Download Weight Loss and Heart Failure: A Nationwide Study of Gastric

10.1161/CIRCULATIONAHA.116.025629
Weight Loss and Heart Failure: A Nationwide Study of Gastric Bypass
Surgery Versus Intensive Lifestyle Treatment
Running Title: Sundström et al.; Weight Loss and Heart Failure
Johan Sundström, MD, PhD1; Gustaf Bruze, PhD2; Johan Ottosson, MD, PhD3;
Claude Marcus, MD, PhD4; Ingmar Näslund, MD, PhD3; Martin Neovius, PhD2
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1
Department of Medical Sciences, Uppsala University, and Uppsala Clinical Research Center
Epidemio
iolo
logy
lo
gy Unit,
Uni
nit,
t,
(UCR), Uppsala, Sweden; 2Department of Medicine, Solna, Clinical Epidemiology
Karolinska Institutet, Stockholm, Sweden; 3Department of Surgery, Faculty of Me
M
Medicine
dicine and
Health, Örebro University, Örebro, Sweden; 4Department of Clinical Science, Intervention and
Te
ech
hno
n lo
ogy
gy,, Karo
olinska Inst
tittutet,
t,, Stockholm,
Sto
ock
c hoolm
lm,, Sweden
Swed
Sw
e en
Technology,
Karolinska
Institutet,
Addres
Address
Addr
esss for
f r Correspondence:
fo
C rrres
Co
e pond
ponden
ence
cee:
Johan Sundström,
Sundström MD,
MD PhD
Department of Medical Sciences
Uppsala University Hospital
751 85 Uppsala, Sweden
Tel: +46704225220
Fax: +4618509297
E-mail: [email protected]
Journal Subject Terms: Obesity; Heart Failure; Cardiomyopathy; Primary Prevention;
Lifestyle
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10.1161/CIRCULATIONAHA.116.025629
Abstract
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Background—Associations of obesity with incidence of heart failure have been observed, but
the causality is uncertain. We hypothesized that gastric bypass surgery leads to lower incidence
of heart failure compared to intensive lifestyle modification in obese people.
Methods—We included obese people without previous heart failure from a Swedish nationwide
registry of people treated with a structured intensive lifestyle program, and the Scandinavian
Obesity Surgery Registry. All analyses used inverse probability weights, based on baseline bodymass index and a propensity score estimated using baseline variables. Treatment groups were
well balanced regarding weight, body mass index and most potential confounders. Associations
of treatment with heart failure incidence, as defined in the National Patient Register, were
analyzed using Cox regression.
Results—The 25,804 gastric bypass surgery patients had on average lost 18.8 kg more weight
after 1 year, and 22.6 kg more after 2 years, than the 13,701 lifestyle modification patients.
During a median of 4.1 years, surgery patients had lower heart failure incidence than lifestyle
modification patients (hazard ratio 0.54, 95% CI 0.36-0.82). A 10 kg achieved weight loss after 1
year was related to a hazard ratio for heart failure of 0.77, 95% confidence interval 0.60 to 0.97,
inn both treatment groups combined. Results were robust in sensitivity analyses.
Conclusions—Gastric bypass surgery was associated with approximately one half
lf the
the incidence
inci
in
cide
ci
denc
de
n e
nc
of heart failure compared with intensive lifestyle modification in this study of twoo llarge
a gee
ar
nationwide registries. We also observed a graded association between increasing weight loss and
decreasing risk of heart failure.
Key-Words:
Key-W
-Wor
-W
ords
or
ds:: he
ds
hheart
arrt failure; bariatric surgery, gastric
gastrricc bypass, low calorie
caloriie diet, weight loss
ca
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10.1161/CIRCULATIONAHA.116.025629
Clinical Perspective
What is new?
x
In this study of nearly 40,000 obese people without previous heart failure from two
Swedish nationwide registries, gastric bypass surgery was associated with approximately
one half the incidence of heart failure compared with intensive lifestyle treatment
including low and very low calorie diets.
x
The associations were to a large extent mediated by weight loss, partly mediated by
effects of treatment on interim atrial fibrillation, diabetes, and hypertension, but likely not
mediated by the effects of treatment on myocardial infarction.
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x
Results were robust in multiple sensitivity analyses.
What are the clinical implications?
x
Taken together with previous Mendelian randomization findings, this supports a causal
effect of obesity on heart failure.
x
With
(currently
W
Wi
th increasing
increa
in
easing obesity prevalence (current
r ntly affecting half
hal
alf a billion
bill
bi
l ion people worldwide)
and heart fa
ffailure
iluuree be
bbeing
in
ng th
thee le
lead
leading
a in
ing caus
cause
se off hos
hospitalization
osspi
pita
tali
ali
liza
zatiionn and
and hhospital
ospi
pita
pi
tall co
ta
cost
costs
s s in W
st
Western
esste
tern
r
societies,
strong
incentive
societie
es, th
the pu
public hhealth
ealth implicationn ooff tthese
hese
see oobservations
b erv
bs
errvation
ons is a str
t ong inc
tr
centiv
cen
ive for
prev
pr
prevention
even
ev
enttionn aand
en
ndd aaggressive
ggress
gg
ssiv
ss
ivee tr
iv
tre
treatment
eatm
men
entt of oobesity.
beesity
ty
y.
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10.1161/CIRCULATIONAHA.116.025629
Introduction
One billion people worldwide are overweight and another half billion obese, and these numbers
are increasing.1 Obesity may lead to heart failure, the leading cause of hospitalization in Western
societies.2 The association was observed in the Framingham Heart Study,3 has since been
confirmed by multiple other observational studies4 and is backed by experimental evidence.5 In
support of a causal association are Mendelian randomization study findings6 and observational
findings of beneficial effects of bariatric surgery on cardiac function in obese patients.7
The effect of substantial intentional weight reduction on heart failure incidence among
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obese people is unknown. Such evidence is unlikely to be produced by randomized trials, as
heart failure is rare in middle-aged, requiring long follow-up in large samples.
We hypothesized that gastric bypass surgery leads to lower incidence of heart failure
compared with intensive lifestyle modification among obese people, due to its larger effect on
weig
weight
ighht loss. We hence
ig
hen
encee aimed
en
aim
imed
ed to
to compare
comp
co
m ar
are the in
incide
incidence
dencee of hheart
eartt ffailure
ea
ailu
ai
lure
re iin
n a na
nati
nationwide
tion
ti
onwi
on
w dee rregistry
egis
eg
isstr
tryy of
o
obe
obese
ese persons tr
trea
treated
eateed w
with
ith a structured
d iintensive
ntensiive llifestyle
iffessty
tyle
le m
modification
odificcationn pprogram
od
rog
ogram ve
og
versus
erssus those inn a
nationwide quality of care registry of bariatric surgeryy who und
derwent gastric bypass surgery.
underwent
Methods
Study design and participants
The cohorts used in the present study were the Scandinavian Obesity Surgery Registry (SOReg)8
and the Itrim Health Database.9, 10
SOReg is a nationwide, prospective electronically captured quality of care registry for
bariatric surgery,8 started in 2007 and estimated to cover 98.5% of all bariatric surgery
procedures in Sweden. For this study, data were available between 2007 and 2012.
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The Itrim Health Database is a registry of individuals treated with low- or very-lowcalorie-diets (LCD/VLCD) and lifestyle modification.9, 10 The registry prospectively collects data
on individuals participating in the commercial weight loss programs at 38 Itrim franchise centers
across Sweden, using a common electronic platform for quarterly follow-up of a standardized set
of clinical variables. For this study, data were available from 2006 to 2013.
In the present study, people aged 18 or older with a baseline BMI between 30 and 49.9
were included from SOReg and Itrim (Supplemental Figure 1). We excluded people who crossed
over from Itrim to SOReg (n=811), and people with missing data on education or marital status
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(n=254). No participant had a history of heart failure before baseline, as determined in the same
way as the outcome, described below.
The study was approved by the regional ethics committee in Stockholm, Sweden. All
analyses were performed on de-identified data.
Interventions
nteerventions
All SOReg
SOReg participants
parrtic
i ip
ipants
ts und
underwent
der
erwent pprimary
rima
mary ga
gastric
astricc bbypass
ypaass
yp
s ffor
or wei
weight
ight lo
loss
oss purposes,
purposess, 96.0%
96.
