Identification and Nutritional Profiling of Hyperpalatable Foods in the Brazilian Food Composition Table Characterization of Hyperpalatable Foods

Identification and Nutritional Profiling of Hyperpalatable Foods in the Brazilian Food Composition Table Characterization of Hyperpalatable Foods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and Nutritional Profiling of Hyperpalatable Foods in the Brazilian Food Composition Table Characterization of Hyperpalatable Foods Catharina Schoen Borba, Larissa David Lemos, Isadora Martins Freitas, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8419384/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract PURPOSE The standardized classification of hyperpalatable foods (HPF) was originally established and validated based on dietary data from the North American population. Evaluating the applicability of this classification within the dietary patterns of other populations allows an assessment of its external validity and the potential for generalization across different cultural and nutritional contexts. Our objective was to investigate the presence and characteristics of hyperpalatable foods (HPFs) in the Nutritional Composition Table of Foods Consumed in Brazil (POF, IBGE). METHODS The foods listed in the nutritional table were analyzed and categorized as hyperpalatable or not, including their categories and their protein, fiber and energy density contents. RESULTS One thousand eight hundred ninety foods were analyzed, of which 1095 (57.9%) were classified as HPFs, 37.8%, 46.8% and 92% of which were considered to have high energy density, high protein content and low fiber content, respectively. Most HPFs were classified in the high fat and sodium group (FSOD, 39.2%), while 4.3% met criteria for multiple categories. CONCLUSION These data demonstrate that more than half of the foods on the table are considered HPFs, but a comprehensive analysis of foods composition revealed their heterogeneity. Notably, most hyperpalatable foods identified in the food composition table exhibited low energy density, challenging the common assumption that hyperpalatability is inherently associated with high energy content. Further research in this area is important to improve the understanding of these foods, their classification, and their adaptation to the food culture of the populations. Food Diet and Nutrition Food Composition Table Food Composition Hyperpalatable Energy Density Figures Figure 1 1 Introduction Hyperpalatable foods (HPFs) can disrupt appetite control and food intake, disrupting homeostatic regulation, and may therefore be associated with nutritional problems [ 1 – 2 ]. Hyperpalatability can be generated by ingredient combinations or by preparation methods that induce a feeling of pleasure when the food is ingested. The synergy between specific ingredients creates an artificially enhanced and more rewarding palatability experience than any single palatable ingredient [ 3 ]. Different definitions for the term "hyperpalatable foods" have been proposed, with varying focus on ingredients or food categories. However, Fazzino et al. [ 3 ] proposed a quantitative classification that allows a clear distinction between HPF and non-HPF foods. According to Gearhardt et al. [ 4 ], individuals can develop dependence on HPF, resulting in uncontrolled eating, because the complex mechanism of regulating food intake and the feeling of satiety during a meal are impaired. However, it is important to consider that, in addition to palatability, several factors can influence the sensory signals of satiation and satiety, such as chewing and the time food remains in the mouth, ingestion time, intestinal flow, and the nutritional composition and sensory characteristics of foods, such as dietary fiber and protein content, flavor, aroma, visual appearance, and texture [ 5 – 8 ]. Research that evaluates food composition and its implications for palatability, signaling satiation and satiety can help in planning more effective nutritional counseling strategies. The IBGE (Brazilian Institute of Geography and Statistics) Nutritional Composition Table of Foods Consumed in Brazil (POF 2008–2009) was prepared using information from detailed records of foods consumed by members of a representative sample of the Brazilian population throughout the country through the 2008–2009 Household Budget Survey. This survey produced a database that provides information on food composition and food consumption and aimed to understand potential inadequacies in Brazilian dietary patterns and contribute to the establishment of national food and nutrition policies and programs. It also serves as an important tool for research and investigation in the area of ​​nutrition in Brazil. To date, no study has identified and evaluated the frequency of the HPF in the Nutritional Composition Table of Foods Consumed in Brazil. Therefore, the present study aims to identify foods that would be classified as HPFs, according to the description by Fazzino et al. [ 3 ], and the frequency of this food group among those consumed by Brazilians. This analysis will allow us to understand the contribution of HPF to the set of foods consumed by Brazilians, as shown in the table in question, in addition to classifying these foods according to their energy density, protein content, and fiber content. 2 Materials and methods The identification of HPF foods and their frequency assessment were performed via the IBGE Food Nutritional Composition Table (POF) 2008–2009. This table consists of 19 major food groups: cereals and legumes; tuberous vegetables; flours, starches, and pastas; coconuts, chestnuts, and nuts; leafy, fruity, and other vegetables; fruits; sugars and confectionery products; salts and condiments; meats and offal; fish and seafood; canned and preserved foods; poultry and eggs; dairy products; baked goods; processed meats; nonalcoholic beverages and infusions; alcoholic beverages; oils and fats; and miscellaneous foods. In the present study, 17 of the 19 groups were evaluated; beverage groups (nonalcoholic beverages, infusions and alcoholic beverages) were excluded because of the lack of literature on the classification of beverages as HPFs. In addition to the groups described, in the IBGE Table, foods were arranged in 16 different forms of preparation: raw, cooked, grilled/embered/barbecued, baked, fried, breaded/breaded, sautéed, red sauce, white sauce, garlic and oil, butter/oil, vinegar, stew, porridge, and soup, in addition to the option "not applicable" when the food had not been prepared in any way. The IBGE Food Nutritional Composition Table is available in the Online Library on the IBGE website [ 8 ]. It is divided into four tables. Table 1 contains information on energy, macronutrients, and fiber; Table 2 contains information on fats and sugars; Table 3 lists the mineral composition; and, finally, Table 4 provides information on the amounts of vitamins present in foods. For this study, the four composition tables were grouped in Microsoft Excel software for food classification and analysis. Table 1 Nonhyperpalatable foods (NHPF) and hyperpalatable foods (HPF) in the different food groups of the nutritional composition table of foods consumed in Brazil - IBGE - POF 2008–2009 Groups NHPF N (%) HPF N (%) Examples of HPF from each group Cereals and legumes 16 (44.4) 20 (55.6) Cooked rice, corn, peas, and beans, popcorn Tuberous vegetables 40 (38.5) 64 (61.5) Boiled potatoes, fried potatoes, braized cassava, cooked cassava Flours, starches and pasta 46 (56.8) 35 (43.2) Mucilon, cornstarch, breakfast cereals, mini pizza, mini pastries, instant noodles Coconuts, chestnuts and walnuts 20 (87.0) 3 (13.0) Almond, Brazil nut, cashew nut Leafy, fruity and other vegetables 165 (85.5) 28 (14.5) Sautéed chicory, breaded cauliflower, sautéed pumpkin, green beans with butter/oil, breaded eggplant Fruits 87 (96.7) 3 (3.3) Breaded banana or with butter/oil Sugars and confectionery products 85 (59.4) 58 (40.6) Ice cream, chocolate, truffle, mousse, coconut candy, peanut brittle, caramelized peanuts, brigadeiro Salts and condiments 15 (65.2) 8 (34.8) Pickled capers, mayonnaise, soy sauce Meat and offal 89 (23.7) 286 (76.3) Roasted filet mignon, grilled rump steak, braized offal, fried pork chop Fish and seafood 67 (75.3) 22 (24.7) Fried fish, stewed fish, fish in white sauce, breaded crab Canned and preserved foods 11 (23.4) 36 (76.6) Olives, pickled mushrooms, pickled sweetcorn, onion cream Poultry and eggs 29 (14.3) 174 (85.7) Grilled, roasted or boiled chicken, nuggets, roast chester, fried chicken egg Dairy products 25 (29.1) 61 (70.9) Whole cow's milk, whole yogurt (any flavor), powdered milk, cheeses, salted butter Bakery products 17 (17.2) 82 (82.8) Salt bread, industrially prepared sliced ​​bread, cheese bread, savory biscuits, sweet biscuits Processed meats 0 (0.0) 111 (100.0) Industrialized beef burger, bacon, sausage, salami, mortadella, ham Oils and fats 6 (100.0) 0 (0.0) Miscellaneous 77 (42.1) 104 (57.5) Pastel, pork rinds, esfirra, hot dog, hamburger (sandwich), bauru, pizza, industrialized pizza NHPF -Nonhyperpalatable food; HPF - hyperpalatable food Table 2 Nonhyperpalatable foods (NHPF) and hyperpalatable foods (HPF) in the different forms of food preparation from the nutritional composition table of foods consumed in Brazil - IBGE - POF 2008–2009 Preparation NHPF N (%) HPF N (%) With garlic and oil 7 (38.9) 11 (61.1) In vinaigrette 17 (73.9) 6 (26.1) Bakeat 44 (34.4) 84 (65.6) With butter/oil 2 (11.1) 16 (88.9) Cocked 91 (46.0) 107 (54.0) Crude 48 (59.3) 33 (40.7) Breaded 12 (24.0) 