Waist circumference, a body shape index, and molecular subtypes of colorectal cancer: A pooled analysis of four cohort studies

Christos V Chalitsios; Georgios Markozannes; Christos Papagiannopoulos; Elom K Aglago; Sonja I Berndt; Daniel D Buchanan; Peter T Campbell; Yin Cao; Andrew T Chan; Niki Dimou; David A Drew; Amy J French; Peter Georgeson; Marios Giannakis; Stephen B Gruber; Marc J Gunter; Tabitha A Harrison; Michael Hoffmeister; Li Hsu; Wen-Yi Huang; Meredith AJ Hullar; Jeroen R Huyghe; Brigid M Lynch; Victor Moreno; Christina C Newton; Jonathan A Nowak; Mireia Obón-Santacana; Shuji Ogino; Conghui Qu; Stephanie L Schmit; Robert S Steinfelder; Wei Sun; Claire E Thomas; Amanda E Toland; Quang M Trinh; Tomotaka Ugai; Caroline Y Um; Bethany Van Guelpen; Syed H Zaidi; Neil Murphy; Ulrike Peters; Amanda I Phipps; Konstantinos K Tsilidis

doi:10.1158/1055-9965.EPI-24-1534

. Author manuscript; available in PMC: 2025 Oct 3.

Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2025 Apr 3;34(4):568–577. doi: 10.1158/1055-9965.EPI-24-1534

Waist circumference, a body shape index, and molecular subtypes of colorectal cancer: A pooled analysis of four cohort studies

Christos V Chalitsios ¹, Georgios Markozannes ^1,², Christos Papagiannopoulos ¹, Elom K Aglago ², Sonja I Berndt ³, Daniel D Buchanan ^4,^5,⁶, Peter T Campbell ⁷, Yin Cao ^8,^9,¹⁰, Andrew T Chan ^11,^12,^13,^14,^15,¹⁶, Niki Dimou ¹⁷, David A Drew ^11,¹³, Amy J French ¹⁸, Peter Georgeson ^4,⁵, Marios Giannakis ^19,^20,²¹, Stephen B Gruber ²², Marc J Gunter ^2,¹⁷, Tabitha A Harrison ^23,⁴¹, Michael Hoffmeister ²⁴, Li Hsu ^23,²⁵, Wen-Yi Huang ³, Meredith AJ Hullar ²³, Jeroen R Huyghe ²³, Brigid M Lynch ^26,²⁷, Victor Moreno ^28,^29,^30,³¹, Christina C Newton ³², Jonathan A Nowak ³³, Mireia Obón-Santacana ^28,^29,^30,³¹, Shuji Ogino ^15,^20,^33,³⁴, Conghui Qu ²³, Stephanie L Schmit ^35,³⁶, Robert S Steinfelder ²³, Wei Sun ²³, Claire E Thomas ²³, Amanda E Toland ³⁷, Quang M Trinh ³⁸, Tomotaka Ugai ¹⁵, Caroline Y Um ³², Bethany Van Guelpen ^39,⁴⁰, Syed H Zaidi ³⁸, Neil Murphy ¹⁷, Ulrike Peters ^23,^41,^*, Amanda I Phipps ^23,^41,^*, Konstantinos K Tsilidis ^1,^2,^*

¹Department of Hygiene and Epidemiology, University of Ioannina, Ioannina, Greece

²Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

³Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.

⁴Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia.

⁵University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, Australia.

⁶Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia.

⁷Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA.

⁸Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA.

⁹Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, Missouri, USA.

¹⁰Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA.

¹¹Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

¹²Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA.

¹³Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.

¹⁴Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA.

¹⁵Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.

¹⁶Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts, USA.

¹⁷Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France.

¹⁸Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.

¹⁹Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

²⁰Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

²¹Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

²²Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte CA, USA.

²³Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.

²⁴Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.

²⁵Department of Biostatistics, University of Washington, Seattle, Washington, USA

²⁶Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia.

²⁷Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.

²⁸Unit of Biomarkers and Suceptibility (UBS), Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), L’Hospitalet del Llobregat, 08908 Barcelona, Spain

²⁹ONCOBELL Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat,08908 Barcelona, Spain

³⁰Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain

³¹Department of Clinical Sciences, Faculty of Medicine and health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L’Hospitalet de Llobregat, 08908 Barcelona, Spain

³²Department of Population Science, American Cancer Society, Atlanta, Georgia.

³³Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA.

³⁴Tokyo Medical and Dental University (Institute of Science Tokyo), Tokyo, Japan.

³⁵Genomic Medicine Institute, Cleveland Clinic, Cleveland, Ohio, USA.

³⁶Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, Ohio, USA.

³⁷Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, Ohio, USA.

³⁸Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

³⁹Department of Diagnostics and Intervention, Oncology Unit, Umeå University, Umeå, Sweden.

