Metabolically Defined Body Size Phenotypes and Risk of Endometrial Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Background: Obesity is a risk factor for endometrial cancer but whether metabolic dysfunction is associated with endometrial cancer independent of body size is not known. Methods: The association of metabolically defined body size phenotypes with endometrial cancer risk was investigated in a nested case–control study (817 cases/ 817 controls) within the European Prospective Investigation into Cancer and Nutrition (EPIC). Concentrations of C-peptide were used to define metabolically healthy (MH; <1st tertile) and metabolically unhealthy (MU; ≥1st tertile) status among the control participants. These metabolic health definitions were combined with normal weight (NW); body mass index (BMI)<25 kg/m2 or waist circumference (WC)<80 cm or waist-to-hip ratio (WHR)<0.8) and overweight (OW; BMI≥25 kg/m2 or WC≥80 cm or WHR≥0.8) status, generating four phenotype groups for each anthropometric measure: (i) MH/NW, (ii) MH/OW, (iii) MU/ NW, and (iv) MU/OW. Results: In a multivariable-adjusted conditional logistic regression model, compared withMH/NWindividuals, endometrial cancer risk was higher among those classified as MU/NW [ORWC, 1.48; 95% confidence interval (CI), 1.05–2.10 and ORWHR, 1.68; 95% CI, 1.21– 2.35] and MU/OW (ORBMI, 2.38; 95% CI, 1.73–3.27; ORWC, 2.69; 95% CI, 1.92–3.77 and ORWHR, 1.83; 95% CI, 1.32–2.54). MH/OW individuals were also at increased endometrial cancer risk compared with MH/NW individuals (ORWC, 1.94; 95% CI, 1.24–3.04). Conclusions: Women with metabolic dysfunction appear to have higher risk of endometrial cancer regardless of their body size. However, OW status raises endometrial cancer risk even among women with lower insulin levels, suggesting that obesityrelated pathways are relevant for the development of this cancer beyond insulin. Impact: Classifying women by metabolic health may be of greater utility in identifying those at higher risk for endometrial cancer than anthropometry per se.World Health OrganizationDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College LondonDanish Cancer SocietyLigue nationale contre le cancerInstitut Gustave RoussyMutuelle Generale de l'Education NationaleInstitut National de la Sante et de la Recherche Medicale (Inserm)Deutsche KrebshilfeHelmholtz AssociationGerman Institute of Human Nutrition PotsdamRehbruecke (DIfE)Federal Ministry of Education & Research (BMBF)Fondazione AIRC per la ricerca sul cancroCompagnia di San PaoloConsiglio Nazionale delle Ricerche (CNR)Dutch Ministry of Public Health, Welfare and Sports (VWS)Netherlands Cancer Registry (NKR)LK Research FundsDutch Prevention Funds Netherlands Organization for Scientific Research (NWO)World Cancer Research Fund (WCRF-ERC) 232997Netherlands GovernmentHealth Research Fund (FIS)-Instituto de Salud Carlos III (ISCIII)Junta de AndaluciaPrincipality of AsturiasBasque GovernmentRegional Government of MurciaRegional Government of NavarraCatalan Institute of OncologyICO (Spain)Swedish Cancer Society Swedish Research CouncilEuropean CommissionCounty Council of SkaneCounty Council of Vasterbotten (Sweden)Cancer Research UK 14136 C8221/A29017 C19335/A21351UK Research & Innovation (UKRI)Medical Research Council UK (MRC)European Commission 1000143 MR/M012190/

from global food systems to individual exposures and mechanisms

Funding Information: This work was supported by Cancer Research UK [Ref: C33493/A29678] and World Cancer Research Fund International [Ref: IIG_FULL_2020_033]. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.Ultra-processed foods (UPFs) have become increasingly dominant globally, contributing to as much as 60% of total daily energy intake in some settings. Epidemiological evidence suggests this worldwide shift in food processing may partly be responsible for the global obesity epidemic and chronic disease burden. However, prospective studies examining the association between UPF consumption and cancer outcomes are limited. Available evidence suggests that UPFs may increase cancer risk via their obesogenic properties as well as through exposure to potentially carcinogenic compounds such as certain food additives and neoformed processing contaminants. We identify priority areas for future research and policy implications, including improved understanding of the potential dual harms of UPFs on the environment and cancer risk. The prevention of cancers related to the consumption of UPFs could be tackled using different strategies, including behaviour change interventions among consumers as well as bolder public health policies needed to improve food environments.publishersversionpublishe

