Abstract
Background:
Evidence suggests a positive association between sugar intake and colorectal cancer (CRC) outcomes. We sought to investigate inflammation and angiogenesis as underlying mechanisms behind increased sugar intake and worse CRC outcomes.
Method:
Pre-surgery serum samples were obtained from 191 patients diagnosed with primary invasive stage I-IV CRC. Biomarkers of inflammation (CRP, SAA, IL-6, IL-8, MCP-1, TNFα) and angiogenesis (VEGFA, VEGFD, sICAM-1 and sVCAM-1) were analyzed (Meso-Scale-Discovery). Fructose, glucose, sucrose, and total sugar intake (calories/day, % total calories) were assessed by FFQ. Pearson’s correlation and multiple linear regression analyses were performed.
Results:
Patients were on average 64 years old, 64% were male, the majority was diagnosed with stage II-III (58%) cancers, and 67% were either overweight or obese. Among normalweight individuals (BMI <25 kg/m2), we observed a significant inverse association between VEGFD and any type of sugar intake in cal/day (sucrose: p=0.01, glucose and fructose: p<0.001) and MCP-1 and fructose intake (p=0.05). The magnitude of reduction in VEGF ranged between −1.24 for sucrose to 4.49 for glucose intake, and −2.64 for fructose intake for MCP-1 levels.
Conclusion:
Sugar intake was associated with some inflammation or angiogenesis biomarkers, among CRC patients; differences were observed by adiposity that warrant further investigation.
Keywords: colorectal cancer, sugar, glucose, fructose, sucrose, diet, nutrition
INTRODUCTION
A link between high sugar intake and worse colorectal cancer (CRC)-specific outcomes including mortality has been reported by several studies [1–5]. A large cohort study of 1,201 patients revealed higher sugar-sweetened beverage and fruit juice consumption were specifically associated with worse mortality outcomes in CRC patients [1]. Similarly, specifically increased refined carbohydrate intake was associated with a higher CRC mortality rate, in a cohort of 1,542 patients followed for 4 years after diagnosis of CRC [5]. Other studies have reported that a higher glycemic load and total carbohydrate intake are associated with decreased disease-free, recurrence-free, and overall survival of CRC and that the associations are modified by BMI, showing stronger associations with higher BMI [1, 2]. Individuals with a higher BMI have generally higher levels of systemic inflammation. This low-grade systemic inflammation in overweight and obese individuals is hypothesized as underlying mechanism of the effect modification by BMI. Keum et al. observed that high insulin scores post CRC diagnosis were associated with a worse CRC survival rate, suggesting that diets rich in high glycemic foods may negatively impact CRC patients [4]. Although available evidence at this point is limited, it does suggest that dietary sugar is playing an adverse role in CRC progression and survival [4]. The underlying mechanisms of the link between sugar intake and CRC remain, yet, unclear.
Inflammation and angiogenesis, hallmarks of cancer, have consistently been associated with CRC outcomes including increased recurrence and decreased survival rates [6–9]. Inflammation aids in proliferation and survival of malignant cells, promotes angiogenesis and metastasis, and undermines immune responses [6]. Some recent research suggests that diets rich in sugar promote systemic inflammation as well as inflammation in the tumor microenvironment [6]. Results demonstrating an association between sugars and inflammatory markers would strengthen the overall evidence base.
The objective of this study was to investigate associations between sugar intake (specifically fructose, glucose, and sucrose), using both total calories (cal) from sugar and total percentage of calories from sugar, and circulating inflammation- and angiogenesis-related biomarkers in prospectively followed colorectal cancer patients.
METHODS
Study population
The present study is conducted as part of the prospective ColoCare Study (ClinicalTrials.gov Identifier: NCT02328677), an international cohort of newly diagnosed stage I–IV colorectal cancer patients (ICD-10 C18–C20) [1]. The ColoCare Study is a multicenter initiative of interdisciplinary research on colorectal cancer outcomes and prognosis and comprises patients recruited at the Fred Hutchinson Cancer Research Center, Seattle (Washington, USA), the National Center for Tumor Diseases (NCT), Heidelberg (Germany), the H. Lee Moffitt Cancer Center and Research Institute, Tampa (Florida, USA), the Cedars-Sinai Medical Center, Los Angeles (CA, USA), the St. Louis University Cancer Center, St. Louis (MO, USA) and the Huntsman Cancer Institute, Salt Lake City (Utah, USA). ColoCare inclusion criteria are as follows: patients first diagnosed with colon or rectal cancer (stages I–IV), age ≥ 18 years, English (U.S. sites) or German (German site) speaking, and mentally/physically able to consent and participate. Subjects meeting the inclusion criteria are recruited for the ColoCare Study prior to tumor surgery. Baseline examination includes anthropometric measurements, biospecimen collection (e.g., blood, stool, urine, saliva and fresh frozen tumor, normal and visceral adipose tissue) and self-administered questionnaires on symptoms, health behaviors and health-related quality of life. Subjects are followed up (a) passively by retrieving medical data from hospital records, and (b) actively at 3, 6, 12, 24, and 36 months after surgery with collection of blood, stool, urine, saliva and questionnaires on symptoms, health behaviors, health-related quality of life and dietary assessment by food frequency questionnaire.