⁴⁰Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.

⁴¹Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA

Joint last authors

^✉

Corresponding author Christos V. Chalitsios, Department of Hygiene and Epidemiology, School of Medicine, University of Ioannina, Ioannina, 45110, Greece, christos.chalitsios@uoi.gr

PMCID: PMC12132105 NIHMSID: NIHMS2056041 PMID: 39898780

Abstract

Background

Waist circumference (WC) and its allometric counterpart, “a body shape index” (ABSI), are risk factors for colorectal cancer (CRC); however, it is uncertain whether associations with these body measurements are limited to specific molecular subtypes of the disease.

Methods

Data from 2,772 CRC cases and 3,521 controls were pooled from four cohort studies within the Genetics and Epidemiology of Colorectal Cancer Consortium. Four molecular markers (BRAF mutation, KRAS mutation, CpG island methylator phenotype, and microsatellite instability) were analysed individually and in combination (Jass-types). Multivariable logistic and multinomial logistic models were used to assess the associations of WC and ABSI with overall CRC risk and in case-only analyses evaluating heterogeneity by molecular subtype, respectively.

Results

Higher WC (OR_{per 5cm}=1.06, 95%CI:1.04–1.09) and ABSI (OR_{per 1-SD}=1.07, 95%CI:1.00–1.14) were associated with elevated CRC risk. There was no evidence of heterogeneity between the molecular subtypes. No difference was observed regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancer, as well as in early and later onset CRC. Associations did not differ in the Jass-type analysis.

Conclusions

Higher WC and ABSI were associated with elevated CRC risk; however, they do not differentially influence all four major molecular mutations involved in colorectal carcinogenesis but underscore the importance of maintaining a healthy body weight in CRC prevention.

Impact

The proposed results have potential utility in colorectal cancer prevention.

INTRODUCTION

Colorectal cancer (CRC) is one of the most common cancers and a leading cause of cancer-related deaths (1). It exhibits substantial genetic and molecular heterogeneity across its subtypes (2). The molecular characterisation of CRC using clinically relevant genetic and epigenetic features holds significant promise for prognosis (3) and treatment considerations (4). Within the landscape of CRC, mutations within the KRAS gene are implicated in driving the growth of colorectal adenomas in a notable subset, ranging from 30% to 40% of sporadic cases (5). Microsatellite instability (MSI), marked by frequent alterations in repetitive DNA sequences, is observed in approximately 10% to 15% of CRC cases and is linked with a more favourable prognosis (6). Furthermore, a subset of MSI-high CRC cases also exhibits features such as the CpG island methylator phenotype (CIMP) and BRAF c.1799T>A (p.V600E) mutations (7).

While obesity is widely recognised as a strong risk factor for CRC (8–10), evidence regarding the impact of obesity and other body dimensions on the development of various molecular subtypes of CRC is more limited. A large pooled observational analysis (11,872 CRC cases) from 11 studies in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) found consistent positive associations between body mass index (BMI) and various molecular CRC subtypes, (11) but did not examine waist circumference (WC). Similarly, a positive association was observed between BMI and CRC risk across molecular subtypes 1 to 4 defined by Jass (combining the four somatic colorectal tumour markers) (12); however, for Jass type 5, associated with familial-like/Lynch syndrome, no significant association with BMI was found, suggesting that obesity might not be a risk factor for CRC development in individuals with this syndrome (11). However, the limitation of BMI as a measurement of adiposity lies in its inability to differentiate between adipose tissue and muscle mass or to discern fat distribution across various body compartments. To address this issue, additional anthropometric measurements such as WC, hip circumference (HC), and their amalgamation in the waist-to-hip ratio (WHR) have been advocated. These measurements offer a more nuanced depiction of body shape and patterns of adiposity accumulation, aiding in the differentiation between abdominal (WC and WHR) and gluteofemoral fat distribution (HC). Similarly to BMI, WC, HC, and WHR have also been associated with a higher risk of CRC (13–16), but their association with the risk by molecular subtypes of CRC is largely unknown.

Moreover, the aforementioned body shape indices are strongly correlated with BMI (17). Adjusting these indices for BMI can lead to multicollinearity issues in the models, affecting the effect of the index of interest, making it harder to identify the independent effect estimate and reducing the power of the model. An alternative approach involves the development of a new, BMI-independent allometric body shape index called “A Body Shape Index” (ABSI) (18). This index is a mathematical transformation of WC normalised to height and weight, making it uncorrelated with these measurements. Limited evidence suggests that ABSI is positively associated with CRC (19,20); however, its effect on the molecular subtypes of CRC remains unknown.

