tobacco smoking is a major cause of morbidity 
 and mortality in 
 developed countries .
there are more than 
 4500 identified chemicals and over 60 
 potential or probable human carcinogens in cigarette smoke .
the complex mixture of cigarette smoke has been 
 classified by iarc ( the international agency for research on cancer ) 
 as a group 1 known human carcinogen .
the 
 institute of medicine ( iom ) in 2000 , at the request of the food and 
 drug administration ( fda ) , considered whether harm reduction approaches 
 through reducing toxin exposures in smoke via modified - risk tobacco 
 products ( mrtps ) could feasibly enhance tobacco control for smokers 
 who will not or could not quit .
recently , the fda has been given 
 legislative authority over tobacco products , including the ability 
 to establish performance standards ( e.g. , regulating the amount of 
 carcinogen emissions and exposure to smokers through product design 
 changes ) and evaluating manufacturers health claims of mrtps .
in the last several years , there has been a renewed 
 interest by the tobacco companies to manufacture these .
while tobacco product design changes developed 
 by the manufacturers or mandated by the fda can be screened in the 
 laboratory for changes in smoke chemical constituents and toxicology , ultimately the impact of such changes must be 
 evaluated in humans using biomarkers .
however , there are only a limited 
 number of biomarkers that assess exposure , and none that have been 
 sufficiently validated for lung cancer risk . given the wide variety of tobacco toxicants , a broad range of biomarkers 
 needs to be developed and validated to assess the impact of cigarette 
 design changes on human exposure and health .
the best available 
 cigarette smoke biomarkers are chemically specific , 
 reflecting only a narrow range of known toxicants . these target polycyclic 
 aromatic hydrocarbons ( e.g. , benzo(a)pyrene ) , tobacco - specific nitrosamines 
 ( e.g. , nnn , nnk ) , aromatic amines , and volatile hydrocarbons ( e.g. , 
 benzene , 1,3-butadiene and acrolein ) . among these , polycyclic 
 aromatic hydrocarbons ( pahs ) and tobacco - specific nitrosamines ( tsnas ) 
 are considered the major causative agents in cigarette smoke contributing 
 to lung cancer , and for cancer risk overall .
acrolein is a suspected human carcinogen and 
 carries the highest level of risk for respiratory effects in experimental 
 animals , while benzene is a known human 
 leukemogen .
metabolomics is an 
  omics technology that provides a simultaneous 
 assessment of numerous molecules ( i.e. , the metabolome ) allowing for 
 the quantification of individual metabolites and the identification 
 of a phenotypic profile for clusters of metabolites .
it provides information about metabolites from exogenous toxin exposures 
 and those from cellular endogenous pathways as a result of the toxic 
 exposures .
in contrast to currently available 
 smoke exposure biomarkers that are mostly chemically specific , metabolomics can provide information allows 
 for broader phenotype assessments incorporating profiles within and 
 across disease pathways . separately ,
metabolomics provide phenotypic 
 information about the cell s environment and mechanistic pathways 
 that genomics and transcriptomics do not .
metabolomics is becoming an important component of systems biology , 
 especially in determining the global metabolic profile by detecting 
 thousands of small and large molecules in various media ranging from 
 cell cultures to human biological fluids such as urine , saliva , and 
 blood .
more and more modern instrumentations 
 and technologies are emerging for the metabolomics research , such 
 as gas chromatography  mass spectrometry ( gc  ms ) , liquid 
 chromatography  mass spectrometry ( lc  ms ) , high - performance 
 liquid chromatography coupled with electrochemical coulometric array 
 ( lceca ) , and nuclear magnetic resonance ( nmr)-based study . each platform has its specific strengths and 
 weaknesses . for example , gc 
ms has great separation efficiency 
 and resolution but analyzes only the volatile metabolic compounds ; lceca has great reproducibility and sensitivity 
 but is low - throughput and provides only limited chemical structure 
 information ; nmr is a fast and reliable nondestructive detector but has poor 
 resolving power and poor sensitivity requiring larger amount of analyte 
 compared to mass spectrometry .
lc  ms is an important tool with great flexibility in metabolomics 
 compared to the other methods .
it is 
 often used to identify the low - abundance metabolites for a targeted 
 study or to obtain the largest metabolomics profile for a global approach . 
 with the development of ultrahigh pressure liquid chromatography ( uplc ) , 
 better chromatographic resolution and peak capacity compared to hplc 