All analyses in this manuscript are based on data collected between December 2010 and May 2014 only at the ColoCare site in Heidelberg, Germany. Eligible patients had available 6-month Food Frequency Questionnaire (FFQ) and stored baseline (pre-surgery) blood samples. The study was approved by the Ethics Committee of the University of Heidelberg and the Institutional Review Board of the University of Utah, and all subjects provided written informed consent.
Blood processing and biomarker assays
Non-fasting blood samples were collected from patients prior to surgery (baseline) at the University Clinic of Heidelberg. Serum was extracted within four hours of blood-draw and stored in aliquots at −80°C until analysis. 500μl of each patient’s serum was shipped on dry ice to Huntsman Cancer Institute (HCI, Salt Lake City, Utah, USA) for analysis. Blood samples used to measure the biomarkers in this study were stored for about 3–6 years at −80 degrees Celsius. Previous validation studies have shown that the measurement of biomarkers that were measured in this study are stable over 3–6 years if stored at −80 degrees Celsius [2–5].
Multiplexed serum-based assays for inflammation markers, including C-reactive protein (CRP), serum amyloid A (SAA), interleukin-6 (IL-6), interleukin-8 (IL-8), monocyte chemoattractant protein 1 (MCP1), and tumor necrosis factor α (TNFα), as well as angiogenesis markers, including vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor D (VEGFD), intercellular adhesion molecule 1 (SICAM-1), and vascular cell adhesion molecule 1 (SVCAM-1) have previously been established on the Mesoscale Discovery Platform (MSD, Rockville, MD, USA) in the Ulrich laboratory at Huntsman Cancer Institute [6]. Blinded patient samples plus intraplate and interplate quality control samples (QC) were assayed for MCP-1, IL-6, IL-8, and TNF-α on the U-PLEX Proinflammatory Combo 1, for CRP, SAA, sICAM-1 and sVCAM-1 on the V-PLEX Vascular Injury Plate 2, and for VEGFA and VEGFD on the V-PLEX Angiogenesis Panel 1. Serum samples were ran at dilutions of 1:1000 (Vascular Injury panel) and 1:8 (Angiogenesis panel), and the serum was freeze-thawed once. MSD sector 2400A (Meso Scale Diagnostics, Rockville, MD, USA) was used to read the plates. Data was analyzed with MSD Discovery Workbench 4.0 software (Meso Scale Diagnostics, Rockville, MD). The overall inter-plate coefficient of variability (CV) was 9.9% and intra-plate CV was 4.6%.
Sugar intake
To assess the participants’ total sugar intake, we used the validated 148-item European Prospective Investigation into Cancer and Nutrition (EPIC) Food Frequency Questionnaire (FFQ) Potsdam, which is semi-quantitative and self-administered [7–10]. The EPIC FFQ reflects the frequency and quantity of food intake for all main food groups during the past 12 months. Two versions of the FFQ with identical questions pertaining to sugar intake were used. [7]. Utilizing the EPIC-Soft software, food items were grouped into 80 classes [11]. The 80 food classes were then assigned into 25 foogroups based on nutrients or culinary usage. The food groups included four groups of vegetable intake, three groups of meat intake, one group of fruit and fish intake, four groups of fat intake, and twelve other food groups (See Supplementary Table 1). Added sugar intake frequency (derived from intake of sweet and confectionary food groups) was evaluated on a scale ranging from “never”, “one time per month or less”, “two to three times per month”, “one to two times per week”, to “three times per week or more.” Portion sizes were defined by household measures, such as measuring cups [10, 12]. The food group data were combined with consumption frequency and standard portion sizes to yield estimates of kcal consumption and grams per day of fat, protein, carbohydrates, fiber, sugar, and micronutrients, as described previously [10, 12]. Intrapersonal validity of self-reported food intakes including sugar assessed with the EPIC FFQ has previously been tested via doubly labeled water, repeated 24 hour recalls, blood and urine sample analyses [21]. They reported moderate (correlation coefficients >0.60) for total energy intake, mono- (e.g., fructose, glucose), and disaccharides (e.g., sucrose) compared with the values measured using 24-hour recalls [21].
Statistical Methods
Descriptive statistics were used to summarize the distributions of the demographic and clinical characteristics (such as age, sex, stage at diagnosis, BMI, smoking status), nutritional intakes (summarized as average kcal intake), and systemic inflammatory markers. Each biomarker was tested for normal distribution through the Shapiro-Wilk test and q-q-plot distributions, and was log2-transformed to prevent heteroscedasticity. Scatter plots were constructed for all continuous variables to detect and eliminate potential outliers.
Mean and standard deviations (SD) were calculated for continuous variables (age, BMI, and biomarker measurements), while frequencies and percentages were determined for categorical variables (sex, smoking, tumor stage, and tumor site).
