Download Is a Diabetes-Linked Amino Acid Signature associated with Beta

DOI: 10.1161/CIRCGENETICS.113.000421
Is a Diabetes-Linked Amino Acid Signature associated with Beta BlockerInduced Impaired Fasting Glucose?
Running title: Cooper-DeHoff et al.; Amino acids and ȕEORFNHU-induced IFG
Rhonda M. Cooper-DeHoff, PharmD, MS1,2,3,4; Wei Hou, PhD6; Liming Weng, PhD1,2,3;
Rebecca A. Baillie, PhD7; Amber L. Beitelshees, PharmD, MPH8; Yan Gong, PhD1,2,3;
Mohamed H.A. Shahin, MS1,2,3; Stephen T. Turner, MD9; Arlene Chapman, MD10; John G.
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
Gums, PharmD1,3,5; Stephen H. Boyle, PhD11; Hongjie Zhu, PhD11; William R. Wikoff,, PhD12;
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Eric Boerwinkle, PhD13; Oliver Fiehn, PhD12; Reginald F. Frye, Ph
1,2,3,4*
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Kaddurah-Daouk, PhD11*; Julie A. Johnson, Pharm
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Genetics
*contributed equally as co-senior authors
Correspondence:
Rhonda M. Cooper-DeHoff, PharmD, MS
Department of Pharmacotherapy and Translational Research
& Center for Pharmacogenomics
University of Florida
Gainesville, FL 32610
Tel: 352-273-6184
Fax: 352-273-6121
E-mail: [email protected]
Journal Subject Codes: [192] Glucose intolerance, [193] Clinical studies
1
DOI: 10.1161/CIRCGENETICS.113.000421
Abstract:
Background - The five amino acid (AA) signature including isoleucine (Ile), leucine (Leu),
valine (Val), tyrosine (Tyr), and phenylalanine (Phe) has been associated with incident diabetes
and insulin resistance. We investigated whether this same AA signature, single nucleotide
polymorphisms (SNPs) in genes in their catabolic pathway, were associated with development of
impaired fasting glucose (IFG) after atenolol treatment.
Methods and Results - Among 234 European American participants enrolled in the
Pharmacogenomic Evaluation of Antihypertensive Responses (PEAR) study and treated with
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
atenolol for 9 weeks, we prospectively followed a nested cohort that had both metabolomics
profiling and genotype data available, for the development of IFG. Wee as
assessed
association
asse
sess
se
ssed
ss
ed th
thee as
asso
ocia
between baseline circulating levels of Ile, Leu, Val, Tyr and Phe, as well
BCAT1
ell as SN
SNPs
Ps iin
n BC
BCAT
A and
PAH with development
levels
d ve
deve
velo
l pm
lo
pmen
ent off IFG. All baseline AA le
en
eve
vels
l were stronglyy aassociated
ssociated with IFG
development.
increment
AAss was
with
nt. Each
nt
Each increm
em
men
nt in
n sstandard
tand
ta
ndar
nd
ardd de
ar
ddeviation
viiat
atio
on off the
he five
fiv
ivee AA
A
was associated
wa
asssoci
as
ciiat
ated
ed
dw
itth thee
following odds
Ile 2.29
oddds ratio
ratio and
and
n 95%
5% confidence
confi
fide
fi
dencce interval
de
in
ntervvall fo
for
or IFG
FG
G bas
based
ased oon
n fu
fully
ullly ad
adjusted
dju
j stted m
model:
odeel:: Il
(1.31-4.01),, Leu 1.80 (1.10
(1.10-2.96),
Vall 1.77
Tyr
0-2
- .9
.96)
6)), Va
V
777 ((1.07-2.92),
1..077-2.992)), Ty
yr 2.
22.13
133 (1.20-3.78)
(1.20
20-3
20
-3.778)) and Phe 2.04 (1.16-3
3.59). The compo
ccomposite
p site p value
vallue was 2x
22x10
100-55. Those
T ose wi
Th
with
ith
h PA
PAH
H ((rs2245360)
rs22
2245
22
453660) AA
AA genotype
g noty
ge
ype
p had the
t
highest incidence
idence
ide
denc
de
ncee of IFG
IFG (p
(p fo
forr tr
tren
trend=0.0003).
end=
d=0.
d=
0 0003
0.
00003
0 )).
Conclusions - Our data provide important insight into the metabolic and genetic mechanisms
underlying atenolol associated adverse metabolic effects.
Clinical Trial Registration - clinicaltrials.gov; Unique Identifier: NCT00246519
Key words: amino acids, impaired glucose tolerance, pharmacogenetics, metabolomics, betablocker
2
DOI: 10.1161/CIRCGENETICS.113.000421
Hypertension and being overweight or obese have previously been identified as risk factors for
diabetes and frequently coexist.1, 2 Impaired fasting glucose (IFG), impaired glucose tolerance
(IGT) and insulin resistance, important early components of cardiometabolic dysfunction, are
also prevalent in those with hypertension 3 and significantly increase the risk for diabetes.4, 5
Eblockers, although very effective blood pressure (BP) lowering agents, are associated with
adverse metabolic effects, including hyperglycemia, IFG, and diabetes, all of which are
associated with adverse cardiovascular consequences long-term.6-8 While the mechanistic
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underpinnings of Eblocker associated adverse metabolic effects are incompletely
nco
c mp
pletely
ly
y under
understood,
erst
er
stooo we
st
bolic eeffects
ffec
ects
ts iss pr
pres
e en in
have previously shown that risk for Eblocker associated adverse metabolic
present
those with and
andd w
without
ithout
u abdominal
ut
abd
bdominal obesity.6
bd
Identification
ntification
nt
tiffication of risk
rissk factors
facctorrs for diabetes
fa
diab
betess has
has been
beenn a focus
f cuss for decades,
fo
deecaades, with
with tthe
he ul
uultimate
ultim
ltim
goal of developing
elopi strateg
eloping
strat
strategies
gie
iess to del
delay
ellay
ay or pr
pprevent
event onse
onset
s t of diabetes
diaabe
b tes in tho
those
hoos at highest risk. B
hose
Based
on data from
m oobservational
bser
bs
erva
er
vati
va
tion
ti
onal
on
al and
and rrandomized
ando
an
domi
do
mize
mi
zedd cl
ze
clin
clinical
inic
in
ical
ic
al ttrials,
rial
ri
alss, cl
al
clin
clinical
inic
in
ical
ic
al ch
char
characteristics
arac
ar
acte
ac
teri
te
rist
ri
stic
st
icss kn
ic
know
known
ownn to
ow
increase risk for drug induced diabetes include age, ethnicity, race, body mass index,
hypertension, stroke, among many others.9
Metabolomics, a rapidly growing field that enables mapping of global biochemical
changes associated with disease or treatment,10 has been used successfully to map pathways
implicated in mechanisms of variation of response to drugs.11 Recently, metabolomics data from
an observational study of the Framingham Offspring cohort identified a small cluster of essential
amino acids (AAs), including baseline levels of three branched-chain amino acids (BCAA),
isoleucine (IIe), leucine (Leu) and valine (Val), and two aromatic amino acids (AAA),
phenylalanine (Phe) and tyrosine (Tyr), as metabolomic risk factors associated with a significant
and independent increased risk for diabetes.12 This same 5 AA cluster has also been identified as
3
DOI: 10.1161/CIRCGENETICS.113.000421
a predictor of insulin resistance in young adults.13
While the clinical characteristics that predict risk for drug induced diabetes are similar to
those that predict diabetes of other etiologies,14, 15 there are currently no data available to assess
whether the same profile of BCAA and AAA that predicted diabetes and insulin resistance risk
might also predict risk for drug induced alterations in glucose status. Likewise, while branched
chain amino-acid transaminase 1, BCAT1 and phenylalanine hydroxylase, PAH, genes that
catabolize BCAA and AA respectively, have been previously associated with diabetes16 and
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
insulin resistance,17 there are no data regarding the associations of single
gle nucleotide
polymorphisms (SNPs) in these genes with drug induced metabolic changes.
