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Impact of Whole, Fresh Fruit Consumption on Energy Intake and Adiposity: A Systematic Review

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Background: The energy content of whole, fresh fruit derives primarily from simple sugars, which are currently under heightened scrutiny for their potential contribution to obesity and chronic disease risk. Yet fruit also has a relatively low energy density, moderate palatability/reward value, and high fiber content, which together may limit energy intake. Although reasoned arguments can be made that fruit is fattening or slimming, the question is best resolved empirically. Methods: Methods were preregistered with PROSPERO (CRD42018111830). The primary outcome is the impact of whole, fresh fruit consumption on measures of adiposity including body weight in randomized controlled trials (RCTs). Secondary outcomes are the impact of whole, fresh fruit consumption on energy intake in RCTs, and the association between whole, fresh fruit consumption and changes in measures of adiposity in prospective observational studies. CENTRAL and PubMed databases were searched through October 2018. Cochrane risk of bias tool was used to assess risk of bias in RCTs, and the GRADE method was used to judge and convey the certainty of conclusions. Reporting follows PRISMA guidelines. Results: RCTs, and particularly those of higher quality, suggest that increasing whole, fresh fruit consumption promotes weight maintenance or modest weight loss over periods of 3–24 weeks (moderate certainty), with limited evidence suggesting that a high intake of fruit favors weight loss among people with overweight or obesity. Consistent with this, single-meal RCTs suggest that consuming whole, fresh fruit tends to decrease energy intake, particularly when consumed prior to a meal or when displacing more energy-dense foods (moderate certainty). Prospective observational studies suggest that habitually higher fruit intake is not associated with weight change, or is associated with modest protection against weight gain, over five or more years. Conclusions: Current evidence suggests that whole, fresh fruit consumption is unlikely to contribute to excess energy intake and adiposity, but rather has little effect on these outcomes or constrains them modestly. Single-meal RCTs, RCTs lasting 3–24 weeks, and long-term observational studies are relatively consistent in supporting this conclusion. Whole, fresh fruit probably does not contribute to obesity and may have a place in the prevention and management of excess adiposity.
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SYSTEMATIC REVIEW
published: 08 May 2019
doi: 10.3389/fnut.2019.00066
Frontiers in Nutrition | www.frontiersin.org 1May 2019 | Volume 6 | Article 66
Edited by:
David Katz,
Yale Griffin Prevention Research
Center, United States
Reviewed by:
Nada Benajiba,
Princess Nourah bint Abdulrahman
University, Saudi Arabia
Cristina Pereira-Wilson,
University of Minho, Portugal
*Correspondence:
Stephan J. Guyenet
s.guyenet@gmail.com
Specialty section:
This article was submitted to
Clinical Nutrition,
a section of the journal
Frontiers in Nutrition
Received: 17 December 2018
Accepted: 23 April 2019
Published: 08 May 2019
Citation:
Guyenet SJ (2019) Impact of Whole,
Fresh Fruit Consumption on Energy
Intake and Adiposity: A Systematic
Review. Front. Nutr. 6:66.
doi: 10.3389/fnut.2019.00066
Impact of Whole, Fresh Fruit
Consumption on Energy Intake and
Adiposity: A Systematic Review
Stephan J. Guyenet*
Self-employed, Seattle, WA, United States
Background: The energy content of whole, fresh fruit derives primarily from simple
sugars, which are currently under heightened scrutiny for their potential contribution
to obesity and chronic disease risk. Yet fruit also has a relatively low energy density,
moderate palatability/reward value, and high fiber content, which together may limit
energy intake. Although reasoned arguments can be made that fruit is fattening or
slimming, the question is best resolved empirically.
Methods: Methods were preregistered with PROSPERO (CRD42018111830). The
primary outcome is the impact of whole, fresh fruit consumption on measures of adiposity
including body weight in randomized controlled trials (RCTs). Secondary outcomes
are the impact of whole, fresh fruit consumption on energy intake in RCTs, and
the association between whole, fresh fruit consumption and changes in measures of
adiposity in prospective observational studies. CENTRAL and PubMed databases were
searched through October 2018. Cochrane risk of bias tool was used to assess risk of
bias in RCTs, and the GRADE method was used to judge and convey the certainty of
conclusions. Reporting follows PRISMA guidelines.
Results: RCTs, and particularly those of higher quality, suggest that increasing whole,
fresh fruit consumption promotes weight maintenance or modest weight loss over
periods of 3–24 weeks (moderate certainty), with limited evidence suggesting that a high
intake of fruit favors weight loss among people with overweight or obesity. Consistent
with this, single-meal RCTs suggest that consuming whole, fresh fruit tends to decrease
energy intake, particularly when consumed prior to a meal or when displacing more
energy-dense foods (moderate certainty). Prospective observational studies suggest that
habitually higher fruit intake is not associated with weight change, or is associated with
modest protection against weight gain, over five or more years.
Conclusions: Current evidence suggests that whole, fresh fruit consumption is unlikely
to contribute to excess energy intake and adiposity, but rather has little effect on
these outcomes or constrains them modestly. Single-meal RCTs, RCTs lasting 3–24
weeks, and long-term observational studies are relatively consistent in supporting this
conclusion. Whole, fresh fruit probably does not contribute to obesity and may have a
place in the prevention and management of excess adiposity.
Keywords: fruit, adiposity, body weight, obesity, energy intake, sugar
Guyenet Fruit, Energy Intake, and Adiposity
INTRODUCTION
Rationale
Worldwide, the total per capita burden of disease continues
to decline, but it does not do so uniformly. Technological
and economic progress have substantially relieved the ancient
burdens of starvation, infectious disease, and accidents, yet they
have ushered in a new era of non-communicable disorders such
as obesity, diabetes, and coronary heart disease (1,2). A key
contributor to these disorders is the overconsumption of energy
and accumulation of excess adipose tissue (36).
Sucrose and other simple sugars with a sweet taste, henceforth
“sugar,” have long been suspected as a culprit in energy
overconsumption and excess adiposity. In 1980, the United States
Department of Agriculture Dietary Guidelines advised the public
to “avoid too much sugar” and, as part of a four-point plan for
reducing weight, “eat less sugar and sweets” (7). Yet scrutiny
of sugar has intensified recently, both within the scientific
community and outside it, with certain researchers and popular
writers arguing that sugar is a particularly potent driver of obesity
and non-communicable disease risk (810). Observational and
experimental findings indeed suggest that in sufficient quantity,
refined sugar can increase energy intake and adiposity (11,12).
The energy content of sweet fruits is primarily in the form of
sugar. If sugar is a particularly potent driver of obesity and non-
communicable disease risk, this raises the possibility that even
whole, fresh fruit may have similar effects, and that conventional
advice to increase fruit consumption may be misguided.
On the other hand, fruit differs from refined sugar-containing
foods in important respects, such as its lower energy density,
lower palatability/reward value, higher fiber content, and higher
concentration of essential and non-essential micronutrients.
Some of these properties are expected to limit energy intake and
adiposity, and together they may render fruit slimming relative
to other commonly-eaten foods. Furthermore, the human
evolutionary lineage has likely consumed substantial quantities
of fruit for tens of millions of years prior to the emergence of
obesity and cardiometabolic disease as common health problems,
suggesting that it is unlikely to be a major contributor to these
conditions (13).
Although reasoned arguments can be made that fruit
is fattening or slimming, the question is best resolved
empirically. Previous reviews have addressed similar topics (14
17), concluding that fruit aids in the prevention of excess
energy intake, is unlikely to increase adiposity, and may reduce
adiposity. However, the current review is the most recent to
comprehensively review the randomized controlled trial (RCT)
and prospective observational literature on whole, fresh fruit
intake specifically. Further, it employs best-practice systematic
review methods including detailed preregistration of methods
with a well-defined search strategy, adherence to PRISMA
reporting guidelines, assessment of study bias using the Cochrane
risk of bias tool, and assessment of certainty of conclusions using
the GRADE method.
Objectives
The objective of this review is to systematically review the
randomized controlled trial and prospective observational
research literature on the impact of whole, fresh fruit
consumption on energy intake and measures of adiposity,
and synthesize available studies to form overall conclusions. All
studies involving human subjects are considered, and RCTs must
report between-group comparisons that isolate differences in
whole, fresh fruit consumption as a variable.
Research Question
What is the impact of whole, fresh fruit consumption on energy
intake and adiposity?
METHODS
Protocol and Registration
A protocol for this review was preregistered in the PROSPERO
systematic review registry prior to initiating literature searches
(CRD42018111830)1, with the exception of brief preliminary
searches used to develop the search method. The primary
outcome is the impact of whole, fresh fruit consumption on
measures of adiposity, as measured by RCTs. A secondary
outcome is the impact of whole, fresh fruit consumption on
energy intake, as measured by RCTs. An additional secondary
outcome is the association of whole, fresh fruit intake with
changes of measures of adiposity, as measured by prospective
observational studies.
Eligibility Criteria
“Fruit” is defined in the common/culinary sense of a sweet,
seed-bearing plant tissue. Fruit varieties that lack seeds are
included. “Whole” and “fresh” denote fruit that has not been
significantly processed, i.e., raw and whole rather than cooked,
pureed, dried, juiced, or powdered. Raw fruit that has been
peeled or cut into bite-sized pieces is included. “Change in
adiposity” is defined as change in body weight and/or other
direct or indirect measures of body fatness, including but not
limited to body fat percentage, body mass index, and waist
circumference. “RCT” is defined as a study that assigns subjects
to different intervention conditions using randomization or
pseudorandomization, such that outcomes can be compared
between randomized groups. “Prospective observational study” is
defined as a non-intervention study that collects data on exposure
variables at an earlier time point, and outcomes at a later time
point, and reports the association between the two.
Studies were required to be (1) RCTs on the impact of whole,
fresh fruit consumption on measures of adiposity including body
weight, (2) RCTs on the impact of whole, fresh fruit consumption
on energy intake, or (3) prospective observational studies on the
association between fruit consumption and body weight and/or
adiposity. Only published studies were considered. No language
restrictions were applied. All RCTs and prospective observational
studies conducted in humans and published through October
2018 were considered.
To be eligible, RCTs must include a between-group difference
in fruit intake. The experimental design must isolate between-
group differences in fruit intake as a variable, without substantial
concurrent between-group differences in other variables such
1https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=111830
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Guyenet Fruit, Energy Intake, and Adiposity
as vegetable intake. An equivalent non-fruit intervention in a
comparison group, such as increased nut intake, is permitted.
Energy intake of diets must not be strictly controlled to permit
differences in energy intake and adiposity to arise. RCTs must
report differences in measures of adiposity, or energy intake,
between groups, or such differences must be calculable from data
available in the manuscript. Changes in weight and/or adiposity
must be measured with a minimum of 2 weeks between baseline
and end line to allow meaningful differences in adiposity to
emerge, while energy intake RCTs can represent any duration.
For observational outcomes, studies must be prospective
observational studies that report the association between the
consumption of fresh, whole fruit consumption and subsequent
changes in measures of adiposity including body weight. During
the course of study selection, the author felt it was appropriate
to add one exclusion criterion that was not prespecified:
observational studies were excluded if they were potentially
confounded by a concurrent intervention. For example, an
observational study that reports the association between fruit
intake and weight change may be confounded if it is conducted
in subjects that received advice to increase fruit intake as part of
a weight loss intervention. This criterion excluded four studies,
whose findings are broadly similar to those that met inclusion
criteria (1821).
Search Strategy
The search strategy was designed in collaboration with Ben
Harnke, Education and Reference Librarian, the University of
Colorado Health Sciences Library. RCT searches were conducted
in the Cochrane controlled register of trials (CENTRAL), and
prospective observational study searches were conducted in
PubMed. CENTRAL compiles RCTs from multiple sources
FIGURE 1 | PRISMA flow diagram summarizing the study identification and selection process.
