Background:

Obesity, a risk factor for colorectal cancer, raises systemic levels of proinflammatory mediators. Whether increased levels also reside in the colons of obese individuals and are accompanied by procancerous alterations in the mucosal transcriptome is unknown.

Methods:

Concentrations of TNFα, IL1β, and IL6 in blood and colonic mucosa of 16 lean and 26 obese individuals were examined. Differences in the mucosal transcriptome between the two groups were defined.

Results:

Plasma IL6 and TNFα were 1.4- to 3-fold elevated in obese subjects [body mass index (BMI) ≥ 34 kg/m2] compared with the lean controls (P < 0.01). Among individuals with BMI ≥ 34 kg/m2 colonic concentrations of IL6 and TNFα were 2- to 3-fold greater than in lean subjects (P < 0.03). In a general linear model, adjusted for NSAID use, colonic IL6 (partial r = 0.41; P < 0.01) and TNFα (partial r = 0.41; P = 0.01) increased incrementally over the entire range of BMIs (18.1–45.7). Regular use of nonsteroidal anti-inflammatory drugs (NSAIDs) was associated with a reduction in colonic IL6 (β = −0.65, P < 0.02). RNA sequencing (NSAID users excluded) identified 182 genes expressed differentially between lean and obese subjects. The two gene networks most strongly linked to changes in expression included several differentially expressed genes known to regulate the procarcinogenic signaling pathways, NFκB and ERK 1/2, in a pattern consistent with upregulation of each in the obese subjects.

Conclusions:

Incremental increases in two major proinflammatory colonic cytokines are associated with increasing BMI, and in the obese state are accompanied by procancerous changes in the transcriptome.

Impact:

These observations delineate means by which an inflammatory milieu may contribute to obesity-promoted colon cancer.

Colorectal cancer is a major public health issue in both economically developing and economically developed nations, with approximately 1.4 million new cases worldwide per year (1). A highly prevalent risk factor for the disease is excess adiposity: those with a body mass index (BMI) of 25–29.9 (overweight) have a relative risk of 1.2–1.5 for developing colorectal cancer compared with lean controls, while those with a BMI of ≥30 (obesity) have a relative risk of 1.5–2.0 (2).

Despite the consistent observations linking excess adiposity and colorectal cancer, the biochemical and molecular mechanisms responsible for this relationship continue to be debated. It has been known for more than two decades that obesity produces a chronic, low-grade state of inflammation that is evident in the bloodstream and adipose tissue (3) and, although other concurrent cellular pathways might also be contributory (4–6), this inflammatory state has been postulated to be an important factor in mediating the procarcinogenic effects of obesity (6). Furthermore, some evidence indicates that it is the visceral pattern of obesity (so-called “android-pattern obesity”) that is most prone to create inflammation (7) and increase cancer risk (8).

Both preclinical and clinical studies support a link between inflammation, activation of precancerous pathways, and the development of a variety of cancers, including colorectal cancer (9). Proinflammatory cytokines such as TNFα, IL6, and IL1β have been observed to potently activate several cell signaling pathways that are implicated in colonic carcinogenesis, such as NFκB, Akt, and Wnt (10–12). Furthermore, these effects of cytokines have been observed in colonocyte cell culture (12, 13) and in the gastrointestinal epithelium of intact animals (14, 15), underscoring the potential relevance of these effects to colorectal carcinogenesis. Clinically, it has been long recognized that the risk of colorectal cancer increases progressively with the duration of chronic inflammation among patients with ulcerative colitis (16). Furthermore, prospective studies have shown that long-term use of aspirin, a prototypical nonsteroidal anti-inflammatory agent, reduces the risk of both colorectal adenomas and cancers (17, 18).

Nevertheless, the idea that obesity-associated inflammation might promote colorectal carcinogenesis rests on the assumption that an inflammatory milieu also resides within the colonic mucosa. Recent studies, including our own, have demonstrated that biochemical and molecular mediators of inflammation are elevated in the colons of obese laboratory rodents compared with lean controls (14, 19, 20). Comparable evidence is lacking in humans, although it has been reported that weight loss in obese women can diminish colonic cytokines (21). The two major goals of this study, therefore, were to establish whether elevated concentrations of proinflammatory cytokines exist in the colons of obese humans and to examine whether the changes in the colonic transcriptome that accompany obesity are relevant to its inclination toward carcinogenesis.