is achieved , and therefore it has become the platform of choice in 
 our study .
there has been a great increase in 
 the research of metabolomics , 
 and a number of studies have shown the utility in assessing human 
 disease risk .
however , while some studies show the utility in urine 
 samples , little has been shown to validate the reproducibility of global 
 metabolomics profile using lc 
thus , this study assesses ultra performance 
 liquid chromatography coupled to quadrupole with time - of - flight mass 
 spectrometry ( uplc  qtof - ms ) method for use in metabolomics 
 and applies the procedure to human samples from smokers and nonsmokers .
all reagents and solvents were 
 of hplc grade . 4-nitrobenzoic acid ( 4-nba ) , debrisoquine ( as debrisoquine 
 sulfate ) , cotinine , ( )-nicotine , 1,11-undecanedicarboxylic acid 
 and 3-hydroxycoumarin were purchased from sigma - aldrich ( st . louis , 
 mo ) ; pseudooxynicotine , 3-hydroxycotinine , and cotinine n - oxide were 
 purchased from toronto research chemicals ( north york , on , canada ) ; 
 acetonitrile ( acn ) and water were purchased from fisher optima grade 
 ( fisher scientific , waltham , ma ) .
i , in order to test the variations of uplc  qtof - ms 
 assay , eight smokers plasma were split into two aliquots ( 5 
 l ) , and one aliquot was sampled consecutively 5 times , immediately 
 followed by the second aliquot sampled three times .
the eight subjects were 
 sampled consecutively , followed by a repeat of the experiment for 
 the first two subjects .
experiment ii takes advantage of a quality control procedure 
 used for ensuring assay consistency during the course of a large sample 
 set analysis . here
, a pooled sample from 7 nonsmoker subjects undergoing 
 therapeutic phlebotomy were placed as every tenth sample among a sample 
 set of 613 independent samples from 160 smokers ; 62 injections of 
 the pooled control were analyzed .
experiment iii was intended to determine if 
 there was a metabolomic 
 result difference between smokers and nonsmokers .
there were nine 
 smokers and ten nonsmokers ; the latter recruited from a three - arm 
 study validating biomarkers in smokers , former smokers , and nonsmokers .
smokers : blood samples were obtained from 160 cross - sectional study of smokers 
 intended to characterize cigarette smoke exposure and the development 
 and validation of biomarkers .
as an eligibility criterion , they had a stable 
 smoking pattern for at least 6 months of 10 cigarettes per day or 
 more .
there were 613 samples from these subjects that were analyzed 
 in a single session .
the samples in the 
 first group , used for the laboratory validation studies for experiment 
 ii , were nonsmokers who were undergoing therapeutic phlebotomy for 
 hemachromatosis or myeloproliferative diseases .
their plasma was pooled 
 and aliquots were interspersed with the smokers samples in 
 every tenth aliquot .
the second group , used to assess metabolomic 
 differences between smokers and nonsmokers for experiment iii , used 
 nonsmokers from a study of biomarker validation in smokers , former 
 smokers , and nonsmokers from the tobacco product assessment consortium 
 ( tobprac ) .
these were healthy persons who smoked less than 100 cigarettes 
 in their lifetime and recruited by irb - approved local media .
demographics of all the participants are listed in supplementary 
 tables 1 to 3 in the supporting information .
blood was collected as serum or plasma ( heparin green top tubes ) 
 as indicated .
sample aliquots were mixed 
 with 195 l of 66% acn containing the internal standards debrisoquine 
 and 4-nba .
the samples were centrifuged at 16000 g for 10 min at 4 c to remove particulates and precipitated 
 proteins .
one - hundred fifty microliters of the supernatant was then 
 transferred into an autosampler vial , followed by uplc 
samples were injected onto a reverse - phase 50  2.1 
 mm acquity 1.7-m c18 column ( waters , milford , ma ) using an 
 acquity uplc system ( waters , milford , ma ) with a gradient mobile phase 
 consisting of 2% acn in water containing 0.1% formic acid ( a ) and 
 2% water in acn containing 0.1% formic acid ( b ) .
each sample was resolved 
 for 10 min at a flow rate of 0.5 ml / min .
the gradient consisted of 
 100% a for 0.5 min then a ramp of curve 6 to 60% b from 0.5 to 4.0 
 min , then a ramp of curve 6 to 100% b from 4.0 to 8.0 min , hold at 
 100% b until 9.0 min , then a ramp of curve 6 to 100% a from 9.0 to 
 9.2 min , followed by a hold at 100% a until 10 min .
mass spectrometry was performed on a q - tof premier ( waters , milford , 
 ma ) operating in either negative - ion ( esi ) or positive - ion 
 ( esi+ ) electrospray ionization mode with a capillary voltage of 3200 
 v and a sampling cone voltage of 20 v in negative mode and 35 v in 
 positive mode . the desolvation gas flow was set to 800 l / h and the 
 temperature was set to 350 c .
the cone gas flow was 25 l / h , 
 and the source temperature was 120 c .
accurate mass was maintained 
 by introduction of lockspray interface of sulfadimethoxine ( 311.0814 
 [ m + h ] or 309.0658 [ m  h ] ) 
 at a concentration of 250 pg/l in 50% aqueous acetonitrile 
 and a rate of 150 l / min .
data were acquired in centroid mode 
 from 50 to 850 m / z in ms scanning . 

