Download Change in Growth Differentiation Factor 15

Clinical Chemistry 59:7
1091–1098 (2013)
Proteomics and Protein Markers
Change in Growth Differentiation Factor 15
Concentrations over Time Independently Predicts
Mortality in Community-Dwelling Elderly Individuals
Kai M. Eggers,1* Tibor Kempf,2 Lars Wallentin,1 Kai C. Wollert,2 and Lars Lind1
BACKGROUND: Growth differentiation factor 15 (GDF15) is emerging as a powerful risk indicator in both
cardiovascular disease patients and communitydwelling individuals. We investigated GDF-15 concentrations and their changes over 5 years in elderly individuals from the community, together with the
underlying conditions and prognostic implications of
these measurements.
METHODS: We analyzed GDF-15 concentrations using
a sandwich immunoassay in participants from the
PIVUS (Prospective Investigation of the Vasculature in
Uppsala Seniors) study. Measurements were performed at both 70 (n ⫽ 1004) and 75 (n ⫽ 813) years of
age. Median follow-up was 8.0 years.
RESULTS: Over time, GDF-15 concentrations increased
by 11.0% (P ⬍ 0.001). These changes were related to
male sex, hypertension, diabetes, heart failure, renal
function, and concentrations of N-terminal pro–Btype natriuretic peptide (NT-proBNP). Significant relationships also emerged between changes in GDF-15
and changes in concentrations of NT-proBNP and
C-reactive protein (CRP) and renal function between
ages 70 and 75. The R2 value of the model including all
covariates was 0.20. GDF-15 concentrations independently predicted all-cause mortality [hazard ratio 4.0
(95% CI 2.7– 6.0)] with results obtained at ages 70 and
75 as updated covariates. Baseline GDF-15 concentrations improved prognostic discrimination and reclassification [C statistic 0.06 (P ⫽ 0.006); integrated discrimination improvement ⫽ 0.030 (P ⫽ 0.004);
category-free net reclassification improvement ⫽
0.281 (P ⫽ 0.006)]. The change in GDF-15 concentrations over time independently predicted even allcause mortality occurring after age 75 [hazard ratio
3.6 (95% CI 2.2– 6.0)].
1
Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala
University, Sweden; 2 Division of Molecular and Translational Cardiology, Department of Cardiology and Angiology, Hannover Medical School, Hannover,
Germany.
* Address correspondence to this author at: Department of Medical Sciences,
Cardiology, Uppsala University, S-751 85 Uppsala, Sweden. Fax ⫹46-18-5066-38; e-mail [email protected].
Received December 8, 2012; accepted February 27, 2013.
CONCLUSIONS: GDF-15
concentrations and their
changes over time are powerful predictors of mortality
in elderly community-dwelling individuals. GDF-15
concentrations increase with aging, and these changes
are explained only partially by cardiovascular risk factors, indicators of neurohumoral activation and inflammation, and renal function. Thus GDF-15 reflects
both cardiovascular and other biological processes
closely related to longevity.
© 2013 American Association for Clinical Chemistry
Growth differentiation factor 15 (GDF-15)3 is a divergent member of the transforming growth factor-␤ cytokine superfamily. GDF-15 is secreted at low concentrations from most tissues but may be overexpressed in
cardiomyocytes and various vascular cell types under
stressful conditions (1 ). Several recent investigations
have demonstrated that circulating GDF-15 concentrations are increased and independently prognostic
across a wide spectrum of cardiovascular diseases, including acute and chronic coronary artery disease, congestive heart failure, and ischemic stroke (2– 6 ).
Even in the general population, GDF-15 has been
shown to be a powerful predictor of mortality (7–9 ).
