ProteomicAnalysisofPlasmaRevealsFatMass ﬂuencesCancer ......Corresponding Author: Johanna W. Lampe, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M4-B402, Seattle,

Research Article

Proteomic Analysis of Plasma Reveals Fat MassInfluencesCancer-Related Pathways inHealthyHumans Fed Controlled Diets Differing inGlycemic LoadCarly B. Garrison1, Yuzheng Zhang1, Sandi L. Navarro1, Timothy W. Randolph1,Meredith A.J. Hullar1, Mario Kratz1, Marian L. Neuhouser1, Daniel Raftery1,2,Paul D. Lampe1, and Johanna W. Lampe1

Abstract

Increased adiposity and diets high in glycemic load(GL) are associated with increased risk of many chronicdiseases including cancer. Using plasma from 80healthy individuals [40 men/40 women, 29 withDXA-derived low fat mass (FM) and 51 with high FM]in a randomized cross-over–controlled feeding trial andarrays populated with 3,504 antibodies, we measuredplasma proteins collected at baseline and end of each oftwo 28-day controlled diets: a lowGLdiet high inwholegrains, legumes, fruits, and vegetables (WG) and a highGL diet high in refined grains and added sugars (RG).Following univariate testing for proteins differing bydiet, we evaluated pathway-level involvement. Amongall 80 participants, 172 proteins were identified asdiffering between diets. Stratifying participants by highand low FM identified 221 and 266 proteins, respec-

tively, as differing between diets (unadjusted P < 0.05).These candidate proteins were tested for overrepresen-tation in Reactome pathways, corresponding to 142 (of291) pathways in the high-FM group and 72 (of 274)pathways in the low-FM group. We observed that thecancer-related pathways, DNA Repair, DNA Replication,and Cell Cycle, were overrepresented in the high-FMparticipants while pathways involved in post-transla-tional protein modification were overrepresented inparticipants with either FM. Although high-GL diets areassociatedwith increased risk of some cancers, our studyfurther suggests that biology associated with consump-tion of GL diets is variable depending on an individual'sadiposity and dietary recommendations related to can-cer prevention be made with the additional consider-ation of an individual's FM.

IntroductionThe human diet is a complex exposure that plays a

recognized role in the risk of several types of cancer (1).Glycemic index (GI) and glycemic load (GL) may be arelevant component of dietary recommendations forcancer prevention due to the known effects of GI/GL onblood glucose and insulin concentrations and the effects

of these on oxidative stress and immune response (2).One study examining GI and colorectal cancer identifiedthat a sedentary lifestyle in addition to a high-GI dietwas associated with a higher risk of colorectal cancerthan an active lifestyle plus high-GI diet or sedentaryand low GI (2). Additional studies have also shownGI and GL as risk factors for colorectal, breast, andpancreas cancer (2, 3). Observational studies suggestthat we need to understand the biological response toa total dietary pattern to provide better public healthrecommendations (4, 5).Increased adiposity is a risk factor for at least 13 types of

cancers (1, 6) and has been shown to increase inflamma-tory responses, with obesity characterized as a "low-gradechronic inflammatory state" (7, 8). In addition, growthfactors and hormones such as insulin-like growth factor 1(IGF1), insulin, and leptin (LEP) are increased with higheradiposity (9). Identifying dietary patterns that contributeto, as well as measures to prevent, the predisposition ofobese and overweight individuals to cancer is a highpriority worldwide (10).

1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center,Seattle, Washington. 2Department of Anesthesiology and Pain Medicine, North-west Metabolomics Research Center, University of Washington, Seattle,Washington.

Note: Supplementary data for this article are available at Cancer PreventionResearch Online (http://cancerprevres.aacrjournals.org/).

Corresponding Author: Johanna W. Lampe, Fred Hutchinson Cancer ResearchCenter, 1100 Fairview Ave N., M4-B402, Seattle, WA 98109. Phone: 1-206-667-6580; Fax: 1-206-667-7850; E-mail: [email protected]

