Introduction
Normal-weight obesity (NWO) is characterised by an adequate body mass index (BMI), and a high percentage of body fat (%BF), which results in an increased risk of developing nutrition-related non-communicable diseases (N-NCDs)(Reference De Lorenzo, Martinoli and Vaia1). In addition to being a primary risk factor strongly associated with N-NCDs, high %BF impairs physical function and reduces quality of life(Reference Snijder, Dam and Visser2). Epidemiological studies highlight the importance of dietary habits in determining body composition, which together with physical activity and genetics, play a fundamental role in the modifiable causes of N-NCDs(Reference Madeira, Silva and Veloso3,Reference Lachat, Otchere and Roberfroid4) .
There are multiple factors contributing to changes in current dietary patterns, including the rising production and consumption of industrially processed foods and beverages(Reference Ludwig5). Changes in food access and quality have been influenced by shifts in production and distribution, as well as social, cultural and economic changes(Reference Monteiro, Moubarac and Cannon6). These changes have led to a significant increase in the prevalence of overweight and obesity in Brazil(7).
Considering the negative changes in the diet quality of the Brazilian population in the last decades(8), healthy dietary habits are being encouraged through clear and accessible information from the national dietary guidelines(9). Based on the information that several dietary guidelines have neglected the impact of industrial processing on overall health(Reference Monteiro10,Reference Monteiro, Levy and Claro11) , a new classification of foods (NOVA) clustering them according to the extent and purpose of their processing was proposed(Reference Monteiro, Levy and Claro11). In this classification, processing is considered the physical, chemical and biological procedures following the harvest of raw food and before it is subjected to any culinary preparation(Reference Monteiro, Levy and Claro11). This new classification was adopted as a theoretical framework for the current Dietary Guidelines for the Brazilian population(9).
Therefore, it is important to identify factors related to the diet of individuals with NWO, as they may present food consumption that favours the excessive accumulation of body fat. Here, we aimed to describe the consumption of ultra- and non-ultra-processed foods by individuals with and without NWO, as well as investigate the association of food and nutrient intake to the presence of NWO. We hypothesised that individuals with NWO have a higher consumption of ultra-processed foods compared to their counterparts and that there are associations between the intake of this type of food and the NWO.
Material and methods
Study design, participants and ethics
This was a cross-sectional study carried out in an academic community in Goiânia, Goiás, Brazil, from January to June 2019. The study was publicised through folders, social networks and e-mails sent to students, employees and professors at the Federal University of Goiás (UFG). Individuals aged between 20 and 59 years and with normal BMI (18⋅50–24⋅99 kg/m²)(12) were eligible for the study. Exclusion criteria were smoking, use of metal implants, limb amputation, intense physical activity, self-reported vitamin and/or mineral supplementation, self-reported acute and/or chronic diseases, self-reported use of lipid-lowering drugs, anti-hypertensives, anti-glycaemic drugs or insulin; pregnant or lactating women; menopause or undergoing hormone replacement therapy; undergoing nutritional monitoring and/or changing usual diet in the last 6 months prior to collection; or missing some stage of data collection. The final sample size consisted of 159 NWO individuals and 65 non-NWO individuals (Fig. 1).
Fig. 1. STROBE flowchart of participants’ recruitment.
This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the Ethics Committee of the Federal University of Goiás (protocol number 2.772.022). Written informed consent was obtained from all subjects.
Data collection
Data were collected at the School of Nutrition of the Federal University of Goiás, considering the availability of the participants. During the first appointment, participants signed the consent form, answered a standardised questionnaire (including information about food consumption), and had the body composition assessed. At the second appointment, participants were contacted by phone calls to provide additional information on food consumption.
Lifestyle assessment included routine habits, such as alcohol consumption and level of physical activity, which was assessed with the short version of the International Physical Activity Questionnaire(Reference Matsudo, Araújo and Matsudo13). The classification included very active, active, irregularly active (A or B) and sedentary.
Body composition assessment
Body composition (percentage of total BF, android BF and gynoid BF) was evaluated using Dual Energy Radiological Absorption (DXA) with a DPX-NT model equipment (General Electric Medical Systems Lunar®, Madison, USA)(14).
Anthropometry
Body mass was measured on a Filizola® digital platform scale, 150 kg maximum load and 0⋅1 kg accuracy (Filizola Shop, São Paulo, Brazil). Height was assessed using a Seca® stadiometer, 220 cm maximum range and 0⋅1 cm accuracy (Seca Deutschland, Hamburg, Germany)(Reference Lohman, Roche and Martorell15).
