Comparison of dimension reduction methods on fatty acids food source study

Author:

Chen Yifan,Miura Yusuke,Sakurai Toshihiro,Chen Zhen,Shrestha Rojeet,Kato Sota,Okada Emiko,Ukawa Shigekazu,Nakagawa Takafumi,Nakamura Koshi,Tamakoshi Akiko,Chiba Hitoshi,Imai Hideyuki,Minami Hiroyuki,Mizuta Masahiro,Hui Shu-Ping

Abstract

AbstractSerum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

Funder

Japan Society for the Promotion of Science, KAKENHI

Integration Research for Agriculture and Interdisciplinary Fields

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

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