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 1 | Primary outcome: impact of whole, fresh fruit consumption on measures of adiposity in RCTs.
Trial Subjects Intervention Duration Weight difference Adiposity difference Notes
Singh et al.
(24)
Men and women with essential
hypertension. 131 randomized; 66
guava, 65 control.
0.5–1.0 kg of fresh guava fruit per day
before meals vs. no intervention
12 weeks 0.4 kg (p=NS) Not reported No power calculation. Not
preregistered. Subjects were asked to
maintain weight.
Singh et al.
(25)
Men and women with essential
hypertension and mild
hypercholesterolemia. 101
randomized; 52 guava; 49 control.
0.5–1.0 kg of fresh guava fruit per day
before meals vs. no intervention
24 weeks 0.1 kg (p=NS) Not reported No power calculation. Not
preregistered.
de Oliveira et
al. (26)
Hypercholesterolemic, overweight,
non-smoking women, 30–50 years of
age. 49 randomized; number in each
group unclear.
3 apples, pears, or oat cookies per day.
Hypocaloric diet for all subjects.
12 weeks 0.33 kg (p=0.003) Not reported No power calculation. Not
preregistered. May have pooled two
fruit groups that were individually
randomized.
Rodriguez et
al. (27)
Women with obesity. 15 randomized;
8 low-fruit; 7 high-fruit.
Low-fruit diet vs. high-fruit diet (goal of 5
vs. 15% of kcal from fructose).
Hypocaloric diet for all subjects.
8 weeks +0.3 kg (p=0.781) 0.4% BF (p=0.231);
3.1 cm WC (p=
0.048); 0.01 WHR (p=
0.395)
No power calculation. Not
preregistered. Very small sample size.
Fujioka et al.
(28)
Men and women with obesity. 91
randomized; 24 grapefruit capsules +
apple juice; 22 placebo +apple juice;
21 placebo +grapefruit juice; 24
placebo +fresh grapefruit.
3x a day prior to meals: Group A,
Grapefruit capsules +7 oz apple juice;
Group B, placebo capsules +7 oz apple
juice; Group C placebo capsules +8 oz
grapefruit juice; Group D placebo capsules
+half a fresh grapefruit. 20–30 min of
walking 3–4X per week for all groups.
12 weeks 1.4 kg vs. group B (p
=0.048); 0.1 kg vs.
group C (p=NS)
NS difference in % BF
(impedance)
Performed a power calculation. Not
preregistered.
Rush et al.
(29)
Healthy men and women. 12
randomized; 6 kiwi; 6 control.
One kiwi fruit per 30 kg body weight vs. no
kiwi fruit. General diet advice and a
pedometer for all subjects.
3 weeks (fruit
intervention)
+0.9 kg (p=NS) Not reported No power calculation. Not
preregistered. Very small sample size.
Madero et al.
(30)
Men and women with overweight or
obesity. 131 randomized; 66
low-fructose diet; 65 natural fructose
diet.
Low-fructose diet (<20 g/d) vs. moderate
natural-fructose diet (50–70 g/d mostly
from fruit). Hypocaloric diet for all subjects.
6 weeks 1.36 (p=0.02) 0.8% BF (p=0.1);
0.15 WHR (p=0.41)
Performed a power calculation.
Preregistered with anthropometric
changes as primary outcome
(NCT00868673).
Dow et al.
(31)
Men and premenopausal women with
overweight or obesity. 74
randomized; 32 control; 42 grapefruit.
Half a fresh grapefruit with each meal (3X
per d) vs. no intervention. All subjects were
assigned a baseline diet low in fruit and
vegetables.
6 weeks 0.5 (p=0.119) 0.55% BF (p=0.337,
impedance); 1.22 cm
WC (p=0.062); 0.0
WHR (p=0.470)
Performed a power calculation.
Preregistered with weight change as
primary outcome (NCT01452841).
(Continued)
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 1 | Continued
Trial Subjects Intervention Duration Weight difference Adiposity difference Notes
Ravn-Haren
et al. (32)
Healthy men and women. 34
randomized; number per group
unclear.
550 g/d whole apple; 22 mg/d apple
pomace; 500 mL/d cloudy apple juice;
500 mL/d clear apple juice; no
intervention. Baseline diet was low in
polyphenols and pectin.
4 weeks +0.15 (p=NS) vs. no
intervention; 0.07 vs.
pomace (p=NS); 0.42
vs. cloudy juice (p=
NS); 0.36 vs. clear
juice (p=NS).
0.0 WHR vs. no
intervention (p=NS);
0.003 WHR vs.
pomace (p=NS);
0.008 WHR vs.
cloudy juice (p=NS);
0.005 WHR vs. clear
juice (p=NS).
No power calculation. Not
preregistered. Very small sample size for
number of groups. High dropout rate.
Agebratt et al.
(33)
Healthy non-obese men and women.
30 randomized; 15 fruit; 15 nuts.
Diet supplementation with 7 kcal/kg body
weight/d of fruit vs. nuts.
8 weeks +0.03 kg (p=0.95) 0.2 cm SAD (p=
0.91); 0.08 l VF (p=
0.16)
Performed a power calculation but
based on a hepatic fat outcome.
Preregistered but not for
anthropometric outcomes
(NCT02227511). Registry page is
sparse.
Kumari et al.
(34)
Men and women 18–25 years old. 45
randomized; 15 guava with peel; 15
peeled guava; 15 no intervention.
400 g guava with skin per day (group A)
vs. 400 g peeled guava per day (group B)
vs. no intervention (group C)
6 weeks 2.0 kg (A vs. C; p=
NR); 2.6 kg (B vs. C;
p=NR)
Not reported No power calculation. Not
preregistered.
Randomized controlled trials reporting the impact of whole, fresh fruit consumption on body weight and adiposity. Weight and adiposity differences represent changes in the fruit intervention group relative to the comparator group.
Additional strengths and limitations of study design are listed in the “notes” column. NS, not statistically significant (p >0.05). NR, not reported; BF, body fat; WC, waist circumference; WHR, waist-to-hip ratio; SAD, sagittal abdominal
diameter; VF, visceral fat.
including PubMed, Embase, Clinicaltrials.gov, handsearches, and
other biomedical resources. Brief preliminary searches were used
to develop the search method by verifying that studies identified
in previous review papers were present (1416). Formal searches
were conducted in November 2018, following preregistration.
The adiposity RCT search employed the following search
terms in CENTRAL: ((Fruit OR fruits):ti,ab OR [mh Fruit])
AND ((weight OR “body mass index” OR BMI OR “waist
circumference” OR “body fat” OR adiposity OR overweight OR
obesOR leanness OR Overnutrition OR “over nutrition”):ti,ab
OR [mh ”Body Weight”] OR [mh “Body Weight Changes”]
OR [mh “Overweight”] OR [mh “Thinness”] OR [mh “Body
Fat Distribution”]).
The energy intake RCT search employed the following search
terms in CENTRAL: ((Fruit OR fruits):ti,ab OR [mh Fruit]) AND
((“energy” OR calorieOR caloric OR kilocalorieOR kcal OR
“joule” OR kilojoule OR kJ):tiab) AND ((intake OR consumOR
ingestOR ate OR eat OR eating):ti,ab OR [mh “Energy Intake”]
OR [mh Eating]).
The prospective observational study search employed
the following search terms in PubMed, in addition to the
“observational study” publication type filter: (Fruit[tiab] OR
fruits[tiab] OR “Fruit”[Mesh:NoExp]) AND (weight[tiab]
OR “body mass index”[tiab] OR BMI[tiab] OR “waist
circumference”[tiab] OR “body fat”[tiab] OR adiposity[tiab]
OR overweight[tiab] OR obes[tiab] OR leanness[tiab] OR
Overnutrition[tiab] OR “over nutrition”[tiab] OR “Body
Weight”[Mesh:NoExp] OR “Body Weight Changes”[Mesh]
OR “Overweight”[Mesh] OR “Thinness”[Mesh] OR “Body
Fat Distribution”[Mesh]) AND (prospective[tiab] OR
cohort[tiab] OR longitudinal[tiab] OR follow up stud[tiab]
OR followup stud[tiab] OR incidence stud[tiab] OR
“Cohort Studies”[Mesh]).
In addition, previous review papers on fruit, energy intake,
and adiposity were hand searched for relevant studies (1416).
Study Selection
After search records were identified, SG examined titles and
abstracts for studies that met inclusion criteria. Potential
candidates were compiled in Excel spreadsheets for examination
of full text. SG then examined the full text of each study to verify
that inclusion criteria were satisfied, resulting in the exclusion of
some studies.
Data Collection
Data were extracted from studies that met inclusion criteria
into Excel spreadsheets and Cochrane Review Manager 5.3. Data
presented in manuscripts were taken at face value and authors
were not contacted for additional information. For RCTs, the
following data were extracted: first author, year of publication,
number and characteristics of subjects, summary of intervention,
duration of intervention, between-group difference in weight
with statistical significance, between-group difference in other
measures of adiposity with statistical significance, Cochrane
risk of bias assessment (based on random sequence generation,
allocation concealment, blinding of participants and personnel,
blinding of outcome assessment, incomplete outcome data,
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Guyenet Fruit, Energy Intake, and Adiposity
selective reporting), additional noteworthy study features. When
not directly reported, between-group differences in measures of
adiposity and energy intake were calculated based on available
data whenever possible.
For observational studies, the following data were extracted:
first author, year, number and characteristics of subjects, measure
of fruit intake, length of follow-up, covariates accounted for,
association between fruit consumption and weight change with
statistical significance, association between fruit consumption
and other measures of adiposity with statistical significance,
additional noteworthy study features. Data from the most
adjusted model that does not include energy intake as a
covariate were generally preferred, since energy intake is a
mechanism by which fruit intake may impact body weight. If
an unadjusted or minimally adjusted model was the only one
available without energy intake as a covariate, the most adjusted
model was selected.
Body mass index measures were only collected if body weight
was not reported, since the two measures are redundant in adults
who have achieved their final height.
Risk of Bias
Risk of bas was estimated using the Cochrane risk of bias tool
for individual studies, according to Cochrane guidelines2and
using Cochrane Review Manager 5.3, and GRADE was used to
judge and communicate the certainty of conclusions for each
outcome3Risk of bias score was used as a criterion to weight
the informativeness of each study when synthesizing conclusions,
and GRADE was used to judge and communicate the certainty of
each conclusion. Risk of bias graphs and summaries were created
using Cochrane Review Manager 5.3.
Synthesis of Results
This review employed a narrative synthesis method whereby
study quality was evaluated and conclusions were informally
weighted according to study quality (informal, i.e., a quantitative
method was not applied to weight the informativeness of
individual studies). Among the three outcomes considered, the
preregistered primary outcome was assigned the highest weight.
Reporting follows PRISMA guidelines (22). Quantitative meta-
analysis of RCTs was judged to be suboptimal in this context,
given the limited number of studies identified, the fact that
the study pool would have been further narrowed by applying
more stringent meta-analysis inclusion criteria, and widely
varying study methods and quality. Although meta-analyses are
commonly performed on fewer than ten studies, typical methods
for accomplishing this do not adequately control the false positive
rate, particularly in the context of high heterogeneity (23).
Finally, given the high risk of bias of most studies identified, the
author believes it is more informative to focus on high-quality
trials than to pool their findings with less informative trials.
2https://handbook-5-1.cochrane.org/chapter_8/8_assessing_risk_of_bias_in_
included_studies.htm
3https://handbook-5-1.cochrane.org/chapter_12/12_2_1_the_grade_approach.
htm.
RESULTS
Study Selection and Characteristics
Figure 1 summarizes the search and study selection process using
the PRISMA flow diagram. 4,264 records were identified in
database searches, 1,671 for body weight RCTs, 848 for energy
intake RCTs, and 1,745 for prospective observational studies.