We have previously reported that GDF-15 concentrations in community-dwelling individuals are closely
related to cardiovascular risk factors and perturbations
of cardiac and vascular function (10 ). However, information on the change in GDF-15 concentrations over
time, underlying cardiovascular and metabolic conditions, and the prognostic implications of changes in the
general population are lacking so far. We therefore investigated these issues in participants from the PIVUS
(Prospective Investigation of the Vasculature in Uppsala Seniors) study. The main hypothesis of our analy-
Previously published online at DOI: 10.1373/clinchem.2012.201210
Nonstandard abbreviations: GDF-15, growth differentiation factor 15; PIVUS,
Prospective Investigation of the Vasculature in Uppsala Seniors; NT-proBNP,
N-terminal pro–B-type natriuretic peptide; CRP, C-reactive protein; cTnI, cardiac
troponin I; eGFR, estimated glomerular filtration rate; LV, left ventricular; IDI,
integrated discrimination improvement; NRI, net reclassification improvement;
OR, odds ratio; HR, hazard ratio.
3
1091
sis was that the changes in GDF-15 concentrations over
time would be of prognostic relevance for future
mortality.
termined the LV ejection fraction. We calculated LV
mass index according to the recommendations of the
American Society of Echocardiography (16 ).
Methods
STATISTICAL ANALYSIS
STUDY DESIGN
All individuals aged 70 years living in Uppsala, Sweden,
were eligible for participation in the PIVUS study. Potential study participants were randomly chosen from
the register of community inhabitants. Of the 2025 individuals invited, 1016 participated in the study, with
baseline investigations starting in April 2001. All participants recorded their medical history and underwent
cardiovascular examinations, including echocardiography and blood sampling for measurement of circulating biomarkers (11 ). Five years after enrollment, all
surviving study participants were invited to a reexamination 1 month after their 75th birthday, and 827
participants (85.8% of all survivors) attended. Written
informed consent was obtained from all study participants, and the study protocol was approved by the local
ethics committee and complies with the Declaration of
Helsinki.
The present analysis was based on 1004 participants with available GDF-15 results at baseline and a
subcohort of 813 participants who also had available
GDF-15 results at 5-year follow-up.
LABORATORY ANALYSIS
By use of an immunoradiometric assay, we measured
GDF-15 concentrations in frozen (not previously
thawed) samples of EDTA-plasma obtained at baseline
and 5-year follow-up. The detection limit of this assay
is 20 ng/L; the assay is linear from 200 to 50 000 ng/L
and has intra- and interassay imprecisions of ⱕ12.2%
(12 ). We measured N-terminal pro–B-type natriuretic
peptide (NT-proBNP) using the Elecsys proBNP sandwich immunoassay on an Elecsys 2010 instrument
(Roche Diagnostics) and C-reactive protein (CRP) on
an Architect ci8200 analyzer (Abbott Laboratories).
We measured cardiac troponin I (cTnI) concentrations
with a high-sensitivity assay (Beckman Coulter) and
dichotomized them at the level of detection of 6.0 ng/L
(13 ), considering that 38.7% of the study participants
had undetectable concentrations. We measured estimated glomerular filtration rate (eGFR) according to
the 4-variable Modification of Diet in Renal Disease
equation (14 ).
ECHOCARDIOGRAPHY
Echocardiography was performed with an Acuson
XP124 cardiac ultrasound unit, as described previously
(15 ). We calculated left ventricular (LV) volumes according to the Teichholz method and, from those, de1092 Clinical Chemistry 59:7 (2013)
Data for continuous variables are presented as median
(25th, 75th percentile) or mean (SD). We used the
Mann–Whitney U-test for between-group comparisons and the Wilcoxon signed rank test for withingroup comparisons of continuous variables. Correlations between continuous variables were determined
by the Spearman rank correlation. Categorical variables are expressed as frequencies and percentages. All
differences in categorical variable frequencies were analyzed with the ␹2 test.