Experimental design

The study protocol was approved by the Tufts University Health Sciences Institutional Review Board, and written informed consent was obtained from all subjects. Prospective subjects were identified through the weekly list of individuals scheduled to undergo routine screening colonoscopy at Tufts Medical Center (Boston, MA). Individuals with BMIs falling within the ranges of 18–25 and 30–50 were recruited to participate in the study. Exclusion criteria included: a past history of cancer (except nonmelanoma skin cancer), a polyposis syndrome, and any current or chronic inflammatory disease or infection. Use of nonsteroidal anti-inflammatory drugs (NSAIDs) was documented, and regular users were defined as those taking a dose of NSAIDs ≥1×/week. The subject population was confined to Caucasian subjects to minimize confounding by race because it is known that this factor modifies the relationship between obesity and inflammation (22).

On the day of the colonoscopy, subjects had height, weight, hip, and waist circumference measured with shoes removed, and fasting blood samples were collected. Eight colonic biopsies were obtained during the colonoscopy from normal-appearing mucosa at the rectosigmoid junction, at least 10 cm from any polyp. Biopsies were immediately placed in an aluminum foil packet suspended on crushed ice, then snap frozen in liquid nitrogen and stored at −80°C for later analysis. Blood samples were spun at 1,000 × g for 15 minutes at 4°C, and plasma was divided into aliquots and stored at −80°C for later analysis.

Cytokines

TNFα, IL1β, and IL6 protein levels were measured in duplicate in plasma and colon using a chemiluminescence system [MesoScale Discovery (MSD)]. The choice of these cytokines was based on prior work indicating that these three cytokines are the most reliably elevated in the colons of obese mice (19, 23, 24). Colonic cytokine and total protein concentrations were measured in lysate preparations isolated from 2–3 colonic biopsies. Lysates were generated using a cell lysate kit (MSD), to which protease (Roche Diagnostics Corp.) and phosphatase inhibitors (Sigma Aldrich) were added. Colonic cytokine concentrations were adjusted for protein concentration using the Bradford assay (Bio-Rad Laboratories) and are reported as pg/mg of protein. The interassay coefficients of variation were <8% for each plasma and colonic assay.

RNA extraction

In order to compare differences in colonic expression due to obesity and exclude the confounding effects of NSAIDs, regular users of NSAIDs were omitted in the RNA sequencing analysis. Nine subjects were randomly selected from each group, matched for age and gender, by an individual blinded to all characteristics of the subjects except for their group assignment, age, and gender. RNA was isolated from one biopsy from each of these 18 individuals using the Ambion RiboPure Kit (Life Technologies). Total RNA was resuspended in 200 μL of RNase/DNase-free water. RNA quality was assessed using the 2100 Bioanalyzer system (Agilent Technologies) to verify that the RNA Integrity Number (RIN) was greater than 8, which it was in each instance.

RNA sequencing analysis

Preparation of the RNA sequence libraries was done using the TruSeq RNA Sample Preparation Kit v2 (Illumina), with 1 μg of input RNA per sample. Quality was assessed using the Fragment Analyzer (Advanced Analytical). Single-end sequencing was performed on the HiSeq 2500 (Illumina). The analysis was run at a multiplexing level sufficient to generate 10–25 million reads per sample. Reads were aligned to the human genome version hg19 using the Tuxedo suite software package, which includes Bowtie, TopHat, and Cufflinks (25). The demultiplexed FASTQ files were generated using CASAVA 1.8.2 (Illumina), and the quality control (QC) reports were generated with FastQC. Samples were considered of acceptable quality if the mean quality score was at least 30 and the percentage of bases with a quality score of ≥30 was at least 90%. One sample did not meet this threshold and was excluded from further analysis, leaving 9 samples in one group and 8 in the other. A mean of 15,744,767 ± 2,745,633 (SEM) reads were generated per sample. Differential expression analysis was conducted using DESeq2, an open-source Bioconductor package (https://bioconductor.org/biocLite.R). Prior to this analysis, it was necessary to convert BAM files generated from the TopHat output to SAM files using Samtools (http://www.htslib.org/). HTSeq-count, an open-source tool (http://www-huber.embl.de/HTSeq), utilizes SAM files to count the number of reads which map to an individual gene. DESeq2 then utilizes these counts to identify differential expressed genes. Only genes with counts in at least 80% of the samples were considered for differential expression analysis. Genes were identified as significantly differentially expressed with q-values (false discovery rates) ≤ 0.10. The fold change in gene expression is indicated by a regularized transformation that mimics a log2 transformation (26).

