Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study.
Gaussian graphical models
dietary patterns
gastric cancer
networks
Journal
Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829
Informations de publication
Date de publication:
23 Apr 2020
23 Apr 2020
Historique:
received:
25
03
2020
revised:
18
04
2020
accepted:
21
04
2020
entrez:
29
4
2020
pubmed:
29
4
2020
medline:
29
4
2020
Statut:
epublish
Résumé
Gaussian graphical models (GGMs) are novel approaches to deriving dietary patterns that assess how foods are consumed in relation to one another. We aimed to apply GGMs to identify dietary patterns and to investigate the associations between dietary patterns and gastric cancer (GC) risk in a Korean population. In this case-control study of 415 GC cases and 830 controls, food intake was assessed using a 106-item semiquantitative food frequency questionnaire that captured 33 food groups. The dietary pattern networks corresponding to the total population contained a main network and four subnetworks. For the vegetable and seafood network, those who were in the highest tertile of the network-specific score showed a significantly reduced risk of GC both in the total population (OR = 0.66, 95% CI = 0.47-0.93,
Identifiants
pubmed: 32340406
pii: cancers12041044
doi: 10.3390/cancers12041044
pmc: PMC7226381
pii:
doi:
Types de publication
Journal Article
Langues
eng
Subventions
Organisme : National Cancer Center, Korea
ID : 1410260
Organisme : National Cancer Center, Korea
ID : 1810980
Organisme : National Cancer Center, Korea
ID : 1910330
Organisme : National Research Foundation of South Korea (NRF)
ID : 2018R1D1A1A09083876
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