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Biological Rhythm Research
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Meal distribution across the day and its relationship with body
composition
Murilo Dattiloa; Cibele Aparecida Crispima; Ioná Zalcman Zimberga; Sérgio Tufika; Marco Túlio de
Melloa
a Departamento de Psicobiologia, Universidade Federal de São Paulo, Sao Paulo, Brazil
First published on: 15 July 2010
To cite this Article Dattilo, Murilo , Crispim, Cibele Aparecida , Zimberg, Ioná Zalcman , Tufik, Sérgio and de Mello,
Marco Túlio(2011) 'Meal distribution across the day and its relationship with body composition', Biological Rhythm
Research, 42: 2, 119 — 129, First published on: 15 July 2010 (iFirst)
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Meal distribution across the day and its relationship with body
composition
Murilo Dattilo, Cibele Aparecida Crispim, Iona
´Zalcman Zimberg,
Se
´rgio Tufik and Marco Tu´lio de Mello*
Departamento de Psicobiologia, Universidade Federal de Sa
˜o Paulo, Sao Paulo, Brazil
(Received 13 January 2010; final version received 24 February 2010)
Evidence has suggested that meal distribution across the day may influence body
composition. This study aimed to evaluate the distribution of energy and
macronutrient intake in healthy men and women, and to correlate it with body
composition. Fifty-two healthy volunteers (24 men), aged 20–45 years old,
participated in the study. Food intake was analyzed by a three-day food record
and anthropometric measurements included body mass, height, body mass index,
body fat percentage, and waist circumference. Positive correlations were found in
men between night fat intake and body mass index, body fat percentage and waist
circumference and negative correlations were seen between morning energy and
macronutrient intake and the same anthropometric variables. These data suggest
that fat intake at night is associated with higher values in anthropometric variables
while morning food intake can be associated with lower values in anthropometric
variables.
Keywords: meal distribution; body mass; waist circumference; obesity; body fat
Introduction
Factors which lead to an increase of body mass have been of interest for several
investigators (World Health Organization 2000; Bes-Rastrollo et al. 2006), as obesity
is highly related to the development of type 2 diabetes mellitus (Tu
¨rkoglu et al. 2003;
Gregg et al. 2005) and cardiovascular diseases (Gregg et al. 2005). There is concise
evidence demonstrating that the type of diet consumed, coupled with an increase in a
sedentary lifestyle, are the main factors responsible for establishing a positive energy
balance and consequent increase of body fat (Astrup 2001).
Although the question ‘‘what to eat?’’ has been extensively studied, little is
known about the best time to eat meals, how it should be distributed across the day,
or even if these aspects are important (Holmba
¨ck et al. 2003; Crispim et al. 2007). It
is well known that several physiological aspects present a circadian rhythm, like the
rate of gastric emptying, intestinal blood flow, renal and hepatic activity (Dunbar
et al. 1989), hormonal responses to food intake (Astrup 2001; Holmba
¨ck et al. 2003),
insulin sensitivity and glucose tolerance (Van Cauter et al. 1991), lipid tolerance
(Arasaradnam et al. 2002) and diet-induced thermogenesis (Romon et al. 1993), with
lower efficiency being observed at night and increased efficiency in the morning.
*Corresponding author. Email: tmello@psicobio.epm.br
Biological Rhythm Research
Vol. 42, No. 2, April 2011, 119–129
ISSN 0929-1016 print/ISSN 1744-4179 online
Ó2011 Taylor & Francis
DOI: 10.1080/09291011003729270
http://www.informaworld.com
Downloaded By: [Tufik, Sergio] At: 15:45 28 April 2011
Thus, the integration of such aspects in response to food intake may contribute to
physiological changes in digestion, absorption, and utilization of nutrients, that,
associated with different values of diet-induced thermogenesis and levels of physical
activity across the day (e.g. higher in the morning and lower at night), could be
capable of influencing long-term body weight regulation.