Overall associations between sugar intake (both in terms of kcal/day and percentage of kcal/day) and inflammation and angiogenesis biomarkers were evaluated using multivariate linear regression models. Models were adjusted for age, sex, meat, vegetable, and fruit intake and stratified by BMI (<25 and ≥25 kg/m2). Associations between patient characteristics, measured biomarkers and sugar intake were calculated using Spearman’s and Pearson’s correlation coefficients, as appropriate both overall and by BMI (<25 and ≥25 kg/m2). Effect modification on a multiplicative scale was tested using interaction terms for each of the potential modifying variables, including BMI, age, and sex. A basic power analysis for a correlation between two variables, such as sugar level and an inflammation variable, indicated that correlations between 0.19 to 0.28 would have statistical power between 0.7 and 0.9 for sample sizes ranging from 129–168, when using a significance level of 0.05. Data analyses was conducted in SAS (Version 9.4, SAS, Cary, NC).
Inclusion and Exclusion Criteria
Similar to previous studies evaluating total energy intake as an exposure, study participants with an estimated total kcal intake of <600kcal/day or > 4000kcal/day were excluded from the final analysis (n=6) [13–16]. Additionally, study participants with improbable beverage intake values (n=1), CRP levels > 50 mg/L (n=4), and CRP levels 1.5 times over the upper reference limit (n=11) were removed from relevant analyses. Those with CRP >1.5 times the upper reference limit and a BMI of > 27.5 kg/m2 were excluded from stratified analyses (n=1), as the higher levels of CRP in these patients could be attributed to higher BMI [17, 18].
RESULTS
Characteristics of the study participants (n=191) are summarized in Table 1. The mean age of study participants was 64 years (range: 27–87 years) and 64% were male. Over half of the study population was overweight with a mean BMI slightly over 26 kg/m2 (range: 13.3–39.6 kg/m2), while 17% percent were obese (BMI >=30 kg/m2). The selected cohort was mostly diagnosed with stage II and III colorectal cancers (58% of study population), and evenly represented colon and rectum tumors. Patients reported an average intake of 2,301 calories, 202 grams of vegetables, 49.5 grams of meat, and 227 grams of fruits per day.
Table 1:
Baseline characteristics of ColoCare study participants (N=191)
| N (%) | |
|---|---|
| Sex | |
| Female | 69 (36) |
| Male | 122 (64) |
| Age (years) | |
| Mean ± SD | 64 ± 12 |
| <50 | 20 (10) |
| 50–60 | 55 (29) |
| >60 | 116 (61) |
| BMI (kg/m 2 ) | |
| Mean ± SD | 26 ± 4 |
| <18.5 | 3 (2) |
| 18.5 – 24.9 | 60 (31) |
| 25 – 29.9 | 94 (50) |
| ≥ 30 | 33 (17) |
| Total calories/day | 2,301 ± 673 |
| Dietary intakes, grams/day | |
| Vegetable intake | 202 ± 119 |
| Meat intakea | 49.5 ± 33.3 |
| Fruit intake | 227 ± 211 |
| Tumor stage | |
| I | 35 (18) |
| II | 64 (32) |
| III | 49 (26) |
| IV | 26 (14) |
| Tumor site | |
| Colon | 86 (45) |
| Rectosigmoid and rectum | 105 (55) |
includes processed and red meat intake
Table 2 summarizes the total sugar intake as well as the mean distributions of the inflammatory and angiogenesis biomarkers of the study participants. The distribution of total intake (in calories/day) of sucrose (63–779 calories/day), glucose (19–370 calories/day), and fructose (22–511 calories/day) varied greatly. As a percentage of total calories, sucrose, glucose, and fructose intake ranged from 0–20%, 0–10%, and 0–20%, respectively. The distribution of many of the systemic inflammatory markers were skewed with long tails, as was expected. However, once the inflammatory markers were log transformed, distribution of all the biomarkers was normalized.
Table 2:
Sugar intake and levels of inflammation and angiogenesis-related biomarkers
| Sugar intake, mean ± SD (range) | |
| Calories/day | |
| Sucrose | 256 ± 119 (63–779) |
| Glucose | 84.0 ± 51.6 (19–370) |
| Fructose | 109 ± 66.0 (22–511) |
| Percentage of calories per day/ total calories | |
| Sucrose | 10% ± 3% (0.0–20) |
| Glucose | 0.0% ± 2% (0.0–10) |
| Fructose | 0.0% ± 2% (0.0–20) |
| Inflammation and angiogenesis-related biomarkers, mean ± SD (range) | |
| CRP (mg/L) | 4.4 ± 4 (0.2–23) |
| SAA (mg/L) | 9.2 ± 15 (0.6–120) |
| sICAM-1 (mg/L) | 0.4 ± 0.16 (0.2–1.5) |
| sVCAM-1 (mg/L) | 0.6 ± 0.19 (0.3–1.6) |
| VEGFA (pg/L) | 796 ± 589 (93–3,506) |
| VEGFD (pg/L) | 857 ± 303 (226–1944) |
| IL-6 (pg/ml) | 1.8 ± 10 (0.2–117) |
| IL-8 (pg/ml) | 26.9 ± 90 (22.4–1116) |
| MCP-1 (pg/ml) | 184 ± 108 (22.4–1116) |
| TNFα (pg/L) | 1.2 ± 0.49 (0.4–3.5) |
Abbreviations: standard deviation (SD), C-reactive protein (CRP), serum amyloid A (SAA), interleukin-6 (IL-6), interleukin-8 (IL-8), monocyte chemoattractant protein 1 (MCP1), and tumor necrosis factor α (TNFα), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor D (VEGFD), intercellular adhesion molecule 1 (SICAM-1), and vascular cell adhesion molecule 1 (SVCAM-), picogram (pg), liter (L), milligram (mg)
The EPIC FFQ divided food into 17 broad groups. Of these, the groups that contributed to sugar intake in the diet were as follows: 1) The main sources of sucrose in the diet were the “sugar and confectionary”, “dairy”, and “fruit” food groups. Together, these groups totaled 68% of total sucrose intake; 2) The main sources of glucose in the diet were the “fruit”, “non-alcoholic beverages”, and “sugar and confectionary” food groups, with a combined contribution of 71% of total glucose intake; and 3). The main contributors of fructose in the diet were the “non-alcoholic beverages”, “fruit”, and “sugar and confectionary” food groups, and these groups contributed to 80% of total fructose intake (data not shown).
