Dietary Patterns, Metabolomics and Colorectal Cancer Risk and Mortality in the Nurses' Health Study and Health Professionals Follow-Up Study
Conditions: Colorectal Cancer, Mortality
Sponsor: Ohio State University Comprehensive Cancer Center

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Document Cover page 
 
Official Title of the study: Dietary Patterns, Metabolomics and Colorectal Cancer Risk and 
Mortality in the Nurses' Health Study and Health Professionals Follow-Up Study 
 
NCT number: not available 
 
Date of the document: originally created on October 1, 2015 
 
History of changes:  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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C. APPROACH  
C1. Aim 1 – K99 phase: To use 3 different approaches to derive 8 dietary patterns and utilize a 
standardized methodology to compare the best diet for overall health with the best diet for CRC 
prevention; and further determine if associations with CRC prevention are mediated by biological 
mechanisms involving inflammation and insulin 
Rationale / Preliminary data 
We have previously reported that several a priori (AHEI, AMED or OMNI) and a posteriori (Western or prudent) 
dietary patterns are associated with overall health (with all-cause mortality as indicator)58,59 and with CRC 
prevention12,60 in separate studies. The current proposal goes further to determine the extent to which diets 
beneficial for overall health are consistent with diets beneficial for CRC prevention. This is important for 
designing effective dietary guidelines for overall health that are optimized for CRC prevention, given that it is 
not feasible to have an optimal diet for every disease. We also increase the variety of dietary patterns by 
including two new patterns – the EDII and CDHR derived using a third and novel approach – the hypothesis-
driven approach, that combines the strengths of both a priori and a posteriori approaches. 
Dietary patterns research has overcome the limitations of single nutrient research to a large extent but not 
completely. Methodological issues have hindered a wider translation of findings from dietary patterns research, 
the major critique being lack of consistency and scientific rigor in the methods. In Aim 1a we will utilize a 
standardized methodology to evaluate the associations of the 8 dietary patterns with all-cause mortality as a 
marker for overall health and with CRC risk, adjusting for the same set of potential confounders, and further 
investigate in Aim 1b several inflammatory markers (CRP, IL6, TNFR, and adiponectin) and hyperinsulinemia 
(C-peptide) as mediators of the association between dietary patterns and CRC risk. This will provide an 
unprecedented opportunity to evaluate the consistency of evidence for the benefits of diet quality for overall 
health and for CRC prevention, supported by strong epidemiological methods and biological plausibility. 
Methodological Approach 
Study Population: The NHS was established in 1976 as a prospective cohort of 121,701 U.S. registered female 
nurses who were aged 30 to 55 years at enrollment.61,62 The HPFS was initiated in 1986 as a prospective 
cohort of 51,529 U.S. male health professionals who were aged 40 to 75 years.63 Blood samples were 
collected from subpopulations of the NHS (n=32,826) in 1989-1990 and HPFS (n=18,225) from 1993 to 1994, 
using similar protocols for all cohorts.64,65  
Assessment of dietary data: Dietary data are updated every four years in the NHS (since 1980), NHS-II (since 
1991) and in the HPFS (since 1986) with a validated semi quantitative food frequency questionnaire (FFQ) 
assessing habitual intake of foods and beverages over the preceding year. Participants reported how often, on 
average, they consumed each food of a standard portion size.66-68 We will use average dietary data 
cumulatively across the multiple FFQs, to reduce within-subject variation and best represent long-term diet.69,70  
Assessment of lifestyle factors: We will calculate cumulative body mass index (BMI), physical activity levels, 
and pack-years smoked.71 Correlation between technician-measured and self-reported weight was r=0.96 in 
1980 and 0.97 in 1986 and did not differ by level of BMI.72 Our methods that measure physical activity and diet 
have been rigorously validated in our cohorts.73-76 We have also routinely collected information regarding a 
CRC family history in a first-degree relative, post-menopausal hormone therapy, and aspirin use.77,78 These 
long-term lifestyle data at multiple time points provide an unprecedented documentation of individual’s lifestyle 
factors and will allow us to account for within-person variation in the effect of lifestyle on CRC risk. 
Assessment of biomarkers: Procedures for the measurement of plasma inflammatory markers (IL6, CRP, 
TNFαR2, and adiponectin), 64,79,80 and plasma insulinemic markers (C-peptide, TG, and HDL)80,81 have been 
described. The intra-assay coefficient of variation from blinded quality control (QC) samples ranged from 2.9% 
to 12.8% for IL6, 1.0% to 9.1% for CRP, 4.0% to 10.0% for TNFαR2, 8.1% to 11.1% for adiponectin, <12% for 
C-peptide and <1.8% for TG and HDL across batches. In the nested case-control studies in which these 
biomarkers were measured, samples from cases and their matched controls were analyzed in the same batch. 
QC samples were randomly interspersed among the case-control samples, and laboratory personnel were 
blinded to QC and case-control status for all assays. Our group has previously documented the stability of 
biomarkers in whole blood for 24 to 48 hours.82 Participants who provided blood samples were cancer free and 
similar to their overall cohort with respect to BMI, parity, age at menarche, and past oral contraceptive use.80  
Ascertainment of CRC incidence83,97,98 and mortality:80,81 Information on incident cancer and other disease 

