The Metabolome-Wide Association Study: A New Look at Human Disease Risk Factors
T
he ongoing revolution in high-throughput genomic screening and the promise of a deeper understanding of genetic variation in relation to common disease have led to the development of the genome-wide association (GWA) study paradigm. The concept is straightforward: to extensively genotype or haplotype large human population cohorts and statistically link specific allelic or other genotypic variation to epidemiological data on, for example, body mass index, cardiovascular disease (CVD) incidence, and diabetes, that were collected simultaneously. This approach has proved highly successful for the detection of common variants associated with a range of diseases and intermediate phenotypes (Nat. Rev. Genet. 2008, 9, 3114-3118). But life (and death), of course, is more complex than mere genetic programming, and most major diseases and their risk factors are products of gene–environment interactions operating at individual and population levels (Mol. Syst. Biol. 2006, 2, 52). To deconvolve these levels of complexity, we have explored the use of molecular spectroscopic approaches that capture environmental and selected genomic influences to investigate the connections between phenotype variation and disease risk factors. We have termed this the metabolome-wide association (MWA) study concept. We recently demonstrated the first proof of principle of this approach in humans for the identification of discriminatory biomarkers at the population level and their associations with disease risk, such as for high blood pressure (BP) (Nature 2008, 453, 396-400). We studied urine samples from 4630 people (sampled twice) from 17 different population subgroups in the U.S., U.K., China, and Japan and extensively modeled the geographical variations in their metabolic phenotypes (or metabotypes) by using multivariate analysis of high-resolution proton-NMR spectral data. The samples were obtained in the INTERMAP epidemiological study (J. Hum. Hypertens. 2003, 17, 591-608), which aims to understand the influence of macro- and micronutrient intakes on human BP variation. We found extensive population metabotype variation even within countries and identified dozens of population-discriminating biomarkers that were highly statistically significant. These biomarkers were from the four key sources of phenotypic variation: dietary, gut microbiomic, xenometabolomic (population drug use), and genetic. Some of these biomarkers were also strongly associated with BP variation. We note that GWA studies have so far been unsuccessful in the discovery of BP-related risk genes in the general population. We examined four of these discriminating biomarkers in more detail, specifically, alanine (linked to diet), formate (folaterelated, one-carbon metabolism and starch breakdown by gut microbes), N-methylnicotinamide (diet), and hippurate (diet and microbiome activity). These biomarkers all have strong environmental connections. Formate excretion is of particular 10.1021/pr8005099
© 2008 American Chemical Society
interest because it was inversely related to the BP of individuals in our study. Formate is closely involved in Cl–exchange in the kidney via the CFEX transporter, which itself is related to a complex series of SLC26 anion exchangers (Curr. Opin. Nephrol. Hypertens. 2007, 16, 484-490) that handle renal ion balance, including Na+ and Cl-. Thus the MWA-generated putative biomarker (formate) is connected by a complex physiological chain to factors (including salt handeling) that are known to link to BP regulation. Therefore, the MWA approach can lead directly to novel, testable physiological questions that relate to disease risk, for example, what is the role of formate in BP regulation and how does it relate to other risk factors such as dietary and urinary Na+ levels? This may give new insights into disease mechanisms and pathophysiology that may ultimately lead to novel drug targets. The MWA approach is highly complementary to GWA studies. MWA studies lead to the discovery of putative biomarkers that are part of generally established metabolic and physiological pathways and are thus amenable to experimentation. GWA studies, on the other hand, may identify a single nucleotide polymorphism (SNP) or a set of SNPs that may be remote from genes and have no obvious functional importance. Deep sequencing efforts and experimental studies are required to establish the significance of such variants, and the extent to which this knowledge will translate into novel therapeutic targets is an open question (Nat. Genet. 2008, 40, 695-701). MWA has the potential to produce more tractable solutions because the biomarker result is already in the “real world” of human physiology and can be associated with defined metabolic pathways. Such biomarkers may be surrogates of the mechanistic process rather than direct reporters of the physiological target. Nevertheless, the biomarkers may point to hitherto unknown aspects of the disease pathophysiology or etiology. Ideally, one would like to have GWA and MWA studies that are fully aligned and integrated, and one could also imagine that in the future there will be proteome-wide association (PWA) and even metagenome-wide association (MGWA) studies to capture even more systemic biological information. However, current technology probably does not allow practical PWA or MGWA studies simply because of the scale of quantitative analysis required, with cohorts of thousands or tens of thousands of participants. Now that is a real challenge for proteomic scientists! A huge untapped potential for MWA exists already. Scores of longitudinal epidemiological studies have been performed over decades, covering a range of diseases (cancer, CVD, neurodegeneration, etc.) with stored biofluid samples and connected epidemiological information on incidence and mortality. Therefore, we can use the MWA approach to retrospectively analyze these legacy samples to find prospective biomarkers for disease development. So, the MWA approach Journal of Proteome Research • Vol. 7, No. 9, 2008 3637
adds enormous value to the massive efforts originally expended to collect and store these samples and offers a unique opportunity to study the rapidly changing phenotypes in populations worldwidessomething that is undoubtedly connected to our changing patterns of disease. With the advent of numerous new “biobank” types of studies, a great opportunity now exists to collect samples for future metabolic, genomic, and proteomic analyses that will impact personalized health care management and public health care policy in the future.
3638
Journal of Proteome Research • Vol. 7, No. 9, 2008
JEREMY K. NICHOLSON and ELAINE HOLMES Department of Biomolecular Medicine, Imperial College London PAUL ELLIOTT Department of Epidemiology and Public Health, Imperial College London
