• History & Evolution
• Biosynthesis & dietary uptake
• Itaconic acid and the microbiome
• Itaconic acid, immunity and inflammation
• Itaconic acid and cardiovascular disease
• Itaconic acid and neurology
• Itaconic acid and oncology
• Itaconic acid and 5P medicine
• References

History & Evolution

1836: discovery (Baup, 1836) | 1840: first synthesis (Crasso, G., 1840) | 1960s: first industrial applications | 2012: first synthesized in mammalian cells (Sugimoto, et al., 2011)

In 1836, Swiss chemist Samuel Baup discovered an unknown compound when experimenting with the distillation of citric acid (Baup, 1836). In 1840, Gustav Crasso synthesized the same compound through the decarboxylation of aconitate, and called it itaconate – an anagram of its precursor (Crasso, 1840). This unsaturated dicarboxylic acid received little attention for almost a hundred years, until 1931, when Japanese mycologist Kinoshita discovered a strain of Aspergillus fungi in salted prune juice that produced the metabolite (Kinoshita, 1932). He named the fungus Aspergillus itaconicus.

By the 1960s, itaconic acid had caught the attention of the polymer industry, thanks to the useful double bond of its methylene group. Now, around 40,000 tons are produced each year, primarily synthesized from Aspergillus terreus (Cordes, 2015).

Scientific interest in itaconate’s biological role was renewed in the early 2010s, when Sugimoto et al. discovered that it could be synthesized in mammalian immune cells (Sugimoto et al., 2011). Since then, itaconic acid’s role as a key player in immunometabolism and inflammatory regulation has become an active area of research (Diotallevi et al., 2021).

Biosynthesis vs. dietary uptake

In humans, itaconic acid is an endogenous metabolite synthesized primarily in the mitochondria of activated macrophages (Peace et al., 2022). It is synthesized by decarboxylating cis-aconitate, an intermediate of the tricarboxylic acid (TCA) cycle. Metabolomics has shown that this occurs through the action of the enzyme cis-aconitase decarboxylase, also known as immune-responsive gene 1 (IRG1 or ACOD1) (Michelucci et al., 2013). Expression of IRG1 is upregulated during infection and inflammation, leading to increased itaconate production. Plasma concentrations are therefore highly variable, depending on immune status.

Humans do not obtain itaconic acid directly from the diet, but dietary factors may affect its production. For example, carbohydrate intake affects the TCA cycle and energy metabolism, which in turn influences the availability of itaconate precursors like cis-aconitate. Under inflammatory conditions, nutrient availability may affect itaconate synthesis.

Itaconate is the biologically active, deprotonated form of itaconic acid, which contains two carboxylic acid groups that lose protons at pH levels above 7. This gives itaconate a double negative charge and makes it the dominant form found in the bloodstream. Structurally, it resembles other dicarboxylates such as succinate, malonate, phosphoenolpyruvate and fumarate (Peace et al., 2022).

Itaconic acid and the microbiome

While itaconic acid is synthesized by environmental microbes, like some Aspergillus species, it does not appear to be produced by gut microbiota in significant amounts. That said, it has been described as a “microbiota-associated metabolite” because its production in the mitochondria of host immune cells is triggered by microbial components such as lipopolysaccharide (LPS) from Gram-negative bacteria (Fedotcheva et al., 2022).

A study using integrated multiomics approaches showed that altered itaconate metabolism leads to significant changes in gut microbiota composition. Using a mouse model, Eberhart et al. (2024) found that in response to a high-fat diet, the itaconate-synthesizing enzyme ACOD1 promotes gut dysbiosis associated with obesity and inflammation, while genetic deletion of ACOD1 protects against metabolic disease (Eberhart et al., 2024). In addition, fecal metagenomics and microbiota transplantation showed that itaconate inhibits growth of Bacteroidaceae. These findings suggest that itaconate plays a role in diet-induced obesity and positions the ACOD1–itaconate pathway as a potential therapeutic target for the inflammatory effects of obesity.

Itaconic acid also inhibits bacterial isocitrate lyase, a key enzyme in the glyoxylate shunt during bacterial infection (Peace, 2022). This limits the growth of pathogens like Mycobacterium tuberculosis, Pseudomonas indigofera and Salmonella. However, some bacteria have developed strategies to protect themselves against itaconate’s antimicrobial properties (Peace, 2022). For example, Pseudomonas aeruginosa, Yersinia pestis and M. tuberculosis can break down itaconate into pyruvate and acetyl-CoA, while P. aeruginosa and Staphylococcus aureus respond to itaconate-induced stress by shifting from LPS to extracellular polysaccharide production, leading to biofilm formation that provides intracellular protection for the bacteria.

See how the MxP® Quant 1000 assay helps scientists investigate the impact of energy metabolism on health by accurately profiling TCA metabolites with targeted metabolomics

Itaconic acid, immunity and inflammation

Itaconic acid plays an important role in immune regulation and inflammation through the antibacterial mechanisms mentioned above. It also modulates the production of reactive oxygen species (ROS), which helps defend against bacterial and viral infections. Itaconate’s antiviral properties have been found to inhibit Zika virus replication (Daniels et al., 2019).

Beyond its antimicrobial activity, itaconic acid also acts as an anti-inflammatory metabolite by targeting key metabolic pathways in activated macrophages. It inhibits succinate dehydrogenase, a key enzyme in the TCA cycle, which alters mitochondrial metabolism and promotes anti-inflammatory cytokine production (Cordes et al., 2016). It also disrupts mitochondrial energy metabolism by disrupting oxidative phosphorylation and fatty acid oxidation. Recent studies have shown that itaconic acid can promote the pentose phosphate pathway, increasing nicotinamide adenine dinucleotide phosphate (NADPH) levels and enhancing NADPH oxidase-mediated ROS production. These ROS contribute to the expression of anti-inflammatory genes, restrict pathogens like Salmonella typhimurium and reduce pro-inflammatory cytokine production (Zhu et al., 2021).

These findings have led to broader recognition of the itaconate pathway in immune regulation. A review by Li et al. (2020) describes the IRG1/itaconate axis as a central feature in immunometabolic regulation, supported by metabolomics data that link itaconate to changes in mitochondrial respiration, oxidative stress and inflammatory signaling in macrophages (Li et al., 2020).

Given these properties, itaconic acid has emerged as a metabolite of interest in immune-mediated diseases. A recent review by Luo et al. (2024) suggests itaconate and its derivatives may play a role in conditions such as rheumatoid arthritis, multiple sclerosis , type 1 diabetes mellitus and autoimmune hepatitis (Luo et al., 2025).

Itaconic acid and cardiovascular disease

Research shows that itaconate may have a protective effect in cardiovascular disease. In models of atherosclerosis, IRG1 deficiency has been shown to increase plaque burden (Harber et al., 2024), while treatment with 4-octyl itaconate (4-OI) reversed plaque inflammation in mice and reduce cardiovascular inflammation in human immune cells (Cyr et al., 2024).

Itaconate also supports cardiac repair after myocardial infarction. A study using targeted metabolomics found that after efferocytosis (the clearing of dead cells which is essential for cardiac repair), altered signaling in macrophages expressing the TREM2 receptor disrupted the TCA cycle (Gong et al., 2024). This resulting increase in itaconate production reduced cardiomyocyte apoptosis and promoted fibroblast proliferation. Increased TREM2 levels led to improved cardiac function.

Itaconic acid and neurology

Neuroinflammation is a well-established driver in the progression of numerous neurological disorders, including Alzheimer’s disease (AD), Parkinson’s disease (PD), multiple sclerosis (MS) and stroke. Itaconate and its derivatives may help counteract this neuroinflammatory activity in the central nervous system through the regulation of immune pathways including Nrf2/KEAP1, ROS production and the NLRP3 inflammasome (Kong et al., 2024).

In LPS-induced neuroinflammation, both itaconate and the related isomer mesaconate have been found to reduce inflammation in the brain and improve synaptic plasticity, linked to memory and cognitive processes (Ohm et al., 2024). A study using untargeted serum metabolomics in PD patients also identified reduced levels of circulating itaconate, suggesting a role in disease-associated immune dysregulation (Paul et al., 2023).

These findings suggest that itaconate may be a promising candidate as a biomarker and therapeutic target in neurological conditions.

Itaconic acid and oncology

Itaconic acid has also been implicated in cancer biology. In peritoneal tumors such as melanoma and ovarian carcinoma, metabolomics techniques have shown an upregulation of itaconic acid (Weiss et al., 2018). Tumor-associated macrophages were found to accumulate itaconate, resulting in increased oxidative phosphorylation and mitochondrial ROS production, and in turn promoting tumor growth. Silencing IRG1 was found to reduce the tumor burden.

However, there is also evidence that itaconic acid has anti-tumor effects (Perkovic et al., 2020) (Wang, Z. et al., 2024). A mouse model showed that dimethyl itaconate suppresses colitis-associated colorectal cancer (Wang et al., 2020). In another animal study, 4-OI was found to induce ferroptosis in the treatment of retinoblastoma (Liu et al., 2022).

Thes findings suggest that more research is needed to fully understand the role of itaconic acid in different types of cancer.

Itaconic acid and 5P medicine

In the context of 5P medicine – predictive, preventive, precision, population-based, participatory medicine – itaconic acid is again relevant due to its role in mediating immune responses and inflammation, both of which contribute to many prevalent chronic diseases. A white paper by biocrates, “Chronic diseases have a common origin”, highlights the underlying role of inflammation in complex chronic disease.

Obesity is a major public health concern and key target for preventive strategies. Dietary itaconate has been shown to reduce visceral fat accumulation in rats, highlighting a potential role in early intervention and metabolic regulation (Sakai et al., 2004).

As a biomarker of disease, itaconate levels in blood or immune cells may signal early inflammatory activity or therapeutic response, as seen in conditions like rheumatoid arthritis (Daly et al., 2020), non-alcoholic fatty liver disease (Weiss et al., 2018) and bacterial infections (Singh et al., 2021). In each of these examples, metabolomics has played a role in providing insights about the presence and behavior of this important metabolite.

Itaconic acid is a promising focus for future research, with omics technologies offering a powerful tool to support 5P medicine.

References

Baup, S.: Ueber eine neue Pyrogen-Citronensäure, und über Benennung der Pyrogen-Säuren überhaupt. (1836) Ann. Pharm | https://doi.org/10.1002/jlac.18360190107.

Cordes, T. et al.: Itaconic Acid: The Surprising Role of an Industrial Compound as a Mammalian Antimicrobial Metabolite. (2015) Annual Review of Nutrition | https://doi.org/10.1146/annurev-nutr-071714-034243.

Cordes, T. et al.: Immunoresponsive Gene 1 and Itaconate Inhibit Succinate Dehydrogenase to Modulate Intracellular Succinate Levels. (2016) J Biol Chem.| https://doi.org/10.1074/jbc.M115.685792.

Crasso, G.: Untersuchungen über das Verhalten der Citronsäure in höherer Temperatur und die daraus hervorgehenden Produkte (1840) Ann. Chem. Pharm. | https://doi.org/10.1002/jlac.18400340104.

Cyr, Y. et al.: The IRG1–itaconate axis protects from cholesterol-induced inflammation and atherosclerosis. (2024) Natl Acad Sci USA | https://doi.org/10.1073/pnas.2400675121.

Daly, R. et al.: Changes in Plasma Itaconate Elevation in Early Rheumatoid Arthritis Patients Elucidates Disease Activity Associated Macrophage Activation. (2020) Metabolites | https://doi.org/10.3390/metabo10060241.

Daniels, B. et al.: The nucleotide sensor ZBP1 and kinase RIPK3 induce the enzyme IRG1 to promote an antiviral metabolic state in neurons.(2019) Immunity | https://doi.org/10.1016/j.immuni.2018.11.017.

Diotallevi, M. et al.: Itaconate as an inflammatory mediator and therapeutic target in cardiovascular medicine. (2021) Biochem Soc Trans, | https://doi.org/10.1042/BST20210269.