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outcomes have been collected through participants, next-of-kin, or death certification. For any report of CRC, 
we seek written permission from study participants or next-of-kin to review their medical records. Study 
physicians review these records and successfully confirm 97% of reported CRC. The National Death Index is 
used to ascertain deaths and any diagnosis of CRC that contributed to death or was a secondary diagnosis. 
Dietary patterns definitions: We will use 3 different approaches to define 8 dietary patterns: 4 a priori patterns 
(AHEI-2010, DASH, AMED, OMNI); 2 a posteriori patterns (Western and prudent); and 2 hypotheses-driven 
patterns (EDII, CDHR). 1. AHEI-2010: The 11 components of the AHEI-2010 include vegetables, fruits, whole 
grains, sugar-sweetened beverages and fruit juice, nuts and legumes, red/processed meat, trans fat, long 
chain (ω-3) fats, polyunsaturated fatty acids, sodium and alcohol. We will award points for intake of each 
dietary component on a scale from 0-10, with 10 indicating adherence, 0 no adherence and intermediate 
scores categorized proportionately.13 2. DASH:84,85 Several different DASH scores exist,6 and we will use the 
one developed (DASH-Fung)86 and applied by my mentors,60 which is most commonly found in the literature 
with US populations. DASH-Fung scores 8 components (7 food groups and 1 nutrient)—each worth 5 points—
for a total of 40 points.86 3. AMED: we will use the AMED score that our group adapted for use in a US 
population.59,87 The score rewards 1 point if intake is above the cohort-specific median for vegetables, 
legumes, fruit, nuts, whole-grain cereals, fish, and MUFAs:SFAs and 1 point for intake below the cohort-
specific median for red and processed meats. In addition, alcohol intake of 5 to 10 g/d for women and 10 to 15 
g/d for men received 1 point. The total score ranges between 0 and 9.87,88 4.OMNI: the OMNI diet is an 
improved DASH diet with partial substitution of carbohydrates with protein, about half from plant sources, or 
with unsaturated fat, predominantly monounsaturated fat.89,90 5. Western pattern and 6. Prudent pattern: we 
will derive these two a posteriori patterns using principal components analysis.91-93 The factors will be rotated 
by an orthogonal transformation to achieve a simpler structure with greater interpretability, and the number of 
factors to retain will be determined using the diagram of eigenvalues.91 The factor score for each pattern will be 
calculated by summing intakes of food groups weighted by their factor loadings. 7. EDII and 8. CDHR: I 
developed and validated these two patterns (described below) as part of my postdoctoral fellowship work. The 
manuscripts have been submitted.  
Preliminary study 1 - Development and validation of an empirical index of dietary inflammatory potential (EDII): 
The goal for developing the EDII was to empirically create a score based on food groups to assess the overall 
inflammatory potential of whole diets. We first calculated average daily intakes (per 1000kcal) of 39 previously 
defined food groups70 from the 1986 and 1990 
FFQs in the NHS. We then applied reduced rank 
regression (RRR) to derive a dietary pattern 
predictive of three inflammatory biomarkers: CRP, 
TNFαR2 and IL6. RRR identifies linear functions of 
predictors (e.g., food groups) that simultaneously 
explain as much variation in the responses of 
interest (e.g., biomarkers) as possible. The first 
factor obtained by RRR was retained for 
subsequent analyses; (we called this the RRR 
dietary pattern). We then used stepwise linear 
regression analyses to identify the most important 
component food groups contributing to the RRR 
dietary pattern with the biomarker response score 
(RRR dietary pattern) as the dependent variable, 
the 39 food groups as independent variables, and a significance level of p=0.05 for entry into, and retention in 
the model. Intakes of the food groups identified in the stepwise linear regression analyses were weighted by 
the beta coefficients derived from the final stepwise regression model and then algebraically summed to 
constitute the EDII score. Higher (more positive) scores indicate more pro-inflammatory diets while lower (more 
negative) scores indicate anti-inflammatory diets. The EDII is comprised of 18 food groups; 9 are anti-
inflammatory and 9 pro-inflammatory. In two independent samples, the NHS-II and HPFS, the EDII significantly 

