Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Sep 5.
Published before final editing as: J Acad Nutr Diet. 2026 Aug 19:156830. doi: 10.1016/j.jand.2026.156830

Dietary fat, inflammation, circulating tumor cells, and circulating tumor DNA in colorectal cancer survivors: An exploratory cross-sectional study

Stephanie LE Compton 1,*, Eunhan Cho 2, Guillaume Spielmann 3, Fanchao Yi 4, Shengping Yang 5, Justin C Brown 6,*
PMCID: PMC13544021  NIHMSID: NIHMS2206265  PMID: 42617694

Abstract

Background:

Inflammation contributes to cancer recurrence through signaling pathways responsible for cell growth and metastasis, impacting circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). A high fat diet may contribute to inflammation directly or indirectly through changes in visceral adipose tissue (VAT), a body composition compartment associated with inflammation.

Objective:

This analysis examined the association between percent energy from fat and circulating markers of inflammation, CTCs, and ctDNA.

Design:

This study was an exploratory secondary analysis of cross-sectional data from the Exercise and Colorectal Cancer Treatment (EXACT) trial conducted from August 2019 to May 2023 at Pennington Biomedical Research Center. Estimated percent energy from fat was collected at baseline using the National Cancer Institute (NCI) Fat Screener. VAT was assessed using dual energy x-ray absorptiometry.

Participants/Setting:

Participants included 57 stage I-III colorectal cancer survivors.

Main Outcome Measures:

Outcomes included eleven inflammatory biomarkers, CTCs, and ctDNA.

Statistical Analyses Performed:

Associations between percent energy from fat and inflammatory and tumor biomarkers was quantified using the Spearman correlation coefficient (r). Spearman partial correlations adjusted for age, sex, race, cancer stage, and moderate-to-vigorous physical activity (MVPA). Fully adjusted models additionally adjusted for VAT.

Results:

In unadjusted analyses, percent energy from fat was associated with soluble interleukin (IL)-6 receptor (sIL-6r) (r = 0.32, 95% Confidence Interval (CI): 0.06-0.53), soluble tumor necrosis factor (TNF) receptor 1 (sTNFr1) (r = 0.32, 95% CI: 0.06-0.53), soluble TNF receptor 2 (sTNFr2) (r = 0.34, 95% CI: 0.09-0.55), and soluble IL-2 receptor alpha (sIL-2rα) (r = 0.29, 95% CI: 0.03-0.51). sTNFr2 remained associated with percent energy from fat in adjusted (r = 0.32, 95% CI: 0.06-0.55) and fully adjusted (r = 0.28, 95% CI: 0.02-0.51) models.

Conclusions:

This exploratory analysis provides insight into the mechanisms by which diet may impact cancer recurrence to support encouraging a healthy dietary pattern.

Keywords: colorectal cancer, inflammation, circulating tumor DNA, diet

INTRODUCTION

Inflammation contributes to solid tumor progression through the activation of tumor growth and metastatic pathways.1,2 In colorectal cancer, inflammation following surgical resection is associated with reduced disease-free and overall survival.3–5 Colorectal cancer commonly sheds cells, known as circulating tumor cells (CTCs), and cellular fragments such as circulating tumor deoxyribonucleic acid (ctDNA).6,7 The presence of CTCs and ctDNA following colorectal cancer treatment is predictive of tumor recurrence and mortality.8–10

Diet following colorectal cancer diagnosis is associated with risk of recurrence and death.11,12 Diet has been investigated as a potential link between inflammation and colorectal cancer recurrence. High fat diets, considered pro-inflammatory13 may modulate chronic inflammation to influence colorectal cancer outcomes. Pro-inflammatory diets are associated with increased risk of all-cause mortality in adults and specifically among colorectal cancer survivors.14,15 A high fat diet may contribute directly or indirectly to inflammation through changes in body composition. For example, adipose tissue dysfunction in obesity induces chronic low-grade inflammation that is associated with increased risk of obesity-related cancers.16 In particular, visceral adipose tissue (VAT) has been linked to chronic inflammation and angiogenesis in colorectal cancer.17,18 However, the mechanisms linking high fat diet, inflammation, and prognostic biomarkers of recurrence in colorectal cancer are understudied. This exploratory analysis assessed associations between percent energy from fat, inflammation, CTCs, and ctDNA and whether VAT is associated with these relationships.

