whole grain and refined grain consumption and the risk of type 2 diabetes: a systematic review and...
TRANSCRIPT
REVIEW
Whole grain and refined grain consumption and the riskof type 2 diabetes: a systematic review and dose–responsemeta-analysis of cohort studies
Dagfinn Aune • Teresa Norat • Pal Romundstad •
Lars J. Vatten
Received: 6 February 2013 / Accepted: 16 September 2013 / Published online: 25 October 2013
� Springer Science+Business Media Dordrecht 2013
Abstract Several studies have suggested a protective
effect of intake of whole grains, but not refined grains on
type 2 diabetes risk, but the dose–response relationship
between different types of grains and type 2 diabetes has
not been established. We conducted a systematic review
and meta-analysis of prospective studies of grain intake
and type 2 diabetes. We searched the PubMed database for
studies of grain intake and risk of type 2 diabetes, up to
June 5th, 2013. Summary relative risks were calculated
using a random effects model. Sixteen cohort studies were
included in the analyses. The summary relative risk per 3
servings per day was 0.68 (95 % CI 0.58–0.81, I2 = 82 %,
n = 10) for whole grains and 0.95 (95 % CI 0.88–1.04,
I2 = 53 %, n = 6) for refined grains. A nonlinear associ-
ation was observed for whole grains, pnonlinearity \ 0.0001,
but not for refined grains, pnonlinearity = 0.10. Inverse
associations were observed for subtypes of whole grains
including whole grain bread, whole grain cereals, wheat
bran and brown rice, but these results were based on few
studies, while white rice was associated with increased
risk. Our meta-analysis suggests that a high whole grain
intake, but not refined grains, is associated with reduced
type 2 diabetes risk. However, a positive association with
intake of white rice and inverse associations between
several specific types of whole grains and type 2 diabetes
warrant further investigations. Our results support public
health recommendations to replace refined grains with
whole grains and suggest that at least two servings of
whole grains per day should be consumed to reduce type 2
diabetes risk.
Keywords Whole grains � Refined grains � Cereals �Type 2 diabetes � Meta-analysis
Introduction
The prevalence of diabetes type 2 is rapidly increasing
worldwide, with an estimated 311 million persons living
with diabetes in 2011 and this number is expected to
increase to 552 million by 2030 [1]. Diabetes patients have
increased risk cardiovascular disease, some cancers, eye
and kidney disease [2]. Total medical costs of diabetes
were estimated at US$245 billion in 2012 in the US [3].
Changes in body weight and physical activity are likely
to contribute to these increased rates [4], but diet may also
influence diabetes risk, directly and indirectly through an
effect on obesity. Whole grains contain endosperm, germ,
and bran, in contrast to refined grains which have the germ
and bran removed during the milling process. Whole grains
have been hypothesized to reduce the risk of type 2 dia-
betes based on their content of fiber, vitamins and minerals
and phytochemicals which may improve insulin sensitivity
and glucose metabolism, and by reducing overweight and
obesity [5]. In contrast, refined grains may increase risk
because of their high glycemic index or glycemic load and
reduced fiber and nutrient content. Several studies of whole
Electronic supplementary material The online version of thisarticle (doi:10.1007/s10654-013-9852-5) contains supplementarymaterial, which is available to authorized users.
D. Aune � P. Romundstad � L. J. Vatten
Department of Public Health and General Practice, Faculty of
Medicine, Norwegian University of Science and Technology,
Trondheim, Norway
D. Aune (&) � T. Norat
Department of Epidemiology and Biostatistics, School of Public
Health, Imperial College London, St. Mary’s Campus, Norfolk
Place, Paddington, London W2 1PG, UK
e-mail: [email protected]
123
Eur J Epidemiol (2013) 28:845–858
DOI 10.1007/s10654-013-9852-5
grain intake in relation to type 2 diabetes risk have reported
inverse associations with higher intake [5–10], but some
found no significant association [11, 12]. Inverse associa-
tions have been reported with intake of specific whole grain
products as well, including brown bread [13–15], whole
grain breakfast cereals [13, 16] and brown rice [7],
although the results are not entirely consistent [17, 18]. In
contrast, most studies of refined grain intake have shown
no association overall [5, 12, 13, 19], although two sug-
gested inverse associations [8, 10], while high intake of
white bread [17] or white rice [7, 20, 21] has been asso-
ciated with increased risk, although not consistently
[17, 22]. Although two previous meta-analyses have been
conducted on whole grains and type 2 diabetes [23, 24], the
optimal intake of whole grains for prevention of type 2
diabetes is not established because the shape of the dose–
response relationship has not been investigated. In addi-
tion, there is increasing evidence suggesting that whole
grains reduces the risk of overweight and obesity and
weight gain [24–30], thus it is possible that body mass
index may be an intermediate factor more than a con-
founder, but it is not known how much of the association
that may be explained by reduced body fatness. We con-
ducted a systematic review and meta-analysis of the evi-
dence from prospective studies with the aim of clarifying
(1) the association between the intake of grains and dif-
ferent types of grains and type 2 diabetes risk, (2) the dose–
response relationship between intake of grains and specific
types of grains and type 2 diabetes risk, and (3) how much
of the association that may be explained by reduced body
fatness.
Methods
Search strategy
We conducted a comprehensive search in the PubMed
database up to June 5th, 2013 for studies of various food
groups and type 2 diabetes risk. The search terms relevant
to this analysis included ‘‘cereal OR breakfast cereal OR
grain OR whole grain OR rice OR bread’’ AND ‘‘diabe-
tes’’. The full search is provided in the Supplementary
Appendix. We also searched the reference lists of all the
studies that were included in the analysis and the reference
lists of published meta-analyses [23, 24].
