Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008-2016)

Fernanda Rauber; Eurídice Martínez Steele; Maria Laura da Costa Louzada; Christopher Millett; Carlos Augusto Monteiro; Renata Bertazzi Levy

doi:10.1371/journal.pone.0232676

Abstract

We examined the association between the consumption of ultra-processed foods and adiposity in a nationally representative sample of the UK adult population. We studied 6,143 participants (19 to 96 years, 51.6% female) sampled by the UK National Diet and Nutrition Survey (2008–16). Food items reported in four-day food diary were classified according to the NOVA system. Multiple linear and logistic regressions were used to evaluate associations between the dietary contribution of ultra-processed foods (sex-specific quartile and continuous) and Body Mass Index (BMI), Waist Circumference (WC) and obesity (BMI>30kg/m²) and abdominal obesity (men: WC≥102cm, women: WC≥88cm) status. Models were adjusted for sociodemographic and lifestyle characteristics. In multivariable analyses, the highest consumption of ultra-processed food was associated with 1.66 kg/m² higher BMI (95%CI 0.96–2.36), 3.56 cm greater WC (95%CI 1.79–5.33) and 90% higher odds for being obese (OR = 1.90, 95%CI 1.39–2.61), compared with the lowest consumption. A 10% increase in the consumption of ultra-processed foods was associated with an increase of 0.38 kg/m² in BMI (95%CI 0.20–0.55), 0.87 cm in WC (95%CI 0.40–1.33) and 18% higher odds of being obese (OR = 1.18, 95%CI 1.08–1.28). The consumption of ultra-processed food was associated with an increase in BMI, WC and prevalence of obesity in both sexes. A dose response relationship was observed in both sexes, with a 10% increase in the consumption of ultra-processed foods being associated with a 18% increase in the prevalence of obesity in men and a 17% increase in women. Higher consumption of ultra-processed food is associated with greater adiposity in the UK adult population. Policy makers should consider actions that promote consumption of unprocessed or minimally processed foods and reduce consumption of ultra-processed foods.

Citation: Rauber F, Steele EM, Louzada MLdC, Millett C, Monteiro CA, Levy RB (2020) Ultra-processed food consumption and indicators of obesity in the United Kingdom population (2008-2016). PLoS ONE 15(5): e0232676. https://doi.org/10.1371/journal.pone.0232676

Editor: David Meyre, McMaster University, CANADA

Received: December 19, 2019; Accepted: April 20, 2020; Published: May 1, 2020

Copyright: © 2020 Rauber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Computerized raw data files and documentation from this survey were obtained under license from the U.K. Data Archive (http://www.esds.ac.uk). Details of how food item classification was accomplished are further explained in previously published papers (Rauber et al, Nutrients. 2018 - see Supplementary Table S1 http://www.mdpi.com/2072-6643/10/5/587/s1).

Funding: This work was supported by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), grant numbers 2015/14900-9, 2016/14302-7 and 2018/19820-1 (FR is a beneficiary of a postdoctoral fellowship). FAPESP had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Increasing prevalence of obesity is driving greater chronic disease and mortality burdens globally [1]. In the United Kingdom (UK), more than a quarter of adults are obese [2]. Concomitantly, ultra-processed food production and consumption are steadily increasing worldwide [3,4]. These products have led to displacement of dietary patterns based on minimally processed foods and freshly prepared meals [5,6]. In a recent analysis of the global trend of ultra-processed foods, the UK had the third highest volume sales of ultra-processed foods per capita (140.7 kg/capita/year) when compared to 80 high- and middle-income countries [7]. Analyses of 2008–2014 national dietary survey reveal that ultra-processed foods already make up more than half of the total dietary energy consumed in the UK [8].

Ultra-processed foods are industrial formulations of food-derived substances (e.g. oils, fats, sugars, starch, protein isolates) that contain little if any whole food and often include ingredients not used in culinary preparations, such as flavourings, colourings, emulsifiers and other cosmetic additives [9]. These formulations are hyper-palatable, attractive, convenient, sold in large packages, and highly marketed [6,10]. Analyses of nationally representative studies conducted in the UK [8], the United States [11], Canada [12] and Brazil [13] have shown strong associations between consumption of ultra-processed foods and dietary nutrient profiles that predict increased risk of obesity and several other diet-related chronic diseases. Moreover, associations between ultra-processed food intake and incidence of diet-related NCDs [14–16], including obesity [17], have been identified in several large prospective cohort studies. A cross-over, randomized controlled trial of healthy adults has shown that diets high in ultra-processed foods increased energy intake, body weight and fat gain, compared with a control diet matched in calories, sugar, fibre, and macronutrients [18].

Cross-sectional studies also have found association between ultra-processed food consumption and obesity. An ecological study across 19 European countries, including UK, found a positive association between national household availability of ultra-processed foods and national prevalence of obesity among adults [19]. Nationally-representative individual-based studies have found cross-sectional associations between the dietary share of ultra-processed foods and obesity among adults in Brazil [20], the United States [21], and Canada [22]. One study using data from the cycles 1–4 (2008–2012) of UK National Diet and Nutrition Survey (NDNS) found no association with body weight but this may reflect that relative energy intake from ultra-processed and processed foods was grouped together [23], as previous research carried out in Brazil found no association between increased consumption of processed foods and obesity [24].

Given the very high levels of ultra-processed food consumption and obesity in the UK, this study used more recent data from the UK NDNS (2008–2016) to evaluate the association between ultra-processed food consumption and a wider set of obesity indicators than previously studied—overall and abdominal obesity, greater body mass index and waist circumference.

Methods

Study design and population

The data source for this study were the National Diet and Nutrition Survey Rolling Programme (NDNS), which is a continuous cross-sectional survey of dietary habits and nutrient intakes of a representative sample of people living in private households in the UK. The current analysis used data collected between February 2008 and August 2016 (years 1–8 combined).

The sample was drawn from a selection of postcodes across the UK. Participants completed a consecutive four-day food diary and participated in an interview to collect background data that included data on demographics, socio-economic characteristics and health behaviours. Participants who completed the food diary on at least 3 days were subsequently visited by a nurse who conducted anthropometric measurements and collected a blood sample. The details of this survey have been previous described [25].

For this study, we included all participants aged 19 years or above (n = 6,155; age range 19 to 96 years). We excluded individuals who had all days of food diary classified as outliers (n = 10). We defined extreme total energy intake outliers as values above the 99th or below the 1st percentiles [26], for each food diary day and age group. No pregnant and breastfeeding women were included. In total, 6,143 participants were eligible for inclusion in the analyses and more than 96% completed the four food diary days.

