Assay for Assessing Heart Failure

Abstract

A method of immunoassay for detecting and/or monitoring a cardiovascular disease in a patient and/or assessing the likelihood of or the severity of a cardiovascular disease in a patient, comprising contacting a biofluid sample from a patient with a monoclonal antibody that specifically binds to a C-terminal epitope of the C5 domain of the α3 chain of type VI collagen, and/or contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal neo-epitope of the N-terminal propeptide of type III collagen.

Claims

1: A method of immunoassay for detecting and/or monitoring a cardiovascular disease in a patient and/or assessing the likelihood of or the severity of a cardiovascular disease in a patient, wherein said method comprises: (i) contacting a biofluid sample from a patient with a monoclonal antibody that specifically binds to a C-terminal epitope of the C5 domain of the α3 chain of type VI collagen, and/or contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal neo-epitope of the N-terminal propeptide of type III collagen, (ii) detecting and determining the amount of binding between each monoclonal antibody used in step (i) and peptides in the sample or samples, and (iii) correlating said amount of binding of each monoclonal antibody as determined in step (ii) with values associated with normal healthy subjects and/or values associated with known disease severity and/or values obtained from said patient at a previous time point and/or a predetermined cut-off value.

2: The method of claim 1, wherein the cardiovascular disease is heart failure.

3: The method of claim 2, wherein the cardiovascular disease is heart failure with a preserved ejection fraction (HFpEF).

4: The method of claim 1, wherein the method is a method for assessing the severity of a cardiovascular disease in a patient that comprises assessing the likelihood of patient mortality and/or hospitalization as a result of the cardiovascular disease and/or a composite of adverse cardiovascular events.

5: The method of claim 1, wherein the patient is a patient undergoing a therapy for the cardiovascular disease.

6: The method of claim 1, wherein step (i) comprises contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal epitope of the C5 domain of the α3 chain of type VI collagen.

7: The method of claim 6, wherein said monoclonal antibody specifically binds to a C-terminus amino acid sequence KPGVISVMGT (SEQ ID NO: 1).

8: The method of claim 7, wherein said monoclonal antibody does not recognize or specifically bind to an elongated version of said C-terminus amino acid sequence which is KPGVISVMGTA (SEQ ID NO: 2), or to a truncated version of said C-terminus amino acid sequence which is KPGVISVMG (SEQ ID NO: 3).

9: The method of claim 1, wherein step (i) comprises contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal neo-epitope of the N-terminal propeptide of type III collagen.

10: The method of claim 9, wherein said monoclonal antibody specifically binds to a C-terminus amino acid sequence CPTGPQNYSP (SEQ ID NO: 14).

11: The method of claim 10, wherein said monoclonal antibody does not recognize or specifically bind to an elongated version of said C-terminus amino acid sequence which is CPTGPQNYSPQ (SEQ ID NO: 15), or to a truncated version of said C-terminus amino acid sequence which is CPTGPQNYS (SEQ ID NO: 16).

12: The method of claim 1, wherein said biofluid is serum or plasma.

13: The method claim 1, wherein said immunoassay is a competition assay or a sandwich assay.

14: The method of claim 1, wherein said immunoassay is a radioimmunoassay or an enzyme-linked immunosorbent assay.

15: A method of monitoring cardiovascular disease and/or assessing the severity of a cardiovascular disease in a patient undergoing treatment with an aldosterone antagonist, wherein said method comprises: (i) contacting a biofluid sample from a patient undergoing treatment with an aldosterone antagonist with a monoclonal antibody that specifically binds to a C-terminal epitope of the C5 domain of the α3 chain of type VI collagen, and/or contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal neo-epitope of the N-terminal propeptide of type III collagen, (ii) detecting and determining the amount of binding between each monoclonal antibody used in step (i) and peptides in the sample or samples, and (iii) correlating said amount of binding of each monoclonal antibody as determined in step (ii) with values associated with normal healthy subjects and/or values associated with known disease severity and/or values obtained from said patient at a previous time point and/or a predetermined cut-off value.

16: The method of claim 15, wherein the aldosterone antagonist is Spironolactone.

17: The method of claim 15, wherein the cardiovascular disease is heart failure.

18: The method of claim 17, wherein the cardiovascular disease is heart failure with a preserved ejection fraction (HFpEF).

19: The method of claim 15, wherein the method is a method for assessing the severity of a cardiovascular disease in a patient that comprises assessing the likelihood of patient mortality and/or hospitalization as a result of the cardiovascular disease and/or a composite of adverse cardiovascular events.

20: The method of claim 15, wherein step (i) comprises contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal epitope of the C5 domain of the α3 chain of type VI collagen.

21: The method of claim 20, wherein said monoclonal antibody specifically binds to a C-terminus amino acid sequence KPGVISVMGT (SEQ ID NO: 1).

22: The method of claim 21, wherein said monoclonal antibody does not recognize or specifically bind to an elongated version of said C-terminus amino acid sequence which is KPGVISVMGTA (SEQ ID NO: 2), or to a truncated version of said C-terminus amino acid sequence which is KPGVISVMG (SEQ ID NO: 3).

23: The method of claim 15, wherein step (i) comprises contacting a biofluid sample from the patient with a monoclonal antibody that specifically binds to a C-terminal neo-epitope of the N-terminal propeptide of type III collagen.

24: The method of claim 23, wherein said monoclonal antibody specifically binds to a C-terminus amino acid sequence CPTGPQNYSP (SEQ ID NO: 14).

25: The method of claim 24, wherein said monoclonal antibody does not recognize or specifically bind to an elongated version of said C-terminus amino acid sequence which is CPTGPQNYSPQ (SEQ ID NO: 15), or to a truncated version of said C-terminus amino acid sequence which is CPTGPQNYS (SEQ ID NO: 16).

26: The method of claim 15, wherein said biofluid is serum or plasma.

27: The method of claim 15, wherein said immunoassay is a competition assay or a sandwich assay.

28: The method of claim 15, wherein said immunoassay is a radioimmunoassay or an enzyme-linked immunosorbent assay.

Description

FIGURES

[0074] FIG. 1A: Hazard ratio for the primary endpoint per standard-deviation change in fibrosis biomarkers in unadjusted analyses (one model per biomarker).

[0075] FIG. 1B: Hazard ratio for the composite endpoint of death or heart failure admission per standard-deviation change in fibrosis biomarkers in unadjusted analyses (one model per biomarker).

[0076] FIG. 2: Kaplan-Meier survival curves for the primary endpoint among subjects stratified by tertiles of Pro-C6 (left) and Pro-C3 (right).

