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REVIEW
Eects of Whole Grain Intake, Compared with
Rened Grain, on Appetite and Energy Intake:
A Systematic Review and Meta-Analysis
Lisa M Sanders,1Yong Zhu,2Meredith L Wilcox,1Katie Koecher,2and Kevin C Maki1,3
1Midwest Biomedical Research, Addison, IL, USA; 2Bell Institute of Nutrition, General Mills, Inc., Minneapolis, MN, USA; and 3Indiana University, Department
of Applied Health Science, School of Public Health, Bloomington, IN, USA
ABSTRACT
Results from observational studies indicate that whole grain (WG) intake is inversely associated with BMI and risk of weight gain. WG intake may
influence energy balance and body composition through effects on appetite and energy intake. To evaluate the impact of WG food consumption
on appetite and energy intake, a systematic review and meta-analysis was performed of results from randomized controlled trials (RCTs) assessing
WG food consumption, appetite, and energy intake in adults. A search of PubMed, Scopus, and Food Science and Technology Abstracts yielded
36 RCTs measuring subjective appetite ratings after consuming WG foods compared with refined grain (RG) controls. Thirty-two of these studies
reported AUCs for subjective appetite (hunger, fullness, satiety, desire to eat, or prospective consumption) and/or energy intake and were included
in the meta-analysis. Pooled estimates from meta-analyses are expressed as standardized mean differences (SMDs). Compared with RG foods, intake
of WG foods resulted in significant differences in AUCs for subjective hunger (SMD: −0.34; 95% CI: −0.46, −0.22; P<0.001), fullness (SMD: 0.49; 95%
CI: 0.31, 0.66; P<0.001), satiety (SMD: 0.33; 95% CI: 0.18, 0.47; P<0.001), and desire to eat (SMD: −0.33; 95% CI: −0.46, −0.20; P<0.001). There
were small, nonsignificant reductions in prospective consumption ratings (P=0.08) and energy intake (P=0.07) with WG intake compared with
RG. These results support the view that consumption of WG foods, compared with RG foods, significantly impacts subjective appetite, and might
partly explain the inverse associations between WG food intake and risk of overweight, obesity, and weight gain over time. PROSPERO registration:
CRD42020148217. Adv Nutr 2021;00:1–19.
Keywords: whole grain, appetite, satiety, hunger, fullness, desire to eat, prospective food consumption, energy intake, meta-analysis, randomized
controlled trials
Introduction
Whole grains (WGs) are intact, ground, cracked, or aked
grain kernels that contain all 3 anatomical components—
endosperm, bran, and germ—in the same relative propor-
tions as they exist in the intact kernel (1,2). WG foods tend
This research was funded by Bell Institute of Nutrition, General Mills, Inc.
Author disclosures: KCM, MLW, and LMS are employees of Midwest Biomedical Research, which
has received research funding from General Mills, Inc., Kellogg Company,and the Quaker
division of PepsiCo. KK and YZ are employees of General Mills, Inc. The funding sponsor
provided comments on early aspects of the study design. Interim analyses and the nal data
were shared with the sponsor prior to publication, but the nal decision for all aspects of study
conduct and manuscript content is that of the authors alone.
Supplemental Tables 1–3 and Supplemental Figures1–6 are available from the
“Supplementary data” link in the online posting of the article and from the same link in the
online table of contents at https://academic.oup.com/advances/.
Address correspondence to KCM (e-mail: kcmaki@iu.edu).
Abbreviations used: GRADE, Grading of Recommendations Assessment, Development and
Evaluation; RCT, randomized controlled trial; RG, rened grain; SMD, standardized mean
dierence; VAS, visual analog scale; WG, whole grain.
to be higher in ber, B vitamins, iron, zinc, magnesium,
and selenium compared with foods made predominantly
with rened grains (RGs) (3). Accordingly, the 2015 Dietary
Guidelines for Americans recommends at least half of daily
grainintaketobefromWGsandincludesWGfoodsinevery
healthyeatingpattern.However,mostAmericanscontinueto
consume more RG foods (e.g., white bread, white rice) than
WG foods (e.g., wholewheat bread, brown rice, oatmeal) (3).
Results from observational studies suggest that higher
intake of WGs is associated with lower risk of weight gain and
incident overweight or obesity (4–6), although, ndings from
short-term (≤16 wk), randomized controlled trials (RCTs)
evaluating the eect of higher WG intake on body weight
have been equivocal (4,7). One of the potential mechanisms
bywhichWGscouldimpactbodyweightoverthelong
term is by suppressing appetite and, consequently, reducing
energy intake. Many WG foods contain quantities of dietary
C
The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition. This is an Open Access article distributed under the terms of the Creative Commons
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ber that have the potential to inuence glucose metabolism
(8–10), gastrointestinal transit (11), and gastrointestinal
hormone secretions (12), all of which have the potential
to impact appetite. Furthermore, fermentation of ber and
other phenolic compounds in WGs by gut microbes can
create secondary metabolites, such as SCFAs, that could
inuence appetite and energy intake (13).
Appetite is typically measured with a series of questions
relating to subjective sensations, such as hunger, fullness,
satiety or satisfaction, desire to eat, and prospective con-
sumption (14,15). Visual analog scales (VAS) with an anchor
termateachendofthescalehavebeenvalidatedasa
method for assessing changes in appetite over time after
food consumption (16). Satiety (How satised are you?) and
fullness (“How full are you?”) are related concepts and often
used interchangeably, depending on the preference of the
investigator, but both questions have been validated (14,
16). Postprandial VAS scores often correlate with subsequent
meal energy intake (15,16). However, VAS scores alone
canbeunreliableasaproxymeasureforsubsequentenergy
intake, so, ideally, both subjective appetite sensations and
energy intake should be measured (17).
Although many clinical trials have investigated the eect
of WG consumption on subjective appetite or energy intake,
due to mixed results and mostly small studies, the totality
of evidence remains unclear. Therefore, the objective of
this systematic review and meta-analysis was to evaluate
the impact of consuming WGs, compared with RGs, on
outcomes related to subjective appetite and energy intake in
RCTs in adults. The primary outcome was hunger AUC and
secondary outcomes were fullness AUC, desire to eat AUC,
satiety AUC, prospective consumption AUC, and energy
intake.
Methods
Literature searches
The Preferred Reporting Items for Systematic Reviews
and Meta-Analyses (PRISMA) guidelines were followed for
performingthesystematicreviewandmeta-analyses(18).
A comprehensive literature search was conducted using the
PubMed database, Scopus, and Food Science & Technology
Abstracts, which covered studies published from 1946
through September 2019. The search was designed to identify
publications of RCTs that examined WG intake from intact
WGs (e.g., rye, oats, quinoa, brown rice, etc.) or foods made
with WGs (e.g., breads, ready-to-eat breakfast cereals, etc.)
and outcomes related to subjective appetite (hunger, fullness,
satiety, desire to eat, prospective consumption), energy
intake, gastric emptying, and appetite-related hormones (e.g.,
ghrelin, leptin). Full search term details are provided in
Supplemental Table 1. Prior to the data analysis, in April
2020, the literature search was performed again in PubMed
only to identify relevant studies published between the
initial search and data analysis. No additional studies were
identied.
Inclusion and exclusion criteria
Inclusion criteria consisted of RCTs conducted in adult
humans (≥18 y of age), English language publications, WG
foods (≥51% of grains being WG) (19,20) as the main inter-
vention compared with RG foods as a control, documented
(or the ability to determine) quantitative intake of WG,
and a measurement of subjective appetite, energy intake,
appetite-related hormones, and/or gastric emptying time.
