j.1467-3010.2012.01977.x
Transcript of j.1467-3010.2012.01977.x
-
7/28/2019 j.1467-3010.2012.01977.x
1/11
-
7/28/2019 j.1467-3010.2012.01977.x
2/11
Keywords: fat, fat-sugar seesaw, macronutrient intakes, NDNS, non-milk extrinsic sugar,
sugars
Introduction
Dietary intake data suggest that fat and sugar have aninverse relationship. Many observational studies show
that, in diets where a large percentage of the reportedenergy intake is provided by fat, a small percentage of
energy in the diet comes from sugar(s) (Baghurst et al.1994; Parnell et al. 2008). McColl (1988) described this
as the fat-sugar seesaw. Furthermore, one of thesestudies noted that consumers with a high sugar, low fat
intake tended to have a lower body mass index thanlow sugar, high fat consumers (Bolton-Smith & Wood-
ward 1994). This led to the suggestion that sugar energydisplaces fat energy from the diet and that, as dietary fat
is more energy dense and potentially more likely toencourage weight gain, high sugar intakes could protect
against obesity (Gibney et al. 1995).Dietary fat has an energy density of 37 kJ/100 g,
which is more than twice that of sugar (16 kJ/100 g)(Holland et al. 1991). Therefore, any increase or
decrease in the amount (weight) of sugar in the diet willhave a smaller effect on the percentage energy from
macronutrients (and total energy) than a corresponding
increase or decrease in the amount of fat. The concept of
the fat-sugar seesaw has been used to suggest that thehealthy eating targets of reducing dietary sugar and fatcannot be achieved simultaneously (Drummond et al.
1996). This led to the conclusion that dietary advicewould be more effective if focused on fat reduction,
while setting no limits on the consumption of carbohy-drates, including sugar (Drummond et al. 1996; Gibson
1997). Because sugar is combined with fats in the manu-facture of many processed foods, an alternative argu-
ment is that high sugar foods can act as a vehicle fordietary fat (Emmett & Heaton 1995; Macdiarmid et al.
1998). Furthermore, Macdiarmid et al. (1995) cast
doubt on the fat-sugar seesaw concept, observing that itdepends on whether macronutrients are calculated andanalysed as percentages of energy intake or weight in
grams.Central to the evidence for a fat-sugar seesaw is the
inverse relationship between intakes of fat and sugar inpeoples diets. Daily dietary intakes show variation,
both between individuals and on an individual basis(e.g. de Castro 1991). These variations tend to produce
a wide range of different correlations between various
macronutrient intakes in individuals, perhaps suggestingthat people respond differently (i.e. that they exhibit
individual differences) to alterations in the macronutri-ent composition of the diet. Having said that, however,
each correlation is poorly estimated from just sevendaily observations (such as when taken from 7-day food
intake records); therefore, it is possible that much of thevariation in correlation is due to sampling rather than
differences in the dietary behaviours of people. More-over, sampling distribution of the correlation coefficient
is complex, usually being described through approxima-tions, such as Fisher (1922) and Konishi (1978).
To test the concept of the fat-sugar seesaw, this studyexamined correlations between dietary macronutrients,
between individuals and on an individual basis over a7-day period using dietary data from the National Diet
and Nutrition Survey (NDNS) for adults, which werecollected in 2000 and 2001 (Henderson et al. 2003).
We also examined relationships between the macronu-trient compositions of foods in the Food Standard
Agencys (FSAs) Nutrient Databank. The NutrientDatabank includes energy and nutrient information on
some 7000 foods, and was used by the NDNSresearchers to calculate energy and nutrient intakes
from the weights of foods and drinks recorded by theparticipants.
Methods
Collection of the NDNS data used here took placebetween July 2000 and June 2001 and included a nation-
ally representative sample of adults aged 1964 yearsfrom England, Wales and Scotland (Henderson et al.
2003). Food intake records, using the 7-day weighedfood intake method, were provided by n = 1724 people.
