 diabetes.

The Dapagliflozin Effect on Cardio-vascular Events trial (DECLARE-TIMI58) was a CVOT with dapagliflozin in17,160 patients with type 2 diab-etes and either multiple risk factors(MRFs) for atherosclerotic CV disease(ASCVD) (59.4%) or established ASCVD(eASCVD) (40.6%) that demonstrated asignificant 17% reduction in one of itstwo dual primary efficacy outcomes ofCV death and hospitalization for heartfailure (3). The main secondary pre-specified renal outcome in DECLARE-TIMI 58 was the composite cardiore-nal outcome, defined as a sustaineddecline of at least 40% in eGFR to <60mL/min/1.73 m2, ESKD, or death fromrenal or CV causes (3). A renal-specificcomposite outcome was similarly pre-defined but excluded death from CVcauses. We previously published sig-nificant reductions in both the cardi-orenal and renal-specific compositeoutcomes (3).

In this secondary exploratory analysis,we present the effect of dapagliflozinon urinary albumin-to-creatinine ratio(UACR), both in the entire trial popula-tion and according to baseline UACRand eGFR categories. We also presentthe effect of dapagliflozin on the cardi-orenal and renal-specific composite out-comes according to baseline UACR.

RESEARCH DESIGN AND METHODS

The DECLARE-TIMI 58 design, partici-pants’ baseline characteristics, mainoutcomes, and main renal results havebeen previously reported (3,4,16,17).Briefly, we recruited patients with type2 diabetes and either MRFs for ASCVD(age $55 years for men or $60 yearsfor women plus one or more of the fol-lowing: dyslipidemia, hypertension, orcurrent tobacco use), or eASCVD (age$40 years and ischemic heart disease,cerebrovascular disease, or peripheralarterial disease). Other inclusion criteriawere HbA1c between 6.5 and 12.0%(47.5–113.1 mmol/mol) and creatinineclearance of $60 mL/min as estimatedby the Cockcroft-Gault equation (18).The institutional review board at eachparticipating site approved the trial pro-tocol, and all participants provided writ-ten informed consent.

Participants were randomly assignedin a double-blinded manner to once-daily dapagliflozin 10 mg or matchingplacebo (1:1). The primary end points ofthe trial, major adverse cardiovascularevents (MACE), a composite of CVdeath, myocardial infarction, or ischemicstroke, achieved noninferiority, and acomposite of CV death or hospitaladmission for heart failure achievedsuperiority (3). Since the trial met onlyone of its dual primary outcomes forsuperiority, all other analyses of addi-tional outcomes should only be consid-ered as hypothesis generating. Thecardiorenal outcome was defined astime to first event of a composite of sus-tained confirmed decrease in eGFR byat least 40% (as confirmed by two testsat the central laboratory at least 4weeks apart) to <60 mL/min/1.73 m2,ESKD (defined as dialysis for $90 days,kidney transplantation, or sustained [i.e.,two measurements at the central labo-ratory at least 4 weeks apart] eGFR of<15 mL/min/1.73 m2), or CV or renaldeath. The renal-specific outcome includedall the components of the cardiorenal out-come except CV death (3).

The serum creatinine and spot urinealbumin and creatinine were measuredat the central laboratories (LabCorp Clini-cal Trials [Covance], Singapore, Geneva,and New York) at screening, baseline, 6months, 12 months, yearly thereafter,and at the end of the trial. eGFR was cal-culated using the Chronic Kidney Disease

Epidemiology Collaboration equation (18).Baseline values and categorization of thesevalues were defined according to the labo-ratory test on the date of randomization.The change from baseline was calculatedfor these parameters, and time to onsetof renal outcomes was calculated accord-ing to the first of the two subsequent lab-oratory assessments.

Participants were divided into prespe-cified subgroups according to theirbaseline eGFR (eGFR $90, <90 to $60,and <60 mL/min/1.73 m2) and accord-ing to their baseline UACR (UACR #15,>15 to <30, $30 to #300, and >300mg/g) (19). Patients with baseline uri-nary albumin below the laboratory’slowest detectable level were recog-nized as a distinct UACR category andgrouped together with patients withUACR #15 mg/g. Due to a change inthe assay used in the central laboratoryto measure urinary albumin, the lowestdetectable level of albumin was modi-fied during the trial from urine albumin<3.0 mg/L since the initiation of thetrial on 25 April 2013 until 30 April2017, and then <7.0 mg/L until the endof the trial on 18 September 2018. Forcalculation of the continuous change inUACR over time and avoid bias due tothe date of enrollment, all measuredvalues of urine albumin <7.0 mg/Lwere recognized as below the detect-able level and assigned a value of7 mg/g UACR for continuous analysis.A sensitivity analysis was performedassigning below detectable measuresof urinary albumin to UACR = 3.5 mg/g(the midpoint of the range). Confirmedsustained change in the categoricalUACR was defined as a change in theUACR categories in two consecutivetests done according to the schedulefor UACR testing at the central labora-tory, as mentioned above.

Statistical AnalysisBaseline characteristics of the four pre-defined subgroups of baseline UACR arereported as absolute numbers and per-centages for categorical variables and asmean and SD or median and interquar-tile range (IQR), as appropriate, forcontinuous variables. We used the x2

test to compare categorical variablesand the Kruskal-Wallis test to comparecontinuous variables between UACRsubgroups. Analyses were performed

1806 Albuminuria in DECLARE-TIMI 58 Diabetes Care Volume 44, August 2021

Dow


THEEK D

ER R

IJKSUN


according to the intention-to-treat prin-ciple, using data from all randomlyassigned participants.Change in the geometric mean UACR

over time were analyzed using mixed mod-els for each baseline UACR and eGFR cate-gory separately, adjusting for treatmentarm, baseline ACE inhibitors (ACEi)/angio-tensin II receptor blockers (ARBs) treat-ment, use of diuretics, baseline HbA1c, visit,the interaction between treatment armand visit, stratification factors (hematuriaand eASCVD/MRF status), and the baselinevalue of the UACR.UACR data were log-transformed

before analysis due to their nonnormaldistribution as was previously done insimilar analyses (20,21). Adjusted least-square means, 95% CIs, and differencesbetween treatments were back-trans-formed to the original scale.Cox proportional hazards models

were used to compare the change incategorical UACR between treatmentarms, both for confirmed sustainedrepeated change, done according toUACR test timelines, and for a singlechange in UACR category. The hazardratio (HR) and 95% CI are reported. Weused Kaplan-Meier curves to demon-strate the risk of deterioration in cate-gorical albuminuria status over timeand compared between treatment armsusing a log-rank test. In addition, thepercentage of participants distributedwithin the UACR categories at baselineand 6 months, among those withreadings at both time points, are pre-sented, and a comparison betweentreatment arms was performed usingthe x2 test.Cox proportional hazards models were

also used to compare treatment arms forrisk of cardiorenal and renal-specific com-posite outcomes according to baselineUACR categories. All Cox models werestratified by baseline ASCVD (i.e., estab-lished disease vs. MRFs) and hematuria(i.e., present vs. absent) at baseline.No adjustments for multiple compari-

sons were made. Analyses were per-formed using SAS 9.4 software (SASInstitute, Cary, NC). DECLARE-TIMI 58 isregistered with ClinicalTrials.gov, clinicaltrial reg. no. NCT01730534.

Data and Resource AvailabilityIndividual participant data will not bemade available. However, we encourage

parties interested in collaboration to con-tact the corresponding author directly forfurther discussions.