Datafiles for the present study were obtained under license from the UK Data Archive (http://www.esds.ac.uk). All relevant research ethics and governance committees approved the survey.

Dietary assessment

Participants were provided with written instructions and asked to record all foods and drinks consumed both at home and outside. Diary days were randomly selected to guarantee balanced representation of all days of the week. The diaries were coded using the in-house dietary assessment software, Diet In Nutrients Out (DINO), with nutrient values provided by the Department of Health’s Nutrient Databank, updated for each survey year [27,28].

We classified all recorded food items according to NOVA food classification system, which considers the physical, biological and chemical methods used during the food manufacturing process [9]. NOVA classifies foods into four groups: 1) unprocessed or minimally processed foods (e.g. fresh, dry or frozen fruits or vegetables; grains, flours and pasta; pasteurized or powder milk, plain yogurt, fresh or frozen meat); 2) processed culinary ingredients (e.g. table sugar, oils, butter, and salt); 3) processed foods (e.g. vegetables in brine, cheese, simple breads, fruits in syrup, canned fish); and 4) ultra-processed foods (e.g. soft drinks, sweet or savoury packaged snacks, confectionery; packaged breads and buns; reconstituted meat products and pre-prepared frozen or shelf-stable dishes)—the focus of this study. More information regarding the NOVA classification can be found elsewhere [9].

We classified foods by considering the NDNS variables ‘Food Number’ and ‘Subsidiary food groups’. When foods were judged to be homemade dishes, we applied the classification to the underlying ingredients in order to ensure more accurate classification. The NDNS database was provided with most food items systematically disaggregated into their individual components and the method adopted to disaggregate the food codes has been described in a previous paper [29]. Despite this, a few composite dishes were not disaggregated into constituent ingredients (less than 4%). In these cases, homemade dishes were categorised according to the main constituent ingredient. Details of how food item classification was undertaken are further explained in previously published papers [8,30].

We used the mean of all available days of food diary for each person to estimate the dietary contribution of ultra-processed foods (as a percentage of total energy intake).

Outcomes assessment

Body Mass Index (BMI) and Waist Circumference (WC) were treated as continuous variables and used to generate measures of obesity and abdominal obesity. Height and weight were measured using a portable stadiometer and weighing scales by trained fieldworkers. WC was measured at the midpoint between the iliac crest and the lower rib to the nearest 0.1 centimetre. BMI was calculated by dividing weight by height squared (kg/m²) and values of ≥30 kg/m² were classified as obesity according to World Health Organization criteria [31]. Abdominal obesity was defined as a WC ≥102 cm for men and ≥88 cm for women [32].

Covariates

Covariates included were age in years, sex, ethnicity (white, non white), region (England North, England Central/Midlands, England South (including London), Scotland, Wales, and Northern Ireland), social class (routine & manual occupations, intermediate occupations, lower managerial & professional occupations, and higher managerial & professional occupations), survey year (years 1–8), physical activity (h/day of moderate or vigorous physical activity), smoking status (yes or no), sleep duration (h/day), and following a diet to lose weight (yes or no). Multiple imputation by chained equations was used to attribute numerical values to physical activity (15.1%), sleep duration (3.4%), social class occupation (3.1%), ethnicity (0.1%), and smoking (0.05%). As predictive variables, we considered sex, age, region, survey year, obesity status and ultra-processed food consumption. The data were imputed ten times, and the results exhibited satisfactory statistical reproducibility according to Monte Carlo error analysis [33].

Statistical analysis

First, we estimated the distribution of total dietary energy intake according to NOVA food groups and subgroups. Then we examined how the mean energy share of ultra-processed foods and prevalence of obesity and high waist circumference (abdominal obesity) varied according to characteristics of the UK population. Differences in the exposure and outcomes by each individual characteristic were evaluated using bivariate linear regressions.

Multiple linear regression analysis was used to evaluate associations between the consumption of ultra-processed foods and BMI (kg/m²) and WC (cm), respectively. The normal probability plot of the residuals was constructed to verify the assumption of normality. Multiple logistic regression analysis was used to determine associations between the consumption of ultra-processed foods and the odds of obesity and abdominal obesity, respectively. We used the relative contribution of ultra-processed foods to total energy intake, both categorized into quartiles (using sex specific cut-offs) and continuous (10% increase in relative intake of ultra-processed foods), as the explanatory variables. For all outcomes, we first fitted a crude model and thereafter a multivariable model adjusted for 1) age, sex, ethnicity, region, survey year, and social class occupation, 2) also including physical activity, smoking, sleep duration and following a diet to lose weight. Test of linear trend was performed by treating quartile of ultra-processed food as an ordinal variable. Analyses were also performed in men and women separately, since previous studies had shown a potential difference between sexes in the association between ultra-processed food consumption and obesity [20,21].

Additional adjustment for total energy intake and consumption of fruits and vegetables were performed in all multivariable analyses.

NDNS study weights were used in all analyses to account for sampling and non-response error. All statistical analyses were carried out using Stata Statistical Software version 14. The p values reported were two-tailed, and a p value of <0.05 was considered statistically significant.

Results

The distribution of total daily energy intake according to NOVA groups and subgroups is presented in Table 1. UK adults aged 19 years or over consumed on average 7,631kj/day (1,823 kcal SE 11.6), 54.3% of which came from ultra-processed foods, 30.7% from unprocessed or minimally processed foods, 10.1% from processed foods, and 4.9% from culinary ingredients.

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Table 1. Distribution of total energy intake according to NOVA food groups.

UK population aged 19 years or over (2008–16).

https://doi.org/10.1371/journal.pone.0232676.t001

The consumption of ultra-processed foods (% of total energy) and the prevalence of obesity and abdominal obesity according to characteristics of the UK population are presented in Table 2. Ultra-processed food consumption was higher among men, those with white British ethnicity and smokers, but lower with increasing age and in higher social class groups. The prevalence of obesity was 26.4%, which increased with age and among those on a diet. Obesity prevalence was lower among higher social class groups and among those with higher physical activity and sleep duration levels. The prevalence of abdominal obesity was 40.7%, which was higher among woman, those with white British ethnicity and among those on a diet. Prevalence of abdominal obesity increased with age but was lower among higher social class groups and among those with higher physical activity and sleep duration levels.

