4 research outputs found
Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Concerns and Approaches for Cohort and Gender Issues in Serum Metabolome Studies
This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.209Mathematical models that reflect the effects of dietary restriction (DR) on the sera
metabolome may have utility in understanding the mechanisms of DR and in applying this
knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility
of identifying biomarkers through metabolome analysis and the validity of our approach
in independent cohorts of 6-month-oId male and female ad libitum fed or DR rats.
Cross-cohort studies showed that cohort-specific effects distorted the dataset The present
study extends these observations across the entire sample set, thereby validating our markers
independently of specific cohorts. Metabolites originally identified in males were examined
in females and vice-versa. DR's effect on the metabolom e is partially gender-specific
and is modulated by environmental factors. DR reduces inter-gender differences in the
metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples
and 39/93 metabolites in the male samples were significantly altered (using our previous
cut-off criteria of p ^ 0.2) by DR. The metabolites modulated by DR present a wide
spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype
is broadly representative of the metabolome and that DR has broad effects on the
metabolome. These studies, coupled with those in the preceding and following reports, also
highlight the utility for consideration of the metabolome as a network of metabolites using
appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed
herein suggest potential cautions, and potential approaches, for identification of multivariate
biomarker profiles that reflect changes in physiological status, such as a metabolism
that predisposes to increased risk of neoplasia
Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Practical Issues in Development of Expert System-Based Classification Models in Metabolomic Studies
This is the publisher's official version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004.8.197Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for
physiological status (e.g., relative risk of developing age-related diseases such as cancer).
Megavariate analysis (unsupervised hierarchical cluster analysis IHCAJ; principal components
analysis [PCAJ) of serum metabolites reproducibly distinguish DR from ad libitum fed
rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than
distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets
of serum metabolites contain sufficient information to construct mathematical models
of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform
distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised
HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models
were built with single cohorts, combined cohorts or mixed samples from previously studied
cohorts as training sets. Both algorithms over-fit models based on single cohort training sets.
KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of
k could not be accurately set in advance). SIMCA models had 100% accuracy within all
training sets, 89% accuracy in test sets, did not appear to over-fit mixed cohort training sets,
and did not require post-hoc modeling adjustments. These data indicate that (i) previously
defined metabolites are robust enough to construct classification models (expert systems)
with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses
outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv)
subtle inter-cohort variability may be a critical issue for high data density biomarker studies
that lack state markers
Development of Biomarkers Based on Diet-Dependent Metabolic Serotypes: Characteristics of Component-Based Models of Metabolic Serotypes
This is the publisher's version, also available electronically from: http://online.liebertpub.com/doi/pdfplus/10.1089/omi.2004Our research seeks to identify a scrum profile, or serotype, that reflects the systemic physiologic
modifications resultant from dietary restriction (DR), in part such that this knowledge
can be applied for biomarker studies. Direct comparison suggests that component-based
classification algorithms consistently out-perform distance-based metrics for studies of nutritional
modulation of metabolic serotype, but are subject to over-fitting concerns. Intercohort
differences in the sera metabolome could partially obscure the effects of DR. Further
analysis now shows that implementation of component-based approaches (also called projection
methods) optimized for class separation and controlled for over-fitting have >97%
accuracy for distinguishing sera from control or DR rats. DR's effect on the metabolome is
shown to be robust across cohorts, but differs in males and females (although some metabolites
are affected in both). We demonstrate the utility of projection-based methods for both
sample and variable diagnostics, including identification of critical metabolites and samples
that are atypical with respect to both class and variable models. Inclusion of non-statistically
different variables enhances classification models. Variables that contribute to these
models are sharply dependent on mathematical processing techniques; some variables that
do not contribute under one paradigm arc powerful under alternative mathematical paradigms.
In practical terms, this information may find purpose in other endeavors, such as
mechanistic studies of DR. Application of these approaches confirms the utility of megavariate
data analysis techniques for optimal generation of biomarkers based on nutritional modulation
of physiological processes