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AbstractObjectiveTo assess the prospective associations between consumption of ultra-processed food and risk of cancer.DesignPopulation based cohort study.Setting and participants104 980 participants aged at least 18 years (median age 42.8 years) from the French NutriNet-Santé cohort (2009-17). Dietary intakes were collected using repeated 24 hour dietary records, designed to register participants’ usual consumption for 3300 different food items. These were categorised according to their degree of processing by the NOVA classification.Main outcome measuresAssociations between ultra-processed food intake and risk of overall, breast, prostate, and colorectal cancer assessed by multivariable Cox proportional hazard models adjusted for known risk factors.ResultsUltra-processed food intake was associated with higher overall cancer risk (n=2228 cases; hazard ratio for a 10% increment in the proportion of ultra-processed food in the diet 1.12 (95% confidence interval 1.06 to 1.18); P for trend<0.001) and breast cancer risk (n=739 cases; hazard ratio 1.11 (1.02 to 1.22); P for trend=0.02). These results remained statistically significant after adjustment for several markers of the nutritional quality of the diet (lipid, sodium, and carbohydrate intakes and/or a Western pattern derived by principal component analysis).ConclusionsIn this large prospective study, a 10% increase in the proportion of ultra-processed foods in the diet was associated with a significant increase of greater than 10% in risks of overall and breast cancer. Further studies are needed to better understand the relative effect of the various dimensions of processing (nutritional composition, food additives, contact materials, and neoformed contaminants) in these associations.Study registrationClinicaltrials.gov NCT03335644.

Food additives (e.g. artificial sweeteners, emulsifiers, dyes, etc.) are ingested by billions of individuals daily. Some concerning results, mainly derived from animal and/or cell-based experimental studies, have recently emerged suggesting potential detrimental effects of several widely consumed additives. Profiles of additive exposure as well as the potential long-term impact of multiple exposure on human health are poorly documented. This work aimed to estimate the usual intake of food additives among participants of the French NutriNet-Santé cohort and to identify and describe profiles of exposure (single substances and mixtures). Overall, 106,489 adults from the French NutriNet-Santé cohort study (2009-ongoing) were included. Consumption of 90 main food additives was evaluated using repeated 24 h dietary records including information on brands of commercial products. Qualitative information (as presence/absence) of each additive in food products was determined using 3 large-scale composition databases (OQALI, Open Food Facts, GNPD), accounting for the date of consumption of the product. Quantitative ingested doses were estimated using a combination of laboratory assays on food matrixes (n = 2677) and data from EFSA and JECFA. Exposure was estimated in mg per kg of body weight per day. Profiles of exposure to food additive mixtures were extracted using Non-negative Matrix Factorization (NMF) followed by k-means clustering as well as Graphical Lasso. Sociodemographic and dietary comparison of clusters of participants was performed by Chi-square tests or linear regressions. Data were weighted according to the national census. Forty-eight additives were consumed by more than 10% of the participants, with modified starches and citric acid consumed by more than 90%. The top 50 also included several food additives for which potential adverse health effects have been suggested by recent experimental studies: lecithins (86.6% consumers), mono- and diglycerides of fatty acids (78.1%), carrageenan (77.5%), sodium nitrite (73.9%), di-, tri- and polyphosphates (70.1%), potassium sorbate (65.8%), potassium metabisulphite (44.8%), acesulfame K (34.0%), cochineal (33.9%), potassium nitrate (31.6%), sulfite ammonia caramel (28.8%), bixin (19.5%), monosodium glutamate (15.1%) and sucralose (13.5%). We identified and described five clusters of participants more specifically exposed to five distinct additive mixtures and one additional cluster gathering participants with overall low additive exposure. Food additives, including several for which health concerns are currently debated, were widely consumed in this population-based study. Furthermore, main mixtures of additives were identified. Their health impact and potential cocktail effects should be explored in future epidemiological and experimental studies.