RESULTS

Of the 17,160 participants of DECLARE-TIMI 58, 16,843 (98.15%) had baselineUACR data. There were 9,067 (53.83%)participants with baseline UACR #15mg/g category, of which 551 (3.30%)had albumin below detectable levels;2,577 (15.30%) with UACR of >15 to<30 mg/g; 4,030 (23.93%) with base-line UACR $30 to #300 mg/g; and1,169 (6.94%) with baseline UACR >300mg/g (Table 1).

Participants with lower baseline UACRcategories were more likely to be femaleand White, had shorter diabetes duration,and were less likely to have a history ofeASCVD, heart failure, or hypertension.Patients with a higher baseline UACR hadhigher mean HbA1c, lower eGFR, andhigher systolic blood pressure. ACEi/ARBsuse was common across all baselineUACR categories (80.0–85.5%) but dif-fered with statistical significance amongUACR categories (P < 0.0001) (Table 1).

Change in the geometric mean inUACR over time by treatment arm ispresented according to the four UACRbaseline subgroups #15, >15 to <30,$30 to #300, and >300 mg/g (Fig.1A–D). At 6 months, the dapagliflozinarm had a statistically significant lowermean UACR compared with placebo inall UACR baseline subgroups (P =0.0033 for UACR #15 mg/g and P <0.0001 for all other subgroups) (Fig.1A–D). Between 6 months and 4 years,UACR in the subgroup of UACR >15mg/g was lower in the dapagliflozinarm than in the placebo arm (Fig.1B–D). A separation of the curves as amarker for effect in the lowest UACRcategory (#15 mg/g) was seen after36 months (P = 0.0140 at 36 months, P< 0.0001 at 48 months) (Fig. 1A). Inthe high-risk category of patients withbaseline proteinuria (UACR >300 mg/g), after a large decrease in meanUACR during the first 6 months oftreatment, the mean UACR remainedstable to decreased during 48 monthsof treatment with dapagliflozin (Fig. 1D).Sensitivity analyses in which belowdetectable levels of albumin were imputeddifferently (UACR = 3.5 mg/g) did notmaterially change outcomes.

Change in the geometric mean inUACR over time by treatment arm is pre-sented according to the three baselineeGFR subgroups eGFR $90, <90–$60,and <60 mL/min/1.73 m2 (Fig. 1E–G). Inall three eGFR subgroups and at all timepoints after baseline, the dapagliflozinarm had a statistically significant lowermean UACR compared with placebo (at4 years P < 0.0001 for all three eGFRsubgroups).

Analysis of confirmed sustained changein the categorical UACR from baseline toend of trial (EOT) demonstrated animprovement in UACR categories for allUACR subgroups with dapagliflozin versusplacebo (Fig. 2A). The improvement withdapagliflozin was statistically significant foreach UACR category separately as well asfor the sum of patients who improved byat least one UACR category (HR 1.45 [95%CI 1.35–1.56], P < 0.0001) and two UACRcategories (HR 1.43 [1.23–1.65], P <0.0001). A statistically significant reductionin the deterioration in UACR categoriesfrom baseline to EOT was also seen withdapagliflozin in most categories (theincrease to UACR >15 mg/g in those withbaseline UACR #15 mg/g was the onlycategory that was numerically but not sta-tistically reduced with dapagliflozin) (Fig.2B). The overall one-category and two-cat-egory deteriorations in UACR were bothreduced with dapagliflozin versus placebo(HR 0.82 [0.77–0.88], P < 0.0001; and HR0.79 [0.69–0.91], P = 0.0007, respectively).

In addition, improvement in categori-cal UACR on one measure from baselineto EOT was increased with dapagliflozincompared with placebo (SupplementaryFig. 1A), while one-time worsening incategorical UACR was greatly reducedwith dapagliflozin (Supplementary Fig.1B).

Looking specifically at the change inthe distribution of UACR categories fromrandomization to 6 months according totreatment arms, there were statisticallysignificant differences between patientstreated with dapagliflozin versus placebo.While at baseline the UACR categoriesdistribution was equal between treat-ment arms (P = 0.99), at 6 months therewas a higher percentage of patientstreated with dapagliflozin than placeboin the UACR #15 mg/g category, at 56%vs. 52%. The opposite was true for the$30 to #300 mg/g category, at 23% vs.25%, and for the >300 mg/g category, at5% vs. 7%, in the dapagliflozin and

care.diabetesjournals.org Mosenzon and Associates 1807

Dow


THEEK D

ER R

IJKSUN






Table 1—Patients’ baseline characteristics in DECLARE-TIMI 58 according to four baseline UACR categories: UACR #15, >15to <30, $30 to #300, and >300 mg/g

UACR #15 mg/g(n = 9,067)

UACR >15 to <30mg/g (n = 2,577)

UACR $30 to #300mg/g (n = 4,030)

UACR >300 mg/g(n = 1,169) P

Demographic characteristicsFemale sex 3,583 (39.5) 1,083 (42.0) 1,292 (32.1) 339 (29.0) <0.0001Age, years, mean (SD) 63.8 (6.6) 64.4 (7.0) 64 (7.1) 63.5 (6.9) 0.0008BMI, kg/m2 31.2 (27.7–35.2) 31.2 (27.7–35.4) 31.5 (28.0–35.5) 32.0 (28.1–36.3) 0.0002BMI, kg/m2, mean (SD) 31.9 (5.9) 32.0 (6.1) 32.1 (6.0) 32.7 (6.2) 0.0002Race

White 7,441 (82.1) 2,000 (77.6) 3,079 (76.4) 860 (73.6)Asian 1,015 (11.2) 402 (15.6) 674 (16.7) 190 (16.3) <0.0001Black 319 (3.5) 81 (3.1) 139 (3.4) 49 (4.2)Other 292 (3.2) 94 (3.6) 138 (3.4) 70 (6.0)

Medical history

Diabetes duration#5 years 2,303 (25.4) 555 (21.5) 750 (18.6) 145 (12.4) <0.0001>5 to #15 years 4,647 (51.3) 1,325 (51.4) 1,997 (49.6) 567 (48.5)>15 years 2,117 (23.3) 697 (27) 1,283 (31.8) 457 (39.1)

eASCVD 3,415 (37.7) 1,037 (40.2) 1,786 (44.3) 578 (49.4) <0.0001History of congestive heartfailure 811 (8.9) 274 (10.6) 437 (10.8) 169 (14.5) <0.0001Hypertension 8,025 (88.5) 2,333 (90.5) 3,690 (91.6) 1,096 (93.8) <0.0001Hyperlipidemia 7,319 (80.7) 2,071 (80.4) 3,217 (79.8) 930 (79.6) 0.5815

CV and glucose-lowering drugused

ACEi/ARB 7,257 (80.0) 2,115 (82.1) 3,316 (82.3) 1,000 (85.5) <0.0001MRAs 413 (4.6) 93 (3.6) 182 (4.5) 59 (5.0) 0.1355Diuretic 3,561 (39.3) 1,051 (40.8) 1,708 (42.4) 517 (44.2) 0.0004Metformin 7,482 (82.5) 2,119 (82.2) 3,308 (82.1) 919 (78.6) 0.013Insulin 3,248 (35.8) 1,073 (41.6) 1,897 (47.1) 656 (56.1) <0.0001Sulfonylurea 3,896 (43.0) 1,137 (44.1) 1,680 (41.7) 489 (41.8) 0.2196DPP-4 inhibitors 1,562 (17.2) 453 (17.6) 646 (16.0) 185 (15.8) 0.1975GLP-1 receptor agonist 383 (4.2) 115 (4.5) 181 (4.5) 50 (4.3) 0.8928