In the current analysis, total energy intake was divided
into seven components: fat, total sugar, intrinsic sugars,non-milk extrinsic sugars (NMES), non-sugar carbohy-drates, protein and alcohol. Here total sugar refers to all
intracellular and extracellular dietary sugars and milksugars, whether these were added to foods or are natu-
rally occurring. Intrinsic sugar is that contained withinthe cell walls of foods (such as in fruits) and milk sugars
(e.g. lactose). Non-milk extrinsic sugars are those notcontained within the cell walls, such as sugar from honey
and table sugar, but excluding milk sugars. Non-sugar
214 G. W. Horgan and S. Whybrow
2012 The Authors
Journal compilation 2012 British Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
3/11
-
7/28/2019 j.1467-3010.2012.01977.x
4/11
the various sugars were (r = -0.40, n = 1724, P < 0.001)for total sugar; (r = -0.22, n = 1724, P < 0.001) for
NMES; and (r = -0.33, n = 1724, P < 0.001) for intrin-sic sugars.
Within-subject associations
Table 2 shows the mean within-subject correlationsbetween the weights (g/day) of the various macronutri-
ents of the reported diet as calculated from each sub-jects seven separate daily intake records. All correlation
values in Table 2 are positive and statistically significant(P < 0.001), indicating not only a lack of fat-sugar
seesaw in the diets of individuals, but the opposite. Thepositive correlations between the macronutrients
(Table 2) show that (on average) on days when subjects
consumed more of one macronutrient than they did onother days, they also consumed more of all of the othermacronutrients. For example, on days when subjects
consumed more fat than they did on average on otherdays, they also consumed more intrinsic sugar, more
NMES and more total sugar, rather than less, as wouldbe predicted by the fat-sugar argument.
Figure 2 shows a histogram of the distribution ofwithin-subject correlations between the weights (g/day)
of fat and total sugar, calculated from each individuals
seven separate daily intake records. The mean cor-relation between the weights (g/day) of fat and total
sugar obtained was positive (r = 0.24, n = 12 068,
P = < 0.001). This differed significantly from zero, thus
indicating that there were more positive than negativecorrelations between fat and total sugar (g/day).
Figure 2 also shows the theoretical distribution ofcorrelations between two variables estimated from
10 000 simulated samples of size 7 (corresponding tothe seven daily food intake records), when the true
correlation between the two variables is r = 0.24. The fitbetween the theoretical distribution of correlations (line
in Fig. 2) and the observed distribution of correlations(bars in Fig. 2) from the subjects data is remarkably
close.
Associations within the Nutrient Databank
Figure 3 illustrates a scatter plot of the amount (g/100 g)
of fat and total sugar in each of the n = 6990 food itemsin the Nutrient Databank that were included in the
analysis. The correlation between fat and total sugar inthe food items of the Nutrient Databank is very weakly
positive (r = 0.03, n = 6990, P = 0.006) and many of thefood items are concentrated in the bottom left of the
plot. This association is also illustrated in log scaleformat for visual emphasis (Fig. 3b). Two clusters can
be seen within this plot, one smaller cluster of foods
containing substantially more of both fat and sugar than
those in the other larger cluster. Closer inspection of theNutrient Databank revealed that items in this smallercluster were mostly the more processed and composite
foodstuffs, whereas the larger cluster consisted more ofraw food ingredients. However, even within the smaller
cluster, which contained foods with a sugar content of atleast 10 g/100 g, there was a weak, but positive, corre-
lation (r = 0.09, n = 1539, P < 0.001) between fat andsugar, whereas this association was negative in the larger
cluster (r = -0.18, n = 5451, P < 0.001).Table 3 shows the level of associations among fat,
sugars, carbohydrate and protein in the n = 6990 foods
in the Nutrient Databank. Fat showed a significant posi-tive correlation with total sugar, NMES, carbohydrateand non-sugar carbohydrate, and a significant negative
correlation with intrinsic sugar. Protein showed a sig-nificant negative correlation with total sugar, intrinsic
sugar, NMES, carbohydrate and non-sugar carbohy-drate. Thus, fat had a positive correlation with all
macronutrients (except intrinsic sugar) whereas proteinhad a positive correlation with fat and a negative cor-
relation with all other macronutrients.