Multiple linear regression models evaluating linear associations between sugar intake and inflammation- and angiogenesis-related biomarkers adjusted by age and sex and stratified by BMI status (<25 kg/m2 vs. ≥25 kg/m2) are outlined in Table 3. Among normal weight individuals (BMI <25 kg/m2), we observed a significant inverse association between sugar intake of any type (sucrose, glucose, fructose) and VEGFD and fructose intake and MCP-1 levels. As such, increasing sugar intake (computed as either as calories/day or a % of total calories) was statistically significantly associated with a decrease in circulating VEGF and MCP-1 levels after adjustment for age, sex, meat, fruit, and vegetable intake. The magnitude of reductions in VEGF were
Table 3:
Multiple linear regression analysis between sugar intake and inflammation- and angiogenesis-related biomarkers stratified by BMI and adjusted for age, sex, meat, fruit, and vegetable intake
| Biomarker | Sucrose (cal/day) | Glucose (cal/day) | Fructose (cal/day) | Sucrose (total cals, %) | Glucose (total cals, %) | Fructose (total cals, %) |
|---|---|---|---|---|---|---|
| BMI <25 kg/m 2 | ||||||
| ß (p) | ß (p) | ß (p) | ß (p) | ß (p) | ß (p) | |
| CRP | −0.30 (0.88) | 0.52 (0.91) | 1.39 (0.75) | −0.03 (0.65) | −0.04 (0.81) | −0.02 (0.90) |
| SAA | −1.08 (0.46) | −2.76 (0.41) | −2.11 (0.52) | −0.03 (0.58) | −0.09 (0.45) | −0.07 (0.56) |
| sICA-1 | −0.37 (0.41) | −0.62 (0.57) | −0.67 (0.52) | −0.01 (0.73) | 0.00 (0.94) | 0.00 (0.92) |
| sVCAM-1 | 0.19 (0.63) | 0.51 (0.58) | 0.44 (0.62) | 0.00 (0.74) | 0.01 (0.71) | 0.01 (0.81) |
| VEGFA | 0.32 (0.73) | 0.21 (0.94) | −0.01 (1.00) | 0.02 (0.65) | 0.00 (0.97) | −0.01 (0.95) |
| VEGFD | −1.24 (0.01) * | −4.49 (<0.001) * | −3.80 (<0.001) * | −0.04 (0.07) | −0.14 (0.02) * | −0.14 (0.01) * |
| IL-6 | −1.02 (0.48) | −4.24 (0.19) | −3.94 (0.21) | −0.03 (0.60) | −0.15 (0.18) | −0.16 (0.17) |
| IL-8 | 0.36 (0.76) | −2.31 (0.42) | −1.97 (0.48) | 0.03 (0.48) | −0.09 (0.35) | −0.10 (0.33) |
| MCP-1 | −1.07 (0.07) | −2.63 (0.06) | −2.64 (0.05) * | −0.04 (0.09) | −0.08 (0.10) | −0.10 (0.06) |
| TNFα | −0.35 (0.62) | −2.17 (0.19) | −1.46 (0.35) | −0.01 (0.79) | −0.06 (0.25) | −0.04 (0.40) |
| BMI ≥ 25kg/m 2 | ||||||
| CRP | 1.64 (0.36) | 4.32 (0.36) | 3.34 (0.44) | 0.10 (0.04) * | 0.14 (0.22) | 0.14 (0.20) |
| SAA | −2.50 (0.17) | −5.82 (0.23) | −5.13 (0.25) | −0.03 (0.61) | −0.08 (0.50) | −0.04 (0.71) |
| sICAM-1 | −0.33 (0.53) | 0.39 (0.79) | 0.34 (0.80) | 0.00 (0.75) | 0.02 (0.64) | 0.02 (0.61) |
| sVCAM-1 | −0.07 (0.89) | 2.52 (0.06) | 1.48 (0.22) | 0.01 (0.67) | 0.08 (0.01) * | 0.06 (0.06) |
| VEGFA | −0.89 (0.42) | −1.55 (0.52) | −1.42 (0.56) | −0.02 (0.59) | −0.04 (0.56) | −0.03 (0.68) |
| VEGFD | −0.46 (0.41) | 0.45 (0.72) | 0.15 (0.91) | −0.01 (0.50) | 0.03 (0.43) | 0.02 (0.58) |
| IL-6 | 0.06 (0.97) | 3.72 (0.19) | 0.93 (0.74) | −0.01 (0.84) | 0.06 (0.39) | −0.03 (0.75) |
| IL-8 | 1.25 (0.47) | 6.72 (0.08) | 5.23 (0.17) | 0.00 (0.95) | 0.12 (0.24) | 0.05 (0.62) |
| MCP-1 | 0.88 (0.24) | 2.26 (0.18) | 2.12 (0.21) | 0.03 (0.23) | 0.05 (0.27) | 0.04 (0.37) |
| TNFα | −0.35 (0.56) | 0.61 (0.64) | 0.27 (0.84) | −0.01 (0.40) | 0.02 (0.64) | 0.00 (0.89) |
Abbreviations: C-reactive protein (CRP), serum amyloid A (SAA), interleukin-6 (IL-6), interleukin-8 (IL-8), monocyte chemoattractant protein 1 (MCP1), and tumor necrosis factor α (TNFα), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor D (VEGFD), intercellular adhesion molecule 1 (SICAM-1), and vascular cell adhesion molecule 1 (SVCAM-1)
denotes copy above kcal/day equates to per 1000 calories of specified sugar per day.
