Metabolites · 9 authors, 5 centres
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This study used mass spectrometry imaging (MSI) to map metabolite distribution in the livers of rats fed a high-fat diet. The researchers found that triglyceride (TG) levels initially rose but returned to control levels by week 14, challenging the reliability of TG as a standalone hyperlipidemia biomarker. Nine functional metabolites, including TG(14:0/14:0/16:0), were identified as differentially expressed and correlated with hyperlipidemia progression.
**Background:** Hyperlipidemia is a metabolic disorder characterized by elevated blood lipids, particularly triglycerides (TG). While TG is a standard biomarker, it remains unclear whether TG levels remain consistently elevated throughout the entire developmental course of hyperlipidemia. Traditional metabolomics lacks spatial information about metabolite distribution in organs. Mass spectrometry imaging (MSI) offers in situ, spatially resolved metabolomic analysis. This study aimed to use MSI to investigate temporal changes in hepatic metabolite distribution in a high-fat diet rat model of hyperlipidemia.
**Methods:** Twenty SPF-grade male SD rats (180–200 g) were randomly divided into a control group (basic diet) and a high-lipid (HL) model group (high-fat diet containing 20% sucrose, 15% lard oil, 1.2% cholesterol, 0.2% sodium cholate, 1.2% calcium hydrogen phosphate, 0.8% stone powder, and 5% casein). After 2 weeks of diet, blood was collected from the inner canthus to confirm successful modeling via serum TG measurement. Rats were fed continuously for 12 additional weeks and then sacrificed. Liver tissues were analyzed by H&E staining (5 μm sections) and by MALDI-MSI (10 μm frozen sections, DHB matrix, 50 μm × 50 μm resolution, m/z 400–900, positive and negative ion modes). MSI data were processed using Imaging MS solution Ver.1.30. Metabolites were annotated against eight databases (KEGG, BioCyc, HMDB, DrugBank, LipidMaps, ChEBI, PubChem, MoNA). Differential metabolites were defined as fold change >1.5 or <0.75, p < 0.05, VIP >1. PLS-DA, volcano plot, Venn diagram, and correlation analyses were performed.
**Key Results:** H&E staining showed that HL rat hepatocytes were swollen with numerous fat vacuoles and diffuse steatosis, while controls had normal liver architecture. TG levels in the HL group were significantly higher than controls at early time points (p < 0.001), but by week 14, there was no significant difference between groups (p > 0.05). Body weights were significantly higher in the HL group. PLS-DA analysis effectively distinguished Control_8W vs. HL_8W, Control_14W vs. Control_8W, Control_14W vs. HL_14W, and HL_14W vs. HL_8W. Volcano plot analysis (fold change >2, p < 0.01, VIP >1) identified: C1 (Control_8W vs. HL_8W): 37 increased, 27 decreased metabolites; C2 (Control_14W vs. Control_8W): 25 increased, 13 decreased; C3 (Control_14W vs. HL_14W): 25 increased, 18 decreased; C4 (HL_14W vs. HL_8W): 27 increased, 9 decreased. Fourteen differential metabolites were identified in key comparisons C2 and C3. Venn diagram analysis of metabolites common to all four comparisons identified 16 metabolites, of which nine were classified into four categories: phosphatidic acids (PA), phosphatidylglycerols (PG), sphingomyelins (SM), and triacylglycerols (TG). The nine identified metabolites were: PA(20:3-OH/i-21:0), PA(20:4-OH/22:6), PG(20:5-OH/i-16:0), PG(22:6-2OH/i-13:0), PG(O-18:0/20:4), PGP(18:3-OH/i-12:0), PGP(PGJ2/i-15:0), SM(d18:0/18:1-2OH), and TG(14:0/14:0/16:0). Correlation analysis showed a negative correlation between TG and most differential metabolites, with correlation coefficients greater than 0.6 (negative correlation less than −0.6).
**Clinical Implications:** This study demonstrates that TG levels in a high-fat diet rat model are not persistently elevated; they decline by week 14 to levels comparable to controls. This finding challenges the reliability of TG as a standalone biomarker for hyperlipidemia and emphasizes the importance of considering animal age or disease stage when using TG as a diagnostic marker. MSI provided in situ, spatially resolved metabolite distribution data that traditional serum or tissue homogenate metabolomics cannot offer. The identification of TG(14:0/14:0/16:0) as a key metabolite closely associated with hyperlipidemia may serve as a more specific biomarker or therapeutic target. The study also highlights the potential of MSI as a powerful tool for visualizing metabolic changes in organ tissues, offering a new pathway for in situ, visualized, and data-rich metabolomics research that could improve understanding of hyperlipidemia mechanisms and inform future prevention and treatment strategies.