hanges. Therefore,
Thereffore, in a
Th
nested cohort
ort from
from the
thee Pharmacogenomic
Pha
harmacogenomic Evaluation
ha
Evaluatio
io
on of Antihypertens
Antihypertensive
nsiv
ns
i e Responses (PEA
(PEAR)
study, we used
used
us
d a targeted
d ““pharmacometabolomics
phaarmaccomet
etaabolom
miccs informed
infform
rmed
ed
d pharmacogenomics”
phaarmaccogen
enomic
en
icss” approach
ic
approoachh 18 to
whet
ethe
et
herr bbaseline
he
aseeli
line
ne llevels
evel
ev
e s of the
th previously
prev
evio
ev
iouusly
io
ly identified
ly
id
den
enti
tif
ti
ifi
fied
fied
d BCAA
BC
CAA
A and
an AAA
AAA and
and SNP
SN
NPs in
investigate whether
SNPs
genes in their
e catabolic pathways,
eir
pathhway
ys,, increase
increase the
th
he odds
oddds off development
d velo
de
l pm
p ent off IFG,
IFG
IF
G, a phenotype
p enotyp
ph
ype
yp
associated with
iith
thh significant
ignifi
ifi nt increased
i
ed
d risk
iskk off ddiabetes
iabbete and
d cardiovascular
cardio
rdi
dio asc llar di
disease ffollo
following
oll
ll iing
ng sshort
term exposure to atenolol.4, 19
Methods
PEAR is a prospective, randomized, parallel group, titration study undertaken to evaluate the
pharmacogenomic determinants of the antihypertensive and adverse metabolic responses to
atenolol and hydrochlorothiazide in hypertensive participants without a history of heart disease
or diabetes. Details regarding study design and enrollment criteria have been previously
published.20 The study is registered at clinicaltrials.gov, NCT00246519,
http://clinicaltrials.gov/ct2/show/NCT00246519.
4
DOI: 10.1161/CIRCGENETICS.113.000421
Study Population
PEAR participants were enrolled from primary care clinics at the University of Florida
(Gainesville, FL), Emory University (Atlanta, GA), and the Mayo Clinic (Rochester, MN).
Because we are seeking to extend findings generated in a primarily White population, this
analysis includes a subset of European American men and women with mild-to-moderate
essential hypertension, between the ages of 17-65, selected for metabolomics profiling and with
available genotype information within the atenolol arm of PEAR (Supplement Figure 1). For
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
metabolomics profiling, patients were divided into quartiles based on the DBP response to
atenolol monotherapy. Equal numbers of patients were selected randomly
mly within
wit
ithi
it
hin each
hi
each
h response
res
espo
quartile using
PROC
SURVEYSELECT
random
n tthe
ng
h P
he
RO
ROC
O SU
S
RVEYSELECT procedure
procedu
durre (simple rando
du
om sampling without
replacement).
n ). P
nt)
Prior
rior to bas
baseline
seline measurements,
meas
a urem
as
men
nts, tthose
hose rreceiving
eceeiv
ivin
ingg tr
in
treatment
reaatmen
en
nt for
foor hypertension
hyypertension
p rtten
pe
nsion
on aat
enrollment hadd aall
lll antihypertensive
antih
hyp
yper
erte
t ns
te
nsiv
i e drugs
druugs discontinued
dr
diisc
scon
onti
on
tinu
ti
nued
d for
forr a m
median
edia
ed
ian
ia
an 277 ((interquartile
inte
terq
te
rquart
rq
rttil
ilee range
rang
ra
nge
[IQR] 19-GD\V3DUWLFLSDQWVZLWK,)*JOXFRVH•PJGODWEDVHOLQHZHUHH[FOXGHGIURP
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this analysis.
s
PEAR Protocol
Written informed consent was obtained voluntarily from all participants, and institutional review
board approval was obtained from all study sites, which included University of Florida, Mayo
Clinic and Emory University. Atenolol was initiated at 50mg, and titrated to 100mg, based on BP
> 120/70 mmHg and tolerability. After at least 6 weeks on the final dose, response was
determined, including BP measurements and laboratory-based assessments, and results described
in this analysis come from this response assessment time point. PEAR is registered at
ClinicalTrials.gov, #NCT00246519.
