Download ORIGINAL COMMUNICATION Lipid, protein and

European Journal of Clinical Nutrition (2002) 56, 37–43
ß 2002 Nature Publishing Group All rights reserved 0954–3007/02 $25.00
www.nature.com/ejcn
ORIGINAL COMMUNICATION
Lipid, protein and carbohydrate intake in relation to
body mass index
A Trichopoulou1*, C Gnardellis1, V Benetou1, P Lagiou1, C Bamia1 and D Trichopoulos1,2
1
Department of Hygiene and Epidemiology, University of Athens Medical School, Athens, Greece; and 2Department of
Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
Objective: To examine whether the same amount of energy intake has different consequences on body mass index (BMI),
depending on the source of energy from specific macronutrients.
Design: Cross-sectional study, in the context of the European Prospective Investigation into Cancer and nutrition (EPIC).
Setting: Communities all over Greece.
Subjects: A total of 27 862 apparently healthy volunteers, men and women, ages 25 – 82 y.
Interventions: None.
Methods: Dietary information was collected through an interviewer-administered semi-quantitative food frequency questionnaire. In the context of a cross-sectional analysis, we calculated changes of BMI per increments of energy intake from protein,
carbohydrates, saturated, polyunsaturated and monounsaturated lipids and ethanol, controlling for mutual confounding and
other confounders, among all participants, and after exclusion of under-reporters and=or those on a diet.
Results: Protein intake was positively associated with BMI. The association was evident when nutrients were not mutually
adjusted for and increased after mutual adjustment among nutrients (b ¼ 0.80 kg=m2 per 418.4 kJ or 100 kcal increment, 95%
confidence interval 0.55 – 1.06 for men, and b ¼ 1.59, 95% CI 1.30 – 1.88 for women), as well as after exclusion of underreporters and=or those on a diet. The effects of other macronutrients were less substantial or consistent.
Conclusions: There is evidence indicating that protein intake is conducive to obesity. Moreover, our data suggest that neither
saturated or monounsaturated lipids nor carbohydrates are likely to play a major role in increasing BMI over and beyond that
indicated by their energy content.
Sponsorship: EPIC-Greece is supported by the Europe Against Cancer Program of the European Commission and the Greek
Ministry of Health.
European Journal of Clinical Nutrition (2002) 56, 37 – 43. DOI: 10.1038=sj=ejcn=1601286
Keywords: protein; lipids; carbohydrates; body mass index; obesity
Introduction
A high body mass index (BMI) increases morbidity and
mortality from several diseases, including cardiovascular
*Correspondence: A Trichopoulou, Department of Hygiene and
Epidemiology, University of Athens Medical School, 75 M. Asias Street,
GR-115 27, Athens, Greece.
E-mail: [email protected]
Guarantor: A Trichopoulou.
Contributors: AT is the principal investigator of the EPIC project in
Greece and generated the working hypothesis. CG and CB undertook
the statistical analyses. VB and PL are EPIC project managers, dealt
with the biomedical and epidemiological issues and were responsible
for the drafting of the paper. DT is the senior epidemiologist of the
EPIC-Greece study.
Received 7 March 1999; revised 30 May 2001;
accepted 6 June 2000
diseases, diabetes mellitus and some forms of cancer
(Garrow, 1986; Bray, 1996; World Cancer Research Fund &
American Institute for Cancer Research, 1997; Seidell,
1998b). In addition, obesity is generally considered as an
aesthetic drawback in most modern societies. Reduced
energy intake and increased physical activity are the only
established approaches to reducing BMI that can be sustained over long periods of time (Klesges et al, 1992;
Nelson & Tucker, 1996; Rising et al, 1994; Schulz & Schoeller,
1994; De Groot & van Staveren, 1995; Parker et al, 1997;
Trichopoulou et al, 2000). There is also an extensive literature, from both animal experiments and human studies,
addressing the question whether specific energy-generating
nutrients affect BMI differentially, after accounting for their
energy content (Slattery et al, 1992; Stubbs et al, 1997;
Macronutrient intake and BMI
A Trichopoulou et al
38
Willett, 1998a; West & York, 1998). There appears to be no
agreement among investigators, with the majority considering that energy from lipids is more conducive to an increased
BMI than energy from other macronutrients (Astrup et al,
1997; Bray & Popkin, 1998; Hill et al, 2000), whereas a
differential effect is not accepted by other investigators
(Willett, 1998a; Seidell, 1998a).