Is the serving size and household measure information on labels clear and standardized? Analysis of the labels of processed foods sold in Brazil

This study aimed to analyze the household measures reported on labels of processed foods, taking into account their adequacy with the type of food and their relationship with the declared serving size. We analyzed the labels of processed foods for sale in a Brazilian supermarket. Serving sizes were assessed according to the parameters of the Brazilian law, and household measures were assessed according to the term used. A chi-square heterogeneity test was performed, and a p value of &lt;0.05 was considered indicative of statistical significance. We analyzed 1,102 processed foods and found that 72% declared the exact reference serving size prescribed by the Brazilian law. We found inappropriate household measures with regard to the way foods are customarily consumed (e.g., 2 ½ cookies) as well as subjective (e.g., 2 pieces) or incomplete (e.g., 1 spoon) measure terms. Household measures expressed as fractions were greater among products with measures that referred to the product’s total weight (e.g., ½ package) and with serving sizes that complied with the Brazilian law (p &lt; 0.001). Therefore, the serving size and household measure information on the labels of Brazilian processed foods are neither appropriate nor standardized. Consequently, this could complicate consumers’ understanding and use of this information.Titulo PT: As informações sobre porção e medida caseira nos rótulos são claras e padronizadas? Uma análise em rótulos de alimentos industrializados brasileirosEsta pesquisa objetivou analisar as medidas caseiras declaradas nos rótulos de alimentos industrializados, considerando sua adequação ao tipo do alimento e à porção declarada no rótulo. Foram analisados os rótulos de alimentos industrializados à venda em um supermercado brasileiro. As porções foram avaliadas conforme os parâmetros definidos pela Legislação Brasileira de Rotulagem Nutricional de Alimentos e as medidas caseiras foram avaliadas conforme o termo utilizado. Foi realizado Teste de Qui quadrado de heterogeneidade, sendo considerado valor-p &lt; 0,05 como indicativo de significância estatística. Foram analisados 1102 alimentos industrializados, desses 72% declararam a porção de referência definida pela legislação brasileira. Encontrou-se medidas caseiras inadequadas à forma de consumo do alimento (2½ biscoitos doces), com termos de mensuração subjetivos (2 pedaços) e incompletos (1 colher). O fracionamento da medida caseira foi estatisticamente maior entre produtos com a medida caseira referente ao peso total (1/2 pacote) e com porção adequada à legislação brasileira (p &lt; 0.001). Portanto, as informações sobre porção e medida caseira nos rótulos de produtos industrializados brasileiros não são precisas nem padronizadas. Como consequência, podem gerar dificuldade no entendimento e no uso dessas informações pelo consumidor brasileiro

Metabolically-Defined Body Size Phenotypes and Risk of Endometrial Cancer in the European Prospective Investigation into Cancer and Nutrition (EPIC)

