Increased heart failure risk in normal-weight people with metabolic syndrome compared with metabolically healthy obese individuals.

Abstract

The purpose of this study was to assess whether the metabolically healthy obese phenotype is associated with lower heart failure (HF) risk compared with normal-weight individuals with metabolic syndrome (MetS).
Obesity and MetS often coexist and are associated with increased HF risk. It is controversial whether obese individuals with normal insulin sensitivity have decreased HF risk.
A total of 550 individuals without diabetes or baseline macrovascular complications were studied during a median follow-up of 6 years. Participants were classified by presence (n = 271) or absence (n = 279) of MetS and by body mass index (body mass index: <25 kg/m(2) = normal weight, n = 177; 25 to 29.9 kg/m(2) = overweight, n = 234; ≥ 30 kg/m(2) = obese, n = 139). MetS was diagnosed with the National Cholesterol Education Program Adult Treatment Panel III criteria. Left ventricular functional capacity, myocardial structure, and performance were assessed echocardiographically.
Body mass index was not associated with increased HF risk. The presence of MetS conferred a 2.5-fold higher HF risk (hazard ratio [HR]: 2.5, 95% confidence interval [CI]: 1.68 to 3.40). Overweight and obese individuals without MetS had the lowest 6-year HF risk (HR: 1.12, 95% CI: 0.35 to 1.33 [corrected] and HR: 0.41, 95% CI: 0.10 to 1.31, respectively) compared with normal-weight individuals with MetS (HR: 2.33, 95% CI: 1.25 to 4.36, p < 0.001). From the individual components of MetS, impaired fasting glucose (HR: 1.09, 95% CI: 1.06 to 1.10), high BP (HR: 2.36, 95% CI: 1.03 to 5.43), low high-density lipoprotein cholesterol (HR: 1.88, 95% CI: 1.29 to -2.77), and central obesity (HR: 2.22, 95% CI: 1.02 to 1.05) were all associated with increased HF risk. Factors commonly associated with MetS such as insulin resistance and inflammation (high-sensitivity C-reactive protein and microalbuminuria) were also independently associated with HF incidence.
In contrast to normal weight insulin-resistant individuals, metabolically healthy obese individuals show decreased HF risk in a 6-year follow-up study.

Full text

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doi:10.1016/j.jacc.2011.04.047
2011;58;1343-1350
J. Am. Coll. Cardiol.
Nicholas Katsilambros, and Christodoulos Stefanadis
Christina Voulgari, Nicholas Tentolouris, Polychronis Dilaveris, Dimitris Tousoulis,

Syndrome Compared With Metabolically Healthy Obese Individuals
Increased Heart Failure Risk in Normal-Weight People With Metabolic
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Heart Failure
Increased Heart Failure Risk in
Normal-Weight People With Metabolic Syndrome
Compared With Metabolically Healthy Obese Individuals
Christina Voulgari, MD, PHD,*† Nicholas Tentolouris, MD, PHD,† Polychronis Dilaveris, MD, PHD,*
Dimitris Tousoulis, MD, PHD,* Nicholas Katsilambros, MD, PHD,† Christodoulos Stefanadis, MD, PHD*
Athens, Greece
Objectives
The purpose of this study was to assess whether the metabolically healthy obese phenotype is associated with
lower heart failure (HF) risk compared with normal-weight individuals with metabolic syndrome (MetS).
Background
Obesity and MetS often coexist and are associated with increased HF risk. It is controversial whether obese indi-
viduals with normal insulin sensitivity have decreased HF risk.
Methods
A total of 550 individuals without diabetes or baseline macrovascular complications were studied during a me-
dian follow-up of 6 years. Participants were classified by presence (n ? 271) or absence (n ? 279) of MetS and
by body mass index (body mass index: ?25 kg/m2? normal weight, n ? 177; 25 to 29.9 kg/m2? overweight,
n ? 234; ?30 kg/m2? obese, n ? 139). MetS was diagnosed with the National Cholesterol Education Program
Adult Treatment Panel III criteria. Left ventricular functional capacity, myocardial structure, and performance
were assessed echocardiographically.
Results
Body mass index was not associated with increased HF risk. The presence of MetS conferred a 2.5-fold higher
HF risk (hazard ratio [HR]: 2.5, 95% confidence interval [CI]: 1.68 to 3.40). Overweight and obese individuals
without MetS had the lowest 6-year HF risk (HR: 1.12, 95% CI: 0.35 to 0.33 and HR: 0.41, 95% CI: 0.10 to 1.31,
respectively) compared with normal-weight individuals with MetS (HR: 2.33, 95% CI: 1.25 to 4.36, p ? 0.001).
