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. 2023 Feb 20;12(4):901.
doi: 10.3390/foods12040901.

Chemometric Discrimination of Cichorium glandulosum Boiss. et Huet and Cichorium intybus L. via Their Metabolic Profiling, Antioxidative, and Hypoglycemic Activities

Maoru Li  1 , Guoyong Xie  2 , Yadong Ding  1 , Ji Ma  1 , Qiuyan Liu  1 , Yuqin Wang  1 , Zan Peng  1 , Jianbo Sun  3 , Jing Shang  1
Affiliations

Chemometric Discrimination of Cichorium glandulosum Boiss. et Huet and Cichorium intybus L. via Their Metabolic Profiling, Antioxidative, and Hypoglycemic Activities

Maoru Li et al. Foods. .

Abstract

Cichorium glandulosum Boiss. et Huet (CG) and Cichorium intybus L. (CI) are widely used as the main raw material of functional food with hepatoprotective and hypoglycemic effects. Due to the lack of comparison on the chemical ingredients and efficacy, they were often used imprecisely and interchangeably. It is necessary to distinguish between them. With the plant metabolomics based on high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (HPLC-QTOF-MS) and multivariate chemometric techniques, the chemical ingredients were characterized and 59 compounds between CG and CI were classified. As for antioxidative and hypoglycemic activities in vitro, CI extraction exhibited better antioxidant activity than CG, while CG extraction showed stronger hypoglycemic activity. Furthermore, a bivariate correlation between the chemical composition and efficacy of the extract was also analyzed, and three differentially strong correlation components between CI and CG were prepared, and the antioxidative and hypoglycemic efficacies were compared in vivo and different active phenotypes were obtained. Finally, we revealed chemical and biological differences between CG and CI, providing a basis for achieving better quality control and developing more effective functional foods.

Keywords: Cichorium glandulosum Boiss. et Huet; Cichorium intybus L.; HPLC-QTOF-MS; activity comparison; plant metabolomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
HPLC fingerprints of CG and CI. (A) CG reference fingerprint chromatogram (CGR) and 24 common peaks; (B) CI reference fingerprint chromatogram (CIR); (C) HPLC fingerprints of CG; (D) HPLC fingerprints of CI; (E) HPLC fingerprints of CI compared to CGR; (F) HPLC fingerprints of CG compared to CIR.
Figure 2
Figure 2
The multivariate analysis of metabolites in CG and CI. (A) Principle component analysis (PCA) plot; (B) score scatter plot of the OPLS-DA model; (C) volcano plot of differential metabolites; (D) differential comparison of metabolite intensity; (E) heat map of CG and CI. Significantly up- and downregulated metabolites are indicated in red and green, respectively. Those without significant difference between the two groups are indicated in grey. A greater absolute value on the horizontal axis indicates a greater foldchange between CG and CI. A greater value on the vertical axis indicates greater significance.
Figure 3
Figure 3
Correlation analysis between activities in vitro and abundance of compounds in CG and CI. (A) Comparison of antioxidant activities and hypoglycemic activities between CG and CI evaluated by IC50. ns p > 0.05, * p < 0.05 compared with CI. (B) The peak intensity of TOP10 components. (C) Spearman’s correlation coefficients to each bioactive compound detected by HPLC. * p < 0.05, ** p < 0.01 compared with peak intensity.
Figure 4
Figure 4
Effect of CGA and CIA on antioxidative and hypoglycemic activities in larval zebrafish. (A) Experimental outline of H2O2 induced larval zebrafish. (B) Experimental outline of GLU+ALX induced larval zebrafish. (C) ROS production showed in fluorescence image and merged with light field image. (D) ROS level quantitated by fluorescence quantitative data. (E) Glucose levels of each treated larval group. Bar indicates means ± SD. **** p < 0.0001 compared with control; ## p < 0.01, ### p < 0.001, #### p < 0.0001 compared with model. p < 0.05 was considered as statistical significance calculated by ANOVA followed by Tukey’s test.
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