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Published in final edited form as: Int J Cancer. 2022 Apr 22;151(3):348–360. doi: 10.1002/ijc.34015

Diabetes mellitus in relation to colorectal tumor molecular subtypes - a pooled analysis of more than 9,000 cases

Sophia Harlid 1,*, Bethany Van Guelpen 1,2, Conghui Qu 3, Björn Gylling 4, Elom K Aglago 5, Efrat L Amitay 6, Hermann Brenner 6,7,8, Daniel D Buchanan 9,10,11, Peter T Campbell 12, Yin Cao 13,14,15, Andrew T Chan 16,17,18,19,20,21, Jenny Chang-Claude 22,23, David A Drew 18, Jane C Figueiredo 24,25, Amy J French 26, Steven Gallinger 27, Marios Giannakis 19,28,29, Graham G Giles 30,31,32, Marc J Gunter 5, Michael Hoffmeister 6, Li Hsu 3,33, Mark A Jenkins 34, Yi Lin 3, Victor Moreno 35,36,37,38, Neil Murphy 5, Polly A Newcomb 3,39, Christina C Newton 40, Jonathan A Nowak 41, Mireia Obón-Santacana 35,36,37, Shuji Ogino 19,20,41,42, John D Potter 3,39,43, Mingyang Song 16,18,44, Robert S Steinfelder 3, Wei Sun 3, Stephen N Thibodeau 45, Amanda E Toland 46, Tomotaka Ugai 20,41, Caroline Y Um 40, Michael O Woods 47, Amanda I Phipps 3,48, Tabitha Harrison 3,48, Ulrike Peters 3,48
PMCID: PMC9251811  NIHMSID: NIHMS1815095  PMID: 35383926

Abstract

Diabetes is an established risk factor for colorectal cancer. However, colorectal cancer is a heterogeneous disease and it is not well understood whether diabetes is more strongly associated with some tumor molecular subtypes than others. A better understanding of the association between diabetes and colorectal cancer according to molecular subtypes could provide important insights into the biology of this association.

We used data on lifestyle and clinical characteristics from the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), including 9,756 colorectal cancer cases (with tumor marker data) and 9,985 controls, to evaluate associations between reported diabetes and risk of colorectal cancer according to molecular subtypes. Tumor markers included BRAF and KRAS mutations, microsatellite instability and CpG island methylator phenotype.

In the multinomial logistic regression model, comparing colorectal cancer cases to cancer-free controls, diabetes was positively associated with colorectal cancer regardless of subtype. The highest OR estimate was found for BRAF-mutated colorectal cancer, n=1,086 (ORfully adj: 1.67, 95% CI: 1.36-2.05), with an attenuated association observed between diabetes and colorectal cancer without BRAF-mutations, n=7,959 (ORfully adj: 1.33, 95% CI: 1.19-1.48). In the case only analysis, BRAF-mutation was differentially associated with diabetes (pdifference=0.03). For the other markers, associations with diabetes were similar across tumor subtypes.

In conclusion, our study confirms the established association between diabetes and colorectal cancer risk, and suggests that it particularly increases the risk of BRAF-mutated tumors.

Keywords: Diabetes, Colorectal Cancer, Subtype

Graphical Abstract

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INTRODUCTION

Metabolic health, and excess body fat in particular, are involved in the development of colorectal cancer 1–3. Individuals with diabetes mellitus, especially those with type 2 diabetes, have an increased risk of developing colorectal cancer 4. This connection is likely independent of shared risk factors between diabetes and colorectal cancer 5. Instead, the association between diabetes and colorectal cancer may depend on other mechanisms such as alterations to the gut microbiome, increased inflammation in the gut, hyperinsulinemia in early stage type 2 diabetes, and activation of cancer promoting pathways 6.

Colorectal cancer is a heterogeneous disease, displaying considerable differences in molecular markers, which correlate with anatomical tumor location and other clinical and patient characteristics. For example, BRAF-mutations are more common in proximal colon cancer, older patients and women, and often co-occur with high-level microsatellite instability (MSI) 7. This raises the question of whether the risk factors for colorectal cancer also vary by tumor molecular markers. Investigations into potentially variable associations of metabolic factors and molecular subtypes of colorectal cancer have been inconsistent, but some reports suggest a possible association between adiponectin and lower risk of KRAS-mutated colorectal cancer 8,9. Adiponectin is known to have anti-inflammatory effects and has been suggested as a potential treatment for obesity and type 2 diabetes 10. Although studies have examined associations between body mass index (BMI) and different colorectal cancer phenotypes 11,12, to our knowledge, no studies have investigated diabetes in relation to the risk of molecular subtypes of colorectal cancer, despite the fact that high BMI is one of the strongest known predictors of type 2 diabetes risk 13.