Note: biomarkers levels were log-2 transformed to prevent homoscedasticity
−1.24 (p=0.01) and −0.04 (p=0.07) for sucrose intake, −4.49 (p<0.001) and −0.14 (p=0.02) for glucose intake, and −3.80 (p<0.001) and −0.14 (p=0.01) for fructose intake (in cal/day and % of total cals, respectively). The magnitude of reduction in MCP-1 levels was −2.64 (p=0.05) for fructose intake in cal/day. Among overweight and obese individuals (BMI ≥25 kg/m2), increased intake of glucose was statistically significantly associated with increased levels of sVCAM-1 and sucrose intake with increased levels of CRP, both when expressed as percentage of total caloric intake.
Overall, no significant correlation was observed between sugar intake and pro-inflammatory and -angiogenesis biomarkers or BMI (Supplementary Table S1). The correlation of sugar intake (sucrose, glucose, fructose) with BMI ranged from −0.04 to 0.05, while that with all measured inflammation and angiogenesis biomarkers ranged from −0.20 to 0.14. Since inflammation levels differ by obesity, we further explored whether correlations between sugar intake (calories/day) and inflammation- and angiogenesis-biomarkers differed by BMI (<25 and ≥25 kg/m2) through stratified analyses, results of which are shown in Supplementary Table S2. After stratifying by BMI, VEGFD and MCP-1 were moderately inversely correlated with sugar intake (sucrose, glucose, fructose) in calories/day such that correlations were statistically significant and ranged from −0.37 to −0.42 for VEGFD and −0.26 to −0.3 for MCP-1 in those with BMI <25 kg/m2. These statistically significant inverse correlations were consistent across sugar types and the magnitude of the correlation was very similar. Further adjustment for total caloric intake did not change the magnitude or the direction of these correlations with VEGFD and MCP-1. Among persons with BMI > 25 kg/m2, only sVCAM-1 was significantly positively correlated with increased glucose and fructose intake as a percentage of total calories (0.29 and 0.22 for glucose and sucrose intake as a % of total calories, respectively; see Supplementary Table S2), however this correlation was only moderately strong. This inflammatory marker was not correlated with sugar intake among those with BMI < 25 kg/m2.
DISCUSSION
To our knowledge, this is the first study to assess the association between sugar intake and circulating inflammation- and angiogenesis-related biomarkers in a prospective cohort of patients diagnosed with colorectal cancer. All samples were collected prior to surgery, eliminating inflammatory effects resulting from treatment. Although we did not observe statistically significant associations between sucrose, fructose, or glucose intake and systemic inflammatory and angiogenesis markers overall, we observed associations between sugar intake and biomarkers of inflammation and angiogenesis (VEGFD and MCP-1) in leaner individuals with BMI <25 kg/m2. We did not observe statistically significant correlations, except for sVCAM-1 and CRP, between sugar intake and selected inflammatory/angiogenesis biomarkers measured among individuals with a BMI over 25 kg/m2.
It has been hypothesized that increased dietary sugar intake may upregulate pro-inflammatory pathways in humans [19]. Fructose is of interest since its metabolism in the liver bypasses a regulatory step – exposure to phosphofructokinase, unlike glucose and galactose [20]. By bypassing phosphofructokinase, a regulatory enzyme in glycolysis, high intake of fructose can result in dyslipidemia [20]. Fructose metabolism also occurs independently of insulin, unlike glucose. Additionally, fructose seems to induce hepatic and extrahepatic insulin resistance [21]. These metabolic findings support the role of fructose in obesity and metabolic syndrome [20], and may predispose to cancer development and progression. However, in a meta-analysis of 13 feeding studies, the effects of sugar intake on clinical inflammation markers were inconclusive [19].