Exclusion criteria included observational studies (cross-
sectional, retrospective or prospective cohorts), case-control
or single-arm studies with no control condition, studies in
animals or in vitro, multicomponent interventions where the
eect of WGs cannot be determined (e.g., intervention with
WGs and additional ber compared with RGs without added
ber), studies comparing dierent types of WGs without an
RG control, studies on individual grain components (e.g.,
bran) or dietary supplements, interventions administered
via tube feeding or enteral nutrition, studies in children
(<18 y of age) or pregnant/lactating women, trials using
medications or supplements known to inuence appetite
or gastric emptying, and studies in subjects with a chronic
disease, with the exception of type 2 diabetes mellitus,
obesity, or metabolic syndrome.
Screening and data extraction
Publications identied in each database using the search
terms were combined and duplicates were removed. First-
level screening of titles and abstracts was completed inde-
pendently by a member of the research team (LMS) using
Abstrackr (http://abstrackr.cebm.brown.edu/). Full texts of
all publications identied as potentially eligible were ob-
tained for further review. Publications that were unclear
with respect to eligibility were resolved by discussion with
the research team. Reference lists from eligible publications
were reviewed to determine any additional studies for
inclusion. Following the full-text review, PICO (population,
intervention, comparator, and outcome) data were extracted
from the eligible studies into a database independently by
1 reviewer (LMS) and veried for accuracy independently
by a second reviewer (MLW). All discrepancies were re-
solved by discussion among the reviewers and referencing
the original publication. Outcomes extracted from eligible
studies included subjective appetite measures, energy intake
(subsequentmealinacutestudiesanddailyintakeforchronic
studies), appetite-related hormones, and gastric emptying.
In studies where outcomes were reported in bar
graphs, Engauge Digitizer software version 4.1 (http:
//markummitchell.github.io/engauge-digitizer/)wasused
to estimate the means and SD or SEM in the graphs for
inclusion in the database. If studies reported measuring
subjective appetite or energy intake but did not report the
data or variability, the corresponding author was contacted
by e-mail to request the quantitative data. One author
responded with additional data which was included in the
data extraction. Two publications (21,22)didnotreport
SDsorSEMsandtheauthorsdidnotrespondtoe-mail
requests, therefore, the SDs for the outcomes were estimated
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as the maximum SD reported by other studies of the same
duration.
For studies where the amount of WG in the nal food
was not documented, the recipes for test foods, or the label
information for commercial products, was reviewed. For
low-moisture foods (e.g., pasta, akes) the percentage WG
in the dry ingredients of the recipe was estimated as the
percentage WG of the nal food. For higher moisture foods
(e.g., bread), the percentage WG in the dry ingredients of the
recipe was estimated as the percentage WG of the nal food
after adjustment for the moisture content. If the moisture
content of the nal food was not provided in the publication,
moisture content was estimated using Food Data Central
from the USDA Agriculture Research Service (23).
Assessment of study quality
Risk of bias for each relevant outcome within a study was
assessed independently by a member of the research team
(LMS) with the Cochrane risk-of-bias tool for randomized
trials (24). The quality of the evidence for each outcome was
assessed through discussion among members of the research
team using the Grading of Recommendations Assessment,
Development and Evaluation (GRADE) method (25).
Statistical analysis
Meta-analyses were completed using MedCalc Statistical
Software version 19.0.5 (MedCalc Software BVBA; https:
//medcalc.org; 2019). Subjective appetite measures were
prespecied as the primary outcome, but because hunger
was the most frequently measured outcome for subjective
appetite, it was selected after the data extraction, but prior to
completion of the meta-analysis, to be the primary outcome
ofthesubjectiveappetitemeasures.Theprimaryanalysisfor
allsubjectiveappetitemeasuresusedpooledSMDestimates
(WG compared with RG control) and 95% CIs for AUCs
of hunger, fullness, satiety, desire to eat, and prospective
consumption. Although all studies used a VAS to measure
appetite and calculate AUCs, some VAS scales diered in
anchoring statements and length (i.e., not all used a 100-mm
line). The use of SMDs allowed pooling of the results from
studies with these dierent approaches. The primary analysis
forenergyintakemeasuresusedpooledSMDestimates(WG
compared with RG control) and 95% CIs for caloric intake.
Statistical signicance for individual study and pooled SMDs
wasdeclaredwhenthe95%CIdidnotincludethenullvalue
of 0 (i.e., Pvalue <0.05). Studies were weighted according
to the inverse of the variance of each study’s eect using
random eects models. Random eects models were chosen
for the primary analyses due to dierences across studies
in key design elements such as subject characteristics and
length of test period. Fixed eects models were completed for
hunger and other appetite measures in the main analysis (i.e.,
not for subgroups) as sensitivity analyses. Because results
did not dier materially between random and xed eects
models, only results from the former are presented. The
magnitude of eect sizes were interpreted as <0.40 =small,
0.40–0.70 =moderate, and >0.70 =large (26). Analyses were
notcompletedforgastricemptyingbecauseonly3studies
with data were available and dierent methodologies were
used for measuring gastric emptying rate and/or time (MRI,
paracetamol, and ultrasound). Analyses for appetite-related
hormones were not completed due to budgetary and time
constraints.
Sensitivity analyses were completed for subjective appetite
measurements to assess the degree to which varying time
frames for determination of AUCs could have impacted the
results. This was achieved by analyzing AUC measurements
<180 min and ≥180 min separately. An additional sensitivity
analysis on the subset of studies requiring calculation of the
WG content was also completed.
Subgroup analyses were performed on subjective appetite
measuresfortypeofWG,amountofWGconsumed(less
than or equal to the median, or greater than the median),
feeding approach (matching available carbohydrates, match-
ing calories and volume), and measurement timing (immedi-
ately after meal, subsequent meal). Similar subgroup analyses
were performed for energy intake with the 1 dierence
of measurement timing (subsequent meal, third meal, or
daily intake). Subgroup analyses were not possible for health
status (e.g., type 2 diabetes), age, gender, or BMI due to an
insucient number of studies or combined reporting within
studies (e.g., overweight and normal weight data combined)
that did not allow for distinct subgroups.
Statistical heterogeneity was assessed using Cochran
QandtheI2statistic. An I2value ≥40% was used to
designate moderate or higher heterogeneity, in accordance
with the recommendations in the Cochrane Handbook (27).
The presence of publication bias was assessed visually by
examining funnel plots measuring the SEM as a function of
the SMD.
Results
A ow diagram summarizing the literature search process
is shown in Figure 1.Thescopeofthisreviewislimited
to subjective appetite and energy intake. Therefore, of the
51 eligible articles included in the data extraction, 36 were
included in the systematic review (13,21,22,28–60)and
32 were included in the meta-analysis of subjective appetite
and/or energy intake. The 4 studies excluded from the meta-
analysis (57–60) reported measuring subjective appetite
AUCs and/or energy intake, but did not show the data and
authors did not respond to e-mail requests for the data.
Study characteristics are presented in Table 1.Thirtyve
(13,21,22,28–47,49–60) of the 36 studies were crossover in
design, with only 1 parallel trial (48), and included data from
794 participants. Four studies included daily consumption
of WGs or RGs for 3 to 8 wk (13,48,51,60), whereas the
remaining studies tested the response to acute intake of WGs
and RGs. WG intake in longer-term feeding studies ranged
from 48 to 145 g/d, and acute studies ranged from 40 g
to 254 g. Subjective appetite was measured only in acute
studies.ThemostcommontypeofWGtestedwasryein
15 publications (21,30,31,34,37,38,40–42,44,48,49,51,55,
57), followed by wheat in 12 publications (22,29,32,33,36,
Whole grain intake and appetite 3
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FIGURE 1 Flow diagram of literature search process for the effect of WGs on subjective appetite and energy intake in adults. FSTA, Food
Science & Technology Abstracts; WG, whole grain.