6.0% off
which were conducted
d laparoscopi
ically.
laparoscopically.
All Itrim participants started with a 3-month weight loss phase with either LCD or VLCD
treatment, determined based on baseline BMI, personal preference, and contraindications.
The structured weight-loss program is presented in detail in the Supplemental Methods.
In short, VLCD treatment was a liquid-based formula diet of 500kcal/day for 3-10 weeks
followed by 2-8 weeks gradual introduction of normal food. In June, 2009, the program was
changed to 600kcal/day for 3 weeks followed by 800kcal/day for up to 9 weeks. The LCD
treatment included 2 calorie-restricted normal food meals and 2 formula-diet meal replacement
sachets per day providing a total caloric intake of about 1200-1500kcal.
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After the weight-loss phase, patients entered a 9-month weight maintenance program of
exercise, dietary advice, and behavioral therapy.
Covariables
Participants were linked to nationwide health registers using the unique Swedish personal
identity number. From these registries, data were obtained on socioeconomic variables, diseases,
and drug treatments. Data sources and definitions using the International Classification of
Diseases (ICD) and Anatomical Therapeutical Chemical (ATC) classification system codes used
are given in the Supplemental Table 1. In SOReg, blood pressure was measured as part of routine
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healthcare; in Itrim, blood pressure was measured using a standardized protocol (Supplemental
Table 1). We imputed missing data for smoking and systolic blood pressure (eachh mi
miss
missing
s in
ingg in
approximately 25% of participants) using multiple imputation from all other complete baseline
data. Baseline characteristics in persons with complete data versus those with any missing data
are pr
presented in Sup
Supplemental
uppllem
up
men
e taal Table
Tabl
Ta
blee 2..
bl
Foll
lloow-up and
ll
d out
utco
ome
Follow-up
outcome
Participants were followed
f llowed in the National
fo
N tional P
Na
Patient
atient Register
Register andd the Causes off Death Re
R
Register
gister
until December 31, 2014. Patients were followed from the treatment date until the first instance
of the outcome, death, emigration, or end of follow-up. Itrim participants who during follow-up
crossed over to bariatric surgery were censored at the cross-over date. During follow-up, 135
participants emigrated, making register-based follow-up complete for 99.5%.
The primary outcome was the first hospitalization for heart failure, ICD-10 I50 as main
reason for hospitalization, retrieved from the National Patient Register. We have previously
shown this outcome to have 95% positive predictive value in a validation study.11 The secondary
outcome was non-ischemic heart failure, defined as the first hospitalization of heart failure that
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was not preceded by a myocardial infarction (censoring patients at time of myocardial
infarction).
In mediation analyses, interim instances of myocardial infarction and atrial fibrillation
were obtained from the National Patient Register, and diabetes and hypertension were defined
based on drug treatment at one-year follow-up.
Statistical analysis
We estimated a propensity score for treatment (surgery/lifestyle), see Supplement for variables
included in the analysis (Supplemental Methods). Common support was observed over the
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majority of the propensity score distribution but 988 persons (2.3%) had to be dropped at the
lower
ower end and 3291 (7.5%) at the upper end of the distribution. We checked for balance
bal
alan
ance
an
ce iin
n
covariables
covariabl
a es between the treatment groups using weighted analyses described below, and using
standardized
tandardized differences in means.
We the
then
en us
used
sed
e coarsened
coar
co
arse
ar
sene
nedd ex
ne
exac
exact
a t ma
m
matching
tching
n 12 too as
ng
assign
ssi
sign
gn iinverse
n er
nv
erse
se pprobability
roba
ro
b bili
lity
li
ty w
weights
eights
ei
t ((IPW)
ts
IPW)
IP
W) to
the
he participants
pa
ts based
bas
ased oon
n baseline
baaseline
see
BMII and
and thee pro
propensity
ope
pens
nsit
ns
ityy score.
it
sccore. T
This
his procedure
proccedure as
pr
assigns
ssign
gns an IPW
PW
too each partic
participant
i ipant ba
bbased
sed on the number of parti
participants
icipants in the two treatment groups in strata off
the variables chosen for the matching. This weight was used in all further analyses. We excluded
11 persons who could not be matched. The resulting study sample consisted of 39,505 persons.
Associations between treatment and weight loss were described using mixed models with
body weight at baseline and one- and two-year follow-up visits as dependent variables, fixed
effects for time and treatment and a random intercept for participant identification.
Associations of treatment with heart failure incidence were estimated using Cox
proportional hazard models. Proportionality of hazards was investigated using Schoenfeld’s tests.
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We analyzed if the association of treatment with heart failure incidence was mediated by
weight loss per se in two ways. Both analyses made use of a weight measurement at the one-year
visit, and start of follow-up was reset to the one-year visit. First, we studied if there was a graded
association between achieved weight loss at one year with the incidence of heart failure. To the
total sample, including both treatment groups, we fit a model that included the three independent
variables of baseline weight, treatment group, and achieved weight loss at one year (modeled
using a linear term due to the limited number of cases). Second, we re-weighted the sample and
included body mass index at one year, in addition to the other weighting variables (baseline
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characteristics of that sample are provided in Supplemental Table 3), in order to investigate if
achieved weight loss was a major mediator of the effect.
We used generalizedd structural equation modelling
modelling to investigate if parts of the
associations between weight loss and incident heart failure were mediated by myocardial
infarction,
nfaarcction, atri
atrial
ial fib
fibrillation
ibrilllattio
ib
ionn (b
(bot
(both
othh as tim
ot
time-updated
me-updat
a ed
at
d variables),
varia
iabl
ia
bles
bl
es),
es
), dia
diabetes
iabe
bete
be
tes or
o hyp
hypertension
yper
yp
e te
er
tens
n ionn (b
ns
(bot
(both
o h at
ot
a
the
he on
one-year visit),
viisi
sit)), each
ch in a separate analyses,
analysess, assu
assuming
sumi
ming
mi
ng ssequential
eq
quenttiaal igno
ignorability
norrab
rability inn tthe
hee
associations of weight lloss
oss with mediators and heart
heart failure and
d no post-weight-l
post-weight-loss
loss confounders.
confoundders
In order to investigate potential competing risk, we constructed a composite outcome
consisting of the first instance of heart failure, myocardial infarction or cardiovascular death.
In a sensitivity analysis, we also adjusted the main model for variables that differed
between the weighted groups (standardized difference in means •0.1). IPW is an effective way
of minimizing selection bias, but relies on a correctly specified propensity score. Therefore, as
sensitivity analyses, we also investigated associations of treatment with heart failure incidence
using Cox analyses stratified by levels of a combination of the propensity score and baseline
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BMI, and using Cox analyses adjusted for restricted cubic splines of the propensity score and
BMI. We also investigated the multiplicative interaction term treatment group*sex.
In a secondary analysis, we restricted the sample to patients usually determined to be
eligible for bariatric surgery (BMI above 40, or BMI between 35 and 40 with significant obesityrelated comorbidity defined as hypertension, diabetes, and previous myocardial infarction).
For comparisons of heart failure rates in the obese samples with a general population
sample, we matched up to 10 comparators from the Swedish Total Population Registry13 to each
patient who had gastric bypass surgery, on birth year, intervention year, sex, and place of
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residence (county, commune, parish).
Two-sided tests with a 5% alpha level were considered statistically signif
significant.
fic
ican
antt. St
an
Stat
Stata
ataa 14
at
was used for all analyses.
Results
Resu
sullts
su
Participant
Part
rtiicipant characteristics
rt
ch
harac
acteeriisticss
In
with
13,701
n this study, 25,8044 persons were treated
d wit
i h gastric bypass surgery and 13
3,7011 with
h lifestyle
modification. Baseline characteristics of the study sample are outlined in Table 1.
Weight and BMI were perfectly balanced between the weighted treatment groups. Minor
differences in systolic blood pressure, income, and use of antihypertensive, antidiabetic, and
lipid-lowering drugs were still apparent between the weighted treatment groups, all favoring the
lifestyle participants (Table 1).
Effects of treatment on weight loss and heart failure
On average, surgery led to 18.8 kg more weight loss than lifestyle treatment at the one-year
follow-up and 22.6 kg more weight loss at the two-year follow-up visit. Development of weight,
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waist circumference and body fat during the first two years of the study are displayed in Figure
1.
