Albuminuria: Associated With Heart Failure Severity and Impaired Clinical Outcomes

Journal Pre-proof Albuminuria: Associated with Heart Failure Severity and Impaired Clinical Outcomes Mony Shuvy, MD, Donna R. Zwas, MD, Chaim Lotan, MD, Andre Keren, MD, Israel Gotsman, MD PII:

S0828-282X(19)31219-X

DOI:

https://doi.org/10.1016/j.cjca.2019.09.001

Reference:

CJCA 3438

To appear in:

Canadian Journal of Cardiology

Received Date: 30 May 2019 Revised Date:

15 August 2019

Accepted Date: 2 September 2019

Please cite this article as: Shuvy M, Zwas DR, Lotan C, Keren A, Gotsman I, Albuminuria: Associated with Heart Failure Severity and Impaired Clinical Outcomes, Canadian Journal of Cardiology (2019), doi: https://doi.org/10.1016/j.cjca.2019.09.001. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society.

Albuminuria: Associated with Heart Failure Severity and Impaired Clinical Outcomes Running title: Albuminuria and outcomes in heart failure

Mony Shuvy1,2 MD, Donna R. Zwas MD 1,2, Chaim Lotan MD 1, Andre Keren MD 1,2

, Israel Gotsman MD 1,2

1

Heart Institute, Hadassah University Hospital, Jerusalem, Israel

2

Heart Failure Center, Clalit Health Services, Israel

Corresponding author: Mony Shuvy MD, Hadassah-Hebrew University Medical Center P.O. Box 12000, Jerusalem 91120, Israel. Telephone- 972-2-6776451, Fax- 972-2-6778190 E-mail: [email protected]

Keywords: Albuminuria; Heart failure; Outcome

Word count: 4182

1

Abstract Background: Urinary albumin to creatinine ratio (UACR) is common in patients with heart failure (HF) and may have an impact on clinical outcome. We evaluated the effect of UACR on clinical outcome in a real-world cohort of patients with HF. Methods: All patients with HF at a health maintenance organization were followed for cardiac-related hospitalizations and death. Results: The study cohort included 4,668 HF patients and was divided into 3 groups based on UACR: normal range albuminuria (2,085 patients; 45%), microalbuminuria (1,769 patients; 38%), and macroalbuminuria (814 patients; 17%). Microalbuminuria and macroalbuminuria were both associated with increasing age, diabetes mellitus, hypertension, peripheral vascular disease, atrial fibrillation, and NYHA class III/IV. Microalbuminuria and macroalbuminuria were directly associated with decreased event-free survival from death as well as death and cardiovascular-hospitalizations. Cox regression analysis after adjustment for significant predictors demonstrated that microalbuminuria was associated with increase in mortality (hazard ratio (HR) 1.18, 95% CI 1.18-1.38, P=0.03) and macroalbuminuria (HR 1.33, 95% CI 1.10-1.61, P<0.001). Albuminuria was also an independent predictor of death and cardiovascular-hospitalizations. Analysis of albuminuria as a continuous parameter (natural logarithm transformed) showed that UACR was an independent predictor of mortality as well as death and cardiovascular-hospitalizations.

Subclinical

albuminuria (UACR 12-29µg/mg) was directly associated with reduced survival. Cox regression analysis using restricted cubic splines demonstrated an independent continuous increase in mortality with increasing albuminuria (P<0.0001 adjusted linear model). Conclusions: Albuminuria provides important information regarding several

2

detrimental processes in HF and is a significant predictor of a worse outcome.

3

Summary Urinary albumin to creatinine ratio (UACR) is common in patients with heart failure (HF). All patients with HF at a health maintenance organization were followed for cardiac-related hospitalizations and death. Microalbuminuria and macroalbuminuria were directly associated with decreased event-free survival from death as well as death and cardiovascular-hospitalizations. Even subclinical albuminuria (UACR 12-29µg/mg) was directly associated with reduced survival. Cox regression analysis demonstrated an independent continuous increase in mortality with increasing albuminuria.

4

Background Heart failure (HF) has emerged as a major epidemic and is a significant public health burden. It is associated with considerable morbidity and mortality1, 2. Increased excretion of albumin in the urine is a marker of the various pathophysiological conditions including systemic inflammation and endothelial and microvascular dysfunction that arise in patients with HF3. Albuminuria also reflects kidney injury which can be caused by chronic HF and by other common comorbidities such as diabetes mellitus and chronic kidney disease4. In the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) Programme, more than 40% of HF patients had albuminuria, which was associated with increased risk of mortality even after adjustment for other prognostic variables5. In the more recent TOPCAT study (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist), 35% had microalbuminuria and 13% had macroalbuminuria and both were independently associated with worse cardiovascular outcomes6. Similar results were obtained in ALiskiren Observation of heart Failure Treatment (ALOFT) study7. In these trials, albuminuria was categorized as microalbuminuria or macroalbuminuria and the latter was associated with worse outcomes5, 6, 8. Although albuminuria is very common among HF population, most data regarding its prognostic impact was obtained from clinical trials rather than real world data. Furthermore, the prognostic impact of different albuminuria levels has not been completely evaluated. To address these issues, we first evaluated the impact of urinary albumin to creatinine ratio (UACR) on clinical outcome in a large real-world cohort of patients with chronic HF. Secondly, we looked at the prognostic impact of specific albuminuria levels on HF outcomes. Methods 5