5
DOI: 10.1161/CIRCGENETICS.113.000421
Laboratory Measurements
At baseline and after completion of atenolol monotherapy, fasting blood samples were collected
for glucose and insulin. Insulin sensitivity status was calculated using the homeostatic model
assessment – insulin resistance (HOMA-IR).21
Biochemical Assays
Glucose was measured in plasma, on an Hitachi 911 Chemistry Analyzer (Roche Diagnostics) at
the central laboratory at the Mayo Clinic, Rochester, MN, using spectrophotometry by an
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
automated enzymatic assay. Plasma insulin was measured using the Access
ccess Ultrasensitive
ve Insulin
I
immunoassay system (Beckman Instruments). All samples were tested
d in du
dduplicate,
pllic
i ate,
t andd ddata
at
eans
ea
ns of
of th
tthee duplicate
d plicate samples.
du
reported aree m
means
Quantificatio
io
on
Amino Acidd Quantification
mples were
wer
ere tran
ansf
sfer
ferre
redd from
m the
the PEAR
PEA
EAR
AR laboratory
labo
labo
b ra
rato
tory
to
ry to
to the
th
he metabolomics
mettabo
me
tab lo
lomi
mics core
mi
cor
oree labor
l bor
la
bo
Plasma samples
transferred
laboratory,
o California,, Davis,
D viis,, where
Da
whhere samples
sampl
ples
l were extracted,
d dderivatized
d,
erivatiized
d and analy
yzed ass
University of
analyzed
22, 23
e iio sll iin
n great
at detail
ddetail.
etail
il 22
Briefly,
Briefl
B
riiefl
fl mass spectrometry
spectrometr
tr et used
sed
daL
Leco
e P
Pegasus
Pegas s IV
IV time
reported previously
of flight (TOF) mass spectrometer with 280qC transfer line temperature, electron ionization at 70V and an ion source temperature of 250qC. Mass spectra were acquired from m/z 85-500 at 20
spectra/sec and 1750 V detector voltage. Quantitative data were normalized to the sum intensities
of all known metabolites and used for statistical investigation.
Genotyping and Quality Control
To explore a potential functional mechanism underlying our observation that the 5 AA signature
was associated with development of IFG following exposure to atenolol, we sought to investigate
genes in common catabolic pathways. Because we had a limited population (n=184) with
available genetic data and that did not have IFG at baseline, we used a candidate gene approach
6
DOI: 10.1161/CIRCGENETICS.113.000421
for this analysis. For the BCAAs, we chose to focus on BCAT1, which encodes the cytosolic
form of the enzyme branched-chain amino acid transaminase. This enzyme catalyzes the
reversible transamination of branched-chain alpha-keto acids to branched-chain L-amino acids
essential for cell growth, and importantly, the aminotransferase step is the first step in the
catabolic process for Ile, Leu and Val. For the AAs, we chose to focus on PAH, which encodes
the enzyme phenylalanine hydroxylase. Phenylalanine hydroxylase catalyzes the hydroxylation
of the aromatic side-chain of phenylalanine to generate tyrosine, and is the rate-limiting enzyme
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
of the metabolic pathway that degrades excess phenylalanine.
Genotypes for SNPs in the BCAT1 and PAH genes were obtained
Omni1M
ned fr
ffrom
om tthe
h O
he
mn
ni1
i1M
M quad
GWAS Beadchip.
The
Omni1M
a chi
adc
h p.
p. T
hee O
mni1M quad is a GWAS chip
chi
h p that used the Infinium
Infi
In
f nium II assay and
genotypes w
GenTrain2
calling
algorithm.
Procedures
were
ere called using
usin
ing BeadStudio
in
Beead
dSt
S udio
o software
sof
ofttwaaree and
an
nd Ge
GenT
nTraain
nT
n2 call
lin
ng al
algori
rith
ri
hm. Pro
Proced
oced
for quality Cont
Control
principal
component
population
C
trool and
and pr
an
pri
inciipa
inci
pall comp
mp
ponentt (P
(PC)
PC)
C analysis
ana
nally
lysi
siss for
for ddetermination
fo
ete
teerm
rmiina
inationn off pop
opul
ulat
lattion
ion
substructuree are described iin
minor
n ddetail
etail
il iinn the supplementary
supp
pplementaryy materi
pp
materials.
ials.
l After
Aft
f er QC
QC procedures,
p ocedures,, m
pr
allele frequency
>0.05,
disequilibrium
pruning,
enc ffilter
ilte off >0
il
0 05 and
d li
linkage
nkk
diseq
di
ilibri
ilib
il
ibrii m and
ndd monogenic
ic SNP
SNP pr
ning
nii a total
of 96 SNPs, 69 SNPs in BCAT1 and 27 SNPs in PAH, were analyzed in the genetic association
study.
Primary Outcome
We examined the association between baseline plasma metabolite level of Ile, Leu, Val, Tyr and
Phe, as well as SNP genotypes and development of IFG, defined as a new occurrence of fasting
JOXFRVH•PJGO24, following treatment with atenolol monotherapy. After a 12 hour fast,
glucose was measured in all participants at baseline, prior to initiation of atenolol, and again at
the end of atenolol therapy. Mean duration of treatment with atenolol was 9 weeks. Participants
ZLWKDIDVWLQJJOXFRVH•PJGODWEDVHOLQHZHUHH[FOXGHGIURPWKLVVWXG\3DUWLFLSDQWVZLWKD
7
DOI: 10.1161/CIRCGENETICS.113.000421
baseline fasting glucose < 100 mg/dl DQGDIDVWLQJJOXFRVH•PJGODWWKHHQGRIDWHQRORO
treatment were considered to have developed IFG. Participants with a baseline fasting glucose <
100 mg/dl and a fasting glucose that remained < 100 mg/dl at the end of atenolol treatment were
considered to have not developed IFG.
