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A cross-cultural comparison of appetitive and dietary responses to food challenges
Food Quality and Preference 15 (2004) 129–136 www.elsevier.com/locate/foodqual
A cross-cultural comparison of appetitive and dietary responses to food challenges Phoebe Lokkoa, Sarah Kirkmeyerb, Richard D. Mattesb,* a Food Research Institute, Accra, Ghana Purdue University, Department of Foods and Nutrition, 212 Stone Hall, W. Lafayette, IN 47907-1264, USA
b
Received 10 June 2002; received in revised form 6 August 2002; accepted 24 February 2003
Abstract Cross-cultural comparisons of appetitive and dietary responses to foods should provide mechanistic insights on the global obesity prevalence pattern. Healthy, normal weight, adult, Americans (N=24) and Ghanaians (N=29) participated in eight food challenges. After an overnight fast, appetite was rated and saliva was collected before and after viewing/smelling the day’s food challenge (peanuts, peanut butter, almonds, chocolate, chestnuts, pickles, rice cakes or no load). Appetitive sensations were recorded for 3 h and diet records were kept 24 h prior to and following food ingestion. Similar changes of hunger were noted across cultures. Discrepancies in cephalic phase salivary flow were observed between groups for all foods except peanuts (all P<0.05), possibly due to differences in food familiarity and local cuisine. Mean (SE) dietary compensation scores were 81 16% and 100 10% in the Americans and Ghanaians, but the latter group had more complete compensation to foods matched to peanuts on weight and volume (both P <0.05) suggesting a greater sensitivity to these food properties. Monotony effects were stronger in the Ghanaians who customarily had less diverse diets. These data reveal population differences in appetitive and dietary responses to foods with potential nutritional implications. # 2003 Elsevier Ltd. All rights reserved. Keywords: Hunger; Saliva; Food; Diet; Human
1. Introduction Obesity has become pandemic (Popkin, 1998). Recognition of this fact holds implications for identifying the underlying causes of the disorder. While population-specific etiologies may exist, there are likely to be broader factors underlying this public health problem. The recency of increases in population adiposity implicates behavioral factors such as changes of energy expenditure and dietary intake. Worldwide trends towards industrialization have reduced the energy cost of occupational activities and have promoted new leisure time practices that further reduce activity (Popkin, 2001). Global changes of diet have been identified and include increased consumption of lower fiber grains, foods higher in fat and sugar, animal products, convenience foods and energy yielding beverages (Beverage Digest Fact Book, 1998; Popkin, 1994, 1998). Still, the question remains as to why these changes have cir* Corresponding author. Tel.: +1-765-494-0662; fax: +1765-4940674. E-mail address: [email protected] (R.D. Mattes). 0950-3293/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00039-9
cumvented the biological control mechanisms that strive to match energy consumption with expenditure. Quantifying variations in the extent to which these trends have influenced the adiposity of different cultures (Caballero, 2001) may hold important insights on the permissive role played by biological factors. One approach for elucidating diet-related biological variability is to assess responses to common dietary challenges in peoples of different cultures. The present project was an incremental first attempt to explore cross-cultural appetitive and dietary responses to foods as a means to understand their potential facilitatory roles in global trends of obesity. While data are extremely incomplete, it appears that the trend for increasing obesity is less pronounced in many Sub-Saharan African countries compared to the USA (Popkin, 1998). This report contrasts responses of Americans and Ghanaians to a common set of food stimuli. Identification of the mechanisms responsible for the increased global incidence of overweight and obesity should also provide important insights for management of complications associated with undernutrition (e.g. reduced quality of life and productivity). This study is part
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of a larger project exploring the effects of peanut consumption on hunger, energy balance and cardiovascular disease risk. The focus on peanuts is based on the fact that, in many developing countries, overweight and underweight coexist (Doak, Adair, Monteeiro, & Popkin, 2000) and there is a need for foods and products that meet the needs of individuals at both extremes of energy balance. Items with high energy density, but strong satiety properties, such as nuts (Kirkmeyer & Mattes, 2000), may serve dual purposes. They may help to curb appetite in those concerned with weight gain and ameliorate hunger pains in individuals with insufficient access to food. Thus, appetitive responses to nuts were contrasted with those to foods varying in sensory and nutritional properties.
2. Methods 2.1. General protocol Eight testing sessions were conducted at weekly intervals. The day prior to each testing session, participants kept a record of all foods consumed. They began a fast at 23:00 h and reported to the test site (a dining roomlike environment adjacent to a kitchen) to complete a set of appetitive questionnaires 3.5 h prior to their customary midday meal time. Saliva samples were collected prior to and during exposure to (viewing and smelling) the day’s test food. The food was then consumed in its entirety within 15 min with 150 ml of water. The test foods were: (1) unsalted cocktail peanuts (Planters, Nabisco Foods, Planters Division, Winston-Salem, North Carolina); (2) low sodium peanut butter (Peter Pan, Hunt Wesson, Inc., Fullerton, California); (3) almonds (bulk, raw); (4) chestnuts (whole chestnuts in water, Clement Faugier, Privas, France); (5) milk chocolate (Hershey Foods, Hershey Chocolate USA, Hershey, Pennsylvania); (6) salt-free and fat-free rice cakes (Quaker Oats Co., Chicago, Illinois); (7) and dill pickles (kosher dill spears, Vlassic Foods Inc., Camden, New Jersey). All foods, except chestnuts, were given as prepared by the manufacturer. The chestnuts were rinsed and baked at 155 C for 30 min. There was also a ‘‘no load’’ condition. Treatments were presented in random order. The weight and energy and macronutrient content of each food used are listed in Table 1. The rationale for selecting the particular test foods has been elaborated upon previously (Kirkmeyer & Mattes, 2000), but basically they were used to highlight features of peanuts that could contribute to their satiety value. The first five foods were items used in a similar way to peanuts and were matched for energy content but varied from the peanuts on another basic property: peanut butter—viscosity (semisolid versus solid), almonds— botanical classification (tree nut versus legume), chestnuts—macronutrient content (primary energy compo-
