Advancing Metabolomics with Metabolon 

A Brief History of Metabolomics

Ever since the 5th century, when Egyptians discovered that ants could detect excessive amounts of glucose in diabetic urine, people have known that metabolites, the small molecule reactants, intermediates, and products of metabolism, reflect human health. However, it wasn’t until the 20th century that the biological significance of metabolites was recognized.

In the 1940s, Roger J. Williams used paper chromatography to show that metabolic patterns not only exist but also differ between individuals. By the 1960s, advancements in GC-MS and NMR technologies enabled analytically robust quantitation of small molecules, which helped establish a connection between changes in metabolite abundances and altered biological processes. By the 1970s, specific metabolic patterns were becoming associated with illnesses such as schizophrenia, inflammatory bowel disease, and cardiovascular disease. The term “metabolome”, used to describe all metabolites within a biological system, first appeared in the scientific literature in 1998, and between 1998 and 2024, more than 100,000 publications that are searchable with the keyword “metabolomics” were added to PubMed.

Today, metabolomics is integrated with other omics technologies, including genomics, transcriptomics, and proteomics, to provide a comprehensive view of biological systems. While genes, transcripts, and proteins represent the potential for cellular processes, metabolites capture the real-time functional state of those processes. Because metabolites are the end products of enzymatic regulatory networks, they integrate inputs from the genome, proteome, microbiome, diet, and environment. This makes them the most proximal molecular indicators of the phenotype, and a critical link between molecular biology and observable physiology.

Metabolon’s Role in Advancing the Field

For the past 25 years, Metabolon has made significant contributions to both metabolomics and other scientific disciplines by continuously generating robust metabolomics data and developing novel analytical methods and data analysis tools. In the following sections, we discuss Metabolon’s technological achievements that have secured its place as an industry-leading metabolomics provider, as well as key areas of study that have advanced because of Metabolon data.

Technological Innovations

Traditionally, metabolites are separated and analyzed using a complex workflow that presents major challenges, including inadequate metabolite pathway coverage, unreliable metabolite identification, and a lack of biological context for data interpretation. Metabolon has integrated key innovations into its LC/MS-based platform that address these challenges, resulting in industry-leading analytical sensitivity and precision.

First, samples are subjected to 4 different chromatographic and ionization methods, each optimized to separate hydrophilic, hydrophobic, basic, and polar compounds. Using 4 complementary methods expands chemical coverage and builds analytic redundancy into the method, so compounds with overlapping chemical characteristics are identified with the highest confidence.

Second, Metabolon has the largest commercial biochemical reference library in the world, containing more than 5,400 metabolites. Importantly, the majority of these have achieved Level 1 identification status – the highest standard of confidence as determined by the Metabolomics Standards Initiative.¹ Although open-source reference libraries tend to be larger, the methods used to identify their entries are both undisclosed and differ across users, leaving room for uncertainty regarding biochemical identity. By containing the largest number of accurately annotated reference points, Metabolon’s library enables us to achieve identification accuracy superior to other untargeted metabolomics platforms.

Third, Metabolon’s proprietary software allows thousands of biochemicals from 70+ pathways to be rapidly and accurately annotated and then aligned with data from the scientific literature. Follow-up manual interpretation of the data allows us to distinguish metabolic perturbations caused by disease from those caused by less relevant factors such as diet and medication. Our bioinformatics platform combined with expert analysis has enabled clinicians to identify various metabolic diseases and characterize disease mechanisms despite medications and dietary factors that typically mask the disease signature, which we will discuss in greater detail in later sections.

By applying these innovations to both independent and client-supported research projects, Metabolon has made significant contributions to many areas of life science. Below are a few key disciplines that stand out.

Population Health

Population health aims to identify and characterize factors that drive poor health in communities of interest to help tailor interventions and improve outcomes. The health of various populations depends on many factors, including genetics, income, access to healthcare and education, living environment, social context, and lifestyle choices. How these factors affect biological processes to result in health problems is not completely understood, and the metabolome is uniquely positioned to help bridge this gap because it integrates genetic and environmental inputs to show the effects of both internal and external determinants. Furthermore, metabolomics can provide phenotypic context to data generated in population health studies and thereby reveal insights to help mitigate health disparities. To date, Metabolon has run upwards of 100,000 samples for population studies and worked with several cohorts that span a wide range of ages, ethnicities, and diseases. Some of these cohorts include Metabolic Syndrome in Men², Rotterdam and EPIC-Norfolk³, German Chronic Kidney Disease (GCKD) and Atherosclerosis Risk in Communities (ARIC)4, Twins UK^5,6, and Health for Life in Singapore (HELIOS)⁷.