Eberhart, T. et al.: ACOD1 deficiency offers protection in a mouse model of diet-induced obesity by maintaining a healthy gut microbiota. (2024) Cell Death Dis. | https://doi.org/10.1038/s41419-024-06483-2.

Fedotcheva, N. et al.: Influence of Microbial Metabolites and Itaconic Acid Involved in Bacterial Inflammation on the Activity of Mitochondrial Enzymes and the Protective Role of Alkalization.(2022) Int J Mol Sci. | https://doi.org/10.3390/ijms23169069.

Gong, S. et al.: TREM2 macrophage promotes cardiac repair in myocardial infarction by reprogramming metabolism via SLC25A53. (2024) Death Differ. | https://doi.org/10.1038/s41418-023-01252-8.

Harber, K. et al.: Targeting the ACOD1-itaconate axis stabilizes atherosclerotic plaques. (2024) Biol. | https://doi.org/10.1016/j.redox.2024.103054.

Kinoshita, K.: Über die Produktion von Itaconsäure und Mannit durch einen neuen Schimmelpilz, Aspergillus itaconicus. (1932) Acta Phytochimica | https://www.scirp.org/reference/referencespapers?referenceid=899199

Kong, X. et al.: The anti-inflammatory effects of itaconate and its derivatives in neurological disorders. (2024) Cytokine & Growth Factor Reviews, | https://doi.org/10.1016/j.cytogfr.2024.07.001.

Li, R. et al.: Itaconate: A Metabolite Regulates Inflammation Response and Oxidative Stress. (2020) Oxid Med Cell Longev.| https://doi.org/10.1155/2020/5404780.

Liu, K. et al.: Induction of autophagy-dependent ferroptosis to eliminate drug-tolerant human retinoblastoma cells. (2022) Death Dis. | https://doi.org/10.1038/s41419-022-04974-8.

Luo, Y. et al.: Metabolic Regulation of Inflammation: Exploring the Potential Benefits of Itaconate in Autoimmune Disorders. (2025) Immunology | https://doi.org/10.1111/imm.13875.

Michelucci, A. et al.: Immune-responsive gene 1 protein links metabolism to immunity by catalyzing itaconic acid production. (2013) PNAS | https://doi.org/10.1073/pnas.1218599110.

Ohm, M. et al.: The potential therapeutic role of itaconate and mesaconate on the detrimental effects of LPS-induced neuroinflammation in the brain. (2024) J Neuroinflammation | https://doi.org/10.1186/s12974-024-03188-3.

Paul, K. et al.: Untargeted serum metabolomics reveals novel metabolite associations and disruptions in amino acid and lipid metabolism in Parkinson’s disease. (2023) Mol Neurodegener. | https://doi.org/10.1186/s13024-023-00694-5.

Peace, C. et al.: The role of itaconate in host defense and inflammation. (2022) J Clin Invest. | https://doi.org/10.1172/JCI148548.

Perkovic, I. et al.: Itaconic acid hybrids as potential anticancer agents. (2020) Mol Divers. | https://doi.org/10.1007/s11030-020-10147-6.

Sakai, A. et al.: Itaconate reduces visceral fat by inhibiting fructose 2,6-bisphosphate synthesis in rat liver. (2004) Nutrition | https://doi.org/10.1016/j.nut.2004.08.007.

Singh, S. et al.: Integrative metabolomics and transcriptomics identifies itaconate as an adjunct therapy to treat ocular bacterial infection. (2021) Rep Med, | https://doi.org/10.1016/j.xcrm.2021.100277.

Sugimoto, M. et al.: Non-targeted metabolite profiling in activated macrophage secretion. (2011) Metabolomics | https://doi.org/10.1007/s11306-011-0353-9.

Wang, Q. et al.: The anti-inflammatory drug dimethyl itaconate protects against colitis-associated colorectal cancer. (2020) Journal of Molecular Medicine | https://doi.org/10.1007/s00109-020-01963-2.

Wang, Z. et al.: Cancer cell-intrinsic biosynthesis of itaconate promotes tumor immunogenicity. (2024) EMBO J. | https://doi.org/10.1038/s44318-024-00217-y.

Weiss, J. et al.: Itaconic acid mediates crosstalk between macrophage metabolism and peritoneal tumors. (2018) J Clin Invest, | https://doi.org/10.1172/JCI99169.

Zhu, X. et al.: Itaconic acid exerts anti-inflammatory and antibacterial effects via promoting pentose phosphate pathway to produce ROS. (2021) Sci Rep. | https://doi.org/10.1038/s41598-021-97352-x.

Using mass spectrometry in metabolic profiling

Metabolomics relies on mass spectrometry (MS), a prevalent methodology known for its high sensitivity, reproducibility, and versatility. MS is a quantitative and qualitative analytical technique that uses the mass-to-charge (m/z) ratio of previously ionized molecules to provide insights into biochemical pathways, metabolic fluxes, and the physiological status of organisms. Due to the diverse physical and chemical properties of compounds such as lipids, amino acids, and organic acids, as well as their widespread and fluctuating distribution across biological matrices, different MS-based technologies are used (Ren et al. 2018; Zhang et al. 2015).

Mass spectrometry, which consists of three main components: an ion source, a mass analyzer, and a detector. Ionization techniques such as electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) ionize molecules from solutions/solid-state, mixtures, while electron ionization (EI) is used to ionize molecules from the gas phase (after gas-chromatography (GC) separation).

Mass analyzers like time-of-flight (TOF), quadrupole, and ion trap separate ions based on their m/z ratios. Detectors such as electron multipliers and photomultiplier tubes then amplify ion signals for analysis. Liquid chromatography-mass spectrometry (LC-MS) combines liquid chromatography separation with MS detection, suitable for complex mixtures, whereas GC-MS offers high resolution for volatile compounds but requires derivatization for non-volatile ones. Each platform has advantages and limitations, guiding their selection for metabolomics studies based on the nature of the analytes and analytical requirements (Ahmad et al. 2020, Hoffmann et al. 2007).

Sample preparation

Effective metabolomic analysis calls for meticulous sample preparation to extract metabolites from the biological matrices, enhance detectability, and mitigate potential interference in subsequent mass spectrometry (MS) analysis.

If you’re unsure about the optimal matrix for your study, consider referring to ‘Which sample matrix should I use for my metabolomics study?’.

While preparation techniques play a crucial role in improving the detection and stability of metabolites, they also introduce complexities and variability into the sample preparation workflow. Sample clean-up procedures are necessary to eliminate unwanted matrix components, ameliorate matrix effects and enhance the sensitivity and selectivity of MS detection (Gong et al. 2017). Despite efforts to optimize sample preparation protocols, striking a balance between maximizing metabolite detection and minimizing sample variability and matrix effects remains challenging (Fan et al. 2012).

Extraction and analysis

Metabolomics analysis relies on various extraction techniques, each tailored to specific metabolite proprieties and sample matrices. Liquid-liquid extraction (LLE), solid-phase extraction (SPE), and protein precipitation are a few examples, each offering distinct advantages and applicability across different sample types (Nováková et al. 2024).

LLE involves partitioning analytes between two immiscible liquid phases, making it ideal for extracting non-polar and moderately polar metabolites. SPE uses solid-phase sorbents to selectively retain analytes, making it applicable to a wide range of sample types with high selectivity (Schomakers et al. 2022, Chetwynd et al. 2015). By contrast, protein precipitation removes proteins using organic solvents or acids, providing a rapid and effective approach for high-throughput analyses.

Derivatization methods enhance metabolite detection and stability, particularly those with low volatility or poor ionization efficiency, but as with preparation techniques, they can complicate the sample preparation workflow (Skov et al 2015).

Data analysis and interpretation

After data preprocessing, which involves peak picking, retention time alignment, and normalization, researchers strive to mitigate systematic variations that may be lurking in the raw data. To facilitate the identification of differences between experimental conditions, the data interpretation phase uses statistical methods, which may include the following (Lamichhane et al. 2018, Cambiaghi et al. 2017): 

• t-tests
• analysis of variance (ANOVA)
• linear mixed models
• multivariate analyses such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA)
• metabolite set analysis such as the hypergeometric test and Fisher Exact test
• data fusion techniques such as joint and individual explained analysis (JIVE) and deeply integrated single-cell omics (DISCO).

A few common challenges to look out for at this stage are handling missing values, managing high data dimensionality, and controlling false discovery rates. Researchers address these through various strategies such as imputation techniques, dimensionality reduction methods, and stringent statistical corrections. Quality control measures and validation strategies are also imperative for ensuring the robustness and reproducibility of results (Perez de Souza et al. 2024, Anwardeen et al. 2023). Multivariate data analysis techniques help find patterns and correlations within metabolomics datasets. Limonciel, 2023, introduces the “STORY Principle”, a systematic framework for interpreting metabolomics data and guiding researchers through each step.

Applications of MS-based metabolomics in recent research publications

Metabolomics can be applied in diagnostics, prognostics, biomarker discovery, treatment monitoring, and understanding disease mechanisms across various fields such as cardiometabolic diseases, diabetology, oncology, and toxicology.

The study of cardiometabolic diseases, including diabetes, is an especially important application of MS in metabolomics research. Huang et al. 2024, investigated the protective effect of Liu-Wei-Di-Huang-Wan (a traditional Chinese formulation composed of six commonly used Chinese herbs) in mice with type 2 diabetes mellitus. They identified 30 endogenous metabolites including 3-hydroxybutyric acid, citric acid, hexadecanoic acid, and octadecanoic acid. They found that treatment with Liu-Wei-Di-Huang-Wan improved metabolic patterns in diabetic kidney disease mice, comparable to gliquidone, by regulating metabolites associated with key metabolic pathways. In another study, Matter-Rieck et al. 2024, showcase the potential role of omentin in regulating systemic metabolism, particularly lipid metabolism, particularly lipid metabolism, and influencing insulin sensitivity, further exploring its involvement in type 2 diabetes. Omentin levels were positively associated with lipids such as phosphatidylcholines (PCs) and acylcarnitines, and negatively associated with certain amino acids.

Epidemiology and genetics offer insights into disease etiology and progression. Rosario et al. 2023, identified significant metabolic and transcriptional dysregulation associated with gastrointestinal adenocarcinomas. The findings reveal alterations in pathways such as steroid metabolism and tryptophan/kynurenine metabolism, with sex-specific differences. Investigating gut microbiota stability, Frost et al. 2021, found that instability, characterized by an increase in potential pathogens like Enterobacteriaceae and a decrease in beneficial bacteria such as Bifidobacteria, was significantly associated with metabolic disorders including diabetes mellitus and fatty liver disease.

Metabolomics is also an important investigative tool in oncology, presenting the metabolic signatures linked to cancer development and treatment response. Zhang N et al. 2024, used LC-MS and GC-MS analysis of plasma metabolites to study biomarkers for neoadjuvant therapy efficacy in HER2 + breast cancer. Significant differences were found between responders and non-responders, with 100 metabolites identified and enriched in 40 pathways. Area under the curve (AUC) values for discriminating groups exceeded 0.910, and 18 metabolites showed potential for monitoring efficacy. In another study, Decker et al. 2024, described connections between cholesterol precursors, oxysterols, and diverse factors in women diagnosed with breast cancer. Their research shows the linkage of cholesterol precursors to metabolic factors like body mass index (BMI), cardiovascular disease (CVD) and oxysterols, particularly those generated via reactive oxygen species (ROS), and key characteristics of breast cancer tumors, including obesity.

Metabolic profiling is increasingly recognized in cardiology studies. A study by Kretzschmar et al. 2024, analyzed plasma metabolites in patients with acute decompensated heart failure (ADHF) and chronic heart failure (CHF). They found distinct metabolic signatures associated with each phenotype, including potential novel biomarkers for heart failure such as 1-methyl histidine and 3-indolepropionic acid. Another interesting study by Andrews et al. 2024, highlights significant changes in lipid metabolism in mice, specifically focusing on alterations in acylcarnitines, amino acids, bile acids, ceramides, sphingomyelins, and triacylglycerols. These findings pinpoint the mechanisms underlying hepatic steatosis and atherosclerosis development in mice fed the hyperhomocysteinemic diet.

