Metabolomics: Deciphering Metabolism

Metabolomics: Deciphering Metabolism

Metabolomics: Deciphering Metabolism

Metabolism — a term you’ve likely encountered numerous times, especially in discussions about diet, weight loss, and sports. Countless internet blogs offer advice on how to enhance or speed up your metabolism to achieve fitness goals, reinforcing the notion that metabolism plays a crucial role in weight management. Indeed, metabolism is a key factor in both weight loss and gain. Yet, despite its ubiquity, if asked to provide a precise definition of “metabolism,” you might find yourself struggling to articulate a clear explanation.

The word “metabolism” has its origins in the Greek term “μεταβολή,” which translates to “change.” According to the Oxford English Dictionary, metabolism can be succinctly defined as “the chemical processes that occur within a living organism in order to maintain life” [1]. While this definition is certainly accurate, it may strike you as overly simplistic, lacking depth and detail. In a more comprehensive context, metabolism encompasses all the chemical reactions required to extract energy from nutrients, as well as the subsequent reactions necessary to apply that energy in support of essential biological functions. This broad scope reveals that metabolism is a sprawling field of study, which is why numerous scientific disciplines, including biochemistry, molecular biology, and physiology, encompass metabolism as an area of investigation. Over the past two decades, the field has experienced a remarkable surge in research capabilities, thanks to the advancements in technology and computational power, exemplified by the emergence of metabolomics.

This article aims to provide a general understanding of the omics sciences, followed by a more detailed exploration of metabolomics. We will delve into the core concepts of metabolomics, examining its definition, history, and the most commonly utilized methods in contemporary research.

Metabolomics is also the foundational module of the Health Optimization and Practice (HOMe/HOPe) Essential Certification and we will discuss why this is the case in detail.

Now, let’s dive into the highly complex but fascinating realm of metabolomics!

Ome and Omics

In biomedical sciences, the suffixes “-ome” and “-omics” frequently refer to something large and complex, usually a field of study that is centered on large-scale data analyses to understand specific aspects of cell biology. Thus, the suffix “-ome” describes a large group of molecules or elements, while “-omics” pertains to the comprehensive investigation of that “-ome,” often involving copious volumes of information [2-4]. The first “ome” mentioned in scientific literature was the genome, although the origin of the word is not clear [2]. On the other hand, the term “genomics” can be attributed to geneticist Thomas H. Roderick in 1986, as he sought to christen a new journal he intended to establish in collaboration with colleagues [5]. A continually expanding list of specialized fields of study, collectively referred to as “omics,” has sprouted:

  • Genomics: A science devoted to the study of the genome, which includes all of an organism’s DNA. Some scholars incorporate epigenomics within the ambit of genomics.
  • Transcriptomics: The transcriptome, which encompasses the sum of all RNA molecules, enables the transmission of genetic information into protein translation and it is the field of study of transcriptomics.
  • Proteomics: Proteomics delves into the proteome, which includes the totality of proteins and their post-translational modifications, including enzymes, peptide hormones, and other signaling molecules.
  • Metabolomics: The discipline’s focal point is the metabolome, encompassing all molecules generated through cellular metabolism. This includes a diverse array of compounds, both endogenous and exogenous, such as sugars, fatty acids, lipids, and small peptides [6]. In other words, metabolomics investigates the activity and status of cellular and organismal metabolism on a global scale [7].

With a clearer understanding of the metabolome and metabolomics, let’s embark on a journey through the history of this scientific field and delve into its fundamental technical foundations to gain a better grasp of its significance and potential.


The term “metabolome” was coined by Stephen G. Oliver in 1998, in a review centered on the systematic analysis of the yeast genome. Within this review, Oliver underscored the significance of profiling the levels of all the metabolites in different mutants of yeast to identify the genes (or groups of genes) involved in the synthesis or metabolism of a specific metabolic product. This, in turn, would facilitate the mapping of those genes in the yeast genome [8]. Metabolome, the term to refer to all the metabolites present in a yeast cell, paralleled the words “genome” and “proteome,” referring to an organism’s complete DNA and proteins, respectively. At the turn of the century, multiple studies began to explore the potential of metabolite profiling in metabolism regulation and other research areas, and metabolomics emerged as the designated term for metabolome research [9,10].