Statistical analyses

Cytokine values were each log-transformed to reduce skewness and when possible data are reported as geometric means. Cytokine concentrations were examined categorically by dividing the 42 subjects into the following three BMI categories: 18–25, 30–33.9, and ≥34 (n = 16,14,12, respectively). An ANCOVA was used to evaluate differences in cytokines between these three categories. Gender, age, presence of colonic polyp, “ever” and “current” tobacco use, and regular NSAID use were examined for potential confounding and effect modification. A general linear model was also constructed to look at the relationship between plasma cytokines and continuous BMI and waist-to-hip ratio, including the same potential confounding variables. Results for plasma IL1β are not presented in Table 2 because it was zero inflated, with almost 50% of the values at zero. Thus, it could not be analyzed with parametric methods and the appropriate nonparametric tests were substantially underpowered because of the modest sample size. All statistical analyses were performed using SAS version 9.3.

Study participants

Seventeen lean (BMI range: 18.1–24.9) and 26 obese (range: 30.0–45.7) subjects between the ages of 45 and 70 years old were enrolled in the study. One lean enrolled subject did not complete the colonoscopy, and data from this individual are not included in the analysis. Demographic and anthropometric features of the 42 subjects who completed the study are displayed in Table 1; neither age or gender distribution differed between the two groups. Thirteen of the 42 subjects were regular users of NSAIDs.

Table 1.

Study enrollment characteristics in the 42 lean and obese subjects

EndpointLeanObeseP
Gender (M,F) 9,7 14,12 0.99 
Age 56.1 ± 1.7 58.6 ± 1.3 0.69 
BMI 22.0 ± 0.5 34.7 ± 0.9 <0.01 
Waist (cm) 31.6 ± 0.7 44.8 ± 0.9 <0.01 
Hip (cm) 36.2 ± 0.7 46.5 ± 0.9 <0.05 
Waist:hip ratio 0.87 ± 0.02 0.97 ± 0.02 <0.05 
EndpointLeanObeseP
Gender (M,F) 9,7 14,12 0.99 
Age 56.1 ± 1.7 58.6 ± 1.3 0.69 
BMI 22.0 ± 0.5 34.7 ± 0.9 <0.01 
Waist (cm) 31.6 ± 0.7 44.8 ± 0.9 <0.01 
Hip (cm) 36.2 ± 0.7 46.5 ± 0.9 <0.05 
Waist:hip ratio 0.87 ± 0.02 0.97 ± 0.02 <0.05 

NOTE: Data represent the mean ± SE.

Abbreviations: F, female; M, male.

Elevated plasma cytokines in obese subjects

There was no effect modification or confounding observed in the association between plasma cytokines and BMI measures by our predetermined list of potential confounders. Plasma IL6 and TNFα concentrations were elevated 3-fold and 1.4-fold in the ≥34 BMI group compared with the lean, respectively (P < 0.01, Table 2). In each instance, the means of the mildly obese group were intermediate in value. Results for plasma IL1β do not appear in Table 2 because it was zero inflated, with nearly 50% of the values at zero.

Table 2.

Geometric means and 95% CIs of cytokine measures by BMI categories in the presence of potential confounders1

