Sharpening Precision Medicine by a Thorough Interrogation of Metabolic Individuality

Abstract

Precision medicine is an active component of medical practice today, but aspirations are to both broaden its reach to a greater diversity of individuals and improve its "precision" by enhancing the ability to define even more disease states in combination with associated treatments. Given complexity of human phenotypes, much work is required. In this review, we deconstruct this challenge at a high level to define what is needed to move closer toward these aspirations. In the context of the variables that influence the diverse array of phenotypes across human health and disease - genetics, epigenetics, environmental influences, and the microbiome - we detail the factors behind why an individual's biochemical (metabolite) composition is increasingly regarded as a key element to precisely defining phenotypes. Although an individual's biochemical (metabolite) composition is generally regarded, and frequently shown, to be a surrogate to the phenotypic state, we review how metabolites (and therefore an individual's metabolic profile) are also functionally related to the myriad of phenotypic influencers like genetics and the microbiota. We describe how using the technology to comprehensively measure an individual's biochemical profile - metabolomics - is integrative to defining individual phenotypes and how it is currently being deployed in efforts to continue to elaborate on human health and disease in large population studies. Finally, we summarize instances where metabolomics is being used to assess individual health in instances where signatures (i.e. biomarkers) have been defined.

Keywords: Diagnostics; Genome Wide Association Study; Inborn Errors of Metabolism; Mass Spectrometry; Metabolomics; Precision Medicine; Untargeted Analysis.

Fig. 1

Complex landscape for predicting allelic variants that impact health. A. Single variant example. Even after delineating a variant that projects a high risk for developing a disease, outcomes can be very different where one individual may succumb to a deleterious variant while another individual with the same variant may remain healthy and never develop disease – the concept of penetrance. B. Multi/polygenic example. Many traits are influenced by a combination of allelic variants and therefore are more challenging to directly map to a trait. In both scenarios, external factors (lifestyle, environment, and the microbiome) frequently are involved in how the effects of these variants play out and whether an individual remains healthy or develops disease.

Fig. 2

Metabolic pathways are at the nexus of all cellular function. Signaling through the central dogma of genes, transcripts, and proteins leads to metabolic pathway regulation. However, it is not always appreciated that metabolism also has a direct effect on posttranslational modifications, epigenetics, and consequently, on many complex biological processes (albeit, less well understood than the flow from genes to metabolic pathways).

Fig. 3

Metabolism (metabolic pathways) are a surrogate of the phenotype. Metabolites are ideally situated to account for phenotypic changes induced by either genes, the environment, the microbiota, or a complex combination of all of these influences.

Fig. 4

Evolving landscape of precision medicine. The majority of clinical tools today limit the ability to fully individualize a treatment for many conditions (left). Big data initiatives (middle) are currently enrolling and profiling thousands of individuals and collecting an immense amount of data at the clinical and molecular level to begin the process of defining individual health, disease, and response to interventions. As signatures (alleles, metabolite profiles) emerge (indicated by colored human silhouettes), they can be deployed as additional profiling and analysis reveals additional clinically useful signatures. The result of these initiatives aspires to offer the potential to define, track, and treat disease on a far more individual basis (far right).

Fig. 5

Metabolomics is a genome “sentinel.” Recent research 1, 2 showed that metabolites are a highly informative “intermediate phenotype” between genes and the phenotype. By illuminating alleles that actively perturb metabolism and reflecting back to the function of the allele, an awareness of metabolism's importance for understanding biology – particularly in the context of genetics – was re-kindled.

Fig. 6

Metabolomic screening to identify disease signatures – inborn errors of metabolism (IEMs). A. Paradigm for how a single sample submitted for a metabolomics screen can screen a multitude of genetic abnoramtilites at once. B. Two examples of patient samples overlaid on metabolic atlas where metabolites that are elevated or reduced relative to a control population are red and blue, respectively. The magnitude of the deviation is noted by the size of the colored sphere. In these examples, a clear pattern emerges from any normal metabolic variation and secondary disease effects to reveal the salient metabolic pathways and either maple syrup urine disease or phenylketonura. Adapted from Miller et al. .