Cancer Prev Res 2019;12:567–78

doi: 10.1158/1940-6207.CAPR-19-0175

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We previously showed that response to a low-GL diethigher in whole grains and fresh fruits and vegetables,compared with a more refined grain high-GL diet, resultedin reduced fasting concentrations of the acute-phase pro-tein C-reactive protein (CRP; ref. 11), reduced IGF1 (12),reduced postprandial glycemic response (13), andimproved satiety (14). These results have suggested thatthese diets could have a broader effect on integratedcellular processes and cellular signaling linked to cancerrisk. Omics approaches, such as proteomics, can interro-gate the effects of dietary changes on a broader scale,allowing us to understand how signaling pathways aremodulated in ways that might affect disease risk.Utilizing proteomics to measure response to exposure

thatmay influence cancer risk has been reported, includingstudies to discover plasma biomarkers of cancerrisk (15–17). Other studies in prospective cohorts haveidentified biomarkers present in plasma years prior tocancer diagnosis, suggesting that differences in certainpathways may reflect increased cancer susceptibili-ty (18, 19). Proteomics approaches have also been usedto examine responses to interventions, including thosetesting diet (20) and drugs (21), as well as to examine theeffect of body mass on overall pathway biology (7). Thesestudies suggest that in the context of interventions, prote-omics can provide insight into the complexity of multipleresponses to a dietary intervention and the possiblemechanisms driving intervention effects. The objective ofthis study was to use proteomic approaches to identifyproteins and pathways that are altered in the plasma ofparticipants fed low- and high-GL diet patterns and toevaluate whether response differed by participant adipos-ity and any potential associated cancer risk.

Materials and MethodsSamplesThe plasma samples used in this study were obtained

from the Carbohydrate and Related Biomarkers (CARB)study. The CARB study was a randomized crossover–controlled feeding trial designed to test the effects of low-versus high-GL diets on known biomarkers of cancer-riskpathways; complete details on recruitment and studydesign have been published previously (11). This trial wasregistered at ClinicalTrials.gov as NCT00622661. Briefly,healthy, nonsmoking men and women, aged 18–45 years,consumed two controlled diets for 28 days each, with atleast a 28-day washout period between intervention per-iods (11). One diet was high in whole grains, fresh fruits,and vegetables, and low in GI carbohydrate sources (WG).The other diet substituted refined grains for whole grainsand provided carbohydrates from mostly high-GI sources(RG). The two diets differed primarily in GL and totaldietary fiber content (22). Mean daily energy intake (esti-mated for each participant based on height, weight, sex,

and usual activity level) and percent energy from carbo-hydrate, protein, and fat was designed to be similar on thetwo diets. Complete details on diet menus and consump-tion have been published previously (11, 12). All foodwasprepared under consistent and carefully controlled condi-tions and provided to the participants. Fasting blood wascollected at the beginning and end of each diet period, andeach participant served as their own control in this cross-over design (11). A total of 82 participants were recruitedfor the CARB study and 80 completed both feeding per-iods (11). In our analysis, three plasma samples collectedin EDTA from each of the 80 participants who completedthe CARB study were used: day 1 of the study (before dietintervention), as well as after 28 days on each diet. At thebaseline clinic visit, all participants completedmeasures ofheight, weight, and body circumference, and underwentdual X-ray absorptiometry (DXA), which yieldedmeasuresof total body fatmass (FM).High FMwas defined using theaccepted cut points of >25% and >32% for men andwomen, respectively (23), consistent with other evalua-tions of effects of FM in the CARB study (11).

Antibody arraysThe antibody arrays were populated with 3,504 distinct

antibodies acquired primarily from commercial supplierssuch as SDIX (now sold by Novus Biologicals), AvivaBiosciences, R&D Systems, Abnova, Sigma-Aldrich, andothers. The 3,504 antibodies correspond to 2,072 differenthuman proteins that participate in diverse signaling path-ways. Details on array fabrication have been previouslyreported (24, 25).

ProteomicsRelative protein levels were detected as described

previously (24–27). Briefly, albumin and IgG were deplet-ed fromplasma, and thedepletedplasmawas concentratedto its original volume, measured for total protein concen-tration, labeledwith the amine-reactive dyesCy3- andCy5-maleimide, and unincorporated dye was removed. Indi-vidual Cy5-labeled participant samples were incubatedwith anequal amountofCy3-labeled reference (a commonpool of plasma comprised of samples collected from fourwomen and three men was used as a reference). Labeledlysates were incubated on arrays for 90 minutes, the arrayswere washed serially to remove excess dye, and thenscanned in an Axon GenePix 4000B microarray scannerand data extracted using GenePix Pro 6.0 Software (Molec-ular Devices).