43,48,54,55,57,58,60). Other WGs tested included barley
(39,46,54,59), oats (28,35,45,52), corn (50,53), rice (47),
buckwheat (56), and quinoa (56).
WG intake and hunger
Overall, 35 comparisons reported in 18 dierent studies (21,
22,28–43) were included in the analysis of the impact of WG
on hunger AUC. Intake of WG foods resulted in signicantly
lowerhungerAUCcomparedwithRGfoods(Figure 2,SMD:
−0.34; 95% CI: −0.46, −0.22; P<0.001) with no signicant
heterogeneity between studies (Q =40.08, P=0.22,
I2=15.17%). A sensitivity analysis showed the timing of
AUC measurement did not substantially impact the results,
with the eect size diering slightly in studies <180 min
or ≥180 min (Supplemental Table 2). A sensitivity analysis
including the subset of studies requiring WG amounts to
be calculated showed a slightly larger eect size than the
analysis including all studies (SMD: −0.45); however, studies
requiring calculation of WG content estimated slightly higher
mean levels of WG intake (92.7 ±5.3 g compared with 88.9 ±
4.4 g), which might contribute to this larger eect. Similar
results were found for all other outcomes.
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TABLE 1 Summary of studies included in the systematic review and meta-analysis of WG intake and subjective appetite AUC and/or energy intake in adults1
Study Population Health status Study design WG exposure2
RG control
exposure
Outcomes
measured
Length of
appetite
testing Effect of WG
Wolever e t al. (28)n=40 Healthy Crossover 40 g WG oats Cream of rice in skim
milk
Hunger 180 min NS hunger
60% male Acute appetite test Fullness NS fullness
39.2 ±13.1 y Matched available CHO Desire to eat NS desire to eat
BMI: 26.5 ±3.1 Breakfast meal Prospec tive
consumption
NS prospective consumption
Costabile et al. (29)n=14 Healthy Crossover 117 g WG wheat in pasta Wheat pasta Hunger 240 min ↓Hunger
50% male Acute appetite test Satiety NS satiety
30 ±2 y Matched available CHO Desire to eat ↓Desire toeat
BMI: 22 ±1 Breakfast meal Energy intake NS energy intake
Energy intake at lunch
Lee et al. (30)n=21 Healthy Crossover 40gWGryeinporridge Wheatbread(55g
portion)
Hunger 240 min ↓Hunger (55 g)
52% male Acute appetite test 55 g WG rye in porri dge Fullness ↑Fullness (55 g)
38.6 ±11.8 y Matched calories Desire to eat NS desire to eat
BMI: 24.9 ±3.3 Breakfast meal
Sandberg et al. (31)n=21
48% male
25.3 ±3.9 y
BMI: 22.7 ±2.3
Healthy Crossover
Subsequent meal appetite test
Matched available CHO
Dinner meal
Energy intake at lunch
134.5 g WG rye from rye flour bread
133.5 g WG rye from flour/kernel blend
bread
Wheat bread Hunger
Satiety
Desire to eat
Energy intake
210 min ↓Hunger
↑Satiety (rye flour bread)
↓Desire to eat (rye flo ur bread)
NS energy intake
Cioffi et al. (32)n=16 Healthy, overweight to
obese
Crossover 100 g WG wheat in pasta Wheat pasta Hunger 240 min NS hunger
44% male Acute appetite test Fullness ↑Fullness
44 ±10 y
BMI: 30.1 ±2.8
Matched calories and volume
Lunch meal
Satiety
Prospec tive
consumption
Energy intake
↑Satiety
↓Prospective consumption
NS energy intake
Cioffi et al. (33)n=8 Healthy, normal weight
to overweight
Crossover 100 g WG wheat in pasta Wheat pasta Hunger 240 min NS hunger
50% male Acute appetite test Fullness NS fullness
39 ±14 y
BMI: 24.7 ±2.7
Matched calories and volume
Lunch meal
Satiety
Prospec tive
consumption
NS satiety
NS prospective consumption
Sandberg et al. (34)n=19 Healthy Crossover 88.8 g WG rye from bread Wheat bread Hunger 180 min ↓Hunger
45% male Subsequent meal appetite test Satiety ↑Satiety
25.6 ±3.5 y Matched availableCHO Desire to eat ↓Desire to eat
BMI: 21.9 ±1.9 Dinner meal
Geliebter et al. (35)n=36 Healthy Crossover 93.6 g WG from oatmeal Corn flakes Hunger 180 min ↓Hunger
50% male 50% normal weight Acute appetite test Fullness ↑Fullness
26–31 y350% overweight Matched calories and volume
Breakfast mealBMI normal weight:
23 ±2males;
22 ±2 females
BMI overweight:
33 ±3males;
36 ±7 females
Gonzalez-Anton
et al. (36)
n=23 Healthy Crossover 90.1 g WG from wheat bread Wheat bread Hunger 180 min NS hunger
55% male Normal weight to
overweight
Acute appetite test Fullness NS fullness
26 ±1 y Matched available CHO Satiety NS satiety
BMI: 23.8 ±0.5 Breakfast meal
Energy intake at lunch
Prospec tive
consumption
NS prospective consumption
NS energy intake
Energy intake
(Continued)
Whole grain intake and appetite 5
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TABLE 1 (Continued)
Study Population Health status Study design WG exposure2
RG control
exposure
Outcomes
measured
Length of
appetite
testing Effect of WG
Johansson et al. (37)n=23 Healthy Crossover 60 g WG from rye crisp bread Wheat crisp bread Hunger 240 min ↓Hunger
30% male Normal weight to
overweight
Acute appetite test Fullness ↑Fullness
60.1 ±12.1 y Matched calories Desire to eat NS desire to eat
BMI: 23.8 ±3.4 Breakfast meal
Forsb erg et al. (21)n=21 Healthy Crossover 76 g WG from rye kernel crisp bread
60.8 g WG from rye kernel crisp bread
Wheat bread (soft) Hunger 240 min ↓Hunger
47% male Normal weight to
overweight
Acute appetite test Satiety ↑Satiety(60.8 g)
39 ±14 y Matched calories Desire to eat ↓Desire to eat
BMI: 23.3 ±3 Breakfastmeal Energy intake ↓Energy intake (60.8 g)
Energy intake at lunch
Hartvigsen et al. (38)n=15 Metabolic syndrome Crossover 90 g WG from rye kernel bread Wheat bread Hunger 270 min ↓Hunger
47% male Acute appetite test Fullness ↑Fullness
62.8 ±4.2 y Matched availableCHO Satiet y ↑Satiety
BMI: 31.1 ±3.2 Breakfast meal
Energy intake at lunch
Prospec tive
consumption
↓Prospective consumption
Energy intake NS energy intake
Johansson et al. (39)n=19 Healthy Crossover 96.8 g WG from boiled barley kernels Wheat bread Hunger 120 min NS hunger
32% male
24.2 ±1.9 y
Subsequent meal appetite test
Matched available CHO
Satiety
Desire to eat
NS satiety
NS desire to eat
BMI: 22.3 ±2 Dinner meal Energy intake ↓Energy intake
Energy intake at lunch