Statistical Analyses
Frequencies and percentages were calculated for categorical baseline characteristics and were
compared by using Chi-square tests between those who did and did not develop IFG. Means and
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
standard deviations or medians and interquartile ranges were calculated
d for continuous baseline
bas
ae
characteristics and were compared using t-tests or Wilcoxon rank sum test
tests,
respectively,
ts, respe
cti
tivelly,
between those
did
and
glucose,
o wh
ose
whoo di
id an
nd did not develop IFG. Change
Chan
Ch
nge and percentt cchange
hange in fasting gluc
c
baseline glucose,
log-transformed
ucose
uc
cos , baselinee iinsulin
nsul
ullin and
and
n baseline
bas
asellin
ne AA
AA levels
lev
evelss were
we e log
og-traansfo
forrmed
fo
d ddue
uee to no
nnonon-normal distribution
across
case/control
model
t uttio
tribut
ion
on and
and compared
com
ompa
pare
red ac
cross
roo cas
se/
e/co
/ ont
ntro
troll ggroups.
rou
ouups
ps. We
We uutilized
t li
ti
lize
zed a re
rregression
gresssi
sion
ion m
od to
od
assess the association
between baseline
with
the
change
adjusted
a
baseli
line AA
A llevel
evell wi
ith
h th
he pe
ppercent
rcent ch
hange
g iin
n gl
gglucose,
ucose,, adj
justt for
age, gender,
BMI,
glucose
r BMI
B
MI baseline
ba li gl
l cose and
ndd ins
iinsulin,
lin
lin andd HOMA
HOMA IR
IIR,
R in
i the
th
h entire
tii cohort
ohh t with
iith
th
h
metabolomics data available (n=150). Whether a participant developed IFG, as a binary outcome
variable, was fitted by multiple logistic regression models. In separate analyses, each baseline
plasma AA level was entered in the logistic model individually as a primary risk predictor.
Because the distribution of those who did and did not develop IFG was significantly different by
gender and previous research has shown that plasma amino acid levels differ significantly by
gender,25 we conducted a subgroup analysis where each baseline plasma AA level as a primary
risk predictor of IFG development was tested separately by gender. The values of AA level were
standardized (mean=0 and standard deviation=1) so that the regression coefficients are
interpreted as odds ratio per standard deviation.12 Odds ratios for AA level predicting IFG were
8
DOI: 10.1161/CIRCGENETICS.113.000421
estimated. For logistic regression analyses, we tested four separate models: 1) unadjusted, 2)
adjusted for baseline age, sex and BMI, 3) model two plus fasting glucose and fasting insulin and
4) model three plus HOMA-IR, to test the effect of insulin resistance. We evaluated the
composite effect of the AAs by combining the individual p values from the model that included
HOMA-IR. Since the BCAA and AAA were all strongly correlated (r = 0.56-0.91, p<0.0001 for
all comparisons), an extension to Fisher’s combination for correlated tests was performed, for
testing significance of the composite p value.26 For amino acid analyses, P values less than 0.05
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
were considered statistically significant.
For pharmacogenomics analyses, deviations from Hardy Weinberg
Equilibrium
berg E
quil
ilib
il
ibrium
i
((HWE)
HW
were assessed
s us
sed
usin
using
in
ng Fisher’s
Fishher
Fi
er’s Exact Test with alpha=0.05.The
alphaa=0
=0.05.The associat
associations
atio
tio
ions of the BCAT1 an
and
n
PAH SNPs and
baseline
and IFG weree evaluated
evalu
lu
uated
d using
ng logistic
loggistticc regression,
reegrresssi
sion
on, adjusting
on
addju
justin
sting
ng for
foor age,, gender,
gen
nder,, bbas
as
glucose, insulin
sulin and
and HOMA
HOM
OMA
A (model
( od
(m
o ell 4 above),
abovee), and
n the
nd
the
h first
firrstt 2 PCs
PCs
Cs for
for ancestry.
anc
ncestr
tryy. To
tr
To account
acco
ac
coun
untt for
f
multiple comparisons,
mparisons,
mpa
p risons,, alpha
alph
phha levels
levels
l for
for the
h SNP
SNP associations
associiatiions were set based
based
d on the total num
number
of SNPs included
cll dded
ed
d in
i the
th
h analysis,
anal
all sis
is which
hhich
ichh was
as 96:
96 0.05/96
0 05
05/9
05/96
/966 = 00.0005.
0005
0005 SNP
SNP genot
genotype
t pe QC
QC andd
genetic association analyses were conducted in PLINK.27 All other analyses were performed by
using SAS v9.3 (SAS institute, Cary, NC).
Results
Pharmacometabolomic Analyses
Among the 234 participants randomized to the atenolol group in the PEAR study, metabolomic
profiling was conducted on 150 (Supplement Figure 1). Of those, 122 did not have IFG at
baseline and are the focus of the metabolomics analysis. A total of 24 participants developed IFG
during an average 9 weeks of treatment with atenolol. Baseline characteristics of the participants
included in the metabolomic analysis are summarized in Table 1 and do not differ from the entire
9
DOI: 10.1161/CIRCGENETICS.113.000421
atenolol cohort (data not shown). While those who developed IFG were more likely to be men,
the cohort was similar in age, body mass index and systolic and diastolic BP, in those who did
and did not develop IFG. Baseline fasting glucose was higher (Table 1), and change in glucose
following treatment with atenolol was significantly greater among those who developed IFG than
those who did not (mean 13.5 mg/dl vs. 2.1 mg/dl, p<0.0001) (Supplement Figure 2A). Median
baseline plasma levels of Ile, Leu, Val and Phe were significantly higher in those who developed
IFG than those who did not, while there was no difference in baseline levels of Tyr (Supplement
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
Figure 2B).
Baseline levels of all five AAs were significantly associated with
ith percentt change
ch
hange
gee iin
n
glucose, even
en after
afte
af
terr ad
te
adjustment
dju
justtment for age, sex, BMI, baseline
baseeline glucose and
ba
ndd insulin and HOMA-IR.
HOMAThe beta coefficients
baseline
AAs
shown
oefffic
oe
i ients of the
he bas
asselin
in
ne fivee A
As aare
ree sho
hownn iinn Table
Tablle 2.
Ta
Association
with
n off amino
ami
min
no acids
aciids wit
ith
it
h impaired
impa
paair
ired fasting
fas
asti
ting
ti
n glucose
glucose
see
Associationn between baseline
odds
baseli
liine AA
AA level
levell (per
( er SD
(p
S increment)) andd od
dds fo
fforr de
ddevelopment
velopm
p ent of IFG
IF
F for
models 1-4 in
Results
Model
i the
th
h ooverall
erall
all
ll cohort
hort iiss summarized
s mmari
rii edd in
in Supplement
S pplement
pll
t Table
Tabl
bl 11. R
Res
llts
ts from
f
Mode
M
odde 4
showed the following odds ratio and 95% confidence interval for IFG: Ile 2.29 (1.31-4.01), Leu
1.80 (1.10-2.96), Val 1.77 (1.07-2.92), Tyr 2.13 (1.20-3.78) and Phe 2.04 (1.16-3.59). (Figure 1).