This study analysed individual-level, harmonised data from CRC cases and controls from the four observational studies within GECCO that had information on WC. We aimed to shed light on the associations of WC and ABSI with the risk of overall CRC and its molecular tumour subtypes.

MATERIALS and METHODS

Study population

This study sample included CRC cases and controls nested in four cohort studies (Cancer Prevention Study-II [CPS-II] (21,22), Health Professionals Follow-up Study [HPFS], Melbourne Collaborative Cohort Study [MCCS] (23), and Nurses’ Health Study [NHS] (24)) within the GECCO with available tumour markers, WC, height, and body mass data (Table S1). All CRC cases were defined as colorectal adenocarcinoma and confirmed by pathological records, medical records, and/or death certificate information. Controls were selected at the time of case selection among individuals without a history of CRC based on study-specific matching criteria. All participants provided written informed consent. A research ethics committee or institutional review board approved each study and the overall project. Additional information on contributing studies is included in the Supplementary material & method.

Outcomes assessment

The outcomes of interest were the overall CRC and tumour molecular subtypes of CRC (based on MSI, CIMP, BRAF, and KRAS mutation status). The latter was defined according to the testing for MSI, CIMP, and mutations in the BRAF and KRAS genes that each study conducted based on their individual protocols.

The included studies used polymerase chain reaction (PCR) to assess microsatellite status. Additionally, IHC was used for a subset of MCCS (25–27) samples. For classification using IHC, tumours lacking nuclear staining in tumour cells for at least one of these proteins were considered to have a positive MSI screening status, and MSI-negative screens were considered microsatellite stable (MSS). The specific markers assessed using PCR-based methods are summarised in Table S2. To harmonise markers across all studies, we created two categories for downstream analyses, MSI-high and non-MSI-high. For studies that categorised MSI status as MSI-high (MSI-H), MSI-low (MSI-L), and MSS, we collapsed MSI-L and MSS into the non-MSI-high category. Tumour classification was based on >4 interpretable markers for MCCS 14, >5 interpretable markers for CPS-II (unless all four markers were unstable, in which case the tumour was classified as MSI), and >7 interpretable markers for NHS and HPFS (25). For these studies, tumours were classified as MSI-high (MSI-H) if 30% or more of the markers showed instability and non-MSI-high if < 30% and > 0% showed instability or if no marker exhibited instability.

Studies used PCR, sequencing, and IHC techniques to assess BRAF and KRAS mutations. Most studies evaluated the V600E mutation in BRAF exon 15 and KRAS mutations in codons 12 and 13, though a few evaluated additional loci. In analyses, we included any mutation identified by at least one study. MCCS applied a fluorescent real-time allele-specific -PCR assay (28) to detect the BRAF V600E mutation. For KRAS mutations in codons 12 and 13, a combination of real-time PCR with high-resolution melting (HRM) analysis and direct Sanger sequencing for positive cases was employed (29). The CPS-II cohort used PCR to evaluate BRAF V600E mutations as well as KRAS mutations in codon 12, 13, and 14. In the HPFS and NHS cohorts, PCR and pyrosequencing were utilized to identify BRAF codon 600 mutations (30–32). Additionally, these cohorts applied real-time PCR and pyrosequencing to detect KRAS mutations in codons 12, 13, 61, and 14 (30,33).

Studies used gene promoter methylation analysis to determine CIMP status. The specific genes assessed in each study are shown in Table S3. Similar to the harmonisation of MSI status, we created two CIMP categories for downstream analyses, CIMP-high and CIMP-low/negative. In instances where studies categorised CIMP-high, CIMP-low, and CIMP-negative, we collapsed CIMP-low and CIMP-negative into the CIMP-low/negative category. HPFS, NHS (31,34), CPS-II, and MCCS (35) used the MethyLight (36) method to determine CIMP status. HPFS, NHS, and CPS-II used a panel of eight genes, and MCCS used a panel of five. The percent of methylated reference (PMR) value was calculated and, for CPS-II a gene was considered positive for methylation when the PMR>10. HPFS and NHS used a PMR cutoff value of >4 for CDKN2A, MLH1, CACNA1G, NEUROG1, RUNX3, SOCS1, and a PMR of >6 for CRABP1 and IGF2. HPFS, NHS, and CPS-II classified tumors with ≥6 methylated markers as CIMP-high, 1–5 markers as CIMP-low, and no markers as CIMP-negative. MCCS classified tumours with >3 methylated markers as CIMP-high and, otherwise, as CIMP-low/negative.

Tumour subtypes consistent with Jass classifications were further defined. (3,12).