the metabolite identifications were confirmed by comparing the retention 
 time under the same chromatographic conditions and by matching the 
 fragmentation pattern of the parent ion from the biological sample 
 to that of the standard metabolite using tandem mass spectrometry 
 ( uplc  qtof - ms / ms ) .
the raw data from uplc  qtof instrument 
 were converted to network common data format ( netcdf ) files .
they 
 were then preprocessed using xcms for 
 peak detection , retention time correction and peak matching to obtain 
 a peak list in which each peak is represented by its m / z value , retention time and intensities ( peak area ) 
 across samples .
preprocessed data sets were analyzed using matlab 
 ( mathworks , natick , ma ) and metaboanalyst ( www.metaboanalyst.ca ) to perform scatter plot , hierarchical 
 clustering analysis and principal component analysis ( pca ) .
r was 
 used for anova ( analysis of variance ) and coefficients of variation 
 analysis in experiment i and random forest classification .
significant 
 features were searched against the madison - qingdao metabolomic consortium 
 database ( mmcd ) and the human metabolome 
 database ( hmdb ) with the mass accuracy 
 of 10 parts per million to identify putative metabolite identifications 
 in experiment iii .
for the metabolomic experiments 
 to provide sound biological insights 
 into pathobiology , it is imperative to demonstrate that the variability 
 of metabolomics measurements is within acceptable limits .
ms platform for the metabolomic 
 analysis of urine samples has been examined from unspecified healthy 
 subjects , which indicated that the within - day 
 reproducibility of uplc  qtof - ms system is sufficient to ensure 
 data quality in global metabolomic studies , after sufficient equilibration 
 of the system . in this study ,
the reproducibility 
 of lc  ms - based metabolomic experiments using blood samples 
 of smokers and nonsmokers is examined . the use of blood , while more 
 difficult to collect compared to urine , is conceptually a better matrix 
 for biomarkers because it is not dependent on renal excretion , for 
 example , a need to adjust for urinary creatinine or stability of a 
 metabolite in urine in vivo and ex vivo . for blood ,
there is a choice of serum or plasma ; the former will 
 include metabolites that result from coagulation and blood processing , 
 and so includes compounds not existing in vivo .
significant 
 differences for plasma versus serum have been noted by wedge et al . , where they found correlations of many metabolites 
 including glycerophosphocholines , creatinine , erythritol and glutamine 
 in the biological pathways with the prognosis of small cell lung cancer 
 in plasma but not in serum , indicating the clinical feasibility of 
 plasma for metabolomics study .
a recent study comparing metabolite 
 profiles in both biofluids has reported a higher concentration of 
 metabolites in serum , and a better reproducibility in plasma . however , the overall reproducibility was good 
 in both biofluids . for smoke exposure in particular ,
more nicotine - related 
 metabolites were seen in serum samples while better stability were 
 observed in the plasma .
the choice of serum versus plasma in our study 
 was dictated by the availability of large volumes of sample needed 
 for the numerous replicate experiments .
the data indicate , however , 
 that the choice for serum versus plasma was not important for the 
 purposes of validation and reproducibility .
the reproducibility 
 analysis is performed in three parts : the first 
 utilizes plasma from 8 smokers in experiment i to analyze the reproducibility 
 of the lc  ms - based metabolomics data over a short period of 
 time . the various factors that affect the quality of the data are 
 examined and their respective contributions to the data variability 
 are analyzed . as part of experiment i , immediately following the multiple 
 injections of the 8 smokers , the samples from the first two subjects 
 were reinjected 5 times and 3 times .
thus , the replicates followed 
 the initial 64 chromatographic runs and 13 h in time .
results of these 
 latter two subjects are compared against the first analyses to examine 
 the effect of running time and potential carry - over on the data .
ms platform over multiple days is 
 examined using experiment ii through a qc analysis similar to the 
 approach by gika , et al . after establishing 
 the reproducibility of lc  ms - based metabolomics data for plasma 
 samples ,
experiment iii was conducted to validate the use of metabolomics 
 for the assessment of smoke exposure , as recommended by hatsukami , 
 et al .
ms - based proteomics 
 experiments , it is assumed that the variability of the intensity of 
 a peak can be attributed to either sample preparation variation or 
 machine measurement . for the two aliquots of each 
 si , i = 1 , 2, ... ,8 in 
 experiment i , their difference is mainly due to the sample preparation 
 variation . and for the repeated injections of each aliquot , their 
 difference is derived from the measurement noise , which results from 
 variability in the chromatography and the mass spectroscopic measurements . 