Finally, in neuroscience, Ge et al. 2024, conducted a metabolomic analysis from 48 patients with disorders of consciousness (DoC), showing distinct metabolic profiles linked to etiology, consciousness levels, and prognosis. Their findings highlight the critical role of phospholipid metabolism and identify potential biomarkers for improved diagnosis and treatment of DoC.

As mass spectrometry evolves, we’ll see more opportunities to apply the methodology within metabolomics studies. If you are considering using this technique in your own study, feel free to contact the biocrates team with any questions.

References

Andrews, S.G. et al.: Diet-Induced Severe Hyperhomocysteinemia Promotes Atherosclerosis Progression and Dysregulates the Plasma Metabolome in Apolipoprotein-E-Deficient Mice (2024) Nutrients | DOI: 10.3390/nu16030330

Anwardeen, N.R.et al.: Statistical methods and resources for biomarker discovery using metabolomics (2023) BMC Bioinformatics 24 | DOI:10.1186/s12859-023-05383-0

Cambiaghi A et al.: Analysis of metabolomic data: tools, current strategies and future challenges for omics data integration (2017) Briefings in Bioinformatics | DOI: 10.1093/bib/bbw031

Causon J.: Review of sample preparation strategies for MS-based metabolomic studies in industrial biotechnology (2016) Analytica Chimica Acta | DOI: 10.1016/j.aca.2016.07.033

Chetwynd J. et al.: Solid-Phase Extraction and Nanoflow Liquid Chromatography-Nanoelectrospray Ionization Mass Spectrometry for Improved Global Urine Metabolomics (2014) Analytical Chemistry | DOI: doi.org/10.1021/ac503769q

Decker, N.S et al.: Associations between lifestyle, health, and clinical characteristics and circulating oxysterols and cholesterol precursors in women diagnosed with breast cancer: a cross-sectional study. (2024) Sci Rep 14 | DOI: 10.1038/s41598-024-55316-x

Fan, T.WM.: Considerations of Sample Preparation for Metabolomics Investigation. (2012) The Handbook of Metabolomics. Methods in Pharmacology and Toxicology | DOI: 10.1007/978-1-61779-618-0_2

Frost F. et al.: Long-term instability of the intestinal microbiome is associated with metabolic liver disease, low microbiota diversity, diabetes mellitus and impaired exocrine pancreatic function (2021) Gut | DOI: 10.1136/gutjnl-2020-322753

Ge Q, et al.: Serum metabolism alteration behind different etiology, diagnosis, and prognosis of disorders of consciousness (2024) Chin Neurosurg J. | DOI: 10.1186/s41016-024-00365-4

Gong Z-G. et al.: The Recent Developments in Sample Preparation for Mass Spectrometry-Based Metabolomics (2017) Critical Reviews in Analytical Chemistry, 47(4), 325–331 | DOI: 10.1080/10408347.2017.1289836

Hoffmann E. et al.: Mass Spectrometry: Principles and Applications (2007) Hoboken, NJ: John Wiley & Sons. | ISBN: 978-0-470-03310-4

Huang JH. et al.: A GC-MS-Based Metabolomics Investigation of the Protective Effect of Liu-Wei-Di-Huang-Wan in Type 2 Diabetes Mellitus Mice (2020) Int J Anal Chem. Aug | DOI: 10.1155/2020/1306439

Javed A et al.: Basic Principles and Fundamental Aspects of Mass Spectrometry (2022) Mass Spectrometry in Food Analysis | DOI: 10.1201/9781003091226-2

Kretzschmar T. et al.: Metabolic Profiling Identifies 1-MetHis and 3-IPA as Potential Diagnostic Biomarkers for Patients With Acute and Chronic Heart Failure With Reduced Ejection Fraction. (2024) Circ Heart Fail | DOI: 10.1161/CIRCHEARTFAILURE.123.010813

Lamichhane S.:Chapter Fourteen – An Overview of Metabolomics Data Analysis: Current Tools and Future Perspectives (2018) Comprehensive Analytical Chemistry | DOI: 10.1016/bs.coac.2018.07.001

Limonciel A. The STORY principle A guide to the biological interpretation of metabolomics (2023) biocrates life sciences gmbh | DOI: 10.1016/bs.coac.2018.07.001

Nováková S. et al.: Comparison of Various Extraction Approaches for Optimized Preparation of Intracellular Metabolites from Human Mesenchymal Stem Cells and Fibroblasts for NMR-Based Study (2024) Metabolites | DOI: 10.3390/metabo14050268

Perez de Souza L. et al.: Computational methods for processing and interpreting mass spectrometry-based metabolomics. (2024) Essays Biochem | DOI: 10.1042/EBC20230019

Ratter-Rieck J.: Omentin associates with serum metabolite profiles indicating lower diabetes risk: KORA F4 Study (2024) BMJ Open Diabetes Research and Care | DOI: 10.1136/bmjdrc-2023-003865

Ren J-L. et al.: Advances in mass spectrometry-based metabolomics for investigation of metabolites (2018) Royal Society of Chemistry | DOI: 10.1039/C8RA01574K

Rosario D. et al.: Systematic analysis of gut microbiome reveals the role of bacterial folate and homocysteine metabolism in Parkinson’s disease (2021) Cell Reports 34 | DOI: 10.1016/j.celrep.2021.108807

Schomakers V. Et al.: Polar metabolomics in human muscle biopsies using a liquid-liquid extraction and full-scan LC-MS (2022) TAR Protocols | DOI: 10.1016/j.xpro.2022.101302

Skov K. et al.: LC–MS analysis of the plasma metabolome—A novel sample preparation strategy (2015) Journal of Chromatography B | DOI: 10.1016/j.jchromb.2014.11.033

Zhang A. et al.: Metabolomics for Biomarker Discovery: Moving to the Clinic (2015) Biomed Res Int. | DOI: 10.1155/2015/354671

Zhang N et al.: Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer. (2024) Breast Cancer Res. | DOI: 10.1186/s13058-024-01813

• History & Evolution
• Biosynthesis vs. dietary uptake
• TMAO and the microbiome
• Is TMAO good or bad?
• TMAO and cardiovascular diseases
• TMAO and diabetes
• TMAO and chronic kidney disease (CKD)
• TMAO and trimethylaminuria (TMAU)

History & Evolution

Trimethylamine N-oxide (TMAO) is an amine oxide and osmolyte commonly found in marine crustaceans and marine fish, protecting them against water pressure, temperature, salinity, and urea (Velasquez et al. 2016).

TMAO concentration appears to increase in fish tissues as they inhabit greater depths in the ocean, allowing them to withstand increasing hydrostatic pressure (Yancey et al. 2014). TMAO metabolizes into trimethylamine (TMA), which is responsible for the pungent smell of degrading seafood.

Recently, interest in TMAO has extended beyond the world of marine biology, with a growing body of research linking it to cardiovascular disease (CVD), insulin resistance, renal dysfunction, cancer, stroke, and inflammation in humans (Velasquez et al. 2016, Ufnal et.al. 2015, Pelletier et al. 2019, Farhangi et al. 2020).

It’s not all bad news though: research points to TMAO having several positive effects too, including circulatory benefits and helping cells maintain volume under stress (Ufnal et al. 2015).

Biosynthesis vs. dietary uptake

TMAO is biosynthesized from TMA, which is generated when gut bacteria metabolize choline, derived from lecithin, L-carnitine and betaine (Janeiro et al. 2018, Rohrmann et al. 2016). TMA is transported to the liver and oxidized into TMAO by the liver enzyme FMO3 (Wang et al. 2011). TMAO is then stored in cells or cleared by the kidneys.

The amount of TMAO found in plasma can be affected by diet, gut microbiota, drugs, and liver enzyme activity (Janeiro et al. 2018). Diet is the main source of TMAO and TMA precursors. TMAO levels increase after consuming foods high in choline, carnitine and lecithin, including red meat, seafood, eggs and dairy (Velasquez et al. 2016). Vegan and vegetarian diets do not seem to select for the gut bacteria needed to metabolize these precursors into TMAO (Koeth et al. 2013).

Small amounts of L-carnitine are synthesized endogenously from lysine and methionine, in the kidney, liver and brain (Cave et al. 2008). Similarly, humans can produce some choline endogenously through hepatic synthesis, though not enough to meet the body’s needs. TMAO is often seen as a waste product of choline, but does appear to play an important role in many biological processes (Ufnal et al. 2015).

TMAO and the microbiome

The composition of the gut microbiome heavily influences TMAO production. TMAO levels are dependent on the presence of specific intestinal microbiota, particularly Deferribacteraceae, Anaeroplasmataceae, Prevotellaceae and Enterobacteriaceae (Velasquez et al. 2016). Plasma levels of TMAO are suppressed when individuals take antibiotics

A short-term crossover feeding trial in healthy young men showed that TMAO production is a function of individual differences in the gut microbiome, and demonstrated differences in how microbiota respond to dietary TMAO and its precursors (Cho et al. 2016). For example, fish consumption led to higher quantities of TMAO metabolites than eggs or beef.

Dietary TMAO was absorbed without needing any processing by gut microbiota. Individuals who produced higher levels of TMAO seemed to have a less diverse gut microbiome.

Is TMAO good or bad?

A major debate in current TMAO research surrounds its role in linking the gut microbiome and cardiovascular disease. The link is there – but literature is unclear on whether TMAO is a bystander or a mediator (Janeiro et al. 2018), and whether it should be sought out or avoided in a healthy diet.

On one hand, TMAO and its precursors are found in foods eaten regularly in heart-healthy Mediterranean diets, particularly seafood. Some research suggests that TMAO could be beneficial for heart function. A study on hypertensive rats found that higher levels of TMAO had no negative effects on circulation, and a low-dose TMAO treatment helped reduce hypertension-related heart disease symptoms (Huc et al. 2018).

However, other studies have shown that increased levels of TMAO are associated with increased risk of CVD. Similarly, higher TMAO levels also appear to correlate with impaired kidney function, and reduced renal clearance of TMAO is associated with an increased risk of CVD.

It is thought that TMAO causes changes in cholesterol and bile acid metabolism leading to cell inflammation, which in turn creates an atherogenic effect, but whether TMAO is a causal factor or not remains unclear (Janeiro et al. 2018, Winther et al. 2021).

There is also a growing body of evidence identifying TMAO as a missing link between gut dysbiosis and other conditions, including colorectal cancer (often linked to red meat consumption). This would suggest TMAO-rich foods should be left off the menu.

TMAO and cardiovascular disease

The connection between TMAO and CVD was first made using an animal model in 2011, which revealed that choline, TMAO and betaine were predictors of CVD risk. When gut bacteria were suppressed, TMAO production was limited, which in turn inhibited atherosclerosis progression (the primary underlying cause of CVD)

Human studies into the relationship between TMAO and atherosclerosis have been generally small or with mixed results (Meyer et al. 2016). In one frequently cited three-year study of over 4000 individuals, high levels of TMAO in plasma correlated with an increased risk of adverse cardiovascular events, independent of traditional risk factors.

However, the validity of these findings has been challenged because of the high prevalence of comorbidities and pre-existing inflammation markers among participants (Meyer et al. 2016).

Interestingly, TMA has not attracted the same attention as a potential risk factor for CVD, despite being a known uremic toxin (Jaworska et al. 2019). Nevertheless, evidence is accumulating to show an association between gut microbes and cardiometabolic disease, and elevated plasma TMAO levels do seem to predict future risk of major adverse cardiac events and CVD (Senthong et al. 2016).

