| 001 | 186551 | ||
| 005 | 20240229145749.0 | ||
| 024 | 7 | _ | |2 doi |a 10.3389/fnut.2022.1035580 |
| 024 | 7 | _ | |2 pmid |a pmid:36590209 |
| 024 | 7 | _ | |2 pmc |a pmc:PMC9800919 |
| 024 | 7 | _ | |a altmetric:140273742 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2023-00004 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 630 |
| 100 | 1 | _ | |a Huybrechts, Inge |b 0 |
| 245 | _ | _ | |a 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. |
| 260 | _ | _ | |a Lausanne |b Frontiers Media |c 2022 |
| 336 | 7 | _ | |2 DRIVER |a article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |b journal |m journal |s 1672673074_4387 |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 520 | _ | _ | |a 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.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).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).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. |
| 536 | _ | _ | |0 G:(DE-HGF)POF4-313 |a 313 - Krebsrisikofaktoren und Prävention (POF4-313) |c POF4-313 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |2 Other |a EPIC |
| 650 | _ | 7 | |2 Other |a Nova |
| 650 | _ | 7 | |2 Other |a biomarkers |
| 650 | _ | 7 | |2 Other |a elaidic acid |
| 650 | _ | 7 | |2 Other |a food processing |
| 650 | _ | 7 | |2 Other |a syringol |
| 700 | 1 | _ | |a Rauber, Fernanda |b 1 |
| 700 | 1 | _ | |a Nicolas, Geneviève |b 2 |
| 700 | 1 | _ | |a Casagrande, Corinne |b 3 |
| 700 | 1 | _ | |a Kliemann, Nathalie |b 4 |
| 700 | 1 | _ | |a Wedekind, Roland |b 5 |
| 700 | 1 | _ | |a Biessy, Carine |b 6 |
| 700 | 1 | _ | |a Scalbert, Augustin |b 7 |
| 700 | 1 | _ | |a Touvier, Mathilde |b 8 |
| 700 | 1 | _ | |a Aleksandrova, Krasimira |b 9 |
| 700 | 1 | _ | |a Jakszyn, Paula |b 10 |
| 700 | 1 | _ | |a Skeie, Guri |b 11 |
| 700 | 1 | _ | |0 P:(DE-He78)0b48ce513fe49013263657450a12f870 |a Bajracharya, Rashmita |b 12 |u dkfz |
| 700 | 1 | _ | |a Boer, Jolanda M A |b 13 |
| 700 | 1 | _ | |a Borné, Yan |b 14 |
| 700 | 1 | _ | |a Chajes, Veronique |b 15 |
| 700 | 1 | _ | |a Dahm, Christina C |b 16 |
| 700 | 1 | _ | |a Dansero, Lucia |b 17 |
| 700 | 1 | _ | |a Guevara, Marcela |b 18 |
| 700 | 1 | _ | |a Heath, Alicia K |b 19 |
| 700 | 1 | _ | |a Ibsen, Daniel B |b 20 |
| 700 | 1 | _ | |a Papier, Keren |b 21 |
| 700 | 1 | _ | |0 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4 |a Katzke, Verena |b 22 |u dkfz |
| 700 | 1 | _ | |a Kyrø, Cecilie |b 23 |
| 700 | 1 | _ | |a Masala, Giovanna |b 24 |
| 700 | 1 | _ | |a Molina-Montes, Esther |b 25 |
| 700 | 1 | _ | |a Robinson, Oliver J K |b 26 |
| 700 | 1 | _ | |a Santiuste de Pablos, Carmen |b 27 |
| 700 | 1 | _ | |a Schulze, Matthias B |b 28 |
| 700 | 1 | _ | |a Simeon, Vittorio |b 29 |
| 700 | 1 | _ | |a Sonestedt, Emily |b 30 |
| 700 | 1 | _ | |a Tjønneland, Anne |b 31 |
| 700 | 1 | _ | |a Tumino, Rosario |b 32 |
| 700 | 1 | _ | |a van der Schouw, Yvonne T |b 33 |
| 700 | 1 | _ | |a Verschuren, W M Monique |b 34 |
| 700 | 1 | _ | |a Vozar, Beatrice |b 35 |
| 700 | 1 | _ | |a Winkvist, Anna |b 36 |
| 700 | 1 | _ | |a Gunter, Marc J |b 37 |
| 700 | 1 | _ | |a Monteiro, Carlos A |b 38 |
| 700 | 1 | _ | |a Millett, Christopher |b 39 |
| 700 | 1 | _ | |a Levy, Renata Bertazzi |b 40 |
| 773 | _ | _ | |0 PERI:(DE-600)2776676-7 |a 10.3389/fnut.2022.1035580 |g Vol. 9, p. 1035580 |p 1035580 |t Frontiers in nutrition |v 9 |x 2296-861X |y 2022 |
| 909 | C | O | |o oai:inrepo02.dkfz.de:186551 |p VDB |
| 910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)0b48ce513fe49013263657450a12f870 |a Deutsches Krebsforschungszentrum |b 12 |k DKFZ |
| 910 | 1 | _ | |0 I:(DE-588b)2036810-0 |6 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4 |a Deutsches Krebsforschungszentrum |b 22 |k DKFZ |
| 913 | 1 | _ | |0 G:(DE-HGF)POF4-313 |1 G:(DE-HGF)POF4-310 |2 G:(DE-HGF)POF4-300 |3 G:(DE-HGF)POF4 |4 G:(DE-HGF)POF |a DE-HGF |b Gesundheit |l Krebsforschung |v Krebsrisikofaktoren und Prävention |x 0 |
| 914 | 1 | _ | |y 2022 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0100 |2 StatID |a JCR |b FRONT NUTR : 2021 |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0200 |2 StatID |a DBCoverage |b SCOPUS |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0300 |2 StatID |a DBCoverage |b Medline |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0501 |2 StatID |a DBCoverage |b DOAJ Seal |d 2021-05-11T13:21:47Z |
| 915 | _ | _ | |0 StatID:(DE-HGF)0500 |2 StatID |a DBCoverage |b DOAJ |d 2021-05-11T13:21:47Z |
| 915 | _ | _ | |0 StatID:(DE-HGF)0030 |2 StatID |a Peer Review |b DOAJ : Blind peer review |d 2021-05-11T13:21:47Z |
| 915 | _ | _ | |0 LIC:(DE-HGF)CCBYNV |2 V:(DE-HGF) |a Creative Commons Attribution CC BY (No Version) |b DOAJ |d 2021-05-11T13:21:47Z |
| 915 | _ | _ | |0 StatID:(DE-HGF)0199 |2 StatID |a DBCoverage |b Clarivate Analytics Master Journal List |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0113 |2 StatID |a WoS |b Science Citation Index Expanded |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0150 |2 StatID |a DBCoverage |b Web of Science Core Collection |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)1110 |2 StatID |a DBCoverage |b Current Contents - Clinical Medicine |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0160 |2 StatID |a DBCoverage |b Essential Science Indicators |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)9905 |2 StatID |a IF >= 5 |b FRONT NUTR : 2021 |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0561 |2 StatID |a Article Processing Charges |d 2022-11-22 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0700 |2 StatID |a Fees |d 2022-11-22 |
| 920 | 1 | _ | |0 I:(DE-He78)C020-20160331 |k C020 |l C020 Epidemiologie von Krebs |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C020-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|
guest ::