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Table 2. Consumption of ultra-processed foods (% of total energy) and prevalence of obesity and abdominal obesity according to characteristics of the UK population aged 19 years or over (NDNS, 2008–16).

https://doi.org/10.1371/journal.pone.0232676.t002

Crude and adjusted analyses of the association between the dietary contribution of ultra-processed foods and adiposity are shown in Table 3. The consumption of ultra-processed foods ranged from ~35% (1st quartile) to ~74% (4th quartile) of total energy intake. The crude prevalence of obesity increased from 22.8% in the first to 31.6% in the fourth quartile of ultra-processed food consumption; while the crude prevalence of abdominal obesity was similar across quartiles (from 41.2% to 41.7%, respectively). In multivariable analyses, the highest consumption of ultra-processed food was associated with 1.66 kg/m² higher BMI (95%CI 0.96–2.36), 3.56 cm greater WC (95%CI 1.79–5.33) and 90% higher odds for being obese (OR = 1.90, 95%CI 1.39–2.61), compared with the lowest consumption. A significant linear trend was observed for the association between quartile of ultra-processed food consumption and the three outcomes (P<0.001). In the full adjusted model, a 10% increase in the consumption of ultra-processed foods was associated with an increase of 0.38 kg/m² in BMI (95%CI 0.20–0.55), 0.87 cm in WC (95%CI 0.40–1.33) and 18% in the odds of obesity (continuous OR = 1.18, 95%CI 1.08–1.28). No association was observed for odds of abdominal obesity.

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Table 3. Crude and adjusted analyses of the association between the dietary contribution of ultra-processed food and indicators of obesity among the UK population aged 19 years or over (NDNS, 2008─16).

https://doi.org/10.1371/journal.pone.0232676.t003

The analyses stratified by sex are shown in Table 4. In women, the crude prevalence of obesity increased from 21.8% in the first to 33.3% in the fourth quartile of ultra-processed food consumption; while the crude prevalence of abdominal obesity increased from 45.7% to 47.3%, respectively. In multivariable analyses, the highest consumption of ultra-processed food was associated with 1.81 kg/m² higher BMI (95%CI 0.81–2.81), 2.82 cm WC (95%CI 0.80–4.85) and double the odds for being obese (OR = 2.09, 95% CI 1.37–3.20), compared with the lowest consumption. In men, the crude prevalence of obesity increased from 24.2% in the first to 30.1% in the last quartile of ultra-processed food consumption; while the crude prevalence of abdominal obesity increased from 35.1% to 36.5%, respectively. In multivariable analyses, the highest consumption of ultra-processed food was associated with 1.35 kg/m² higher BMI (95%CI 0.42–2.26), 3.35 cm WC (95%CI 1.71–6.99), and 1.62 odds for being obese (95%CI 1.02–2.57). A significant linear trend was observed for the association between quartile of ultra-processed food consumption and the three outcomes (P<0.05) in women and men. In the full adjusted model, a 10% increase in the consumption of ultra-processed foods was associated with an increase in BMI, WC and prevalence of obesity in both sexes. No association was observed for odds of abdominal obesity in both sexes.

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Table 4. Crude and adjusted analyses of the association between the dietary contribution of ultra-processed food and indicators of obesity among the UK population aged 19 years or over, stratified by sex (NDNS, 2008–16).

https://doi.org/10.1371/journal.pone.0232676.t004

In the sensitivity analysis, additional adjustment for total energy intake and for consumption of fruits and vegetables had little effect on the magnitude of the associations (S1 Table).

Discussion

Findings from this nationally representative cross-sectional sample of UK adults show that higher consumption of ultra-processed food is associated with greater BMI, WC and odds of being obese. We identified a dose response relationship with a 10% increase in the consumption of ultra-processed foods being associated with a 18% and 17% increase in the prevalence of obesity in men and women, respectively.

Our results corroborate the findings from large, population-based cross-sectional and cohort studies that the consumption of ultra-processed foods is associated with obesity [16,20–22, 34], regardless of the contribution of these foods to the total diet (ranging from 25% in Brazil to 56% in the United States). Conversely, one study using data from the 2008–2012 UK NDNS found no association between relative energy intake from ultra-processed and moderately processed foods combined and body weight [23]. We point out that the food classification used by the mentioned study displays important differences with the one applied in our own that should be considered. More importantly, the group of processed foods, which include cheeses, vegetables and legumes preserved in brine, cured/canned meat and fish (but not sausage), were grouped together with ultra-processed foods. One analysis in Brazil showed that household purchase of ultra-processed foods was associated with greater prevalence of obesity, whereas processed foods were not [24]. Using ultra-processed foods as a specific group, rather than grouped with processed foods, may justify the difference in the findings from our study compared to other UK study [23].

Several mechanisms may explain the relationship between higher consumption of ultra-processed foods and obesity. Studies have shown strong associations between consumption of ultra-processed foods and dietary nutrient profiles known to increase the risk of several diet-related chronic diseases [8,11–13]. Diets based on these foods are energy-dense, high in free sugars, saturated and trans fats, and depleted in protein, fibre and most micronutrients [8,11–13]. A recent randomized controlled study of 20 healthy adults showed that diets high in ultra-processed foods increased dietary content of carbohydrates and fats, energy intake, body weight and fat gain, compared with a control diet matched in calories, sugar, fibre, and macronutrients [18].

These findings suggest that ultra-processed foods appear to promote overconsumption although the mechanism is not clear. Other characteristics related to the foods and/or the industrial processes, such as the greater deconstruction of the original food matrix and the cosmetic additives added to these products, have been associated with changes in the composition and metabolic behaviour of the gut microbiota that promote inflammatory diseases [35–38], with potential important implications for body weight and adiposity [39]. Moreover, ultra-processed foods are typically packaged in plastics, and several plasticizers such as bisphenol A have been shown to be associated with obesity [40,41].

The potential differences between sexes in the association between ultra-processed food consumption and obesity are supported by previous studies conducted in Brazil and the United States [20,21]. The reasons for the observed sex differences are unclear but may be related to the fact that women are more predisposed to adverse metabolic effects of rapidly digested, carbohydrate-rich foods than men [42]. Moreover, there are known gender difference in the type of ultra-processed foods consumed, with sweetened products more likely to be consumed among women (see Supporting Information–S2 Table). Therefore, this higher consumption of ultra-processed foods rich in sugars, in combination with the higher sensitivity to the hyper-glycaemic effects of these foods [43,44], might explain larger effects of ultra-processed foods on adiposity in women. Finally, the absence of a positive association between ultra-processed food consumption and abdominal obesity may be due to overall reduced statistical power consequence of dichotomizing a continuous outcome variable [45].

