VOLUME 16 NUMBER 1 | JANUARY 2008 | www.obesityjournal.org
behavior and psychology
nature publishing group
See commentary page 5
Dietary Fat Increases Energy Intake
Across the Range of Typical Consumption
in the United States
William Donahoo1, Holly R. Wyatt2, Joanna Kriehn1, Jennifer Stuht2, Fang Dong2,
Patrick Hosokawa2, Gary K. Grunwald2, Susan L. Johnson2, John C. Peters3 and James O. Hill2
Objective: The fat content of a diet has been shown to affect total energy intake, but controlled feeding trials have
only compared very high (40% of total calories) fat diets with very low (20% of total calories) fat diets. This study was
designed to measure accurately the voluntary food and energy intake over a range of typical intake for dietary fat.
Methods and Procedures: Twenty-two non-obese subjects were studied for 4 days on each of three diets, which
included core foods designed to contain 26, 34, and 40% fat, respectively of total calories and ad lib buffet foods of
similar fat content. All diets were matched for determinants of energy density except dietary fat. Subjects consumed
two meals/day in an inpatient unit and were provided the third meal and snack foods while on each diet. All food
provided and not eaten was measured by research staff.
Results: Voluntary energy intake increased significantly as dietary fat content increased (P = 0.008). On the 26%
dietary fat treatment, subjects consumed 23.8% dietary fat (core and ad lib foods combined) and 2,748 ± 741 kcal/day
(mean ± s.d.); at 34% dietary fat, subjects consumed 32.7% fat and 2,983 ± 886 kcal/day; and at 40% dietary fat
subjects consumed 38.1% fat and 3,018 ± 963 kcal/day.
Discussion: These results show that energy intake increases as dietary fat content increases across the usual range
of dietary fat consumed in the United States. Even small reductions in dietary fat could help in lowering total energy
intake and reducing weight gain in the population.
Obesity (2008) 16, 64–69. doi:10.1038/oby.2007.31
The public is confused about how to eat in order to maintain
a healthy body weight and in particular about how much fat
vs. carbohydrate to include in a healthy diet. The popularity
of low-fat diets in the 1970s and 1980s (1) was replaced by the
popularity of low carbohydrate diets following the publication
of Atkins’ New Diet Revolution (2) in 1992.
While many popular diet books are based on advocating a
specific diet composition for weight loss, it makes more sense
to base population diet composition recommendations on the
ability to help maintain weight and avoid weight gain or, for
people who have lost weight, to avoid regaining weight. In
this sense, the best diet for weight management would be one
that is feasible to maintain over time and that minimizes the
chances of positive energy balance.
One of the most significant influences of diet composition is
on voluntary energy intake. There is a very consistent finding
that when given ad libitum access to food, subjects eat more
total energy when the fat content of the diet is ≥40% vs. ≤20%
(3–5). This may be because of the higher energy density of
high-fat diets (6–9).
There are no controlled feeding trials, using weighed food
measures, that have examined the systematic impact of die-
tary fat level on energy intake across the range of dietary fat
intake normally consumed by Americans (10–12). There are
many intervention trials that have varied the dietary fat level
in outpatient settings where food intake was measured by self-
reporting (13–15) that suggest that the level of dietary fat can
affect food intake. However, dietary self reports may not pro-
vide accurate information about energy intake (16). Further, it
is not clear whether the effect of dietary fat on energy intake is
due solely to its higher energy density or whether it has impact
independent of energy density. This study was undertaken to
investigate further the impact of diet composition on ad libi-
tum energy intake, focusing particularly on the typical range of
free-living western diets.
1Kaiser Permanente Colorado, Denver, Colorado, USA; 2Center for Human Nutrition, University of Colorado School of Medicine, Denver, Colorado, USA;
3Procter & Gamble Company, Cincinnati, Ohio, USA. Correspondence: Holly R. Wyatt (Holly.Wyatt@uchsc.edu)
Received 10 January 2007; accepted 30 July 2007. doi:10.1038/oby.2007.31
Obesity | VOLUME 16 NUMBER 1 | JANUARY 2008 65
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Methods and Procedures
Twenty-two healthy, non-smoking, men (n = 15) and women (n = 7)
with a BMI between 21 and 28 kg/m2 were enrolled in the study.