38 (76.0) Stew 18 (23.1) 60 (76.9) Fried 23 (16.5) 116 (83.5) Grilled/embered/barbecue 25 (32.5) 52 (67.5) Porridge 3 (60.0) 2 (40.0) Dairy-based sauce 4 (18.2) 18 (81.8) Tomato-based sauce 22 (37.9) 36 (62.1) Not applicable 435 (49.7) 441 (50.3) Sautéed 29 (30.9) 65 (69.1) Soup 15 (60.0) 10 (40.0) Table 3 Frequencies of nonhyperpalatable and hyperpalatable foods according to their energy density, protein content and fiber content Total food items NHPF HPF p a Energy density High - N (%) 588 (31.1) 174 (21.9) 414 (37.8) p < 0.0001 Low- N (%) 1,302 (68.9) 621 (78.1) 681 (62.2) Protein content High- N (%) 778 (41.2) 266 (33.5) 512 (46.8) p < 0.0001 Low - N (%) 1,112 (58.8) 529 (66.5) 583 (53.2) Fiber content Low - N (%) 1,683 (89.0) 675 (84.9) 1,008 (92.0) p < 0.0001 Source - N (%) 133 (7.0) 67 (8.4) 66 (6.0) High- N (%) 74 (4.0) 53 (6.7) 21 (2.0) Energy density - high (≥ 250 kcal/100 g) and low (< 250 kcal/100 g); protein content - high (≥ 30% protein) and low (< 30% protein); fiber content - high (≥ 6 g of fiber in 100 g of product), source (3 to 5.9 g of fiber in 100 g of product) and low (< 3 g of fiber in 100 g of product); a Pearson's chi-square test, with Yates correction, p value < 0.05. Table 4 Frequencies of hyperpalatable foods according to energy density, protein content and fiber content NHPF FSOD FS CSOD COMBINED Energy density High - N (%) 174 (21.9) 279 (37.6) 74 (50.0) 22 (17.7) 39 (48.1) Low - N (%) 621 (78.1) 463 (62.4) 74 (50.0) 102 (82.3) 42 (51.9) Protein content High - N (%) 266 (33.5) 504 (67.9) 5 (3.4) 1 (0.8) 2 (2.5) Low - N (%) 529 (66.5) 238 (32.1) 143 (96.6) 123 (99.2) 79 (97.5) Fiber content Low - N (%) 675 (84.9) 720 (97.1) 131 (88.5) 99 (79.8) 58 (71.6) Source -N (%) 67 (8.4) 15 (2.0) 15 (10.1) 16 (12.9) 20 (24.7) High - N (%) 53 (6.7) 7 (0.9) 2 (1.4) 9 (7.3) 3 (3.7) Total 795 (42.1) 742 (39.2) 148 (7.8) 124 (6.6) 81 (4.3) Energy density - high (≥ 250 kcal/100 g) and low (< 250 kcal/100 g); protein content - high (≥ 30% protein) and low (< 30% protein); fiber content - high (≥ 6 g of fiber in 100 g of product), source (3 to 5.9 g of fiber in 100 g of product) and low (< 3 g of fiber in 100 g of product); FSOD - hyperpalatable food - fat + sodium; FS - hyperpalatable food - fat + sugar; CSOD - hyperpalatable food - carbohydrate + sodium; COMBINED: (FSOD + CSOD) and (FSOD + FS). The database generated by combining the tables was used to classify foods according to their palatability, fiber content, protein content, and energy density. The classification of HPFs followed the criteria proposed by Fazzino et al. [ 3 ], who grouped them into three categories: fat + sodium (FSOD), for foods with > 25% of calories from fat and ≥ 0.30% sodium; fat + simple sugar (FS), for foods with > 20% of calories from fat and > 20% from simple sugars; and carbohydrate + sodium (CSOD), for foods with > 40% of calories from carbohydrates (excluding fiber and simple sugars) and ≥ 0.20% sodium. Foods that belonged to more than one category, such as FSOD + CSOD and FSOD + FS, were grouped into the "COMBINED" category. The fiber content of foods was classified on the basis of the RDC 54/2012 of ANVISA (National Health Surveillance Agency), which provides supplementary nutritional information, defining the declarations of some nutritional properties, including the classification regarding the fiber content of the food [ 9 ]. Therefore, foods were classified as "low fiber" when they had less than 3 g of fiber per 100 g of product; as "source of fiber" when they had 3 to 5.9 g of fiber per 100 g of product; and as "high fiber" when the amount of fiber was equal to or greater than 6 g per 100 g of food. The amount of protein in foods was analyzed as described by Esmaeili et al. [ 10 ], in which foods were classified as "low protein" when the food was composed of less than 30% protein and as "high protein" when the food was composed of 30% or more protein. The classification of foods, in relation to energy density, was carried out on the basis of the average value of the range described in the recommendation of the World Cancer Research Fund/American Institute for Cancer Research, with foods that have an energy density of less than 250 kcal per 100 g of the product being classified as “low energy density” and foods that have a density greater than or equal to 250 kcal per 100 g being classified as “high energy density” [ 11 ]. The analysis and classification of the foods in the table were carried out by a trained research team composed of nutrition professionals and graduate students. Before data analysis, all team members received specific training on data access procedures and data recording. Nutrient values and food characteristics used for classification into HPFs, as well as for determining energy density and protein and fiber contents, were recorded in a shared document for consistency and reference. Weekly meetings were held to review the entries and address any uncertainties. The team members were instructed to document all the questions and the corresponding food items for later verification. A final review of all records was conducted by a senior researcher. Statistical analysis was performed via SPSS software, version 20.0. Measures of central tendency and dispersion were calculated, in addition to comparing means via Student's t tests and assessing associations between variables via Fisher's exact test or Pearson's chi-square test, with Yates' correction. The variables are presented as absolute and relative frequencies. A 5% level of statistical significance was adopted. 3 Results One thousand ninety-five (57.8%) foods were considered hyperpalatable in the evaluation of the IBGE Table. Tables 1 and 2 show the frequency of the foods classified as hyperpalatable and nonhyperpalatable (NHPF), as well as examples of foods classified as hyperpalatable, separated by major food groups and by preparation methods. Among the different food groups, the most prominent in terms of the frequency of hyperpalatable foods was processed meats, 100% of which were classified as hyperpalatable, followed by poultry and eggs (85.7%) and bread and bakery items (82.8%). Examples of foods in these groups include processed meats, chicken nuggets, and processed bread. The preparation methods with butter/oil, fried foods, or dairy-based sauce presented hyperpalatable frequencies of 88.9%, 83.5%, and 81.8%, respectively. In addition to the different preparation techniques, the IBGE table includes the option "not applicable", indicating that a preparation method is considered unique, which corresponds to 50.6% of the total foods registered; 50.3% of these foods with the option "not applicable" were considered HPF. According to the classification of hyperpalatable food groups proposed by Fazzino et al. [ 3 ] (Fig. 1 ), the most frequent categories were NHPF and FSOD, representing 795 (42.1%) and 742 (39.3%) of the foods listed in the table, respectively. Some foods included in the FSOD group are pizza, hamburgers, hot dogs, sausages, and meats. NHPF and HPF foods were also evaluated according to their energy density, protein content, and fiber content, as described in Table 3 . In terms of energy density, 588 (31.1%) of all the foods in the IBGE Table had high energy density. When comparing the distribution of foods according to energy density and hyperpalatability, we found that among HPF foods, there were more foods with high energy density (414 foods, 37.8%) than among NHPF foods (174 foods, 21.9%; p < 0.0001). In terms of protein content, in the HPF group, 583 foods (53.2%) presented a low protein content, and among the NHPFs, 529 foods (66.5%; p < 0.0001) presented a high protein content. With respect to fiber content analysis, only 11% of the total foods in the IBGE Table were considered sources of or had high fiber content. In the HPF, only 8.0% of foods were classified as sources of or high in fiber, whereas in the NHPF, the percentage was greater (15.1%; p < 0.0001). Among the HPF foods classified as sources of or high in fiber, we have vegetables prepared with butter and/or oil, almonds, chestnuts, and peanut butter. Table 4 describes the categorizations of HPF according to the groups proposed by Fazino et al. [ 3 ] and their relationships with energy density, protein content, and fiber content. The difference is evident in relation to the HPF categories and energy density; foods in the FS category stand out as the main high-calorie foods (50%). This food group includes chocolates, truffles, brigadeiros, caramelized peanuts, sweet biscuits, and some types of cakes. Moreover, the CSOD category of the HPF predominates among those with low energy density (82.3%), as do the low-calorie foods (78.1%). Some examples of CSOD foods with low energy density are corn, risotto, some forms of potato preparation, cassava, and polenta. With respect to protein content in the HPF, a predominance of FSOD foods can be observed among the high-protein foods, whereas in the other categories (FS, CSOD, and COMBINED), there is a higher frequency of low-protein foods. With respect to the HPF, in terms of fiber content, the combined and CSOD categories stand out as high-fiber foods or sources, with 23 and 25 foods, respectively, including light farofa, some cereal bars, corn, and legumes such as beans. On the other hand, FSOD has a lower frequency of high-fiber foods. 