BMI Group
18–2530–33.934+Ptrend
Plasma 
 IL6 0.8 (0.6–1.0)a 1.4 (1.0–1.9)b 2.4 (1.7–3.4)b <0.001 
 IL6 + Age + Sex 0.8 (0.6–1.0)a 1.3 (1.0–1.8)a 2.5 (1.8–3.5)b <0.001 
 IL6 + Age + Sex + NSAID 0.8 (0.6–1.1)a 1.3 (1.0–1.8)a,b 2.3 (1.6–3.3)b <0.001 
 IL6 + All Confounders1 0.8 (0.6–1.0)a 1.3 (1.0–1.7)b 2.3 (1.7–3.3)b <0.001 
 TNFα 2.5 (2.2–2.8)a 2.9 (2.6–3.3)a,b 3.5 (3.0–4.0)b 0.003 
 TNFα + Age + Sex 2.5 (2.2–2.8)a 3.0 (2.6–3.4)a,b 3.4 (3.0–3.9)b 0.002 
 TNFα + Age + Sex + NSAID 2.5 (2.2–2.8)a 3.0 (2.6–3.4)a,b 3.4 (2.9–3.9)b 0.007 
 TNFα + All Confounders1 2.4 (2.1–2.7)a 3.0 (2.6–3.4)a,b 3.4 (2.9–3.9)b 0.002 
Colonic 
 IL6 4.1 (2.9–5.9) 4.2 (2.8–6.2) 7.3 (4.7–11.2) 0.088 
 IL6 + Age + Sex 4.1 (2.9–5.9) 3.9 (2.5–5.9) 7.6 (4.9–11.8) 0.093 
 IL6 + Age + Sex + NSAID 3.6 (2.5–5.0)a 3.8 (2.6–5.6)a 9.2 (6.0–14.0)b 0.008 
 IL6 + All Confounders1 3.6 (2.5–5.2)a 3.8 (2.6–5.7)a 9.2 (5.9–14.2)b 0.013 
 TNFα 23.9 (16.5–34.6) 29.5 (19.9–43.7) 48.8 (31.3–76.1) 0.027 
 TNFα + Age + Sex 23.9 (16.7–34.1)a 26.3 (17.7–39.2)a,b 52.4 (33.9–81.0)b 0.028 
 TNFα + Age + Sex + NSAID 21.4 (15.1–30.3)a 25.9 (17.8–37.7)a 61.7 (40.0–95.2)b 0.004 
 TNFα + All Confounders1 22.7 (15.9–32.3)a 26.4 (18.2–38.4)a 62.1 (40.3–95.8)b 0.011 
 IL1β 31.5 (22.1–44.9) 30.7 (21.0–44.8) 50.9 (32.5–79.8) 0.22 
 IL1β + Age + Sex 31.5 (22.1–45.0) 28.3 (19.1–42.1) 54.8 (34.6–86.9) 0.215 
 IL1β + Age + Sex + NSAID 29.0 (20.4–41.3) 28.3 (19.3–41.5) 62.2 (39.1–99.0) 0.083 
 IL1β + All Confounders1 29.5 (20.6–42.1) 28.0 (19.1–40.8) 60.8 (38.4–96.4) 0.163 
BMI Group
18–2530–33.934+Ptrend
Plasma 
 IL6 0.8 (0.6–1.0)a 1.4 (1.0–1.9)b 2.4 (1.7–3.4)b <0.001 
 IL6 + Age + Sex 0.8 (0.6–1.0)a 1.3 (1.0–1.8)a 2.5 (1.8–3.5)b <0.001 
 IL6 + Age + Sex + NSAID 0.8 (0.6–1.1)a 1.3 (1.0–1.8)a,b 2.3 (1.6–3.3)b <0.001 
 IL6 + All Confounders1 0.8 (0.6–1.0)a 1.3 (1.0–1.7)b 2.3 (1.7–3.3)b <0.001 
 TNFα 2.5 (2.2–2.8)a 2.9 (2.6–3.3)a,b 3.5 (3.0–4.0)b 0.003 
 TNFα + Age + Sex 2.5 (2.2–2.8)a 3.0 (2.6–3.4)a,b 3.4 (3.0–3.9)b 0.002 
 TNFα + Age + Sex + NSAID 2.5 (2.2–2.8)a 3.0 (2.6–3.4)a,b 3.4 (2.9–3.9)b 0.007 
 TNFα + All Confounders1 2.4 (2.1–2.7)a 3.0 (2.6–3.4)a,b 3.4 (2.9–3.9)b 0.002 
Colonic 
 IL6 4.1 (2.9–5.9) 4.2 (2.8–6.2) 7.3 (4.7–11.2) 0.088 
 IL6 + Age + Sex 4.1 (2.9–5.9) 3.9 (2.5–5.9) 7.6 (4.9–11.8) 0.093 
 IL6 + Age + Sex + NSAID 3.6 (2.5–5.0)a 3.8 (2.6–5.6)a 9.2 (6.0–14.0)b 0.008 
 IL6 + All Confounders1 3.6 (2.5–5.2)a 3.8 (2.6–5.7)a 9.2 (5.9–14.2)b 0.013 
 TNFα 23.9 (16.5–34.6) 29.5 (19.9–43.7) 48.8 (31.3–76.1) 0.027 
 TNFα + Age + Sex 23.9 (16.7–34.1)a 26.3 (17.7–39.2)a,b 52.4 (33.9–81.0)b 0.028 
 TNFα + Age + Sex + NSAID 21.4 (15.1–30.3)a 25.9 (17.8–37.7)a 61.7 (40.0–95.2)b 0.004 
 TNFα + All Confounders1 22.7 (15.9–32.3)a 26.4 (18.2–38.4)a 62.1 (40.3–95.8)b 0.011 
 IL1β 31.5 (22.1–44.9) 30.7 (21.0–44.8) 50.9 (32.5–79.8) 0.22 
 IL1β + Age + Sex 31.5 (22.1–45.0) 28.3 (19.1–42.1) 54.8 (34.6–86.9) 0.215 
 IL1β + Age + Sex + NSAID 29.0 (20.4–41.3) 28.3 (19.3–41.5) 62.2 (39.1–99.0) 0.083 
 IL1β + All Confounders1 29.5 (20.6–42.1) 28.0 (19.1–40.8) 60.8 (38.4–96.4) 0.163 

1Confounders: age, sex, ever smoked (y/n), polyp present (y/n), and NSAID use (y/n).

a,bdifferent superscript letters denote signficantly different means, P < 0.05.