[0077] FIG. 3: Kaplan-Meier survival curves for the composite endpoint of death or heart failure admission among subjects stratified by tertiles of Pro-C6 (left) and Pro-C3 (right).

EXAMPLES

Example 1—Antibody Development for Pro-C6

[0078] A monoclonal antibody specific for Pro-C6 was developed as described in WO 2016/156526 (Nordic Bioscience, incorporated herein by reference) using the last 10 amino acids of the type VI collagen α3 chain (i.e. the C-terminus sequence .sup.3168‘KPGVISVMGT’.sup.3177(SEQ ID No: 1)) as an immunogenic peptide. Briefly, 4-6-week-old Balb/C mice were immunized subcutaneously with 200 μl emulsified antigen with 60 μg of the immunogenic peptide. Consecutive immunizations were performed at 2-week intervals in Freund's incomplete adjuvant, until stable sera titer levels were reached, and the mice were bled from the 2nd immunization on. At each bleeding, the serum titer was detected and the mouse with highest antiserum titer and the best native reactivity was selected for fusion. The selected mouse was rested for 1 month followed by intravenous boosting with 50 μg of immunogenic peptide in 100 μl 0.9% sodium chloride solution 3 days before isolation of the spleen for cell fusion.

[0079] Mouse spleen cells were fused with SP2/0 myeloma fusion partner cells. The fusion cells were raised in 96-well plates and incubated in the CO2-incubator. Here standard limited dilution was used to promote monoclonal growth. Cell lines specific to the selection peptide and without cross-reactivity to either elongated peptide (KPGVISVMGTA (SEQ ID No: 2), Chinese Peptide Company, China) or truncated peptide (KPGVISVMG (SEQ ID No: 3), American Peptide Company, USA) were selected and sub-cloned. At last the antibodies were purified using an IgG column.

[0080] The antibodies generated were sequenced and the CDRs determined.

[0081] The sequence of the chains are as follows (CDRs underlined and in bold):

TABLE-US-00015 Heavy Chain Sequence (mouse IgG1 isotype) (SEQ ID No: 30) EVQLQQSGPVMVKPGTSVKTSCKASGYTFTDFNMNWVKQSHGKSLE WIGAINPHNGATSYNQKFSGKATLTVDKSSSTAYMELNSLTSDDSA VYYCARWGNGKNSWGQGTTLTVSSAKTTPPSVYPLAPGSAAQTNSM VTLGCLVKGYFPEPVTVTWNSGSLSSGVHTFPAVLQSDLYTLSSSV TVPSSTWPSETVTCNVAHPASSTKVDKKIVPRDCGCKPCICTVPEV SSVFIFPPKPKDVLTITLTPKVTCVVVDISKDDPEVQFSWFVDDVE VHTAQTQPREEQFNSTFRSVSELPIMHQDWLNGKEFKCRVNSAAFP APIEKTISKTKGRPKAPQVYTIPPPKEQMAKDKVSLTCMITDFFPE DITVEWQWNGQPAENYKNTQPIMDTDGSYFVYSKLNVQKSNWEAGN TFTCSVLHEGLHNHHTEKSLSHSPGK CDR-H1: (SEQ ID No: 7) DFNMN CDR-H2: (SEQ ID No: 8) AINPHNGATSYNQKFSG CDR-H3: (SEQ ID No: 9) WGNGKNS Light Chain Sequence (mouse Kappa isotype) (SEQ ID No: 31) DVVMTQTPLSLPVNLGDQASISCRSSQRIVHSNGITFLEWYLQKPG QSPKLLIYRVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGLYY CFQGSHVPLTFGAGTRLELKRADAAPTVSIFPPSSEQLTSGGASVV CFLNNFYPKDINVKWKIDGSERQNGVLNSWTDQDSKDSTYSMSSTL TLTKDEYERHNSYTCEATHKTSTSPIVKSFNRNEC CDR-L1: (SEQ ID No: 4) RSSQRIVHSNGITFLE CDR-L2: (SEQ ID No: 5) RVSNRFS CDR-L3: (SEQ ID No: 6) FQGSHVPLT

Example 2—Antibody Development for Pro-C3

[0082] A monoclonal antibody specific for Pro-C3 was developed as described in WO 2014/170312 (Nordic Bioscience, incorporated herein by reference) using sequence 145′-CPTGPQNYSP-′153 (SEQ ID No: 14) of the α1 chain PIIINP as an immunogenic peptide. Briefly, generation of monoclonal antibodies was initiated by subcutaneous immunization of 4-5 week old Balb/C mice with 200 μl emulsified antigen and 50 μg PIIINP neo-epitope C-terminus sequence (OVA-CGG-CPTGPQNYSP (SEQ ID No: 32)) using Freund's incomplete adjuvant. The immunizations were repeated every 2 weeks until stable serum titer levels were reached. The spleen cells were fused with SP2/0 myeloma cells to produce hybridoma, and cloned in culture dishes using the semi-medium method. The supernatants were screened for reactivity against calibrator peptide and native material in an indirect ELISA using streptavidin-coated plates. Biotin-CGG-CPTGPQNYSP (SEQ ID No: 33) was used as screening peptide, while the free peptide CPTGPQNYSP (SEQ ID No: 14) was used as calibrator to test for further specificity of clones.

[0083] Native reactivity and affinity of the antibody was assessed using different biological materials such as urine, serum, and amniotic fluid (AF) from both humans and rats in a preliminary ELISA using 2 ng/ml biotinylated peptide on streptavidin-coated microtiter plates and the supernatants from growing monoclonal hybridoma cells. Antibody specificity was tested in a preliminary assay using deselection and elongated peptides (i.e. calibrator peptide with ten amino acid substitutions and calibrator peptide with one additional amino acid at the cleavage site, respectively). The isotype of the monoclonal antibodies was determined using the Clonotyping System-HRP kit, cat. 5300-05 (Southern Biotech, Birmingham, Ala., USA). The subtype was determined to be an IgG2 subtype.

[0084] The antibodies generated were sequenced and the CDRs determined.