4 Discussion This study represents an original investigation of the identification and characterization of HPFs among set foods consumed by the Brazilian population. This is an important area of ​​research, especially considering the increasing rates of obesity and diet-related diseases. In our study, we analyzed the Nutritional Composition Table of Foods Consumed in Brazil, which was produced from dietary records of a Brazilian population sample. One thousand ninety-five foods met the low-calorie criteria, according to the publication by Fazzino et al. [ 3 ], accounting for 57.9% of the foods. This finding is similar to data obtained in an American study conducted by Fazzino et al. [ 3 ], in which the quantitative low-calorie classification was applied to the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS), a representative dataset of the American food system. In the FNDDS, 7,757 foods were included, excluding beverages, and of these, 4,795 (62%) foods were identified as HPFs. When comparing our data with those of Fazzino et al. [ 3 ], we observed similarities in the distribution of foods across the HPF categories (FSOD, FS, and CSOD). In the Brazilian study, the FSOD category had the highest number of foods included, accounting for 67.8% of the HPF, followed by the FS category, accounting for 13.5%, and the CSOD category, accounting for 11.3%. In the American study, the FSOD category was also the most common category, representing 70% of the HPF, followed by the FS category, with 25%, and the CSOD category, with 16%.3 However, it is important to emphasize that the methodology for classifying within the HPF category adopted in the two studies presents some differences. In the present study, foods that met the conditions for more than one category were grouped under the designation COMBINED, whereas in the North American study, foods that met the conditions for more than one group were included in more than one group simultaneously. These methodological differences may influence the interpretation of the results obtained, especially when considering the representation of foods in the groups and the prevalence of categories. The evidence suggests a correlation between food palatability and increased appetite, followed by increased intake. Therefore, there is a direct relationship between food palatability, increased intake, and the propensity to overeat [ 12 ]. Stinson et al. [ 13 ] reported that high fat and simple sugar contents were strongly associated with overeating and weight gain. DiFeliceantonio et al. [ 14 ] reported that, compared with foods that contain these ingredients alone, foods that combine fat and carbohydrates have an increased reinforcing capacity and exert a supraadditive effect on the brain's reward system. In our analysis, we observed that 7.8% of the foods listed in the table are high in simple sugars and fat, whereas 4.3% are classified as COMBINED, such as pizza, instant noodles, and stuffed cookies. That is, they have characteristics that allow them to be classified into more than one HPF category and may have a greater reinforcing capacity than other foods in each group. The presence of protein and fiber in food affects gastrointestinal flow, influencing digestion and, consequently, the feeling of fullness. Fiber, particularly soluble fiber, plays a key role in increasing viscosity during gastric digestion, which contributes to reduced gastric flow and the interaction of food with gastric enzymes [6; 14]. This phenomenon results in a delay in the absorption of nutrients, prolonging the time of gastric emptying and the subsequent release of hormones related to satiety, promoting a feeling of fullness [10; 16–17]. Moreover, proteins play an important role in the secretion of satiety hormones, with the rate and amount of amino acid absorbed in the gastrointestinal tract directly influencing the duration of satiety. Therefore, combining protein and fiber in foods and meals can be an effective strategy for reducing appetite and prolonging the feeling of fullness [ 18 – 19 ]. Since HPFs can stimulate appetite by circumventing satiety mechanisms but protein and fiber stimulate satiety and reduce appetite [7; 20], the presence of high fiber and protein contents in foods considered HPF could balance the hyperphagia effects caused by high palatability. In this study, 569 (46.8%) of the HPF foods had high protein contents, and 87 (8%) of them were sources of or had high fiber contents. Therefore, evaluating the different characteristics of HPFs expands the knowledge about this food group and can help guide healthier choices for a pleasurable diet. In the present study, 37.8% (414) of the foods considered HPF had high energy density, whereas Sutton et al. [ 21 ] described a frequency of 58.7% (2453/4177) of HPF foods with high energy density in an analysis of foods from the American food system. The literature has indicated that the energy density of foods is intrinsically related to their palatability due to the significant presence of fats, sugars and/or other caloric ingredients, which tend to provide an intense and rewarding sensory experience [ 21 – 22 ]. In contrast, foods with low energy density and rich in fiber tend to provide greater satiation with lower caloric intake [ 22 – 23 ]. In our study, we found that 681 (62.2%) of the HPFs had a low energy density and were foods that are commonly used in the Brazilian diet, such as rice, beans, meat, and eggs, which are of extreme nutritional importance [ 24 – 25 ]. The results of this research show that highly processed foods predominate among the foods reported by participants in a representative sample of the Brazilian population through the 2008–2009 Household Budget Survey. González et al. [ 24 ] reported a relationship between emotionally motivated eating and highly processed food consumption, promoting a positive energy balance. This suggests an association between the consumption of these foods and emotionally influenced eating behaviors, reflecting cultural learning and the contextual environment. Food influences the response to and expression of emotions, which in turn affect food choices. However, the exact reasons for the consumption of highly processed foods are not yet fully understood, generating conflicting data [23; 26]. However, our analysis revealed that, in addition to fattier and more palatable foods such as pizza and hamburgers, many traditional Brazilian foods, often recommended for consumption, such as meats, rice, and beans, can also be classified as HPFs, especially when prepared with oils, butter, or sauces high in fat and sodium. These findings highlight the complexity of food classification and highlight the need for further research to assess the relationships among HPF consumption, eating behavior, and the health impacts of this consumption. However, it is essential to consider the limitations inherent to this study when interpreting its results. The accuracy of the data from the national table is one of the main limitations, as nutritional values ​​can vary depending on factors such as food origin, preparation methods, and geographic region, reflecting the diversity of the Brazilian diet. Another relevant point is the IBGE table's approach to total sodium content, which includes both natural and added sodium, the latter being estimated on the basis of the Nutrition Data System for Research (NDSR), an American database. Furthermore, most preparation methods for hyperpalatable foods are based on American data, which may not accurately reflect the eating habits of the Brazilian population. Finally, the classification of beverages represented an additional limitation, as it was not possible to include them in the analyses due to the lack of specific literature to guide their categorization as hyperpalatable, taking into account the difference in palatability compared with that of solid foods. However, this study has significant strengths: it is the first to conduct a comprehensive analysis of the composition of hyperpalatable foods in the Brazilian context, considering fiber, protein, and energy density contents, as well as including a detailed analysis of the broad range of foods consumed in the country. Unlike studies limited to ingredient combinations, this research also examines the variability in the characteristics of hyperpalatable foods and their preparation methods, providing a unique and relevant baseline for future research on the impact of these foods on the health of the population. The findings of this study provide a comprehensive analysis of the characteristics of the HPFs listed in the IBGE table, providing information on their nutritional composition and heterogeneity. These findings demonstrate the importance of evaluating HPFs not only on the basis of palatable ingredient combinations but also on their characteristics, such as energy density and protein and fiber contents. Understanding food characteristics can help develop effective strategies to promote healthy food choices and address the challenges associated with overconsumption. We emphasize the importance of further research and monitoring of the HPF to improve the understanding of these foods and their definition, categorization, and adaptation to and adaptation to the food culture of populations. