Metabonomics and Lipidomics: Correlation and Difference

As integral components of systems biology, metabolomics and lipidomics investigate low-molecular-weight metabolites and lipid species, respectively, examining their structural diversity, functional roles, and involvement in physiological mechanisms. While sharing methodological approaches and analytical targets, these disciplines diverge substantially in their investigative priorities and practical implementations. This review systematically compares and contrasts metabolomics and lipidomics, elucidating their synergistic relationships and unique attributes to enhance comprehension of their distinct contributions to biological research. By delineating their complementary strengths and application scopes, we aim to underscore their collective significance in advancing mechanistic insights into complex biological systems.

Foundational Definitions

1. Metabolomics

Metabolomics is a scientific discipline focused on analyzing the composition, concentration variations, and temporal dynamics of low-molecular-weight metabolites (typically<1,500 Da) within biological systems. These metabolites—encompassing amino acids, carbohydrates, organic acids, nucleotides, and signaling molecules—serve as direct indicators of cellular metabolic activity.

2. Lipidomics

Lipidomics investigates the structural diversity, functional roles, and biological interactions of lipid species, including glycerolipids, phospholipids, sphingolipids, sterols, and fatty acids. Beyond their role as membrane constituents, lipids regulate critical processes such as cellular signaling, energy homeostasis, and metabolic modulation.

Suggested framework for high-throughput clinical targeted metabolomics and lipidomics studies (Anh NK et al., 2024).

Correlation between metabonomics and lipidomics

Metabolomics and lipidomics, while distinct in their primary focuses, exhibit profound interdisciplinary connections through shared analytical frameworks, overlapping molecular targets, and complementary applications in biomedical research. Below, we expand on their synergistic relationships across three critical dimensions:

1. Overlapping Analytical Targets

Shared Molecular Classes

• Lipid species such as fatty acids (e.g., palmitic acid, oleic acid) and glycerolipids (e.g., triacylglycerols) are dually classified as both lipids and metabolites. This duality positions lipidomics as a specialized subset of metabolomics, enabling integrated analyses of lipid-related metabolic pathways (e.g., β-oxidation, lipogenesis).

Integrated Metabolic Networks

Lipid metabolism is enzymatically and functionally intertwined with central carbon metabolism:

• Carbohydrate-lipid crosstalk: Excess glucose drives de novo lipogenesis via acetyl-CoA.
• Amino acid-lipid interactions: Methionine metabolism regulates S-adenosylmethionine (SAMe)-dependent phospholipid methylation.
• Energy homeostasis: Mitochondrial β-oxidation of fatty acids fuels the TCA cycle, linking lipid catabolism to ATP production.

2. Methodological Parallels and Adaptations

Mass Spectrometry (MS) Platforms

• LC-MS: Dominates both fields for untargeted profiling (e.g., polar metabolites, phospholipids) and targeted quantification (e.g., acylcarnitines, sphingomyelins).
• GC-MS: Preferred for volatile metabolites (e.g., short-chain fatty acids) and derivatized lipids (e.g., fatty acid methyl esters).
• Imaging MS: MALDI-MSI spatially resolves metabolites and lipids in tissues, revealing colocalization patterns in diseases like atherosclerosis.

Integrated analytical method for metabolomics and lipidomics in the mouse brain based on UPLC-Q-TOF-MS (Xiong F et al., 2023)

Nuclear Magnetic Resonance (NMR)

• Structural elucidation: Identifies double-bond positions in unsaturated lipids and stereochemistry of metabolites.
• Quantitative profiling: Absolute quantification of high-abundance species (e.g., lactate in metabolomics; cholesterol in lipidomics).

Emerging Hybrid Approaches

• Ion mobility-MS: Enhances lipid isomer separation (e.g., sn-position variants) and metabolite identification.
• Multi-omics integration: Combines metabolomic flux analysis with lipid turnover rates to model systemic metabolic dynamics.