[0085] The sequence of the chains are as follows (CDRs underlined and in bold):

TABLE-US-00016 Heavy Chain Sequence (mouse IgG2A isotype) (SEQ ID No: 34) EVQLQQSGPEVLKPGASVKMSCKASGYTFINYVIHWLKQKAGQGPEW IGYMNPYNDVPKNNAKFRGKARLTSDRSSTTAYMELNSLTSEDSAVY YCARGGFFGPLSYWGQGTLVTVSAAKTTAPSVYPLAPVCGDTTGSSV TLGCLVKGYFPEPVTLTWNSGSLSSGVHTFPAVLQSDLYTLSSSVTV TSSTWPSQSITCNVAHPASSTKVDKKIEPRGPTIKP CDR-H1: (SEQ ID No: 20) GYTFINYVIH CDR-H2: (SEQ ID No: 21) YMNPYNDVPKNNAKFRG CDR-H3: (SEQ ID No: 22) GGFFGPLSY Light Chain Sequence (mouse Kappa isotype) (SEQ ID No: 35) DVLMTQTPLSLSVSLGDQASISCRSSQNIVYSNGDTYFEWYLQKPGQ SPKLLIYKVSQRFSGVPDRFSGSGSGTDFTLKISRVETEDLGVYYCF QGAHDPPAFGGGTKLELKRADAAPTVSIFPPSSEQLTSGGASVVCFL NNFYPKDINVKWKIDGSERQNGVLNSWTDQDSKDSTYSMSSTLTLTK DEYERHNSYTCEATHKTSTSPIVKSFNRNEC CDR-L1: (SEQ ID No: 17) RSSQNIVYSNGDTYFE CDR-L2: (SEQ ID No: 18) KVSQRFS CDR-L3: (SEQ ID No: 19) FQGAHDPPA

Example 3—Antibody Development for C4M

[0086] A monoclonal antibody specific for C4M was developed as previously described in Sand et. al..sup.38 (incorporated herein by reference) using the N-terminal neo-epitope sequence 162′-ILGHVPGMLL-′171 (SEQ ID No: 27) generated by MMP-12 cleavage between amino acids 161 and 162 of the α1 chain of type IV collagen as an immunogenic peptide. Briefly, generation of monoclonal antibodies was initiated by immunization of four to six-week-old Balb/C mice subcutaneously with 200 μl emulsified antigen and 50 μg of the immunogenic peptide (ILGHVPGMLL-GGC-KLH (SEQ ID No: 36)) using Freund's incomplete adjuvant. Immunizations were performed every 2nd week until stable sera titer levels were reached. The mouse with highest serum titer was selected for fusion. The mouse was rested for one month and then boosted intravenously with 50 μg of immunogenic peptide in 100 μl 0.9% sodium chloride solution three days before isolation of the spleen for cell fusion. Mouse spleen cells were fused with SP2/0 myeloma fusion partner cells. The resulting hybridoma cells were cloned using a semi-solid medium method, transferred into 96-well microtiter plates for further growth and incubated in a CO2 incubator. Standard limited dilution was used to promote monoclonal growth.

[0087] Native reactivity and peptide affinity of the monoclonal antibodies were evaluated by displacement of native samples (human, rat, and mouse serum, plasma, and urine) in a preliminary indirect ELISA using a biotinylated peptide (ILGHVPGMLL-K-biotin (SEQ ID No: 37)) on streptavidin-coated microtiter plates and the supernatant from the growing monoclonal hybridoma. Specificities of the clones to the free peptide (ILGHVPGMLL (SEQ ID No: 27)), a nonsense peptide, and an elongated peptide (EILGHVPGMLL (SEQ ID No: 28)) were tested. Isotyping of the monoclonal antibodies was performed using a SBA Clonotyping System-HRP kit. The monoclonal antibody was purified from collected supernatant of the selected clones using HiTrap protein G columns and subsequently labeled with horseradish peroxidase (HRP) using the Lightning link HRP labeling kit, according to the manufacturer's instructions.

[0088] The monoclonal antibody with the best native reactivity, peptide affinity, and stability was chosen from the antibody-producing clones generated after fusion between mouse spleen cells and myeloma cells. The clones selected were of the IgG1 subtype and the antibodies showed reactivity to healthy human, rat, and mouse serum, as well as human plasma EDTA, and showed no reactivity to the elongated peptide or nonsense peptide.

Example 4—PRO-C3 Immunoassay

[0089] PRO-C3 was measured using an enzyme-linked immunosorbent assay (ELISA) developed at Nordic Bioscience, as described in WO2014/170312, and as also detailed in other publications.sup.19. Briefly, these procedures were as follows:

[0090] A 96-well streptavidin-coated ELISA plate from Roche, cat.11940279, was coated with the biotinylated peptide Biotin-CGG-CPTGPQNYSP (SEQ ID No: 33) dissolved in coater buffer (50 mM PBS-BTE+10% sorbitol, pH 7.4), incubated for 30 min at 20° C. in the dark and subsequently washed in washing buffer (20 mM Tris, 50 mM NaCl, pH 7.2). Thereafter 20 μl of peptide calibrator or sample were added to appropriate wells, followed by 100 μl of HRP-conjugated monoclonal antibody NB61N-62 dissolved in incubation buffer (50 mM PBS-BTB+10% LiquidII (Roche), pH 7.4) and the plate was incubated for 20 hours at 4° C. and washed. Finally, 100 μl tetramethylbenzinidine (TMB) (Kem-En-Tec cat.: 4380H) was added, the plate was incubated for 15 min at 20° C. in the dark and in order to stop the reaction, 100 μl of stopping solution (1% H.sub.2SO.sub.4) was added and the plate was analyzed in the ELISA reader at 450 nm with 650 nm as the reference (Molecular Devices, SpectraMax M, CA, USA). A calibration curve was plotted using a 4-parametric mathematical fit model.

Example 5—PRO-C6 Immunoassay

[0091] PRO-C6 was measured using an enzyme-linked immunosorbent assay (ELISA) developed at Nordic Bioscience, as described in WO2016/156526, and as also detailed in other publications.sup.39. Briefly, these procedures were as follows:

[0092] ELISA-plates used for the assay development were Streptavidin-coated from Roche (cat.: 11940279). All ELISA plates were analyzed with the ELISA reader from Molecular Devices, SpectraMax M, (CA, USA). We labeled the selected monoclonal antibody with horseradish peroxidase (HRP) using the Lightning link HRP labeling kit according to the instructions of the manufacturer (Innovabioscience, Babraham, Cambridge, UK). A 96-well streptavidin plate was coated with biotinylated synthetic peptide biotin-KPGVISVMGT (SEQ ID No: 38) (Chinese Peptide Company, China) dissolved in coating buffer (40 mM Na.sub.2HPO.sub.4, 7 mM KH.sub.2PO.sub.4, 137 mM NaCl, 2.7 mM KCl, 0.1% Tween 20, 1% BSA, pH 7.4) and incubated 30 minutes at 20° C. 20 μL of standard peptide or samples diluted in incubation buffer (40 mM Na.sub.2HPO.sub.4, 7 mM KH.sub.2PO.sub.4, 137 mM NaCl, 2.7 mM KCl, 0.1% Tween 20, 1% BSA, 5% Liquid II, pH 7.4) were added to appropriate wells, followed by 100 μL of HRP conjugated monoclonal antibody 10A3, and incubated 21 hour at 4° C. Finally, 100 μL tetramethylbenzinidine (TMB) (Kem-En-Tec cat.4380H) was added and the plate was incubated 15 minutes at 20° C. in the dark. All the above incubation steps included shaking at 300 rpm. After each incubation step the plate was washed five times in washing buffer (20 mM Tris, 50 mM NaCl). The TMB reaction was stopped by adding 100 μL of stopping solution (1% H.sub.2SO.sub.4) and measured at 450 nm with 650 nm as the reference.