Clalit Health Services is the largest health maintenance organization (HMO) in Israel. It has a central computerized database in which all members have a complete digital record. The database includes demographics, comprehensive clinical data, diagnoses, and all laboratory data undertaken in a single centralized laboratory of the HMO. We identified and retrieved electronically from the computerized database all members with a diagnosis of HF as coded by the database in Jerusalem using the International Classification of Diseases, Ninth Revision (ICD-9) code 428. Data was retrieved from January 2017. Patients were followed for clinical events including cardiovascular hospitalizations and death until to January 2019. 7106 patients had a diagnosis of HF. Natriuretic peptides are not routinely performed in Israel and were not available for analysis. All hospitalizations in cardiac and internal medicine departments including cardiac and internal intensive care units were retrieved and analysed. Data on mortality was retrieved from the National Census Bureau. The Institutional Committee for Human Studies of Clalit Health Services, approved the study protocol. Biochemical analyses were performed at the HMO single centralized core laboratory with routine standardized methodologies on fresh samples of blood obtained after an overnight fast. Urinary albumin to creatinine ratio (µg/mg) was measured from a spot morning urine sample. Glucose levels were measured in plasma, and all other biochemical analyses were performed on serum. The laboratory is authorized to perform tests according to the international quality standard ISO-9001. The study cohort was stratified according to UCAR: normal range albuminuria (<30µg/mg), microalbuminuria (30-300 µg/mg) and macroalbuminuria (>300µg/mg). Patients with missing values of UCAR (2,438; 34%) were excluded from the analysis, and their baseline characteristics are presented in Supplementary Table S1. These patients were older, with a higher prevalence of dementia and were less likely to be 6

treated. SPSS version 17.0 for Windows (SPSS Inc., Chicago, Illinois, USA) and R Statistical Software version 3.0.1 for Windows (R Development Core Team) were used for the analyses. Comparison of the clinical characteristics was performed using the Mann-Whitney U test for continuous variables and the Chi-Square Test for categorical variables. Clinical predictors were transformed where appropriate. Log10 was used for logarithmic transformations with the exception of estimated glomerular filtration rate (eGFR) that a square root transformation was used. Follow-up time was calculated using Kaplan-Meier estimate of potential follow-up9. Kaplan-Meier curves, with the log-rank test, were used to compare survival according to UACR levels. Multivariate Cox proportional hazards regression analysis was used to evaluate independent variables that determined survival. Parameters included in the multivariate Cox regression analysis incorporated age, gender and other clinically significant parameters as well as clinical or laboratory parameters that were significant on univariable analysis with the addition of significant drug therapy in separate models. Restricted cubic spline multivariable cox regression analysis was performed to evaluate the relationship between UACR as a continuous parameter and mortality. Proportionality assumptions of the Cox regression models were evaluated by log–log survival curves and with the use of Schoenfeld residuals. An evaluation of the existence of confounding or interactive effects was made between variables and their possible collinearity. A p value of <0.05 was considered statistically significant. Results Clinical parameters The study cohort included 4,668 HF patients and was divided into 3 groups based on UACR: normal range albuminuria (2,085 patients; 45%), microalbuminuria (1,769 patients; 38%), and macroalbuminuria (814 patients; 17%). The distribution of UACR 7

in the cohort is presented in Figure 1. Subgroup analysis (Supplementary Table S2) demonstrated that microalbuminuria was highly prevalent in patients with diabetes, hypertension and reduced GFR (40%, 40% and 32%, respectively). However, microalbuminuria was also highly prevalent in patients without these diseases (33%, 26%, 38%, respectively). Even patients without any of these diagnoses had a 22% prevalence of microalbuminuria. Table 1 presents the demographics and clinical parameters of the patients stratified according to the UACR groups. In general, cardiovascular comorbidities were more common in patients with albuminuria, and the prevalence of diabetes mellitus was one of most remarkable differences between the groups: 59%, 73% and 85% for normal range albuminuria, microalbuminuria and macroalbuminuria respectively, p<0.001. Microalbuminuria and macroalbuminuria were both associated with increasing age, diabetes mellitus, hypertension, peripheral vascular disease and atrial fibrillation. UACR was directly associated with the severity of the HF as reflected by the NYHA class. Patients with advanced disease (NYHA III/IV) had a significantly higher UACR compared to patients with milder disease (NYHA I/II), (50 (18-208)) vs. 32 (11-129) respectively, P<0.001). A higher percentage of patients with microalbuminuria and macroalbuminuria were in NYHA class III/IV (43% and 49% respectively versus 34% in patients with normal range albuminuria, P<0.001). Furthermore, the Charlson Comorbidity index was directly associated with albuminuria. Albuminuria was also associated with higher creatinine, glucose, uric acid and increased C-reactive protein. Patients with macroalbuminuria had the lowest estimated glomerular filtration rate (51 mL/min per 1.73m2), were less treated with angiotensin converting enzyme inhibitors (ACE-I) or angiotensin receptor blockers (ARB) and spironolactone, but more with furosemide. Patients with microalbuminuria were more likely to be treated with ACE-I or ARB and more with