The aim of the present study was to investigate self-reported diabetes status in relation to molecular tumor traits in colorectal cancer (BRAF and KRAS mutations, MSI status and CpG island methylator phenotype (CIMP)). To achieve this, we used pooled data and tissue samples from the Colorectal Cancer Family Registry (CCFR) and the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), comprising a total of 9,756 colorectal cases, with tumor marker data, and 9,985 colorectal cancer free controls, all with information about self-reported diabetes status.

METHODS

Study Participants

The study population consisted of individuals diagnosed with colorectal cancer and controls from observational studies within GECCO and the CCFR, with available tumor marker data and self-reported information about diabetes status (not distinguishing between type 1 and type 2) (Supplementary Materials and Supplementary Table S1). Specifically, 9,756 CRC cases had characterization of MSI status, BRAF and KRAS mutations, and CIMP status.

Studies with MSI, BRAF, KRAS, and CIMP data included the Seattle, Ontario, Australia, and Mayo Clinic sites from the Colon Cancer Family Registry (CCFR), the Cancer Prevention Study-II (CPS-II), the German Darmkrebs: Chancen der Verhütung durch Screening Study (DACHS), the Swedish centers of the European Prospective Investigation into Cancer and Nutrition study (EPIC_Sweden), the Health Professionals Follow-up Study (HPFS), the Melbourne Collaborative Cohort Study (MCCS), the Newfoundland Colorectal Cancer Registry (NFCCR), the Nurses’ Health Study (NHS) and the Northern Sweden Health and Disease Study (NSHDS, an EPIC partner but with no overlap with EPIC-Sweden in this study).

Tumor Marker Data

Collection and Harmonization of MSI Status CIMP Status, and BRAF and KRAS Mutations

Data collection and harmonization of GECCO and CCFR tumor marker data have been described elsewhere 14,15. Details on the analytical approach are described in the Supplementary Materials.

Briefly, MSI testing was primarily conducted using polymerase chain reaction (PCR) following the guidelines of the National Cancer Institute Bethesda Consensus Panel (CCFR, CPS-II, MCCS, NHS) 16 with ≥ 4 interpretable markers typically required to classify tumors (Supplemental Table S2). DACHS used a mononucleotide panel of 4 markers. Tumors were classified as MSI-high if at least 30% of the markers showed instability and non-MSI-high if less than 30% of the makers showed instability. Other studies used immunohistochemistry (NSHDS, EPIC-Sweden and subsets of CCFR and MCCS).

CIMP status was determined using methylation analyses as described in the Supplementary Materials and Supplementary Table S3. Briefly, the CCFR, CPS-II, HPFS, MCCS, NSHDS, EPIC Sweden and NHS used MethyLight to determine CIMP status. CPS-II, HPFS, NSHDS, EPIC Sweden and NHS used an 8-gene panel; CCFR and MCCS used a 5-gene panel. DACHS determined CIMP status using a different 5-gene panel and methods described by Warth et al. 17. We created 2 CIMP categories for this analysis: CIMP-high and CIMP-low/negative. In instances in which studies categorized tumors as CIMP-high, CIMP-low, and CIMP-negative, we combined CIMP-low and CIMP-negative into the CIMP-low/negative category.

Studies assessed BRAF and KRAS mutations using PCR, sequencing, and immunohistochemistry (Supplementary Materials). Most studies evaluated BRAF via c.1799T>A (V600E) mutations in exon 15 and KRAS via mutations in codons 12 and 13, although any mutation identified by one of the studies in BRAF and KRAS genes was included.

We further defined five combined colorectal tumor subtypes consistent with previously suggested classifications 18–20: Type 1 (MSI-high, CIMP-high, BRAF-mutated, KRAS-wildtype), Type 2 (non MSI-high, CIMP-high, BRAF-mutated, KRAS-wildtype), Type 3 (non MSI-high, CIMP-low/negative, BRAF-wildtype, KRAS-mutated), Type 4 (non MSI-high, CIMP-low/negative, BRAF-wildtype, KRAS-wildtype), Type 5 (MSI-high, CIMP-low/negative, BRAF-wildtype, KRAS-wildtype. Given our large sample size, we grouped additional marker combinations together into eleven additional types including: Type 6 (non MSI-high, CIMP-low/negative, BRAF-mutated, KRAS-wildtype), Type 8 (non MSI-high, CIMP-high, BRAF-wildtype, KRAS-mutated), Type 9 (MSI-high, CIMP-low/negative, BRAF-wildtype, KRAS-mutated), Type 11 (non MSI-high, CIMP-low/negative, BRAF-wild type, KRAS-mutated), and Type 14 (MSI-high, CIMP-high, BRAF-wild type, KRAS-wild type). Subtypes with fewer than 50 cases were dropped from analyses or were grouped together with the other marker combinations. These included: Type 7 (non MSI-high, CIMP-high, BRAF-wildtype, KRAS-wildtype), Type 10 (MSI-high, CIMP-high, BRAF-wildtype, KRAS-wildtype), Type 12 (MSI-high, CIMP-low/negative, BRAF-mutated, KRAS-wildtype), Type 13 (MSI-high, CIMP-low/negative, BRAF-mutated, KRAS-mutated), Type 15 (MSI-high, CIMP-high, BRAF-wildtype, KRAS-mutated), and Type 16 (MSI-high, CIMP-high, BRAF-mutated, KRAS-mutated). A summary of the combined marker classifications can be found in Figure 1 and Supplementary Table S4.