NWO classification cut-off points
Individuals with normal BMI (18⋅50–24⋅99 kg/m2) were separated by sex and age group and classified according to their %BF determined by DXA. The groups were women with NWO – %BF >30 %(Reference De Lorenzo, Martinoli and Vaia1) and men with NWO – %BF >19 %(Reference Kosmala, Derzhko and Przewlocka-Kosmala16).
Food consumption analysis
The food consumption was evaluated using three 24 h recalls (R24h) on non-consecutive days and including a weekend day(Reference Tucker17), following the Multiple Pass Method (MPM)(Reference Moshfegh, Rhodes and Baer18). To mitigate collection bias, during face-to-face and telephone interactions, trained researchers used strategies to quantify the food servings, such as a photographic manual and household measuring utensils(Reference Crispim, Fisberg and Almeida19). Then, this information was converted from household measures to grams(20) and the data were analysed using the Nutrition Data System for Research (NDS-R®) software (Nutrition Coordinating Center, University of Minnesota, Minneapolis – Saint Paul, USA)(21). Once the food consumption database was generated, the researchers established food standardizations, especially for those considered to be regional, since the NDS-R® provides information from international food composition tables.
The NOVA food classification
Foodstuffs were categorised by a registered dietitian (RD) according to the methodology proposed by Monteiro et al. (Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23) . Foods were classified into four groups according to the extension and purpose of the processing during production. Processing was defined as the methods and techniques used by the food and beverage industry to modify and convert them into food products, as well as the methods used to preserve and increase shelf life. Procedures performed during culinary preparation were not considered in the classification. After food categorisation, another RD inspected the entire list of foods to ensure the reliability of the categories. To classify the list of foods, RDs remained blind regarding the classification of individuals with and without NWO.
Group 1: fresh or minimally processed foods
The first group included those foods that have undergone minimal modification to increase shelf life and allow longer storage. Another purpose of minimal processing is to facilitate or diversify the culinary preparation of food, which include cleaning, portioning and removing non-edible parts. Other processes include grating, peeling, compression, simple bottling, drying, cooling, freezing, pasteurisation, fermentation, fat reduction, vacuum, gas and simple packaging(Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23) .
Group 2: culinary ingredients
The second group included substances extracted from group 1 or from nature that are consumed as necessary ingredients for culinary preparation. The main processes include pressing, grinding, spraying, drying and refining. The purpose of this processing is to provide products to be used in residential kitchens and restaurants to season, prepare or preserve foods from the first group. Rarely, ingredients in this category will be consumed in the absence of foods from group 1(Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23) .
Group 3: processed foods
The third group included foods prepared by adding salt or sugar and, occasionally, oil, vinegar or another ingredient from the second group to a food from the first group. These processes include preservation, cooking and non-alcoholic fermentation. In this category, the primary objective is to increase the food shelf life or modify its flavour(Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23) .
Group 4: ultra-processed foods
This group consists of industrial formulations produced with five or more ingredients. These ingredients mostly include substances and additives added during the manufacturing of processed foods. These substances are not common in culinary preparations as their purpose is to simulate sensory attributes of foods from the first group. The main objective of ultra-processing is to produce industrial ready-to-eat products to minimise preparation and replace foods from the first and third groups(Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23) .
Statistical analyses and justification of sample size
Double-entry databases were built to check consistency and the analysis was performed using R software version 4⋅1⋅1(24). First, the energy contribution of each NOVA classification group and subgroup was calculated. The proportion of energy of each group or subgroup in relation to total energy consumption was then calculated separately for every individual, and for the NWO and non-NWO individuals.
Total diet was divided into two dietary fractions: (1) ultra-processed foods and (2) non-ultra-processed foods (represented by fresh or minimally processed foods, culinary ingredients and processed foods). Nutrient intake was calculated for (1) total diet of NWO and non-NWO individuals; (2) ultra-processed and non-ultra-processed foods of the total sample; (3) ultra-processed foods of NWO and non-NWO individuals and (4) non-ultra-processed foods of NWO and non-NWO individuals.
Data distribution was assessed using the Shapiro–Wilk test. NWO and non-NWO individuals and the ultra- and non-ultra-processed fractions were compared using a Student's t test or Mann–Whitney test, according to the data distribution. Comparison of frequencies was performed using the χ2 test or Fisher's exact tests.
Logistic linear regression models were built to analyse the associations between the presence of NWO and food intake variables using the backward strategy for adjustment. Nutrients from ultra- and non-ultra-processed foods that could associate with NWO included energy, protein, carbohydrate, total and added sugars, total fat, alcohol, saturated fatty acids, trans fatty acids, cholesterol, fibre, calcium, iron and sodium.