Individuals between the ages of 25 and 40 years were recruited by writ-
ten advertisements which were distributed throughout the University
of Colorado Health Sciences Center and the surrounding community.
The research study was approved by the Institutional Review Board at
the University of Colorado Health Sciences Center and the Scientific
Advisory Committee of the General Clinical Research Center. After
providing informed consent, interested participants completed a series
of questionnaires to ensure that they met eligibility criteria. The ques-
tionnaires used were: menstrual cycle questionnaire, Eating Inventory
(17), The Center for Epidemiological Studies—Depression Scale (18)
and Eating Attitude Test (EATS-26) (19). We eliminated any subjects
who displayed high levels of dietary restraint (>10 for women and >8 for
men on the restraint subscale of the Eating Inventory), and those who
reported high depression scores (>20 on the Center for Epidemiological
Studies—Depression Scale questionnaire). All women participated in
the feeding protocols during the early follicular phase of the menstrual
cycle to control for any potential interaction between appetite and men-
strual cycle phase.
Subjects who met the eligibility criteria were scheduled for a screening
visit where they were required to return 3-day food diaries and 7-day
physical activity diaries. Food records were reviewed by a registered dieti-
tian. Subjects were excluded if their typical dietary fat intake was <25%
or >35% of total energy intake. Subjects were excluded if they consumed
more than one drink/day of alcohol or had one episode of consuming
three or more drinks of alcohol during the 4 days of recording food
intake. They were also excluded if they consumed more than 24 oz/day
of sugar-containing soda or if they did not routinely consume breakfast
(they should have eaten breakfast at least 3 of the 4 days of recording
Following screening, participants were randomly assigned to one of six
cohorts, each containing four subjects. In a randomized, crossover design,
each cohort consumed the controlled diets for 4 consecutive days for each
of three fat levels: 26, 34, and 40%, respectively of total calories. The order
of fat level was randomly determined for each subject. The diets were
developed to have similar levels of other determinants of energy density,
namely water and fiber. Thus, differences in energy density of the diets
were due only to dietary fat content. There was a 3-week washout period
between dietary fat levels. Subjects were not aware of the composition of
the diets they were consuming.
Each participant was asked to initially consume half of his/her esti-
mated energy needs for each meal in a “core” meal. In preliminary work,
we found that this method allowed for achieving the desired level of
diet composition while still providing subjects flexibility in total calories
consumed. Estimated energy needs were determined using the following
formula: Estimated energy needs = 1.5 × (372 + 23.9 × fat-free mass). This
core meal consisted of foods containing 15% protein, the target level of fat
and the remainder from carbohydrate. The energy level was held constant
for a subject across all three diets. After the participants consumed their
core meal, they were offered an individual buffet containing several foods
with a fat content within 10% of the target for the diet. The amount and
macronutrient composition of ad libitum food consumed at each meal
as well as during two optional snacks/day were measured by research
staff. The research staff controlled the number of dishes available and
ensured that offerings included both sweet and savory foods. In most
cases the same foods (with different levels of fat) were used for each diet
condition. For example, yogurt with varying fat levels was provided with
each diet. Individuals consumed their breakfast and dinner with others in
their cohort at the General Clinical Research Center dining room. They
consumed lunch and snacks on their own without supervision. Subjects
were queried each morning about any food eaten on their own.
Lunch and snacks were pre-weighed and prepared for subjects each
day. Subjects received lunch and snacks after breakfast. All wrappers, con-
tainers and uneaten food were returned to the General Clinical Research
Center to be weighed. At that time, subjects were asked about their con-
sumption of lunch items and whether anyone else ate any of the food.