Laboratory and clinicalmeasurements

HbA1c, % 7.9 (7.3–8.8) 8.1 (7.5–9.1) 8.3 (7.5–9.3) 8.4 (7.6–9.6) <0.0001HbA1c, %, mean (SD) 8.1 (1.1) 8.4 (1.2) 8.5 (1.3) 8.6 (1.3) <0.0001eGFR, mL/min/1.73 m2,

mean (SD) 85.9 (15.0) 86.2 (15.7) 84.6 (17.0) 80.7 (18.3) <0.0001eGFR (CKD-EPI) categories

<60 mL/min/1.73 m2 508 (5.6) 178 (6.9) 381 (9.5) 167 (14.3) <0.000160 to <90 mL/min/1.73 m2 4,156 (45.8) 1,111 (43.1) 1,761 (43.7) 554 (47.4)$90 mL/min/1.73 m2 4,403 (48.6) 1,288 (50.0) 1,887 (46.8) 448 (38.3)

Blood pressureSystolic, mmHg 132.5 (122.5–142.5) 135 (125.0–145.0) 137.5 (127.0–147.5) 142 (132.0–154.0) <0.0001Systolic, mmHg,

mean (SD) 132.9 (14.8) 135.4 (15.2) 137.3 (15.6) 142.7 (16.1) <0.0001Diastolic, mmHg 78 (71.0–83.5) 79 (71.5–84.5) 79 (71.5–85.0) 80 (74.0–86.5) <0.0001Diastolic, mmHg,

mean (SD) 77.6 (9.0) 78.2 (9.1) 78.2 (9.2) 79.9 (9.3) <0.0001Total cholesterol, mg/dL 163 (138.0–194.0) 163 (138.0–195.0) 162 (137.0–193.0) 171 (142.0–206.0) <0.0001Total cholesterol, mg/dL,

mean (SD) 168.7 (43.2) 168.6 (42.6) 168.7 (45.1) 178.2 (53.3) <0.0001Fasting triglycerides, mg/dL 141 (104.0–197.0) 149 (107.0–208.0) 155 (112.0–224.0) 160.5 (114.0–238.0) <0.0001Fasting triglycerides, mg/dL,

mean (SD) 168.4 (120.9) 175.7 (112.9) 192.6 (153.2) 211.1 (191.3) <0.0001

Categorical data are shown as n (%) and continuous data as median (IQR) or as indicated otherwise. The P value between UACR subgroupswas calculated using the x2 test to compare categorical variables and the Kruskal-Wallis test to compare continuous variables. CKD-EPI,Chronic Kidney Disease Epidemiology Collaboration; DPP-4, dipeptidyl peptidase 4; GLP-1, glucagon-like peptide 1; MRAs, mineralocorticoidreceptor antagonists.


Dow


THEEK D

ER R

IJKSUN


placebo arm, respectively (P < 0.0001)(Supplementary Table 1).Kaplan-Meier curves for new onset of

UACR >15 mg/g in patients with a base-line UACR #15 mg/g did not achievestatistical significance (log-rank P =0.0536) (Supplementary Fig. 2A). Kap-lan-Meier curves for new onset of UACR$30 mg/g in patients with a baselineUACR <30 mg/g (Supplementary Fig.2B) and new onset of UACR $300 mg/gin patients with a baseline UACR <300mg/g (Supplementary Fig. 2C) demon-strated an improvement with dapagliflo-zin compared with placebo (log-rank P< 0.0001 for both).The cardiorenal event rates in the

placebo arm in participants with UACR#15 mg/g versus those with UACR >15to <30 mg/g were 3.1% and 4.9% (P <0.0001), and the renal-specific eventrates in the placebo arm were 1.3% and2.4% (P < 0.0001) for the UACR #15mg/g versus those with UACR >15 to<30 mg/g, respectively. Together these

findings demonstrate an increasedrisk for both outcomes with higherbaseline UACR categories, even in thenormoalbuminuria range. The cardiore-nal outcome was reduced with dapagli-flozin for all UACR $30 mg/g subgroups(P < 0.0125, Pinteraction = 0.0327) whilethe renal-specific outcome was reducedwith dapagliflozin versus placebo for allUACR subgroups (P < 0.05, Pinteraction =0.480) (Fig. 3).

CONCLUSIONS

In this exploratory analysis of the resultsfrom DECLARE-TIMI 58, dapagliflozinreduced the deterioration of UACR,regardless of baseline eGFR and UACR,even in the category of UACR #15 mg/g.Dapagliflozin increased the likelihood ofcategorical improvement in UACR anddecreased the risk for categorical UACRdeterioration. This improvement wasalready demonstrated in the first postran-domization UACR test at 6 months. We

also demonstrated a decreased risk forcardiorenal outcome with dapagliflozin forthose with baseline UACR $30 mg/g. Inaddition, a decreased risk for renal-specific outcomes with dapagliflozin wasdemonstrated for all baseline UACRcategories.

SGLT2i have been previously demon-strated to reduce albuminuria by 30–40%(22–24), and various mechanisms havebeen proposed to explain this effect. Theseinclude an increase in natriuresis, a con-traction in plasma volume, and a reductionin single nephron hyperfiltration (25).Reduction in hyperfiltration has been sug-gested to result from sodium delivery tothe macula densa, thereby restoring glo-merular pressure to physiological levels(26,27). The decrease in nephron perfusionback to normal levels may cause reducedwall tension and shear stress (28), leadingto the deactivation of proinflammatorycytokines and a possible reduction in renalfibrosis (29). Moreover, SGLT2i have beenshown to decrease renal cortical hypoxia

��

��

�

�

�

�

�

�

�

�

�

p =0.0033 p =0.4420 p =0.1098 p =0.0140 p <.0001

0

5

10

0 6 12 24 36 480 6 12 24 36 48month of follow−up

Mea

n U

AC

R (

mg/

g)

� �Dapagliflozin Placebo

UACR<=15 mg/g

4747 4541 4363 4097 3839 3319

4766 4593 4431 4186 4005 3475Dapagliflozin

Placebo

Sample Size per Time Point

�

�

�

�

�

�

�

�

�

�

�

�

p <.0001 p <.0001 p <.0001 p <.0001 p <.0001

0

10

20

30


Mea

n U

AC

R (

mg/

g)


15<UACR<30 mg/g

1109 1060 1017 924 867 737

1087 1046 1007 956 879 726Dapagliflozin

Placebo


A B

�

�

�

�

�

�

�

�

� �

�

�

p <.0001p <.0001 p <.0001 p <.0001 p <.0001

0

25

50

75

100


Mea

n U

AC

R (

mg/

g)


30<=UACR<=300 mg/g

1985 1881 1785 1641 1504 1225

1985 1903 1836 1696 1588 1330Dapagliflozin

Placebo


�

�

�

�

�

�

�

� � �

�

�

p <.0001 p <.0001 p =0.0001 p =0.0004 p <.0001

0

200

400

600

800


Mea

n U

AC

R (

mg/

g)


UACR>300 mg/g

573 544 511 444 403 328

594 551 530 493 459 371Dapagliflozin

Placebo


C D

Figure 1—Change in UACR over time by treatment arm at baseline, 6 months, and 1, 2, 3, and 4 years in the group of patients with baseline UACR#15 mg/g (A), baseline UACR >15 to <30 mg/g (B), baseline UACR$30 to#300 mg/g (C) and baseline UACR >300 mg/g (D), and in the group ofpatients with baseline eGFR $90 mL/min/1.73 m2 (E), baseline eGFR <90 to $60 mL/min/1.73 m2 (F), and baseline eGFR <60 mL/min/1.73 m2

(G). Shown are point estimates and 95% confidence intervals of geometric mean back-transformed to the original scale.