With the growing scientific appeal of e-epidemiology, concerns arise regarding validity and reliability of Web-based self-reported data. The objectives of the present study were to assess the validity of Web-based self-reported weight, height, and resulting body mass index (BMI) compared with standardized clinical measurements and to evaluate the concordance between Web-based self-reported anthropometrics and face-to-face declarations. A total of 2513 participants of the NutriNet-Santé study in France completed a Web-based anthropometric questionnaire 3 days before a clinical examination (validation sample) of whom 815 participants also responded to a face-to-face anthropometric interview (concordance sample). Several indicators were computed to compare data: paired t test of the difference, intraclass correlation coefficient (ICC), and Bland-Altman limits of agreement for weight, height, and BMI as continuous variables; and kappa statistics and percent agreement for validity, sensitivity, and specificity of BMI categories (normal, overweight, obese). Compared with clinical data, validity was high with ICC ranging from 0.94 for height to 0.99 for weight. BMI classification was correct in 93% of cases; kappa was 0.89. Of 2513 participants, 23.5% were classified overweight (BMI≥25) with Web-based self-report vs 25.7% with measured data, leading to a sensitivity of 88% and a specificity of 99%. For obesity, 9.1% vs 10.7% were classified obese (BMI≥30), respectively, leading to sensitivity and specificity of 83% and 100%. However, the Web-based self-report exhibited slight underreporting of weight and overreporting of height leading to significant underreporting of BMI (P<.05) for both men and women: -0.32 kg/m² (SD 0.66) and -0.34 kg/m² (SD 1.67), respectively. Mean BMI underreporting was -0.16, -0.36, and -0.63 kg/m² in the normal, overweight, and obese categories, respectively. Almost perfect agreement (ie, concordance) was observed between Web-based and face-to-face report (ICC ranged from 0.96 to 1.00, classification agreement was 98.5%, and kappa 0.97). Web-based self-reported weight and height data from the NutriNet-Santé study can be considered as valid enough to be used when studying associations of nutritional factors with anthropometrics and health outcomes. Although self-reported anthropometrics are inherently prone to biases, the magnitude of such biases can be considered comparable to face-to-face interview. Web-based self-reported data appear to be an accurate and useful tool to assess anthropometric data.

Background To date, no consensus has emerged on the most appropriate front-of-pack (FOP) nutrition label to help consumers in making informed choices. We aimed to compare the effectiveness of the label formats currently in use: nutrient-specific, graded and simple summary systems, in a large sample of adults. Methods The FOP label effectiveness was assessed by measuring the label acceptability and understanding among 13,578 participants of the NutriNet-Sante cohort study, representative of the French adult population. Participants were exposed to five conditions, including four FOP labels: Guideline Daily Amounts (GDA), Multiple Traffic Lights (MTL), 5-Color Nutrition Label (5-CNL), Green Tick (Tick), and a \"no label\" condition. Acceptability was evaluated by several indicators: attractiveness, liking and perceived cognitive workload. Objective understanding was assessed by the percentage of correct answers when ranking three products according to their nutritional quality. Five different product categories were tested: prepared fish dishes, pizzas, dairy products, breakfast cereals, and appetizers. Differences among the label effectiveness were compared with chi-square tests. Results The 5-CNL was viewed as the easiest label to identify and as the one requiring the lowest amount of effort and time to understand. GDA was considered as the least easy to identify and to understand, despite being the most attractive and liked label. All FOP labels were found to be effective in ranking products according to their nutritional quality compared with the \"no label\" situation, although they showed differing levels of effectiveness (p<0.0001). Globally, the 5-CNL performed best, followed by MTL, GDA and Tick labels. Conclusions The graded 5-CNL label was considered as easy to identify, simple and rapid to understand; it performed well when comparing the products' nutritional quality. Therefore, it is likely to present advantages in real shopping situations where choices are usually made quickly.