Our study has several strengths. We used the NOVA food classification system, which has been recognised as a valid tool for nutrition and public health research and policy [4, 46]. We also used the most updated version of NOVA that classifies processed foods and ultra-processed foods separately. In addition, we used data from the NDNS, a large and nationally representative sample of the UK population, increasing external validity of results. Notably, the disaggregation of dishes into underlying ingredients provided a more precise estimate of the dietary contributions of ultra-processed foods. Moreover, NDNS uses a high-quality dietary assessment method which provides detailed analysis of different foods consumed, which considered variations in consumption between different days of the week, as well as the seasonal variation.

Some limitations should be noted. Although food diaries are recognised to be one of the most comprehensive methods for assessing dietary intake, underreporting of some foods (particularly unhealthy foods) is a potential issue. Previous studies suggest that individuals with obesity may underreport consumption of foods with caloric sweeteners [47] such as desserts and sweet baked goods [48,49]. This social desirability bias may lead to underestimation of the dietary contribution of ultra-processed foods or dilution of the association between ultra-processed food consumption and adiposity. Notwithstanding, NDNS data were obtained through optimal methods for collecting dietary intake [50], which helped to reduce missing information. Though NDNS collects limited information indicating food processing (i.e., product brands and place of meals), for a small number of specific food items such as pizza there was insufficient information for classification purposes. In those cases, we choose the most commonly consumed alternative (culinary preparation or manufactured product) [8,30]. Finally, due to the cross-sectional nature of our data, reverse causality cannot be excluded. However, these findings are consistent with limited prospective data that reported consumption of ultra-processed foods and subsequent excess weight in two cohort studies [16, 35]. Due to the observational nature of our study, residual confounding cannot be ruled out.

The analyses presented here suggest that actions to reduce the consumption of ultra-processed foods could produce important public health benefits. Brazil [51], Uruguay [52] and Canada [53] have included the concept of ultra-processed foods in their food-based dietary guidelines. Recently, France set a goal of 20% reduction in consumption of ultra-processed foods by 2022 [54]. Actions to reduce consumption of some categories of ultra-processed foods has been also adopted in some countries. For instance, Mexico introduced fiscal measures, such as a tax on sugar-sweetened beverages and junk food, and studies already demonstrated a significant decline in the purchases of those products [55,56]. Voluntary agreements between industry and government have been shown repeatedly to be ineffective in improving public health [6]. This is confirmed by recent UK experience where the early stages of the government’s sugar reduction programme, which challenged the food industry to voluntarily cut sugar in some products, has produced only slow progress toward proposed targets [57]. Therefore, more radical measures that change the availability, price and marketing of products know to affect health is needed.

In conclusion, our findings suggest that ultra-processed foods may be contributing to the high rates of obesity in the UK and supports previous findings from Brazil, United States, Canada, and Spain. Several strategies should be employed to achieve necessary reductions in ultra-processed food consumption, including adequate food labelling, advertising regulation, and pricing policies. In addition, future dietary guidelines for the UK population should take food processing into account.

Supporting information

S1 Table. Crude and adjusted analyses of the association between the dietary contribution of ultra-processed food and indicators of obesity among the UK population aged 19 years or over, additional adjustment (NDNS, 2008–16).

https://doi.org/10.1371/journal.pone.0232676.s001

(DOC)

S2 Table. Distribution of type of ultra-processed foods according to sex.

UK population aged 19 years or over (2008–16).

https://doi.org/10.1371/journal.pone.0232676.s002

(DOC)