8

furosemide compared with patients with normal range albuminuria. Multivariable linear regression demonstrated that age, diabetes mellitus, hypertension, decreased eGFR,

atrial

fibrillation

and

NYHA

class

III/IV

were

associated

with

microalbuminuria (Supplementary Table S3). Albuminuria and clinical outcomes The overall 2 year-mortality rate was 19.6%. Survival rate by Kaplan-Meier analysis demonstrated that microalbuminuria and macroalbuminuria were directly associated with reduced survival: 85.2±0.8% vs. 77.5±1.0% vs. 74.2±1.5, for normal range albuminuria, microalbuminuria, and macroalbuminuria respectively p<0.001; Figure 2A). Similarly, microalbuminuria and macroalbuminuria were also directly associated with decreased event-free survival from death or cardiovascular-hospitalizations (39.5±1.1% vs. 28.5±1.1% vs. 18.9±1.4%, for normal range albuminuria, microalbuminuria,

macroalbuminuria

respectively

p<0.001;

Figure

2B).

Multivariable Cox regression analysis after adjustment for significant predictors demonstrated that albuminuria was a significant predictor of mortality (Table 2). After adjustment for other significant predictors (see Table 2 for predictors included), microalbuminuria was associated with an increase in mortality compared to normal range albuminuria, with a hazard ratio (HR) of 1.18, 95% confidence interval (CI) 1.18-1.38, P=0.03. Macroalbuminuria was also associated with mortality compared with normal range albuminuria (HR 1.33, 95% CI 1.10-1.61, P<0.001). Inclusion of HF medications demonstrated an even stronger association with a direct relation between microalbuminuria as well as macroalbuminuria and mortality (Table 3). Microalbuminuria and macroalbuminuria were also significant independent predictors of the combined endpoint of death or cardiovascular-hospitalizations with a graded increased risk with increasing albuminuria (Table 3). Analysis of albuminuria as a

9

continuous parameter (Ln transformed) after adjustment for other predictors including medications (outlined in Table 3) demonstrated that Ln UACR was an independent predictor of mortality (HR 1.07, 95% CI 1.02-1.12, P<0.001) as well as death and heart failure hospitalizations (HR 1.09, 95% CI 1.07-1.12, P<0.001). Subgroup analysis revealed that there were no significant interactions with albuminuria as a predictor of death or of the combined end-point in any of the relevant subgroups of patients. No interaction was demonstrated with diabetes, hypertension or reduced eGFR. This would suggest that microalbuminuria as well as macroalbuminuria are significant predictors of outcome across all patients with HF. Subclinical microalbuminuria We performed a sensitivity analysis of the group of patients with UACR below 30 µg/mg (considered ‘normal’). In this group, the median value of UCAR was 12 µg/mg, and patients were divided into 2 groups: normal UACR defined as UACR<12µg/mg (1018 patients), and subclinical albuminuria defined as UACR 1229µg/mg (1067 patients). Patients with subclinical albuminuria were older (78 vs. 71 years old, p<0.01), had more diabetes mellitus (64% vs. 53%, p<0.01), hypertension (86% vs. 75%, p<0.01), and more likely to have NYHA class III/IV (39% vs. 28%, p<0.01), compared with patients with normal UACR. Predictors for developing subclinical albuminuria included older age, female gender, diabetes mellitus, hypertension, decreased eGFR and atrial fibrillation (Supplementary Table S3). Subclinical albuminuria was directly associated with reduced survival compared with normal UACR, 82.7±1.2% vs. 87.9±1.0%, P<0.001 respectively, as well as decreased event-free survival from death or cardiovascular-hospitalizations 35.0±1.5% vs. 44.3±1.6%, P<0.001 (Supplementary Figure S1). Cox regression analysis showed that subclinical microalbuminuria was a predictor of death (HR 1.49, 95% CI 1.18-1.87,