Background: Obesity is a risk factor for endometrial cancer but whether metabolic dysfunction is associated with endometrial cancer independent of body size is not known. Methods: The association of metabolically defined body size phenotypes with endometrial cancer risk was investigated in a nested case-control study (817 cases/ 817 controls) within the European Prospective Investigation into Cancer and Nutrition (EPIC). Concentrations of C-peptide were used to define metabolically healthy (MH; _1st tertile) status among the control participants. These metabolic health definitions were combined with normal weight (NW); body mass index (BMI)_25 kg/m2 or WC >_80 cm or WHR >_0. 8) status, generating four phenotype groups for each anthropometric measure: (i) MH/NW, (ii) MH/OW, (iii) MU/ NW, and (iv) MU/OW. Results: In a multivariable-adjusted conditional logistic regression model, compared with MH/NW individuals, endometrial cancer risk was higher among those classified as MU/NW [ORWC, 1.48; 95% confidence interval (CI), 1.05-2.10 and ORWHR, 1.68; 95% CI, 1.21- 2.35] and MU/OW (ORBMI, 2.38; 95% CI, 1.73-3.27; ORWC, 2.69; 95% CI, 1.92-3.77 and ORWHR, 1.83; 95% CI, 1.32-2.54). MH/OW individuals were also at increased endometrial cancer risk compared with MH/NW individuals (ORWC, 1.94; 95% CI, 1.24-3.04). Conclusions: Women with metabolic dysfunction appear to have higher risk of endometrial cancer regardless of their body size. However, OW status raises endometrial cancer risk even among women with lower insulin levels, suggesting that obesity related pathways are relevant for the development of this cancer beyond insulin. Impact: Classifying women by metabolic health may be of greater utility in identifying those at higher risk for endometrial cancer than anthropometry per se

results from the prospective EPIC cohort study

Funding Information: This work was supported by Cancer Research UK (C33493/A29678), World Cancer Research Fund International (IIG_FULL_2020_033), and the Institut National du Cancer (INCa number 2021–138). The coordination of EPIC is financially supported by the IARC and the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the UK National Institute for Health and Care Research Imperial Biomedical Research Centre. The national cohorts are supported by the Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM; France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF; Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC–Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund, Statistics Netherlands (Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology (ICO; Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); and Cancer Research UK (14136 to EPIC–Norfolk; C8221/A29017 to EPIC–Oxford) and Medical Research Council (1000143 to EPIC–Norfolk; MR/M012190/1 to EPIC–Oxford; UK). Where authors are identified as personnel of the International Agency for Research on Cancer or WHO, they are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer or WHO. Publisher Copyright: © 2023 World Health Organization UPDATE NOTICE Correction to Lancet Planet Health 2023; 7: e219–32. The Lancet Planetary Health. 2023;7(5):e357. Scopus ID: 85158098931Background: Food processing has been hypothesised to play a role in cancer development; however, data from large-scale epidemiological studies are scarce. This study investigated the association between dietary intake according to amount of food processing and risk of cancer at 25 anatomical sites using data from the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Methods: This study used data from the prospective EPIC cohort study, which recruited participants between March 18, 1991, and July 2, 2001, from 23 centres in ten European countries. Participant eligibility within each cohort was based on geographical or administrative boundaries. Participants were excluded if they had a cancer diagnosis before recruitment, had missing information for the NOVA food processing classification, or were within the top and bottom 1% for ratio of energy intake to energy requirement. Validated dietary questionnaires were used to obtain information on food and drink consumption. Participants with cancer were identified using cancer registries or during follow-up from a combination of sources, including cancer and pathology centres, health insurance records, and active follow-up of participants. We performed a substitution analysis to assess the effect of replacing 10% of processed foods and ultra-processed foods with 10% of minimally processed foods on cancer risk at 25 anatomical sites using Cox proportional hazard models. Findings: 521 324 participants were recruited into EPIC, and 450 111 were included in this analysis (318 686 [70·8%] participants were female individuals and 131 425 [29·2%] were male individuals). In a multivariate model adjusted for sex, smoking, education, physical activity, height, and diabetes, a substitution of 10% of processed foods with an equal amount of minimally processed foods was associated with reduced risk of overall cancer (hazard ratio 0·96, 95% CI 0·95–0·97), head and neck cancers (0·80, 0·75–0·85), oesophageal squamous cell carcinoma (0·57, 0·51–0·64), colon cancer (0·88, 0·85–0·92), rectal cancer (0·90, 0·85–0·94), hepatocellular carcinoma (0·77, 0·68–0·87), and postmenopausal breast cancer (0·93, 0·90–0·97). The substitution of 10% of ultra-processed foods with 10% of minimally processed foods was associated with a reduced risk of head and neck cancers (0·80, 0·74–0·88), colon cancer (0·93, 0·89–0·97), and hepatocellular carcinoma (0·73, 0·62–0·86). Most of these associations remained significant when models were additionally adjusted for BMI, alcohol and dietary intake, and quality. Interpretation: This study suggests that the replacement of processed and ultra-processed foods and drinks with an equal amount of minimally processed foods might reduce the risk of various cancer types. Funding: Cancer Research UK, l'Institut National du Cancer, and World Cancer Research Fund International.publishersversionpublishersversionpublishe