Study selection
To be included, the study had to have a prospective design
and to investigate the association between the intake of
grains and type 2 diabetes risk. Estimates of the relative
risk (hazard ratio, risk ratio) had to be available with the
95 % confidence intervals in the publication and for the
dose–response analysis, a quantitative measure of intake
and the total number of cases and person-years had to be
available in the publication. We identified 28 publications
that reported on intake of grains in relation to diabetes
[5–23, 31–39]. Three publications were excluded because
no risk estimates were provided [35, 36, 39], two publi-
cations were excluded because they were cross-sectional
studies [37, 38] and four because they were duplicates
[31–34]. One publication [23] was included only in the
sensitivity analysis with and without adjustment for BMI
because the most recent publication [7] from these two
studies did not provide results both adjusted and unadjusted
for BMI. In addition several publications from the same
studies reported on different grain items and all were
included in the analyses, but each study was only included
once in the analysis of the relevant grain variable.
Data extraction
We extracted the following data from each study: The first
author’s last name, publication year, country where the
study was conducted, the study name, follow-up period,
sample size, gender, age, number of cases, dietary assess-
ment method (type, number of food items and whether it
had been validated), exposure, quantity of intake, RRs and
95 % CIs for the highest versus the lowest grain intake and
variables adjusted for in the analysis.
Statistical methods
To take into account within and between studies hetero-
geneity we used random effects models to estimate sum-
mary RRs and 95 % CIs for the highest versus the lowest
level of grain intake and for the dose–response analysis
[40]. The average of the natural logarithm of the RRs was
estimated and the RR from each study was weighted by the
inverse of its variance. A two-tailed p \ 0.05 was con-
sidered statistically significant.
We used the method described by Greenland and
Longnecker [41] for the dose–response analysis and com-
puted study-specific slopes (linear trends) and 95 % CIs
from the natural logs of the RRs and CIs across categories
of grain intake. The method requires that the distribution of
cases and person-years or non-cases and the RRs with the
variance estimates for at least three quantitative exposure
categories are known. We estimated the distribution of
cases or person-years in studies that did not report these,
but reported the total number of cases/person-years [42].
The median or mean level of grain intake in each category
of intake was assigned to the corresponding relative risk for
each study. For studies that reported grain intake by ranges
of intake we estimated the midpoint for each category by
846 D. Aune et al.
123
calculating the average of the lower and upper bound.
When the highest or lowest category was open-ended we
assumed the open-ended interval length to be the same as
the adjacent interval. In studies that reported the intakes in
grams per day we used 30 g as a serving size for recal-
culation of the intakes to a common scale (servings per
day) [43]. We used 158 g as a serving size for intake of
white rice and brown rice consistent with a recent study
[44]. The dose–response results in the forest plots are
presented for a 3 serving per day increment [43]. We
examined a potential nonlinear dose–response relationship
between grain intake and type 2 diabetes by using frac-
tional polynomial models [45]. We determined the best
fitting second order fractional polynomial regression
model, defined as the one with the lowest deviance. A
likelihood ratio test was used to assess the difference
between the nonlinear and linear models to test for non-
linearity [46]. The intake in the reference category was
subtracted from the intake in each category for the linear
dose–response analysis, but not for the nonlinear dose–
response analysis.
Heterogeneity between studies was assessed by the Q
test and I2 [47]. I2 is the amount of total variation that is
explained by between study variation. I2 values of
approximately 25, 50 and 75 % are considered to indicate
low, moderate and high heterogeneity, respectively.
Publication bias was assessed with Egger’s test [48] and
Begg’s test [49] with the results considered to indicate
publication bias when p \ 0.10. We conducted sensitivity
analyses excluding one study at a time to ensure that the
results were not simply due to one large study or a study
with an extreme result, when there were at least 5 studies in
the analysis. The statistical analyses were conducted using
Stata, version 10.1 software (StataCorp, College Station,
TX, USA).
Results
We identified sixteen cohort studies (nineteen publications)
that were included in the analyses of grain intake and type
2 diabetes risk [5–23] (Table 1; Fig. 1). Seven studies were
from the US, six were from Europe, two from Asia and one
was from Australia (Table 1).
Whole grains
Ten cohort studies (8 publications) [5–12] were included in
the analysis of total whole grain intake and type 2 diabetes
risk and included 19,829 cases among 385,868 participants.
One of the studies only reported a continuous result and
was not included in the high versus low analysis [11]. The
summary RR for high versus low intake was 0.74 (95 % CI
0.71–0.78, I2 = 0 %, pheterogeneity = 0.43) (Supplementary
Figure 1). The summary RR per 3 servings per day was
0.68 (95 % CI 0.58–0.81, I2 = 82 %, pheterogene-
ity \ 0.0001) (Fig. 2a). The summary RR ranged from 0.65
(95 % CI 0.56–0.77) when excluding the EPIC-Potsdam
study to 0.72 (95 % CI 0.63–0.83) when excluding the
Nurses’ Health Study 1. There was no evidence of small
study bias with Egger’s test, p = 0.49 or with Begg’s test,
p = 0.37. There was evidence of a nonlinear association
between whole grain intake and type 2 diabetes risk,
pnonlinearity \ 0.0001, with a steeper reduction in risk when
increasing intake from low levels and most of the benefit
was observed up to an intake of two servings per day
(Fig. 2b, Supplementary Table 1).
Refined grains
Six studies [5, 8, 12, 13, 19] reported on refined grain
intake and type 2 diabetes and included 9,545 cases among
258,078 participants. The summary RR for high versus low
intake of refined grains was 0.94 (95 % CI 0.82–1.09,
I2 = 64 %, pheterogeneity = 0.02) (Supplementary Figure 2).