10

P<0.001). Subclinical albuminuria was also a predictor of death and cardiovascular hospitalizations (HR 1.30, 95% CI 1.16 -1.45, P<0.001). Adjustments for significant parameters demonstrated that subclinical albuminuria was not a significant independent predictor of death or the combined end-point of death and cardiovascular hospitalizations. This would suggest that while subclinical albuminuria was not an independent predictor of outcome in this specific analysis, it is not ‘normal’ and is associated with more advanced disease and a significant marker for adverse outcomes. An additional sensitivity analysis evaluating albuminuria as a continuous parameter using restricted cubic splines was performed. Knots were allocated at albuminuria of 12, 30, 300 µg/mg. Cox regression analysis demonstrated a direct relationship between the decrease in albumin and mortality. This analysis demonstrated that any increase in albuminuria was a predictor of mortality with a continuous increase in the risk with increasing albuminuria (Figure 3A), P<0.0001 for the linear model. After adjustment for significant parameters included in Table 2, demonstrated that there was a direct linear relationship between albuminuria and mortality along the whole spectrum of albuminuria, and this was independently significant (Figure 3B), P<0.0001 for the linear model. This analysis is consistent with the previous analysis that albuminuria is not benign and has clinical significance even in the subclinical range. Discussion In this real-world cohort of HF patients, albuminuria was highly prevalent and was a significant predictor of death as well as death and cardiovascular hospitalizations. This direct relationship between albuminuria and clinical outcome appears to be relevant across the entire spectrum of albuminuria levels. Moreover, the present study suggests that even low range albuminuria that might be considered a normal

11

finding among HF population conveys an additional predictive value with a significant negative impact on clinical outcome. Kidney injury marked by albuminuria, is associated with numerous detrimental biological processes that are present in HF and pertain to a worse outcome10. Albuminuria in HF patients might be due to kidney injury caused by concomitant comorbidities such as hypertension11 and diabetes mellitus and indeed more than 80% of patients with macroalbuminuria in our cohort were diabetic. However, even patients without any of these diagnoses had a 22% prevalence of microalbuminuria, suggesting that albuminuria is part of the kidney injury related to HF. Factors that promote albuminuria in HF include reduced renal perfusion and increased venous pressure, glomerular hypertension and tubulointerstitial hypoxia, leading to loss of glomerular integrity and tubular damage. Once renal function decreases, additional factors like oxidative stress and inflammation further aggravate endothelial and vascular injuries forming a vicious cycle that accelerates cardiac injury12. Therefore, albuminuria is a summation of numerous deleterious factors in HF patients and would be expected to give important prognostic information in HF. In addition, we found that NYHA class III/IV were both independently associated with albuminuria, stressing the association between HF severity and albuminuria. The detrimental effect of albuminuria is well known in diabetes13 and hypertension14 even in the general population15. Consequently international guidelines recommend screening for microalbuminuria among patients with diabetes16 or hypertension17. Despite the expected detrimental effect of albuminuria on outcome in HF, there is no consensus or recommendations regarding screening for albuminuria in HF as there is sparse data in the literature regarding this parameter as a prognostic factor in real-life HF18.

12

Most data on the prognostic impact of albuminuria in HF population is obtained from clinical trials. Analysis of 1175 participants from the TOPCAT study suggested that macroalbuminuria and microalbuminuria were independently associated with the adverse outcomes6. Similarly, the Chronic Heart Failure Analysis and Registry in the Tohoku District 2 (CHART 2) study evaluated 2,465 consecutive patients with HF and preserved ejection fraction showing significantly higher mortality among patients with albuminuria regardless of eGFR levels19. Interestingly, the subjects in the Studies Of Left Ventricular Dysfunction (SOLVD) trial suggested that proteinuria was associated with significantly reduced survival only in patients with an elevated BUN/Cr ratio, while in patients with a normal BUN/Cr, proteinuria failed to incur a survival disadvantage20. Our findings suggest that albuminuria was an outcome predictor in different subgroups of HF population including diabetic and non-diabetic patients and in patients with and without CKD. An interesting and clinically important finding in the present study was that subclinical albuminuria has a prognostic implication in chronic HF. Even mild albuminuria predicted a worse clinical outcome. Similar findings in the healthy population was reported in the Atherosclerosis Risk in Communities (ARIC) prospective cohort study suggested that albuminuria is an independent risk factor for HF and death with a linear correlation across the entire range of UACR15. This finding is plausible biologically, as albuminuria appears to be a continuous variable that reflects kidney injury and other abnormalities and therefore also supposedly ‘normal’ albuminuria levels characterizes the clinical state of the HF patient and has prognostic implications. These findings of a prognostic importance of albuminuria, and the association of it with HF clinical status raise the question regarding targeting albuminuria as a 13

therapeutic avenue in HF population. We believe that assessing albuminuria levels in HF patients is important and should be considered, as this parameter is associated with numerous detrimental factors in HF including functional capacity and additionally, provides important prognostic information, as demonstrated in the present study. With regard to targeting it as therapeutic goal, data is limited. This parameter has been evaluated among diabetic patients. de Boer et al. followed long standing type 1 diabetes patients, and found that remission from microalbuminuria to normoalbuminuria was not associated with reduced cardiovascular death21. However, in the Reduction in End Points in Noninsulin-Dependent Diabetes Mellitus with the Angiotensin II Antagonist Losartan (RENAAL) study, RAS inhibitor–induced reductions in albuminuria were associated with lower cardiovascular death22. These diverse results may be explained by different populations and different ranges of albuminuria. More relevant, in HF patients, the recent TOPCAT analysis showed that among HF with preserved ejection fraction patients, spironolactone significantly reduced albuminuria and

improved

outcomes23. There is a definite need for further studies to assess the impact of reducing albuminuria in patients with HF. Importantly, our findings suggest that despite their increased risk, patients with macroalbuminuria were less treated with evidence-based agents: ACE-I or ARB. This observation, in which patients at high risk for adverse events receive less-intensive treatment than do patients at lower risk is referred as the “treatment risk paradox”. Patients related factors (frailty, and functional status) and physician factors (misjudgement of patient risk at baseline) appear to contribute to this phenomenon24-26. Several potential limitations of this study merit consideration. The present study was an observational study. Data regarding clinical parameters and drug therapy was