Figure 1.

Figure 1.

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Case-control associations between individuals reporting diabetes and individuals not reporting diabetes with risk of colorectal cancer subtypes defined by combined marker status. Error bars represent 95% confidence intervals. Adjusted for: study, sex, age at crc diagnosis, energy intake, family history, BMI, red meat, processed meat, vegetables, fruit, alcohol, smoking, exercise and aspirin/NSAID use.

1P-values were calculated using multinomial logistic regression, comparing colorectal cancer cases to cancer free controls separately for each defined Jass-type with more than 50 cases included.

2Pdifference was calculated using multinomial logistic regression, comparing cases of each Jass-type to all additional cases not belonging to that type.

Exposure Data

Data collection and harmonization of GECCO and CCFR epidemiologic data have been described elsewhere 14,15,21. Briefly, demographic and environmental risk factor data were self-reported at in-person interviews or via structured self-administered questionnaires. Data were collected at study entry, or one to two years prior to sample ascertainment. A multistep iterative data-harmonization procedure was applied, reconciling each study’s unique protocols and data collection instruments. Multiple quality-control checks were performed, and outlying values of variables were truncated to the minimum or maximum value of an established range for each variable. Variables were combined into a single dataset with common definition, standardized coding, and standardized permissible values.

Diabetes status was obtained through self-reported answers to questions about diabetes diagnoses (summarized in Supplementary Table S1) and includes, but does not distinguish between, both type 1 and type 2. We defined age at the time of a colorectal cancer diagnosis for cases and time of enrolment for controls. Missing covariate data were assumed to be missing at random, conditional on observed data, and were imputed using mean imputation.

Statistical Analyses

We used multinomial models to estimate odds ratios (OR) and 95% confidence intervals (CIs) for the association between diabetes and the risk of each molecular tumor marker among colorectal cancer cases, defined as MSI-high versus non-MSI-high, CIMP-high versus low/negative, and BRAF or KRAS mutated versus non-mutated. To test for differences related to subtype within the case-only analysis we used unconditional logistic regression. In the combined marker analysis, Type 4 (non-MSI-high, CIMP-low/negative, BRAF-wildtype, KRAS-wildtype) was used as a reference group in the case-only analysis, whereas in the polytomous analysis, cancer-free controls were used as the reference group. Both analyses used multinomial logistic regression to compare each molecular pathological subtype to the reference group. We also used multinomial logistic regression to estimate the association between diabetes and risk of colorectal cancer stratified by tumor location (colon, rectum, proximal colon, distal colon) and sex (male and female), and compared case-combinations of marker and tumor site with controls. For case-only analyses, we compared marker combinations stratified by tumor site.

Minimally adjusted models (presented in the supplementary materials) included study, age and sex as covariates, and fully adjusted models additionally included energy intake, family history of colorectal cancer, body mass index (BMI), red and processed meat consumption, vegetable consumption, fruit consumption, alcohol use, smoking status, exercise and aspirin/NSAID use. Variables were first selected based on their theoretical relevance as potential confounders of an association between diabetes and colorectal cancer risk. We then performed a forward direction selection of suitable covariates resulting in fiber intake being dropped from the analysis. For polytomous and case-only analyses of the primary subtypes (MSI, BRAF, KRAS and CIMP-status), we considered a two sided p-value of <0.05 to be significant (in both minimally and fully adjusted models). However, when testing associations between diabetes and combined marker subtypes, we used the alpha of 0.5 % as recommended by Benjamin et al. 22. All analyses were performed using R version 4.0.0 (R Foundation for Statistical Computing, Vienna).

