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Review
. 2024 Aug 30;73(S1):S165-S183.
doi: 10.33549/physiolres.935443.

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

S Rakusanova  1 , T Cajka
Affiliations
Review

Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease

S Rakusanova et al. Physiol Res. .

Abstract

Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.

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Conflict of interest statement

Conflict of Interest: There is no conflict of interest.

Figures

Fig. 1
Fig. 1
Metabolomics and lipidomics workflow.
Fig. 2
Fig. 2
(A) Example of sample extraction using MTBE, methanol, and water [41], leading to two phases for subsequent metabolomics and lipidomics platforms. (B) Example of a typical LC-MS sequence during metabolomics and lipidomic analysis, consisting of solvent injection for general platform equilibration, followed by a system suitability test (SST), platform equilibration using pooled QC samples, analysis of method blanks (BL), a diluted series of QC samples (SD), randomized study samples with regular QC sample injections after every 10 study samples. (C) Example of different LC-MS platforms [41,42] for metabolomic and lipidomic analysis in relation to the XlogP (predicted octanol/water partition coefficient) range of subgroups of polar metabolites and complex lipids.
Fig. 3
Fig. 3
(A) Example of MS-DIAL software [56] used for processing lipidomics data acquired using the RPLC-ESI(–)-MS [41], with annotated PC 16:0_18:2 in human serum. Using ammonium acetate and acetic acid as mobile phase modifiers led to the detection PC 34:2 as an acetate adduct ([M+CH3COO]−) (m/z 816.576). The MS/MS spectrum of PC 34:2 provided a fragment ion [M–CH3]− (m/z 742.539) and a series of fragments for elucidating fatty acyl chains (e.g., m/z 255.233 for 16:0 and m/z 279.233 for 18:2). The use of the underscore “_” indicates certainty in the composition of the fatty acyl constituents but not their specific placement on the glycerol backbone. (B) Example of MS-FINDER software [60] used for the structure elucidation of an unknown compound (m/z 189.1597, retention time 4.57 min) in human serum acquired using the HILIC-ESI(+)-MS platform [41], with tentative annotation as N6,N6,N6-trimethyl-L-lysine.
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