During a median follow-up of 4.1 years (range 0.0 to 9.0), 73 new heart failure cases
occurred with an incidence rate of 5.4 per 10,000 person-years at risk (95% confidence interval
3.5 to 8.7). Of these, 29 occurred in the lifestyle group and 44 in the surgery group, with
incidence rates of 7.6 (3.4 to 21.3) and 4.1 (3.1 to 5.6) per 10,000 person-years at risk,
respectively. Surgical treatment was hence associated with lower incidence of heart failure than
lifestyle treatment (hazard ratio 0.54, 95% confidence interval 0.36 to 0.82). Cumulative hazards
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are presented in Figure 2. The absolute five-year risk of heart failure was 0.4 % among the
lifestyle
ifestyle modification patients and 0.2% among surgery patients.
The incidence of heart failure in the age-, sex- and place-ofplace-of-residence-matched
f residence-matched general
population sample was 3.0 (2.7 to 3.4) per 10,000 person-years at risk.
Mediation
Me
edi
diation anal
analyses
alysses
al
e
In
n aanalyses
n lyses using
na
usin
ingg on
in
oneone-year
-year achieved
ac
weight
weiight loss
lo
osss and
an setting
sett
se
ttin
tt
ing start
in
st of
of follow-up
folllo
ow-up to the
h one-year
he
one-yearr
visit, 31,347
311,3477 persons participated and 500 cases of he
hheart
art failure occurred. We ffound
ound a ddoseoseresponse relationship between one-year weight loss and risk of heart failure with a hazard ratio
for a 10 kg weight loss of 0.77, 95% confidence interval 0.60 to 0.97, in a model adjusting for
baseline weight and treatment fitted to the two treatment groups combined (Figure 3). In an
analysis weighted also for BMI at one year, no association of treatment group with risk of heart
failure was seen (hazard ratio 1.09, 95% confidence interval 0.62 to 1.92), supporting that the
achieved weight loss is a major mediator of the effect.
Among 39,114 persons without myocardial infarction before baseline, 67 heart failure
cases occurred without an interim myocardial infarction. Surgery was associated with lower
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10.1161/CIRCULATIONAHA.116.025629
incidence also of this non-ischemic heart failure (hazard ratio 0.48, 95% confidence interval 0.31
to 0.74).
The association of treatment with heart failure in the total sample was not mediated by
the effects of treatment on myocardial infarction to any substantial extent, but appeared to be
partly mediated by effects of treatment on interim atrial fibrillation, diabetes and hypertension
(Table 2).
Sensitivity analyses
In a sensitivity analysis investigating potential competing risk, we included 39,114 persons
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without prior heart failure or myocardial infarction. Of these, 244 suffered a composite endpoint
of a first instance of heart failure, myocardial infarction or cardiovascular death (69
(669 experienced
expe
ex
peri
pe
rien
ri
ence
en
ced
ce
heart failure, 113 a myocardial infarction, and 72 died from cardiovascular disease; patients
could expe
experience
p rience more than one outcome). The surgery group experienced a lower rate also of
this
hiss composite
compositee endpoint;
endp
dpoiint
nt;; hazard
hazaard rratio
ha
atio
at
i 00.58
.58 (95%
.5
% cconfidence
onfide
denc
de
ncee in
nc
iinterval
teerval
rval
a 0.4
0.46
.46 to
o 00.74).
.74)
.7
4).. Off tthe
4)
he 1113
13
myoc
myocardial
ocardial inf
oc
infarctions,
far
arcttio
onss, 51 occurred
occurred in th
the
he lifest
lifestyle
tylee m
modification
odif
od
ific
if
icat
atio
on group
gr p (rate
(raate 9.2 pe
per
er 10
10,000
0,000
person-years at risk
risk;
k; 95
95% confidence interval 4.2 to 23.7), and 62 iinn the surgery group (rate 5.9
59
5.
per 10,000 person-years at risk; 95% confidence interval 4.6 to 7.7).
In a sensitivity analysis adjusting the main model for variables that differed between the
weighted groups (use of antihypertensive, antidiabetic, and lipid-lowering drugs, loop diuretics,
education level and inclusion year), results were similar to the main model (hazard ratio 0.45,
95% confidence interval 0.27 to 0.76). In sensitivity analyses, we substituted the weighted
analyses for models stratified according to matching weights, with essentially the same results
(hazard ratio 0.46, 95% confidence interval 0.23 to 0.92). We also performed analyses adjusting
for splines of baseline BMI and the propensity score instead. This again produced similar results
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(hazard ratio 0.47, 95% confidence interval 0.24 to 0.93). No deviation from proportionality of
hazards was observed (all p>0.9).
In a model including a multiplicative interaction term treatment group*sex, similar main
results were observed (hazard ratio 0.37, 95% confidence interval 0.19 to 0.72), and no
treatment*sex interaction (p=0.17).
In a sample of 21,620 persons typically determined to be eligible for bariatric surgery
(BMI above 40, or BMI between 35 and 40 with significant obesity-related comorbidity), 57
cases of heart failure occurred. In this sample, surgery was associated with a hazard ratio of 0.53,
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95% confidence interval 0.35 to 0.82, for heart failure.
Discussion
Principal observations
In
n tthis
hiis study off a large
lar
argee sample
sam
ampl
am
plee of
pl
o obese
obe
b see ppeople
eoplee wi
w
without
thoutt previous
th
prev
pr
evio
ev
iouss hheart
io
e rt
ea
r ffailure
a lure
ai
re ffrom
room twoo Sw
Swed
Swedish
edis
ed
ih
is
nationwide
nati
ion
o wide registries,
reg
gisstrriees, gastric
gastriic bypass surgery
sur
urgery was
was associated
assoc
ocia
oc
iate
ia
tedd with
with a nea
nearly
arlly hhalved
alved inc
incidence
n id
nc
dence of
heart fail
failure
i ure comparedd to intensive lifestyle treatment including LCD/VLCD.
LC
CD/VL
V CD
D. Th
The
he associations
were to a large extent mediated by weight loss, partly mediated by effects of treatment on interim
atrial fibrillation, diabetes, and hypertension, but likely not mediated by the effects of treatment
on myocardial infarction. Results were robust in multiple sensitivity analyses.
Comparisons with previous studies
Associations of obesity with incident heart failure are well-established,3, 4, 6 but the effect of
substantial weight loss on incident heart failure is unknown. Patient series have suggested
beneficial effects on cardiac function7 and prognosis14 of bariatric surgery in patients with heart
failure, but other studies have shown an inverse association of BMI with mortality among people
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with heart failure.15, 16 The present study only included people without heart failure at baseline
and hence gives no implications for treatment of obese people with heart failure.
Potential mechanisms
The association of treatment with heart failure was mainly mediated by weight loss, as supported
by the strong dose-response relationship and the absence of a treatment effect when accounting
for one-year weight loss in the models.
Bariatric surgery, compared to conventional treatment, has been associated with lower
incidence of myocardial infarctions17 and improvement in several other risk factors for heart
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failure, including diabetes, hypertriglyceridemia, and high blood pressure18 in the nonandomized prospective Swedish Obese Subjects study. All of these may contribute
contribut
utee to the
the
randomized
associations observed in the present study,19, 20 but in our data the effects appeared less likely to
be mediated by the effects of treatment on myocardial infarction (although myocardial infarction
rate
atee w
was
as lowerr aamong
mongg ssurgery
mo
urge
ur
g ry ppatients),
ge
atie
at
ient
ie
n s)), and mo
m
more
ree llikely
ikel
elyy by eeffects
el
fffecctss ooff tr
treatm
treatment
men
entt on int
interim
terrim aatrial
trial
tr
fibrillation,
fibr
ril
illa
l tion, diabetes
diab
abettess aand
ab
n blo
nd
blood
ood pressure.
e. This iss in
n line
lin
ne with
with
t the
thhe known
kn n substantial
suubs
b tantial effects
efffeccts of
24
bariatric surgery on diabetes
dia
i betes prevention2211 and remission.22-24
It should, however,
h wever, be noted that
ho
the mediation analyses had low and differential statistical power.