4,201 records were excluded on the basis of title and abstract
contents, leaving 63 potentially eligible studies; 16 body weight
RCTs, 10 energy intake RCTs, and 37 observational studies. Many
studies were excluded on first pass because they did not isolate
whole, fresh fruit intake as a variable; for example, they reported
associations between combined fruit/vegetable intake and weight
outcomes. Upon inspection of full-text manuscripts, 41 studies
met inclusion criteria; 11 body weight RCTs, 5 energy intake
RCTs, and 25 observational studies. Reasons for exclusion were
that studies were duplicates (n=3), interventions were not long
enough to meet inclusion criteria (n=1), interventions involved
processed rather than whole, fresh fruit (n=2), studies did not
report data that are pertinent to this review (n=8), observational
studies were potentially confounded by a concurrent intervention
(n=4), and observational studies reported data in a cross-
sectional rather than prospective manner (n=4).
RCTs reporting the impact of whole, fresh fruit consumption
on body weight and adiposity were published between 1992
and 2016 and are summarized in Table 1. Of the 11 RCTs
identified, two included fewer than 20 subjects, four included 21–
50 subjects, two included 51–100 subjects, and three included
more than 100 subjects. Interventions represented a variety of
fruit types, but guava (n=3), apple (n=2), and grapefruit (n
=2) were named more commonly than other fruit. The quantity
of fruit varied widely, with the highest intake representing a
goal of 15 percent of energy intake from fruit-derived fructose,
which implies 30 percent of total energy intake from fruit.
Interventions lasted 3–24 weeks, with 6 and 12 weeks being
most common. All RCTs reported weight changes, and six
also reported changes in other measures of adiposity. Four
trials reported performing a power calculation as part of trial
design, and three trials were preregistered, of which two were
preregistered for an adiposity-related outcome.
RCTs reporting the impact of whole, fresh fruit consumption
on energy intake were published between 2003 and 2016 and
are summarized in Table 2. Of the 5 RCTs identified, two
included fewer than 20 subjects, two included 21–50 subjects, and
one included more than 50 subjects. Interventions represented
several fruit types, but apple (n=2) was named more commonly
than other fruit. As in the body weight RCTs (some of which
also met inclusion criteria for energy intake), the quantity of
fruit varied widely, with the highest intake representing a goal
of 15 percent of calorie intake from fruit-derived fructose,
which implies 30 percent of total energy intake from fruit.
Interventions lasted between one meal and 12 weeks, with single-
meal and 8-week trials being the most common. Three trials used
self-report methods to measure energy intake, two of which used
a 3-day weighed food record; food intake in the other two trials
was directly measured by investigators. The latter two trials were
single-meal studies. Three trials reported performing a power
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TABLE 2 | Secondary outcome: impact of whole, fresh fruit consumption on energy intake in RCTs.
Trial Subjects Intervention Duration Energy intake difference Measurement
method
Notes
de Oliveira et al.
(26)
Hypercholesterolemic, overweight,
non-smoking women, 30 to 50 years
of age. 49 randomized; number in
each group unclear.
3 apples, pears, or oat cookies per
day. Hypocaloric diet for all subjects.
12 weeks 22 kcal (p=NR) daily Food frequency
questionnaire and
3-day dietary record
No power calculation. Not
preregistered. No
between-group p-value provided
for EI. May have pooled two fruit
groups that were individually
randomized.
Rodriguez et al.
(27)
Women with obesity. 15 randomized;
8 low-fruit; 7 high-fruit.
Low-fruit diet vs. high-fruit diet (goal
of 5 vs. 15% of kcal from fructose).
Hypocaloric diet for all subjects.
8 weeks +47 kcal (p=NS) daily 3-day dietary record No power calculation. Not
preregistered. Very small sample
size
Flood-Obbagy and
Rolls (35)
Men and women 18–45 years old. 59
randomized; crossover design.
Isocaloric preload with apple, apple
sauce, apple juice with added fiber,
apple juice, or no preload. Followed
by an ad libitum test meal.
Single meal 187 kcal vs. no preload (p<
0.0001); 91 kcal vs. apple sauce
(p<0.02); 152 kcal vs. apple
juice with fiber (p<0.02); 178
kcal vs. apple juice (p<0.02).
Figures represent total meal energy
intake including preload.
Weighed by
investigators
Performed a power calculation.
Not preregistered.
James et al. (36) Healthy pre-menopausal women. 12
randomized; crossover design.
Isocaloric mixed berries vs.
confectionary snack, followed by an
ad libitum test meal.
Single meal 134 kcal (p<0.001) Weighed by
investigators
Performed a power calculation.
Not preregistered.
Agebratt et al. (33) Healthy non-obese men and women.
30 randomized; 15 fruit; 15 nuts.
Diet supplementation with 7 kcal/kg
body weight/d of fruit vs. nuts.
8 weeks 216 kcal (p=0.37) 3-day weighed dietary
record
Performed a power calculation
but based on hepatic fat
outcome. Preregistered but not
for energy intake
(NCT02227511). Registry page
is sparse.
Randomized controlled trials reporting the impact of whole, fresh fruit consumption on energy intake. Energy intake differences represent the fruit intervention group relative to the comparator group. Additional strengths and limitations
of study design are listed in the “notes” column. NS, not statistically significant (p >0.05); NR, not reported.
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Guyenet Fruit, Energy Intake, and Adiposity
calculation as part of study design, although one was powered for
a hepatic fat outcome. Only one trial was preregistered, also for a
hepatic fat outcome.
Prospective observational studies reporting the impact of
whole, fresh fruit consumption on body weight and adiposity
change were published between 2002 and 2018 and are
summarized in Table 3. Of the 25 studies identified, seven
included fewer than 1,000 subjects, six included 1,000–10,000
subjects, seven included 10,001–50,000 subjects, and five
included more than 50,000 subjects. Follow-up length ranged
from 6 months to 24 years. Eleven studies reported weight
changes, and 18 reported changes in other measures of adiposity.
None were preregistered, and only one applied an adjustment for
multiple comparisons (Bonferroni correction) to control family-
wise error rate when testing several hypotheses using a single
data set.
Risk of Bias
Figure 2 presents the Cochrane risk of bias graph, and Figure 3
presents the Cochrane risk of bias summary for RCTs reporting
the impact of whole, fresh fruit consumption on body weight
and other measures of adiposity. Six of 11 included trials are
at an unclear risk of bias from random sequence generation
and allocation concealment due to incomplete description of the
randomization process, while the other five are at a low risk of
bias. All 11 trials are at a high risk of bias due to lack of blinding
of participants, an unavoidable consequence of including whole,
fresh fruit consumption as an intervention. This leaves open
the possibility that part of the impact of fruit observed in these
trials is attributable to placebo effects. The risk of bias due to
blinding of outcome assessment is low in all 11 trials, not because
investigators were consistently blinded to treatment assignment,
but because there is a low risk of bias in measuring body weight
due to its simple and objective nature. Nine of 11 trials had a low
risk of attrition bias due to low dropout rates, while the other two
were at high risk due to high dropout rates. Nine of 11 trials were
at an unclear risk of selective reporting bias due to insufficient
information about initial study design resulting from a lack of
detailed preregistration information, while the other two were at
low risk due to detailed preregistration. The risk of other bias was
fairly evenly split between low (n=3), unclear (n=4), and high
(n=4) risk of bias. The two studies with a low risk of other bias
were scored as such because they offered detailed preregistration
information with primary outcomes relevant to adiposity.
Body weight RCTs that scored most favorably on the Cochrane
risk of bias tool are Madero et al. (30), Dow et al. (31), Agebratt
et al. (33), Singh (25), Singh et al. (24), and Kumari et al. (34)
(Figure 3). Each received a score of “low risk” in four or five of
seven domains.
Figure 4 presents the Cochrane risk of bias graph, and
Figure 5 presents the Cochrane risk of bias summary for RCTs
reporting the impact of whole, fresh fruit consumption on
energy intake. This literature fared more poorly in risk of bias
scoring than the body weight RCT literature. All energy intake
RCTs are at an unclear risk of bias from random sequence
generation, and four of five are at an unclear risk of bias from
allocation concealment. This is due to incomplete description
of the randomization process. For the same reason as the body
weight RCTs, all energy intake RCTs are at a high risk of bias due
to lack of blinding of participants, leaving open the possibility
of placebo effects. The risk of bias due to blinding of outcome
assessment is high in three of five energy intake RCTs due to
reliance on self-reported measures of energy intake, and low
in two of five due to directly measured energy intake. Four of
five trials had a low risk of attrition bias due to low dropout
rates. All trials were at an unclear risk of selective reporting
bias due to insufficient information about initial study design
resulting from a lack of detailed preregistration information.
The risk of other bias was unclear in four of five trials due to
insufficient information.
Energy intake RCTs that scored most favorably on the
Cochrane risk of bias tool are Flood-Obbagy and Rolls (35), James
et al. (36), and Agebratt et al. (33) (Figure 5), although none
received a score of “low risk” in more than two of seven domains.
Although observational studies were not assessed using the
Cochrane risk of bias tool, features that are informative of
bias risk will nevertheless be considered here. All observational
studies included in this review used self-report methods
to measure fruit intake, most commonly food frequency
questionnaires (Table 3). This introduces a substantial source
of error that may also introduce an unknown degree of bias.
Validation studies suggest that the Pearson correlation coefficient
between fruit intake measured by food frequency questionnaires
and 7-day weighed food record is 0.50–0.67, implying that 65–
75 percent (R2) of the variability in fruit intake identified by
7-day weighed food record is not captured by food frequency
questionnaire (62). Although it has been argued that self-report
error is randomly distributed and should not bias associations,
the author is uncertain to what extent this argument is correct,
and in which contexts.
Observational studies are inherently more limited than RCTs
as tools for causal inference because relationships between
exposure and outcome variables may be confounded by other
variables. For example, between-person variation in fruit and
vegetable intake has a genetic component and “individuals
genetically predisposed to low fruit and vegetable consumption
may be predisposed to higher [body mass index],” suggesting
a possible source of bias that could be both important
and difficult to correct (63). For this reason among others,
observational relationships between fruit intake and body weight
changes can be difficult to interpret. Most of the studies
represented in Table 3 adjusted extensively for confounding
variables in an attempt to limit confounding bias. However, it
is difficult to be certain that the most impactful confounding
variables were measured and appropriately incorporated into
multivariate models.
In addition, it is difficult to be certain that observational
relationships were not overadjusted, attenuating the measured
association between fruit intake and body weight/adiposity
outcomes. In this regard, it is notable that many studies
adjusted for energy intake, which seems suboptimal for the
purposes of this review since modified energy intake may be
a key intermediate variable between fruit intake and body
weight/adiposity outcomes.
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 3 | Secondary outcome: association of whole, fresh fruit intake with changes in measures of adiposity in prospective observational studies.
Study Subjects Intake measure Follow-up
length
Weight change Adiposity change Covariates Notes
Schulz et
al. (37)
17,369 German
non-smoking men
and women
Food frequency
questionnaire
2 years Not reported Men: OR per 100 g daily intake for small wt
gain 1.04 (p=NS); OR for large wt gain
0.94 (p=NS); OR for small wt loss 1.05 (p
=NS); OR for large wt loss 1.03 (p=NS).
Women: OR per 100 g intake for small wt
gain, 0.94 (p=NS); OR for large wt gain
0.94 (p=NS); OR for small wt loss 1.01 (p
=NS); OR for large wt loss 1.03 (p=NS).
Age, initial body weight and height,
education, weight history, medication,
menopause, life and health contentment,
dietary change, physical activity, prevalent
diabetes and thyroid disease
Not adjusted for multiple
comparisons. Not
preregistered.
Field et al.