nent is carbohydrate versus fat) and chocolate—sensory (sweet versus salty snack). The pickles were matched to the peanuts on weight (90 g) and the rice cakes were matched to the peanuts on preingestive volume (37.5 cm3). To ensure participants in both countries were tested with identical foods, all items were purchased at one site in the US. Consequently, some items were less familiar to the Ghanaian participants. To minimize this potential source of bias, eligibility was based, in part, on positive ratings for all items in screening taste tests. Hedonic ratings during testing ranged between 1.38 and 4.86 (1=extremely palatable on a 13-point category scale) for all foods except the pickles and did not differ between groups. The pickles were rated positively by both groups [mean (SE) 2.54 (0.8) U.S.; 6.38 (0.6) Ghana], but differed between them (P < 0.001). Participants completed appetitive questionnaires immediately after food ingestion and at 15, 30, 60, 90, 120, 150 and 180 min after ingestion. Dietary records were kept for the following 24 h. The protocol was approved by the Purdue University Human Subjects Committee. 2.2. Subjects Individuals meeting the following eligibility criteria were recruited by public advertisement: body mass index 18–25 kg/m2, weight stable (no gain or loss > 1.5 kg in past 3 months), 18–30 years of age, no chronic health disorder, not taking medication known to influence appetite, not pregnant or lactating, no allergy to test foods, not on a special diet or health program, control over the purchase and preparation of the majority of foods consumed and regular consumers of breakfast. The sample was comprised of 29 participants (15 male and 14 female) from Ghana (Accra). They were 23.9 0.4 years old (mean S.D.) with a mean BMI of 21.3 0.4 kg/m2. Twenty-four (11 male and 13 female) participants were enrolled in the USA (W. Lafayette, IN). The US subjects were 22.1 0.5 years old with a mean BMI of 21.2 0.4 kg/m2. Participants differed in ethnic and racial composition, but all were drawn from urban university populations Table 1 Weight, energy content and macronutrient composition of challenge foods Food
Weight (g)
Energy (KJ)
Fat (g)
Carbohydrate (g)
Protein (g)
Peanuts Peanut butter Almonds Chocolate Chestnuts Pickles Rice cakes No load
87.5 70.8 80.4 105.0 235.8 90.0 7.4 0
2092 2092 2092 2092 2092 63 126 0
43.8 45.0 45.0 30.0 0.0 0.0 0.0 0
18.8 15.0 16.1 52.5 104.7 3.0 7.0 0
21.9 20.0 15.6 7.5 7.0 0.0 1.0 0
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2.3. Appetitive questions Hunger, fullness, desire to eat and prospective consumption were recorded on 13-point category scales. Descriptors ranged from ‘‘not at all’’ to ‘‘extremely.’’ Participants also indicated the strength of their desire to consume foods from a list of 39 items comprising the challenge foods and others representing various nutrient contents (high protein, fat or carbohydrate), sensory properties (predominantly salty or sweet) or food groups (nuts, fruits/vegetable, beverages). Clusters of these items were used to evaluate the specificity of monotony effects after each experimental food was consumed. 2.4. Dietary records Prior to beginning the study, participants were trained to keep dietary records, were provided standard response forms and used food models to estimate portion size. Each dietary record was reviewed with the participant to ensure its accuracy and completeness. Participants were not aware of the study’s purpose to minimize the potential for biased reporting. The dietary records kept by the US participants were analyzed by a single individual with the Nutritionist IV nutrient database (First Databank, San Bruno, CA). The Ghanaian records were analyzed with tables of the nutrient content of local foods (Ayeson & Ankrah, 1995; Holland et al., 1993). To assess the accuracy of the records, each participant’s basal metabolic rate was estimated using published equations (Shils et al., 1999). Using these values, reported 24-h intakes the day before and day of load ingestion for each food were then compared to calculated 99.7% confidence limit cut-offs for energy intake (Goldberg et al., 1991). Less than 3.2% of records did not fall within plausible ranges. 2.5. Cephalic phase salivary response Resting and stimulated saliva samples were collected by the method of Christensen and Navazesh (1984). Participants expectorated all saliva in their mouth (this sample was discarded). Then they tilted their heads forward with mouth open over a preweighed test tube. Saliva was allowed to flow freely for 2 min, with no mechanical action, then all remaining salvia was expectorated into the tube. This was discarded and another 3-min collection was obtained and weighed to determine the flow rate. This was followed by a similar collection procedure while participants viewed and smelled the food they were about to consume. 2.6. Statistical analyses Data were collected on category scales but regarded as sufficiently robust to be subjected to parametric ana-
lyses (O’Mahony, 1986). Data were analyzed by twoway repeated measures analysis of variance. Nationality was the between group factor. Energy intake was the index variable used for power analyses because it was expected to be the most variable and, as a consequence, yield the most conservative sample size estimate. Power analysis (Cohen & Cohen, 1983) indicated a sample of 24 would permit detection of a treatment effect that accounted for 10% of the within subject variance in energy intake with 85% power at the 5% level of probability for within subject tests. A sample of this size permits detection of a standardized difference of one at the 5% probability level with 90% power for between group comparisons. Associations between measures were assessed by Pearson correlation coefficients.
3. Results 3.1. Hunger Baseline hunger ratings were similar across treatments in both sample populations and did not differ between populations. Fig. 1 presents the change of hunger sensations at hourly intervals over the 180-min period following experimental food ingestion. No significant population differences were observed at any time point on the no-load treatment day or following ingestion of peanuts, peanut butter, almonds or chocolate. The initial decline in hunger after chestnut consumption tended to be greater in the Ghanaians [t(51)=1.77, P=0.083]. Hunger ratings were significantly lower at the 1 [t(51)=2.77, P < 0.01] and 2-h [t(51)=2.51, P < 0.02] time points after pickle ingestion and the 3-h [t(51)=2.17, P < 0.04] time point after rice cakes in the Ghanaian sample compared to the Americans. They also tended to be lower at the 3-h time point for pickles [t(51)=1.72, P=0.091]. Table 2 contains values of the least squares regression line slopes representing the recovery of hunger ratings from the initial drop following experimental food consumption to the 3-h time point. No significant differTable 2 MeanSE of the slope of the least squares regression line representing the recovery of hunger ratings from the initial drop following experimental food consumption to the 3-h time point
Peanuts Peanut butter Almonds Chestnuts Chocolate Pickles Rice cakes No load
American
Ghanaian
1.070.18 1.090.20 1.180.16 1.460.20 1.120.18 1.350.18 1.140.19 0.870.14
1.410.19 1.180.14 1.120.19 1.780.16 1.340.13 1.360.14 1.010.16 0.980.12
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Fig. 1. Change of self-reported hunger sensations at hourly intervals over the 180-min period following experimental food ingestion. The only significant population differences occurred at the 3-h time point after rice cakes and at hours 1 and 2 after pickle ingestion where ratings from the Ghanaians were lower.
ences in the slopes were observed for any treatment between populations. However, for the Ghanaian population, the slope of the hunger ratings was significantly higher after chestnuts compared to each of the other treatments [F(7,196)=4.00, P < 0.001]. 3.2. Cephalic phase salivary response Mean (averaged over the eight treatment days) resting salivary flow rates were significantly higher in the Ghanaians (1.52 0.19 g/min) compared to the US (0.88 0.11 g/min) sample [t(51)=2.98, P=0.005]. In neither population did the replicate collection on the no load treatment day result in an increment in salivary flow. For all other treatments, except chestnut exposure in the US sample and pickle exposure in the Ghanaian sample, cognitive, visual and olfactory stimulation led to a significant increment in flow rate (all P40.015). Fig. 2 displays data on the change of salivary flow rates following visual and odor stimulation with the experi-
Fig. 2. Change of salivary flow rate while viewing and smelling challenge foods relative to unstimulated flow. For all treatments, except chestnut exposure in the US sample and pickle exposure in the Ghanaian sample, stimulation lead to a significant increment in flow rate. Significant population differences in stimulated flow rates were observed for peanut butter, almonds, chestnuts, chocolate, pickles and rice cakes. Differences after peanut ingestion and the replicate collections for the no load treatment were not significantly different.