Example 6—C4M Immunoassay

[0093] C4M was measured using an enzyme-linked immunosorbent assay (ELISA) developed at Nordic Bioscience, as described in Sand et al.sup.38. Briefly, these procedures were as follows:

[0094] A 96-well streptavidin-coated microtiter plate (cat. no. 11940279, Roche Diagnostics, Hvidovre, Denmark) was coated with 100 μl biotinylated peptide (ILGHVPGMLL-K-biotin (SEQ ID No: 37)) dissolved in coating buffer (50 mM Tris, containing 1% bovine serum albumin, 0.1% Tween-20, and 0.4% bronidox (BTB), pH 8.0) and incubated for 30 minutes at 20° C. 20 μl standard peptide or sample dissolved in assay buffer (50 mM Tris-BTB, pH 8.0) was added to appropriate wells, followed by 100 μl of conjugated monoclonal antibody diluted in assay buffer and incubated 1 hour at 20° C. Finally, 100 μl tetramethylbenzinidine (TMB) (cat. no. 4380H, Kem-En-Tec, Taastrup, Denmark) was added and the plate was incubated 15 minutes at 20° C. The TMB reaction was stopped by adding 100 μl stopping solution (1% H2SO4). All incubation steps were performed in the dark with shaking at 300 rpm and followed by five washes in washing buffer (20 mM Tris, 50 mM NaCl, pH 7.2). The results were analysed spectrophotometrically at 450 nm with 650 nm as the reference using an ELISA microplate reader (VersaMax, Molecular Devices, Sunnyvale, Calif., USA). A standard curve was performed by serial dilution of the standard peptide and plotted using a 4-parametric mathematical fit model.

Example 7—Biomarker Analysis in TOPCAT Samples

[0095] In this study, the relationship between neoepitope biomarkers of type III, IV and VI collagen formation (Pro-C3, Pro-C4 and Pro-C6 respectively) and degradation (C3M, C4M and C6M, respectively) and subject outcomes, among subjects with HFpEF enrolled in the TOPCAT trial, was assessed.

[0096] Methods

[0097] Study Population

[0098] The study used data and biosamples from the TOPCAT Trial obtained from the National Heart, Lung, and Blood Institute. The parent trial data are available to other researchers through the National Institutes of Health Biolincc website.

[0099] The design of the TOPCAT trial and the general characteristics of the study population have been described in previous publications.sup.46-42. Briefly, TOPCAT was a multi-center, international, randomized, double-blind, placebo-controlled trial of spironolactone that enrolled 3445 adults with HFpEF across >270 clinical sites in 6 countries from August 2006 until January 2012. The primary results of the trial have been previously published.sup.42. All study participants provided written informed consent.

[0100] Inclusion criteria for TOPCAT were as follows: age 50 years; diagnosis of HF based on at least 1 HF symptom at the time of study screening and at least 1 HF sign within the 12 months before screening; left ventricular EF≥45% (per local reading); at least 1 HF hospitalization in the 12 months before study screening or BNP (B-type natriuretic peptide) >100 pg/mL or NT-proBNP (N-terminal pro-BNP) >360 pg/mL (in the absence of an alternative explanation for elevated natriuretic peptide level) within the 60 days before screening; and serum potassium <5.0 mmol/L before randomization.sup.46,42.

[0101] Exclusion criteria have been published in detail previously.sup.40 but included severe systemic illness with a life expectancy of <3 years, significant chronic pulmonary disease, infiltrative or hypertrophic cardiomyopathy, constrictive pericarditis, previous cardiac transplant or LV assist device, known chronic hepatic disease, severe chronic kidney disease (defined as estimated glomerular filtration rate [eGFR]<30 mL/min per 1.73 m.sup.2 or serum creatinine ≥2.5 mg/dL), a history of significant hyperkalemia, known intolerance to aldosterone antagonists, and recent myocardial infarction, coronary artery bypass grafting, or percutaneous coronary intervention.

[0102] The primary goal of the trial was to determine if spironolactone was associated with a reduction in the composite outcome of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. All HF hospitalizations were adjudicated by a clinical end point committee at Brigham and Women's Hospital, blinded to study-drug assignments, according to prespecified criteria, as previously described.sup.40. In this analysis, we examined the relationship between biomarkers and tissue fibrosis and: (1) The primary endpoint, as defined above; (2) A composite endpoint of death or heart failure hospitalization, which is increasingly utilized in HFpEF studies.sup.43.

[0103] Given significant regional variations in the trial population.sup.6, the analysis in the present study was limited to subjects enrolled in the Americas.

[0104] Biomarker Assays

[0105] Stored plasma samples were obtained from Biolincc for all participants enrolled in the Americas who had stored plasma from the baseline examination (n=206).

[0106] Specific biomarkers of collagen formation (Pro-C3, Pro-C4 and Pro-C6) and degradation (C3M, C4M and C6M) were measured using enzyme-linked immunosorbent assays (ELISA). The Pro-C3, Pro-C6 and C4M ELISAs were carried out as described supra (see Examples 4, 5 and 6, respectively). Pro-C4 is a known biomarker of collagen type IV formation, and the Pro-C4 ELISA was carried out in the manner described in Leeming et al.sup.44. C3M and C6M are known biomarkers of collagen type III degradation and collagen type VI degradation, respectively, and the C3M and C6M ELISAs were carried out in the manner described in Barascuk et al.sup.45 and Juhl et al.sup.46, respectively.

[0107] NT-proBNP levels were measured using a validated Luminex Bead-Based multiplexed assay (Bristol Myers-Squibb; Ewing Township, N.J.).

