Evidence for obesity paradox in patients with acute coronary syndromes: A report from the Swedish Coronary Angiography and Angioplasty Registry

Abstract

Aims:
The obesity paradox refers to the epidemiological evidence that obesity compared with normal weight is associated with counter-intuitive improved health in a variety of disease conditions. The aim of this study was to investigate the relationship between body mass index (BMI) and mortality in patients with acute coronary syndromes (ACSs).

Methods and results:
We extracted data from the Swedish Coronary Angiography and Angioplasty Registry and identified 64 436 patients who underwent coronary angiography due to ACSs. In 54 419 (84.4%) patients, a significant coronary stenosis was identified, whereas 10 017 (15.6%) patients had no significant stenosis. Patients were divided into nine different BMI categories. The patients with significant stenosis were further subdivided according to treatment received such as medical therapy, percutaneous coronary intervention (PCI), or coronary artery by-pass grafting. Mortality for the different subgroups during a maximum of 3 years was compared using Cox proportional hazards regression with the lean BMI category (21.0 to <23.5 kg/m(2)) as the reference group. Regardless of angiographic findings [significant or no significant coronary artery disease (CAD)] and treatment decision, the underweight group (BMI <18.5 kg/m(2)) had the greatest risk for mortality. Medical therapy and PCI-treated patients with modest overweight (BMI category 26.5-<28 kg/m(2)) had the lowest risk of mortality [hazard ratio (HR) 0.52; 95% CI 0.34-0.80 and HR 0.64; 95% CI 0.50-0.81, respectively]. When studying BMI as a continuous variable in patients with significant CAD, the adjusted risk for mortality decreased with increasing BMI up to ~35 kg/m(2) and then increased. In patients with significant CAD undergoing coronary artery by-pass grafting and in patients with no significant CAD, there was no difference in mortality risk in the overweight groups compared with the normal weight group.

Conclusion:
In this large and unselected group of patients with ACSs, the relation between BMI and mortality was U-shaped, with the nadir among overweight or obese patients and underweight and normal-weight patients having the highest risk. These data strengthen the concept of the obesity paradox substantially.

Full text

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CLINICAL RESEARCH
Coronary artery disease
Evidence for obesity paradox in patients
with acute coronary syndromes: a report
from the Swedish Coronary Angiography and
Angioplasty Registry
Oskar Angera
˚s1*, Per Albertsson1, Kristjan Karason1, Truls Ra
˚munddal1,
Go
¨ran Matejka1, Stefan James2, Bo Lagerqvist2, Annika Rosengren1,
and Elmir Omerovic1
1
Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; and
2
Uppsala Clinical Research
Center, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
Received 22 February 2012; revised 11 June 2012; accepted 27 June 2012; online publish-ahead-of-print 4 September 2012
See page 330 for the editorial comment on this article (doi:10.1093/eurheartj/ehs237)
Aims The obesity paradox refers to the epidemiological evidence that obesity compared with normal weight is associated
with counter-intuitive improved health in a variety of disease conditions. The aim of this study was to investigate the
relationship between body mass index (BMI) and mortality in patients with acute coronary syndromes (ACSs).
Methods
and results
We extracted data from the Swedish Coronary Angiography and Angioplasty Registry and identified 64 436 patients
who underwent coronary angiography due to ACSs. In 54 419 (84.4%) patients, a significant coronary stenosis was
identified, whereas 10 017 (15.6%) patients had no significant stenosis. Patients were divided into nine different BMI
categories. The patients with significant stenosis were further subdivided according to treatment received such as
medical therapy, percutaneous coronary intervention (PCI), or coronary artery by-pass grafting. Mortality for the dif-
ferent subgroups during a maximum of 3 years was compared using Cox proportional hazards regression with the
lean BMI category (21.0 to ,23.5 kg/m
2
) as the reference group. Regardless of angiographic findings [significant or no
significant coronary artery disease (CAD)] and treatment decision, the underweight group (BMI ,18.5 kg/m
2
) had
the greatest risk for mortality. Medical therapy and PCI-treated patients with modest overweight (BMI category
26.5 – ,28 kg/m
2
) had the lowest risk of mortality [hazard ratio (HR) 0.52; 95% CI 0.34– 0.80 and HR 0.64; 95%
CI 0.50–0.81, respectively]. When studying BMI as a continuous variable in patients with significant CAD, the
adjusted risk for mortality decreased with increasing BMI up to 35 kg/m
2
and then increased. In patients with sig-
nificant CAD undergoing coronary artery by-pass grafting and in patients with no significant CAD, there was no dif-
ference in mortality risk in the overweight groups compared with the normal weight group.