In the context of the Greek segment of the European
Prospective Investigation into Cancer and nutrition (EPIC),
we have undertaken a large cross-sectional study, relying on
validated questionnaires for nutritional intake and physical
activity, and standardised somatometric measurements. The
objective of this study was to assess whether the same
amount of energy intake has different consequences on
BMI, calculated as the ratio of weight in kilograms over
square of height in metres. depending on the source of
energy from protein, carbohydrates, various forms of dietary
lipids or ethanol.
Methods
A validated semi-quantitative food frequency questionnaire
was administered, from 1994 to 1999, by specially trained
interviewers to 27 862 apparently healthy adults, men and
women, aged 25 – 82 y. The study subjects were recruited
from regions all over Greece in order to participate in the
Greek component of the EPIC study. EPIC is a multi-country
prospective cohort study investigating the role of nutrition
and other lifestyle and environmental factors in the aetiology of cancer and other chronic diseases (Riboli, 1992).
Each participant signed an informed consent form before
enrolment.
Somatometric measurements concerning weight, height,
sitting height, as well as waist and hip circumferences were
also undertaken. Subjects were measured without shoes,
lightly clothed and without any restrictive underwear.
The semi-quantitative food frequency questionnaire was
validated during the pilot phase of the study (Gnardellis et al,
1995; Katsouyianni et al, 1997). The questions covered the
average frequency of consumption of approximately 150
food items and beverages over a period of 1 y. Standard
portion sizes were used for the estimation of consumed
quantities (Trichopoulou, 1992; Gnardellis et al, 1995).
Nutrient intakes were calculated through a food composition
database adapted to accommodate the characteristics of the
Greek diet (Trichopoulou, 1992). Intakes were expressed as
averages per day, taking into account seasonal variation
(Katsouyianni et al, 1997). For this paper, only energy-generating nutrients (protein, carbohydrates, saturated, polyunsaturated and monounsaturated dietary lipids and ethanol)
were considered.
Occupational and leisure-time activities were assessed by
the use of a special section of the lifestyle questionnaire,
which was also used for the overall evaluation of physical
activity level. Details have been given elsewhere (Trichopoulou et al, 2000). Briefly, the average time per day spent on
European Journal of Clinical Nutrition
job, household, sporting and other activities, as well as on
sleep, was calculated. Each activity was assigned a MET value
(the ratio of the metabolic rate associated with a given
activity to the resting metabolic rate) recorded in published
tables (Ainsworth et al, 1993). The time spent in each of the
above activities was multiplied by the MET value of that
activity, and all MET-hour products were summed to give a
total MET-hour score for the day (Martinez et al, 1997). This
score essentially corresponds to the amount of energy per
kilogram of body weight expended by an individual during
an average day.
When energy intake is held constant, increasing energy
intake from a particular nutrient presupposes reduction of
energy intake from one or more of the remaining energygenerating nutrients. Because this complicates interpretation, we have considered as reliable only findings that were
shown to be both robust in the different models and consistent in the various study subgroups (eg those defined by
age, smoking status, extent of under-reporting etc).
Multiple linear regression was used to evaluate the effects
of energy-generating nutrients and ethanol on BMI, whilst
adjusting mutually, as well as for possible confounding
variables such as age (in specified age-groups as shown in
Table 1, categorically), smoking habits (current smokers, exsmokers and never smokers, categorically), educational level
expressed as years of schooling (in four groups as shown in
Table 2, categorically) and energy expenditure (in 5 METhours per day increments, continuously). The large sample
size assures the validity of the normal distribution approximation for statistical testing and estimation. Separate
models were fitted for men and women (all non-pregnant),
as well as for all participants and those who remained after
exclusion of under-reporters (ie subjects with a calculated
energy intake less than 1.14 of the basal metabolic rate;
Schofield, 1985; Goldberg et al, 1991; Shetty et al, 1996;
Gnardellis et al, 1998) and=or subjects on any kind of diet.
The parameter coefficients for men and women were compared, when needed, through standard statistical procedures.