Rosen et al. (40)n=10 Healthy Crossover 98.5 g WG from rye bread Endosperm rye
bread
Wheat bread
Hunger 120 min4↓Hunger (porridge)
50% male Acute appetite test 106.6 g WG from rye kernel por ridge Fullness ↑Fullness (porridge)
26 ±1.1 y Matched available CHO 97.1 g WG from wheat kernel porridge Desire to eat ↓Desire to eat (porridge)
BMI: 22.6 ±0.4 Breakfast meal Energy intake ↓Energy intake (rye porridge)
Energy intake at lunch
Rosen et al. (41)n=20 Healthy Crossover 84.7 g WG from commercial rye bread Wheat bread Hunger 180 min ↓Hunger (Evolo)
50% male Acute appetite test 83.2 g WG from Amilo rye bread Fullness NS fullness
26.7 ±0.9 y Matchedavailable CHO 82 g W G fromEvolo r yebread Desire to eat NS desire to eat
BMI: 22.2 ±0.39 Breakfast meal 82.7 g WG from Picasso rye bread
80.1gWGfromVicelloryebread
82.8 g WG from Kaskelott rye bread
Rosen et al. (42)n=14 Healthy Crossover 118.2 g WG from D. Zlote rye bread Wheat bread Hunger 180 min ↓Hunger (Nikita, Rekrut)
50% male Acute appetite test 125.7 g WG from H. Loire rye bread Fullness ↑Fullness
23.6 ±0.5 y Matchedavailable CHO 120.7 g W Gfrom N ikita rye bread Desire to eat NS desire to eat
BMI: 22 ±0.5 Breakfastmeal 122.2 g WG from Rekrut r ye bread
122.9 g WG from Amilo rye bread
(Continued)
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TABLE 1 (Continued)
Study Population Health status Study design WG exposure2
RG control
exposure
Outcomes
measured
Length of
appetite
testing Effect of WG
Kristensen et al. (43)n=16 Healthy Crossover 83.6 g WG from wheat pasta Wheat bread Hunger 180 min NS hunger
38% male Acute appetite test Wheat pasta Fullness NS fullness
24.1 ±3.8 y
BMI: 21.7 ±2.2
Matched available CHO and
calories
Breakfast meal
Energy intake at lunch
Satiety
Prospec tive
consumption
Energy intake
NS satiety
NS prospective consumption
NS energy intake
Solah et al. (22)n=22 Healthy Crossover 53.1 g WG from boiled bulgur kernels HA rice Hunger 240 min NS hunger
Gender, age, BMI not
reported
Acute appetite test 53.1 g WG from steamed bulgur kernels Fullness NR fullness
Matched calories and volume 53.1 g WG from boiled Turkish bulgur
kernels
Desire to eat NR desire to eat
Breakfast meal Prospec tive
consumption
NR prospective consumption
Rosen et al. (44)n=12 Healthy Crossover 66.1 g WG from rye bread Endosperm rye
bread
Wheat bread
Endosperm rye
porridge
Wheat porridge
Satiety 180 min NS satiety
75% male Acute appetite test 51.1 g WG from rye porridge
25.3 ±0.8 y Matched availableCHO
BMI: 23.1 ±0.6 Breakfast meal
Hlebowiczet al. (45)n=12 Healthy Crossover 42.5 g WG from oat flakes Corn flakes Satiety 120 min NS satiet y
50% male Acute appetite test
28 ±4 y Matched calories and volume
BMI: 22 ±2 Breakfast meal
Granfeldtetal.(46)n=10 Healthy Crossover 89 g WG from hull-less barley kernel
porridge
76.2 g WG from hulled barley kernel
porridge
90.6 g WG from hull-less HA barley kernel
porridge
87.7 g WG from hulled HA barley kernel
porridge
Wheat bread Satiety 180 min ↑Satiety (hull-less porridge,
hull-less HA porridge, hulled
HA porridge)
50% male Acute appetite test
34 ±8 y Matched available CHO
BMI: 21.2 ±2 Breakfastmeal
79.1 g WG from hulled barley flour
porridge
90.3 g WG from hull-less HA barley flour
porridge
Hamad et al. (47)n=20 Healthy and type 2
diabetes
Crossover 70 g WG from long-grain brown rice Long-grain white
rice
Hunger 120 min NS all outcomes (for type 2
diabetes)
NR hunger
NR fullness
NR desire to eat
NS prospective consumption
40% male Acute appetite test Fullness
25.4 ±2 y male Matched available CHO Desire to eat
24.7 ±1.9 y female Breakfast meal Pros pectiv e
consumptionBMI: 23.02 ±1.62
male; 22.39 ±1.32
female
(Continued)
Whole grain intake and appetite 7
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TABLE 1 (Continued)
Study Population Health status Study design WG exposure2
RG control
exposure
Outcomes
measured
Length of
appetite
testing Effect of WG
Roager et al. (13)n=50 Overweight or obese, ≥1
of: impaired fasting
glucose, dyslipidemia,
hypertension
Crossover Target ≥75gWG/dfromvarietyofWG
foods
Targ et <10 g WG/d
from variety of RG
foods
Energy intake NA NS energy intake
36% male Daily consumption for 8 wk
48.6 ±11.1 y Ad libitum intake
BMI: 28.9 ±3.6 Energy intake over the day
Suhr et al. (48)n=24/arm Overweight to
moderately obese
Parallel No target intake No target intake Energy intake NA NS energy intake
WG rye: 46% male Daily consumption for 6 wk Mean intake WG rye: 124 ±11.8 g/d Mean intake WG:
5.4 ±12.6 g/d53 ±8.9 y Ad libitum intake Mean intake WG wheat: 145 ±12.1 g/d
BMI: 28 ±1.9 Energy intake over the day
WG wheat: 42%
male; 48.2 ±9.9 y;
BMI: 27.7 ±1.9
RG control: 50%
male; 51.8 ±9y;
BMI: 27.8 ±2
Ibrugger et al. (49)n=12 Healthy Crossover 90 g WG from rye kernel porridge Wheat bread Energy intake NA ↓Energy intake
100% male
25.6 ±3.9 y
Matched available CHO and
calories
BMI: 23.1 ±1.2 Dinner meal
Energy intake at lunch
Luhovyy et al. (50)n=30 Healthy Crossover 50g WG from HA corn cook ie Wheat cookie Energy intake NA NS energy intake
100% male Matched calories
22.9 ±0.6 y Preload to lunch
BMI: 22.6 ±0.3 Energy intake at lunch
Isaksson et al. (51)n=24 Healthy Crossover 55 g WG from rye porridge Wheat bread Energy intake NA NS energy intake
21% male Normal weight to
moderately
overweight
Daily consumption for 3 wk—
breakfast meal only
Breakfast meals
Matched calories
33 ±13 y
BMI: 23.4 ±2.2
Energy intake over the day
Zafar et al. (52)n=12 Healthy Crossover 87 g WG from oatmeal Wheat bread Energy intake NA ↓Energy intake
0% males Matched available CHO
Age, BMI not
reported
Breakfast meal
Energy intake at lunch
Anderson et al. (53) Study 1: Healthy Crossover 50 g WG from HA corn in soup HA corn starch in
soup
Energy intake NA NS energy intake (30 min)
n=17 Matched calories and volume ↓Energy intake (120 min)
100% males Preload to lunch
20.2 ±0.1 y Energy intake at lunch (Study 1 =
30 min; Study 2 =120 min)BMI: 22.5 ±0.3
Study 2: n=16
100% males
20.9 ±0.3 y
BMI: 22.5 ±0.4
(Continued)
