University of Groningen Galectin-3 in heart failure van der Velde, … · 2017-01-04 ·...

University of Groningen

Galectin-3 in heart failurevan der Velde, Allart Rogier

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:2017

Link to publication in University of Groningen/UMCG research database

Citation for published version (APA):van der Velde, A. R. (2017). Galectin-3 in heart failure: From biomarker to target for therapy. [Groningen]:Rijksuniversiteit Groningen.

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).

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: 18-10-2020

https://www.rug.nl/research/portal/en/publications/galectin3-in-heart-failure(a8dc89a8-17a8-49b3-9d14-ae0714222882).html

https://www.rug.nl/research/portal/en/publications/galectin3-in-heart-failure(a8dc89a8-17a8-49b3-9d14-ae0714222882).html

Chapter 2Biomarkers for Risk Prediction in Acute Decompensated Heart Failure

AR van der VeldeWC MeijersRA de Boer

Current Heart Failure Reports (2014); 11: 246-259

Chapter 2

28

ABsTRACT

Risk prediction in patients admitted with acute decompensated heart failure (ADHF) remains a challenge. Biomarkers may improve risk prediction, which in turn may help to better inform patients regarding short-term and long-term prognosis, therapy and care. Most data on biomarkers have been derived from patient cohorts with chronic heart failure. In ADHF, currently, risk tools largely rely on common clinical and biochemical parameters. However, ADHF is not a single disease. It presents in various manners and different etiologies may underlie ADHF, which are reflected by different biomarkers. In the last decade, many studies have reported the prognostic value of these biomarkers. These studies have attempted to describe a value for statistical modeling, e.g., reclas-sification indices, in an effort to report incremental value over a clinical model or the “gold standard”. However, the overall incremental predictive value of biomarkers has been modest compared to already existing clinical models. Natriuretic peptides, e.g., (NT-pro)BNP, are the benchmark, but head-to-head comparisons show that there are novel biomarkers with comparable prognostic value. Multimarker strategies may pro-vide superior risk stratification. Future studies should elucidate cost-effectiveness of single or combined biomarker testing. The purpose of this review was to provide an update on current biomarkers and to identify new promising biomarkers that can be used in prognostication of acute heart failure.

29

Biomarkers for Risk Prediction in Acute Decompensated Heart Failure

Chap

ter 2

inTRoduCTion

Heart failure (HF) is a complex disease, and its underlying pathophysiology is multifacto-rial. Patients who are hospitalized for a rapid worsening of preexistent HF or acute decom-pensated heart failure (ADHF), are difficult to treat, their mortality rate remains high [1••], and it remains a challenge to predict the risk of individual patients [2]. ADHF is associated with a number of individual characteristics including clinical, biochemical, demographic variables and comorbidity [3•]. For risk prediction after admission for ADHF, biomarkers may be of additional value to assess risk. “Biomarkers” can be any objectively measured biological marker that reflects normal biological processes, pathogenic processes, or phar-macologic responses to therapeutic interventions [4•]. It is difficult to identify the perfect biomarker, primarily because HF has various different causes, which may be reflected by an array of different biomarkers. Thus, it remains a challenge to identify individual prognostic variables to guide clinical decision making. Nevertheless, biomarkers may be used as a tool, which can help doctors to better inform patients regarding therapy and care options. Accurate prognostic evaluation may aid in identifying patients who are at high risk and need more intensive monitoring, while those estimated lower risk, may be monitored less intensively. The purpose of this review is to provide an update on current biomarkers as predictors for outcome in ADHF, and to discuss new promising biomarkers, including HE4/WAP4C, syndecan-1 and urinary C-type natriuretic peptide, and also a new technique called bioelectrical impedance vector analysis (BIVA).

BiomARkeRs FoR HF Risk sTRATiFiCATion: mARkeRs oF undeRlyinG PATHoPHysioloGy

Several biomarkers for ADHF reflect the underlying pathophysiology of HF. Commonly, incident HF is preceded by a cardiovascular event. This event may happen suddenly as in myocardial infarction (MI), or may be slow and progressive, as in hypertension. Over time, the heart becomes progressively damaged due to maladaptive changes, often referred to as cardiac remodeling. In the end, the sequelae of events will impair the contractile function resulting in reduced cardiac output. Before this point, the human body will try to compensate, predominantly with neurohormonal activation [5]. Activa-tion of the sympathetic nervous system with increased release of epinephrine leads to increased contractility and increased vascular resistance. Similarly, activation of the renin-angiotensin-system (RAAS) and increased release of antidiuretic hormone (ADH) leads to vasoconstriction and increased cardiac output. However, prolonged increase in angiotensin II also induces deleterious effects such as fibrosis via aldosterone, while norepinephrine induces cardiac remodeling and hypertrophy. In concert with apoptosis

Chapter 2

30

of cardiac cells, this (mal)adaptive response on the long term contributes to adverse cardiac remodeling [6]. Once the delicate balance in the failing heart is disturbed, it will lead to congestion by fluids, which is clinically reflected as acute decompensated heart failure. This is accompanied by volume overload of the left ventricle (LV) leading to increased myocardial wall stretch. Each step of progression to heart failure is reflected by different kinds of biomarkers (Fig. 1).

mARkeRs oF neuRoendoCRine ACTiVATion

mR-proAdm

In response to increased ventricular wall stress and hemodynamic changes, adreno-medullin (ADM), a peptide hormone, is secreted by myocardial tissue and endothelial cells [7]. ADM stimulates natriuresis and vasodilatation, which is mediated by cAMP and nitric oxide, thereby decreasing afterload and increasing cardiac output [8, 9•]. Elevated levels of this peptide hormone have been associated with the severity of HF [10]. How-ever, ADM is difficult to measure due to its short half-life, instability in plasma and fast clearance from circulation [11, 12]. Midregional pro-adrenomedullin (MR-proADM) is a stable precursor of ADM and its levels mirror those of ADM [12]. Elevated levels of MR-proADM have also shown to be predictive for adverse outcome in ADHF patients [13–15, 16•], and some studies even suggest that MR-proADM is superior in predicting mortality, compared to BNP or NT-proBNP in patients with ADHF (Table 1) [16•, 17].

Copeptin

As a result of changes in osmolality and decreases in circulatory pressures, arginine vasopressin (AVP, also known as ADH) is chronically released by the posterior pituitary gland in patients with HF in order to preserve cardiac output [18•]. AVP interacts with renal collecting ducts (via the V2-receptor) and maintains vascular tonus (via the V1a-receptor), thereby regulating blood pressure. Higher levels of AVP are related to severity of disease in patients with HF [19]. However, AVP is difficult to measure due to its instability and fast clearance rate [20]. Copeptin is the C-terminal segment of the AVP-prohormone and is a stable surrogate marker for AVP [21]. It is also released by the hypothalamus, in the same amounts as AVP [22] and is a sensitive marker for HF progres-sion [23]. In patients with acute decompensated HF, copeptin was found to be predictive for all-cause mortality at 1-year [24•] and also at 24 months [25]. The predictive value was similar to MR-proADM, C-terminal endothelin-1 precursor fragment, midregional proatrial natriuretic peptide (MR-proANP), and B-type natriuretic peptide (BNP) [16•]. Serial measurement of copeptin was found to be an independent predictor for death

31


Chap

ter 2

Myoc

ardi

al in

jury

Neur

oend

ocrin

e act

ivatio

n RA

AS

SNS

AVP

Incr

ease

d ve

nous

pr

essu

re

Card

iac re

mod

eling

Ap

optos

is Hy

pertr

ophy

Fib

rosis

S

T2

Inflam

matio

n GDF-

15

Synd

ecan

-1

HE4

Myoc

ardi

al st

retc

h In

crea

sed

rena

l in

ters

titial

pre

ssur

e Na

triure

tic pe

ptide

s NG

AL

Cysta

tin-C

Ur

inary

C-typ

e NP

MRpr

oADM

Bi

oelec

trical

impe

danc

e vec

tor

analy

sis

Chro

mogr

anin

A Co

pepti

n

Decr

ease

d LV

func

tion

CRP

IL-

6 Pr

ocalc

itonin

Trop

onin

Gale

ctin-

3

Figu

re 1

: Pa

thop

hysi

olog

y of

hea

rt fa

ilure

, whi

ch is

refl e

cted

by

diff e

rent

bio

mar

kers

.

Chapter 2

32 33


Chap

ter 2

AUC

NRI

aIn

crem

enta

l val

ue o

ver:

(Mod

el)

Endp

oint

Follo

w-u

pAu

thor

Stud

yN

Card

iom

yocy

te s

tret

ch

MR-

proA

NP

0.75

7.4

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

0.67

23 %

*Cl

inic

al m

odel

All-

caus

e m

orta

lity

5 ye

arSe

rond

e M

F et

al.

[82•

•]Bi

omar

coeu

rs, F

INN

-AKV

A30

6

NT-

proB

NP

0.84

12.9

%Cl

inic

al m

odel

All-

caus

e m

orta

lity

1 ye

arSc

rutin

io D

et a

l. [1

52]

0.75

9.1

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

0.81

7 %

Clin

ical

risk

fact

ors

All-

caus

e m

orta

lity

90 d

ays

Eurli

ngs

LW e

t al.

[55]

PRID

E60

3

0.56

11 %

*Cl

inic

al m

odel

All-

caus

e m

orta

lity

5 ye

arSe

rond

e M

F et

al.

