Precision Metabolism: Hitting the Mark

Despite being in the age of information technology, with a wealth of molecular data at our fingertips, relatively simple clinical parameters such as BMI and fasting glucose are used to diagnose complex metabolic disorders. These clinical markers have stood the test of time and are affordable. However, given that many patients taking top-selling drugs fail to benefit from their prescriptions (Schork, 2015xSchork, N.J. Nature. 2015; 520: 609–611Crossref | PubMed | Scopus (152)See all ReferencesSchork, 2015), we are sharply reminded that a “one size fits most” approach may not always be effective for diagnosing and treating heterogeneous diseases such as the metabolic syndrome. The need for individualized therapies has prompted various countries, including the United States, the United Kingdom, and China, to launch “Precision Medicine” initiatives, recognizing the need to collect and analyze big data from the population at large to ultimately benefit the health of sub-populations and individuals. In the United States, this has paved the way to an all-inclusive research program led by the NIH, “All of Us,” tapping into the rich diversity of the United States population, which promises to collect and analyze lifestyle, environmental, and biological data from one million volunteers in order to cover a wide array of health conditions.The hope of understanding and treating the patient as an individual rather than as part of a generic class has started to materialize. Given their tractability, rare monogenic diseases, such as the extreme hyperphagia and obesity of two patients with proopiomelanocortin deficiency being treated with a melanocortin-4 receptor agonist (Kuhnen et al., 2016xKuhnen, P., Clement, K., Wiegand, S., Blankenstein, O., Gottesdiener, K., Martini, L.L., Mai, K., Blume-Peytavi, U., Gruters, A., and Krude, H. N. Engl. J. Med. 2016; 375: 240–246Crossref | PubMed | Scopus (33)See all ReferencesKuhnen et al., 2016), have seen success stories. Long-term strategies will be aimed at deciphering more complex and heterogeneous pathologies, ranging from cancer to metabolic disorders. In 2011, the National Research Council of the United States called for a “knowledge network,” which layers and connects complex factors, ultimately building a new “taxonomy of disease” from which a patient can be diagnosed through the integration of omics data. The omics include exposomes (exposure-omics), metabolomes, genomes, epigenomes, and microbiomes (NRC, 2011xToward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. National Research Council. See all ReferencesNRC, 2011). In this Special Issue, we introduce the concept of “Precision Metabolism” and review our quiver of complex factors that need to be integrated to individually target metabolic health and disease.Maggi and colleagues kick off the issue with one of the most basic differences between individuals: sex. In their essay they argue that, with evolutionary pressure driving sex divergence and positive selection on females to adapt their energy metabolism to their reproductive needs, sex differences are intricately weaved into the pathology of metabolic disorders. Focusing on immune-metabolic crosstalk, Elinav and colleagues tackle the complex dynamic equilibrium between diet, host genome, gut microbome, and the immune response from conception through birth to old age. With the march of time, an individual’s metabolism shifts, as does his or her immune state; they propose the intriguing possibility of harnessing the immune system as a means of personalized treatment of some metabolic disorders. Leulier and colleagues review diet, host physiology, and microbiota within an integrative framework from model organisms to humans, and propose a theoretical concept, the nutritional geometry framework, for personalized diet optimization. This concept is applied in a research article in this issue, in which Piper and colleagues (2017)xPiper, M.D., Soultoukis, G., Blanc, E., Mesaros, A., Herbert, S., Juricic, P., He, X., Atanassov, I., Salmonowicz, H., Yang, M. et al. Cell Metab. 2017; 25: 610–621Abstract | Full Text | Full Text PDF | PubMed | Scopus (9)See all References)Piper and colleagues (2017) use the genomic information of an organism to define its dietary amino acid requirements, and show that exome-based designer diets optimize fitness in flies and mice.Although one would intuitively think that understanding the underlying genetic basis for obesity would be helpful, Loos and Janssens take a sobering look at where we are in understanding the polygenic basis of obesity risk. Though we have nearly 200 common genetic variants associated with obesity, we are still coming up short in our predictive capacity compared to traditional parameters, such as family history and childhood obesity. As we are now realizing, however, epigenetics provides an additional layer of complexity on top of our genetics, as our “non-genetic molecular legacy of prior environmental exposures.” Two Perspectives, one by Rando and colleagues, and one by Patti and colleagues, review the complex links between metabolism and epigenetic modifications and multigenerational disease links transmitted through germ cells. Nielsen and colleagues close the issue from a systems perspective, bringing into focus the unprecedented availability of big data for integrative analysis, and take us back to the individual, looking at the immense value of N-of-1 clinical trials with large cohorts.Sixteen years since the publication of the first draft of the human genome, we are watching the arrow of precision medicine fly toward the bull’s-eye of metabolic health.