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, 453 (7193), 396-400

Human Metabolic Phenotype Diversity and Its Association With Diet and Blood Pressure


Human Metabolic Phenotype Diversity and Its Association With Diet and Blood Pressure

Elaine Holmes et al. Nature.


Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.


Figure 1 |
Figure 1 |. Hierarchical cluster analysis using group average linkage based on median 1H NMR urine spectra, by population sample and gender (n = 4,630).
Data for first 24-h urinary specimens. The hierarchical cluster analysis (HCA) algorithm produces a dendrogram showing the overall similarity/dissimilarity between population samples. Similarity index is normalized to intercluster distance. The similarity index measures the multivariate distance between clusters. A similarity of one indicates zero distance between clusters; a value of zero indicates the maximum intercluster separation seen in the data. Each branch of the dendrogram defines a subcluster; population samples within subclusters are more similar to each other than to those in other subclusters.
Figure 2 |
Figure 2 |. Plots of cross-validated principal components analysis scores (n = 4,630). a,
Pseudo three-dimensional plot for principal components (PC) 1–3; b, PC2 versus PC1; c, PC3 versus PC1; d, PC3 versus PC2. Median1H NMR spectra of the first 24-h urine specimens stratified by country and by gender, female (triangles) and male (squares). R2x = 74.2% (percentage variation in the NMR data explained by the model); Q2× 5 49.6% (percentage variation in the NMR data predictable by the model from cross validation). The cross-validated scores values for the first three components are available in Supplementary Information. Symbols in b–c as in a. Key: 1, Beijing; 2, Guangxi; 3, Shanxi; 4, Aito Town; 5, Sapporo; 6, Toyama; 7, Wakayama; 8, Belfast; 9, West Bromwich; 10, Baltimore; 11, Chicago; 12, Corpus Christi Hispanic; 13, Corpus Christi non-Hispanic; 14, Honolulu; 15, Jackson; 16, Minneapolis; 17, Pittsburgh.
Figure 3 |
Figure 3 |. O-PLS-DA scores and loadings plots (bootstrap analyses) for participants reporting high vegetable/low animal protein and low vegetable/high animal protein intakes, first 24-h urinary specimens.
Plots (one orthogonal component) compare top and bottom quartiles, adjusted for sample, age and sex, from a, East Asian, and b, western population samples. Loadings plots from the O-PLS-DA bootstrap analyses are shown with discriminatory metabolites labelled (see Methods for metabolite selection criteria) for c, East Asian and d, western participants. Analyses are after removal of metabolic outliers using the 95% Hotelling’s T2 statistic in the initial PCA. The plots show the number of participants, the number of components used in each model and the Q2Y values (percentage variation in the protein subgroup assignment predictable by the model from cross validation).

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