PurposeWhile intensive modern food systems have significant unfavourable impacts on health and the environment, new sustainable food consumption trends have been emerging in recent years. This study identified recent dietary trends over a 4-year period in terms of overall dietary patterns and organic foods consumption and associated socio-demographic determinants.MethodsFood intakes were assessed among 18,108 participants of the NutriNet-Santé cohort in 2014 and 2018. A food frequency questionnaire was used to estimate organic and conventional food consumption. Change in food consumption, quality of the diet (assessed by the adherence to the French national guidelines), plant-based diet using published scores, organic food intake were evaluated in regard with various socio-demographic factors. The paired student t test to compare dietary intake and the Kruskal–Walllis test to consider socio-demographic characteristics were used.ResultsConsumption of meat and processed meat decreased respectively by 5.09 g/day (SD 51.15) and 1.12 g/day (SD 26.05). The average total consumption of organic products increased by 12% (+ 93 g/day) while consumption of organic fish and seafood (− 1.4 g/day), poultry (− 1 g/day), processed meat (− 0.3 g/day) and meat (− 3.3 g/day) decreased. The dietary towards healthier diets was more pronounced in certain population subgroups. For example, females, young individuals and postgraduate participants were more likely to increase their consumption of healthful plant and animal-based foods, organic foods and to improve the overall nutritional quality of their diets during the follow-up period than their counterparts.ConclusionOur results indicate a slight inflexion towards healthier and plant-based diets over a 4-year period at least in some segments of the population. A decrease in the consumption of animal products and an increase in the consumption of healthful plant-based foods and organic foods suggests a potential trend towards more sustainable diets among certain subgroups. The environmental impacts of these changes need to be assessed in further works as well as the way to sustain and improve them, in particular those who do not initiate sustainable transition.

This review summarises existing evidence on the impact of organic food on human health. It compares organic vs. conventional food production with respect to parameters important to human health and discusses the potential impact of organic management practices with an emphasis on EU conditions. Organic food consumption may reduce the risk of allergic disease and of overweight and obesity, but the evidence is not conclusive due to likely residual confounding, as consumers of organic food tend to have healthier lifestyles overall. However, animal experiments suggest that identically composed feed from organic or conventional production impacts in different ways on growth and development. In organic agriculture, the use of pesticides is restricted, while residues in conventional fruits and vegetables constitute the main source of human pesticide exposures. Epidemiological studies have reported adverse effects of certain pesticides on children’s cognitive development at current levels of exposure, but these data have so far not been applied in formal risk assessments of individual pesticides. Differences in the composition between organic and conventional crops are limited, such as a modestly higher content of phenolic compounds in organic fruit and vegetables, and likely also a lower content of cadmium in organic cereal crops. Organic dairy products, and perhaps also meats, have a higher content of omega-3 fatty acids compared to conventional products. However, these differences are likely of marginal nutritional significance. Of greater concern is the prevalent use of antibiotics in conventional animal production as a key driver of antibiotic resistance in society; antibiotic use is less intensive in organic production. Overall, this review emphasises several documented and likely human health benefits associated with organic food production, and application of such production methods is likely to be beneficial within conventional agriculture, e.g., in integrated pest management.