mental foods versus resting values and replicate trials for the no load condition. Significant population differences in incremental flow rates were observed for peanut butter [t(51)=2.48, P=0.016], almonds [t(51)=3.94, P < 0.001], chestnuts [t(51)=3.30, P=0.002], chocolate [t(51)=2.34, P=0.024], pickles [t(51)=2.09, P=0.042] and rice cakes [t(51)=3.78, P=0.001]. Differences after peanut consumption and the replicate collections for the no load treatment were not significantly different. 3.3. Dietary intake Mean 24-h energy intakes on the days preceding food challenges (control) and on the days of food ingestion (experimental) are presented in Fig. 3. Energy
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Fig. 3. Mean (SE) 24-h energy intakes on the day preceding food challenges (control) and on the day of food challenges (experimental). No significant differences were observed between the pretreatment day and challenge day for any condition in the US sample. In the Ghanaian sample, energy intake was significantly lower on the challenge day with rice cakes and pickles relative to their respective pre-challenge days and the days 500 kcal food challenges were provided.
intake on the challenge day includes the energy provided by the respective food. No significant differences were observed between the pretreatment day and challenge day for any condition in the US sample. In the Ghanaian sample, no significant differences were observed between the pre-challenge day and day of a food challenge for the energy-matched feeds or for the sequential days with no load. However, energy intake was significantly lower on the challenge day with rice cakes [t(25)=3.16, P=0.004) and pickles [t(25)=2.29, P=0.031]. Repeated measures ANOVAs indicated there were no significant differences in 24-h energy intake across pre-challenge days, between groups or a group by treatment interaction. There was a significant difference in 24-h energy intake across treatments on the challenge days [F(7,336)=5.88, P < 0.001]. Energy intake on days rice cakes and pickles were consumed
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were significantly lower than on days when any of the 500kcal food challenges were consumed by the Ghanaians (both P < 0.002). To assess dietary compensation, a repeated measures analysis of variance was conducted with free-feeding energy intake data (i.e. excluding the contribution of the load on experimental days). A significant effect of time [F(1,48)=167.8, P < 0.001] and a significant time by food interaction [F(7,336)=3.1, P < 0.005] were observed. Free-feeding energy intake decreased on experimental as compared to the prior days for all energy-matched loads (all P40.002). No significant differences in intake were observed for the weight, volume and no-load treatments. This pattern held for the Americans except no difference was noted for the chestnut treatment. Significant reductions were observed for all loading days for the Ghanaians. Dietary energy compensation was quantified as [(baseline 24h energy intake+ 500 kcal) (free-feeding intake including the food challenge)/500 kcal]100. A value of 100% represents an adjustment of free-feeding intake that exactly offsets the energy contributed by the challenge food (i.e. perfect compensation). Values below 100% indicate incomplete compensation so, total daily energy intake would be increased. Values greater than 100% signify over-compensation so total daily intake would be less than customary. The percent compensation values for each energy-matched load for the American and Ghanaian participants were comparable: peanuts—104.2%, 130.7%; peanut butter—151.0%, 104.0%; almonds—56.9%, 55.7%; chestnuts—56.7%, 113.5%; and chocolate—88.6% and 87.2% respectively. Due, in part to high variance, none of the deviations from perfect compensation for the 500-kcal loads were statistically significant. Mean compensation (averaged over the five energy-matched food challenge days) was 91.5 17.5% in the US sample and 98.2 9.8% in Ghana. This difference was not statistically significant. Because the weight and volume loads provided an insignificant energy challenge, energy compensation scores were not calculated for these treatments. Over the course of the study, participants completed 16 24-h diet records. The mean number of unique foods (i.e. the same food eaten more than once on a day was counted only once) consumed by participants from the USA and Ghana was 10.85 0.49 and 8.32 0.24, respectively [t(51)=4.88, P < 0.001]. Only 42% of the US sample consumed less than 10 foods per day whereas the proportion was 90% among the Ghanaian sample. 3.4. Monotony effect The monotony effects of the energy-matched foods were evaluated by obtaining ‘‘desire to eat’’ ratings for the same food and 38 others representative of groups in
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the food guide pyramid over a 180-min period. Analyses compared changes of ratings for the same food with changes of ratings for all other items and various subgroups including items that were: high fat, high protein, high carbohydrate, nuts, fruits, vegetables, salty snacks, sweet snacks and beverages. A monotony effect was defined as a statistically significantly greater decline in desire to eat more of the challenge food relative to the desire to eat the various alternative groups of foods. By this definition, similar monotony effects were observed between the two samples for peanuts, peanut butter and chocolate whereas marked cultural differences emerged for almonds and chestnuts. More specifically, the peanuts led to a monotony effect compared to all foods and items that were high fat, high protein, and other nuts in both the US and Ghanaian samples. The peanut butter lead to a monotony effect relative to all foods and high fat, high protein and high carbohydrate items in both countries. In the US sample, an effect was also noted relative to salty snacks and, in the Ghana sample, effects were observed relative to sweet snacks, fruits and vegetables as well. Chocolate consumption led to a monotony effect relative to all foods and high fat, high protein, fruit, vegetable and salty snack items in both countries. A significant difference was also observed compared to high carbohydrate foods only in Ghana. In contrast to these largely parallel responses, the almonds did not exert a monotony effect in the US sample, but did compared to all other food groupings in the Ghanaian sample. Similarly, chestnuts led to a monotony effect only relative to high carbohydrate foods in the USA but a robust effect relative to all other groupings in Ghana. With the exception of the almonds in the USA, all foods lead to a significantly greater desire to consume beverages relative to consumption of other foods. The challenge foods may have increased thirst, but this was not measured.
4. Discussion Global changes in the prevalence of obesity are suggestive of etiologies that cross cultural and geographic boundaries. It has been proposed that high and increased consumption of foods that evoke weak appetitive responses and compensatory dietary responses [e.g., high fat foods (Blundell et al., 1996; Lawton et al., 1993), energy-yielding beverages (Dimeglio & Mattes, 2000; Mattes, 1996)] facilitates passive over-consumption and positive energy balance. The present study explored the acute appetitive and dietary responses to a common set of foods in peoples of disparate cultures who were educated, urban dwellers with comparable, normal BMIs to gain insights on possible explanations for their discrepant patterns of weight change (Popkin, 1998).