METHODS

Study Design

This exploratory, cross-sectional analysis used data from the Exercise and Colorectal Cancer Treatment (EXACT) trial, a randomized, parallel-group, controlled design conducted at a single site (Pennington Biomedical Research Center, Baton Rouge, LA, USA).19 Briefly, the EXACT trial was sponsored by the National Cancer Institute (NCI) to characterize the biological mechanisms by which exercise may prevent cancer recurrence and death in patients with stage I-III colorectal cancer.19,20 Potentially eligible study participants were identified using the Louisiana Tumor Registry and were contacted via mail and telephone. Between August 2019 and February 2023, 60 participants were enrolled, and primary data collection was completed in May 2023. The trial followed Good Clinical Practice21 and the ethical principles in the Declaration of Helsinki. The Institutional Review Board at Pennington Biomedical Research Center approved the protocol and all study materials. Participants provided written informed consent and physician approval prior to randomization. The study was registered on ClinicalTrials.gov as NCT03975491. Trial design19 and the primary outcomes20 have been previously described.

Participants

Eligible participants were adults (aged ≥ 18 years) with histologically confirmed stage I-III colon cancer. Participants completed curative-intent tumor resection and cancer-directed therapy (e.g., chemotherapy and radiation) at least 30 days before enrollment and had no additional surgery planned during the intervention period. Participants had no contraindication to exercise as determined by the Physical Activity Readiness Questionnaire (PAR-Q).22 All participants received approval to participate from a qualified healthcare provider. Participants were excluded if they had evidence of metastatic disease, were undergoing concurrent active treatment for other cancers, were enrolled in other clinical trials of lifestyle interventions, or were unwilling to comply with study requirements. Additional inclusion and exclusion criteria have been previously described.19

Clinical Characteristics

Tumor characteristics such as primary tumor site, tumor staging (American Joint Committee on Cancer, 8th edition)23, and time since treatment completion were abstracted from the Louisiana Tumor Registry or review of medical records. Participant demographics were obtained using questionnaires per National Institutes of Health requirements.

Percent Energy from Fat

Prior to randomization, participants completed the Pennington Center Longitudinal Study (PCLS)24 standardized lifestyle questionnaire administered on paper containing questions regarding physical activity levels, diet, and socioeconomic status. This questionnaire contained the NCI Fat Screener to assess percent energy from fat.25,26 Participants who did not complete the NCI Fat Screener were not included in the analysis.

Inflammatory Markers

At baseline, participants underwent a fasting blood draw (≥ 8 hours only water) after abstaining from alcohol consumption or structured exercise for 24 hours. Blood was centrifuged as Ethylenediaminetetraacetic Acid (EDTA)-preserved plasma and serum and stored at −80°C. Inflammation measures included high-sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6), soluble IL-6 receptor (sIL-6r), tumor necrosis factor-alpha (TNFα), soluble TNFα receptors 1 and 2 (sTNFr1, sTNFr2), interleukin-2 (IL-2), soluble IL-2 receptor-alpha (sIL-2Rα), interleukin-10 (IL-10), interleukin-23 (IL-23), and soluble receptor for advanced glycation end-products (sRAGE). These pro- and anti-inflammatory cytokines and soluble receptors were chosen based on interactions with signaling networks in the Janus kinase/signal transducers and activators of transcription (JAK-STAT) and nuclear factor kappa B (NF-κB) pathways and their role in disease initiation and progression in colorectal cancer. 2,27 Additionally, sRAGE, an anti-inflammatory decoy receptor for RAGE, was included based on its inverse association with high fat diets and waist circumference.28–30 hs-CRP was quantified using a chemiluminescent assay (IMMULITE, Siemens, St. Paul, MN, USA). IL-6, sIL-6r, TNFa, sTNFr1, sTNFr2, IL-2, sIL-2Rα, IL-10, IL-23, and sRAGE were quantified using a MILLIPLEX multiplex magnetic bead panel (Millipore Sigma, Burlington, VT, USA), with an inter-assay coefficient of variation of <15% and sensitivity of 0.11 pg/mL. The intra-and inter-assay coefficients of variation were <5% and <15% for all analytes, respectively. Masked quality-control samples are interspersed among cases. All assays were conducted by lab personnel blinded to the randomized group assignment.

Tumor Endpoints

Tumor endpoints were quantified using peripheral blood samples collected into cell-free DNA collection tubes (Streck Cell-Free BCT, La Vista, NE, USA).31 CTCs were quantified within 48 hours of collection using a geometrically enhanced differential immunocapture (GEDI) platform.32 GEDI is a microfluidic platform that utilizes antibody-coated obstacles to capture rare cells within the blood. To capture CTCs, obstacles were coated with an antibody specific to epithelial cell adhesion molecule (EpCAM), an epithelial cell-specific marker. Captured cells were stained with DAPI and fluorescently labeled antibodies to the leukocyte marker CD45 and the epithelial cell marker pan-CK. A technician blinded to the randomized group assignment counted pan-CK+/DAPI+/CD45- EpCAM captured cells with intact cellular morphology as CTCs. Tumor fraction was quantified using a comprehensive, versatile, scalable platform to measure ctDNA.33 Ultra-low pass whole-genome sequencing of cfDNA was performed to an average genome-wide fold coverage of ≈ 0.1 × to determine the presence of detectable tumor DNA. Cell-free DNA was analyzed using ichorCNA, which uses a probabilistic hidden Markov model to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the tumor fraction of an ultra-low-pass whole genome sequencing sample.34

Body Composition

Body composition was measured at baseline using whole-body dual-energy x-ray absorptiometry (DXA, Hologic Discovery; Apex v 4.02 software) and used to quantify visceral adipose tissue (VAT) mass (kg). Height and weight were used to calculate body mass index (BMI, kg/m2).