[0108] Statistical Analyses

[0109] Participant characteristics were summarized using mean (SD) for normally distributed variables and median (interquartile range) for non-normally distributed continuous variables. Categorical variables are expressed as counts (percentages). Subjects enrolled in the Americas who had available samples for measurement of the biomarkers of interest vs. those who did not were compared. The non-paired t test for normally-distributed variables, the Kruskal-Wallis test for non-normally distributed variables and the chi-square test or Fisher's exact test, as appropriate, for categorical variables were used.

[0110] The relationship between biomarkers and the primary outcome (cardiovascular death, aborted cardiac arrest, or heart failure hospitalization), as well as the composite of HF hospitalization or all-cause death, were assessed using Cox regression. Kaplan-Meier survival curves for tertiles of each biomarker were constructed, and these were compared using the log-rank test. Adjusted Cox models were built, as appropriate, to assess whether unadjusted associations are independent of confounders, including: (1) the MAGGIC risk score, which incorporates multiple demographic, clinical and laboratory variables (Model 1).sup.47; (2) The MAGGIC risk score plus NT-proBNP levels (Model 2); (3) Important individual clinical covariates chosen a priori, including age, sex, diabetes mellitus status, estimated glomerular filtration rate, systolic blood pressure (SBP), and NYHA class III/IV and history of myocardial infarction (Model 3). Hazard ratios for all biomarkers are standardized (expressed per standard-deviation increase, or 1-point increased in the z score) in order to provide an intuitive comparison between the biomarkers.

[0111] Finally, interactions between the pre-randomization level of each biomarker and randomized treatment with spironolactone were tested, as predictors of the endpoints mentioned above. When an interaction was found, stratified survival analyses were performed according to the median value of the biomarker, in which the effect of spironolactone treatment was assessed.

[0112] Statistical significance was defined as a 2-tailed P value<0.05. All probability values presented are 2-tailed. Statistical analyses were performed using the Matlab statistics and machine learning toolbox (Matlab 2016b, the Mathworks; Natwick, Mass.) and SPSS for Mac v22 (SPSS Inc., Chicago, Ill.).

[0113] Results

[0114] A comparison of trial participants enrolled in the Americas who did vs. those who did not have available frozen biosamples for biomarker measurements is shown in Table 1. There were no significant differences between the subgroups in age or sex. Subjects with available samples demonstrated a slightly greater proportion of white (85.44 vs. 77.36%) and a slightly lower proportion of black (12.62 vs. 17.7%) participants. The prevalence of NYHA class history of myocardial infarction, stroke, peripheral arterial disease or diabetes mellitus did not differ between the subgroups, whereas the prevalence of COPD was lower and the prevalence of hypertension and atrial fibrillation was greater among participants with available samples. Antihypertensive medication, diuretic, glucose-lowering agent and ACE inhibitor/ARB use did not defer between the groups. Subjects with available samples were more often receiving stains.

TABLE-US-00017 TABLE 1 General characteristics of study participants with vs. without available plasma samples. Numbers represent Mean (SD), Median (IQR) or counts (%) Participants Participants without with available available samples samples P (n = 1559) (n = 206) value Demographic Characteristics Age, years 72 (64, 79) 72 (64, 79)   0.9054  Male Sex 770 (49.39%) 113 (54.85%)   0.1405  Race <0.0001  White 1206 (77.36%) 176 (85.44%)   0.0082  Black 276 (17.70%) 26 (12.62%)   0.0687  Asian 18 (1.15%) 1 (0.49%)   0.7162* Other 67 (4.30%) 3 (1.46%)   0.0495  BMI, kg/m2 32.8 (27.9, 38.5) 33.1 (28.5, 37.8)   0.5387  Heart rate, bpm 68 (61, 76) 66.5 (60, 76)   0.0456  Systolic BP, mmHg 130 (118, 139) 124 (114, 136)   0.0039  Diastolic PB, mmHg 70 (62, 80) 70 (62, 78)   0.0428  Medical History NYHA class III-IV 540 (34.70%) 80 (38.83%)   0.2434  Myocardial 313 (20.09%) 46 (22.33%)   0.4529  Infarction Stroke 143 (9.18%) 15 (7.28%)   0.3703  COPD 269 (17.27%) 22 (10.68%)   0.0167  Hypertension 1392 (89.35%) 195 (94.66%)   0.0170  Peripheral Arterial 183 (11.75%) 24 (11.65%)   0.9681  Disease Atrial Fibrillation 640 (41.08%) 102 (49.51%)   0.0212  Diabetes Mellitus 692 (44.42%) 96 (46.60%)   0.5531  Medication Use Beta Blockers 1215 (77.98%) 172 (83.50%)   0.0698  Calcium Channel 600 (38.51%) 81 (39.32%)   0.8225  Blockers Diuretics 1385 (88.90%) 187 (90.78%)   0.4153  Glucose-lowering 628 (40.31%) 91 (44.17%)   0.2885  agents ACE Inhibitors or 1240 (79.59%) 154 (74.76%)   0.1094  ARBs Statins 995 (63.86%) 153 (74.27%)   0.0032 

[0115] Relationship Between Baseline Biomarkers of Tissue Fibrosis and Outcomes

[0116] FIG. 1A shows standardized hazard ratios for all examined fibrosis biomarkers for the primary endpoint in unadjusted analyses (one model per biomarker). FIG. 1B shows corresponding standardized hazard ratios for death or heart failure admission. In these analyses, pro-C6 (HR=1.90; 95% CI=1.54-2.34; P<0.0001) and pro-C3 (HR=1.57; 95% CI=1.28-1.94; P<0.0001) strongly predicted the trial primary endpoint. Similarly, pro-C6 (HR=1.94; 95% CI=1.60-2.35; P<0.0001) and pro-C3 (HR=1.56; 95% CI=1.29-1.89; P<0.0001) predicted the composite endpoint of death or heart failure admission.

[0117] FIG. 2 shows Kaplan-Meier survival curves for the primary endpoint corresponding to the tertiles of Pro-C6 (left) and Pro-C3 (right), respectively. Pro-C6 stratified subjects across a broad range of absolute risk. There was graded pronounced reduction in event-free survival from the lowest tertile (Pro-C6<11.0 ng/ml) to the highest tertile (Pro-C6>16.0 ng/ml) of Pro-C6. For Pro-C3, only the highest tertile (Pro-C3>14.0 ng/ml) demonstrated a pronounced reduced event-free survival. A similar pattern was found for death or heart failure admission, as shown in FIG. 3.