we refer to the variability due to biological differences among subjects 
 as  inter - individual variation  . for the first 8 biological 
 subjects
s1s8 , it can be shown that there is minimal sample 
 preparation variability and measurement noise but large differences 
 due to the interindividual variation .
the intensity of each 
 peak from the peak - wise variation in the metabolomics data can be 
 modeled as1where yijk is the log transformation of the observed intensity of the ith biological subject ( i = 1 , 2 , ... , 8) , 
 the jth aliquot ( j = 1 , 2 ) , and 
 the kth injection ( k = 1,2, ... ,5 
 if j = 1 , k = 1,2,3 if j
= 2 ) ;  represents the average concentration of each peak ; 
 i , j(i ) and k(ij ) follow the normal distribution with mean zero 
 and variances b2 , s2 , and m2 respectively , and i , j(i ) and k(ij ) are mutually independent .
a 
 random effects analysis of variance ( anova ) model was used to estimate 
 the variations b2 ,
s2 , and m2 , which characterize the contributions of the interindividual 
 difference , sample preparation difference and measurement noise to 
 the variability of the data .
the variability due to chromatography 
 and machine measurement error was first 
 assessed by visually inspecting the 
 total ion chromatograms ( tics ) of the repeated runs from the same 
 aliquot .
, the assessment of the variability 
 will not be affected by different choices of preprocessing schemes . 
 in figure 1 , the tics of the five runs from 
 the first aliquot of s1 are compared with each other . it was found 
 that there was perfect qualitative reproducibility for all five runs 
 from the same biological subject and aliquot .
. then the peak list ( list of peaks ) from preprocessing 
 was used 
 to assess the variability among the repeated injections from an aliquot . 

for the first aliquot of biological subject s1 to s8 , the logarithmic 
 intensities of all the peaks from the first injection were compared 
 against the logarithmic intensities of all the peaks from the other 
 injections using scatter plots in figure 2 . 

the scatter plots of the peak intensities from two injections are 
 largely on the diagonal line and exhibits a high correlation ( pearson 
 correlation = 0.98 ; p < 0.001 ) , indicating a high 
 resemblance between the repeated injections .
since the five repeated 
 injections span a time of 57.5 min , it is evident that the lc 
scatter 
 plots of logarithmic intensities of the five injections of the first 
 aliquot of s1 to s8 represents ( a ) first injections vs second injections , 
 ( b ) first injections vs third injections , ( c ) first injections vs 
 fourth injections , and ( d ) first injections vs fifth injections .
the variance calculated from the anova model and 
 the coefficient 
 of variation were compared along with m / z values and retention times to see if the measurement error of the 
 lc  ms system is more significant over a particular region of 
 chromatogram and mass spectrum , as sample preparation variation and 
 interindividual variation should not be affected by chromatogram or 
 mass measurement . generally , the largest variation appears at the 
 beginning ( less than 25 seconds ) of the chromatogram run ( figure 3 ) .
the reason for the large variation at the beginning 
 of the chromatogram may be attributed to the high aqueous gradient 
 of the reverse - phase chromatography that can not retain the very polar 
 compounds or hydrophilic metabolites in the beginning of the elution .
if this area is considered to be important , 
 it likely can be resolved by using hilic ( hydrophilic interaction 
 liquid chromatography ) .
the variation 
 toward the end of the chromatogram is often observed partly due to 
 baseline shift and can result in an overestimate of the intensities 
 of the analytes .
thus , we have discarded 
 the last two minutes of the chromatography during the preprocessing 
 of the data . as shown in figure 3 ,
the retention 
 time of peaks only go up to 480 s , where the variability of data is 
 moderate .
the variation caused by the measurement noise shows a significant 
 negative correlation ( spearman correlation = 0.66 ; p < 0.001 , see supplementary figure 1 , supporting information ) with peak intensity .
it is understandable 
 as the noise effect is less severe on high - intensity peaks and it 
 is relatively easy to detect and quantify high - intensity peaks .
overall , the mean cv for the measurement 
 noise was 0.02 , which ranged from 0.003 to 0.297 .
similar to the above , 
 the greatest cvs were in the areas at the beginning of the chromatogram . 