Considering the lack of population-based studies with individuals with NWO in Brazil, the detectable effect size of the sample was settled a posteriori. Our sample size had a power of 80 % to detect, at a significance level of 5 %, a Cohen's effect size of magnitude 0⋅41. A significance level of 5 % was considered for all analyses.
Results
The final sample consisted of 224 individuals with normal BMI, 159 (71⋅0 %) with high %BF (NWO group) and 65 with normal %BF (non-NWO group). Most subjects were female in both groups (P = 0⋅376). Furthermore, the groups did not differ with respect to marital status (P = 0⋅455), socioeconomic classification (P = 0⋅094), age (P = 0⋅506), height (P = 0⋅744) or physical activity level (P = 0⋅295). Individuals with NWO had higher levels of education (P = 0⋅046), weight (P = 0⋅019), BMI (P = <0⋅0001) and %BF (P = <0⋅0001). All descriptive data are presented in Table 1.
Table 1. Socioeconomic, lifestyle, anthropometric and body composition variables of individuals in the NWO and non-NWO groups
Values are presented as mean ± sd, median (IQR) or absolute (relative) frequencies.
NWO, normal-weight obesity; BMI, body mass index; PAL, physical activity level; ME, metabolic equivalents; %BF DEXA, percentage of body fat assessed by Dual Energy Radiological Absorption; % BF Android, percentage of android body fat; % BF Gynoid, percentage of gynoid body fat; A/G, ratio between android/gynoid body fat.
Bold values indicate significant differences between groups.
* Student's t test or Mann–Whitney test, χ2 test or Fisher's exact test.
For NOVA classification, 49⋅4 % of the energy came from ‘Fresh or minimally processed foods’, 10⋅6 % from ‘Fats and Culinary Ingredients’, 12⋅8 % from ‘Processed foods’ and 27⋅2 % from ‘Ultra-processed foods’ (Table 2).
Table 2. Absolute and relative mean daily energy intake according to the NOVA food groups for the total sample (n 224)
Values are mean ± sd.
%, percentage in relation to the total energy intake.
* Adapted from (Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23).
When individuals were separated into groups, those in the NWO group exhibited overall lower energy intake (P = 0⋅030), but also lower mean energy from ‘fresh or minimally processed foods’, specifically for rice (P = 0⋅037), beans (P = 0⋅002) and fruits (P = 0⋅026) when compared with those in the non-NWO group. There were no differences in energy intake from ‘fats and culinary ingredients’ (P = 0⋅727), ‘processed foods’ (P = 0⋅345) and ‘ultra-processed foods’ (P = 0⋅874) between the two groups. Although the total energy intake from ultra-processed foods did not differ between the groups, there was a difference in energy intake from savoury crackers and snacks (P = 0⋅010), which was higher in the non-NWO group; and from sausages (P = 0⋅023), which was higher in the NWO group (Table 3).
Table 3. Total energy intake according to the NOVA food groups of individuals in the NWO (n 159) and non-NWO (n 65) groups
Values are mean ± sd.
%, percentage in relation to the total energy intake.
Student's t test or Wilcoxon test.
NWO, normal-weight obesity.
Bold values indicate significant differences between groups.
* Adapted from (Reference Monteiro, Cannon and Levy22,Reference Monteiro, Cannon and Levy23).
Next, we evaluated selected nutrients in relation to the overall diet. Differences between NWO and non-NWO individuals were found only for dietary fibre intake in which higher values were observed for the non-NWO group. When NWO and non-NWO groups were compared in relation to the ultra-processed food group no differences were observed for any nutrient. However, in the non-ultra-processed food group, NWO individuals exhibited lower consumption of energy, protein, carbohydrates, dietary fibre, calcium, iron, sodium and sugar compared with the non-NWO subjects (Table 4).
Table 4. Nutrient content of the overall diet and two NOVA groups of individuals in the NWO (n 159) and non-NWO (n 65) groups
Values are mean ± sd.
NWO, normal-weight obesity; UP, ultra-processed foods; non-UP, non-ultra-processed foods; SFA, saturated fatty acid; TFA, trans fatty acids.
Student's t test or Mann–Whitney test (P-value <0⋅05).
* Different from NWO group in overall diet.
¶ Different from UP in overall food groups.
† Different from NWO in non-UP food group.