Exposure and confounder assessment

Demographic factors were self-reported through in-person interviews or via questionnaires at the time of recruitment. Dietary variables were ascertained using food frequency questionnaires. A multistep iterative process was employed for data harmonisation, aligning the distinct protocols and data collection tools of each study. Rigorous quality control checks were conducted, and outliers in variable values were adjusted to the nearest minimum or maximum within a predefined acceptable range for each variable. Variables were combined into a single dataset with common definitions, standardised coding, and standardised permissible values. All participants had body mass, height, and WC measured at enrolment by trained staff adhering to standardised protocols. In our study, the exposures of interest were WC (in cm) and ABSI. We calculated ABSI based on WC adjusted for height and body mass defined as $WC (m) * {(body mass (kg))}^{- \frac{2}{3}} * {(height (m))}^{\frac{5}{6}}$ (18).

Statistical analysis

WC and ABSI were modelled continuously (per 5cm and 1 standard deviation [SD] (=8.04 ABSI score), respectively) and categorically in sex-specific tertiles defined among controls only. Logistic regression analysis was performed to estimate the odds ratio (OR) of the association between WC, ABSI, and overall CRC. Multinomial logistic regression models were used to estimate the relative risk ratio (RRR) for the association between WC, ABSI, and CRC subtypes defined by tumour markers (MSI-H vs MSS/MSI-L, CIMP high vs low or negative, and BRAF or KRAS mutated vs wild-type) and Jass-types. Logistic regression models were performed to assess the differences in the associations of WC and ABSI with CRC across molecularly defined subtypes among cases only. In the case-only analyses, we also conducted a logistic regression analysis defining Jass-type 4 as the reference group to evaluate differences among Jass-types. Jass types with at least 50 cases were assessed in relation to WC and ABSI. The multivariable models included study population, age, sex, smoking status, education, and red meat intake.

The main analysis was based on the population with complete information. Subgroup analyses were conducted by sex, cancer anatomical site (proximal colon, distal colon, rectum), and by early (≤55 yrs. old) and later (>55 yrs. old) onset CRC. Four sensitivity analyses were undertaken. In the first, the main models were additionally adjusted for family history of CRC, history of endoscopy, energy intake, and consumption of alcohol, processed meat, fruits, and vegetables. In the second, the main models were additionally adjusted for physical activity (only two of the four included studies had available data), but the results did not change. In the third, missing values for WC and all a priori–determined CRC risk factors (Table 1) were imputed using multiple imputations by chained equations (MICE) (37) with five imputed datasets and five iterations using the random forest method. Diagnostic plots suggest a good performance of this method (Figure S1–S3). Finally, we calculated the association of WC and ABSI with the overall CRC risk by study without observing any different findings.

Table 1.

Baseline characteristics of cases and controls.

Characteristics	Cases (N = 2,772)	Controls (N = 3,521)