Previous studies have evaluated the role of sugar intake in cancer incidence as well as prognosis, including that of colorectal cancer [22]. A higher intake of sugars has been linked to an elevated risk of colorectal cancer, however, results for associations between dietary sugar intake and survival after a colorectal cancer diagnosis have been unequivocal [23]. Zhu et al. reported no association between high sugar consumption and disease-free survival of CRC; however, the investigators assessed diet only in the year prior to diagnosis [24]. In contrast, Fung et al. demonstrated increased sugar-sweetened beverage intake to lower overall mortality among women with CRC [25]. Increased sugar consumption has also been associated with increased CRC recurrence in two previous studies, and this effect was modified by BMI [26, 27].
Of note, past studies have only evaluated sugar consumption in relation to systemic and tumor-specific inflammation. Here, we also report on associations with biomarkers of angiogenesis [28]. VEGF is a known angiogenesis biomarker [29]. Previous research has associated increased VEGF levels with worse CRC prognosis [30]. In this study, we observed an inverse association between both VEGF and MCP-1 and sugar intake among those with a BMI <25m2/kg. MCP-1 is a chemokine monocyte chemoattractant protein that promotes processes involved in tumor development, progression, and invasion [31]. Elevated sugar intake has been suggested to trigger pro-inflammatory and subsequently, tumor-promoting activities. Hence, contrary to our results, a positive association between increased sugar intake and VEGF, MCP-1 would have been expected. As no previously published study has evaluated the association between higher dietary sugar intake and these angiogenesis biomarkers among cancer patients, evidence supporting or contradicting our results is lacking. MCP-1 expression is suggested to be induced by VEGF, which aligns with our other observed associations [28]. Further studies, including investigation of this observation in animal models, are warranted. Obesity, increased amounts of visceral adipose tissue, and poor diet are all known to increase systemic inflammation and CRC risk [32]. In patients with a BMI ≥ 25kg/m2, increased sugar intake may act predominately to increase inflammation and, in turn, influence cancer risk and progression. Consistent with this hypothesis, in the current report, we observed positive correlation between increased intake of fructose and glucose with sVCAM-1, a biomarker for angiogenesis.
Our study is limited by the assessment of dietary intake via FFQ [7, 8]. In general, sugar intake would be expected to be underreported in an FFQ based on previous validation. The FFQ used in this study was previously validated against two 24-hour dietary recalls, presenting valid results for self-reported total energy intake and sugar intake-related nutrients [9, 10]. Additionally, the reported sugar intake of our patients aligns with average sugar intakes of the general German population (61–78g/day) [40]. Although the FFQ was collected at 6 months post-surgery, it assesses dietary habits over 12 months. However, we recognize that patients may have changed their diet in response to their cancer diagnosis. Therefore, there is a possibility of measurement error in our assessment of sugar intake. Several other food components besides dietary sugar can affect the inflammation axis through their pro- or anti-inflammatory properties [33]. Hence, there is a possibility of residual confounding by measured as well as unmeasured confounders. There is also a possibility of measurement error for the biomarkers measured in this study. However, we used a well-validated panel of biomarkers that has been shown to have good intra- and inter-batch reliability [12, 13]. Besides, any bias that may be introduced due to the lab measurement error would be non-differential and therefore would only bias our results towards the null. Our study has several strengths. The ColoCare Study is a well-annotated cohort of CRC patients with comprehensive FFQ data. The large study sample size allowed for stratification by BMI. Additionally, availability of biospecimens at diagnosis enabled measurement of a set of ten different biomarkers to assess inflammation and angiogenesis.
In summary, the current study aimed to evaluate the association of dietary sugar intake patterns with systemic inflammatory and angiogenesis markers in a prospective cohort of CRC patients. No associations were seen between sugar intake patterns and systemic inflammatory and angiogenesis markers overall. In stratified analyses, we observed moderate inverse correlations (that reached statistical significance) between sugar intake and anti-inflammatory biomarkers, particularly VEGFD and MCP1, in leaner individuals with a BMI < 25kg/m2. This study enhances our understanding of the role of dietary factors, particularly sugar intake, in inflammation among patients with colorectal cancer.
Supplementary Material
ACKNOWLEDGEMENTS
We thank our collaborators on the ColoCare recruitment, Jenny Chang-Claude, and Michael Hoffmeister. We are grateful to all the study staff who have made this study possible, especially Torsten Kölsch, Susanne Jakob, Stefanie Skender, Werner Diehl, Rifraz Farook, Anett Brendel, Marita Wenzel and Renate Skatula. We also thank Heiner Boeing, PhD, MPH from the German Institute of Human Nutrition for his support of the nutritional analyses.
Funding
This study was supported by National Institutes of Health (NIH) U01 CA206110, NIH K07 CA222060, R01 CA207371, and R01 CA189184, the German Consortium of Translational Cancer Research (DKTK) the German Cancer Research Center (Division of Preventive Oncology, C. M. Ulrich), and the Matthias Lackas Foundation. C. Himbert is supported by NIH R01 CA211705, the Stiftung LebensBlicke and Claussen-Simon Stiftung, Germany. C. Himbert, Lin T, Warby C.A., J. Ose, and C.M. Ulrich are funded by the Huntsman Cancer Foundation.