When the p values from the individual AAs were combined, the composite p value was 2x10-5
for Model 4, indicating a very strong association between this five AA metabolite profile and
odds for IFG following atenolol treatment. In subgroup analysis of odds for IFG according to
gender, with the covariates from Model 4, in women (n=6 developed IFG and n=62 did not
develop IFG), the ORs ranged from 1.18-3.67, however none of the associations reach statistical
significance likely due to the small number of women that developed IFG. In men (n=18
developed IFG and n=36 did not develop IFG), the ORs ranged from 1.75-2.47, and baseline
10
DOI: 10.1161/CIRCGENETICS.113.000421
levels of Ile, Tyr and Phe were associated with significantly increased adjusted odds for
developing IFG. Data summarizing adjusted odds for IFG according to gender are presented in
Supplement Table 2.
Pharmacogenetic Analysis
Patients with the PAH rs2245360 AA genotype had the highest incidence of IFG (41.7%),
compared with patients with AG genotype (15.2%) and GG genotype (7.4%), p for
trend=0.0003, which achieved statistical significance (Figure 2). In analyses of odds per allele
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
for developing IFG after atenolol monotherapy, PAH rs2245360 had adjusted ORs of 3.51
3.51,
51, 95%
51
confidence interval 1.62-7.63, p = 0.0015. None of the BCAT1 SNPs tested
estedd achieved
achi
hieved
hi
d Bonferroni
Bon
onffe
corrected statistical
significance
t issti
tatis
t ca
call si
sign
g iffic
icance for development of IIFG.
F .
FG
Conclusions
n
ns
We have shown,
first
h
hown,
for the fi
irs
rstt time,
time
ti
m , that
me
t at baseline
th
b se
ba
seli
line
li
ne plasma
plaasm
smaa levels
leveels
l of
of Ile,
Ile,
e Leu,
Leu
e , Va
Val,
l Tyr and Phe,
Phee as
well as a gene
catabolic
strongly
with
developing
ene
ne iin
n a ca
cata
tabo
boli
licc pathway,
li
p th
path
pa
t wa
wayyy,, are
are stro
st
trong
ngly
gly
y aassociated
ssoc
ss
ocia
iate
tedd wi
ith increased
incr
in
crea
ease
sedd od
odds
dds
ds of
of de
deve
develo
velo
lo
IFG in hypertensive participants, treated relatively short-term (9 weeks) with atenolol, a
commonly prescribed ȕ blocker. Importantly, IFG is an independent predictor of diabetes,4 and
adding measures of anthropometrics and/or insulin resistance status into prediction models is
often more informative for assessing diabetes risk.28 Here, we show that baseline levels of 4 of
the 5 AAs investigated are significantly different in those who do and do not develop IFG, and
the AAs can further inform a prediction model for drug induced IFG, an early metabolic risk
phenotype in the diabetes continuum. Our data extend recent findings from the Framingham
Offspring study,12 an observational study primarily in Whites, that showed a strong association
between these AAs and incident diabetes.
Overweight, obesity, glucose intolerance and insulin resistance have been closely linked
11
DOI: 10.1161/CIRCGENETICS.113.000421
through many important biochemical and regulatory pathways.29 Knowledge that higher blood
concentrations of Ile, Leu, Val, Tyr and Phe are elevated in people with obesity, insulin
resistance or diabetes is not new.30 The continued rise in prevalence of risk conditions for
diabetes in the last 2 decades has increased the need to better understand all relevant underlying
pathways. Recent developments in metabolite profiling or metabolomics, have provided insight
into additional biochemical pathways that play an important role in glucose and insulin
regulation.31 In a study comparing lean and obese individuals, BCAA and AAA were recently
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
recognized as a metabolite cluster strongly associated with insulin sensitivity.
sitivity.
y 32 Additiona
Additionally,
nall
na
BCAA have been associated with coronary artery disease 33 and diabetes.
tes.122 W
Wee hhave
ave furt
further
rthhe
rt
he
B AA
A as a sig
gni
n ficant predictor for ateno
nolo
no
lol-induced IFG,, iin
n a population of middlemii
confirmed BCAA
significant
atenolol-induced
aged, otherwise
wisse healthy, hy
hype
hypertensive
erttensi
siive individuals,
ind
n iv
nd
vid
duaalss, pr
providing
rovid
din
ingg aadditional
dddittional
al evi
evidence
viidencce fo
for
or its uutility
till as
nt riskk biomarker.
bioomar
bi
arke
k r.
ke
r In
In an
a analysis
ana
naaly
lysis st
stra
rati
ra
t fi
ti
fied
ed
d by
by gender,
geend
nder
er, Hu
er
uff
f ma
mann, eett al
l, sh
how
owed
ed
d tthat
tha
h a
ha
an important
stratified
Huffman,
al,
showed
cluster of large
arge
g neutral AA’s
AA’
A’s (Leu/Ile
A’
( eu/I
(L
/ le
/I
l rati
ratio,
io, V
Val,
al,
l, P
Phe,
he, T
Try,
ry, pproline
roliine andd hhistidine)
istiidi
dine)) alongg with uric
acid was a significant
ignifi
ifi nt independent
indd
dent predictor
dict off insulin
di
iins
n li
lin sensitivity
sensiti
itii iitt in
in both
bothh men andd women.
omenn 225 In
our subgroup analysis evaluating the 5 AA signature stratified by gender, we confirmed an
association with increased odds for IFG in men, and saw similar, though non-significant trends in
women. Our inability to confirm an association in women is likely due to the small number of
women who developed IFG in our study, and warrants further investigation in a larger cohort of
women treated with atenolol.