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predicted concentrations of several biomarkers (IL6, CRP, TNFαR2, adiponectin and an overall inflammatory 
marker score as construct validators) (Table 2). 
Preliminary study 2 - Development and validation of three indices of dietary insulinemic potential: We 
developed the three indices based on food groups, to assess the insulinemic potential of whole diets: 1) the 
empirical dietary index for hyperinsulinemia (EDIH), 2) the empirical dietary index for insulin resistance (EDIR) 
and 3) the composite dietary index for hyperinsulinemia and insulin resistance (CDHR). We used dietary and 
biomarker data (C-peptide, TG and HDL) in the NHS to 
develop the three indices, by fitting 2 separate stepwise 
linear regression models to identify the most important 
component food groups contributing to hyperinsulinemia 
(with C-peptide as the dependent variable) and to insulin 
resistance (ratio of TG/HDL as the dependent variable), 
the 39 food groups as independent variables, and a 
significance level of p=0.1 for entry into, and retention in 
the model. Intakes of the food groups identified in the 
stepwise linear regression analyses were weighted by 
the beta coefficients derived from the final stepwise 
regression model and then algebraically summed to constitute the scores. To create the composite score, we 
algebraically summed up the different components of both the EDIH and the EDIR to constitute the CDHR. 
Higher scores on all 3 indices indicate more highly insulinemic diets (hyperinsulinemia or insulin resistance) 
while lower scores indicate low insulinemic or insulin sensitive diets. The EDHR comprises 24 food groups; 12 
are anti-insulinemic and 12 pro-insulinemic. In the HPFS, the EDII significantly predicted concentrations of the 
overall insulinemic marker score (combination of C-peptide and TG/HDL) as the construct validator (Figure 1) 
and of C-peptide and TG/HDL in separate models. 
Statistical Analysis: The primary analysis for Aim 1a will prospectively assess associations between each of 
the 8 dietary patterns and all-cause mortality, and with incident CRC, using Cox proportional hazards 
regression, with separate models for men and women and in pooled analyses. For CRC, we will investigate 
associations by anatomic subsite (proximal colon, distal colon, rectum). After >30 years of follow-up, we have 
confirmed 31,286 all-cause deaths in the NHS and 12,555 in the HPFS.94 Also, we have identified 1,764 CRC 
cases (823 proximal, 521 distal, and 379 rectal cancers) in NHS, and in the HPFS, we documented 1,640 CRC 
cases (640 proximal, 574 distal, and 426 rectal cancers).95-97 Person-time (in months) for each participant will 
be calculated from the date of return of the baseline questionnaire in 1980 for women and 1986 for men, until 
the date of death from any cause or until CRC diagnosis, or June 1, 2012 whichever came first. Cumulatively 
updated average dietary pattern scores will be calculated using information from all available FFQs up to the 2-
year interval prior to the follow-up cycle in which death or CRC diagnosis was confirmed. We will categorize 
the dietary patterns into quintiles and use the lowest quintile as reference, stratifying the Cox models on 
calendar year, age in months, to estimate hazard ratios (HR) and 95% confidence intervals (CI) to determine 
risk of dying from any cause and risk of developing CRC in higher quintiles of dietary patterns. Test for trend 
will be performed by modeling the dietary pattern scores as continuous variable and deriving the p-value for 
trend from the Wald test. In multivariable analyses, we will adjust for: BMI, physical activity, age, energy intake, 
race/ethnicity, educational status, smoking status, family history of CRC, NSAIDs use and additionally for 
menopausal status and postmenopausal hormone use in women. We will assess effect modification by BMI 
and physical activity, and incorporate 2-6 year time lags to reduce the potential for reverse causation. Based 
on the magnitude of risk explained, we will select the best diet in each of the three approaches (a priori, a 
posteriori and hypothesis-driven) and conduct a comparative analysis to derive the overall best diet for a 
healthful lifestyle that maximizes CRC prevention. 
Aim 1b will be investigated using dietary and biomarker data from the two cohorts in a cross-sectional study. 
The 8 dietary patterns will be the main exposures while CRC diagnosis will be the outcome of interest. We will 
calculate dietary patterns from the 2 FFQs closest to the period of blood draw. That is, the 1986 and 1990 

