Applications of Lipidomics in Nutrition and Health

Lipids can influence lipid metabolic pathways in the body. Objective and accurate identification of potential lipid biomarkers in the process from health to disease progression, so as to develop dietary guidelines for disease prevention and health maintenance, is of great importance to promoting human health.

Lipidomics applied to the nutritional study of polyunsaturated fatty acids (PUFA)

LDL-C/HDL-C, total cholesterol and triglyceride levels are indicators for monitoring cardiovascular diseases. PUFA, especially w-3 PUFA, has the effect of lowering serum cholesterol and triglycerides, inhibiting LDL-C synthesis and increasing HDL-C levels. In turn, HDL-C maintains endothelial vascular reactivity, inhibits endothelial cell apoptosis, improves resistance to oxidative stress, and reduces the expression of adhesion factor-related genes in blood vessels and arteries. In addition, in studies related to Alzheimer's disease, docosahexaenoic acid (DHA) content in brain tissue was found to be significantly reduced, and the ability of patients' liver to synthesize DHA was also affected. w-3 PUFA can alter the composition of cell membrane phospholipids, interrupt the efficacy of lipid rafts to achieve anti-inflammatory effects, and inhibit the signal transduction of nuclear transcription factors-such as κB, reduce the expression of inflammatory genes, and promote the expression of the anti-inflammatory transcription factor NR1C3 and the binding of G proteins to GPR120 receptors.

An omics discipline with a key role in nutritionFig.1 An omics discipline with a key role in nutrition (De et al., 2020)

Lipidomics applied to the nutritional study of plant sterols

Phytosterols have anti-inflammatory, metabolic, prostate disease prevention, and anti-cancer effects. The exact mechanism by which it reduces LDL-C in the blood is not known. It has been speculated that the competition between plant sterols and cholesterol inhibits the absorption of cholesterol in the human body, and plant sterols and cholesterol must be dissolved into particle clusters before they can be absorbed by the body.

Lipidomics applied to disease research

With the development of lipidomics, lipid molecules have become important biomarkers. For example, plasma levels of triglycerides, short-chain fatty acids, free fatty acids, ceramides, and phosphatidylcholine are elevated in patients with hyperlipidemia, while levels of lysolecithin (LPC) (16:0) are decreased. The decrease in LPC (16:0) is consistent with its modulation by peroxisome proliferator-activated receptor α. Free fatty acids are associated with endothelium-dependent nitric oxide production and nuclear factor κB signaling activity. Ceramides control nutrient uptake and anabolism in tissues. The high sensitivity of molecular changes in lipids facilitates early diagnosis and effective prevention.

Lipids have an important role in cellular signal transduction and tissue physiology studies. Lipids in the brain determine the location and function of protein distribution in cell membranes and therefore can influence synaptic fluxes in neurons. Disturbances in lipid metabolism are closely associated with many neurological disorders, including schizophrenia and neurodegenerative diseases such as Alzheimer's disease, Parkinson's syndrome, and Niemann-Pick disease.

Lipidomics for CVDFig 2. Lipidomics for CVD (Pechlane et al., 2016)

Analytical methods for lipids

Sample pretreatment methods: liquid-liquid extraction (LLE) and solid-phase extraction (SPE) are often used.

Mass spectrometry detection techniques: Self-connected injection methods and chromatography and chromatography-mass spectrometry coupling techniques are often used. Self-acceptance mass spectrometry has the advantage of fast analysis, but matrix effects can occur when analyzing biological samples with complex matrices.

Combining the separation advantages of chromatography with the identification advantages of mass spectrometry can effectively reduce the matrix effects that other components may have on the target compounds. The complex system is separated by chromatography and then entered into the mass spectrometry detector, which can improve the reliability of the mass spectrometry scan data.

Gas chromatography (GC) is commonly used to analyze the composition of small molecular weight lipids such as fatty acids. The availability of high-temperature GC stationary phases has made it possible to analyze high boiling point compounds by GC. However, high temperatures have a destructive effect on lipids with relatively high unsaturates, making them unsuitable for the analysis of lipids containing long-chain polyunsaturated fatty acids. GC is also effective in separating fatty acid isomers and cis-trans structures of unsaturated fatty acid double bonds, and full two-dimensional gas chromatography coupled in series with gas chromatography of different polarities is an effective method for the analysis of polyunsaturated long-chain fatty acids.

The closed environment of liquid chromatography (HPLC) effectively prevents lipid degradation, and almost all classes of lipid molecules can be separated by high-performance liquid chromatography. Normal phase chromatography (NPLC) is often used in a complementary manner to reversed-phase chromatography (RPLC), which is suitable for the separation of strongly polar lipids depending on the polarity of the lipid head group, while RPLC is often used for the separation of lipid molecules containing the same head group with different fatty acyl chains, and is suitable for the separation of weakly and moderately polar lipids.

Lipidomics data analysis: Lipidomics data processing mainly includes signal filtering, metabolic feature extraction, chromatography and mass spectrometry matching, missing value filtering and complementary values, signal normalization, compound characterization and quantification, and other steps. The processed data also needs to be analyzed by multivariate statistical analysis, such as data scalarization processing, pattern recognition and characteristic variable selection, and finally data visualization.


  1. de Castro, M. L., & Quiles-Zafra, R. (2020). Lipidomics: An omics discipline with a key role in nutrition. Talanta, 219, 121197.
  2. Pechlaner, R., Kiechl, S., & Mayr, M. (2016). Potential and caveats of lipidomics for cardiovascular disease.
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