Another potential mode of action is a direct effect of weight loss on cardiac stress, such
as hemodynamic load. Cardiac troponin-I is a biomarker of cardiac stress and an important risk
marker for heart failure.25 Recent data have shown that gastric bypass reduces cardiac troponin-I
concentration, as compared to intensive lifestyle intervention.26
In addition to the mediation by effects on risk factors, there may be effects of weight loss
on obesity cardiomyopathy per se. Fatty acids are the major oxidative fuel for the heart,27 and
myocardial fDWW\DFLGȕ-oxidation is mainly regulated by peroxisome proliferator-activated
13
10.1161/CIRCULATIONAHA.116.025629
receptor-D (PPAR-D).28 ,QH[SHULPHQWDOPRGHOVREHVLW\LPSDLUVP\RFDUGLDOIDWW\DFLGȕoxidation regulation.29 This leads to lipid deposition in cardiomyocytes, contractile
dysfunction,29 myocardial fibrosis,28 and cardiomyocyte apoptosis.5, 30 In experimental models,
weight loss leads to an upregulation of myocardial PPARs, improved lipid metabolism, and
increased ejection fraction.31
In the recent EMPA-REG OUTCOME study, empagliflozin treatment led to a modest
weight loss and a strong preventive effect against heart failure in persons with diabetes.32 In light
of the present study, it should be considered whether it is the energy loss and consequent weight
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
loss with the sodium/glucose cotransporter-2 inhibitors that underpins their protection against
heart failure. In the present study, surgically treated patients on average lost 20 kgg m
oree th
or
than
an
more
patients in the lifestyle program. It will be important to follow the full effect of this on the
ncidence of heart failure over longer time periods.
incidence
Stre
ren
re
ngths and
d limitations
lim
mit
i attio
ions
n
ns
Strengths
Stre
eng
ngths include
includ
ude two
ud
tw
wo largee prospectively
prospect
c iv
ct
i el
ely desi
igned
ed nationwide
nat
atio
at
ionwid
io
ide registries
reegistriies of
of bariatric
bariatrric su
surgery and
an
nd
Strengths
designed
ntensive lifestyle treatment with a program that is a relevant comparator to gastric bypass
intensive
surgery,33 and a validated outcome ascertained using nationwide health registries with complete
coverage and negligible loss to follow-up.
Weaknesses include the observational nature of the study. It is unlikely that randomized
trials of bariatric surgery will provide evidence for a relatively rare event such as heart failure.
Further, the low number of outcomes is a limitation. In spite of balance in most of the available
baseline factors between the treatment groups after weighting, residual confounding may have
affected our observations. This would likely result in a conservative bias as participants in the
lifestyle program had higher educational level and income, and can be assumed to be more health
14
10.1161/CIRCULATIONAHA.116.025629
conscious. They are also likely more motivated, as they paid out-of-pocket for their treatment,
whereas the government paid for 90% of the bariatric surgery procedures. Further, the gastric
bypass surgery group had worse clinical profile in terms of diabetes, dyslipidemia and
hypertension. The statistical modelling accounted well for differences between groups, and
results were robust in sensitivity analyses further adjusting for group differences and using other
statistical techniques. We did not have data on ethnicity, and it is reasonable to assume that the
participants were predominantly Caucasian. Generalizability to other ethnic groups is unknown.
We also lacked data on exercise and dietary advice as well as compliance to such advice in the
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
surgery group, and measurements of physical activity in either group. Further, generalizability to
other bariatric surgery methods than gastric bypass is unknown, and it should be nnoted
oted
ot
ed tthat
hatt the
ha
the
preferred type of bariatric surgery may differ over time and between geographic regions.
Unsurprisingly, the incidence of heart failure was higher in both of the obese samples
than
han
n in
in the in the
the age-,
age
g -, sexsex
ex- and
an
nd place-of-residence-matched
plac
pl
ace-oofac
f resideenc
n e-maatc
tche
h d ge
he
ggeneral
neeraal po
popu
population,
p lati
tion
ti
on,, likely
on
liike
k ly
y explained
exp
xpla
laain
ined
by higher
hig
igher prev
prevalence
val
alen
nce ooff obes
obesity-related
esity-relatedd rrisk
es
isk fa
factors.
actorrs. Ne
Neve
Nevertheless,
vert
ve
rthe
h lesss, the lo
low
w hheart
eart fai
failure
ilur
ure rate inn
this
his sample led
l d to a mere 0.2% five-year riskk difference
le
diifference (corresponding to a number needed to
treat of 534) in this comparison of two groups that both had an effective weight loss treatment.
Conclusions
Gastric bypass surgery was associated with a nearly halved incidence of heart failure compared
to an intensive lifestyle modification program including LCD/VLCD, in this study of nationwide
registries including almost 40,000 individuals. We also observed a dose-response relationship
between weight loss and risk of heart failure. Taken together with Mendelian randomization
findings, this supports a causal effect of obesity on heart failure, the leading cause of
hospitalization in Western societies.
15
10.1161/CIRCULATIONAHA.116.025629
Acknowledgements
MN co-designed the study, and is the principal investigator of the SOReg/Itrim study; JS
designed the study, performed statistical analyses, interpreted the data and wrote the first draft of
the manuscript. All authors contributed to interpreting the results and contributed with significant
intellectual input in the manuscript. JS, GB, and MN had full access to all the data in the study
and takes responsibility for the integrity of the data and the accuracy of the data analysis. The
corresponding author had final responsibility for the decision to submit for publication.
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Sources of Funding
Research reported in this publication was supported by the National Institute of Diabetes
Diaabe
bete
tees An
Andd
Digestive And Kidney Diseases of the National Institutes of Health under Award Number
R01DK105948. The content is solely the responsibility of the authors and does not necessarily
eprres
esent the of
off
fici
cial
ci
a vviews
iews
ie
w off th
ws
thee Na
N
tion
onal Ins
sti
titu
utees of Health.
Hea
ealt
lth. A
lt
u ho
ut
h rs of th
this
is sstudy
tudy w
tu
eree also
er
als
represent
official
National
Institutes
Authors
were
upp
pppor
o ted by tthe
he S
wedi
dish R
esearch Co
ouncil (M
MN:22013
13-3
13
-377
-3
7 0;
77
0; JS: 2010–1078).
2010–
0–10078). The
Thhe funder
fun
under off the
the
supported
Swedish
Research
Council
(MN:2013-3770;
tudy had no role in
i study design, ddata
ata collection, da
ddata
ta analysis, data interpretation, or writing of
study
the report.
Disclosures
CM, MN and JS report receiving consulting fees (modest) for participation in the scientific
advisory committee of Itrim. IN is the previous director of the Scandinavian Obesity Surgery
Registry. JO is its current director. GB has nothing to disclose.
16
10.1161/CIRCULATIONAHA.116.025629
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19
Table 1. Baseline characteristics
Gastric Bypass Surgery Lifestyle
(n=25,804)
(n=13,701)
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Difference surgery – lifestyle Standardized
differences
in means
Age (years)
41.3 (41.2 to 41.4)
41.5 (41.3 to 41.7)
-0.2 (-0.4 to 0.0)
0.006
Male sex (%)
23.7 (23.2 to 24.2)
22.3 (22.1 to 23.5)
0.9 (0.0 to 1.8)
0.055
Weight (kg)
119.1 (118.9 to 119.3) 119.0 (118.7 to 119.3) 0.1 (-0.3 to 0.5)
0.029
Body-mass index (kg/m2)
41.5 (41.4 to 41.5)
41.4 (41.4 to 41.5)
0.0 (-0.1 to 0.1)
0.023
Systolic blood pressure (mmHg)
134.9 (134.6 to 135.1) 133.9 (133.5 to 134.2) 1.0 (0.5 to 1.4)
0.050
Married (%)
43.2 (42.6 to 43.8)
44.3 (43.5 to 45.1)
-1.1 (-2.1 to -0.0)
-0.022
Current smoker (%)
16.2 (15.6 to 16.8)
17.1 (16.4 to 17.7)
-0.9 (-1.8 to 0.0)
-0.028
Prior myocardial infarction (%)
1.1 (0.9 to 1.2)
0.8 (0.7 to 1.0)
0.2 (0.0 to 0.4)
0.036
Prior atrial fibrillation (%)
1.0 (0.8 to 1.1)
1.0 (0.8 to 1.2)
-0.0 (-0.2 to 0.2)
-0.003
Prior valve disease (%)
0.1 (0.1 to 0.2)
0.1 (0.1 to 0.2)
0.0 (-0.0 to 0.1)
0.035
Prior substance abuse (%)
0.8 (0.7 to 0.9)
0.9 (0.8 to 1.1)
-0.1 (-0.3 to 0.1)
-0.024
Lipid-lowering drugs (%)
13.9 (13.5 to 14.3)
10.4 (9.9 to 10.9)
3.5 (2.9 to 4.2)
0.123
Antihyperglycemic drugs (%)
15.0 (14.6 to 15.4)
9.4 (8.9 to 9.9)
5.6 (5.0 to 6.2)
0.256
Oral antihyperglycemic drugs (%) 13.8 (13.4 to 14.2)
9.1 (8.6 to 9.6)
4.7 (4.0 to 5.3)
0.233
Insulin
ulin (%)
6.5 (6.2 to 6.8)
1.9 (1.7 to 2.2)
4.6 (4.2 to 5.0)
0.262
0.26
Aspirin
in (%)
6.9 (6.6 to 7.2)
4.9 (4.5 to 5.2)
2.0 (1.4 to 2.4)
00.091
.09
Thyroid
oid hormones (%)
9.5 (9.1 to 9.9)
8.0 (7.6 to 8.5)
1.5 (0.9 to 2.1)
0.054
0.05
0.