Array statistical analysisFor each antibody, the fold change of signal (red

channel) compared with reference (green channel), theM value, was calculated as log2(Rc/Gc); where Rc is redcorrected, and Gc is green corrected using the "normexp"background correction method (28). Technical sources

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of variation were normalized by loess procedures formicroarrays using R limma package (RRID:SCR_010943;ref. 29), including within-array print-tip loess andbetween-arrays reference channel quartile normaliza-tion. Following normalization, triplicate features weresummarized using their median. All statistical analyseswere conducted on M values.Linear mixed models (LMM; ref. 30) were fit for each

antibody using the pre-intervention baseline and twopost-diet timepoint measurements for each of the 80participants. After initially adjusting for baseline valuesfor each participant, the two post-diet timepoint mea-surements in this crossover study were accounted for bya random-effect term per participant in the LMM, per-forming repeated-measures analysis and allowing eachparticipant to serve as their own control. The baselineprotein concentrations prior to diet intervention and thediet sequence, together with sex, age, fat mass by DXA(defined as 0 if

Of the proteins identified in the analysis of all 80participants, 134 (78%) overlapped with proteins identi-fied in the two FM categories (Fig. 1A). The two analysesby FM group contained 48 proteins in common, 16 ofwhichwere detected bymore thanone antibody in the low-and/or high-FM analysis (Fig. 1A). Among these 48,25 (52%) appeared higher in the same diet in each analysis(i.e., higher in RG diet or higher in WG diet in bothFM groups; Fig. 1B). Leptin (LEP), a hormone used as amarker in obesity that is involved in energy homeostasisand a key player in body weight control (37), was higherafter the consumption of the WG diet as compared withRG in both stratified and unstratified analyses (P < 0.01 inFM-stratified analyses, P < 0.05 in all 80-participantanalysis; Fig. 1C; Supplementary Tables S1A–S1C). As ourprevious work in these participants found no significantdiet difference in plasma LEP concentration when employ-ing a different assay (11), we wanted to confirm ourfindings by using an additional assay that employed thesame antibody as was on our array. We validated thespecificity of the antibody used on our array and recapit-ulated our observation using aWestern immunoblot assay,confirming higher expression of LEP in plasma after con-sumption of the WG diet compared with RG, regardless ofFM (P < 0.05; Fig. 1D). Of the 48 proteins in commonbetween the high- and low-FM analyses, 23 (48%)appeared higher in opposite diets between FM analyses(Fig. 1B). One of these 23 proteins, EGFR, was higher afterconsumption of the RG diet among participants with highFM (P < 0.05), whereas among participants with low FM it

was higher after the WG diet (P < 0.05; SupplementaryFig. S2A; Supplementary Tables S1A–S1C). We wanted toconfirm that we were seeing the soluble form of thetransmembrane protein EGFR (sEGFR, �100kDa) in theparticipants' plasma, as the overexpression of EGFR iscommonly observed in cancers (38) and could indicatea change in cancer-related predisposition in our parti-cipants. We observed that the primary size of EGFR mea-sured in participant plasma was approximately 100 kDacompared with full-length EGFR around 160 kDa (Sup-plementary Fig. S2B). While not tested for significance, aswe only examined samples from two participants in eachFM group, in addition to identifying the form of EGFRpresent in our samples, we also observed higher signal afterconsumption of the RG diet (compared with WG) insamples from high-FM individuals and higher signal afterconsumption of the WG diet in samples from low-FMindividuals, consistent with our array results.

Stratified FM analyses identify distinct pathwaysWe first explored pathways of interest, according to our

hypergeometric test, in all 80 participants. For identifica-tion purposes, all Reactome pathways are italicized. Sixty-nine pathways were identified as overrepresented at FDR

were closely related to the initiation and/or predispositionof cancer. First, the pathway group Immune System (Sup-plementary Fig. S3D) had six pathways of the AdaptiveImmune System overrepresented by proteins differingbetween diet in the high-FM group (Fig. 3A and B). Threeof the Adaptive Immune System pathways are subpathwaysto theClass I MHCmediated antigen processing & presentationtertiary pathway. Second, some of the secondary SignalTransduction (Supplementary Fig. S3E) cancer-relatedpathway categories, including Signaling by WNT, con-tained predominantly high-FM overrepresented pathways(Fig. 4A and B).

