001     136655
005     20240229105052.0
024 7 _ |a 10.1093/ajcn/nqy074
|2 doi
024 7 _ |a pmid:29924298
|2 pmid
024 7 _ |a 0002-9165
|2 ISSN
024 7 _ |a 0095-9871
|2 ISSN
024 7 _ |a 1938-3207
|2 ISSN
024 7 _ |a 1938-3215
|2 ISSN
024 7 _ |a altmetric:43919643
|2 altmetric
037 _ _ |a DKFZ-2018-01124
041 _ _ |a eng
082 _ _ |a 570
100 1 _ |a Assi, Nada
|b 0
245 _ _ |a Metabolic signature of healthy lifestyle and its relation with risk of hepatocellular carcinoma in a large European cohort.
260 _ _ |a Bethesda, Md.
|c 2018
|b Soc.
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 1536311881_14297
|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
520 _ _ |a Studies using metabolomic data have identified metabolites from several compound classes that are associated with disease-related lifestyle factors.In this study, we identified metabolic signatures reflecting lifestyle patterns and related them to the risk of hepatocellular carcinoma (HCC) in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort.Within a nested case-control study of 147 incident HCC cases and 147 matched controls, partial least squares (PLS) analysis related 7 modified healthy lifestyle index (HLI) variables (diet, BMI, physical activity, lifetime alcohol, smoking, diabetes, and hepatitis) to 132 targeted serum-measured metabolites and a liver function score. The association between the resulting PLS scores and HCC risk was examined in multivariable conditional logistic regression models, where ORs and 95% CIs were computed.The lifestyle component's PLS score was negatively associated with lifetime alcohol, BMI, smoking, and diabetes, and positively associated with physical activity. Its metabolic counterpart was positively related to the metabolites sphingomyelin (SM) (OH) C14:1, C16:1, and C22:2, and negatively related to glutamate, hexoses, and the diacyl-phosphatidylcholine PC aaC32:1. The lifestyle and metabolomics components were inversely associated with HCC risk, with the ORs for a 1-SD increase in scores equal to 0.53 (95% CI: 0.38, 0.74) and 0.28 (0.18, 0.43), and the associated AUCs equal to 0.64 (0.57, 0.70) and 0.74 (0.69, 0.80), respectively.This study identified a metabolic signature reflecting a healthy lifestyle pattern which was inversely associated with HCC risk. The metabolic profile displayed a stronger association with HCC than did the modified HLI derived from questionnaire data. Measuring a specific panel of metabolites may identify strata of the population at higher risk for HCC and can add substantial discrimination compared with questionnaire data. This trial was registered at clinicaltrials.gov as NCT03356535.
536 _ _ |a 313 - Cancer risk factors and prevention (POF3-313)
|0 G:(DE-HGF)POF3-313
|c POF3-313
|f POF III
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed,
700 1 _ |a Gunter, Marc J
|b 1
700 1 _ |a Thomas, Duncan C
|b 2
700 1 _ |a Leitzmann, Michael
|b 3
700 1 _ |a Stepien, Magdalena
|b 4
700 1 _ |a Chajès, Véronique
|b 5
700 1 _ |a Philip, Thierry
|b 6
700 1 _ |a Vineis, Paolo
|b 7
700 1 _ |a Bamia, Christina
|b 8
700 1 _ |a Boutron-Ruault, Marie-Christine
|b 9
700 1 _ |a Sandanger, Torkjel M
|b 10
700 1 _ |a Molinuevo, Amaia
|b 11
700 1 _ |a Boshuizen, Hendriek
|b 12
700 1 _ |a Sundkvist, Anneli
|b 13
700 1 _ |a Kühn, Tilman
|0 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
|b 14
|u dkfz
700 1 _ |a Travis, Ruth
|b 15
700 1 _ |a Overvad, Kim
|b 16
700 1 _ |a Riboli, Elio
|b 17
700 1 _ |a Scalbert, Augustin
|b 18
700 1 _ |a Jenab, Mazda
|b 19
700 1 _ |a Viallon, Vivian
|b 20
700 1 _ |a Ferrari, Pietro
|b 21
773 _ _ |a 10.1093/ajcn/nqy074
|g Vol. 108, no. 1
|0 PERI:(DE-600)1496439-9
|n 1
|p 117-126
|t The American journal of clinical nutrition
|v 108
|y 2018
|x 1938-3207
909 C O |o oai:inrepo02.dkfz.de:136655
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 14
|6 P:(DE-He78)0907a10ba1dc8f53f04907f54f6fdcfe
913 1 _ |a DE-HGF
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-313
|2 G:(DE-HGF)POF3-300
|v Cancer risk factors and prevention
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF3
|b Gesundheit
914 1 _ |y 2018
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b AM J CLIN NUTR : 2015
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Thomson Reuters Master Journal List
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b AM J CLIN NUTR : 2015
920 1 _ |0 I:(DE-He78)C020-20160331
|k C020
|l Epidemiologie von Krebserkrankungen
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C020-20160331
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21