We defined the change in GDF-15 concentrations from age 70 to 75 first, as the ln-transformed
relative difference between concentrations at each
time. This change criterion was chosen because it
approximated a normal distribution to a greater extent than other relative or absolute change criteria
(Shapiro–Wilk W-test statistic ⫽ 0.95). Second, we
defined different change patterns considering published data on the biological variability of GDF-15
(17 ): decreasing, change in GDF-15 concentrations
ⱕ⫺20%; increasing, change in GDF-15 concentrations ⱖ40%; or unchanged, change in GDF-15 concentrations between ⫺19% and 39%. Independent
predictors of changes were identified by multiple
linear regression analysis and multivariable regression analysis, as appropriate. Tested covariates included sex, hypertension (defined as blood pressure
⬎140/90 mmHg at rest or antihypertensive treatment), diabetes [defined as fasting blood glucose
⬎110 mg/dL (6.1 mmol/L) or antidiabetic treatment
including diet], HDL cholesterol, LDL cholesterol,
current smoking, previous smoking, body mass index, abnormal electrocardiographic findings [defined as presence of ST-segment depression (Minnesota codes 4-1 and 4-2), T-wave inversion
(Minnesota codes 5-1, 5-2, or 5-3), pathologic
Q-waves (Minnesota code 1-1), or left bundle
branch block (Minnesota code 7-1) on a conventional 12-lead electrocardiogram (18 )], previous
myocardial infarction, self-reported heart failure,
previous coronary revascularization, previous
stroke, NT-proBNP, CRP, cTnI (ⱖ6.0 ng/L), eGFR,
LV ejection fraction, LV mass index, intercurrent
cardiovascular events (defined as hospitalizations
for myocardial infarction, stroke, or coronary revascularization occurring between the examinations at
baseline and 5-year follow-up), and GDF-15 at baseline. If necessary, continuous variables were lntransformed to achieve a normal distribution. The
adjusted models included all covariates demonstrat-
GDF-15 Concentrations in the Elderly
ing a univariate association to ln(GDF-15) at P ⱕ
0.10. In an extended model, we also included the
changes in concentrations of NT-proBNP and CRP
and the eGFR (defined as the absolute difference of
ln-transformed results at both measurements) from
age 70 to 75 as covariates.
The primary outcome examined in this analysis
was all-cause mortality. In secondary analyses, we assessed the associations of GDF-15 with cardiovascular
and noncardiovascular mortality. Cardiovascular mortality was defined as death resulting from myocardial
infarction, stroke, heart failure, other documented cardiovascular diseases, sudden death, and death not
clearly attributable to noncardiovascular disease. Information on outcome was obtained from the Swedish
Registry on Mortality and the medical records for Uppsala County, and outcome data were complete for all
participants. Two follow-up periods were considered:
from the baseline investigation until censor date at end
of December 2010 [median 8.0 (25th, 75th percentile
7.1, 8.9) years], and from the 5-year follow-up examinations until censor date [2.9 (2.1, 3.8) years], which
enabled us to investigate the prognostic implications of
changes in GDF-15 concentrations.
The prognostic value of ln-transformed GDF-15
was assessed by Cox proportional hazard models adjusting for established cardiovascular risk indicators:
sex, hypertension, diabetes, HDL cholesterol, LDL
cholesterol, current smoking, body mass index, previous cardiovascular disease, ln(CRP), and ln(eGFR).
Additional adjustment was made for cTnI ⱖ6.0 ng/L
and ln(NT-proBNP). In these models, we included
ln(GDF-15) concentrations, as well as all confounders
from both age 70 and 75 in an updated covariate fashion. This procedure creates 2 rows for each observation
in the data set, 1 for the period between ages 70 and 75,
and 1 for the period between age 75 and the censor
date. With this approach, information on measurements performed at both age 70 and 75 could be used to
increase the precision of the prognostic estimates and
also take changes over time into account in an appropriate fashion. The models assessing the prognostic implications of changes in GDF-15 concentrations over
time applied the same set of covariates, including
ln(GDF-15) at baseline.