TMAO and diabetes

The emerging association between gut dysbiosis and insulin resistance is another area of interest in TMAO research (Øellgaard et al. 2017). One systematic review and meta-analysis looked at TMAO levels and diabetes risk, with a combined dataset of more than 15,000 participants.

This revealed a robust association between high circulating concentrations of TMAO and increased prevalence of diabetes mellitus: people with high circulating concentrations of TMAO were 50% more likely to have diabetes than those with lower levels, and people with diabetes had higher levels of TMAO than nondiabetic people.

In animal models, dietary TMAO impaired glucose tolerance and inhibited insulin signaling. Again, the precise mechanism remains unclear (Zhuang et al. 2019, Winther et al. 2021).

TMAO and chronic kidney disease (CKD)

Gut dysbiosis is linked with an increased risk of kidney disease, particularly when TMAO and choline levels are elevated (Velasquez et al. 2016). In one prospective cohort study of CKD patients, mortality risk was 2.8 times higher in those with higher TMAO levels In another study, patients with end-stage renal disease on dialysis had TMAO levels forty times higher than the control group (Hai et al. 2015).

While CKD is known to alter the gut microbiome, there is mounting evidence to suggest that TMAO is not only a biomarker for CKD, but also contributes to disease progression This points to diet, microbiome, hepatic TMA metabolism, and TMAO behavior as potential therapeutic targets.

A study in germ-free mice versus mice colonized with gut microbiota showed that the gut microbiota influences the host’s development of hypertensive organ damage, with TMAO and other microbial metabolites such as short-chain fatty acids (SCFAs) contributing to the phenotype (Avery et al., 2023).

TMAO and trimethylaminuria (TMAU)

Trimethylaminuria, or ‘fish odor syndrome,’ is caused when a mutation of FMO3 prevents the conversion of TMA into TMAO. Individuals with TMAU excrete TMA, instead of TMAO, which causes a fishy smell in urine, sweat, and breath (Velasquez et al. 2016).

TMAU is inherited when both parents carry FMO3-altering genes, and can be triggered by diet and stress. It also affects more women than men, suggesting a link with sex hormones. Treatment usually focuses on avoiding foods rich in TMA or TMA precursors, suppressing TMA production in the gut with antibiotics, and taking supplements to enhance FMO3 enzyme activity and reduce TMA concentration (National Human Genome Research Institute 2018).

Learn more about the roles of TMAO, TMA, and other microbial metabolites in complex chronic diseases such as cancer, Alzheimer’s disease, depression, inflammatory bowel disease, multiple sclerosis and diabetes in our whitepaper “Complex chronic diseases have a common origin”.

References

Advantages

Urine offers several advantages over other body fluids in medical and metabolic investigations (3):

• Sample collection is non-invasive and relatively easy. This makes urine sampling ideal for large-scale epidemiological and longitudinal studies as well as for pediatric and geriatric populations.

• Sample handling and storage is simple. Fresh samples can generally be kept on ice or at 4 °C for up to 8 hours before centrifugation. The supernatant should then be stored at -80 °C (4).

• Urine is typically available in larger quantities than plasma or serum, which can be especially advantageous for studies requiring larger amounts of sample material.

• Like plasma or serum (but unlike feces or tissue) samples, urine is a homogenous matrix, meaning there is no variation or gradient within a sample.

• Urine is mostly free from interfering proteins or lipids that may affect metabolite extraction, ionization of target metabolites, or mask the presence of low-abundance metabolites (5).

• Urine responds rapidly to dietary and lifestyle factors, disease, medications, and other influences, making it possible to monitor acute changes in metabolism and see how the body responds to different conditions or interventions.

• Because urine reflects both systemic and renal metabolism, it is useful for monitoring the effect of food, drugs, and chemical or pollutant exposure (6).

• Urine contains higher concentrations of substances such as organic acids, neurotransmitters, hormones, and gut microbial metabolites that are often undetectable in other biofluids (7).

Challenges

However, urine sampling is not without its challenges:

• Urine contains extremely high salt concentrations and high levels of urea. In addition, the waste products of foods and beverages, environmental pollutants, medicines and endogenous waste metabolites can be present at very high concentrations. This makes metabolite quantification in urine technically complex (5).

• Fluid intake has major effects on the concentration of urinary metabolites. Creatinine normalization goes some way to address this, but creatinine excretion itself varies depending on nutrition, kidney function, age and overall health.

• Metabolite concentrations in urine also vary depending on diurnal rhythm, fasting time and last meal content. Mitigation of these effects is most successful with the “second midstream morning urine” or “24-hour urine” collection methods. While it is not practical to standardize last meals, it is crucial to standardize collection methods and fasting time across the study (8).

• Many plasma or serum metabolites cannot be quantified in urine samples because their concentrations are too low to detect. Lipids in particular are almost completely absent. However, the metabolic picture provided by small molecules analysis in urine marries very well with measurements of broader panels (including lipids) in matching blood-derived samples.

Because of the differences between urine and blood-based matrices, the metabolomics solutions developed for plasma and serum cannot be simply applied to urine sample measurement. For this reason, biocrates has recently launched a urine extension for the MxP® Quant 500 and AbsoluteIDQ® p180 kits. This includes adapted calibration standards, urine quality control samples, urine-like zero samples, and optimized methods for a more accurate quantification of urinary metabolites.

Application of urine metabolomics

Some of the key application areas of urine metabolomics include:

Disease diagnosis and biomarker discovery

Urine samples are widely used for diagnosis of kidney diseases and metabolic diseases, and for diagnosis and biomarker discovery in several types of cancer (9, 10) and neurological disorders (11).

Drug efficacy and safety

Urine is used to monitor the effect of drugs on metabolism and identify potential adverse reactions or nephrotoxic effects.

Toxicology

Urine metabolomics can be used to detect and explore the mechanism of action in metabolite changes following exposure to toxins or environmental pollutants (12, 13).

Athletic and sports performance

Although urine metabolomics reveals the effects of training, performance and recovery on metabolism, the most common application is the detection of prohibited substances, i.e., doping control.

Nutrition and diet

Urine metabolomics can be used to investigate the impact of specific diets, nutrients, or dietary interventions on metabolism and monitor adherence to specific dietary regimens.

Urine metabolomics and precision nutrition

Urine metabolomics is attracting interest particularly in nutrition and diet research. Our white paper on complex diseases discusses the role of diet in many complex chronic diseases in the Western world, like type 2 diabetes, Alzheimer’s disease, inflammatory bowel disease (IBD) and cancer. Given the link between gut microbiota and disease metabolism, modifying the microbiome through dietary habits and consumption of pre- and probiotics may be an effective route to better overall health (14, 15).

Nutrient metabolism and dietary effects vary between individuals (16), which opens the door to personalized dietary recommendations based on an individual’s metabolism characteristics. Precision nutrition uses omics to analyze an individual’s response to different foods or dietary patterns and identify the most effective dietary or lifestyle changes to prevent or treat particular diseases (17).

Urine metabolomics aids precision nutrition research in the following ways:

• Assessing individual dietary intake and adherence: Specific urinary profiles were linked with adherence to the Alternative Healthy Eating Index (18). By analyzing the metabolite profiles in urine, researchers can identify markers associated with the consumption of specific foods or nutrients to monitor dietary habits and make necessary adjustments (19).

• Tracking response to dietary interventions: The ability to monitor how an individual’s metabolism responds to different diets or supplementation protocols supports the optimization of dietary strategies for weight management, blood sugar control or other health goals (19, 20).

• Identifying metabolic phenotypes (metabotypes): Metabolic phenotypes are specific patterns of metabolite concentrations associated with an individual’s response to dietary components. This information can be used to categorize individuals into different metabolic subgroups and tailor dietary recommendations.

• Monitoring nutrient status: Urine metabolomics can detect markers of nutrient deficiency or excess, which helps to assess malnutrition severity and inform individualized dietary modifications. Long-term longitudinal tracking of the nutrient status is particularly useful in chronic disease management.

• Identifying biomarkers: Urine metabolomics can help identify biomarkers associated with specific diet-related health conditions or disease risk factors. These biomarkers can be used to assess an individual’s risk for certain diseases and develop personalized dietary strategies to mitigate that risk (19, 21).

Overall, urine metabolomics provides a wealth of information about an individual’s metabolism and how it responds to dietary factors. This information can help individuals make more informed dietary choices and optimize their nutrition for better health outcomes.

Clinical relevance of urine metabolomics in precision nutrition

The National Institutes of Health promote precision nutrition as the best method to formulate clinically relevant diet plans for individuals and populations that share similar physiological, behavioral or sociocultural traits (22). Precision nutrition studies produce convincing results to demonstrate links between dietary change and several complex diseases. Below are a few examples that demonstrate the relevance of precision nutrition driven by urine metabolomics for a broad range of chronic diseases:

Cardiovascular disease

The North Karelia project found that dietary changes can significantly decrease coronary heart disease mortality in the general population (23). An elevated risk of atherosclerosis is linked to higher gut microbiota production of trimethylamine (TMA), which is readily detectable in urine but less so in plasma. Subjects who produce comparatively high amounts of TMA from red meat received the dietary advice to reduce their red meat intake, thus reducing their risk for cardiovascular events (20).

Epilepsy

Akiyama et al. investigated the effect of a ketogenic diet on epilepsy using plasma and urine metabolomics (24). Results showed a metabolic shift from glucose-based to fat-based energy generation associated with increased urine concentrations of 3-hydroxybutyric acid, a known fasting marker, and other organic acids. The altered energy source proved to be effective for a subset of epilepsy patients, indicating that energy metabolism and neuronal function are closely linked.

IBD

Another prospective study followed 20 ulcerative colitis patients for 12 months. Urine metabolomics revealed significant nutrition-related differences between patients with and without relapse that were sufficient to discriminate between these groups (25).

Obesity

A urine metabolomics-based study analyzed metabolomes and microbial features to understand the associated metabolic pathways and the effect of lifestyle interventions on pediatric obesity. After an eight-week weight-reduction lifestyle modification program, the responder group showed significantly decreased urinary myristic acid levels in correlation with an improved Bacteroidetes to Firmicutes gut bacteria ratio, indicating reduced fatty acid biosynthesis. Another finding was that high weight among the non-responders was associated with low urinary levels of hippuric acid, a metabolite resulting mainly from gut bacterial processing of polyphenols, suggesting that there are benefits of consuming polyphenol-rich plants on the weight that depend on the gut microbiome (26).

These examples show that the diet-microbiome-metabolome axis has an important role in linking macronutrient consumption and disease, and that urine metabolomics can help elucidate the metabolic pathways involved.

A major limitation of nutritional studies is that diet assessment often relies on self-reports, such as 24-hour dietary recalls or dietary diaries that are vulnerable to subjectivity, errors in estimated portion size and accidental omissions. Estimated prevalence of misreporting with these tools is around 30–88% (27). Urine metabolomics can help solve this problem.

Garcia-Perez et al. showed that urinary metabolic profiles developed in a controlled environment can be used to assess adherence to dietary patterns in free-living populations without the need to collect dietary data (23). Moreover, the Food Biomarker Alliance developed an objective biomarker system to define urine and blood biomarkers of food intake (BFIs) (28). This metabolite inventory aims to determine exactly what a person has eaten, how much they have eaten, and how it has been metabolized (17). Urinary BFIs have been identified for intake of meat (29), citrus fruit (30), fish (17) and coffee (31). In all cases, BFIs must be measured using targeted metabolomic methods (17).

Continued urine-based dietary pattern analysis in population-based programs and epidemiological studies will enhance understanding the relation between diet and disease and will improve the knowledge base for physicians to provide effective individualized precision nutrition recommendations.

Interested in incorporating urine metabolomics into your study? Find out more about the new urine extension for the biocrates MxP® Quant 500 and AbsoluteIDQ® p180 kits.