3. Convergent Translational Applications

1. Disease Mechanism Investigation

Metabolomics and lipidomics synergistically investigate metabolic dysregulation in pathological conditions such as malignancies, diabetes mellitus, and cardiovascular disorders. These approaches enable:

• Biomarker Discovery: Identification of diagnostic signatures.e.g., This investigation employed untargeted metabolomic and lipidomic profiling to compare septic and healthy canines, revealing distinct host metabolic perturbations characteristic of sepsis pathogenesis. Key pathways including acetone aldehyde catabolism, ketogenesis, and glucose-alanine cycling showed significant enrichment, indicative of systemic metabolic reprogramming. Notably, metabolites including 13,14-dihydro-15-ketoprostaglandin A2 and 12(13)-DiHOME, alongside lipid species such as sphingomyelin (SM) and lysophosphatidylcholine (LPC), emerged as potential diagnostic and prognostic biomarkers. Non-surviving subjects exhibited elevated concentrations of inflammation-associated metabolites such as indole sulfate, highlighting their role in sepsis progression. These findings offer novel therapeutic targets and mechanistic insights for the clinical management of canine sepsis (Montague B et al,, 2022).
• Mechanistic Elucidation: Uncovering pathway-specific disruptions. e.g., This investigation employed integrated metabolomic and lipidomic profiling to delineate the molecular pathways underlying type 1 diabetes (T1D)-associated cognitive dysfunction. In male T1D murine models, region-specific metabolic perturbations were identified, with the frontal cortex exhibiting significantly elevated lipid peroxidation levels compared to the hippocampus. These metabolic disruptions correlated with oxidative stress and microglia-driven neuroinflammatory responses, ultimately contributing to neuronal degeneration. Notably, the frontal cortex demonstrated heightened susceptibility to diabetes-induced metabolic dysregulation, positioning it as a vulnerable neuroanatomical locus for T1D-related cognitive decline. These findings provide mechanistic insights critical for developing region-specific therapeutic interventions targeting metabolic and inflammatory pathways in diabetic encephalopathy (Xiong F et al., 2023).

2. Pharmaceutical Development

Both disciplines critically evaluate pharmacological interventions through:

• Metabolic Impact Assessment: Monitoring drug-induced shifts in central carbon metabolism. e.g., This investigation elucidated the pharmacological mechanism underlying Qiliqiangxin (QLQX) capsule-mediated cardiac functional enhancement using a post-myocardial infarction heart failure rat model integrated with metabolomic and lipidomic profiling. QLQX administration effectively normalized dysregulated plasma metabolites, notably fatty acid species (FA18:2, FA18:3), while reducing biomarkers of cardiac stress (NT-proBNP, HS-TnT). Mechanistically, QLQX upregulated critical metabolic pathways including unsaturated fatty acid synthesis, branched-chain amino acid catabolism, and glycerophospholipid remodeling. These findings demonstrate that QLQX exerts cardioprotective effects by orchestrating amino acid-lipid metabolic networks, offering experimental validation for metabolism-targeted therapeutic strategies in chronic heart failure management (Liu K et al., 2024).
• Metabolic signal analysis: analyzing the molecular signals of drug metabolism. e.g., This investigation delineated the pathophysiological mechanisms and potential biomarkers underlying statin-associated muscle symptoms (SAMS) through integrated metabolomic and lipidomic profiling. A cohort of 67 patients (28 SAMS cases vs. 39 tolerant controls) was analyzed, revealing distinct metabolic perturbations in SAMS patients. Key findings included elevated plasma linoleic acid (18:2) concentrations, reductions in phospholipid and ether-linked lipid species, diminished acylcarnitine levels, and dysregulation of tryptophan/tyrosine metabolism. Pathway analysis identified significant dysregulation in the urea cycle, branched-chain fatty acid β-oxidation, and carnitine biosynthesis pathways. These observations implicate mitochondrial energy metabolism impairment and pro-inflammatory lipid signaling (e.g., arachidonic acid pathway activation) as central drivers of SAMS pathology. By establishing a metabolic dysregulation-SAMS symptom axis, this study provides a foundation for developing mechanism-targeted therapeutic strategies (Garrett TJ et al., 2023).
• Drug evaluation: to detect the therapeutic effect of drugs and the possibility of targets. e.g., This investigation comprehensively assessed the therapeutic efficacy and mechanistic pathways of WLM extract using a dextran sulfate sodium (DSS)-induced ulcerative colitis murine model. WLM administration notably alleviated UC-associated clinical manifestations, including colonic atrophy and histopathological damage, while suppressing pro-inflammatory cytokine production. Multi-omics integration delineated four principal therapeutic axes: Metabolomic profiling identified 21 dysregulated metabolites linked to glycerophospholipid metabolism, arachidonic acid signaling, and citric acid cycle perturbations. Lipidomic screening revealed 60 differentially abundant lipid species, indicative of systemic lipid homeostasis disruption. Gut microbiota analysis demonstrated WLM-mediated microbial remodeling, characterized by enrichment of Limosilactobacillus and Akkermansia alongside suppression of Helicobacter and Streptococcus genera. qPCR validation confirmed WLM's capacity to downregulate inflammatory gene expression, modulate energy/lipid metabolic networks, and enhance intestinal mucosal barrier repair. These multi-modal insights establish WLM as a multi-target therapeutic agent for UC intervention, bridging metabolic regulation, microbial homeostasis, and mucosal barrier restoration (Huang H et al., 2024).

