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

被引:35
作者
Huybrechts, Inge [1 ]
Rauber, Fernanda [2 ,3 ]
Nicolas, Genevieve [1 ]
Casagrande, Corinne [1 ]
Kliemann, Nathalie [1 ]
Wedekind, Roland [1 ]
Biessy, Carine [1 ]
Scalbert, Augustin [1 ]
Touvier, Mathilde [4 ]
Aleksandrova, Krasimira [5 ,6 ]
Jakszyn, Paula [7 ,8 ]
Skeie, Guri [9 ]
Bajracharya, Rashmita [10 ]
Boer, Jolanda M. A. [11 ]
Borne, Yan [12 ]
Chajes, Veronique [1 ]
Dahm, Christina C. [13 ]
Dansero, Lucia [14 ]
Guevara, Marcela [15 ,16 ,17 ]
Heath, Alicia K. [18 ]
Ibsen, Daniel B. [13 ,19 ,20 ,21 ]
Papier, Keren [22 ]
Katzke, Verena [10 ]
Kyro, Cecilie [23 ]
Masala, Giovanna [24 ]
Molina-Montes, Esther [25 ,26 ,27 ,28 ]
Robinson, Oliver J. K. [29 ]
de Pablos, Carmen Santiuste [26 ,30 ]
Schulze, Matthias B. [31 ,32 ]
Simeon, Vittorio [33 ]
Sonestedt, Emily [12 ]
Tjonneland, Anne [23 ]
Tumino, Rosario [34 ]
van der Schouw, Yvonne T. [35 ]
Verschuren, W. M. Monique [11 ,35 ]
Vozar, Beatrice [1 ]
Winkvist, Anna [36 ,37 ]
Gunter, Marc J. [1 ]
Monteiro, Carlos A. [3 ,38 ]
Millett, Christopher [2 ,39 ]
Levy, Renata Bertazzi [2 ,3 ]
机构
[1] Int Agcy Res Canc, Nutr & Metab Branch, Lyon, France
[2] Univ Sao Paulo, Sch Med, Dept Prevent Med, Sao Paulo, Brazil
[3] Univ Sao Paulo, Ctr Epidemiol Res Nutr & Hlth, Sao Paulo, Brazil
[4] Sorbonne Paris Nord Univ, Univ Paris Cite CRESS, Epidemiol & Stat Res Ctr,CNAM, Nutr Epidemiol Res Team EREN,INSERM U1153,INRAE U, Paris, France
[5] Leibniz Inst Prevent Res & Epidemiol BIPS, Dept Epidemiol Methods & Etiol Res, Bremen, Germany
[6] Univ Bremen, Human & Hlth Sci, Bremen, Germany
[7] Catalan Inst Oncol ICO IDIBELL, Unit Nutr & Canc, Canc Epidemiol Res Programme, Barcelona, Spain
[8] Ramon Llull Univ, Blanquerna Sch Hlth Sci, Barcelona, Spain
[9] UiT Arctic Univ Norway, Dept Community Med, Tromso, Norway
[10] German Canc Res Ctr, Heidelberg, Germany
[11] Natl Inst Publ Hlth & Environm RIVM, Ctr Nutr Prevent & Hlth Serv, Bilthoven, Netherlands
[12] Lund Univ, Fac Med, Dept Clin Sci Malmo, Nutr Epidemiol, Lund, Sweden
[13] Aarhus Univ, Dept Publ Hlth, Aarhus, Denmark
[14] Univ Turin, Ctr Biostat Publ Hlth C BEPH, Dept Clin & Biol Sci, Turin, Italy
[15] Inst Salud Publ Navarra, Pamplona, Spain
[16] Ctr Invest Biomed Red Epidemiol & Salud Publ CIBE, Madrid, Spain
[17] Navarra Inst Hlth Res IdiSNA, Pamplona, Spain
[18] Imperial Coll London, Sch Publ Hlth, Dept Epidemiol & Biostat, London, England
[19] Steno Diabet Ctr Aarhus, Aarhus, Denmark
[20] Univ Cambridge, MRC Epidemiol Unit, Sch Clin Med, Cambridge, England
[21] Univ Copenhagen, Dept Nutr Exercise & Sports, Frederiksberg, Denmark
[22] Univ Oxford, Nuffield Dept Populat Hlth, Canc Epidemiol Unit, Oxford, England
[23] Danish Canc Soc Res Ctr, Danish Canc Soc, Copenhagen, Denmark
[24] Inst Canc Res, Clin Epidemiol Unit, Prevent & Clin Network ISPRO, Florence, Italy
[25] Univ Granada, Dept Nutr & Food Sci, Campus Cartuja, Granada, Spain
[26] CIBER Epidemiol & Publ Hlth CIBERESP, Madrid, Spain
[27] Inst Invest Biosanitaria IbsGRANADA, Granada, Spain
[28] Univ Granada, Biomed Res Ctr, Inst Nutr & Food Technol INYTA Jose Mataix, Granada, Spain
[29] Imperial Coll London, MRC Ctr Environm & Hlth, Sch Publ Hlth, London, England
[30] IMIB Arrixaca, Dept Epidemiol, Murcia Reg Hlth Council, Murcia, Spain
[31] German Inst Human Nutr Potsdam Rehbrucke, Dept Mol Epidemiol, Nuthetal, Germany
[32] Univ Potsdam, Inst Nutr Sci, Potsdam, Germany
[33] Vanvitelli Univ, Dipartimento Salute Mentale & Fis & Med Prevent, Naples, Italy
[34] AIRE ONLUS, Hyblean Assoc Epidemiol Res, Ragusa, Italy
[35] Univ Utrecht, Univ Med Ctr Utrecht, Julius Ctr Hlth Sci & Primary Care, Utrecht, Netherlands
[36] Umea Univ, Dept Publ Hlth & Clin Med, Sustainable Hlth, Umea, Sweden
[37] Univ Gothenburg, Sahlgrenska Acad, Dept Internal Med & Clin Nutr, Gothenburg, Sweden
[38] Univ Sao Paulo, Sch Publ Hlth, Dept Nutr, Sao Paulo, Brazil
[39] Imperial Coll London, Sch Publ Hlth, Publ Hlth Policy Evaluat Unit, London, England
来源
FRONTIERS IN NUTRITION | 2022年 / 9卷
基金
英国医学研究理事会; 英国科研创新办公室;
关键词
food processing; Nova; EPIC; biomarkers; elaidic acid; syringol; CONSUMPTION; OBESITY; QUALITY; RISK; DIET; POPULATIONS; PROFILES; PATTERNS;
D O I
10.3389/fnut.2022.1035580
中图分类号
R15 [营养卫生、食品卫生]; TS201 [基础科学];
学科分类号
100403 ;
摘要
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.
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