The importance of metabolomics, along with omics disciplines in general, hinges on their capacity to employ high-throughput techniques, generating vast datasets. In the case of metabolomics, a single experiment can encompass the measurement of the levels of hundreds or even thousands of metabolites in a given cell or tissue sample. Subsequently, bioinformatic software efficiently analyzes this wealth of information to generate a profile, or a snapshot, of all the metabolites present in the sample [7]. Without these powerful tools, obtaining the same data would take hundreds of biochemical assays and significantly more time to assess the results.

To underscore the profound significance of the metabolomic approach, consider this: The first comprehensive chart of the cellular metabolome was crafted by Donald Nicholson in 1955. He depicted all the metabolic pathways known at that time by using stencils, and the final picture included a mere 20 metabolic pathways [11]. In contrast, contemporary metabolomics experiments provide insights into the levels of hundreds of metabolites, map them across known metabolic pathways, and even determine the flux of a specific molecule through a particular metabolic pathway, highlighting the groundbreaking strides achieved in this field.

Targeted vs. untargeted approach

Now that we have a firm grasp of the concepts surrounding the metabolome and metabolomics, let’s imagine ourselves as scientists, so put your lab coat on. Imagine that we are investigating how a specific drug affects the metabolome of cancer cells. To achieve this, we opt to analyze the metabolites in treated cells in comparison to untreated ones. In this scenario, we encounter two distinct approaches. The first one involves quantifying the levels of every metabolite present in a cell, aiming to elucidate how the drug influences the metabolome. Through this method, we may potentially identify specific metabolic pathways that the drug affects. The second approach involves evaluating only metabolites from specific metabolic pathways, based on previous knowledge. For instance, if we have evidence indicating that our drug impacts nucleotide metabolism, it would be more efficient to focus on assessing the levels of metabolites directly linked to nucleotide biosynthesis or the nucleotide salvage pathway.

The first approach, also called the untargeted approach, operates in a non-hypothesis-driven manner since it lacks prior ideas or established knowledge supporting the experimental setup. In contrast, the latter approach, known as the targeted approach, is hypothesis-driven, as we base our scientific question on previous knowledge or hypotheses [7].

  • Untargeted approach: This approach focuses on measuring and comparing as many signals as possible across a sample set. Subsequently, these signals are matched with metabolite profiles stored within metabolomics databases. This screening-oriented method is useful to discover novel metabolites present in specific diseases or to identify newly engineered metabolic pathways [7,12].
  • Targeted approach: In contrast, the targeted approach centers on the accurate identification and quantitation of a predefined set of metabolites within biological samples. Typically, this set of metabolites is selected in accordance with the scientific question or tailored to the dimensions of an existing metabolite library. While this approach can yield more profound insights, it relies heavily on prior knowledge and hypotheses [7,12].

Metabolic Flux Analyses

In the context of the targeted approach, we have the capability to quantify the levels of metabolites at specific time points, akin to capturing snapshots of metabolites at precise moments. This allows us to compare these snapshots and discern how the levels of particular metabolites fluctuate under different conditions or at various time intervals within the cytoplasm. To draw an analogy with traffic, it is similar to tallying the types and number of vehicles within a city. However, this data alone does not unveil the existence of traffic jams or the destination of police cars, for example. To delve deeper into these aspects, we turn to flux analysis [13,14].