Conclusion In this large and unselected group of patients with ACSs, the relation between BMI and mortality was U-shaped, with
the nadir among overweight or obese patients and underweight and normal-weight patients having the highest risk.
These data strengthen the concept of the obesity paradox substantially.
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Keywords Obesity paradox †Acute coronary syndrome †Body mass index †SCAAR
Introduction
In the general population, obesity is associated with increased mor-
tality.
1
Obese individuals have a higher frequency of cardiovascular
risk factors such as hypertension, hyperlipidaemia, and diabetes.
Therefore, these individuals have higher cardiovascular
disease-related morbidity and mortality rate. As weight reduction
is associated with improved risk factor profile, the guidelines for
*Corresponding author. Tel: +46 31 3421000, Fax: +46 31 823762, Email: oskar.angeras@vgregion.se
Published on behalf of the European Society of Cardiology. All rights reserved. &The Author 2012. For permissions please email: journals.permissions@oup.com
European Heart Journal (2013) 34, 345–353
doi:10.1093/eurheartj/ehs217
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primary prevention of cardiovascular diseases recommend weight
loss in overweight and obese individuals.
2
Despite limited scientific
evidence, this recommendation has also been extended to the
guidelines for secondary prevention of coronary artery disease
(CAD)
3–5
and heart failure (HF).
6
In fact, a number of epidemio-
logical studies suggest that obesity may confer protection in
some common disease settings. This was first shown in patients
with end-stage renal failure, in whom obesity constituted a favour-
able prognostic factor.
7
This led to the proposal of the obesity
paradox.
8–10
Subsequently, obesity paradox has been reported
in cardiac conditions including HF,
11
atrial fibrillation,
12
sudden
cardiac death,
13
and CAD.
14
It has also been confirmed by
several meta-analyses,
9,15
which support the view that obesity, in
certain clinical conditions, could lead to a favourable prognosis.
9,15
Acute coronary syndromes (ACSs) are among the most import-
ant causes of mortality and reduced quality of life in modern
Western societies. Some studies indicate that obesity paradox is
also present in patients with ACSs. However, this evidence is rela-
tively weak as it is based on retrospective studies.
16
Therefore, the
aim of this study was to evaluate the relationship between body
mass index (BMI) and mortality in one of the largest-to-date
ACS population based on the prospectively collected data from
the Swedish Coronary Angiography and Angioplasty Registry
(SCAAR).
17
Methods
Study design
In this prospective registry study, we established a cohort of all con-
secutive patients admitted to Swedish hospitals, during the period
May 2005–December 2008, with ACSs such as unstable angina,
ST-elevation myocardial infarction (STEMI), and non-STEMI and who
underwent coronary angiography. Non-STEMI, STEMI, and unstable
angina were defined according to the criteria established by the Euro-
pean Society of Cardiology.
18,19
Patient population
The data were collected from SCAAR, which was established in 1992.
This registry currently contains information about all coronary angio-
graphies and percutaneous coronary interventions (PCIs) performed
in Sweden.
17
Each catheterization procedure is described with 50
angiographies and 200 PCI variables that includes both demographic
and procedure-related data. The registry is sponsored by the
Swedish Health Authorities and does not receive any funding from
commercial interests. Details about patients’ weight and height (mea-
sured or self-reported) were entered into the SCAAR starting from
May 2005. All patients in Sweden who were admitted with ACSs
and underwent angiography during the period May 2005 – December
2008 were included in the analysis. The information about patients’
characteristics and co-morbidities were based on the data extracted
from the patients’ medical records.
Statistics
Primary analysis
The primary outcome was all-cause mortality in the patients who had
significant stenosis (.50% diameter narrowing) in one or more coron-
ary arteries. The patients with significant CAD were divided into the
subgroups according to the physician’s initial decision for treatment
strategy such as coronary artery bypass grafting (CABG), PCI, or
medical therapy. The treatment strategy was defined based on
intention-to-treat decision following the index catheterization.
BMI is defined as the weight divided by length in meters squared.