Table 1 Mean daily energy and macronutrient intakes
n
Women Men All
958 766 1724
Mean ( SD) Mean ( SD) Mean ( SD)
Energy (MJ) 8.03 (2.52) 8.22 (2.65) 8.11 (2.58)
Energy (kcal) 1921(603) 1967(634) 1940 (617)
Ener gy intake: BMR 1.37 (0.46) 1 .07 (0.36) 1 .24 (0.44)
CHO (g) 232.2 (78.5) 236.5 (81.4) 234.1 (79.8)
Fat (g) 71.1 (27.0) 73.6 (29.1) 72.2 (28.0)
Protein (g) 72.9 (23.3) 76.0 (31.1) 74.3 (27.1)
Alcohol (g) 15.6 (21.4) 14.6 (19.8) 15.2 (20.7)
Total sugar (g) 100.7 (46.9) 102.0 (47 .8) 101.3 (47 .3)
Intr insic sugar (g) 38.4 (20.2) 38.3 (20.2) 38.3 (20.2)
NMES (g) 62.3 (39.8) 63.7 (40.9) 62.9 (40.3)
CHO (% energy) 46.5 (7.1) 46.3 (7.4) 46.4 (7.2)
Fat (% energy) 32.6 (6.0) 32.8 (6.4) 32.7 (6.2)
Protein (% ener gy) 15.7 (3.1) 16.0 (3.7) 15.8 (3.4)
Alcohol (% energy) 5.3 (6.7) 4.9 (6.3) 5.1 (6.5)Total sugar (% ener gy) 20 .0 (6.6) 19.8 (6.7) 19.9 (6.6)
Intr insic sugar (% ener gy) 7 .9 (3.9) 7.7 (3.9) 7.8 (3.9)
NMES (% energy) 12.1 (6.2) 12.1 (6.4) 12.1 (6.3)
BMR, basal metabolic rate; CHO, carbohydrate; NMES, non-milk extrinsic
sugar; SD, standard deviation; % energy, percentage energy (see Methods).
216 G. W. Horgan and S. Whybrow
2012 The Authors
Journal compilation 2012 British Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
5/11
Discussion
Principal findings
In this study of the correlations between fat and sugar(s)in the diets of NDNS subjects, and the FSAs Nutrient
Databank, which was used to calculate energy and
nutrient intakes from the weights of foods and drinksrecorded by the subjects, the correlations between fat
and sugar(s) were calculated in five ways: (1) betweenindividuals in percentage terms (percentage energy); (2)
between individuals in absolute amounts (g); (3)between individuals in amount (g) relative to BMR; (4)
within individuals over 7 days; and (5) between food
Fat
0 100 200 300 400 50 100 150 200
50
100
150
200
0
100
200
300
400
Sugar
Non-sugar CHO
0
100
200
300
50 100 150 200
50
100
150
20
0
0 100 200 300
Protein
Figure 1 Scatter plot matrix of individual means (g/day) of the macronutrient components.To reduce clutter, axes are marked at the top and bottom, and
left and right, on alternate plots. CHO, carbohydrate.
Fat and sugar relationship in the NDNS 217
2012 The Authors
Journal compilation 2012 Br itish Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
6/11
items in the Nutrient Databank. Only when examining
the correlation between fat and sugar(s) between indi-
viduals in percentage terms (percentage energy) was thefat-sugar seesaw evident; in all other methods, the cor-relation between fat and sugar(s) was positive.
Results showed that in a free-living human popula-tion, macronutrient intakes do not exhibit a fat-sugar
seesaw effect when expressed in absolute terms (i.e.weight in g). There was no inverse relationship between
any one macronutrient and another, and specificallythere was no inverse relationship between fat and intrin-
sic sugar, NMES or total sugar, but rather there was a
Table 2 Within-subject correlations for the macronutrient components of the diet (g/day)
Fat Total sugar Intrinsic sugar NMES CHO Non-sugar CHO Protein
Fat r = 1.00
Total sugar r = 0.24 r = 1.00
Intrinsic sugar r = 0.15 r = 0.51 r = 1.00
Non-milk extrinsic sugars r = 0.23 r = 0.85 r = 0.11 r = 1.00
Carbohydrate r = 0.42 r = 0.68 r = 0.42 r = 0.58 r = 1.00
Non-sugar carbohy dr ate r = 0.41 r = 0.20 r = 0.20 r = 0.16 r = 0.79 r = 1.00
Protein r = 0.50 r = 0.19 r = 0.24 r = 0.13 r = 0.36 r = 0.36 r = 1.00
For all correlations, n = 12 068, P < 0.001.
NMES, non-milk extrinsic sugar; CHO, carbohydrate.