Several sets of linear regression models were fitted, but the
results were generally similar although estimates of the
parameters may have occasionally varied. For clarity, we
present here only results from the main two sets of these
models. In the first set of models, the effects on BMI of
energy intake from each energy-generating nutrient is
shown, controlling for age, educational level (years of
schooling), tobacco smoking, energy expenditure and total
energy intake, but not for the other energy-generating nutrients. In each of these models, energy intake from a certain
macronutrient was evaluated using standard methods as
recommended by Willett and Stampfer (1986) and Willett
(Willett, 1998b), but no attempt was made to adjust for
energy intake from the remaining energy-generating nutrients. In the second set of models, energy intakes from all the
energy-generating nutrients were simultaneously included,
adjusting also for age, educational level (years of schooling),
tobacco smoking and energy expenditure. In the later
Macronutrient intake and BMI
A Trichopoulou et al
Table 1
39
Distribution of study participants, body mass index (BMI, in kg=m2) and tobacco smoking status, by age and gender
Men
Women
BMI
BMI
Tobacco smoking (%)
Tobacco smoking (%)
Cut-offs of quartiles
Age
groups
Cut-offs of quartiles
n
1st
2nd
3rd
Never
smokers
Past
smokers
Current
smokers
n
1st
2nd
3rd
Never
smokers
Past
smokers
Current
smokers
2072
4252
2398
2761
11483
25.2
25.7
26.0
25.6
25.6
27.3
27.8
28.4
28.1
27.9
29.7
30.3
31.0
30.7
30.4
24.3
19.9
27.9
29.8
24.8
18.6
29.9
37.6
46.9
33.6
57.1
50.1
34.5
23.3
41.7
2760
5669
4099
3851
16379
22.6
24.7
26.8
27.4
25.1
25.0
27.7
29.8
30.3
28.5
28.3
31.4
33.1
33.5
32.1
43.1
62.9
87.5
93.6
72.9
11.3
10.9
4.9
3.6
7.7
45.6
26.2
7.7
2.9
19.3
II. Excluding under-reporters and=or those on a diet
< 40
1302
24.9
27.0
29.4
40 – 54
2349
25.5
27.7
30.0
55 – 64
1347
25.9
28.3
30.8
65 þ
1428
25.4
27.9
30.5
Total
6426
25.4
27.7
30.1
23.0
19.6
28.1
32.4
24.9
17.5
25.8
35.2
42.3
29.7
59.5
54.7
36.7
25.3
45.3
1615
3057
1897
1588
8157
22.2
24.6
26.8
27.2
24.6
24.2
27.5
29.7
30.0
27.9
27.0
30.8
32.9
33.0
31.4
44.7
65.3
88.3
94.3
72.2
10.3
9.3
4.1
3.4
7.2
45.0
25.4
7.6
2.3
20.6
I. All participants
< 40
40 – 54
55 – 64
65 þ
Total
Table 2 Percentage distribution of study participants by age, gender and educational level (years of schooling)
Men
Educational level (years of schooling) (%)a
Women
Educational level (years of schooling) (%)b
Age group
5y
6 – 11 y
12 y
13 y
5y
6 – 11 y
12 y
13 y
I. All participants
< 40
40 – 54
55 – 64
65 þ
Total
0.2
1.5
21.1
36.3
13.7
16.7
36.9
54.1
56.1
41.4
23.2
12.9
7.5
3.4
11.3
59.9
48.7
17.2
3.9
33.4
0.4
4.0
44.6
56.2
25.8
32.5
48.9
36.6
38.3
40.6
28.4
19.7
10.9
3.6
15.2
38.6
27.3
7.4
1.5
18.2
those on a diet
18.7
23.4
42.3
12.1
56.7
7.1
55.7
2.9
43.5
11.3
57.7
44.2
11.7
2.5
30.9
0.6
3.9
47.6
57.9
23.9
34.9
53.4
37.5
37.2
42.9
26.9
18.2
8.7
2.7
14.7
37.6
24.4
5.9
1.5
18.3
II. Excluding under-reporters and=or
< 40
0.2
40 – 54
1.4
55 – 64
24.4
65 þ
38.6
Total
14.3
a
For 20 (0.2%) and 6 (0.1%) of the male participants in the upper and the lower panel, respectively, the educational level was
unknown.
For 42 (0.3%) and 20 (0.3%) of the female participants in the upper and the lower panel, respectively, the educational level
was unknown.
b
models, total energy intake was not included to avoid collinearity — the so-called energy partition model (Willett,
1998b). All the analyses were performed with the STATA
statistical package (Stata Corporation, 1999).
In the Results section, all partial regression coefficients
express changes in BMI for an increment of energy intake
from the respective nutrient of 418.4 kJ or 100 kcal.
Results
Tables 1 and 2 show the distribution of study participants,
BMI, tobacco smoking status and educational status (years of
schooling), by age and gender, for all participants, and after
exclusion of energy intake under-reporters and=or those on a
diet of any type. The high prevalence of smoking among
men and among younger women is well known for the
Greek population (Table 1). There are, by design, more
women than men in the study and about one half in both
genders claims to be on a diet of some type and=or are
considered as under-reporters of energy intake (Table 1).