[82•

•]Bi

omar

coeu

rs, F

INN

-AKV

A30

6

BNP

0.75

5.5

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

0.60

4 %

*Cl

inic

al m

odel

All-

caus

e m

orta

lity

5 ye

arSe

rond

e M

F et

al.

[82•

•]Bi

omar

coeu

rs, F

INN

-AKV

A30

6

infla

mm

atio

n

CRP

0.75

5.3

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

HE4

0.75

31 %

*Cl

inic

al m

odel

+ B

NP

All-

caus

e m

orta

lity

+ H

F re

hosp

italiz

atio

n18

mon

ths

De

Boer

RA

et a

l. [1

38•]

COAC

H56

7

Card

iac

rem

odel

ing

Gal

ectin

-30.

7042

.6 %

*Cl

inic

al m

odel

+ B

NP

HF

Reho

spita

lizat

ion

30 d

ays

Mei

jers

WC

et a

l. [5

3]CO

ACH

, PRI

DE,

UM

D H

-232

5890

2

0.82

19 %

Clin

ical

risk

fact

ors

All-

caus

e m

orta

lity

90 d

ays

Eurli

ngs

LW e

t al.

[55]

PRID

E60

3

ST-2

0.77

25.5

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

30 d

ays

Lass

us J

et a

l. [1

25••]

MO

CA s

tudy

5306

0.77

10.3

%Cl

inic

al p

redi

ctio

n m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

Synd

ecan

-1N

.A.

48.5

%*

Clin

ical

risk

fact

ors

All-

caus

e m

orta

lity

+ H

F re

hosp

italiz

atio

n in

HFP

EF18

mon

ths

Trom

p J e

t al.

[144

•]CO

ACH

567

neu

roen

docr

ine

acti

vati

on

Cope

ptin

0.83

37 %

Adju

sted

mod

el +

NT-

proB

NP

All-

caus

e m

orta

lity

30 d

ays

Poto

cki M

et a

l. [1

27]

287

All-

caus

e m

orta

lity

90 d

ays

Mai

sel A

et a

l. [1

53]

BACH

tria

l55

7

MR-

proA

DM

N.A

.8.

9 %

Best

clin

ical

mod

elA

ll-ca

use

mor

talit

y90

day

sM

aise

l A e

t al.

[16•

]BA

CH tr

ial

466

Chapter 2

32 33


Chap

ter 2

(con

tinue

d)

AUC

NRI

aIn

crem

enta

l val

ue o

ver:

(Mod

el)

Endp

oint

Follo

w-u

pAu

thor

Stud

yN

0.80

28.7

%Cl

inic

al m

odel

All-

caus

e m

orta

lity

30 d

ays

Lass

us J

et a

l. [1

25••]

MO

CA s

tudy

5306

0.76

9.1

%Cl

inic

al m

odel

All-

caus

e m

orta

lity

1 ye

arLa

ssus

J et

al.

[125

••]M

OCA

stu

dy53

06

Rena

l inj

ury

NG

AL

0.73

29.8

%Cl

inic

al m

odel

All-

caus

e m

orta

lity

+ H

F re

hosp

italiz

atio

n30

day

sM

aise

l A e

t al.

[123

•]G

ALL

AN

T tr

ial

Chapter 2

34

or transplantation in an univariate analysis. However, after adjustment for troponin T in multivariate analysis, this association was diminished [26•].

Chromogranin A

Chromogranin A is believed to reflect the activity of the central nervous system [27•] and is produced by adrenergic and neuroendocrine cells [28]. Chromogranin A is a prohormone, which is cleaved after posttranslational modification in vasostatins and catestatin. These peptides negatively regulate neuroendocrine function of the adjacent cells: vasostatin inhibits vasoconstriction and catestatin inhibits the release of catechol-amines [29]. Patients with HF have higher levels of chromogranin A and these levels are associated with severity of HF and were found to be an independent predictor for mortality in patients with ADHF [30•].

mARkeRs oF CARdiAC RemodelinG

Cardiac remodeling is about the process of cellular changes of the myocardium and includes myocyte hypertrophy, fibrosis and apoptosis or necrosis [31, 32••]. Biomarkers of cardiac remodeling with established prognostic value include: ST2, galectin-3, and GDF-15.

(soluble) sT2

Following volume overload and mechanical strain, more ST2 mRNA is released from cardiomyocytes and fibroblasts [33, 34, 35••]. ST2 exists in multiple isoforms including ST2L; a transmembrane receptor, and soluble ST2 (sST2); a circulating form of ST2 [36]. ST2L exerts its effect via IL-33, a ligand of ST2, and has antifibrotic and antihypertrophic effects [37]. However, sST2 can bind IL-33, acting as a decoy receptor, and blocks the antihypertrophic and antifibrotic effects of IL-33 [38]. In this way, sST2 is the component of ST2 with deleterious effects. The sST2 levels are higher in patients with heart failure and have been shown to be associated with poor prognosis in patients with ADHF [39, 40] and add prognostic value to NT-proBNP and hsTropT [41•]. The sST2 levels are also significantly correlated with left ventricular end systolic volumes and dimensions and left ventricular ejection fraction (LVEF) [42]. Moreover, elevated levels of sST2 identify patients with a more decompensated profile and more progressive cardiac remodeling [43]. Serial measurements of sST2 add prognostic information to baseline measure-ments [44, 45•].

35


Chap

ter 2

Galectin-3

In response to cardiac stress (e.g., pressure overload), galectin-3 is released by activated macrophages in the heart to stimulate production of collagens, causing fibrosis and subsequent ventricular dysfunction [46, 47•, 48•]. A key feature of galectin-3 is cell-cell adhesion [49], and, therefore, it also plays an important role in tumor growth, metastasis [50], and inflammation [51]. The prognostic value of galectin-3 has been extensively studied and galectin-3 was found to be an independent predictor for mortality [52] and rehospitalization [53] in patients with ADHF. ROC analysis for predicting mortality was superior to NT-proBNP and apelin [54•]. However, some studies did not find this association in multivariate analysis after correction for eGFR or NT-proBNP [54•, 55]. Serial measurements in patients with ADHF also provide additional prognostic value [56]. Galectin-3 is not only an interesting biomarker because of its prognostic value; interestingly, it may also serve as a potential target for therapy. Experimental studies have shown that pharmacological inhibition of galectin-3 attenuates cardiac fibrosis in animal models [48•, 57••]. These two features render galectin-3 a promising biomarker and protein for further study in HF biology.

GdF-15

Cardiovascular events, like ischemia, pressure overload or atherosclerosis, induce a rapid upregulation of GDF-15 in cardiomyocytes [58•, 59], although GDF-15 may also colocal-ize in macrophages [60]. However, circulating GDF-15 is produced mostly outside the heart and, therefore, is not cardio-specific. GDF-15 is a member of the TGF-β superfamily and has shown to have anti-inflammatory and antihypertrophic effects [58•, 59]. This cardioprotective factor has shown to be a promising predictor for outcome in chronic heart failure patients [45•, 61–64]. GDF-15 has also shown to be a predictor in HF pa-tients with a preserved ejection fraction (HFpEF) [65]. Serial measurements show that increasing levels of GDF-15 over time predict adverse outcomes [63]. However, data in ADHF patients are lacking.

mARkeRs oF myoCARdiAl sTReTCH

In ADHF, increased filling pressures induce myocardial stretch, and, as a result, natri-uretic peptides (NPs) are released from cardiomyocytes into the plasma. NPs have been extensively studied in both acute and chronic heart failure, and have been shown to be powerful predictors of outcome [66••]. NPs include atrial natriuretic peptide (ANP), which is mainly released from atrial cardiomyocytes, and B-type natriuretic peptide (BNP), mainly released from ventricular cardiomyocytes. Both ANP and BNP have natri-uretic, diuretic, and vasodilatory effects. Secondary functions include inhibition of the

Chapter 2

36

RAAS system and endothelin-1 secretion [67]. ANP and BNP are the biologically active peptides but their prognostic performance is less strong compared to their precursor peptides. N-terminal pro B-type natriuretic peptide (NT-proBNP) is a part of the precur-sor peptide BNP and is more stable and has a longer half-life [68]. The same applies for ANP. ANP has a short half-life and measurements are unreliable [69•]. N-terminal pro atrial natriuretic peptide (NT-proANP) is more stable but also prone to degradation [69•]. Another precursor of ANP, midregional pro atrial-type natriuretic peptide (MR-proANP) is a more stable alternative and has also shown its predictive value [69•, 70].

For diagnosis of ADHF, BNP and NT-proBNP are equally effective [71]. However, this is different for prognosis. Compared to clinical assessment (history taking and physical examination) or New York Heart Association (NYHA) symptoms, NT-proBNP is superior in predicting outcome [72, 73]. Serial measurements of NT-proBNP have also been shown to have incremental prognostic value, both the relative change as well as the absolute value [74].