Dow


THEEK D

ER R

IJKSUN







due to a reduction in the energy require-ment of proximal tubular cells (30) and incontrary to increased renal medullary hyp-oxia causing an increase in the expressionof hypoxia-inducible factors and erythro-poietin (31).

Compared with placebo, dapagliflozintreatment reduced UACR across allbaseline eGFR and UACR categories,including those with UACR #15 mg/gand those with eGFR $90 mL/min/1.73m2, during �4 years of follow-up. Theresults indicate a beneficial effect fordapagliflozin on UACR as early as 6months following treatment initiation.Dapagliflozin decreased UACR comparedwith placebo after 6 months for mostbaseline eGFR and UACR categories,except for the UACR #15 mg/g sub-groups. In the placebo arm at 6 months,UACR values in the subgroups of UACR>15 mg/g seemed lower comparedwith baseline, a phenomenon that maybe partially explained by regression tothe mean, placebo-effect, or adjustmentof background medications. Nonethe-less, in all these subgroups, the

reduction in UACR in the dapagliflozinarm was significantly larger, which testi-fies to the effect of the drug. Analysisof the distribution between subgroupsof UACR after 6 months of treatmentcompared with baseline demonstratedan increase in the percentage ofpatients with UACR #15 mg/g in thedapagliflozin treatment arm, while thepercentage of patients with UACR $30mg/g was increased in the placebo arm.These findings add important informa-tion to the findings from the BI 10773(Empagliflozin) Cardiovascular OutcomeEvent Trial in Type 2 Diabetes MellitusPatients (EMPA-REG OUTCOME) trialand the Canagliflozin CardiovascularAssessment Study (CANVAS) program,which had smaller populations with nor-moalbuminuria and did not divide thisgroup category into two subgroups(1,2,20,21). The population in DECLARE-TIMI 58 was larger than previous trialsand included a higher percentage ofparticipants both without eASCVD andwith normal kidney function and UACRat baseline (3). The length of follow-up

in the trial was also longer, with amedian follow-up of 4.2 (IQR 3.9–4.4)years compared with 3.1 (IQR 2.2–3.6)years of follow-up and 2.6 years (IQR2.0–3.4) of treatment duration in theEMPA REG and 188 (SD 106) weeks inCANVAS and 108 (SD 20) weeks in Can-agliflozin Cardiovascular AssessmentStudy-Renal (CANVAS-R) (1–3). Similarto previous trials, the effect of dapagli-flozin on UACR was in addition to wide-spread treatment with ACEi/ARBs(81.3% of participants) (3).

Unlike previous trials, we groupedour population into four categories ofbaseline UACR, dividing the large groupof patients with normal albuminuria atbaseline (11,644 patients, 69.1% ofpatients with baseline UACR measure-ments) into those with UACR #15 ver-sus those with UACR >15 to <30 mg/g.The greater representation of patientswith normal albumin excretion com-pared with previous trials allowed us tobetter define subtle changes within thisimportant group of patients, whichaccording to prior publications represent

��

�

�

�

�

�

�

�

�

�

�

p <.0001 p <.0001 p <.0001 p <.0001 p <.0001

0

10

20

30


Mea

n U

AC

R (

mg/

g)


60<=eGFR<90 mL/min/1.73m²

3812 3643 3474 3215 2998 2552

3772 3606 3485 3267 3075 2612Dapagliflozin

Placebo


�

� �

�

�

�

�

�

��

�

�

p <.0001 p =0.0050 p <.0001 p <.0001 p <.0001

0

10

20

30


Mea

n U

AC

R (

mg/

g)


eGFR>=90 mL/min/1.73m²

3957 3782 3640 3387 3157 2681

4070 3929 3792 3589 3415 2922Dapagliflozin

Placebo


E F

�

�

�

�

�

�

�

�

�

�

�

�

p <.0001 p =0.0003 p =0.0002 p =0.0001 p <.0001

0

20

40

60


Mea

n U

AC

R (

mg/

g)


eGFR<60 mL/min/1.73m²

645 601 562 504 458 376

589 557 526 474 440 367Dapagliflozin

Placebo


G

Figure 1—Continued.


Dow


THEEK D

ER R

IJKSUN


50–70% of the general population ofpatients with type 2 diabetes (32–34). Wewere able to demonstrate an improve-ment in UACR deterioration even in thisgroup of patients with UACR #15 mg/g.The division into four categories also con-forms to the current knowledge that bothincreased renal and CV risk do not beginat UACR $30 mg/g, but rather at muchlower levels of UACR (35–37), and to thecurrent Kidney Disease: Improving GlobalOutcomes (KDIGO) recommendation todivide the range of normoalbuminuria intotwo separate groups (19). The data pre-sented here further emphasize the associa-tion between higher levels of UACR withinthe normoalbuminuria range and increasedrisk for adverse renal events.

Studying the categorical changes inUACR, we found that patients treatedwith dapagliflozin were more likely toexperience a categorical improvementand were less likely to experience dete-rioration. The observation was consis-tent when defined as at least onecategorical shift, or at least two shifts,and remained stable when calculated asa single measurement change or as sus-tained change. Although albuminuria-based end points are limited by highday-to-day variability, recent analysesindicated that similar drug effects areachieved when comparing single andconfirmed measurements (38). The sin-gle measurement analysis may sufferfrom increased “noise” but benefits

from a higher number of events, result-ing in a possible increase in statisticalpower (39). Time wise, dapagliflozinreduced the rate of new onset micro-or macroalbuminuria relatively earlyduring the trial, and the separationbetween the populations was main-tained throughout (Supplementary Fig.2B and C). Considering these findings,the analyses of the change in UACRboth as a continuous and categoricalvariable provide a comprehensive pic-ture, emphasizing the beneficial effectfor dapagliflozin on urinary albuminexcretion across all baseline UACR andeGFR categories.

Albumin excretion rate is a clinicallyuseful surrogate marker for severity of

Dapagliflozin (N=8582) Placebo (N=8578)

UACR to <=300 in subjects with >300 at BL

UACR to <30 in subjects with 30−300 at BL

UACR to <=15in subjects with >15−<30 at BL

stable UACR at <=15in subjects with <=15 at BL

UACR of at least 1−step improvement in subjects with >15 at BL

UACR of 2−step Improvement in subjects with >=30 at BL

n

282

774

618

3660

1674

422

N

594

2017

1281

4538

3892

2611

n/N(%)

47.5%

38.4%

48.2%

80.7%

43.0%

16.2%

n

175

576

507

3543

1258

301

N

575

2013

1296

4529

3884

2588

n/N(%)

30.4%

28.6%

39.1%

78.2%

32.4%

11.6%

Hazard ratio (95% CI)

1.82 (1.51, 2.20)

1.46 (1.31, 1.62)

1.32 (1.17, 1.48)

1.06 (1.01, 1.11)

1.45 (1.35, 1.56)

1.43 (1.23, 1.65)

Cox p−value

<0.0001

<0.0001

<0.0001

0.0195

<0.0001

<0.0001

0.80 1.0 1.4 1.8<−−− favored Placebo −−− −−− favored Dapagliflozin −−−>

Dapagliflozin (N=8582) Placebo (N=8578)

UACR to >15 in subjects with <=15 at BL

UACR to >=30 in subjects with <30 at BL

UACR to >300 in subjects with 30−300 at BL

UACR of at least 1−step deterioration in subjects with <=300 at BL

UACR 2−step deterioration in subjects with <30 at BL

n

1078

772

156

1630

385

N

4538

5819

2017

7836

5819

n/N(%)

23.8%

13.3%

7.7%

20.8%

6.6%

n

1137

959

284

1931

481

N

4529

5825

2013

7838

5825

n/N(%)

25.1%

16.5%

14.1%

24.6%

8.3%

Hazard ratio (95% CI)

0.94 (0.86, 1.02)

0.79 (0.72, 0.87)

0.52 (0.43, 0.64)

0.82 (0.77, 0.88)

0.79 (0.69, 0.91)

Cox p−value

0.1319

<0.0001

<0.0001

<0.0001

0.0007

0.40 0.55 0.75 1.0 1.2 <−−− favored Dapagliflozin −−− −−− favored Placebo −−−>

A

B

Figure 2—Change in confirmed sustained categorical UACR (mg/g) from baseline (BL) to EOT in dapagliflozin vs. placebo arm. A: Improvement inUACR categories. B: Deterioration in UACR categories.