[0118] In models that included both Pro-C6 and Pro-C3, pro-C6 was independently predictive of the primary endpoint (HR=1.84; 95% CI=1.36-2.47; P<0.0001) and of death/HF admission (HR=1.92; 95% CI=1.46-2.53; P<0.0001). In contrast, in these models, Pro-C3 was not significantly associated with either the primary endpoint (HR=1.06; 95% CI=0.76-1.46; P=0.74) or death/HF admission (HR=1.01; 95% CI=0.75-1.37; P=0.93). Similarly, in models that included both pro-C6 (as a continuous variable) and a pro-C3 level>14 ng/mL (highest tertile of distribution, expressed as a binary variable), pro-C6, but not pro-C3 status was independently predictive of the primary endpoint and of death/HF admission.

[0119] Subsequent adjusted analyses were performed for pro-C6 only (Table 2). In models that adjusted for the MAGGIC risk score, ProC6 strongly predict the primary endpoint (HR=1.88; 95% CI=1.52-2.33; P<0.0001) and death/HF admission (HR=1.91; 95% CI=1.57-2.33; P<0.0001). The hazard ratios for Pro-C6 were very similar when additional adjustment for NT-proBNP was performed (adjusted Model 2, Table 2). When adjusted for Pro-C6, NT-ProBNP became only weakly predictive of the outcome, whereas the MAGGIC risk score became non-predictive of the primary endpoint or of death/HF admission.

[0120] Similarly, in models that adjusted for age, sex, diabetes mellitus status, estimated glomerular filtration rate, SBP, NYHA class III/IV and history of myocardial infarction (adjusted Model 3, Table 2), ProC6 strongly predicted the primary endpoint (HR=1.81; 95% CI=1.44-2.27; P<0.0001) and the endpoint of death or HF admission (HR=1.84; 95% CI=1.49-2.26; P<0.0001).

[0121] As shown in Table 2, for the primary endpoint, the Harrel's C-statistic was much greater for a model including only pro-C6 alone (0.705) than for models including the MAGGIC risk score (0.552), the MAGGIC risk score plus BNP (0.582), or a combination of clinical variables included in adjusted model 3 (0.64). Similarly, for the death/heart failure-related hospitalization, the Harrel's C-statistic was much greater for a model including only pro-C6 alone (0.707) than for models including the MAGGIC risk score (Adjusted model 1: 0. 0.571), the MAGGIC risk score plus BNP (Adjusted model 2: 0.602), or a combination of clinical variables (Adjusted model 3: 0.623). Accordingly, the addition of pro-C6 to models already containing the MAGGIC risk score, the MAGGIC risk score plus BNP, or a combination of clinical variables, resulted in marked improvements in the Harrel's C-statistic (Table 2).

TABLE-US-00018 TABLE 2 Relationship between pro-C6 levels and the incidence of the primary endpoint and of death or HF admission in various models. Harrel's c Harrel's c (with (without Model HR (95% CI) P value ProC6)* ProC6)* Primary Endpoint Unadjusted 1.90 (1.54-2.34) <0.0001 — 0.705 (0.034) Adjusted Model 1 1.88 (1.52-2.33) <0.0001 0.552 (0.041) 0.699 (0.036) Adjusted Model 2 1.87 (1.51-2.33) <0.0001 0.552 (0.043) 0.706 (0.035) Adjusted Model 3 1.81 (1.44-2.27) <0.0001  0.64 (0.042) 0.724 (0.031) Death or HF admission Unadjusted 1.94 (1.60-2.35) <0.0001 — 0.707 (0.031) Adjusted Model 1 1.91 (1.57-2.33) <0.0001 0.571 (0.037) 0.698 (0.033) Adjusted Model 2 1.90 (1.55-2.32) <0.0001 0.602 (0.039) 0.709 (0.032) Adjusted Model 3 1.84 (1.49-2.26) <0.0001 0.623 (0.039) 0.715 (0.03)  *The number in parentheses represents the standard error of the estimate. Adjusted Model 1: adjusted for the MAGGIC risk score. Adjusted Model 2: adjusted for the MAGGIC risk score and NT-proBNP levels. Adjusted Model 3: adjusted for age, sex, diabetes mellitus, estimated glomerular filtration rate, systolic blood pressure (SBP), NYHA class III/IV and history of myocardial infarction.

[0122] Interactions with Randomized Arm

[0123] Significant interactions between baseline levels of C4M and randomized treatment arm were found as predictors of the primary endpoint (P for C4M-treatment arm interaction=0.0061) and of death/HF admission (P for C4M-treatment arm interaction=0.0063), indicating a more favorable response to treatment among participants with lower C4M levels at baseline. No interactions with treatment arm were found for the other examined fibrosis biomarkers.

[0124] Discussion

[0125] The relationship between biomarkers of ECM turnover, measured at baseline among participants enrolled in the TOPCAT trial was studied. It was demonstrated that pro-C6 and pro-C3, biomarkers of fibrogenesis assessed by type VI and III collagen formation, respectively, predicted the risk of incident cardiovascular events, as well as a composite of all-cause death/HF-related hospitalization in this population. Pro-C6, in particular, was a strong independent predictor of these outcomes and stratified subjects across a broad range of absolute risk. Pro-C6 alone performed better as a predictor of outcomes than the MAGGIC risk score, NT-ProBNP or a combination of clinical variables. The addition of pro-C6 to the MAGGIC risk score, with or without additional adjustment for NT-proBNP levels, resulted in a marked increase in the Harrel's C-statistic, a measure of model fit and discrimination which is analogous to the receiver-operator characteristic curve. In addition, an interaction between levels of C4M, a biomarker of collagen type IV degradation (present predominantly in the vascular basement membrane), and the risk reduction associated with randomization to spironolactone vs. placebo was found. In these post-hoc analyses, subjects with higher C4M levels appeared to derive greater benefit from randomization to spironolactone. These findings support an important role for tissue fibrosis in HFpEF, and identify biomarkers of ECM turnover that are readily measured, and could be implemented in various settings for risk stratification in this population.

[0126] In the present study, high levels of pro-C6 were strongly predictive of outcomes. It is important to note the pronounced prognostic power of this biomarker, which largely exceeded that of the MAGGIC risk score, the MAGGIC risk score plus NT-proBNP levels, and a combination of key clinical variables. In addition, pro-C6 markedly improved the discrimination of models that already included these prognostic factors; in contrast, the addition of standard predictors to a model already containing pro-C6 resulted in minimal improvements in model fit. Therefore, pro-C6 appears to be a particularly strong and robust independent predictor of outcomes in HFpEF. Pro-C6 may thus be useful in the diagnosis of HFpEF, for the identification of good candidates for antifibrotic therapies, and/or for monitoring and characterizing the efficacy of such therapies.