Plasma IL6 and TNFα concentrations increased incrementally with BMI (r = 0.52, P < 0.001; r = 0.43, P < 0.01, respectively). In contrast, no significant relationships were observed between plasma TNFα or IL6 and the waist-to-hip ratio. Regular NSAID use did not confound the relationship between plasma cytokines and adiposity, regardless of whether BMI was expressed as a categorical or as a continuous variable. Furthermore, general linear modeling with other potential confounders did not significantly alter the relationship between plasma IL6 and TNFα and BMI (Table 2).

Elevated colonic cytokines in obese subjects

General linear models demonstrated that regular NSAID use was a significant confounder in the relationship between colonic concentrations of IL6 and TNFα and BMI, whereas gender, age, presence of a colonic polyp, and “current” or “ever” tobacco use were not. After adjustment for NSAID use, those subjects with a BMI ≥ 34 had significantly greater colonic IL6 and TNFα concentrations, by approximately 2- to 3-fold, than those in the 18–25 category (P < 0.03), and in the instance of both cytokines those in the 30–33.9 category had intermediate values (Table 2). In contrast, no significant association existed between the concentrations of these two colonic cytokines and the waist-to-hip ratio. No significant changes in colonic IL1β were observed with increases in either BMI or waist-to-hip ratio.

The Ptrend values in Table 2 underscore the fact that in both plasma and colon these two cytokines rise incrementally across the range of BMIs that were studied. The table also highlights the fact that, in the colon but not in the plasma, including regular use of NSAIDs in the model strengthens the association between BMI and both TNFα and IL6. In addition, regular NSAID use was a significant independent predictor of colonic IL6 (β = −0.65, P < 0.02) in the model. After correction for regular NSAID use, the general linear model describing the relationship between colonic IL1β and BMI approached, but did not achieve, the threshold for statistical significance (Table 2, P = 0.08).

Interestingly, there were no significant correlations between plasma cytokine concentrations and the concentration of the corresponding colonic cytokine (P > 0.30 for all three cytokines).

Alterations in the transcriptional profile of colonic mucosa

RNA sequencing of colonic mucosa from obese and lean subjects identified 182 genes whose differential expression between the two groups was significant (q < 0.10; see Supplementary Table S1). From this list of 182 differentially expressed genes, the 10 genes possessing the largest differential in expression were identified. Among those 10, 3 genes (BIRC2, DES, and MYD88) appear in the two top-ranked networks by Ingenuity Pathway Analysis (IPA; Qiagen; see Fig. 1A and next paragraph). Expression of these three genes in the lean and obese groups were then assessed by quantitative real-time PCR (ABI 7300, Applied Biosystems/Thermo Fisher Scientific; primer sets: Supplementary Table S2), and for each gene a significant differential in gene expression was observed and in the same direction as described by RNA-sequencing.

Figure 1.

Top-scoring biologic networks identified by Ingenuity Pathway Analysis. The top-scoring networks in the Ingenuity Pathway Analysis were most closely associated with cell-to-cell signaling (A) and cancer and gene expression (B). Genes differentially expressed in the colon of obese versus lean subjects (q-value < 0.10) are enclosed in a rectangle; genes enclosed in ovals are not differentially expressed but are part of the biologic network. The intensity of red shading indicates the magnitude of downregulation in obese individuals and the intensity of green shading indicates the magnitude of upregulation in obese subjects, with darker shades indicating greater expression differential. Solid lines indicate a direct molecular relationship and dotted lines indicate an indirect molecular relationship.

Figure 1.

Top-scoring biologic networks identified by Ingenuity Pathway Analysis. The top-scoring networks in the Ingenuity Pathway Analysis were most closely associated with cell-to-cell signaling (A) and cancer and gene expression (B). Genes differentially expressed in the colon of obese versus lean subjects (q-value < 0.10) are enclosed in a rectangle; genes enclosed in ovals are not differentially expressed but are part of the biologic network. The intensity of red shading indicates the magnitude of downregulation in obese individuals and the intensity of green shading indicates the magnitude of upregulation in obese subjects, with darker shades indicating greater expression differential. Solid lines indicate a direct molecular relationship and dotted lines indicate an indirect molecular relationship.