The summary RR per 3 servings per day was 0.95 (95 % CI
0.88–1.04, I2 = 53 %, pheterogeneity = 0.06) (Fig. 3a). The
summary RR ranged from 0.93 (95 % CI 0.86–1.00) when
the Nurses’ Health Study 1 was excluded to 0.98 (95 % CI
0.90–1.08) when the Women’s Health Initiative was
excluded. There was no evidence of small study bias with
Fig. 1 Flow-chart of study selection
Whole grain and refined grain consumption 847
123
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calc
ium
,m
agnes
ium
,
fruit
,veg
etab
les,
fish
,B
MI
Bre
ad47.1
ver
sus
0
g/d
ay
0.8
5(0
.64–1.1
4)
Noodle
s225
ver
sus
41.3
g/d
ay
0.8
9(0
.68–1.1
7)
Ric
e,w
560
ver
sus
165
g/d
ay
1.6
5(1
.06–2.5
7)
Bre
ad60
ver
sus
4g/d
ay0.9
9(0
.73–1.3
4)
Noodle
s176.9
ver
sus
29.0
g/d
ay
1.1
5(0
.83–1.5
8)
Fis
her
etal
2009
[11],
Ger
man
y
Euro
pea
n
Pro
spec
tive
Inves
tigat
ion
into
Can
cer
and
Nutr
itio
n–
Pots
dam
study
1994/
1998–2005,
7.1
yea
rs
foll
ow
-up
2,3
18
m&
w,
age
35–65
yea
rs:
724
case
s
Val
idat
edF
FQ
,
148
food
item
s
Whole
gra
ins,
rs7903146
CC
gen
oty
pe
Per
50
g/d
ay0.8
6(0
.75–0.9
9)
Age,
sex,
BM
I,w
aist
circ
um
fere
nce
,
educa
tion,
occ
upat
ional
acti
vit
y,
sport
s,
smokin
g,
alco
hol,
red
mea
t,pro
cess
edm
eat,
low
-fat
dai
ry,
butt
er,
mar
gar
ine,
veg
etab
le
fat,
tota
len
ergy
Whole
gra
ins,
rs7903146
CT
?
TT
gen
oty
pe
Per
50
g/d
ay1.0
8(0
.96–1.2
3)
Whole grain and refined grain consumption 849
123
Ta
ble
1co
nti
nu
ed
Auth
or,
publi
cati
on
yea
r[R
ef.
no.]
,co
untr
y
Stu
dy
nam
eF
oll
ow
-up
per
iod
Stu
dy
size
,
gen
der
,ag
e,
num
ber
of
case
s
Die
tary
asse
ssm
ent
Exposu
reQ
uan
tity
RR
(95%
CI)
Adju
stm
ent
for
confo
under
s
de
Munte
r
etal
2007
[23],
US
A
Nurs
es’
Hea
lth
Stu
dy
1
1984–2002,
18
yea
rsfo
llow
-
up
73,3
27
w,
age
37–65
yea
rs:
4,7
47
case
s
Val
idat
edF
FQ
,
116
food
item
s
Whole
gra
ins
31.2
ver
sus
3.7
g/d
ay
0.7
5(0
.68–0.8
3)
Age,
smokin
gst
atus,
physi
cal
acti
vit
y,
alco
hol,
HR
T,
OC
use
,F
H–T
2D
M,
coff
ee,
sugar
-sw
eete
ned
soft
dri
nks,
fruit
punch
,
tota
len
ergy,
pro
cess
edm
eat,
PU
FA
/SF
A
rati
o,
BM
I
Bra
n9.6
ver
sus
0.6
g/d
ay
0.7
2(0
.65–0.8
0)
Ger
m1.5
ver
sus
0.2
g/d
ay
0.8
3(0
.75–0.9
2)
de
Munte
r
etal
2007
[23],
US
A
Nurs
es’
Hea
lth
Stu
dy
2
1991–2003,
12
yea
rsfo
llow
-
up
88,4
10
wag
e
26–46
yea
rs:
2,7
39
case
s
Val
idat
edF
FQ
,
131
food
item
s
Whole
gra
ins
39.9
ver
sus
6.2
g/d
ay
0.8
6(0
.72–1.0
2)
Age,
smokin
gst
atus,
physi
cal
acti
vit
y,
alco
hol,
HR
T,
OC
use
,F
H–T
2D
M,
coff
ee,
sugar
-sw
eete
ned
soft
dri
nks,
fruit
punch
,
tota
len
ergy,
pro
cess
edm
eat,
PU
FA
/SF
A
rati
o,
BM
I
Bra
n12.0
ver
sus
1.1
g/d
ay
0.8
4(0
.71–1.0
0)
Ger
m1.9
ver
sus
0.3
g/d
ay
1.0
0(0
.85–1.1
7)
Vil
legas
etal
2007
[20],
Chin
a
Shan
ghai
Wom
en’s
Hea
lth
Stu
dy
1996/
2000–2004,
5
yea
rsfo
llow
-
up
64,1
17
w,
age
40–70
yea
rs:
1,6
08
case
s
Val
idat
edF
FQ
,
77
food
item
s
Ric
e300
ver
sus\
200
g/d
ay
1.7
8(1
.48–2.1
5)
Age,
ener
gy
inta
ke,
BM
I,W
HR
,sm
okin
g
stat
us,
alco
hol,
physi
cal
acti
vit
y,
inco
me
level
,ed
uca
tion
level
,occ
upat
ion,
hyper
tensi
on
Sta
ple
food
item
s
(ric
e,noodle
s,
stea
med
bre
ad,
bre
ad)
Quin
tile
5ver
sus
11.3
7(1
.11–1.6
9)
Sch
ulz
eet
al
2007
[14],
Ger
man
y
Euro
pea
n
Pro
spec
tive
Inves
tigat
ion
into
Can
cer
and
Nutr
itio
n–
Pots
dam
study
1994/
1998–2005,
7
yea
rsfo
llow
-
up
9,7
02
m
&15,3
65
w,
age
35–65
yea
rs:
844
case
s
Val
idat
edF
FQ
,
146
food
item
s
Whole
gra
inbre
ad80.2
ver
sus
4.4
g/d
ay
0.7
8(0
.62–0.9
7)
Age,
sex,
BM
I,sp
ort
sac
tivit
ies,
educa
tion,
cycl
ing,
occ
upat
ional
acti
vit
y,
smokin
g,
alco
hol,
tota
len
ergy
inta
ke,
wai
st
circ
um
fere
nce
,P
UF
A:S
FA
rati