[0127] A particularly interesting finding of the present study is the highly significant interaction between C4M and the risk modification associated with randomized treatment with spironolactone. These findings support the notion that biomarkers of collagen turnover can identify individuals who benefit from spironolactone. The present study is the first that reports an interaction between biomarkers of collagen turnover and the reduction in the risk of clinical events associated with spironolactone therapy. It was found that lower C4M levels were associated with a greater reduction in risk associated with spironolactone randomization. C4M is a marker of collagen degradation; therefore, lower levels indicate reduced degradation and thus increased collagen accumulation, which is a therapeutic target of spironolactone.

[0128] In addition to blood vessels, collagen IV is also present in the glomerular basement membrane that prevents the leakage of plasma proteins into the urine. Interestingly, however, C4M was not associated with albuminuria in the present cohort. Similarly, in contrast to the pronounced interaction between changes in C4M and spironolactone, no interaction was found between proteinuria and spironolactone treatment in a recent analysis of the TOPCAT trial.sup.48. Therefore, the interaction between C4M and spironolactone effects are unlikely to be mediated by glomerular basement membrane degradation.

[0129] In summary, fibrogenesis assessed by Pro-C6 is strongly and independently predictive of a poor prognosis in HFpEF. In contrast, low levels of C4M appear to identify patients with HFpEF who exhibit particularly favorable responses to aldosterone antagonists (mineralocorticoid-receptor antagonists).

[0130] In this specification, unless expressly otherwise indicated, the word ‘or’ is used in the sense of an operator that returns a true value when either or both of the stated conditions is met, as opposed to the operator ‘exclusive or’ which requires that only one of the conditions is met. The word ‘comprising’ is used in the sense of ‘including’ rather than in to mean ‘consisting of’. All prior teachings acknowledged above are hereby incorporated by reference. No acknowledgement of any prior published document herein should be taken to be an admission or representation that the teaching thereof was common general knowledge in Australia or elsewhere at the date hereof.

REFERENCES

[0131] 1. Lam C S, Donal E, Kraigher-Krainer E, Vasan R S. Epidemiology and clinical course of heart failure with preserved ejection fraction. Eur J Heart Fail 2011; 13:18-28. [0132] 2. Lloyd-Jones D M, Hong Y, Labarthe D et al. Defining and setting national goals for cardiovascular health promotion and disease reduction: the American Heart Association's strategic Impact Goal through 2020 and beyond. Circulation 2010; 121:586-613. [0133] 3. Lam C S, Donal E, Kraigher-Krainer E, Vasan R S. Epidemiology and clinical course of heart failure with preserved ejection fraction. Eur J Heart Fail 2011; 13:18-28. [0134] 4. Chirinos J A. Deep Phenotyping of Systemic Arterial Hemodynamics in HFpEF (Part 2): Clinical and Therapeutic Considerations. J Cardiovasc Transl Res 2017; 10:261-274. [0135] 5. Rommel K P, von Roeder M, Latuscynski K et al. Extracellular Volume Fraction for Characterization of Patients With Heart Failure and Preserved Ejection Fraction. J Am Coll Cardiol 2016; 67:1815-25. [0136] 6. Mohammed S F, Hussain S, Mirzoyev S A, Edwards W D, Maleszewski J J, Redfield M M. Coronary microvascular rarefaction and myocardial fibrosis in heart failure with preserved ejection fraction. Circulation 2015; 131:550-9. [0137] 7. Chirinos J A, Akers S R, Trieu L et al. Heart Failure, Left Ventricular Remodeling, and Circulating Nitric Oxide Metabolites. J Am Heart Assoc 2016; 5. [0138] 8. Su M Y, Lin L Y, Tseng Y H et al. CMR-verified diffuse myocardial fibrosis is associated with diastolic dysfunction in HFpEF. JACC Cardiovasc Imaging 2014; 7:991-7. [0139] 9. Mohammed S F, Majure D T, Redfield M M. Zooming in on the Microvasculature in Heart Failure With Preserved Ejection Fraction. Circ Heart Fail 2016; 9. [0140] 10. Richards A M. Circulating Biomarkers of Cardiac Fibrosis: Do We Have Any and What Use Are They? Circ Heart Fail 2017; 10. [0141] 11. Lin L Y, Wu C K, Juang J M et al. Myocardial Regional Interstitial Fibrosis is Associated With Left Intra-Ventricular Dyssynchrony in Patients With Heart Failure: A Cardiovascular Magnetic Resonance Study. Sci Rep 2016; 6:20711. [0142] 12. Duca F, Kammerlander A A, Zotter-Tufaro C et al. Interstitial Fibrosis, Functional Status, and Outcomes in Heart Failure With Preserved Ejection Fraction: Insights From a Prospective