Study
CPS-II	860 (31)	1,003 (28.5)
HPFS	629 (22.7)	602 (17.1)
MCCS	490 (17.7)	674 (19.1)
NHS	793 (28.6)	1,242 (35.3)
Age ¹ , mean (SD) yrs.	70 (9)	70 (9)
Unknown	5 (0.2)	0
Sex
Males	1,328 (47.9)	1,447 (41.1)
Females	1,444 (52.1)	2,074 (58.9)
Smoking status
Never smoker	1,131 (40.8)	1,613 (45.8)
Former smoker	1,329 (47.9)	1,585 (45)
Current smoker	245 (8.8)	278 (7.9)
Unknown	67 (2.4)	45 (0.7)
Body mass index, Kg/m²
Mean (SD)	26.7 (4.5)	25.5 (4.3)
Underweight (<18.5)	28 (1)	43 (1.2)
Normal (18.5 to <24.9)	983 (35.5)	1,487 (42.2)
Overweight (25 to 30)	1,125 (40.6)	1,355 (38.5)
Obese (>30)	521 (18.8)	545 (15.5)
Unknown	115 (4.1)	91 (2.6)
Waist circumference, mean (SD), cm	92 (14)	90 (13)
Unknown	396 (14)	365 (10)
A Body Shape Index, mean (SD)	80 (8)	79 (8)
Unknown	475	439
Dietary intake, mean (SD)
Red meat, servings/day	0.9 (0.7)	0.8 (0.6)
Unknown	136 (4.9)	159 (4.5)
Processed meat, servings/day	0.3 (0.3)	0.2 (0.3)
Unknown	274 (9.9)	245 (7)
Fruits, servings/day	2 (2.1)	2.1 (2.1)
Unknown	219 (7.9)	312 (8.9)
Vegetables, servings/day	3.1 (2.3)	3.2 (2.2)
Unknown	219 (7.9)	312 (8.9)
Fiber, g/day	20 (10)	20 (10)
Unknown	251 (9.1)	199 (5.7)
Education level
8^th degree or less + less than high school graduate	350 (12.6)	462 (13.1)
High school graduate or completed GED	269 (9.7)	29.7 (8.4)
Some college or technical school	647 (23.3)	553 (15.7)
College graduate + Graduate degree	1,392 (50.2)	2,178 (61.9)
Unknown	114 (4.1)	31 (0.9)
First-degree relative with CRC	200 (7.2)	203 (5.8)
Unknown	352 (12.7)	332 (9.4)
Location of CRC
Proximal colon	1,265 (45.6)	-
Distal colon	794 (28.6)	-
Rectum (including rectosigmoid junction)	670 (24.2)	-
Unknown	43 (1.6)	-
CRC stage
Stage 1 or local	765 (27.5)	-
Stage2/3 or regional	1,376 (49.6)	-
Stage 4 or distant	276 (10)	-
Unknown	355 (12.8)	-
*BRAF*
Wild type	2,148 (77.5)	-
Mutated	379 (13.7)	-
Unknown	245 (8.8)	-
*KRAS*
Wild type	1,549 (55.9)	-
Mutated	856 (30.9)	-
Unknown	367 (13.2)	-
Microsatellite instability (MSI)
Stable/low	2,114 (76.3)	-
High	408 (14.7)	-
Unknown	250 (9)	-
CpG island methylator phenotype (CIMP)
Low/negative	2,031 (73.3)	-
High	530 (19.1)	-
Unknown	211 (7.6)	-
Jass type (combined subtypes)
[1] MSI-high, CIMP-high, BRAF mut, KRAS wt	183 (2.5)	-
[2] MSS/MSI-low, CIMP-high, BRAF mut, KRAS wt	67 (2.4)	-
[3] MSS/MSI-low, CIMP-low/neg, BRAF wt, KRAS mut	669 (24.1)	-
[4] MSS/MSI-low, CIMP-low/neg, BRAF wt, KRAS wt	932 (33.6)	-
[5] MSI-high, CIMP-low/neg, BRAF wt, KRAS wt	48 (1.7)	-
[6] MSS/MSI-low, CIMP-low/neg, BRAF mut, KRAS wt	63 (2.3)	-
[7] MSS/MSI-low, CIMP-low/neg, BRAF mut, KRAS mut	4 (0.1)	-
[8] MSS/MSI-low, CIMP-high BRAF wt, KRAS wt	35 (1.3)	-
[9] MSS/MSI-low, CIMP-high, BRAF wt, KRAS mut	70 (2.5)	-
[10] MSS/MSI-low, CIMP-high, BRAF mut, KRAS mut	1 (0)	-
[11] MSI-high, CIMP-low/neg, BRAF wt, KRAS mut	32 (1.2)	-
[12] MSI-high, CIMP-low/neg, BRAF mut, KRAS wt	13 (0.5)	-
[13] MSI-high, CIMP-low/neg, BRAF mut, KRAS mut	1 (0)	-
[14] MSI-high, CIMP-high, BRAF wt, KRAS wt	58 (2.1)	-
[15] MSI-high, CIMP-high, BRAF wt, KRAS mut	9 (0.3)	-
[16] MSI-high, CIMP-high, BRAF mut, KRAS mut	3 (0.1)	-
Unknown	584 (21.1)	-

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Abbreviations: CPSII, Cancer Prevention Study II; CRC, Colorectal Cancer; HPFS, Health Professionals Follow-up Study; MCCS, Melbourne Collaborative Cohort Study; NHS, Nurses’ Health Study; SD, standard deviation

Age at diagnosis (cases) and selection (controls).

We used Bonferroni-corrected P values (0.05/12=0.004 corresponding to 4 subtyping markers being tested x 3 analyses: men-only, women-only, both sexes combined) to assess the statistical significance of the primary subtypes (MSI, CIMP-status BRAF, and KRAS). For the other analyses, we considered a P value less than 0.05 as statistically significant. Analyses were performed using R v4.3.1 (R Foundation for Statistical Computing, Vienna, Austria).

Data availability

Data described in the manuscript, code book, and analytic code will be made available upon request and permission. Please contact gecco@fredhutch.org 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.

RESULTS

Study population characteristics

This study population comprised 2,772 CRC cases and 3,521 controls with the same mean age (70 years old) (Table 1). Individuals with a CRC diagnosis compared to controls were more likely to be males (47.9% vs. 41.1%), former or current smokers (56.7% vs 52.9%), and obese (18.8% vs. 15.5%). Participants with CRC were also more likely to have a first-degree relative with CRC (7.2% vs 5.8%), a higher mean WC (92 [14] cm vs. 90 [13] cm) and ABSI (80 [8] vs. 79 [8]). Among cases, 14.7% were MSI-H (n=408), 19.1% were CIMP-high (n=530), 13.7% were BRAF-mutated (n=379), and 30.9% were KRAS-mutated (n=856), with the type 4 (MSS/MSI-low, CIMP-, BRAF wt, KRAS wt) (33.6%) being the most frequent Jass-type.