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Ethical Approval Statement This study did not involve the collection or analysis of data directly related to human participants. The research was based exclusively on a secondary database containing information on food composition, without any identifiable personal data. Therefore, ethical approval by an institutional review board or research ethics committee was not needed. Funding Statement The authors declare that no specific funding was received for the execution of this study. The research was conducted using data from publicly available databases and therefore did not require financial support for data collection or analysis. Amanda Jacobsen and Larissa Lemos received individual research scholarships from the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS), Brazil. Author Contribution **CSB:** Writing – original draft, visualization, software, methodology, investigation, formal analysis, data curation, conceptualization. **LDL:** Writing – original draft, methodology, investigation, formal analysis, data curation. **IMF:** Methodology, Investigation, Formal analysis, Data curation. **AGJ:** Methodology, Investigation, Formal analysis, Data curation. **VC:** Writing – review & editing, software, resources, conceptualization, data curation. **JPG:** Writing – review & editing, Resources, Conceptualization, Data curation, Formal analysis. **SA** : Writing – review & editing, Project administration, Methodology, Formal analysis, Data curation, Conceptualization supervision, Project administration. Acknowledgement Acknowledgments: AGJ and LDL received individual research scholarships from the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS) and Federal University of Health Science of Porto Alegre, Brazil. Data Availability Publicly available in a repository:Instituto Brasileiro de Geografia e Estatística (2011) Pesquisa de orçamentos familiares 2008–2009: tabela de composição nutricional dos alimentos consumidos no Brasil. IBGE. Accessed 26 Jun 2025 and are available at the following URL: https://biblioteca.ibge.gov.br/index.php/biblioteca-catalogo?view=detalhes&id=250002. References Brutman J, Davis JF, Sirohi S. Behavioral and neurobiological consequences of hedonic feeding on alcohol drinking. Curr Pharm Des. 2020;26:2309–15. https://doi.org/10.2174/1381612826666200206092231 . Dixon JB. The effect of obesity on health outcomes. Mol Cell Endocrinol. 2010;316:104–8. https://doi.org/10.1016/j.mce.2009.07.008 . Fazzino TL, Rohde K, Sullivan DK. Hyperpalatable foods: development of a quantitative definition and application to the US food system database. Obesity. 2019;27:1761–8. https://doi.org/10.1002/oby.22639 . Gearhardt AN, Davis C, Kuschner R, Brownell KD. 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Characteristics of dietary fibers relevant to appetite and energy intake outcomes in human intervention trials. Am J Clin Nutr. 2017;106:747–54. https://doi.org/10.3945/ajcn.117.157172 . Barati M, Javanmardi F, Jabbari M, Mokari-Yamchi A, Farahmand F, et al. An in silico model to predict digestion-resistant and bioactive peptide content of dairy products. LWT. 2020;130:109616. https://doi.org/10.1016/j.lwt.2020.109616 . Morell P, Fiszman S. Revisiting the role of protein-induced satiation and satiety. Food Hydrocoll. 2017;68:199–210. https://doi.org/10.1016/j.foodhyd.2016.08.003 . Kohanmoo A, Faghih S, Akhlaghi M. Effect of short- and long-term protein consumption on appetite and appetite-regulating gastrointestinal hormones. Physiol Behav. 2020;226:113123. https://doi.org/10.1016/j.physbeh.2020.113123 . Sutton CA, Stratton M, L’Insalata AM, Fazzino TL. Ultraprocessed, hyperpalatable, and high energy density foods: prevalence and distinction across 30 years in the United States. Obesity. 2024;32:166–75. https://doi.org/10.1002/oby.23897 . Poppitt SD, Prentice AM. Energy density and its role in the control of food intake: evidence from metabolic and community studies. Appetite. 1996;26:153–74. https://doi.org/10.1006/appe.1996.0013 . Cummings JR, Schiestl ET, Tomiyama AJ, Mamtora T, Gearhardt AN. Highly processed food intake and immediate and future emotions in everyday life. Appetite. 2022;169:105868. https://doi.org/10.1016/j.appet.2021.105868 . González CEF, Chávez-Servín JL, De La Torre-Carbot K, Ronquillo González D, Aguilera Barreiro MDLA, Ojeda Navarro LR. (2022) Relationship between emotional eating, consumption of hyperpalatable energy-dense foods, and indicators of nutritional status: a systematic review. J Obes 2022:4243868. https://doi.org/10.1155/2022/4243868 Ministério da Saúde. (2014) Guia alimentar para a população brasileira , 2nd edn. Ministério da Saúde, Brasília. http://www.saude.gov.br/bvs . Accessed 26 Jun 2025. Pandolfi E, Sacripante R, Cardini F. Food-induced emotional resonance improves emotion recognition. PLoS ONE. 2016;11:e0167462. https://doi.org/10.1371/journal.pone.0167462 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 May, 2026 Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 10 Feb, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 26 Dec, 2025 Editor assigned by journal 26 Dec, 2025 Submission checks completed at journal 23 Dec, 2025 First submitted to journal 21 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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12:42:46","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":116003,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8419384/v1/5b0c2b6ef6e35f00602652ea.html"},{"id":99316854,"identity":"43f7bc93-3ed2-4630-b062-df04568a8a12","added_by":"auto","created_at":"2025-12-31 16:29:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15268,"visible":true,"origin":"","legend":"\u003cp\u003eRelative frequency of categorization of hyperpalatable and non-hyperpalatable foods.\u003c/p\u003e\n\u003cp\u003eLegend: NHPF – Non-hyperpalatable food; FSOD – Hyperpalatable food – Fat + Sodium; FS – Hyperpalatable food – Fat + Sugar; CSOD – Hyperpalatable food – Carbohydrate + Sodium; COMBINED: FSOD + CSOD – fat + sodium and carbohydrate + sodium FSOD + FS – Fat + sodium and fat + sugar.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8419384/v1/5bab69ed440c11d974f262c3.png"},{"id":99323465,"identity":"d189d5b8-0d90-4dd1-8fad-eb53fcccd984","added_by":"auto","created_at":"2025-12-31 16:45:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":689040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8419384/v1/1b30fdd8-7398-454e-8ac7-ac2285cb02ce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Nutritional Profiling of Hyperpalatable Foods in the Brazilian Food Composition Table Characterization of Hyperpalatable Foods","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHyperpalatable foods (HPFs) can disrupt appetite control and food intake, disrupting homeostatic regulation, and may therefore be associated with nutritional problems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Hyperpalatability can be generated by ingredient combinations or by preparation methods that induce a feeling of pleasure when the food is ingested. The synergy between specific ingredients creates an artificially enhanced and more rewarding palatability experience than any single palatable ingredient [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Different definitions for the term \"hyperpalatable foods\" have been proposed, with varying focus on ingredients or food categories. However, Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] proposed a quantitative classification that allows a clear distinction between HPF and non-HPF foods.\u003c/p\u003e \u003cp\u003eAccording to Gearhardt et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], individuals can develop dependence on HPF, resulting in uncontrolled eating, because the complex mechanism of regulating food intake and the feeling of satiety during a meal are impaired. However, it is important to consider that, in addition to palatability, several factors can influence the sensory signals of satiation and satiety, such as chewing and the time food remains in the mouth, ingestion time, intestinal flow, and the nutritional composition and sensory characteristics of foods, such as dietary fiber and protein content, flavor, aroma, visual appearance, and texture [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Research that evaluates food composition and its implications for palatability, signaling satiation and satiety can help in planning more effective nutritional counseling strategies.\u003c/p\u003e \u003cp\u003eThe IBGE (Brazilian Institute of Geography and Statistics) Nutritional Composition Table of Foods Consumed in Brazil (POF 2008\u0026ndash;2009) was prepared using information from detailed records of foods consumed by members of a representative sample of the Brazilian population throughout the country through the 2008\u0026ndash;2009 Household Budget Survey. This survey produced a database that provides information on food composition and food consumption and aimed to understand potential inadequacies in Brazilian dietary patterns and contribute to the establishment of national food and nutrition policies and programs. It also serves as an important tool for research and investigation in the area of ​​nutrition in Brazil. To date, no study has identified and evaluated the frequency of the HPF in the Nutritional Composition Table of Foods Consumed in Brazil. Therefore, the present study aims to identify foods that would be classified as HPFs, according to the description by Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and the frequency of this food group among those consumed by Brazilians. This analysis will allow us to understand the contribution of HPF to the set of foods consumed by Brazilians, as shown in the table in question, in addition to classifying these foods according to their energy density, protein content, and fiber content.