Abstract B21: Relationship of the global metabolome in blood to smoking behavior: A pilot study

Abstract

Abstract Introduction: Better biomarkers of cancer risk are needed for smokers to enhance early detection and support the FDA as it considers cigarette performance standards and evaluate health claims for modified tobacco products. This study will identify new biomarkers through metabolomics, a powerful method to identify numerous metabolites and profiles. Study Design: We utilized a previously conducted epidemiology study of well-characterized smokers. These subjects completed an extensive interview and smoked two cigarettes, one hour apart, in a smoking laboratory. Blood and CO measurements were collected before and after each cigarette. Plasma is available for metabolomic profiling. A pilot study was conducted on 5 light smokers and 5 heavy smokers’ plasma from the study and evaluated whether number of cigarettes per day mediates the influence of global metabolome. Hypothesis: We hypothesized that the number of cigarettes per day exerts their influence on phenotype through global metabolome. Methods: Using UPLC-TOFMS in our pilot study, we first analyzed global metabolomic profiles of 5 light smokers and 5 heavy smokers’ plasma and evaluated whether number of cigarettes per day mediate the influence of global metabolomic profiles. The data obtained for multivariate data analyses using Scripps’ XCMS. t-test, SVM (Support Vector Machine analysis), and Random Forest from the Metaboanalyst (www.metaboanalyst) were used for feature selection. The OOB (out-of-bag) error was used to measure the performance of Random Forest classification and the performance for SVMs was estimated using leave-one-out cross validation (LOOCV). Results: In our results, 1199 peaks in the positive mode and 497 peaks in the negative mode were detected and after feature selection, 816 and 361 peaks were selected in the positive mode and negative mode, separately. Those significant features from all three methods (t-test, SVM and Random Forest) were pooled together for identification using Metlin, MMCD and HMDB databases to obtain the KEGG IDs, and the pooled KEGG IDs were used for Ingenuity Pathways Analysis (Ingenuity@ Systems, www.ingenuity.com). Our preliminary results suggest that smoking behavior is associated with several biological pathways such as cell death, gene expression, cell-cell interaction, and lipid metabolism. The Pathways Analysis also reveals several liver, heart and renal toxicity and altered bile acid biosynthesis, phospholipid degradation due to heavy smoking. Future Studies: We will further evaluate whether smoking topography and inhalation, as measured by a nicotine boost and CO boost, and whether nicotine metabolism is associated with smokers’ global metabolome by level of smoking. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B21.