05
Antihypertensive
hypertensive drugs (%)
30.0 (29.4 to 30.6)
26.5 (25.8 to 27.2)
3.5 (2.5 to 4.4)
0.11
0.118
Beta-blockers
ta-blockers (%)
14.2 (13.8 to 14.6)
13.9 (13.3 to 14.5)
0.3 (-0.4 to 1.0)
0.03
0.033
Calcium
lcium channel blockers (%)
9.4 (9.0 to 9.7)
6.9 (6.5 to 7.4)
2.5 (1.9 to 3.0)
0.113
0.11
RAAS
AAS blockers
blo
ock
c errs (%
(%)
22.0 (21.5 to 22.5)
18.3
.3
3 (17.7 to 19.0)
3.7 (2.9
(2 to 4.5)
0.12
0.123
Thiazide
iazide
de ddiuretics
iureticss ((%)
%)
6·5 (6·2 to 6·8)
5·9
9 (5·5 to 6·3)
0·6 (0
(0·1 to 1·1)
0.018
0.01
Loop diuretics
diu
uretics
ree
(%)
7.7
7..7 (7.4
(7.4
4 to
to 8.1)
1)
5.1
1 (4.7
(4.7
7 to
to 55.5)
.5)
5)
2.6 (2
2.
(2.1 to
o 3.
3.1)
1)
00.112
0.
11
Education
atio
on
-0
-0.154
0.15
<10
0 ye
years
ear
ars (%)
177 (17.4 to 18
17.8
18.2)
14
14.4
4.4
4 (13
(13.9
13.9
13
.9
9 tto
o 14.9
14
14.9)
4 9)
3.4 (2
(2.9 to
o 3.9)
0.
0.104
.10
10 to 122 years
yea
e rs (%)
%)
61.0
61
.0 (60.5
.5 tto
o 61
61.5)
59.8
8 (59
(59.3
59.3 to 60.3)
59
60.3
60
3)
1.1 (0.9
(0 to
to 1.4)
1 4)
1.
0.
0.019
.01
>12
2 ye
year
years
arss (%
((%))
21
21.2
.2
2 (20.8
8 tto
o 21
21.7)
1.7
7)
25
25.8
5.8
8 ((25.1
255.1 to 26
26.4))
-4.
-4.5
4 5 ((-5.3
-5.3
3 to -3.8)
8))
-0.
-0.097
0 09
Income
me (1000 USD/year)
30
30.5
5 (30
(30.2
2 to 30
30.6)
6)
32
32.5
5 (32
(32.11 to 32.8)
32 8)
-2.1
2 1 (-2.5
( 2 5 to -1.7)
1 7)
-0.093
0 09
Inclusion year
0.260
2006 (%)
0.4 (0.3 to 0.5)
0.4 (0.3 to 0.4)
0.0 (-0.0 to 0.0)
-0.021
2007 (%)
2.8 (2.7 to 3.0)
2.8 (2.6 to 3.0)
0.0 (-0.1 to 0.1)
-0.017
2008 (%)
9.1 (8.7 to 9.4)
9.2 (8.9 to 9.5)
0.1 (-0.2 to 0.4)
-0.015
2009 (%)
13.3 (12.9 to 13.6)
13.1 (12.8 to 13.5)
0.1 (-0.2 to 0.4)
0.016
2010 (%)
23.1 (22.7 to 23.5)
23.0 (22.6 to 23.5)
0.1 (-0.1 to 0.3)
0.018
2011 (%)
26.3 (25.9 to 26.8)
26.4 (26.0 to 26.9)
-0.1 (-0.3 to 0.1)
0.037
2012 (%)
24.9 (24.4 to 25.4)
25.2 (24.6 to 25.8)
-0.3 (-1.0 to 0.4)
-0.042
Data are means or percent, difference, and standardized differences in means between surgery and lifestyle groups (95%
confidence intervals) from an inverse probability-weighted sample.
10.1161/CIRCULATIONAHA.116.025629
Table 2. Mediation analysis
Occurrence of the Hazard ratio (95% confidence P for an indirect effect of
potential mediator interval) for treatment adjusted the potential mediator
during follow-up for the potential mediator
N (%)
0.54 (0.36 to 0.82)
130 (0.3)
0.54 (0.35 to 0.82)
0.18
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Main model
Adjusted for interim
myocardial infarction
Adjusted for interim
641 (1.6)
0.63 (0.41 to 0.96)
atrial fibrillation
Adjusted for use of diabetes 1,235 (3.1)
0.64 (0.42 to 0.97)
drugs at one-year follow-up
Adjusted for use of blood
6,353 (16.1)
0.65 (0.43 to 0.99)
pressure-lowering drugs at
one-year follow-up
P-values are from generalized structural equation models
21
<0.0001
<0.0001
<0.0001
10.1161/CIRCULATIONAHA.116.025629
Figure Legends
Figure 1. Changes in weight, waist circumference and body fat during the first two years of
the study. Data are from an inverse probability-weighted sample. Error bars are 95% confidence
intervals. Body fat data were not available for the gastric bypass surgery patients.
Figure 2. Cumulative hazard of heart failure in persons treated with lifestyle or gastric
bypass surgery. Data are from an inverse probability-weighted sample.
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Figure 3. Hazard ratio of heart failure in relation to achieved weight loss at one
onee year.
yea
ear.
r
Data are from an inverse probability-weighted sample of both lifestyle and gastric bypass
urgery pa
ppatients
tients combined. The model included the three variables: baseline weight, treatment
surgery
grou
up, and achieved
ach
hie
ieveed we
w
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ig
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year. S
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nter
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Weight Loss and Heart Failure: A Nationwide Study of Gastric Bypass Surgery Versus
Intensive Lifestyle Treatment
Johan Sundström, Gustaf Bruze, Johan Ottosson, Claude Marcus, Ingmar Näslund and Martin
Neovius
Downloaded from http://circ.ahajournals.org/ by guest on June 18, 2017
Circulation. published online March 3, 2017;
Circulation is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231
Copyright © 2017 American Heart Association, Inc. All rights reserved.
Print ISSN: 0009-7322. Online ISSN: 1524-4539
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circ.ahajournals.org/content/early/2017/03/03/CIRCULATIONAHA.116.025629
Data Supplement (unedited) at:
http://circ.ahajournals.org/content/suppl/2017/03/03/CIRCULATIONAHA.116.025629.DC1
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SUPPLEMENTAL MATERIAL
Weight loss and heart failure: a nationwide study of gastric bypass surgery
versus intensive lifestyle treatment
Johan Sundström,1 Gustaf Bruze,2 Johan Ottosson,3 Claude Marcus,4 Ingmar Näslund,3 Martin Neovius2
1
Department of Medical Sciences, Uppsala University, and Uppsala Clinical Research center (UCR). Uppsala, Sweden
2
Department of Medicine, Solna, Clinical Epidemiology Unit, Karolinska Institutet, Stockholm, Sweden
3
4
Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
Department of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden
Table of Contents
Item
Description
Supplemental Methods
Weight-loss program
2
Statistical methods
2
Supplemental Table 1
Variable sources and definitions
3
Supplemental Table 2
4
Supplemental Figure 1
Baseline characteristics in persons with complete
data vs those with any missing data
Baseline characteristics of sample re-weighted
also by one-year BMI
Flow chart for definition of the study sample
Supplemental figure titles and legends
Supplemental Figure 1
7
Supplemental Table 3
Page
5
6
Weight loss and heart failure
Sundström et al.