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003eThe identification of HPF foods and their frequency assessment were performed via the IBGE Food Nutritional Composition Table (POF) 2008\u0026ndash;2009. This table consists of 19 major food groups: cereals and legumes; tuberous vegetables; flours, starches, and pastas; coconuts, chestnuts, and nuts; leafy, fruity, and other vegetables; fruits; sugars and confectionery products; salts and condiments; meats and offal; fish and seafood; canned and preserved foods; poultry and eggs; dairy products; baked goods; processed meats; nonalcoholic beverages and infusions; alcoholic beverages; oils and fats; and miscellaneous foods. In the present study, 17 of the 19 groups were evaluated; beverage groups (nonalcoholic beverages, infusions and alcoholic beverages) were excluded because of the lack of literature on the classification of beverages as HPFs. In addition to the groups described, in the IBGE Table, foods were arranged in 16 different forms of preparation: raw, cooked, grilled/embered/barbecued, baked, fried, breaded/breaded, saut\u0026eacute;ed, red sauce, white sauce, garlic and oil, butter/oil, vinegar, stew, porridge, and soup, in addition to the option \"not applicable\" when the food had not been prepared in any way.\u003c/p\u003e \u003cp\u003eThe IBGE Food Nutritional Composition Table is available in the Online Library on the IBGE website [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is divided into four tables. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e contains information on energy, macronutrients, and fiber; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e contains information on fats and sugars; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e lists the mineral composition; and, finally, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides information on the amounts of vitamins present in foods. For this study, the four composition tables were grouped in Microsoft Excel software for food classification and analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNonhyperpalatable foods (NHPF) and hyperpalatable foods (HPF) in the different food groups of the nutritional composition table of foods consumed in Brazil - IBGE - POF 2008\u0026ndash;2009\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNHPF\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHPF\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExamples of HPF from each group\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCereals and legumes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCooked rice, corn, peas, and beans, popcorn\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTuberous vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoiled potatoes, fried potatoes, braized cassava, cooked cassava\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlours, starches and pasta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMucilon, cornstarch, breakfast cereals, mini pizza, mini pastries, instant noodles\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoconuts, chestnuts and walnuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlmond, Brazil nut, cashew nut\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeafy, fruity and other vegetables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e165 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSaut\u0026eacute;ed chicory, breaded cauliflower, saut\u0026eacute;ed pumpkin, green beans with butter/oil, breaded eggplant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreaded banana or with butter/oil\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSugars and confectionery products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIce cream, chocolate, truffle, mousse, coconut candy, peanut brittle, caramelized peanuts, brigadeiro\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalts and condiments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (65.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePickled capers, mayonnaise, soy sauce\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeat and offal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e89 (23.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e286 (76.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRoasted filet mignon, grilled rump steak, braized offal, fried pork chop\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish and seafood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67 (75.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFried fish, stewed fish, fish in white sauce, breaded crab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanned and preserved foods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (76.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOlives, pickled mushrooms, pickled sweetcorn, onion cream\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry and eggs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrilled, roasted or boiled chicken, nuggets, roast chester, fried chicken egg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (70.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhole cow's milk, whole yogurt (any flavor), powdered milk, cheeses, salted butter\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBakery products\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82 (82.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSalt bread, industrially prepared sliced ​​bread, cheese bread, savory biscuits, sweet biscuits\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed meats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndustrialized beef burger, bacon, sausage, salami, mortadella, ham\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOils and fats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscellaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104 (57.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePastel, pork rinds, esfirra, hot dog, hamburger (sandwich), bauru, pizza, industrialized pizza\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNHPF -Nonhyperpalatable food; HPF - hyperpalatable food\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNonhyperpalatable foods (NHPF) and hyperpalatable foods (HPF) in the different forms of food preparation from the nutritional composition table of foods consumed in Brazil - IBGE - POF 2008\u0026ndash;2009\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreparation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNHPF\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHPF\u003c/p\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith garlic and oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (61.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn vinaigrette\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (26.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBakeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84 (65.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith butter/oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (88.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCocked\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107 (54.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (59.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (40.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreaded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (76.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (76.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (83.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrilled/embered/barbecue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (67.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePorridge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy-based sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (81.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomato-based sauce\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (62.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e435 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e441 (50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaut\u0026eacute;ed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (69.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequencies of nonhyperpalatable and hyperpalatable foods according to their energy density, protein content and fiber content\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal food items\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNHPF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHPF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ep\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnergy density\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e588 (31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e174 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e414 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow- N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,302 (68.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e621 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e681 (62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProtein content\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh- N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e778 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e266 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e512 (46.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c9\" namest=\"c8\" rowspan=\"2\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,112 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e529 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e583 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFiber content\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1,683 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e675 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1,008 (92.