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      Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé)

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          Abstract

          Objective

          To assess the prospective associations between consumption of ultra-processed foods and risk of cardiovascular diseases.

          Design

          Population based cohort study.

          Setting

          NutriNet-Santé cohort, France 2009-18.

          Participants

          105 159 participants aged at least 18 years. Dietary intakes were collected using repeated 24 hour dietary records (5.7 for each participant on average), designed to register participants’ usual consumption of 3300 food items. These foods were categorised using the NOVA classification according to degree of processing.

          Main outcome measures

          Associations between intake of ultra-processed food and overall risk of cardiovascular, coronary heart, and cerebrovascular diseases assessed by multivariable Cox proportional hazard models adjusted for known risk factors.

          Results

          During a median follow-up of 5.2 years, intake of ultra-processed food was associated with a higher risk of overall cardiovascular disease (1409 cases; hazard ratio for an absolute increment of 10 in the percentage of ultra-processed foods in the diet 1.12 (95% confidence interval 1.05 to 1.20); P<0.001, 518 208 person years, incidence rates in high consumers of ultra-processed foods (fourth quarter) 277 per 100 000 person years, and in low consumers (first quarter) 242 per 100 000 person years), coronary heart disease risk (665 cases; hazard ratio 1.13 (1.02 to 1.24); P=0.02, 520 319 person years, incidence rates 124 and 109 per 100 000 person years, in the high and low consumers, respectively), and cerebrovascular disease risk (829 cases; hazard ratio 1.11 (1.01 to 1.21); P=0.02, 520 023 person years, incidence rates 163 and 144 per 100 000 person years, in high and low consumers, respectively). These results remained statistically significant after adjustment for several markers of the nutritional quality of the diet (saturated fatty acids, sodium and sugar intakes, dietary fibre, or a healthy dietary pattern derived by principal component analysis) and after a large range of sensitivity analyses.

          Conclusions

          In this large observational prospective study, higher consumption of ultra-processed foods was associated with higher risks of cardiovascular, coronary heart, and cerebrovascular diseases. These results need to be confirmed in other populations and settings, and causality remains to be established. Various factors in processing, such as nutritional composition of the final product, additives, contact materials, and neoformed contaminants might play a role in these associations, and further studies are needed to understand better the relative contributions. Meanwhile, public health authorities in several countries have recently started to promote unprocessed or minimally processed foods and to recommend limiting the consumption of ultra-processed foods.

          Study registration

          ClinicalTrials.gov NCT03335644.

          Related collections

          Most cited references 35

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          Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations.

           Joel A Black (2000)
          To re-state the principles underlying the Goldberg cut-off for identifying under-reporters of energy intake, re-examine the physiological principles and update the values to be substituted into the equation for calculating the cut-off, and to examine its use and limitations. New values are suggested for each element of the Goldberg equation. The physical activity level (PAL) for comparison with energy intake:basal metabolic rate (EI:BMR) should be selected to reflect the population under study; the PAL value of 1.55 x BMR is not necessarily the value of choice. The suggested value for average within-subject variation in energy intake is 23% (unchanged), but other sources of variation are increased in the light of new data. For within-subject variation in measured and estimated BMR, 4% and 8.5% respectively are suggested (previously 2.5% and 8%), and for total between-subject variation in PAL, the suggested value is 15% (previously 12.5%). The effect of these changes is to widen the confidence limits and reduce the sensitivity of the cut-off. The Goldberg cut-off can be used to evaluate the mean population bias in reported energy intake, but information on the activity or lifestyle of the population is needed to choose a suitable PAL energy requirement for comparison. Sensitivity for identifying under-reporters at the individual level is limited. In epidemiological studies information on home, leisure and occupational activity is essential in order to assign subjects to low, medium or high PAL levels before calculating the cut-offs. In small studies, it is desirable to measure energy expenditure, or to calculate individual energy requirements, and to compare energy intake directly with energy expenditure.
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            Ultra-Processed Food Products and Obesity in Brazilian Households (2008–2009)