Importantly, evidence also exists suggesting that these AAs may play a causal role in
metabolic dysregulation. A study evaluating the effect of a high fat diet with or without BCAA
supplementation in rats demonstrated that the high fat plus BCAA diet resulted in a higher rate of
insulin resistance than the high fat diet alone.32 Similarly, a small study of healthy men who were
12
DOI: 10.1161/CIRCGENETICS.113.000421
infused a solution containing 18 AAs showed that AAs impair both insulin-mediated suppression
of glucose production and insulin-stimulated glucose disposal in skeletal muscle.34 Together,
these data suggest that these metabolites contribute to the development of metabolic
dysregulation and are not just innocent bystanders in the metabolic disease continuum.35 Our
data from a relatively overweight/obese hypertensive population suggest that treatment with
atenolol may be another environmental exposure that has interactions between BCAA, AAA and
glucose regulation, as we observed significantly increased odds for IFG, an important early
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
predictor for diabetes, associated with all amino acids tested.
PAH, on chromosome 12 (12q22-q24.2) has been associated with alt
altered
lttered
d met
metabolic
tab
bol
oliic
ic
h African
he
Afr
f iccan American
Ame
meri
r can cohort from the Insulin
Insu
suli
su
lin Resistance At
the
h rosclerosis Family Study,
status. In the
Atherosclerosis
age between the
ag
he 12q22-24.2
12q
2qq22
22-224.2 region
regiion (PAH)
re
((PA
PA
AH) andd ac
acut
u e in
ut
iinsulin
nsuliin response
reespponsse
se too glucose
gluccose was
strong linkage
acute
Linkage
kage
ka
age ttoo fasting
f st
fa
stin
tin
i g insulin
in in has
insuli
ha previously
prrev
evio
ious
io
usly
ly been
ly
bee
eenn reported
repo
re
portted
po
d nnear
earr 12
ea
12q23
2q23
q23 in tthe
q2
he A
he
Amish
mis
mi
is as
observed.17 Linka
well.36 Our observation of a significant
siign
g ifficant difference
diff
di
fference iin
ff
n de
ddevelopment
velopm
p ent off IFG
FG accordingg to PAH
H
rs2245360 genotype
genott pe ssuggests
ggests
t thi
this
his gene ma
may play
pla
pll an iimportant
m rt t ffunctional
nctional
tii all role
oll iinn metabolic
etab
boli
lic risk.
ȕ-adrenergic blockers have long been recommended as first-line therapy for the treatment
of hypertension, especially in patients with coronary artery disease.37 HoZHYHUȕ-blockers have
been implicated in altering glucose homeostasis, primarily through inhibition of pancreatic
insulin secretion and promoting insulin resistance. ȕ-receptor selectivity appears to play a role in
the degree of downstream metabolic effects, which include not only glucose increases but also
weight gain and dyslipidemia. Nonselective and higher-dose selective agents result in the largest
adverse metabolic changes.38 1HZHUȕ-blockers including nebivolol and carvedilol appear to
minimally affect glucose homeostasis and improve insulin sensitivity.39, 40 While the mechanisms
XQGHUO\LQJWKLVGLIIHUHQWLDOHIIHFWRQJOXFRVHUHPDLQXQFOHDUWKH\OLNHO\H[WHQGEH\RQGȕ
13
DOI: 10.1161/CIRCGENETICS.113.000421
blockade. $WHQROROWKHȕEORFNHUXVHGWRWUHDWparticipants in PEAR is cardioselective, and the
100mg dose used is the usual dose used to treat hypertension. We observe that AAs and PAH, a
gene previously associated with insulin sensitivity, are also associated with atenolol-associated
IFG. These data suggest the underlying mechanisms of drug-induced and primary dysmetabolism
may be the same. Drugs may be an environmental trigger in patients with metabolic risk factors.
There are a few limitations of our study that are worthy of mention. First, while we
targeted our analysis on a metabolite cluster previously identified for risk of diabetes in a
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
population of Whites, we conducted our analyses only in a cohort of European American
Americans.
an
ns.
Because our cohorts is relatively small, particularly for analyses by gender,
results
should
nder, th
these result
ts sh
be replicated
WUHDWHGZLWKȕEORFNHUVand
e in oother
ed
ther
th
er ppopulations
oppul
u ations with hypertension,
hypertension
onn, untreated
untreated and WU
UHD
HDWHGZLWKȕEORFNHUV
other drugs tthat
metabolic
risks,
while
did
haat possess adverse
adverrsee m
e abol
et
o ic ri
ol
iskss, forr cconfirmation.
onf
nfir
nf
irma
ir
m tion
ma
on. Second,
Seco
ondd, whi
hii e we
hile
we di
id observe
obb
a significant
with
atenolol,
cannot
n association
nt
asso
sooci
ciat
a ionn with
at
wiith
t IFG
IFG
FG in our
ou
u cohort
coho
h rt treated
ho
treat
atted
dw
ith
it
th at
tenol
ollol
ol,
l we can
anno
an
not exclude
no
excl
clud
ludde th
tthat
h
other metabolites
findings
European
b
bolites
are also pplaying
layiingg a role.
rolle. Third,
Thi
h rdd, our fi
nddings
g were iidentified
d ntif
de
ifie
if
i d among
g Europe
pe
American hhypertensive
individuals,
only
generalizable
pertensi
rt sii e iindi
ndi
di id als
ls treated
tr tedd with
iith
th
h atenolol
te loll andd as ssuch
ch
h are onl
l generali
ali
li abl
able
ble
among similar patient populations. +RZHYHUXVHRIȕEORFNHUVLQJHQHUal, and atenolol
specifically is highly prevalent. In 2010, more than 36 million prescriptions were filled for
atenolol or atenolol combinations in the US.41 Lastly, further investigation is warranted to 1)
determine whether this 5 amino acid signature is causal in the development of IFG or is simply a
marker of insulin resistance and impaired beta cell function and 2) confirm our pharmacogenetic
association and to extend this finding to other antihypertensive agents associated with
hyperglycemia, including thiazide diuretics and other race and ethnic groups at high risk for
metabolic dysfunction.
