Home > Publications database > Evaluation of protein and amino acid intake estimates from the EPIC dietary questionnaires and 24-h dietary recalls using different food composition databases. > print

001     177215
005     20240229133733.0
024 7 _ |a 10.1016/j.numecd.2021.09.012
|2 doi
024 7 _ |a pmid:34696945
|2 pmid
024 7 _ |a 0939-4753
|2 ISSN
024 7 _ |a 1590-3729
|2 ISSN
024 7 _ |a altmetric:115881452
|2 altmetric
037 _ _ |a DKFZ-2021-02349
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Iguacel, Isabel
|b 0
245 _ _ |a Evaluation of protein and amino acid intake estimates from the EPIC dietary questionnaires and 24-h dietary recalls using different food composition databases.
260 _ _ |a New York, NY [u.a.]
|c 2022
|b Elsevier
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1642495860_14848
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2022 Jan;32(1):80-89
520 _ _ |a This study aimed to expand the European Prospective Investigation into Cancer and Nutrition (EPIC) nutrient database (ENDB) by adding amino acid (AA) values, using the U.S. nutrient database (USNDB). Additionally, we aimed to evaluate these new protein and AA intake estimates from the EPIC dietary questionnaires (DQ) and 24-h dietary recalls (24-HDR) using different matching procedures.Dietary energy, protein and AA intakes were assessed via DQ and 24-HDR by matching with the USNDB food composition table. Energy and protein intakes calculated using USNDB matching were compared with those calculated using ENDB, that uses country specific food composition tables. Pearson correlations, Cohen's weighted kappa statistic and Bland-Altman plots were used to compare data resulting from USNDB matching with our reference from ENDB matching. Very high correlations were found when comparing daily energy (r = 0.99) and dietary protein intakes (r = 0.97) assessed via USNDB with those obtained via ENDB (matching for DQ and 24-HDR). Significant positive correlations were also found with energy and protein intakes acquired via 24-HDRs in the EPIC calibration sample.Very high correlations between total energy and protein intake obtained via the USDA matching and those available in ENDB suggest accuracy in the food matching. Individual AA have been included in the extended EPIC Nutrient database that will allow important analyses on AA disease prospective associations in the EPIC study.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
650 _ 7 |a 24-h dietary recall
|2 Other
650 _ 7 |a Amino acid intakes
|2 Other
650 _ 7 |a Dietary questionnaire
|2 Other
650 _ 7 |a Food composition tables
|2 Other
650 _ 7 |a Validity
|2 Other
700 1 _ |a Perez-Cornago, Aurora
|b 1
700 1 _ |a Schmidt, Julie A
|b 2
700 1 _ |a Van Puyvelde, Heleen
|b 3
700 1 _ |a Travis, Ruth
|b 4
700 1 _ |a Casagrande, Corinne
|b 5
700 1 _ |a Nicolas, Genevieve
|b 6
700 1 _ |a Riboli, Elio
|b 7
700 1 _ |a Weiderpass, Elisabete
|b 8
700 1 _ |a Ardanaz, Eva
|b 9
700 1 _ |a Barricarte, Aurelio
|b 10
700 1 _ |a Bodén, Stina
|b 11
700 1 _ |a Bruno, Eleonora
|b 12
700 1 _ |a Ching-López, Ana
|b 13
700 1 _ |a Dagfinn, Aune
|b 14
700 1 _ |a Jensen, Torill E
|b 15
700 1 _ |a Ericson, Ulrika
|b 16
700 1 _ |a Johansson, Ingergerd
|b 17
700 1 _ |a Ma Huerta, José
|b 18
700 1 _ |a Katzke, Verena
|0 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4
|b 19
|u dkfz
700 1 _ |a Kühn, Tilman
|0 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
|b 20
700 1 _ |a Sacerdote, Carlotta
|b 21
700 1 _ |a Schulze, Matthias B
|b 22
700 1 _ |a Skeie, Guri
|b 23
700 1 _ |a Ramne, Stina
|b 24
700 1 _ |a Ward, Heather
|b 25
700 1 _ |a Gunter, Marc J
|b 26
700 1 _ |a Huybrechts, Inge
|b 27
773 _ _ |a 10.1016/j.numecd.2021.09.012
|g p. S093947532100452X
|0 PERI:(DE-600)2050914-5
|n 1
|p 80-89
|t Nutrition, metabolism and cardiovascular diseases
|v 32
|y 2022
|x 0939-4753
909 C O |p VDB
|o oai:inrepo02.dkfz.de:177215
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 19
|6 P:(DE-He78)fb68a9386399d72d84f7f34cfc6048b4
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 20
|6 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-01-30
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-01-30
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NUTR METAB CARDIOVAS : 2021
|d 2022-11-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-17
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2022-11-17
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2022-11-17
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

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21