Waist circumference and individual molecular subtypes

Higher WC (OR_{per 5cm} = 1.06, 95% CI 1.04 to 1.09) and ABSI (OR_{per 1-SD} = 1.07, 95% CI 1.00 to 1.14) were linearly associated with elevated overall CRC risk in adjusted models (Tables 2 and 3). There were no significant differences by sex (p_difference= 0.1017) regarding the impact of WC, the association with increased ABSI was stronger in males (OR_{per 1-SD} = 1.16, 95% CI 1.03 to 1.32) than females (OR_{per 1-SD} = 1.03, 95% CI 0.94 to 1.11) (p_difference= 0.0320). WC was positively associated with CRC risk for all individual molecular subtypes in both sexes having a nearly identical RRR. ABSI was not found to be linearly associated with any individual molecular subtypes in females but with all molecular subtypes in males compared to controls. No evidence of heterogeneity was observed for both anthropometric measures in case-only analyses. There was no difference regarding the influence of WC and ABSI on the four major molecular mutations involved in colorectal carcinogenesis in each of the three anatomical sites (proximal colon, distal colon, and rectum) (Table S4–S9) and in early and later onset CRC (Table S10–S11).

Table 2.

Association between waist circumference, colorectal cancer, and its molecular subtypes^{a, b, c}.

		Microsatellite instability		CpG island methylator phenotype		BRAF		KRAS

Exposure	Overall CRC OR (95%CI)	MSS/MSI-L RRR (95%CI)	MSI-H RRR (95%CI)	CIMP-low/negative RRR (95%CI)	CIMP-high RRR (95%CI)	BRAF-wild type RRR (95%CI)	BRAF-mutated RRR (95%CI)	KRAS-wild type RRR (95%CI)	KRAS-mutated RRR (95%CI)

Both sexes
N cases	2,206	1,678	330	1,616	417	1,706	303	1,235	681
Tertile 1^d	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Tertile 2^d	1.23 (1.07, 1.42)	1.27 (1.09–1.49)	1.14 (0.85–1.55)	1.28 (1.10–1.51)	1.13 (0.86–1.48)	1.32 (1.13–1.54)	1.08 (0.80–1.47)	1.21 (1.02–1.45)	1.46 (1.17–1.83)
Tertile 3^d	1.44 (1.25, 1.67)	1.52 (1.30–1.78)	1.42 (1.05–1.91)	1.45 (1.24–1.71)	1.41 (1.08–1.85)	1.56 (1.33–1.82)	1.35 (0.99–1.82)	1.49 (1.25–1.78)	1.61 (1.29–2.02)
P_trend	8.77E-07	2.60E-07	0.0182	6.54E-06	0.009	6.39E-08	0.048	5.07E-06	5.85E-05
Per 5cm	1.06 (1.04–1.09)	1.07 (1.04–1.10)	1.06 (1.01–1.11)	1.06 (1.03–1.09)	1.06 (1.01–1.10)	1.07 (1.04–1.10)	1.06 (1.01–1.11)	1.06 (1.03–1.09)	1.08 (1.04–1.12)
P-value	1.04E-06	4.39E-07	0.0249	1.41E-05	0.0107	6.87E-07	0.0190	2.3E-05	2.04E-05
P_difference	NA	0.7850		0.7521		0.8321		0.4452

Males
N cases	1,116	897	121	851	155	925	102	611	358
63–91.4cm	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
91.5–99.9cm	1.04 (0.84, 1.29)	1.06 (0.84–1.32)	1.23 (0.73–2.10)	1.06 (0.84–1.33)	1.01 (0.64–1.62)	1.08 (0.86–1.36)	0.97 (0.57–1.66)	0.90 (0.70–1.16)	1.55 (1.11–2.16)
100–155.6cm	1.49 (1.20, 1.84)	1.51 (1.20–1.90)	1.85 (1.10–3.10)	1.47 (1.17–1.85)	1.68 (1.08–2.63)	1.52 (1.21–1.91)	1.46 (0.86–2.48)	1.44 (1.12–1.85)	1.91 (1.37–2.67)
P_trend	9.25E-05	1.57E-04	0.0146	0.0005	0.0122	0.0001	0.130	0.0016	0.0002
Per 5cm	1.10 (1.06–1.15)	1.10 (1.05–1.15)	1.21 (0.83–1.76)	1.10 (1.05–1.15)	1.13 (1.04–1.22)	1.04 (1.01–1.08)	1.03 (0.98–1.09)	1.09 (1.04–1.15)	1.13 (1.07–1.20)
P-value	1.38E-06	1.69E-05	0.3180	2.43E-05	0. 0045	0.0192	0.1850	0.0002	2.04E-05
P_difference	NA	0.2171		0.5662		0.7512		0.3340