Fat-sugar correlations
Correlation
Frequency
1.0 0.5 0.0 0.5 1.0
0.0
0.2
0.4
0.6
0.8
Figure 2 Histogram of within-individual correlations for fat and
total-sugar intake (g/day), calculated for each subject. The theoretical
sampling variation for a population with a constant correlation is also
shown as the superimposed smooth curve.
0.01
0.1
1
10
100
0.01 0.1 1 10 100
Fat (g/100g)
Totalsugar(g/100g)
(a)
(b)
0
10
20
30
40
50
60
70
80
90
100
0 20 40 60 80 100
Totalsugar(g/100g)
Fat (g/100g)
Figure 3 (a) Fat and sugar amount per 100 g in all items in the National
Diet and Nutrition Survey food composition database. (b) The same plot
on a log scale.
218 G. W. Horgan and S. Whybrow
2012 The Authors
Journal compilation 2012 British Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
7/11
mild positive one. In most cases, this lack of inverserelationship was not a consequence of dietary behav-
iour; rather it is possibly connected to the macronutrientcomposition of specific food items that the study par-
ticipants chose to consume. Given that mean within-subject correlation between absolute intakes (i.e. weight
in g) of fat and total sugar was positively associated, itcan be concluded that fat and sugar do not displace each
other in absolute terms (i.e. gram for gram); at least this
was the case in free-living individuals self-reporting their
habitual diets. Our results indicate that on days whenmore of one macronutrient (e.g. fat) is consumed com-pared with other days, then there is also a tendency to
consume more of the other macronutrient [in this casesugar(s)] as well.
Comparison with other studies
Numerous epidemiological studies have shown an
inverse relationship between sugar and fat consumptionwhen macronutrients are expressed as their percentage
contribution to total energy intake (e.g. Baghurst et al.
1994; Bolton-Smith & Woodward 1994; Gibney et al.1995; Alexy et al. 2003; Dwyer et al. 2003); indeed,such relationships were observed in the current analysis.
Furthermore, when Australian adults were divided intoquintiles (fifths) of percentage energy intake from fat,
Baghurst et al. (1994) found significant negative trendsof percentage energy intake from fats and sugars.
Baghurst et al. (1994) grouped sugars differently than inthe current analysis, but they reported significant nega-
tive trends of percentage energy intake from fats and
simple sugars (monosaccharides and disaccharideswhether occurring naturally or added to foods and
equivalent to the sum of intrinsic sugars and NMESreported in the current paper), natural sugars (all sugars
other than those added to processed foods) in both menand women, and for added sugars (all sugars that had
been added to processed foods or added by the con-sumer) in men, but not women. Calculating absolute
intakes as weight in g of sugars from the publishedvalues of Baghurst et al. (1994) shows that the weight of
simple sugars, natural sugars and added sugars for men,but not women, also decreases with quintiles of increas-ing percentage energy from fat. Thus, the data presented
by Baghurst et al. (1994) appear to show a fat-sugarseesaw even when sugar is expressed as weight and fat
as a percentage of energy intake, at least in men.When the NDNS data used in the current analysis
were divided up into quintiles of percentage energy fromfat using the same methods as Baghurst et al. (1994),
similar relationships to those reported by Baghurst et al.(1994) were apparent; weight of intrinsic sugars (g),
total sugars (g) and percentage energy from NMES
decreased with increasing quintiles of percentage energyintake from fat (values not shown here), suggestive of afat-sugar seesaw. This is an apparent contradiction to
the results of the regression analysis, conducted on thesame data, showing positive correlations between fat
and sugar(s) (Table 2), and contesting the fat-sugarseesaw; the weight of fat and sugar(s) in the diet appears
to have contradictory relationships, being negativewhen intakes of sugar(s) are considered against quintiles
of the percentage energy intake from fat, yet positive
Table 3 Food database correlations in the macronutrient composition of foods (expressed as g/100 g of food)
Fat Total sugar Intrinsic sugar Extrinsic sugar CHO Non-sugar CHO Protein
Fat r = 1.00
P < 0.001
Total sugar r = 0.03 r = 1.00
P = 0.006 P < 0.001
Intrinsic sugar r = -0.02 r = 0.38 r = 1.00
P = 0.041 P < 0.001 P < 0.001
NMES r = 0.04 r = 0.96 r = 0.11 r = 1.00
P < 0.001 P < 0.001 P < 0.001 P < 0.001
CHO r = 0.09 r = 0.72 r = 0.27 r = 0.70 r = 1.00
P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001
Non-sugar CHO r = 0.10 r = 0.07 r = 0.02 r = 0.07 r = 0.74 r = 1.00
P < 0.001 P < 0.001 P = 0.086 P < 0.001 P < 0.001 P < 0.001
Protein r = 0.17 r = -0.24 r = -0.12 r = -0.22 r = -0.20 r = -0.06 r = 1.00
P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001
For all correlations, n = 6990.