Characterization of the degree of food processing in the European Prospective Investigation into Cancer and Nutrition: Application of the Nova classification and validation using selected biomarkers of food processing

Background: Epidemiological studies have demonstrated an association between the degree of food processing in our diet and the risk of various chronic diseases. Much of this evidence is based on the international Nova classification system, which classifies food into four groups based on the type of processing: (1) Unprocessed and minimally processed foods, (2) Processed culinary ingredients, (3) Processed foods, and (4) “Ultra-processed” foods (UPF). The ability of the Nova classification to accurately characterise the degree of food processing across consumption patterns in various European populations has not been investigated so far. Therefore, we applied the Nova coding to data from the European Prospective Investigation into Cancer and Nutrition (EPIC) in order to characterize the degree of food processing in our diet across European populations with diverse cultural and socio-economic backgrounds and to validate this Nova classification through comparison with objective biomarker measurements. Methods: After grouping foods in the EPIC dataset according to the Nova classification, a total of 476,768 participants in the EPIC cohort (71.5% women; mean age 51 [standard deviation (SD) 9.93]; median age 52 [percentile (p)25– p75: 58–66] years) were included in the cross-sectional analysis that characterised consumption patterns based on the Nova classification. The consumption of food products classified as different Nova categories were compared to relevant circulating biomarkers denoting food processing, measured in various subsamples (N between 417 and 9,460) within the EPIC cohort via (partial) correlation analyses (unadjusted and adjusted by sex, age, BMI and country). These biomarkers included an industrial transfatty acid (ITFA) isomer (elaidic acid; exogenous fatty acid generated during oil hydrogenation and heating) and urinary 4-methyl syringol sulfate (an indicator for the consumption of smoked food and a component of liquid smoke used in UPF). Results: Contributions of UPF intake to the overall diet in % grams/day varied across countries from 7% (France) to 23% (Norway) and their contributions to overall % energy intake from 16% (Spain and Italy) to >45% (in the UK and Norway). Differences were also found between sociodemographic groups; participants in the highest fourth of UPF consumption tended to be younger, taller, less educated, current smokers, more physically active, have a higher reported intake of energy and lower reported intake of alcohol. The UPF pattern as defined based on the Nova classification (group 4;% kcal/day) was positively associated with blood levels of industrial elaidic acid (r = 0.54) and 4-methyl syringol sulfate (r = 0.43). Associations for the other 3 Nova groups with these food processing biomarkers were either inverse or non-significant (e.g., for unprocessed and minimally processed foods these correlations were –0.07 and –0.37 for elaidic acid and 4-methyl syringol sulfate, respectively). Conclusion: These results, based on a large pan-European cohort, demonstrate sociodemographic and geographical differences in the consumption of UPF. Furthermore, these results suggest that the Nova classification can accurately capture consumption of UPF, reflected by stronger correlations with circulating levels of industrial elaidic acid and a syringol metabolite compared to diets high in minimally processed foods

Predicted basal metabolic rate and cancer risk in the European Prospective Investigation into Cancer and Nutrition