However, the percent change in NT-proBNP seems to be more important than the ab-solute value at discharge [75]. The PROTECT study also showed that biomarker-guided therapy in patients with acute heart failure was useful: a 50 % reduction in NT-proBNP was associated with almost 50 % reduction of events [76]. Future studies should confirm this in an acute HF cohort. For patients with chronic heart failure, NT-proBNP guided therapy remains controversial. Some suggested that adding serial measurements of NT-proBNP to optimal clinical management may not improve outcome [77•]. Further-more, posttreatment or discharge values of NTpro-BNP are more predictive for adverse outcome compared to NT-proBNP levels at hospital admission [75, 78]. The ongoing Guiding Evidence Based Therapy Using Biomarker Intensified Treatment (GUIDE-IT; Clini-calTrials.gov Identifier: NCT01685840) trial is expected to provide more definite answers if natriuretic peptide guided therapy is helpful in systolic heart failure. In conclusion, both absolute values as well as relative changes of NT-proBNP during hospitalization are informative.

When NPs are used for risk prediction, certain confounders have to be taken into ac-count. Renal failure will increase NP levels [79] and obese patients tend to have lower levels of NPs [80]. Because of the beneficial effects of NPs, it was hypothesized that systemic infusion of NPs will have favorable effects in patients. However, recombinant NPs as a therapeutic did not lead to less mortality [81•].

MR-proANP is a relatively new player and has shown strong prognostic value at least comparable to NT-proBNP or BNP [24•]. For long-term prognosis, MR-proANP seems to be more predictive than NT-proBNP or BNP [82••]. Serial measurements of MR-proANP added prognostic information, also in a multivariate analysis after adjustment for cofac-tors [26•]. Further investigation is warranted to assess if MR-proANP indeed outperforms NT-proBNP, which is the gold standard nowadays.

37


Chap

ter 2

mARkeRs oF inFlAmmATion

Heart failure is considered as a proinflammatory state, and furthermore, infection is an important precipitating factor for ADHF, especially in the elderly. Therefore, markers of infection have been considered as predictors for prognosis [83].

C-Reactive Protein (CRP)

Progression of heart failure occurs, at least in part, by the effect of proinflammatory cytokines like IL-6 and TNF-alpha [84]. IL-6 induces release of CRP, synthesized by the liver [85]. CRP is recognized as one of the best markers reflecting inflammation and has a function in the activation of the complement system [86]. Levels of high sensitivity (hs)CRP are higher in patients with HF and are associated with mortality and rehospi-talization in patients with ADHF [87, 88]. However, CRP is less reliable as a predictor for prognosis in the treatment of HF patients with secondary infections, and more reliable as a predictor for outcomes in noninfected patients [89•].

Procalcitonin

In infective states, especially bacterial infections, procalcitonin is upregulated by endotoxin stimuli but it can also be triggered in a noninfective inflammatory state by proinflammatory cytokines, such as IL-6 or TNF [90, 91]. This implies that procalcitonin levels are not only increased in pneumonia or sepsis, but can also be elevated in cardio-genic shock and acute coronary syndrome (ACS) [92, 93]. Procalcitonin is a precursor peptide of calcitonin, and is released from the liver and peripheral blood mononuclear cells [91]. Following (bacterial) infection, procalcitonin levels reach a fast peak (within eight hours), even before C-reactive protein [94]. Procalcitonin has been shown to be an independent predictor for 30-day mortality in ADHF [95•]. However, another study did not find this association with all-cause mortality [96]. Whether procalcitonin is a strong predictor for outcome in acute heart failure remains uncertain and more studies are needed to answer this question. Procalcitonin can be helpful to identify patients with pneumonia [97] and elevated levels (>0.5 ng/ml) have a specificity of 0.99 for diagnosis of systemic infection [98], so that current procalcitonin studies in HF primarily address the coexistence of pneumonia.

il-6

IL-6 is a proinflammatory cytokine, released in response to infection [99•] IL-6 can bind IL-6R, a membrane receptor. This complex activates gp130, which induces intracellular inflammatory signaling pathways [100]. IL-6 is an important mediator of the acute phase response and induces release of CRP [101]. IL-6 is significantly correlated with 180-day mortality [102] and is predictive for mortality in patients with ADHF [103, 104•, 105].

Chapter 2

38

mARkeRs oF myoCyTe injuRy

Troponin

During admission for ADHF, increased myocardial wall stress and elevated left ventricular end diastolic pressure exist, which lead to decreased myocardial perfusion and subse-quent myocyte necrosis and apoptosis [106•]. As a result of this (subclinical) myocardial injury, troponin can be released from the cytosol of cardiomyocytes into the circulation [107]. Troponin is a cardiac regulatory protein and controls actin-myosin interaction [108]. The troponin complex consists of an I, T, and C complex, which can be found on the actin filament [109]. Troponin C is not specific for the heart; however, troponin T & I are specific markers for myocardial injury [108] and can be used to confirm the diagnosis of ACS [110] and to estimate infarct size [111]. Troponin has a high specificity; however, there are certain factors that can interfere with circulating levels of troponin, such as renal failure, sex, age, and LV hypertrophy [112]. Troponin I and T were both found to be important prognostic factors in patients with ADHF [113••]. Patients with elevated levels of troponin had lower (systolic) blood pressure, lower LV ejection fraction, and higher in-hospital mortality (8.0 % vs 2.7 %) [113••]. In comparing high sensitivity troponins, troponin T was found to be a better predictor for outcome compared to troponin I [114•]. Serial measurements of troponin were also found to be predictive for outcome. Poor outcomes were observed in patients with increasing troponin levels after hospital admission and treatment for ADHF [115].

mARkeRs oF RenAl injuRy

Renal insufficiency is common in patients with acute heart failure. Worsening renal func-tion and acute kidney injury are both being associated with a worse prognosis [116], and acute kidney injury occurs in 30-50 % of patients hospitalized for ADHF and is associated with prolonged length of stay, increased hospitalization costs, and increased mortal-ity [117•, 118]. For this reason, markers of renal injury like cystatin C and Neutrophil Gelatinase-Associated Lipocalin (NGAL) may be useful in identifying high-risk patients.

Cystatin C

Cystatin C is a cysteine protease inhibitor that is released from all functioning cells. It is constantly produced and is freely filtered by the glomerulus. Because it is not actively secreted by renal tubules, it is considered an ideal endogenous marker of renal (glomer-ular) function [119, 120]. It has been reported that high cystatin C levels are associated with a higher risk for mortality and/or HF readmission, and also after multivariate adjust-ments [121]. A prospective, observational, multicenter study conducted in hospitalized

39


Chap

ter 2

patients with acute heart failure showed that cystatin C remained a significant predictor for outcome after adjustment for established biochemical predictors of adverse events such as creatinine, estimated glomerular filtration rate (Cockcroft-Gault), hemoglobin, sodium, and NT-proBNP [122]. Cystatin C levels of ADHF patients were much higher in these patients compared to ambulatory patients with chronic HF. It was speculated that these findings probably reflect a more advanced kidney dysfunction in patients with ADHF, resulting in a poor prognosis in this population.

nGAl

Neutrophil gelatinase-associated lipocalin (NGAL) is a 25-kDa protein secreted by renal tubular cells, leucocytes, and epithelial cells. It is released in response to ischemic or toxic injury and is a measure of acute kidney injury and precedes creatinine elevation [123•]. ADHF patients with elevated serum NGAL levels at admission have a higher risk that is associated with the development of worsening renal function [124]. NGAL has been shown to be a strong prognostic indicator of 30 days outcome at discharge for patients admitted to the hospital for ADHF [123•]. NGAL improved reclassification over BNP with 29.8 %, but in a model containing only BNP without clinical variables (Table 1).

HeAd-To-HeAd ComPARisons And mulTimARkeR APPRoACHes

Nowadays, a large number of biomarkers is available for estimating prognosis after ADHF. Comparing these biomarkers for risk prediction might be difficult, because each of them is reflecting a different pathophysiology, but they may help to identify which marker, or which combination of markers, provides most incremental value. Several studies have examined the prognostic value of multiple biomarkers in the same study.

The MOCA trial studied the incremental value of biomarkers in a multinational obser-vational cohort on ADHF and was comprised of 5,306 patients [125••]. Several biomarkers provided incremental value in risk prediction compared to the clinical prediction model (age, gender, blood pressure, estimated glomerular filtration rate, heart rate, and sodium and hemoglobin levels). For prediction of 30-day mortality, MR-proADM showed the highest incremental value in risk prediction (NRI: 28.7 %) (Table 1). For 1-year prognosis, sST2 was found to be the best predictor (NRI: 10.3 %). Also dual biomarker combinations were studied. For 30-day mortality, the combination of CRP and MR-proADM yielded the highest incremental value in predicting prognosis (NRI: 36.8 %) and for 1-year mortality the combination of CRP and sST2 was found to be the best predictor (NRI: 20.3 %).

In the BACH trial, a prospective, multicenter study of 1,641 patients presenting with dyspnea at the emergency department, MR-proADM again was found to be the best

Chapter 2

40

predictor for mortality at several time points compared to BNP and NT-proBNP. However, for prediction of rehospitalization, BNP was still superior [16•].

Two large cohorts, Biomarcoeurs and FINN-AKVA, studied the prognostic value of natriuretic peptides of patients with ADHF [82••]. MR-proANP was found to be the best predictor for 5-year mortality (NRI: 23 %). Gegenhuber et al., measured several promis-ing biomarkers for risk prediction in 137 patients with ADHF [24•]. For the prediction of 1-year mortality, MR-proANP had the highest predictive value (AUC: 0.73), before BNP (AUC: 0.72), MR-proADM (AUC: 0.71), and copeptin (AUC: 0.69).