o,
MU
FA
:SF
Ara
tio,
carb
ohydra
te,
mag
nes
ium
Sim
mons
etal
2007
[15],
UK
Euro
pea
n
Pro
spec
tive
Inves
tigat
ion
into
Can
cer
and
Nutr
itio
n–
Norf
olk
study
1993/
1998–2000,
4.6
yea
rs
foll
ow
-up
25,6
33
m&
w,
age
40–79
yea
rs:
417
case
s
Val
idat
edF
FQ
,W
hole
mea
l/bro
wn
bre
ad
C1
ver
sus\
1
port
ion/d
ay
0.7
2(0
.53–0.9
7)
Unad
just
ed
Koch
aret
al
2007
[16],
US
A
Physi
cian
s’
Hea
lth
Stu
dy
1
1981/
1983–2002,
19.1
yea
rs
foll
ow
-up
21,1
52
m,
mea
n
age
53
yea
rs:
1,9
58
case
s
FF
Q,
NA
Bre
akfa
stce
real
sC
7ver
sus
0se
rv/
wee
k
0.6
9(0
.60–0.7
9)
Age,
smokin
g,
vit
amin
inta
ke,
alco
hol,
veg
etab
les,
physi
cal
acti
vit
yB
MI
Whole
gra
ins
cere
als
C7
ver
sus
0se
rv/
wee
k
0.6
0(0
.50–0.7
1)
Refi
ned
cere
als
C7
ver
sus
0se
rv/
wee
k
0.9
5(0
.73–1.3
0)
Van
Dam
etal
2006
[6],
US
A
Bla
ckW
om
en’s
Hea
lth
Stu
dy
1995–2003,
8
yea
rsfo
llow
-
up
41186
w,
age
21–69
yea
rs:
1,9
64
case
s
Val
idat
edF
FQ
,
68
food
item
s
Whole
gra
ins
1.2
9ver
sus
0.0
3
serv
/day
0.6
9(0
.60–0.7
9)
Age,
tota
len
ergy,
BM
I,sm
okin
gst
atus,
stre
nous
physi
cal
acti
vit
y,
alco
hol,
par
enta
l
his
tory
of
DM
,ed
uca
tion,
coff
ee,
sugar
-
swee
tened
soft
dri
nk,
pro
cess
edm
eat,
red
mea
t,lo
w-f
atdai
ry
850 D. Aune et al.
123
Ta
ble
1co
nti
nu
ed
Auth
or,
publi
cati
on
yea
r[R
ef.
no.]
,co
untr
y
Stu
dy
nam
eF
oll
ow
-up
per
iod
Stu
dy
size
,
gen
der
,ag
e,
num
ber
of
case
s
Die
tary
asse
ssm
ent
Exposu
reQ
uan
tity
RR
(95%
CI)
Adju
stm
ent
for
confo
under
s
Hodge
etal
2004
[17],
Aust
rali
a
Mel
bourn
e
Coll
abora
tive
Cohort
Stu
dy
1990/1
994–N
A,
4yea
rs
foll
ow
-up
31,6
41
m&
w,
age
40–69
yea
rs:
365
case
s
FF
Q,
121
food
item
s
Cer
eal
C41
ver
sus\
20
tim
es/w
eek
1.0
5(0
.73–1.5
2)
Age,
sex,
countr
yof
bir
th,
physi
cal
acti
vit
y,
FH
–D
M,
alco
hol
inta
ke,
educa
tion,
wei
ght
chan
ge
inth
ela
st5
yea
rs,
ener
gy
inta
ke,
BM
I,W
HR
Bre
akfa
stce
real
C7.0
ver
sus\
0.0
1
tim
es/w
eek
1.0
1(0
.75–1.3
5)
Ric
eC
2.5
ver
sus\
1.0
tim
es/w
eek
0.9
3(0
.68–1.2
7)
Bre
adC
18.0
ver
sus\
6.0
tim
es/w
eek
1.1
2(0
.79–1.5
8)
Whit
ebre
adC
7.0
ver
sus\
0.5
tim
es/w
eek
1.1
3(0
.86–1.5
0)
Whole
-mea
lbre
adC
17.5
ver
sus\
0.5
tim
es/w
eek
0.8
6(0
.63–1.1
8)
Sav
ory
cere
al
pro
duct
s
C1.5
ver
sus\
0.5
tim
es/w
eek
1.2
2(0
.89–1.6
9)
Pas
taC
3.0
ver
sus\
0.5
tim
es/w
eek
0.8
6(0
.60–1.2
3)
Oth
erce
real
C11.0
ver
sus\
2.0
tim
es/w
eek
0.7
9(0
.56–1.1
0)
Monto
nen
etal
2003
[8],
Fin
land
Fin
nis
hM
obil
e
Cli
nic
Hea
lth
Exam
inat
ion
Surv
ey
1966/
1972–1995,
23
yea
rs
foll
ow
-up
2,2
86
m&
2,0
30
w,
age
40–69
yea
rs:
52/1
02
case
s
Die
tary
his
tory
inte
rvie
w,
[100
food
item
s
Tota
lgra
in340–1535
ver
sus
10–181
g/d
ay
0.3
8(0
.19–0.7
7)
Age,
sex,
geo
gra
phic
area
,sm
okin
g,
BM
I,
inta
ke
of
ener
gy,
fruit
,ber
ries
and
veg
etab
les
Whole
gra
in238–1321
ver
sus
0–109
g/d
ay
0.6
5(0
.36–1.1
8)
Rye
182–1026
ver
sus
0–58
g/d
ay
0.6
5(0
.36–1.1
8)
Oth
erw
hole
gra
in76–632
ver
sus
0–5
g/d
ay
1.1
4(0
.69–1.8
7)
Refi
ned
gra
in111–567
ver
sus
0–45
g/d
ay
0.6
2(0
.36–1.0
6)
Refi
ned
gra
infr
om
whea
t
91–389
ver
sus
0–33
g/d
ay
0.6
9(0
.41–1.1
7)
Fung
etal
2002
[19],
US
A
Hea
lth
Pro
fess
ional
s
Foll
ow
-up
Stu
dy
1986–1998,
12
yea
rsfo
llow
-
up
42,8
98
m,
age
40–75
yea
rs:
1,1
97
case
s
Val
idat
edF
FQ
,
131
food
item
s
Whole
gra
ins
3.2
ver
sus
0.4
serv
/day
0.7
0(0
.57–0.8
5)
Age,
per
iod,
physi
cal
acti
vit
y,
ener
gy
inta
ke,
mis
sing
FF
Q,
smokin
g,
FH
–D
M,
alco
hol
inta
ke,
fruit
inta
ke,
veg
etab
lein
take,
BM
IR
efined
gra
ins
4.1
ver
sus
0.8
1.0
8(0
.87–1.3
3)
Whole grain and refined grain consumption 851
123
Ta
ble
1co
nti
nu
ed
Auth
or,
publi
cati
on
yea
r[R
ef.