Dow


THEEK D

ER R

IJKSUN




kidney disease. Treatments that improvealbuminuria status are associated with areduction in the progression of CKD(13). Clinically, improvement in albumin-uria status serves as a positive prognos-tic factor for adverse CV and renaloutcomes, while increased albuminexcretion serves as a warning sign (14).We previously reported a 24% decreasein the composite cardiorenal outcomeof the trial (40% sustained decrease ineGFR, ESKD, and renal or CV death)(3,4). Here we demonstrated that theimprovement was most pronounced inthose patients with higher albuminuriaat baseline (Pinteraction = 0.0327), how-ever this analysis was not adjustedfor the differences in the subgroupspopulation. This stands in contrast tothe renal-specific composite outcome(defined as all of the above but withoutCV death), in which the improvementwith dapagliflozin was independent ofbaseline UACR (Pinteraction = 0.4801).These results emphasize that dapagliflo-zin improved renal outcomes in allpatients, but improvement of compositecardiorenal outcomes was achieved inpatients who already had renal damage,as evidenced by an increased UACR.This finding widens the newer findingsfrom the Canagliflozin and Renal Events

in Diabetes with Established NephropathyClinical Evaluation (CREDENCE) (5) andDapagliflozin And Prevention of Adverseoutcomes in Chronic Kidney Disease(DAPA-CKD) (6) trials, both of which dem-onstrated a lower rate of both renal andcardiorenal outcomes in different popula-tions of patients with CKD, with (5,6) andwithout (6) type 2 diabetes.

While treatment with dapagliflozinmay improve prognosis even when initi-ated in patients with kidney markers inthe normal-healthy range, the low rateof adverse renal events in this popula-tion may require longer duration oftreatment to demonstrate dapagliflo-zin’s full effect. These results, along withthe improvement demonstrated in therenal-specific outcome for all UACR sub-groups, emphasize the way in whichDECLARE-TIMI 58 was able to add sup-porting information to the renal-specificoutcomes trials (CREDENCE, DAPA-CKD,EMPA-KIDNEY and others) (4–6,40)regarding the effect of SGLT2i in thehealthier population of patients withtype 2 diabetes that is a large part ofthe population with type 2 diabetes inour daily practice but not well repre-sented in renal outcomes trials.

These analyses must be viewed ashypothesis generating, since one of the

dual primary efficacy outcomes (MACE)was not achieved and because DECLARE-TIMI 58 was a CV outcome trial ratherthan a renal outcome trial. Though Afri-can American and Hispanic patients areat high risk for CKD, the limited numberof subjects enrolled from these categoriesprecludes a definitive understanding ofany race- or ethnicity-based differences inoutcomes or treatment effects (41).Another limitation of our trial was thatwe tested UACR only as a single sample,rather than an average of two to threesamples, and only 6 months from base-line and thereafter once yearly. The eGFRdynamics in DECLARE-TIMI 58, includingthe early drop following dapagliflozin initi-ation, are not included in this analysis. Anadditional limitation is the relatively lownumber of patients in the highest risk cat-egory of albuminuria (1,169 patients withUACR >300 mg/g at baseline, <7% ofthe entire trial population), reflected inthe relatively small number of renalevents. However, this can also be seen asa possible strength of the trial, as this ismore representative of the general popu-lation of patients with type 2 diabetesworldwide (32–34).

In conclusion, in the large populationof patients with type 2 diabetes andlow renal risk in DECLARE-TIMI 58, we

Dapagliflozin Placebo

Cardiorenal composite endpoint

UACR <=15 mg/g

15 < UACR < 30 mg/g

30 <= UACR <= 300 mg/g

300 < UACR mg/g

Renal−Specific composite endpoint

UACR <=15 mg/g

15 < UACR < 30 mg/g

30 <= UACR <= 300 mg/g

300 < UACR mg/g

n/N (%)

123/4538 (2.7%)

68/1281 (5.3%)

113/2017 (5.6%)

59/594 (9.9%)

33/4538 (0.7%)

17/1281 (1.3%)

39/2017 (1.9%)

31/594 (5.2%)

KM event rate

2.6%

5.2%

5.5%

9.5%

0.7%

1.3%

2.0%

4.8%

n/N (%)

145/4529 (3.2%)

69/1296 (5.3%)

152/2013 (7.6%)

105/575 (18.3%)

60/4529 (1.3%)

35/1296 (2.7%)

66/2013 (3.3%)

75/575 (13%)

KM event rate

3.1%

4.9%

7.2%

17.7%

1.3%

2.4%

3.3%

12.8%

Hazard Ratio (95% ci)

0.84 (0.66, 1.07)

1.01 (0.72, 1.41)

0.73 (0.57, 0.94)

0.52 (0.38, 0.72)

0.54 (0.35, 0.83)

0.50 (0.28, 0.89)

0.59 (0.39, 0.87)

0.38 (0.25, 0.58)

P value

0.1543

0.9539

0.0125

<0.0001

0.0048

0.0190

0.0082

<0.0001

P Value for interaction

0.0327

0.4801

0.25 0.50 1.0 1.5 <−−− favored Dapagliflozin −−− −−−favored Placebo −−−>

Figure 3—Treatment effect of dapagliflozin vs. placebo on composite cardiorenal and renal-specific outcomes according to baseline UACR catego-ries of#15, >15 to <30,$30 to#300, and >300 mg/g. Cox model with stratification factor (baseline hematuria status and eASCVD or MRF sta-tus). KM, Kaplan-Meier.


Dow


THEEK D

ER R

IJKSUN


were able to demonstrate a significantpositive long-term effect of dapagliflozinon UACR, irrespective of baseline eGFRand UACR, and even in patients withnormoalbuminuria at baseline. We alsodemonstrated a reduction in renal-spe-cific outcomes across all baseline UACRcategories. This reduction in UACR andrenal outcomes with dapagliflozin wasachieved on top of >80% use of ACEi/ARBs. The possible association betweenthe positive effect of dapagliflozin onalbuminuria and its positive effect onthe cardiorenal and renal-specific out-comes in DECLARE-TIMI 58 remain tobe further analyzed.