Emerging evidence suggests that a metabolic profile associated with obesity may be a more relevant risk factor for some cancers than adiposity per se. Basal metabolic rate (BMR) is an indicator of overall body metabolism and may be a proxy for the impact of a specific metabolic profile on cancer risk. Therefore, we investigated the association of predicted BMR with incidence of 13 obesity-related cancers in the European Prospective Investigation into Cancer and Nutrition (EPIC). BMR at baseline was calculated using the WHO/FAO/UNU equations and the relationships between BMR and cancer risk were investigated using multivariable Cox proportional hazards regression models. A total of 141,295 men and 317,613 women, with a mean follow-up of 14 years were included in the analysis. Overall, higher BMR was associated with a greater risk for most cancers that have been linked with obesity. However, among normal weight participants, higher BMR was associated with elevated risks of esophageal adenocarcinoma (hazard ratio per 1-standard deviation change in BMR [HR1-SD]: 2.46; 95% CI 1.20; 5.03) and distal colon cancer (HR1-SD: 1.33; 95% CI 1.001; 1.77) among men and with proximal colon (HR1-SD: 1.16; 95% CI 1.01; 1.35), pancreatic (HR1-SD: 1.37; 95% CI 1.13; 1.66), thyroid (HR1-SD: 1.65; 95% CI 1.33; 2.05), postmenopausal breast (HR1-SD: 1.17; 95% CI 1.11; 1.22) and endometrial (HR1-SD: 1.20; 95% CI 1.03; 1.40) cancers in women. These results indicate that higher BMR may be an indicator of a metabolic phenotype associated with risk of certain cancer types, and may be a useful predictor of cancer risk independent of body fatness

Characterization of the degree of food processing in the European Prospective Investigation into Cancer and Nutrition: Application of the Nova classification and validation using selected biomarkers of food processing

Background: Epidemiological studies have demonstrated an association between the degree of food processing in our diet and the risk of various chronic diseases. Much of this evidence is based on the international Nova classification system, which classifies food into four groups based on the type of processing: (1) Unprocessed and minimally processed foods, (2) Processed culinary ingredients, (3) Processed foods, and (4) Ultra-processed foods (UPF). The ability of the Nova classification to accurately characterise the degree of food processing across consumption patterns in various European populations has not been investigated so far. Therefore, we applied the Nova coding to data from the European Prospective Investigation into Cancer and Nutrition (EPIC) in order to characterize the degree of food processing in our diet across European populations with diverse cultural and socio-economic backgrounds and to validate this Nova classification through comparison with objective biomarker measurements. Methods: After grouping foods in the EPIC dataset according to the Nova classification, a total of 476,768 participants in the EPIC cohort (71.5% women; mean age 51 [standard deviation (SD) 9.93]; median age 52 [percentile (p)25-p75: 58-66] years) were included in the cross-sectional analysis that characterised consumption patterns based on the Nova classification. The consumption of food products classified as different Nova categories were compared to relevant circulating biomarkers denoting food processing, measured in various subsamples (N between 417 and 9,460) within the EPIC cohort via (partial) correlation analyses (unadjusted and adjusted by sex, age, BMI and country). These biomarkers included an industrial transfatty acid (ITFA) isomer (elaidic acid; exogenous fatty acid generated during oil hydrogenation and heating) and urinary 4-methyl syringol sulfate (an indicator for the consumption of smoked food and a component of liquid smoke used in UPF). Results: Contributions of UPF intake to the overall diet in % grams/day varied across countries from 7% (France) to 23% (Norway) and their contributions to overall % energy intake from 16% (Spain and Italy) to >45% (in the UK and Norway). Differences were also found between sociodemographic groups; participants in the highest fourth of UPF consumption tended to be younger, taller, less educated, current smokers, more physically active, have a higher reported intake of energy and lower reported intake of alcohol. The UPF pattern as defined based on the Nova classification (group 4;% kcal/day) was positively associated with blood levels of industrial elaidic acid (r = 0.54) and 4-methyl syringol sulfate (r = 0.43). Associations for the other 3 Nova groups with these food processing biomarkers were either inverse or non-significant (e.g., for unprocessed and minimally processed foods these correlations were -0.07 and -0.37 for elaidic acid and 4-methyl syringol sulfate, respectively). Conclusion: These results, based on a large pan-European cohort, demonstrate sociodemographic and geographical differences in the consumption of UPF. Furthermore, these results suggest that the Nova classification can accurately capture consumption of UPF, reflected by stronger correlations with circulating levels of industrial elaidic acid and a syringol metabolite compared to diets high in minimally processed foods