Supporting this hypothesis, previous studies have suggested that the period of the
day when meals are consumed may significantly influence body composition (Baecke
et al. 1983). In animals, food intake during the 12-h light phase is associated with a
significant weight gain in comparison with mice fed during the 12-h dark phase (Arble
et al. 2009). In humans, this hypothesis was cited by epidemiological investigations of
the circadian distribution of energy intake, which have suggested that obese
individuals consume a greater proportion of energy in the latter half of the day
when compared with lean subjects, both in children (Maffeis et al. 2000) and adults
(Bellisle et al. 1988; Fricker et al. 1990). Furthermore, studies involving night workers
have also indicated that these individuals present a greater risk of developing obesity
(Geliebter et al. 2000; Morikawa et al. 2007; Suwazono et al. 2008), and eating food at
night is a possible contributory factor (van Amelsvoort et al. 1999).
From another perspective, some previous data have indicated that the
consumption of meals in the morning, like breakfast meals, may have an important
role in health promotion (Song et al. 2005; Huang et al. in press). As mentioned
before, the morning period is associated with a higher metabolic and physiological
efficiency, and is particularly satiating, contributing to reduce the total amount
ingested for the day (de Castro 2004). Moreover, some research has demonstrated
that consumption of meals in the morning contributes to body weight reduction,
preventing weight gain and hence the development of obesity (Song et al. 2005; Kant
et al. 2008; Huang et al. in press; Patro and Szajewska in press).
Taking that into consideration, the present study aimed to evaluate the
distribution of energy and macronutrient intake in healthy men and women, and
to correlate it with body composition variables. With these analyses, we expect to
obtain data that allows a better explanation of the relationship between eating a
meal at different times of the day and body composition.
Methods
Subjects
The sample consisted of 24 men and 28 women, between 19 and 45 years old
(27.2 +5.9 and 28.8 +6.6 years old, respectively). All individuals were sedentary
according to the Baecke et al. (1982) questionnaire and without health problems, like
dyslipidemia, diabetes, cardiovascular disease, hypertension and sleep disturbance,
according to medical evaluation, clinical and polysomnographic tests. Participation in
the study was voluntary after signing a written informed consent. Furthermore, all
individuals knew that they could interrupt their participation in the study at any given
moment, without any cost. The present study was approved by the Committee of
Ethics in Research of the Universidade Federal de Sa
˜o Paulo, under protocol #0592/07.
Food intake evaluation
The volunteers were instructed to provide as much detail as possible of the food and
fluids consumed, including brand names and recipes for home-cooked foods. Portion
120 M. Dattilo et al.
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sizes were estimated using common household measurements such as cups, glasses,
bowls, teaspoons, and tablespoons in addition to individual food items/units. The
volunteers discussed their reported food intake with a qualified nutritionist and the
information was amended to include additional explanations and details, thus
improving the accuracy of the information obtained. Nutwin version 1.5 software
(Universidade Federal de Sa
˜o Paulo, Brazil) was used for the quantitative analysis of
energy and nutrient intake.
Body composition
Body mass was measured with a scale with 0.1 kg precision (Plenna
1
, Plenna
Especialidades Ltda, Brazil) and height with a stadiometer fixed to the wall with
0.1 cm precision (Sanny
1
, American Medical do Brasil Ltda, Brazil). Body mass
divided by the squared height was used to calculate the body mass index (BMI) in
kg/m
2
.
For the evaluation of body composition, body densities were determined
according to the formulas proposed by Jackson and Pollock (1978) and Jackson
et al. (1980), for men and women, respectively. Skinfold fat was measured at the
chest, axilla, triceps, subscapula, abdomen, supra-illac, and thigh using a Lange
1
skinfold caliper (Beta Technology Incorporated, USA). Body fat percentage (%BF)
was obtained using the formula proposed by Siri (1961). The measurements of waist
circumference (WC) were taken midway between the lowest rib and the iliac crest
with an inelastic measuring tape (Sanny
1
, American Medical do Brasil Ltda, Brazil).