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FFQs for the NHS, and the 1990 and 1994 FFQs for HPFS, averaging data across the 2 FFQs. We will then 
conduct mediation analyses, with the main objective of identifying the direct and indirect effects of each dietary 
pattern on CRC development, i.e., the effect of the dietary pattern that acts through the proposed mediators 
(CRP, TNFαR2, IL6, adiponectin, C-peptide) (indirect effect) and the effects that are unexplained by these 
same mediators (direct effects). The traditional approach to mediation analysis is based on adjusting for the 
mediator in standard regression models to estimate the direct effect, however, this approach may lead to 
flawed conclusions.98 We will therefore use the regression-based approach described by VanderWeele and 
Vansteelandt99 to estimate the direct and indirect effects of each potential mediator one at a time and 
cumulative effect for multiple mediators. This approach consists of fitting one model for the mediator, and 
another for the outcome. The approach has the advantage of assessing several mediators in the same model 
which would efficiently assess the potential interrelatedness of these mediators. We will use the SAS macro 
developed by the same authors99 to implement the mediation analysis. All statistical tests will be two sided, and 
we will consider 95% confidence intervals not including one as statistically significant.  
Expected outcomes for Aim 1 and impact on the field: To our knowledge, an evaluation of the consistency of 
evidence for the benefits of diet quality for overall health and for CRC prevention, based on a standardized 
methodology and plausible biological mechanisms has not previously been conducted. With the completion of 
this study, we anticipate an increased ability to widely translate findings from dietary patterns research and to 
design guidelines for healthful lifestyles that are optimized for CRC prevention.  
C2. Aim 2 – R00 phase: To identify the set of metabolites predicted by both inflammatory diets and by 
circulating inflammatory markers and then evaluate the association of this unique inflammatory 
metabolome with CRC risk. 
Aim 3 – R00 phase: To identify the set of metabolites predicted by both insulinemic diets and by 
circulating insulin markers and then evaluate the association of the unique insulinemic metabolome 
with CRC risk. 
Rationale 
Metabolomics, being the sum of the total metabolic processes in a cell, and situated downstream to all the 
other “omics,”49,100 is uniquely suited to assess metabolic responses to dietary stimuli. Few studies have 
integrated metabolomics data in nutritional epidemiology and have determined the metabolic phenotype of 
behavioral/psychological dietary preferences.49,50 The observed metabolic “fingerprint” or metabolome provides 
evidence for a link between specific dietary patterns and metabolic phenotypes. Additionally, no prior study has 
integrated dietary patterns with biomarker and metabolomics data to determine the metabolic phenotype of 
dietary behavior corroborated by relevant biomarkers. Integrating dietary, biomarker and metabolomics data 
provides an opportunity to derive unique metabolomes that may help to clearly differentiate between CRC 
cases and non-cases within the context of CRC early detection or diagnosis. 
Methodological Approach 
Assessment of metabolite data: plasma metabolites were measured as peak areas by a targeted LC-MS 
metabolomics platform directed by Dr. Clary Clish at the Broad Institute of the Massachusetts Institute of 
Technology and Harvard University (Cambridge, MA). Metabolite profiling methods were developed using 
reference standards of metabolites to determine chromatographic retention times, MS multiple reaction 
monitoring transitions, declustering potentials and collision energies.101 In prior pilot work,102 our group 
quantified 257 metabolites from archived plasma to evaluate metabolite interassay reproducibility, 
reproducibility with delayed processing, and within-person reproducibility over time. Interassay reproducibility 
was assessed with CVs from 60 duplicate plasma samples donated by participants in the NHS and HPFS, and 
20 QC pool plasma replicates. CVs were <20% for 92% of metabolites and generally were similar by plasma 
anticoagulant type (heparin or EDTA) and fasting time. Metabolite reproducibility over a 24- to 48-h processing 
delay (n=48 samples) and within-person reproducibility over 1–2 years (n=80 samples) were assessed using 