Close modal

IPA analysis was used to identify biologic networks (and their functions) that were enriched in the differentially expressed genes obtained from the DESeq2 analysis. Top networks were identified based upon a network score (a measure of the probability of finding a greater number of differentially expressed genes than a set threshold within a set of n genes randomly selected from IPA's Global Molecular Network). Within a network composed of 35 molecules, for example, a network score of 6 indicates a 1 in one million [i.e.: −log(10−6) = 6] chance of obtaining a network containing the same number of molecules when randomly picking 35 molecules (https://www.qiagenbioinformatics.com/ipa-training/). The top two biological networks that emerged (network score for each = 25) relate to the following functions: “Cell-to-Cell Signaling and Interaction, Hair and Skin Development and Function, Embryonic Development” and “Cancer, Organismal Injury and Abnormalities, Gene Expression” (Table 3; Fig. 1A and B). These two networks are of particular interest because they include several differentially expressed genes that are either regulators or effectors of cell signaling cascades pivotal in colonic carcinogenesis, including the NFκB, MAPK-ERK, p53, and Wnt pathways.

Table 3.

Top IPA categories enriched amongst the genes differentially expressed in the colonic epithelium of obese compared with lean individuals

Top five IPA networks
Network IDNetwork functionsScore
Cell-To-Cell Signaling and Interaction, Hair and Skin Development and Function, Embryonic Development 25 
Cancer, Organismal Injury and Abnormalities, Gene Expression 25 
Tissue Development, Cellular Growth and Proliferation, Cancer 21 
DNA Replication, Recombination, and Repair, Cellular Assembly and Organization, Cell Death and Survival 17 
Cellular Movement, Cell-To-Cell Signaling and Interaction, Hematologic System Development and Function 13 
Top five IPA networks
Network IDNetwork functionsScore
Cell-To-Cell Signaling and Interaction, Hair and Skin Development and Function, Embryonic Development 25 
Cancer, Organismal Injury and Abnormalities, Gene Expression 25 
Tissue Development, Cellular Growth and Proliferation, Cancer 21 
DNA Replication, Recombination, and Repair, Cellular Assembly and Organization, Cell Death and Survival 17 
Cellular Movement, Cell-To-Cell Signaling and Interaction, Hematologic System Development and Function 13 

NOTE: The score is generated from a hypergeometric distribution and calculated with a right-tailed Fisher exact test. For instance, a network score of 25 indicates a 1 in 1,025 chance of obtaining the same network by chance when randomly selecting the same number of molecules in the network.

The focus of our study was to investigate whether explicit evidence of inflammation exists on a biochemical level in the colonic mucosa of the obese human and to define associated changes in gene expression. Our observations establish, for the first time, that the colonic concentrations of two major proinflammatory cytokines in humans are associated, in an incremental fashion, with increasing BMI. When examined categorically, colonic TNFα and IL6 levels were approximately 2- to 3-fold as great among those with a BMI ≥34 than in lean individuals and when examined as continuous variables colonic cytokine concentrations rose incrementally over the entire range of BMIs that were examined. Incorporating regular use of NSAIDs in the models strengthened the association between colonic IL6, TNFα, and BMI, indicative of the suppressive effect that NSAIDs had on colonic cytokine levels.

It is not clear why a significant relationship between colonic IL1β and BMI was not evident (or, at most, was greatly attenuated compared with the other two cytokines). A feature that distinguishes IL1β from TNFα and IL6 is that it is synthesized as a precursor molecule and must be cleaved by IL1β-converting enzyme (ICE; caspase-1) to achieve its final, active form (27). It is therefore feasible that either caspase-1 or its inflammasome platform were not sufficiently activated by obesity to observe increases in the fully processed cytokine.

In this study, colonic TNFα and IL6 were elevated incrementally in association with increasing BMI over the entire range of BMIs that were examined (18.1–45.7). Thus, even a modest degree of excess adiposity is accompanied by a discernible state of biochemical inflammation in the colon. This is consistent with the observation that even modest degrees of adiposity in the “overweight” range (BMI 25–29.9) produce a demonstrable rise in the risk of colorectal cancer (2). Indeed, some have argued that the incremental increase in the risk of colon cancer begins with BMIs exceeding 22.5 (28).

A metric of visceral adiposity, waist-to-hip ratio, did not correlate with either plasma or colonic cytokine concentrations in this study. Some have argued that visceral adiposity, as opposed to a peripheral pattern of obesity, is more likely to incite systemic inflammation (29) and therefore is more predictive of obesity-associated cytokinemia than overall measures of obesity, and that the waist-to-hip ratio is an independent predictor of systemic inflammation (30). In vitro observations demonstrating that visceral adipose tissue secretes more IL6 than subcutaneous adipose tissue support this concept (7). Although the lack of an association with a measure of visceral adiposity in this study suggests that obesity produces a state of biochemical inflammation in the colon regardless of the state of visceral adiposity waist-to-hip ratio is far from a perfect measure of visceral adiposity (31), so this gauge of visceral adiposity may simply not have been accurate enough to detect its relationship with plasma or colonic cytokines.