DELCARATION OF INTEREST STATEMENT
There are no conflicts of interests to be declared. Dr. Ulrich has as cancer center director oversight over research funded by several pharmaceutical companies, but has not received funding directly herself.
REFERENCES
- 1.Ulrich CM, Gigic B, Bohm J, Ose J, Viskochil R, Schneider M, Colditz GA, Figueiredo JC, Grady WM, Li CI, et al. : The ColoCare Study: A Paradigm of Transdisciplinary Science in Colorectal Cancer Outcomes. Cancer Epidemiol Biomarkers Prev 2019, 28:591–601. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Navarro SL, Brasky TM, Schwarz Y, Song X, Wang CY, Kristal AR, Kratz M, White E, Lampe JW: Reliability of serum biomarkers of inflammation from repeated measures in healthy individuals. Cancer Epidemiol Biomarkers Prev 2012, 21:1167–1170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lee S-A, Kallianpur A, Xiang Y-B, Wen W, Cai Q, Liu D, Fazio S, Linton MF, Zheng W, Shu XO: Intra-individual Variation of Plasma Adipokine Levels and Utility of Single Measurement of These Biomarkers in Population-Based Studies. Cancer Epidemiology Biomarkers & Prevention 2007, 16:2464–2470. [DOI] [PubMed] [Google Scholar]
- 4.Eschen O, Christensen JH, Dethlefsen C, Schmidt EB: Cellular Adhesion Molecules in Healthy Subjects: Short Term Variations and Relations to Flow Mediated Dilation. Biomarker insights 2008, 3:57–62. [PMC free article] [PubMed] [Google Scholar]
- 5.Hardikar S, Song X, Kratz M, Anderson GL, Blount PL, Reid BJ, Vaughan TL, White E: Intraindividual variability over time in plasma biomarkers of inflammation and effects of long-term storage. Cancer Causes Control 2014, 25:969–976. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Ulrich CM, Boehm J, Warby CA, Lin T, Salou M, Gigic B, Scherer D, Nattenmueller J, Ose J, Zielske L, et al. : Body fatness and adipose tissue subtypes are associated with biomarkers of inflammation and angiogenesis in colorectal cancer patients: the ColoCare study. In AACR MEG special conference: Improving Cancer Risk Prediction for Prevention and Early Detection2016 [Google Scholar]
- 7.Nothlings U, Hoffmann K, Bergmann MM, Boeing H: Fitting portion sizes in a self-administered food frequency questionnaire. J Nutr 2007, 137:2781–2786. [DOI] [PubMed] [Google Scholar]
- 8.Boeing H, Bohlscheid-Thomas S, Voss S, Schneeweiss S, Wahrendorf J: The relative validity of vitamin intakes derived from a food frequency questionnaire compared to 24-hour recalls and biological measurements: results from the EPIC pilot study in Germany. European Prospective Investigation into Cancer and Nutrition. Int J Epidemiol 1997, 26 Suppl 1:S82–90. [DOI] [PubMed] [Google Scholar]
- 9.Kroke A, Klipstein-Grobusch K, Voss S, Möseneder J, Thielecke F, Noack R, Boeing H: Validation of a self-administered food-frequency questionnaire administered in the European Prospective Investigation into Cancer and Nutrition (EPIC) Study: comparison of energy, protein, and macronutrient intakes estimated with the doubly labeled water, urinary nitrogen, and repeated 24-h dietary recall methods. The American Journal of Clinical Nutrition 1999, 70:439–447. [DOI] [PubMed] [Google Scholar]
- 10.Gigic B, Boeing H, Toth R, Bohm J, Habermann N, Scherer D, Schrotz-King P, Abbenhardt-Martin C, Skender S, Brenner H, et al. : Associations Between Dietary Patterns and Longitudinal Quality of Life Changes in Colorectal Cancer Patients: The ColoCare Study. Nutr Cancer 2018, 70:51–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Voss S, Charrondiere UR, Slimani N, Kroke A, Riboli E, Wahrendorf J, Boeing H: [EPIC-SOFT a European computer program for 24-hour dietary protocols]. Z Ernahrungswiss 1998, 37:227–233. [DOI] [PubMed] [Google Scholar]
- 12.Engeset D, Alsaker E, Ciampi A, Lund E: Dietary patterns and lifestyle factors in the Norwegian EPIC cohort: the Norwegian Women and Cancer (NOWAC) study. Eur J Clin Nutr 2005, 59:675–684. [DOI] [PubMed] [Google Scholar]
- 13.Mazzeo T, Roncoroni L, Lombardo V, Tomba C, Elli L, Sieri S, Grioni S, Bardella MT, Agostoni C, Doneda L, et al. : Evaluation of a Modified Italian European Prospective Investigation into Cancer and Nutrition Food Frequency Questionnaire for Individuals with Celiac Disease. J Acad Nutr Diet 2016, 116:1810–1816. [DOI] [PubMed] [Google Scholar]
- 14.Ruttgers D, Fischer K, Koch M, Lieb W, Muller HP, Jacobs G, Kassubek J, Nothlings U: Association of food consumption with total volumes of visceral and subcutaneous abdominal adipose tissue in a Northern German population. Br J Nutr 2015, 114:1929–1940. [DOI] [PubMed] [Google Scholar]