The GALLANT trial showed that the renal marker NGAL was a significant predictor for events in multivariable analyses while neither eGFR nor creatinine reached significance. Furthermore, NGAL improved reclassification over BNP by a net 10.3 % [123•].

Zairis et al., studied 577 patients with ADHF. Cox regression modeling showed that BNP was the best predictor, compared to cardiac troponin I and hs-CRP. The combina-tion of these biomarkers improved C-statistics from 0.70 to 0.82 [126•]. Potocki et al., performed a prospective, observational cohort study in 287 patients with acute dyspnea [127]. AUC was highest for copeptin (0.83), followed by NT-proBNP (0.76), and BNP (0.63). After inclusion of copeptin to the adjusted model (including NT-proBNP), NRI was 37 %. Finally, a small study of 79 patients reported BNP to be the best predictor for mortality in univariate analysis, compared to proinflammatory cytokines, including IL-6 [128].

GendeR diFFeRenCes

In biomarkers for acute heart failure, sex based differences have been described. For instance, levels of natriuretic peptides are higher in healthy women, compared to healthy men [129]. This may partly be explained by estrogen-mediated stimulation and androgen-mediated suppression [130, 131] and; therefore, it was suggested to use sex specific reference limits [129]. However, in decompensated state, these sex based differ-ences are not significantly different. This may be explained by the higher percentage of women with HFpEF, which is accompanied by lower values of natriuretic peptides [132]. Besides natriuretic peptides, levels of biomarkers that are related to cardiac remodeling or inflammation (syndecan-1, CRP, GDF-15, IL-6) are found to be significantly lower in women compared to men [133•]. However, the fibrosis marker galectin-3 generally shows higher values in healthy women [132, 134•]. In contrast, another fibrotic marker, sST2, is significantly lower in women, which might be due to the effects of sex hormones [135]. Recently, a new intriguing biomarker was also discovered with prognostic value specifi-cally for women [136]. Proneurotensin was found to be associated with development of cardiovascular disease and cardiovascular mortality, but only in women. Proneurotensin

41


Chap

ter 2

is a stable precursor of neurotensin, and regulates satiety, but more research is needed to establish its role in development of cardiovascular disease in women.

new deVeloPmenTs

He4/wAP4C

Recently, human epididymis protein 4 (HE4) or whey acidic protein four-disulphide core (WAP4C) was identified as one of the most upregulated genes in cultured myofibro-blasts. HE4 is an established biomarker for epithelial ovarian cancer and is known for its antiprotease and anti-inflammatory function [137]. HE4 is not cardiospecific and is associated with GDF-15, and in this regard may play a role in the immune response. In an analysis of the COACH trial, a multicenter randomized study with 567 patients admit-ted for ADHF, HE4 was associated with HF severity and outcome. Furthermore, HE4 was shown to be an independent predictor for HF outcome and improved risk classification (NRI: 31 %, over clinical model + BNP) [138•]. A recent animal study showed that ad-ministration of HE4-neutralizing antibodies had protective effects in preventing fibrosis [139••]. These results show that HE4 could be a potential new biomarker for (acute) heart failure, and may serve as a new therapeutic target to prevent fibrosis.

urinary C-type natriuretic Peptide

In HF, renal dysfunction is common and also an independent predictor of outcome in patients with ADHF [140]. However, the prognostic value of renal tubular injury is not reflected in current estimates of kidney function. Recently, urinary C-type natriuretic peptide was discovered as a biomarker for HF. This urinary peptide is produced both in the kidney as well as in the endothelium of renal tubules and plasma levels of a specific isoform are elevated in patients with ADHF [141]. Urinary C-type natriuretic peptide is a venodilator and undergoes downstream cleavage into NT-proCNP and CNP53, which is further processed to NT-CNP53. NT-CNP53 has a longer half-life and is more stable compared to the active CNP [142]. In a study of 58 patients with ADHF, urinary NT-CNP53 excretion was found to be predictive for mortality and all-cause rehospitalization or death [143•], even after adjustment for age, urinary protein/creatinine ratio and plasma NT-proBNP. In the same study, urinary KIM-1 and NGAL excretion were no significant predictors for outcome. Additionally, NT-CNP53 showed incremental prognostic value to plasma NT-proBNP for the combined endpoint of death and rehospitalization.

syndecan-1

Syndecan-1 is a transmembrane receptor and a member of the proteoglycan family. It is associated with fibrotic and inflammatory diseases and is involved in matrix-matrix inter-

Chapter 2

42

actions. In a recent study, syndecan-1 was found to be a predictor for clinical outcome in patients HFpEF, but not in patients with reduced ejection fraction (HFrEF), independent of other known heart failure risk factors [144•]. Multivariable regression analysis showed a positive correlation with markers of fibrosis and remodeling (galectin-3, ST2, periostin) but not with markers for inflammation. Syndecan-1 also improved risk classification in patients with HFpEF after adding to the established model of clinical risk factors (NRI: 48.5 %). Syndecan-1 could serve as a potential predictor for outcome specifically in patients with HFpEF, but clearly more studies are needed to confirm this.

Bioelectrical impedance Vector Analysis (BiVA)

In healthy individuals, total body water (TBW) comprises approximately 60 % of body weight and can be found intracellular and extracellular. One third of TBW is extracellular, both in interstitial fluid and in intravascular fluid. Estimation of body fluid status was found to be important for risk stratification in ill patients [145], but it is often difficult to assess. Bioimpedance analysis is a noninvasive technique to estimate volume status and body composition. This is measured by bioelectrical impedance, where resistance was found to be inversely related to TBW. BIVA is now considered to be the superior method to assess TBW and was recently tested in 270 patients with acute heart failure. BIVA showed significant prognostic value in univariate and multivariate analysis for a 30-day follow-up of cardiac events and improved prognostic value to BNP, particularly in the BNP ‘grey zone’ (100-400 pg/ml) [146, 147•]. BIVA is a promising new technique for risk prediction in acute heart failure. More and larger studies on this topic should elucidate if BIVA could serve as a new prognostic marker in acute heart failure.

ConClusion

Numerous studies have demonstrated the clinical utility of different biomarkers in acute and chronic HF. Currently, NPs are routinely used in HF care, but there are several novel biomarkers. Biomarkers should be critically reviewed and should fulfill certain criteria [148]. Baseline and repeated measurements should be possible and at reasonable cost. Secondly, the biomarker should provide information that is not already available from clinical assessment. During the last decade, (NT-pro) BNP was the benchmark as prognostic biomarker for outcome in patients with ADHF. However, recent head-to-head comparisons show that MR-proANP and MR-proADM are promising biomarkers that could maybe replace (NT-Pro) BNP as the benchmark.

Novel biomarkers should demonstrate at least comparable diagnostic or prognostic value over currently available assays [149]. Multiple studies nowadays report a net reclassification index, which make it easy to report the incremental prognostic value of

43


Chap

ter 2

a biomarker (Table 1). Nevertheless, it remains difficult to find a single biomarker that fulfills all the needs for the evaluation of prognosis in patients with ADHF. Some bio-markers have shown their predictive value, however this remains limited over already existing clinical parameters [125••]. This also accounts for multimarker studies where only modest prognostic improvements were found compared to only traditional risk factors [150]. A good clinical prediction model often has high prognostic value with corresponding high AUC values [125••] so that improving above and beyond a good model by adding a biomarker is difficult. Therefore, current studies are focusing on com-binations of biomarkers for risk prediction in acute heart failure. Ultimately, multimarker strategy may provide superior risk stratification compared to single biomarker measure-ment [55], particularly when biomarkers from distinct pathophysiologic pathways are combined, providing ‘orthogonal’ information to one another.

In the future, discovery of novel biomarkers may be achieved by proteomics and metabolomics, although these omics-based techniques have yet to yielded new bio-markers of acute heart failure. There are promising results in cardiac transplantation [151•], and; hence, future studies could yield new interesting biomarkers for acute heart failure. Future research should also elucidate if a multimarker strategy is cost-effective. Furthermore, biomarker guided trials are underway. If monitoring of biomarkers are of incremental value in patients with acute heart failure, they should provide more clarity.

Chapter 2

44

ReFeRenCes

Papers of particular interest have been highlighted as:• Of importance•• Of major importance 1. •• Follath F, Yilmaz MB, Delgado JF, et al. Clinical presentation, management and outcomes in

the Acute Heart Failure Global Survey of Standard Treatment (ALARM-HF). Intensive Care Med 2011,37:619-626

ALARM-HF was a large survey among 4,953 patients with AHF in 9 different countries and described characteristics and management of acute heart failure in these patients.

2. Rudiger A, Harjola VP, Muller A, et al. Acute heart failure: clinical presentation, one-year mortality and prognostic factors. Eur J Heart Fail 2005,7:662-670

3. • Harjola VP, Follath F, Nieminen MS, et al. Characteristics, outcomes, and predictors of mortality at 3 months and 1 year in patients hospitalized for acute heart failure. Eur J Heart Fail 2010,12:239-248

The EuroHeart Failure Survey (EHFS) II evaluated 3- and 12-month mortality in 2,981 patients with acute heart failure from 30 different countries and identified clinical risk markers.

4. • Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred defini-tions and conceptual framework. Clin Pharmacol Ther 2001,69:89-95

The Biomarkers Definitions Working Group defined biomarkers as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention.