14

based on a digitized database. Although this database was validated and found to be highly accurate, not all data could be verified. While we tried to adjust for clinically relevant parameters, not all clinical parameters were available and it is impossible to adjust for all variables that may affect outcome. In particular, data on natriuretic peptide levels were not available. In addition, the UACR was closely related to numerous other parameters related to severity of the HF, and even though we adjusted for these parameters in multivariable models, it impossible to completely preclude that part of the predictive value of the UACR is related to it representing the severity of the HF. In addition, the cohort was a community-based cohort and the findings may not be applicable in more advanced or hospital-based HF cohorts. Finally, urine analysis was not available in all patients. To address this issue and to have a better perspective, we evaluated the features of patients with missing data and found that they were older, with more dementia and less likely to be treated and therefore were at a higher risk regardless of the levels of albuminuria. In conclusion, albuminuria levels provide important information regarding several detrimental processes in HF and is a significant predictor of a worse outcome in these patients. Routine evaluation for albuminuria should be considered in HF patients.

Disclosures: None.

Sources of funding: None.

15

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McDonald MA, Ashley EA, Fedak PWM, et al. Mind the Gap: Current Challenges and Future State of Heart Failure Care. Can J Cardiol. 2017;33:1434-1449.

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Dobre D, Nimade S, de Zeeuw D. Albuminuria in heart failure: what do we really know? Curr Opin Cardiol. 2009;24:148-154.

4.

Weir MR. Microalbuminuria and Cardiovascular Disease. CJASN. 2007;2:581-590.

5.

Jackson CE, Solomon SD, Gerstein HC, et al. Albuminuria in chronic heart failure: prevalence and prognostic importance. The Lancet. 2009;374:543-550.

6.

Selvaraj S, Claggett B, Shah SJ, et al. Prognostic Value of Albuminuria and Influence of Spironolactone in Heart Failure With Preserved Ejection Fraction. CIRC-HEART FAIL. 2018;11.

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Jackson CE, MacDonald MR, Petrie MC, et al. Associations of albuminuria in patients with chronic heart failure: findings in the ALiskiren Observation of heart Failure Treatment study. Eur J Heart Fail. 2011;13:746-754.

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Watanabe H, Iino K, Ito H. Subclinical microalbuminuria as a predictor of heart failure prognosis. Circ J.78:2838-2839.

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Schemper M, Smith TL. A note on quantifying follow-up in studies of failure time. Control. Clin. Trials. 1996;17:343-346.

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Arques S, Ambrosi P. Human Serum Albumin in the Clinical Syndrome of Heart Failure. J Card Fail. 2011;17:451-458.

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Ross BA, Wald R, Goldstein MB, et al. Relationships Between Left Ventricular Structure and Function According to Cardiac MRI and Cardiac Biomarkers in End-Stage Renal Disease. Can J Cardiol . 2017;33:501-507.

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Stehouwer CDA, Smulders YM. Microalbuminuria and Risk for Cardiovascular Disease: Analysis of Potential Mechanisms. JASN. 2006;17:2106-2111.

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de Zeeuw D, Remuzzi G, Parving H-H, et al. Albuminuria, a Therapeutic Target for Cardiovascular Protection in Type 2 Diabetic Patients With Nephropathy. Circulation. 2004;110:921-927.

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Huang M, Matsushita K, Sang Y, Ballew SH, Astor BC, Coresh J. Association of Kidney Function and Albuminuria With Prevalent and Incident Hypertension: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2015;65:58-66.

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Blecker S, Matsushita K, Köttgen A, et al. High-Normal Albuminuria and Risk of Heart Failure in the Community. Am J Kidney Dis. 2011;58:47-55.

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Chamberlain JJ, Johnson EL, Leal S, Rhinehart AS, Shubrook JH, Peterson L. Cardiovascular Disease and Risk Management: Review of the American Diabetes Association Standards of Medical Care in Diabetes 2018. Ann Intern Med. 2018;168:640.

17.

Williams B, Mancia G, Spiering W, et al. 2018 Practice Guidelines for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. J Hypertens. 2018;36:2284-2309.

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Ezekowitz JA, O'Meara E, McDonald MA, et al. 2017 Comprehensive Update of the Canadian Cardiovascular Society Guidelines for the Management of Heart Failure. Can J Cardiol. 2017;33:1342-1433.

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Miura M, Shiba N, Nochioka K, et al. Urinary albumin excretion in heart failure with preserved ejection fraction: an interim analysis of the CHART 2 study. Eur J Heart Fail. 2012;14:367-376.