the correlation coefficient for the cv in relation to peak intensities 
 is shown in figure 4 ( spearman correlation 
 = 0.785 ; p < 0.001 ) .
those metabolites 
 with cv more than 10% have been removed from the analysis for biomarker 
 discovery .
distribution of the estimated measurement error in cv ( % ) over m / z values and retention times .
the dot size and color corresponds to the 
 cv value : the larger the dot , and the brighter the color of the dot , 
 the larger the cv value of the individual peak .
scatter plot of the estimated measurement noise in coefficient 
 of variation ( % ) versus the mean intensities of peaks over s1 to s8 .
the scatter plot also was used to examine the variability 
 due to 
 sample preparation .
the repeated injections from each aliquot were 
 averaged to obtain the mean intensity of each peak for an aliquot . 

the logarithmic intensities of all peaks of the first aliquot of the 
 eight biological subjects were compared with the logarithmic intensities 
 of peaks of the second aliquot of the biological subjects .
the two 
 aliquots show a high degree of resemblance as the intensities are 
 largely on the diagonal line ( supplementary figure 2 , supporting information ) and exhibits a high correlation 
 ( pearson correlation = 0.99 ; p < 0.001 ) .
the results 
 demonstrate that the sample preparation does not significantly affect 
 the quality of the lc  ms data .
hierarchical clustering of the 
 8 subjects each with 8 injections ( five plus three injections ) from 
 experiment i is shown in figure 5a .
the result confirms that the variability due to 
 sample preparation is generally equal to or smaller than the measurement 
 error , so it is sometimes masked by the measurement error .
hierarchical 
 clustering of all metabolites ( a ) from subject 1 ( s1 ) 
 to subject 8 ( s8 ) and ( b ) from s1 to s8 plus the analytical replicates 
 s1 and s2. heat map colors represent relative values , 
 in which red represents values above the mean , black represents the 
 mean , and green represents values below the mean of a row ( metabolite ) 
 across all columns ( samples ) .
in experiment i , the first two biological subjects 
 s1 and s2 were repeated at the end of the experiment as s1 
 and s2 , with separate sample preparations and injections . 
 between s1 ( s2 ) and s1 ( s2 )
, there were 56 other sample 
 runs , and they were more than 10 h separated in time .
the effects 
 of run time and carry - overs on data variability can be evaluated by 
 comparing s1 vs s1 and s2 vs s2. the logarithmic intensities 
 of the four aliquots from s1 and s2 were compared with those of the 
 four aliquots from s1 and s2. a high degree of resemblance 
 is evident between s1 , s2 and s1 , s2 ( supplementary 
 figure 3 , supporting information ) and exhibits 
 a high correlation ( pearson correlation = 0.97 ; p < 0.001 ) , which showcases that the lc 
ms platform is stable 
 after a moderate time period ( more than 10 h ) .
we then analyzed 
 a new hierarchical clustering adding experimental replicates ( s1 
 and s2 ) that were run at the end of the experiment , and the 
 result showed that the same sample s analytical replicates 
 were clustered together ( s1 to s1 , and s2 to s2 , see 
 figure 5b ) .
thus , the increased running time 
 and carry - overs of the sample analyzed between them does not significantly 
 affect the data reproducibility .
the estimated variances are shown 
 as the box plot in figure 6 and the variance 
 and coefficient variation are summarized in table 1 .
the results confirm that the variations between measurement 
 error and sample preparation variation are comparable , while they 
 are smaller than the interindividual variations , even if these replicates 
 were analyzed 13 h apart .
box - plot of the estimated variances due to interindividual 
 variation , 
 sample preparation and measurement noise . from the model equation ( 1 )
, 
 we can deduce 
 that the variance of the mean intensity of all the biological subjects 
 is2or more general3where i is 
 number of biological subjects , j is the number of 
 aliquots per subject and k is the number of injections 
 per aliquot .
this means that the variability of the mean of the observed 
 peak intensity is dominated by the variation because of interindividual 
 difference .
thus , the most effective way to reduce the variability 
 in lc  ms data is to increase the number of biological subjects 
 under investigation , given the total number of sample runs .
because 
 of this understanding , in experiment ii , only one aliquot per subject 
 and one injection per aliquot are used for the lc  ms - based 
 metabolomics analysis of 613 biological subjects .
experiment ii provides the quality control 
 data from a large metabolomics study of smokers ( n = 160 ) with up to four separate blood draws before and after two 
 cigarettes each ( 613 samples ) . for the quality control ,
62 pooled 
 healthy control samples were prepared by mixing equal volumes ( 400 
 l ) of plasma from seven nonsmokers and were inserted as every 
 10th sample of the run .
the pooled aliquot is assumed to be homogeneous and 
 repeatedly injected across the entire lc 
ii , 62 pooled 
 quality control samples were injected during the analysis of 675 samples , 
 including the 62 pooled samples .
the analyses were conducted uninterrupted 
 over 138 h. the peak list is acquired after peak detection and alignment 
 using xcms .
the preprocessing may help to reduce the variation in data due to 
 instrument noise as only peak regions are considered in the analysis . 