The patients were divided into nine different BMI categories accord-
ing to the National Institute of Health – AARP cohort
16
:,18.5 kg/m
2
(underweight), 18.5 to ,21.0 kg/m
2
(normal weight); 21.0 to
,23.5 kg/m
2
(normal weight); 23.5 to ,25.0 kg/m
2
(normal
weight); 25.0 to ,26.5 kg/m
2
(overweight); 26.5 to ,28.0 kg/m
2
(overweight); 28.0 to ,30.0 kg/m
2
(overweight); 30.0 to ,35.0 kg/
m
2
(obese) and ≥35.0 kg/m
2
(obese). Baseline characteristics of
patients across the categories were examined by
x
2
tests for linear
trend for nominal variables and by the Jonckheere – Terpstra test
for trend for continuous variables to account for the ordinal
nature of the BMI categories.
Unadjusted survival was examined using a Kaplan – Meier survival
curve and the log-rank test. To evaluate the association between
BMI and mortality, multivariable-adjusted hazard ratios (HR) were
calculated using Cox proportional-hazards regression models for
each treatment strategy. All potential confounders listed in Table 1
were entered into the model. The BMI category 21 – ,23.5 kg/m
2
was considered the reference category and statistical significance
was set at P,0.05. All tests were two sided. Interactions between
BMI category with age, and BMI category and sex were tested
using the likelihood ratio test. The assumption of proportional
hazards for each covariate was reviewed separately by the means
of log-minus-log survival plots.
The database was scrutinized for missing data. Logistic regression
showed that a number of variables were associated (P,0.05) with
missing data including diabetes, previous myocardial infarction (MI),
previous HF, previous stroke, chronic obstructive pulmonary
disease, hyperlipidaemia, hypertension, smoking habits, and dementia.
This relationship indicates that the presence of missing data was not
completely random. Thus, in addition to the complete case analysis,
we applied multiple imputation method to estimate the missing
data
20,21
and performed Cox proportional hazards regression with
the imputed data set under the assumption that missing data are
missing at random. Multiple imputation was implemented using the
same covariates as in the main model with addition of cumulative
hazard and event indicator.
22
Cumulative hazard was estimated
with the Nelson–Allens test using STATA software (version 12, Sta-
taCorp, College Station, TX, USA). IBM SPSS missing data module
software (version 20, IBM Corporation, New York, NY, USA) was
used for the imputation procedure with 10 imputed data sets. The
imputation procedure and subsequent Cox proportional hazards re-
gression estimation was performed according to the Rubin’s
protocol.
23
The continuous risk relationship between BMI and all-cause mortal-
ity was analysed by entering BMI as a continuous variable into fractional
polynomial Cox proportional-hazards regression
24
adjusted for the
covariates used in the main analysis with the addition of treatment
strategy. This analysis was performed using STATA software.
Secondary analysis
Three types of secondary analyses were performed. First, we exam-
ined the relationship between BMI and mortality in patients who
received a cardiac catheterization but were not diagnosed with CAD
(,50% diameter narrowing or normal coronaries). Second, we
explored the mortality data as all-cause mortality, in-hospital mortality,
30-day mortality, and 3-year mortality. Third, we examined the rela-
tionship between BMI category and hospitalization for MI, HF, and
stroke after the index procedure.
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Table 1 Baseline characteristics of 38 667 acute coronary syndrome patients with significant coronary artery disease
BMI group (kg/m
2
)
<18.5
(n5344)
18.5 to <21
(n51578)
21 to <23.5
(n55146)
23.5 to <25
(n56113)
25 to <26.5
(n56286)
26.5 to <28
(n55456)
28 to <30
(n55604)
30 to <35
(n56383)
≥
35
(n51757)
Pvalue
Age mean; +D ( years) 71.2 +11 71.4 +11 69.7 +11 68.9 +11 67.3 +11 66.5 +10 65.7 +11 64.4 +11 62.0 +11 ,0.001