4.1. Hunger Marked similarities in hunger responses to the food challenges were observed in the two populations. Both groups reported greater initial reductions after ingestion of the energy-matched loads relative to the weight- and volume-matched loads. Differences in the sensory properties and macronutrient contents did not result in differential responses between foods or populations. While there are many cognitive, environmental and physiological factors that influence hunger in free-living individuals, the present observations suggest an important influence of a food’s energy content on the hunger response it elicits. The similarity in responses to the pickles and rice cakes is notable given that they differ widely in energy density (0.1 cal/g for cucumbers and 3.9 cal/g for rice cakes). This raises a question about the saliency of this property as recently proposed (Rolls & Barnett, 2001). With the exception of chestnuts in the Ghanaian sample, the rate of return of hunger following food ingestion was consistent across foods and between populations. This is suggestive of common biological processes in the two populations and a basic property of hunger responses to food challenges. The latter being that food ingestion primarily influences the initial magnitude of hunger suppression with less impact on recovery. Despite the use of foods varying in sensory properties, energy density, and nutrient content, there was no evidence of accelerated recovery to low energy foods or prolonged suppression to foods high in energy, weight or fiber. This has been noted elsewhere (e.g. Kirkmeyer & Mattes, 2000; Raben et al., 1997; Westerterp-Plantanga et al., 1997). One cultural difference in hunger responses was observed. The Ghanaians exhibited greater initial declines of hunger after the chestnut, pickle and rice cake loads compared to the Americans. The pickles and rice cakes were lower in energy, but matched to the peanuts on a weight and volume basis, respectively while the chestnut challenge was considerably larger and heavier than the other energy-matched foods since its primary source of energy is carbohydrate rather than fat. Thus, the findings suggest the Ghanaians were more responsive to weight and volume cues. This difference provides an opportunity to explore the influence of these properties in energy balance. One hypothesis warranting consideration is that the much wider use of energy modified products in the USA, has disrupted learned associations between energy content and other food properties (e.g. portion size) by Americans. The present 24-h energy intake data support this hypothesis. Energy compensation was consistent across challenge foods in the Americans, but was lower for the weight and volume matched foods in the Ghanaians.
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4.2. Cephalic phase salivary response The cephalic phase salivary response reportedly provides an ‘‘objective’’ index of an individual’s level of hunger, the palatability of a food and the anticipated consequence of food ingestion (Mattes, 2000; Wooley & Wooley, 1973; Wooley & Wooley, 1981). Our inclusion of this measure was to determine if responses coincided with a food’s energy content, macronutrient composition or energy density. No clear pattern of salivary responses consistent with the first two food properties was observed in either culture. However, a sharp cultural difference emerged related to food weight and volume where the pattern of responses was essentially complimentary rather than consistent. Peanuts and pickles led to the highest responses by the Americans, but the lowest responses by the Ghanaians. Given saliva’s role to protect the oral cavity through buffering of acids and facilitation of mastication of hard items with sharp edges that may damage epithelial tissues, the American responses were closer to expectations. We can only speculate on explanations for these differences. One possibility for the lower flow rate following pickle exposure among the Ghanaians may be a stress response since pickles were novel to this group. Stressrelated activation of the sympathetic nervous system reduces flow rate (Bradley, 1991). Cultural differences in peanut use offer another explanation. Unlike in the United States, a high proportion of peanuts are consumed as sauces and soups in Ghana where the need for mastication is much reduced. 4.3. Monotony The desire for diet diversity may enhance or compromise diet quality. A more varied diet is more likely to contain the full range of needed nutrients. However, where diet diversity is limited, a decline in acceptability of these foods, due to monotony, can compromise diet quality. This study explored the effect of the challenge foods on the willingness to ingest more of that food relative to 38 others. One notable observation was that the monotony effect was not nutrient-specific. Following consumption of the peanuts, peanut butter and almonds (Ghanaians only), all good sources of fat and protein, other foods rich in fat and protein were not comparably affected. The energy from chestnuts is derived largely from carbohydrate, but the desirability of other carbohydrate-rich foods was not comparably reduced. The sensory properties, familiarity and energy density of foods also elicited little monotony effect. Thus, our findings, after acute challenges, suggest that the inclusion of a desirable, new, high protein food to the diet (e.g. peanuts) will not necessarily result in displacement of other protein-rich sources. It also suggests that this will not occur with fat where a substitution may be desirable.
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The Ghanaians showed a stronger monotony effect than the Americans. After consumption of every challenge food (except peanuts where the response was similar) the Ghanaians reported a significantly greater reduction of desire to eat the challenge food than groupings of other foods. If dietary diversity is psychologically rewarding, this greater specificity in response may be related to the fact that the Ghanaians customarily ate about 25% fewer foods per day than the Americans. That is, relative to the Americans, the Ghanaians may be more sensitized to the problem of monotony and ensure greater variety by maintaining the acceptability of other foods when ingesting one to satiety. 4.4. Dietary compensation Although dietary compensation tended to be less precise in the Americans (91.5 versus 98.2% in the Ghanaians), the difference was not statistically significant. The level of compensation is higher than that often observed for solid foods in food challenge studies [mean of about 67% (Mattes, 1996)]. However, this may stem from the fact that both groups were comprised of normal weight individuals, who obviously have maintained energy balance over their lifetimes. Importantly, the pattern of compensation errors was similar in the two groups (e.g. greatest for almonds in both groups). This supports the likelihood of common response mechanisms. Chronic feeding trials will be required to establish the veracity and implications of this observation. Taken together, these data indicate greater commonalities than differences in appetitive and dietary responses to the food challenges in the two populations. As a result, they are likely to express similar responses to global changes in the nature of the food supply and lifestyles. The one exception may be a greater sensitivity to volume and weight properties of foods among the Ghanaians. The reliability of this finding, the basis for this population difference and its health implications warrant further evaluation in lean and obese individuals as it may provide an insight for development of approaches to curb the obesity pandemic.
Acknowledgements This work was supported by a grant from the United States Agency for International Development (no. RD309-022/4092094).