Funding and Duality of Interest. T.A.Z.reports a research grant from Deutsche For-schungsgemeinschaft (ZE 1109/1-1). J.P.D.reports support from U.S. Food and DrugAdministration. DECLARE-TIMI 58 was initiallyfunded by AstraZeneca and Bristol-MyersSquibb; by the time of publication AstraZe-neca was the sole funder (NCT01730534).O.M. declares advisory board membershipfrom AstraZeneca, Novo Nordisk, Eli Lilly,Sanofi, Merck Sharp & Dohme, BoehringerIngelheim, and BOL Pharma; speakers bureauhonorarium from AstraZeneca, Novo Nordisk,Eli Lilly, Sanofi, Merck Sharp & Dohme, Boeh-ringer Ingelheim, and Jansen; and researchgrants from Novo Nordisk and AstraZeneca.S.D.W. reports grants from AstraZeneca duringthe conduct of the study, grants from Amgenand Sanofi, grants and personal fees fromArena, AstraZeneca, Bristol-Myers Squibb,Daiichi Sankyo, Eisai, Eli Lilly, and Janssen;grants, personal fees, and other from Merck;personal fees from Aegerion, Allergan,AngelMed, Boehringer Ingelheim, Boston Clin-ical Research Institute, Icon Clinical, Lexicon,Servier, St. Jude Medical, and XOMA, outsidethe submitted work; and is a member of theTIMI Study Group, which has received institu-tional research grant support through Brig-ham and Women’s Hospital from Abbott,Amgen, Anthos Therapeutics, Aralez, Astra-Zeneca, Bayer HealthCare Pharmaceuticals,Inc., Daiichi Sankyo, Eisai, Intarcia, MedI-mmune, Merck, Novartis, Pfizer, Quark Phar-maceuticals, Regeneron Pharmaceuticals, Inc.,Roche, Siemens Healthcare Diagnostics, Inc.,Takeda, The Medicines Company, and ZoraBiosciences. H.J.L.H. reports grants and otherfrom AstraZeneca, AbbVie, and BoehringerIngelheim, and other from CSL Behring, Bayer,Chinook, Gilead, Merck, Novo Nordisk, Jans-sen, Mitsubishi Tanabe, and Retrophin. J.P.D.reports personal fees from AstraZeneca dur-ing the conduct of the study; personal feesfrom Sanofi, Bayer, CSL Behring, Novo Nor-disk/ICON Clinical Research, Amgen, andIronwood Pharmaceuticals; other from Collab-orative Study Group, and personal fees fromBoehringer-Ingelheim. A.C. reports grants

from Novo Nordisk and AstraZeneca, and per-sonal fees from Novo Nordisk, AstraZeneca,Abbot, Eli Lilly, Sanofi, Boehringer Ingelheim,Merck Sharp & Dohme, GlucoMe, and MedialEarly Sign. E.L.G. and S.A.M. report grantsfrom AstraZeneca during the conduct of thestudy and are members of the TIMI StudyGroup, which has received institutionalresearch grant support through Brigham andWomen’s Hospital from Abbott, Amgen,Anthos Therapeutics, Aralez, AstraZeneca,Bayer HealthCare Pharmaceuticals, Inc., Daii-chi Sankyo, Eisai, Intarcia, MedImmune,Merck, Novartis, Pfizer, Quark Pharmaceuti-cals, Regeneron Pharmaceuticals, Inc., Roche,Siemens Healthcare Diagnostics, Inc., Takeda,The Medicines Company, and Zora Bioscien-ces. A.R. and I.Y. report consultation fees fromAstraZeneca and Novo Nordisk. T.A.Z. T.A.Zreports research grants from the Austrian Sci-ence Funds and the German Research Foun-dation, honoraria for serving on advisoryboards from Boehringer Ingelheim, personalfees from AstraZeneca, Boehringer Ingelheim,and Sun Pharmaceutical Industries, and edu-cational grants from Eli Lilly and Company.I.A.M.G.-N., A.M.L., M.F., and P.A.J. areemployees at BioPharmaceuticals R&D, Astra-Zeneca, Gothenburg, Sweden. D.L.B. disclosesadvisory board: Cardax, CellProthera, CerenoScientific, Elsevier PracticeUpdate Cardiology,Level Ex, Medscape Cardiology, MyoKardia,PhaseBio, PLx Pharma, and Regado Bioscien-ces; board of directors: Boston VA ResearchInstitute, Society of Cardiovascular PatientCare, and TobeSoft; chair: American HeartAssociation Quality Oversight Committee;data monitoring committees: Baim Institutefor Clinical Research (formerly Harvard ClinicalResearch Institute, for PORTICO trial, funded bySt. Jude Medical, now Abbott), Cleveland Clinic(including for the CENTERA THV System in Inter-mediate Risk Patients Who Have Symptomatic,Severe, Calcific, Aortic Stenosis Requiring AorticValve Replacement [ExCEED] trial, funded byEdwards), Contego Medical (chair, ProtectionAgainst Emboli During Carotid Artery StentingUsing the Neuroguard IEP System [PERFORMANCE2]), Duke Clinical Research Institute, Mayo Clinic,Mount Sinai School of Medicine (for the Edoxa-ban Compared to Standard Care After Heart ValveReplacement [ENVISAGE] trial, funded by DaiichiSankyo), and Population Health Research Institute;honoraria: Baim Institute for Clinical Research(formerly Harvard Clinical Research Institute;Triple Therapy With Warfarin in Patients WithNonvalvular Atrial Fibrillation Undergoing Percuta-neous Coronary Intervention [RE-DUAL PCI] clinicaltrial steering committee funded by BoehringerIngelheim; Study to Investigate CSL112 in SubjectsWith Acute Coronary Syndrome [AEGIS-II]) execu-tive committee funded by CSL Behring), BelvoirPublications (editor in chief, Harvard Heart Letter),Duke Clinical Research Institute (clinical trial steer-ing committees, including for the Prostate Cancerand Cardiovascular Disease [PRONOUNCE] trial,funded by Ferring Pharmaceuticals), HMP Global(editor in chief, Journal of Invasive Cardiology),Journal of the American College of Cardiology(guest editor; associate editor), K2P (co-chair, inter-disciplinary curriculum), Level Ex, Medtelligence/

ReachMD (Continuing Medical Education [CME]steering committees), MJH Life Sciences, Popu-lation Health Research Institute (for the Cardio-vascular Outcomes for People Using AnticoagulationStrategies [COMPASS] operations committee,publications committee, steering committee,and USA national co-leader, funded by Bayer),Slack Publications (chief medical editor, Cardiol-ogy Today’s Intervention), and WebMD (CMEsteering committees); other: Clinical Cardiology(deputy editor), National Cardiovascular DataRegistry Acute Coronary Treatment and Inter-vention Outcomes Network (NCDR-ACTION)Registry Steering Committee (chair), and Veter-ans Administration Clinical Assessment, Report-ing and Tracking System for Cath Labs (VACART) Research and Publications Committee(chair); research funding: Abbott, Afimmune,Amarin, Amgen, AstraZeneca, Bayer, BoehringerIngelheim, Bristol-Myers Squibb, Cardax, Chiesi,CSL Behring, Eisai, Ethicon, Ferring Pharmaceuti-cals, Forest Laboratories, Fractyl, Idorsia, Iron-wood, Ischemix, Lexicon, Eli Lilly, Medtronic,MyoKardia, Pfizer, PhaseBio, PLx Pharma,Regeneron, Roche, Sanofi, Synaptic, and TheMedicines Company; royalties: Elsevier (editor,Cardiovascular Intervention: A Companion toBraunwald’s Heart Disease); site co-investigator:Biotronik, Boston Scientific, CSI, St. Jude Medi-cal (now Abbott), and Svelte; trustee: AmericanCollege of Cardiology; and unfunded research:FlowCo, Merck, Novo Nordisk, and Takeda.L.A.L. has received research funding from, hasprovided CME on behalf of, and/or has acted asan advisor to AstraZeneca, Bayer, BoehringerIngelheim, Eli Lilly, GSK, Janssen, Lexicon,Merck, Novo Nordisk, Sanofi, and Servier.D.K.M. has received personal fees from AstraZe-neca, Boehringer Ingelheim, Janssen, Lexicon,Merck & Company, Merck Sharp & Dohme,Novo Nordisk, Sanofi, Eisai, Esperion, GlaxoS-mithKline, Eli Lilly USA, Pfizer, Metavant,Applied Therapeutics, Afimmune, and CSL Beh-ring. J.P.H.W. reports grants, personal fees, andother from AstraZeneca and Novo Nordisk;other from Astellas, Janssen, Sanofi, RhythmPharmaceuticals, Wilmington Healthcare, and EliLilly; personal fees and other from BoehringerIngelheim, Napp, and Mundipharma; and grantsand personal fees from Takeda. M.S.S. reportsgrants and personal fees from Amgen, AnthosTherapeutics, AstraZeneca, Intarcia The Medi-cines Company, MedImmune, and Merck; per-sonal fees from Althera, Bristol-Myers Squibb,CVS Caremark, DalCor, Dr. Reddy’s Laboratories,Dyrnamix, and IFM Therapeutics; and grantsfrom Bayer, Daiichi-Sankyo, Eisai, Novartis, Pfizerand Quark Pharmaceuticals, and is a memberof the TIMI Study Group, which has alsoreceived institutional research grant supportthrough Brigham and Women’s Hospital from:Abbott, Regeneron, Roche, and Zora Bioscien-ces. I.R. declares advisory board membershipfrom AstraZeneca, Eli Lilly and Company, MerckSharp & Dohme, Novo Nordisk, Inc., and Sanofi;consultant fees from AstraZeneca, Insuline Med-ical, Medial EarlySign Ltd., CamerEyes Ltd.,Exscopia, Orgenesis Ltd., BOL Pharma, GlucomeLtd., DarioHealth, Diabot, Concenter BioPharma,and CuraLife Ltd.; speakers bureau honorariumfrom AstraZeneca, Eli Lilly and Company, Merck


