Effect of antihypertensive drugs on selected plasma long-chain fatty acids.

<p>Plasma metabolite level is presented as relative units: the median of all analyzed samples was set to 1. Box-and-whisker plots are presented. P values <0.05 from Wilcoxon signed-rank test are included. P, placebo (mean of three periods); A, amlodipine; B, bisoprolol; H, hydrochlorothiazide; L, losartan.</p

Effect of antihypertensive drugs on selected plasma acylcarnitines.

<p>Plasma metabolite level is presented as relative units: the median of all analyzed samples was set to 1. Box-and-whisker plots are presented. P values <0.05 from Wilcoxon signed-rank test are included. P, placebo (mean of three periods); A, amlodipine; B, bisoprolol; H, hydrochlorothiazide; L, losartan.</p

Correlation of the change of plasma cysteinylglycine and hexadecanedioate levels with the antihypertensive effect of amlodipine.

<p>Correlation coefficients (r) and P values from partial correlation, calculated with normalized metabolite change values and controlling for metabolite baseline level, are included. dASBP, change of 24-hour ambulatory systolic blood pressure; dADBP, change of 24-hour ambulatory diastolic blood pressure.</p

Characteristics of the study subjects.

<p>Characteristics of the study subjects.</p

Bladder Cancer Biomarker Discovery Using Global Metabolomic Profiling of Urine

<div><p>Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.</p></div

Workflow diagram of biomarker candidate selection from both cohort 1 and cohort 2 data sets.

<p>Abbreviations: HX, BCa negative but with history of BCa; Hema, BCa negative presenting with hematuria.</p

Receiver Operating Characteristic curves for a 6-biomarker algorithm.

<p>An algorithm, utilizing the candidate biomarkers palmitoyl sphingomyelin, lactate, gluconate, adenosine, 2-methylbutyrylglycine and guanidinoacetate was trained using the cohort-1 data set and then tested on the cohort-2 data set. ROC curves with AUCs are displayed for the training set (A.) and the test set (B.).</p

Cohort 1 derived candidate biomarker set heatmap for BCa vs. control groups.

<p>Red fill cells indicate metabolites with higher mean levels in BCA urines than in non-BCa controls at a p≤0.05 significance. Green cells indicate lower levels in BCa relative to control urines at a p≤0.05 significance. Statistical q-values and profiling results for all other named compounds measured in cohort 1 samples are presented inn <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115870#pone.0115870.s001" target="_blank">S1 Table</a>.</p

Random Forest analysis of cohort 2 sample data using 25 metabolites selected from cohort.

<p>Metabolites are rank-ordered by their mean decrease accuracy score. A higher mean decrease accuracy value indicates a greater predictive value. The 6 boxed data points represent top performing metabolites summarized in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115870#pone-0115870-g006" target="_blank">Fig. 6</a>.</p

Comparison of statistically significant metabolites from cohorts 1 and 2.

<p>Comparisons are for all BCA positive urines versus combine BCA negative controls. Dark red and dark green cells represent fold differences with a p≤0.05. Light green cell with blue text represents p≤0.1. BLQ: below limit of quantitation; NA: not applicable.</p