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@ARTICLE{Huybrechts:186551,
      author       = {I. Huybrechts and F. Rauber and G. Nicolas and C.
                      Casagrande and N. Kliemann and R. Wedekind and C. Biessy and
                      A. Scalbert and M. Touvier and K. Aleksandrova and P.
                      Jakszyn and G. Skeie and R. Bajracharya$^*$ and J. M. A.
                      Boer and Y. Borné and V. Chajes and C. C. Dahm and L.
                      Dansero and M. Guevara and A. K. Heath and D. B. Ibsen and
                      K. Papier and V. Katzke$^*$ and C. Kyrø and G. Masala and
                      E. Molina-Montes and O. J. K. Robinson and C. Santiuste de
                      Pablos and M. B. Schulze and V. Simeon and E. Sonestedt and
                      A. Tjønneland and R. Tumino and Y. T. van der Schouw and W.
                      M. M. Verschuren and B. Vozar and A. Winkvist and M. J.
                      Gunter and C. A. Monteiro and C. Millett and R. B. Levy},
      title        = {{C}haracterization of the degree of food processing in the
                      {E}uropean {P}rospective {I}nvestigation into {C}ancer and
                      {N}utrition: {A}pplication of the {N}ova classification and
                      validation using selected biomarkers of food processing.},
      journal      = {Frontiers in nutrition},
      volume       = {9},
      issn         = {2296-861X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2023-00004},
      pages        = {1035580},
      year         = {2022},
      abstract     = {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.},
      keywords     = {EPIC (Other) / Nova (Other) / biomarkers (Other) / elaidic
                      acid (Other) / food processing (Other) / syringol (Other)},
      cin          = {C020},
      ddc          = {630},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:36590209},
      pmc          = {pmc:PMC9800919},
      doi          = {10.3389/fnut.2022.1035580},
      url          = {https://inrepo02.dkfz.de/record/186551},
}