In conclusion, we have extended the previous findings associating BCAA and AAAs
14
DOI: 10.1161/CIRCGENETICS.113.000421
with incident diabetes, to atenolol-induced IFG development. Our findings are important as they
suggest novel biomarkers for the identification of those individuals at risk of developing
antihypertensive treatment-induced diabetes. They may also provide insights to help better
understand the mechanisms of E-blocker-induced dysglycemia. While pharmacogenomics has
shown to be informative with regard to drug:gene interactions for ȕEORFNHU%3UHVSRQVH42 this
study represents one of the first to employ a targeted pharmacometabolomic investigation
combined with a pharmacogenomics investigation informed by the pharmacometabolomic
Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
findings for the antihypertensive drug-induced dysglycemia phenotype.
e. With the prevalence
p evalen
pr
enc of
en
overweight, obesity, insulin resistance, hypertension and diabetes on the
he ri
rise
ise not onl
only
nlyy in
n tthe
he US,
but worldwide,
wide,
e, understanding
und
nder
erst
stan
st
nding as many aspects of the
th
he mechanisticc unde
underpinnings
deerpinnings of drug in
induced
adverse metabolic
taaboolic effectss aass ppossible
ossibl
b e is iimportant.
bl
mp
portaantt. Pharmacometabolomics
Phaarmac
acom
ac
o ettabolom
om
om
miccs is a new,
new
w, bu
but
ut rap
rapidly
p
e that
eld
th
hatt has
has promi
mise iinn defining
mi
defi
finiing pat
thways implicated
impllicatted
d in mechanisms
mech
chanisms off variation
ch
variiatiion of
growing field
promise
pathways
response to th
ther
therapies
erap
er
apie
ap
iess an
andd co
comp
compliments
mpli
mp
lime
ment
me
ntss information
nt
info
in
form
rmat
rm
atio
at
ionn gained
gain
ga
ined
ed from
fro
rom
m a ph
phar
pharmacogenomics
arma
ar
maco
ma
coge
co
geno
ge
nomi
no
mics
cs appr
aapproach.
pprr
pp
h may bbe off immense
i
l as the
h focus
f
f drug
d
h
d a
Together, they
value
for
therapy
moves towards
personalized approach.
Acknowledgments: This study was presented at the American Heart Association 2012 Scientific
Sessions meeting, Los Angeles, California, in abstract form.
Funding Sources: This work was funded by the Pharmacometabolomics Research Network
(RC2 GM092729) and the NIH Pharmacogenomics Research Network (U01 GM074492).
Additional funding includes: K23 HL086558 (RMC-D), K23 HL091120 (ALB), and grants from
the NIH National Center for Research Resources to the University of Florida (UL1 TR000064),
Emory University (UL1 TR000454), and Mayo Clinic (UL1 TR000135).
Conflict of Interest Disclosures: RK-D holds patents in the metabolomics field. All others have
none.
15
DOI: 10.1161/CIRCGENETICS.113.000421
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8
Table 1 Baseline
a eliine character
ase
characteristics
erristiics off the
th co
ccohort
ohoort w
with
itth me
metabolomics
metabo
bolo
bo
lom
lo
miccs data,
dataa, according
acc
ccordiing
cc
n too IFG
G sta
status
a
Characteristic
i
istic
European American
A
i
((n, %))
Women (n,%)
Current smoker (n,%)
Mean age, years
Mean BMI, kg/m2
Mean waist circ., cm
Mean SBP, mmHg
Mean DBP, mmHg
Median Fasting glucose, mg/dL
Median Fasting insulin, μIU/mL
Mean Potassium, meq/L
Median HOMA IR
Develope
ped IFG
Developed
(n=2
(n
=24)
=2
4))
(n=24)
Diid Not Develop IFG
Did
((n=98)
(n
n=98)
24, 10
24
1000
98, 10
98
1000
6, 25*
3, 13
52.5 (9)
30.5 (4.5)
98.5 (11)
143 (9)
92 (5)
93 (86.0-98.0)*
8.3 (4.8-11.2)
4.5 (0.40)
1.86 (1.06-2.56)
62, 63*
13, 13
49.6 (10)
30.3 (6.2)
96.6 (13)
145(10)
92 (6)
88.3 (83.5-91.5)*
6.6 (4.9-9.6)
4.3 (0.34)
1.42 (1.05-1.97)
Continuous variables that are normally distributed (age, BMI, waist circumference, SBP and DBP) are presented as
mean (standard deviation). Continuous variables such as glucose, insulin and HOMA are not normally distributed
and are presented as median (interquartile range). Categorical variables were presented as number and percentages.
BMI=body mass index, SBP=systolic blood pressure, DBP=diastolic blood pressure, HOMA-IR=homeostatic model
assessment-insulin resistance. * denotes a p value < 0.05 by using Chi-square or Wilcoxon rank sum test to compare
frequency between those who did and did not develop IFG.
19
DOI: 10.1161/CIRCGENETICS.113.000421
Baseline AA
Table 2: Association between baseline and percent change in glucose following treatment with
atenolol. Beta coefficients were from multiple regression adjusted for baseline glucose, baseline
insulin, HOMA-IR, gender, age and body mass index (BMI).
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Beta Coefficient (SE)
P value
Ile
0.31 (0.08)
0.0003
Leu
0.28 (0.08)
0.001
Val
0.24 (0.08)
0.004
Tyr
0.21 (0.068)
0.01
Phe
0.18 (0.08)
0.03
03
SE= standard error, Ile=isoleucine, Leu=leucine, Val=valine, Tyr=tyrosine, Phe=p
Phe=phenylalanine
phe
heny
nyla
ny
lala
la
lani
la
nine
ni
ne
Figure Legends:
gends:
Figure 1. Odds ratio and 95% Confidence Intervals (per standard deviation) for baseline amino
acid level and development of impaired fasting glucose (IFG) from logistic regressions adjusted
for age, gender, BMI, baseline fasting glucose and insulin and HOMA-IR (model 4), n=122.
BMI = Body Mass Index, HOMA-IR=Homeostatic Model Assessment – Insulin Resistance,
Ile=isoleucine, Leu=leucine, Val=valine, Tyr=tyrosine, Phe=phenylalanine
Figure 2. From the genotyped population, incidence of impaired fasting glucose according to the
top PAH SNP, n=184. IFG=impaired fasting glucose.
20
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Downloaded from http://circgenetics.ahajournals.org/ by guest on May 6, 2017
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Is a Diabetes-Linked Amino Acid Signature associated with Beta Blocker-Induced Impaired
Fasting Glucose?
Rhonda M. Cooper-DeHoff, Wei Hou, Liming Weng, Rebecca A. Baillie, Amber L. Beitelshees, Yan
Gong, Mohamed H.A. Shahin, Stephen T. Turner, Arlene Chapman, John G. Gums, Stephen H.