Just as a sprawling metropolis comprises a labyrinthine network of roads, subway lines, and railway tracks, metabolism represents a huge, complex network of biochemical reactions spanning diverse metabolic pathways that are intricately interconnected. Certain metabolites from one metabolic reaction may serve as precursor metabolites for other reactions or pathways. Depending on both external and internal cellular conditions, metabolism dynamically shifts from one pathway to another, sometimes rerouting metabolites to fulfill the evolving metabolic requirements of the cell. In instances where we seek to study the flow of metabolites through various pathways to comprehend how cells or tissues adapt to differing conditions, substrates can be labeled or marked with stable isotopes like 13C and 2H. This tagging allows us to “trace” these metabolites and infer the metabolic transformations they undergo as well as the enzymatic reactions involved [15]. Metabolic flux analysis emerges as a potent tool for unraveling alterations in metabolism under disease conditions or identifying which metabolic pathways exhibit heightened activity following specific stimuli.

Analytical technologies

Initially, a metabolomic sample contains a highly complex mixture of analytes. To facilitate subsequent detection, metabolites should be first separated from each other. Following separation, metabolites undergo analysis and quantification by employing detection procedures. Broadly speaking, metabolomic analyses hinge on separation techniques coupled with detection methods. The most prevalent separation methodologies are gas chromatography (GC) and high-performance liquid chromatography (HPLC), also known simply as liquid chromatography (LC), while the most frequently utilized detection techniques are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy, often referred to as magnetic resonance spectroscopy (MRS) [16,17].

  • Gas Chromatography-Mass Spectrometry (GC-MS): In this approach, GC, a common laboratory method used to separate and analyze the chemical compounds of a sample, is used to fractionate the metabolites of the mixture. This is coupled with MS, a technique capable of identifying and quantifying the metabolites present in the sample by measuring the mass-to-charge ratio of ions [16,17].
  • Liquid Chromatography- or High-Performance Liquid Chromatography-Mass Spectrometry (LC-MS/HPLC-MS): HPLC utilizes specialized pumps to expedite the transit of samples through the chromatography column. It is currently the prevailing technique in the field of metabolomics [16,17], and it is often coupled with MS.
  • Nuclear Magnetic Resonance (NMR) Spectroscopy: In contrast to the aforementioned methods, NMR spectroscopy does not require a previous separation of metabolites, thus preserving sample activity post-analysis. This detection method is based on the application of a strong magnetic field to the sample, identifying metabolites through their distinctive response to the magnetic field [18]. Notably, NMR spectroscopy is the preferred detection method for untargeted analyses, offering distinct advantages in this context [19].

Statistical and Bioinformatic Analysis

Conducting a metabolomic analysis will generate a vast dataset and information that demands comprehensive scrutiny to derive meaningful insights and conclusions. Such analyses require computational tools, with a plethora of software options at one’s disposal [20]. Bioinformatic analyses in metabolomics include, among others, statistical tests, data visualization plots, hierarchical clustering, and pathway enrichment analyses, which are similar to those commonly used in genomics [7]. These bioinformatic tools empower researchers to navigate through datasets that would be impractical for manual analysis, thereby enhancing the comprehension and visualization of metabolic profiles.

Metabolomics in Health Optimization Medicine and Practice (HOMe/HOPe)

Genomics measures what can happen. Transcriptomics and proteomics both measure what appears to be happening. But it is metabolomics that measures what has happened and what is happening now. Therefore, it is immediately and clinically actionable.

A recent paper even called metabolomics the “Stethoscope of the 21st Century” because it is poised to change clinical practice dramatically… not only from a disease perspective (as you’ll find in the paper here) but also because of its potential to transform and optimize health.

At HOMe/HOPe, we measure at the level of the metabolome. To be more precise, we measure endogenous and exogenous metabolites and correct for subtle deficiencies and toxicities using network-wide range shifting, the latter meaning that we move all nutrient and hormone levels to those found at the optimal age range of 21 to 30 years old.

The HOMe/HOPe framework sets disease aside and instead focuses on the health of your basic cell using the science of metabolomics. When optimized, this is the true foundation of all health and wellness!


In tandem with other omics disciplines, metabolomics represents a potent avenue for exploring the realms of biochemistry and molecular biology, particularly within the domain of cell metabolism. The applications of metabolomic data span a wide array of fields, including cancer research, diabetes, obesity, immunology, neuroscience, and many more. And not just for diseases and conditions!