Post-translational protein modificationWhile many of the cancer-related pathways identified

in the overrepresentation pathway analysis of high-FMparticipants were not identified in the low-FMparticipants,

post-translational protein modification had numerouspathways overrepresented for proteins differing betweendiets in both FM groups. Post-translational protein modi-fication, a secondary pathway in Metabolism of proteins(Supplementary Fig. S3F), contained eight pathwaysoverrepresented for proteins differing between diet inthe high-FM participants and four in the low-FM parti-cipants (Fig. 5A and B). Two pathways, Post-translationalprotein modification and O-linked glycosylation, were iden-tified in both high- and low-FM participants. In the high-FM group, 82% of the proteins differing between diets inthe Post-translational protein modification pathway hadhigher expression after consumption of the RG diet(50% in low-FM group; Supplementary Tables S1B andS1C; Supplementary Table S3; Supplementary Table S4).Diseases of glycosylation, a secondary pathway in Disease(Supplementary Fig. 3G), contained six pathways

Figure 1.

Proteins identified as differentially expressed betweenWG and RG diets in both high- and low-FM groups.A, Venn diagram of all proteins identified asdifferentially expressed between diets and the group they are in. B,Differentially expressed proteins in common in response to diet in the high- and low-FMgroups. The color bar indicates the coefficient (between�1.12 and 0.97) of the selected proteins in the LMM analysis (P < 0.05) with proteins that are higher inthe RG diet in blue and those higher in theWG diet in red. � Indicates more than one antibody different between diets in the low-FM group (P < 0.05), ^ indicatesmore than one antibody different between diets in the high-FM group (P < 0.05), and � indicates more than one antibody different between diets and higher indifferent diets (P < 0.05), with the coefficient belonging to the antibody with the lowest P value. C,M values of the LEP results from the array.D, Immunoblotshowing higher expression of the LEP protein in WG compared with RG diet (P < 0.05) in both high-FM (HFM) and low-FM (LFM) participants. < denotes samplesfrom amale and , denotes samples from a female participant. HELA cell lysate served as a negative (�) control. LEP protein served as a positive (þ) control.

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overrepresented for proteins differing between dietsin the high-FM participants and seven in the low-FMparticipants (Fig. 5C). Six pathways overlapped betweenthe high and low-FM overrepresented pathways, includ-ing Diseases of glycosylation and Diseases associated withO-glycosylation of proteins. Diseases of glycosylation had 88%of proteins higher after the RG diet in the high-FM group(50% in low-FM group; Supplementary Tables S1B andS1C; Supplementary Table S3; Supplementary Table S4).

DiscussionHere, we demonstratemeasurable proteomic differences

in plasma of healthy adults after 4 weeks of consumptionof RG- versus WG-controlled diets. Proteomic differencesdue to diet were more robustly detected after stratifyingparticipants based on participant DXA–measured FM.Onestriking observation was that proteomic differencesobserved in the two FM protein analyses were largelyunique, with only approximately 10% of proteins identi-fied per group overlapping and similarly higher with thesame diet. One such protein, leptin, showed higher plasmalevels after the WG diet in both FM groups. Leptin is anadipokine with a key role in the regulation of energyhomeostasis and food intake (37). We previously reportedthat participants in the CARB study found the WG diet

more satiating then the RG diet (14). We did not see adiet difference in leptin in a previous analysis in theseparticipants, even when stratified by participant FM (11),possibly due to the use of different antibodies and detec-tion platforms. Because most of the diet-differences inproteins identified in our analyses were unique to eitherhigh or low-FM participants, we hypothesized that therewere differing biological responses to the diets based onFM. Pathway analysis was used to test this hypothesisand showed the following: (i) pathway overrepresentationanalysis in the high-FM group identified many key pro-cesses in the initiation and predisposition of cancerthat were altered by diet only in the high-FM participantsand (ii) differing expression of proteins by diet in post-translational protein modification pathways (a processobserved in the detection of many cancer types) occurredin both FM groups.Pathway analysis among individuals with a high FM

uniquely identified pathways involved in Cell Cycle,DNA Repair, and DNA Replication as overrepresented forproteins differently expressed between the WG and RGdiets.DNARepair, Cell Cycle, andDNAReplicationpathwaysare critical for cell replication, and disruption in any ofthese processes can lead to cancer initiation (39). Sustainedalterations in these cancer-related pathways could lead tothe development of cancer (39). As individuals with high

Figure 2.

Few pathways overrepresented for proteins differing by diet overlap between all participant, high- and low-FM groups, with some primary pathway categoriesspecific to one FM group.A, Venn diagram of the pathways identified for each FM group. B, The number of identified pathways in each primary pathway categorydiffers between the high- and low-FM groups with some pathways, such as Cell Cycle, DNA Repair, and DNA Replication specific to the high-FM group. Identifiedpathways in the high-FM group are in dark green, pathways in the low-FM group are in light green. Colored pathways were identified at FDR < 0.05.