From the individual components of MetS, impaired fasting glucose (HR: 1.09, 95% CI: 1.06 to 1.10), high BP
(HR: 2.36, 95% CI: 1.03 to 5.43), low high-density lipoprotein cholesterol (HR: 1.88, 95% CI: 1.29 to ?2.77), and
central obesity (HR: 2.22, 95% CI: 1.02 to 1.05) were all associated with increased HF risk. Factors commonly
associated with MetS such as insulin resistance and inflammation (high-sensitivity C-reactive protein and mi-
croalbuminuria) were also independently associated with HF incidence.
Conclusions
In contrast to normal weight insulin-resistant individuals, metabolically healthy obese individuals show decreased
HF risk in a 6-year follow-up study.(J Am Coll Cardiol 2011;58:1343–50) © 2011 by the American College of
Cardiology Foundation
Heart failure (HF) is one of the leading causes of morbidity
and mortality, and its prevalence continues to increase
despite the decrease in cardiovascular death rates (1). Al-
though prevention of HF is complex, several risk factors
have been identified as consistently associated with its
development, including age, sex, left ventricular (LV) hy-
pertrophy and dysfunction, diabetes, hypertension, smok-
ing, physical inactivity, dyslipidemia, and obesity. In addi-
tion to recent improvements in the management of classic
cardiovascular risk factors, parameters such as obesity and
insulin resistance are poised to play an important role in the
development of future cardiovascular events. Although obe-
sity is currently considered an established determinant of
HF, the mechanisms by which it is translated into an
increased HF risk are still unclear (2).
See page 1351
Currently, there is strong interest in metabolically healthy
obese people, who, despite their body fat, display a favorable
metabolic profile characterized by high levels of insulin sensi-
tivity and a favorable blood pressure (BP), glycemic, lipids, and
inflammation profile (3). However, it remains controversial
by Sonela Skenderi on October 2, 2011
From the *First Department of Cardiology, Athens University Medical School,
Hippokration Hospital, Athens, Greece; and the †First Department of Propaedeutic
Medicine, Athens University Medical School, Laiko General Hospital, Athens,
Greece. All authors have reported that they have no relationships relevant to the
contents of this paper to disclose.
Manuscript received January 24, 2011; revised manuscript received April 14, 2011,
accepted April 27, 2011.
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Journal of the American College of Cardiology
© 2011 by the American College of Cardiology Foundation
Published by Elsevier Inc.
Vol. 58, No. 13, 2011
ISSN 0735-1097/$36.00
doi:10.1016/j.jacc.2011.04.047
content.onlinejacc.org

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whether this healthier metabolic
profile translates into a lower car-
diovascular risk compared with
normal-weight individuals with
metabolic syndrome (MetS) (4).
We investigated the indepen-
dent associations of MetS, insulin
resistance, inflammatory markers,
and lifestyle measures with inci-
dent HF beyond obesity and other
established cardiovascular risk fac-
tors in a population without dia-
betes and baseline macrovascular
complications.
Methods
Study population. The study population consisted of 550
of 944 individuals (58.3%) without diabetes enrolled in a
prospective, community-based study designed to evaluate
the impact of MetS on HF risk. Participants were recruited
between 2003 and 2005. At baseline, inclusion criteria
required that subjects were free of clinically apparent mac-
rovascular disease. Therefore, we excluded all subjects with
a history of chronic stable angina, echocardiographically
documented acute and chronic HF, hemodynamically sig-
nificant cardiac valvular disease, and peripheral vascular and
cerebrovascular disease. Subclinical coronary artery disease
was excluded at baseline in 454 participants (82.5%) who
agreed to undergo quantitative coronary angiography. Pa-
tients with chronic kidney disease estimated by a glomerular
filtration rate of ?60 ml/min, infections, and acute or
chronic inflammatory diseases were also excluded. This was
further applied to patients treated with nonsteroidal anti-
inflammatory medications or corticosteroids in the previous
3 months. The study was approved by the hospital review
board, and all study participants gave written informed
consent.
Baseline examination. Standardized questionnaires were
used to obtain information about smoking and medication
use. Body mass index (BMI) was calculated as BMI ?
weight (kg)/height2(m2) from weight measured to the
nearest 0.5 kg and height to the nearest 0.1 cm. Obesity was
defined as a BMI ?30 kg/m2and overweight as BMI 25 to
29.9 kg/m2, respectively. Hypertension was defined as systolic
BP?140mmHgand/ordiastolicBP?90mmHgand/oruse
of antihypertensive medications.
Standardized physical activity questionnaires were used
for the assessment of the average amount of time per week
engaged in aerobic exercise activities and the energy ex-
pended for each activity in metabolic equivalent (MET)
hours per week (MET-h/week) (5).