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Dietary fat and appetite control in obese subjects: weak effects on satiation and satiety. International Journal of Obesity, 17(7), 409–416. Mattes, R. D. (1996). Dietary compensation by humans for supplemental energy provided as ethanol or carbohydrate in fluids. Physical Behaviour, 59(1), 179–187. Mattes, R. D. (2000). Nutritional implications of the cephalic-phase salivary response. Appetite, 34, 177–183. O’Mahony, M. (1986). Sensory evaluation of food: statistical methods and procedures. New York: Marcel Dekker. Popkin, B. M. (1994). The nutrition transition in low-income countries: an emerging crisis. Nutrional Review, 52, 285–298. Popkin, B. M. (2001). The nutrition transition and obesity in the developing world. Journal of Nutrition, 131, 871 S-873S. Popkin, B. M., & Doak, C. M. (1998). The obesity epidemic is a worldwide phenomenon. Nutrional Review, 56(4), 106–114. Raben, A., Andersen, K., Karberg, M. A., Holst, J. J., & Astrup, A. (1997). Acetylation of or b-cyclodextrin addition to potato starch: beneficial effect on glucose metabolism and appetite sensations. American Journal of Clinical Nutrition, 66, 304–324. Rolls, B., & Barnett, R. (2001). Volumetrics. New York: Harper Collins. Shils, M. E., Olson, J. A., Shiken, M., & Ross, A. C. (1999). Modern nutrition in Health and Disease 9th ed. Baltimore: Wilkins & Wilkins. pp. A-60. Westerterp-Plantenga, M. S., Wijckmans-Duijsens, N. E. G., Verboeket-Van de Venne, W. P. H. G., de Graaf, K., Weststrate, J. A., & Van Het Hof, K. H. (1997). Diet-induced thermogenesis and satiety in humans after full-fat and reduced-fat meals. Physiological Behaviour, 61(2), 343–349. Wooley, S. C., & Wooley, O. W. (1973). Salivation to the sight and thought of food: a new measure of appetite. Psychosomatic Medicine, 35, 136–142. Wooley, O. W., & Wooley, S. C. (1981). Relationship of salivation in humans to deprivation, inhibition and the encephalization of hunger. Appetite, 2, 331–350.
A cross-cultural comparison of appetitive and dietary responses to food challenges Phoebe Lokkoa, Sarah Kirkmeyerb, Richard D. Mattesb,* a Food Research Institute, Accra, Ghana Purdue University, Department of Foods and Nutrition, 212 Stone Hall, W. Lafayette, IN 47907-1264, USA
b
Received 10 June 2002; received in revised form 6 August 2002; accepted 24 February 2003
Abstract Cross-cultural comparisons of appetitive and dietary responses to foods should provide mechanistic insights on the global obesity prevalence pattern. Healthy, normal weight, adult, Americans (N=24) and Ghanaians (N=29) participated in eight food challenges. After an overnight fast, appetite was rated and saliva was collected before and after viewing/smelling the day’s food challenge (peanuts, peanut butter, almonds, chocolate, chestnuts, pickles, rice cakes or no load). Appetitive sensations were recorded for 3 h and diet records were kept 24 h prior to and following food ingestion. Similar changes of hunger were noted across cultures. Discrepancies in cephalic phase salivary flow were observed between groups for all foods except peanuts (all P<0.05), possibly due to differences in food familiarity and local cuisine. Mean (SE) dietary compensation scores were 81 16% and 100 10% in the Americans and Ghanaians, but the latter group had more complete compensation to foods matched to peanuts on weight and volume (both P <0.05) suggesting a greater sensitivity to these food properties. Monotony effects were stronger in the Ghanaians who customarily had less diverse diets. These data reveal population differences in appetitive and dietary responses to foods with potential nutritional implications. # 2003 Elsevier Ltd. All rights reserved. Keywords: Hunger; Saliva; Food; Diet; Human
1. Introduction Obesity has become pandemic (Popkin, 1998). Recognition of this fact holds implications for identifying the underlying causes of the disorder. While population-specific etiologies may exist, there are likely to be broader factors underlying this public health problem. The recency of increases in population adiposity implicates behavioral factors such as changes of energy expenditure and dietary intake. Worldwide trends towards industrialization have reduced the energy cost of occupational activities and have promoted new leisure time practices that further reduce activity (Popkin, 2001). Global changes of diet have been identified and include increased consumption of lower fiber grains, foods higher in fat and sugar, animal products, convenience foods and energy yielding beverages (Beverage Digest Fact Book, 1998; Popkin, 1994, 1998). Still, the question remains as to why these changes have cir* Corresponding author. Tel.: +1-765-494-0662; fax: +1765-4940674. E-mail address: [email protected] (R.D. Mattes). 0950-3293/03/$ - see front matter # 2003 Elsevier Ltd. All rights reserved. doi:10.1016/S0950-3293(03)00039-9
cumvented the biological control mechanisms that strive to match energy consumption with expenditure. Quantifying variations in the extent to which these trends have influenced the adiposity of different cultures (Caballero, 2001) may hold important insights on the permissive role played by biological factors. One approach for elucidating diet-related biological variability is to assess responses to common dietary challenges in peoples of different cultures. The present project was an incremental first attempt to explore cross-cultural appetitive and dietary responses to foods as a means to understand their potential facilitatory roles in global trends of obesity. While data are extremely incomplete, it appears that the trend for increasing obesity is less pronounced in many Sub-Saharan African countries compared to the USA (Popkin, 1998). This report contrasts responses of Americans and Ghanaians to a common set of food stimuli. Identification of the mechanisms responsible for the increased global incidence of overweight and obesity should also provide important insights for management of complications associated with undernutrition (e.g. reduced quality of life and productivity). This study is part
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of a larger project exploring the effects of peanut consumption on hunger, energy balance and cardiovascular disease risk. The focus on peanuts is based on the fact that, in many developing countries, overweight and underweight coexist (Doak, Adair, Monteeiro, & Popkin, 2000) and there is a need for foods and products that meet the needs of individuals at both extremes of energy balance. Items with high energy density, but strong satiety properties, such as nuts (Kirkmeyer & Mattes, 2000), may serve dual purposes. They may help to curb appetite in those concerned with weight gain and ameliorate hunger pains in individuals with insufficient access to food. Thus, appetitive responses to nuts were contrasted with those to foods varying in sensory and nutritional properties.