Females
N cases	1,090	781	209	765	262	781	201	624	133
53.3–76.2cm	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
76.3–88.9cm	1.46 (1.19–1.78)	1.58 (1.27–1.98)	1.12 (0.77–1.62)	1.59 (1.27–1.99)	1.23 (0.87–1.73)	1.65 (1.32–2.06)	1.17 (0.80–1.71)	1.62 (1.27–2.06)	1.43 (1.05–1.95)
89–152.4cm	1.40 (1.15–1.72)	1.57 (1.25–1.97)	1.20 (0.83–1.73)	1.46 (1.16–1.83)	1.26 (0.89–1.77)	1.62 (1.29–2.03)	1.29 (0.89–1.88)	1.57 (1.23–2.01)	1.42 (1.04–1.94)
P_trend	0.0016	0.0002	0.332	0.002	0.186	7.59E-05	0.175	0.0006	0.032
Per 5cm	1.04 (1.01–1.08)	1.05 (1.02–1.09)	1.01 (0.96–1.07)	1.04 (1.01–1.08)	1.03 (0.98–1.09)	1.11 (1.06–1.16)	1.09 (0.99–1.20)	1.05 (1.01–1.08)	1.05 (1.00–1.10)
P-value	0.0059	0.0022	0.6250	0.0105	0.1850	2.29E-06	0.8910	0.0136	0.0378
P_difference	NA	0.2394		0.9714		0.9701		0.9960

Open in a new tab

Controls are used as the reference for all effect sizes. CI, confidence interval; CIMP, CpG island methylator phenotype; CRC, colorectal cancer; MSI, microsatellite instability; MSS, microsatellite stable; OR. Odds Ratio; RRR, relative risk ratio.

The models are adjusted for study, age (continuous), sex (when not stratified), smoking status, education, and red meat intake (servings/d).

Case-only analyses used to calculate P_difference.

Based on sex-specific tertiles among controls only.

Table 3.

Association between ABSI, colorectal cancer, and its molecular subtypes^{a, b, c}.

		Microsatellite instability		CpG island methylator phenotype		BRAF		KRAS

Exposure	Overall CRC OR (95%CI)	MSS/MSI-L RRR (95%CI)	MSI-H RRR (95%CI)	CIMP-low/negative RRR (95%CI)	CIMP-high RRR (95%CI)	BRAF-wild type RRR (95%CI)	BRAF-mutated RRR (95%CI)	KRAS-wild type RRR (95%CI)	KRAS-mutated RRR (95%CI)

Both sexes
N cases	2,143	1,636	321	1,576	398	1,661	294	1,206	664
Tertile 1^d	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Tertile 2^d	0.96 (0.83–1.10)	0.96 (0.82–1.12)	0.92 (0.69–1.25)	0.95 (0.82–1.12)	1.03 (0.77–1.36)	0.94 (0.81–1.10)	1.09 (0.80–1.49)	1.01 (0.85–1.19)	0.88 (0.71–1.10)
Tertile 3^d	1.14 (0.99–1.33)	1.24 (1.05–1.46)	1.00 (0.74–1.37)	1.12 (0.95–1.32)	1.22 (0.91–1.62)	1.19 (1.01–1.40)	1.22 (0.88–1.68)	1.23 (1.03–1.48)	1.19 (0.95–1.49)
P_trend	0.0475	0.0083	0.9670	0.16	0.1624	0.0277	0.2394	0.0214	0.0972
Per 1-SD	1.07 (1.00–1.14)	1.09 (1.01–1.18)	1.05 (0.91–1.20)	1.05 (0.98–1.14)	1.08 (0.96–1.22)	1.08 (1.00–1.16)	1.10 (0.95–1.26)	1.08(1.00–1.18)	1.09 (0.98–1.20)
P-value	0.0454	0.0194	0.5273	0.1749	0.2191	0.0484	0.1898	0.064	0.1193
P_difference	NA	0.4921		0.6871		0.7177		0.8645