Energy per gram information on the nutrition label of packaged food
and ProNutrasoftware (Princeton, NJ) were used to calculate energy and
Throughout the study, participants were asked to wear a pedometer
(Accusplit AE120; Accusplit, San Jose, CA), to determine physical activ-
ity through walking. It is possible that diet composition could affect the
usual amount of physical activity. Each subject recorded total steps/
day throughout the study. Subjects were given a hand-held computer
on which to rate the palatability of the diet following each meal. Visual
analogue scales (100 mm) were used for the ratings (21).
Body composition was determined for all subjects using a Hologic
(Bedford, MA) dual energy X-ray absorptiometry (22). Subjects were
tested after voiding and wearing gowns.
Data analysis was performed with SAS software (SAS Institute, Cary,
NC). Subject characteristics at baseline were compared between men
and women using independent sample t-tests. The primary outcome
was ad libitum energy intake, averaged over the 4 days on a given dietary
condition. Secondary outcomes including dietary fat level, food weight,
and energy density were analyzed in the same way. Separate analyses
were carried out for core foods, ad libitum buffet foods, and total intake.
Repeated measures analysis of variance with dietary fat level (26, 34,
and 40%) as a within-subject factor was carried out using linear mixed
models with a heterogeneous compound symmetry covariance using
SAS PROC MIXED. The effect of treatment order was also considered
by including a variable for treatment order in the model. These methods
account for the correlation due to repeated measures of subjects, allow
different variances for the three dietary fat levels, and provide valid han-
dling of the occasional missing observations for reasons not related to
treatments or outcomes (23). Contrasts were used within these meth-
ods to estimate dietary fat effects. Statistical significance was set at P <
0.05. Results are presented as means and s.d. unless otherwise noted.
Visual analogue scale ratings of hunger before the meal, fullness after
the meal, and average palatability of foods at each meal were averaged
over the 4 days on each diet treatment, and were compared using repeated
measures analysis of variance as above.
subject characteristics and completion/drop outs
Table 1 shows characteristics of subjects. Subjects were gener-
ally young and of normal weight, with ages ranging from 26
to 38 for women and 25 to 39 for men, and BMI ranging from
20.7 to 24.1 kg/m2 for women and 21.6 to 27.6 kg/m2 for men.
table 1 subject characteristics (mean (s.d.))
Men (n = 15) Women (n = 7)
Age (years) 30.5 (4.6) 29.4 (4.2)
Height (cm) 180.0 (5.3) 168.6 (7.6)*
Weight (kg)76.7 (9.4) 63.8 (4.5)*
BMI (kg/m2) 23.6 (1.7) 22.4 (1.7)
%Body fat 16.0% (10.7)29.8% (2.8)*
Average steps walked per day
7,286 (2,729)7,247 (1,778)
*P < 0.05.
VOLUME 16 NUMBER 1 | JANUARY 2008 | www.obesityjournal.org
behavior and psychology
Twenty of the twenty-two subjects completed all three dietary
fat levels. Due to scheduling difficulties, one female and one
male completed only two of the three levels.
The top panel of Table 2 shows the characteristics of the core
foods in each diet. We were successful within 0.6 percentage
points in achieving the levels of dietary fat intended for each
condition. We were also successful in achieving equal amounts
of energy from core foods across diets, with the mean energy
content of core foods varying by only 23 kcal across diets. The
statistical significance of this small difference (P < 0.0001) is
due to the accuracy and small variability of the energy content
across diets for individual subjects. Due to the substitution of
fat for carbohydrate, energy density increased across treat-
ments (P < 0.0001) and, in order for energy content to remain
constant, food weight decreased with increasing fat level (P <
0.0001). Other determinants of energy density (i.e., non-
calorics, mainly water and fiber) were also very similar across
diets, varying by only 0.4 percentage points (data not shown).