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Spearman and intraclass correlation coefficients (ICCs). Approximately 75% of metabolites were reproducible 
over delays in processing of blood samples (Spearman or ICC ≥0.75, comparing immediate and 24-h delayed 
processing). Carbohydrates and purine/pyrimidine derivatives were most adversely affected by the processing 
delay; and 90% of metabolites were reproducible over 1–2 years within individuals (Spearman or ICC ≥0.4).102 
174 metabolites passed these QC criteria and will be included in the analysis for Aims 2 and 3. We have used 
this platform in a nested case-control study of pancreatic cancer, with the novel finding that the 3 branched 
chain amino acids (isoleucine, leucine and valine) were associated with a higher risk of pancreatic cancer (RR, 
top vs. bottom quintile of sum of 3 amino acids=2.13; 95%CI=1.43-3.15), independent of other risk factors.103 
Derivation of metabolomic fingerprint scores and statistical analysis: (metabolomic fingerprint is used 
interchangeably with metabolomic signature, profile, or pattern). Table 3 lists the sample sizes for participants 
with biomarker (CRP, TNFαR2, IL6, adiponectin, and C-peptide) data from several matched case-control 
studies, nested within the NHS and HPFS. Participants were matched on cohort, sex, year of birth, smoking 
status (never, former, current), and fasting status (<8 hours, ≥8 hours). In addition to the two dietary pattern 
scores (EDII and CDHR), we will create two different overall biomarker scores for each participant as follows:  
Overall inflammatory marker score (infMS) = z-score (logIL6) + z-score (logCRP) + z-score (log 
TNFαR2) – z-score (logAdiponectin); 
Overall insulinemic marker score (insMS) = z-score (logC-peptide) + z-score (logTG/logHDL).  
Prior to analysis, concentrations of all metabolites will be log transformed and values normalized to have 
mean: 0, standard deviation: ±1. The two dietary pattern 
scores (EDII and CDHR) and the two overall biomarker 
scores (infMS and insMS) will be trichotomized into tertiles. 
All data will then be merged and participants randomized to 
either the training dataset or the validation dataset in a 2 to 
1 manner. We will use the training dataset to develop the 
metabolomic fingerprint scores, and then assess their 
validity in the validation dataset. We will construct four 
separate multivariable-adjusted Full Canonical Discriminant 
Analysis models, to create the metabolomic fingerprint 
scores, with all 174 metabolites as dependent variables 
and the highest and lowest tertiles of the dietary pattern scores and two overall biomarker scores as 
independent variables (one in each of the four models). To maximize the degree of discrimination, we will 
exclude participants in the middle tertiles. The canonical coefficients from the principal components’ 
eigenvector encapsulate the greatest degree of discrimination. As such, all 174 metabolites contribute to the 
computation of the Full Canonical Variable. The metabolomic fingerprint score will include the 20 metabolites 
with the largest canonical discriminant coefficients (10 largest negative and 10 largest positive), identified by 
the full canonical discriminant analysis. Ten was determined as the cutoff for inclusion in the metabolomic 
fingerprint score as we deemed that the identification of 20 (potentially related) metabolites would be sufficient 
for biological interpretation of the results. The multivariable-adjusted models will include case/control status, 
cohort, quintiles of caloric intake, smoking status, and age at blood collection. We will not adjust for BMI or 
physical activity because we showed in preliminary studies 1 and 2 that 
these variables played a more mediating or modification role than a 
confounding role in the association between inflammatory and insulinemic 
dietary patterns and respective biomarkers (Tabung et al, submitted). We 
will therefore assess effect modification by BMI and physical activity. 
To evaluate the performance of the discrimnatory variables (i.e., the 4 metabolomic fingerprint scores), we will 
compute them in the validation dataset, and then conduct four separate un-adjusted and multivariable-adjusted 
ANCOVAs for each discriminatory variable, with tertiles of each of EDII, CDHR, infMS and insMS as the 