- 15.McKeown NM, Troy LM, Jacques PF, Hoffmann U, O’Donnell CJ, Fox CS: Whole- and refined-grain intakes are differentially associated with abdominal visceral and subcutaneous adiposity in healthy adults: the Framingham Heart Study. Am J Clin Nutr 2010, 92:1165–1171. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Moskal A, Pisa PT, Ferrari P, Byrnes G, Freisling H, Boutron-Ruault MC, Cadeau C, Nailler L, Wendt A, Kuhn T, et al. : Nutrient patterns and their food sources in an International Study Setting: report from the EPIC study. PLoS One 2014, 9:e98647. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Wener MH, Daum PR, McQuillan GM: The influence of age, sex, and race on the upper reference limit of serum C-reactive protein concentration. J Rheumatol 2000, 27:2351–2359. [PubMed] [Google Scholar]
- 18.Pierce BL, Neuhouser ML, Wener MH, Bernstein L, Baumgartner RN, Ballard-Barbash R, Gilliland FD, Baumgartner KB, Sorensen B, McTiernan A, Ulrich CM: Correlates of circulating C-reactive protein and serum amyloid A concentrations in breast cancer survivors. Breast Cancer Res Treat 2009, 114:155–167. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Della Corte KW, Perrar I, Penczynski KJ, Schwingshackl L, Herder C, Buyken AE: Effect of Dietary Sugar Intake on Biomarkers of Subclinical Inflammation: A Systematic Review and Meta-Analysis of Intervention Studies. Nutrients 2018, 10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Tappy L, Le KA: Metabolic effects of fructose and the worldwide increase in obesity. Physiol Rev 2010, 90:23–46. [DOI] [PubMed] [Google Scholar]
- 21.Dirlewanger M, Schneiter P, Jequier E, Tappy L: Effects of fructose on hepatic glucose metabolism in humans. Am J Physiol Endocrinol Metab 2000, 279:E907–911. [DOI] [PubMed] [Google Scholar]
- 22.Michaud DS, Fuchs CS, Liu S, Willett WC, Colditz GA, Giovannucci E: Dietary glycemic load, carbohydrate, sugar, and colorectal cancer risk in men and women. Cancer Epidemiol Biomarkers Prev 2005, 14:138–147. [PubMed] [Google Scholar]
- 23.van Zutphen M, Kampman E, Giovannucci EL, van Duijnhoven FJB: Lifestyle after Colorectal Cancer Diagnosis in Relation to Survival and Recurrence: A Review of the Literature. Curr Colorectal Cancer Rep 2017, 13:370–401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Zhu Y, Wu H, Wang PP, Savas S, Woodrow J, Wish T, Jin R, Green R, Woods M, Roebothan B, et al. : Dietary patterns and colorectal cancer recurrence and survival: a cohort study. BMJ Open 2013, 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Fung TT, Kashambwa R, Sato K, Chiuve SE, Fuchs CS, Wu K, Giovannucci E, Ogino S, Hu FB, Meyerhardt JA: Post diagnosis diet quality and colorectal cancer survival in women. PLoS One 2014, 9:e115377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Fuchs MA, Sato K, Niedzwiecki D, Ye X, Saltz LB, Mayer RJ, Mowat RB, Whittom R, Hantel A, Benson A, et al. : Sugar-sweetened beverage intake and cancer recurrence and survival in CALGB 89803 (Alliance). PLoS One 2014, 9:e99816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Meyerhardt JA, Sato K, Niedzwiecki D, Ye C, Saltz LB, Mayer RJ, Mowat RB, Whittom R, Hantel A, Benson A, et al. : Dietary Glycemic Load and Cancer Recurrence and Survival in Patients with Stage III Colon Cancer: Findings From CALGB 89803. JNCI Journal of the National Cancer Institute 2012, 104:1702–1711. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Liu H, Heaney AP: Refined fructose and cancer. Expert Opin Ther Targets 2011, 15:1049–1059. [DOI] [PubMed] [Google Scholar]
- 29.Saharinen P, Tammela T, Karkkainen MJ, Alitalo K: Lymphatic vasculature: development, molecular regulation and role in tumor metastasis and inflammation. Trends Immunol 2004, 25:387–395. [DOI] [PubMed] [Google Scholar]
- 30.Di Caro G, Carvello M, Pesce S, Erreni M, Marchesi F, Todoric J, Sacchi M, Montorsi M, Allavena P, Spinelli A: Circulating Inflammatory Mediators as Potential Prognostic Markers of Human Colorectal Cancer. PLoS One 2016, 11:e0148186. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yadav A, Saini V, Arora S: MCP-1: chemoattractant with a role beyond immunity: a review. Clin Chim Acta 2010, 411:1570–1579. [DOI] [PubMed] [Google Scholar]
- 32.Marmol I, Sanchez-de-Diego C, Pradilla Dieste A, Cerrada E, Rodriguez Yoldi MJ: Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer. Int J Mol Sci 2017, 18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.World Cancer Research Fund: Food Nutrition, Physical Activity, and the Prevention of Cancer: A global Perspective. Washington, D.C.: AICR; 2007. [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.