this assesses reproducibility of the chromatography , quantitation 
 of the spectroscopy , and run - to - run carryover and cross - contamination 
 while running as a batch in a chromatographic system .
the reproducibility 
 of the quality control replicates was analyzed through unsupervised 
 principal component analysis ( pca ) by metaboanalyst .
pca has been 
 shown to be an effective approach to visualize high - dimensional data 
 by projecting the data point into a low - dimension space .
if a certain 
 degree of platform stability has been attained , the qc samples should 
 cluster tightly together in the pca score plot .
pca of the experimental 
 samples and qc samples has revealed a pattern as shown in figure 7 , which gives an indication about the reproducibility 
 of the data .
although there are some variations among the qc samples , 
 they occupy a relatively constrained space in the pca score plot . 
 when we compare our study ( 675 injections with 62 qcs , total run time 
 138 h ) with the validation guideline for urine samples from gika et 
 al .
( 130 injections with 16 
 qcs , total run time 29 h ) , the performance of our qc samples showed 
 little variation in tight clustering until samples run later in time . 

the deviation from the qc cluster might be due to the time - related 
 drift in instrumental performance over a long time span in the large 
 scale study , or the matrix effect due to the 
 largely peptide / protein - based plasma samples compare to urine .
figure 8 is the hierarchical 
 clustering analysis of samples and qcs shown as a heatmap using pearson s 
 correlation for the similarity measure ( distance ) , and clustering 
 algorithms using ward s linkage ( clustering to minimize the 
 sum of squares of any two clusters ) .
the result demonstrated that 
 our platform is capable of discriminating the metabolome from clusters 
 of qc samples and experimental samples , which again provides confidence 
 in the quality of our pooled healthy controls throughout the run .
scores 
 plot between the selected principle components ( pcs ) showed 
 difference between samples and qcs in their metabolomic profiles . 

clustering result shown as heatmap ( distance measure using 
 pearson , 
 and clustering algorithm using ward ) of the pooled qc samples and 
 experimental samples .
there is wide interindividual variation for smoking behavior , which 
 is affected by several factors such as race , gender , psychological factors and genetic background .
metabolomic profiling may identify differences in exposure and response , 
 reflecting inherent biological differences .
metabolomics has 
 the power to simultaneously detect carcinogen metabolites and endogenous 
 metabolites affected by smoke exposure .
thus , both exposure and effect 
 can be assessed . to examine the feasibility of a metabolomic study 
 in assessing smoke exposure , experiment iii of 9 smokers and 10 nonsmokers 