2. Methods 2.1. General protocol Eight testing sessions were conducted at weekly intervals. The day prior to each testing session, participants kept a record of all foods consumed. They began a fast at 23:00 h and reported to the test site (a dining roomlike environment adjacent to a kitchen) to complete a set of appetitive questionnaires 3.5 h prior to their customary midday meal time. Saliva samples were collected prior to and during exposure to (viewing and smelling) the day’s test food. The food was then consumed in its entirety within 15 min with 150 ml of water. The test foods were: (1) unsalted cocktail peanuts (Planters, Nabisco Foods, Planters Division, Winston-Salem, North Carolina); (2) low sodium peanut butter (Peter Pan, Hunt Wesson, Inc., Fullerton, California); (3) almonds (bulk, raw); (4) chestnuts (whole chestnuts in water, Clement Faugier, Privas, France); (5) milk chocolate (Hershey Foods, Hershey Chocolate USA, Hershey, Pennsylvania); (6) salt-free and fat-free rice cakes (Quaker Oats Co., Chicago, Illinois); (7) and dill pickles (kosher dill spears, Vlassic Foods Inc., Camden, New Jersey). All foods, except chestnuts, were given as prepared by the manufacturer. The chestnuts were rinsed and baked at 155 C for 30 min. There was also a ‘‘no load’’ condition. Treatments were presented in random order. The weight and energy and macronutrient content of each food used are listed in Table 1. The rationale for selecting the particular test foods has been elaborated upon previously (Kirkmeyer & Mattes, 2000), but basically they were used to highlight features of peanuts that could contribute to their satiety value. The first five foods were items used in a similar way to peanuts and were matched for energy content but varied from the peanuts on another basic property: peanut butter—viscosity (semisolid versus solid), almonds— botanical classification (tree nut versus legume), chestnuts—macronutrient content (primary energy compo-
nent is carbohydrate versus fat) and chocolate—sensory (sweet versus salty snack). The pickles were matched to the peanuts on weight (90 g) and the rice cakes were matched to the peanuts on preingestive volume (37.5 cm3). To ensure participants in both countries were tested with identical foods, all items were purchased at one site in the US. Consequently, some items were less familiar to the Ghanaian participants. To minimize this potential source of bias, eligibility was based, in part, on positive ratings for all items in screening taste tests. Hedonic ratings during testing ranged between 1.38 and 4.86 (1=extremely palatable on a 13-point category scale) for all foods except the pickles and did not differ between groups. The pickles were rated positively by both groups [mean (SE) 2.54 (0.8) U.S.; 6.38 (0.6) Ghana], but differed between them (P < 0.001). Participants completed appetitive questionnaires immediately after food ingestion and at 15, 30, 60, 90, 120, 150 and 180 min after ingestion. Dietary records were kept for the following 24 h. The protocol was approved by the Purdue University Human Subjects Committee. 2.2. Subjects Individuals meeting the following eligibility criteria were recruited by public advertisement: body mass index 18–25 kg/m2, weight stable (no gain or loss > 1.5 kg in past 3 months), 18–30 years of age, no chronic health disorder, not taking medication known to influence appetite, not pregnant or lactating, no allergy to test foods, not on a special diet or health program, control over the purchase and preparation of the majority of foods consumed and regular consumers of breakfast. The sample was comprised of 29 participants (15 male and 14 female) from Ghana (Accra). They were 23.9 0.4 years old (mean S.D.) with a mean BMI of 21.3 0.4 kg/m2. Twenty-four (11 male and 13 female) participants were enrolled in the USA (W. Lafayette, IN). The US subjects were 22.1 0.5 years old with a mean BMI of 21.2 0.4 kg/m2. Participants differed in ethnic and racial composition, but all were drawn from urban university populations Table 1 Weight, energy content and macronutrient composition of challenge foods Food
Weight (g)
Energy (KJ)
Fat (g)
Carbohydrate (g)
Protein (g)
Peanuts Peanut butter Almonds Chocolate Chestnuts Pickles Rice cakes No load
87.5 70.8 80.4 105.0 235.8 90.0 7.4 0
2092 2092 2092 2092 2092 63 126 0
43.8 45.0 45.0 30.0 0.0 0.0 0.0 0
18.8 15.0 16.1 52.5 104.7 3.0 7.0 0
21.9 20.0 15.6 7.5 7.0 0.0 1.0 0
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2.3. Appetitive questions Hunger, fullness, desire to eat and prospective consumption were recorded on 13-point category scales. Descriptors ranged from ‘‘not at all’’ to ‘‘extremely.’’ Participants also indicated the strength of their desire to consume foods from a list of 39 items comprising the challenge foods and others representing various nutrient contents (high protein, fat or carbohydrate), sensory properties (predominantly salty or sweet) or food groups (nuts, fruits/vegetable, beverages). Clusters of these items were used to evaluate the specificity of monotony effects after each experimental food was consumed. 2.4. Dietary records Prior to beginning the study, participants were trained to keep dietary records, were provided standard response forms and used food models to estimate portion size. Each dietary record was reviewed with the participant to ensure its accuracy and completeness. Participants were not aware of the study’s purpose to minimize the potential for biased reporting. The dietary records kept by the US participants were analyzed by a single individual with the Nutritionist IV nutrient database (First Databank, San Bruno, CA). The Ghanaian records were analyzed with tables of the nutrient content of local foods (Ayeson & Ankrah, 1995; Holland et al., 1993). To assess the accuracy of the records, each participant’s basal metabolic rate was estimated using published equations (Shils et al., 1999). Using these values, reported 24-h intakes the day before and day of load ingestion for each food were then compared to calculated 99.7% confidence limit cut-offs for energy intake (Goldberg et al., 1991). Less than 3.2% of records did not fall within plausible ranges. 2.5. Cephalic phase salivary response Resting and stimulated saliva samples were collected by the method of Christensen and Navazesh (1984). Participants expectorated all saliva in their mouth (this sample was discarded). Then they tilted their heads forward with mouth open over a preweighed test tube. Saliva was allowed to flow freely for 2 min, with no mechanical action, then all remaining salvia was expectorated into the tube. This was discarded and another 3-min collection was obtained and weighed to determine the flow rate. This was followed by a similar collection procedure while participants viewed and smelled the food they were about to consume. 2.6. Statistical analyses Data were collected on category scales but regarded as sufficiently robust to be subjected to parametric ana-
lyses (O’Mahony, 1986). Data were analyzed by twoway repeated measures analysis of variance. Nationality was the between group factor. Energy intake was the index variable used for power analyses because it was expected to be the most variable and, as a consequence, yield the most conservative sample size estimate. Power analysis (Cohen & Cohen, 1983) indicated a sample of 24 would permit detection of a treatment effect that accounted for 10% of the within subject variance in energy intake with 85% power at the 5% level of probability for within subject tests. A sample of this size permits detection of a standardized difference of one at the 5% probability level with 90% power for between group comparisons. Associations between measures were assessed by Pearson correlation coefficients.
3. Results 3.1. Hunger Baseline hunger ratings were similar across treatments in both sample populations and did not differ between populations. Fig. 1 presents the change of hunger sensations at hourly intervals over the 180-min period following experimental food ingestion. No significant population differences were observed at any time point on the no-load treatment day or following ingestion of peanuts, peanut butter, almonds or chocolate. The initial decline in hunger after chestnut consumption tended to be greater in the Ghanaians [t(51)=1.77, P=0.083]. Hunger ratings were significantly lower at the 1 [t(51)=2.77, P < 0.01] and 2-h [t(51)=2.51, P < 0.02] time points after pickle ingestion and the 3-h [t(51)=2.17, P < 0.04] time point after rice cakes in the Ghanaian sample compared to the Americans. They also tended to be lower at the 3-h time point for pickles [t(51)=1.72, P=0.091]. Table 2 contains values of the least squares regression line slopes representing the recovery of hunger ratings from the initial drop following experimental food consumption to the 3-h time point. No significant differTable 2 MeanSE of the slope of the least squares regression line representing the recovery of hunger ratings from the initial drop following experimental food consumption to the 3-h time point
Peanuts Peanut butter Almonds Chestnuts Chocolate Pickles Rice cakes No load
American
Ghanaian
1.070.18 1.090.20 1.180.16 1.460.20 1.120.18 1.350.18 1.140.19 0.870.14
1.410.19 1.180.14 1.120.19 1.780.16 1.340.13 1.360.14 1.010.16 0.980.12
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Fig. 1. Change of self-reported hunger sensations at hourly intervals over the 180-min period following experimental food ingestion. The only significant population differences occurred at the 3-h time point after rice cakes and at hours 1 and 2 after pickle ingestion where ratings from the Ghanaians were lower.