(38)
14,918 US boys
and girls ages
9–14
Food frequency
questionnaire
3 years Not reported Boys: 0.0 SD BMI z-score (p=NS); Girls:
0.0 SD BMI z-score (p=NS)
Age, age squared, Tanner stage, height
change, baseline weight, physical activity,
and inactivity
Not adjusted for multiple
comparisons. Not
preregistered.
Newby et
al. (39)
1,379 US boys
and girls ages 2–5
Food frequency
questionnaire
6–12 months +0.02 kg/year per daily
serving (p=0.41)
Not reported Age, sex, ethnicity, residence, level of
poverty, maternal education, birth weight,
food groups
Not adjusted for multiple
comparisons. Not
preregistered.
Drapeau et
al. (40)
248 Canadian
men and women
3-day dietary
record
6 years 0.18 kg per 1 percent
increase in fruit intake
(p=0.03)
0.16 % BF per 1 percent increase in fruit
intake (p=0.01); 0.19 cm waist
circumference per 1 percent increase in
fruit intake (p=0.03); 1.05 mm sum of 6
skinfold thicknesses per 1 percent
increase in fruit intake (p=0.01)
Age, baseline body weight, or adiposity
indicators, changes in daily physical
activity level
Not adjusted for multiple
comparisons. Not
preregistered.
He et al.
(41)
74,063 US female
health
professionals ages
38–63
Food frequency
questionnaire
12 years Not reported OR for obesity 0.76 in highest vs. lowest
quintile of fruit intake change (p=0.0007);
OR for major weight gain 0.73 in highest
vs. lowest quintile of fruit intake change (p
=0.03)
Age, year of follow-up, change in physical
activity, change in cigarette smoking
status, changes in alcohol consumption
and caffeine intake, change in use of
hormone replacement therapy, changes in
energy-adjusted intakes of saturated fat,
polyunsaturated fat, monounsaturated fat,
trans-unsaturated fatty acid, protein, total
energy, baseline BMI
Not adjusted for multiple
comparisons. Not
preregistered.
Koh-
Banerjee et
al. (42)
27,082 US male
health
professionals ages
40–75
Food frequency
questionnaire
8 years 2.51 kg per 20 g/d
increase in fruit fiber (p
<0.001)
Not reported Age, baseline fruit intake, smoking,
baseline weight, and baseline values and
changes in refined grains, calories, total
physical activity, alcohol, protein, and
trans, saturated, monounsaturated, and
polyunsaturated fats
Not adjusted for multiple
comparisons. Not
preregistered.
Nooyens
et al. (43)
288 Dutch men
ages 50–65 years
Food frequency
questionnaire
5 years 0.02 kg/year
per serving increase of
fruit per week (p=
0.03)
0.03 cm/year WC per serving increase of
fruit per week (p<0.01)
Retirement status, type of job, interaction
between retirement and type of job, age,
smoking, baseline fruit intake, physical
activity, intake of potatoes, breakfast,
sugar-sweetened beverages, fiber density
Not adjusted for multiple
comparisons. Not
preregistered.
Sanchez-
Villegas et
al. (44)
6,319 Spanish
male and female
university
graduates
Food frequency
questionnaire
28 months 0.09 kg in highest vs.
lowest tertile of fruit
intake (p=0.46)
Not reported Age, sex, baseline BMI, smoking, physical
activity, alcohol consumption, energy,
intake, change in dietary habits, physical
activity, intake of cereals, vegetables,
legumes, fish, nuts, meat, full-fat dairy,
olive oil, red wine
Not adjusted for multiple
comparisons. Not
preregistered.
(Continued)
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 3 | Continued
Study Subjects Intake measure Follow-up
length
Weight change Adiposity change Covariates Notes
te Velde et
al. (45)
168 Dutch men
and women
Dietary history
interview
24 years Not reported 0.71 BMI units in highest vs. lowest
quartile of fruit intake (p=NS); 0.16 mm
sum of 6 skinfolds in highest vs. lowest
quartile of fruit intake (p=NS)
Sex, bone age at 13 years, total energy
intake, physical activity, tobacco use, fiber
intake
Not adjusted for multiple
comparisons. Not
preregistered.
Vioque et
al. (46)
206 Spanish men
and women ages
15–80
Food frequency
questionnaire
10 years Not reported OR 0.62 for weight gain >3.41 kg over 10
years in highest vs. lowest quartile of fruit
intake (p=0.211)
Sex, age, educational level, BMI, time
spent watching TV, presence of disease,
baseline height, energy intake,
energy-adjusted intakes of protein,
saturated fat, monounsaturated fat,
polyunsaturated, fiber, caffeine, alcohol
Not adjusted for multiple
comparisons. Not
preregistered.
Buijsse et
al. (47)
89,432 Danish,
German, UK,
Italian, and Dutch
men and women
Food frequency
questionnaire
6.5 years 0.016 kg/y per
additional 100 g fruit
intake (p<0.05)
Not reported Age, sex, cohort, product term UK-Nor X
fruit/vegetable intake, years of follow-up,
baseline weight, baseline height, change in
smoking status, baseline physical activity
(dummy variables), education, alcohol
intake, postmenopausal status,
postmenopausal hormone use
Not adjusted for multiple
comparisons. Not
preregistered.
Halkjaer et
al. (48)
42,696 Danish
men and women
ages 50–64
Food frequency
questionnaire
5 years Not reported Men: 0.07 cm WC per additional 60
kcal/d fruit intake (p<0.001). Women:
0.10 cm WC per additional 60 kcal/d
fruit intake (p<0.07).
Baseline waist circumference, body mass
index, age, smoking, sport, hours of sport,
energy intake from wine, beer, and spirits,
baseline intake of 21 food and beverage
groups
Not adjusted for multiple
comparisons. Not
preregistered.
Berz et al.
(49)
2,327 US girls age
9 years
3-day dietary
record
10 years Not reported 2.1 BMI units in highest vs. lowest fruit
intake (p<0.001)
Race, height, SES, physical activity level,
television viewing and video game playing,
and total energy
Not adjusted for multiple
comparisons. Not
preregistered.
Mozaffarian
et al. (50)
120,877 US male
and female health
professionals
Food frequency
questionnaire
12–20 years 0.22 kg per 4-year
period per 1-serving
increase (p<0.001)
Not reported Age, baseline BMI, sleep duration,
changes in physical activity, alcohol use,
television watching, smoking, vegetables,
nuts, dairy, potatoes, grains,
sugar-sweetened beverages, fruit juice,
diet beverages, sweets, meats, trans fat,
fried foods
Not adjusted for multiple
comparisons. Not
preregistered.
Romaguera
et al. (51)
48,631 Danish,
German, UK,
Italian, and Dutch
men and women
Food frequency
questionnaire
5.5 years Not reported 0.04 cm/y WC (adjusted for BMI) per
100 kcal increment of fruit intake (p<
0.001)
Energy intake, age, baseline weight,
baseline height, baseline WC (adjusted for
BMI), smoking, alcohol intake, physical
activity, education, follow-up duration,
menopausal status, hormone replacement
therapy
Not adjusted for multiple
comparisons. Not
preregistered.
Mirmiran et
al. (52)
1,930 Iranian men
and women ages
19–70
Food frequency
questionnaire
3 years 0.42 kg in highest vs.
lowest quartile of fruit
intake (p=0.01)
0.53 cm WC in highest vs. lowest
quartile of fruit intake (p=0.006)
Sex, age at baseline, BMI, education,
smoking, physical activity, total energy
intake, dietary carbohydrate, fat, protein
Not adjusted for multiple
comparisons. Not
preregistered.
(Continued)
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 3 | Continued
Study Subjects Intake measure Follow-up
length
Weight change Adiposity change Covariates Notes
Vergnaud
et al. (53)
373,803 Danish,
French, German,
Greek, Italian,
Dutch, Norwegian,
Spanish, Swedish,
and UK men and
women
Food frequency
questionnaire
5 years Men: 0.001 kg/y per
additional 100 g fruit
intake (p=0.75).
Women: 0.001 kg/y
per additional 100 g
fruit intake (p=0.20).
Not reported Age, education, physical activity, change
in smoking status, BMI at baseline,
follow-up time, energy intake, energy
intake from alcohol, plausibility of total
energy intake reporting, vegetable intake
Not adjusted for multiple
comparisons. Not
preregistered. Intake data
were calibrated using 24-h
dietary recall data.
Bayer et al.
(54)
1,252 girls and
boys age 6
Parental
questionnaire
4 years Not reported +0.014 unit BMI z-score in high vs. low
fruit consumers (p=NS). +0.033 unit BMI
z-score for decreased fruit intake (p=
0.808). 0.126 unit BMI z-score for
increased fruit intake (p=0.348).
Physical activity, cluster Not adjusted for multiple
comparisons. Not
preregistered.
Bertoia et
al. (55)
133,468 US male
and female health
professionals
Food frequency
questionnaire
13–14 years
(mean)
0.24 kg per 4-year
period per 1-serving
increase (p<0.05)
Not reported Baseline age, BMI, change in smoking
status, physical activity, hours of sitting or
watching TV, hours of sleep, fried
potatoes, juice, whole grains, refined
grains, fried foods, nuts, whole-fat dairy,
low-fat dairy, sugar-sweetened beverages,
sweets, processed meats, non-processed
meats, trans fat, alcohol, seafood
Not adjusted for multiple
comparisons. Not
preregistered. Similar to
Mozaffarian et al. (50).
de Munter
et al. (56)
23,108 Swedish
men and women
ages 18–84
Questionnaire 8 years Not reported Men: 0.07 BMI units in “daily” vs. “less
than daily” fruit intake group (p=NS); RR
0.89 for overweight incidence (p=NS);
RR 0.90 for obesity incidence (p=NS).
Women: +0.02 BMI units in “daily” vs.
“less than daily” fruit intake group (p=
NS); RR 0.94 for overweight incidence (p
=NS); RR 0.88 for obesity incidence (p=
NS).
Age, education, physical activity, alcohol
intake, smoking
Not adjusted for multiple
comparisons. Not
preregistered.
Rautiainen
et al. (57)
18,146 US female
health
professionals aged
45
Food frequency
questionnaire
15.9 years 0.01 kg in highest vs.
lowest quintile of fruit
intake (p=0.46)
HR 0.87 for obesity or overweight in
highest vs. lowest quintile of fruit intake (p
=0.01)
“Age, randomization treatment
assignment, physical activity, history of
hypercholesterolemia or hypertension,
smoking status, postmenopausal status,
postmenopausal hormone use, alcohol
use, multivitamin use, energy intake,
baseline BMI”
Not adjusted for multiple
comparisons. Not
preregistered.
Hur et al.
(58)
770 Korean male
and female
children and
adolescents
3-day dietary
record
4 years Not reported 0.08 unit BMI z-score per g/d fruit sugar
(p<0.05). 0.60% BF per g/d fruit sugar
(p=NS).
Not adjusted for multiple
comparisons. Not
preregistered.
(Continued)
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Guyenet Fruit, Energy Intake, and Adiposity
TABLE 3 | Continued
Study Subjects Intake measure Follow-up
length
Weight change Adiposity change Covariates Notes
Bel-Serrat
et al. (59)
2,755 Irish boys
and girls ages
6–10
Parental food
frequency
questionnaire
3 years Not reported OR 2.16 for developing
overweight/obesity in “sometimes/never”
fresh fruit vs. “every day/most days” group
(p<0.01). 0.04 unit BMI z-score in
“sometimes/never” fresh fruit vs. “every
day/most days” group (p=NS).
Energy intake, income, sex, age Not adjusted for multiple
comparisons. Not
preregistered.