random forests analysis was used to perform supervised classification 
 and feature selection , and top 50 important features were selected 
 with 100% accuracy by multidimensional scaling plot ( figure 9a ) and can be visualized according to their rank 
 importance ( figure 9b ) . to preliminarily identify 
 the metabolites in experiment iii ,
the madison - qingdao metabolomic 
 consortium database and the human metabolome database were searched ; 
 169 putative features from the positive mode and 53 from the negative 
 mode were identified .
then , 12 candidate metabolites were manually 
 selected based on their putative identifications and availability 
 of chemical standards for comparisons , as listed in table 2 .
validation for these candidate metabolites were 
 then done by acquiring ms / ms spectra . as expected , the ms / ms spectra 
 of nicotine , cotinine , 3-hydroxycotinine and cotinine n - oxide were 
 verified and matched well with those from authentic compounds in our 
 serum samples ( figure 10 and supplementary 
 figure 4 , supporting information ) .
, it is primarily metabolized to cotinine by cytocrome 
 p450 2a6 ( cyp2a6 ) enzyme and further metabolized by the same enzyme 
 to 3-hydroxycotinine .
thus , the ratio of 
 cotinine and 3-hydroxycotinine reflect a stable cyp2a6 metabolic 
 activity for nicotine metabolism .
pseudooxynicotine , 
 1,11-undecanedicarboxylic acid and 3-hydroxycoumarin were also verified 
 by ms / ms identification .
pseudooxynicotine is an amino ketone product 
 of nicotine by soil bacteria and was reported to 
 be the direct precursor to the tobacco - specific lung carcinogen nnk 
 in the bacterial systems .
that the incubation of nicotine 
 with human liver microsomes produced pseudooxynicotine through 2-hydroxylation 
 of nicotine but has not been identified 
 in humans . since microflora has been found to play a crucial role 
 in human metabolome , tobacco - related metabolites metabolized 
 by bacteria could have contributions to carcinogenesis that had nt 
 been thought of before .
nicotine - related metabolites including nicotine , 
 cotinine , 3-hydroxycotinine , cotinine n - oxide ( a minor metabolite 
 of nicotine ) and pseudooxynicotine validated by ms / ms were all significantly 
 higher in intensity among smokers compare to virtually zero among 
 nonsmokers ( figure 11 ) . in our results ,
3-hydroxycoumarin 
 and 11-undecanedicarboxylic acid were seen in both groups but higher 
 in peak areas among nonsmokers compare to smokers ( figure 11 ) .
3-hydroxycoumarin is the metabolic product of 
 the natural compound coumarin which can be found in plants and spices . 

metabolism of coumarin in humans is mainly carried out by cyp2a6 to 
 7-hydroxycoumarin but also can be metabolized 
 by cyp3a4 to 3-hydroxycoumarin .
nicotine 
 is mainly metabolized by cyp2a6 enzyme , and thus could increase bioactivation 
 of cyp2a6 and the 7-:3-hydroxycoumarin ratio among smokers . however , 

most of the reports on 3-hydroxycoumarin were done in rodent models 
 and thus needs further clarification in human .
11-undecanedicarboxylic 
 acid is a dicarboxylic acid with a 13-carbon dibasic acid ( tridecanedioic ) 
 occurring in plant and animal tissues .
the relationship of 11-undecanedicarboxylic 
 acid to cigarette smoke is still unknown , maybe through altering the 
 carboxylic acid metabolism in the tobacco leaf or during the manufacture 
 of cigarettes .
( a ) forest 
 accuracies 
 were calculated and the sample classifications were presented by multidimensional 
 scaling ( mds ) plot . in this plot ,
nonsmokers ( red ) and smokers ( blue ) 
 were well separated in serum samples .
( b ) visualization of the top 
 metabolites across all samples identifies the rank importance of the 
 ions .
ms / ms spectrum from authentic compounds of cotinine ( a1 ) , 
 hydroxycotinine 
 ( b1 ) , pseudooxynicotine ( c1 ) , and 1,11-undecanedicarboxylic acid ( d1 ) . 

ms / ms spectrum obtained from serum samples of smoker ( a2 , b2 , c2 ) 
 or nonsmoker ( d2 ) were also presented for comparison .
box plots of peak areas for seven candidate metabolites 
 among smokers 
 and nonsmokers .
our results further show the feasibility and potential 
 of the metabolomics 
 profiling to identify new biomarkers of cigarette smoke exposure and 
 lung cancer risk . given that cigarette smoke is a complex mixture , 
 it is possible that metabolic profiles will provide additional information 
 for tobacco - related disease risk different from existing chemically 
 specific biomarkers , such as those reported by hecht et al .
also , broad detection methods such as used here 
 can screen for unanticipated changes in smoke exposure as cigarette 
 designs change .
for example , the technology used to decrease some 
 constituents might increase those of others ( e.g. , for the eclipse 
 cigarette , acrolein and co increased ) .
another reason for needing a broad biomarker 
 such as a metabolomics profile of exposure and risk is to help tailor 
 prevention and early detection strategies for former and current smokers . 