Boyle, Hongjie Zhu, William R. Wikoff, Eric Boerwinkle, Oliver Fiehn, Reginald F. Frye, Rima
Kaddurah-Daouk and Julie A. Johnson
Circ Cardiovasc Genet. published online March 13, 2014;
Circulation: Cardiovascular Genetics is published by the American Heart Association, 7272 Greenville Avenue, Dallas,
TX 75231
Copyright © 2014 American Heart Association, Inc. All rights reserved.
Print ISSN: 1942-325X. Online ISSN: 1942-3268
The online version of this article, along with updated information and services, is located on the
World Wide Web at:
http://circgenetics.ahajournals.org/content/early/2014/03/13/CIRCGENETICS.113.000421
Data Supplement (unedited) at:
http://circgenetics.ahajournals.org/content/suppl/2014/03/13/CIRCGENETICS.113.000421.DC1
Permissions: Requests for permissions to reproduce figures, tables, or portions of articles originally published in
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SUPPLMEMENTAL MATERIAL
Supplement Methods
Genotyping and Quality Control
Participants were excluded if sample genotype call rates were below 95% and SNPs
were excluded if genotype call rates were below 90%. The genotype data were not
reclustered after QC filters, but the genotype and sample call rate was recalculated after
QC. Sample contamination was detected by checking gender mismatches using X
chromosome genotype data and cryptic relatedness was estimated by pairwise identityby-descent (IBD) analysis implemented using PLINK
(http://pngu.mgh.harvard.edu/purcell/plink/).1 Heterozygosity was also assessed using
PLINK, by estimating the inbreeding coefficient, F. After the QC procedures, the total
SNP call rate in the remaining individuals was 99.519%. Hardy-Weinberg equilibrium
was assessed using with chi-square tests with one degree of freedom.
To address the issue of population substructure and admixture in our racially and
ethnically diverse population, a Principal Component Analysis (PCA) was performed in
all subjects on a linkage disequilibrium (LD) pruned dataset using the EIGENSTRAT
method.2 Race/ethnic groups were confirmed with PCA clustering results. If race/ethnic
category disagreed strongly, patients were re-categorized to reflect the PCA result,
considered to better reflect genetic ancestry. The top principal components (PCs 1-2)
that provided the best separation of ancestry clusters were selected to be included as
covariates for analysis.
Supplement References
1.
2.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al.
Plink: A tool set for whole-genome association and population-based linkage
analyses. Am J Hum Genet. 2007;81:559-575.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D.
Principal components analysis corrects for stratification in genome-wide
association studies. Nat Genet. 2006;38:904-909.
Supplement Table 1
Association between baseline amino acid level (per standard deviation) and odds for
developing impaired fasting glucose following treatment with atenolol
Ile
Leu
Val
Tyr
Phe
Model 1
OR (95% CI),
p value
Model 2
OR (95% CI),
p value
Model 3
OR (95% CI),
p value
1.91 (1.23-2.96),
0.004
1.69 (1.10-2.58),
0.017
1.68 (1.09-2.59),
0.020
1.64 (1.06-2.54),
0.028
1.62 (1.03-2.54),
0.037
1.64 (1.03-2.62),
0.037
1.46 (0.93-2.28),
0.101
1.54 (0.97-2.45),
0.068
1.68 (1.05-2.71),
0.032
1.67 (1.03-2.71),
0.039
2.28 (1.29-4.01),
0.004
1.76 (1.07-2.88),
0.025
1.76 (1.06-2.91),
0.027
2.03 (1.16-3.56),
0.014
2.01 (1.14-3.54),
0.016
Model 4
OR (95% CI),
p value
2.29 (1.31-4.01),
0.0034
1.80 (1.10-2.96),
0.019
1.77 (1.07-2.92),
0.025
2.13 (1.20-3.78),
0.010
2.04 (1.16-3.59),
0.014
Model 1 = unadjusted, model 2 = model 1+ age, sex, BMI, model 3 = model 2 +
baseline glucose and baseline insulin, model 4 = model 3 + HOMA IR. Each amino acid
level is included in the models as a continuous variable. Values are odds ratios per
standard deviation (95% confidence intervals) and p values for impaired fasting glucose
from an unadjusted logistic regression model and conditional logistic regression models
adjusted for baseline fasting glucose and baseline fasting insulin or baseline fasting
glucose and baseline fasting insulin. Ile=isoleucine, Leu=leucine, Val=valine,
Tyr=tyrosine, Phe=phenylalanine
Supplement Table 2
Association between baseline amino acid level (per standard deviation) and odds for
developing impaired fasting glucose following treatment with atenolol according to
gender
Women
Men
n=68 (6 developed IFG and 62
n=54 (18 developed IFG and 36 did
did not develop IFG)
not develop IFG)
OR (95% CI), p value
OR (95% CI), p value
Ile
3.67 (0.84-16.35), 0.084
2.06 (1.09-3.89), 0.025
Leu
1.90 (0.51-7.05), 0.339
1.75 (0.97-3.16), 0.063
Val
1.36 (0.52-3.58), 0.531
1.85 (0.97-3.54), 0.062
Tyr
1.18 (0.38-3.66), 0.780
2.47 (1.15-5.32), 0.020
Phe
1.36 (0.46-4.05), 0.576
2.25 (1.07-4.72), 0.032
Values are odds ratios (OR) per standard deviation (95% confidence intervals [CI]) and
p values for impaired fasting glucose from logistic regression models adjusted for age,
body mass index, baseline fasting glucose, baseline fasting insulin and HOMA IR. Each
amino acid level is included in the models as a continuous variable. Ile=isoleucine,
Leu=leucine, Val=valine, Tyr=tyrosine, Phe=phenylalanine
SUPPLEMENT FIGURE LEGENDS
Figure 1. Flow diagram of participants included in the analyses.
Figure 2. In the 122 participants with metabolomics data and without impaired fasting
glucose at baseline, A. Change in glucose following treatment with atenolol according to
those who did and did not develop IFG, B. Baseline amino acid levels according to
those who did and did not develop IFG for Isoleucine, Leucine, Valine, Tyrosine and
Phenylalanine. The data in B are presented as ion counts (measurement unit).
Horizontal bars are median and 25th and 75% percentile. Whiskers are 5th and 95th
percentile.
Supplement Figure 1