no.]
,co
untr
y
Stu
dy
nam
eF
oll
ow
-up
per
iod
Stu
dy
size
,
gen
der
,ag
e,
num
ber
of
case
s
Die
tary
asse
ssm
ent
Exposu
reQ
uan
tity
RR
(95%
CI)
Adju
stm
ent
for
confo
under
s
Liu
etal
2000
[13],
US
A
Nurs
es’
Hea
lth
Stu
dy
1
1984–1994,
10
yea
rsfo
llow
-
up
75,5
21
w,
age
38–63
yea
rs:
1,8
79
case
s
FF
Q,
126
food
item
s
Tota
lgra
inQ
uin
tile
5ver
sus
10.7
5(0
.63–0.8
9)
Age,
BM
I,physi
cal
acti
vit
y,
cigar
ette
smokin
g,
alco
hol
inta
ke,
FH
–D
M2
ina
1st
deg
ree
rela
tive,
use
of
mult
ivit
amin
sor
vit
amin
Esu
pple
men
ts,
tota
len
ergy
inta
ke
Whole
gra
in2.7
0ver
sus
0.1
3
serv
/day
ay
0.7
3(0
.63–0.8
5)
Refi
ned
gra
inQ
uin
tile
5ver
sus
11.1
1(0
.94–1.3
0)
Refi
ned
/whole
gra
inra
tio
Quin
tile
5ver
sus
11.2
6(1
.08–1.4
6)
Dar
kbre
adC
1/d
ayver
sus
alm
ost
nev
er
0.7
7(0
.66–0.9
0)
Whole
-gra
in
bre
akfa
stce
real
C1/d
ayver
sus
alm
ost
nev
er
0.6
6(0
.55–0.8
0)
Popco
rnC
1/d
ayver
sus
alm
ost
nev
er
0.8
8(0
.59–1.3
1)
Cooked
oat
mea
lC
1/d
ayver
sus
alm
ost
nev
er
0.7
3(0
.35–1.5
4)
Bro
wn
rice
5–6/w
eek
ver
sus
alm
ost
nev
er
0.4
7(0
.15–1.4
5)
Whea
tger
m5–6/w
eek
ver
sus
alm
ost
nev
er
0.8
5(0
.52–1.3
7)
Bra
n5–6/w
eek
ver
sus
alm
ost
nev
er
0.5
4(0
.41–0.7
2)
Oth
ergra
ins
\1/w
eek
ver
sus
alm
ost
nev
er
0.7
7(0
.63–0.9
4)
Mey
eret
al
2000
[5],
US
A
Iow
aW
om
en’s
Hea
lth
Stu
dy
1986–1992,
6
yea
rsfo
llow
-
up
35,9
88
w,
age
55–69
yea
rs:
1,1
41
case
s
Val
idat
edF
FQ
,
127
food
item
s
Tota
lgra
ins
41.5
ver
sus
9.5
serv
/wee
k
0.6
8(0
.54–0.8
7)
Age,
tota
len
ergy
inta
ke,
BM
I,W
HR
,
educa
tion,
pac
k-y
ears
of
smokin
g,
alco
hol
inta
ke,
physi
cal
acti
vit
yW
hole
gra
ins
20.5
ver
sus
1.0
serv
/wee
k
0.7
9(0
.65–0.9
6)
Refi
ned
gra
ins
29.5
ver
sus
3.5
serv
/wee
k
0.8
7(0
.70–1.0
8)
adj.
adju
stm
ent,
BM
Ibody
mas
sin
dex
,D
Mdia
bet
esm
elli
tus,
FF
Qfo
od
freq
uen
cyques
tionnai
re,
FH
fam
ily
his
tory
,m
men
,N
Anot
avai
lable
,W
HR
wai
st-t
o-h
ipra
tio,
ww
om
en
852 D. Aune et al.
123
Egger’s test, p = 1.00 or with Begg’s test, p = 1.00. There
was no evidence of a nonlinear association between refined
grain intake and type 2 diabetes risk, pnonlinearity = 0.10
(Fig. 3b, Supplementary Table 2).
