Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease
- A. uters, H. Krude
- N. Engl. J. Med
Despite being in the age of information technology, with a wealth of molecular data at our fingertips, relatively simple clinical parameters suchasBMIand fastingglucoseareused todiagnose complexmetabolic disorders. These clinicalmarkers have stood the test of timeand are affordable. However, given that many patients taking top-selling drugs fail to benefit from their prescriptions (Schork, 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, andChina, to launch ‘‘PrecisionMedicine’’ 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 researchprogram ledby theNIH, ‘‘All ofUs,’’ tapping into the richdiversityof theUnitedStatespopulation,whichpromises tocollect 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 (K€ uhnen et al., 2016), have seen success stories. Longterm 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, andmicrobiomes (NRC, 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 issuewith one of themost basic differencesbetween individuals: sex. In their essay they argue that, with evolutionary pressure driving sex divergence and positive selection on females to adapt their energymetabolism 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 fromconception throughbirth to old age.With themarchof time, an individual’smetabolismshifts, asdoeshis or her immunestate; they propose the intriguing possibility of harnessing the immune system as ameans of personalized treatment of somemetabolic disorders. Leulier and colleagues review diet, host physiology, andmicrobiota within an integrative framework frommodel 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, inwhichPiper and colleagues (2017) use the genomic information of anorganism todefine 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.