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"2\" nameend=\"c9\" namest=\"c8\" rowspan=\"3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e133 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e67 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e66 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh- N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e74 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e53 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e21 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c10\" namest=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eEnergy density - high (\u0026ge;\u0026thinsp;250 kcal/100 g) and low (\u0026lt;\u0026thinsp;250 kcal/100 g); protein content - high (\u0026ge;\u0026thinsp;30% protein) and low (\u0026lt;\u0026thinsp;30% protein); fiber content - high (\u0026ge;\u0026thinsp;6 g of fiber in 100 g of product), source (3 to 5.9 g of fiber in 100 g of product) and low (\u0026lt;\u0026thinsp;3 g of fiber in 100 g of product); \u003csup\u003ea\u003c/sup\u003ePearson's chi-square test, with Yates correction, p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFrequencies of hyperpalatable foods according to energy density, protein content and fiber content\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNHPF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFSOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCSOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eCOMBINED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c8\" namest=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnergy density\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e174 (21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e279 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e22 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e39 (48.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e621 (78.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e463 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e102 (82.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e42 (51.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProtein content\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e504 (67.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e529 (66.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143 (96.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e123 (99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e79 (97.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFiber content\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e675 (84.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e720 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131 (88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e99 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e58 (71.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource -N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e16 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e20 (24.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh - N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3 (3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e795 (42.1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e742 (39.2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e148 (7.8)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003e124 (6.6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003e81 (4.3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eEnergy density - high (\u0026ge;\u0026thinsp;250 kcal/100 g) and low (\u0026lt;\u0026thinsp;250 kcal/100 g); protein content - high (\u0026ge;\u0026thinsp;30% protein) and low (\u0026lt;\u0026thinsp;30% protein); fiber content - high (\u0026ge;\u0026thinsp;6 g of fiber in 100 g of product), source (3 to 5.9 g of fiber in 100 g of product) and low (\u0026lt;\u0026thinsp;3 g of fiber in 100 g of product); FSOD - hyperpalatable food - fat\u0026thinsp;+\u0026thinsp;sodium; FS - hyperpalatable food - fat\u0026thinsp;+\u0026thinsp;sugar; CSOD - hyperpalatable food - carbohydrate\u0026thinsp;+\u0026thinsp;sodium; COMBINED: (FSOD\u0026thinsp;+\u0026thinsp;CSOD) and (FSOD\u0026thinsp;+\u0026thinsp;FS).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e The database generated by combining the tables was used to classify foods according to their palatability, fiber content, protein content, and energy density. The classification of HPFs followed the criteria proposed by Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], who grouped them into three categories: fat\u0026thinsp;+\u0026thinsp;sodium (FSOD), for foods with \u0026gt;\u0026thinsp;25% of calories from fat and \u0026ge;\u0026thinsp;0.30% sodium; fat\u0026thinsp;+\u0026thinsp;simple sugar (FS), for foods with \u0026gt;\u0026thinsp;20% of calories from fat and \u0026gt;\u0026thinsp;20% from simple sugars; and carbohydrate\u0026thinsp;+\u0026thinsp;sodium (CSOD), for foods with \u0026gt;\u0026thinsp;40% of calories from carbohydrates (excluding fiber and simple sugars) and \u0026ge;\u0026thinsp;0.20% sodium. Foods that belonged to more than one category, such as FSOD\u0026thinsp;+\u0026thinsp;CSOD and FSOD\u0026thinsp;+\u0026thinsp;FS, were grouped into the \"COMBINED\" category.\u003c/p\u003e \u003cp\u003eThe fiber content of foods was classified on the basis of the RDC 54/2012 of ANVISA (National Health Surveillance Agency), which provides supplementary nutritional information, defining the declarations of some nutritional properties, including the classification regarding the fiber content of the food [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, foods were classified as \"low fiber\" when they had less than 3 g of fiber per 100 g of product; as \"source of fiber\" when they had 3 to 5.9 g of fiber per 100 g of product; and as \"high fiber\" when the amount of fiber was equal to or greater than 6 g per 100 g of food. The amount of protein in foods was analyzed as described by Esmaeili et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], in which foods were classified as \"low protein\" when the food was composed of less than 30% protein and as \"high protein\" when the food was composed of 30% or more protein. The classification of foods, in relation to energy density, was carried out on the basis of the average value of the range described in the recommendation of the World Cancer Research Fund/American Institute for Cancer Research, with foods that have an energy density of less than 250 kcal per 100 g of the product being classified as \u0026ldquo;low energy density\u0026rdquo; and foods that have a density greater than or equal to 250 kcal per 100 g being classified as \u0026ldquo;high energy density\u0026rdquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe analysis and classification of the foods in the table were carried out by a trained research team composed of nutrition professionals and graduate students. Before data analysis, all team members received specific training on data access procedures and data recording. Nutrient values and food characteristics used for classification into HPFs, as well as for determining energy density and protein and fiber contents, were recorded in a shared document for consistency and reference. Weekly meetings were held to review the entries and address any uncertainties. The team members were instructed to document all the questions and the corresponding food items for later verification. A final review of all records was conducted by a senior researcher.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed via SPSS software, version 20.0. Measures of central tendency and dispersion were calculated, in addition to comparing means via Student's t tests and assessing associations between variables via Fisher's exact test or Pearson's chi-square test, with Yates' correction. The variables are presented as absolute and relative frequencies. A 5% level of statistical significance was adopted.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003eOne thousand ninety-five (57.8%) foods were considered hyperpalatable in the evaluation of the IBGE Table. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the frequency of the foods classified as hyperpalatable and nonhyperpalatable (NHPF), as well as examples of foods classified as hyperpalatable, separated by major food groups and by preparation methods. Among the different food groups, the most prominent in terms of the frequency of hyperpalatable foods was processed meats, 100% of which were classified as hyperpalatable, followed by poultry and eggs (85.7%) and bread and bakery items (82.8%). Examples of foods in these groups include processed meats, chicken nuggets, and processed bread. The preparation methods with butter/oil, fried foods, or dairy-based sauce presented hyperpalatable frequencies of 88.9%, 83.5%, and 81.8%, respectively. In addition to the different preparation techniques, the IBGE table includes the option \"not applicable\", indicating that a preparation method is considered unique, which corresponds to 50.6% of the total foods registered; 50.3% of these foods with the option \"not applicable\" were considered HPF.\u003c/p\u003e \u003cp\u003eAccording to the classification of hyperpalatable food groups proposed by Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the most frequent categories were NHPF and FSOD, representing 795 (42.1%) and 742 (39.3%) of the foods listed in the table, respectively. Some foods included in the FSOD group are pizza, hamburgers, hot dogs, sausages, and meats.