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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

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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).

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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

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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.

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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.

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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

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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

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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.

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