References

1. Bouatra S. et al.: The human urine metabolome. (2013) PLoS One | https://doi.org/10.1371/journal.pone.0073076

2. The Urine Metabolome Database [cited 2023 Sep 18]. Available from: URL: https://urinemetabolome.ca/statistics.

3. Ledinger S.: Which sample matrix should I use for my metabolomics study? (2022) biocrates life cciences ag 
[cited 2023 Sep 1].| Available from: URL: https://biocrates.com/metabolomics-study-sample-matrix/.

4. Stevens VL et al.: Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review. (2019) Metabolites | Available from: URL: https://www.mdpi.com/2218-1989/9/8/156.

5. Brezmes J. et al.: Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.(2022) Int J Mol Sci | https://doi.org/10.3390/ijms231911171.

6. Holmes E. et al.: Detection of urinary drug metabolite (xenometabolome) signatures in molecular epidemiology studies via statistical total correlation (NMR) spectroscopy. (2007) Anal Chem | https://doi.org/10.1021/ac800859x

7. Zhang Z. et al.: Urine Analysis has a Very Broad Prospect in the Future. (2022) Front. Anal. Sci. | https://doi.org/10.3389/frans.2021.812301

8. Lehmann R.: From bedside to bench-practical considerations to avoid pre-analytical pitfalls and assess sample quality for high-resolution metabolomics and lipidomics analyses of body fluids. (2021) Anal Bioanal Chem | https://doi.org/10.1007/s00216-021-03450-0.

9. Erben V. et al.: Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms. (2021) Diagnostics (Basel) | https://doi.org/10.3390/diagnostics11030561.

10. Krossa S. et al.: Detectable biomarkers in urine for prostate cancer prognosis. (2019) European Urology Supplements | https://doi.org/10.1016/S1569-9056(19)33331-7

11. Yilmaz A. et al.: Targeted Metabolic Profiling of Urine Highlights a Potential Biomarker Panel for the Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment: A Pilot Study. (2020) Metabolites | https://doi.org/10.3390/metabo10090357.

12. Imam SZ et al.: Changes in the metabolome and microRNA levels in biological fluids might represent biomarkers of neurotoxicity: A trimethyltin study. (2018) Exp Biol Med (Maywood) | https://doi.org/10.1177/1535370217739859.

13. Li Y. et al.: Metabolomic insights into the lasting impacts of early-life exposure to BDE-47 in mice. (2020) Environ Pollut | https://doi.org/10.1016/j.envpol.2020.114524

14. Mills S. et al.: Precision Nutrition and the Microbiome Part II: Potential Opportunities and Pathways to Commercialisation. (2019) Nutrients | https://doi.org/10.3390/nu11071468.

15. Mills S. et al.: Precision Nutrition and the Microbiome, Part I: Current State of the Science. (2019) Nutrients | https://doi.org/10.3390/nu11040923.

16. Zeevi D. et al.: Personalized Nutrition by Prediction of Glycemic Responses. (2015) Cell | https://doi.org/10.1016/j.cell.2015.11.001.

17. LeVatte M. et al.: Applications of Metabolomics to Precision Nutrition. (2022) Lifestyle Genom 2022 | https://doi.org/10.1159/000518489

18. Brennan L. et al.: Role of metabolomics in the delivery of precision nutrition. (2023) Redox Biol | https://doi.org/10.1016/j.redox.2023.102808

19. Wang DD et al.: Precision nutrition for prevention and management of type 2 diabetes. (2018) The Lancet Diabetes & Endocrinology | https://doi.org/10.1016/S2213-8587(18)30037-8

20. Tebani A. et al.: Paving the Way to Precision Nutrition Through Metabolomics. (2019) Front Nutr | https://doi.org/10.3389/fnut.2019.00041

21. Cho K. et al.: Combined untargeted and targeted metabolomic profiling reveals urinary biomarkers for discriminating obese from normal-weight adolescents. (2017) Pediatric Obesity | https://doi.org/10.1111/ijpo.12114

22. Rodgers GP et al.: Precision Nutrition-the Answer to “What to Eat to Stay Healthy”. (2020) JAMA |  https://doi.org/10.1001/jama.2020.13601

23. Garcia-Perez I et al.: Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial. (2017) The Lancet Diabetes & Endocrinology | https://www.thelancet.com/journals/landia/article/piis2213-8587(16)30419-3/fulltext

24. Akiyama M et al.: Comprehensive study of metabolic changes induced by a ketogenic diet therapy using GC/MS- and LC/MS-based metabolomics. (2023) Seizure | https://doi.org/10.1016/j.seizure.2023.03.014

25. Keshteli AH et al.: Dietary and metabolomic determinants of relapse in ulcerative colitis patients: A pilot prospective cohort study. (2017) World J Gastroenterol | https://doi.org/10.3748/wjg.v23.i21.3890

26. Lee Y et al.: Serum, Urine, and Fecal Metabolome Alterations in the Gut Microbiota in Response to Lifestyle Interventions in Pediatric Obesity: A Non-Randomized Clinical Trial. (2023) Nutrients | https://doi.org/10.3390/nu15092184

27. Poslusna K et al.: Misreporting of energy and micronutrient intake estimated by food records and 24 hour recalls, control and adjustment methods in practice. (2009) Br J Nutr | https://doi.org/10.1017/S0007114509990602

28. Brouwer-Brolsma EM et al.: Combining traditional dietary assessment methods with novel metabolomics techniques: present efforts by the Food Biomarker Alliance. (2017) Proc Nutr Soc | https://doi.org/10.1017/s0029665117003949

29. Cross AJ et al.: Urinary biomarkers of meat consumption. (2011) Cancer Epidemiol Biomarkers Prev | https://doi.org/10.1158/1055-9965.epi-11-0048

30. Lloyd AJ et al.: Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption. (2011) Br J Nutr | https://doi.org/10.1158/1055-9965.epi-11-0048

31. Heinzmann SS et al.: 2-Furoylglycine as a Candidate Biomarker of Coffee Consumption. (2015) J Agric Food Chem | https://doi.org/10.1158/1055-9965.epi-11-0048

• History & evolution
• Biosynthesis vs. dietary uptake
• Circulating cholesterol and cardiovascular disease
• Cholesterol as a precursor
• Cholesterol and membranes
• Cholesterol and neurology
• Cholesterol and cancer

History and evolution

1769: first discovery (Olson 1998) | 1929: discovery of lipoproteins | 1951: first synthesis of cholesterol

Cholesterol is a lipid found in all animal cell membranes, contributing to cell structure and integrity, membrane fluidity, and transportation of signaling molecules (Goldstein et al. 2009). It is also a precursor to steroid hormones, bile acids and vitamin D.

The study of cholesterol has a long history. It was first discovered in 1769 by Poulletier de la Salle, who observed a waxy substance in bile and gallstones (Olson 1998). The name is derived from “cholesterine”, a name given to it by Chevreul in 1815 based on the Ancient Greek words for bile (chole) and solid (stereos). By the early 19th century, cholesterol was becoming better understood as a component of blood plasma.

Cholesterol lipoproteins were discovered in 1929, with the first substance isolated later shown to be high-density lipoprotein (HDL) (Olson 1998). The second of the two main cholesterol-carrying lipoproteins, low-density lipoprotein (LDL), was discovered in 1949, when John Gofman took advantage of the Manhattan Project’s ultracentrifuge technology to investigate “protein X” (LDL) (Siri-Tarino et al. 2006).

In the 1960s and 70s, the protein components and receptors of cholesterol lipoproteins were identified, allowing a greater understanding of its role in the fat transport system. This led to the “cholesterol wars”, where controversy surrounding the role of lipids (specifically dietary cholesterol) in atherosclerosis and coronary heart disease dominated lipid research for the remainder of the 20th century, arguably hindering medical progress (Kuijpers 2021).

Biosynthesis vs. dietary uptake

Around 70% of cholesterol in humans is synthesized endogenously, with the remaining 30% obtained via the diet (Bourgin et al 2020). Biosynthesis of cholesterol occurs in the liver through either de novo or exogenous biosynthesis. De novo biosynthesis is a complex process, starting with acetyl-coenzyme A (CoA), derived via oxidation reactions (Tsuchiya et al. 2010). Acetyl-CoA condenses to 3-hydroxy-3-methylglutaryl-CoA (HMG-CoA). This is catalyzed by HMG-CoA synthase and reduced by HMG-CoA reductase to produce mevalonate. Mevalonate is then converted to squalene, a precursor of sterols, via a series of enzymatic reactions. A further phase of enzymatic reactions converts these sterols into cholesterol in the endoplasmic reticulum.

In the exogenous pathway, dietary cholesterol comes from steroids in animal tissues, with the main sources including eggs, meat and dairy products. To be absorbed, ingested cholesterol esters must be hydrolyzed by pancreatic enzymes to form free fatty acids (FFAs) (Lecerf 2011). Around 1000 mg of cholesterol reaches the intestinal lumen each day, from both endogenous and exogenous pathways.

Cholesterol homeostasis is maintained via feedback loops regulated by transcriptional factors, liver X receptors (LXR, which regulate cholesterol excretion) and farnesoid X receptors (FXR, which regulate bile acid metabolism) (Soliman 2018).

Circulating cholesterol and cardiovascular disease

Cholesterol is excreted from the liver into biliary fluids, which then enter the digestive system. Around half of this cholesterol is recycled via the small intestine (Cohn 2010). HDL helps transport excess fat from the tissues and blood to the liver, where it can be excreted from the body. LDL does the opposite, transporting fat to the tissues.

Cholesterol gets mixed reviews, with HDL often labelled as “good” cholesterol and LDL considered “bad”. Elevated HDL may have a protective effect, and too little can increase the risk of cardiovascular disease. Conversely, elevated LDL levels can cause a build-up of fats in the blood, causing clots and cardiovascular disease (Soliman 2018). However, both play an essential role in human health, and an imbalance of either can have deleterious effects, so it is now commonly accepted that the names “good” and “bad” cholesterol make little sense.

Cholesterol levels in extracellular vesicles (EVs) have attracted attention as potential diagnostic markers of cardiovascular disease, cancer and other diseases (Pfrieger et al. 2018). EVs contain cholesterol metabolites such as 27-hydroxycholesterol, which has been associated with breast cancer cell lines (Roberg-Larsen 2017). These membrane-enclosed structures released by cells may also be a method of drug delivery, by transporting proteins, genes, microRNAs and viruses to treat disease (Pfrieger et al. 2018).

Cholesterol as a precursor

Cholesterol is a precursor of the following metabolites:

• bile acids (such as cholic acid), which aid digestion, contribute to the absorption of lipophilic nutrients in the intestine, and support gene and metabolism regulation;
• steroid hormones (such as cortisol and sex hormones) which regulate numerous physiological functions and metabolic processes, and are synthesized from cholesterol in the adrenal gland and gonads through the common precursor steroid pregnenolone, using the LDL/LDL-receptor pathway (Hu et al. 2010);
• vitamin D, which helps regulate calcium and phosphate levels and supports bone, dental, muscular and paracrine functions;
• oxysterols, which are oxygenated derivatives of cholesterol that play a role in bile acid synthesis, the immune system and bone marrow function (Oguro 2019).

Cholesterol and membranes

Cholesterol is an essential building block of cell membranes, interacting with phospholipid fatty-acid chains to influence membrane fluidity and integrity. It does this primarily through sphingolipid rafts. Lipid rafts help regulate cell signaling, membrane transport and protein trafficking, though precisely how they affect cholesterol transport is still unknown (Cerqueira et al. 2016, Wang et al. 2020). The involvement of cholesterol-enriched lipid rafts has been observed in studies of viruses including influenza, human immunodeficiency virus (HIV), Newcastle disease virus (NDV), herpes and others.

Cholesterol is an important component of the myelin sheath, along with sphingomyelins (Poitelon et al. 2020). The myelin sheath surrounds nerve cell axons, which makes it relevant to the study of diseases related to the central nervous system and neurophysiology.