Female; n(%) 218 (63) 736 (47) 1804 (35) 1565 (26) 1345 (21) 1255 (23) 1253 (22) 1882 (30) 688 (39) ,0.001
Prior PCI; n(%) 33 (9.6) 133 (8.4) 482 (9.4) 687 (11.2) 750 (11.9) 670 (12.3) 741 (13.2) 930 (14.6) 274 (15.6) ,0.001
Prior CABG; n(%) 18 (5.2) 134 (8.5) 429 (8.3) 527 (8.6) 633 (10.1) 516 (9.5) 579 (10.3) 641 (10.0) 171 (9.7) ,0.001
Diabetes; n(%) 31 (9.0) 166 (10.5) 635 (13.8) 844 (13.8) 982 (15.6) 1047 (19.2) 1261 (22.5) 1876 (29.4) 726 (41.3) ,0.001
Current smoker; n(%) 141 (41.0) 473 (30.0) 1342 (26.1) 1395 (22.8) 1319 (21.0) 1182 (21.7) 1193 (21.3) 1395 (21.9) 437 (24.9) ,0.001
Former smoker; n(%) 76 (22.1) 403 (25.5) 1496 (29.1) 1963 (32.1) 2269 (36.1) 1947 (35.7) 2207 (39.4) 2569 (40.2) 655 (37.3) ,0.001
Treated hypertension; n(%) 148 (43.0) 651 (41.3) 2228 (43.3) 2790 (45.6) 2970 (47.2) 2802 (51.4) 2988 (53.3) 3865 (60.6) 1203 (68.5) ,0.001
Treated hyperlipidaemia;
n(%)
122 (35.5) 641 (40.6) 2175 (42.3) 2657 (43.5) 2882 (45.8) 2595 (47.6) 2871 (51.2) 3420 (53.6) 1035 (58.9) ,0.001
Previous myocardial
infarction; n(%)
109 (31.7) 417 (26.4) 1244 (24.2) 1479 (24.2) 1567 (24.9) 1370 (25.1) 1487 (26.5) 1706 (26.7) 534 (30.4) ,0.001
Stroke; n(%) 48 (14.0) 144 (9.1) 403 (7.8) 413 (6.8) 452 (7.2) 378 (6.9) 355 (6.3) 425 (6.7) 119 (6.8) ,0.001
Kidney failure; n(%) 10 (2.9) 43 (2.7) 94 (1.8) 103 (1.7) 77 (1.2) 94 (1.7) 96 (1.7) 104 (1.6) 40 (2.3) 0.32
Heart failure; n(%) 29 (8.4) 138 (8.7) 361 (7.0) 363 (5.9) 370 (5.9) 315 (5.8) 376 (6.7) 498 (7.8) 189 (10.8) 0.001
Cancer; n(%) 30 (8.7) 57 (3.6) 169 (3.3) 174 (2.8) 203 (3.2) 146 (2.7) 151 (2.7) 148 (2.3) 34 (1.9) ,0.001
Peripheral vascular disease;
n(%)
33 (9.6) 129 (8.2) 252 (4.9) 285 (4.7) 260 (4.1) 168 (3.1) 232 (4.1) 242 (3.8) 56 (3.2) ,0.001
Dementia; n(%) 0 (0) 6 (0.4) 7 (0.1) 10 (0.2) 10 (0.2) 5 (0.1) 7 (0.1) 6 (0.1) 1 (0.1) 0.05
Chronic obstructive
pulmonary disease; n(%)
78 (22.7) 180 (11.4) 407 (7.9) 394 (6.4) 374 (5.9) 317 (5.8) 358 (6.4) 476 (7.5) 213 (12.1) 0.03
Indication
Unstable angina/
non-STEMI; n(%)
223 (64.8) 1120 (71.0) 3686 (71.6) 4406 (72.1) 4610 (73.3) 4075 (74.7) 4338 (77.4) 4969 (77.8) 1442 (82.1) ,0.001
STEMI; n(%) 120 (34.9) 456 (28.9) 1440 (28.0) 1699 (27.8) 1665 (26.5) 1371 (25.1) 1260 (22.5) 1404 (22.0) 309 (17.6) ,0.001
Other; n(%) 1 (0.3) 2 (0.1) 20 (0.4) 8 (0.1) 11 (0.2) 10 (0.2) 6 (0.1) 10 (0.2) 6 (0.3) 0.22
Angiographic findings
One vessel disease; n(%) 133 (38.7) 615 (39.0) 2068 (40.2) 2397 (39.2) 2495 (39.7) 2158 (39.6) 2245 (40.1) 2650 (41.5) 750 (42.7) 0.005
Multi-vessel disease; n(%) 211 (61.3) 963 (61.0) 3078 (59.8) 3716 (60.8) 3791 (60.3) 3298 (60.4) 3359 (59.9) 3733 (58.5) 1007 (57.3) 0.005
Primary decision
PCI; n(%) 258 (75.0) 1156 (73.3) 3817 (74.2) 4606 (75.3) 4741 (75.4) 4068 (74.6) 4200 (74.9) 4843 (75.9) 1267 (72.1) 0.54
CABG; n(%) 39 (11.3) 206 (13.1) 691 (13.4) 846 (13.8) 877 (14.0) 786 (14.4) 830 (14.8) 899 (14.1) 246 (14.0) 0.04
Medical therapy; n(%) 47 (13.7) 216 (13.7) 638 (12.4) 661 (10.8) 668 (10.8) 602 (11.0) 574 (10.2) 641 (10.0) 224 (13.9) 0.002
Evidence for obesity paradox in patients with acute coronary syndromes 347
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Results
Patient characteristics
Between May 2005 and December 2008, a total of 64 436 patients
underwent catheterization for suspected ACSs in Sweden. Missing
data for one or more variables occurred in 18 743 (29.1%). The
variable most often missing was BMI (15 159) followed by
smoking status (4588), hyperlipidaemia (2040), hypertension
(1663), diabetes (642), prior PCIs (46), and prior CABG (28). Im-
plausible BMI values (defined as BMI .70 or ,11 kg/m
2
) were
present in 27 patients and were treated as missing data. In the
total 45 693 patients who had complete data, 38 667 had significant
stenosis at angiography whereas 7026 did not. Patients excluded
from the complete case analysis due to missing data were older,
had more co-morbidities, more STEMI, and a higher mortality
rate (HR 1.65, 95% CI 1.53 – 1.77) than patients who were
included. The mean time of follow-up for the study cohort was