In the final regression model, energy and added sugar from ultra-processed foods were associated with the presence of NWO. Each additional 1 kcal of energy consumption and 1 g of added sugar consumption was associated with a 1⋅02 odds ratio of belonging to the NWO group. Total fat from non-ultra-processed foods was associated with the presence of NWO, with each additional 1 g of total fat intake resulting in a 1⋅09 odds ratio of belonging to the NWO group. Additionally, negative associations were observed between consumption of carbohydrates and total fats from ultra-processed foods and NWO. Each additional 1 g of carbohydrate consumption was associated with a 0⋅93 odds ratio of belonging to the NWO group, and each additional 1 g of total fat consumption was associated with a 0⋅83 odds ratio of belonging to the NWO group (Table 5).
Table 5. Associations between independent variables and the presence of normal-weight obesity (n 224)
Logistic regression with estimate of odds ratio (OR) and the respective confidence interval (95 % CI), with presence/absence of NWO as the outcome. se, standard error; CI, confidence interval; UP, ultra-processed foods.
Variables included in the models were: NWO (yes/no) as the dependent variable; age (years), sex (male/female), energy (kcal) and nutrients [protein (g), carbohydrate (g), total sugars (g), added sugars (g), dietary fibre (g), fat (g), saturated fatty acids (g), trans fatty acids (g), cholesterol (mg), calcium (mg), iron (mg), sodium (mg)], and alcohol (g) from ultra-processed foods; energy and nutrients [(energy (kcal) and nutrients [protein (g), carbohydrate (g), total sugars (g), added sugars (g), dietary fibre (g), fat (g), saturated fatty acids (g), trans fatty acids (g), cholesterol (mg), calcium (mg), iron (mg), sodium (mg)], and alcohol (g) from non-ultra-processed foods, as independent variables.
Bold values indicate significant differences between groups.
Discussion
This is the first study to analyse food consumption according to the NOVA classification of adults with NWO in Brazil. The results suggest that individuals with NWO, despite presenting with lower energy intake compared with the non-NWO counterparts (P = 0⋅030), consumed less fresh food, such as rice, beans and fruits. In addition, a positive association between energy and added sugar intake from ultra-processed foods and a negative association between total fat intake from non-ultra-processed foods with the presence of NWO were observed. These results support the evidence that increased consumption of some dietary components widely found in ultra-processed foods may be associated with harmful outcomes resulting from excess body fat.
In an analysis of a historical series from ten cycles of the National Health and Nutrition Examination Survey (NHANES 1999–2018), US children and adolescents exhibited an increase (from 61⋅4 to 67⋅0 %) in consumption of total energy from ultra-processed foods and a reduction (from 28⋅0 to 23⋅5 %) in the intake of fresh or minimally processed foods(Reference Wang, Martínez Steele and Du25). Over the last decades, there has been a marked increase in the consumption of ultra-processed foods in several countries, regardless of socioeconomic status. Studies have suggested that this scenario significantly contributes to increased rates of cancer(Reference Fiolet, Srour and Sellem26), dyslipidaemias(Reference Mozaffarian, Hao and Rimm27) and obesity(Reference Rauber, Campagnolo and Hoffman28) worldwide.
As expected, our study found that ultra-processed foods had higher energy, carbohydrate, fat, sodium, total sugar and added sugars content, but lower protein, cholesterol and dietary fibre compared to non-ultra-processed foods. Although there was no significant difference in ultra-processed food consumption between the NWO and non-NWO groups, there was a positive association between energy and added sugar consumption from ultra-processed foods and the presence of NWO. This finding suggests that a higher intake of energy and added sugar from ultra-processed foods may increase the risk of developing NWO. It is important to note that ultra-processed foods are typically high in calories and added sugars and have been linked to various negative health outcomes. These findings highlight the need to reduce the consumption of ultra-processed foods, and to choose fresh and minimally processed foods instead. Furthermore, the fact that even small changes in energy and added sugar intake can increase the likelihood of developing NWO underscores the importance of making dietary changes to prevent this condition. These findings can have important implications for public health policies aimed at reducing the prevalence of NWO and related diseases.
Regular consumption of ultra-processed foods is associated with an increased risk of mortality from all causes, as well as with impaired intake of whole grains, fruits and vegetables, while consuming leafy vegetables, fruits and other vegetables is associated with lower body weight(Reference Rico-Campà, Martínez-González and Alvarez-Alvarez29–Reference Martínez Steele, Popkin and Swinburn31). Several mechanisms are involved in the relationship between high consumption of ultra-processed foods and excess adiposity. In general, ultra-processed foods have an ingredient profile considered to be obesogenic compared with fresh and minimally processed foods. These foods tend to be rich in energy, sugars, fats and sodium, while having low levels of dietary fibre, proteins and micronutrients due to the degree of processing(Reference Martínez Steele, Popkin and Swinburn31,Reference Monteiro, Cannon and Levy32) . Moreover, they contain chemical additives that can modify the gut microbiota and contribute to a pro-inflammatory state(Reference Zinöcker and Lindseth33,Reference Wahlqvist34) . Additionally, these foods are typically stored in plastic containers, which may contain bisphenol A associated with obesity development(Reference Heindel, Newbold and Schug35).