RESULTS

The main characteristics of the study participants are described in Table 1. Individuals reporting a diabetes diagnosis were generally more likely than those not reporting diabetes to be male (p<0.01) and to use aspirin regularly (p<0.01), and less likely to have a reported family history of colorectal cancer, especially among colorectal cancer cases (p<0.01). Participants with diabetes were also more often non-drinkers (p<0.01), were more likely to exercise (p<0.01), had a history of smoking (p<0.01) and were more often obese (p<0.01). These relationships were consistent among both colorectal cancer cases and controls.

TABLE 1.

Participant Characteristics

CRC Cases Controls
Characteristics Diabetes
Diabetes
No Yes p1 No Yes p1
Total 8639 1117 9101 883
Study, n (row %) <0.01 <0.01
 CCFR_Australia 713 (94.1) 45 (5.9) 175 (96.2) 7 (3.8)
 CCFR_Ontario 1073 (91.3) 102 (8.7) 1178 (90.1) 121 (9.9)
 CCFR_Seattle 1612 (87.5) 231 (12.5) 705 (93.0) 53 (7.0)
 CPSII 798 (93.0) 60 (7.0) 901 (93.0) 68 (7.0)
 DACHS 1879 (81.4) 430 (18.6) 2940 (86.0) 481 (14.0)
 EPIC_Sweden 142 (96.6) 5 (3.4) 384 (99.7) 1 (0.3)
 HPFS1 241 (96.0) 10 (4.0) 251 (98.8) 3 (1.2)
 HPFS2 354 (93.7) 24 (6.3) 192 (93.7) 13 (6.3)
 MCCS 467 (95.3) 23 (4.7) 658 (97.6) 16 (2.4)
 NFCCR 405 (78.9) 108 (21.1) 401 (86.1) 65 (13.9)
 NHS1 204 (95.8) 9 (4.2) 749 (97.5) 19 (2.5)
 NHS2 519 (89.5) 61 (10.5) 283 (90.1) 31 (9.9)
 NSHDS 232 (96.3) 9 (3.7) 284 (98.2) 5 (1.8)
Age of CRC diagnosis <0.01 <0.01
 Mean (range) 62.57 (20-96) 67.27 (27-90) 65.75 (20- 102) 69.74 (27- 97)
Sex, n (column %) <0.01 <0.01
 Female 4157 (48.1) 451 (40.4) 4505 (49.5) 318 (36)
 Male 4482 (51.9) 666 (59.6) 4596 (50.5) 565 (64)
Family History of CRC, n (column%) <0.01 0.38
 No 6594 (76.3) 902 (80.8) 7656 (84.1) 780 (88.3)
 Yes 1571 (18.2) 169 (15.1) 947 (10.4) 87 (9.9)
Aspirin, n (column %) <0.01 <0.01
 No 5949 (68.9) 668 (59.8) 5404 (59.4) 446 (50.5)
 Yes 1976 (22.9) 412 (36.9) 2736 (30.1) 419 (47.5)
Dietary intake, Mean (range)
 Energy intake, kcal/day 2046 (357-4958) 2155 (310-4959) 0.01 1964 (379-4958) 2043 (482-4684) 0.06
 Redmeat, servings/day 0.72 (0-8) 0.77 (0-5) 0.01 0.67 (0-8) 0.75 (0-5.2) <0.01
 Process meat, servings/day 0.48 (0-4) 0.66 (0-2.9) <0.01 0.45 (0-4) 0.64 (0-2.5) <0.01
 Vegetable, servings/day 2.21 (0-20) 1.77 (0-14) <0.01 2.24 (0-18) 1.76 (0.03-17) <0.01
 Fruit, servings/day 1.6 (0-20) 1.41 (0-9) <0.01 1.69 (0-20) 1.36 (0-11) <0.01
 Fiber g/day 22.5 (3.2-80) 23.4 (5.6-70.4) 0.11 22.7 (1.8-80) 23.0 (3.8-80) 0.65
Alcohol intake, n (column %) <0.01 <0.01
 >28g/d 1074 (12.4) 124 (11.1) 946 (10.4) 89 (10.1)
 1-28g/d 3854 (44.6) 412 (36.9) 4624 (50.8) 393 (44.5)
 Nondrinker 3135 (36.3) 539 (48.3) 3078 (33.8) 356 (40.3)
Smoke ever, n (column %) <0.01 <0.01
 No 3597 (41.6) 419 (37.5) 4311 (47.4) 324 (36.7)
 Yes 4810 (55.7) 672 (60.2) 4547 (50) 533 (60.4)
Exercise, n (column %) <0.01 <0.01
 No 332 (3.8) 25 (2.2) 427 (4.7) 20 (2.3)
 Yes 2642 (30.6) 471 (42.2) 3892 (42.8) 532 (60.2)
BMI, n (%) <0.01 <0.01
 Normal 3096 (35.8) 157 (14.1) 3792 (41.7) 180 (20.4)
 Overweight 3613 (41.8) 459 (41.1) 3778 (41.5) 396 (44.8)
 Obese 1632 (18.9) 469 (42) 1307 (14.4) 287 (32.5)
Stage, n (%) 0.19
 Stage 1 or local 1746 (20.2) 219 (19.6) - -
 Stage 2/3 or regional 4735 (54.8) 687 (61.5) - -
 Stage 4 or distant 928 (10.7) 124 (11.1) - -
Site, n (%) 0.02
 Proximal 3173 (36.7) 460 (41.2) - -
 Distal 2525 (29.2) 298 (26.7) - -
 Rectal 2759 (31.9) 339 (30.3) - -
BRAF, n (%) 0.02 -
 Wildtype 7053 (81.6) 906 (81.1) - -
 Mutated 937 (10.8) 149 (13.3) - -
KRAS, n (%) 0.74 -
 Wildtype 4675 (54.1) 603 (54) - -
 Mutated 2315 (26.8) 306 (27.4) - -
MSI, n (%) 0.92 -
 non MSI-high 6843 (79.2) 877 (78.5) - -
 MSI-high 1152 (13.3) 149 (13.3) - -
CIMP, n (%) 0.49 -
 Low/Negative 5741 (66.5) 737 (66) - -
 High 1162 (13.5) 159 (14.2) - -
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1