Dow


THEEK D

ER R

IJKSUN


Sharp & Dohme, Novo Nordisk, Inc., and Sanofi;and stock/shareholder interests from GlucomeLtd., Orgenesis Ltd., DarioHealth, CamerEyesLtd., Diabot, and BOL Pharma. D.L.B. receivedhonoraria from American College of Cardiology(ACC) (senior associate editor, Clinical Trials andNews, ACC.org; vice-chair, ACC AccreditationCommittee), Canadian Medical and SurgicalKnowledge Translation Research Group (clinicaltrial steering committees), Society of Cardiovas-cular Patient Care (secretary/treasurer), NationalCardiovascular Data Registry Acute CoronaryTreatment and Intervention Outcomes Network(NCDR-ACTION) Registry Steering Committee(chair), and Veterans Administration ClinicalAssessment, Reporting and Tracking System forCath Labs (VA CART) Research and PublicationsCommittee (chair). No other potential conflictsof interest relevant to this article werereported.Author Contributions. O.M., S.D.W., H.J.L.H,J.P.D., A.C., A.R., M.S., I.Y., T.A.Z., I.A.M.G.-N.,A.M.L., M.F., D.L.B., L.A.L., D.K.M., J.P.H.W., M.S.S.,and I.R. contributed to data interpretation. O.M.,S.D.W., H.J.L.H, J.P.D., A.C., A.R., M.S., I.Y., T.A.Z.,I.A.M.G.-N., A.M.L., M.F., D.L.B., L.A.L., D.K.M.,J.P.H.W., M.S.S., and I.R. contributed to the writingof the report. O.M., S.D.W., H.J.L.H, J.P.D.,I.A.M.G.-N., A.M.L., M.F., M.S.S., and I.R. contrib-uted to the study design. O.M., S.D.W., A.C.,E.L.G., A.R., M.S., I.Y., S.A.M., I.A.M.G.-N., A.M.L.,M.F., P.A.J., D.L.B., L.A.L., D.K.M., J.P.H.W., M.S.S.,and I.R. contributed to data analysis. O.M.,S.D.W., E.L.G., A.R., M.S., I.Y., S.A.M., I.A.M.G.-N.,A.M.L., M.S.S., and I.R. designed the figures.O.M., S.D.W., M.S., I.A.M.G.-N., A.M.L., M.S.S., andI.R. did the literature search. O.M., S.D.W.,I.A.M.G.-N., A.M.L., M.F., M.S.S., and I.R. contrib-uted to data collection. O.M. and I.R. are theguarantors of this work and, as such, had fullaccess to all the data in the study and takeresponsibility for the integrity of the data and theaccuracy of the data analysis.Prior Presentation. Parts of this study werepresented in abstract form at the 79th Scien-tific Sessions of the American Diabetes Associ-ation, San Francisco, CA, 7–11 June 2019.

References1. Wanner C, Inzucchi SE, Lachin JM, et al.; EMPA-REG OUTCOME Investigators. Empagliflozin andprogression of kidney disease in type 2 diabetes. NEngl J Med 2016;375:323–3342. Neal B, Perkovic V, Mahaffey KW, et al.; CANVASProgram Collaborative Group. Canagliflozin andcardiovascular and renal events in type 2 diabetes.N Engl JMed 2017;377:644–6573. Wiviott SD, Raz I, Bonaca MP, et al.;DECLARE–TIMI 58 Investigators. Dapagliflozinand cardiovascular outcomes in type 2 diabetes.N Engl J Med 2019;380:347–3574. Mosenzon O,Wiviott SD, Cahn A, et al. Effectsof dapagliflozin on development and progressionof kidney disease in patients with type 2diabetes: an analysis from the DECLARE-TIMI 58randomised trial. Lancet Diabetes Endocrinol2019;7:606–6175. Perkovic V, Jardine MJ, Neal B, et al.;CREDENCE Trial Investigators. Canagliflozin and

renal outcomes in type 2 diabetes andnephropathy. N Engl J Med 2019;380:2295–23066. Heerspink HJL, Stef�ansson BV, Correa-Rotter R,et al.; DAPA-CKD Trial Committees and Investigators.Dapagliflozin in patients with chronic kidney disease.N Engl JMed 2020;383:1436–14467. Cannon CP, Pratley R, Dagogo-Jack S, et al.;VERTIS CV Investigators. Cardiovascular outcomeswith ertugliflozin in type 2 diabetes. N Engl J Med2020;383:1425–14358. Tuttle KR, Bakris GL, Bilous RW, et al. Diabetickidney disease: a report from an ADA ConsensusConference. Diabetes Care 2014;37:2864–28839. Yamanouchi M, Furuichi K, Hoshino J, et al.;Research Group of Diabetic Nephropathy, theMinistry of Health, Labour and Welfare, andthe Japan Agency for Medical Researchand Development. Nonproteinuric versus proteinuricphenotypes in diabetic kidney disease: a propensityscore-matched analysis of a nationwide, biopsy-based cohort study. Diabetes Care 2019;42:891–90210. Chronic Kidney Disease Prognosis Consortium,Matsushita K, van der Velde M, Astor BC, et al.Association of estimated glomerular filtration rate andalbuminuriawith all-cause and cardiovascularmortalityin general population cohorts: a collaborative meta-analysis. Lancet 2010;375:2073–208111. van der VeldeM,Matsushita K, Coresh J, et al.;Chronic Kidney Disease Prognosis Consortium.Lower estimated glomerular filtration rate andhigher albuminuria are associated with all-causeand cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts. Kidney Int2011;79:1341–135212. Coresh J, Heerspink HJL, Sang Y, et al.; ChronicKidney Disease Prognosis Consortium and ChronicKidney Disease Epidemiology Collaboration. Change inalbuminuria and subsequent risk of end-stage kidneydisease: an individual participant-level consortiummeta-analysis of observational studies. Lancet DiabetesEndocrinol 2019;7:115–12713. Heerspink HJL, Greene T, Tighiouart H, et al.;Chronic Kidney Disease Epidemiology Collaboration.Change in albuminuria as a surrogate endpoint forprogression of kidney disease: a meta-analysis oftreatment effects in randomised clinical trials.Lancet Diabetes Endocrinol 2019;7:128–13914. Schmieder RE, Mann JFE, Schumacher H,et al.; ONTARGET Investigators. Changes inalbuminuria predict mortality and morbidity inpatients with vascular disease. J Am Soc Nephrol2011;22:1353–136415. American Diabetes Association. 11.Microvascularcomplications and foot care: Standards ofMedical Carein Diabetes—2021. Diabetes Care 2021;44(Suppl.1):S151–S16716. Wiviott SD, Raz I, Bonaca MP, et al. Thedesign and rationale for the Dapagliflozin Effecton Cardiovascular Events (DECLARE)-TIMI 58Trial. Am Heart J 2018;200:83–8917. Raz I, Mosenzon O, BonacaMP, et al. DECLARE-TIMI 58: participants’ baseline characteristics.DiabetesObesMetab 2018;20:1102–111018. Schwandt A, Denkinger M, Fasching P, et al.Comparison of MDRD, CKD-EPI, and Cockcroft-Gault equation in relation to measured glomerularfiltration rate among a large cohort with diabetes.J Diabetes Complications 2017;31:1376–138319. Levey AS, de Jong PE, Coresh J, et al. Thedefinition, classification, and prognosis of chronic