21 months (SD +13 months). Patient characteristics according
to the different BMI categories at the time of cardiac catheteriza-
tion are shown in Table 1. It was observed that obese patients
were more likely to be younger, have hyperlipidaemia, hyperten-
sion, and diabetes mellitus, but were less likely to smoke and to
have high-risk coronary anatomy. The underweight patients were
more likely to be females and the indication for angiography was
more often STEMI.
Survival
Primary analysis
During follow-up, 3018 (4.7%) deaths were registered in patients
with significant CAD. Unadjusted survival is presented as a
Kaplan–Meier curve in Figure 1. This demonstrates substantial dif-
ference in mortality between the different BMI categories (P,
0.001; log-rank test for trend). Patients who were underweight
with BMI ,18.5 kg/m
2
had the highest risk followed by patients
with normal weight, whereas overweight patients had the lowest
risk. The unadjusted HR ranged from 2.94 (2.25– 3.83) to 0.52
(0.44–0.61) compared with the reference group. The same
pattern was present in ACS patients with significant coronary
artery stenosis treated with PCIs. In this group, the highest mortal-
ity rate was also in the underweight patients (12.4%) followed by
the patients with normal BMI whereas the lowest mortality was
in the overweight and obese patients (3.9%). The adjusted HR
ranged from 2.31 (1.67–3.21) to 0.66 (0.53– 0.82) compared
with the reference group (Figure 2A). In patients who were
treated with CABG or medical therapy alone the differences in
HR were smaller (Figure 2Band C). Examination of BMI as a
Figure 1 Kaplan – Meier survival curve for patients with acute coronary syndrome and significant coronary artery disease according to the
different BMI categories.
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continuous variable (Figure 3) using fractional polynomial Cox re-
gression demonstrated a U-shaped association between BMI and
adjusted all-cause mortality, where mortality decreased with in-
creasing BMI between 30 and 40 kg/m
2
, and then began to increase
again at a BMI of .40 kg/m
2
. The results of Cox-regression based
on the estimation after the multiple imputation procedure have
shown congruent data when compared with the unadjusted and
adjusted complete case-analysis models in patients with significant
stenosis (Table 2). There was no interaction between BMI category
and age, and BMI category and sex. There was no difference in
mortality between the BMI categories regarding in-hospital mortal-
ity and 30-day mortality (Figure 4).
Secondary analysis
The baseline characteristics of patients without significant stenosis
are shown in Table 3. The distribution of co-morbidity between the
BMI categories is quite similar to the cohort with stenosis.
However, this subgroup had younger patients, a higher proportion
of women, and fewer co-morbidities. Adjusted HR are shown in
Figure 5. In this analysis, only underweight patients had a significant-
ly higher HR compared with the reference group in both the
adjusted and the unadjusted models.
Among the patients with significant CAD there was no differ-
ence between the BMI categories with regard to hospitalization
for MI, HF, and stroke after the index procedure (data not shown).
Discussion
The most important result from our study is that overweight and
obese patients with ACSs had lower mortality rate compared with
patients with normal BMI. This was independent of the treatment
strategy until up to 3 years after hospitalization. This large obser-
vational study with prospectively collected data strengthens the
existing evidence and increases the awareness of obesity paradox.
An inverse relationship between obesity and all-cause and car-
diovascular mortality has previously been described in patients
with CAD.
8–14
In fact, in two recent large observational studies
obesity paradox has also been associated in patients with
ACSs.
14,25
Our study verifies and emphasizes this phenomenon,
but it does not offer any evident explanation for the paradox.