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Brisco MA, Zile MR, ter Maaten JM, et al. The risk of death associated with proteinuria in heart failure is restricted to patients with an elevated blood urea nitrogen to creatinine ratio. INT J CARDIOL. 2016;215:521-526.

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de Boer IH, Gao X, Cleary PA, et al. Albuminuria Changes and Cardiovascular and Renal Outcomes in Type 1 Diabetes: The DCCT/EDIC Study. CJASN. 2016;11:1969-1977.

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de Zeeuw D, Remuzzi G, Parving H-H, et al. Proteinuria, a target for renoprotection in patients with type 2 diabetic nephropathy: Lessons from RENAAL. Kidney Int. 2004;65:2309-2320.

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Nayor M, Larson MG, Wang N, et al. The association of chronic kidney disease and microalbuminuria with heart failure with preserved vs. reduced ejection fraction. Eur J Heart Fail. 2017;19:615-623.

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McAlister FA, Oreopoulos A, Norris CM, et al. Exploring the Treatment-Risk Paradox in Coronary Disease. JAMA Intern Med. 2007;167:1019-1025.

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Figure legend Figure 1. Histogram of urine albumin to creatinine ratio levels in the heart failure cohort. Figure 2. Kaplan Meier survival analysis according to urine creatinine to albumin ratio levels. [2A] Increasing urine albumin to creatinine ratio levels were directly associated with reduced survival; Log rank P<0.001. [2B] Increasing urine albumin to creatinine ratio levels were directly associated with reduced event-free survival from death or cardiovascular-hospitalizations; Log rank P<0.001. Figure 3. Mortality as a function of urine albumin to creatinine ratio as a continuous variable using restricted cubic splines. Knots were allocated at UACR of 12, 30, 300 µg/mg. [3A] Cox regression analysis with hazard ratio for mortality [with 95% CI]. There was a continuous increase in the risk with increasing albuminuria, P<0.0001 for the linear model. [3B] Cox regression analysis for mortality after adjustment, P <0.0001 for the adjusted model. Variables included in the model included age, gender, NYHA class, diabetes, hypertension, ischemic heart disease, atrial fibrillation, log-transformed serum urea levels, square root-transformed estimated glomerular filtration rate, serum sodium and hemoglobin. Supplementary Figure S1. Kaplan Meier survival analysis according to urine creatinine to albumin ratio levels including subclinical levels (UACR 12-29µg/mg). [A] Increasing urine albumin to creatinine ratio levels were directly associated with reduced survival. Subclinical albuminuria was directly associated with reduced survival compared with normal levels. Log rank P<0.001. [B] Increasing urine albumin to creatinine ratio levels were directly associated with reduced event-free survival from death or cardiovascular-hospitalizations including subclinical albuminuria; Log rank P<0.001.

19

-1-

Table 1. Demographics and clinical characteristics of patients with heart failure according to urinary albumin to creatinine ratio levels.

Variable

Normal (N=2085)

MicroMacroalbuminuria albuminuria (N=1769) (N=814)

Total (N=4668)

P Value

Age (Years)

75 (65-83)

78 (69-85)

74 (66-83)

76 (67-84)

<0.001

Gender (Male)

1207 (58)

934 (53)

477 (59)

2618 (56)

0.002

NYHA Class III/IV

568 (34)

625 (43)

312 (49)

1505 (40)

<0.001

6.0 (4.0-7.0)

6.5 (5.0-8.0)

7.0 (5.0-8.0)

6.0 (5.0-8.0)

<0.001

Diabetes mellitus

1221 (59)

1292 (73)

693 (85)

3206 (69)

<0.001

Hypertension

1690 (81)

1611 (91)

769 (94)

4070 (87)

<0.001

Hyperlipidemia

1914 (92)

1664 (94)

783 (96)

4361 (93)

<0.001

Ischemic Heart Disease

1429 (69)

1248 (71)

586 (72)

3263 (70)

0.14

Prior Myocardial Infarction

941 (45)

816 (46)

373 (46)

2130 (46)

0.82

33 (2)

45 (3)

21 (3)

99 (2)

0.07

Atrial fibrillation

770 (37)

789 (45)

276 (34)

1835 (39)

<0.001

Prior Stroke/ transient ischemic attack

404 (19)

443 (25)

241 (30)

1088 (23)

<0.001

Peripheral vascular disease

252 (12)

294 (17)

184 (23)

730 (16)

<0.001

Chronic obstructive lung disease

449 (22)

428 (24)

188 (23)

1065 (23)

0.14

Depression

351 (17)

295 (17)

118 (14)

764 (16)

0.28

Dementia

194 (9)

198 (11)

94 (12)

486 (10)

0.08

Dialysis

19 (0.9)

53 (3)

113 (14)

185 (4)

<0.001

Malignancy

398 (19)

445 (25)

170 (21)

1013 (22)

<0.001

Body mass index (kg/m2)

29 (26-33)

29 (26-34)

30 (26-34)