Research conducted in Brazil, Chile and Sweden has shown that the purchase of ultra-processed foods has increased significantly in comparison to non-ultra-processed foods(Reference Martins, Levy and Claro36–Reference Juul and Hemmingsson38). This trend is not unique to these countries but is rather a global phenomenon that warrants attention. It is crucial to study the impact of consuming ultra-processed foods on the health of individuals and populations, regardless of age and social class. For instance, a recent cohort study involving over 348 000 subjects aged 25 to 70 from nine European countries found that consuming ultra-processed foods was associated with weight gain (0⋅12 kg/5 years, 95 % CI 0⋅09–0⋅15)(Reference Cordova, Kliemann and Huybrechts39). These results highlight the need to prioritise the consumption of fresh and minimally processed foods to reduce the rates of obesity and other N-NCDs rates.
Previous studies have focused on specific aspects of the association between excess body fat and food consumption. For instance, a study of 4786 Finnish adults found that low consumption of meat and soft drinks was associated with a lower risk of NWO, while high alcohol and sugar intake, as well as low dietary fibre intake, were risk factors(Reference Männistö, Harald and Kontto40). Our study's findings are consistent with these results, as individuals with NWO in our sample showed lower dietary fibre intake and higher consumption of processed meats compared to non-NWO subjects. However, no study to our knowledge has evaluated the dietary intake of individuals with NWO according to the NOVA classification. Understanding how different types of processed foods contribute to NWO is crucial to designing effective interventions to prevent and manage this condition.
Our study has several strengths. First, the NOVA classification of foods, despite some criticism, has been widely recommended and it is an important tool for the study of nutrition and public health policies. In addition, the classification of foods into two large subgroups (ultra- and non-ultra-processed) enabled a better understanding of which foods are associated with NWO. Studying individuals with a condition that still requires further investigation (NWO) contributes to an analysis of the consequences of inadequate food consumption, primarily associated with body composition. Finally, the NOVA classification of all foods was carried out by a researcher and rigorously inspected by a second, thus reducing possible bias.
Regarding the study limitations, despite adequate statistical power for inference, the sample size of our study limits the generalisability of our results to other populations. The lack of consensus on cut-off points for NWO classification could also be a limitation, as the results may vary based on the selected %BF values. Nevertheless, we believe that using the cut-off values of >19 % for men and >30 % for women provide a more accurate representation of our studied population, and our findings are consistent with previous NWO research. Additionally, the use of a software based on US nutritional composition tables to assess food consumption in Brazil may introduce potential inaccuracies. However, we standardised the foods, particularly those typical of the local diet, to minimise distortions in the assessment of food consumption.
In conclusion, the overall consumption of ultra-processed foods did not differ between individuals with and without NWO. However, the NWO group consumed more processed meats and had a lower intake of energy and added sugars from non-ultra-processed foods than the non-NWO group. Additionally, significant associations were observed between nutrients from ultra-processed foods and the presence of NWO. While both groups had fresh and minimally processed foods as the basis of their diets, the non-NWO group consumed more non-ultra-processed foods than those with NWO. These findings underscore the importance of the Brazilian dietary guidelines’ golden rule, which advises against consuming ultra-processed foods and recommend making fresh and minimally processed foods the foundation of one's diet.
Acknowledgements
The authors would like to thank all the volunteers and the data collection team for all the help.
A. C. S: Writing – Original Draft, Investigation. A. F. F. P.: Writing – Original Draft, Investigation. L. B. S.: Conceptualisation, Methodology, Formal analysis; Writing – Review & Editing. A. S. G. C.: Conceptualisation, Methodology, Formal analysis; Writing – Review & Editing. C. C.: Conceptualisation; Writing – Original Draft, Writing – Review & Editing; Supervision. All authors read and approved the final version of the manuscript.
This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Master's (ACS and AFFP) students received scholarships from the Brazilian funding agencies ‘Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)’ and ‘Fundação de Amparo à Pesquisa do Estado de Goiás (FAPEG)’.
The authors have no conflict of interest to declare.
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