The level of education is, as expected, remarkably high in
younger compared to older Greek people. Thus, among men,
59.9% of those younger than 40 y have more than 12 y of
schooling, whereas, among those 65 y or older, the corresponding figure is 3.9% (Table 2). The increase of BMI until
about the age of 65 (eg from a median of 27.3 and 25.0 kg=m2
among men and women, respectively, younger than 40 y, to
a median of 28.4 and 29.8 kg=m2 among men and women,
European Journal of Clinical Nutrition
Macronutrient intake and BMI
A Trichopoulou et al
40
respectively, 55 to 64-y-old) is in line with expectations. So is
the association of under-reporting with increased BMI (Pvalue from t-test for unequal variances less than 0.001).
Table 3 presents quartiles of total energy intake (in kJ) and
percentage energy intake from specific nutrients by gender,
for all participants, and after exclusion of energy intake
under-reporters and=or those on a diet of any type. Again,
the data are compatible to expectations, because Greeks are
known to consume more than 40% of total energy intake
from lipids, particularly in the form of olive oil (Trichopoulou et al, 1993). Note that the values in Table 3 denote the
quartiles of energy intake from the different nutrients.
Table 3 Cut-off points of quartiles of total energy intake (in kJ) and of percentage of energy intake from specific
nutrients by gendera
Men
Quartiles
Women
Quartiles
1st
2nd
3rd
1st
2nd
3rd
7821.8
13.1
34.5
41.5
10.9
4.9
19.1
0.9
9597.3
14.1
38.3
45.0
12.6
5.5
21.8
3.2
11713.8
15.2
42.1
48.4
14.2
6.9
24.3
7.0
6280.6
13.3
36.5
43.8
11.3
5.1
20.1
0.0
7655.6
14.3
39.9
46.9
13.0
5.7
23.0
0.4
9266.2
15.4
43.4
49.9
14.7
7.5
25.3
1.3
II. Excluding under-reporters and=or those on a diet
Energy
9339.6
10775.1
Protein (%)
13.1
14.1
Carbohydrates (%)
34.4
38.1
Lipids (%)
41.6
44.9
Saturated
11.4
12.8
Polyunsaturated
4.9
5.5
Monounsaturated
18.7
21.5
Ethanol (%)
1.3
3.7
12584.0
15.0
41.6
48.0
14.3
7.2
23.8
7.6
7483.7
13.3
36.5
44.5
11.9
5.2
20.0
0.0
8553.9
14.2
39.6
47.3
13.4
5.7
22.8
0.4
9966.6
15.1
42.8
49.9
15.0
8.1
25.0
1.6
I. All participants
Energy
Protein (%)
Carbohydrates (%)
Lipids (%)
Saturated
Polyunsaturated
Monounsaturated
Ethanol (%)
a
Note that the values denote the quartiles of energy intake from the different nutrients. Therefore, the figures in the columns
do not add up to 100% and the sums increase, as they should, from quartile 1 to quartile 3.
Table 4 Partial regression coefficients (b) (and 95% confidence intervals) expressing changes of body mass index (BMI) for an increment of 418.4 kJ or
100 kcal of energy intake from the specified nutrient, controlling for age, educational level (years of schooling), tobacco smoking, energy expenditure and
energy intake (left side), or, controlling for age, educational level (years of schooling), tobacco smoking, energy expenditure and mutually among the
nutrients (right side)
Without mutual adjustment among nutrients
Men
I. All
Protein
Carbohydrates
Saturated
Polyunsaturated
Monounsaturated
Ethanol
0.76
7 0.09
0.29
0.18
0.06
7 0.07
II. Excluding under-reporters and=or
Protein
0.83
Carbohydrates
7 0.06
Saturated
0.32
Polyunsaturated
0.22
Monounsaturated
0.001
Ethanol
7 0.07
a
0.55,
7 0.15,
0.15,
0.05,
7 0.02,
7 0.11,
b
Men
95% CI
Women
b
95% CI
b
95% CI
0.98
7 0.03
0.42
0.31
0.15
7 0.03
1.35
7 0.27
0.12
0.56
0.08
7 0.83
1.08,
7 0.35,
7 0.05,
0.41,
7 0.03,
7 0.99,
1.62
7 0.18
0.30
0.71
0.18
7 0.67
0.80
7 0.09
7 0.18
0.13
0.03
7 0.03
a
r ¼ 0.17
0.55, 1.06
7 0.14, 7 0.04
7 0.37, 0.01
0.004, 0.26
7 0.05, 0.11
7 0.08, 0.01
P < 0.0001
1.59
7 0.26
7 0.81
0.56
0.12
7 0.80
r ¼ 0.38
1.30, 1.88
7 0.32, 7 0.20
7 1.02, 7 0.61
0.41, 0.70
0.03, 0.21
7 0.95, 7 0.64
P < 0.0001
those on a diet
0.56, 1.10
7 0.13, 0.01
0.14, 0.50
0.06, 0.37
7 0.11, 0.11
7 0.12, 7 0.02
1.13
7 0.10
7 0.15
0.56
7 0.13
7 0.62
0.81,
7 0.19,
7 0.35,
0.40,
7 0.25,
7 0.79,
1.44
0.001
0.05
0.71
7 0.01
7 0.46
0.91
7 0.01
7 0.06
0.21
0.03
0.02
r ¼ 0.22
0.58, 1.23
7 0.08, 0.05
7 0.30, 0.18
0.05, 0.37
7 0.07, 0.14
7 0.03, 0.06
P < 0.0001
1.77
7 0.07
7 0.88
0.64
0.12
7 0.54
r ¼ 0.48
1.43, 2.11
7 0.14, 7 0.001
7 1.11, 7 0.65
0.48, 0.79
0.01, 0.22
7 0.70, 7 0.37
P < 0.0001
r is the multiple correlation coefficient.