References

1. Global Burden of Disease Study 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017; 390:1423–1459.
- View Article
- Google Scholar
2. National Health Service. Government Statistical Service. Statistics on Obesity, Physical Activity and Diet—England, 2017: Report. London, UK: National Health Service, 2017. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/613532/obesphys-acti-diet-eng-2017-rep.pdf (accessed on 30 January 2018).
3. Stuckler D, McKee M, Ebrahim S, Basu S. Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PLoS Med. 2012; 9(6):e1001235. pmid:22745605
- View Article
- PubMed/NCBI
- Google Scholar
4. Pan American Health Organization. Ultra-processed Food and Drink Products in Latin America: Trends, Impact on Obesity, Policy Implications. Washington, DC: Pan American Health Organization, 2015.
5. Monteiro CA, Cannon G. The impact of transnational "big food" companies on the South: a view from Brazil. PLoS Med. 2012; 9(7):e1001252. pmid:22802732
- View Article
- PubMed/NCBI
- Google Scholar
6. Moodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T et al. Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet. 2013;381(9867):670–9. pmid:23410611
- View Article
- PubMed/NCBI
- Google Scholar
7. Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019;20(Suppl 2):10–19.
- View Article
- Google Scholar
8. Rauber F, da Costa Louzada ML, Steele EM, Millett C, Monteiro CA, Levy RB. Ultra-Processed Food Consumption and Chronic Non-Communicable Diseases-Related Dietary Nutrient Profile in the UK (2008–2014). Nutrients. 2018; 10(5). pii: E587. pmid:29747447
- View Article
- PubMed/NCBI
- Google Scholar
9. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019; 22(5):936–941. pmid:30744710
- View Article
- PubMed/NCBI
- Google Scholar
10. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA. 2011; 305(13):1352–1353. pmid:21467290
- View Article
- PubMed/NCBI
- Google Scholar
11. Martínez Steele E, Popkin BM, Swinburn B, Monteiro CA. The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Popul Health Metr. 2017; 15(1):6. pmid:28193285
- View Article
- PubMed/NCBI
- Google Scholar
12. Moubarac JC, Batal M, Louzada ML, Martinez Steele E, Monteiro CA. Consumption of ultra-processed foods predicts diet quality in Canada. Appetite. 2017; 108:512–520. pmid:27825941
- View Article
- PubMed/NCBI
- Google Scholar
13. Louzada MLDC, Ricardo CZ, Steele EM, Levy RB, Cannon G, Monteiro CA. The share of ultra-processed foods determines the overall nutritional quality of diets in Brazil. Public Health Nutr. 2018; 21(1):94–102. pmid:28714425
- View Article
- PubMed/NCBI
- Google Scholar
14. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ. 2019; 365:l1451. pmid:31142457
- View Article
- PubMed/NCBI
- Google Scholar
15. Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018; 360:k322. pmid:29444771
- View Article
- PubMed/NCBI
- Google Scholar
16. Mendonça RD, Pimenta AM, Gea A, de la Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes AC et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr. 2016; 104(5):1433–1440. pmid:27733404
- View Article
- PubMed/NCBI
- Google Scholar
17. Mendonça RD, Lopes AC, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-Processed Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The Seguimiento Universidad de Navarra Project. Am J Hypertens. 2017; 30(4):358–366. pmid:27927627
- View Article
- PubMed/NCBI
- Google Scholar
18. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019; 30(1):67–77.e3. pmid:31105044
- View Article
- PubMed/NCBI
- Google Scholar
19. Monteiro CA, Moubarac JC, Levy RB, Canella DS, Louzada MLDC, Cannon G. Household availability of ultra-processed foods and obesity in nineteen European countries. Public Health Nutr. 2018; 21(1):18–26. pmid:28714422
- View Article
- PubMed/NCBI
- Google Scholar
20. Louzada ML, Baraldi LG, Steele EM, Martins AP, Canella DS, Moubarac JC et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 2015; 81:9–15. pmid:26231112
- View Article
- PubMed/NCBI
- Google Scholar
21. Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excess weight among US adults. Br J Nutr. 2018; 120(1):90–100. pmid:29729673
- View Article
- PubMed/NCBI
- Google Scholar
22. Nardocci M, Leclerc BS, Louzada ML, Monteiro CA, Batal M, Moubarac JC. Consumption of ultra-processed foods and obesity in Canada. Can J Public Health. 2019; 110(1):4–14. pmid:30238324
- View Article
- PubMed/NCBI
- Google Scholar
23. Adams J, White M. Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008–12). Int J Behav Nutr Phys Act. 2015;12:160. pmid:26684833
- View Article
- PubMed/NCBI
- Google Scholar
24. Canella DS, Levy RB, Martins AP, Claro RM, Moubarac JC, Baraldi LG et al. Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS One. 2014; 9(3):e92752 pmid:24667658
- View Article
- PubMed/NCBI
- Google Scholar
25. Public Health England. National Diet and Nutrition Survey Results from Years 1, 2, 3 and 4 (Combined) of the Rolling Programme (2008/2009–2011/2012). London, UK: Public Health England, 2014.
26. Nielsen SJ, Adair L. An alternative to dietary data exclusions. J Am Diet Assoc 2007; 107(5):792–799. pmid:17467374
- View Article
- PubMed/NCBI
- Google Scholar
27. Fitt E, Cole D, Ziauddeen N, Pell D, Stickley E, Harvey A et al. DINO (Diet In Nutrients Out)—an integrated dietary assessment system. Public Health Nutr. 2015; 18(2):234–241. pmid:24674815
- View Article
- PubMed/NCBI
- Google Scholar
28. Public Health England. McCance and Widdowson’s the composition of foods integrated dataset 2015. London, UK: Public Health England, 2015.
29. Fitt E, Mak TN, Stephen AM, Prynne C, Roberts C, Swan G, et al. Disaggregating composite food codes in the UK National Diet and Nutrition Survey food composition databank. Eur J Clin Nutr 2010;64 Suppl 3:S32–36.
- View Article
- Google Scholar
30. Rauber F, Louzada MLDC, Martinez Steele E, Rezende LFM, Millett C, Monteiro CA et al. Ultra-processed foods and excessive free sugar intake in the UK: a nationally representative cross-sectional study. BMJ Open 2019; 9(10):e027546. pmid:31662351
- View Article
- PubMed/NCBI
- Google Scholar
31. World Health Organization. Body Mass Index (BMI) classifications. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (accessed on 30 January 2019).
32. World Health Organization. Waist circumference and waist–hip ratio: report of a WHO expert consultation. Geneva, Switzerland: World Health Organization, 2011.
33. White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011; 30(4):377–399. pmid:21225900
- View Article
- PubMed/NCBI
- Google Scholar
34. Canhada SL, Luft VC, Giatti L, Duncan BB, Chor D, Fonseca MJMD et al. Ultra-processed foods, incident overweight and obesity, and longitudinal changes in weight and waist circumference: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2019 Oct 17:1–11. pmid:31619309
- View Article
- PubMed/NCBI
- Google Scholar
35. Zinöcker MK, Lindseth IA. The Western Diet-Microbiome-Host Interaction and Its Role in Metabolic Disease. Nutrients. 2018; 10(3):pii: E365. pmid:29562591
- View Article
- PubMed/NCBI
- Google Scholar
36. Payne AN, Chassard C, Lacroix C. Gut microbial adaptation to dietary consumption of fructose, artificial sweeteners and sugar alcohols: implications for host–microbe interactions contributing to obesity. Obes Rev. 2012; 13(9):799–809. pmid:22686435
- View Article
- PubMed/NCBI
- Google Scholar
37. Pearlman M, Obert J, Casey L. The Association Between Artificial Sweeteners and Obesity. Curr Gastroenterol Rep. 2017; 19(12):64. pmid:29159583
- View Article
- PubMed/NCBI
- Google Scholar
38. Suez J, Korem T, Zeevi D, et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514(7521):181–6. pmid:25231862
- View Article
- PubMed/NCBI
- Google Scholar
39. Wahlqvist ML. Food structure is critical for optimal health. Food Funct. 2016; 7(3):1245–50. pmid:26667120
- View Article
- PubMed/NCBI
- Google Scholar
40. Thayer KA, Heindel JJ, Bucher JR, Gallo MA. Role of environmental chemicals in diabetes and obesity: a National Toxicology Program Workshop Review. Environ Health Perspect. 2012;120(6):779–789. pmid:22296744
- View Article
- PubMed/NCBI
- Google Scholar
41. Heindel JJ, Newbold R, Schug TT. Endocrine disruptors and obesity. Nat Rev Endocrinol. 2015; 11(11):653–661. pmid:26391979
- View Article
- PubMed/NCBI
- Google Scholar
42. Mirrahimi A, Chiavaroli L, Srichaikul K, Augustin LS, Sievenpiper JL, Kendall CW et al. The role of glycemic index and glycemic load in cardiovascular disease and its risk factors: a review of the recent literature. Curr Atheroscler Rep. 2016; 16 (1), 381.
- View Article
- Google Scholar
43. Fan J, Song Y, Wang Y, Hui R, Zhang W.Dietary glycemic index, glycemic load, and risk of coronary heart disease, stroke, and stroke mortality: a systematic review with meta-analysis. Plos One. 2012; 7(12): e52182. pmid:23284926
- View Article
- PubMed/NCBI
- Google Scholar
44. Youn S, Woo HD, Cho YA, Shin A, Chang N, Kim J. Association between dietary carbohydrate, glycemic index, glycemic load, and the prevalence of obesity in Korean men and women. Nutr Res. 2012; 32(3): 153–159. pmid:22464801
- View Article
- PubMed/NCBI
- Google Scholar
45. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006; 332(7549): 1080. pmid:16675816
- View Article
- PubMed/NCBI
- Google Scholar
46. Food and Agriculture Organization of the United Nations. Guidelines on the collection of information on food processing through food consumption surveys. Rome, Italy: Food and Agriculture Organization of the United Nations, 2015.
47. Bingham S, Luben R, Welch A, Tasevska N, Wareham N, Khaw KT. Epidemiologic assessment of sugars consumption using biomarkers: comparisons of obese and non-obese individuals in the European Prospective Investigation of Cancer Norfolk. Cancer Epidemiol Biomarkers Prev. 2007; 16(8):1651–4. pmid:17684141
- View Article
- PubMed/NCBI
- Google Scholar
48. Lafay L, Mennen L, Basdevant A, Charles MA, Borys JM, Eschwège E et al. Does energy intake underreporting involve all kinds of food or only specific food items? Results from the Fleurbaix Laventie Ville Sante (FLVS) study. Int J Obes Relat Metab Disord. 2000; 24:1500–6. pmid:11126348
- View Article
- PubMed/NCBI
- Google Scholar
49. Pryer JA, Vrijheid M, Nichols R, Kiggins M, Elliott P. Who are the ‘‘low energy reporters” in the dietary and nutritional survey of British adults? Int J Epidemiol. 1997; 26: 146–54. pmid:9126514
- View Article
- PubMed/NCBI
- Google Scholar
50. Public Health England. Dietary data collection and editing. In National Diet and Nutrition Survey. Results from years 1–4 (combined) of the Rolling Programme (2008/2009–2011/2012). London, UK: Public Health England, 2014. https://www.gov.uk/government/statistics/national-diet-and-nutrition-survey-results-from-years-1-to-4-combined-of-the-rolling-programme-for-2008-and-2009-to-2011-and-2012 (accessed on 15 January 2018).
51. Monteiro CA, Cannon G, Moubarac J-C, Martins APB, Martins CA, Garzillo J, et al. Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil. Public Health Nutr. 2015; 18(13):2311–22. pmid:26205679
- View Article
- PubMed/NCBI
- Google Scholar
52. Ministerio de Salud. Guia alimentaria para la poblacion uruguaya. Para una alimentacion saludable, compartida y placentera. Uruguay: Ministerio de Salud, 2016.
53. Minister of Health. Government of Canada. Canada’s food guide. Ottawa, Canada: Minister of Health, 2019.
54. Fiolet T. Quoi dans mon assiette: Objectifs du PNNS 2018–2022. 2018. Available from: https://quoidansmonassiette.fr/objectifs-pnns-2018-2022-pour-politique-nutritionnelle-et-sante-en-france/ (accessed on 30 January 2019).
- View Article
- Google Scholar
55. Batis C, Rivera JA, Popkin BM, Taillie LS. First-year evaluation of Mexico’s tax on nonessential energy-dense foods: an observational study. PLoS Med. 2016; 13(7):e1002057. pmid:27379797
- View Article
- PubMed/NCBI
- Google Scholar
56. Colchero MA, Popkin BM, Rivera JA, Ng SW. Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ. 2016; 352:h6704. pmid:26738745
- View Article
- PubMed/NCBI
- Google Scholar
57. Public Health England. First measure of industry progress to cut sugar unveiled [press release]. London, UK: Public Health England, 2018.