There is huge potential for metabolomic testing to also cultivate and create a true foundation of health, and this is the focus of the first module of the Essential HOMe/HOPe Certification. The concept is simple: Set disease and conditions aside (at least, at first) and optimize the metabolome using network-wide range shifting. From there, everything else (including treating disease) gets easier!

Written by Ferran Riaño-Canalias, PhD



  1. Metabolism. In: Oxford Dictionary of English. Oxford Uni. Press; 2010.

  2. Yadav SP. The Wholeness in Suffix -omics, -omes, and the Word Om. J Biomol Tech JBT. 2007;18(5):277.

  3. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi:10.1186/s13059-017-1215-1

  4. Vailati-Riboni M, Palombo V, Loor JJ. What Are Omics Sciences? In: Ametaj BN, ed. Periparturient Diseases of Dairy Cows. Springer International Publishing; 2017:1-7. doi:10.1007/978-3-319-43033-1_1

  5. Kuska B. Beer, Bethesda, and Biology: How “Genomics” Came Into Being. JNCI J Natl Cancer Inst. 1998;90(2):93-93. doi:10.1093/jnci/90.2.93

  6. Subedi P, Moertl S, Azimzadeh O. Omics in Radiation Biology: Surprised but Not Disappointed. Radiation. 2022;2(1):124-129. doi:10.3390/radiation2010009

  7. Liu X, Locasale JW. Metabolomics: A Primer. Trends Biochem Sci. 2017;42(4):274-284. doi:10.1016/j.tibs.2017.01.004

  8. Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends Biotechnol. 1998;16(9):373-378. doi:10.1016/S0167-7799(98)01214-1

  9. Thomas GH. Metabolomics breaks the silence. Trends Microbiol. 2001;9(4):158. doi:10.1016/s0966-842x(01)02010-8

  10. Fiehn O. Metabolomics--the link between genotypes and phenotypes. Plant Mol Biol. 2002;48(1-2):155-171.

  11. Dagley S, Nicholson DE. An introduction to metabolic pathways. Introd Metab Pathw. Published online 1970. Accessed September 4, 2023.

  12. Bingol K. Recent Advances in Targeted and Untargeted Metabolomics by NMR and MS/NMR Methods. High-Throughput. 2018;7(2):9. doi:10.3390/ht7020009

  13. Sauer U, Zamboni N. From biomarkers to integrated network responses. Nat Biotechnol. 2008;26(10):1090-1092. doi:10.1038/nbt1008-1090

  14. Hiller K, Metallo CM. Profiling metabolic networks to study cancer metabolism. Curr Opin Biotechnol. 2013;24(1):60-68. doi:10.1016/j.copbio.2012.11.001

  15. Zamboni N, Saghatelian A, Patti GJ. Defining the Metabolome: Size, Flux, and Regulation. Mol Cell. 2015;58(4):699-706. doi:10.1016/j.molcel.2015.04.021

  16. Brown M, Dunn WB, Dobson P, et al. Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. The Analyst. 2009;134(7):1322. doi:10.1039/b901179j

  17. Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R. Metabolomics for laboratory diagnostics. J Pharm Biomed Anal. 2015;113:108-120. doi:10.1016/j.jpba.2014.12.017

  18. Martin JD. Nuclear Magnetic Resonance Spectroscopy. In: Between Making and Knowing. WORLD SCIENTIFIC; 2020:561-569. doi:10.1142/9789811207631_0049

  19. Emwas AHM. The Strengths and Weaknesses of NMR Spectroscopy and Mass Spectrometry with Particular Focus on Metabolomics Research. In: Bjerrum JT, ed. Metabonomics. Vol 1277. Methods in Molecular Biology. Springer New York; 2015:161-193. doi:10.1007/978-1-4939-2377-9_13

  20. Spicer R, Salek RM, Moreno P, Cañueto D, Steinbeck C. Navigating freely-available software tools for metabolomics analysis. Metabolomics. 2017;13(9):106. doi:10.1007/s11306-017-1242-7