We classified participants into 3 groups of physical
activity levels at each assessment: those engaged in ?7.5
MET-h/week (equivalent to ?150 min/week of moderate-
intensity physical activity), the minimum recommended by
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the American Heart Association; 7.5 to ?21 MET-h/week;
and ?21 MET-h/week (equivalent to ?420 min/week of
moderate-intensity activity) (6).
Fasting plasma glucose and insulin levels were measured
at baseline. Insulin levels were determined by a radioimmu-
noassay method using the Biosure Human Insulin Specific
RIA Kit (Biosure, Belgium). As an index of insulin resis-
tance, the Homeostasis Model Assessment index was cal-
culated using the formula (fasting glucose [mmol/l]) ?
(fasting insulin [?U/ml])/22.5 (7). Urinary creatinine and
albumin were measured by radioimmunoassay (Pharmacia
and Upjohn Diagnostics, Uppsala, Sweden) in a single 24-h
urine collection after excluding proteinuria due to urinary
tract infection by microscopic examination and culture.
Participants were categorized into 2 groups based on the
baseline urinary albumin (mg)/creatinine (g) ratio: 1) nor-
mal, ratio ?30; and 2) microalbuminuria, ratio 30 to 299.9
(8). Glycated A1c hemoglobin was measured with a latex
immunoagglutination inhibition method (Bayer Health-
Care LLC, Elkhart, Indiana) with a nondiabetic range of
4.0% to 6.0%. High-sensitivity C-reactive protein was
determined using ADVIA 1650 (Bayer HealthCare).
MetS was defined using the National Cholesterol Edu-
cation Program Adult Treatment Panel III criteria (9).
Participants with 3 or more of these criteria were considered
to have MetS: abdominal obesity given as waist circumfer-
ence (?102 cm in men and ?88 cm in women), serum
triglycerides ?150 mg/dl, high-density lipoprotein choles-
terol ?40 mg/dl in men and ?50 mg/dl in women, BP
?130/85 mm Hg or use of antihypertensive medications,
fasting glucose ?100 mg/dl. The studied population was
classified into 2 groups: those who met the MetS definition
(n ? 271, 54.2%) and those who did not (n ? 279, 55.8%).
LV structure and function were determined by complete
2-dimensional, Doppler echocardiographic examination per-
formed at baseline with a Hewlett-Packard Sonos 5500 Ultra-
sound System (Hewlett-Packard, Andover, Massachusetts).
Follow-up and outcome parameters. Median follow-up
time was 6.0 years (interquartile range: 4.9 to 7.6 years).
Each participant was contacted every 6 to 9 months to
inquire about all interim hospital admissions, cardiovascular
outpatient diagnoses, and deaths. Medical records and
information were successfully obtained for the 487 subjects
(83.2%). Subjects who were lost to follow-up (n ? 52, 9.5%)
were younger (p ? 0.01) but did not differ in sex (p ? 0.70)
from those who were followed. Hospitalized cardiovascular
event and outpatient cardiovascular diagnostic encounter
information was successfully obtained for an estimated 98%
of the participants.
The primary endpoint of this study was HF. Endpoint
criteria were: 1) HF identified by the study physician on the
basis of symptoms and signs (e.g., shortness of breath, fatigue,
reduced exercise tolerance requiring initiation or an increase in
dose if previously prescribed for another cause [i.e., hyperten-
sion] of a loop diuretic, angiotensin-converting enzyme inhib-
itor, or angiotensin II receptor blocker therapy, or evidence-
by Sonela Skenderi on October 2, 2011
Abbreviations
and Acronyms
BMI ? body mass index
BP ? blood pressure
CI ? confidence interval
HF ? heart failure
HR ? hazard ratio
LV ? left ventricular
MET ? metabolic
equivalent
MetS ? metabolic
syndrome
1344 Voulgari et al.
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based beta-blocker therapy); and 2) objective evidence of
structural or functional heart; disease defined as echocardio-
graphic documentation of systolic dysfunction (LV ejection
fraction (LVEF) ?40%) or diastolic dysfunction (10). Dia-
stolic dysfunction was defined as LV mass ?88 g/m2in
women and ?102 g/m2in men, or E/A ?1 or mitral (E wave)
deceleration time ?200 ms, or Tei index ?0.40 (11).
Statistical methods. Statistical analyses were performed
using programs available in the SPSS version 15.0 statistical
package (SPSS Inc., Chicago, Illinois). Data are presented
as mean ? SD or median (interquartile range) for contin-
uous variables and number (percentage) for categorical
variables. Differences in baseline characteristics of partici-
pants in whom HF developed or did not develop were tested
with a Student t test (continuous variables with normal
distribution) or chi-square test (categorical variables). Cox
proportional hazards models were used to analyze the
association of risk factors with incident HF. Two sets of
models were used: model 1, unadjusted analysis, and model
2, adjusted for established risk factors of HF including age,
sex, fasting glucose, hypertension, lipids, baseline LV hy-
pertrophy and function, current cigarette smoking, and
physical inactivity. Each novel risk factor was included in a
separate model with the established risk factors in model 2.