Myelin cannot be synthesized without cholesterol and contains the largest pool of free cholesterol in the body (Poitelonet al. 2020). Most cholesterol in myelin is derived from de novo synthesis in oligodendrocytes and astrocytes, since blood-based cholesterol cannot cross the blood-brain barrier.

Cholesterol and neurology

Around a quarter of the body’s cholesterol is contained in the brain (Björkhem et al. 2004). As noted, cholesterol availability is a crucial factor in brain development and neuronal function. Changes in cholesterol metabolism in the central nervous system have been implicated in neurologic disorders, including Alzheimer’s disease (AD), Huntington’s disease and Parkinson’s disease (Zhang et al. 2015).

Several studies have linked hypercholesterinemia and lipoprotein isoforms such as ApoE4 to AD. One study showed that lower levels of cholesterol precursors may reduce the neuroprotective effect on mitochondrial energy metabolism, while non-enzymatically produced metabolites may contribute to inflammation, pointing to cholesterol metabolism as a potential therapeutic target in AD (Varma et al. 2021).

Smith-Lemli-Opitz syndrome is a rare recessive disorder caused by mutations in the gene encoding enzyme 7-dehydrocholesterol reductase. Patients with severe cases can experience intellectual disabilities, delayed motor and language development, and sleep disorders, thought to be exacerbated by low brain cholesterol levels (Cunniff et al. 1997).

Cholesterol and cancer

Cholesterol has a known impact on carcinogenic activity, including tumor development, apoptosis, and angiogenesis (Cruz et al. 2013). Several studies have investigated cholesterol metabolism as a potential therapeutic target for cancer treatment. Inhibiting cholesterol synthesis via the mevalonate pathway and delivering anti-cancer agents via lipoprotein transport are two examples.

One study used the drug ezetimibe, in combination with dietary interventions, to show that elevated circulating cholesterol levels promote prostate cancer tumor growth (Solomon et al. 2009). Reduced circulating cholesterol was shown to slow tumor growth.

Other studies have taken a “Trojan horse” approach to exploit cancer cells’ high cholesterol requirements, to deliver anti-cancer agents to cancer cells (Cruz et al. 2013). Further research is needed to understand more about these different strategies.

References

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Bourgin M. et al.: Exploring the Bacterial Impact on Cholesterol Cycle: A Numerical Study. (2020) Frontiers in Microbiology | https://doi.org/10.3389/fmicb.2020.01121

Cerqueira N. et al.: Cholesterol Biosynthesis: A Mechanistic Overview. (2016) Biochemistry | https://doi.org/10.1021/acs.biochem.6b00342

Cohn J. et al.: Dietary Phospholipids and Intestinal Cholesterol Absorption. (2010) Nutrients | https://doi.org/10.3390/nu2020116

Cruz P. et al. : The role of cholesterol metabolism and cholesterol transport in carcinogenesis: a review of scientific findings, relevant to future cancer therapeutics.” ( 2013) Frontiers in Pharmacology | https://doi.org/10.3389/fphar.2013.00119

Cunniff C. et al.: Clinical and biochemical spectrum of patients with RSH/Smith-Lemli-Opitz syndrome and abnormal cholesterol metabolism. (1997) American Journal of Medical Genetics | https://doi.org/10.1007/978-1-4614-1037-9_218

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Hu J. et al.: Cellular cholesterol delivery, intracellular processing and utilization for biosynthesis of steroid hormones.” (2010) Nutrition & Metabolism | https://doi.org/10.1186/1743-7075-7-47

Kuijpers P.: History in medicine: the story of cholesterol, lipids and cardiology.” (2021) e-Journal of Cardiology Practice | https://www.escardio.org/Journals/E-Journal-of-Cardiology-Practice/Volume-19/history-in-medicine-the-story-of-cholesterol-lipids-and-cardiology]

Lecerf J. et al.: Dietary cholesterol: from physiology to cardiovascular risk. (2011) British Journal of Nutrition | https://doi.org/10.1017/S0007114511000237

Oguro H.: The Roles of Cholesterol and Its Metabolites in Normal and Malignant Hematopoiesis. (2019) Frontiers in Endocrinology | https://doi.org/10.3389/fendo.2019.00204

Olson R.: Discovery of the Lipoproteins, Their Role in Fat Transport and Their Significance as Risk Factors. (1998) The Journal of Nutrition | https://doi.org/10.1093/jn/128.2.439S

Pfrieger F.: Cholesterol and the journey of extracellular vesicles. (2018) Journal of Lipid Research | https://doi.org/10.1194/jlr.R084210

Poitelon Y. et al.: Myelin Fat Facts: An Overview of Lipids and Fatty Acid Metabolism. (2020) Cells | https://doi.org/10.3390/cells9040812

Roberg-Larsen H. et al.: Mass spectrometric detection of 27-hydroxycholesterol in breast cancer exosomes. (2017) The Journal of Steroid Biochemistry and Molecular Biology | https://doi.org/10.1016/j.jsbmb.2016.02.006

Siri-Tarino P.: The early years of lipoprotein research: from discovery to clinical application. (2006) Journal of Lipid Research | https://doi.org/10.1194/jlr.R069575

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Zhang J.et al.: Cholesterol metabolism and homeostasis in the brain. (2015) Protein Cell | https://doi.org/10.1007/s13238-014-0131-3

• History & Evolution
• Biosynthesis vs. dietary uptake
• Structure and nomenclature
• Phosphatidylcholines and membrane properties
• Phosphatidylcholines and inflammation
• Phosphatidylcholines, surfactant and the lungs
• Sex differences in phosphatidylcholine levels
• Phosphatidylcholines and central nervous system infections

History and evolution

1846: discovery of lecithin (Vance 2014) | 1954: identification of pathways for biosynthesis (Zeisel 2012) | 1990s: phosphatidylcholine shown to be essential for human health

Phosphatidylcholines (PCs) are one of the most abundant glycerophospholipids found in animal and plant eukaryotic cell membranes (van der Veen et al. 2017). PCs were first identified as a component of egg yolk in 1846 by Theodore Gobley, who named them “lecithin”, after the Greek word for egg yolk (lekithos) (Vance 2014). In 1862, Adolph Strecker found that heating lecithin from bile produced a substance he called “choline”.

Lecithin was later identified as phosphatidylcholine and the two terms were often used interchangeably, though PCs are part of the broader lecithin family (Zeisel 2012).

Pathways for the biosynthesis of PCs were discovered in the 1950s. The existence of various PC molecules with fatty acyl chains of varying chain lengths and saturation statuses gave rise to a more detailed nomenclature for this family of lipids detailed below.

PCs are present in multiple tissues, including brain and nerve, and can also act as an emulsifier in the lungs. They are often referred to as membrane lipids, but animal and human studies have revealed roles for PCs in energy metabolism, lipoprotein transport and cell signaling (van der Veen et al. 2017).

Biosynthesis vs. dietary uptake

PCs are found in foods high in lecithin, such as egg yolks, soybeans, sunflower seeds, meat and fish. Dietary PC is metabolized in the small intestine by pancreatic and mucosal enzymes to generate 1-lyso-phosphatidylcholine, which then enters various pathways for lipid and glucose metabolism and fat storage (Nilsson et al. 2019).

Dietary PC is the main source of choline, an essential nutrient that supports lipid and amino acid metabolism and contributes to cell membrane structure (Tang et al. 2013). Choline also acts as precursor for the neurotransmitter acetylcholine, which supports brain and muscle function.

PCs are synthesized primarily in the endoplasmic reticulum (ER) through two main pathways: the cytidine 5-diphosphocoline (CDP-choline) or Kennedy pathway, and the phosphatidylethanolamine methyl transferase (PEMT) pathway.

In the CDP-choline pathway, cytidine triphosphate activates phosphocholine and converts it to diacylglyceride (DG) to form PC (Moessinger et al. 2014). Cytoplasmic cytidylyltransferase and cholinephosphotransferase-1 mediate the reaction (Henneberry et al. 2002). This pathway accounts for around 70% of PC synthesis in the liver (Vance 2014).

The remaining 30% of PC synthesis occurs through the PEMT pathway (Moessinger et al. 2014). Here, S-adenosyl methionine (SAM) methylates phosphatidylethanolamine (PE) to form PC. This pathway is also the main route to PC synthesis in bacteria.

Intestinal PCs, whether derived from dietary uptake, bile or de novo synthesis, are hydrolyzed by phospholipase A2 (PLA2) to lysoPCs and fatty acids, then absorbed by enterocytes (Kennelly et al. 2018). This is known as the Lands cycle. The reverse reaction is also a route to PC synthesis.

Structure and nomenclature

Phospholipids include two main categories: glycerophospholipids and sphingolipids. PCs are a member of the former, along with PEs, phosphatidylserines (PSs), phosphatidylinositols (PIs), and cardiolipins (Li et al. 2015).

PCs have a phosphocholine head group linked to two fatty acyl side chains by a glycerol backbone. They can be sub-classified into diacyls, alkylacyls or alkenylacyls, depending on the type of bond at the sn-1 position (first carbon of the glycerol backbone).

Scientific nomenclature for PCs varies. At biocrates, we use a short nomenclature where the name of the PC denotes the type of bond linking the fatty acyl groups to the glycerol backbone, and the number of carbon atoms and double bonds. For example:

• PC aa C32:1 describes a PC with two fatty acyl chains adding up to 32 carbon atoms and one double bond. The ‘aa’ indicates that both moieties at the sn-1 and sn-2 positions are fatty acyl residues bound to the glycerol backbone by ester bonds.
• PC ae C44:6 describes a PC with two fatty acyl chains adding up to 44 carbon atoms and six double bonds. The ‘ae’ denotes that one of the moieties, either in the sn-1 or sn-2 position, is a fatty alcohol residue bound by an ether bond.
• LysoPC a C18:0 describes a lysoPC with a fatty acyl chain with 18 carbon atoms and no double bonds. The ‘a’ indicates that the moiety usually at the sn-1 position is a fatty acid residue bound to the glycerol backbone by an ester bond.

Lysophosphatidylcholines are produced when one of the fatty acyl groups in PCs is removed by phospholipase.

Phosphatidylcholines and membrane properties

PCs are the most abundant phospholipid in cell membranes, accounting for 40–50% of total cellular phospholipids (van der Veen et al. 2017). Mammalian cells contain large and diverse populations of PCs, derived from the remodeling action of phospholipases and lysophospholipid acyltransferases.

PCs are commonly found in the outer layer of the cell membrane, while other glycerophospholipids (PE, PS, PI) are predominant in the inner membrane leaflet. Intracellular transport is not yet fully understood. PC-specific transfer protein and non-specific lipid transfer proteins may play a role, but animal studies suggest that neither accounts for the entirety of PC movement (Vance 2014).

PCs contribute to cell function and structure. They are central to membrane-mediated cell signaling, protein synthesis, cholesterol homeostasis and triglycerides storage and secretion (Lagace et al. 2013).

Phosphatidylcholines and inflammation

PCs are involved in the early stages of the inflammatory cascade. When PCs with a C20:4 fatty acyl group are cleaved to form lysoPCs, this releases arachidonic acid (FA (20:4)). This specific fatty acid is the precursor for a family of active lipids called eicosanoids. These include prostaglandins and leukotrienes that serve as signaling molecules during inflammation .

LysoPCs also have a role in modulating the immune response through the activation and transportation of immune cells. These functions have been associated with inflammatory diseases such as diabetes, obesity, atherosclerosis, cancer and rheumatoid arthritis (Dei Cas et al. 2020).

A high ratio of lysoPCs to PCs may indicate increased enzyme activity associated with progression of inflammatory conditions, such as osteoarthritis (Zhai et al. 2019). The amount of PC affects the size and dynamics of lipid droplets in immune cells and this variation in lipid activity can trigger stress responses. The location of PC synthesis in the ER may affect the etiology of diseases that arise from ER dysfunction (Lagace et al. 2013).