while this study will not directly assess biomarkers for lung cancer 
 risk , the first step in developing these is the development of biomarkers 
 of smoke exposures .
metabolomics provides information about 
 the metabolic status of 
 living systems and can provide phenotypic information about the cell s 
 environment and mechanistic pathways , as well as having clinical utility 
 including as a risk biomarker .
it is inexpensive and high - throughput , 
 and has the potential to identify new biomarkers of cigarette smoke 
 exposure and the consequent disease risk .
such biomarkers will assist 
 in the evaluation of tobacco products and performance standards , and 
 for identifying smokers at risk for lung cancer . upon the basis of 
 our study using uplc  qtof - ms , low variability
is observed in 
 measurement variation and sample preparation variation . when applying 
 qc samples to the large scale study , qc clustering presents high stability 
 of the platform but not the ones run later in time .
various 
 factors including the stability and maintenance of individual 
 instrument , sample preparation techniques , and the choice of column 
 could affect the performance of the chromatography result . therefore , 
 we recommend that for future metabolomics studies , especially large 
 scale epidemiological studies , a separate experiment to assess the 
 variability of the platform before applying precious human samples 
 is needed so as not to exceed the results reported herein . after determining 
 the required sample size ,
the number of aliquots and injections needed 
 according to the variability of the specific platform , a pooled qc 
 sample set to be run in between the experimental samples throughout 
 the run is crucial in order to further assess the repeatability of 
 the experiment .
after preprocessing and analyzing the data , metabolites 
 with cv no more than 1015% should be considered for the biomarker 
 discovery .
metabolomics can potentially be used to better characterize 
 smoking - related 
 disease risks to enhance prevention and early detection methods , and 
 by the fda in the regulation of tobacco products .
the recent authority 
 to the fda over tobacco products allows the fda to mandate product 
 performance standards governing smoke exposure to toxic constituents 
 and also requires the fda to evaluate manufacture s health 
 claims for modified - tobacco products of purported reduced exposure .
the fda decisions , however , will need to be supported 
 by scientific studies and a better understanding of cigarette smoke 
 toxicology .
it also will require support through human studies and 
 biomarkers that reflect the complex exposure to tobacco smoke . today , 
 though , there are only a few biomarkers of exposure and no validated 
 biomarkers of cancer risk .
as proof of 
 principle , several new biomarkers were identified herein that have 
 not thus far considered to be biomarkers of tobacco smoke exposure . 

thus , metabolomics has the potential to develop a metabolic phenotype 
 in healthy smokers of smoke exposure and disease risk .
 metabolomics is likely an ideal tool to assess tobacco 
 smoke exposure 
 and the impact of cigarette smoke on human exposure and health . to 
 assess reproducibility and feasibility of this by uplc  
 qtof - ms , 
 three experiments were designed for the assessment of smokers 
 blood . 
 experiment i was an analysis of 8 smokers with 8 replicates . 
 
 experiment ii was an analysis of 62 pooled quality control ( qc ) samples 
 from 7 nonsmokers plasma placed as every tenth sample among 
 a study of 613 samples from 160 smokers . finally , to examine the feasibility 
 of metabolomic study in assessing smoke exposure , experiment iii consisted 
 of 9 smokers and 10 nonsmokers serum to evaluate differences 
 in their global metabolome . 
 there was minimal measurement and sample 
 preparation variation in all experiments , although some caution is 
 needed when analyzing specific parts of the chromatogram . 
 when assessing 
 qc samples in the large scale study , qc clustering indicated high 
 stability , reproducibility , and consistency . 
 finally , in addition 
 to the identification of nicotine metabolites as expected , there was 
 a characteristic profile distinguishing smokers from nonsmokers . 
 metabolites 
 selected from putative identifications were verified by ms / ms , showing 
 the potential to identify metabolic phenotypes and new metabolites 
 relating to cigarette smoke exposure and toxicity . 
Introduction
Materials and Methods
Results and Discussion
Conclusions