Statistical analysis
Data were analyzed with Statistica 7.0 (StatSoft, Inc., Tulsa, OK, USA). All values
were expressed as mean +standard deviation (SD). For gender comparisons
between food intake, Student’s t-tests for independent samples were used. Pearson’s
correlation coefficients were used to assess the association between food intake
variables (proportions of overall intake ingested during each period) and
anthropometric measurements (BMI, %BF, WC). ANOVA with repeated measure-
ments was used to compare the energy intake of meals and energy and macronutrient
intake in accordance with morning (breakfast and mid-morning snack), afternoon
(lunch and mid-afternoon snack) and night (dinner and supper) periods. When
significant differences were obtained, the tests were followed by Bonferroni’s
corrections for multiple comparisons. Statistical tests when P0.05 were accepted
as significant.
Results
Table 1 presents the anthropometric characteristics of the subjects. The sample was
composed of young adults and mean BMI and WC for both genders indicates
normal ranges.
Although the values of energy intake were higher in men in relation to women
(2697.6 +870.6 kcal and 1865.5 +502.1 kcal; P50.01, respectively), no
significant differences between genders in macronutrient intake (expressed per kg
body weight and also as a percentage of total intake) were identified (Table 2).
Biological Rhythm Research 121
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Figure 1 depicts the energy distribution of the meals eaten during the course of
the day in men and women. Differences were obtained for women [F(5,35) ¼9.71,
P50.01], and post-hoc analyses showed that lunch was significantly higher than
breakfast (P¼0.01), mid-morning snack (P50.01), mid-afternoon snack
(P50.01), and supper (P¼0.01), whereas dinner was higher than mid-morning
snack (P¼0.07). For men, no differences were seen for any meal [F(5,20) ¼1.20,
P¼0.35].
Figure 2 shows data from the daily meals divided into three periods of the day
(morning, afternoon and night) and gender, in accordance with energy (kcal),
carbohydrate (g), protein (g), and fat (g) intake. Energy intake was higher in the
afternoon and night than in the morning, for men [F(2,46) ¼12.6, P50.01] and
women [F(2,50) ¼12.19, P50.01]. Carbohydrate intake at night was higher than
in the morning for men [F(2,46) ¼4.67, P¼0.01] and afternoon intake was higher
than in the morning for women [F(2,50) ¼7.15, P50.01]. Protein intake at
afternoon and night was higher than in the morning, for men [F(2,46) ¼19.39,
P50.01] and women, whereas afternoon was higher than night for women [F
(2,50) ¼18.16, P50.01]. Fat intake in the afternoon and night was higher than in
the morning, for men [F(2,46) ¼16.11, P50.01] and women [F(2,50) ¼7.43,
P50.01].
In Table 3, meal intakes are distributed into 3 periods – morning, afternoon and
night – and the respective correlations of each period with %BF, BMI and WC are
presented for both genders. Among men, significant negative correlations were
observed for total energy and macronutrient intake in the morning and a positive
Table 1. Descriptive data of the subjects.
Variables Men (n ¼24) Women (n ¼28)
Age (yrs) 27.2 +5.9 28.8 +6.6
Height (cm) 175.0 +6.2 161.5 +5.6
Body mass (kg) 76.6 +14.7 58.0 +8.3
BMI (kg/m
2
) 24.9 +4.2 22.2 +2.6
Body fat (%) 19.4 +7.7 22.0 +5.1
WC (cm) 84.9 +11.9 71.8 +6.6
BMI, Body Mass Index; WC, Waist circumference.
Table 2. Food intake data for men and women.