Total grains and subtypes of grains
Fewer studies had reported on total grains and subtypes of
grains. The summary RR for high versus low total grain
intake was 0.74 (95 % CI 0.58–0.93) [5, 8, 13, 17] with
moderate heterogeneity, I2 = 60 %, pheterogeneity = 0.06
(Supplementary Figure 3). The summary RR per 3 servings
per day was 0.83 (95 % CI 0.75–0.91, I2 = 36 %, phetero-
geneity = 0.19) (Supplementary Figure 4a). There was evi-
dence of a nonlinear association between total grain intake
and type 2 diabetes, pnonlinearity = 0.001, and the reduction
in risk was steeper at the lower and higher end of the
intake, with a slight flattening at intermediate intakes
(Supplementary Figure 4b, Supplementary Table 3). The
summary RR for high versus low intake was 0.82 (95 % CI
0.72–0.94, I2 = 50 %, pheterogeneity = 0.11, n = 4) for
whole grain bread [5, 13, 14, 17], 0.66 (95 % CI 0.57–0.77,
I2 = 35 %, pheterogeneity = 0.21, n = 3) for whole grain
cereals [5, 13, 16], 0.76 (95 % CI 0.69–0.84, I2 = 30 %,
pheterogeneity = 0.24, n = 3) for wheat bran [7], 0.97 (95 %
CI 0.86–1.10, I2 = 59 %, pheterogeneity = 0.09, n = 3) for
wheat germ [7], 0.89 (95 % CI: 0.81–0.97, I2 = 0 %,
pheterogeneity = 0.40, n = 3) for brown rice [7], 1.17 (95 %
CI: 0.93–1.47, I2 = 78 %, pheterogeneity \ 0.0001, n = 7)
for white rice [7, 17, 20–22], and 0.82 (95 % CI 0.56–1.18,
n = 2) for total cereals [16, 17] (Table 2). Nonlinear
associations were observed for whole grain bread, pnonlin-
earity = 0.01, whole grain cereals, pnonlinearity \ 0.0001,
wheat bran, pnonlinearity = 0.007, and brown rice, pnonlinear-
ity = 0.02, and consistent with the analysis of overall
whole grain intake, the reduction in risk was steepest when
increasing the intake from low levels (Supplementary
Figure 5a-d). We were not able to fit a nonlinear curve for
A
B
0.4
0.6
0.8
1.0
1.2
RR
0 1 2 3 4 5
Whole grains (serving/day)
Best fitting fractional polynomial95% confidence interval
Relative Risk
.25 .5 .75 1 1.5
Study Relative Risk (95% CI)
Ericson, 2013 0.77 ( 0.63, 0.94)
Parker, 2013 0.83 ( 0.69, 0.99)
Wirström, 2013 0.68 ( 0.41, 1.12)
Sun, 2010, HPFS 0.66 ( 0.55, 0.79)
Sun, 2010, NHS1 0.46 ( 0.39, 0.56)
Sun, 2010, NHS2 0.69 ( 0.54, 0.88)
Fisher, 2009 0.96 ( 0.81, 1.13)
van Dam, 2006 0.41 ( 0.30, 0.56)
Montonen, 2003 0.75 ( 0.48, 1.17)
Meyer, 2000 0.77 ( 0.63, 0.93)
Overall 0.68 ( 0.58, 0.81)
Fig. 2 Whole grains and type 2 diabetes. Summary estimates were
calculated using a random-effects model
B
A
0.4
0.6
0.8
1.0
1.2
RR
0 1 2 3 4 5 6 7
Refined grains (servings/day)
Best fitting fractional polynomial95% confidence interval
Relative Risk
.25 .5 .75 1 1.5
Study
Relative Risk
(95% CI)
Ericson, 2013 0.98 ( 0.85, 1.13)
Parker, 2013 0.89 ( 0.82, 0.96)
Montonen, 2003 0.66 ( 0.43, 1.00)
Fung, 2002 1.03 ( 0.86, 1.22)
Liu, 2000 1.07 ( 0.95, 1.20)
Meyer, 2000 0.93 ( 0.79, 1.08)
Overall 0.95 ( 0.88, 1.04)
Fig. 3 Refined grains and type 2 diabetes. Summary estimates were
calculated using a random-effects model
Whole grain and refined grain consumption 853
123
white rice, possibly due to large differences in the intake
between studies.
Subgroup and sensitivity analyses
There was no significant heterogeneity between subgroups
in analyses of whole grains and type 2 diabetes stratified by
gender, duration of follow-up, geographic area, number of
cases and adjustment for confounding factors and inverse
associations were apparent in most subgroups, although
they were not always statistically significant (Table 3).
Although the test for heterogeneity was not significant,
pheterogeneity = 0.15, the association appeared to be slightly
stronger in the American studies than among the European
studies.
Because BMI may be an intermediate variable we also
restricted the analysis to the five studies (four publications)
that had presented risk estimates both adjusted and not
adjusted for BMI [10, 12, 19, 23]. The summary RR per 3
servings per day increase in whole grain intake was 0.69
(0.60–0.80, I2 = 58 %, pheterogeneity = 0.05) with BMI
adjustment (and this was similar to the result from the main
analysis) and 0.53 (95 % CI 0.41–0.69, I2 = 88 %, pheter-
ogeneity \ 0.001) without BMI adjustment (Fig. 4a) and
there were similar differences in the results by BMI
adjustment in the nonlinear analysis (Fig. 4b).
Discussion
Our meta-analysis supports the hypothesis that a high
whole grain and total grain intake protects against type 2
diabetes with a 32 and 17 % reduction in the relative risk
per 3 servings per day, but we found no association
between overall refined grain intake and type 2 diabetes
risk. There was evidence of a nonlinear inverse association
between whole grains and total grains and type 2 diabetes
with most of the reduction observed when increasing the
intake up to 2 servings per day for whole grain intake,
while for total grains there was also a steep reduction in
relative risk when increasing intake from low levels,
followed by a slight flattening of the curve with interme-
diate intakes and a steeper reduction at higher intakes.
However, the inverse association with high total grain
intake should be interpreted with caution as it was based on
relatively few studies, and is likely to be driven by higher
whole grain intake since there was no association with
overall refined grain intake. A positive association was
observed with intake of white rice. In addition, we found
that several subtypes of whole grains including whole grain
cereals, brown bread and brown rice were associated with
reduced risk, but these analyses were based on few studies
and need further confirmation.