5. Lee CS, Tkacs NC. Current concepts of neurohormonal activation in heart failure: mediators and mechanisms. AACN Adv Crit Care 2008,19:364-85; quiz 386-7

6. Misra A, Mann DL. Treatment of heart failure beyond practice guidelines. Role of cardiac remodel-ing. Circ J 2008,72 Suppl A:A1-7

7. Jougasaki M, Burnett JC,Jr. Adrenomedullin: potential in physiology and pathophysiology. Life Sci 2000,66:855-872

8. Sata M, Kakoki M, Nagata D, et al. Adrenomedullin and nitric oxide inhibit human endothelial cell apoptosis via a cyclic GMP-independent mechanism. Hypertension 2000,36:83-88

9. • Lainchbury JG, Troughton RW, Lewis LK, Yandle TG, Richards AM, Nicholls MG. Hemodynamic, hormonal, and renal effects of short-term adrenomedullin infusion in healthy volunteers. J Clin Endocrinol Metab 2000,85:1016-1020

This article describes the hemodynamic effects of systemic infusion of adrenomedullin up to patho-physiological plasma levels in healthy volunteers.

10. Nicholls MG, Charles CJ, Lainchbury JG, et al. Adrenomedullin in heart failure. Hypertens Res 2003,26 Suppl:S135-40

11. Meeran K, O’Shea D, Upton PD, et al. Circulating adrenomedullin does not regulate systemic blood pressure but increases plasma prolactin after intravenous infusion in humans: a pharmacokinetic study. J Clin Endocrinol Metab 1997,82:95-100

12. Morgenthaler NG, Struck J, Alonso C, Bergmann A. Measurement of midregional proadreno-medullin in plasma with an immunoluminometric assay. Clin Chem 2005,51:1823-1829

13. Travaglino F, Russo V, De Berardinis B, et al. Thirty and ninety days mortality predictive value of admission and in-hospital procalcitonin and mid-regional pro-adrenomedullin testing in patients with dyspnea. Results from the VERyfing DYspnea trial. Am J Emerg Med 2014,32:334-341

45


Chap

ter 2

14. Cinar O, Cevik E, Acar A, et al. Evaluation of mid-regional pro-atrial natriuretic peptide, procalcito-nin, and mid-regional pro-adrenomedullin for the diagnosis and risk stratification of dyspneic ED patients. Am J Emerg Med 2012,30:1915-1920

15. Shah RV, Truong QA, Gaggin HK, Pfannkuche J, Hartmann O, Januzzi JL,Jr. Mid-regional pro-atrial natriuretic peptide and pro-adrenomedullin testing for the diagnostic and prognostic evaluation of patients with acute dyspnoea. Eur Heart J 2012,33:2197-2205

16. • Maisel A, Mueller C, Nowak RM, et al. Midregion prohormone adrenomedullin and prognosis in patients presenting with acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial. J Am Coll Cardiol 2011,58:1057-1067

The BACH trial was a prospective, multicenter, international study of 1,641 ADHF patients presenting with dyspnea at the emergency department and studied the diagnostic and prognostic value of vari-ous biomarkers.

17. Peacock WF, Nowak R, Christenson R, et al. Short-term mortality risk in emergency department acute heart failure. Acad Emerg Med 2011,18:947-958

18. • Chatterjee K. Neurohormonal activation in congestive heart failure and the role of vasopressin. Am J Cardiol 2005,95:8B-13B

This review focuses on the different components of neurohormonal activation in heart failure. 19. Francis GS, Benedict C, Johnstone DE, et al. Comparison of neuroendocrine activation in patients

with left ventricular dysfunction with and without congestive heart failure. A substudy of the Studies of Left Ventricular Dysfunction (SOLVD). Circulation 1990,82:1724-1729

20. Stoiser B, Mortl D, Hulsmann M, et al. Copeptin, a fragment of the vasopressin precursor, as a novel predictor of outcome in heart failure. Eur J Clin Invest 2006,36:771-778

21. Morgenthaler NG, Struck J, Alonso C, Bergmann A. Assay for the measurement of copeptin, a stable peptide derived from the precursor of vasopressin. Clin Chem 2006,52:112-119

22. Struck J, Morgenthaler NG, Bergmann A. Copeptin, a stable peptide derived from the vasopressin precursor, is elevated in serum of sepsis patients. Peptides 2005,26:2500-2504

23. Meijer E, Bakker SJ, Halbesma N, de Jong PE, Struck J, Gansevoort RT. Copeptin, a surrogate marker of vasopressin, is associated with microalbuminuria in a large population cohort. Kidney Int 2010,77:29-36

24. • Gegenhuber A, Struck J, Dieplinger B, et al. Comparative evaluation of B-type natriuretic peptide, mid-regional pro-A-type natriuretic peptide, mid-regional pro-adrenomedullin, and Copeptin to predict 1-year mortality in patients with acute destabilized heart failure. J Card Fail 2007,13:42-49

This study compares the prognostic value of different novel biomarkers and concludes some have similar predictive properties compared with BNP for 1-year all-cause mortality.

25. Neuhold S, Huelsmann M, Strunk G, et al. Prognostic value of emerging neurohormones in chronic heart failure during optimization of heart failure-specific therapy. Clin Chem 2010,56:121-126

26. • Miller WL, Hartman KA, Grill DE, Struck J, Bergmann A, Jaffe AS. Serial measurements of midre-gion proANP and copeptin in ambulatory patients with heart failure: incremental prognostic value of novel biomarkers in heart failure. Heart 2012,98:389-394

The authors studied serial monitoring of MR-proANP and copeptin and concluded this was useful for detecting the highest-risk outpatients with HF.

27. • Taupenot L, Harper KL, O’Connor DT. The chromogranin-secretogranin family. N Engl J Med 2003,348:1134-1149

This is an extensive review about the functions and mechanisms of the chromogranin-secretogranin family.

Chapter 2

46

28. Mazza R, Imbrogno S, Tota B. The interplay between chromogranin A-derived peptides and cardiac natriuretic peptides in cardioprotection against catecholamine-evoked stress. Regul Pept 2010,165:86-94

29. Pieroni M, Corti A, Tota B, et al. Myocardial production of chromogranin A in human heart: a new regulatory peptide of cardiac function. Eur Heart J 2007,28:1117-1127

30. • Dieplinger B, Gegenhuber A, Struck J, et al. Chromogranin A and C-terminal endothelin-1 pre-cursor fragment add independent prognostic information to amino-terminal proBNP in patients with acute destabilized heart failure. Clin Chim Acta 2009,400:91-96

In 137 patients with acute destabilized heart failure, increased chromogranin A and CT-proET-1 plasma concentrations add independent prognostic information to NT-proBNP.

31. Teiger E, Than VD, Richard L, et al. Apoptosis in pressure overload-induced heart hypertrophy in the rat. J Clin Invest 1996,97:2891-2897

32. •• Kong P, Christia P, Frangogiannis NG. The pathogenesis of cardiac fibrosis. Cell Mol Life Sci 2014,71:549-574

This review discusses the molecular pathways that are involved in myocardial fibrosis. 33. Weinberg EO, Shimpo M, De Keulenaer GW, et al. Expression and regulation of ST2, an inter-

leukin-1 receptor family member, in cardiomyocytes and myocardial infarction. Circulation 2002,106:2961-2966

34. Shimpo M, Morrow DA, Weinberg EO, et al. Serum levels of the interleukin-1 receptor family member ST2 predict mortality and clinical outcome in acute myocardial infarction. Circulation 2004,109:2186-2190

35. •• Adams KF,Jr, Fonarow GC, Emerman CL, et al. Characteristics and outcomes of patients hospital-ized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). Am Heart J 2005,149:209-216

The ADHERE registry describes characteristics and outcome of 100,000 patients hospitalized for acute decompensated heart failure.

36. Tago K, Noda T, Hayakawa M, et al. Tissue distribution and subcellular localization of a variant form of the human ST2 gene product, ST2V. Biochem Biophys Res Commun 2001,285:1377-1383

37. Sanada S, Hakuno D, Higgins LJ, Schreiter ER, McKenzie AN, Lee RT. IL-33 and ST2 comprise a criti-cal biomechanically induced and cardioprotective signaling system. J Clin Invest 2007,117:1538-1549

38. Seki K, Sanada S, Kudinova AY, et al. Interleukin-33 prevents apoptosis and improves survival after experimental myocardial infarction through ST2 signaling. Circ Heart Fail 2009,2:684-691

39. Januzzi JL,Jr, Peacock WF, Maisel AS, et al. Measurement of the interleukin family member ST2 in patients with acute dyspnea: results from the PRIDE (Pro-Brain Natriuretic Peptide Investigation of Dyspnea in the Emergency Department) study. J Am Coll Cardiol 2007,50:607-613

40. Mueller T, Dieplinger B, Gegenhuber A, Poelz W, Pacher R, Haltmayer M. Increased plasma concen-trations of soluble ST2 are predictive for 1-year mortality in patients with acute destabilized heart failure. Clin Chem 2008,54:752-756

41. • Pascual-Figal DA, Manzano-Fernandez S, Boronat M, et al. Soluble ST2, high-sensitivity troponin T- and N-terminal pro-B-type natriuretic peptide: complementary role for risk stratification in acutely decompensated heart failure. Eur J Heart Fail 2011,13:718-725

This study investigated the use of ST2 and hs-Troponin and conclude these markers provide superior risk stratification over NT-proBNP.