CHO, carbohydrate; NMES, non-milk extrinsic sugar.
Fat and sugar relationship in the NDNS 219
2012 The Authors
Journal compilation 2012 Br itish Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
8/11
when considered against intakes of fat expressed asweights (g). This apparent paradox is a result of the
seesaw in percentage energy from fat and absoluteamount of sugar (g) being an artefact rather than a real
dietary effect; this is simply a weaker version of thecorrelation, expressed as percentage energy from fat and
percentage energy from sugar that arises from the mereoperation of taking percentages instead of using weight
in the analysis. Furthermore, if sugar, fat, protein andnon-sugar carbohydrate are independent variables (i.e.no seesaw effect), then sugar (g) and the percentageenergy from fat will be negatively correlated. These will
even be negatively correlated when there is a weak posi-tive correlation between sugar (g) and fat (g). The
reason for this is that expressing one (or more) of themacronutrients as a percentage, whether percentage
contribution to energy or total weight, introduces adependency between the macronutrients. Consider, for
example, a range of theoretical foods that comprise onlyfat and sugar in variable amounts. When the macronu-
trient composition of these foods is expressed as a per-centage of total energy content of the food, there must
be a perfect negative correlation (or seesaw) betweenpercentage energy from fat and percentage energy from
sugar, because the total must equal to 100% and foodsthat have a high percentage energy from fat must have a
low percentage energy from sugar, and vice versa. If
further macronutrients (non-sugar carbohydrate andprotein) are introduced into the model, the same effect
remains in operation, albeit less strongly. The mecha-
nism for this is that foods that have a high percentageenergy from fat must have a low percentage energy fromthe sum of the remaining macronutrients (in this case
sugar, non-sugar carbohydrate and protein).At 37 kJ/100 g, the energy density of dietary fat is
approximately 2.3 times that of sugar (16 kJ/100 g)(Holland et al. 1991). This has the effect of strength-
ening the correlation between fat and sugar when one,or indeed both, are expressed as their percentage con-
tribution to total energy; the association would still benegative if fat and sugar had the same energy densities
because the different energy densities of fat and sugar
are constants and have a simple scaling effect. Theabove negative relationships between percentageenergy from fat and weight (g) of sugar(s) are evident
even in dietary data fabricated from random numbersemphasizing that the fat-sugar seesaw is a mathemati-
cal artefact rather than a dietary effect. This can beseen by using the random number function of a
spreadsheet program to generate fat, sugar, non-sugarcarbohydrate and protein values for theoretical foods
and plotting percentage energy from fat against weight
(g) of sugar. The authors may be contacted for such aspreadsheet.
That fat and sugar, or even fat and carbohydrate, arereciprocally related in percentage terms in the diet is
almost inevitable because they are the main energy-providing macronutrients in typical Western style diets.
The contribution of the other main macronutrient(protein) to daily energy intakes tends to show less
variation. When expressed as absolute amounts(g/100 g), there is a positive relationship between fat
and sugar in peoples diets (Emmett & Heaton 1995;Macdiarmid et al. 1995; Drewnowski et al. 1997), and
as reported here and elsewhere (Stubbs et al. 2001) therelationship between fat and sugar in ready-to-eat foods
in food composition tables is far less evident (see below).In the present study, clear positive relationships were
evident among the macronutrients (g) in the recordeddietary intake data. When macronutrient intakes were
expressed as weight (g), and energy requirements weretaken into account, albeit approximately by calculating
intake relative to estimated BMR, the correlationsbetween pairs of macronutrients were all positive. Thus,
as expected, people with higher energy requirementstended to achieve the necessary higher energy intakes by
consuming more of all the macronutrients and not bydiscernibly selecting more of one and less of another as
a seesaw effect would suggest.