Our meta-analysis has limitations which affect the
interpretation of the results. The main limitation is the low
number of cohort studies available apart from the total
whole grain analysis. Further studies are therefore needed
before firm conclusions can be made for the remaining
exposures. Although it is possible that the inverse associ-
ation between whole grain intake and type 2 diabetes could
be due to unmeasured or residual confounding by other
lifestyle factors we found that the association persisted in
several subgroup analyses where such factors had been
adjusted for. There was high heterogeneity in the dose–
response analysis of whole grains and type 2 diabetes,
although not in the comparison of the highest versus the
lowest intake. There was less heterogeneity in studies
conducted among men than among women, but there was
no significant heterogeneity between these subgroups, or
when stratified by number of cases, duration of follow-up
or adjustment for confounding factors. A slightly stronger
association was observed in the American studies than
among the European studies, but there was also no sig-
nificant heterogeneity by geographic location, suggesting
that this finding could be due to chance. Because of the low
number of studies our ability to test for publication bias
may have been limited, however, there was no indication of
asymmetry in the funnel plots. In addition, because of the
low number of studies with very high intakes of whole
grains and total grains, the results in the high ranges ([3
servings for whole grains, and[7 servings for total grains)
were based on relatively few datapoints and should be
Table 2 Subtypes of grains and type 2 diabetes risk
Type of grain High versus low comparison Dose-response analysis
N RR (95 % CI) I2 Pheterogeneity Dose N RR (95 % CI) I2 Pheterogeneity
Whole grain bread 4 0.81 (0.74–0.89) 0 0.60 Per 3 serv/day 3 0.74 (0.56–0.98) 44.1 0.17
Whole grain breakfast cereal 3 0.72 (0.55–0.93) 77.8 0.01 Per 1 serv/day 3 0.73 (0.59–0.91) 80.3 0.006
Brown rice 3 0.89 (0.81–0.97) 50 0.11 Per 0.5 serv/day 3 0.87 (0.78–0.97) 26.1 0.26
Wheat bran 3 0.76 (0.69–0.84) 30 0.24 Per 10 g/day 3 0.79 (0.72–0.87) 49.1 0.14
Wheat germ 3 0.97 (0.86–1.10) 59 0.09 Per 2 g/day 3 0.98 (0.87–1.11) 50.1 0.14
White rice 7 1.17 (0.93–1.47) 78.1 \0.0001 Per 1 serv/day 6 1.23 (1.15–1.31) 21.4 0.27
854 D. Aune et al.
123
interpreted with caution. Measurement errors in the expo-
sure assessment are known to bias effect estimates, but
because we only included prospective cohort studies such
measurement errors are most likely to have resulted in
attenuation of the association between whole grain intake
and type 2 diabetes risk. None of the studies published to
date have corrected their results for measurement error.
The definition of whole grains differed in some of the
studies (Supplementary Table 4) with several American
studies considering breakfast cereals to be made of whole
grains if the product contained C25 % whole grain or bran
by weight [5, 7, 13, 19, 23], while one Swedish study used
C50 % as a cut-off point [9]. Several other studies did not
state how whole grains were defined, thus it is difficult to
assess whether the differing definitions might have influ-
enced the results. Further studies using biomarkers of
Table 3 Subgroup analyses of whole intake and type 2 diabetes, dose–response
Whole grains, 3 servings per day
n RR (95 % CI) I2 (%) Pha Ph
b
All studies 10 0.68 (0.58–0.81) 81.9 \0.0001
Duration of follow-up
\10 years follow-up 5 0.72 (0.56–0.93) 82.3 \0.0001 0.26
C10 years follow-up 5 0.65 (0.53–0.79) 75.3 0.003
Sex
Men 3 0.70 (0.61–0.81) 0 0.53 0.43/0.723
Women 7 0.64 (0.51–0.80) 82.0 \0.0001
Men and women 2 0.93 (0.79–1.09) 1.5 0.31
Geographic location
Europe 4 0.84 (0.72–0.97) 23.8 0.27 0.15
America 6 0.62 (0.51–0.77) 84.0 \0.0001
Number of cases
Cases \1,000 3 0.88 (0.73–1.06) 13.6 0.31 0.32
Cases 1,000–\2,000 3 0.64 (0.46–0.89) 84.5 0.002
Cases C2.000 4 0.65 (0.50–0.83) 85.1 \0.0001
Adjustment for confounders
Body mass index Yes 10 0.68 (0.58–0.81) 81.9 \0.0001 NC
No 0
Physical activity Yes 9 0.68 (0.57–0.81) 83.9 \0.0001 0.78
No 1 0.75 (0.48–1.17)
Smoking Yes 10 0.68 (0.58–0.81) 81.9 \0.0001 NC
No 0
Alcohol Yes 8 0.68 (0.56–0.82) 85.9 \0.0001 0.84
No 2 0.72 (0.52–1.01) 0 0.78
Coffee Yes 1 0.41 (0.30–0.56) 0.07
No 9 0.72 (0.61–0.84) 78.7 \0.0001
Red and/or processed meat Yes 5 0.61 (0.46–0.83) 90.5 \0.0001 0.23
No 5 0.78 (0.71–0.87) 0 0.94
Dairy products Yes 3 0.70 (0.47–1.05) 90.9 \0.0001 0.94
No 7 0.67 (0.57–0.78) 69.0 0.004
Fruits and/or vegetables Yes 5 0.66 (0.53–0.82) 80.6 \0.0001 0.74
No 5 0.71 (0.55–0.91) 82.0 \0.0001
Energy intake Yes 9 0.68 (0.57–0.82) 83.9 \0.0001 0.99
No 1 0.68 (0.41–1.12)
a P for heterogeneity within each subgroup2 P for heterogeneity between subgroups with meta-regression analysis3 P for heterogeneity between men and women (excluding studies with both genders)
NC not calculable
Whole grain and refined grain consumption 855
123
whole grain intake could be useful to assess the impact of
measurement errors in the dietary assessment [50] and any
further studies on dietary whole grain intake should report
the definition of whole grain foods used in the analysis for
comparison between studies.