47


Chap

ter 2

42. Shah RV, Chen-Tournoux AA, Picard MH, van Kimmenade RR, Januzzi JL. Serum levels of the inter-leukin-1 receptor family member ST2, cardiac structure and function, and long-term mortality in patients with acute dyspnea. Circ Heart Fail 2009,2:311-319

43. Shah RV, Januzzi JL,Jr. ST2: a novel remodeling biomarker in acute and chronic heart failure. Curr Heart Fail Rep 2010,7:9-14

44. Breidthardt T, Balmelli C, Twerenbold R, et al. Heart failure therapy-induced early ST2 changes may offer long-term therapy guidance. J Card Fail 2013,19:821-828

45. • Gaggin HK, Szymonifka J, Bhardwaj A, et al. Head-to-head comparison of serial soluble ST2, growth differentiation factor-15, and highly-sensitive troponin T measurements in patients with chronic heart failure. JACC Heart Fail 2014,2:65-72

This article performed a head-to-head comparison of 3 promising biomarkers in chronic heart failure and concluded only serial ST2 measurements added prognostic information to baseline values.

46. Sharma UC, Pokharel S, van Brakel TJ, et al. Galectin-3 marks activated macrophages in failure-prone hypertrophied hearts and contributes to cardiac dysfunction. Circulation 2004,110:3121-3128

47. • de Boer RA, Voors AA, Muntendam P, van Gilst WH, van Veldhuisen DJ. Galectin-3: a novel media-tor of heart failure development and progression. Eur J Heart Fail 2009,11:811-817

This review describes the mechanisms of galectin-3 in development of heart failure. 48. • Calvier L, Miana M, Reboul P, et al. Galectin-3 mediates aldosterone-induced vascular fibrosis.

Arterioscler Thromb Vasc Biol 2013,33:67-75 The authors conclude that aldosterone might induce fibrosis via a galectin-3 dependent pathway. 49. Sasaki T, Brakebusch C, Engel J, Timpl R. Mac-2 binding protein is a cell-adhesive protein of the

extracellular matrix which self-assembles into ring-like structures and binds beta1 integrins, col-lagens and fibronectin. EMBO J 1998,17:1606-1613

50. Song L, Tang JW, Owusu L, Sun MZ, Wu J, Zhang J. Galectin-3 in cancer. Clin Chim Acta 2014,431C:185-191

51. Henderson NC, Sethi T. The regulation of inflammation by galectin-3. Immunol Rev 2009,230:160-171

52. Carrasco-Sanchez FJ, Aramburu-Bodas O, Salamanca-Bautista P, et al. Predictive value of serum galectin-3 levels in patients with acute heart failure with preserved ejection fraction. Int J Cardiol 2013,169:177-182

53. Meijers W.C., Januzzi J.L., deFilippi C. et al. Elevated Plasma Galectin-3 is Associated with Near-Term Rehospitalization in Heart Failure: A Pooled Analysis of 3 Clinical Trials. American Heart Journal 2014,

54. • van Kimmenade RR, Januzzi JL,Jr, Ellinor PT, et al. Utility of amino-terminal pro-brain natriuretic peptide, galectin-3, and apelin for the evaluation of patients with acute heart failure. J Am Coll Cardiol 2006,48:1217-1224 This substudy of the PRIDE demonstrated the clinical utility of galectin-3 for the first time, showing that galectin-3 was the best predictor for 60-day mortality.

55. Eurlings LW, Sanders-van Wijk S, van Kimmenade R, et al. Multimarker strategy for short-term risk assessment in patients with dyspnea in the emergency department: the MARKED (Multi mARKer Emergency Dyspnea)-risk score. J Am Coll Cardiol 2012,60:1668-1677

56. van der Velde AR, Gullestad L, Ueland T, et al. Prognostic value of changes in galectin-3 levels over time in patients with heart failure: data from CORONA and COACH. Circ Heart Fail 2013,6:219-226

57. •• Yu L, Ruifrok WP, Meissner M, et al. Genetic and pharmacological inhibition of galectin-3 prevents cardiac remodeling by interfering with myocardial fibrogenesis. Circ Heart Fail 2013,6:107-117

Chapter 2

48

This article showed that galectin-3 could also serve as a “target for therapy”. Inhibition of galectin-3 resulted in attenuation of fibrosis in animal models of heart failure.

58. • Xu J, Kimball TR, Lorenz JN, et al. GDF15/MIC-1 functions as a protective and antihypertrophic factor released from the myocardium in association with SMAD protein activation. Circ Res 2006,98:342-350

The authors identified GDF-15 as a novel antagonist of hypertrophic response. 59. Kempf T, Eden M, Strelau J, et al. The transforming growth factor-beta superfamily member

growth-differentiation factor-15 protects the heart from ischemia/reperfusion injury. Circ Res 2006,98:351-360

60. de Jager SC, Bermudez B, Bot I, et al. Growth differentiation factor 15 deficiency protects against atherosclerosis by attenuating CCR2-mediated macrophage chemotaxis. J Exp Med 2011,208:217-225

61. Kempf T, von Haehling S, Peter T, et al. Prognostic utility of growth differentiation factor-15 in patients with chronic heart failure. J Am Coll Cardiol 2007,50:1054-1060

62. Foley PW, Stegemann B, Ng K, et al. Growth differentiation factor-15 predicts mortality and morbidity after cardiac resynchronization therapy. Eur Heart J 2009,30:2749-2757

63. Anand IS, Kempf T, Rector TS, et al. Serial measurement of growth-differentiation factor-15 in heart failure: relation to disease severity and prognosis in the Valsartan Heart Failure Trial. Circula-tion 2010,122:1387-1395

64. Lok DJ, Klip IT, Lok SI, et al. Incremental prognostic power of novel biomarkers (growth-differenti-ation factor-15, high-sensitivity C-reactive protein, galectin-3, and high-sensitivity troponin-T) in patients with advanced chronic heart failure. Am J Cardiol 2013,112:831-837

65. Izumiya Y, Hanatani S, Kimura Y, et al. Growth differentiation factor-15 is a useful prognostic marker in patients with heart failure with preserved ejection fraction. Can J Cardiol 2014,30:338-344

66. •• Doust JA, Pietrzak E, Dobson A, Glasziou P. How well does B-type natriuretic peptide predict death and cardiac events in patients with heart failure: systematic review. BMJ 2005,330:625

This systematic review concludes that BNP is a strong prognostic factor in patients with heart failure. 67. Brunner-La Rocca HP, Kaye DM, Woods RL, Hastings J, Esler MD. Effects of intravenous brain natri-

uretic peptide on regional sympathetic activity in patients with chronic heart failure as compared with healthy control subjects. J Am Coll Cardiol 2001,37:1221-1227

68. Kimura K, Yamaguchi Y, Horii M, et al. ANP is cleared much faster than BNP in patients with con-gestive heart failure. Eur J Clin Pharmacol 2007,63:699-702

69. • Moertl D, Berger R, Struck J, et al. Comparison of midregional pro-atrial and B-type natriuretic peptides in chronic heart failure: influencing factors, detection of left ventricular systolic dysfunc-tion, and prediction of death. J Am Coll Cardiol 2009,53:1783-1790

MR-proANP was found to be a better predictor for death in patients with chronic heart failure, com-pared to BNP and NT-proBNP.

70. Morgenthaler NG, Struck J, Thomas B, Bergmann A. Immunoluminometric assay for the midregion of pro-atrial natriuretic peptide in human plasma. Clin Chem 2004,50:234-236

71. Mueller T, Gegenhuber A, Poelz W, Haltmayer M. Diagnostic accuracy of B type natriuretic peptide and amino terminal proBNP in the emergency diagnosis of heart failure. Heart 2005,91:606-612

72. Maisel A, Hollander JE, Guss D, et al. Primary results of the Rapid Emergency Department Heart Failure Outpatient Trial (REDHOT). A multicenter study of B-type natriuretic peptide levels, emergency department decision making, and outcomes in patients presenting with shortness of breath. J Am Coll Cardiol 2004,44:1328-1333

49


Chap

ter 2

73. Baggish AL, van Kimmenade RR, Pinto Y, et al. New York Heart Association class versus amino-terminal pro-B type natriuretic peptide for acute heart failure prognosis. Biomarkers 2010,15:307-314

74. Bayes-Genis A, Santalo-Bel M, Zapico-Muniz E, et al. N-terminal probrain natriuretic peptide (NT-proBNP) in the emergency diagnosis and in-hospital monitoring of patients with dyspnoea and ventricular dysfunction. Eur J Heart Fail 2004,6:301-308

75. Bettencourt P, Azevedo A, Pimenta J, Frioes F, Ferreira S, Ferreira A. N-terminal-pro-brain natri-uretic peptide predicts outcome after hospital discharge in heart failure patients. Circulation 2004,110:2168-2174

76. Januzzi JL,Jr, Rehman SU, Mohammed AA, et al. Use of amino-terminal pro-B-type natriuretic peptide to guide outpatient therapy of patients with chronic left ventricular systolic dysfunction. J Am Coll Cardiol 2011,58:1881-1889

77. • Schou M, Gustafsson F, Videbaek L, et al. Adding serial N-terminal pro brain natriuretic peptide measurements to optimal clinical management in outpatients with systolic heart failure: a multi-centre randomized clinical trial (NorthStar monitoring study). Eur J Heart Fail 2013,15:818-827

When serial measurements of NT-proBNP were added to optimal clinical management, this was not associated with improved outcome.