8 Sanders et al.
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TABLE 1 (Continued)
Study Population Health status Study design WG exposure2
RG control
exposure
Outcomes
measured
Length of
appetite
testing Effect of WG
Schroeder et al. (54)n=50 Healthy Crossover 84 g WG from barley hot cereal and snack
mix78.5 g WG from wheat hot cereal
and snack mix
Rice hot cereal Hunger 240 min NS hunger
26% male Normal weight to obe se Acute appetite test Fullness NS fullness
31 ±11 y Matched calories and volume Desire to eat NS desire to eat
BMI: 23 ±3 Breakfast meal and mid-morning
snack
Prospec tive
consumption
NS prospective consumption
NS energy intake
Energy intake at lunch Energy intake
Isaksson et al. (55)n=22 Healthy Crossover 162 g WG from rye porr idge (breakfast)
and wheat pasta (lunch)
62 g WG from rye porr idge (breakfast
only)
Wheat bread
(breakfast) and
wheat pasta
(lunch)
Energy intake NS energy intake
36% male Normal weight to
moderately
overweight
Matched calories
40.7 ±14.7 y Breakfast and lunch meal
BMI: 23.2 ±2.4 Energy intake at dinner
Berti et al. (56) Study 1: buckwheat Healthy Crossover Study 1: 111.3 g WG from buckwheat
pasta (large portion)
55.7 g WG from buckwheat pasta (small
portion)
Study 2: 419.8 g WG from quinoa risotto
(large portion)
211.6 g WG from quinoa risotto (small
portion)
Study 1: wheat pasta
(large and small
portions)
Study 2: White rice
(large and small
portions)
Energy intake NA NS energy intake
n=14 Matched volume
100% males Mid-morning snack
24 ±2.6 y Energy intake at lunch
BMI: 22.3 ±2.7
Study 2: quinoa
n=12
100% males
25.4 ±2.2 y
BMI: 23 ±1.9
Breen et al. (57)5n=10 Type 2 diabetes Crossover 54.1 g WG from buttermilk wheat bread Wheat bread Hunger 270 min NS hunger
60% male Acute appetite test 135.4 g WG from pumpernickel r yebread Fullness NS fullness
53.9 ±5.5 y Matched available CHO Satiety NS satiety
BMI: 35.1 ±7.5 Breakfast meal Prospec tive
consumption
NS prospective consumption
Holt et al. (58)5n=10 Healthy Crossover 79.8 g WG from wheat bread Wheat bread Hunger 120 min NR hunger
30% males Acute appetite test Fullness NR fullness
23.5 ±6.2 y Matched calories
BMI: 22.1 ±1.3 Breakfast Meal
Nilsson et al. (59)5n=17 Healthy Crossover 105.3 g WG from barley kernel bread Wheat bread Satiety 180 min ↑Satiety (high β-glucan barley
bread)65% males Subsequent meal appetite test 124.4 g WG from cut barley kernel bread
25.9 ±3.2 y Matched available CHO 139.3 g WG from HA barley bread
BMI: 22.5 ±2.1 Dinner meal 253.9 g WG from high β-glucan barley
bread
52.6 g WG from barley kernel bread
Bodinham et al. (60)5n=14 Healthy Crossover 48 g WG from wheat bread Wheat bread Energy intake NA NS energy intak e
36% male Daily consumption for 3 wk
26 ±1.4 y Energy intake over the day
BMI: 21. 8 ±0.8
1CHO, carbohydrate; HA, high-amylose; NA, not applicable; NR, not reported; NS, not significant (P>0.05); RG, refined grain; WG, whole grain.
2Reported or calculated values.
3Range of means provided because age reported by gender and weight status.
4120 min measurement =AUC0–60min +AUC60–120min
5Included in systematic review but data not available to include in meta-analysis.
Whole grain intake and appetite 9
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FIGURE 2 Forest plot of the meta-analysis on the effect of WG intake on hunger in adults. Values are the standardized mean differences
(SMDs) for hunger AUC between WG intake and RG intake (21,22,28–43). Comm., commercial; Ctrl., control; RG, refined grain; var., variety;
WG, whole grain.
Findings from the subgroup analyses for hunger AUC are
shown in Table 2.HungerwaslowerintheWGgrouprelative
to the control when the test and control conditions were
matched by available carbohydrate (P<0.001). However,
studies matched by calories or matched by calories and
volume did not show a signicant dierence in hunger AUC
between WG and RG controls.
Three studies not included in the meta-analysis measured
hunger AUC but did not report the data. All of these studies
(47,54,57) did not show a signicant eect of WG on hunger
AUCcomparedwithRG.
WG intake and fullness
Twe lve stud i e s ( 28,30,32,33,35–38,40–43)with25com-
parisons were included in the analysis of the eect of WG on
fullness.IntakeofWGfoodsresultedinsignicantlygreater
fullness AUC compared with RG foods (Figure 3; SMD: 0.49;
95% CI: 0.31, 0.66; P<0.001) with moderate heterogeneity
between studies (Q =37.95, P=0.035, I2=36.76%). A
sensitivity analysis showed the timing of AUC measurement
impacted the eect size, with studies measuring fullness at
<180 min having a greater eect size than studies measuring
at ≥180 min (Supplemental Table 2).
Subgroup analyses’ results for fullness AUC are shown
in Table 2. There was a signicant positive eect of WG
on fullness in studies matched by available carbohydrate (P
<0.001), but not in studies that matched calories. There
were insucient studies matched by calories and volume to
include as a subgroup. Subgroup analyses were not possible
for acute compared with subsequent meal studies because all
studies were acute.
Five studies (22,47,54,57,58) not included in the meta-
analysis measured fullness AUC but did not report the data.
Three studies (47,54,57) did not nd a signicant eect of
WG on fullness AUC compared with RG. One of the studies
only calculated fullness AUC to use in a satiety index (58)and
thus did not statistically compare the AUC values between
treatments. Solah et al. (22)didnotreportthestatistical
results for fullness AUC.
WG intake and satiety
Thirteen studies (21,29,31–34,36,38,39,43–46)with
24 comparisons were included in the analysis of the eect of
WG on satiety. There was a signicant positive eect of WG
on satiety AUC (Figure 4, SMD: 0.33; 95% CI: 0.18, 0.47; P
<0.001) with no signicant heterogeneity between studies
(Q =15.40, P=0.88, I2=0.00%). A sensitivity analysis
showed the timing of AUC measurement did not impact the
eect size although there were only 2 studies that measured
satiety AUC for <180 min (Supplemental Table 2).