Supplemental Methods
Weight-loss program
The VLCD treatment was a liquid-based formula diet of 500kcal/day for 3-10 weeks
(125kcal/sachet, 4 sachets/day, approved as sole source VLCD by the Swedish National Food
Agency) followed by 2-8 weeks gradual introduction of normal food. Early introduction of
normal food occurred if patients were satisfied with the achieved weight loss or reached
BMI<25. In June, 2009, the program was changed to 600kcal/day for 3 weeks followed by
800kcal/day for up to 9 weeks.
The LCD treatment included 2 calorie-restricted normal food meals and 2 formula-diet
meal replacement sachets per day providing a total caloric intake of about 1200-1500kcal.
After the weight- loss phase, patients entered a 9-month weight maintenance program of
exercise (circuit training at the center 2-3 times/week for 30-45 minutes, and pedometer use to
encourage walking), dietary advice, and behavioral therapy (a structured program of 20 1- hour
group sessions, initiated during the weight-loss phase). There were also face-to-face
counselling sessions throughout the program.
Statistical methods
Variables included in the propensity score analysis were baseline variables age, systolic blood
pressure, smoking, previous myocardial infarction, previous atrial fibrillation, weight, BMI,
educational level, marital status, income, sex, antihypertensive drugs, lipid-lowering drugs,
antidiabetic drugs, previous substance abuse, previous valve disease, and treatment year, with
non-linear modelling of some variables, and interaction terms for several variables including
BMI.
2
2
1
Weight loss and heart failure
Sundström et al.
Supplemental Table 1. Variable sources and definitions
Variables obtained from nationwide health registries
Atrial fibrillation
ICD-10: I48·9, diagnosed in ambulatory or in-patient care
Myocardial infarction
ICD-10: I21, diagnosed in in-patient care
Valve disease
ICD-10: I34, I35, I36, I37, diagnosed in in-patient care
Heart failure
ICD-10: I50, diagnosed in in-patient care, only as main
cause of hospitalization
Substance abuse
ICD-10: F10-F19, diagnosed in ambulatory or in-patient
care
Lipid-lowering drug treatment
ATC: C10
Antidiabetic drug treatment
ATC: A10
Aspirin treatment
ATC: B01AC06
Thyroid hormone treatment
ATC: H03A
Antihypertensive drug
ATC: C02, C03A, C03B, C03EA01, C07 except
treatment
C07AA07, C08C, C09
Beta-receptor blocker treatment ATC: C07 except C07AA07
Calcium antagonist treatment
ATC: C08C
RAAS blocker treatment
ATC: C09
Thiazide diuretic treatment
ATC: C03A, C03B, C03EA01
Loop diuretic treatment
ATC: C03C
Income level
Mean of previous 2 calendar years from income & taxation
register
Education level
Age
Sex
Marital status
Emigration date
Death date
Source
National Patient
Register
Prescribed Drug
Register
(dispensed
prescriptions)
Income and
Taxation Register
Education Register
Total Population
Register
Causes of Death
Register
Variables obtained in the SOReg or Itrim registries
Baseline weight, waist
Screening weight was used for the SOReg participants,
circumference & body-mass
since almost all are treated with VLCD in the months
index
before surgery. Weight at start of intervention was used for
Itrim participants.
Intervention date
Date of first visit in Itrim. Date of surgery in SOReg.
Systolic blood pressure
According to the Itrim protocol, coffee and smoking
should be avoided 30 minutes before the measurement.
Blood pressures were measured in the left upper arm using
an Omron 1240 oscillometric device with the cuff at heart
level, in the sitting position with the back against the
backrest and without crossed legs, after 5 minutes’ rest. If
the first recording was elevated (SBP>=140 mmHg or
DBP>=90 mmHg), a second measurement was obtained;
the mean of those two measurements recorded. Registered
in the SOReg protocol, without further instructions.
Smoking
Structured questionnaire in Itrim and SOReg.
Weight & waist circumference
Structured protocol in Itrim and SOReg.
during follow-up
Percent body fat during follow-up Structured protocol in Itrim, using a Tanita body impedance
meter.
3
SOReg Itrim
2
1
Weight loss and heart failure
Sundström et al.
Supplemental Table 2. Baseline characteristics in persons with complete data vs those with any
missing data
Age (years)
Male sex
Weight (kg)
Baseline body-mass index (kg/m2)
Systolic blood pressure (mmHg)
Married (%)
Current smoker (%)
Prior myocardial infarction (%)
Prior atrial fibrillation (%)
Prior valve disease (%)
Prior substance abuse (%)
Lipid-lowering drugs (%)
Antidiabetic drugs (%)
Antihypertensive drugs (%)
Education
<10 years (%)
10 to 12 years (%)
>12 years (%)
Income (1000 USD/year)
Complete data
(n= 23,700)
41.4 (41.2 to 41.5)
23.3 (22.7 to 23.8)
118.7 (118.5 to 118.9)
41.4 (41.3 to 41.4)
134.4 (134.2 to 134.6)
43.7 (43.1 to 44.3)
16.5 (16.0 to 16.9)
0.9 (0.7 to 1.0)
0.9 (0.8 to 1.0)
0.1 (0.0 to 0.1)
0.9 (0.8 to 1.0)
12.3 (11.9 to 12.7)
12.4 (12.0 to 12.8)
28.4 (27.9 to 29.0)
Any missing data
(n=15,805)
41.3 (41.1 to 41.5)
23.7 (23.0 to 24.4)
119.8 (119.5 to 120.1)
41.6 (41.5 to 41.6)
135.7 (134.7 to 136.7)
43.3 (42.5 to 44.1)
17.5 (15.9 to 19.1)
1.2 (1.0 to 1.3)
1.1 (0.9 to 1.3)
0.2 (0.1 to 0.3)
0.8 (0.6 to 0.9)
13.4 (12.9 to 14.0)
14.2 (13.7 to 14.8)
29.5 (28.7 to 30.2)
Difference
complete – missing
0.1 (-0.2 to 0.3)
-0.4 (-1.3 to 0.5)
-1.1 (-1.5 to -0.7)
-0.2 (-0.3 to -0.1)
-1.3 (-2.3 to -0.3)
0.4 (-0.6 to 1.4)
-1.1 (-2.7 to 0.6)
-0.3 (-0.5 to -0.1)
-0.2 (-0.4 to -0.0)
-0.1 (-0.2 to -0.0)
0.1 (-0.1 to 0.3)
-1.2 (-1.9 to -0.5)
-1.9 (-2.6 to -1.2)
-1.0 (-1.9 to -0.1)
16.3 (15.8 to 16.7)
60.5 (60.0 to 61.0)
23.3 (22.8 to 23.8)
31.1 (30.9 to 31.4)
17.2 (16.7 to 17.8)
60.7 (60.2 to 61.2)
22.0 (21.4 to 22.6)
30.9 (30.6 to 31.2)
-1.0 (-1.6 to -0.4)
-0.3 (-0.4 to -0.1)
1.2 (0.5 to 1.9)
0.2 (-0.2 to 0.6)
Data are means or percent and difference between persons with complete data on all variables and those with
any missing data (95% confidence intervals) from an inverse probability-weighted sample.
4
Weight loss and heart failure
Sundström et al.