Physical Activity

At baseline, participants wore an Actigraph CT3X+ (Actigraph, Pensacola, FL, USA) on their waist for seven consecutive days to assess physical activity levels. Moderate-to-vigorous physical activity (MVPA) minutes per day was calculated using validated cut points.35,36

Statistical Analysis

Baseline measures prior to randomization were used for this analysis. Of the 60 participants included in the EXACT trial, 57 completed the NCI Fat Screener and were included in this analysis. Descriptive characteristics of the study population are presented as means ± standard deviation (SD) for continuous variables and as counts and proportions for categorical variables. Inflammatory biomarkers are presented as geometric means ± standard deviation (SD). The unadjusted strength of association between percent energy from fat, inflammation, and tumor markers were quantified using the Spearman correlation coefficient (r) with 95% confidence intervals (CI) calculated using Fisher’s z-transformation.37 Adjusted models were quantified using Spearman partial correlations, calculated using rank-based residuals to control for covariates age, sex, race, cancer stage, and MVPA. Fully adjusted models additionally included VAT. Confidence intervals for partial Spearman correlations were obtained using nonparametric bootstrap resampling. All statistical tests are two-sided. Statistical analyses were conducted using SAS Software Windows version 9.4 (SAS Institute Inc.).38 Given the exploratory nature of this analysis, results were not adjusted for multiple testing.

RESULTS

Baseline Characteristics

Baseline characteristics are shown in Table 1. The mean ± SD age was 60.7 ± 11.0 years, 37 (65%) were female, 42 (74%) were white, and 44 (77%) had completed treatment for primary colon cancer. The mean ± SD body mass index (BMI) was 32.1 ± 5.8 kg/m2, MVPA minutes was 23.0 ± 27.1, and VAT was 0.8 ± 0.3 kg. On average, participants consumed a percent energy from fat of 33.2% ± 3.7%. Baseline levels of inflammatory markers, CTCs, and ctDNA are shown in Table 2.

Table 1:

Baseline characteristics of the Exercise and Colorectal Cancer Treatment (EXACT) trial participants (N=57)

Characteristic Overall
Age (years) (mean ± SD) 60.7 ± 11.0
Sex n (%)
 Male 20 (35)
 Female 37 (65)
Race n (%)
 White 42 (74)
 Black 10 (18)
 Othera 5 (8)
Primary cancer site n (%)
 Colon 44 (77)
 Rectal 13 (23)
Cancer stage n (%)
 I 15 (27)
 II 16 (28)
 III 26 (45)
Time since treatment completion (months) (mean ± SD) 36.0 ± 27.0
Body mass index (kg/m2) (mean ± SD) 32.1 ± 5.8
Moderate-to-vigorous physical activity minutes per day (mean ± SD)b 23.0 ± 27.1
Visceral adipose tissue (kg) (mean ± SD) 0.8 ± 0.3
Predicted calories from fat (%) (mean ± SD) 33.2 ± 3.7
Open in a new tab
a

Other includes Asian, Other/Multiple, or Don’t Know

b

Participants wore an Actigraph GT3X+ triaxial accelerometer on their waist for seven consecutive days to assess physical activity levels

Table 2:

Baseline measures of inflammation, circulating tumor cells, and circulating tumor DNA in the Exercise and Colorectal Cancer Treatment (EXACT) trial (N=57)

Marker Geometric mean ± SD.
High-sensitivity c-reactive protein (hsCRP) (mg/L) 2.4 ± 2.1
Interleukin-6 (IL-6) (pg/mL)a 4.7 ± 4.0
Soluble interleukin-6 receptor (sIL-6r) (pg/mL)b 17903.9 ± 1.5
Tumor necrosis factor alpha (TNFα) (pg/mL)c 8.1 ± 1.7
Soluble tumor necrosis factor alpha receptor 1 (sTNFr1) (pg/mL)d 826.0 ± 2.0
Soluble tumor necrosis factor alpha receptor 2 (sTNFr2) (pg/mL)e 5917.5 ± 1.5
Interleukin-2 (IL-2) (pg/mL)f 3.8 ± 1.7
Soluble interleukin-2 receptor alpha (sIL-2rα) (pg/mL)g 435.8 ± 1.4
Interleukin-10 (IL-10) (pg/mL)h 24.2 ± 2.8
Interleukin-23 (IL-23)(pg/mL)i 305.1 ± 2.1
Soluble receptor for advanced glycation end-products (sRAGE) (pg/mL)j 180.2 ± 2.0
Circulating tumor cells (CTCs) (cells/mL) 1.5 ± 2.0
Circulating tumor DNA (ctDNA) (%) 0.011 ± 0.007
Open in a new tab
a