Nutritional composition of food intake Men (n ¼24) Women (n ¼28) P
x
EI (kcal) 2697.6 +870.6 1865.5 +502.1 50.001
Kcal (kcal/kg) 36.9 +15.5 32.8 +10.1 0.27
Total fat intake (%EI) 31.5 +5.9 30.7 +5.2 0.60
Total fat intake (g/kg) 1.2 +0.5 1.1 +0.5 0.44
Total carbohydrate intake (%EI) 51.9 +8.2 52.8 +5.3 0.64
Total carbohydrate intake (g/kg) 4.9 +2.7 4.3 +1.5 0.33
Total protein intake (%EI) 16.6 +3.9 16.6 +5.4 0.95
Total protein intake (g/kg) 1.5 +0.6 1.3 +0.4 0.15
EI, Energy intake.
x
Comparison done by using Student’s t test.
122 M. Dattilo et al.
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correlation was found between fat intake at night and %BF, BMI, and WC; for
women, a single significant positive correlation was found in the afternoon for %BF.
For the correlations between energy intake in each period and overall intake,
significant results were obtained only for the total sample (Figure 3). In summary,
correlations with small magnitude were found in the morning. In the afternoon, the
values indicated a negative correlation (r¼70.29, P50.05), whereas at night, the
correlations were positive (r¼0.34, P50.05), suggesting that ingesting a high
proportion of the daily intake in the afternoon was associated with lower overall
Figure 1. Comparison of the energy contents of the six daily meals in men and women.
*Different from breakfast, mid-morning snack, mid-afternoon snack, and supper in men,
P50.01.
#
Different from mid-morning snack in women, P50.01.
Figure 2. Nutritional composition of the different meals in men and women in accordance
with the period of day. *Different from morning, in men.
#
Different from morning, in women.
¥
Different from night, in women.
Biological Rhythm Research 123
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intake and that ingesting a high proportion of daily intake in at night was associated
with higher overall intake.
Discussion
Although studies developed with the same aim as ours are limited in the literature,
we expected to obtain data to support the hypothesis that food intake at night might
Table 3. Correlation between food intake data and anthropometric variables in the morning,
afternoon and night periods.
Men (n ¼24) Women (n ¼28)
%BF BMI WC %BF BMI WC
Kcal
Morning 70.57 70.60 70.60 70.20 70.11 70.03
Afternoon 0.22 0.31 0.23 0.21 0.17 0.18
Night 0.31 0.25 0.32 0.01 70.05 70.14
Carbohydrate
Morning 70.59 70.61 70.61 70.09 0.02 0.02
Afternoon 0.24 0.24 0.14 70.11 0.01 0.03
Night 0.11 70.01 0.09 0.01 70.02 70.18
Protein
Morning 70.40 70.42 70.43 70.23 70.11 70.04
Afternoon 0.19 0.29 0.34 0.41 0.26 0.20
Night 0.13 0.09 0.20 0.12 0.09 70.09
Fat
Morning 70.44 70.49 70.47 70.28 0.10 70.09
Afternoon 0.02 0.15 0.09 0.19 70.02 70.18
Night 0.46 0.55 0.50 70.04 70.10 70.06
%BF, Body fat percentage; BMI, Body Mass Index; WC, Waist Circumference. Bold correlation
coefficients: P50.05.
Figure 3. Correlation between energy intake in each period and overall intake. *P50.05.
124 M. Dattilo et al.
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contribute to increases in %BF, BMI, and WC. In fact, we observed a positive
correlation between fat intake at night and these variables, an issue that highlights
several points related to health, but another important find of this study was the
negative correlation of total energy and all macronutrient intake in the morning and
such anthropometric variables, similarly with previous data that emphasizes the
importance of food intake in this period, like breakfast.
Several physiological aspects are marked by a circadian rhythm, many of which
are associated with post-prandial metabolism. However, evidence that guides specific
modifications in food intake distribution across the day are very scarce (Waller et al.
2004). According to the ‘‘lipogenic–lipolytic’’ theory of Armstrong (1980), daytime
food intake is associated with glucose metabolism and fat deposition, and nocturnal
(sleep) fasting with fat metabolism. So, rhythmic alterations of post-prandial
responses at night, associated with metabolism peculiarities during sleep, indicates
that the body is preparing to sleep, presenting a lower capacity to deal with food in
this period.