Thus, changes in energy density were due solely to changes in
the macronutrient composition (% dietary fat).
ad libitum intake
The middle panel of Table 2 shows ad libitum intake from the
buffet. Dietary fat as a percent of energy paralleled the increase
for the core diets, due to the design of the buffets in having
similar fat levels as the core foods. The percentage of fat was
several points lower than for the core foods, due apparently to
subjects’ selection of buffet foods. Food weight remained stable
(P = 0.51) and ad lib energy intake increased (P = 0.052) across
the diets, due to the increase in energy density (P < 0.0001).
Variability of all quantities was much greater for ad lib intake
than for core foods.
The bottom panel of Table 2 shows the total intake from core
foods and buffet combined. Dietary fat levels paralleled the tar-
get levels but were slightly lower due to the lower fat content of
the subjects’ buffet items. By repeated measures analysis of var-
iance, the energy contents of the diets were significantly differ-
ent (P = 0 031). On the 26% fat diet, the average energy intake
was 2,748 ± 741 kcal/day (mean ± s.d.). This increased by 6%
on the 34% fat diet (2,983 ± 886 kcal/day, P = 0.091 compared
to the 26% fat diet) and by 10% on the 40% fat diet (3,018 ±
963 kcal/day, P = 0.011 compared to the 26% fat diet). Energy
intake did not differ significantly between the 34 and 40% diets
(P = 0.35). Figure 1 shows these results. When tested as a lin-
ear trend, the average energy intake increased significantly as
the % fat in the diet increased (P = 0.008), by 19 ± 7 kcal/day
(mean ± s.e.m.) for each percentage point of dietary fat level.
Food weight did not change significantly across the diets (P =
0.33) but energy density increased with dietary fat level (P <
0.0001), accounting for the increase in energy intake.
There were no significant differences in the visual analogue scale
palatability ratings among the three diets, either when examin-
ing the three meals (breakfast, lunch, and dinner) separately or
combining data from all meals (P > 0.06 for all). Table 3 shows
the means, standard deviations, and P values for palatability of
meals, hunger before meals, and fullness after meals.
hunger and fullness
There were no significant differences among the three diets
in the visual analogue scale hunger ratings before meals sepa-
rately or when combining data from all meals (P > 0.08 for all).
Fullness ratings after meals did not differ significantly among
the three diets when meals were combined (P = 0.28) or when
breakfast and lunch were examined separately (P > 0.73 for
table 2 characteristics of core foods, ad lib intake, and total
intake (mean (s.d.))
Dietary fat target level
(percent of energy)
Ad lib buffet
Total daily intake
Dietary fat level
Average energy intake (kcal/day)
P = 0.091 vs.
P = 0.011 vs.
P = 0.35 vs.
Figure 1 The average energy intake (kcal/day) for each level of dietary fat.
Obesity | VOLUME 16 NUMBER 1 | JANUARY 2008 67
behavior and psychology
both). There was a significant difference across diets in fullness
after dinner (P = 0.03), with subjects reporting slightly greater
fullness on the 40% fat diet (78.7 ± 13.2 versus 74.6 ± 11.9 and
75.0 ± 9.6 on 26 and 34% fat diets), but this may be a result of
doing several significance tests in this analysis.
The mean number of steps recorded per day on the 26, 34,
and 40% diets, were 8,077 ± 3,014, 8,028 ± 2,593, and 8,832 ±
3,089, respectively (mean ± s.d.), and these did not differ sig-
nificantly across diets (P = 0.15). This suggests that the normal
physical activity was not significantly affected by the changes
in diet composition.
These results show that the amount of fat in the diet can affect
voluntary energy intake and can be important for body weight
regulation. The study extends previous research (7–9) by
showing that even within the typical range of consumption in
western diets, total ad libitum energy intake increases as the
fat content of the diet increases. Reducing dietary fat in the
population should reduce total energy intake.