p-values from a chi-square test for categorical variables and ANOVA for continuous variables.

In the case-control analysis, we observed an association between diabetes and colorectal cancer (ORall crc (fully adj.) =1.34, 95% CI: 1.21-1.48). This relationship was consistent across all molecular subtypes as shown in Table 2. However, it was stronger in cases with BRAF-mutated tumors (ORfully adj. =1.67, 95% CI: 1.36-2.05) as compared to tumors without BRAF mutations (ORfully adj. =1.33, 95% CI: 1.19-1.48) (pdifference=0.03) (Table 2 and Supplementary Table S5). For additional comparisons of subtype-specific associations, differences were not significant.

Table 2.

Associations between diabetes and risk of different molecular subtypes of colorectal cancer

graphic file with name nihms-1815095-t0003.jpg
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1

Adjusted for: study, sex, age at crc diagnosis, energy intake, family history, BMI, red meat, processed meat, vegetables, fruit, alcohol, smoking, exercise and aspirin/NSAID use.

2

Error bars represent 95% confidence intervals.

3

Multinomial logistic regression was used to compare colorectal cancer cases to cancer free controls separately for each molecular pathological subtype (polytomous analysis, p).

4

Multinomial logistic regression was used to compare cases of each molecular pathological subtype to all additional cases not belonging to that subtype (case only analysis, p-difference).

Analyses were then further stratified by tumor sub-site and sex (Supplementary Table S6). We observed significant differences between the association of diabetes and colorectal cancer by BRAF-mutation status for colon (pdifference=0.04) and proximal colon cancers (pdifference=0.02). The difference between BRAF-mutated and wild-type OR point estimates was also retained for rectal tumors (but not for distal tumors) and among both men and women, but without reaching significance (Supplementary Table S6). However, the number of BRAF-mutated tumors in the rectum was much lower than in the colon, which may explain why the observed difference in the association between diabetes and colorectal cancer risk by BRAF-mutation status was not statistically significant. The association between diabetes and colorectal cancer risk did not differ by other tumor markers in any of the stratified analyses.

Results from analyses of combined marker subtypes are presented in Figure 1 and Supplementary Table S7. Among the ten subtype combinations tested, diabetes was statistically significantly associated with risk of five different types (1, 3, 4, 5 and 9) in case-control analyses, but only type 4 remained significant after adjustments for multiple comparisons. No significant differences in subtype-specific associations were noted in case-only analyses.

DISCUSSION

This large collaborative effort is the first study to investigate the impact of diabetes on subtypes of colorectal cancer, while also considering both distinct molecular markers and tumor subtypes based on marker combinations. Our primary finding was a statistically significant difference in the strength of the association between reported diabetes status and colorectal cancer by BRAF status, with a stronger association for BRAF-mutated than non-mutated tumors. This was consistent in both our minimally and fully adjusted models. Reporting a diabetes diagnosis was more strongly associated with BRAF-mutated tumors in both the proximal colon and rectum indicating that tumor location does not explain this result.