Ample evidence suggests positive effects of species diversity on ecosystem functioning and services in natural and agricultural landscapes. Less obvious and even contested are the effects of such diversity on human well‐being. This state of art partly stems from methodological difficulties to evaluate and quantify these effects and imprecise conceptual frameworks. Here we propose a conceptual framework that links different aspects of diversity, particularly species and genetic richness, to ecosystem functioning, ecosystem services and disservices, and different aspects of well‐being. We review current approaches for the study of diversity–well‐being relationships and identify shortcomings and principle obstacles, mainly stemming from theoretical premises that are too imprecise. We discuss five basic methodological approaches to link diversity to well‐being: matrix models, indirect inference, Price partitioning, structural equation modelling, and environmental inference. We call for a stricter terminology with respect to the different aspects of functioning, multifunctionality and well‐being and highlight the need to evaluate each step in the different pathways from diversity to well‐being. A full understanding of ecological constraints on human well‐being requires consideration of trade‐offs in diversity effects, of contrasting perceptions of well‐being, and of ecosystem disservices. We also call for appropriate long‐term socio‐ecological research platforms to gather relevant data about ecosystem functioning and well‐being across space and time. Read the free Plain Language Summary for this article on the Journal blog. Zusammenfassung Zahlreiche Befunde deuten auf eine positive Auswirkung der Artenvielfalt auf das Funktionieren und die Leistungen von Ökosystemen in Natur‐ und Agrarlandschaften hin. Weniger offensichtlich und sogar umstritten sind die Auswirkungen einer solchen Vielfalt auf das menschliche Wohlergehen. Diese Wissenslücke ist teilweise auf methodologische Schwierigkeiten bei der Bewertung und Quantifizierung dieser Effekte und auf ungenaue konzeptionelle Rahmenbedingungen zurückzuführen. Wir schlagen hier einen konzeptionellen Rahmen vor, der verschiedene Aspekte der Vielfalt, insbesondere des Arten‐ und genetischen Reichtums, mit Ökosystemfunktionen, Ökosystemleistungen und ‐dysfunktionen und verschiedenen Aspekten des Wohlergehens verbindet. Wir beleuchten aktuelle Ansätze zur Untersuchung von Beziehungen zwischen Vielfalt und Wohlergehen und identifizieren Mängel und prinzipielle Hindernisse, die hauptsächlich auf zu ungenauen theoretischen Prämissen beruhen. Wir diskutieren fünf grundlegende methodologische Ansätze, um Diversität mit Wohlergehen zu verknüpfen: Matrixmodelle, indirekte Inferenz, Price‐Partitionierung, Pfadanalysen und Umweltinferenz. Wir plädieren für eine strengere Terminologie in Bezug auf die verschiedenen Aspekte des Funktionierens, der Multifunktionalität und des Wohlergehens und betonen die Notwendigkeit, jeden Schritt auf den verschiedenen Pfaden von der ökologischen Vielfalt zum Wohlergehen zu bewerten. Ein vollständiges Verständnis der ökologischen Einschränkungen des menschlichen Wohlergehens erfordert die Berücksichtigung der Balance bei Diversitätseffekten, von gegensätzlichen Wahrnehmungen des Wohlergehens und von Ökosystemdysfunktionen. Wir fordern auch geeignete langfristige sozio‐ökologische Forschungsplattformen, um relevante Daten über das Funktionieren von Ökosystemen in Bezug auf das menschliche Wohlergehen in Raum und Zeit zu sammeln. Abstraktné Množstvo dôkazov poukazuje na pozitívny vplyv diverzity na fungovanie ekosystémov a ich úlohy v prírodnej a poľnohospodárskej krajine. Menej zrejmé a dokonca otázne sú ale účinky takejto diverzity na blahobyt ľudí. Tento stav čiastočne vyplýva z metodických ťažkostí pri posudzovaní týchto účinkov a nepresných koncepčných rámcov. V tejto práci navrhujeme koncepčný rámec, ktorý spája rôzne aspekty diverzity s fungovaním ekosystémov, jej úlohou, negatívnymi účinkami a blahobytom. Preskúmame súčasné prístupy k štúdiu vzťahov medzi diverzitou a blahobytom a identifikujeme nedostatky a zásadné prekážky, ktoré vyplývajú najmä z príliš nepresných teoretických východísk. V práci rozoberáme päť základných metodologických prístupov k spojeniu diverzity a blahobytu a to: maticové modely, nepriamu inferenciu, rozdelenie podľa ceny, modelovanie štrukturálnych rovníc a inferenciu podľa prostredia. Žiadame o presnejšiu terminológiu s ohľadom na rôzne aspekty fungovania, multifunkčnosti a blahobytu a zdôrazňujeme potrebu vyhodnotiť každý krok na jednotlivých cestách od diverzity k ľudskému blahobytu. Úplné pochopenie ekologických obmedzení ľudského blahobytu si vyžaduje, aby sa zohľadňovali kompromisy v účinkoch diverzity, kontrastného vnímania blahobytu a negatívnych účinkov ekosystémových služieb. Tiež žiadame o vytvorenie vhodných dlhodobých sociálno‐ekologických výskumných platforiem, ktoré by zhromažďovali relevantné údaje o fungovaní ekosystémov a o ľudskom blahobyte v priestore a čase. Abstrakt Wiele dowodów wskazuje na pozytywny wpływ różnorodności na funkcjonowanie ekosystemów i ich usługi w krajobrazach naturalnych i rolniczych. Mniej oczywiste, a nawet kwestionowane są skutki takiej różnorodności dla dobrostanu człowieka. Ten stan rzeczy wynika częściowo z trudności metodologicznych w ocenie tych skutków oraz nieprecyzyjnych ram koncepcyjnych. W tym miejscu proponujemy ramy koncepcyjne, które łączą różne aspekty różnorodności z funkcjonowaniem ekosystemu, usługami i nie‐zasługami ekosystemu oraz dobrostanem. Dokonujemy przeglądu obecnych podejść do badania relacji różnorodność ‐ dobrostan i identyfikujemy braki i zasadnicze przeszkody, wynikające głównie ze zbyt nieprecyzyjnych założeń teoretycznych. Omawiamy pięć podstawowych podejść metodologicznych do powiązania różnorodności z dobrostanem: modele macierzowe, wnioskowanie pośrednie, podział Price'a, modelowanie równań strukturalnych i wnioskowanie środowiskowe. Wzywamy do wprowadzenia bardziej rygorystycznej terminologii w odniesieniu do różnych aspektów funkcjonowania, wielofunkcyjności i dobrostanu oraz podkreślamy potrzebę oceny każdego kroku na różnych ścieżkach od różnorodności do dobrostanu. Pełne zrozumienie ekologicznych ograniczeń dobrostanu człowieka wymaga uwzględnienia kompromisów w zakresie skutków różnorodności, kontrastowego postrzegania dobrostanu oraz usług ekosystemów. Wzywamy również do stworzenia odpowiednich długoterminowych platform badawczych o charakterze społeczno‐ekologicznym w celu zebrania odpowiednich danych na temat funkcjonowania ekosystemów i dobrostanu w przestrzeni i czasie. Read the free Plain Language Summary for this article on the Journal blog.