29 (26-34)

0.14

Pulse (beats per minute)

72 (64-80)

72 (65-80)

73 (65-80)

72 (64-80)

0.09

Charlson Score

Prior coronary bypass surgery

Systolic blood pressure (mmHg)

125 (116-135) 130 (120-140) 132 (122-147) 128 (119-139)

<0.001

-2-

Diastolic blood pressure (mmHg)

71 (64-78)

71 (64-79)

72 (65-80)

71 (64-79)

0.003

Urine Albumin / Creatinine ratio

12 (7.0-19)

80 (47-142)

708 (4501,170)

38 (13-161)

<0.001

Creatinine (mg/dL)

0.9 (0.8-1.2)

1.1 (0.8-1.4)

1.3 (1.0-2.0)

1.0 (0.8-1.4)

<0.001

Estimated glomerular filtration rate (mL/min per 1.73m2)*

77 (57-97)

64 (47-85)

51 (31-71)

68 (48-90)

<0.001

Urea (mg/dL)

41 (32-56)

49 (37-68)

62 (44-98)

47 (35-66)

<0.001

Laboratory Data

Sodium (mEq/L)

140 (138-142) 140 (138-142) 140 (138-142) 140 (138-142)

<0.001

Potassium (mEql/L)

4.6 (4.3-4.9)

4.7 (4.3-5.0)

4.7 (4.4-5.1)

4.6 (4.3-5.0)

<0.001

Hemoglobin (g/dL)

13 (12-14)

12 (11-14)

12 (11-14)

13 (11-14)

<0.001

7.4 (6.1-8.8)

7.4 (6.2-9.0)

7.7 (6.2-9.1)

7.4 (6.1-8.9)

0.006

15 (14-16)

15 (14-16)

15 (14-16)

15 (14-16)

<0.001

Glucose (mg/dL)

108 (96-131)

116 (98-147)

131 (104-173)

113 (98-145)

<0.001

Hemoglobin A1c (%)

6.1 (5.6-6.9)

6.4 (5.7-7.5)

6.9 (6.1-8.4)

6.3 (5.7-7.4)

<0.001

Uric Acid (mg/dL)

6.2 (5.0-7.4)

6.5 (5.3-7.9)

6.9 (5.7-8.2)

6.4 (5.2-7.8)

<0.001

TSH (mIU/L)

2.1 (1.4-3.2)

2.3 (1.5-3.5)

2.3 (1.5-3.5)

2.2 (1.4-3.4)

0.005

Iron (µg/dL)

62 (45-82)

56 (41-72)

53 (39-68)

58 (42-76)

<0.001

White blood count (x109/L) Red Cell Distribution Width (%)

Transferrin (mg/dL)

259 (229-297) 253 (218-294) 231 (194-267) 251 (218-291)

<0.001

Transferrin Saturation (%)

17 (12-24)

16 (11-21)

16 (12-22)

16 (12-22)

<0.001

Ferritin (ng/ml)

74 (34-144)

77 (36-162)

109 (53-247)

80 (37-166)

<0.001

Calcium (mg/dL)

9.3 (9.0-9.6)

9.3 (8.9-9.6)

9.2 (8.8-9.5)

9.3 (9.0-9.6)

<0.001

Phosphorus (mg/dL)

3.4 (3.1-3.8)

3.5 (3.1-3.9)

3.6 (3.3-4.1)

3.5 (3.1-3.9)

<0.001

Magnesium (mg/dL)

2.1 (1.9-2.3)

2.1 (1.9-2.3)

2.1 (1.8-2.3)

2.1 (1.9-2.3)

0.16

Triglycerides (mg/dL)

123 (91-170)

123 (89-173)

143 (100-199)

126 (91-176)

<0.001

Low-density lipoprotein (mg/dL)

81 (65-103)

77 (61-99)

79 (60-100)

79 (63-101)

<0.001

Albumin (g/dL)

4.0 (3.8-4.2)

3.9 (3.7-4.1)

3.8 (3.5-4.0)

3.9 (3.7-4.2)

<0.001

C-Reactive Protein (mg/dL)

0.5 (0.2-1.3)

0.6 (0.3-1.6)

0.8 (0.3-2.1)

0.6 (0.3-1.5)

<0.001

-3-

Alanine transaminase (IU)

17 (12-23)

16 (12-21)

14 (11-20)

16 (12-22)

<0.001

Alkaline Phosphatase (IU)

85 (70-106)

90 (71-112)

96 (76-124)

89 (71-111)

<0.001

Total Bilirubin (mg/dL)

0.6 (0.5-0.8)

0.6 (0.5-0.8)

0.5 (0.4-0.7)

0.6 (0.4-0.8)

<0.001

25 (18-41)

27 (18-48)

28 (19-54)

26 (18-45)

<0.001

ACE-I/ARB

1700 (82)

1494 (84)

662 (81)

3856 (83)

0.03

Beta blockers

1631 (78)

1393 (79)

662 (81)

3686 (79)

0.18

Spironolactone

840 (40)

690 (39)