            Background Production and consumption of industrially processed food and drink products have risen in parallel with the global increase in overweight and obesity and related chronic non-communicable diseases. The objective of this study was to analyze the relationship between household availability of processed and ultra-processed products and the prevalence of excess weight (overweight plus obesity) and obesity in Brazil. Methods The study was based on data from the 2008–2009 Household Budget Survey involving a probabilistic sample of 55,970 Brazilian households. The units of study were household aggregates (strata), geographically and socioeconomically homogeneous. Multiple linear regression models were used to assess the relationship between the availability of processed and ultra-processed products and the average of Body Mass Index (BMI) and the percentage of individuals with excess weight and obesity in the strata, controlling for potential confounders (socio-demographic characteristics, percentage of expenditure on eating out of home, and dietary energy other than that provided by processed and ultra-processed products). Predictive values for prevalence of excess weight and obesity were estimated according to quartiles of the household availability of dietary energy from processed and ultra-processed products. Results The mean contribution of processed and ultra-processed products to total dietary energy availability ranged from 15.4% (lower quartile) to 39.4% (upper quartile). Adjusted linear regression coefficients indicated that household availability of ultra-processed products was positively associated with both the average BMI and the prevalence of excess weight and obesity, whereas processed products were not associated with these outcomes. In addition, people in the upper quartile of household consumption of ultra-processed products, compared with those in the lower quartile, were 37% more likely to be obese. Conclusion Greater household availability of ultra-processed food products in Brazil is positively and independently associated with higher prevalence of excess weight and obesity in all age groups in this cross-sectional study.
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              The Nutrinet-Santé Study: a web-based prospective study on the relationship between nutrition and health and determinants of dietary patterns and nutritional status

              Background Nutrition-related chronic diseases such as cardiovascular diseases and cancer are of multiple origin, and may be due to genetic, biologic, behavioural and environmental factors. In order to detangle the specific role of nutritional factors, very large population sample cohort studies comprising precisely measured dietary intake and all necessary information for accurately assessing potential confounding factors are needed. Widespread use of internet is an opportunity to gradually collect huge amounts of data from a large sample of volunteers that can be automatically verified and processed. The objectives of the NutriNet-Santé study are: 1) to investigate the relationship between nutrition (nutrients, foods, dietary patterns, physical activity), mortality and health outcomes; and 2) to examine the determinants of dietary patterns and nutritional status (sociological, economic, cultural, biological, cognitive, perceptions, preferences, etc.), using a web-based approach. Methods/design Our web-based prospective cohort study is being conducted for a scheduled follow-up of 10 years. Using a dedicated web site, recruitment will be carried out for 5 years so as to register 500 000 volunteers aged ≥ 18 years among whom 60% are expected to be included (having complete baseline data) and followed-up for at least 5 years for 240 000 participants. Questionnaires administered via internet at baseline and each year thereafter will assess socio-demographic and lifestyle characteristics, anthropometry, health status, physical activity and diet. Surveillance of health events will be implemented via questionnaires on hospitalisation and use of medication, and linkage with a national database on vital statistics. Biochemical samples and clinical examination will be collected in a subsample of volunteers. Discussion Self-administered data collection using internet as a complement to collection of biological data will enable identifying nutrition-related risks and protective factors, thereby more clearly elucidating determinants of nutritional status and their interactions. These are necessary steps for further refining nutritional recommendations aimed at improving the health status of populations.
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                Author and article information

                Contributors
                Role: PhD candidate in epidemiology
                Role: assistant professor of nutritional epidemiology
                Role: senior researcher in nutritional epidemiology
                Role: junior researcher in nutritional epidemiology
                Role: senior researcher in nutritional epidemiology
                Role: PhD candidate in epidemiology
                Role: PhD candidate in epidemiology
                Role: postdoctoral researcher in epidemiology
                Role: professor of nutrition and public health
                Role: senior researcher in nutritional epidemiology
                Role: professor of nutrition and public health
                Role: assistant professor of nutrition and public health
                Role: senior researcher in nutritional epidemiology, and head of the EREN Team
                Journal
                BMJ
                BMJ
                BMJ-UK
                bmj
                The BMJ
                BMJ Publishing Group Ltd.
                0959-8138
                1756-1833
                2019
                29 May 2019
                : 365
                Affiliations
                [1 ]Sorbonne Paris Cité Epidemiology and Statistics Research Center (CRESS), Inserm U1153, Inra U1125, Cnam, University of Paris 13, Nutritional Epidemiology Research Team (EREN), Bobigny, France
                [2 ]MOISA, University of Montpellier, INRA, CIRAD, CIHEAM-IAMM, Montpellier SupAgro, Montpellier, France
                [3 ]Public Health Department, Avicenne Hospital, AP-HP, Bobigny, France
                [4 ]Department of Nutrition, School of Public Health, University of São Paulo, São Paulo, Brazil
                Author notes
                Correspondence to: B Srour b.srour@ 123456eren.smbh.univ-paris13.fr (or @bernardsrour on Twitter)
                Article
                srob046135
                10.1136/bmj.l1451
                6538975
                31142457
                Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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