Two arguments have been put forth to account for the existence
of obesity paradox. First is that the obesity paradox is a mere con-
sequence of one or several confounding factors present in the
obese population. The second argues that the explanation has to
be found in the biology of the obese phenotype itself, which
means we need to define known or to detect yet unknown pro-
tective biological pathways that protect obese patients with cardio-
vascular diseases from premature death.
In accordance with the previous studies, our data show that
obese patients are younger and have less severe CAD at the
Figure 2 Adjusted risk for mortality (95% CI) in patients with
acute coronary syndrome and significant coronary artery disease
in whom the treatment decision was (A) PCI (n¼28 956),
(B) CABG (n¼5420) and (C) Medical therapy (n¼4291)
according to the different BMI categories. Patients with missing
variables are not included.
Figure 3 Adjusted fractional polynomial Cox proportional-
hazards regression (95% CI, shaded area) with continuous risk re-
lationship between BMI and all-cause mortality in patients with
acute coronary syndrome and significant coronary artery disease.
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time of cardiac catheterization. As obese patients with ACSs are
younger,
26,27
they are more likely to be referred to experts in sec-
ondary prevention
28
and to receive treatment for co-morbidities.
The obese patients also tend to have higher blood pressure,
which may lead to more aggressive use of disease-modifying med-
ications such as ACE-inhibitors and beta-blockers—the pharmaco-
logical treatment particularly beneficial for secondary prevention.
However, Oreopoulos et al.
14
were not able to demonstrate
that obese patients with CAD are being more aggressively
treated with statins, beta-blockers, ACE-inhibitors, and nitrates.
Unfortunately, we were not able to address this issue as our data-
base does not contain information about adequacy of
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Table 2 Adjusted and unadjusted hazard ratios (95% CI) of cumulative mortality according to the BMI group from different Cox proportional hazards regression
models in patients with acute coronary syndrome and significant stenosis on angiography
BMI group (kg/m
2
)
n<18.5 18.5 to 21 21 to <23.5 23.5 to <25 25 to <26.5 26.5 to <28 28 to <30 30 to <35 >35
Unadjusted analysis 41 731 2.94 (2.27 – 3.83) 1.31 (1.08 – 1.59) 1.0 0.76 (0.65–0.88) 0.62 (0.53– 0.73) 0.52 (0.44 – 0.62) 0.55 (0.46 – 0.65) 0.52 (0.44 – 0.61) 0.75 (0.60 – 0.94)
Adjusted
a
complete case analysis 38 667 1.90 (1.41 – 2.55) 1.13 (0.92 – 1.40) 1.0 0.79 (0.67–0.93) 0.75 (0.63– 0.89) 0.63 (0.52 – 0.76) 0.71 (0.60 – 0.86) 0.66 (0.55 – 0.79) 1.04 (0.81 – 1.34)
Unadjusted multiple imputation 54 419 2.77 (2.13–3.61) 1.26 (1.05–1.52) 1.0 0.81 (0.69 – 0.94) 0.72 (0.63 – 0.84) 0.66 (0.57 – 0.77) 0.67 (0.57–0.78) 0.62 (0.53–0.73) 0.73 (0.58– 0.92)
Adjusted
a
multiple imputation 54 419 2.04 (1.57–2.04) 1.13 (0.93–1.34) 1.0 0.85 (0.73– 0.99) 0.82 (0.71 – 0.95) 0.76 (0.65 – 0.88) 0.78 (0.66–0.92) 0.75 (0.64–0.89) 0.98 (0.78– 1.24)
a
Adjustments are made for age, gender, prior PCI, prior CABG, diabetes mellitus, smoking status, treated hypertension, treated hyperlipidaemia, previous MI, prior stroke, prior kidney failure, prior heart failure, prior cancer, prior peripheral
vascular disease, prior dementia, prior chronic obstructive pulmonary disease, indication for coronary angiography, angiographical finding, and primary treatment decision.
Figure 4 Adjusted risk for (A) in-hospital, (B) 30-day, and
(C) 3-year mortality (95% CI) in patients with acute coronary syn-
drome and significant coronary artery disease according to the
different BMI categories.