3. Nutritional Science Advancements

Integrated metabolomic-lipidomic strategies advance dietary research by:

• Lipid-Specific Modulation: Assessing lipidome adaptations to nutritional interventions. e.g., This investigation evaluated the therapeutic efficacy of DAG as a substitute for TAG in managing hyperuricemia among athletes. Integrated lipidomic and metabolomic profiling revealed that DAG supplementation markedly decreased serum uric acid concentrations in study participants, concurrent with elevated xanthine and phosphatidylcholine levels and reduced acylcarnitine abundance. The therapeutic mechanisms involved suppression of ROS and urate biosynthesis, enhancement of phospholipid-mediated uric acid excretion, and optimization of ammonia recycling pathways. These effects collectively disrupted the pathogenic cycle linking hyperuricemia, oxidative stress, and mitochondrial dysfunction, highlighting DAG's potential as a dietary intervention for metabolic regulation in athletic populations (Zhang F et al., 2024).

The difference between metabonomics and lipidomics

1. Divergence in Analytical Targets

Metabolomics

• Scope: Examines the comprehensive profile of small-molecule metabolites (molecular weight<1,500 Da), including intermediates and end products of cellular processes. Key analytes include amino acids (e.g., glutamine, alanine), carbohydrates (e.g., glucose, fructose), organic acids (e.g., citrate, lactate), nucleotides (e.g., ATP, GTP), and signaling molecules (e.g., serotonin, dopamine).
• Functional Relevance: Provides insights into global metabolic states, reflecting real-time cellular activity and systemic physiological responses.

Lipidomics

• Scope: Concentrates exclusively on lipid species, categorized into eight major classes: fatty acyls (e.g., palmitate), glycerolipids (e.g., triacylglycerols), glycerophospholipids (e.g., phosphatidylcholine), sphingolipids (e.g., ceramides), sterols (e.g., cholesterol), prenol lipids (e.g., ubiquinone), saccharolipids, and polyketides.
• Functional Relevance: Focuses on lipid-driven processes such as membrane architecture, intracellular signaling (e.g., eicosanoid pathways), and energy storage.

2. Distinct Research Priorities

Metabolomics

• Dynamic Profiling: Tracks temporal fluctuations in metabolite pools using techniques like stable isotope-resolved metabolomics (SIRM) to map flux through pathways such as glycolysis, the TCA cycle, and amino acid biosynthesis.
• Systems Biology Integration: Correlates metabolic shifts with transcriptomic and proteomic data to model whole-body metabolic adaptations in conditions like obesity or sepsis.

Lipidomics

• Structural Resolution: Characterizes lipid subspecies diversity, including regioisomers (e.g., sn-1 vs. sn-2 acyl chain positions) and double-bond localization (e.g., ω-3 vs. ω-6 fatty acids).
• Spatiotemporal Mapping: Utilizes imaging mass spectrometry (e.g., MALDI-MSI) to visualize lipid distribution in tissues, revealing compartment-specific roles in diseases like atherosclerosis or Alzheimer's.