Introduction: Because of its ease of collection, urine is one of the most commonly used matrices for metabolomics studies. However, unlike other biofluids, urine exhibits tremendous variability that can introduce confounding inconsistency during result interpretation. Despite many existing techniques to normalize urine samples, there is still no consensus on either which method is most appropriate or how to evaluate these methods.Objectives: To investigate the impact of several methods and combinations of methods conventionally used in urine metabolomics on the statistical discrimination of two groups in a simple metabolomics study.Methods: We applied 14 different strategies of normalization to forty urine samples analysed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). To evaluate the impact of these different strategies, we relied on the ability of each method to reduce confounding variability while retaining variability of interest, as well as the predictability of statistical models.Results: Among all tested normalization methods, osmolality-based normalization gave the best results. Moreover, we demonstrated that normalization using a specific dilution prior to the analysis outperformed post-acquisition normalization. We also demonstrated that the combination of various normalization methods does not necessarily improve statistical discrimination.Conclusions: This study re-emphasized the importance of normalizing urine samples for metabolomics studies. In addition, it appeared that the choice of method had a significant impact on result quality. Consequently, we suggest osmolality-based normalization as the best method for normalizing urine samples.

AbstractObjectiveTo assess the prospective associations between consumption of ultra-processed foods and risk of cardiovascular diseases.DesignPopulation based cohort study.SettingNutriNet-Santé cohort, France 2009-18.Participants105 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 measuresAssociations 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.ResultsDuring 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.ConclusionsIn 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 registrationClinicalTrials.gov NCT03335644.

Background The Nutri-Score summary graded front-of-pack nutrition label has been identified as an efficient tool to increase the nutritional quality of pre-packed food purchases. However, no study has been conducted to investigate the effect of the Nutri-Score on the shopping cart composition, considering the type of foods. The present paper aims to investigate the effect of the Nutri-Score on the type of food purchases, in terms of the relative contribution of unpacked and pre-packed foods, or the processing degree of foods. Methods Between September 2016 and April 2017, three consecutive randomized controlled trials were conducted in three specific populations – students ( N  = 1866), low-income individuals ( N  = 336) and subjects suffering from cardiometabolic diseases ( N  = 1180) – to investigate the effect of the Nutri-Score on purchasing intentions compared to the Reference Intakes and no label. Using these combined data, the proportion of unpacked products in the shopping carts, as well as the distribution of products across food categories taking into account the degree of processing (NOVA classification) were assessed by trials arm. Results The shopping carts of participants simulating purchases with the Nutri-Score affixed on pre-packed foods contained higher proportion of unpacked products – especially raw fruits and meats, i.e. with no FoPL –, compared to participants purchasing with no label (difference of 5.93 percentage points [3.88–7.99], p -value< 0.0001) or with the Reference Intakes (difference of 5.27[3.25–7.29], p-value< 0.0001). This higher proportion was partly explained by fewer purchases of pre-packed processed and ultra-processed products overall in the Nutri-Score group. Conclusions These findings provide new insights on the positive effect of the Nutri-Score, which appears to decrease purchases in processed products resulting in higher proportions of unprocessed and unpacked foods, in line with public health recommendations.