While we only evaluated three different levels of dietary fat,
there appears to be a linear relationship between the fat in the
diet and voluntary energy intake across the range of dietary fat
tested, as shown in Figure 1. Since the pattern of ad libitum
energy intake across dietary fat levels was as expected, given
results in other studies across wider ranges of dietary fat, and
was significant across the entire range (26–40%), we believe
the lack of significance between individual fat levels (e.g., 26%
vs. 34% and 34% vs. 40%) indicates there were an insufficient
number of subjects to detect such small differences. These data
would suggest that each 1% reduction in dietary fat within this
range would, on average, reduce energy intake by ~20 kcal/day.
For example, reducing dietary fat by 5% would be expected
to reduce voluntary energy intake by ~100 kcal/day. Hill et al.
(24) calculated that this reduction in energy intake should be
sufficient to stop most of the gradual weight gain occurring
in the population. For those not experiencing gradual weight
gain, this would be sufficient to produce some weight loss.
Thus, a 5% reduction in the fat intake of the population could
be a strategy for at least stopping the gradual weight gain of the
population and perhaps lowering the average BMI.
The 2005 Dietary Guidelines for Americans recommends
that dietary fat should comprise no more than 35% of total
energy intake (25). This is an increase in the upper limit of die-
tary fat by ~5% over the previous guidelines (26), and accord-
ing to our study results, could lead to an increase in energy
intake and weight gain in the population.
Studies of body weights on low-fat diets in free-living indi-
viduals eating ad libitum are mixed. Astrup et al. (27) reviewed
19 intervention trials and found that 18 of 19 showed a lower
body weight with low vs. higher fat diets. Alternatively, Willett
(28) reviewed very long-term trials of low-fat diets and argued
that differences in fat consumption within the range of 18–40%
have no lasting impact on body fatness in the long term. The
mixed results could be because of poor compliance to low-fat
diets over the long term.
In contrast to studies in free-living subjects, controlled
laboratory studies are consistent in showing that increases in
dietary fat promote positive energy balance. These studies,
including the present study, are consistent in that they show
that as dietary fat increases voluntary energy intake increases
(3–5). When studied in a whole-room calorimeter, 24-h energy
balance becomes more positive with increased dietary fat (3,4).
Further, excess energy in the form of dietary fat is stored more
efficiently in the body than excess energy from carbohydrate
(29). Dietary fat has been suggested to contribute to a small
reduction in total energy expenditure, since fat has a lower
thermic effect on food than carbohydrate (30). This results
of this work, considered together, suggest that high-fat diets
increase the likelihood of excessive energy intake and positive
energy balance, and that excess fat is stored more efficiently
than excess carbohydrate. There is a need for carefully con-
trolled intervention studies, where dietary adherence is high
, to examine the impact of alteration in dietary fat levels on
body weight and body fat. Donnelly et al. (31) recently con-
ducted one such study in college students and showed that
energy intake increased significantly as dietary fat increased
from <25% (low fat) to 28–32% (medium fat) to >35% (high
fat). Body weight gain also increased with increasing levels of
dietary fat. That study, taken together with the present results,
strongly supports the conclusion that increasing dietary fat is
associated with increasing energy intake and increasing likeli-
hood of weight gain.
Dietary fat level has also been shown to play a role in regain-
ing of weight after sustained weight loss. Individuals in the
National Weight Control Registry, a registry of successful
weight loss maintainers, report consuming a diet containing
~24% of total energy from fat during weight loss maintenance
table 3 Vas palatability of meals, hunger before meals, and
fullness after meals (mean (s.d.))
Palatability of meals (VAS, 0–100)
P value for
Palatability of meals
Hunger before meals
Fullness after meals
VAS, visual analogue scale.
VOLUME 16 NUMBER 1 | JANUARY 2008 | www.obesityjournal.org
behavior and psychology
(32). Further, an increase in dietary fat is one of the best pre-
dictors of weight regain over time in these individuals (33).
