Figure 1.
Study schematic and weight loss patterns in obese, sedentary women undergoing a fitness and weight loss intervention to assess changes in the plasma metabolome.
Blood was drawn in the overnight-fasted and post-OGTT conditions, during each of pre-intervention and post-intervention Test Weeks identically controlled for dietary intake, weight maintenance, and physical activity. To match menstrual cycle stage within individuals, Test Week 2 was after an intervention period ranging from 14-17 weeks (14 week time point mean is depicted for clarity). Of 15 women completing Test Week 1, 12 remained throughout the study to finish Test Week 2.
Table 1.
Body mass, fitness, and glucose homeostasis indices in previously sedentary obese women following a weight loss and fitness intervention.
Figure 2.
Subject Scores Plot derived from a partial least squares discriminate analysis (PLS-DA) model using temporal variance in identified plasma metabolites over the course of an OGTT in women.
Each time point is represented by a different colored oval surrounding subject-time point groupings (see legend), and the score of each subject is represented as a symbol within time point cluster. Both pre- and post-intervention data were used to generate this plot (see Results). Note that variation in metabolite levels at each of the time points led to separation (discrimination) of clusters from one another, with the exception of the final 2 time points for which subject scores were similar.
Figure 3.
A chemical similarity network of identified metabolites was used to visualize OGTT-associated changes in metabolite levels following an OGTT in women.
Vertices represent metabolites that are connected by edges (lines) based on chemical similarity (Tanimoto similarity >0.7). Loadings on the first latent variable in the PLS model for metabolic changes correlated with time during the OGTT are mapped to vertex size (absolute loading) and color is used to display the direction of the change (sign of loading: red, increase; blue, decrease; gray, unclear change or differential change in pre- vs. post-intervention conditions).
Table 2.
Overnight-fasted concentrations of identified and as-yet unidentified plasma metabolitesa that were significantly altered by a weight loss and fitness intervention in adult women.
Figure 4.
Post-OGTT excursions in plasma concentrations of metabolites that displayed a significant difference in area-under-the-curve (AUC) when comparing the pre- and post-intervention phases (red and green lines, respectively) of a weight loss and fitness regimen in women.
Values are quantion peak heights for each of the individual metabolites.
Figure 5.
Post-OGTT excursions in plasma concentrations of very long chain fatty acid (VLCFA) metabolites that are products of the Elongation of very long chain fatty acid (Elovl) enzymes.
Illustrated are temporal changes in metabolites when comparing the pre- and post-intervention phases (red and green lines, respectively) of a weight loss and fitness regimen in women. Values are quantion peak heights for each of the individual metabolites. Also shown is the set of reactions catalyzed by ELOVL including metabolites detected in this study.
Table 3.
Post-OGTT excursions (AUC) of plasma identified and as-yet unidentified metabolitesa and serum insulin significantly changed by a weight loss and fitness intervention in adult women.
Figure 6.
Subject Scores Plots derived from PLS-DA models using either overnight-fasted or post-OGTT (area-under-the-curve, AUC) metabolite variances, illustrating that differences in select metabolic features successfully discriminate subjects based on intervention phase.
Each symbol represents the score for a single subject during the pre- or post-intervention phases (red and green, respectively). For both models, the best discrimination of phases was evident along the latent variable 1 axis (LV1). Note that one subject's score from the post-intervention phase overlapped with the scores cluster of the pre-intervention subjects, indicating similarity in fasted metabolite pattern with pre-intervention phase women (also see Results).
Table 4.
Plasma metabolite and endocrine parameters used to generate a combined PLS-DA model that best discriminates the pre- and post- weight loss and fitness intervention states in adult women.
Figure 7.
Subject Scores Plot (A) and Variable Loadings Plot (B) derived from a combined PLS-DA model using both overnight-fasted and post-OGTT (area-under-the-curve, AUC) metabolite variances.
The model was calculated using a combination of the most robust metabolic features included in separate PLS-DA modeling of the fasted and post-OGTT states (Table 4). Each symbol in the Scores Plot represents the score for a single subject during the pre- or post-intervention phases (red and green, respectively). The best discrimination of phases was evident along the latent variable 1 axis (LV1), and the contribution of individual metabolic phenotype factor variance toward separation of the groups along LV1 is depicted in the Loadings Plot.
Table 5.
Correlations of interest between Matsuda insulin sensitivity index and fasting plasma or post-OGTT metabolite area-under-the-curve (AUC) in adult women either pre- or post-weight loss and fitness intervention.a