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TABLE 2 Subgroup analyses for the eect of WGs on subjective appetite in adults1
Outcome and subgroups
Number of
compar-
isons/studies
Subjects
(WG/control)
Effect estimate SMD
(95% CI)2I2(%) Pvalue2
Hunger AUC
Type of WG
Rye 22/9 759 (378/381) −0.42 (−0.57, −0.26) 12.04 <0.001
Wheat 10/7 330 (165/165) −0.27 (−0.52, −0.02) 22.14 <0.031
Amount of WG (median split)
≤88.8 g 18/8 748 (374/374) −0.20 (−0.34, −0.06) 0.00 0.006
>88.6 g 17/10 531 (267/264) −0.53 (−0.72, −0.35) 16.07 <0.001
Feeding approach
Matched available CHO 24/11 821 (409/412) −0.44 (−0.59, −0.30) 10.65 <0.001
Matched calories 7/4 276 (138/138) −0.10 (−0.33, 0.14) 0.00 0.421
Matched calories and volume 6/4 246 (123/123) −0.22 (−0.47, 0.03) 0.00 0.078
Measurement timing
Acute appetite test 31/15 1096 (548/548) −0.35 (−0.49, −0.21) 24.80 <0.001
Subsequent meal appetite test 4/3 183 (90/93) −0.35 (−0.64, −0.06) 0.00 0.019
Fullness AUC
Type of WG
Rye 17/6 531 (265/266) 0.54 (0.32, 0.75) 35.77 <0.001
Wheat 6/5 170 (85/85) 0.53 (0.12, 0.94) 42.56 0.012
Amount of WG (median split)
≤90.0 g 13/6 496 (248/248) 0.33 (0.12, 0.54) 28.12 0.002
>90.0 g 12/6 357 (178/179) 0.69 (0.43, 0.94) 28.72 <0.001
Feeding approach
Matched available CHO 19/7 609 (304/305) 0.57 (0.37, 0.77) 34.09 <0.001
Matched calories 5/3 194 (97/97) 0.19 (−0.18, 0.57) 43.81 0.315
Satiety AUC
Type of WG
Rye 10/5 351 (173/178) 0.31 (0.07, 0.55) 22.38 0.011
Wheat 6.5 178 (89/89) 0.22 (−0.07, 0.51) 0.00 0.141
Barley 7/2 156 (77/79) 0.46 (0.15, 0.76) 0.00 0.004
Amount of WG (median split)
≤88.3 g 12/5 323 (160/163) 0.21 (−0.004, 0.42) 0.00 0.055
>88.3 g 12/9 386 (191/195) 0.42 (0.23, 0.63) 0.00 <0.001
Feeding approach
Matched available CHO 19/9 561 (277/284) 0.37 (0.21, 0.53) 0.00 <0.001
Matched calories 4/2 146 (73/73) 0.08 (−0.24, 0.40) 0.00 0.631
Matched calories and volume 3//3 66 (33/33) 0.36 (−0.11, 0.84) 0.00 0.132
Measurement timing
Acute appetite test 20/10 526 (265/261) 0.31 (0.14, 0.48) 0.00 <0.001
Subsequent meal appetite test 4/3 183 (90/93) 0.38 (0.09, 0.67) 0.00 0.011
Desire to eat AUC
Type of WG
Rye 21/8 727 (363/364) −0.36 (−0.50, −0.21) 0.00 <0.001
Amount of WG (median split)
≤88.8 g 13/6 552 (276/276) −0.23 (−0.40, −0.07) 0.00 0.006
>88.8 g 13/5 341 (170/171) −0.50 (−0.72, −0.28) 6.56 <0.001
Feeding approach
Matched available CHO 20/8 681 (340/341) −0.42 (−0.57, −0.27) 0.00 <0.001
Matched calories 5/3 212 (106/106) −0.07 (−0.33, 0.20) 0.00 0.623
Measurement timing
Acute appetite test 21/8 711 (355/356) −0.34 (−0.50, −0.19) 14.12 <0.001
Subsequent meal appetite test 4/3 182 (91/91) −0.35 (−0.64, −0.06) 0.00 0.018
Prospective consumption AUC
Type of WG
Wheat 5/4 150 (75/75) −0.15 (−0.51, 0.21) 19.32 0.406
Amount of WG (median split)
≤86.8 g 4/3 184 (92/92) −0.17 (−0.46, 0.12) 0.00 0.240
>86.8 g 4/4 116 (58/58) −0.40 (−1.02, 0.23) 62.96 0.213
Feeding approach
Matched avail CHO 6/5 258 (129/129) −0.21 (−0.53, 0.11) 38.83 0.202
1CHO, carbohydrate; RG, refined grain; SMD, standardized mean difference; WG, whole grain.
2Effect estimates and Pvalues from random effects models.
Whole grain intake and appetite 11
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FIGURE 3 Forest plot of the meta-analysis on the effect of WG intake on fullness in adults. Values are the standardized mean differences
(SMDs) for fullness AUC between WG intake and RG intake (28,30,32,33,35,36,38–43). Comm., commercial; Ctrl., control; RG, refined
grain; var., variety; WG, whole grain.
Findings of the subgroup analyses for satiety AUC are
shown in Table 2. There was a positive eect on satiety AUC
with WG rye (P=0.011) and WG barley (P=0.004), but not
WG wheat. There was also a signicant positive eect of WG
when tested at amounts greater than the median (88.25 g), but
not less than or equal to the median. A signicant positive
eect of WG on satiety was determined in studies with test
and control conditions matched by available carbohydrate (P
<0.001), but not when matched by calories or calories and
volume.
Two studies (57,59) not included in the meta-analysis
measured satiety AUC but did not report the data. Breen et
FIGURE 4 Forest plot of the meta-analysis on the effect of WG intake on satiety in adults. Values are the standardized mean differences
(SMDs) for satiety AUC between WG intake and RG intake (21,29,31–34,36,38,39,43–46). Ctrl., control; HAWG, high-amylose whole grain;
RG, refined grain; var., variety; WG, whole grain.
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FIGURE 5 Forest plot of the meta-analysis on the effect of WG intake on desire to eat in adults. Values are the standardized mean
differences (SMDs) for desire to eat AUC between WG intake and RG intake (21,28–31,34,37,39–42). Comm., commercial; Ctrl., control;
RG, refined grain; var., variety; WG, whole grain.
al. (57)didnotndasignicanteectofWGwheatbread
or WG rye bread on satiety AUC compared with RG wheat
breadinastudyofsubjectswithtype2diabetes.Nilsson
et al. (59)foundahigh–β-glucan WG barley variety had a
signicantly positive eect on satiety AUC compared with
RG wheat; however, other WG barley treatments were not
signicantly dierent from RG wheat.
WG intake and desire to eat
Twenty-ve comparisons reported in 11 dierent studies (21,
28–31,34,37,39–42) were included in the analysis of the
impact of WGs on desire to eat AUC. Intake of WG foods
resulted in a signicantly lower desire to eat AUC compared
with RG foods (Figure 5;SMD:−0.33; 95% CI: −0.47,
−0.20; P<0.001) with no signicant heterogeneity between
studies (Q =23.97, P=0.46, I2=0.00%). A sensitivity
analysis showed the timing of AUC measurement did not
substantially impact the results, with the eect size diering
slightly in studies <180 min or ≥180 min (Supplemental
Table 2).
Results of subgroup analyses for desire to eat AUC are
shown in Table 2. Desire to eat was lower in the WG group
relative to the control when the test and control conditions
matched by available carbohydrate (P<0.001), but not when
matched by calories.
Three studies (22,47,54) not included in the meta-
analysis measured desire to eat AUC but did not report the
data. Two studies (47,54) did not nd a signicant eect of
WGs on desire to eat AUC compared with RGs. The other
study (22) did not report the statistical results for desire to
eat AUC.
WG intake and prospective consumption
Eight comparisons reported in 7 dierent studies (28,32,33,
36,38,43,47) were included in the analysis of the impact of
WG on prospective consumption AUC. There was no eect
of WG on prospective consumption AUC (Figure 6;SMD:
−0.25; 95% CI: −0.52, 0.03) with no signicant heterogeneity
between studies (Q =9.91, P=0.19, I2=29.37%).
Sensitivity analyses could not be performed because all
studies measured prospective consumption at a time frame
≥180 min. There were no signicant eects of WGs on
prospective consumption AUC in any of the subgroups
(Table 2).