BRAF mutations, found in 8-12 % of colorectal cancers, are generally associated with a poor prognosis and are more common in proximal tumors 23–25. BRAF is a serine-threonine kinase activated by KRAS as part of the MAPK signaling cascade 26. Both BRAF and KRAS are oncogenes that are commonly mutated in colorectal cancer, and mutations in these genes are often considered mutually exclusive. Previous studies have found some evidence that medical drugs can differentially affect the risk of different molecular subtypes. For example, a study from 2013 27 found that aspirin use (which has consistently been shown to decrease colorectal cancer risk 28) seemed to specifically lower the risk of BRAF-non mutated colorectal cancer but not BRAF-mutated colorectal cancer. More related to diabetes, a study from 2012 29 found that metformin, an antidiabetic drug previously shown to have anti-tumour activity in several different cancers 30, did not affect BRAF-mutated melanoma cells, but instead seemed to accelerate their growth in a xenograft mice model of melanoma. The authors suggested that the accelerated growth of the BRAF-mutated tumors could be attributed to improved angiogenesis. Although this is in line with our own results, where individuals reporting diabetes had a higher incidence of BRAF-mutated tumors compared to other molecular markers, later studies have been unable to replicate the findings 31. In addition, at least one study 32 specifically examining the association between metformin and colon or colorectal cancer, but not by subtype, did not report any evidence of metformin acting differently depending on tumor site. It should also be noted that although metformin is commonly prescribed to individuals with type 2 diabetes mellitus, we lacked information about metformin use among our participants.

Colorectal cancer is known to often develop through a specific number of events along the so-called conventional pathway, which is a multistep process initiated by mutations in APC, KRAS or BRAF genes. It is also well known that other important pathways exist, such as the serrated pathway and the alternate pathway, which have other characteristics as well as distinct risk factors 33. For example, the serrated pathway is associated with older age at onset, female sex and smoking, and is also characterized primarily by BRAF mutations and CIMP-high status34,35. Previous studies originating from the GECCO consortia have investigated whether other established risk factors for colorectal cancer are associated with specific molecular subtypes, sometimes supporting development through distinct pathways. One recent study focused on dietary factors did show some evidence of heterogeneity related to fruit and especially fiber intake 15. In polytomous analyses, higher fruit intake was associated with a decreased risk of developing BRAF-mutated tumors. High fiber intake, on the other hand, was associated with decreased risk of non-MSI-high, CIMP-low/negative, BRAF-wildtype, and KRAS-wildtype subtypes, although none of these associations retained significance in case-only analyses. In another study, it was found that smoking was strongly associated with subtype combinations that included CIMP-high and MSI-high tumors 36. As a result of these findings, the authors suggest that smoking might specifically increase the risk of tumors developing through the serrated pathway. Our study however, despite showing an increased risk of developing BRAF-mutated tumors, did not find any evidence of diabetes resulting in any pathway specific development. Both previous studies and ours underline the importance of using large enough sample sets to be able to adequately assess patterns of associations that differ depending on molecular subtype.

Metabolic dysfunction, often defined as the presence of three or more of the criteria for metabolic syndrome (central obesity, hypertension, dysglycemia, and dyslipidemia), is associated with an increased risk of developing both diabetes (type 2) and colorectal cancer. Several studies have aimed to assess the relationship between metabolic abnormalities (such as high body mass index (BMI), hypertension and dysglycemia) and colorectal cancer, focusing on metabolic syndrome, inflammation and specific colorectal cancer subtypes 37–39. One of several plausible links between diabetes and colorectal cancer could be related to insulin resistance and hyperinsulinemia, a state of heightened insulin levels and a hallmark of untreated type 2 diabetes in its earlier natural history. High insulin levels have been linked to an increased risk of multiple cancers, including colorectal cancer 40. This may relate to its growth promoting effects as well as its ability to increase circulating IGF1 levels 41. Several studies have also identified a link between markers of heightened insulin levels (for example C-peptide) and colorectal cancer risk 42,43, but without finding any evidence of heterogeneity 44. However, the most important metabolic factor likely to affect both diabetes and colorectal cancer risk is obesity and the relationship between BMI and colorectal cancer has also shown consistent directions of associations across studies, although just as for hyperglycemia, evidence of heterogeneity between subtypes has been somewhat inconsistent 11,45–51. In the current study however, we find some evidence of diabetes increasing the risk of specifically BRAF-mutated tumors, but taking into account previous studies, this subtype-specific risk difference is probably not related to the shared metabolic risk factors connecting diabetes and colorectal cancer.