European Journal of Clinical Nutrition
Women
95% CI
b
With mutual adjustment among nutrients
Macronutrient intake and BMI
A Trichopoulou et al
41
Therefore, the figures in the columns of Table 3 do not add
up to 100% and the sums increase, as they should, from
quartile 1 to quartile 3.
Table 4 presents the partial regression coefficients expressing changes of BMI per 418.4 kJ (100 kcal) increments of
energy intake from a particular nutrient, adjusting for age,
educational level (years of schooling), tobacco smoking,
energy expenditure and total energy intake (left side), or
for age, educational level (years of schooling), tobacco smoking, energy expenditure and energy intake from the remaining energy-generating nutrients simultaneously (right side).
The coefficients are presented when all participants have
been considered (upper panel), as well as, after exclusion of
under-reporters and=or those on a diet of any type (lower
panel). The striking finding in this table is the significant
positive association between protein intake and BMI. The
association is evident when nutrients are not mutually
adjusted for (ie left upper side of Table 4) and tends to
increase after mutual adjustment for nutrients (right upper
side of Table 4), as well as after the exclusion of underreporters and=or those on a diet of any type (lower panel
of Table 4). The positive association of protein with BMI is
also substantial, because the respective partial regression
coefficients are considerably larger, in absolute terms, than
those of all other nutrients. The lack of independence
hinders formal statistical testing, but it is noteworthy that
the confidence interval of the partial regression coefficient
for protein does not overlap with those of the partial regression coefficients for the other macronutrients. There is also
evidence that the protein partial regression coefficient is
larger among women than among men (P 0.001 for the
significance of interaction term between gender and protein
intake in all models involving mutual adjustment among
nutrients).
With respect to carbohydrates, there is evidence of an
inverse association that persists after mutual adjustment
among nutrients. However, the association is not significant
when under-reporters and=or those on a diet are excluded
and, in all instances, the regression coefficients are, in
absolute terms, 10 times smaller than those concerning
protein.
With respect to saturated lipids, the evidence is conflicting and, thus, unconvincing. There is evidence for a significant positive association when nutrients are not mutually
adjusted for among men, but the association becomes
inverse, and significantly so among women, after mutual
adjustment among nutrients (P < 0.001). With respect to
polyunsaturated lipid intake, there is evidence of a positive,
statistically significant, but weak association with BMI, the
evidence being generally weaker among men, compared to
women. Regarding the intake of monounsaturated fat its
effect, although generally not significant, appears to be
positive and statistically significant among women, after
adjustment for other macronutrients (right side, upper and
lower panel of Table 4). When lipids are considered together,
as total lipid intake, the respective regression coefficient is, as
expected, a weighted average of the three constituent
groups, but conveys no additional information.
Finally, the evidence for ethanol intake appears compatible with an inverse association with BMI among women,
whereas the association among men is weaker especially in
regression models where all energy-generating nutrients are
mutually adjusted for (right side of Table 4).
Discussion
The present study is based on data that are both observational and cross-sectional in nature, and thus has the drawbacks inherent in these approaches (Willett, 1998b;
Trichopoulou et al, 2000). However, efficient analyses of
cross-sectional data represent a valuable initial step in identifying relationships between diet and health-related outcomes. Moreover, intervention and prospective cohort
studies have their own weaknesses. Thus, they can rarely
be large enough to allow the documentation of other than
very strong nutritional associations. Furthermore, they
depend on long-term dietary compliance (intervention studies) or frequently repeated dietary measurements (observational cohort studies). Observational cross-sectional
investigations can thus be useful, particularly when they
are large and rely on strict protocols and standardised instruments and procedures. This study is one of the largest in this
field and has relied on questionnaires that have been
detailed, standardised, validated and administered by specially trained interviewers (Gnardellis et al, 1995; Katsouyianni et al, 1997; Pols, et al, 1997).