Mumena et
al. (60)
336 Caribbean (St.
Kitts and Nevis,
Trinidad and
Tobago) boys and
girls ages 6–10
24-h dietary recall 18 months Not reported Children who became overweight or
obese had a lower fruit intake at baseline
than children who did not (p=0.017)
Measurement round, follow-up length,
age, sex, baseline z-BMI, baseline
abdominal obesity status, school
socioeconomic status, school location,
household ownership
Adjusted for multiple
comparisons (Bonferroni).
Not preregistered.
Okop et al.
(61)
800 South African
men and women
Food frequency
questionnaire
4.5 years Not reported OR 1.47 of weight gain 5% in “seldom or
no daily fruit” vs. “daily fruit” (p<0.05)
N/A Not adjusted for multiple
comparisons. Not
preregistered.
Prospective observational studies reporting the association between whole, fresh fruit consumption and body weight and adiposity changes. Additional strengths and limitations of study design are listed in the “notes” column. NS, not
statistically significant (p >0.05); NR, not reported; OR, odds ratio; RR, relative risk; HR, hazard ratio; BMI, body mass index; BF, body fat; WC, waist circumference.
An additional potential source of bias is that none of the
observational studies reported preregistration, leaving open the
possibility that outcome selection and data analysis methods were
(perhaps inadvertently) guided toward preferred outcomes. The
great diversity of analytic methods represented in these studies
amplifies this concern because it demonstrates that the possible
space of analytic methods is vast (64). Preregistration narrows
this space a priori.
Finally, only one observational study reported adjusting
significance tests for multiple comparisons, e.g., Bonferroni
correction. The more hypothesis tests that are performed on a
single data set, the higher the likelihood of a false positive finding.
Since the commonly accepted false positive rate in the biomedical
research community is 5 percent, testing ten hypotheses yields a
40 percent risk of that at least one of the ten tests will return a false
positive finding. Many of the observational studies identified here
tested ten or more hypotheses, and most represent datasets that
have been analyzed using many statistical tests in other contexts
(64). Overall, the risk of bias in the observational literature
considered here appears quite high and must limit the strength
of causal inferences drawn from it.
Synthesized Findings
Body Weight RCTs
The primary outcome of this review is the impact of whole,
fresh fruit consumption on measures of adiposity including
body weight, as measured by RCTs. Given the limited number
of studies identified, which would be further reduced by more
stringent meta-analysis inclusion criteria, and large variation
of methodology and study quality, qualitative synthesis appears
most appropriate.
Of the 11 RCTs included, seven reported numerical reductions
of body weight as a result of increased fruit consumption
relative to a comparison group, while four reported numerical
increases of weight (Table 1). Among trials that reported
numerical reductions of body weight, three comparisons were
statistically significant (26,28,30), while none of the comparisons
suggesting weight increases achieved statistical significance. This
is consistent with the possibility that the weight increases
result from random chance. In support of this possibility,
studies that reported non-significant weight increases from fruit
consumption tended to be statistically underpowered, with as few
as six subjects per group (Table 1).
Among the two trials that reported performing a power
calculation a priori and were preregistered with measures of
adiposity as the primary outcome, Madero et al. (30) reported a
significant weight loss of 1.36 kg over 6 weeks of substantially
increased fruit consumption (30), while Dow et al. (31) reported
a non-significant weight loss of 0.5 kg over 6 weeks of modestly
increased fruit consumption (31). In addition, Madero et al. (30)
received the lowest risk of bias score of all studies considered,
and Dow et al. (31) tied for the second lowest risk of bias.
In contrast, three of the four trials reporting non-significant
increases of body weight from increased fruit consumption were
among those with the highest risk of bias, while the fourth, which
reported a non-significant and negligible weight gain of 0.03 kg
over 8 weeks, was among those with relatively lower risk of bias
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Guyenet Fruit, Energy Intake, and Adiposity
FIGURE 2 | Risk of bias graph for the primary outcome: the impact of whole, fresh fruit consumption on measures of body weight and adiposity in RCTs. Bars
illustrate the proportion of trials that received a particular risk of bias score in each risk of bias domain.
(Figure 3). It is evident that the trials reporting weight reductions
tend to have higher methodological quality than those reporting
weight increases, suggesting that the former should be viewed as
more informative.
Six trials reported measures of adiposity other than body
weight, including body fat percentage, waist circumference,
waist-to-hip ratio, sagittal abdominal diameter, and visceral
fat volume (Table 1). Only one measure achieved statistical
significance, a waist circumference reduction of 3.1 cm
over 8 weeks of a high-fruit diet (27). However, this
finding is questionable due to the study’s small sample
size, high risk of bias, and apparent lack of correction for
multiple comparisons.
Since Madero et al. (30) and Dow et al. (31) appear to
surpass the others in methodological quality and relevance to
adiposity modification, these will be discussed in detail and will
contribute disproportionately to the overall conclusions of this
review (30,31). These two trials are particularly relevant to
adiposity modification because all subjects had overweight or
obesity. Madero et al. (30) was preregistered with anthropometric
changes as the primary outcome, and sample size was selected
using a power calculation. Sample size was larger than all
but one other trial, which had the same number of subjects.
This trial also received the lowest risk of bias score among
the 11 trials identified, with low risk in all categories except
participant blinding, in which high risk is unavoidable due to the
impossibility of blinding subjects, and allocation concealment,
which was judged as unclear because it was not described in
the manuscript.
One hundred and thirty one men and women with overweight
or obesity were randomly assigned to eat a low-fructose diet
(<20 g/d) vs. a natural-fructose diet (50–70 g/d) in which most
of the fructose was from whole, fresh fruit, for 6 weeks. The
latter is approximately equivalent to five to eight whole medium
apples per day, or eleven to fifteen whole oranges per day.
After 6 weeks, the natural-fructose group had lost 1.36 kg more
weight than the low-fructose group (p=0.02). The trial also
reported non-significant reductions of body fat percentage and
waist-to-hip ratio in the natural-fructose group relative to the
low-fructose group.
Dow et al. (31) was preregistered with body weight change as
the primary outcome, and sample size was selected using a power
calculation (31). This trial tied for the second-lowest risk of bias
score among the 11 trials identified, with low risk of bias in four of
seven domains (Figure 3). Its risk of bias in participant blinding
was judged as high, which is unavoidable. Its risk of bias due to
randomization and allocation concealment are unclear due to a
lack of information in the manuscript.
Seventy-four men and premenopausal women with
overweight or obesity were randomly assigned to eat half a
fresh grapefruit three times per day prior to each meal, vs. no
intervention, for 6 weeks. In addition, all subjects were assigned
to a baseline diet low in fruit and vegetables. After 6 weeks, the
grapefruit group had lost 0.5 kg more weight than the control
group, but this difference was not statistically significant (p=
0.119). Between-group differences in body fat percentage (+0.55
percent), waist circumference (1.22 cm), and waist-to-hip ratio
(0.0) were also non-significant.
Informally weighting the strength of findings according to
study quality and risk of bias, the overall RCT literature suggests
that increasing whole, fresh fruit consumption promotes weight
maintenance or modest weight loss over periods of 3–24 weeks,
with limited evidence suggesting that high intakes of fruit lead to
weight loss among people with overweight or obesity. The overall
quality of evidence according to the GRADE method is moderate,
indicating that the true effect of fruit consumption on measures
of adiposity is probably close to the effect estimated in the RCTs
considered here, particularly those of higher quality.
Energy Intake RCTs
A secondary outcome of this review is the impact of whole, fresh
fruit consumption on measures of energy intake, as measured by
RCTs. Given the small number of studies identified, and large
variation of methodology and study quality, qualitative synthesis
appears most appropriate.
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Guyenet Fruit, Energy Intake, and Adiposity
FIGURE 3 | Risk of bias summary for individual trials contributing to the
primary outcome: the impact of whole, fresh fruit consumption on measures of
body weight and adiposity in RCTs. Colored dots represent low risk (green),
unclear risk (yellow), and high risk (red) in each risk of bias domain for each trial.
Of the five RCTs included, four reported numerical reductions
of energy intake as a result of increased fruit consumption relative
to a comparison group, while one reported a numerical increase
of energy intake (Table 2). Among trials that reported numerical
reductions of energy intake, two comparisons were statistically
significant (35,36), while the comparison suggesting numerically
increased energy intake reported a small-magnitude effect (+47
kcal/d) that did not achieve statistical significance (27). This
is consistent with the possibility that the finding of increased
energy intake resulted from random chance. In support of this
possibility, this study was likely statistically underpowered, with
seven and eight subjects per group (Table 1). It did not report
performing an a priori power calculation and was also at high
risk of bias due to its use of a self-reported measure of energy
intake, and not blinding subjects (unavoidable) or investigators
(avoidable) (Figure 5).
Only one energy intake trial was preregistered, and its primary
outcome was a change of hepatic fat content rather than energy
intake (33). Among the three trials that reported performing a
power calculation a priori, two reported statistically significant
single-meal reductions of energy intake of 134–187 kcal from
meals including a fruit preload relative to no preload or a
confectionary snack (35,36), and the third reported a non-
significant 216-kcal reduction of daily energy intake from a high-
fruit diet relative to a high-nut diet (33). The former two trials
were the only two to employ direct measurement of energy
intake by investigators rather than self-reported intake (Table 2).
The three trials that performed power calculations received the
most favorable risk of bias scores among the five trials identified,
although none of the five trials received a low risk score in more
than two of seven domains (Figure 5).
Consistent with the adiposity RCTs, it is evident that the trials
reporting energy intake reductions, and particularly statistically
significant ones, tend to have higher methodological quality than
the trial reporting energy intake increase, suggesting that the
former should be viewed as more informative. However, it is
notable that the three trials reporting non-significant effects,
while relying on self-reported data, were the only three with
follow-up periods longer than a single meal (Table 2).
Flood-Obbagy and Rolls (35) and James et al. (36) appear to
surpass the other trials in methodological quality due to direct
measurement of energy intake, lower risk of bias score than the
other three, and sufficient statistical power supported by a priori
power calculations (35,36). Flood-Obbagy and Rolls (35) also has
the largest sample size of the five trials identified, and its effective
sample size is amplified by its crossover design. These two trials
will be discussed in detail and will contribute disproportionately
to the overall conclusions of this review. Although these trials
may provide a higher level of certainty than the others, they are
less relevant to energy intake control in the context of obesity
because their subjects were either lean (35) or mildly overweight
(36) on average.
Flood-Obbagy and Rolls (35) received one of the lowest risk
of bias scores among the five trials identified, although none of
the trials were at a low risk of bias. It received a low risk of bias
score in blinding of outcome assessment due to directly measured
energy intake, and low risk of attrition bias due to low attrition
(Figure 5). It received an unclear risk of bias score for random
sequence generation, allocation concealment, selective reporting,
and other bias due to insufficient information in the manuscript
and the absence of preregistration. It received a high risk of bias
score in participant blinding, in which high risk is unavoidable.
Fifty-nine men and women 18–45 years old, with body mass
index of 23.7 kg/m2(M) and 24.3 kg/m2(W), received five
food preloads in random order on different days, followed after
15 min by an ad libitum test meal of cheese tortellini, tomato
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Guyenet Fruit, Energy Intake, and Adiposity
FIGURE 4 | Risk of bias graph for a secondary outcome: the impact of whole, fresh fruit consumption on energy intake in RCTs. Bars illustrate the proportion of trials
that received a particular risk of bias score in each risk of bias domain.
sauce, and water (35). Preload conditions were whole apple, apple
sauce, apple juice with added soluble fiber (pectin), apple juice, or
no preload. All preloads were adjusted to contain equal energy
(125 kcal) and weight (266 g) except the no-preload control.
Total meal energy intake as directly measured by investigators,
including preload, was lowest in the whole apple condition and
highest in the no-preload condition, with a highly statistically
significant difference of 187 kcal (Table 2). Energy intake in
the whole apple condition was also significantly lower than all
other conditions. Total meal energy intake increased with each
processing step away from whole fresh apples, in the following
order: whole apples <apple sauce <apple juice with fiber <
apple juice.