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independent variable in each validation model. Participants in the middle tertiles will be included. We will then 
analyze least square means ± SEM of the metabolomic fingerprint scores among tertiles of the independent 
variables, adjusting for multiple comparisons using the Bonferroni approach (0.05/3=0.017).  
Matched nested case-control study to evaluate CRC risk: Metabolomic assays are currently being conducted 
for 1,056 CRC cases and 1,056 matched controls for the 174 identified metabolites as part of funded grants 
(NHS: P01 CA 087969, CA 186107; HPFS: CA 167552) (see assessment of metabolite data).  We will 
compute the 4 validated metabolomic fingerprint scores and the 2 common metabolome profiles developed 
and categorize into quintiles. The date of blood donation will be used as baseline for follow-up (1989-90 for 
NHS and 1993-94 for HPFS). We will employ multivariable conditional 
logistic regression models to determine the risk of CRC in quintiles of 
the 2 common metabolome profiles (Fig. 2), using the lowest quintile as 
reference. In secondary analyses, we will utilize each of the 4 
metabolomic fingerprint scores in the models to evaluate CRC risk and 
compare findings with the main analyses (2 common metabolome 
profiles).  
Expected outcomes for aims 2 and 3 and impact on the field: We 
anticipate identifying key metabolic pathways in CRC based on unique 
metabolomes that are the joint effect of dietary preferences and 
circulating biomarkers. In addition, we expect to find metabolites of 
uncertain, and possible pathogenic significance related to CRC risk. 
Therefore we will uncover potentially actionable metabolomes and 
specific metabolites for prevention and early detection. Findings from these aims will identify the biological 
mechanisms by which dietary patterns relate to CRC risk, and along with our findings from Aim 1, we will 
develop a more comprehensive understanding of how to improve the efficiency of dietary recommendations 
optimized for CRC prevention. 
Sample size and statistical power considerations (all Aims): Table 3 illustrates case counts up to 2012 for all-
cause death and CRC in NHS and HPFS, including the magnitude of association that can be achieved with 
80% power given the sample sizes for the various hypotheses.104-106  We will thus have ample power to detect 
significant associations. We also anticipate large precision (narrow 95%CIs) in these analyses.  
Limitations and alternative strategies (all Aims):  Bias due to incomplete follow-up is possible. Thus we devote 
great effort to follow-up, with 93% cumulative follow-up. Differential CRC screening intensity could create bias, 
but we do not expect endoscopy rates to vary appreciably by exposure status. Misclassification of exposures is 
always present to some degree. We have documented 
extensively the validity of our dietary questionnaire, which is 
further enhanced by repeated administrations. The data from 
our validation/calibration studies and studies of within-person 
variation of biomarkers allow us to adjust RRs and CIs for 
measurement error using methods we have developed.107-110 
We also conducted extensive pilot studies to assess 
metabolites based on our a priori QC criteria, and excluded metabolites that did not pass these criteria.102  
Confounding due to unmeasured or imperfectly measured covariates can be present. Thus, we strive to assess 
any plausible confounding variables and will conduct extensive multivariable-adjusted analyses. 
Representativeness of a single blood sample to reflect long-term levels is a potential concern. However, our 
data above indicate that the ICCs over 2-3 years for most proposed metabolites are 0.48-0.72.  
Summary and future research directions: Despite advances in screening, CRC remains the third most 
commonly diagnosed cancer and the second-leading cause of cancer death in the US. In this unique cohort 
with data on dietary and lifestyle factors and biological specimens, we will use an innovative, integrated 

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approach to evaluate hypotheses related to inflammation-mediated, insulin-mediated and metabolic signaling 
pathways. The findings will elucidate the etiology of CRC and identify potential strategies for prevention, and 
early detection. In addition, the cross-cutting nature of our hypotheses across aims will allow for a productive 
synergy and further our understanding of CRC etiology. Dr. Tabung will acquire an important set of skills 
necessary to achieve his long term goal of establishing an independent interdisciplinary research program to 
elucidate the role of diet in cancer prevention and control, including the identification of plausible underlying 
biological mechanisms. We expect to identify metabolomes or specific metabolites generated by diet, 
inflammation, insulin or their complex interactions, which are up- or downregulated in specific biological 
pathways and that may be important to further investigate in the context of CRC prevention and control. These 
data will form the basis of my future R01 in Year 4.  
 

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