232 (29)

1762 (38)

<0.001

Furosemide

1314 (63)

1291 (73)

641 (79)

3246 (70)

<0.001

Thiazide

301 (14)

271 (15)

116 (14)

688 (15)

0.68

Digoxin

120 (6)

124 (7)

38 (5)

282 (6)

0.05

Amiodarone

359 (17)

316 (18)

108 (13)

783 (17)

0.01

Aspirin

1233 (59)

998 (56)

511 (63)

2742 (59)

0.008

Gamma-glutamyltransferase (IU) Medication

Data is presented as median (inter-quartile range) for continuous variables and counts (percentages) for categorical variables. P value by the Kruskal Wallis Test for continuous variables and the Chi-Square Test for categorical variables. Diabetes mellitus defined as fasting plasma glucose ≥ 126 mg/dL or glucose lowering treatment, hypertension as blood pressure > 140/90 mmHg measured on several occasions or anti-hypertensive treatment and hyperlipidemia as low density lipoprotein > 130 mg/dL, fasting serum triglycerides > 200 mg/dL or lipid lowering treatment. * Estimated Glomerular Filtration Rate was calculated using the modified Modification of Diet in Renal Disease (MDRD) equation (175 * serum creatinine–1.154 * age–0.203. For females a correction factor is used multiplying by 0.742.)

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Table 2. Predictors of mortality by Cox regression analysis. Univariable

Multivariable

Hazard Ratio (95% CI)

P Value

Hazard Ratio (95% CI)

P Value

Age (years)

1.02 (1.01-1.02)

<0.001

1.05 (1.04-1.06)

<0.001

Gender (Male)

0.89 (0.84-0.94)

<0.001

1.26 (1.09-1.45)

0.002

NYHA III/IV

1.73 (1.63-1.85)

<0.001

1.34 (1.16-1.55)

<0.001

Diabetes Mellitus

1.31 (1.24-1.39)

<0.001

1.11 (0.95-1.30)

0.18

Hypertension

1.89 (1.73-2.06)

<0.001

0.98 (0.75-1.28)

0.91

Ischemic Heart Disease

1.10 (1.04-1.17)

0.002

0.88 (0.76-1.02)

0.09

Atrial Fibrillation

1.43 (1.36-1.52)

<0.001

1.33 (1.16-1.52)

<0.001

Urea (mg/dL)*

3.82 (3.35-4.36)

<0.001

5.82 (3.57-9.50)

<0.001

eGFR** (mL/min per 1.73m2)

0.89 (0.88-0.90)

<0.001

1.07 (1.01-1.14)

0.02

Sodium (mEq/L)

0.97 (0.96-0.98)

<0.001

0.96 (0.94-0.97)

<0.001

Hemoglobin (g/dL)

0.86 (0.85-0.87)

<0.001

0.85 (0.82-0.89)

<0.001

<0.001

Urine Albumin / Creatinine ratio

Normal (<30mcg/mg) Microalbuminuria (30300mcg/mg) Macroalbuminuria (>300mcg/mg)

1.0 (Reference)

0.009 1.0 (Reference)

1.59 (1.37-1.84)

<0.001

1.18 (1.02-1.38)

0.03

1.89 (1.59-2.26)

<0.001

1.33 (1.10-1.61)

0.003

Data is presented as hazard ratio (95% confidence interval), P value. * Log-transformed ** Square root-transformed

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Table 3. Hazard ratio for clinical outcome according to Urine Albumin / Creatinine ratio by Cox regression analysis. Urine Albumin / Creatinine ratio Normal (<30mcg/mg)

Microalbuminuria (30-300mcg/mg)

Macroalbuminuria (>300mcg/mg)

P-value

1.0 (Reference)

1.59 (1.37-1.84) <0.001

1.89 (1.59-2.26) <0.001

<0.001

1.0 (Reference) 1.0 (Reference)

1.18 (1.02-1.38) 0.03 1.23 (1.05-1.43) 0.009

1.33 (1.10-1.61) 0.003 1.31 (1.08-1.59) 0.006

1.0 (Reference)

1.36 (1.26-1.47) <0.001

1.77 (1.61-1.95) <0.001

1.0 (Reference) 1.0 (Reference)

1.16 (1.07-1.25) <0.001 1.16 (1.07-1.26) <0.001

1.43 (1.29-1.58) <0.001 1.44 (1.30-1.60) <0.001

Death Univariable Multivariable Multivariable and Drugs

0.009 0.008

Death and cardiovascular hospitalization Univariable Multivariable Multivariable and Drugs

<0.001 <0.001 <0.001

Data is presented as hazard ratio (95% confidence interval), P value. Parameters that were included in the multivariable analysis model were age, gender, ischemic heart disease, diabetes, hypertension, atrial fibrillation, log-transformed serum urea levels, square root-transformed estimated glomerular filtration rate, hemoglobin, serum sodium. Parameters that were included in the multivariable and drugs analysis included the above parameters and the drug treatment with angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, beta blocker, furosemide, spironolactone and aspirin.