Changes in phospholipid ratios can also affect energy production and have been associated with metabolic disorders such as obesity, diabetes and atherosclerosis (van der Veen J, et al., 2017). For example, PCs are central to the established association between a PC metabolite through microbial metabolism, trimethylamine-N-oxide (TMAO), and increased risk of cardiovascular disease (Tang et al. 2013).

Phosphatidylcholines, surfactant and the lungs

PCs comprise around 80% of surfactant lipids in the lungs (Agassandian et al. 2013). The majority are in disaturated form, as dipalmitoylphosphatidylcholine (DPPC). Saturated PCs are essential components of pulmonary surfactants due to their ability to lower the surface tension on alveolar structures in the lungs, which can inhibit lung expansion and cause pulmonary edema.

This makes PCs an interesting subject for the study of respiratory disease. Surfactant proteins have been well studied as a treatment for respiratory distress syndrome in neonates (Speer et al. 2013). Efficacy of surfactant treatment in adult respiratory conditions is less well established, but studies point to a possible role in modulating the immune response in pulmonary disease (Wang et al. 2021).

Lipidomic analyses have shown links between circulating lipids, including PCs, and the severity of COVID-19 (Pimentel et al. 2021). Individuals with metabolic comorbidities have been reported to be at greater risk of more severe COVID-19. Therefore, PCs may be relevant both through their role in the immune cascade and for their surfactant properties.

Sex differences in phosphatidylcholine levels

Sex-based differences in the human blood metabolome are reasonably well established. Several metabolomic investigations have shown that women have higher levels of PCs than men (Barupal et al. 2019). A 2011 study of more than 3000 participants in the Cooperative Health Research in the Augsburg Region (KORA) cohort found sex differences for up to 78% of metabolites, including PCs (Mittelstrass et al. 2011). Concentrations of PCs were found to be significantly higher in females than in males, while the reverse was true of lysoPCs.

A 2017 study found similar results: women tended to have higher levels of PCs, while men were found to have higher concentrations of lysoPCs (Trabado et al. 2017). In the same study, older subjects were found to have higher plasma levels of PCs than younger subjects.

These findings suggest that PCs concentrations may contribute to sex differences in susceptibility for many chronic diseases.

Phosphatidylcholines and central nervous system infections

PC levels may be a useful mechanism for distinguishing bacterial and viral central nervous system (CNS) infections. A 2021 study used targeted metabolomics to develop lipid profiles for bacterial meningitis, viral meningitis or encephalitis, and noninflamed controls (Al-Mekhlafi et al. 2021). PCs were found to be significantly elevated in the cerebrospinal fluid (CSF) of patients with bacterial meningitis, compared to both viral infection and controls. Ten robust biomarkers were identified, with four of the top five PCs showing better results than standard CSF parameters.

In bacterial meningitis, changes in PCs were more strongly correlated with local CNS disease than systemic inflammation, which suggests dysfunction of the blood-CSF barrier leading to cell death.

Learn more about the roles of PCs and other phospholipids in complex chronic diseases such as cancer, Alzheimer’s disease, depression, inflammatory bowel disease, multiple sclerosis and diabetes in our whitepaper “Complex chronic diseases have a common origin”.

References

Agassandian M. et al.: Surfactant phospholipid metabolism. (2013) Biochimica et Biophysica Acta | https://doi.org//10.1016/j.bbalip.2012.09.010

Al-Mekhlafi A. et al.: Elevated Free Phosphatidylcholine Levels in Cerebrospinal Fluid Distinguish Bacterial from Viral CNS Infections.(2021) Cells | https://doi.org/10.3390/cells10051115

Barupal D. et al.: The circulating lipidome is largely defined by sex descriptors in the GOLDN, GeneBank and the ADNI studies. (2019) bioRxiv | https://doi.org/10.1101/731448

Dei Cas M. et al.: Functional Lipids in Autoimmune Inflammatory Diseases.(2020) International Journal of Molecular Sciences | https://doi.org/10.3390/ijms21093074

Henneberry A. et al.: The Major Sites of Cellular Phospholipid Synthesis and Molecular Determinants of Fatty Acid and Lipid Head Group Specificity. (2002) Molecular Biology of the Cell | https://doi.org/10.1091/mbc.01-11-0540

Kennelly J. et al.: Intestinal de novo phosphatidylcholine synthesis is required for dietary lipid absorption and metabolic homeostasis. (2018) Journal of Lipid Research | https://doi.org/10.1194/jlr.M087056

Lagace T. et al.: The role of phospholipids in the biological activity and structure of the endoplasmic reticulum. (2013) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamcr.2013.05.018

Li J. et al.: A review on phospholipids and their main applications in drug delivery systems. (2015) Asian Journal of Pharmaceutical Sciences | https://doi.org//10.1016/j.ajps.2014.09.004

Mittelstrass K.et al.: Discovery of Sexual Dimorphisms in Metabolic and Genetic Biomarkers. (2011) PLoS Genetics | https://doi.org/10.1371/journal.pgen.1002215

Moessinger C. et al.: Two different pathways of phosphatidylcholine synthesis, the Kennedy Pathway and the Lands Cycle, differentially regulate cellular triacylglycerol storage. (2014) BMC Cell Biology | https://doi.org/10.1186/s12860-014-0043-3

Nilsson, Å. et al.: Pancreatic and mucosal enzymes in choline phospholipid digestion. (2019) American Journal of Physiology | https://doi.org/doi.org/10.1152/ajpgi.00320.2018

Pimentel L. et al.: Cholesterol, inflammation, and phospholipids: COVID-19 share traits with cardiovascular disease. (2021) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbalip.2020.158839

Speer C. et al.: Surfactant therapy: past, present and future. (2013) Early Human Development | https://doi.org/10.1016/S0378-3782(13)70008-2

Tang W. et al.: Intestinal Microbial Metabolism of Phosphatidylcholine and Cardiovascular Risk. (2013) The New England Journal of Medicine | https://doi.org/10.1056/NEJMoa1109400

Trabado S. et al.: The human plasma-metabolome: Reference values in 800 French healthy volunteers; impact of cholesterol, gender and age. (2017) PLoS ONE | https://doi.org/10.1371/journal.pone.0173615

van der Veen J. et al.: The critical role of phosphatidylcholine and phosphatidylethanolamine metabolism in health and disease. (2017) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamem.2017.04.006

Vance, D.: Phospholipid methylation in mammals: from biochemistry to physiological function. (2014) Biochimica et Biophysica Acta | https://doi.org/10.1016/j.bbamem.2013.10.018

Wang S. et al.: The Role of Pulmonary Surfactants in the Treatment of Acute Respiratory Distress Syndrome in COVID-19. (2021) Frontiers in Pharmacology | https://doi.org/10.3389/fphar.2021.698905

Zeisel, S.: A brief history of choline. (2012) Annals of Nutrition and Metabolism | https://doi.org/10.1159/000343120

Zhai G. et al.: Serum lysophosphatidylcholines to phosphatidylcholines ratio is associated with symptomatic responders to symptomatic drugs in knee osteoarthritis patients. (2019) Arthritis Research & Therapy | https://doi.org/10.1186/s13075-019-2006-8

History & Evolution

1866: discovery and synthesis of indole (Van Order et al. 1942) | 1869: chemical structure identified

Indole was first discovered during the process of making dyes with indigo in the mid-nineteenth century. German chemist Adolf von Baeyer first isolated indole through a reaction of indigo, sulfuric acid and sulfuric anhydride (Gribble 2016). The metabolite gets its name from the words “indigo” and “oleum,” because it was the product of treating indigo dye with oleum.

Indole is the main metabolite produced by gut bacteria during tryptophan metabolism (Jaglin et al. 2018), and is a precursor of metabolites that can be both beneficial and harmful for the host. Indoles, also known as benzopyrroles, are heterocyclic compounds found widely in nature. They have a double-ring structure, with one benzene and one pyrrole ring. This structure is of great interest in medicinal chemistry, as it can be used as a scaffold for new drugs, including treatments for cancer, liver disease, diabetes, hypertension, inflammation, and depression (Kaushik et al. 2013, Dorababu 2020).

Solid at room temperature, indole has a pungent odor, commonly associated with the smell of feces and coal tar. Interestingly, it has a pleasant floral odor in very small concentrations, which makes it a perfect ingredient for perfumes and even a flavor enhancer in chocolate.

Indole is also found in plant-based psychedelic drugs including psilocybin and dimethyltryptamine.

Biosynthesis vs. dietary uptake

In humans, indole is synthesized in the intestine as a product of tryptophan metabolism. Tryptophan, one of the nine essential amino acids, cannot be synthesized by humans, and is therefore primarily obtained through dietary sources, particularly cruciferous vegetables, dairy and meat products (Hubbard et al. 2015, Friedman 2018). Tryptophan is mostly absorbed in the small intestine and liver, but any that reaches the colon can be catabolized to indole derivatives, such as indoleamine 2,3-dioxygenase 1 (IDO1), which generates indole metabolites and alters gut function.

Indole and the microbiome

Gut bacteria activate ingested tryptophan to trigger three main catabolic pathways, including the indole pathway, the kynurenine pathway and the serotonin pathway (Taleb 2019). Due to the different catabolic enzymes present in different bacteria, a variety of tryptophan catabolites are produced, including indole (Hendrikx & Schnabi, 2019). For example, Clostridium sporogenes metabolizes tryptophan to release the enzyme tryptophanase (Roager et al. 2018). This produces 3-indolepropionic acid (3-IPA), which binds to the pregnane X receptor (PXR) in intestinal cells. Once absorbed into the bloodstream, IPA is distributed around the body.

Another pathway involves Lactobacillus, which metabolizes tryptophan into indole-3-aldehyde (I3A). This activates the aryl hydrocarbon receptor (AhR) in intestinal immune cells, which in turn triggers the production of the immuno-protective cytokine, interleukin-22 (IL-22).

However, while some bacteria elicit the production of indoles that generate a positive effect on the gut microbiome, indole can also have a less beneficial effect when metabolized in the liver into indoxyl sulfate, a uremic toxin. Too much can increase the risk of kidney dysfunction and vascular disease.

Indole, the intestine, and inflammation

Studies have shown that indole and its derivatives interact with the gut epithelium. Indole and I3A may help to maintain intestinal mucosal homeostasis, while IPA improves gastrointestinal barrier function through the activation of PXR, which inhibits inflammation (Zhao et al. 2019, Taleb 2019).

This is a two-way relationship: intestinal bacteria affect the pathways by which indole derivatives are produced, while concentrations of indole derivatives can affect the proliferation and function of gut microbiota communities.

Indole has also been found to have anti-inflammatory properties, with several studies showing that indole reduces mucosal inflammation (Knudsen et al. 2021). I3A has been shown to influence lipolysis and reduce the hepatic inflammatory response in the presence of AhR (Krishnan et al. 2018).

In the kynurenine pathway, tryptophan is degraded by indole derivatives such as IDO, triggered by proinflammatory cytokines. The tryptophan/kynurenine ratio a useful indicator of IDO activity, often used as a marker of inflammation and immune response in diseases including COVID-19 (Thomas et al. 2020).

Similarly, IDO modulates the immune response to tumor cells, which makes it a useful target for cancer therapies (Uyttenhove et al. 2003). Interestingly, people who eat diets high in tryptophan seem to have lower rates of cancer. Several anticancer drugs, such as vincristine and vinblastine, are indoles, while indole derivatives continue to be explored as possible cancer-prevention targets.

Indole derivatives and cellular signaling

Many indole derivatives are AhR ligands, which determine the reactivity of AhR.
AhR activation can affect immune regulation, intestinal homeostasis and carcinogenesis (Hubbard et al. 2015). AhR activity seems to be very high in some tumor cells, which may be linked to its role in inflammatory signaling. AhR could be an indicator of cancer aggressiveness: there are both positive and negative correlations of AhR signaling and poor prognosis, depending on the type of cancer (Saito et al. 2014). An increase in the kynurenine/tryptophan ratio has also been linked to cancer progression (Hubbard et al. 2015).