We constructed Kaplan–Meier curves to illustrate the timing of events. The incremental value of
GDF-15 to a risk model based on established cardiovascular risk indicators was investigated by calculation of the C statistics, with comparison of differences
by the method described by DeLong et al. (19 ). In addition, we assessed the integrated discrimination improvement (IDI) and the category-free net reclassification improvement (NRI) as described by Pencina and
colleagues (20, 21 ).
Table 1. Clinical characteristics of the study
population.a
Male sex
502 (50.0)
Hypertension
725 (72.2)
Diabetes
115 (11.5)
Current smoking
108 (10.8)
Previous smoking
414 (41.2)
Body mass index, kg/m
2
Abnormal electrocardiographic findings
27.0 (4.4)
171 (17.1)
History
Myocardial infarction
67 (6.7)
Heart failure
35 (3.5)
PCI/CABGb
54 (5.4)
Stroke
34 (3.4)
Biochemistry
HDL cholesterol, mmol/L
1.5 (0.4)
LDL cholesterol, mmol/L
3.4 (0.9)
cTnI ⱖ6.0 ng/L
615 (61.3)
NT-proBNP, ng/L
109.7 (64.5, 181.8)
CRP, mg/L
1.2 (0.6, 2.3)
eGFR, mL 䡠 min⫺1 䡠 (1.73 m2)⫺1
79.0 (65.5, 95.1)
Echocardiographyc
LVMI, g/m2
92.9 (26.8)
LVEF, %
66.5 (8.0)
a
Categorical data are n (%); continuous data are median (25th, 75th
percentile) or mean (SD).
b
PCI, percutaneous coronary intervention; CABG, coronary artery bypass
graft; LVMI, LV mass index; LVEF, LV ejection fraction.
c
n ⫽ 825.
In all tests, a 2-tailed P value ⬍0.05 was considered
significant. The software packages SPSS 19.0 (SPSS
Inc) and Stata 11 (Stata Corp) were used for statistical
analyses.
Results
GDF-15 CONCENTRATIONS AT BASELINE AND THEIR RELATION
TO CLINICAL CHARACTERISTICS
Results for GDF-15 were available in 1004 participants at baseline and ranged from 450 to 9338 ng/L,
median 1135 (95% CI 948 –1390) ng/L. The clinical
characteristics of the studied population are demonstrated in Table 1, and the univariate associations of
these variables to baseline GDF-15 concentrations
are presented in Supplemental Table 1, which accompanies the online version of this article at
http://www.clinchem.org/content/vol59/issue7.
Clinical Chemistry 59:7 (2013) 1093
Table 2. Multiple linear regression: variables associated with change in GDF-15 concentrations.a
Model 1 (n ⴝ 813)
Clinical variables
␤
P
0.02
Model 2 (n ⴝ 682)
␤
P
0.082
0.05
Model 3 (n ⴝ 800)
␤
P
Male sex
0.092
0.077
0.03
Hypertension
0.111
0.002
0.115
0.004
0.089
0.008
Diabetes
0.147
⬍0.001
0.103
0.01
0.156
⬍0.001
⫺0.036
0.36
⫺0.038
0.39
⫺0.026
0.48
Body mass index
0.069
0.06
0.023
0.57
0.039
0.27
Heart failure
0.088
0.02
0.132
0.001
ln(NT-proBNP)
0.133
⬍0.001
HDL cholesterol
0.099
0.003
0.112
0.002
Change in NT-proBNP
0.138
⬍0.001
Change in CRP
0.169
⬍0.001
ln(eGFR)
0.091
0.01
0.087
0.03
Change in eGFR
LV mass indexb
R2
0.038
0.09
⫺0.082
0.05
⫺0.305
⬍0.001
0.38
0.07
0.20
Model 1 was adjusted for ln(GDF-15) at baseline and candidate covariates with an univariate association to the change in GDF-15 concentrations at P ⱕ 0.10,
excluding echocardiographic variables. Model 2 was adjusted as model 1 plus: including echocardiographic candidate covariates with an univariate association
to the change in GDF-15 concentrations at P ⱕ 0.10, excluding ln(NT-proBNP). Model 3 was adjusted as model 1 plus: change in NT-proBNP, change in CRP,
ln(CRP) at baseline, and change in eGFR. Changes in NT-proBNP, CRP, and the eGFR were defined as the absolute difference between ln-transfomed concentrations
at baseline and 5-year follow-up.