Results of Cox proportional hazards models are reported
as hazard ratios and 95% confidence intervals (CIs). All
HRs are calculated and reported for 1 SD increase or
decrease in continuous variables or transfer from 1 level to
another of categorical variables, unless stated otherwise.
Participants who were lost to follow-up (9.5%) were cen-
sored at the time of the last follow-up. Those who did not
experience HF were censored at either 6 years or the last
date of follow-up before 6 years. To evaluate how much of
the association of obesity with incident HF was related to
MetS, systemic inflammation and insulin resistance and vice
versa, we compared the regression coefficient for obesity and
MetS before and after adjusting for these variables.
Results
Baseline characteristics of the studied participants by
BMI and the presence of MetS. At study entry, compared
with normal-weight and overweight individuals, obese sub-
jects had a higher incidence of MetS, were more likely to
smoke cigarettes than overweight, but not more than
normal-weight, individuals. The presence of inflammatory
markers was significantly higher in obese compared with
normal-weight and overweight subjects. MetS was strongly
related to BMI, with 38.4% of individuals with a normal
BMI having MetS compared with 45.7% among overweight
and 69% among obese subjects. Mean systolic BP also
correlated with BMI such that obese subjects had higher
values than normal-weight subjects. However, the presence
of high systolic BP was lower in obese than in normal-
weight individuals (78.1% vs. 83.8%, p ? 0.01). Obese
subjects had pronounced central fat distribution, but lower
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high-density lipoprotein cholesterol levels, increased insulin
resistance, and inflammation profile, except for microalbu-
minuria, compared with normal-weight and overweight
individuals (Table 1).
Baseline LV structure and function. Because studied
participants had no baseline history of heart disease, the
frequency of individuals with abnormal LV structure and
function was likely to be less than in the general population.
Therefore, 95.4% of our studied population had LVEF
?50%, and this proportion was 89.2% (n ? 165) among the
185 participants in whom HF developed. On the other
hand, among the 550 studied participants, 78 participants
(14.1%) had LV hypertrophy, and among participants in
whom HF developed, 48 participants (25.9%) had LV
hypertrophy at baseline (Table 2). Moreover, among 106
patients for whom data on LV function at the time of HF
diagnosis were available, 52 patients (49%) had LVEF
?40%, and 54 participants (51%) had LVEF ?50%.
Incident HF by levels of BMI and metabolic status.
From the 550 participants studied, HF developed in 185 (80
male/73 female; age, 58.5 ? 7.9 years) during follow-up.
Among the patients in whom HF developed, the presence
of LV hypertrophy was significantly higher in all BMI
groups with MetS compared with those without MetS
(7.3% vs. 36.1%, p ? 0.001). The presence of MetS also
increased the percentage of patients in whom diastolic
dysfunction with preserved LVEF developed in all BMI
groups (5.2% vs. 46.4%, p ? 0.001). The same was true for
those in whom both diastolic and systolic dysfunction
developed (1.8% vs. 9.1%, p ? 0.001). Among the individ-
uals without MetS, systolic dysfunction was present only in
the normal-weight group.
Figure 1 separates drug-naïve patients in whom HF
developed who required initiation of therapy as opposed to
those in whom new HF symptoms, signs, and echocardio-
graphic evidence developed while taking medication, ac-
cording to BMI and MetS groups. The percentage of
patients requiring initiation or adjustment of HF medica-
tion was significantly higher in all BMI subgroups with the
presence of MetS.
At baseline, participants in whom HF developed were
more likely to be older, obese with central fat distribution,
current smokers or ex-smokers, hypertensive, and with
impaired fasting glucose. Moreover, at baseline, the major-
ity (44.3%) of those in whom HF developed expended ?7.5
MET-h/week on physical activity, and only 3.8%, ?21
MET-h/week. In contrast, among the population in whom
HF did not develop, the majority (63.2%) had baseline
moderate-intensity physical activity (7.5 to ?21 MET-h/
week). Finally, the prevalence of MetS at baseline was
significantly higher in the studied population in whom HF
developed (Table 2).
