A systems medicine approach to managing chronic disease
Given the incontrovertibility of systems biology, why is systems medicine not foundationally used in clinical care? Here we define systems medicine and outline the challenges for its widespread adoption.
Image credit: David Oritz
What is Systems Medicine?
A system is a set of interconnected parts that work together. By this simple definition, the human body is certainly a system. According to systems theory, systems share several properties: adaptation, boundary, centralisation, complexity, components, consistency, coupling, dynamism, environment, integrations, interfaces, interrelations, resilience, structure, and subsystems. In comparison, there are fewer components to the relational aspect of a system: input, output, process, feedback and environment. In short, systems medicine is the integrative analysis of input, output, process, feedback, and environment as the basis for managing health and disease.
With the gastrointestinal tract as one of the boundaries of the human system, input is that which interacts with the body from the environment. Let us take food as an example. Input is the breakdown products of digestion that interact with the lining of the gut and cross into the circulation, such as peptides, saccharides, fatty acids, micronutrients, alongside microorganisms and chemical pollutants. Microbiome-produced compounds such as short chain fatty acids, peptidoglycan and lipopolysaccharide, pharmaceutical drugs, pollen on mucosal surfaces, personal care products on skin, particulate matter in the respiratory tract, touch, sunlight, trauma and compassion are examples of wider inputs. Process is any pathway of interaction - mechanical, biochemical and electrical, while elimination and excretion via faeces, urine, sweat, and respiratory gases represent output. Finally, biochemical messengers and nerve signals constitute feedback.
Challenge of Complexity
How can we possibly gather and synthesise all the input, output, process, feedback and environment data? Herein lies the first hurdle to the adoption of systems medicine. Currently, we analyse clinical data in a serial fashion; we interpret one laboratory result and then another. Complexity is the limiting factor to performing systems analysis using analogue means. Computational tools that use large, robust, reliable data sets are needed.
Unprecedented amounts of raw data have been generated over two decades of multi-omics research in metabolomics, genomics, proteomics, lipidomics and more recently, exposomics(1). Alongside, there has been the development of mathematical frameworks for analysing networks, such as Biochemical Systems Theory for biochemical reactions(2). Presently, disease-centred omics studies are not uncommon in the scientific literature(3).
Challenge of Translation
How to deal with terabytes of multi-omics data available in open, standardised databases is the second hurdle to the adoption and dissemination of systems medicine, extracting what is useful and making it accessible in a clinical setting (4).
Health Tech Solution
In the age of big data, at the intersection of systems biology and the health and wellness industry, and with a biomarkers market worth $30 billion, all driving innovation, we are at the cusp of producing a big data derived clinical decision tool for systems medicine that will disrupt healthcare. Enter the Tricorder from Star Trek - the data and processing capability is at hand, such a device is no longer just a dream!
References
(1) Sun J, et al. A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: where exposomics meets metabolomics. Environmental International 2022:158;106941. doi.org/10.1016/j.envint.2021-106941
(2) Voit EO and Radivoyevitch T. Biochemical systems analysis of genome-wide expression data. Bioinformatics 2000;16(11): 1023-1037
(3) Qi Z, et al. Computational systems analysis of dopamine metabolism. Plos One 2008;3(6):e2444. doi.10.1371/journal.pone.0002444
(4) Weston AD and Hood L. Systems biology, proteomics, and the future of health care: towards predictive, preventative, and personalized medicine. Journal of Proteome Research 2004;3:179-196
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