The estimated correlations between fat and totalsugar varied greatly between subjects (Fig. 2). For
example, for some people (i.e. those with positive cor-
relations between fat and total sugar), on the days onwhich they consumed more fat they also consumedmore total sugar, whereas for others (i.e. those with
negative correlations between fat and total sugar) when-ever they consumed more fat they consumed less total
sugar. Thus, at face value, Figure 2 could be taken tosuggest that there are different dietary behaviour phe-
notypes, one in which the fat-sugar seesaw operated intheir diet selection and the other in which it does not.
However, this could all be explained by sampling varia-tion. The fit between the theoretical range of correla-
tions (line in Fig. 2) and the observed range of
correlations (bars in Fig. 2) in the subjects is remarkablyclose. Thus, it is not possible to infer variation at all inwithin-subject macronutrient correlation.
Associations within the Nutrient Databank
In the analysis when the contributions of fat and sugar
were plotted against each other (Fig. 3b), thereappeared to be two main clusters of foods stemming
from within the Nutrient Databank. Closer inspection
220 G. W. Horgan and S. Whybrow
2012 The Authors
Journal compilation 2012 British Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
9/11
revealed that the relationship between fat and sugartended to differ between the two cluster groups, such
that, foods that made up the smaller cluster were typi-cally the more processed foods, which tend to have
relatively large amounts of both sugar and fat. Com-mercially available snack foods tend to be mixtures of
fats and sugars (e.g. chocolate), or fats and starches(e.g. biscuits). They tend to be low in protein and have
a low moisture content (Whybrow et al. 2007). Com-monly within the food supply chain, sugar acts as a
vehicle for fat by increasing the palatability of high-fat foods (Emmett & Heaton 1995), whereas less-
processed foods (such as fruits) and raw foodingredients (such as margarine, cooking oils, flour and
table sugar) tend to be composed predominantly of fat,starch or sugar. Another group of foods that can be
high in fat with little or no sugar, but with a relativelylarge amount of protein, is meat (and some fish). In
processed foods, sugar(s) tend to be positively associ-ated with fat, while in raw foods/ingredients they tend
not to be. The correlations between fat and sugar in thetwo clusters are consistent with this; being positive in
the smaller cluster and negative in the larger cluster.Overall, these clusters may be reflected in the associa-
tions in the overall diet, depending on how much thediet is comprised of processed foods or less-
processed foods. Accordingly, there may be a different
relationship between fat and sugar in the context of theoverall diet dependent upon whether or not the sugar
comes from processed or raw foods. However, the
overall relationship between fat and sugar(s) in thediets of individuals in the NDNS was positive.
In this study, a seesaw effect was not evident from the
assessment of the dietary behaviour of free-living sub-jects not undergoing any specific dietary interventions.
Having said that, a seesaw effect may arise in subjectsattempting to modify their weight by altering their diet.
This is because, while weight loss diets have many strat-egies, reducing fat or carbohydrate intake is often sug-
gested. Under these circumstances, individuals mightcompensate by increasing their intake of other macro-
nutrients, in absolute terms, relative to what they were
consuming beforehand. However, this does not appearto be the case for ad libitum low-fat (Carmichael et al.1998) or low-carbohydrate diets (Samaha et al. 2003),
although more rigorous manipulations, such as Atkinstype diets, which severely reduce the amount of carbo-
hydrate consumed in the diet, do result in an increase inthe intake of other macronutrients (Gardner et al.
2007). The overall conclusion seems to be that compen-sation is weak, while supplementing the diet with either
fat or sugar does not produce macronutrient-specific
compensation (Raben et al. 2002; Mazlan et al. 2006;Whybrow et al. 2007).
The relationship between fat and sugar may also bedifferent in populations whose diets comprise very few
processed foods. It has been suggested that a decreasein the consumption of extrinsic sugars could also bring
about a decrease in fat consumption because of thepositive association between these two nutrients in
many processed foods (Emmett & Heaton 1995). Thelack of association between fat and sugar in less-
processed foods suggests that in diets with few pro-cessed foods, a decrease in sugar intake may lead to an
increase in fat intake. In part, this would dependon how raw foods and ingredients are combined in
the diet.Comparing dietary composition in percentage terms
alone can be misleading. It is a feature of compositionaldata to introduce negative correlations between macro-
nutrients, because when any set of independent vari-ables is expressed as a percentage of the total, these
percentages are more likely than not to be negativelycorrelated. In such circumstances, the correlation is a
statistical artefact, which gives little real informationabout how the original variables relate to one another,
and only when dietary intakes are examined as absolutevalues (g), as well as percentages, can the interrelation-
ships between the different macronutrients be studied
fully.