[ref1] 1. Global Burden of Disease Study 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017; 390:1423–1459.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. National Health Service. Government Statistical Service. Statistics on Obesity, Physical Activity and Diet—England, 2017: Report. London, UK: National Health Service, 2017. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/613532/obesphys-acti-diet-eng-2017-rep.pdf (accessed on 30 January 2018).

[ref3] 3. Stuckler D, McKee M, Ebrahim S, Basu S. Manufacturing epidemics: the role of global producers in increased consumption of unhealthy commodities including processed foods, alcohol, and tobacco. PLoS Med. 2012; 9(6):e1001235. pmid:22745605
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref4] 4. Pan American Health Organization. Ultra-processed Food and Drink Products in Latin America: Trends, Impact on Obesity, Policy Implications. Washington, DC: Pan American Health Organization, 2015.

[ref5] 5. Monteiro CA, Cannon G. The impact of transnational "big food" companies on the South: a view from Brazil. PLoS Med. 2012; 9(7):e1001252. pmid:22802732
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref6] 6. Moodie R, Stuckler D, Monteiro C, Sheron N, Neal B, Thamarangsi T et al. Profits and pandemics: prevention of harmful effects of tobacco, alcohol, and ultra-processed food and drink industries. Lancet. 2013;381(9867):670–9. pmid:23410611
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref7] 7. Vandevijvere S, Jaacks LM, Monteiro CA, Moubarac JC, Girling-Butcher M, Lee AC et al. Global trends in ultraprocessed food and drink product sales and their association with adult body mass index trajectories. Obes Rev. 2019;20(Suppl 2):10–19.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref8] 8. Rauber F, da Costa Louzada ML, Steele EM, Millett C, Monteiro CA, Levy RB. Ultra-Processed Food Consumption and Chronic Non-Communicable Diseases-Related Dietary Nutrient Profile in the UK (2008–2014). Nutrients. 2018; 10(5). pii: E587. pmid:29747447
View Article
PubMed/NCBI
Google Scholar

[22] View Article

[23] PubMed/NCBI

[24] Google Scholar

[ref9] 9. Monteiro CA, Cannon G, Levy RB, Moubarac JC, Louzada ML, Rauber F et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019; 22(5):936–941. pmid:30744710
View Article
PubMed/NCBI
Google Scholar

[26] View Article

[27] PubMed/NCBI

[28] Google Scholar

[ref10] 10. Ludwig DS. Technology, diet, and the burden of chronic disease. JAMA. 2011; 305(13):1352–1353. pmid:21467290
View Article
PubMed/NCBI
Google Scholar

[30] View Article

[31] PubMed/NCBI

[32] Google Scholar

[ref11] 11. Martínez Steele E, Popkin BM, Swinburn B, Monteiro CA. The share of ultra-processed foods and the overall nutritional quality of diets in the US: evidence from a nationally representative cross-sectional study. Popul Health Metr. 2017; 15(1):6. pmid:28193285
View Article
PubMed/NCBI
Google Scholar