During the 6-year follow-up period, a consistent pattern
was seen between BMI, MetS, and HF incidence. Among
participants without MetS, HF incidence was 15.6% in
those with a normal BMI, 14.2% in those overweight, and
by Sonela Skenderi on October 2, 2011
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9.3% in those obese. These rates were much lower than
those documented in patients with MetS (63.2%, 47.7%,
and 54.2%, respectively). These relationships of MetS, but
not BMI, being associated with HF incidence persisted and
remained statistically significant after adjustment for age,
sex, current smoking, physical inactivity, lipids, glycemic,
and inflammation profile. Compared with participants
without MetS, subjects with MetS who were normal
weight, overweight, or obese with MetS had approximately
2.3, 2.6, and 2.1 times higher adjusted odds of having HF.
Obese subjects without MetS had the lowest HF risk
compared with normal-weight individuals with MetS
(Table 3, Fig. 2).
Among the 5 criteria used in the definition of MetS,
serum triglycerides were not significant predictors of inci-
dent HF. Fasting hyperglycemia, hypertension, and the
presence of central obesity had the highest predictive value
among MetS components. Inflammation markers were also
significant predictors of incident HF in the studied popu-
lation (Table 3).
Discussion
The main finding of the present study is that after adjust-
ment for well-known cardiovascular risk factors, MetS was
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independently and significantly associated with an increased
6-year incidence of HF in a population without diabetes and
baseline macrovascular complications. Obesity status or in-
creased BMI were not independent predictors of 6-year HF-
risk in this studied population. Moreover, obese participants
without MetS displayed the lowest risk of incident HF
comparedwithnormal-weightparticipantswithMetS.Finally,
hypertension, central obesity, and inflammation were demon-
strated as the strongest possible mediators of the independent
association between MetS and 6-year incidence of HF.
The presence of obesity-related metabolic disturbances
varies widely among obese individuals. Previous studies
described a unique subset of obese individuals who seem to
be protected or more resistant to the development of
metabolic abnormalities associated with obesity. These in-
dividuals are known as metabolically healthy but obese, and,
despite their body fatness, they display a favorable metabolic
and inflammation profiles (3). In the present study, almost
one-third of obese subjects did not have MetS, whereas a
parallel proportion of normal-weight subjects (38.4%) had
MetS. Compared with previous reports that used identical
definition criteria in Mediterranean populations (12), our
results probably indicate an increase in the prevalence of the
different obesity phenotypes in the nondiabetic population.
by Sonela Skenderi on October 2, 2011
Baseline Demographics and Clinical Characteristics of the Studied Cohort According to BMI and Metabolic Status
Table 1Baseline Demographics and Clinical Characteristics of the Studied Cohort According to BMI and Metabolic Status
Characteristics
Normal
(<24.9 kg/m2)
(n ? 177)
Overweight
(25–29.9 kg/m2)
(n ? 234)
Obese
(>30 kg/m2)
(n ? 139)
Normal Weight
vs. Overweight,
p Value
Normal Weight
vs. Obese,
p Value
Overweight
vs. Obese,
p Value
Age, yrs 60.3 ? 10.459.8 ? 9.060.0 ? 9.20.610.770.83
Female/male103 (58.2)/74 (41.8)135 (57.7)/99 (42.3) 74 (53.2)/65 (46.8)1.00 0.46 0.49
MetS, yes/no 68 (38.4)/109 (61.6)107 (45.7)/127 (54.3) 96 (69.0)/43 (31.0)0.15
?0.001
?0.001
MetS: NCEP-ATP III score (0–5)* 3 (3–5) 4 (3–5)4 (3–5)0.03
?0.001
?0.001
Statins65 (36.7)43 (18.4) 46 (33.0)0.070.970.04
ACEIs/ARBs27 (15.3)52 (22.2)53 (38.1)0.230.0040.02
Smoking30 (16.9)21 (8.9)22 (15.8)0.020.840.02
Waist, cm87.7 ? 9.3 97.4 ? 9.2108.2 ? 8.6
?0.001
?0.001
?0.001
Glucose, mg/dl103.5 ? 14.5105.7 ? 13.6110.4 ? 13.70.180.030.05
Triglycerides, mg/dl127.4 ? 73.5136.3 ? 66.7156.7 ? 74.7 0.230.001 0.003
HDL cholesterol, mg/dl 49.0 ? 12.9 47.0 ? 12.444.3 ? 11.20.130.0020.03
Systolic blood pressure, mm Hg128.6 ? 18.6132.6 ? 20.4137.0 ? 19.40.05
?0.0010.02
High waist44 (24.8) 113 (48.3)135 (97.1)
?0.001
?0.001
?0.001
High glucose62 (35.0)94 (40.2)55 (39.6)0.460.530.95
High triglycerides56 (31.6)81 (34.6)58 (41.7)0.450.060.13
Low HDL cholesterol91 (51.4) 102 (43.5)85 (61.2)0.140.07
?0.001
High blood pressure92 (51.9) 143 (61.1)92 (66.2) 0.080.010.22
Total cholesterol, mg/dl197.4 ? 44.4195.9 ? 42.9 193.8 ? 42.6 0.740.480.62
LDL cholesterol, mg/dl122.9 ? 37.5121.6 ? 38.5118.1 ? 37.10.800.230.24
Albumin, mg/24 h* 8.0 (3.0–218.6)8.0 (3.0–139.3)9.2 (2.9–175.0)0.580.090.12
Microalbuminuria31 (17.5)51 (36.7)57 (24.4)0.10
?0.0010.04
HOMA-IR*2.74 (1.16–3.89)3.11 (0.98–4.43)3.96 (0.91–6.36)0.10
?0.0010.001
hs-CRP, mg/dl3.13 ? 0.244.91 ? 0.525.50 ? 0.430.32
?0.0010.001
Values are mean ? SD or as n (%). *Median values (interquartile range). High glucose is defined as fasting glucose ?100 mg/dl; low HDL cholesterol is ?40 mg/dl in men and ?50 mg/dl in women; high
blood pressure is ?130/85 mm Hg or use of antihypertensive medications; NCEP-ATP-III score is the sum of the metabolic components; high waist defined as waist circumference ?102 cm in men and
?88 cm in women; high triglycerides defined as plasma triglycerides ?150 mg/dl.