An important strength of this study is the ability to pool data from multiple observational studies with readily available information on diabetes status. This pooling of datasets enabled us to detect even modestly differential associations between diabetes status and risk of colorectal cancer by molecular subtypes. However, there are also limitations that have to be taken into account when interpreting the study results. First, we were not able to distinguish between type 1 and type 2 diabetes, or to take into account use of specific medications such as metformin. Time and duration of diabetes diagnosis was also lacking. These are all factors that can have important implications and result in misclassification bias. However, the size of our study, and the fact that type 2 diabetes makes up more than 90% of all diabetes cases 52, makes it less likely that these have substantially distorted our results. There may also be selection bias related to the cases included in the pooled analysis or to tissue availability potentially depending on tumor stage and size 53, a limitation that has been previously described 15. Finally, we did not apply any formal adjustment for multiple comparisons to our primary analyses. Although adding such adjustments would not affect the associations of risk, as they are all highly significant, the reported difference in OR estimates between BRAF-mutated and non-mutated tumors would fall slightly below the significance threshold (accounting for tests of four different subtypes). This should be considered when interpreting the findings. It is worth noting that individuals reporting diabetes had a higher risk of all subtypes of colorectal cancer and the difference related to BRAF-mutated tumors only affected the magnitude of the risk, but the direction remained the same.

In summary, our study confirms the established association between diabetes and colorectal cancer risk and suggests that it especially increases the risk of BRAF-mutated tumors. Additional studies to confirm our finding, and possibly determine if the subtype specific association could be attributed to any particular component (such as metformin treatment), are needed before further conclusions can be drawn.

Supplementary Material

suppl table
supinfo

Novelty and Impact:

Analyses in this study confirm that diabetes is associated with colorectal cancer across established molecular subtypes and that it may especially increase the risk of BRAF-mutated colorectal tumors.

Acknowledgements:

CCFR: The Colon CFR graciously thanks the generous contributions of their study participants, dedication of study staff, and the financial support from the U.S. National Cancer Institute, without which this important registry would not exist. The authors would like to thank the study participants and staff of the Seattle Colon Cancer Family Registry and the Hormones and Colon Cancer study (CORE Studies).

CPS-II: The authors thank the CPS-II participants and Study Management Group for their invaluable contributions to this research. The authors would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program.

DACHS: We thank all participants and cooperating clinicians, and everyone who provided excellent technical assistance.

Harvard cohorts (HPFS, NHS): The study protocol was approved by the institutional review boards of the Brigham and Women’s Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. We would like to thank the participants and staff of the HPFS and NHS for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The authors assume full responsibility for analyses and interpretation of these data.

NSHDS investigators thank the Västerbotten Intervention Programme, the Northern Sweden MONICA study, the Biobank Research Unit at Umeå University and Biobanken Norr at Region Västerbotten for providing data and samples and acknowledge the contribution from Biobank Sweden, supported by the Swedish Research Council.

Funding

Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO): National Cancer Institute, National Institutes of Health, U.S. Department of Health and Human Services (U01 CA137088, R01 CA059045, R01CA201407). Genotyping/Sequencing services were provided by the Center for Inherited Disease Research (CIDR) contract number HHSN268201700006I and HHSN268201200008I. This research was funded in part through the NIH/NCI Cancer Center Support Grant P30 CA015704. Scientific Computing Infrastructure at Fred Hutch funded by ORIP grant S10OD028685.

The Colon Cancer Family Registry (CCFR, www.coloncfr.org) is supported in part by funding from the National Cancer Institute (NCI), National Institutes of Health (NIH) (award U01 CA167551). Support for case ascertainment was provided in part from the Surveillance, Epidemiology, and End Results (SEER) Program and the following U.S. state cancer registries: AZ, CO, MN, NC, NH; and by the Victoria Cancer Registry (Australia) and Ontario Cancer Registry (Canada). The CCFR Set-1 (Illumina 1M/1M-Duo) and Set-2 (Illumina Omni1-Quad) scans were supported by NIH awards U01 CA122839 and R01 CA143247 (to GC). The CCFR Set-3 (Affymetrix Axiom CORECT Set array) was supported by NIH award U19 CA148107 and R01 CA81488 (to SBG). The CCFR Set-4 (Illumina OncoArray 600K SNP array) was supported by NIH award U19 CA148107 (to SBG) and by the Center for Inherited Disease Research (CIDR), which is funded by the NIH to the Johns Hopkins University, contract number HHSN268201200008I. Additional funding for the OFCCR/ARCTIC was through award GL201-043 from the Ontario Research Fund (to BWZ), award 112746 from the Canadian Institutes of Health Research (to TJH), through a Cancer Risk Evaluation (CaRE) Program grant from the Canadian Cancer Society (to SG), and through generous support from the Ontario Ministry of Research and Innovation. The SFCCR Illumina HumanCytoSNP array was supported in part through NCI/NIH awards U01/U24 CA074794 and R01 CA076366 (to PAN). The content of this manuscript does not necessarily reflect the views or policies of the NCI, NIH or any of the collaborating centers in the Colon Cancer Family Registry (CCFR), nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government, any cancer registry, or the CCFR.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. This study was conducted with Institutional Review Board approval.