b
Assessed in model 2 in the 825 participants with complete echocardiographic examinations at baseline.
a
CHANGE IN GDF-15 CONCENTRATIONS OVER TIME AND ITS
PREDICTORS
In total, 813 participants had available results for
GDF-15 from both baseline and 5-year follow-up.
None of these individuals had suffered from an intercurrent cardiovascular event within the 28 days preceding the follow-up visit. Considering baseline characteristics, participants in the follow-up examinations
tended to be somewhat healthier than nonparticipants,
in particular in terms of lower prevalence of diabetes
and current smoking, lower LV mass index, and lower
concentrations of NT-proBNP (see online Supplemental Table 2).
GDF-15 concentrations at baseline and 5-year
follow-up correlated strongly (r ⫽ 0.70; P ⬍ 0.001).
Median concentrations increased by 11.0%, from 1102
(938 –1348) ng/L at baseline to 1238 (991–1697) ng/L
at 5-year follow-up (P ⬍ 0.001). By multiple linear regression with the change in GDF-15 concentrations as
the dependent variable, male sex, hypertension, diabetes, self-reported heart failure, ln(eGFR), and ln(NTproBNP) were independently and consistently associated with the changes in GDF-15 (Table 2; models 1
and 2). In an extended model that also included biomarker results obtained at age 75, additional significant
relationships emerged between changes in GDF-15 and
changes in NT-proBNP, CRP, and eGFR (Table 2;
1094 Clinical Chemistry 59:7 (2013)
model 3). The R2 value of model 3 was 0.20. Intercurrent cardiovascular events (n ⫽ 85, 10.5%) were not
associated with an increase in GDF-15 concentrations
upon univariate analysis. Neither were the initiation
of lipid-lowering medication (n ⫽ 98, 12.1%), antihypertensive medication (n ⫽ 248, 30.5%), or coronary revascularization (n ⫽ 27, 3.3%) between the
examinations at baseline and 5-year follow-up associated with decreasing GDF-15 concentrations (data
not shown).
Applying our predefined criteria, 176 participants (21.6%) had increasing concentrations
(ⱖ40%), 576 participants (70.9%) had unchanged
concentrations (⫺19% to 39%), and 61 participants
(7.5%) had decreasing concentrations (ⱕ⫺20%).
Adjusting according to model 1, increasing GDF-15
concentrations were independently associated with
diabetes [odds ratio (OR) 1.9 (95% CI 1.1–3.2), P ⫽
0.02] and hypertension [OR 1.6 (95% CI 1.0 –2.5),
P ⫽ 0.04], whereas decreasing GDF-15 concentrations were independently and inversely associated
with ln(NT-proBNP) [OR 0.7 (95% CI 0.5– 0.9), P ⫽
0.02].
MORTALITY IN RELATION TO GDF-15 CONCENTRATIONS
In total, 111 participants (11.0%) died during the total
follow-up period. Cardiovascular death occurred in 37
GDF-15 Concentrations in the Elderly
Table 3. Cox proportional hazard regression model evaluating the risk of mortality using ln(GDF-15) at
both 70 and 75 years in a model for updated covariates together with established cardiovascular risk
indicators (n ⴝ 1794).