Over the years, Metabolon data has identified numerous metabolic signatures associated with likely outcomes in populations at risk of developing certain diseases. For example, the metabolome is known to reflect the interplay between the genome and environmental exposures and may therefore provide insights into the pathogenesis of diseases with complex mechanisms of onset, such as major depression. In one study, the authors aimed to identify metabolites associated with depression by performing a metabolome-wide association analysis in 13,596 participants from 5 European-based cohorts characterized for depression.³ Using high-level statistical analyses and machine learning, they identified 8 metabolites significantly associated with depression, all of which were derived from food or were products of host and gut microbial metabolism of food-derived products. These findings highlighted important actionable targets for the prevention of depression that could be modified by dietary interventions and serve as the focus of follow-up studies. In another study, the authors aimed to identify metabolites that could serve as markers of kidney function complementary to estimated glomerular filtration rate (eGFR).⁸ They used global metabolomics profiling to identify 493 small molecules in human serum, then measured associations of these molecules with eGFR in approximately 3000 participants from the KORA F4 and Twins UK cohorts. After correcting for multiple testing, 6 metabolites showed pairwise correlation with established indicators of kidney function, which were validated in the African American Study of Kidney Disease (AASK) cohort. Overall, this study revealed a comprehensive list of metabolites associated with kidney function that could complement or potentially improve eGFR measurements.^9,10 Several other studies have applied metabolomics to large at-risk populations with the goal of better understanding mechanisms that drive disease onset, improve diagnostic success of the disease, or define disease phenotypes more precisely so that treatment strategies may be better tailored.^11-20

Rare Diseases

Rare diseases are genetic conditions that individually affect fewer than 200,000 people but altogether affect an estimated 250-450 million people worldwide including 25-30 million Americans. Nearly all of the approximately 7,000 known rare diseases present with non-specific symptomologies, and targeted diagnostic testing is mostly limited to the 63 conditions on the Recommended Uniform Screening Panel. These factors have made genomic sequencing the first line diagnostic for most rare diseases. Unfortunately, despite decades of advancement, the diagnostic yield of genomic sequencing remains limited by our incomplete understanding of rare variant associations and by a lack of robust methods to interpret phenotype-variant associations at scale. Because of these limitations, most people who suffer from a rare disease are subjected to a diagnostic odyssey, sometimes waiting years before receiving a conclusive diagnosis and appropriately targeted care.

Metabolon has repeatedly demonstrated the standalone utility of untargeted metabolomics in diagnosing and identifying novel biomarkers of inborn errors of metabolism (IEMs), a subset of rare diseases. For some indications, including GABA-transaminase deficiency, AADC deficiency, and VLCAD deficiency, Metabolon data distinguished metabolic signatures of those IEMs from signature changes induced by diet and medication, to enable clear diagnoses.^21-23 Metabolon data has also identified and helped clinically validate novel metabolic signatures of several IEMs, including transketolase deficiency, transaldolase deficiency, dihydropteridine reductase deficiency, urocanic aciduria, early infantile epileptic encephalopathies, and Zellweger spectrum disorders.^24-30 Our findings have shown that metabolomics data can inform the type of targeted genomic testing needed to confirm an IEM diagnosis, and also showed that compared to conventional methods of screening for IEMs, global metabolomics profiling provides a 6-fold higher diagnostic yield and identifies a broader spectrum of IEMs.^31-32 Many of these IEM signatures identified by Metabolon data have since been used to aid in diagnosing these conditions, resulting in a shorter time to diagnosis and more timely deployment of targeted care.

Precision Medicine

Precision medicine is a relatively new approach to treatment that accounts for human individuality in genes, environment, and lifestyle when assessing one’s disease risk, prognosis, and individualized therapy. Metabolon data has helped demonstrate the benefits of using metabolomics to prognosticate patients, identify novel therapeutic targets, and predict which patients are most likely to respond to a given treatment.

For example, large-scale metabolic profiling is a promising approach to developing precision treatment strategies for asthma. In one study, data from 14,000 individuals across 4 asthma cohorts were analyzed, and 17 steroid metabolites were shown to be significantly reduced in patients with prevalent asthma.³³ The largest reductions in cortisol were associated with inhaled corticosteroid (ICS) treatment. However, ICS treatment was also associated with significantly increased fatigue and anemia compared to non-ICS treatment. These findings suggest that adrenal suppression in asthma patients who receive ICS treatment may have a larger impact on quality of life than previously recognized. Regular cortisol monitoring of this patient population may help achieve the optimal balance between minimizing both asthma symptoms and the adverse effects of adrenal suppression.³⁴ In another study aimed at understanding associations between age at menopause and age-related cognitive decline, perturbations in specific biochemical pathways were shown to be predictive of brain aging and cognition. These data may help to identify women at higher risk of age-related cognitive impairment and inform clinical decision-making.³⁵ Through various additional studies, Metabolon data has helped characterize patient populations that would benefit from precision treatments and improve disease diagnosis based on phenotypic traits and predict disease onset from personalized metagenomic signatures.^36-46

Conclusions

Metabolon data has played an instrumental role in advancing scientific discovery and our understanding of several scientific disciplines, and the studies discussed here are by no means comprehensive. Metabolomics will continue to serve as an important tool for identifying novel therapeutic targets, improving diagnostic and prognostic tools, and gaining a better understanding of mechanisms that govern physiological and pathophysiological processes. For as long as metabolomics is used in this capacity, Metabolon will continue to serve as an industry leader in this field through innovation and discovery.

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