To convert pg/mL interleukin-6 to pmol/L, multiply by 0.0481

b

To convert pg/mL soluble interleukin-6 receptor to pmol/L, multiply by 0.0237

c

To convert pg/mL tumor necrosis factor alpha to pmol/L, multiply by 0.0575

d

To convert pg/mL soluble tumor necrosis factor alpha receptor 1 to pmol/L, multiply by 0.0182

e

To convert pg/mL soluble tumor necrosis factor alpha receptor 2 to pmol/L, multiply by 0.0133

f

To convert pg/mL interleukin-2 to pmol/L, multiply by 0.0667

g

To convert pg/mL soluble interleukin-2 receptor alpha to pmol/L, multiply by 0.0403

h

To convert pg/mL interleukin-10 to pmol/L, multiply by 0.0556

i

To convert pg/mL interleukin-23 to pmol/L, multiply by 0.0169

j

To convert pg/mL soluble receptor for advanced glycation end-products to pmol/L, multiply by 0.0294

Association of diet with inflammatory biomarkers, CTCs, and ctDNA

Percent energy from fat was associated with inflammatory biomarkers and tumor markers as shown in Table 3. In the unadjusted model, percent energy from fat was positively associated with sIL-6r (r = 0.32, 95% CI: 0.06-0.53), sTNFr1 (r = 0.32, 95% CI: 0.06-0.53), sTNFr2 (r = 0.34, 95% CI: 0.09-0.55), and sIL-2rα (r = 0.29, 95% CI: 0.03-0.51). In the adjusted and fully adjusted models, only sTNFr2 remained associated with percent energy from fat (adjusted: r = 0.32, 95% CI: 0.06-0.55; fully adjusted: r = 0.28, 95% CI: 0.02-0.51). There were no significant associations between percent energy from fat and CTCs or ctDNA.

Table 3:

Spearman correlations between percent energy from fat, inflammation, circulating tumor cells, and circulating tumor DNA in the Exercise and Colorectal Cancer Treatment (EXACT) trial (N=57)

Soluble factor Percent energy from fat
Unadjusted Adjusted (age, sex, race, cancer stage, MVPAa) Fully adjusted (age, sex, race, cancer stage, MVPAa, VATb)
r 95% CI r 95% CI r 95% CI
High-sensitivity c-reactive protein (hsCRP) (mg/L) −0.04 −0.30-0.22 −0.007 −0.32-0.29 0.006 −0.32-0.31
Interleukin-6 (IL-6) (pg/mL)c 0.19 −0.09-0.43 0.16 −0.17-0.45 0.12 −0.21-0.46
Soluble interleukin-6 receptor (sIL-6r) (pg/mL)d 0.32 0.06-0.53 0.26 −0.03-0.51 0.24 −0.04-0.50
Tumor necrosis factor alpha (TNFα) (pg/mL)e 0.23 −0.04-0.46 0.16 −0.14-0.42 0.06 −0.23-0.35
Soluble tumor necrosis factor alpha receptor 1 (sTNFr1) (pg/mL)f 0.32 0.06-0.53 0.25 −0.009-0.48 0.20 −0.07-0.44
Soluble tumor necrosis factor alpha receptor 2 (sTNFr2) (pg/mL)g 0.34 0.09-0.55 0.32 0.06-0.55 0.28 0.02-0.51
Interleukin-2 (IL-2) (pg/mL)h 0.15 −0.15-0.42 0.04 −0.26-0.35 0.001 −0.29-0.33
Soluble interleukin-2 receptor alpha (sIL-2rα) (pg/mL)i 0.29 0.03-0.51 0.24 −0.03-0.47 0.20 −0.05-0.42
Interleukin-10 (IL-10) (pg/mL)j −0.01 −0.27-0.25 −0.02 −0.30-0.28 −0.08 −0.37-0.23
Interleukin-23 (IL-23)(pg/mL)k −0.08 −0.33-0.18 −0.11 −0.39-0.21 −0.17 −0.44-0.15
Soluble receptor for advanced glycation end-products (sRAGE) (pg/mL)l 0.23 −0.07-0.48 0.25 −0.09-0.53 0.22 −0.10-0.51
Circulating tumor cells (CTCs) (cells/mL) 0.19 −0.09-0.43 0.22 −0.03-0.46 0.22 −0.03-0.47
Circulating tumor DNA (ctDNA) (%) −0.03 −0.29-0.24 −0.01 −0.32-0.30 −0.02 −0.34-0.29
Open in a new tab
a