An interesting difference between plasma and colonic cytokine concentrations was that regular NSAID use was associated with diminished IL6 in the colonic mucosa, as evidenced by a significantly negative beta value, whereas NSAIDs did not seem to affect circulating concentrations. Other investigators have similarly observed no decrease, or even an increase, in plasma cytokines with NSAID use (32, 33). Furthermore, in this study, no significant correlations were observed between plasma and colonic cytokine concentrations. Thus, plasma and colonic cytokine concentrations not only differ in regard to their response to NSAIDs, but other factors determining the levels of these molecules in the two tissues appear to be somewhat distinct. Other investigators have reported no association between plasma levels of cytokines and the presence of colonic adenomas (34), underscoring the idea that systemic measures of biochemical inflammation may not be suitable for predicting the likelihood of developing inflammation-related neoplasms in the colon.

Pathway analysis of the 182 differentially expressed genes identified changes in cell signaling networks that have previously been shown to participate in the evolution of colorectal neoplasia. Included among the functions of the two highest scoring biologic networks are “cell-to-cell signaling and interactions,” “cancer,” and the “regulation of gene expression,” each of which are processes intimately linked to carcinogenesis.

It is notable that within the network identified as a regulator of “cell-to-cell signaling” (Fig. 1A, Network #1) “NFκB complex” and “ERK1/2” assume central positions. NFκB activation is procarcinogenic in many settings, promoting cellular proliferation and the activation of several proto-oncogenes (35). A seminal role played by overactivation of NFκB signaling was first demonstrated to promote carcinogenesis in colitis-associated colon cancers but is now recognized to be instrumental in >50% of all human colon cancers (36, 37). Genes that encode intrinsic proteins of the NFκB complex (i.e., NFκB1, NFκB2, RELA, RELB, and REL) do not appear in this network, most likely because the extent to which this complex drives NFκB signaling is controlled by posttranslational phosphorylation of these proteins and their immediate upstream IκB regulators rather than by up- and downregulation of gene expression (38). However, in this network several differentially expressed genes are present that either directly or indirectly regulate the NFκB signaling complex: fibrillarin (FBL), myeloid differentiation primary response 88 (MYD88), desmin (DES), and cellular inhibitor of apoptosis protein 1 (BIRC2). Desmin (DES) is observed to be downregulated in the obese subjects, a molecular event previously demonstrated to increase NFκB signaling (39). Conversely, in Network #1 MYD88 and BIRC2 are significantly upregulated in obese subjects and these proteins are known to activate NFκB signaling (40, 41). The changes in DES, MYD88, and BIRC2 expression are therefore all consistent with the hypothesis that obesity induces procarcinogenic transcriptional changes in the colonic mucosa. It is also worth noting that NFκB signaling upregulates proinflammatory genes such as IL1β and TNFα (42). Thus, the activation of NFκB signaling through the upregulation of genes such as MYD88 and BIRC2 has both carcinogenic and inflammatory consequences, and may thereby create a positive feedback loop that magnifies its own effects.

Like NFκB, ERK1/2 signaling is activated by post-translational phosphorylation of the molecule (43), and expression of the gene is not differentially expressed in this network. However, contained in Network #1 is differential expression of several regulators of ERK. ERK is a component of a family of serine/threonine kinases which are involved in cell migration, adhesion, progression, proliferation, and survival, and it is a major regulator of the oncogenic Ras pathway (44). Our transcriptional analysis identified two differentially expressed genes that modulate ERK activity: RAPTOR and IL6R. RAPTOR is a negative regulator of ERK activity through its regulatory role in mTOR activity (45). As such, the reduction in RAPTOR expression observed in the obese colons would be expected to increase oncogenicity due to ERK activation. Conversely, an increase in the expression of the IL6R was present in the obese colon, which is an alteration in expression that incites an increase in ERK activity (46). Thus, the alterations in RAPTOR and IL6R expression that accompanied obesity are also consistent with pro-carcinogenic molecular changes in the human colon.