A protective effect of whole grain consumption against
type 2 diabetes is biologically plausible and several mech-
anisms may operate to reduce the risk. Several studies have
reported inverse associations between whole grain intake
and prospective weight gain [25–30] and we found that the
size of the association between whole grains and type 2
diabetes was about 1/3 stronger when the analyses were not
adjusted for BMI compared with adjustment for BMI
(RR = 0.53 vs. 0.69, respectively) [10, 12, 19, 23]. Thus,
reduced body fatness may explain part, but not all of the
protective effect of whole grains against type 2 diabetes risk.
The results of the nonlinear analysis stratified by adjustment
for BMI suggest that reduced body fatness may explain a
larger part of the association at higher levels compared with
lower levels of whole grain intake as the association
appeared to have a more linear shape in analyses without
adjustment for BMI than when adjusted for BMI. Whole
grains are an important source of cereal fiber, phytochemi-
cals, vitamins and minerals. High whole grain intake has
been associated with greater insulin sensitivity and lower
fasting insulin concentration and this was observed for dark
breads, and in particular high-fiber cereals [51]. Intake of
cereal fiber, but not fruit or vegetable fiber, has been asso-
ciated with reduced type 2 diabetes risk in a meta-analysis of
prospective studies [14]. Greater intake of soluble fiber
reduces the rate of gastric emptying and leads to a slower
blood glucose and insulin response [52–54]. However,
whole grains contain more insoluble fiber, thus other
mechanisms are probably involved than just the latter.
Intake of rye bread has been shown to result in a lower
postprandial insulin response and this was found to be
independent of its fiber content [55]. In addition, high intake
of whole grains may reduce risk of type 2 diabetes by
reducing concentrations of inflammatory markers including
plasminogen activator inhibitor type 1 and C-reactive pro-
tein [56–60] and liver enzymes including gamma-gluta-
myltransferase and aspartate aminotransferase [56], as
higher concentrations of these proteins may increase type 2
diabetes risk [61–63]. In addition, a high intake of whole
grains and cereal fiber has been associated with greater
blood concentrations of adiponectin [57, 64], a cytokine that
increases insulin sensitivity and reduces inflammation [65].
Further studies are needed to explore potential mechanisms
that could explain the nonlinear associations observed.
Our meta-analysis also has several strengths. Because
we based our analysis on prospective cohort studies recall
bias is not likely to explain our findings, and the possibility
for selection bias is reduced. Although the number of
studies was moderate they included up to 19,800 cases and
385,000 participants and we therefore had adequate sta-
tistical power to detect moderate associations. We con-
ducted several subgroup analyses and observed that the
inverse association persisted in most subgroup analyses,
and the findings were also robust in sensitivity analyses
where each study was excluded one at a time. We quan-
tified the association between grain intake and type 2 dia-
betes by conducting linear and nonlinear dose–response
analyses and found that most of the benefit of whole grains
on type 2 diabetes risk is observed with an intake of at least
2 servings per day (60 g/day). However, if whole grains
reduce body fatness and body mass index is a mediating
factor, further reductions in the risk may be observed with
higher intakes. Increasing whole grain intakes is also likely
to reduce the risk of cardiovascular disease [66],
B
A
0.4
0.6
0.8
1.0
1.2
RR
0 1 2 3 4
Whole grains (serv/day)
without BMI adjustment 95% CIwith BMI adjustment 95% CI
Relative Risk
.1 .25 .5 .75 1 1.5
Study Relative Risk (95% CI)
with BMI adjustment
Ericson, 2013 0.77 ( 0.63, 0.94)
Parker, 2013 0.76 ( 0.64, 0.91)
de Munter, 2007, NHS1 0.53 ( 0.43, 0.64)
de Munter, 2007, NHS2 0.73 ( 0.55, 0.97)
Fung, 2000 0.70 ( 0.57, 0.85)
Subtotal 0.69 ( 0.60, 0.80)
no BMI adjustment
Ericson, 2013 0.75 ( 0.62, 0.91)
Parker, 2013 0.60 ( 0.50, 0.72)
de Munter, 2007, NHS1 0.35 ( 0.29, 0.42)
de Munter, 2007, NHS2 0.48 ( 0.36, 0.64)
Fung, 2000 0.56 ( 0.46, 0.68)
Subtotal 0.53 ( 0.41, 0.69)
Fig. 4 Whole grains and type 2 diabetes, with and without adjust-
ment for BMI. Summary estimates were calculated using a random-
effects model
856 D. Aune et al.
123
overweight and obesity [24–30] and colorectal cancer [43],
and it is possible that there are greater benefits for these
outcomes with even higher intakes.
In summary, our meta-analysis suggests that a high intake
of whole grains, but not refined grains, is associated with
reduced type 2 diabetes risk. However, a positive association
with intake of white rice and inverse associations between
several specific types of whole grains and type 2 diabetes
warrant further investigations. Our results support public
health recommendations to replace refined grains with whole
grains and suggest that at least two servings of whole grains
per day should be consumed to reduce type 2 diabetes risk.
Acknowledgement DA designed the project, conducted the literature
search and analyses and wrote the first draft of the paper. DA, TN, PR,
LJV interpreted the data and revised the subsequent drafts for important
intellectual content and approved the final version of the paper to be
published. The authors declare that there is no duality of interest
associated with this manuscript. This project has been funded by Liai-
son Committee between the Central Norway Regional Health Authority
(RHA) and the Norwegian University of Science and Technology
(NTNU). We thank Ulrika Ericson for clarifying the definition of high-
fibre cereals and breads in the Malmo Diet and Cancer cohort.
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