Moderate-to-vigorous physical activity

b

Visceral adipose tissue

c

To convert pg/mL interleukin-6 to pmol/L, multiply by 0.0481

d

To convert pg/mL soluble interleukin-6 receptor to pmol/L, multiply by 0.0237

e

To convert pg/mL tumor necrosis factor alpha to pmol/L, multiply by 0.0575

f

To convert pg/mL soluble tumor necrosis factor alpha receptor 1 to pmol/L, multiply by 0.0182

g

To convert pg/mL soluble tumor necrosis factor alpha receptor 2 to pmol/L, multiply by 0.0133

h

To convert pg/mL interleukin-2 to pmol/L, multiply by 0.0667

i

To convert pg/mL soluble interleukin-2 receptor alpha to pmol/L, multiply by 0.0403

j

To convert pg/mL interleukin-10 to pmol/L, multiply by 0.0556

k

To convert pg/mL interleukin-23 to pmol/L, multiply by 0.0169

l

To convert pg/mL soluble receptor for advanced glycation end-products to pmol/L, multiply by 0.0294

DISCUSSION

In this exploratory, cross-sectional analysis, percent energy from fat was associated with sTNFr2. Participants consumed an average percent energy from fat (Table 1) within the recommendations from the 2020-2025 Dietary Guidelines for Americans (DGAs).39 Percent energy from fat was positively associated with sIL-6r, sTNFr1, sTNFr2, and sIL-2rα in unadjusted models, but only sTNFr2 remained associated in adjusted and fully adjusted models.

A high percent energy from fat may reflect a more Western dietary pattern, which has been associated with both increased inflammation13 and a higher risk of cancer recurrence and mortality in colorectal cancer survivors.11 In this analysis, sTNFr2 was associated with percent energy from fat even after adjustment for covariates and VAT, suggesting that this relationship occurs independently of VAT. TNF-α signaling occurs through both TNFr1 and TNFr2, with TNFr1 mediating apoptotic pathways40 and TNFr2 mediating proliferative pathways that are pro-tumorigenic.41,42 While these pathway differences may be potential explanations for the nonsignificant relationship between percent energy from fat, TNF, and TNFr1, it may also be that the ability to see differences was underpowered for TNF and TNFr1 given the higher levels observed across participants for TNFr2 (Table 2). Nonetheless, TNFr2 has been significantly associated with overall mortality (Hazard Ratio (HR) = 1.48, 95% CI: 1.02-2.16, P trend = 0.006) but not colorectal cancer specific mortality (HR = 1.23, 95% CI: 0.72-2.08, P-trend 0.34) in the Nurses’ Health Study and Health Professionals Follow-Up Study.43 Additional research is needed to confirm these relationships and understand the clinical relevance of the results.

The majority of the relationships between percent energy from fat and inflammatory markers were null when adjusted for covariates. The inflammatory marker panel included in this analysis contained both pro-inflammatory and anti-inflammatory markers. Pro-inflammatory cytokines and their receptors such as CRP, IL-6, sIL-6r, and IL-2 have previously been investigated in colorectal cancer for their role in disease initiation and progression.2 Inflammatory cytokine IL-23 is associated with activation of immune cells and cytokine production in inflammatory bowel diseases and colon cancer.44 IL-10 may have both tumor-suppressive and tumor-promoting inflammatory properties.45 sRAGE, acting as an anti-inflammatory decoy receptor for the pro-inflammatory RAGE, is inversely related to waist circumference and high fat diets,28–30 but may also increase if there is overstimulation of cell surface RAGE.46 The null results observed suggests that dietary percent energy from fat may not impact these inflammatory pathways in colorectal cancer survivors, or this study was underpowered to detect an effect. Additional research is needed to explore these relationships further and to include multiple aspects of dietary intake.

A novel aspect of this analysis was the investigation of relationship between percent energy from fat and CTCs and ctDNA, biomarkers that assess minimal residual disease to define disease recurrence risk in colorectal cancer.6 These results did not reveal a relationship between percent energy from fat and CTCs or ctDNA. Variability in the cancer type, cancer stage, and time since treatment completion in the cohort may have impacted these results. It is also unclear whether percent energy from fat, a specific aspect of dietary intake, would have a clinically meaningful impact on tumor markers outside of the whole dietary pattern. Future research should prioritize investigating diet with biomarkers of disease recurrence risk.