Strengths and limitations of the studyOne issue that casts a shadow over all dietary analysissurveys (such as the NDNS used here) is the misreport-
ing of food intake. For example, it is well known thatpeople tend to report lower intakes than they have con-
sumed, to eat less than they habitually do and even tochange their pattern of consumption (Macdiarmid &
Blundell 1998; Stubbs et al. 2003). With regard tounder-reporting, Emmett and Heaton (1995) found that
while sugar and fat intakes were positively related whenpeople reporting low energy intakes were included in the
analysis, the relationship became non-significant when
low energy reporting was accounted for. The authorsattributed this to biased misreporting of certain foods.Moreover, misreporting is often clearly evident when
dietary intakes are implausibly low for someone inassumed energy balance (Black 2000). Unfortunately,
however, there is no satisfactory way to remove misre-porting effects from a dietary survey. Omitting subjects
with lower reported intakes only causes bias and higherreported intakes are also affected by misreporting,
because they are more likely in those with higher activity
Fat and sugar relationship in the NDNS 221
2012 The Authors
Journal compilation 2012 Br itish Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
10/11
levels. For these reasons, we did not attempt any adjust-ment or editing of the data to deal with misreporting. To
check whether misreporting might have influenced ourresults, we looked for associations with misreporting
indicators (i.e. reported energy intake relative to esti-mated energy requirements) and found none; there was
no significant association between within-subject fat-sugar correlation and energy intake relative to estimated
energy requirements.The NDNS data used here was collected over a
decade ago. Accordingly, the macronutrient composi-tion of some processed foods will have changed over
that time, particularly the fat and sugar content offoods. Such changes can influence the strength of asso-
ciations between fat and sugar in the foods peoplechoose to consume and can also influence overall diet.
These are, however, unlikely to alter the direction of theassociations.
Conclusions
In the diets of free-living populations, macronutrientintakes do not show a seesaw relationship when
expressed in absolute terms (i.e. g/100 g). There is noindication that people strictly choose between fats and
sugar, or that higher intakes of one macronutrient areassociated with lower intakes of the other, as might be
interpreted from the fat-sugar seesaw argument.Comparing diet composition in percentage terms
alone can be misleading. It is only when dietary intakesare examined as absolute values, as well as relative to
their contribution to energy, that the interrelationshipsbetween the different macronutrients can be studied
fully.
Acknowledgements
This work was supported by The Scottish Governments
Rural and Environment Science and Analytical ServicesDivision (RESAS).
Conflict of interest
The authors have no conflict of interest.
References
Alexy U, Sichert-Hellert W & Kersting M (2003) Associations
between intake of added sugars and intakes of nutrients and food
groups in the diets of German children and adolescents. British
Journal of Nutrition 90: 4417.
Baghurst KI, Baghurst PA & Record SJ (1994) Demographic and
dietary profiles of high and low fat consumers in Australia.
Journal of Epidemiological and Community Health 48:
2632.
Black AE (2000) Critical evaluation of energy intake using the
Goldberg cut-off for energy intake: basal metabolic rate. A practi-
cal guide to its calculation, use and limitations. International
Journal of Obesity 24: 111930.Bolton-Smith C & Woodward M (1994) Dietary composition and
fat to sugar ratios in relation to obesity. International Journal of
Obesity 18: 8208.
Carmichael HE, Swinblirn BA & Wilson MR (1998) Lower fat
intake as a predictor of initial and sustained weight loss in obese
subjects consuming an otherwise ad libitum diet. Journal of the
American Dietetic Association 98: 359.
De Castro JM (1991) Weekly rhythms of spontaneous nutrient
intake and meal pattern of humans. Physiology & Behavior 50:
72938.
Drewnowski A, Henderson SA, Shore AB et al. (1997) The fat-
sucrose seesaw in relation to age and dietary variety of French
adults. Obesity Research 5: 5118.Drummond S, Kirk TR & De Looy A (1996) Are dietary recom-
mendations for dietary fat reduction achievable? International
Journal of Food Sciences and Nutrition 47: 2216.