James et al. (36) received a risk of bias score identical to
Flood-Obbagy and Rolls (35) for similar reasons (Figure 5) (36).
Twelve healthy pre-menopausal women, with body mass index of
26.6 kg/m2, received two preloads in random order on different
days, followed after 60 min by an ad libitum test meal of pasta
with Bolognese sauce and olive oil. Preload conditions were fresh
mixed berries vs. soft berry-flavored candies and were matched
for energy content (65 kcal). The two preloads also contained
a similar amount of sugar (12.1 vs. 15.5 g). Energy intake at
the test meal, as directly measured by investigators, was 134
kcal lower in the mixed berry condition than in the candy
condition (p<0.001).
Informally weighting the strength of findings according to
study quality and risk of bias, the overall RCT literature suggests
that increasing whole, fresh fruit consumption reduces energy
intake, particularly when consumed prior to a meal or instead
of more energy-dense foods. However, these findings have
uncertain relevance to energy intake control in obesity because
the most informative trials were conducted in subjects who were
lean or modesty overweight. In addition, the most informative
trials used single-meal designs, limiting conclusions about the
impact of fruit consumption on long-term energy intake. The
overall quality of evidence according to the GRADE method
is moderate, indicating that the true effect of pre-meal fruit
consumption on short-term energy intake in people without
obesity is probably close to the effect estimated in the RCTs
considered here. Longer-term effects, and those in people with
obesity, are less certain. Nevertheless, the energy intake RCT
literature is broadly consistent with findings from the body
weight RCT literature.
Prospective Observational Studies
A secondary outcome of this review is the association between
whole, fresh fruit consumption and measures of adiposity
including body weight, as measured by prospective observational
studies. Of the 25 studies identified, 11 reported weight changes
over time, and of those, ten reported that people who consumed
larger amounts of fruit gained numerically less weight over time
(or lost more weight) than people who consumed less fruit
(Table 3). Seven of these associations were statistically significant,
all suggesting that higher fruit intake is associated with superior
weight control over time.
Eighteen studies reported changes in measures of adiposity
other than weight over time, 12 of which reported statistically
significant differences between higher and lower consumers of
fruit (Table 3). Among these 12, all reported that markers of
adiposity in people who consume larger amounts of fruit tend to
increase less over time (or decline more rapidly) than in people
who consume less fruit. Although the Cochrane risk of bias tool
was not applied to these studies, as discussed previously they
all appear to be at a high risk of bias due to a combination of
unavoidable and potentially avoidable design features.
Nevertheless, some studies appear more informative than
others. Bertoia et al. (55) [similar to Mozaffarian et al. (50)] and
Vergnaud et al. (53) will be discussed further due to the fact that
they have the largest sample sizes of the 11 studies identified, they
rely on contextually-validated food frequency questionnaires,
they have relatively long follow-up periods, and together they
represent men and women of 11 nations (35,36).
Bertoia et al. (55) compiled data from three cohorts
representing 133,468 US male and female health professionals
(55). Diet assessment was performed using food frequency
questionnaires administered at 4-year intervals for a mean of
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Guyenet Fruit, Energy Intake, and Adiposity
FIGURE 5 | Risk of bias summary for individual trials contributing to a
secondary outcome: the impact of whole, fresh fruit consumption on energy
intake in RCTs. Colored dots represent low risk (green), unclear risk (yellow),
and high risk (red) in each risk of bias domain for each trial.
13–14 years. Analyses controlled for a wide variety of diet and
lifestyle factors (Table 3), and although they did not control for
demographic variables such as income and education, included
cohorts were fairly homogeneous in these respects. Notably,
analyses did not control for energy intake, which is preferable
because energy intake is likely a mediating variable between
whole, fresh fruit intake and adiposity.
In contrast to most other studies identified, Bertoia et al.
(55) examined the association between changes in self-reported
fruit intake and changes in measures of adiposity. In other
words, if a person reported increasing fruit intake over the
course of the follow-up period, were they also less likely to gain
weight over time? Although this “change-on-change” method
remains fundamentally observational, it may avoid some of the
confounding potential of traditional nutritional epidemiology
study designs (65). The study reported that a one-serving increase
of daily fruit intake was associated with a highly statistically
significant 0.24 kg reduction of body weight per 4-year period,
and did not report associations with other measures of adiposity.
Vergnaud et al. (53) represents 373,803 Danish, French,
German, Greek, Italian, Dutch, Norwegian, Spanish, Swedish,
and UK men and women, making it the largest cohort of the
11 studies identified (53). Diet assessment was performed using
food frequency questionnaires and the duration of follow-up was
5 years. In contrast to Bertoia et al. (55) but similar to most other
nutritional epidemiology studies, Vergnaud et al. (53) reports the
association between baseline self-reported fruit intake and weight
change over a 5-year period. Analyses controlled for several
basic diet, lifestyle, and demographic factors, including energy
intake (Table 3). Systematic underestimation or overestimation
of dietary intakes between study centers was addressed using a
dietary calibration study. The study reported that 100 g higher
daily fruit intake was associated with a non-significantly lower
rate of weight gain of 0.001 kg per year in men and women, and
did not report associations with other measures of adiposity.
Informally weighting the strength of findings according to
study quality, the overall prospective observational literature
suggests that habitually higher fruit intake is associated with
no effect on weight, or modest protection against weight gain.
Although these findings must be interpreted cautiously due to
limitations of study design, they are broadly consistent with
the findings of energy intake and body weight RCTs discussed
previously and may suggest that the short- to medium-term
effects observed in RCTs persist in the long term.
DISCUSSION
Summary of Main Findings
The primary outcome of this review is the impact of whole,
fresh fruit consumption on measures of adiposity including
body weight, as measured by RCTs. Overall, these RCTs suggest
that increasing intake of whole, fresh fruit promotes weight
maintenance or modest weight loss over periods of 3–24 weeks.
High intakes of whole, fresh fruit in people with obesity
may promote some degree of weight loss. RCTs provide little
information about more direct measures of adiposity such as
body fat percentage. The strength of evidence supporting this
conclusion is moderate, indicating that the true effect of fruit
consumption on measures of adiposity is probably close to the
effect estimated here.
Secondary outcomes of RCTs reporting the impact of fruit
consumption on energy intake, and prospective observational
studies reporting associations between fruit intake and measures
of adiposity, were broadly consistent with the primary outcome.
The strength of evidence supporting conclusions regarding
energy intake RCTs is moderate, while the prospective
observational findings did not receive a GRADE assessment but
are likely at high risk of bias. As with the primary outcome, RCTs
and prospective observational studies of higher quality tend to
support the hypothesis that higher intakes of whole, fresh fruit
either do not impact weight or modestly attenuate weight gain
over time.
Limitations
Limitations of this review relate both to the review itself, and
to the studies that underlie it. Although quantitative pooling
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Guyenet Fruit, Energy Intake, and Adiposity
of RCT data appeared suboptimal in this context due to the
limited number of studies and considerable heterogeneity in
study methods and quality, narrative synthesis is inherently
more subjective than quantitative meta-analysis. The author
endeavored to limit the potential for bias by preregistering a
detailed research plan and adhering to widely accepted defined
methods for assessing and reporting evidence, such as the
Cochrane risk of bias tool, the GRADE method, and PRISMA
guidelines. The author attempted to be transparent in methods
and reasoning so the reader may form his or her own views.
In addition, the greater subjectivity of narrative reviews may be
counterbalanced in some instances by a superior ability to focus
on high-quality studies rather than diluting their evidence value
by pooling them with lower-quality studies. Finally, the author
was not funded for this work and has no connection with Big
Fruit, eliminating this potential source of real or perceived bias.
An additional limitation of this review is that due to resource
constraints, study selection, data extraction, risk of bias scoring,
and GRADE evaluation were performed by one person. The
Cochrane handbook for Systematic Reviews of Interventions
recommends that systematic reviews be conducted by at least two
people to reduce the risk of errors4.
The conclusions of this review are also limited by the
underlying evidence. Although 11 RCTs were available for the
primary outcome of adiposity, most had serious limitations
of sample size, lack of preregistration, and/or risk of bias.
Conclusions of the primary outcome of this review rest
disproportionately on two high-quality trials. Energy intake
RCTs were fewer in number and tended to be lower-quality
than adiposity RCTs. Prospective observational studies typically
had serious limitations including lack of preregistration, lack of
correction for multiple comparisons, and potential confounding
and overcorrection, which together raise substantial concerns
of bias. Some of these limitations are inherent to observational
methods, while others are potentially avoidable. Nevertheless, the
consistency of findings across the three primary and secondary
outcomes is somewhat reassuring.
CONCLUSIONS
Consistent with earlier reviews on this topic (14,17), available
evidence suggests that increasing intake of whole, fresh
fruit probably does not increase adiposity, but instead has
either no impact on adiposity or constrains it modestly.
Findings consistent with this hypothesis are observed in studies
representing single meals, 3–24 week periods, and periods of
five or more years. Although some uncertainty remains, these
findings support increasing the consumption of whole, fresh fruit
4https://handbook-5-1.cochrane.org/chapter_2/2_3_4_1_the_importance_of_a_
team.htm
as part of a multi-factor approach to controlling excess energy
intake and adiposity. These findings also suggest that if the sugar
content of fruit favors increased energy intake and adiposity,
this is outweighed by its other properties, such as lower calorie
density, moderate palatability/reward value, higher fiber content,
and micronutrient content, at least when consumed as part of
typical diet patterns.
These findings support existing recommendations by
organization such as the US Department of Agriculture and
the World Health Organization to increase fruit consumption
as a public health measure (66,67). Although increasing
consumption of whole, fresh fruit is unlikely to have a large
impact on population obesity rates on its own, it may make a
positive contribution as part of a broader public health strategy
for obesity control. Similarly, healthcare providers should not
expect large changes in adiposity as a result of increasing whole,
fresh fruit consumption alone, but it is reasonable to include
it as part of a broader package of slimming diet and lifestyle
behaviors. Furthermore, it is unlikely to cause adiposity gain
despite its sugar content, at least as part of a typical mixed diet.
Increasing intake of whole, fresh fruit may be more effective as
an adiposity control measure when presented as a replacement
for calorie-dense dessert foods.
Several opportunities for reducing the uncertainty of
conclusions on this topic are apparent. Additional high-quality
RCTs with changes in adiposity as the preregistered primary
endpoint would be useful, particularly if they report an accurate
measure of total and regional adiposity such as dual-energy
X-ray absorptiometry. High-quality energy intake RCTs,
preregistered and with complete description of randomization
processes, involving direct measurement of energy intake in
people with obesity over longer periods of time would also
contribute substantially. Additional energy intake RCTs could
also compare the impact of fruit intake in different contexts,
such as pre-meal vs. intra-meal vs. after-meal intake, to identify
the most effective strategy for energy intake control. Finally,
prospective observational studies that use accurate measurement
instruments, are conducted according to a preregistered
research plan, and adjust for multiple comparisons would
reduce uncertainty.
AUTHOR CONTRIBUTIONS
SG is the sole contributor to this manuscript, aside from its search
strategy, which was designed in collaboration with Ben Harnke.
ACKNOWLEDGMENTS
Many thanks to Ben Harnke, Education and Reference Librarian
at the University of Colorado Health Sciences Library, for his
generous help in designing the review’s search strategy.
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Conflict of Interest Statement: This research was not conducted in association
with any public or private organization. SG is the author of a general-audience
book on the neurobiology of overeating, The Hungry Brain, which does not
currently yield royalties but may in the future. This book mentions fruit but does
not place a special emphasis on it. SG is the co-creator of a weight management
program, the Ideal Weight Program, from which he receives revenue. This
program permits the consumption of fruit but does not place a special emphasis
on it.