[0143] Cardiac Magnetic Resonance Imaging Study. Circ Cardiovasc Imaging 2016; 9. [0144] 13. Roy C, Slimani A, de Meester C et al. Associations and prognostic significance of diffuse myocardial fibrosis by cardiovascular magnetic resonance in heart failure with preserved ejection fraction. J Cardiovasc Magn Reson 2018; 20:55. [0145] 14. Schelbert E B, Fridman Y, Wong T C et al. Temporal Relation Between Myocardial Fibrosis and Heart Failure With Preserved Ejection Fraction: Association With Baseline Disease Severity and Subsequent Outcome. JAMA Cardiol 2017. [0146] 15. Nielsen M J, Karsdal M A, Kazankov K et al. Fibrosis is not just fibrosis-basement membrane modelling and collagen metabolism differs between hepatitis B- and C-induced injury. Aliment Pharmacol Ther 2016; 44:1242-1252. [0147] 16. Haykowsky M J, Kouba E J, Brubaker P H, Nicklas B J, Eggebeen J, Kitzman D W. Skeletal muscle composition and its relation to exercise intolerance in older patients with heart failure and preserved ejection fraction. Am J Cardiol 2014; 113:1211-6. [0148] 17. Rossignol P, Ferreira J P, Zannad F. Fibrosis mechanistic phenotyping and antifibrotic response determination with biomarkers in heart failure: one single biomarker may not fit all settings. Eur J Heart Fail 2018; 20:1300-1302. [0149] 18. Ravassa S, Trippel T, Bach D et al. Biomarker-based phenotyping of myocardial fibrosis identifies patients with heart failure with preserved ejection fraction resistant to the beneficial effects of spironolactone: results from the Aldo-DHF trial. Eur J Heart Fail 2018; 20:1290-1299. [0150] 19. Nielsen M J, Nedergaard A F, Sun S et al. The neo-epitope specific PRO-C3 ELISA measures true formation of type III collagen associated with liver and muscle parameters. Am. J. Transl. Res. 2013; 5: 303-315. [0151] 20. Nielsen M J, Veidal S S, Karsdal M A et al. Plasma Pro-C3 (N-terminal type III collagen propeptide) predicts fibrosis progression in patients with chronic hepatitis C. Liver Int. 2015; 35: 429-437. [0152] 21. Daniels S J, Leeming D J, Eslam M et al. ADAPT: An algorithm incorporating PRO-C3 accurately identifies patients with NAFLD and advanced fibrosis. Hepatology 2018. [0153] 22. Leeming D J, Karsdal M A, Byrjalsen I et al. Novel serological neo-epitope markers of extracellular matrix proteins for the detection of portal hypertension. Aliment. Pharmacol. Ther. 2013; 38: 1086-96. [0154] 23. Jansen C, Leeming D J, Mandorfer M et al. PRO-C3-Levels in Patients with HIV/HCV-Co-Infection Reflect Fibrosis Stage and Degree of Portal Hypertension. Avila M A, ed. PLoS One 2014; 9: e108544. [0155] 24. Karsdal M A, Hjuler S T, Luo Y et al. Assessment of liver fibrosis progression and regression by a serological collagen turnover profile. Am. J. Physiol. Gastrointest. Liver Physiol. 2019; 316: G25-G31. [0156] 25. Nielsen S H, Mygind N D, Michelsen M M et al. Accelerated collagen turnover in women with angina pectoris without obstructive coronary artery disease: An iPOWER substudy. Eur. J. Prev. Cardiol. 2018; 25: 719-727. [0157] 26. Hansen J F, Juul Nielsen M, Nyström K et al. PRO-C3: a new and more precise collagen marker for liver fibrosis in patients with chronic hepatitis C. Scand. J. Gastroenterol. 2018; 53: 83-87. [0158] 27. Park J, Scherer P E, Calle E et al. Adipocyte-derived endotrophin promotes malignant tumor progression. J. Clin. Invest. 2012; 122: 4243-4256. [0159] 28. Lee C, Kim M, Lee J H et al. COL6A3-derived endotrophin links reciprocal interactions among hepatic cells in the pathology of chronic liver disease. J. Pathol. 2018. [0160] 29. Sun K, Park J, Gupta O T et al. Endotrophin triggers adipose tissue fibrosis and metabolic dysfunction. Nat. Commun. 2014; 5: 3485. [0161] 30. Park J, Morley T S, Scherer P E. Inhibition of endotrophin, a cleavage product of collagen VI, confers cisplatin sensitivity to tumours. EMBO Mol. Med. 2013; 5: 935-48. [0162] 31. Bu D, Crewe C, Kusminski C M et al. Human endotrophin as a driver of malignant tumor growth. JCI Insight 2019. [0163] 32. Rasmussen D G K, Hansen T W, von Scholten B J et al. Higher Collagen VI Formation Is Associated With All-Cause Mortality in Patients With Type 2 Diabetes and Microalbuminuria. Diabetes Care 2018: dc172392. [0164] 33. Fenton A, Jesky M D, Ferro C J et al. Serum endotrophin, a type VI collagen cleavage product, is associated with increased mortality in chronic kidney disease. Aguilera A I, ed. PLoS One 2017; 12: e0175200. [0165] 34. Rasmussen D G K, Fenton A, Jesky M et al. Urinary endotrophin predicts disease progression in patients with chronic kidney disease. Sci. Rep. 2017; 7: 17328. [0166] 35. Karsdal M A, Henriksen K, Genovese F et al. Serum endotrophin identifies optimal responders to PPARγ agonists in type 2 diabetes. Diabetologia 2017; 60. [0167] 36. Timpl R. Macromolecular organization of basement membranes. Curr Opin Cell Biol 1996; 8:618-24. [0168] 37. Jayadev R, Sherwood D R. Basement membranes. Curr Biol 2017; 27:R207-R211. [0169] 38. Sand J M, Larsen L, Hogaboam C et al. MMP mediated degradation of type IV collagen alpha 1 and alpha 3 chains reflects basement membrane remodeling in experimental and clinical fibrosis—validation of two novel biomarker assays. PLoS One 2013; 8:e84934. [0170] 39. Sun S, Henriksen K, Karsdal M A et al. Collagen Type III and VI Turnover in Response to Long-Term Immobilization. PLoS One 2015; 10: e0144525. [0171] 40. Desai A S, Lewis E F, Li R et al. Rationale and design of the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial: A randomized, controlled study of spironolactone in patients with symptomatic heart failure and preserved ejection fraction. Am. Heart J. 2011; 162: 966-972.e10. [0172] 41. Pfeffer M A, Claggett B, Assmann S F et al. Regional variation in patients and outcomes in the Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist (TOPCAT) trial. Circulation 2015; 131:34-42. [0173] 42. Pitt B, Pfeffer M A, Assmann S F et al. Spironolactone for heart failure with preserved ejection fraction. N Engl J Med 2014; 370:1383-92. [0174] 43. Lam C S P, Gamble G D, Ling L H et al. Mortality associated with heart failure with preserved vs. reduced ejection fraction in a prospective international multi-ethnic cohort study. Eur Heart J 2018; 39:1770-1780. [0175] 44. Leeming D J, Nielsen M J, Dai Y et al. Enzyme-linked immunosorbent serum assay specific for the 7S domain of Collagen Type IV (P4NP 7S): A marker related to the extracellular matrix remodeling during liver fibrogenesis. Hepatol Res 2012; 42:482-93. [0176] 45. Barascuk N, Veidal S S, Larsen L et al. A novel assay for extracellular matrix remodeling associated with liver fibrosis: An enzyme-linked immunosorbent assay (ELISA) for a MMP-9 proteolytically revealed neo-epitope of type III collagen. Clin Biochem 2010; 43:899-904. [0177] 46. Juhl P, Bay-Jensen A C, Karsdal M, Siebuhr A S, Franchimont N, Chavez J. Serum biomarkers of collagen turnover as potential diagnostic tools in diffuse systemic sclerosis: A cross-sectional study. PLoS One 2018; 13:e0207324. [0178] 47. Pocock S J, Ariti C A, McMurray J J et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 2013; 34:1404-13. [0179] 48. Selvaraj S, Claggett B, Shah S J et al. Prognostic Value of Albuminuria and Influence of Spironolactone in Heart Failure With Preserved Ejection Fraction. Circ Heart Fail 2018; 11:e005288.