be modified through dietary manipulation to alter risk over time, afield classified as
nutri-metabolomics (Bordoni and Capozzi 2014 ). This approach provides a list of
“top”discriminatory metabolites associated with risk for a select disease. Examples
wherein metabonomic and/or metabolomic profiles have been used to estimate
disease risks include but are not limited to associations with preterm birth and
restricted growth in the Rhea mother–child cohort study (Maitre et al. 2014 ), type 2
diabetes (Bergman et al. 2014 ), incident coronary heart disease (Vaarhorst et al.
2014 ), hypertension (Nikolic et al. 2014 ), and chronic renal disease (Kobayashi
et al. 2014 ).
In addition to metabolomics and related“omics”as a source of new diet-related
biomarker discovery, research evaluating microbiota-associated biomarkers of dietary
intervention response has also received expanded attention in recent years. For
example, a study designed to evaluate the effect of probiotic supplementation on
cardiovascular disease risk demonstrated a significant change in gut microbiota that
corresponded to a favorable change in total and LDL-cholesterol (DiRienzo 2014 ). In
a review of the topic, gut microbiota, as measured by lipopolysaccharide-binding
proteinconcentrations, were associated with metabolic syndrome and showed promise
for response to dietary manipulation (Xiao and Zhao 2014 ),similartoresearchsug-
gesting that gut microbiota are linked to obesity and dietary substrate utilization
(Gangarapu et al. 2014 ).
Summary
Biomarkers of diet and/or nutritional exposures have been developed and effec-
tively utilized in nutritional epidemiology and clinical trial research (Pfeiffer et al.
2013 ). Yet, the majority of observational studies continue to rely almost entirely on
self-reported dietary intake estimates. The decision to use self-reported data is
largely driven by the costs, both personnel and assay-related, that are associated
with measuring biomarkers. Further, despite the improvement in accuracy of
exposure estimation when biomarkers are employed, for biomarkers other than
recovery biomarkers, precision remains sub-optimal. As thefield of metabolomics
and nutri-metabonomics evolves, it is expected that both the cost and accuracy of
exposure estimates will improve, making biomarkers the most broadly applied
methodology for evaluating diet–disease associations (Llorach et al. 2012 )
including nutrient bioactivity and efficacy in modulating disease (Rubio-Aliaga
et al. 2012 ). In all likelihood, biomarker efforts will have the greatest impact when
combined with more sophisticated and accurate self-report of intake and attention to
genetic variance in exposure response.
188 T.E. Crane and C.A. Thomson