[34] View Article

[35] PubMed/NCBI

[36] Google Scholar

[ref12] 12. Moubarac JC, Batal M, Louzada ML, Martinez Steele E, Monteiro CA. Consumption of ultra-processed foods predicts diet quality in Canada. Appetite. 2017; 108:512–520. pmid:27825941
View Article
PubMed/NCBI
Google Scholar

[38] View Article

[39] PubMed/NCBI

[40] Google Scholar

[ref13] 13. Louzada MLDC, Ricardo CZ, Steele EM, Levy RB, Cannon G, Monteiro CA. The share of ultra-processed foods determines the overall nutritional quality of diets in Brazil. Public Health Nutr. 2018; 21(1):94–102. pmid:28714425
View Article
PubMed/NCBI
Google Scholar

[42] View Article

[43] PubMed/NCBI

[44] Google Scholar

[ref14] 14. Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, Andrianasolo RM et al. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). BMJ. 2019; 365:l1451. pmid:31142457
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref15] 15. Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, Méjean C et al. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. BMJ. 2018; 360:k322. pmid:29444771
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref16] 16. Mendonça RD, Pimenta AM, Gea A, de la Fuente-Arrillaga C, Martinez-Gonzalez MA, Lopes AC et al. Ultraprocessed food consumption and risk of overweight and obesity: the University of Navarra Follow-Up (SUN) cohort study. Am J Clin Nutr. 2016; 104(5):1433–1440. pmid:27733404
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref17] 17. Mendonça RD, Lopes AC, Pimenta AM, Gea A, Martinez-Gonzalez MA, Bes-Rastrollo M. Ultra-Processed Food Consumption and the Incidence of Hypertension in a Mediterranean Cohort: The Seguimiento Universidad de Navarra Project. Am J Hypertens. 2017; 30(4):358–366. pmid:27927627
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref18] 18. Hall KD, Ayuketah A, Brychta R, Cai H, Cassimatis T, Chen KY et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019; 30(1):67–77.e3. pmid:31105044
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref19] 19. Monteiro CA, Moubarac JC, Levy RB, Canella DS, Louzada MLDC, Cannon G. Household availability of ultra-processed foods and obesity in nineteen European countries. Public Health Nutr. 2018; 21(1):18–26. pmid:28714422
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref20] 20. Louzada ML, Baraldi LG, Steele EM, Martins AP, Canella DS, Moubarac JC et al. Consumption of ultra-processed foods and obesity in Brazilian adolescents and adults. Prev Med. 2015; 81:9–15. pmid:26231112
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref21] 21. Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excess weight among US adults. Br J Nutr. 2018; 120(1):90–100. pmid:29729673
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref22] 22. Nardocci M, Leclerc BS, Louzada ML, Monteiro CA, Batal M, Moubarac JC. Consumption of ultra-processed foods and obesity in Canada. Can J Public Health. 2019; 110(1):4–14. pmid:30238324
View Article
PubMed/NCBI
Google Scholar

[78] View Article

[79] PubMed/NCBI

[80] Google Scholar

[ref23] 23. Adams J, White M. Characterisation of UK diets according to degree of food processing and associations with socio-demographics and obesity: cross-sectional analysis of UK National Diet and Nutrition Survey (2008–12). Int J Behav Nutr Phys Act. 2015;12:160. pmid:26684833
View Article
PubMed/NCBI
Google Scholar

[82] View Article

[83] PubMed/NCBI

[84] Google Scholar

[ref24] 24. Canella DS, Levy RB, Martins AP, Claro RM, Moubarac JC, Baraldi LG et al. Ultra-processed food products and obesity in Brazilian households (2008–2009). PLoS One. 2014; 9(3):e92752 pmid:24667658
View Article
PubMed/NCBI
Google Scholar

[86] View Article

[87] PubMed/NCBI

[88] Google Scholar

[ref25] 25. Public Health England. National Diet and Nutrition Survey Results from Years 1, 2, 3 and 4 (Combined) of the Rolling Programme (2008/2009–2011/2012). London, UK: Public Health England, 2014.

[ref26] 26. Nielsen SJ, Adair L. An alternative to dietary data exclusions. J Am Diet Assoc 2007; 107(5):792–799. pmid:17467374
View Article
PubMed/NCBI
Google Scholar

[91] View Article

[92] PubMed/NCBI

[93] Google Scholar

[ref27] 27. Fitt E, Cole D, Ziauddeen N, Pell D, Stickley E, Harvey A et al. DINO (Diet In Nutrients Out)—an integrated dietary assessment system. Public Health Nutr. 2015; 18(2):234–241. pmid:24674815
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref28] 28. Public Health England. McCance and Widdowson’s the composition of foods integrated dataset 2015. London, UK: Public Health England, 2015.

[ref29] 29. Fitt E, Mak TN, Stephen AM, Prynne C, Roberts C, Swan G, et al. Disaggregating composite food codes in the UK National Diet and Nutrition Survey food composition databank. Eur J Clin Nutr 2010;64 Suppl 3:S32–36.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref30] 30. Rauber F, Louzada MLDC, Martinez Steele E, Rezende LFM, Millett C, Monteiro CA et al. Ultra-processed foods and excessive free sugar intake in the UK: a nationally representative cross-sectional study. BMJ Open 2019; 9(10):e027546. pmid:31662351
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref31] 31. World Health Organization. Body Mass Index (BMI) classifications. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (accessed on 30 January 2019).

[ref32] 32. World Health Organization. Waist circumference and waist–hip ratio: report of a WHO expert consultation. Geneva, Switzerland: World Health Organization, 2011.

[ref33] 33. White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011; 30(4):377–399. pmid:21225900
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref34] 34. Canhada SL, Luft VC, Giatti L, Duncan BB, Chor D, Fonseca MJMD et al. Ultra-processed foods, incident overweight and obesity, and longitudinal changes in weight and waist circumference: the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Public Health Nutr. 2019 Oct 17:1–11. pmid:31619309
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref35] 35. Zinöcker MK, Lindseth IA. The Western Diet-Microbiome-Host Interaction and Its Role in Metabolic Disease. Nutrients. 2018; 10(3):pii: E365. pmid:29562591
View Article
PubMed/NCBI
Google Scholar

[117] View Article

[118] PubMed/NCBI

[119] Google Scholar

[ref36] 36. Payne AN, Chassard C, Lacroix C. Gut microbial adaptation to dietary consumption of fructose, artificial sweeteners and sugar alcohols: implications for host–microbe interactions contributing to obesity. Obes Rev. 2012; 13(9):799–809. pmid:22686435
View Article
PubMed/NCBI
Google Scholar