Two studies (54,57) not included in the meta-analysis
measured prospective consumption AUC but did not report
the data. One study (57) in subjects with type 2 diabetes
FIGURE 6 Forest plot of the meta-analysis on the effect of WG
intake on prospective consumption in adults. Values are the
standardized mean differences (SMDs) for prospective
consumption AUC between WG intake and RG intake (32,33,36,
38,43,47,61). Ctrl., control; RG, refined grain; var. variety; WG,
whole grain.
Whole grain intake and appetite 13
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FIGURE 7 Forest plot of the meta-analysis on the effect of WG intake on energy intake in adults. Values are the standardized mean
differences (SMDs) for caloric intake between WG intake and RG intake (13,29,31,32,36,38–40,43,48–56). Ctrl., control; RG, refined grain;
WG, whole grain.
did not nd a signicant eect of WG wheat bread or WG
rye bread on prospective consumption AUC compared with
RG wheat bread. The other study (54) found no signicant
dierence in prospective consumption between WG barley
or WG wheat and RG rice.
WG intake and energy intake
The impact of WGs on energy intake included 29 compar-
isons from 17 dierent studies (13,29,31,32,36,38–40,
43,48–56). There was a small, nonsignicant reduction in
energy intake following WG consumption (Figure 7;SMD:
−0.11; 95% CI: −0.23, 0.01; P=0.070) and no signicant
heterogeneity between studies (Q =30.25, P=0.35,
I2=7.44%). There was a signicant eect of WGs on energy
intake when the amount of WGs fed was >90.1 g (P=0.006),
the median amount among all studies, but this eect was not
observed with amounts ≤90.1 g. No other subgroup analyses
showed a signicant eect on energy intake (Ta b le 3 ).
Two studies (21,60) not included in the meta-analysis
measured energy intake but did not report the data. One
study reported no signicant eect of WGs compared with
RGs on energy intake at a subsequent meal (60). Forsberg
et al. (21) also reported no signicant dierence between
WGs and RGs in energy intake at a subsequent meal with a
large breakfast (∼600 kcal), but a signicant eect of WGs on
energy intake with a smaller breakfast (∼375 kcal).
Quality of evidence
The quality of evidence as assessed by GRADE criteria is
summarized in Tab le 4 . Overall, the evidence for subjective
measuresofappetiteandenergyintakewasratedasmoderate
(low for prospective consumption). The evidence rating
was downgraded due to concerns about risk of bias in
the studies and possible publication bias. Sources of bias
in the studies were typically inadequate description of
randomization procedures or allocation concealment and
inability to blind the treatments. There was also an indication
of possible publication bias suggesting that smaller studies
with results that did not conrm the main study hypothesis
were not as likely to be published. Risk-of-bias assessment
on the outcomes of individual studies and funnel plots for
the main outcomes are shown in Supplemental Table 3 and
Supplemental Figures 1–6.
Discussion
The results of this meta-analysis of RCTs suggest that
intake of WG foods reduces hunger and desire to eat and
increases fullness and satiety compared with RG foods.
There was no signicant eect of WGs on energy intake
at subsequent meals or across the day compared with RG
foods from the subgroup analyses, although there was a
small, nonsignicant reduction in energy intake in the
main analysis when data were pooled (P=0.07). To our
knowledge, this is the rst systematic review and meta-
analysis to evaluate the eect of WG intake compared with
RG intake on subjective appetite measures and energy intake.
Several meta-analyses have evaluated the relation of WG
intake to body weight, and observational data tend to support
an inverse relation (4–6). However, RCTs with intervention
periods of a few weeks up to a few months have shown
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TABLE 3 Subgroup analyses for the eect of WGs on energy intake in adults1
Outcome and subgroups
Number of
compar-
isons/studies
Subjects
(WG/control)
Effect estimate
SMD (95% CI)2I2(%) Pvalue2
Energy intake
Type of WG
Rye 9/7 311 (156/155) −0.09 (−0.30, 0.13) 0.00 0.440
Wheat 8/7 326 (162/162) −0.09 (−0.30, 0.12) 0.00 0.407
Other312/8 518 (259/259) −0.17 (−0.45, 0.11) 57.00 0.227
Amount of WG (median split)
≤90.1 g 15/11 660 (330/330) −0.01 (−0.18, 0.16) 16.94 0.928
>90.1 g 12/7 411 (207/204) −0.27 (−0.46, −0.08) 0.00 0.006
Feeding approach
Matched available CHO 13/9 395 (197/198) −0.13 (−0.32, 0.07) 0.00 0.196
Matched calories 9/6 372 (188/184) −0.08 (−0.28, 0.12) 0.00 0.435
Measurement timing
Daily intake 4/3 236 (120/116) −0.17 (−0.42, 0.09) 0.00 0.191
Subsequent meal intake 21/14 811 (405/406) −0.10 (−0.26, 0.06) 25.32 0.228
Third meal intake43/2 108 (54/54) −0.13 (−0.51, 0.24) 0.00 0.478
1CHO, carbohydrate; SMD, standardized mean difference; WG, whole grain.
2Effect estimates and Pvalues from random effects models.
3Other category includes barley, buckwheat, corn, oat, and quinoa. There were not enough studies in individual grains to run a separate subgroup analysis.
4Third meal refers to the next meal consumed after the subsequent meal (e.g., breakfast =test meal, lunch =subsequent meal, dinner =third meal).
mixedeectsofWGintakeonbodyweightchange(4,7,13,
62,63).
WG intake was associated with signicant reductions in
appetite ratings, with eects that were small to moderate in
magnitude. There was also a small, nonsignicant reduction
in energy intake that only became signicant at high levels of
WG intake (above the median level of 90.1 g). Taken together,
these results suggest that WGs are able to reduce subjective
appetite following a meal, but not enough to signicantly
impact acute energy intake at a subsequent meal or across the
day. Also, considering the possible publication bias favoring
studies that show benecial eects of WGs on energy intake,
it is likely that WGs have little impact on short-term energy
intake, except perhaps at high levels of WG intake. Studies
that assessed chronic consumption of WGs on energy intake
werefewandonly3to8wkinduration(13,48,51). The
longest RCT (13) did not nd a signicant impact of WGs on
dailyenergyintakecomparedwithanRGdiet,buttherewas
asignicantchangeinbodyweightandbodycompositionin
the WG diet that correlated with a change in energy intake.
Longer-term studies (>16 wk) might be able to determine
whethersmallchangesinenergyintakeassociatedwithWG
consumption can have cumulative long-term eects that
explain dierences in body weight reported in observational
studies.
The diversity in studies allowed for several subgroup
analyses. Firstly, the type of WG tested in the studies
often varied, with WG r ye and WG wheat being the most
TABLE 4 Quality of evidence included in the systematic review and meta-analysis of WGs on subjective appetite measures and energy
intake in adults, based on GRADE approach1
Outcome Risk of bias2Inconsistency3Indirectness Imprecision Publication bias4Decision5
Hunger Some concerns Consistent No serious
indirectness
No serious
imprecision
Possible ⊕⊕⊕∅Moderate
Fullness Some concerns Moderate No serious
indirectness
No serious
imprecision
Possible ⊕⊕⊕∅Moderate
Satiety Some concerns Consistent No serious
indirectness
No serious
imprecision
Undetected ⊕⊕⊕∅Moderate
Desire to eat Some concerns Consistent No serious
indirectness
No serious
imprecision
Possible ⊕⊕⊕∅Moderate
Prospective
consumption
Some concerns Moderate No serious
indirectness
Moderate
imprecision
Unable to
determine6
⊕⊕∅∅ Low
Energy intake Low Consistent No serious
indirectness
Moderate
imprecision
Possible ⊕⊕⊕∅Moderate
1GRADE, Grading of Recommendations Assessment, Development and Evaluation; WG, whole grain.