Copyright © 2019 Guyenet. This is an open-access article distributed under the terms
of the Creative Commons Attribution License (CC BY). The use, distribution or
reproduction in other forums is permitted, provided the original author(s) and the
copyright owner(s) are credited and that the original publication in this journal
is cited, in accordance with accepted academic practice. No use, distribution or
reproduction is permitted which does not comply with these terms.
Frontiers in Nutrition | www.frontiersin.org 19 May 2019 | Volume 6 | Article 66
... National school FV programs have been shown to increase FV consumption among children [6,7,9], but our understanding of their effect on childhood obesity outcomes is limited [8,10]. Meta-analyses and systematic reviews of randomized controlled trials (RCTs) indicate that increased FV consumption may promote weight loss and prevent weight gain [11,12], as the FVs consumed may substitute for more energy-dense foods AU : IchangedconsumingFVmaysubstitu [13,14]. However, school food provision, such as school lunch programs, could increase weight [15]. ...
... Our lack of observed evidence for a benefit from the FFV policy is supported by a much smaller Norwegian intervention study evaluating the association of 1 school year of FFV provision in Norwegian schools with overweight [10,19]. Findings from a meta-analysis and a systematic review of RCTs indicate beneficial effects of FV consumption on weight outcomes [11,12]; however, the interventions evaluated are heterogenous in regard to complexity, setting, and/or target populations, e.g., those with chronic conditions [11]. Moreover, studies evaluating the effect of various dietary interventions and policies on childhood obesity usually include additional components beyond FV provision [15,[27][28][29][30]. Two recently published systematic reviews reported improvements in childhood Results are presented by sex and cohort (including pooled) and for each model. ...
... Including data on additional variables such as attitudes, values, and FV consumption at the individual level may aid the understanding of potential mechanisms of how FFV policies act. Additionally, as provision of FVs may contribute to promoting healthy eating habits, future work should evaluate whether a FFV policy contributes to longer-term healthy eating habits and thereby prevents OW/OB in adulthood [12]. ...
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Background School free fruit and vegetable (FFV) policies are used to promote healthy dietary habits and tackle obesity; however, our understanding of their effects on weight outcomes is limited. We assess the effect of a nationwide FFV policy on childhood and adolescent weight status and explore heterogeneity by sex and socioeconomic position.Methods and findingsThis study used a quasi-natural experimental design. Between 2007 and 2014, Norwegian combined schools (grades 1-10, age 6 to 16 years) were obligated to provide FFVs while elementary schools (grades 1-7) were not. We used 4 nationwide studies (n = 11,215 children) from the Norwegian Growth Cohort with longitudinal or cross-sectional anthropometric data up to age 8.5 and 13 years to capture variation in FFV exposure. Outcomes were body mass index standard deviation score (BMISDS), overweight and obesity (OW/OB), waist circumference (WC), and weight to height ratio (WtHR) at age 8.5 years, and BMISDS and OW/OB at age 13 years. Analyses included longitudinal models of the pre- and post-exposure trajectories to estimate the policy effect. The participation rate in each cohort was >80%, and in most analyses
... Subjects with low HDL values showed higher fruit consumption; however, this value could be due to the fact that only a subject in the group with the condition reported a high fruit intake (16.27 equivalents vs. 3.43 maximum range in the group without disease). It should be noted that even with said consumption, the subject was not overweight and did not have abdominal obesity, as reported in other studies [60,61]. It has been reported that an excess of simple sugars (glucose, fructose, and sucrose) contributes directly to an increase in lipogenesis, higher concentrations of LDL and VLDL, and therefore a decrease in circulating HDL [62]. ...
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The purpose of this study was to analyze the association between components of the diet, metabolic risks, and the serum concentrations of adiponectin and interleukin-6 (IL-6). With prior informed consent, an analytical cross-sectional study was carried out with 72 students in their first year of university. The subjects had a mean age of 19.2 ± 1.0 years and body mass index of 23.38 ± 4.2, and they were mainly women (80.6%). Sociodemographic, anthropometric, and dietary data and metabolic risk factors were evaluated, and biochemical parameters and adipocytokines were also considered. The data were analyzed using means, ranges, and correlations, as well as principal components. In general, the protein, fat, and sodium intake were higher than the international dietary recommendations, and deficiencies in vitamins B5 and E, potassium, phosphorus, selenium, and zinc were observed. The most frequently observed metabolic risks were insulin resistance and hypoalphalipoproteinemia. IL-6 was positively correlated with lipid and protein intake. Adiponectin showed a positive correlation with high-density lipoprotein and a negative correlation with insulin, weight, and waist, while the adiponectin pattern was similar to that of vitamins E and A, which decreased with increasing intake of calories, macronutrients, and sodium. In general, a hypercaloric diet that was high in protein, fat, and sodium and deficient in vitamins, mainly fat-soluble, was associated with a lower concentration of adiponectin and a higher concentration of IL-6, which favor the presence of metabolic risks, including insulin resistance. Intervention studies are required to evaluate the dietary intake of metabolic markers in young people without comorbidities, which will lay the foundation for implementing prevention strategies.
... One cohort study reported lower healthcare charges for men with the highest fruit intake [6], although this study was not nationally representative and examined the quantity of the fruits consumed, not variety [6]. A systematic review found no association between whole, fresh fruit consumption and excess energy intake or adiposity [35]. There are likely further confounding factors, such as food insecurity which has a complex relationship with being bodyweight and healthcare expenditures in older adults [36], as well as other sociodemographic and lifestyle factors influencing the changes in diet quality over time [37]. ...
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Healthcare costs are lower for adults who consume more vegetables; however, the association between healthcare costs and fruit and vegetable varieties is unclear. Our aim was to investigate the association between (i) baseline fruit and vegetable (F&V) varieties, and (ii) changes in F&V varieties over time with 15-year healthcare costs in an Australian Longitudinal Study on Women’s Health. The data for Survey 3 (n = 8833 women, aged 50–55 years) and Survey 7 (n = 6955, aged 62–67 years) of the 1946–1951 cohort were used. The F&V variety was assessed using the Fruit and Vegetable Variety (FAVVA) index calculated from the Cancer Council of Victoria’s Dietary Questionnaire for Epidemiological Studies food frequency questionnaire. The baseline FAVVA and change in FAVVA were analysed as continuous predictors of Medicare claims/costs by using multiple regression analyses. Healthy weight women made, on average, 4.3 (95% confidence interval (CI) 1.7–6.8) fewer claims for every 10-point-higher FAVVA. Healthy weight women with higher fruit varieties incurred fewer charges; however, this was reversed for women overweight/obese. Across the sample, for every 10-point increase in FAVVA over time, women made 4.3 (95% CI 1.9–6.8) fewer claims and incurred $309.1 (95% CI $129.3–488.8) less in charges over 15 years. A higher F&V variety is associated with a small reduction in healthcare claims for healthy weight women only. An increasing F&V variety over time is associated with lower healthcare costs.
... The significant improvements in energy, sodium, and fruit intake achieved by the pooled intervention group compared with the control at six months align with results from other dietary interventions [11,[57][58][59]. This suggests that adults with overweight or obesity can change these aspects of dietary intake in the short term when provided with evidenced-based advice. ...
Article
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This three-arm randomised controlled trial evaluated whether (1) a multi-component weight loss intervention targeting diet, physical activity (PA), and sleep was effective at improving dietary intake over six months and 12 months, compared with a control, and (2) the enhanced diet, PA, and sleep intervention was more effective at improving dietary intake than the traditional diet and PA intervention. A total of 116 adults (70% female, 44.5 years, BMI 31.7 kg/m2) were randomised to either traditional diet and PA intervention; enhanced diet, PA, and sleep intervention; or wait-list control. To examine between-group differences, intervention groups were pooled and compared with the control. Then, the two intervention groups were compared. At six months, the pooled intervention group consumed 1011 fewer kilojoules/day (95% CI −1922, −101), less sodium (−313.2 mg/day; 95% CI −591.3, −35.0), and higher %EI from fruit (+2.1%EI; 95% CI 0.1, 4.1) than the controls. There were no differences in intake between the enhanced and traditional groups at six months. At 12 months, the pooled intervention and control groups reported no significant differences. However, compared to the traditional group, the enhanced reported higher %EI from nutrient-dense foods (+7.4%EI; 95% CI 1.3, 13.5) and protein (+2.4%EI; 95% CI 0.1, 4.6), and reduced %EI from fried/takeaway foods (−3.6%EI; 95% CI −6.5, −0.7), baked sweet products (−2.0%EI; 95% CI −3.6, −0.4), and packaged snacks (−1.1%EI; 95% CI −2.2, −0.3). This weight loss intervention reduced total energy and sodium intakes as well as increased fruit intake in adults at six months. The enhanced intervention group reported improved dietary intake relative to the traditional group at 12 months.
... Furthermore, many fruits, including apples, are rich in flavonoids, a class of phytochemicals which are reported to improve insulin sensitivity, potentially by decreasing apoptosis and promoting proliferation of pancreatic β-cells, and reduce muscular inflammation and oxidative stress (29,30). Moreover, fruit intake may indirectly influence T2DM risk by preventing or managing excess adiposity, possibly via higher dietary fibre contributing towards satiety (31). Interestingly, although there was evidence that higher banana intakes may be associated with a lower risk of diabetes at 5 years, banana intakes were not significantly associated with measures of glucose tolerance and insulin sensitivity at baseline. ...
Article
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Background Fruit, but not fruit juice, intake is inversely associated with type 2 diabetes mellitus (T2DM). However, questions remain about the mechanisms by which fruits may confer protection. Aims were to examine associations between intake of fruit types and 1) measures of glucose tolerance and insulin sensitivity and 2) diabetes at follow-up. Methods Among participants of the Australian Diabetes, Obesity and Lifestyle Study, fruit and fruit juice intake was assessed by food frequency questionnaire at baseline. Associations between fruit and fruit juice intake and 1) fasting plasma glucose, 2-h post-load plasma glucose, HOMA2 of β-cell function (HOMA2-%β), HOMA2 of insulin sensitivity (HOMA2-%S), and fasting insulin levels at baseline and 2) the presence of diabetes at follow-up (5 and 12 years) were assessed using restricted cubic splines in logistic and linear regression models. Results This population of 7,675 Australians (45% males) had a mean±SD age of 54±12 years at baseline. Total fruit intakes were inversely associated with serum insulin and HOMA2-%β, and positively associated with HOMA2-%S at baseline. Compared to participants with the lowest intakes (quartile 1), participants with moderate total fruit intakes (quartile 3) had a 36% lower odds of having diabetes at 5 years [OR (95% CI): 0.64 (0.44, 0.92)], after adjusting for dietary and lifestyle confounders. Associations with 12-year outcomes were not statistically significant. Conclusion A healthy diet including whole fruits, but not fruit juice, may play a role in mitigating T2DM risk.
... Dessa forma, ao serem consumidas, podem proporcionar sensação de saciedade por um período de tempo maior. Assim, acredita-se que a substituição de alimentos de alta densidade energética por alimentos de baixa densidade energética, como frutas e hortaliças, poderia, potencialmente, diminuir a energia total consumida ao longo do dia e impactar no balanço energético 8 . ...