Indole itself is a major intercellular signal molecule in the microbiome (Lee et al. 2010). It can affect drug resistance, virulence of pathogenic bacteria, plasmid stability and biofilm formation. For example, an extracellular signal is just one of indole’s many functions in relation to E. coli.

Indole derivatives and neurology

Indole derivatives have been shown to have both neuroprotective and neurotoxic effects. IPA may have a neuroprotective effect, which makes indole an interesting subject of study in the search for treatments for Alzheimer’s disease (AD) (Pappolla 2021). IPA has been shown to protect neurons and neuroblastoma cells against damage caused by amyloid beta-protein (Abeta), an amino acid peptide involved in AD progression. Treatments focused on inducing higher levels of IPA in microbiota are being explored to promote brain health in older people.

Indole is also found in neurotransmitters such as serotonin, which plays a crucial role in brain and cognitive function. Unregulated serotonin levels can lead to depression, and both synthetic and plant-based indole alkaloids have been investigated for use in antidepressant drugs (Hamid et al. 2017). Plant-based indoles such as psilocybin and dimethyltryptamine are attracting growing interest as potential treatment for many psychological and mood disorders, due to the psychotropic effects (Hamid et al. 2017).

However, indoles are also associated with neurotoxic effects. One animal study found that excessive levels of indole were associated with reduced motor activity and increased anxiety, suggesting that people whose gut microbiota produces too much indole could be prone to mood disorders (Jaglin et al. 2018).

Indoxyl-sulfate and cardiovascular diseases

Research links indole derivatives to cardiometabolic and cardiovascular disease (Taleb 2019). Often this starts in the gut-liver axis. In patients with chronic kidney disease, indoxyl sulfate and p-cresol sulfate (a product of tyrosine metabolism) can accumulate in the kidneys. These uremic toxins are associated with increased risk of cardiovascular disease (Taleb 2019). I3S has also been shown to increase the risk of cardiovascular disease in renal patients (Hubbard et al. 2015).

Some indole derivatives have also been shown to attenuate cardiotoxicity in cancer patients by inducing autophagic cell death in cancer cells (Bi et al. 2018).

Learn more about the roles of indole and its derivatives in complex chronic diseases such as cancer, Alzheimer’s disease, depression, inflammatory bowel disease, multiple sclerosis and diabetes in our whitepaper “Complex chronic diseases have a common origin”.

References

Bi, W. et al.: Indole Alkaloid Derivative B, a Novel Bifunctional Agent That Mitigates 5-Fluorouracil-Induced Cardiotoxicity. (2018) ACS Omega | https://doi.org/10.1021/acsomega.8b02139

Dorababu, A.: Indole – a promising pharmacophore in recent antiviral drug discovery. (2020) RSC Medicinical Chemistry | https://doi.org/10.1039/d0md00288g

Friedman, M.:  Analysis, Nutrition, and Health Benefits of Tryptophan. (2018) International Journal of Tryptophan Research | https://doi.org/10.1177/1178646918802282

Gribble, G.: Indole Ring Synthesis: From Natural Products to Drug Discovery. (2016) Wiley Online Library | https://doi.org/10.1002/9781118695692

Hamid, H. et al.: Indole Alkaloids from Plants as Potential Leads for Antidepressant Drugs: A Mini Review. (2017) Frontiers in Pharmacology | https://doi.org/10.3389/fphar.2017.00096

Hendrikx, T. et al.: Indoles: metabolites produced by intestinal bacteria capable of controlling liver disease manifestation. (2019) Journal of Internal Medicine | https://doi.org/10.1111/joim.12892

Hubbard, T. et al.: Indole and Tryptophan Metabolism: Endogenous and Dietary Routes to Ah Receptor Activation. (2015) Drug Metabolism and Disposition | https://doi/org/10.1124/dmd.115.064246

Inman, M. et al.: Indole synthesis – something old, something new. (2013) Chemical Science | https://doi.org/10.1039/C2SC21185H

Jaglin, M. et al.:  Indole, a Signaling Molecule Produced by the Gut Microbiota, Negatively Impacts Emotional Behaviors in Rats. (2018) Frontiers in Neuroscience | https://doi.org/10.3389/fnins.2018.00216

Kaushik, N. et al.: Biomedical Importance of Indoles. (2013) Molecules | https://doi.org/10.3390/molecules18066620

Knudsen, C. et al.: Hepatoprotective Effects of Indole, a Gut Microbial Metabolite, in Leptin-Deficient Obese Mice. (2021) Journal of Nutrition | https://doi.org/10.1093/jn/nxab032

Krishnan, S. et al.: Gut Microbiota-Derived Tryptophan Metabolites Modulate Inflammatory Response in Hepatocytes and Macrophages. (2018) Cell Reports | https://doi.org/10.1016/j.celrep.2018.03.109

Lee, J. et al.: Indole as an intercellular signal in microbial communities. (2010) FEMS Microbiology Reviews | https://doi.org/10.1111/j.1574-6976.2009.00204.x

Pappolla, M. et al.: Indoles as essential mediators in the gut-brain axis. Their role in Alzheimer’s disease. (2021) Neurobiology of Disease | https://doi.org/10.1016/j.nbd.2021.105403

Roager, H. et al.: Microbial tryptophan catabolites in health and disease. (2018) Nature Communications | https://doi.org/10.1038/s41467-018-05470-4

Saito, R. et al.: Aryl hydrocarbon receptor in breast cancer—a newly defined prognostic marker. (2014) Hormonal Cancer | https://doi.org/10.1007/s12672-013-0160-z

Taber, D. et al.: Indole synthesis: a review and proposed classification. (2011) Tetrahedron | https://doi.org/10.1016/j.tet.2011.06.040

Taleb, S.: Tryptophan Dietary Impacts Gut Barrier and Metabolic Diseases. (2019) Frontiers in Immunology | https://doi.org/10.3389/fimmu.2019.02113

Thomas, T. et al.: COVID-19 infection alters kynurenine and fatty acid metabolism, correlating with IL-6 levels and renal status. (2020) JCI Insight | https://doi.org/10.1172/jci.insight.140327

Uyttenhove, C. et al.: Evidence for a tumoral immune resistance mechanism based on tryptophan degradation by indoleamine 2,3-dioxygenase. (2003) Natural Medicine | https://doi.org/10.1038/nm934

Van Order, R. et al.: Indole (1942) Chemical Reviews | https://doi.org/10.1021/cr60095a004

Zhao, ZH. et al.: Indole-3-propionic acid inhibits gut dysbiosis and endotoxin leakage to attenuate steatohepatitis in rats. (2019) Experimental & Molecular Medicine | https://doi.org/10.1038/s12276-019-0304-5

• Biosynthesis and dietary uptake
• Creatinine and renal function
• Creatinine precursor and neurology
• Creatinine and cardiovascular diseases
• Creatinine and malnutrition

Creatinine is a non-protein nitrogenous waste product generated during energy breakdown in muscles. It is found in plasma, serum and urine (Washington and Van Hoosier et al. 2012). A product of creatine and phosphocreatine breakdown (Burke et al. 1999), it can similarly act as a measure of kidney function (Maynard 1997).

Biosynthesis and dietary uptake

In humans, creatinine is formed primarily in muscle tissue from the precursor creatine, and released in the circulation before being cleared by the kidneys. However, diet is a strong influencer of the overall creatinine concentration in the body. Cooked red meat is a great source of creatinine (Hosten 1990) due to the transformation of creatine into creatinine during the heating process.

Thus, individuals with a diet rich in red meat or other protein sources tend to have higher creatinine levels than those who eat less protein (Samra and Abcar 2012). Dietary creatinine has also been found to be the substrate of several bacterial strains able to degrade creatinine in human stool samples.

These strains include Pseudomonas eisenbergii, ovalis and Clostridium welchii (Ten Krooden and Owens 1975). Most circulating creatinine is filtered by the kidneys and excreted from the body through urine, although up to 34% of creatinine is degraded in the gut, in a process referred to as non-renal clearance (Huang et al. 1982).

Creatinine and renal function

Creatinine is perhaps best known to clinicians and biologists as an indicator of renal function and as the go-to molecule to perform normalization in urine samples. Removed from the blood by glomerular filtration in the kidneys, and only moderately secreted back into the circulation in the nephron (Shemesh et al. 1986), creatinine remains one of the best known candidate molecules to calculate the glomerular filtration rate (GFR).

GFR is measured by combining serum and urine creatinine levels, while estimated GFR calculations are based on serum measurements only (Deray et al 1987).

While creatinine-based estimates can be influenced by factors such as age and diet, the ease of measurement of creatinine in biofluids remains an advantage compared to more exact methods requiring, for instance, long and tedious infusions of inulin for the patient (Diskin 2007). The normal circulating concentration of creatinine is considered to be around 1 mg/dL in healthy adults, with similar variation ranges between females and males.

Creatinine is excreted in the urine at a rate of about 88-128 mL/minute in females, and about 97-137 mL/minute in males (Hosten 1990). Excretion and GFR are however strongly influenced by age, with serum creatinine progressively decreasing, while GFR increases, mirroring a progressive and irreversible decline in renal function (Jones and Burnett 1974).

Another link between creatinine and renal physiology is the use of the serum levels of the enzyme creatine kinase as an indicator skeletal muscle damage from strenuous exercise or rhabdomyolysis, a condition where skeletal muscle is degraded. Such situations can lead to a large strain on the kidneys for the elimination of muscle breakdown products and possibly to renal failure (Clarkson et al. 2006).

Creatinine precursor and neurology

Creatine, the precursor of creatinine with a dreadfully similar name, is a product of arginine metabolism via the combination of arginine and glycine in the kidneys, small intestine or pancreas, followed by methylation in the liver.

Around 90% of the creatine released by the liver in the circulation is absorbed and stored in muscles as phosphocreatine. It is estimated that about 2% of these stores are consumed daily to form creatinine (Brosnan et al. 2011; Dossetor 1966; Hosten 1990).

Several rare diseases grouped under the name “cerebral creatine deficiency syndromes” share severe neurological and developmental symptoms, and a reduction of creatine levels either from impaired creatine transport into the muscle and brain, or from an incapacity to synthetize creatine from arginine (Schulze 2003).

Impaired creatine synthesis is a major impediment in muscle and brain development as it diminishes the activity of the enzyme creatine kinase, which not only catalyses the inter-conversion of phosphocreatine and creatine, but can also produce a molecule of much needed ATP in the process.

Creatine has even been suggested as a potential treatment against cognitive decline and has shown some success to improve short-term memory and reasoning in healthy volunteers (Avgerinos et al. 2018).

Creatinine and cardiovascular diseases

High circulating levels of creatine kinase are also used in the diagnosis of myocardial infarction (Fan et al. 2017). In hypertensive individuals, high levels of serum creatinine are associated with higher mortality rates (Shulman et al. 1989). Similarly, risk of stroke increases as the concentration of serum creatinine rises.

These elevated levels are often the only predictor of all-cause and overall cardiovascular disease deaths (Friedman 1991; Matts et al. 1993), though they do not seem to be associated with cancer and other non-cardiovascular deaths (Wannamethee et al. 1997).

Creatinine and malnutrition

Research on creatinine has provided original applications based on the relative stability of this metabolite in blood and urine under homeostatic conditions. As creatinine is primarily viewed as a product of muscular degradation, a decrease in creatinine levels is thought to reflect a reduction in lean body mass, or muscle mass, available for creatinine synthesis.

This concept has been used to study the effects of malnutrition in children and adults (Hari et al. 2007). In addition, creatinine-based indicators were developed, such as the creatinine height index (CHI) (Walser 1987). The CHI uses the daily urinary creatinine excretion of patients and compares them to those of other individuals of the same height and sex (Viteri and Alvarado 1970).

A variation on this theme is the creatinine arm index suggested by Van Hoeyweghen et al. (1992) with the same principle adapted to reduce the influence of age on the results.

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