2Ranked down primarily for inadequate description of allocation concealment and lack of blinding.
3Based on I2using thresholds in Cochrane Handbook for Systematic Reviews of Interventions, Version 6. The Cochrane Collaboration, 2019. Available at:
https://training.cochrane.org/handbook/current.
4Based on visual analysis of funnel plots.
5Symbols are suggested representations of quality of evidence from GRADE Handbook (https://gdt.gradepro.org/app/handbook/handbook.html).
6Only 8 studies and a minimum of 10 studies are generally needed to evaluate a funnel plot.
Whole grain intake and appetite 15
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frequently tested and both signicantly reducing hunger and
desire to eat and increasing fullness compared with RGs.
Interestingly, there were fewer studies on WGs such as oat
and barley, which are rich in the viscous, fermentable ber β-
glucan and may impact appetite and energy intake dierently
from wheat and rye with primarily nonviscous and poorly
fermentable bers. Fermentation of ber and other phenolic
compounds in WGs by gut microbes can produce metabo-
lites, such as SCFAs, that could inuence appetite and energy
intake beyond the subsequent meal (13). Furthermore,
viscous bers have been shown to slow gastric emptying
and prolong the release of cholecystokinin in response to
a fat-containing meal, possibly contributing to enhanced
feelings of satiety (35,64). The studies on oat and barley
in this meta-analysis had mixed results regarding subjective
appetite and energy intake so more studies in these WGs are
needed.
Using a median split to create a dichotomy of “higher”
and “lower” WG intake, the results suggest small eect sizes
with lower levels of WGs and moderate eect sizes with
higher levels of WGs. Of note, the quantities of WGs fed
in most studies were relatively high, with medians in the
range of 85 to 90 g. This is higher than typical consumption
and recommendations for intake. Daily WG consumption in
the United States is typically ≤16 g, and recommendations
suggest 48 g/d (3,65). Intake is somewhat higher in Europe,
ranging from 23 to 36 g/d, but even populations in northern
Europe with the highest intake (37–58 g/d) are still on the
lowendofthelevelstestedinthesestudies(65). Several
studies included these high levels to achieve 50 g of available
carbohydrate from the test food. One study fed >200 g WGs
in a test food and noted it was necessary to achieve 50 g of
available carbohydrate, but also acknowledged that >25% of
thesubjectswereunabletonishconsumingtheentiretest
product (59). Thus, more studies are needed at lower, realistic
levels of WGs recommended for a healthy diet.
The subgroup analyses also showed dierences in appetite
responses based on the feeding design. When interventions
were matched for available carbohydrate, WGs were signif-
icantly dierent from RGs for hunger, fullness, and desire
to eat. However, when studies were matched for calories or
caloriesandvolume(nonewerematchedforvolumealone),
there was not a signicant dierence between WG and RG
conditions. Because calories and volume both impact subjec-
tive appetite (66), this shows the importance of considering
both factors in studies on food ingredients and appetite. WG
foods have less available carbohydrates than RG foods due
to their ber content; therefore, studies that match available
carbohydrates often fed a greater volume of food in the WG
condition than the RG condition, which could contribute to
greaterfeelingsoffullnessorlowerfeelingsofhunger.Indeed,
several of the investigators commented on the dierence
in portion size and calories potentially contributing to the
ndings (29,36,38,42,56). Although matching available
carbohydrate might yield a more mechanistic understanding
of the inuence of glycemic response on appetite, it does not
reect typical consumption patterns or recommendations,
which suggest substitutions, such as exchanging a slice of
white bread for a slice of whole wheat bread. Substitution of
foods containing WGs for similar foods made with RGs often
results in increased dietary ber intake and reduced energy
density, both of which have been associated with lower
daily energy consumption and less weight gain over time
(66). Almost half (14 of 32) of studies in the current meta-
analysis matched available carbohydrates and did not match
conditions for calories or volume. Accordingly, there is a
need for more studies matching calories and volume between
WG and RG conditions to provide greater clarity regarding
drivers of dierences in indicators of appetite associated with
WG food consumption.
Interestingly, studies that measured appetite at a sub-
sequent meal fed ≥11 h after WG consumption showed
signicant eects on hunger, satiety, and desire to eat,
similar in magnitude to dierences observed in acute meal
studies. The levels of WGs fed were also similar to acute
meal studies, suggesting a potential long-term eect of WGs
on appetite that could be mediated by slowing digestion
or the fermentation of bers from WGs in the colon.
Nilsson et al. (59) showed that an evening meal with
WGs signicantly reduced the gastric emptying rate at a
subsequent standardized breakfast meal, which could have
contributed to the greater feelings of satiety after breakfast.
Fermentation metabolites, such as SCFAs, have been shown
to stimulate the production of appetite-related hormones,
such as glucagon-like peptide-1 and peptide YY (67,61,
68). A meta-analysis has shown glucagon-like peptide-1 to
increase feelings of fullness and reduce energy intake in
humans (69). These potential mechanisms by which WGs
might impact long-term appetite and energy intake should
be further investigated.
The strengths of the current systematic review and meta-
analysis include a comprehensive search of 3 databases to
ensure broad coverage of the literature and the inclusion of a
number of subgroup and sensitivity analyses that have helped
identify hypotheses for additional research and gaps in the
available evidence. However, the systematic review and meta-
analysis was also limited by poor reporting of the amount
of WGs in the studies, which resulted in the exclusion of
several studies and 17 of 36 studies requiring calculations
to estimate WG content based on recipes. Additional studies
were excluded that measured subjective appetite but did not
calculate the AUC. The present analysis also only included
studiesthatprovidedWGfoodswhere≥51% of the grain
was WG. There was also an indication of possible publication
bias suggesting that smaller studies with results that did not
conrm the main study hypothesis were not as likely to
be published. Finally, there were no studies in participants
with type 2 diabetes that met the inclusion criteria and
provided data for the meta-analysis. Furthermore, only
1 study in the meta-analysis was completed in participants
with metabolic syndrome (38). Thus, more investigation is
needed in these populations to determine whether eects of
WG consumption on appetite diers between groups with
and without metabolic dysregulation.
16 Sanders et al.
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In summary, the results from this systematic review
and meta-analysis show that consumption of WG foods,
compared with RG foods, modestly but signicantly reduced
hunger and desire to eat and increased fullness and satiety,
but showed only a small, nonsignicant reduction in energy
intake at the next meal or across the day. Thus, although
it is plausible that eects of WG consumption on appetite
and subsequent energy intake contribute to the associations
of greater WG intake with lower risks for weight gain and
overweight or obesity reported in observational studies,
additional research, especially with longer feeding periods,
will be needed before rm conclusions can be drawn in this
regard. More studies are warranted to further clarify the
eects of dierent WG types and of consumption in amounts
consistent with current recommendations. Particular atten-
tion should be paid to the research design to ensure calories
and volume are matched because these factors can greatly
inuence appetite and subsequent energy intake.
Acknowledgments
Theauthors’responsibilitieswereasfollows—LMS,YZ,KK,
andKCM:contributedtotheconceptionanddesignof
the systematic review and meta-analysis; LMS: reviewed the
publications, extracted the data, and conducted the risk of
bias assessment; MLW: veried the accuracy of the extraction
andanalyzedthedata;LMSandKCM:interpretedthe
data, performed the GRADE assessment, and prepared the
manuscript; and all authors: read and approved the nal
manuscript.
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