3. Methodological Variations

Metabolomics Workflow

• Analytical Platforms:
  • LC-MS: Ideal for polar metabolites (e.g., amino acids) with hydrophilic interaction chromatography (HILIC).
  • GC-MS: Targets volatile metabolites (e.g., short-chain fatty acids) post-derivatization.
  • NMR: Non-destructive analysis for absolute quantification of high-abundance metabolites (e.g., glucose in serum).
• Sample Preparation:
  • Protein precipitation (e.g., methanol/acetonitrile) to isolate metabolites.
  • Freeze-drying for biofluid stabilization.
• Data Analysis:
  • Pathway enrichment tools (e.g., MetaboAnalyst) to identify dysregulated routes like purine metabolism in gout.

Lipidomics Workflow

• Analytical Platforms:
  • LC-MS: Reversed-phase chromatography for nonpolar lipids (e.g., triglycerides).
  • MALDI-MS: High-throughput lipid imaging in tissues.
  • Shotgun Lipidomics: Direct infusion for comprehensive lipid class profiling.
• Sample Preparation:
  • Chloroform-methanol extraction (Folch/Bligh-Dyer) for lipid enrichment.
  • Solid-phase extraction (SPE) to fractionate lipid classes (e.g., separating phospholipids from neutral lipids).
• Data Analysis:
  • Lipid-specific software (e.g., LipidSearch) for acyl chain annotation and oxidation site identification.

If you want to know more about lipidomics, please refer to "Lipidomics: A Comprehensive Overview".

References

1. Xiong F, Gong K, Xu H, Tu Y, Lu J, Zhou Y, He W, Li W, Li C, Zhao L, Gao H, Zheng H. "Optimized integration of metabolomics and lipidomics reveals brain region-specific changes of oxidative stress and neuroinflammation in type 1 diabetic mice with cognitive decline." J Adv Res. 2023 Jan;43:233-245. doi: 10.1016/j.jare.2022.02.011
2. Montague B, Summers A, Bhawal R, Anderson ET, Kraus-Malett S, Zhang S, Goggs R. "Identifying potential biomarkers and therapeutic targets for dogs with sepsis using metabolomics and lipidomics analyses." PLoS One. 2022 Jul 8;17(7):e0271137. doi: 10.1371/journal.pone.0271137
3. Liu K, Xiong Y, Fan Y, Li S, Wu L, Chen H, Wang X. "Research on the mechanism of the anti-myocardial infarction effect of the Qiliqiangxin capsule on heart failure rats via nontargeted metabolomics and lipidomics." BMC Cardiovasc Disord. 2024 Dec 31;24(1):762. doi: 10.1186/s12872-024-04423-8
4. Garrett TJ, Puchowicz MA, Park EA, Dong Q, Farage G, Childress R, Guingab J, Simpson CL, Sen S, Brogdon EC, Buchanan LM, Raghow R, Elam MB. "Effect of statin treatment on metabolites, lipids and prostanoids in patients with Statin Associated Muscle Symptoms (SAMS)." PLoS One. 2023 Dec 15;18(12):e0294498. doi: 10.1371/journal.pone.0294498
5. Zhang F, Lim WLF, Huang Y, Lam SM, Wang Y. "Lipidomics and metabolomics investigation into the effect of DAG dietary intervention on hyperuricemia in athletes." J Lipid Res. 2024 Sep;65(9):100605. doi: 10.1016/j.jlr.2024.100605
6. Huang H, Jiang J, Fan Y, Ding X, Li F, Liu C, Ji L. "Non-targeted metabolomics and pseudo-targeted lipidomics combined with gut microbes reveal the protective effects of Causonis japonica (Thunb.) Raf. in ulcerative colitis mice." Front Cell Infect Microbiol. 2024 Oct 15;14:1397735. doi: 10.3389/fcimb.2024.1397735