Supplemental Table 3. Baseline characteristics of sample re-weighted also by one-year BMI
Age (years)
Male sex
Weight (kg)
Baseline body-mass index (kg/m2)
One-year body-mass index (kg/m2)
Systolic blood pressure (mmHg)
Married (%)
Current smoker (%)
Prior myocardial infarction (%)
Prior atrial fibrillation (%)
Prior valve disease (%)
Prior substance abuse (%)
Lipid-lowering drugs (%)
Antidiabetic drugs (%)
Antihypertensive drugs (%)
Education
<10 years (%)
10 to 12 years (%)
>12 years (%)
Income (1000 USD/year)
Surgery
(n=22,694)
41.5 (41.4 to 41.7)
25.2 (24.6 to 25.8)
119.5 (119.2 to 119.7)
41.4 (41.4 to 41.5)
29.2 (29.1 to 29.2)
135.0 (134.7 to 135.3)
43.0 (42.4 to 43.7)
15.5 (14.9 to 16.1)
1.0 (0.9 to 1.2)
1.0 (0.9 to 1.1)
0.2 (0.1 to 0.2)
0.8 (0.6 to 0.9)
13.7 (13.2 to 14.1)
14.0 (13.6 to 14.5)
30.3 (29.7 to 30.9)
Lifestyle
(n=8,653)
40.4 (40.2 to 40.6)
20.8 (20.1 to 21.5)
119.7 (119.4 to 120.0)
41.3 (41.3 to 41.4)
29.7 (29.6 to 29.8)
133.1 (132.8 to 133.4)
45.7 (44.8 to 46.5)
16.5 (15.8 to 17.2)
0.7 (0.6 to 0.9)
0.4 (0.3 to 0.5)
0.0 (-0.0 to 0.01)
0.6 (0.5 to 0.8)
8.2 (7.7 to 8.7)
7.7 (7.2 to 8.1)
23.0 (22.2 to 23.7)
Difference
surgery – lifestyle
1.1 (0.9 to 1.4)
4.4 (3.5 to 5.3)
-0.2 (-0.7 to 0.2)
0.1 (0.1 to 0.2)
-0.5 (-0.6 to -0.4)
1.9 (1.4 to 2.3)
-2.6 (-3.7 to -1.5)
-1.0 (-2.0 to -0.04)
0.3 (0.1 to 0.5)
0.6 (0.4 to 0.8)
0.1 (0.1 to 0.2)
0.1 (-0.1 to 0.3)
5.5 (4.8 to 6.2)
6.4 (5.7 to 7.0)
7.3 (6.4 to 8.3)
16.8 (16.3 to 17.3)
61.8 (61.3 to 62.3)
21.4 (20.8 to 21.9)
30.6 (30.4 to 30.9)
14.4 (13.9 to 14.9)
61.0 (60.5 to 61.6)
24.6 (23.9 to 25.3)
30.7 (30.3 to 40.0)
2.4 (1.8 to 3.0)
0.8 (0.6 to 1.0)
-3.2 (-4.0 to -2.4)
-0.02 (-0.4 to 0.4)
Data are means or percent and difference between surgery and lifestyle groups (95% confidence
intervals) from an inverse probability-weighted sample.
5
Weight loss and heart failure
Sundström et al.
Supplemental Figure 1.
6
Sundström et al.
Weight loss and heart failure
Supplemental Figure titles and legends
Supplemental Figure 1. Flow chart for definition of the study sample
Intervention years 2006-2013. BMI, body-mass index.
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Dr Carolyn Lam: Welcome to Circulation on the Run, your weekly podcast summary and backstage pass to the journal and its editors. I'm Dr. Carolyn Lam, associate editor from the National Heart Center and Duke National University of Singapore. Our feature paper this week really adds to our understanding of the cause/effect relationship between obesity and heart failure, this time by comparing the effects of gastric bypass surgery versus intensive lifetime treatment on heart failure risk. Before we talk about that, though, let me give you your summary of this week's journal. The first paper brings us one step closer to understanding cardiac recovery in response to mechanical unloading by left ventricular assist devices and it does this by showing that this process may involve the transverse tubular system, which is a micro structural feature of ventricular cardiomyocytes important for contractility and consisting of tubular invaginations of the sarcolemma predominantly located at the Z‐lines of sarcomeres. This transverse tubular system is crucial for efficient excitation contraction coupling by bringing L‐type calcium channels in the sarcolemma in proximity to clusters of ryanodine receptors in the sarcoplasmic reticulum. In the current study by co‐corresponding authors, Dr. Seidel and Drakos and Sachse from University of Utah, the authors studied left ventricular biopsies obtained from five donors and 26 patients with chronic heart failure undergoing implantation of left ventricular assist devices or LVAD's. They used three dimensional confocal microscopy and computational image analysis to assess the transverse tubular system's structure, density, and distance of ryanodine receptor clusters to the sarcolemma. They found that the majority of heart failure myocytes showed remarkable transverse tubular system remodeling, particular sheet‐like invaginations of the sarcolemma, which is previously unknown phenotype. This sheet‐like transverse tubular system remodeling led to increased distances of ryanodine receptors to the sarcolemma causing heterogeneous intracellular calcium release and consequently inefficient excitation contraction coupling. High degrees of transverse tubular remodeling at the time of LVAD implantation was associated with absence of functional cardiac recovery during mechanical unloading, whereas preserved transverse tubular systems structure was associated with recovery. In summary, cardiac recovery during unloading may require an intact transverse tubular system at the time of LVAD implantation. And characterizing this system may help to identify patients with a high probability of functional cardiac recovery in response to mechanical unloading. There have been a proliferation of algorithms based in high sensitivity assays for cardiac troponins for the diagnosis or exclusion of myocardial infarction. All Need Help? mailto:[email protected]
Get this transcript with table formatting these algorithms have the potential to overwhelm clinicians with options. Well, there is help in this week's issue with two observational studies directly comparing the diagnostic performances of multiple high‐sensitivity troponin testing strategies. Now, before I describe these two studies in detail, here are some important reminders. Remember that as of early 2017, although high‐sensitivity troponin assays are routinely used in many regions of the world, they are not available in the United States. Thus, the specific algorithms discussed here are not applicable with the contemporary sensitive assays that are presently used in the United States. Next, let's remind ourselves that both the United States and European professional guidelines recommend serial measurement of cardiac troponins at presentation or zero hours and three to six hours later with additional testing beyond six hours in patients who have electrocardiographic changes, or intermediate or high clinical risk features. The 2015 European Society of Cardiology Guidelines also included an alternative strategy reducing the sampling interval to one hour when using a high sensitivity troponin assay with a validated zero and one hour algorithm based on the 99 percentile cutoff of these high sensitivity troponin assays. Now to the two studies in the current issue, which tie together the expanding evidence with direct comparisons of several of the strategies using the same high sensitivity cardiac troponin assay by Abbott. Dr. Chapman and colleagues from the royal infirmary of Edinburgh, United Kingdom, compared the standard ECS zero and three hour strategy based on the 99th percentile upper reference limit at both time points with the high sensitivity troponin in the evaluation of patients with acute coronary syndrome, or high stakes algorithm, and that would be a zero, three, and six hour algorithm that incorporates a zero hour criteria and at a very low cutoff of five nanogram per liter and a three hour criterion that directs patients with either a rising concentration or with an absolute concentration above the upper reference limit to additional testing. Among 1,218 patients with suspected myocardial infarction, the high stakes algorithm delivered both a higher proportion ruled out for myocardial infarction at zero hours and a higher negative predictive value of 99.5% versus 97.9%. The ESC pathway missed 18 index and two recurrent myocardial infarction events, whereas the high stakes pathway missed two index and two recurrent myocardial infarction events. These findings demonstrate the value of adding a very low zero hour cutoff to facilitate earlier rule out as well as the value of a delta criterion to exclude increasing values among patients that progress to three hour sampling. In the next study, first author, Dr. Boeddinghaus, corresponding author Dr. Mueller and colleagues from University Hospital of Basel, Switzerland compared the ESC alternative zero and one hour strategy with three other approaches COTR135_17 For Review
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using either a single cutoff at zero hours, or the one hour strategy. Among 2,828 patients with symptoms suspicious for myocardial infarction and no ST elevation, each of these four approaches delivered a negative predicted value above 99% comparing favorably to the ESC zero and three hour algorithm that had a negative predictive value of 98.4%. Now, although each of the strategies performed similarly among patients presenting more than two hours after symptom onset, among the early presenters, the negative predictive value and sensitivity were diminished using the single zero hour cutoff of five nanograms per liter. The authors concluded that the single cutoff strategy, the one hour algorithm, and the zero and one hour algorithm, allow the triage towards rule out of myocardial infarction in more than half of consecutive patients presenting with suspected MI to the emergency department. However, the single cutoff strategy should not be used in patients presenting early after chest pain onset. These papers are discussed in an excellent editorial, which also puts everything in perspective by Dr. David Morrow from Brigham and Women’s Hospital in Boston, Massachusetts. I particularity want to refer all of you to the figure that's found in its editorial which really helps you to understand the different strategies involved. The final study tells us about potential death averted and serious adverse events occurred from the adoption of the SPRINT intensive blood pressure regimen in the United States. As a reminder, the systolic blood pressure intervention trial, or SPRINT demonstrated a 27% reduction in all caused mortality with a systolic blood pressure goal of less than 120 versus less than 140 mm Hg among American adults at high cardiovascular risk, but without diabetes, stroke, or heart failure. In the current study, Dr. Bress and colleagues from the University of Utah School of Medicine applied the SPRINT eligibility criteria to the 1999 to 2006 National Health and Nutrition Examination Survey or NHANES and linked this with the national death index through December, 2011. They found that if fully implemented in eligible US adults, intensive blood pressure treatment was projected to prevent about 107,500 deaths and 46,100 of heart failure per year. But, you also give rise to about 56,100 episodes of hypertension. 34,400 episodes of syncope, 43,400 serious electrolyte disorders, and 88,700 of acute kidney injury per year compared to standard blood pressure treatment. Thus, they take home message is careful patients selection and implementation are important because intensive treatment while preventing deaths is associated with increased risks of hypertension, syncope, electrolyte abnormalities and acute kidney injury. Well, that brings us to a close for the summaries, now for our feature discussion. COTR135_17 For Review