While this analysis is exploratory and provides evidence to support future studies, the results are in line with clinical recommendations47 to encourage a healthy dietary pattern in colorectal cancer survivors by reducing foods high in fat and saturated fat to positively impact inflammation. Given that tumor recurrence proceeds most deaths in the first decade following a colorectal cancer diagnosis,48 identifying and recommending interventions by which to reduce this risk are imperative. The results of this analysis should be confirmed in observational studies of larger cohorts and tested in randomized controlled trials to identify causal relationships in order to strengthen clinical guidelines.

There are several limitations to this analysis. This analysis is cross-sectional and was not prespecified in the trial’s statistical analysis plan; therefore, these results should be considered hypothesis generating and exploratory. Based on the exploratory nature of this analysis, no adjustments for multiple comparison were used, which may have impacted the results. The observational nature of this study means there could be residual confounding that impacted the results. The PCLS lifestyle questionnaire was derived from multiple validated questionnaires and contained only one complete screening tool (the NCI Fat Screener)25,26, which limited the ability to assess the whole diet or dietary patterns. Since the full dietary pattern was not assessed, the full extent of the relationships between diet, inflammation, and tumor markers may not have been detected. Additionally, self-reported assessments of diet have known biases and limitations in participant recall. It is also plausible that different aspects of diet interact differently within the inflammatory cascade. Future research is needed in this area to elucidate which foods, food groups, and dietary patterns contribute to inflammatory biomarkers and which may be modifiable for reducing inflammation in colorectal cancer survivors. A limited sample size may reduce the generalizability of these results. Quantification of ctDNA may have been below quantification thresholds, impacting the results. Alternative approaches include the use of negative controls and technologies that are sensitive to detect low tumor fraction, such as whole-genome sequencing of cfDNA. Variation in the cancer type, cancer stage, and time since treatment completion may have influenced the inflammation and tumor marker results.

This study also has several strengths. Inflammation and tumor markers were prespecified endpoints in the EXACT trial, meaning data was available for the entire participant sample. The trial population was representative of the demographics of the underlying population in the recruitment area.19 A majority of participants completed the NCI Fat Screener. Visceral adipose tissue was assessed using DXA, and moderate-to-vigorous physical activity was assessed objectively using accelerometry.

CONCLUSIONS

In stage I-III colorectal cancer survivors, percent energy from fat was associated with sTNFr2 independent of VAT. This exploratory analysis provides insight into the mechanisms by which diet may impact cancer recurrence to support clinical recommendations encouraging a healthy dietary pattern. Future research is needed to further interrogate these relationships and test the hypothesis that diet may reduce inflammation, ctDNA, and CTCs.

Research Snapshot.

Research Question:

In colorectal cancer survivors, is percent energy from fat related to inflammatory and tumor biomarkers, and are these relationships influenced by visceral adipose tissue?

Key Findings:

In this exploratory cross-sectional analysis of stage I-III colorectal cancer survivors, percent energy from fat remained associated with soluble tumor necrosis factor (TNF) receptor 2 after adjustment for covariates and VAT, while other relationships were attenuated.

Funding/Support:

This work was supported by the National Cancer Institute of the National Institutes of Health (R00 CA218603) and the American Institute for Cancer Research.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest: None

Contributor Information

Stephanie L.E. Compton, Postdoctoral Research Fellow, AdventHealth Translational Research Institute, 301 E Princeton St, Orlando, FL 32804.

Eunhan Cho, Postdoctoral Research Fellow, AdventHealth Translational Research Institute, 301 E Princeton St, Orlando, FL 32804.

Guillaume Spielmann, Associate Professor, School of Kinesiology, Louisiana State University, 112 Huey P Long Field House, Baton Rouge, LA, 70802.

Fanchao Yi, Senior Data Scientist Biostatistics, AdventHealth Translational Research Institute, 301 E Princeton St, Orlando, FL 32804.

Shengping Yang, Assistant Professor, Biostatistics, Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA, 70808.

Justin C. Brown, Senior Investigator, AdventHealth Translational, Research Institute, 301 E Princeton St, Orlando, FL 32804.

Data Availability Statement

Data are available for download at the Pennington Biomedical Nutrition Obesity Data Repository (https://my.pbrc.edu/NORC/NORCRepository/).