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNHPF and HPF foods were also evaluated according to their energy density, protein content, and fiber content, as described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In terms of energy density, 588 (31.1%) of all the foods in the IBGE Table had high energy density. When comparing the distribution of foods according to energy density and hyperpalatability, we found that among HPF foods, there were more foods with high energy density (414 foods, 37.8%) than among NHPF foods (174 foods, 21.9%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In terms of protein content, in the HPF group, 583 foods (53.2%) presented a low protein content, and among the NHPFs, 529 foods (66.5%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) presented a high protein content. With respect to fiber content analysis, only 11% of the total foods in the IBGE Table were considered sources of or had high fiber content. In the HPF, only 8.0% of foods were classified as sources of or high in fiber, whereas in the NHPF, the percentage was greater (15.1%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Among the HPF foods classified as sources of or high in fiber, we have vegetables prepared with butter and/or oil, almonds, chestnuts, and peanut butter.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the categorizations of HPF according to the groups proposed by Fazino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and their relationships with energy density, protein content, and fiber content. The difference is evident in relation to the HPF categories and energy density; foods in the FS category stand out as the main high-calorie foods (50%). This food group includes chocolates, truffles, brigadeiros, caramelized peanuts, sweet biscuits, and some types of cakes. Moreover, the CSOD category of the HPF predominates among those with low energy density (82.3%), as do the low-calorie foods (78.1%). Some examples of CSOD foods with low energy density are corn, risotto, some forms of potato preparation, cassava, and polenta. With respect to protein content in the HPF, a predominance of FSOD foods can be observed among the high-protein foods, whereas in the other categories (FS, CSOD, and COMBINED), there is a higher frequency of low-protein foods. With respect to the HPF, in terms of fiber content, the combined and CSOD categories stand out as high-fiber foods or sources, with 23 and 25 foods, respectively, including light farofa, some cereal bars, corn, and legumes such as beans. On the other hand, FSOD has a lower frequency of high-fiber foods.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study represents an original investigation of the identification and characterization of HPFs among set foods consumed by the Brazilian population. This is an important area of ​​research, especially considering the increasing rates of obesity and diet-related diseases. In our study, we analyzed the Nutritional Composition Table of Foods Consumed in Brazil, which was produced from dietary records of a Brazilian population sample. One thousand ninety-five foods met the low-calorie criteria, according to the publication by Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], accounting for 57.9% of the foods. This finding is similar to data obtained in an American study conducted by Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], in which the quantitative low-calorie classification was applied to the United States Department of Agriculture (USDA) Food and Nutrient Database for Dietary Studies (FNDDS), a representative dataset of the American food system. In the FNDDS, 7,757 foods were included, excluding beverages, and of these, 4,795 (62%) foods were identified as HPFs.\u003c/p\u003e \u003cp\u003eWhen comparing our data with those of Fazzino et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], we observed similarities in the distribution of foods across the HPF categories (FSOD, FS, and CSOD). In the Brazilian study, the FSOD category had the highest number of foods included, accounting for 67.8% of the HPF, followed by the FS category, accounting for 13.5%, and the CSOD category, accounting for 11.3%. In the American study, the FSOD category was also the most common category, representing 70% of the HPF, followed by the FS category, with 25%, and the CSOD category, with 16%.3 However, it is important to emphasize that the methodology for classifying within the HPF category adopted in the two studies presents some differences. In the present study, foods that met the conditions for more than one category were grouped under the designation COMBINED, whereas in the North American study, foods that met the conditions for more than one group were included in more than one group simultaneously. These methodological differences may influence the interpretation of the results obtained, especially when considering the representation of foods in the groups and the prevalence of categories.\u003c/p\u003e \u003cp\u003eThe evidence suggests a correlation between food palatability and increased appetite, followed by increased intake. Therefore, there is a direct relationship between food palatability, increased intake, and the propensity to overeat [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Stinson et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported that high fat and simple sugar contents were strongly associated with overeating and weight gain. DiFeliceantonio et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] reported that, compared with foods that contain these ingredients alone, foods that combine fat and carbohydrates have an increased reinforcing capacity and exert a supraadditive effect on the brain's reward system. In our analysis, we observed that 7.8% of the foods listed in the table are high in simple sugars and fat, whereas 4.3% are classified as COMBINED, such as pizza, instant noodles, and stuffed cookies. That is, they have characteristics that allow them to be classified into more than one HPF category and may have a greater reinforcing capacity than other foods in each group.\u003c/p\u003e \u003cp\u003eThe presence of protein and fiber in food affects gastrointestinal flow, influencing digestion and, consequently, the feeling of fullness. Fiber, particularly soluble fiber, plays a key role in increasing viscosity during gastric digestion, which contributes to reduced gastric flow and the interaction of food with gastric enzymes [6; 14]. This phenomenon results in a delay in the absorption of nutrients, prolonging the time of gastric emptying and the subsequent release of hormones related to satiety, promoting a feeling of fullness [10; 16\u0026ndash;17]. Moreover, proteins play an important role in the secretion of satiety hormones, with the rate and amount of amino acid absorbed in the gastrointestinal tract directly influencing the duration of satiety. Therefore, combining protein and fiber in foods and meals can be an effective strategy for reducing appetite and prolonging the feeling of fullness [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Since HPFs can stimulate appetite by circumventing satiety mechanisms but protein and fiber stimulate satiety and reduce appetite [7; 20], the presence of high fiber and protein contents in foods considered HPF could balance the hyperphagia effects caused by high palatability. In this study, 569 (46.8%) of the HPF foods had high protein contents, and 87 (8%) of them were sources of or had high fiber contents. Therefore, evaluating the different characteristics of HPFs expands the knowledge about this food group and can help guide healthier choices for a pleasurable diet.\u003c/p\u003e \u003cp\u003eIn the present study, 37.8% (414) of the foods considered HPF had high energy density, whereas Sutton et al. [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] described a frequency of 58.7% (2453/4177) of HPF foods with high energy density in an analysis of foods from the American food system. The literature has indicated that the energy density of foods is intrinsically related to their palatability due to the significant presence of fats, sugars and/or other caloric ingredients, which tend to provide an intense and rewarding sensory experience [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, foods with low energy density and rich in fiber tend to provide greater satiation with lower caloric intake [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In our study, we found that 681 (62.2%) of the HPFs had a low energy density and were foods that are commonly used in the Brazilian diet, such as rice, beans, meat, and eggs, which are of extreme nutritional importance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of this research show that highly processed foods predominate among the foods reported by participants in a representative sample of the Brazilian population through the 2008\u0026ndash;2009 Household Budget Survey. Gonz\u0026aacute;lez et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] reported a relationship between emotionally motivated eating and highly processed food consumption, promoting a positive energy balance. This suggests an association between the consumption of these foods and emotionally influenced eating behaviors, reflecting cultural learning and the contextual environment. Food influences the response to and expression of emotions, which in turn affect food choices. However, the exact reasons for the consumption of highly processed foods are not yet fully understood, generating conflicting data [23; 26]. However, our analysis revealed that, in addition to fattier and more palatable foods such as pizza and hamburgers, many traditional Brazilian foods, often recommended for consumption, such as meats, rice, and beans, can also be classified as HPFs, especially when prepared with oils, butter, or sauces high in fat and sodium. These findings highlight the complexity of food classification and highlight the need for further research to assess the relationships among HPF consumption, eating behavior, and the health impacts of this consumption.