DACHS: This work was supported by the German Research Council (BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, CH 117/1-1, HO 5117/2-1, HE 5998/2-1, KL 2354/3-1, RO 2270/8-1 and BR 1704/17-1), the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, and the German Federal Ministry of Education and Research (01KH0404, 01ER0814, 01ER0815, 01ER1505A and 01ER1505B).

EPIC: The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Générale de l’Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS) - Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom).

Harvard cohorts (HPFS, NHS): HPFS is supported by the National Institutes of Health (P01 CA055075, UM1 CA167552, U01 CA167552, R01 CA151993, R35 CA197735, and R35 CA253185), and NHS by the National Institutes of Health (R01 CA137178, P01 CA087969, UM1 CA186107, R01 CA151993, R35 CA197735, and R35 CA253185).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 396414 and 1074383 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry and the Australian Institute of Health and Welfare, including the National Death Index and the Australian Cancer Database.

NFCCR: This work was supported by an Interdisciplinary Health Research Team award from the Canadian Institutes of Health Research (CRT 43821); the National Institutes of Health, U.S. Department of Health and Human Services (U01 CA74783); and National Cancer Institute of Canada grants (18223 and 18226). The authors wish to acknowledge the contribution of Alexandre Belisle and the genotyping team of the McGill University and Génome Québec Innovation Centre, Montréal, Canada, for genotyping the Sequenom panel in the NFCCR samples. Funding was provided to Michael O. Woods by the Canadian Cancer Society Research Institute.

NSHDS: The research was supported by Biobank Sweden through funding from the Swedish Research Council (VR 2017-00650, VR 2017-01737), the Swedish Cancer Society (CAN 2017/581), Region Västerbotten (VLL-841671, VLL-833291), Knut and Alice Wallenberg Foundation (VLL-765961), and the Lion’s Cancer Research Foundation (several grants) and Insamlingsstiftelsen, both at Umeå University.

Conflict of Interest

Marios Giannakis reports potential financial conflicts of interest through research funding from Bristol-Myers Squibb, Merck, Servier and Janssen unrelated to this study. Victor Moreno reports grants from Agency for Management of University and Research Grants (AGAUR) of the Catalan Government grant 2017SGR723; Instituto de Salud Carlos III grant PI17-00092; Spanish Association Against Cancer (AECC) Scientific Foundation grant GCTRA18022MORE, Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), action Genrisk. Jonathan Nowak reports prior research funding (for pilot projects) from NanoString and Illumina and ongoing research funding (for technology development) from Akoya Biosciences, unrelated to this study. Mireia Obón-Santacana reports a post-doctoral fellowship from the Spanish Association Against Cancer Scientific Foundation (AECC; POSTD037OBÓN).

List of abbreviations:

BMI

Body Mass Index

CCFR

Colorectal Cancer Family Registry

CIMP

CpG island methylator phenotype

CPS-II

Cancer Prevention Study-II

CRC

Colorectal Cancer

DACHS

the German Darmkrebs: Chancen der Verhütung durch Screening Study

EPIC

the European Prospective Investigation into Cancer and Nutrition study

GECCO

Genetics and Epidemiology of Colorectal Cancer Consortium

HPFS

the Health Professionals Follow-up Study

MCCS

the Melbourne Collaborative Cohort Study

MSI

microsatellite instability

NFCCR

the Newfoundland Colorectal Cancer Registry

NHS

the Nurses’ Health Study

NSHDS

the Northern Sweden Health and Disease Study

OR

Odds Ratio

Footnotes

All other authors declare no conflicts of interest.

Publisher's Disclaimer: Disclaimer

Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

Ethics Statement

All study participants had provided informed consent and included studies had obtained ethical approval from their respective research ethics committee or institutional review board.

Data Availability Statement

Tumor marker and epidemiologic data is available upon request and permission. Please contact gro.hctuhderf@occeg to request the standardized proposal form. The principal investigators of each contributing study will evaluate and approve the proposal, and data access will be managed centrally. Further information is available from the corresponding authors upon request.

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Associated Data

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

Supplementary Materials

suppl table
NIHMS1815095-supplement-suppl_table.xls (214.5KB, xls)
supinfo
NIHMS1815095-supplement-supinfo.pdf (445.9KB, pdf)

Data Availability Statement

Tumor marker and epidemiologic data is available upon request and permission. Please contact gro.hctuhderf@occeg to request the standardized proposal form. The principal investigators of each contributing study will evaluate and approve the proposal, and data access will be managed centrally. Further information is available from the corresponding authors upon request.

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