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Review
. 2021 Apr 19;20(1):37.
doi: 10.1186/s12937-021-00692-7.

A review of statistical methods for dietary pattern analysis

Junkang Zhao  1 , Zhiyao Li  1 , Qian Gao  1 , Haifeng Zhao  2 , Shuting Chen  1 , Lun Huang  1 , Wenjie Wang  1 , Tong Wang  3
Affiliations
Review

A review of statistical methods for dietary pattern analysis

Junkang Zhao et al. Nutr J. .

Abstract

Background: Dietary pattern analysis is a promising approach to understanding the complex relationship between diet and health. While many statistical methods exist, the literature predominantly focuses on classical methods such as dietary quality scores, principal component analysis, factor analysis, clustering analysis, and reduced rank regression. There are some emerging methods that have rarely or never been reviewed or discussed adequately.

Methods: This paper presents a landscape review of the existing statistical methods used to derive dietary patterns, especially the finite mixture model, treelet transform, data mining, least absolute shrinkage and selection operator and compositional data analysis, in terms of their underlying concepts, advantages and disadvantages, and available software and packages for implementation.

Results: While all statistical methods for dietary pattern analysis have unique features and serve distinct purposes, emerging methods warrant more attention. However, future research is needed to evaluate these emerging methods' performance in terms of reproducibility, validity, and ability to predict different outcomes.

Conclusion: Selection of the most appropriate method mainly depends on the research questions. As an evolving subject, there is always scope for deriving dietary patterns through new analytic methodologies.

Keywords: Clustering analysis; Compositional data analysis; Data mining; Dietary patterns; Dietary quality scores; Factor analysis; Least absolute shrinkage and selection operator; Principal component analysis; Reduced rank regression; Treelet transform.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
The principal component analysis with D food group variables. Each PC is a linear combination of D food groups and corresponds to a dietary pattern
Fig. 2
Fig. 2
The k-means clustering with n individuals and g clusters. The individuals with similar dietary characteristics are assigned to one cluster
Fig. 3
Fig. 3
The finite mixture model with n individuals and g clusters. Each individual is only assigned to the cluster with the highest probability
Fig. 4
Fig. 4
A cluster tree produced by the treelet transform with five food group variables. As the dashed line goes up, the cutting level moves away from the root, so the factor loadings become more sparse
Fig. 5
Fig. 5
The reduced rank regression with D food group variables and g intermediate response variables (M). Each PC corresponding to a dietary pattern is a linear combination of D food groups which explaining as much variance (Vmax) in M as possible. D is larger than g
Fig. 6
Fig. 6
The decision tree generated by the C4.5 algorithm
Fig. 7
Fig. 7
The least absolute shrinkage and selection operator. The number of points at which the dashed line intersects the curve represents the number of nonzero coefficients D. The smaller λ, the larger D
Fig. 8
Fig. 8
CoDa-dendrogram of PBs with six food group variables. Each PB corresponds to a dietary pattern. The closer the contact point is to a food, the more of that food is relatively more abundant
See this image and copyright information in PMC

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