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We are discussing obesity and heart failure. Now, we've heard of the obesity paradox, but we also know that obesity may be a risk factor for heart failure and the study today really puts perspective on this and is really one of the largest most convincing studies I've read on this topic. I am so pleased to have the person corresponding author, Dr. Johan Sundstrom from Uppsala University Hospital in Sweden. Welcome, Johan. Dr Johan Sundstrom: Thank you, lovely to talk to you. Dr Carolyn Lam: And especially pleased to have back on the show again, Dr. Torbjorn Omland from University of Oslo, Norway. Hi, welcome back, Torbjorn. Dr Torbjorn Omland: Thank you very much. It's a great pleasure being here. Dr Carolyn Lam: Johan, you know what? Could you just start by telling us about your study? Dr Johan Sundstrom: So, we were fortunate enough to have two great databases here in Sweden. One was the obesity surgery registry called SOREG in which all people have a gastric bypass surgery, for people who are registered. And we also have a company called Itrim who provide intensive lifestyle program, which takes people down on average about 11 kilos, and they have a very structured database as well. So, we were able to pull this data in order to try and understand the effects of intentional weight loss to two different levels of weight loss, what that does to the heart failure incidence. This is a bit of a comparative effectiveness study, so it's of course necessary to make the examples as similar as possible to apply exclusion criteria. We took away everyone who had a body mass index of less than 30 and above 50 and then we applied propensity scores to those two data sets and we had to trim the data sets a little bit further in order to get so called region of common support, which means that we were left with two samples who could have either had surgery or a lifestyle intervention. And then we applied an inverse probability weighting scheme to that. It's statistically complicated but what that does, is it's a matching, but it's not as complicated as matching. With matching, you just give people a weight of 1 or 0, but this gives people other weights as well. So, we end up with characteristics that were very similar at baseline. So, we tried to mimic as close as possible what a randomized clinical trial looks like, but of course we did it posthoc and it’s observational. So, we get our table one, sort of, in this paper that shows very similar characteristics of the two groups. So, what we did then is we noted what happened to the people in these two groups in terms of heart failure incidence and we followed them in our national inpatient registry. So, all the Swedish citizens get a personal identification number so we can use that to follow people in our patient registry. So, we know exactly what drugs people will collect from pharmacies, and we know what they died from, and we know all of their hospitalizations. And we COTR135_17 For Review
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previously validated their heart failure diagnosis in the Swedish Inpatient Registry and we noted that you were in a pretty good position if you were hospitalized with heart failure as the main cause of hospitalization and we noted that people who had agreed to do surgery, had about half the incidence of heart failure than people who were in the intensive lifestyle program. We also noted, if you looked at the achieved weight loss one year after baseline, we noted that a ten kilo weight loss after one year was related to about a 23% lower risk of heart failure. So we noted a litany of association between the achieved weight loss and heart failure incidence. It should said, though, that heart failure in this age group, they are only 41 on average, 41 years old. Heart failure's still very unusual at this age, even in many of these people. We only had 73 cases of heart failure. So, the exact numbers need to be taken with a pinch of salt and have wide confidence intervals around them. Dr Carolyn Lam: Johan, this is exactly why I'm so impressed with your data. First you showed a dose response relationship between the weight loss and risk of heart failure. You also show that it's not an event that occurs very often and so, it would be very difficult to imagine doing a randomized controlled trial for example in this setting and having to wait very long for these events. So, it really goes to show your observational data are extremely important. And I really like the way you took the pains to describe how you tried to overcome the differences that exist between the groups and try to make it as much resembling a randomized trial setting as you could. So, maybe I could turn it over to you, Torbjorn. Could you tell us what you think the implications of this paper are? Dr Torbjorn Omland: First, I will say that that this paper has all the characteristics of a very high quality study. It's a very timely topic that interests a lot of people. The paper's very well written. It's a large sample size as you said and it was very clinically meaningful difference between the groups and that translated into very clear and robust answers. So, I think that this has every mark of high quality paper. But, of course, the very important question is how will this translate into actions? How can we use this information to prevent problems? We know heart failure is a very prevalent disease, especially in the elderly and although the incidence was lower here, I think my question for Johan at least is what would be the next step? What changes can we implement to reduce heart failure among the obese? Dr Johan Sundstrom: That's a great question. I think in this study puts a little piece of the puzzle on the table and that's trying to add a little more evidence towards a causal association between obesity and heart failure. I'm not sure about what we can offer these patients and what will be the translation to lower heart failure incidence in the long run. Of course, we need to follow this sample for longer to have more heart failure cases, because I don't think we've seen the full impact of weight loss in these two samples. We might need to follow them into older age where they would have a higher heart failure incidence. COTR135_17 For Review
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But, how to tackle obesity, I think we'll need accommodate population strategies and high risk strategies. I think if the general consensus in the scientific community after reading this and other important papers, is that there's causal link between obesity and heart failure, then we might need to understand that people who are obese and who have shortness of breath and perhaps swelling or what not, may not just be having low fitness, they might actually behaving signs of heart failure. So, I think as a sort of increased diligence on heart failure, these people might be one thing. But, we didn't really study that. So, I wouldn't draw conclusion. But, otherwise I think it's more of a causal inference piece of the puzzle that we've laid rather than a clinical care piece of the puzzle. Dr Torbjorn Omland: No, I agree, and here you won't to make any recommendations in regards to what interventions you should recommend particularly based on this particular study. Dr Johan Sundstrom: No, because I think there are so many other things that need to be taken into account when it comes to treatment of obesity. Heart failure is actually one of the uncommon outcomes in this age group. We're looking at other outcomes after they present. Myocardial infarction, ventral fibrillation and mortality are actually much more common. So, I think a lot of other data should go into decisions on how to treat patients, not just for heart failure, which is still fairly uncommon at this age. Dr Carolyn Lam: Going back to the other question that Torbjorn asked, do you think that this question still needs to be answered in any way? You've got the Mendelian randomization data. Now, you've got your data. Do you think it's still a question of whether obesity is a risk factor for heart failure? And just in case there's any confusion out there, would you put that together with the so called obesity paradox in heart failure? Dr Johan Sundstrom: To answer the first one, I think we're not going to have any randomized evidence. Treatment of heart failure with intensive programs and prevention of heart failure ... It needs for huge samples that I don't think we're going to have any much better observational evidence anytime soon either. So, we can probably set that question aside a little bit. But, when it comes to the obesity paradox, first of all that's not what we studied here. We didn't have anyone with heart failure in this sample. We included all those people. We can only speculate. I'm a clinical epidemiologist myself, but I'm envious of people who have animal and other models because I think there's a lot more work to do in terms of ppars and and lipid metabolism in obesity and in heart failure. So, I think there'll be more interesting experimental research to come that can help us answer the obesity paradox. Dr Carolyn Lam: Please don't forget to tell your friends about this podcast, and tune in again next week. COTR135_17 For Review
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