Hyperlipidemia is a condition characterized by abnormal levels of lipids in the body, typically presenting as elevated total cholesterol (TC) and/or triglycerides (TG), along with abnormal levels of high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C). As a disorder of lipid metabolism, hyperlipidemia can lead to various cardiovascular diseases such as coronary heart disease, stroke, and atherosclerosis. Elevated lipid levels are recognized as a significant risk factor for cardiovascular diseases (CVDs). Clinically, hyperlipidemia can be classified into hypercholesterolemia, hypertriglyceridemia, mixed hyperlipidemia, and low high-density lipoprotein cholesterolemia.
Understanding the pathogenesis of hyperlipidemia, identifying more sensitive biomarkers, developing safe and effective novel lipid-lowering drugs, and elucidating their mechanisms of action have become important directions of research. Currently, the LIPID MAPS database has cataloged over 40,000 lipid species, making systematic research on these lipids essential. Lipidomics, as an emerging discipline, employs various modern scientific techniques to study the lipidome. Lipidomics utilizes methods such as targeted analysis, profiling, and imaging to compare overall changes in lipid metabolism networks, clarify the characteristics of lipid metabolism under different physiological and pathological conditions, explore their relationship with related diseases, identify and authenticate key biomarkers, ultimately revealing the pathogenesis of diseases, discovering therapeutic drug targets, and lead compounds.
In recent years, lipidomics has become an important method in research related to hyperlipidemia.
Lipidomics Analysis Methods
The basic research process of lipidomics involves several steps, including sample collection and pretreatment, separation and identification, data acquisition and processing, and biological pathway analysis. Among these, the development of analysis methods is crucial. Main methods include Raman spectroscopy, high-performance liquid chromatography (HPLC), gas chromatography (GC), mass spectrometry (MS), chromatography-mass spectrometry (LC-MS), ion mobility-mass spectrometry (IM-MS), mass spectrometry imaging (MSI), capillary electrophoresis-mass spectrometry (CE-MS), and nuclear magnetic resonance (NMR). Of these, liquid chromatography-mass spectrometry (LC-MS) is particularly significant due to its ability to combine the separation characteristics of liquid chromatography with the identification advantages of mass spectrometry, making it the primary analytical method in lipidomics.
Each method has its own strengths and weaknesses, and their suitability varies. Gas chromatography-mass spectrometry (GC-MS) is effective for analyzing volatile lipid components but requires sample derivatization for non-volatile lipid components, limiting its application to complex lipid compounds. Supercritical fluid chromatography-mass spectrometry (SFC-MS), which commonly employs supercritical carbon dioxide as a mobile phase, is well-suited for lipid component analysis due to its strong solvent capabilities. Ion mobility-mass spectrometry (IM-MS) allows for the separation of lipid isomers, while mass spectrometry imaging (MSI) enables visual analysis. Selection of the appropriate method in lipidomics research depends on specific research needs and conditions.
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Lipidomics Analysis Workflow
Sample Pre-treatment
Biological samples used for lipid analysis include blood, urine, saliva, tissues, and cell culture fluids. Lipid extraction from these samples typically employs methods such as single organic solvent extraction, liquid-liquid extraction, and solid-phase extraction. Liquid-liquid extraction, the most widely used method, includes techniques like the Folch method, Bligh-Dyer method, and methyl-tert-butyl ether (MTBE) method, all applicable for extracting various lipid types.
Data Acquisition
Lipidomics data acquisition can be categorized into non-targeted and targeted strategies. Non-targeted lipidomics analyzes the overall lipid changes in biological samples without bias, relying on high-resolution mass spectrometers for qualitative and relative quantitative analysis of lipids, reflecting the patterns and trends of lipid changes to establish lipid profiles. This approach often combines high-resolution mass detection in data-independent or data-dependent acquisition modes. In contrast, targeted lipidomics focuses on the accurate qualitative and quantitative analysis of specific lipid classes, characterized by high sensitivity and specificity, typically employed for analyzing key metabolic pathways or targets. This method frequently utilizes ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry (UHPLC-QQQ-MS), adopting multiple reaction monitoring (MRM) for data collection.
Lipid Structure Identification
Lipids vary in chemical structure, necessitating the determination of their basic skeletons, fatty acyl chain compositions, unsaturation levels, and isomer differentiation during identification. In mass spectrometry-based lipidomics, samples are directly infused into the spectrometer or chromatographically separated before analysis. Collision-induced dissociation or high-energy collisional dissociation fragments lipid molecules, with various scanning modes capturing precursor and product ion information. Comparing multistage fragmentation spectra with standard spectra or matching them against existing lipid spectrum databases aids in inferring lipid classes and structures. Identifying lipids becomes challenging due to the diversity of lipid subclasses, complex structures, and isomers formed by different acyl chain linkage positions, double bond numbers and locations, and functional group stereoisomerism. Analyzing characteristic fragment ions' relative intensities in secondary mass spectra facilitates distinguishing or identifying isomers, such as lysophospholipids differing in sn-position fatty acyl locations. The Paternò-Büchi reaction coupled with tandem mass spectrometry can identify double bond positions and numbers in lipid molecules. Moreover, isotopic peak exclusion is necessary to reduce false positives in lipid structure identification.