The analysis of data from observational nutritional studies
is not straightforward and several issues remain controversial. Prominent among them is the inability to specifically
evaluate the role of changes in the intake of any particular
nutrient alone, while controlling for total energy intake. It
has been pointed out in the literature (Wacholder et al, 1994;
Willett, 1998b) that assessment of differential effects of
energy-generating nutrients on BMI (or other outcomes)
through standard regression procedures is not straightforward. This is because keeping total energy intake constant
and evaluating the consequences on BMI of increasing
energy intake from a particular nutrient presupposes reduction of energy intake from one or more of the remaining
energy-generating nutrients.
Additional complications arise from the varying extent of
misclassification in the reported food consumption and the
estimated nutrient intake. Sources of misclassification may
be gender or age-related or may be unidentifiable; moreover
they may have different consequences depending on
whether a particular nutrient is the focus of the analysis or
serves as a confounder to be controlled for (Willett, 1998b).
Given these problems and uncertainties surrounding
nutritional observational data and their analysis on the
one hand, and the difficulty of undertaking large long-term
nutritional interventions on the other, it seems prudent to
rely for aetiological inferences only on empirical associations
European Journal of Clinical Nutrition
Macronutrient intake and BMI
A Trichopoulou et al
42
that are strong and consistent. Thus we have investigated the
effect of protein, carbohydrates, various forms of dietary
lipids and ethanol on BMI by means of different linear
regression models, and we have considered as reliable only
findings that were shown to be both (i) robust in the
different models, (ii) consistent when under-reporters (Gnardellis et al, 1998) and=or participants on a diet of any type
were excluded from the analysis, and (iii) consistent in
subgroups defined by age, smoking status, etc (data not
shown). Under these criteria and contrary to expectations,
the strongest and most robust association in these data was
the positive one between protein intake and BMI, after
adjusting for demographic variables, tobacco smoking,
total energy intake and physical activity. Given the very
large sample size, the slight positive skewness of the distribution of BMI does not affect the validity of results relying on
traditional analysis of variance and multiple regression and,
in any case, log transformation of BMI generated essentially
identical results.
There were other findings of interest in the present investigation, although these were less consistent (Table 4). Thus,
the positive association between protein intake and BMI is
substantially and significantly stronger among women. Polyunsaturated lipids appear to be positively associated with
BMI in both genders, but the association, although statistically significant, is generally much weaker than that concerning protein. Lastly, and again mostly among women,
there is evidence that, for a given total energy intake,
ethanol intake is inversely associated with BMI. Nevertheless, we are reluctant to embark on biological speculation on
the basis of these results. The large size of the study can make
results of minor magnitude and questionable importance
statistically significant, and the apparent gender interaction
that seems to magnify most findings among women could be
attributed to more accurate reporting by women. The alternative, that in some way female metabolism is more discriminatory in its response to energy intake from different
nutrients, does not have strong scientific foundations. In a
parsimonious way, we interpret our findings as suggestive
that protein intake is likely to increase BMI in both genders,
whereas intake of carbohydrates, saturated lipids, monounsaturated lipids and ethanol are rather unlikely to affect BMI,
when total energy intake and expenditure are kept constant.
We cannot, however, reject the hypothesis that polyunsaturated lipid intake may increase BMI.
The possibility that energy-generating nutrients may
affect obesity in humans in ways over and beyond those
accounted for by their energy content has been intensively
investigated and several major reviews have evaluated the
collective evidence. Most authors are inclined to believe that
dietary lipid intake promotes obesity in humans (Astrup et al,
1997; Bray and Popkin, 1998; Hill et al, 2000) as it appears to
do in laboratory animals (West & York, 1998). There are,
however, other investigators who consider the evidence
inconclusive, because of the inherent limitations in designing and analysing the studies addressing this complex issue
European Journal of Clinical Nutrition
(Willett, 1998a; Seidell, 1998a). The present investigation
does not provide evidence that saturated or monounsaturated fat tend to increase obesity, when energy intake is
accounted for. Moreover, the results of the present study
are compatible with carbohydrates having no positive association with obesity, in line with the results reported by
other investigators (Nelson and Tucker, 1996; Stubbs et al,
1997). Lastly, ethanol intake was not positively associated
with obesity after controlling for energy intake; in fact, an
inverse association was evident, particularly among women,
possibly explained in terms of socio-cultural norms rather
than metabolic mechanisms.
