Dataset Information

Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study.

ABSTRACT: 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, p for trend = 0.018) and in males (OR = 0.55, 95% CI = 0.34-0.89, p for trend = 0.012). Most importantly, the fruit pattern network was inversely associated with the risk of GC for the highest tertile (OR = 0.56, 95% CI = 0.38-0.81, p for trend = 0.002). The identified vegetable and seafood network and the fruit network showed a protective effect against GC development in Koreans.

SUBMITTER: Gunathilake M

PROVIDER: S-EPMC7226381 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study.

Gunathilake Madhawa M Lee Jeonghee J Choi Il Ju IJ Kim Young-Il YI Kim Jeongseon J

Cancers 20200423 4

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 dieta ...[more]

PMID: 32340406

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Project description:BackgroundGraphical Gaussian models are popular tools for the estimation of (undirected) gene association networks from microarray data. A key issue when the number of variables greatly exceeds the number of samples is the estimation of the matrix of partial correlations. Since the (Moore-Penrose) inverse of the sample covariance matrix leads to poor estimates in this scenario, standard methods are inappropriate and adequate regularization techniques are needed. Popular approaches include biased estimates of the covariance matrix and high-dimensional regression schemes, such as the Lasso and Partial Least Squares.ResultsIn this article, we investigate a general framework for combining regularized regression methods with the estimation of Graphical Gaussian models. This framework includes various existing methods as well as two new approaches based on ridge regression and adaptive lasso, respectively. These methods are extensively compared both qualitatively and quantitatively within a simulation study and through an application to six diverse real data sets. In addition, all proposed algorithms are implemented in the R package "parcor", available from the R repository CRAN.ConclusionIn our simulation studies, the investigated non-sparse regression methods, i.e. Ridge Regression and Partial Least Squares, exhibit rather conservative behavior when combined with (local) false discovery rate multiple testing in order to decide whether or not an edge is present in the network. For networks with higher densities, the difference in performance of the methods decreases. For sparse networks, we confirm the Lasso's well known tendency towards selecting too many edges, whereas the two-stage adaptive Lasso is an interesting alternative that provides sparser solutions. In our simulations, both sparse and non-sparse methods are able to reconstruct networks with cluster structures. On six real data sets, we also clearly distinguish the results obtained using the non-sparse methods and those obtained using the sparse methods where specification of the regularization parameter automatically means model selection. In five out of six data sets, Partial Least Squares selects very dense networks. Furthermore, for data that violate the assumption of uncorrelated observations (due to replications), the Lasso and the adaptive Lasso yield very complex structures, indicating that they might not be suited under these conditions. The shrinkage approach is more stable than the regression based approaches when using subsampling.

Dataset Information

Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study.

Publications

Identification of Dietary Pattern Networks Associated with Gastric Cancer Using Gaussian Graphical Models: A Case-Control Study.

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