kidney disease: a KDIGO Controversies Conferencereport. Kidney Int 2011;80:17–2820. Cherney DZI, Zinman B, Inzucchi SE, et al.Effects of empagliflozin on the urinary albumin-to-creatinine ratio in patients with type 2diabetes and established cardiovascular disease:an exploratory analysis from the EMPA-REGOUTCOME randomised, placebo-controlled trial.Lancet Diabetes Endocrinol 2017;5:610–62121. Perkovic V, de Zeeuw D, Mahaffey KW, et al.Canagliflozin and renal outcomes in type 2 diabetes:results from the CANVAS Program randomised clinicaltrials. Lancet Diabetes Endocrinol 2018;6:691–70422. Heerspink HJL, Johnsson E, Gause-Nilsson I, CainVA, Sj€ostr€om CD. Dapagliflozin reduces albuminuriain patients with diabetes and hypertension receivingrenin-angiotensin blockers. Diabetes Obes Metab2016;18:590–59723. Fioretto P, Stefansson BV, Johnsson E, Cain VA,Sj€ostr€om CD. Dapagliflozin reduces albuminuria over2 years in patients with type 2 diabetes mellitus andrenal impairment. Diabetologia 2016;59:2036–203924. Cherney D, Lund SS, Perkins BA, et al. Theeffect of sodium glucose cotransporter 2 inhibitionwith empagliflozin on microalbuminuria andmacroalbuminuria in patients with type 2 diabetes.Diabetologia 2016;59:1860–187025. Heerspink HJL, Perkins BA, Fitchett DH, HusainM,Cherney DZI. Sodium glucose cotransporter 2 inhibitorsin the treatment of diabetes mellitus: cardiovascularand kidney effects, potential mechanisms, and clinicalapplications. Circulation2016;134:752–77226. Tonneijck L, Muskiet MHA, Smits MM, et al.Glomerular hyperfiltration in diabetes: mechanisms,clinical significance, and treatment. J Am SocNephrol 2017;28:1023–103927. van Bommel EJM, Muskiet MHA, vanBaar MJB, et al. The renal hemodynamiceffects of the SGLT2 inhibitor dapagliflozinare caused by post-glomerular vasodilatationrather than pre-glomerular vasoconstrictionin metformin-treated patients with type 2diabetes in the randomized, double-blind REDtrial. Kidney Int 2020;97:202–21228. Lytvyn Y, Bjornstad P, van Raalte DH, HeerspinkHL, Cherney DZI. The new biology of diabetic kidneydisease—mechanisms and therapeutic implications.Endocr Rev 2020;41:202–23129. Vallon V, Thomson SC. Targeting renalglucose reabsorption to treat hyperglycaemia:the pleiotropic effects of SGLT2 inhibition.Diabetologia 2017;60:215–22530. Tsimihodimos V, Filippatos TD, Elisaf MS. SGLT2inhibitors and the kidney: effects and mechanisms.DiabetesMetab Syndr 2018;12:1117–112331. O’Neill J, Fasching A, Pihl L, Patinha D, Franz�enS, Palm F. Acute SGLT inhibition normalizes O2tension in the renal cortex but causes hypoxia in therenal medulla in anaesthetized control and diabeticrats. Am J Physiol Renal Physiol 2015;309:F227–F23432. Parving HH, Lewis JB, Ravid M, Remuzzi G;DEMAND investigators. Prevalence and riskfactors for microalbuminuria in a referred cohortof type II diabetic patients: a global perspective.Kidney Int 2006;69:2057–206333. Young BA, Katon WJ, Von Korff M, et al. Racialand ethnic differences in microalbuminuriaprevalence in a diabetes population: the pathwaysstudy. J Am SocNephrol 2005;16:219–228


Dow


THEEK D

ER R

IJKSUN


34. Newman DJ, Mattock MB, Dawnay ABS,et al. Systematic review on urine albumin testingfor early detection of diabetic complications.Health Technol Assess 2005;9:iii–vi, xiii-16335. Scirica BM, Mosenzon O, Bhatt DL, et al.Cardiovascular outcomes according to urinaryalbumin and kidney disease in patients with type 2diabetes at high cardiovascular risk: observationsfrom the SAVOR-TIMI 53 Trial. JAMA Cardiol2018;3:155–16336. Blecker S,Matsushita K, K€ottgen A, et al. High-normal albuminuria and risk of heart failure in thecommunity. Am J Kidney Dis 2011;58:47–55

37. Astor BC, Matsushita K, Gansevoort RT, et al.;Chronic Kidney Disease Prognosis Consortium.Lower estimated glomerular filtration rate andhigher albuminuria are associated with mortalityand end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts.Kidney Int 2011;79:1331–134038. Kr€opelin TF, de Zeeuw D, Andress DL, et al.Number and frequency of albuminuria measure-ments in clinical trials in diabetic nephropathy.Clin J Am Soc Nephrol 2015;10:410–41639. Kr€opelin TF, de Zeeuw D, Remuzzi G, BilousR, Parving H-H, Heerspink HJL. Determining the

optimal protocol for measuring an albuminuriaclass transition in clinical trials in diabetic kidneydisease. J Am Soc Nephrol 2016;27:3405–341240. EMPA-KIDNEY (The Study of Heart and KidneyProtection With Empagliflozin). ClinicalTrials.gov.Accessed 9 May 2020. Available from https://clinicaltrials.gov/ct2/show/NCT0359411041. Vart P, Powe NR, McCulloch CE, et al.; Centersfor Disease Control and Prevention Chronic KidneyDisease Surveillance Team. National trends in theprevalence of chronic kidney disease among racial/ethnic and socioeconomic status groups, 1988-2016. JAMANetwOpen 2020;3:e207932


Dow


THEEK D

ER R

IJKSUN


https://clinicaltrials.gov/ct2/show/NCT03594110

https://clinicaltrials.gov/ct2/show/NCT03594110

the effect of dapagliflozin on albuminuria in declare-timi 58

Documents