In contrast, we found evidence that protein intake is
strongly positively associated with obesity after controlling
for energy intake in different ways. The finding is not
entirely original (Slattery et al, 1992; Rolland-Cachera et al,
1995) and in fact, it has been hypothesised that protein
intake may be the main underlying reason for the increasing
prevalence of obesity in the Western world (McCarty, 2000).
Nevertheless, the present study provides the strongest
empirical evidence yet that protein intake may be a crucial
determinant of obesity in humans.
In conclusion, we have found evidence indicating that
protein intake is conducive to obesity. No other association,
positive or inverse, in these data was sufficiently strong and
consistent to allow reliable biological speculation. Moreover,
these data indicate that neither saturated or monounsaturated fat nor carbohydrates are likely to play a major role in
increasing BMI over and beyond their energy content. Since
both proteins and polyunsaturated lipids represent a relatively small fraction of total energy intake, it would appear
that qualitative alterations of diet may have little effect on
BMI at the individual level. On the other hand, at a population level, the relatively high protein and polyunsaturated
lipid intake of Americans in the United States could explain,
in part, the expanding obesity problem in this country and
perhaps other countries as well.
Acknowledgements
The European Prospective Investigation into Cancer and
nutrition (EPIC) is co-ordinated by the International
Agency for Research on Cancer (IARC) and supported by
the Europe Against Cancer Program of the European Commission. The Greek segment of the EPIC study is also supported by the Greek Ministry of Health.
References
Ainsworth BE, Haskell WL & Leon AS (1993): Compendium of
physical activities: classification of energy costs of human physical
activities. Med. Sci. Sports Exerc. 25, 71 – 80.
Astrup A, Toubro S, Raben A & Skov AR (1997): The role of low-fat
diets and fat substitutes in body weight management: what have
we learned from clinical studies? J. Am. Dietet. Assoc. 97(Suppl 7),
S82 – S87.
Bray GA (1996): Obesity. In Present Knowledge in Nutrition, ed. EE
Ziegler & LJ Filer, pp 19 – 32. Washington, DC: ILSI Press.
Macronutrient intake and BMI
A Trichopoulou et al
Bray GA & Popkin BM (1998): Dietary fat intake does affect obesity!
Am. J. Clin. Nutr. 68, 1157 – 1173.
De Groot LC & van Staveren WA (1995): Reduced physical activity
and its association with obesity. Nutr. Rev. 53, 11 – 13.
Garrow JS (1986): Obesity. In Human Nutrition and Dietetics, ed. LSP
Davidson, R Passmore & MA Eastwood, pp 465 – 479. Edinburgh:
Churchill Livingstone.
Gnardellis C, Trichopoulou A, Katsouyianni K, Polychronopoulos E,
Rimm EB & Trichopoulos D (1995): Reproducibility and validity of
an extensive semi-quantitative Food Frequency Questionnaire
among Greek school teachers. Epidemiology 6, 74 – 77.
Gnardellis C, Boulou C & Trichopoulou A (1998): Magnitude determinants and impact of under-reporting of energy intake in a
cohort study in Greece. Public Hlth. Nutr. 1, 131 – 137.
Goldberg GR, Black AE, Jebb SA, Cole TJ, Murgatroyd PR, Coward WA
& Prentice AM (1991): Critical evaluation of energy intake data
using fundamental principles of energy physiology: 1. Derivation
of cut-off values to identify under-recording. Eur. J Clin. Nutr. 45,
569 – 581.
Hill JO, Melanson EL & Wyatt HT (2000): Dietary fat intake and
regulation of energy balance: implications for obesity. J. Nutr.
130(Suppl 2S), 284S – 288S.
Katsouyianni K, Rimm EB, Gnardellis C, Trichopoulos D, Polychronopoulos E & Trichopoulou A (1997): Reproducibility and relative
validity of an extensive semi-quantitative Food Frequency Questionnaire using dietary records and chemical markers among
Greek school teachers. Int. J. Epidemiol. 26(Suppl 1), 118 – 127.
Klesges RC, Klesges LM, Haddock CK & Eck LH (1992): A longitudinal
analysis of the impacts of dietary intake and physical activity on
weight change in adults. Am. J. Clin. Nutr. 55, 812 – 822.
Martinez ME, Giovannucci E, Spiegelman D, Hunter DJ, Willett WC
& Colditz GA (1997): Leisure-time physical activity, body size, and
colon cancer in women. Nurses’ Health Study Research Group. J.
Natl. Cancer Inst. 89, 948 – 955.
McCarty MF (2000): The origins of Western obesity: a role for animal
protein? Med. Hypoth. 54, 488 – 494.
