Comprehensive metabolite quantitative assay based on alternate metabolomics and lipidomics analyses

An accurate and robust multicomponent quantitative analysis method: Molar mass coefficient method

Multicomponent quantitative analysis (MCQA) is necessary for comprehensively characterizing the quality of complex samples, including medicines, foods. However, the limited supply of reference substances and the high costs associated with testing hinder the application of the MCQA using the external standard (ES) method. Here we propose a Molar Mass Coefficient (MMC) method for the quantification of multiple compounds with identical chromophore utilizing a single reference compound (SRC) by a UV detector. This method involves establishment of a linear equation of SRC (yr=axr+b) with a standard compound r, followed by the derivation of quantitative equations for other components i using the deduced formula (yi=(Mr/Mi)axi+b). The MMC method introduces only two known physical parameters, specifically the molar mass of compounds r and i, which ensures both the accuracy and practicality of this method. The feasibility of the MMC method was demonstrated with a set of simulated samples containing 17 flavonoids standard compounds, and further validated with samples of Scutellariae Radix and Ginkgo dry extract. The newly established method could accurately quantify flavonoids, yielding results and robustness consistent with the traditional ES method. Notably, for the contents of baicalin and kaempferol, when compared to the results obtained by ES method, the relative standard deviations (RSD%) for the commonly used method were 5.72 % and 4.96 %, respectively, while these values fall down to 0.73 % and 1.02 % when employing the MMC method. The MMC method exhibited significant advantages over existing methods, including high accuracy, robustness and low cost, implying its broad application in different fields.

Reference Intervals of Serum Metabolites and Lipids of a Healthy Chinese Population Determined by Liquid Chromatography-Mass Spectrometry

Background: Metabolomics serves as a very useful tool for elucidating disease mechanisms and identifying biomarkers. Establishing reference intervals (RIs) of metabolites in a healthy population is crucial to the application of metabolomics in life sciences and clinics. Methods: We enrolled 615 healthy Chinese adults aged between 21 and 85 years. Their health status was ascertained through clinical examinations, biochemical parameters, and medical history. Targeted metabolomics and lipidomics analyses were applied to quantify 705 metabolites and lipids in the serum, establishing RIs and investigating the effect of sex and age on the metabolome and lipidome. Results: This study is the first large-scale effort in China to establish RIs for metabolites in the apparently healthy population. We found that most of the sex-related metabolites, including amino acids, acyl-carnitines and triacylglycerols, had higher concentrations in males, while the other sex-related lipids showed higher concentrations in females. Most of the age-related metabolites increased with age, including those associated with protein synthesis, nitric oxide synthesis, energy metabolism, and lipid metabolism. Conclusions: This study gives the reference intervals of the healthy Chinese metabolome and lipidome and their relationship with sex and age, which facilitates life sciences and precision medicine, especially for disease research and biomarker discovery.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

Polarity-extended liquid chromatography-triple quadrupole mass spectrometry for simultaneous hydrophilic and hydrophobic metabolite analysis

Although various metabolomic methods have been reported in recent years, simultaneous detection of hydrophilic and hydrophobic metabolites in a single analysis remains a technical challenge. In this study, based on the combination of hydrophilic interaction liquid chromatography (HILIC) and reversed phase liquid chromatography (RPLC), an online two-dimensional liquid chromatography/triple quadrupole mass spectrometry method (2D-LC/TQMS) was developed for the simultaneous analysis of hydrophilic and hydrophobic metabolites of various biological samples. The method can measure 417 biologically important metabolites (e.g., amino acids and peptides, pyrimidines, purines, monosaccharides, fatty acids and conjugates, organic dicarboxylic acids, and others) with logP values ranging from -10.3 to 21.9. The metabolites are involved in a variety of metabolic pathways (e.g., purine metabolism, pyrimidine metabolism, tyrosine metabolism, galactose metabolism, gluconeogenesis, and TCA cycle). The developed method has good intra- and inter-day reproducibility (RSD of retention time <2%, RSD of peak area <30%), good linearity (R2 > 0.9) and wide linear range (from 0.0025 μg/mL to 5 μg/mL). The applicability of the method was tested using different biological samples (i.e., plasma, serum, urine, fecal, seminal plasma and liver) and it was found that 208 (out of 417) identical metabolites were detected in all biological samples. Furthermore, the metabolomic method was applied to a case/control study of urinary of bladder cancer. Thirty differential metabolites were identified that were involved in carbohydrate and amino acid metabolism.