ACEI ? angiotensin-converting enzyme inhibitor; ARB ? angiotensin II receptor blocker; BMI ? body mass index; HDL ? high-density lipoprotein; HOMA-IR ? Homeostasis Model Assessment for Insulin
Resistance equation; hs-CRP ? high-sensitivity C-reactive protein; LDL ? low-density lipoprotein; MetS ? metabolic syndrome; NCEP-ATP-III score ? National Cholesterol Education Program Adult Treatment
Panel III.
1346Voulgari et al.
Heart Failure in the Metabolically Healthy Obese
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Moreover, the presence of MetS in the normal-weight
subgroup was associated with an increased incidence of
central obesity, fasting hyperglycemia, hypertension,
dyslipidemia, insulin resistance, and subclinical inflam-
mation compared with the absence of MetS in the obese
individuals. In accordance with previous reports (13),
when compared with normal-weight individuals with
MetS, a higher percentage of obese individuals with a
healthy metabolic profile were nonsmokers and met the
current physical activity guidelines.
Although 1 previous study that investigated the impact of
MetS on incident HF in a cohort of older (70 years and
older) adults failed to demonstrate a significant relationship
between MetS and incident HF risk in the subgroup of
subjects without a history of cardiovascular disease (14), we
demonstrated a significant association between MetS and
incident HF in a younger population (50 years and older).
This is in accordance with another community-based study
that also determined MetS as a significant and independent
predictor of incident HF in a population of middle-aged
men without a baseline cardiovascular disease history (15).
Although in the analysis of the MESA (Multi-Ethnic
Study of Atherosclerosis) (16), after adjustment for known
cardiovascular risk factors, only 2 components of MetS—
abdominal adiposity and hyperglycemia—remained inde-
pendent predictors of HF and MetS did not, we found
independent associations among all MetS components and
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incident HF, except high serum triglycerides. This could be
due to sex and ethnicity differences and partly due to the
inclusion of patients with diabetes in the MESA study.
Moreover, as previously suggested in epidemiological stud-
ies of type 2 diabetes and HF, insulin resistance may be
more important in the development of idiopathic dilated
cardiomyopathy than in the development of HF from
ischemic heart disease (17,18). In other studies, insulin
resistance accounted for ?90% of the association between
MetS and HF risk (19,20). In accordance with these
previous reports, overweight and obese individuals without
MetS in our studied population did not present an increased
HF risk.
Therearenumerouspotentialpathophysiologicmechanisms
underlying the relationship between insulin resistance and HF.