Data Analysis
Lipidomics data analysis encompasses mass spectrometry data processing and statistical analysis. Mass spectrometry data processing involves steps like peak filtering, alignment, and normalization, ultimately reporting extracted mass-to-charge ratios, retention times, and areas of all detected peaks. Statistical analysis then assesses these data, including statistical tests and multivariate statistical analysis, to select differential metabolites for bioinformatics analysis of lipid network pathways. Most existing pathway tools, such as KEGG, Ingenuity, and MetaCore, are suitable for elucidating lipid metabolic pathways.
Application of Lipidomics in the Study of Hyperlipidemia
Lipidomics in Disease Mechanism Research
Lipidomics, the comprehensive analysis of a cell or organism's lipid profile, is increasingly pivotal in understanding the intricate mechanisms of diseases such as familial hypercholesterolemia (FH). FH patients are characterized by high cholesterol levels from birth, predisposing them to premature atherosclerotic cardiovascular diseases. Through high-throughput nuclear magnetic resonance technology, researchers can delve into the lipid metabolic alterations between FH children and healthy counterparts. It's been observed that FH children have higher levels of apolipoprotein B, lipids, and lipoprotein subclasses. Furthermore, changes in high-density lipoprotein (HDL) particles, including increased cholesteryl ester levels and decreased free cholesterol and phospholipids, suggest impaired reverse cholesterol transport in FH children. Lipidomics also highlights the close relationship between hyperlipidemia in pregnant women and complications such as gestational diabetes, preeclampsia, and spontaneous preterm birth. Using LC/MS technology, significant differences were found in the lipidome of pregnant women with and without hyperlipidemia, including elevated bilirubin and deoxycholic acid levels in the former, indicating a potential link to abnormal bile secretion during pregnancy.
Flowchart of the integrated strategy platform. Part 1: The mechanism of berberine (BBR) in the treatment of hyperlipidemia (HLP) was comprehensively described based on targeted lipidomics. Part 2: The network pharmacology approach was developed to identify targets of BBR for the treatment of HLP. Part 3: Potential links between biomarkers and hub genes (Chen et al., 2022).
Exploring Biomarkers in Hyperlipidemia Through Lipidomics
The exploration for more sensitive and specific biomarkers to diagnose and monitor hyperlipidemia is becoming increasingly important in medical research and clinical practice. Hyperlipidemia, characterized by elevated levels of lipids in the blood, poses a significant risk for cardiovascular diseases. However, the biomarkers currently used in clinical settings often only show significant alterations in cases of severe dyslipidemia, limiting their effectiveness in early diagnosis and the management of the disease at a more treatable stage.
The advent of liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF/MS) technology has been a game-changer in the search for novel biomarkers. This sophisticated analytical technique allows for the detailed examination of serum fatty acid composition, leading to the identification of 16 potential biomarkers in the serum of hypertriglyceridemia mouse models. These include five fatty acid amides, two fatty acid esters, and nine nitro and halogenated fatty acids. The identification of these compounds is a promising step forward, though it requires further structural confirmation to validate their roles as biomarkers for hyperlipidemia.
The comparison of plasma lipid profiles between hyperlipidemia patients (including those with liver depression and spleen deficiency syndrome) and healthy volunteers has unveiled significant metabolic alterations. Changes in the levels of phosphatidylcholine, phosphatidylethanolamine, and ceramides were observed. These lipids play crucial roles in cellular processes, and their dysregulation can reflect underlying pathophysiological changes associated with hyperlipidemia and its complications.
The findings from these studies underscore the potential of lipidomic profiling as a powerful tool for uncovering novel biomarkers. By offering a more nuanced understanding of the lipid alterations associated with different syndromes within hyperlipidemia, lipidomics opens the door to personalized medicine approaches. This could enable clinicians to tailor treatments to the specific lipidomic profile of an individual, potentially improving outcomes by addressing the disease's pathology more directly and efficiently.
The Role of Lipidomics in Therapeutic Drug Action and Mechanism Studies
Statins, commonly prescribed for high cholesterol, sometimes cause severe adverse reactions. Utilizing UPLC/MS technology to compare lipidomic changes between healthy individuals and hypercholesterolemia patients after rosuvastatin treatment has shed light on the mechanisms behind these adverse reactions. The findings suggest that abnormal fat oxidation or drug-induced mitochondrial dysfunction may be at the root. Furthermore, comparing the lipid changes between dose-escalation of atorvastatin and combination therapy with atorvastatin and fenofibrate using UPLC-QTOF/MS technology revealed distinct lipid metabolic changes. Most notably, a reduction in various glycerolipid metabolites and ceramides, and an increase in sphingomyelins were observed in the combination therapy group. These results provide insight beyond conventional lipid profiles, offering a new basis for research into combination therapies.
Reference
- Chen, Yuting, et al. "Integrated lipidomics and network pharmacology analysis to reveal the mechanisms of berberine in the treatment of hyperlipidemia." Journal of Translational Medicine 20.1 (2022): 412.