Nelson LH & Tucker LA (1996): Diet composition related to body fat
in a multivariate study of 203 men. J. Am. Diet. Assoc. 96, 771 –
777.
Parker DR, Gonzalez S & Derby CA (1997): Dietary factors in relation
to weight change among men and women from two southeastern
New England communities. Int. J. Obes. Relat. Metab. Disord. 21,
103 – 109.
Pols MA, Peeters PHM, Cocke M, Slimani N, Bueno-de-Mesquita HB
& Collette HJA (1997): Estimation of reproducibility and relative
validity of questions included in the EPIC physical activity questionnaire. Int. J. Epidemiol. 26, Sl8l – Sl89.
Riboli F (1992): Nutrition and Cancer: Background and rationale of
the European Prospective Investigation into Cancer and Nutrition
(EPIC). Ann. Oncol. 3, 783 – 791.
Rising R, Harper IT, Fontvielle AM, Ferraro RT, Spraul M & Ravussin F
(1994): Determinants of total daily energy expenditure: variability
in physical activity. Am. J. Clin. Nutr. 59, 800 – 804.
Rolland-Cachera MF, Deheeger M, Akrout M & Bellisle F (1995):
Influence of macronutrients on adiposity development: a follow
up study of nutrition and growth from 10 months to 8 y of age. Int.
J. Obes. Relat. Metab. Disord. 19, 573 – 578.
Schofield WN (1985): Predicting basal metabolic rate, new standards
and review of previous work. Hum. Nutr. Clin. Nutr. 39C(Suppl 1),
5 – 41.
Schulz LO & Schoeller DA (1994): A compilation of total daily energy
expenditure and body weights in healthy adults. Am. J. Clin. Nutr.
60, 676 – 681.
Seidell JC (1998a): Dietary fat and obesity: an epidemiologic perspective. Am. J. Clin. Nutr. 67(Suppl 3), 546S – 550S.
Seidell JC (1998b): Societal and personal costs of obesity. Exp. Clin.
Endocrinol. Diabetes 106(Suppl 2), 7 – 9.
Shetty PS. Henry CJK, Black AE & Prentice AM (1996): Energy
requirements of adults: an update on basal metabolic rates
(BMRs) and physical activity levels (PALs). Eur. J. Clin. Nutr.
50(Suppl 1), S11 – S23.
Slattery ML, McDonald A, Bild DE, Caan BJ, Hilner JE, Jacobs DR Jr &
Liu K (1992): Associations of body fat and its distribution with
dietary intake, physical activity, ethanol, and smoking in blacks
and whites. Am. J. Clin. Nutr. 55, 943 – 949.
Stata Corporation (l999): Intercooled Stata 6.0 for Windows 98=95=NT.
College Station, TX: Stata Corporation.
Stubbs RJ, Prentice AM & James WP (1997): Carbohydrates and
energy balance. Ann. N.Y. Acad. Sci. 819, 44 – 69.
Trichopoulou A (1992): Composition of Greek Foods and Dishes (in
Greek and English). Athens: Athens School of Public Health.
Trichopoulou A, Toupadaki N, Tzonou A, Katsouyanni K, Manousos
O, Kada E & Trichopoulos D (1993): The macronutrient composition of the Greek diet: estimates derived from six case-control
studies. Eur. J. Clin. Nutr. 47, 549 – 558.
Trichopoulou A, Gnardellis CH, Lagiou A, Benetou V & Trichopoulos
D (2000): Body mass idex in relation to energy intake and expenditure among adults in Greece. Epidemiology 11, 333 – 336.
Wacholder S, Schatzkin A, Freedman LS, Kipnis V, Hartman A &
Brown CC (1994): Can energy adjustment separate the effects of
energy from those of specific macronutrients? Am. J. Epidemiol.
140, 848 – 855.
West DB & York B (1998): Dietary fat, genetic predisposition, and
obesity: lessons from animal models. Am. J. Clin. Nutr. 67, 505S –
512S.
Willett W & Stampfer MJ (1986): Total energy intake: implications
for epidemiologic analyses. Am. J. Epidemiol. 124, 17 – 27.
Willett WC (1998a): Is dietary fat a major determinant of body fat?
Am. J. Clin. Nutr. 67,(Suppl 3), 556S – 562S.
Willett WC (1998b): Nutritional Epidemiology. New York: Oxford
University Press.
World Cancer Research Fund & American Institute for Cancer
Research (1997): Energy and related factors. In Food Nutrition and
the Prevention of Cancer: a Global Perspective, pp 366 – 375. World
Cancer Research Fund and American Institute for Cancer
Research.
43
European Journal of Clinical Nutrition