REFERENCES

  • 1.Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–867. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Yasui K, Shida D, Nakamura Y, Ahiko Y, Tsukamoto S, Kanemitsu Y. Postoperative, but not preoperative, inflammation-based prognostic markers are prognostic factors in stage III colorectal cancer patients. British Journal of Cancer. 2021;124(5):933–941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Cheng E, Shi Q, Shields AF, et al. Association of inflammatory biomarkers with survival among patients with stage III colon cancer. JAMA oncology. 2023;9(3):404–413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Wesselink E, Balvers MG, Kok DE, et al. Levels of inflammation markers are associated with the risk of recurrence and all-cause mortality in patients with colorectal cancer. Cancer Epidemiology, Biomarkers & Prevention. 2021;30(6):1089–1099. [DOI] [PubMed] [Google Scholar]
  • 6.Malla M, Loree JM, Kasi PM, Parikh AR. Using circulating tumor DNA in colorectal cancer: current and evolving practices. Journal of Clinical Oncology. 2022;40(24):2846–2857. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Bartolomucci A, Nobrega M, Ferrier T, et al. Circulating tumor DNA to monitor treatment response in solid tumors and advance precision oncology. NPJ Precision Oncology. 2025;9(1):84. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bork U, Rahbari NN, Schölch S, et al. Circulating tumour cells and outcome in non-metastatic colorectal cancer: a prospective study. British journal of cancer. 2015;112(8):1306–1313. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Galizia G, Gemei M, Orditura M, et al. Postoperative detection of circulating tumor cells predicts tumor recurrence in colorectal cancer patients. Journal of Gastrointestinal Surgery. 2013;17(10):1809–1818. [DOI] [PubMed] [Google Scholar]
  • 10.Mauri G, Vitiello PP, Sogari A, et al. Liquid biopsies to monitor and direct cancer treatment in colorectal cancer. British Journal of Cancer. 2022;127(3):394–407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Meyerhardt JA, Niedzwiecki D, Hollis D, et al. Association of dietary patterns with cancer recurrence and survival in patients with stage III colon cancer. Jama. 2007;298(7):754–764. [DOI] [PubMed] [Google Scholar]
  • 12.Chen Q-Y, Keum N, Giovannucci EL. Postdiagnosis dietary and lifestyle factors and mortality outcomes among colorectal cancer patients: a meta-analysis. JNCI: Journal of the National Cancer Institute. 2025:djaf098. [DOI] [PubMed] [Google Scholar]
  • 13.Christ A, Lauterbach M, Latz E. Western diet and the immune system: an inflammatory connection. Immunity. 2019;51(5):794–811. [DOI] [PubMed] [Google Scholar]
  • 14.Wesselink E, Boshuizen HC, van Lanen A-S, et al. Dietary and lifestyle inflammation scores in relation to colorectal cancer recurrence and all-cause mortality: a longitudinal analysis. Clinical Nutrition. 2024;43(9):2092–2101. [DOI] [PubMed] [Google Scholar]
  • 15.Tabung FK, Liu L, Wang W, et al. Association of dietary inflammatory potential with colorectal cancer risk in men and women. JAMA oncology. 2018;4(3):366–373. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Quail DF, Dannenberg AJ. The obese adipose tissue microenvironment in cancer development and progression. Nature Reviews Endocrinology. 2019;15(3):139–154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Himbert C, Ose J, Nattenmüller J, et al. Body fatness, adipose tissue compartments, and biomarkers of inflammation and angiogenesis in colorectal cancer: the ColoCare study. Cancer Epidemiology, Biomarkers & Prevention. 2019;28(1):76–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Liesenfeld DB, Grapov D, Fahrmann JF, et al. Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study. The American journal of clinical nutrition. 2015;102(2):433–443. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Compton SL, Yang S, Maniscalco LS, et al. A randomized trial of aerobic exercise in colorectal cancer: Rationale, design, recruitment, and exercise adherence results. Contemporary Clinical Trials. 2024;146:107702. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Brown JC, Compton SL, Kang A, et al. Effects of exercise on inflammation, circulating tumor cells, and circulating tumor DNA in colorectal cancer. Journal of Sport and Health Science. 2025:101036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.(CITI) CITI. Good Clinical Practice (GCP). Accessed November 5, 2025. https://about.citiprogram.org/series/good-clinical-practice-gcp/
  • 22.Shephard RJ. Readiness for physical activity. President’s Council on Physical Fitness and Sports; 1994. [Google Scholar]
  • 23.Amin Mahul B. SBE, Greene Frederick L., Byrd David R., et al. AJCC Cancer Staging Manual, 8th Edition. vol 8. Springer; 2017. [Google Scholar]
  • 24.ClinicalTrials.gov. Pennington Center Longitudinal Study (PCLS). Accessed November 7, 2025. https://clinicaltrials.gov/study/NCT00959270
  • 25.Thompson FE, Midthune D, Subar AF, Kipnis V, Kahle LL, Schatzkin A. Development and evaluation of a short instrument to estimate usual dietary intake of percentage energy from fat. Journal of the American Dietetic Association. 2007;107(5):760–767. [DOI] [PubMed] [Google Scholar]
