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Why Metabolomics and Metabolon’s Library Coverage: A Compelling Rationale

Thoughts From Ranga

Just over two years ago, I accepted the opportunity to oversee the science at Metabolon as the Chief Scientific Officer of an established metabolomics company. This role aligned perfectly with my conviction that high-quality metabolomic profiles linked to extremely well-curated clinical cohorts are foundational to the aspirational goals of drug developers, as well as academic and clinical researchers, to improve overall health outcomes. The reliability of high-quality data generation is critical to unlocking the unknowns in our understanding of disease mechanisms, identifying appropriate targets for drug development and biomarkers, and demonstrating the promise of precision medicine.  My opinion that metabolomics holds THE KEY to the overall success in each of these endeavors continues to strengthen based on the accumulating scientific literature linking metabolites to phenotypic causality; the subsequent inclusion of gene and protein information then increases our potential to delineate clear cause-to-treatment roadmaps in precision medicine. 

Two examples below highlight (a) why metabolomics and (b) the importance of deep metabolomic library coverage in more rapidly identifying significant discoveries with potentially major health implications, the extent and significance of which have yet to be determined. In 2015, using metabolomic profiles across multiple population cohorts, hexadecanedioate was identified as a circulating metabolite that directly influences blood pressure and is associated with all-cause mortality1. This study effectively established a causal role of hexadecanedioate on blood pressure in preclinical models by demonstrating its influence on the vascular adrenergic pathway, the most fundamental physiological mechanism regulating systolic blood pressure. In a follow-up study, the inclusion of genetic information led to the identification of associated genes, one encoding a transporter and the other an enzyme, determined to have roles biochemical synthesis and pathways influencing hexadecanedioate levels in the blood2.   

A recent publication reported that a terminal metabolite of niacin (a B vitamin often used to treat high-cholesterol), specifically N1-methyl-4-pyridone-3-carboxamide (4PY), was causal for vascular inflammation and associated with a higher incidence of major adverse cardiovascular events (MACE)3. In this case, the linkage between outcome (MACE) and cause (niacin) was established from population cohort datasets.  Untargeted metabolomics was used to establish profiles in a discovery cohort, wherein spectral information was used to identify unknown metabolites associated with increased MACE risk.  Two of the unknowns were subsequently identified through a process of systematic structural elucidation as niacin metabolites N1-methyl-2-pyridone-5-carboxamide (2PY) and N1-methyl-4-pyridone-3-carboxamide (4PY).  The addition of GWAS analysis and experimental validations identified the causal enzymatic pathway responsible for the generation of 2PY and 4PY; that 4PY was responsible for vascular inflammation and risk of MACE. The important discovery was the direct influence of 4PY in increasing expression and adherence of soluble vascular adhesion molecule 1 (sVCAM-1) to vascular endothelium in a rodent model.  The results from the study provide a mechanism that partly explains the observations that increased niacin consumption is associated with cardiovascular events despite lowering cholesterol.

There are several important points to consider: 

First and foremost, these discoveries would not have occurred without metabolomics. Even though the metabolome is closest to the phenotype, however it is not widely adopted within most scientific investigations. In the above examples, the discovery process links clinical phenotype to metabolomic profiles to establish the biological extremes of the cause-and-effect spectrum. The addition of GWAS-based analytics added to the robustness of the discoveries in the sequential delineation of the mechanistic specificity and risk profiles for the phenotype tested.  Furthermore, in the niacin study, the application of Mendelian Randomization (MR) analysis did not identify the association between the increase of 2PY and 4PY and the risk of MACE and other cardiovascular phenotypes.  Given the widespread use of MR analysis in large datasets to identify causality, the lack of association in a study is most often a function of low statistical power and commonly encountered in genetic analysis. 

In the hexadecanedioate discovery study, metabolomic profiles for the discovery and replication cohort were generated using Metabolon’s Global Discovery Platform. Hexadecanedioate is one of the 5400+ metabolites in Metabolon’s chemical library, and its presence is routinely reported in biological samples.   Readily available information on the hexadecanedioate structure, synthesis, and commercial sourcing facilitated the rapid hypothesis testing and validation of its role in influencing blood pressure via the adrenergic pathway. 

In contrast, the discovery of 2PY and 4PY, in the Ferrell publication3, utilized spectral information from untargeted metabolomics to identify metabolite peaks with characteristic features linked to the phenotypic endpoint of interest.  The structure was eventually elucidated using systematic comparisons of spectral information in available databases and standard approaches in the field of metabolomics research. The biochemistry of niacin, including the generation of 2PY and 4PY, has been previously elucidated; both metabolites have been part of Metabolon’s chemical library since 2011.  We can only speculate that running the study samples on the Metabolon Discovery Platform would have identified these metabolites, resulting in time and cost efficiencies leading up to the publication. This, however, highlights the advantages of running metabolomic profiling with a service provider with established large metabolite library coverage as part of their deliverable. 

The broader implications of these findings are yet to be realized. In September 2023, the World Health Organization reported details on the significant impact of hypertension, headlining it as a “silent killer” afflicting an estimated 1.3 billion individuals worldwide4 (WHO report, 2023). In the USA, there has been a progressive increase in hypertension diagnosis within the aging population; half the population is either unaware or is inadequately managed for blood pressure5,6. High blood pressure is the most common modifiable risk for cardiovascular disease manifestations as effective monitoring and management has been associated with improvements in cardiovascular risk and comorbidities. There is a distinct need to investigate the role of metabolites in population cohorts to identify markers such as hexadecanedioate and 4PY that potentially represent the “missing heritability” factors with the potential to influence disease onset, progression, and outcomes, particularly in diseases with long trajectories of progression over time.  

References

  1. Menni C, Graham D, Kastenmüller G, et al. Metabolomic Identification of a Novel Pathway of Blood Pressure Regulation Involving Hexadecanedioate. Hypertension. 2015;66(2):422-429. doi:10.1161/HYPERTENSIONAHA.115.05544 
  2. Menni C, Metrustry SJ, Ehret G, et al. Molecular pathways associated with blood pressure and hexadecanedioate levels. PLoS One. 2017;12(4):e0175479. doi:10.1371/journal.pone.0175479 
  3. Ferrell M, Wang Z, Anderson JT, et al. A terminal metabolite of niacin promotes vascular inflammation and contributes to cardiovascular disease risk. Nat Med. 2024;30(2):424-434. doi:10.1038/s41591-023-02793-8 
  4. Geneva: World Health, Organization. Global Report on Hypertension: The Race against a Silent Killer. Accessed March 8, 2024. https://www.who.int/publications-detail-redirect/9789240081062 
  5. Bromfield SG, Bowling CB, Tanner RM, et al. Trends in Hypertension Prevalence, Awareness, Treatment, and Control Among US Adults 80 Years and Older, 1988–2010. J Clin Hypertens (Greenwich). 2014;16(4):270-276. doi:10.1111/jch.12281 
  6. Sakhuja S, Colvin CL, Akinyelure OP, et al. Reasons for uncontrolled blood pressure among US adults: Data from the US National Health and Nutrition Examination Survey. Hypertension. 2021;78(5):1567-1576. doi:10.1161/HYPERTENSIONAHA.121.17590 
Ranga Sarangarajan, Ph.D.
Ranga leads Metabolon’s R&D teams to deliver metabolomics data and insights that expand and accelerate the impact of life sciences research in all its applications, including biopharma and diagnostics.

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