78. Waldo SW, Beede J, Isakson S, et al. Pro-B-type natriuretic peptide levels in acute decompensated heart failure. J Am Coll Cardiol 2008,51:1874-1882

79. Mueller C, Laule-Kilian K, Scholer A, et al. B-type natriuretic peptide for acute dyspnea in patients with kidney disease: insights from a randomized comparison. Kidney Int 2005,67:278-284

80. Das SR, Drazner MH, Dries DL, et al. Impact of body mass and body composition on circulating levels of natriuretic peptides: results from the Dallas Heart Study. Circulation 2005,112:2163-2168

81. • O’Connor CM, Starling RC, Hernandez AF, et al. Effect of nesiritide in patients with acute decom-pensated heart failure. N Engl J Med 2011,365:32-43

Infusion of nesiritide in a randomized controlled trial in 7,141 patients did not affect the rate of death or hospitalization.

82. •• Seronde MF, Gayat E, Logeart D, et al. Comparison of the diagnostic and prognostic values of B-type and atrial-type natriuretic peptides in acute heart failure. Int J Cardiol 2013,168:3404-3411

This study of 710 patients evaluated the diagnostic and prognostic value of 4 natriuretic peptides and concluded that MR-proANP had the best prognostic value at 5 years.

83. Testa G, Della-Morte D, Cacciatore F, et al. Precipitating factors in younger and older adults with decompensated chronic heart failure: are they different? J Am Geriatr Soc 2013,61:1827-1828

84. Steele IC, Nugent AM, Maguire S, et al. Cytokine profile in chronic cardiac failure. Eur J Clin Invest 1996,26:1018-1022

85. Pasceri V, Willerson JT, Yeh ET. Direct proinflammatory effect of C-reactive protein on human endothelial cells. Circulation 2000,102:2165-2168

86. Du Clos TW, Mold C. C-reactive protein: an activator of innate immunity and a modulator of adap-tive immunity. Immunol Res 2004,30:261-277

87. Zairis MN, Tsiaousis GZ, Georgilas AT, et al. Multimarker strategy for the prediction of 31 days cardiac death in patients with acutely decompensated chronic heart failure. Int J Cardiol 2010,141:284-290

88. Kalogeropoulos AP, Tang WH, Hsu A, et al. High sensitivity C-reactive protein in acute heart failure: Insights from the ASCEND-HF trial. J Card Fail 2014,

89. • Lourenco P, Paulo Araujo J, Paulo C, et al. Higher C-reactive protein predicts worse prognosis in acute heart failure only in noninfected patients. Clin Cardiol 2010,33:708-714

Chapter 2

50

CRP has prognostic value in acute decompensated heart failure patients without infection but not in patients with infectious complications.

90. Delevaux I, Andre M, Colombier M, et al. Can procalcitonin measurement help in differentiat-ing between bacterial infection and other kinds of inflammatory processes? Ann Rheum Dis 2003,62:337-340

91. Picariello C, Lazzeri C, Valente S, Chiostri M, Gensini GF. Procalcitonin in acute cardiac patients. Intern Emerg Med 2011,6:245-252

92. Picariello C, Lazzeri C, Valente S, Chiostri M, Attana P, Gensini GF. Kinetics of procalcitonin in cardiogenic shock and in septic shock. Preliminary data. Acute Card Care 2010,12:96-101

93. Sinning CR, Sinning JM, Schulz A, et al. Association of serum procalcitonin with cardiovascular prognosis in coronary artery disease. Circ J 2011,75:1184-1191

94. Meisner M, Adina H, Schmidt J. Correlation of procalcitonin and C-reactive protein to inflam-mation, complications, and outcome during the intensive care unit course of multiple-trauma patients. Crit Care 2006,10:R1

95. • Travaglino F, Russo V, De Berardinis B, et al. Thirty and ninety days mortality predictive value of admission and in-hospital procalcitonin and mid-regional pro-adrenomedullin testing in patients with dyspnea. Results from the VERyfing DYspnea trial. Am J Emerg Med 2014,32:334-341

In patients with dyspnea, procalcitonin and MR-proADM improved risk stratification and manage-ment.

96. Naffaa M, Makhoul BF, Tobia A, et al. Procalcitonin and interleukin 6 for predicting blood culture positivity in sepsis. Am J Emerg Med 2014,32:448-451

97. Maisel A, Neath SX, Landsberg J, et al. Use of procalcitonin for the diagnosis of pneumonia in patients presenting with a chief complaint of dyspnoea: results from the BACH (Biomarkers in Acute Heart Failure) trial. Eur J Heart Fail 2012,14:278-286

98. Hausfater P, Garric S, Ayed SB, Rosenheim M, Bernard M, Riou B. Usefulness of procalcitonin as a marker of systemic infection in emergency department patients: a prospective study. Clin Infect Dis 2002,34:895-901

99. • Frangogiannis NG, Smith CW, Entman ML. The inflammatory response in myocardial infarction. Cardiovasc Res 2002,53:31-47

This review describes the different components of the inflammatory response following myocardial infarction.

100. Schuett H, Luchtefeld M, Grothusen C, Grote K, Schieffer B. How much is too much? Interleukin-6 and its signalling in atherosclerosis. Thromb Haemost 2009,102:215-222

101. Rose-John S, Scheller J, Elson G, Jones SA. Interleukin-6 biology is coordinated by membrane-bound and soluble receptors: role in inflammation and cancer. J Leukoc Biol 2006,80:227-236

102. Milo-Cotter O, Cotter-Davison B, Lombardi C, et al. Neurohormonal activation in acute heart failure: results from VERITAS. Cardiology 2011,119:96-105

103. Lassus JP, Harjola VP, Peuhkurinen K, et al. Cystatin C, NT-proBNP, and inflammatory markers in acute heart failure: insights into the cardiorenal syndrome. Biomarkers 2011,16:302-310

104. • Miettinen KH, Lassus J, Harjola VP, et al. Prognostic role of pro- and anti-inflammatory cytokines and their polymorphisms in acute decompensated heart failure. Eur J Heart Fail 2008,10:396-403

Pro- and anti-inflammatory cytokines were measured in 423 patients with acute decompensated heart failure. IL-6, IL-10 and TNF-α provided important prognostic information in these patients.

105. Pudil R, Tichy M, Andrys C, et al. Plasma interleukin-6 level is associated with NT-proBNP level and predicts short- and long-term mortality in patients with acute heart failure. Acta Medica (Hradec Kralove) 2010,53:225-228

51


Chap

ter 2

106. • Mann DL, Bristow MR. Mechanisms and models in heart failure: the biomechanical model and beyond. Circulation 2005,111:2837-2849

This review describes heart failure in different clinical model systems, like a cardiorenal model, a hemodynamic model and a neurohormonal model.

107. Agewall S, Giannitsis E, Jernberg T, Katus H. Troponin elevation in coronary vs. non-coronary disease. Eur Heart J 2011,32:404-411

108. Adams JE,3rd, Bodor GS, Davila-Roman VG, et al. Cardiac troponin I. A marker with high specificity for cardiac injury. Circulation 1993,88:101-106

109. Gaze DC, Collinson PO. Multiple molecular forms of circulating cardiac troponin: analytical and clinical significance. Ann Clin Biochem 2008,45:349-355

110. Thygesen K, Alpert JS, White HD, et al. Universal definition of myocardial infarction. Circulation 2007,116:2634-2653

111. Steen H, Giannitsis E, Futterer S, Merten C, Juenger C, Katus HA. Cardiac troponin T at 96 hours af-ter acute myocardial infarction correlates with infarct size and cardiac function. J Am Coll Cardiol 2006,48:2192-2194

112. Sato Y, Fujiwara H, Takatsu Y. Cardiac troponin and heart failure in the era of high-sensitivity as-says. J Cardiol 2012,60:160-167

113. •• Peacock WF,4th, De Marco T, Fonarow GC, et al. Cardiac troponin and outcome in acute heart failure. N Engl J Med 2008,358:2117-2126

Troponin levels were measured in 84,872 patients who were hospitalized for acute decompensated heart failure. A positive troponin test was associated with higher in-hospital mortality.

114. • de Antonio M, Lupon J, Galan A, et al. Head-to-head comparison of high-sensitivity troponin T and sensitive-contemporary troponin I regarding heart failure risk stratification. Clin Chim Acta 2013,426:18-24

In a head-to-head comparison between troponin T and troponin I, troponin T showed better perfor-mance and was a better predictor for death.

115. Kuwabara Y, Sato Y, Miyamoto T, et al. Persistently increased serum concentrations of cardiac tro-ponin in patients with acutely decompensated heart failure are predictive of adverse outcomes. Circ J 2007,71:1047-1051

116. Damman K, Jaarsma T, Voors AA, et al. Both in- and out-hospital worsening of renal function predict outcome in patients with heart failure: results from the Coordinating Study Evaluating Outcome of Advising and Counseling in Heart Failure (COACH). Eur J Heart Fail 2009,11:847-854

117. • Damman K, Navis G, Voors AA, et al. Worsening renal function and prognosis in heart failure: systematic review and meta-analysis. J Card Fail 2007,13:599-608

This systematic review and meta-analysis concludes that worsening renal function predicts substan-tially higher rates of mortality and hospitalization in patients with heart failure.