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Table 3 Baseline characteristics of 7026 acute coronary syndrome patients without significant coronary artery disease
BMI group (kg/m
2
)
<18.5
(n5122)
18.5 to <21
(n5443)
21 to <23.5
(n51059)
23.5 to <25
(n51044)
25 to <26.5
(n5992)
26.5 to <28
(n5913)
28 to <30
(n5916)
30 to <35
(n51154)
≥
35
(n5384)
Pvalue
Age mean; +SD (years) 65.9 +12 64.9 +12 64.1 +12 63.7 +12 63.8 +12 63.7 +11 62.6 +12 61.7 +11 58.7 +12 ,0.001
Female; n(%) 96 (78.7) 331 (74.7) 656 (61.9) 545 (52.2) 501 (50.5) 463 (50.7) 434 (47.4) 610 (52.9) 220 (57.3) ,0.001
Previous PCI; n(%) 5 (4.1) 30 (6.8) 74 (7.0) 92 (8.8) 89 (9.0) 110 (12.0) 113 (12.3) 137 (11.9) 39 (10.2) ,0.001
Previous CABG; n(%) 0 (0) 1 (0.2) 11 (1.0) 7 (0.7) 5 (0.5) 9 (1.0) 9 (1.0) 14 (1.2) 4 (1.0) 0.07
Diabetes; n(%) 4 (3.3) 24 (5.4) 80 (7.6) 75 (7.2) 91 (9.2) 114 (12.5) 128 (14.0) 235 (20.4) 134 (34.9) ,0.001
Current smoker; n(%) 43 (35.2) 122 (27.5) 215 (20.3) 202 (19.3) 158 (15.9) 141 (15.4) 133 (14.5) 187 (16.2) 76 (19.8) ,0.001
Former smoker; n(%) 34 (27.9) 120 (27.1) 294 (27.8) 308 (29.5) 332 (33.5) 312 (34.2) 321 (35.1) 420 (36.4) 132 (34.4) ,0.001
Treated hypertension; n(%) 41 (33.6) 149 (33.6) 396 (37.4) 433 (41.5) 446 (45.0) 454 (49.7) 466 (45.0) 645 (55.9) 227 (59.1) ,0.001
Treated hyperlipidaemia; n(%) 38 (31.1) 161 (36.3) 385 (36.4) 426 (40.8) 423 (42.6) 386 (42.3) 437 (47.8) 544 (47.1) 202 (52.6) ,0.001
Previous myocardial infarction; n(%) 9 (7.4) 56 (12.6) 144 (13.6) 140 (13.4) 160 (16.1) 141 (15.4) 150 (16.4) 218 (18.9) 64 (16.7) ,0.001
Stroke; n(%) 8 (6.6) 28 (6.3) 54 (5.1) 40 (3.8) 52 (5.2) 33 (3.6) 34 (3.7) 47 (4.1) 20 (5.2) 0.07
Kidney failure; n(%) 2 (1.6) 1 (0.2) 14 (1.3) 9 (0.9) 4 (0.4) 13 (1.4) 6 (0.7) 10 (0.9) 5 (1.3) 0.91
Heart failure; n(%) 10 (8.2) 30 (6.8) 57 (5.4) 48 (4.6) 48 (4.8) 51 (5.6) 46 (5.0) 78 (6.8) 48 (12.5) 0.007
Cancer; n(%) 2 (1.6) 26 (5.9) 37 (3.5) 30 (2.9) 27 (2.7) 23 (2.5) 29 (3.2) 30 (2.6) 6 (1.6) 0.01
Peripheral vascular disease; n(%) 3 (2.5) 11 (2.5) 20 (1.9) 16 (1.5) 16 (1.6) 22 (2.4) 16 (1.7) 26 (2.3) 7 (1.8) 0.83
Dementia; n(%) 0 (0) 2 (0.5) 0 (0) 0 (0) 0 (0) 0 (0) 1(0.1) 1 (0.1) 0 (0) 0.54
Chronic obstructive pulmonary disease; n(%) 35 (28.7) 71 (16.0) 117 (11.0) 101 (9.7) 75 (7.6) 76 (8.3) 97 (10.6) 125 (10.8) 66 (17.2) 0.15
Indication
Unstable angina/non-STEMI; n(%) 99 (81.1) 388 (87.6) 937 (88.5) 891 (85.3) 867 (87.4) 800 (87.6) 836 (91.4) 1060 (91.9) 356 (92.7) ,0.001
STEMI; n(%) 22 (18.0) 49 (11.1) 115 (10.9) 148 (14.2) 121 (12.2) 107 (11.7) 75 (8.2) 90 (7.8) 28 (7.3) ,0.001
Other; n(%) 1 (0.8) 6 (1.4) 7 (0.7) 5 (0.5) 4 (0.4) 6 (0.7) 4 (0.4) 4 (0.3) 0 (0) 0.02
Evidence for obesity paradox in patients with acute coronary syndromes 351
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pharmacological treatment before and after catheterization, which
is an important limitation of the study. However, the positive asso-
ciation between guideline-recommended treatment and obesity
has not yet been unequivocally established.
