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TitleMetabolic Phenotyping in Personalized and Public Healthcare
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Page 206

200 Metabolic Phenotyping in Personalized and Public Healthcare

The remaining features had no probable matches, suggesting uncharacterized
metabolic intermediates, unknown conjugates, or environmental chemicals not
present in the KEGG database. After excluding nonclassified chemicals, the
top three categories included pesticides (11%), lipids (9.3%), and carcinogens
(9.3%). Phytochemical metabolites were also present, representing 8.6% of the
matches. The identified metabolites were consistent with exposure to DDT and
co-exposure to other pesticides (Table 7.3). Two metabolites of DDT were pre-
sent within the significant features—chlorobenzoic acid, a bacterial degradation
product of DDT, and DDMU, a degradation product of DDT. Only a limited
number of the significant features were identified as endogenous metabolites and
included metabolites involved in amino acid pathways, co-factor metabolism,
lipids, and fatty acid intermediates. Metabolic intermediates in tyrosine metabo-
lism were also present, including 5-O-(1-carboxyvinyl)-3-phosphoshikimate and
fructose-1,6-bisphosphate. CPAA is converted to dihydroxyphenylacetate through
an oxidoreductase enzyme, which is a metabolic intermediate in phenylalanine
and tyrosine metabolism. Therefore, associations with tyrosine metabolism were
expected, and the presence of metabolites related to tyrosine metabolism sup-
port the use of metabolic phenotyping to detect biologically relevant associations.
Although it is not possible to determine if these metabolites are correlated as a
result of co-exposures, co-transport on lipoproteins, or similar detoxification path-
ways, these results provide insight into the distribution of previously uncharacter-
ized environmental chemicals in a representative healthy population.

7.6.3.3 Network Analysis
To identify metabolic pathway associations with the significant features, a cor-
relation network analysis was completed using the 71 m/z features selected by

FIGURE 7.9 Distribution of plasma chlorophenylacetic acid levels measured in the 153 healthy
individuals using high-resolution metabolomics and reference standardization [20]. Used with per-
mission from Oxford University Press.

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Population Phenotyping, Exposomics, and MWAS Chapter | 7 201

the MWAS and the raw metabolomics data. The resulting metabolic network
maps are provided in Fig. 7.11 for correlation thresholds of |r| ≥ 0.7 and 0.5 at
FDR ≤ 5%. At the more stringent correlation threshold of 0.7, 154 features were
found to be correlated with the CPAA-MWAS selected metabolic chemicals.
The presence of metabolite clusters in Figure 7.11 suggests different patterns
of metabolic associations and can be representative of chemicals from different
sources, varied metabolic disposition, and dissimilar metabolic targets. After

FIGURE 7.10 Metabolic feature association with plasma chlorophenylacetic acid (CPAA).
Pairwise Pearson correlation analysis identified 71 metabolic features significantly associated with
CPAA. Sorting by retention time indicated that these were primarily polar metabolites (A), since
the use of C18 stationary phase with reverse phase chromatography would retain nonpolar metabo-
lites such as lipids more strongly than polar chemicals. Correlations were predominantly positively
associated with CPAA levels, suggesting possible co-exposures additional metabolites of dichlo-
rodiphenyltrichloroethane, or increased enzymatic activity.

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