118. Sarraf M, Masoumi A, Schrier RW. Cardiorenal syndrome in acute decompensated heart failure. Clin J Am Soc Nephrol 2009,4:2013-2026

119. Laterza OF, Price CP, Scott MG. Cystatin C: an improved estimator of glomerular filtration rate? Clin Chem 2002,48:699-707

120. Fliser D, Ritz E. Serum cystatin C concentration as a marker of renal dysfunction in the elderly. Am J Kidney Dis 2001,37:79-83

121. Manzano-Fernandez S, Boronat-Garcia M, Albaladejo-Oton MD, et al. Complementary prognostic value of cystatin C, N-terminal pro-B-type natriuretic Peptide and cardiac troponin T in patients with acute heart failure. Am J Cardiol 2009,103:1753-1759

Chapter 2

52

122. Lassus J, Harjola VP, Sund R, et al. Prognostic value of cystatin C in acute heart failure in relation to other markers of renal function and NT-proBNP. Eur Heart J 2007,28:1841-1847

123. • Maisel AS, Mueller C, Fitzgerald R, et al. Prognostic utility of plasma neutrophil gelatinase-associated lipocalin in patients with acute heart failure: the NGAL EvaLuation Along with B-type NaTriuretic Peptide in acutely decompensated heart failure (GALLANT) trial. Eur J Heart Fail 2011,13:846-851

In this study, NGAL was measured in 186 patients with acute decompensated heart failure. Plasma NGAL was found to be a strong predictor for 30-days outcome and improved reclassification over BNP.

124. Aghel A, Shrestha K, Mullens W, Borowski A, Tang WH. Serum neutrophil gelatinase-associated lipocalin (NGAL) in predicting worsening renal function in acute decompensated heart failure. J Card Fail 2010,16:49-54

125. •• Lassus J, Gayat E, Mueller C, et al. Incremental value of biomarkers to clinical variables for mor-tality prediction in acutely decompensated heart failure: the Multinational Observational Cohort on Acute Heart Failure (MOCA) study. Int J Cardiol 2013,168:2186-2194

In the MOCA study, 5,306 patients with acute decompensated heart failure were included. Biomarkers including sST2, MR-proADM, natriuretic peptides and CRP added prognostic value to clinical risk fac-tors for predicting mortality.

126. • Zairis MN, Tsiaousis GZ, Georgilas AT, et al. Multimarker strategy for the prediction of 31 days cardiac death in patients with acutely decompensated chronic heart failure. Int J Cardiol 2010,141:284-290

This multimarker study concluded that an increasing number of elevated biomarkers, increased the risk of cardiac death gradually.

127. Potocki M, Breidthardt T, Mueller A, et al. Copeptin and risk stratification in patients with acute dyspnea. Crit Care 2010,14:R213

128. Boffa GM, Zaninotto M, Sartor R, et al. Interleukin-6 and tumor necrosis factor-alpha as biochemi-cal markers of heart failure: a head-to-head clinical comparison with B-type natriuretic peptide. J Cardiovasc Med (Hagerstown) 2009,10:758-764

129. Redfield MM, Rodeheffer RJ, Jacobsen SJ, Mahoney DW, Bailey KR, Burnett JC,Jr. Plasma brain natriuretic peptide concentration: impact of age and gender. J Am Coll Cardiol 2002,40:976-982

130. Hong M, Yan Q, Tao B, et al. Estradiol, progesterone and testosterone exposures affect the atrial natriuretic peptide gene expression in vivo in rats. Biol Chem Hoppe Seyler 1992,373:213-218

131. Deng Y, Kaufman S. The influence of reproductive hormones on ANF release by rat atria. Life Sci 1993,53:689-696

132. Ho JE, Gona P, Pencina MJ, et al. Discriminating clinical features of heart failure with preserved vs. reduced ejection fraction in the community. Eur Heart J 2012,33:1734-1741

133. • Meyer S, van der Meer P, van Deursen VM, et al. Neurohormonal and clinical sex differences in heart failure. Eur Heart J 2013,34:2538-2547

Female patients presenting with heart failure have a different clinical presentation and better out-come compared to male patients.

134. • de Boer RA, van Veldhuisen DJ, Gansevoort RT, et al. The fibrosis marker galectin-3 and outcome in the general population. J Intern Med 2012,272:55-64

In this large community-based cohort, galectin-3 was associated with age and various risk factors of cardiovascular disease, with a strong gender interaction.

135. Coglianese EE, Larson MG, Vasan RS, et al. Distribution and clinical correlates of the interleukin receptor family member soluble ST2 in the Framingham Heart Study. Clin Chem 2012,58:1673-1681

53


Chap

ter 2

136. Melander O, Maisel AS, Almgren P, et al. Plasma proneurotensin and incidence of diabetes, cardio-vascular disease, breast cancer, and mortality. JAMA 2012,308:1469-1475

137. Montagnana M, Danese E, Giudici S, et al. HE4 in ovarian cancer: from discovery to clinical applica-tion. Adv Clin Chem 2011,55:1-20

138. • de Boer RA, Cao Q, Postmus D, et al. The WAP four-disulfide core domain protein HE4: a novel biomarker for heart failure. JACC Heart Fail 2013,1:164-169

In the COACH trial, HE4 improved net reclassification when it was added to the clinical model and was correlated with markers of heart failure severity.

139. •• LeBleu VS, Teng Y, O’Connell JT, et al. Identification of human epididymis protein-4 as a fibroblast-derived mediator of fibrosis. Nat Med 2013,19:227-231

HE4 was found to be a new potential biomarker of (renal) fibrosis and could serve as a new therapeutic target.

140. Smith GL, Lichtman JH, Bracken MB, et al. Renal impairment and outcomes in heart failure: sys-tematic review and meta-analysis. J Am Coll Cardiol 2006,47:1987-1996

141. Del Ry S, Passino C, Maltinti M, Emdin M, Giannessi D. C-type natriuretic peptide plasma levels increase in patients with chronic heart failure as a function of clinical severity. Eur J Heart Fail 2005,7:1145-1148

142. Wu C, Wu F, Pan J, Morser J, Wu Q. Furin-mediated processing of Pro-C-type natriuretic peptide. J Biol Chem 2003,278:25847-25852

143. • Zakeri R, Sangaralingham SJ, Sandberg SM, Heublein DM, Scott CG, Burnett JC,Jr. Urinary C-type natriuretic peptide: a new heart failure biomarker. JACC Heart Fail 2013,1:170-177

Urinary C-type natriuretic peptide was detected in patients with heart failure and may have clinical utility as a biomarker.

144. • Tromp J, van der Pol A, Klip IT, et al. The Fibrosis Marker Syndecan-1 and Outcome in Heart Failure Patients with Reduced and Preserved Ejection Fraction. Circ Heart Fail 2014,

Syndecan-1 levels correlate with biomarkers of fibrosis and was a predictor for outcome in patients with HFpEF but not in HFrEF.

145. Castillo-Martinez L, Colin-Ramirez E, Orea-Tejeda A, et al. Cachexia assessed by bioimpedance vector analysis as a prognostic indicator in chronic stable heart failure patients. Nutrition 2012,28:886-891

146. Di Somma S, Navarin S, Giordano S, et al. The emerging role of biomarkers and bio-impedance in evaluating hydration status in patients with acute heart failure. Clin Chem Lab Med 2012,50:2093-2105

147. • Di Somma S, Lalle I, Magrini L, et al. Additive diagnostic and prognostic value of Bioelectrical Impedance Vector Analysis (BIVA) to brain natriuretic peptide ‘grey-zone’ in patients with acute heart failure in the emergency department. Eur Heart J Acute Cardiovasc Care 2014,

In 270 patients with acute heart failure, Bioelectrical Impedance Vector Analysis provided additive 30-day prognostic value compared to BNP.

148. Morrow DA, de Lemos JA. Benchmarks for the assessment of novel cardiovascular biomarkers. Circulation 2007,115:949-952

149. van Kimmenade RR, Januzzi JL,Jr. Emerging biomarkers in heart failure. Clin Chem 2012,58:127-138

150. Wang TJ, Gona P, Larson MG, et al. Multiple biomarkers for the prediction of first major cardiovas-cular events and death. N Engl J Med 2006,355:2631-2639

Chapter 2

54

151. • Cohen Freue GV, Meredith A, Smith D, et al. Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation. PLoS Comput Biol 2013,9:e1002963

Proteomics has led to discovery of new biomarkers for various diseases, providing new treatments and diagnostics. This proposed computational pipeline is applicable to other biomarker proteomic studies.

152. Scrutinio D, Ammirati E, Guida P, et al. Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and valida-tion of the ADHF/NT-proBNP risk score. Int J Cardiol 2013,168:2120-2126

153. Maisel A, Xue Y, Shah K, et al. Increased 90-day mortality in patients with acute heart failure with elevated copeptin: secondary results from the Biomarkers in Acute Heart Failure (BACH) study. Circ Heart Fail 2011,4:613-620

University of Groningen Galectin-3 in heart failure van der Velde, … · 2017-01-04 ·...

Documents

Transcript of University of Groningen Galectin-3 in heart failure van der Velde, … · 2017-01-04 ·...