Article
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Resumo: O objetivo do estudo foi estimar a prevalência da coocorrência de fatores de risco obesogênicos em adolescentes brasileiros e características sociodemográficas e familiares associadas. É um estudo transversal, com dados da Pesquisa Nacional de Saúde do Escolar, 2009 (n = 53.274). A variável resposta (coocorrência de fatores de risco obesogênicos) variou de zero a cinco e foi obtida com base no somatório dos comportamentos de risco: consumo diário de refrigerantes; consumo diário de guloseimas; consumo insuficiente de frutas; consumo insuficiente de hortaliças e atividade física insuficiente. As variáveis explicativas foram sexo, idade, cor da pele, região brasileira, escore de bens e serviços do domicílio, escolaridade materna, tipo de escola, composição familiar, monitoramento parental e realização de refeições principais com os responsáveis. Utilizou-se o diagrama de Venn para a análise exploratória e a regressão logística ordinal com modelo de odds proporcionais parciais para análise multivariada. Verificou-se maior chance de ocorrência simultânea dos fatores nos adolescentes do sexo feminino, naqueles que tinham menor monitoramento parental e que realizavam menor número de refeições com os responsáveis. Em contraste, adolescentes que residiam em regiões mais desenvolvidas do país e aqueles cujas mães possuíam maior escolaridade apresentaram menor chance de coocorrência de fatores de risco obesogênicos. Ações que estimulem mais envolvimento e supervisão familiar no comportamento alimentar podem promover impacto positivo na prevenção da obesidade em adolescentes brasileiros, principalmente em regiões menos desenvolvidas e em lares com mães de menor escolaridade.
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Background: We investigated the association between various food groups and obesity in Japanese patients with type 2 diabetes. Methods: 2070 patients with type 2 diabetes who attended 26 diabetes clinics throughout Japan were analyzed and were divided into obese and non-obese groups. Intakes of food groups determined by a food frequency questionnaire were compared. Odds ratios for obesity for quartiles of individual food groups were calculated using a logistic regression model. Results: Non-obese patients consumed a larger variety of food groups than obese patients, with the diets of non-obese individuals closer to the traditional Japanese diet characterized by fish, seaweed, and soybeans/soy products. Among 21 food groups, low vegetable intake and high sweets intake were the most strongly associated with obesity in both men and women. Low intake of both fruits and vegetables and the combination of high intake of sweets and low intake of fruits were associated with obesity. Conclusions: Food groups and their combinations that were strongly associated with obesity in Japanese patients with type 2 diabetes were identified. Our findings also suggested an inverse association between the traditional Japanese diet and obesity.
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Objective The objective of this study is to assess changes in the dimensions of the food system and consumption associated with body weight variations during the first month’s lockdown in Peruvian adults in Metropolitan Lima. Methods A cross-sectional study conducted during the first months of lockdowns in Peru. 694 adults completed a web-based survey about changes experienced in the process of acquiring food during lockdown, changes in their intake and self-perceived body weight. A multinomial logistic regression analysis was conducted to evaluate the factors associated with changes in body weight. Results Weight gain was perceived in 38% of the participants and 22.8% perceived weight loss. 39.2% did not perceive changes in their weight. Risk factors for body weight gain were increased alcohol consumption (OR=4.510, 95% CI 1.764 to 11.531) and decreased fruit consumption (OR=2.129, 95% CI 1.290 to 3.515), while decreasing cereal intake (OR=0.498, 95% CI 0.269 to 0.922) and choosing nutritious food as a driver for purchase (OR=0.512, 95% CI 0.320 to 0.821) were found to be protective against gaining weight. Decreasing food intake during the pandemic (OR=2.188, 95% CI 1.348 to 3.550) and having to miss important foods (OR=2.354, 95% CI 1.393 to 3.978), were associated with weight loss. Conclusions During confinement, weight gain was mostly associated with food consumption and personal food system factors. Meanwhile, weight loss was associated with external food system factors.
Article
According to FAO information, during the last decades great efforts have been made by authorities and specialists for the development of public policy and to spread the benefits of adopting healthy diets and physical activities, aiming to reduce the incidence of non-transmissible chronic diseases, which are the main cause of death worldwide. In spite of its healthy status, the consumption of vegetable products (fruits, vegetables, grains, and plants) has been staying below the expected level in most countries, forcing the public policy makers to implement ways to encourage consumers to include more vegetable products in their diets. Aware of this reality, the food industry continuously seeks for innovative ways of contributing to increase the consumption of fruits and vegetables. This review proposes to show the consequences of functionality and nutrition macrotrends for fruit and vegetable consumption, and the technological challenges and alternatives found for the development of processed products containing such raw materials. © 2022, Sociedade Brasileira de Ciencia e Tecnologia de Alimentos, SBCTA. All rights reserved.
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Background/objectives: To prospectively investigate the predictors of overweight/obesity and of body mass index (BMI) change in schoolchildren. Subjects/methods: A cohort of 2755 Irish children (53.7% girls), aged 6-10 years at baseline, participating in the Childhood Obesity Surveillance Initiative were included. Height and weight were objectively measured and BMI was calculated. Overweight/obesity were defined according to the International Obesity Task Force cut-offs. Prevalence of overweight/obesity at baseline and follow-up, incidence of overweight/obesity and changes in BMI over time were computed. Lifestyle indicators were parentally reported with a questionnaire. Predictors of overweight/obesity were investigated by multivariate mixed logistic regression models. Results: Baseline BMI (OR = 6.46, 95% CI = 4.56-9.17) and school socioeconomic level (OR = 2.12, 95% CI = 1.16-3.86) were associated with higher risk of becoming overweight/obese. Children with no savoury snacks consumption (OR = 0.22, 95% CI = 0.07-0.69), or with frequent sports/dancing clubs attendance (OR = 0.41, 95% CI = 0.19-0.88) had lower odds of overweight/obesity at follow-up. Poor fruit intake was associated with higher risk of overweight/obesity (OR = 2.16, 95% CI = 1.23-3.78). Conclusions: Initial BMI was the main predictor of subsequent overweight/obesity in schoolchildren, followed, to a lesser extent, by school socioeconomic status. Although lifestyle behaviours appeared to have lower effect as compared with prior BMI, children with healthier lifestyle behaviours were at reduced risk of overweight/obesity at follow-up. Programmes that aim at preventing the onset of overweight and obesity need to be a priority given the existing difficulties to reverse this condition later in life.
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Objectives: This study examines the prospective association between sugar-sweetened beverages (SSB) consumption and change in body weight over a 4-5-year period in a socio-economically disadvantaged South African population. Methods: This is a longitudinal study involving 800 adults (212 men, 588 women); 247 from the original METS (Modelling the Epidemiological Transition Study) cohort (N = 504) and 553 of the original 949 members of the PURE (Prospective Urban and Rural Epidemiology) Study. Both cohorts were drawn from low-income, socio-economically disadvantaged communities. Mean follow-up duration and age were 4.5 (SD 0.45) and 50.0 (SD 11.8) years, respectively. Harmonised measurements included body mass index, self-reported moderate-to-vigorous physical activity, and intake of meat, snacks and 'take-aways', fruits and vegetables and SSB (in servings/week). Multivariate logistic regression models were developed to determine the extent to which SSB consumption predicted relative weight gain, after controlling for potential confounders and known predictors. Results: Nearly a third (29%) of participants had a relative weight change ≥5.0%; higher in the non-obese compared to the obese group (32% vs. 25%; p = 0.026). The average SSB consumption was 9.9 servings/week and was higher in the food insecure compared to the food secure group (11.5 vs. 9.0 servings/week; p = 0.006); but there were no differences between women and men (10.3 vs. 9.1 servings/week; p = 0.054). Mean SSB consumption was higher in the group who gained ≥5% weight compared to those who did not (11.0 vs. 8.7; p = 0.004). After adjustment, SSB consumption of 10 or more servings/week was associated with a 50% greater odds of gaining at least 5% body weight (AOR: 1.50, 95% CI (1.05-2.18)). Conclusion: These results show that higher intake of SSB predicts weight gain in a sample of South Africans drawn from low-income settings. Comprehensive, population-wide interventions are needed to reduce SSB consumption in these settings.
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Background To examine predictors of increasing overweight among children in two developing countries. Methods Primary school children (6–10 y at baseline, n = 336) and their caregivers. Longitudinal data were collected in 2012, with follow-up 18 months later. Data on children’s height, weight and dietary intake were collected within 8 primary public schools in Trinidad and 7 schools in St. Kitts. Caregivers’ demographic and anthropometric data were also collected. Results At baseline, children’s age and sex and caregivers’ BMI, age, and marital status and reported dietary intake were similar across all weight groups. The incidence of overweight and obesity among children was 8.8% and 8.1%, respectively. Dietary intake at baseline was not related to becoming overweight or obese. Similarly there were no differences in reported intake among children who became overweight or obese except that they consumed fewer fruits (0.54±0.92 vs. 0.98±1.66, p = 0.017). Misreporting of energy intake was higher among overweight/obese children as compared to those who were not overweight/obese (27% vs. 17%, p = 0.047). The baseline predictors of increasing BMI (adjusted) of the children were older age, higher baseline BMI z-score and higher height-for-age (HFA) z-score; caregiver BMI, children’s energy intake (with adjustment for misreporting) did not predict changes in children’s BMI. Conclusions The increasing prevalence of overweight/obesity among children is a serious problem in the Caribbean. Heavier children are at elevated risk of continued rapid increase in their weight status, pointing to the need for early intervention.
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Background/objectives: The cardiovascular benefit of increasing fruit and vegetable (F&V) intake following diagnosis of diabetes remains unknown. We aimed to describe how quantity and variety of F&V intake, and plasma vitamin C, change after diagnosis of type 2 diabetes and examine whether these changes are associated with improvements in cardiovascular risk factors. Subjects/methods: A total of 401 individuals with screen-detected diabetes from the ADDITION-Cambridge study were followed up over 5 years. F&V intake was assessed by food frequency questionnaire and plasma vitamin C at baseline, at 1 year and at 5 years. Linear mixed models were used to estimate associations of changes in quantity and variety of F&V intake, and plasma vitamin C, with cardiovascular risk factors and a clustered cardiometabolic risk score (CCMR), where a higher score indicates higher risk. Results: F&V intake increased in year 1 but decreased by year 5, whereas variety remained unchanged. Plasma vitamin C increased at 1 year and at 5 years. Each s.d. increase (250g between baseline and 1 year and 270g between 1 and 5 years) in F&V intake was associated with lower waist circumference (-0.92 (95% CI: -1.57, -0.27) cm), HbA1c (-0.11 (-0.20, -0.03) %) and CCMR (-0.04 (-0.08, -0.01)) at 1 year and higher high-density lipoprotein (HDL)-cholesterol (0.04 (0.01, 0.06) mmol/l) at 5 years. Increased plasma vitamin C (per s.d., 22.5 μmol/l) was associated with higher HDL-cholesterol (0.04 (0.01, 0.06) mmol/l) and lower CCMR (-0.07 (-0.12, -0.03)) between 1 and 5 years. Conclusions: Increases in F&V quantity following diagnosis of diabetes are associated with lower cardiovascular risk factors. Health promotion interventions might highlight the importance of increasing, and maintaining increases in, F&V intake for improved cardiometabolic health in patients with diabetes.European Journal of Clinical Nutrition advance online publication, 19 October 2016; doi:10.1038/ejcn.2016.180.
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Obesity is exponentially increasing regardless of its preventable characteristics. The current measures for preventing obesity have failed to address the severity and prevalence of obesity, so alternative approaches based on nutritional and diet changes are attracting attention for the treatment of obesity. Fruit contains large amounts of simple sugars (glucose, fructose, sucrose, etc.), which are well known to induce obesity. Thus, considering the amount of simple sugars found in fruit, it is reasonable to expect that their consumption should contribute to obesity rather than weight reduction. However, epidemiological research has consistently shown that most types of fruit have anti-obesity effects. Thus, due to their anti-obesity effects as well as their vitamin and mineral contents, health organizations are suggesting the consumption of fruit for weight reduction purposes. These contradictory characteristics of fruit with respect to human body weight management motivated us to study previous research to understand the contribution of different types of fruit to weight management. In this review article, we analyze and discuss the relationships between fruit and their anti-obesity effects based on numerous possible underlying mechanisms, and we conclude that each type of fruit has different effects on body weight.
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