ences in the slopes were observed for any treatment between populations. However, for the Ghanaian population, the slope of the hunger ratings was significantly higher after chestnuts compared to each of the other treatments [F(7,196)=4.00, P < 0.001]. 3.2. Cephalic phase salivary response Mean (averaged over the eight treatment days) resting salivary flow rates were significantly higher in the Ghanaians (1.52 0.19 g/min) compared to the US (0.88 0.11 g/min) sample [t(51)=2.98, P=0.005]. In neither population did the replicate collection on the no load treatment day result in an increment in salivary flow. For all other treatments, except chestnut exposure in the US sample and pickle exposure in the Ghanaian sample, cognitive, visual and olfactory stimulation led to a significant increment in flow rate (all P40.015). Fig. 2 displays data on the change of salivary flow rates following visual and odor stimulation with the experi-
Fig. 2. Change of salivary flow rate while viewing and smelling challenge foods relative to unstimulated flow. For all treatments, except chestnut exposure in the US sample and pickle exposure in the Ghanaian sample, stimulation lead to a significant increment in flow rate. Significant population differences in stimulated flow rates were observed for peanut butter, almonds, chestnuts, chocolate, pickles and rice cakes. Differences after peanut ingestion and the replicate collections for the no load treatment were not significantly different.
mental foods versus resting values and replicate trials for the no load condition. Significant population differences in incremental flow rates were observed for peanut butter [t(51)=2.48, P=0.016], almonds [t(51)=3.94, P < 0.001], chestnuts [t(51)=3.30, P=0.002], chocolate [t(51)=2.34, P=0.024], pickles [t(51)=2.09, P=0.042] and rice cakes [t(51)=3.78, P=0.001]. Differences after peanut consumption and the replicate collections for the no load treatment were not significantly different. 3.3. Dietary intake Mean 24-h energy intakes on the days preceding food challenges (control) and on the days of food ingestion (experimental) are presented in Fig. 3. Energy
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Fig. 3. Mean (SE) 24-h energy intakes on the day preceding food challenges (control) and on the day of food challenges (experimental). No significant differences were observed between the pretreatment day and challenge day for any condition in the US sample. In the Ghanaian sample, energy intake was significantly lower on the challenge day with rice cakes and pickles relative to their respective pre-challenge days and the days 500 kcal food challenges were provided.
intake on the challenge day includes the energy provided by the respective food. No significant differences were observed between the pretreatment day and challenge day for any condition in the US sample. In the Ghanaian sample, no significant differences were observed between the pre-challenge day and day of a food challenge for the energy-matched feeds or for the sequential days with no load. However, energy intake was significantly lower on the challenge day with rice cakes [t(25)=3.16, P=0.004) and pickles [t(25)=2.29, P=0.031]. Repeated measures ANOVAs indicated there were no significant differences in 24-h energy intake across pre-challenge days, between groups or a group by treatment interaction. There was a significant difference in 24-h energy intake across treatments on the challenge days [F(7,336)=5.88, P < 0.001]. Energy intake on days rice cakes and pickles were consumed
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were significantly lower than on days when any of the 500kcal food challenges were consumed by the Ghanaians (both P < 0.002). To assess dietary compensation, a repeated measures analysis of variance was conducted with free-feeding energy intake data (i.e. excluding the contribution of the load on experimental days). A significant effect of time [F(1,48)=167.8, P < 0.001] and a significant time by food interaction [F(7,336)=3.1, P < 0.005] were observed. Free-feeding energy intake decreased on experimental as compared to the prior days for all energy-matched loads (all P40.002). No significant differences in intake were observed for the weight, volume and no-load treatments. This pattern held for the Americans except no difference was noted for the chestnut treatment. Significant reductions were observed for all loading days for the Ghanaians. Dietary energy compensation was quantified as [(baseline 24h energy intake+ 500 kcal) (free-feeding intake including the food challenge)/500 kcal]100. A value of 100% represents an adjustment of free-feeding intake that exactly offsets the energy contributed by the challenge food (i.e. perfect compensation). Values below 100% indicate incomplete compensation so, total daily energy intake would be increased. Values greater than 100% signify over-compensation so total daily intake would be less than customary. The percent compensation values for each energy-matched load for the American and Ghanaian participants were comparable: peanuts—104.2%, 130.7%; peanut butter—151.0%, 104.0%; almonds—56.9%, 55.7%; chestnuts—56.7%, 113.5%; and chocolate—88.6% and 87.2% respectively. Due, in part to high variance, none of the deviations from perfect compensation for the 500-kcal loads were statistically significant. Mean compensation (averaged over the five energy-matched food challenge days) was 91.5 17.5% in the US sample and 98.2 9.8% in Ghana. This difference was not statistically significant. Because the weight and volume loads provided an insignificant energy challenge, energy compensation scores were not calculated for these treatments. Over the course of the study, participants completed 16 24-h diet records. The mean number of unique foods (i.e. the same food eaten more than once on a day was counted only once) consumed by participants from the USA and Ghana was 10.85 0.49 and 8.32 0.24, respectively [t(51)=4.88, P < 0.001]. Only 42% of the US sample consumed less than 10 foods per day whereas the proportion was 90% among the Ghanaian sample. 3.4. Monotony effect The monotony effects of the energy-matched foods were evaluated by obtaining ‘‘desire to eat’’ ratings for the same food and 38 others representative of groups in
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the food guide pyramid over a 180-min period. Analyses compared changes of ratings for the same food with changes of ratings for all other items and various subgroups including items that were: high fat, high protein, high carbohydrate, nuts, fruits, vegetables, salty snacks, sweet snacks and beverages. A monotony effect was defined as a statistically significantly greater decline in desire to eat more of the challenge food relative to the desire to eat the various alternative groups of foods. By this definition, similar monotony effects were observed between the two samples for peanuts, peanut butter and chocolate whereas marked cultural differences emerged for almonds and chestnuts. More specifically, the peanuts led to a monotony effect compared to all foods and items that were high fat, high protein, and other nuts in both the US and Ghanaian samples. The peanut butter lead to a monotony effect relative to all foods and high fat, high protein and high carbohydrate items in both countries. In the US sample, an effect was also noted relative to salty snacks and, in the Ghana sample, effects were observed relative to sweet snacks, fruits and vegetables as well. Chocolate consumption led to a monotony effect relative to all foods and high fat, high protein, fruit, vegetable and salty snack items in both countries. A significant difference was also observed compared to high carbohydrate foods only in Ghana. In contrast to these largely parallel responses, the almonds did not exert a monotony effect in the US sample, but did compared to all other food groupings in the Ghanaian sample. Similarly, chestnuts led to a monotony effect only relative to high carbohydrate foods in the USA but a robust effect relative to all other groupings in Ghana. With the exception of the almonds in the USA, all foods lead to a significantly greater desire to consume beverages relative to consumption of other foods. The challenge foods may have increased thirst, but this was not measured.