Our data suggest a possible interaction between fat intake and metabolic
alterations that are observed at night, such as fat tolerance reduction, which is linked
to insulin resistance (Arasaradnam et al. 2002), and a reduced physical activity level,
contributing together for fat deposition as adipose tissue. Fat metabolism shows
distinct features compared to glucose and amino acid metabolism, that is, unlike
these, increased fat intake is not accompanied by an increase in its oxidation,
favoring lipogenesis and weight gain (Schutz et al. 1989). Moreover, carbohydrate
intake at night may not be directly implicated with lipogenesis de novo (fat synthesis
from non-lipid substrates), but it can contribute to weight gain and fat deposition by
acting as ‘‘sparing’’ of fat oxidation (Schutz et al. 1989). In this way, excessive caloric
intake during the evening hours is problematic (Russ et al. 1984) and avoiding high-
density foods in this period, like high fat food, might aid in reducing overall intake
and may be useful in dietary interventions for overweight and obesity (de Castro
2009).
Nowadays, this issue may be highlighted in front of the widespread use of
artificial lighting that has allowed people to remain active and eat late into the night
(de Castro 2004), like night workers, for example. Individuals who work at night,
that curiously present a higher prevalence of obesity (Waterhouse et al. 2003;
Ishizaki et al. 2004), dyslipidemia (Lennernas et al. 1994), and type 2 diabetes
mellitus (Gottlieb et al. 2005), are influenced by several factors, endogenously and
exogenously. With regard to exogenous factors, food intake can be focused and it is
well described that night workers have an increased consumption of snacks and
small, ‘‘convenience’’ meals during the shift itself (Lennernas et al. 1995), that are
usually high in fat content. In fact, our study did not include night workers in the
sample, but our data can be extrapolated, for instance, to this population as a tool
for nutritional strategies and interventions to reduce and prevent health problems
linked, partially, to food intake.
Recent studies have suggested that food intake during the morning period can be
involved with weight gain prevention in adults (Cho et al. 2003; Song et al. 2005;
Kant et al. 2008; Huang et al. in press) and youth (Alexander et al. 2009;
Kontogianni et al. 2010; Patro and Szajewska in press). The fact that we found an
inverse association between food intake in this period and BMI, %BF, and WC,
corroborate directly with these findings. However, most of the studies utilized BMI
as a tool for determination of nutritional status, while we observed these results for
Biological Rhythm Research 125
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%BF and WC. Song et al. (2005) investigated the association between breakfast
intake and BMI, using data from the National Health and Nutrition Examination
Survey (NHANES) 1999–2000, and observed that women who consumed breakfast
showed a significantly lower risk of presenting BMI 425 kg/m
2
after adjustments
for age, race, smoking, energy intake, exercise and body mass control. In this same
way, Kant et al. (2008) evaluated data from NHANES 1999–2000, 2001–2002 and
2003–2004 and observed that individuals who consumed breakfast had lower energy
density based on the 24-h food record, indicating that food intake during the
morning may contribute to the reduction of total energy intake and contribute to
weight maintenance and fat loss. Furthermore, women (but not men) who ate
breakfast had a lower BMI compared with those who did not (27.9 +0.2 kg/m
2
versus 29.4 +0.4 kg/m
2
,P¼0.001).
Because of strong evidence suggesting that breakfast consumption is a protection
factor against weight gain, both in adults and children, it is important to describe the
paper published by Alexander et al. (2009) that used magnetic resonance imaging as
a tool for body composition evaluation. In this study, the authors observed that
breakfast omission was associated with increased adiposity, specifically intra-
abdominal adipose tissue in 93 overweight Latino youth (10–17 years old).
Moreover, they postulated that interventions focused on increasing breakfast
consumption are warranted.