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FM are at increased risk for some cancers (1, 6), findingthat the difference between consuming a WG or RG dietcould lead to the change in expression of proteinsinvolved in these pathways highlights the need to furtherstudy diet in relation to the link between obesity andcancer. Overall, we saw that DNA Repair pathways weremore active (had higher protein expression) after con-sumption of the RG diet compared with the WG diet inthe high-FM participants. One protein that was higherafter the consumption of the RG diet and is a member ofDNA Repair pathways, proliferating cell nuclear antigen(PCNA), acts to recruit DNA damage repair proteins thatallow completion of DNA replication after DNA dam-age (40). Note that because Cell Cycle and DNA Replica-tion pathways are highly interconnected, the proteins

that identify a change in these pathways are the same.Three proteins in DNA Replication and Cell Cycle path-ways, PSMD7, PSMB5, and PSMB9 are components ofthe proteasome (41). In addition, UBC, a protein inDNA Replication and Cell Cycle pathways, is involved inprotein degradation via the proteasome (42). Protea-some activity is essential for progression through thecell cycle and regulation of DNA replication (41, 42).Currently, there are several proteasome inhibitors beingused in cancer therapy (43).Inflammation, a product of the innate immune system,

is a proposed mechanism for the association betweenobesity and cancer (7). Obesity is associated with low-grade inflammation (7, 8), and chronic low-grade inflam-mation also tends to contribute to a more active adaptive

Figure 3.

Adaptive Immune System pathwaysoverrepresented for proteinsdiffering by diet werepredominantly identified in thehigh-FM group. A, Identifiedpathways in the high-FM group arein dark green rectangles, pathwaysin the low-FM group are in lightgreen rectangles. The pathwayhierarchy to the primary pathwaycategory is shown on the far left.B, Colored pathways were identifiedat FDR < 0.05.






immune system (44). Of the seven adaptive immunesystem pathways overrepresented by proteins differing bydiet in our participants, six are overrepresented in the high-FM group. The secondary pathway group Signaling byWNThad nine pathways overrepresented by proteins differingby diet in our participants and all nine were overrepre-sented in the high-FM group (two were also overrepresent-ed in the low-FM group). Wnt signaling, a critical normaldevelopmental pathway, when altered can contribute tocancer initiation and is an important regulator of can-cer (45). Previous studies have shown a direct link betweenWnt signaling and colorectal cancer, as well as highlightedevidence of Wnt signaling in other cancers (45).

Post-translational modification involves additionalcovalent modifications to proteins after biosynthesis.Post-translational modification alters the functionalproperties of proteins, increases diversity of the prote-ome, and can impact almost all parts of cell biologyand pathogenesis (46). Routinely used cancer biomar-kers (e.g., CA125, CA15-3, CA19-9, PSA, and CEA forovarian, breast, pancreatic, prostate, and colon cancer,respectively) are glycoproteins (47, 48). Our data suggestthat glycosylation pathways are more active (i.e., havehigher protein expression) with the RG diet comparedwith the WG diet, particularly in individuals with highFM. This may be plausible given the higher GL of our RG

Figure 4.

Signaling byWNT pathways overrepresented forproteins differing by diet were predominantlyidentified in the high-FM group. A, Identifiedpathways in the high-FM group are in dark greenrectangles, pathways in the low-FM group are inlight green rectangles. The pathway hierarchy tothe primary pathway category is shown on the farleft. B, Colored pathways were identified atFDR < 0.05.

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diet and is supported by previous studies showing areduction in glycosylation with a low-GI diet in patientswith diabetes (49).The parent study fromwhich these samples were derived

was designed in a manner that strengthens the impact ofthe results we present here. All foods were consistentlyprepared under controlled conditions; all participants

received and consumed the same foods; participants wereinstructed tomaintain similar physical activity levels acrossboth diet intervention periods and to avoidweight change;both controlled diets were eucaloric and each participantreceived both diets—all characteristics of our crossoverstudy that allowed us to measure plasma proteomicchanges that were specific to diet differences within an

Figure 5.