[121] View Article

[122] PubMed/NCBI

[123] Google Scholar

[ref37] 37. Pearlman M, Obert J, Casey L. The Association Between Artificial Sweeteners and Obesity. Curr Gastroenterol Rep. 2017; 19(12):64. pmid:29159583
View Article
PubMed/NCBI
Google Scholar

[125] View Article

[126] PubMed/NCBI

[127] Google Scholar

[ref38] 38. Suez J, Korem T, Zeevi D, et al. Artificial sweeteners induce glucose intolerance by altering the gut microbiota. Nature. 2014; 514(7521):181–6. pmid:25231862
View Article
PubMed/NCBI
Google Scholar

[129] View Article

[130] PubMed/NCBI

[131] Google Scholar

[ref39] 39. Wahlqvist ML. Food structure is critical for optimal health. Food Funct. 2016; 7(3):1245–50. pmid:26667120
View Article
PubMed/NCBI
Google Scholar

[133] View Article

[134] PubMed/NCBI

[135] Google Scholar

[ref40] 40. Thayer KA, Heindel JJ, Bucher JR, Gallo MA. Role of environmental chemicals in diabetes and obesity: a National Toxicology Program Workshop Review. Environ Health Perspect. 2012;120(6):779–789. pmid:22296744
View Article
PubMed/NCBI
Google Scholar

[137] View Article

[138] PubMed/NCBI

[139] Google Scholar

[ref41] 41. Heindel JJ, Newbold R, Schug TT. Endocrine disruptors and obesity. Nat Rev Endocrinol. 2015; 11(11):653–661. pmid:26391979
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref42] 42. Mirrahimi A, Chiavaroli L, Srichaikul K, Augustin LS, Sievenpiper JL, Kendall CW et al. The role of glycemic index and glycemic load in cardiovascular disease and its risk factors: a review of the recent literature. Curr Atheroscler Rep. 2016; 16 (1), 381.
View Article
Google Scholar

[145] View Article

[146] Google Scholar

[ref43] 43. Fan J, Song Y, Wang Y, Hui R, Zhang W.Dietary glycemic index, glycemic load, and risk of coronary heart disease, stroke, and stroke mortality: a systematic review with meta-analysis. Plos One. 2012; 7(12): e52182. pmid:23284926
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref44] 44. Youn S, Woo HD, Cho YA, Shin A, Chang N, Kim J. Association between dietary carbohydrate, glycemic index, glycemic load, and the prevalence of obesity in Korean men and women. Nutr Res. 2012; 32(3): 153–159. pmid:22464801
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref45] 45. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ. 2006; 332(7549): 1080. pmid:16675816
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref46] 46. Food and Agriculture Organization of the United Nations. Guidelines on the collection of information on food processing through food consumption surveys. Rome, Italy: Food and Agriculture Organization of the United Nations, 2015.

[ref47] 47. Bingham S, Luben R, Welch A, Tasevska N, Wareham N, Khaw KT. Epidemiologic assessment of sugars consumption using biomarkers: comparisons of obese and non-obese individuals in the European Prospective Investigation of Cancer Norfolk. Cancer Epidemiol Biomarkers Prev. 2007; 16(8):1651–4. pmid:17684141
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref48] 48. Lafay L, Mennen L, Basdevant A, Charles MA, Borys JM, Eschwège E et al. Does energy intake underreporting involve all kinds of food or only specific food items? Results from the Fleurbaix Laventie Ville Sante (FLVS) study. Int J Obes Relat Metab Disord. 2000; 24:1500–6. pmid:11126348
View Article
PubMed/NCBI
Google Scholar

[165] View Article

[166] PubMed/NCBI

[167] Google Scholar

[ref49] 49. Pryer JA, Vrijheid M, Nichols R, Kiggins M, Elliott P. Who are the ‘‘low energy reporters” in the dietary and nutritional survey of British adults? Int J Epidemiol. 1997; 26: 146–54. pmid:9126514
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref50] 50. Public Health England. Dietary data collection and editing. In National Diet and Nutrition Survey. Results from years 1–4 (combined) of the Rolling Programme (2008/2009–2011/2012). London, UK: Public Health England, 2014. https://www.gov.uk/government/statistics/national-diet-and-nutrition-survey-results-from-years-1-to-4-combined-of-the-rolling-programme-for-2008-and-2009-to-2011-and-2012 (accessed on 15 January 2018).

[ref51] 51. Monteiro CA, Cannon G, Moubarac J-C, Martins APB, Martins CA, Garzillo J, et al. Dietary guidelines to nourish humanity and the planet in the twenty-first century. A blueprint from Brazil. Public Health Nutr. 2015; 18(13):2311–22. pmid:26205679
View Article
PubMed/NCBI
Google Scholar

[174] View Article

[175] PubMed/NCBI

[176] Google Scholar

[ref52] 52. Ministerio de Salud. Guia alimentaria para la poblacion uruguaya. Para una alimentacion saludable, compartida y placentera. Uruguay: Ministerio de Salud, 2016.

[ref53] 53. Minister of Health. Government of Canada. Canada’s food guide. Ottawa, Canada: Minister of Health, 2019.

[ref54] 54. Fiolet T. Quoi dans mon assiette: Objectifs du PNNS 2018–2022. 2018. Available from: https://quoidansmonassiette.fr/objectifs-pnns-2018-2022-pour-politique-nutritionnelle-et-sante-en-france/ (accessed on 30 January 2019).
View Article
Google Scholar

[180] View Article

[181] Google Scholar

[ref55] 55. Batis C, Rivera JA, Popkin BM, Taillie LS. First-year evaluation of Mexico’s tax on nonessential energy-dense foods: an observational study. PLoS Med. 2016; 13(7):e1002057. pmid:27379797
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref56] 56. Colchero MA, Popkin BM, Rivera JA, Ng SW. Beverage purchases from stores in Mexico under the excise tax on sugar sweetened beverages: observational study. BMJ. 2016; 352:h6704. pmid:26738745
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref57] 57. Public Health England. First measure of industry progress to cut sugar unveiled [press release]. London, UK: Public Health England, 2018.

Figures

Abstract

Introduction

Methods

Study design and population

Dietary assessment

Outcomes assessment

Covariates

Statistical analysis

Results

Discussion

Supporting information

S1 Table. Crude and adjusted analyses of the association between the dietary contribution of ultra-processed food and indicators of obesity among the UK population aged 19 years or over, additional adjustment (NDNS, 2008–16).

S2 Table. Distribution of type of ultra-processed foods according to sex.

References