Dwyer JT, Michell P, Cosentino C et al. (2003) Fat-sugar see-saw in
school lunches: impact of a low fat intervention. Journal of Ado-
lescent Health 32: 42835.
Emmett PM & Heaton KW (1995) Is extrinsic sugar a vehicle for
dietary-fat? Lancet 345: 153740.
Fisher RA (1922) On the probable error of a coefficient of correla-
tion deduced from a small sample. Metron 1: 332.
Gardner CD, Kiazand A, Alhassan S et al. (2007) Comparison of
the atkins, zone, ornish, and LEARN diets for change in weight
and related risk factors among overweight premenopausal
women: the A TO Z weight loss study: a randomized trial.Journal of the American Medical Association 297: 96977.
Gibney MJ, Sigman-Grant M, Stanton JL Jr. et al. (1995) Consump-
tion of sugars. American Journal of Clinical Nutrition 62: 178S
94S.
Gibson S (1997) Obesity: is it related to sugar in childrens diets?
Nutrition & Food Science 97: 1847.
Henderson L, Gregory J, Irving K et al. (2003) The National Diet
and Nutrition Survey: Adults Aged 19 to 64 Years. Vol. 2:
Energy, Protein, Carbohydrate, Fat and Alcohol Intake. London:
The Stationery Office.
Holland B, Welch AA, Unwin ID et al. (1991) The Composition of
Foods, Cambridge, The Royal Society of Chemistry and Ministry
of Agriculture, Fisheries and Food.
Konishi S (1978) An approximation to the distribution of the
sample correlation coefficient. Biometrika 65: 6546.
Macdiarmid J & Blundell J (1998) Assessing dietary intake: who,
what and why of under-reporting. Nutrition Research Reviews
11: 23153.
Macdiarmid JI, Cade JE & Blundell JE (1995) Extrinsic sugar as
vehicle for dietary fat. Lancet346: 6967.
Macdiarmid JI, Vail A, Cade JE et al. (1998) The sugar-fat relation-
ship revisited: differences in consumption between men and
women of varying BMI. International Journal of Obesity 22:
105361.
222 G. W. Horgan and S. Whybrow
2012 The Authors
Journal compilation 2012 British Nutrition Foundation Nutrition Bulletin, 37, 213223
-
7/28/2019 j.1467-3010.2012.01977.x
11/11
Mazlan N, Whybrow S, Horgan G et al. (2006) Effects of increas-
ing increments of fat and sugar-rich snacks into the diet on
energy and macronutrient intake in lean and overweight men.
British Journal of Nutrition 96: 596606.
Mccoll KA (1988) The sugar-fat seesaw. British Nutrition Founda-
tion Nutrition Bulletin 13: 1149.
Parnell W, Wilson N, Alexander D et al. (2008) Exploring the rela-
tionship between sugars and obesity. Public Health Nutrition 11:8606.
R Development Core Team 2010. R: a language and environment
for statistical computing. R Foundation for Statistical Computing.
Vienna, Austria.
Raben A, Vasilaras TH, Moller AC et al. (2002) Sucrose compared
with artificial sweeteners: different effects on ad libitum food
intake and body weight after 10 wk of supplementation in over-
weight subjects. American Journal of Clinical Nutrition 76:
7219.
Samaha FF, Iqbal N, Seshadri P et al. (2003) A low-carbohydrate as
compared with a low-fat diet in severe obesity. New England
Journal of Medicine 348: 207481.
Schofield WN (1985) Predicting basal metabolic rate, new standards
and review of previous work. Human Nutrition: Clinical Nutri-
tion 39: 541.
Stubbs RJ, Mazlan N & Whybrow S (2001) Carbohydrates, appe-
tite and feeding behavior in humans. Journal of Nutrition 131:2775S81S.
Stubbs RJ, Oreilley L, Fuller Z et al. (2003) Detecting and Model-
ling Mis-Reporting of Food Intake in Adults. Food Standards
Agency: London.
Whybrow S, Mayer C, Kirk TR et al. (2007) Effects of two-weeks
mandatory snack consumption on energy intake and energy
balance. Obesity Research 15: 67385.
Fat and sugar relationship in the NDNS 223
2012 The Authors
Journal compilation 2012 Br itish Nutrition Foundation Nutrition Bulletin, 37, 213223