Other possible explanations for obesity paradox are that obesity
may protect against malnutrition and energy wastage post-
revascularization and that altered neuroendocrine status in obese
patients may play a role in modulating progression of pathologic
cardiac remodelling after MI. The size of the coronary vessels
increases with increasing BMI and small vessels is a risk factor for
worse outcome after PCI and CABG.
29
Despite the significant differ-
ences in baseline characteristics, our findings regarding the inverse
association between BMI and outcomes persisted after multivariate
adjustments in several models, suggesting an independent associ-
ation between BMI and mortality risk. However, it is important to
keep in mind that inverse association between BMI and outcomes
is U-shaped with increasing risk in patients with morbid obesity.
Indeed, this finding has been independently reported by others.
14,25
In addition to the suggested explanations for obesity paradox,
we tentatively propose that obesity may protect against malignant
ventricular arrhythmias during and after MI and therefore decrease
the risk for sudden death. This hypothesis is indirectly supported
by clinical evidence from patients with HF secondary to AMI,
13
with cautious support from our data. During the follow-up
period, overweight and obese patients did not differ in the fre-
quency of hospitalization for HF, MI, and stroke—common
causes of death in this population—suggesting that obesity is not
associated with lower risk for these clinical events. By process of
elimination, these observations strengthen the hypothesis that
obesity may protect against malignant ventricular arrhythmias as
it is another frequent cause of mortality in patients with CAD.
Thus far, the growing evidence for the existence of obesity
paradox has had no impact on the current guidelines for secondary
prevention in CAD. The European Society of Cardiology and the
American College of Cardiology/American Heart Association rec-
ommend a BMI of ,25 kg/m
2
in their guidelines of secondary pre-
vention strategies.
4,5
We believe that no evidence exists that
proves weight reduction in itself has a positive prognostic value
after ACSs. Actually, some evidence suggests that weight loss
after ACSs might in fact have a negative effect.
30
We believe that
given the current state of our knowledge, obesity paradox requires
much more attention and deserves to be recognized in the guide-
lines. However, our study should not be interpreted as a support
for status quo in obese patients. Instead we think that multidiscip-
linary scientific approach to obesity paradox, both clinical and pre-
clinical (including experiments investigating hypothetically
protective pathways), may lead us to important discoveries that
we can use to improve treatment of ACSs, HF, and arrhythmias.
Indeed, experimental evidence is emerging suggesting that
adipose tissue as the largest endocrine organ
31
produces hor-
mones (e.g. leptin, adiponectin, resistin) that may have cardiopro-
tective effects in MI.
32 –35
There is considerable evidence
demonstrating that leptin and adiponectin have direct cardiopro-
tective effects. These hormones possess anti-inflammatory, anti-
apoptotic, anti-hypertrophic effects and reduce infarct size.
36 –38
All these effects may lower arrhythmogenicity in the infarcted
myocardium and therefore potentially prevent sudden death.
There are six limitations that need to be addressed. First, this is
an observational study and as such it provides only associative evi-
dence, not causative. We cannot rule out the possibility of selec-
tion bias, residual confounding and survival bias as only surviving
hospitalized patients are included in the registry. On the other
hand, the observational nature of our study provides real-world
data on the largest cohort studied to date. Second, SCAAR does
not contain data on pharmacological treatment and we were not
able to adjust for the possible differences known to have impact
on clinical outcome. Third, although BMI is the most commonly
used measure of obesity, it does not directly distinguish between
adipose and lean tissue or central and peripheral adiposity.
Fourth, we were unable to control for the role of unintentional
weight loss. Our risk-adjusted analysis, however, did include age,
smoking status, history of malignancy, dementia, renal failure, HF,
and chronic obstructive pulmonary disease, which are all important
factors that could lead to involuntary weight loss. Fifth, we did not
have data on cause-specific mortality. Finally, 30% of the patients
had missing data. These patients had higher mortality and therefore
their exclusion from the analysis might have produced biased
results. However, results from the multiple imputation model
were congruent with the data from the complete case analysis.
In conclusion, we found that among ACS patients the relation
between BMI and mortality is U-shaped, with the nadir among
overweight or obese patients, and underweight and normal-weight
patients having the highest risk. Therefore, these data strengthen
the concept of obesity paradox substantially.
Conflict of interest: none declared.
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