The heart may become less energy efficient in the setting of
insulinresistance,withdecreasedglucoseuseandincreasedfree
fatty acid use. This metabolic deregulation may increase
susceptibility to injury, such as pressure overload or ischemia
and thus promote deleterious renin-angiotensin-aldosterone
system activation. Evidence of an insulin-resistant cardiomy-
opathy, independent of pressure or volume loading influences,
is currently also emerging (21). Cardiac oxidative stress is often
observed coincident with insulin resistance, and there is accu-
mulating evidence that reactive oxygen species mediate dele-
terious effects in the insulin-resistant heart (22). Reduced
mitochondrial oxidative capacity may also contribute to cardiac
by Sonela Skenderi on October 2, 2011
Distribution of the Baseline Characteristics of theParticipants With and Without the Development of HF
Table 2
Participants With and Without the Development of HF
Distribution of the Baseline Characteristics of the
Without HF (n ? 365) With HF (n ? 185) p Value
Female/male183 (50.1)/182 (49.9) 88 (47.6)/97 (52.4)0.6
Age, yrs
BMI, kg/m2
55.4 ? 8.658.5 ? 7.9
?0.001
27.9 ? 3.928.7 ? 4.30.04
Waist circumference, cm
Normal weight (BMI ?25 kg/m2)
Overweight (BMI 25–29.9 kg/m2)
Obesity (BMI ?30 kg/m2)
97.0 ? 11.6101.6 ? 11.7
?0.001
117 (32.1)60 (32.4) 0.6
165 (45.2) 69 (37.3)0.04
83 (22.7) 56 (30.3) 0.008
Cigarette smoking
Current38 (10.4)28 (15.1) 0.2
Former64 (17.5)47 (25.4)0.04
Never 263 (72.1) 110 (59.5)0.007
Normal glucose (fasting ?100 mg/dl) 148 (40.3)26 (14.1)
?0.001
Hypertension77 (21.0) 103 (56.0)
?0.001
Mean arterial pressure, mm Hg*92.5 ? 12.399.4 ? 11.9
?0.001
Diastolic blood pressure, mm Hg74.5 ? 10.678.4 ? 11.0
?0.001
Physical activity, MET-h/week11.6 ? 5.9 9.1 ? 4.5
?0.001
?7.5 MET-h/week80 (21.9)82 (44.3)
?0.001
7.5 to ?21 MET-h/week 231 (63.2)96 (51.9)0.014
?21 MET-h/week
Left ventricular mass, g/m2
54 (14.9) 7 (3.8)
?0.001
103.9 ? 13.3115.5 ? 19.1
?0.001
Left ventricular hypertrophy, %103 (28.2)85 (45.9)0.02
Left ventricular ejection fraction, %65.3 ? 2.8 64.2 ? 2.6 0.01
MetS138 (37.8) 149 (80.5)
?0.001
Values are n (%) or mean ? SD. *Impaired fasting glucose was defined as fasting glucose of 100 to 125 mg/dl. †Mean arterial pressure:
2/3·diastolic blood pressure ? 1/3·systolic blood pressure.
BMI ? body mass index; HF ? heart failure; MET-h/week ? metabolic equivalent hours per week.
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growth remodeling and dysfunction (23). Experimental data
link both hyperglycemia and hyperinsulinemia with increased
sympathetic nervous system activation, a key pathophysiologic
mechanism in HF (24). Furthermore, the increased intramyo-
cardial triglyceride content in patients with impaired glucose
tolerance may lead to lipotoxicity and cardiomyocyte apoptosis,
ultimately leading to cardiac dysfunction (25).
Inflammation mediators were significant and independent
predictors of incident HF and were significantly associated
with MetS in all BMI groups in our study population. In
accordance with our findings, recent evidence suggests that the
relationship between overweight/obesity and incident HF is
mainly mediated by obesity-related metabolic, inflammatory
(16,26), and hormonal (27) changes. Moreover, obesity is
Figure 1 HF According to Obesity and Metabolic Status
The percentage of drug-naïve patients in whom heart failure (HF) developed requiring medication initiation (yellow bars) compared with those in whom new clinical symp-
toms, signs, and echocardiographic evidence of left ventricular dysfunction developed while taking medication (purple bars). MetS ? metabolic syndrome; NW ? normal
weight (BMI [body mass index]: ?25 kg/m2); OW ? overweight (BMI: 25 to 29.9 kg/m2); Obese: BMI ?30 kg/m2).
Relationship Among the 5 Metabolic Criteria Used in the Definition of MetS,the Presence of MetS, BMI, and HF Incidence During the 6-Year Follow-Up Period
Table 3
the Presence of MetS, BMI, and HF Incidence During the 6-Year Follow-Up Period
Relationship Among the 5 Metabolic Criteria Used in the Definition of MetS,
Metabolic Componentsn HF Incidence, n (%)Unadjusted HRAdjusted HR* 95% CIp Value
High fasting glucose 173 100 (57.8)1.081.091.06–1.10
?0.001
High blood pressure 333120 (36.0)5.334.863.30–8.61
?0.001
High waist circumference 313 106 (33.8)1.032.201.02–1.05
?0.001
Low HDL cholesterol27587 (31.6)1.541.881.29–2.770.001
High triglycerides19953 (26.6)1.111.180.74–1.66 NS
hs-CRP ?1.5 mg/dl 10343 (19.5) 1.55 1.521.31–1.82
?0.001
Microalbuminuria 13963 (45.3) 2.492.511.66–3.74
?0.001
BMI GroupMetS Pre-Incidence of HF, n (%)
Normal No 10917 (15.6)1.00 1.00——
Normal Yes 6843 (63.2) 2.34 2.331.25–4.36 0.007
Overweight No 12718 (14.2)0.901.120.35–1.330.36
OverweightYes10751 (47.7)2.682.661.73–4.13
?0.001
ObeseNo43 4 (9.3)0.260.410.10–1.31 0.49
Obese Yes 9652 (54.2)2.022.131.29–3.17 0.002
*Adjusted for all the factors associated with HF incidence: age, sex, impaired glucose tolerance, dyslipidemia, hypertension, current cigarette smoking, physical inactivity, left ventricular hypertrophy and
function on echocardiography. High fasting glucose level is ?100 mg/dl; high blood pressure is ?130/85 mm Hg; high waist circumference is ?102 cm in men and waist ?88 cm in women; low HDL
cholesterol is ?40 mg/dl in men and ?50 mg/dl in women; high triglyceride level is ?150 mg/dl; microalbuminuria is a urine albumin-to-creatinine ratio of 30 to 299.9 mg/dl.