Metabolism of proteins andDisease pathwaysoverrepresented for proteins differing by diet overlapbetween analysis groups. A, Identified pathways inthe high-FM group are in dark green rectangles,pathways in the low-FM group are in light greenrectangles, and all 80 participants are in yellow.Pathways that overlap between groups are shown asa gradient. The pathway hierarchy to the primarypathway category is shown on the far left. B, High andlow-FM Post-translational protein modification andC, Diseases of glycosylation pathways were identifiedat FDR < 0.05.






individual. In addition, all participants were healthy withnormal fasting blood glucose levels. By stratifying partici-pants, we did lose statistical power with smaller FM groupsample sizes; however, we gained insights into FM-dependent responses. In addition, the distribution of menand women and age differed between the FM groups, butwe adjusted for sex and age in our LMM to limit theseconfounding factors. Overall, the participants in the studywere younger (ages 18–45 years), thus our results may notbe generalizable to older individuals with long-term obe-sity. Furthermore, the platform we utilized to measure theproteomic response, while a powerful way to measurethousands of proteins in the blood, does not cover allpossible human proteins. We attempted to overcome thisby using overrepresentation pathway analysis, allowing usto identify pathways overrepresented for the proteins iden-tifiedonour arrays as differingbetweenWGandRGdiets inthe high- and low-FM groups. One could surmise thatpathway analysis in general might have a cancer bias; wespecifically usedReactomepathways, as they are frequentlycross-referenced with other pathway resources, providingone of the most comprehensive and all-encompassingpathway databases (34).In conclusion, proteomic differences in the biological

response to consumption of a WG as compared with a RGdiet were measurable in the plasma of healthy participantsand allowed for us to employ pathway analysis to deter-mine biological processes that were affected by the con-sumptionof onediet versus the other.Weobserved strikingdiet differences betweenparticipantswith lowandhigh FMinpathways previously implicated in cancer predispositionand initiation. For example, in both the high- and low-FMgroups, we uncovered post-translational protein modi-fication pathway alterations between theWGandRGdiets.However, pathways involved in cell cycle, DNA repair/replication, the adaptive immune system, and WNT sig-naling were overrepresented solely in high-FM partici-pants. Altogether, we identified potential mechanisms ofdietary patterns that can affect cancer predisposition and

initiation in a susceptible obese population. Our datasuggest that an individual's level of adiposity affects thephysiologic response to a WG or RG diet and that furtherstudies of diet recommendations for cancer preventionmay need to consider the impact of adiposity on response.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

DisclaimerThe content of this report is solely the responsibility of the authors

and does not necessarily represent the official views of the NIH.

Authors' ContributionsConception and design: C.B. Garrison, S.L. Navarro, M. Kratz,M.L. Neuhouser, D. Raftery, P.D. Lampe, J.W. LampeDevelopment of methodology: C.B. Garrison, P.D. Lampe,J.W. LampeAcquisition of data (provided animals, acquired and managedpatients, provided facilities, etc.): C.B. Garrison, M.L. NeuhouserAnalysis and interpretation of data (e.g., statistical analysis,biostatistics, computational analysis): C.B. Garrison, Y. Zhang,S.L. Navarro, T.W. Randolph, M. Kratz, P.D. Lampe, J.W. LampeWriting, review, and/or revision of the manuscript: C.B. Garrison,S.L. Navarro, T.W. Randolph,M.A.J. Hullar,M. Kratz,M.L.Neuhouser,D. Raftery, P.D. Lampe, J.W. LampeAdministrative, technical, or material support (i.e., reporting ororganizing data, constructing databases): M.L. Neuhouser,J.W. LampeStudy supervision: M.L. Neuhouser, P.D. Lampe, J.W. Lampe

AcknowledgmentsThis work was funded through the NIH [R01 CA192222, U54

CA116847, P30 CA015704].

The costs of publication of this article were defrayed in part by thepayment of page charges. This articlemust therefore be herebymarkedadvertisement in accordance with 18 U.S.C. Section 1734 solely toindicate this fact.

Received March 29, 2019; revised June 7, 2019; accepted June 24,2019; published first July 2, 2019.

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2019;12:567-578. Published OnlineFirst July 2, 2019.Cancer Prev Res Carly B. Garrison, Yuzheng Zhang, Sandi L. Navarro, et al. Differing in Glycemic LoadCancer-Related Pathways in Healthy Humans Fed Controlled Diets Proteomic Analysis of Plasma Reveals Fat Mass Influences

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ProteomicAnalysisofPlasmaRevealsFatMass ﬂuencesCancer ......Corresponding Author: Johanna W. Lampe, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M4-B402, Seattle,