All-cause mortality
HR (95% CI)
z score
Cardiovascular mortality
P
HR (95% CI)
z score
Noncardiovascular mortality
P
HR (95% CI)
z score
P
ln(GDF-15)
4.0 (2.7–6.0)
6.80
⬍0.001
2.3 (1.1–5.0)
2.09
0.04
5.1 (3.2–8.2)
6.81
⬍0.001
Male sex
1.2 (0.7–1.5)
0.87
0.39
1.2 (0.7–1.6)
0.52
0.61
1.2 (0.5–1.6)
0.65
0.52
Hypertension
0.8 (0.5–1.1)
⫺1.37
0.17
2.1 (0.9–5.2)
1.61
0.11
0.5 (0.3–0.9)
⫺2.63
Diabetes
1.1 (0.6–1.9)
0.34
0.74
1.2 (0.4–3.1)
0.32
0.75
1.1 (0.6–2.1)
0.24
0.81
Body mass index
1.0 (0.9–1.0)
⫺1.89
0.06
0.9 (0.8–1.0)
⫺2.00
0.05
1.0 (0.9–1.0)
⫺0.99
0.32
HDL cholesterol
1.2 (0.7–2.0)
0.75
0.46
0.8 (0.3–2.0)
⫺0.50
0.62
1.4 (0.8–2.6)
1.06
0.29
LDL cholesterol
1.0 (0.8–1.2)
⫺0.18
0.86
1.0 (0.7–1.5)
⫺0.11
0.91
1.0 (0.8–1.3)
⫺0.05
0.96
Current smoking
1.6 (1.0–2.6)
1.75
0.08
2.2 (1.0–5.1)
1.86
0.06
1.4 (0.7–2.5)
0.97
0.33
ln(CRP)
1.1 (0.9–1.3)
1.09
0.28
0.9 (0.6–1.2)
⫺0.83
0.41
1.2 (1.0–1.5)
1.94
0.05
ln(eGFR)
1.2 (0.6–2.4)
0.45
0.65
0.7 (0.2–2.7)
⫺0.50
0.62
1.4 (0.6–3.4)
0.80
0.43
Previous cardiovascular disease
1.7 (1.0–2.8)
2.11
0.04
1.8 (0.7–4.2)
1.27
0.20
1.6 (0.9–2.9)
1.44
0.15
participants (3.7%) and noncardiovascular death in 74
participants (7.4%). The incidence of all-cause mortality across increasing quartiles of GDF-15 was 4.8%
(n ⫽ 12), 8.3% (n ⫽ 21), 10.0% (n ⫽ 25), and 21.0%
(n ⫽ 53) (P ⬍ 0.001).
According to Cox regression analysis with
ln(GDF-15) concentrations at both 70 and 75 years in
an updated covariate fashion and adjusting for established cardiovascular risk indicators (n ⫽ 1794),
ln(GDF-15) independently predicted all-cause mortality with a hazard ratio (HR) of 4.0 (95% CI 2.7– 6.0),
P ⬍ 0.001 (Table 3). Additional adjustment for ln(NTproBNP) and cTnI ⱖ6.0 ng/L did not affect this association considerably [3.7 (2.4 –5.0), P ⬍ 0.001]. Adjusting for cardiovascular risk indicators, the HR for
cardiovascular mortality was 2.3 (1.1–5.0), P ⫽ 0.04,
and the HR for noncardiovascular mortality, 5.1 (3.2–
8.2), P ⬍ 0.001.
Baseline GDF-15 concentrations also improved
discrimination and reclassification regarding all-cause
mortality when added to a risk model based on established cardiovascular risk indicators. The C statistic increased from 0.65 to 0.71 (P ⫽ 0.006), the IDI was
0.030 (P ⫽ 0.004), and the category-free NRI was 0.281
(P ⫽ 0.006). Addition of GDF-15 to our risk model
would have resulted in the correct up-classification of
52.7% of participants who died and the correct downclassification of 61.3% of survivors. In addition, a good
goodness-of-fit was seen for the model including
GDF-15 and cardiovascular risk indicators (Hosmer–
Lemeshow test, P ⫽ 0.59).
