Challenges and Opportunities of Metabolic Profiling

Metabolomics is a comprehensive study of metabolites and metabolism – it measures simultaneously the levels of a large number of metabolites in a biosample, providing a snapshot of the metabolic activity in the sample. The metabolite levels are determined largely by genetics1, but are also influenced by environmental exposures, including the gut microbiome, diet, and physical activity. With the characterization of metabolites in relation to cardiovascular health, disease, and interventions, metabolomics has the potential to identify novel biomarkers for diagnostics and drug targets for therapy, uncover pathobiology of disease, and monitor treatment response.

Metabolomics technologies are continually evolving. As they become more sensitive, they are able to distinguish more and more metabolites². A recent study using ultra-high resolution metabolomics yielded close to 10,000 distinct signals between the mass-to-charge ratio (m/z) range of 85 to 1275³. Beyond detecting these unique spectral signals, however, identification of the chemical structure remains a significant challenge. Typically, only a fraction can be identified and the process can take weeks or months of analysis². Several spectral databases exist to aid in the identification⁴. The Human Metabolome Database lists over 40,000 metabolites that have been detected, or that are expected to be present, in biosamples (hmdb.ca). In this AHA statement on metabolomics in cardiovascular disease (CVD), the authors provide an in-depth overview of the many challenges regarding technology, processing, standardization and analytics that need to be addressed for metabolomics to have the expected considerable impact on diagnostics and therapeutics.

Despite the existing challenges, metabolomics is already providing new insight into etiology, biomarkers, and interventions for CVD. Trimethylamine N-oxide (TMAO) is an example of one identified metabolite that is providing insight into all of the above. Animal studies have shown it to be an atherotoxin5 that disrupts cholesterol balance⁶ (etiology). It correlates strongly with subsequent adverse myocardial events⁷ (biomarker), and it is a byproduct of trimethylamine, which gut microorganisms produce in response to meat and phospholipids in the diet⁵. As such, it represents a mechanism of atherosclerosis that could be treated or prevented by management of diet and/or the microbiome (intervention). Indeed two novel protein targets to reduce its levels have been identified⁸.

Similarly, metabolomics studies have shown that branched-chain amino acids (BCAA), which are insulin analogues, associate with insulin resistance^9-11, and predict development of diabetes¹². Obesity is not always associated with metabolic complications, and in metabolically healthy individuals with overweight/obesity, BCAA are lower than in those with complications¹³. Their levels can be affected by both diet and the gut microbiome¹⁴. Animal studies have shown that a high-fat diet with BCAA caused insulin resistance, while high-fat diet alone or standard chow with BCAA did not¹⁰. BCAA are also associated with coronary artery disease, even after adjusting for other CVD-risk factors^{15, 16}.

Metabolomics studies may unveil common metabolites key to other health complications that are associated with cardiometabolic disturbances. For example, systemic inflammation is proposed to be related to both atherosclerosis and dementia^17-20. Recently, small subsets of metabolites were identified that predicted both incident coronary artery disease²¹ and Alzheimer’s disease²² in large-scale prospective studies. One circulating lysophosphatidylcholine, PC 18:2/0:0, was common to both subsets. Lysophosphatidylcholines are glycerophosphocholine metabolites. They are lipid species that are involved in the modulation of systemic inflammation; they influence signaling and phagocytic functions of immune cells, as well as their migratory and adhesive behaviour and thus their capacity to cross endothelial barriers^23-25.

Metabolomics is also redefining dyslipidemia. Traditionally assessed by ‘high-abundance’ lipids, such as cholesterols and triacylglycerols, which circulate at millimolar blood levels, recent research in older individuals suggests that ‘low-abundance’ lipids, including glycerophosphocholine metabolites, may improve the prediction of CVD outcomes^{21, 26-28}. These lipids circulate at micro- or nano-molar blood levels^29-32, and advances in mass spectrometry enable their quantification. An untargeted lipidomics study identified several lipid species that predicted CVD outcomes; a few were glycerophosphocholine metabolites²¹. Recently, one of the profiled species, that was associated with pre-clinical CVD-risk factors (adiposity and triacylglycerols)²¹ in older adults, was also reported to be associated with higher CVD-risk factors in a much younger population-based cohort of adolescents³³. Further, using targeted lipidomics in the adolescent cohort, novel glycerophosphocholine metabolites were identified and quantified that showed even stronger associations with these risk factors³³. Further research is needed to assess the clinical relevance of these novel metabolites to CVD outcomes.

Further, metabolomics will be influential in drug development and precision medicine. For example, individual patient responses to drugs can be highly variable. Metabolomics can be used to assess drug metabolism, toxicity, and patient compliance, and to identify responders/non-responders (reviewed in³⁴). Combined with the potential of metabolomics to provide biomarkers of disease risk, this ability to find effective drugs with optimal dosing will enable personalized plans for prevention and treatment, respectively.

Moving forward, as clearly stated in this AHA statement on metabolomics in CVD, methodological and analytic standards must be implemented, and high throughput technologies advanced. Metabolomic profiling of large, well phenotyped cohorts is needed to characterize the influence of intrinsic (e.g., genotype, sex, age, etc.) and extrinsic (e.g., diet, exercise, smoking, etc.) factors. Prospective studies are required to characterize metabolomic variations associated with lifestyle changes (i.e., in diet, physical activity, smoking, and medications) and comorbidities, and to distinguish metabolomics signatures that are causes vs. consequences of disease. Replication/validation studies are key to the generalizability of findings. While metabolomics will clearly aid in identifying biological pathways and risk prediction, it may also enable disease prevention through identification of ‘protective’ and ‘harmful’ metabolites and their sources (endogenous vs. exogenous). Determining the functional roles of novel metabolites in health and disease are needed to identify their relevance to clinical outcomes. It is well established that CVD has a long pre-clinical phase, which may start as early as during childhood and adolescence³⁵. Conceivably, longitudinal studies could ascertain whether certain metabolomic profiles in youth, prior to disease onset, could identify those at increased risk of developing CVD, opening a window for prevention. Each of these considerations are relevant not only for ongoing research, but also must be addressed before translation to clinical practice will be an option.