4. Discussion Global changes in the prevalence of obesity are suggestive of etiologies that cross cultural and geographic boundaries. It has been proposed that high and increased consumption of foods that evoke weak appetitive responses and compensatory dietary responses [e.g., high fat foods (Blundell et al., 1996; Lawton et al., 1993), energy-yielding beverages (Dimeglio & Mattes, 2000; Mattes, 1996)] facilitates passive over-consumption and positive energy balance. The present study explored the acute appetitive and dietary responses to a common set of foods in peoples of disparate cultures who were educated, urban dwellers with comparable, normal BMIs to gain insights on possible explanations for their discrepant patterns of weight change (Popkin, 1998).
4.1. Hunger Marked similarities in hunger responses to the food challenges were observed in the two populations. Both groups reported greater initial reductions after ingestion of the energy-matched loads relative to the weight- and volume-matched loads. Differences in the sensory properties and macronutrient contents did not result in differential responses between foods or populations. While there are many cognitive, environmental and physiological factors that influence hunger in free-living individuals, the present observations suggest an important influence of a food’s energy content on the hunger response it elicits. The similarity in responses to the pickles and rice cakes is notable given that they differ widely in energy density (0.1 cal/g for cucumbers and 3.9 cal/g for rice cakes). This raises a question about the saliency of this property as recently proposed (Rolls & Barnett, 2001). With the exception of chestnuts in the Ghanaian sample, the rate of return of hunger following food ingestion was consistent across foods and between populations. This is suggestive of common biological processes in the two populations and a basic property of hunger responses to food challenges. The latter being that food ingestion primarily influences the initial magnitude of hunger suppression with less impact on recovery. Despite the use of foods varying in sensory properties, energy density, and nutrient content, there was no evidence of accelerated recovery to low energy foods or prolonged suppression to foods high in energy, weight or fiber. This has been noted elsewhere (e.g. Kirkmeyer & Mattes, 2000; Raben et al., 1997; Westerterp-Plantanga et al., 1997). One cultural difference in hunger responses was observed. The Ghanaians exhibited greater initial declines of hunger after the chestnut, pickle and rice cake loads compared to the Americans. The pickles and rice cakes were lower in energy, but matched to the peanuts on a weight and volume basis, respectively while the chestnut challenge was considerably larger and heavier than the other energy-matched foods since its primary source of energy is carbohydrate rather than fat. Thus, the findings suggest the Ghanaians were more responsive to weight and volume cues. This difference provides an opportunity to explore the influence of these properties in energy balance. One hypothesis warranting consideration is that the much wider use of energy modified products in the USA, has disrupted learned associations between energy content and other food properties (e.g. portion size) by Americans. The present 24-h energy intake data support this hypothesis. Energy compensation was consistent across challenge foods in the Americans, but was lower for the weight and volume matched foods in the Ghanaians.
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4.2. Cephalic phase salivary response The cephalic phase salivary response reportedly provides an ‘‘objective’’ index of an individual’s level of hunger, the palatability of a food and the anticipated consequence of food ingestion (Mattes, 2000; Wooley & Wooley, 1973; Wooley & Wooley, 1981). Our inclusion of this measure was to determine if responses coincided with a food’s energy content, macronutrient composition or energy density. No clear pattern of salivary responses consistent with the first two food properties was observed in either culture. However, a sharp cultural difference emerged related to food weight and volume where the pattern of responses was essentially complimentary rather than consistent. Peanuts and pickles led to the highest responses by the Americans, but the lowest responses by the Ghanaians. Given saliva’s role to protect the oral cavity through buffering of acids and facilitation of mastication of hard items with sharp edges that may damage epithelial tissues, the American responses were closer to expectations. We can only speculate on explanations for these differences. One possibility for the lower flow rate following pickle exposure among the Ghanaians may be a stress response since pickles were novel to this group. Stressrelated activation of the sympathetic nervous system reduces flow rate (Bradley, 1991). Cultural differences in peanut use offer another explanation. Unlike in the United States, a high proportion of peanuts are consumed as sauces and soups in Ghana where the need for mastication is much reduced. 4.3. Monotony The desire for diet diversity may enhance or compromise diet quality. A more varied diet is more likely to contain the full range of needed nutrients. However, where diet diversity is limited, a decline in acceptability of these foods, due to monotony, can compromise diet quality. This study explored the effect of the challenge foods on the willingness to ingest more of that food relative to 38 others. One notable observation was that the monotony effect was not nutrient-specific. Following consumption of the peanuts, peanut butter and almonds (Ghanaians only), all good sources of fat and protein, other foods rich in fat and protein were not comparably affected. The energy from chestnuts is derived largely from carbohydrate, but the desirability of other carbohydrate-rich foods was not comparably reduced. The sensory properties, familiarity and energy density of foods also elicited little monotony effect. Thus, our findings, after acute challenges, suggest that the inclusion of a desirable, new, high protein food to the diet (e.g. peanuts) will not necessarily result in displacement of other protein-rich sources. It also suggests that this will not occur with fat where a substitution may be desirable.
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The Ghanaians showed a stronger monotony effect than the Americans. After consumption of every challenge food (except peanuts where the response was similar) the Ghanaians reported a significantly greater reduction of desire to eat the challenge food than groupings of other foods. If dietary diversity is psychologically rewarding, this greater specificity in response may be related to the fact that the Ghanaians customarily ate about 25% fewer foods per day than the Americans. That is, relative to the Americans, the Ghanaians may be more sensitized to the problem of monotony and ensure greater variety by maintaining the acceptability of other foods when ingesting one to satiety. 4.4. Dietary compensation Although dietary compensation tended to be less precise in the Americans (91.5 versus 98.2% in the Ghanaians), the difference was not statistically significant. The level of compensation is higher than that often observed for solid foods in food challenge studies [mean of about 67% (Mattes, 1996)]. However, this may stem from the fact that both groups were comprised of normal weight individuals, who obviously have maintained energy balance over their lifetimes. Importantly, the pattern of compensation errors was similar in the two groups (e.g. greatest for almonds in both groups). This supports the likelihood of common response mechanisms. Chronic feeding trials will be required to establish the veracity and implications of this observation. Taken together, these data indicate greater commonalities than differences in appetitive and dietary responses to the food challenges in the two populations. As a result, they are likely to express similar responses to global changes in the nature of the food supply and lifestyles. The one exception may be a greater sensitivity to volume and weight properties of foods among the Ghanaians. The reliability of this finding, the basis for this population difference and its health implications warrant further evaluation in lean and obese individuals as it may provide an insight for development of approaches to curb the obesity pandemic.
Acknowledgements This work was supported by a grant from the United States Agency for International Development (no. RD309-022/4092094).
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