0.009
MORTALITY IN RELATION TO THE CHANGE IN GDF-15
CONCENTRATIONS
Among the 813 participants with available GDF-15 results from both baseline and 5-year follow-up, 32
deaths (3.9%) occurred between the 5-year follow-up
examinations and censor date at the end of December
2010. Given the limited number of deaths, we assessed
only all-cause mortality in this part of the analysis.
Applied as a continuous variable, the change in
GDF-15 concentrations over time exhibited a significant association with all-cause mortality in a model
adjusted for sex and ln(GDF-15) at baseline [HR 3.6
(95% CI 2.2– 6.0), P ⬍ 0.001]. The risk estimate was
not considerably affected after additional adjustment
for either established cardiovascular risk indicators
[3.8 (2.7– 6.1), P ⬍ 0.001] or cardiovascular risk indicators ln(NT-proBNP) and cTnI ⱖ6.0 ng/L [3.6 (2.0 –
6.5), P ⬍ 0.001]. The IDI of the change in GDF-15
concentrations in a model adjusting for established
cardiovascular risk indicators and ln(GDF-15) at baseline was 0.042 (P ⫽ 0.006), and the category-free NRI
was 0.468 (P ⫽ 0.009) with a corresponding correct
up-classification of 59.4% of participants who died and
a correct down-classification of 64.4% of survivors.
Applying our predefined GDF-15 change criteria,
2 participants (3.3%) with decreasing concentrations
and 14 participants (2.4%) with unchanged concentrations died. Sixteen participants (9.1%) with increasing
concentrations died; Fig. 1 demonstrates that the risk
for all-cause mortality was significantly increased in
these participants compared to those with unchanged
GDF-15 concentrations.
Clinical Chemistry 59:7 (2013) 1095
Fig. 1. Cumulative incidence of all-cause mortality after 5-year follow-up in relation to the relative change in
GDF-15 concentrations.
Decreasing GDF-15 concentrations, change ⱕ⫺20%; unchanged GDF-15 concentrations, change between ⫺19% and 39%;
increasing GDF-15 concentrations, change ⱖ40%. Analyses were adjusted for sex and ln(GDF-15) at baseline.
Discussion
The results from our study demonstrate that GDF-15 is
a powerful predictor of mortality in elderly individuals
from the community. As such, our data are consistent
with recent investigations (7–9 ) but also present new
evidence, as they demonstrate that both single GDF-15
results and changes in GDF-15 concentrations over
time are prognostically important.
We and others have previously reported that
GDF-15 concentrations in the general population are
reflective of cardiovascular risk factors and perturbations in cardiac and vascular function (7, 8, 10 ). In the
current longitudinal study, changes in GDF-15 concentrations were associated with similar entities, i.e.,
cardiovascular risk factors (male sex, hypertension, diabetes), indices of neurohumoral activation (baseline
NT-proBNP concentrations and their changes over
time), and indices of inflammation (changes in CRP
concentrations over time). Our results support the notion that GDF-15 integrates information on several relevant aspects and pathways in cardiovascular disease.
The prominent anti-apoptotic, antihypertrophic, and
anti-inflammatory actions of GDF-15 in cardiovascular disease models (22–24 ) indicate that this cytokine
exerts protective effects in the context of acute cardiovascular injury. Whether chronic increases in GDF-15
concentrations in elderly individuals play an adaptive
or maladaptive role remains to be investigated.
1096 Clinical Chemistry 59:7 (2013)
In the present study, GDF-15 demonstrated strong
associations with mortality risk, independent of established clinical and biochemical risk indicators, and enhanced prognostic discrimination and reclassification.