CI ? confidence interval; HR ? hazard ratio; other abbreviations as in Tables 1 and 2.
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highly correlated with insulin resistance, which may in part
potentiate the link between obesity and HF (15). Therefore,
mechanisms beyond a positive caloric balance, such as inflam-
mation and hormonal release, possibly determine the patho-
logic metabolic consequences in obesity.
Study limitations. A small number of patients had high-
sensitivity C-reactive protein values ?1.5 mg/dl and were
included in the analysis. Therefore, the conclusions regarding
the possible mechanistic role of systemic inflammation in the
development of incident HF should be cautiously interpreted.
Further studies are required for additional insights into this
pathophysiologic link. Moreover, despite the longer median
follow-upduration,comparedwiththatofpreviousstudies,the
rather small number of patients in whom HF developed did
not permit a separate cohort analysis that could have increased
the study’s statistical power. This may be explained by the
exclusion from baseline of individuals with diabetes or macro-
vascular complications.
Conclusions
The emergence of HF as a global public health problem
with a high prevalence, high morbidity, and extraordinary
cost underscores the urgency of efforts to identify and
modify risk factors for incident HF (17). The findings of the
present study readdress the importance of MetS as a highly
prognostic marker of future HF risk, whereas obesity alone
appears to confer little independent value in cardiovascular
risk stratification. Focus on the growing, interrelated epi-
demics of obesity and MetS is warranted because it is
established that lifestyle interventions can decrease the risk
of these syndromes. Furthermore, appropriate medical
treatment of hypertension, dyslipidemia, and hyperglycemia
in those at risk of HF is an essential component of
prevention. Therefore, the evaluation of metabolic status
with the National Cholesterol Education Program Adult
Treatment Program III guidelines should be considered in
cardiovascular risk stratification, regardless of weight status.
Moreover, our findings indicate that normal-weight indi-
viduals with MetS have significantly increased risk of the
development of HF. Similarly, overweight and obese people
without MetS have a relatively low risk of the development
of HF. As a result, a healthier overweight/obese metabolic
profile may translate into a lower risk of cardiovascular
morbidity and HF incidence. Despite the uncertainty re-
garding the exact degree of protection related to the
metabolically healthy obese status, ongoing research on the
identification of underlying factors and mechanisms associ-
ated with this phenotype will eventually enable us to
understand the factors that predispose, delay, or protect
individuals from future HF risk.
Reprint requests and correspondence: Dr. Christina Voulgari,
Athens University Medical School, 24 Olenou Street, 11362 Kypseli,
Athens, Greece. E-mail: c_v_24@yahoo.gr.
Figure 2Kaplan-Meier Curves of Incident HF
(A) Kaplan-Meier curve of the 6-year incidence of heart failure (HF) in the normal-
weight group. The presence of metabolic syndrome (MetS) (?) (blue line),
increased the incidence of HF compared with patients without MetS, MetS (?)
(green line). (B) Kaplan-Meier curve of the 6-year incidence of HF in the over-
weight group. The presence of MetS (blue line) increased the incidence of HF
compared with patients without MetS (green line). (C) Kaplan-Meier curve of the
6-year incidence of HF in the obese group. The presence of MetS (blue line)
increased the incidence of HF compared with patients without MetS (green line).
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Key Words: heart failure y inflammation y lifestyle y metabolic
syndrome y obesity.
1350 Voulgari et al.
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doi:10.1016/j.jacc.2011.04.047
2011;58;1343-1350
J. Am. Coll. Cardiol.
Nicholas Katsilambros, and Christodoulos Stefanadis
Christina Voulgari, Nicholas Tentolouris, Polychronis Dilaveris, Dimitris Tousoulis,

Syndrome Compared With Metabolically Healthy Obese Individuals
Increased Heart Failure Risk in Normal-Weight People With Metabolic
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