\u003c/p\u003e \u003cp\u003eHowever, it is essential to consider the limitations inherent to this study when interpreting its results. The accuracy of the data from the national table is one of the main limitations, as nutritional values ​​can vary depending on factors such as food origin, preparation methods, and geographic region, reflecting the diversity of the Brazilian diet. Another relevant point is the IBGE table's approach to total sodium content, which includes both natural and added sodium, the latter being estimated on the basis of the Nutrition Data System for Research (NDSR), an American database. Furthermore, most preparation methods for hyperpalatable foods are based on American data, which may not accurately reflect the eating habits of the Brazilian population. Finally, the classification of beverages represented an additional limitation, as it was not possible to include them in the analyses due to the lack of specific literature to guide their categorization as hyperpalatable, taking into account the difference in palatability compared with that of solid foods. However, this study has significant strengths: it is the first to conduct a comprehensive analysis of the composition of hyperpalatable foods in the Brazilian context, considering fiber, protein, and energy density contents, as well as including a detailed analysis of the broad range of foods consumed in the country. Unlike studies limited to ingredient combinations, this research also examines the variability in the characteristics of hyperpalatable foods and their preparation methods, providing a unique and relevant baseline for future research on the impact of these foods on the health of the population.\u003c/p\u003e \u003cp\u003eThe findings of this study provide a comprehensive analysis of the characteristics of the HPFs listed in the IBGE table, providing information on their nutritional composition and heterogeneity. These findings demonstrate the importance of evaluating HPFs not only on the basis of palatable ingredient combinations but also on their characteristics, such as energy density and protein and fiber contents. Understanding food characteristics can help develop effective strategies to promote healthy food choices and address the challenges associated with overconsumption. We emphasize the importance of further research and monitoring of the HPF to improve the understanding of these foods and their definition, categorization, and adaptation to and adaptation to the food culture of populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthical Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not involve the collection or analysis of data directly related to human participants. The research was based exclusively on a secondary database containing information on food composition, without any identifiable personal data. Therefore, ethical approval by an institutional review board or research ethics committee was not needed.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e \u003cp\u003eThe authors declare that no specific funding was received for the execution of this study. The research was conducted using data from publicly available databases and therefore did not require financial support for data collection or analysis. Amanda Jacobsen and Larissa Lemos received individual research scholarships from the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS), Brazil.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**CSB:** Writing \u0026ndash; original draft, visualization, software, methodology, investigation, formal analysis, data curation, conceptualization. **LDL:** Writing \u0026ndash; original draft, methodology, investigation, formal analysis, data curation. **IMF:** Methodology, Investigation, Formal analysis, Data curation. **AGJ:** Methodology, Investigation, Formal analysis, Data curation. **VC:** Writing \u0026ndash; review \u0026amp;amp; editing, software, resources, conceptualization, data curation. **JPG:** Writing \u0026ndash; review \u0026amp;amp; editing, Resources, Conceptualization, Data curation, Formal analysis. **SA** : Writing \u0026ndash; review \u0026amp;amp; editing, Project administration, Methodology, Formal analysis, Data curation, Conceptualization supervision, Project administration.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledgments: AGJ and LDL received individual research scholarships from the Research Support Foundation of the State of Rio Grande do Sul (FAPERGS) and Federal University of Health Science of Porto Alegre, Brazil.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePublicly available in a repository:Instituto Brasileiro de Geografia e Estat\u0026iacute;stica (2011) Pesquisa de or\u0026ccedil;amentos familiares 2008\u0026ndash;2009: tabela de composi\u0026ccedil;\u0026atilde;o nutricional dos alimentos consumidos no Brasil. 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Accessed 26 Jun 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandolfi E, Sacripante R, Cardini F. Food-induced emotional resonance improves emotion recognition. PLoS ONE. 2016;11:e0167462. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0167462\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0167462\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Food, Diet and Nutrition, Food Composition Table, Food Composition, Hyperpalatable, Energy Density","lastPublishedDoi":"10.21203/rs.3.rs-8419384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8419384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePURPOSE\u003c/h2\u003e \u003cp\u003eThe standardized classification of hyperpalatable foods (HPF) was originally established and validated based on dietary data from the North American population. Evaluating the applicability of this classification within the dietary patterns of other populations allows an assessment of its external validity and the potential for generalization across different cultural and nutritional contexts. Our objective was to investigate the presence and characteristics of hyperpalatable foods (HPFs) in the Nutritional Composition Table of Foods Consumed in Brazil (POF, IBGE).\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eThe foods listed in the nutritional table were analyzed and categorized as hyperpalatable or not, including their categories and their protein, fiber and energy density contents.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eOne thousand eight hundred ninety foods were analyzed, of which 1095 (57.9%) were classified as HPFs, 37.8%, 46.8% and 92% of which were considered to have high energy density, high protein content and low fiber content, respectively. Most HPFs were classified in the high fat and sodium group (FSOD, 39.2%), while 4.3% met criteria for multiple categories.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eThese data demonstrate that more than half of the foods on the table are considered HPFs, but a comprehensive analysis of foods composition revealed their heterogeneity. Notably, most hyperpalatable foods identified in the food composition table exhibited low energy density, challenging the common assumption that hyperpalatability is inherently associated with high energy content. Further research in this area is important to improve the understanding of these foods, their classification, and their adaptation to the food culture of the populations.\u003c/p\u003e","manuscriptTitle":"Identification and Nutritional Profiling of Hyperpalatable Foods in the Brazilian Food Composition Table Characterization of Hyperpalatable Foods","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 12:42:41","doi":"10.21203/rs.3.rs-8419384/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-13T18:30:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T22:13:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251545522973630706485397967053677868717","date":"2026-02-10T13:26:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314581440722384252613500735788655033041","date":"2026-02-08T21:19:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-26T11:57:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T11:53:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-24T04:42:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrire","date":"2025-12-21T20:26:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrire","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Nutrire](https://www.springer.com/journal/41110)","snPcode":"41110","submissionUrl":"https://submission.nature.com/new-submission/41110/3","title":"Nutrire","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8396701d-df31-4c27-8d21-c2c8a183d4df","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-13T18:30:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-23T20:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 12:42:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8419384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8419384","identity":"rs-8419384","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}