Others have reported that much of the impact of high-fat
diets on energy intake could be due to the high energy den-
sity of these diets (34). For example, Stubbs et al. (4) found
that subjects consumed ~285 kcal/day more on a 40% fat diet
as compared to a 20% fat diet when the energy density of food
was not controlled. When these diets were studied by Stubbs et
al. at a similar energy density, there was no difference in volun-
tary energy intake (6). G.K.G. et al. (35) showed that even small
differences in water and fiber content can have large effects on
energy density and energy intake in covert feeding trials. In
studies of individual foods it has been reported (36–38) that
high-fat foods tend to be low in moisture, resulting in fur-
ther increases in energy density. Thus, in the real world, foods
higher in fat may be even higher in energy density than those
we used in our diets. However, it is difficult to replicate this
correlation realistically between fat and moisture in laboratory
studies. In the present study, our more conservative strategy
was to control for the major determinants of energy density
other than dietary fat–fiber and water. Further, the diets did
not differ in palatability. In this way we can attribute the dif-
ferences in energy intake to the differences in the proportion
of fat in the diet.
Subjects in this study did not eat alone but rather consumed
meals with other subjects. The number of people present at the
meals was constant across the study. There is evidence that the
number of people present at a meal may influence the amount
eaten at that meal (39). We chose this method because we
believe that most people consume meals not alone, but with
others. Energy intake may have been different if the subjects
had eaten alone, but there is no reason to think that the effects
of diet composition would not be similar.
Clearly, dietary fat content is not the only factor affecting
body weight. An effective strategy to address obesity will also
have to focus on increasing physical activity. While this was not
the focus of the present study, substantial other data suggest
that increases in physical activity are important in preventing
weight gain and weight regain following weight loss (40–44).
For example, individuals who are most successful in weight
loss maintenance report eating a low-fat diet (~24%) and
engaging in ~60 min/day of physical activity (32). Both low-fat
diets and increased physical activity would affect energy bal-
ance in a way that would make it easier to match energy intake
to energy expenditure. Additionally, increases in dietary fiber
may be associated with decreased weight gain (45).
These results do not support the benefits gained by a low
carbohydrate diet, per se, in long-term weight control. In fact,
the results can be interpreted to conclude that total energy
intake increases as dietary carbohydrate is lowered. The pri-
mary data in support of the benefits of low carbohydrate diets
come from studies of weight loss (46–48). There are no ran-
domized controlled trials that have found low carbohydrate
diets to be superior to low-fat diets in preventing weight gain.
Some have suggested that particular types of carbohydrates
(e.g., high glycemic index foods) may contribute to excessive
energy intake and obesity (49,50). Sloth et al. (51) found
no differences in body weight between low and high glyc-
emic diets fed ad libitum. However, no studies have assessed
energy intake in a controlled setting with high and low glyc-
emic index diets.
Data from the present study, collected under controlled
conditions, suggest that dietary fat contributes to a metabolic
state that favors positive energy balance. The observation that
body weight is not always reduced in long-term low-fat diet
interventions seems to imply poor compliance with low-fat
regimes. Thus, the long-term success of fat reduction in miti-
gating weight gain and supporting weight loss will depend on a
food supply that provides choices that are both lower in fat and
good tasting. Our results would suggest that even small reduc-
tions in dietary fat, which might be possible without dramatic
adverse effects on food preferences, could help in preventing
weight gain in the population.
In summary, these results are consistent with the notion that
decreasing dietary fat even within the ranges typically con-
sumed in Western diets could decrease the risk of consuming
excess energy and could be an important factor in countering
the gradual weight gain seen in the population. Whatever die-
tary advice is provided to the public must be accompanied by
advice for engaging in regular physical activity. Based on these
results, a low-fat diet combined with regular physical activity
could be the foundation for a lifestyle that helps prevent exces-
sive weight gain.
This research was supported by National Institutes of Health grants R37
DK042549 (to J.O.H.), P30 DK048520 (to J.O.H.), and MO1 RR00051.
The authors declared no conflict of interest.
© 2008 The Obesity Society
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