In accordance with the studies previously cited (Cho et al. 2003; Song et al. 2005;
Kant et al. 2008; Alexander et al. 2009; Kontogianni et al. 2010; Huang et al. in
press; Patro and Szajewska in press), it is becoming more concrete that food intake in
the morning is a protection against weight gain. Contrary to taking a meal at night,
food intake in the morning seems to contribute to lower energy storage as body fat,
due to increased daytime activity (Segal et al. 1985). Moreover, according to Romon
et al. (1993) food intake in the morning period (9:00 am) is associated with a greater
thermal effect than intake in the afternoon (5:00 pm) and night (1:00 am) periods;
this difference leads to greater daily energy expenditure and favors the maintenance
of body mass. A study conducted by de Castro (2004) also indicates that meal
distribution during the course of the day may influence the energy value of the food
eaten; the greater the proportion of food intake in the morning period, the smaller
the total daily energy intake, as well as the energy intake in the night period. The
same author has suggested that the morning period is associated with a greater
efficiency of the satiety signal than in the night period, so contributing to a smaller
daily energy intake and the prevention of body mass increase. In the present study,
though we did not observe significant correlations between energy intake in the
morning and overall intake, a negative correlation was seen between the proportion
ingested in the afternoon and overall intake, suggesting that afternoon period also
contributes to a smaller daily energy intake. Furthermore, we observed a positive
correlation between the proportion ingested at night and overall intake, corroborat-
ing directly with de Castro’s findings (2004).
Little is known about the impact that distribution of energy and nutrients
throughout the day has on body mass regulation. In fact, acute experimental
protocols have found that the time of day that meals are consumed can promote
different metabolic and hormonal responses. So, we support the idea that eating at a
‘‘wrong’’ moment can contribute to weight gain and changes in body composition in
the long-term as an adaptation of the body, whereas eating at the ‘‘right’’ moment
can contribute to weight loss and weight gain prevention due to better post-prandial
126 M. Dattilo et al.
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response. In this study, although it was a descriptive evaluation, we corroborate with
previous data in the literature, allowing new perspectives for future research. The
fact that we did not observe significant correlations in women remains unknown, but
some differences in postprandial responses between genders (e.g., meal tolerance,
that is, the ability of the body to bring blood glucose back to basal levels following a
meal because of circadian control) may be present, since there is evidence supporting
reduced effective response in women when standard meals are presented, whereas
male subjects’ response is more stable (Ahmed et al. 1976; Nuttall et al. 1985).
Conclusions
Our data indicate that, at least for men, a greater fat intake at night may be
associated with higher %BF, BMI, and WC, whereas food intake in the morning
period is associated with lower anthropometric variables. Moreover, intake in the
afternoon can reduce the total amount ingested for the day, and that intake in the
night can result in greater overall daily intake. Based on these findings, special
attention needs to be given to fat intake in the night period, giving priority to food
intake in the first period of the day, being a positive tool in dietetic planning and a
possible anti-obesity complementary strategy for the general population, and night
workers, that are individuals who have several health disorders associated, in part,
with the circadian distribution of food. Further studies, with larger samples, should
be developed to better explain these results and with other populations, such as obese
individuals, since food habits are much modified.
Acknowledgements
The authors thank all volunteers for their participation in the study, Jim Waterhouse, Hanna
Karen Moreira Antunes, Everald Van Cooler, and Nadine Bressan for their support and
assistance, and the support of Associac¸ a
˜o Fundo de Incentivo a
`Psicofarmacologia (AFIP),
Centro de Estudos em Psicobiologia e Exercı
´cio (CEPE), Centro de Estudo Multidisciplinar
em Sonoleˆncia e Acidentes (CEMSA), CEPID/SONO-FAPESP (#998/1430373), CNPq
(501567/2007-0), CAPES, FAPESP (2009/11056-1), UNIFESP, FADA, and FADA/
UNIFESP.
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