The present commentary contains a clear and simple guide designed to identify ultra-processed foods. It responds to the growing interest in ultra-processed foods among policy makers, academic researchers, health professionals, journalists and consumers concerned to devise policies, investigate dietary patterns, advise people, prepare media coverage, and when buying food and checking labels in shops or at home. Ultra-processed foods are defined within the NOVA classification system, which groups foods according to the extent and purpose of industrial processing. Processes enabling the manufacture of ultra-processed foods include the fractioning of whole foods into substances, chemical modifications of these substances, assembly of unmodified and modified food substances, frequent use of cosmetic additives and sophisticated packaging. Processes and ingredients used to manufacture ultra-processed foods are designed to create highly profitable (low-cost ingredients, long shelf-life, emphatic branding), convenient (ready-to-consume), hyper-palatable products liable to displace all other NOVA food groups, notably unprocessed or minimally processed foods. A practical way to identify an ultra-processed product is to check to see if its list of ingredients contains at least one item characteristic of the NOVA ultra-processed food group, which is to say, either food substances never or rarely used in kitchens (such as high-fructose corn syrup, hydrogenated or interesterified oils, and hydrolysed proteins), or classes of additives designed to make the final product palatable or more appealing (such as flavours, flavour enhancers, colours, emulsifiers, emulsifying salts, sweeteners, thickeners, and anti-foaming, bulking, carbonating, foaming, gelling and glazing agents).

We described the contribution of ultra-processed foods in the U.K. diet and its association with the overall dietary content of nutrients known to affect the risk of chronic non-communicable diseases (NCDs). Cross-sectional data from the U.K. National Diet and Nutrition Survey (2008–2014) were analysed. Food items collected using a four-day food diary were classified according to the NOVA system. The average energy intake was 1764 kcal/day, with 30.1% of calories coming from unprocessed or minimally processed foods, 4.2% from culinary ingredients, 8.8% from processed foods, and 56.8% from ultra-processed foods. As the ultra-processed food consumption increased, the dietary content of carbohydrates, free sugars, total fats, saturated fats, and sodium increased significantly while the content of protein, fibre, and potassium decreased. Increased ultra-processed food consumption had a remarkable effect on average content of free sugars, which increased from 9.9% to 15.4% of total energy from the first to the last quintile. The prevalence of people exceeding the upper limits recommended for free sugars and sodium increased by 85% and 55%, respectively, from the lowest to the highest ultra-processed food quintile. Decreasing the dietary share of ultra-processed foods may substantially improve the nutritional quality of diets and contribute to the prevention of diet-related NCDs.

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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 (sexspecific quartile and continuous) and Body Mass Index (BMI), Waist Circumference (WC) and obesity (BMI>30kg/m 2) 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/m 2 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/m 2 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.

Objective The objective of this study was to examine the associations between ultra-processed food consumption and risk of obesity among UK adults. Methods Participants aged 40–69 years at recruitment in the UK Biobank (2006–2019) with dietary intakes collected using 24-h recall and repeated measures of adiposity––body mass index (BMI), waist circumference (WC) and percentage of body fat (% BF)––were included (N = 22,659; median follow-up: 5 years). Ultra-processed foods were identified using the NOVA classification and their consumption was expressed as a percentage of total energy intake. Multivariable Cox proportional hazards regression models were used to estimate hazard ratios (HR) of several indicators of obesity according to ultra-processed food consumption. Models were adjusted for sociodemographic and lifestyle characteristics. Results 947 incident cases of overall obesity (BMI ≥ 30 kg/m2) and 1900 incident cases of abdominal obesity (men: WC ≥ 102 cm, women: WC ≥ 88 cm) were identified during follow-up. Participants in the highest quartile of ultra-processed food consumption had significantly higher risk of developing overall obesity (HR 1.79; 95% CI 1.06─3.03) and abdominal obesity (HR 1.30; 95% CI 1.14─1.48). They had higher risk of experiencing a ≥ 5% increase in BMI (HR 1.31; 95% CI 1.20─1.43), WC (HR 1.35; 95% CI 1.25─1.45) and %BF (HR 1.14; 95% CI 1.03─1.25), than those in the lowest quartile of consumption. Conclusions Our findings provide evidence that higher consumption of ultra-processed food is strongly associated with a higher risk of multiple indicators of obesity in the UK adult population. Policy makers should consider actions that promote consumption of fresh or minimally processed foods and reduce consumption of ultra-processed foods.

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OBJETIVO Descrever características da alimentação dos participantes da coorte NutriNet Brasil imediatamente antes e na vigência da pandemia de covid-19. MÉTODOS Os dados deste estudo provêm de coorte de adultos criada para investigar prospectivamente a relação entre alimentação e morbimortalidade por doenças crônicas não transmissíveis no Brasil. Para este estudo, foram selecionados os primeiros participantes (n = 10.116) que responderam por duas vezes a questionário simplificado sobre sua alimentação no dia anterior, a primeira vez ao ingressar no estudo, entre 26 de janeiro e 15 de fevereiro de 2020, e a segunda entre 10 e 19 de maio de 2020. O questionário indaga sobre o consumo de marcadores de alimentação saudável (hortaliças, frutas e leguminosas) e não saudável (alimentos ultraprocessados). Comparações de indicadores baseados no consumo desses marcadores antes e na vigência da pandemia são apresentadas para o conjunto da população estudada e segundo sexo, faixa etária, macrorregião de residência e escolaridade. Testes qui-quadrado e testes t foram utilizados para comparar proporções e médias, respectivamente, adotando-se p < 0,05 para identificar diferenças significantes. RESULTADOS Para o conjunto dos participantes, identificou-se aumento modesto, porém estatisticamente significante, no consumo de marcadores de alimentação saudável e estabilidade no consumo de marcadores de alimentação não saudável. Esse padrão favorável de mudanças na alimentação com a pandemia se repetiu na maior parte dos estratos sociodemográficos. Padrão menos favorável de mudanças, com tendência de aumento no consumo de marcadores de alimentação saudável e não saudável, foi observado nas macrorregiões Nordeste e Norte e entre pessoas com menor escolaridade, sugerindo desigualdades sociais na resposta à pandemia. CONCLUSÕES Caso confirmada, a tendência de aumento no consumo de alimentos ultraprocessados nas regiões economicamente menos desenvolvidas e por pessoas com menor escolaridade preocupa, pois a ingestão desses alimentos eleva o risco de obesidade, hipertensão e diabetes, cuja presença aumenta a gravidade e a letalidade da covid-19.