Even the changes in GDF-15 over time were independently predictive despite the rather short observation
period with the highest mortality seen in participants
with the most pronounced increases. Notably, intercurrent cardiovascular events after the baseline investigations did not contribute to increasing GDF-15 concentrations over time, and only 20% of the variability
of the changes in GDF-15 concentrations was explained by the cardiovascular risk indicators and biomarkers entered into the multiple regressions.
Moreover, GDF-15 predicted both cardiovascular
and noncardiovascular mortality in line with data from
previous investigations (7, 25 ). Our results indicate
that GDF-15 is probably related not only to cardiovascular abnormalities but also to other prognostically adverse pathobiological processes. For example, GDF-15
concentrations are increased in renal failure, an entity
associated with an increased risk for both cardiovascular and noncardiovascular death (26 ). Increased
GDF-15 concentrations are also seen in rheumatoid
arthritis (27 ) and several malignancies (28 ) and have
been related to shortened telomere length, an indicator
of premature aging (23 ). These entities share some
pathobiological pathways with cardiovascular disease
(e.g., oxidative stress and inflammation) that might in-
GDF-15 Concentrations in the Elderly
duce GDF-15 via p53 signaling, the main driver of
GDF-15 transcription (29 ). Although the design of our
study does not permit further exploration of these issues, our data suggest that the prognostic importance
of GDF-15 concentrations is mediated by mechanisms
beyond cardiovascular disease.
Some limitations to this analysis should be acknowledged. The PIVUS study was limited to whites
aged 70 years. We are therefore unable to evaluate the
impact of age and ethnicity on GDF-15 concentrations
and its changes. In addition, the selection of such an
elderly population inevitably mitigates the powerful
influence of age on risk, thereby increasing the relative
prognostic importance of other covariates, including
circulating biomarkers. However, our data are strikingly similar to results from age-adjusted analyses from
the Rancho Bernardo Study, assessing another community population (7 ). The participants in the 5-year
follow-up examinations represented a somewhat
healthier subset of the entire study population, which is
unavoidable in longitudinal studies assessing elderly
populations. Results obtained from this cohort therefore cannot be extrapolated to the entire study population, but likely underestimate the true associations between GDF-15 concentrations and outcome.
We lack more detailed data on the cause of death in
participants dying from noncardiovascular causes.
Finally, we cannot exclude the possibility that our
modeling approach might have affected the prognostic estimates of GDF-15 given the somewhat low
event rates for some outcomes.
In conclusion, GDF-15 concentrations and their
changes over time independently predicted both car-
diovascular and noncardiovascular mortality in an elderly population from the community. GDF-15 concentrations increased with aging, and these changes
were explained only partially by cardiovascular risk factors, renal function, inflammation, and neurohumoral
activation. Our data therefore indicate that GDF-15
and its changes reflect both cardiovascular perturbations and independent pathobiological processes that
are closely related to prognosis.
Author Contributions: All authors confirmed they have contributed to
the intellectual content of this paper and have met the following 3 requirements: (a) significant contributions to the conception and design,
acquisition of data, or analysis and interpretation of data; (b) drafting
or revising the article for intellectual content; and (c) final approval of
the published article.
Authors’ Disclosures or Potential Conflicts of Interest: Upon manuscript submission, all authors completed the author disclosure form.
Disclosures and/or potential conflicts of interest:
Employment or Leadership: None declared.
Consultant or Advisory Role: L. Wallentin, Roche Diagnostics; K.C.
Wollert, Roche Diagnostics.
Stock Ownership: None declared.
Honoraria: K.C. Wollert, Roche Diagnostics.
Research Funding: K.M. Eggers, Swedish Heart-Lung Foundation
and Swedish Society of Medicine; K.C. Wollert, Roche Diagnostics.
Expert Testimony: None declared.
Patents: T. Kempf, EP1884777; L. Wallentin, EP06118464.4; K.C.
Wollert, EP2047275B1.
Role of Sponsor: The funding organizations played no role in the
design of study, choice of enrolled patients, review and interpretation
of data, or preparation or approval of manuscript.
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