The other network ranking highest in the IPA analysis (Fig. 1B, Network #2) is also highly relevant to colorectal carcinogenesis. The CTNNB1 gene, which encodes β-catenin, and GSK3β gene assume central positions in this network, as does TP53, which encodes p53 (Fig. 1B). β-Catenin is the proximate activator of canonical Wnt signaling in colonic carcinogenesis and GSK3-β assists in regulating the degradation of β-catenin (47). When inappropriately overactivated Wnt signaling is generally regarded as one of the earliest steps in the molecular carcinogenesis in 85+% of spontaneous colorectal cancers (47). Likewise, the p53 protein plays a number of important tumor suppressor functions in determining whether colonocytes undergo malignant degeneration (48). The fact that expression of these three genes were not themselves altered by obesity is not mechanistically informative because their activities are largely controlled by posttranslational phosphorylation, protein–protein interactions, and subcellular localization (49–51). However, contained with the Network #2 is indirect evidence for both Wnt activation and hinderance of p53 tumor suppressor activity. The gene for ephrin type-B receptor 3 (EPHB3), a target gene suppressed by β-catenin/Tcf4-mediated signaling and postulated to be a tumor suppressor gene in human colorectal carcinogenesis (52), was significantly downregulated in the obese subjects. In human colorectal neoplasms EphB protein expression decreases incrementally in parallel with stepwise increases in the stage of carcinogenesis, from aberrant crypt foci to adenomas to carcinomas (52); furthermore, its functional role as a suppressor of tumorigenesis is evidenced by the fact that mice whose expression of EPHB3 has been knocked out experience greatly enhanced colonic tumorigenesis (52). Thus, suppression of EphB3 activity may be one avenue through which obesity-induced activation of Wnt promotes neoplastic transformation. Network #2 also indicates that the gene that encodes RAD51 is downregulated in the colons of those with obesity: this is potentially important because one of the many antitransformational activities performed by p53 is its cooperation with RAD51 in repairing DNA damage through homologous recombination (53). Nevertheless, additional mechanistic studies will be needed to confirm that these indirect indicators of Wnt activation and hindrance of p53 activity are present in the colons of the obese.

This translational study does have limitations. The size of the study population is modest, and therefore results need to be confirmed within the context of a larger study. Inter-racial differences in cytokine levels are known to exist (22), so to minimize the confounding effect of this variable this study was confined to Caucasian subjects. Thus, it is unclear at this point whether the observed principles would apply to non-Caucasian populations. A larger study would therefore allow for stratification of the subject population, and thereby determine whether gender- and/or race-specific effects exist. Further mechanistic studies will be needed to establish whether the elevated colonic cytokines and cellular pathways identified in this observational study contribute causally to the procarcinogenic effects of adiposity. Also, whether the elevated colonic cytokines originate from colonocytes or some cell population in the lamina propria is not defined by this study.

In summary, this study demonstrates that the concentrations of two important proinflammatory cytokines in the colon rise incrementally over a wide range of BMI values and that regular use of NSAIDs diminish the concentration of colonic IL6 over the entire range of BMIs that were studied. Although the extent to which obesity increases colonic cytokine levels is modest, the chronicity of exposure produced by obesity and the autoamplifying nature of inflammation produced by inflammatory cytokines (54, 55) underscore the potential impact that such changes may have. In addition, the transcriptome analysis provides evidence that protransformational transcriptional changes in the colonic mucosa accompany obesity that are quite relevant to colonic carcinogenesis. Given the cross-sectional nature of this study, the results cannot prove that the observed changes in the colonic transcriptome are due to the rise in cytokines. Indeed, other metabolic alterations that accompany obesity, such as hyperinsulinemia and changes in adipokine secretion, have been shown to stimulate some of the same pathways observed in this study (56, 57), so it is likely that multiple factors are contributing to the altered colonic transcriptome. Observations from this study nevertheless underscore the potential contribution that the establishment of an inflammatory milieu in the colonic mucosa may play in explaining the enhanced risk of colon cancer due to obesity.

No potential conflicts of interest were disclosed.

Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture.

Conception and design: J.W. Crott, Z. Liu, J.B. Mason

Development of methodology: K. Leung, L.D. Parnell, J.B. Mason

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): A.C. Pfalzer, K. Leung, J.W. Crott, S.J. Kim, A.K. Tai, P.E. Garcia, J.B. Mason

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): A.C. Pfalzer, J.W. Crott, A.K. Tai, L.D. Parnell, F.K. Kamanu, Z. Liu, G. Rogers, J.B. Mason

Writing, review, and/or revision of the manuscript: A.C. Pfalzer, J.W. Crott, L.D. Parnell, F.K. Kamanu, G. Rogers, J.B. Mason

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): F.K. Kamanu, G. Rogers, J.B. Mason

Study supervision: J.B. Mason

This work was funded in part by the Agricultural Research Service of the United States Department of Agriculture, Cooperative Agreement #58-1950-0-014. We especially thank Drs. Peter and Joan Cohn and the HNRCA Directors Student Innovation Fund for contributing support for this study.

We thank the physicians and nurses of the colonoscopy suite at Tufts Medical Center for generously donating their time and expertise. We also thank the staff of the Tufts University Core Facility Genomics Core for their assistance in performing the RNA sequencing.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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