  • 26.Thompson FE, Midthune D, Williams GC, et al. Evaluation of a short dietary assessment instrument for percentage energy from fat in an intervention study. The Journal of nutrition. 2008;138(1):193S–199S. [DOI] [PubMed] [Google Scholar]
  • 27.Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. nature. 2008;454(7203):436–444. [DOI] [PubMed] [Google Scholar]
  • 28.Tayyib NA, Ramaiah P, Alshahrani SH, et al. Soluble receptor for advanced glycation end products (sRAGE) is associated with obesity rates: a systematic review and meta-analysis of cross-sectional study. BMC endocrine disorders. 2023;23(1):275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Feng Z, Du Z, Shu X, et al. Role of RAGE in obesity-induced adipose tissue inflammation and insulin resistance. Cell Death Discovery. 2021;7(1):305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Goldberg T, Cai W, Peppa M, et al. Advanced glycoxidation end products in commonly consumed foods. Journal of the American Dietetic Association. 2004;104(8):1287–1291. [DOI] [PubMed] [Google Scholar]
  • 31.Kang Q, Henry NL, Paoletti C, et al. Comparative analysis of circulating tumor DNA stability In K3EDTA, Streck, and CellSave blood collection tubes. Clinical biochemistry. 2016;49(18):1354–1360. [DOI] [PubMed] [Google Scholar]
  • 32.Gleghorn JP, Pratt ED, Denning D, et al. Capture of circulating tumor cells from whole blood of prostate cancer patients using geometrically enhanced differential immunocapture (GEDI) and a prostate-specific antibody. Lab on a Chip. 2010;10(1):27–29. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Adalsteinsson VA, Ha G, Freeman SS, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. Nature communications. 2017;8(1):1324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ha G, Roth A, Khattra J, et al. TITAN: inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome research. 2014;24(11):1881–1893. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Freedson PS, Melanson E, Sirard J. Calibration of the computer science and applications, Inc. accelerometer. Medicine & science in sports & exercise. 1998;30(5):777–781. [DOI] [PubMed] [Google Scholar]
  • 36.Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Medicine & Science in Sports & Exercise. 2008;40(1):181–188. [DOI] [PubMed] [Google Scholar]
  • 37.Pearson K VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. Philosophical Transactions of the Royal Society of London Series A, containing papers of a mathematical or physical character. 1896;(187):253–318. [Google Scholar]
  • 38.SAS Software for Windows. Version 9.4. Inc. SI. 2021. [Google Scholar]
  • 39.U.S. Department of Agriculture and U.S. Department of Health and Human Services. Dietary Guidelines for Americans, 2020-2025, 9th Edition. DietaryGuidelines.gov. [Google Scholar]
  • 40.Micheau O, Tschopp J. Induction of TNF receptor I-mediated apoptosis via two sequential signaling complexes. cell. 2003;114(2):181–190. [DOI] [PubMed] [Google Scholar]
  • 41.Tartaglia LA, Weber RF, Figari IS, Reynolds C, Palladino MA Jr, Goeddel DV. The two different receptors for tumor necrosis factor mediate distinct cellular responses. Proceedings of the National Academy of Sciences. 1991;88(20):9292–9296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Sheng Y, Li F, Qin Z. TNF receptor 2 makes tumor necrosis factor a friend of tumors. Frontiers in immunology. 2018;9:1170. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Babic A, Shah SM, Song M, et al. Soluble tumour necrosis factor receptor type II and survival in colorectal cancer. British journal of cancer. 2016;114(9):995–1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Neurath MF. IL-23 in inflammatory bowel diseases and colon cancer. Cytokine & growth factor reviews. 2019;45:1–8. [DOI] [PubMed] [Google Scholar]
  • 45.Carlini V, Noonan DM, Abdalalem E, et al. The multifaceted nature of IL-10: regulation, role in immunological homeostasis and its relevance to cancer, COVID-19 and post-COVID conditions. Frontiers in immunology. 2023;14:1161067. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Erusalimsky JD. The use of the soluble receptor for advanced glycation-end products (sRAGE) as a potential biomarker of disease risk and adverse outcomes. Redox Biology. 2021;42:101958. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Rock CL, Thomson CA, Sullivan KR, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors. CA: a cancer journal for clinicians. 2022;72(3):230–262. [DOI] [PubMed] [Google Scholar]
  • 48.Sargent DJ, Patiyil S, Yothers G, et al. End points for colon cancer adjuvant trials: observations and recommendations based on individual patient data from 20,898 patients enrolled onto 18 randomized trials from the ACCENT Group. Journal of Clinical Oncology. 2007;25(29):4569–4574. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data are available for download at the Pennington Biomedical Nutrition Obesity Data Repository (https://my.pbrc.edu/NORC/NORCRepository/).

RESOURCES