Males
N cases	1,089	874	119	833	150	899	101	598	348
6.9–10	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
10.1–10.6	0.96 (0.78–1.19)	0.97 (0.77–1.21)	0.93 (0.57–1.52)	0.97 (0.77–1.21)	1.10 (0.69–1.77)	1.00 (0.79–1.25)	0.97 (0.58–1.62)	0.96 (0.75–1.23)	1.08 (0.78–1.61)
10.7–17	1.18 (0.94–1.48)	1.28 (1.00–1.62)	0.96 (0.57–1.61)	1.18 (0.92–1.50)	1.26 (0.78–2.06)	1.30 (1.03–1.66)	0.86 (0.49–1.52)	1.16 (0.88–1.52)	1.62 (1.16–2.28)
P_trend	0.1252	0.0389	0.8721	0.1762	0.3441	0.0228	0.6030	0.2639	0.0029
Per 1-SD	1.16 (1.03–1.32)	1.20 (1.05–1.38)	1.11 (0.83–1.48)	1.14 (0.99–1.31)	1.31 (1.03–1.66)	1.20 (1.05–1.38)	1.11 (0.81–1.22)	1.18 (1.01–1.38)	1.29 (1.08–1.54)
P-value	0.0188	0.0067	0.4771	0.0632	0.0306	0.0065	0.5197	0.0329	0.0055
P_difference	NA	0.5891		0.2327		0.5796		0.3599

Females
N cases	1,054	762	202	743	248	762	193	608	316
6–9	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
9.1–9.76	0.96 (0.79–1.17)	0.96 (0.77–1.19)	0.91 (0.63–1.33)	0.95 (0.76–1.18)	0.98 (0.68–1.40)	0.90 (0.72–1.12)	1.16 (0.78–1.73)	1.05 (0.83–1.33)	0.77 (0.57–1.04)
9.77–14.9	1.12 (0.92–1.37)	1.21 (0.97–1.51)	1.04 (0.71–1.52)	1.08 (0.86–1.36)	1.19 (0.83–1.69)	1.10 (0.88–1.37)	1.44 (0.97–2.14)	1.31 (1.03–1.68)	0.91 (0.67–1.24)
P_trend	0.2647	0.0955	0.8406	0.4892	0.3085	0.4033	0.073	0.0279	0.5542
Per 1-SD	1.03 (0.94–1.11)	1.04 (0.95–1.14)	1.02 (0.87–1.19)	1.02 (0.93–1.12)	1.01 (0.88–1.17)	1.02 (0.93–1.12)	1.09 (0.93–1.27)	1.04 (0.94–1.15)	1.00 (0.88–1.13)
P-value	0.5603	0.3555	0.7838	0.7009	0.8905	0.6508	0.2935	0.4277	0.9734
P_difference	NA	0.5746		0.7498		0.4557		0.5897

Open in a new tab

The models are adjusted for study, age (continuous), sex (when not stratified), smoking status, education, and red meat intake (servings/d).

Case-only analyses used to calculate P_difference.

Based on sex-specific tertiles among controls only.

When the data were re-analysed after missing values were imputed, identical findings between WC, ABSI, and individual molecular subtypes of CRC were found (Table S12 & S13). Additional adjustments for family history of CRC, history of endoscopy, energy intake, and consumption of alcohol, processed meat, fruits, and vegetables also resulted in unchanged estimates (Table S14).

Waist circumference, ABSI, and combined molecular subtypes

Five of 16 possible combined CRC subtypes had 50 or more cases and were included in the analysis. Analysing by Jass types of CRC, a positive association with increased WC was observed for type 3 (alternate pathway) (RRR_{per 5cm increase} = 1.08, 95%CI 1.04 to 1.12; P=0.0001) and type 4 (traditional adenoma-carcinoma pathway) (RRR_{per 5cm increase} = 1.06, 95%CI 1.02 to 1.10; P=0.0014) CRC compared to controls (Figure 1A). For ABSI, these associations were also very similar but with wider Cis (Figure 1B). There were no differences by Jass type compared to type 4 in the case-only analysis. After conducting multiple imputation, we observed similar results for WC and ABSI (Figure S4).

Figure 1. — Forest plots of Jass classified types of colorectal cancer. Association between (A) waist circumference (per 5 cm increase), (B) a body shape index (per 1-SD increase), and Jass classified types of colorectal cancer.

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

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

Abbreviations: CIMP, CpG island methylator phenotype; CRC, colorectal cancer; MSI, microsatellite instability; MSS, microsatellite stable; RRR, relative risk ratio.

DISCUSSION

In this study of pooled individual-level data from 2,772 CRC cases and 3,521 controls within four cohorts, higher WC and ABSI were associated with a higher CRC risk. ABSI was not found to be linearly associated with any individual molecular subtypes in females but with all molecular subtypes in males compared to controls. There was no evidence of heterogeneity within each molecular subtype defined by BRAF and KRAS mutation, CIMP, and MSI status. We did not find any difference regarding the influence of WC and ABSI on the four major molecular markers in proximal colon, distal colon, and rectal cancer, as well as in early and later-onset CRC. Associations were consistent in the Jass-type analysis.

Our results support existing evidence suggesting an association between obesity, as indicated by WC, and an increased risk of CRC (14). Most prior studies have only investigated BMI (38–40) and only a few studies have examined the association between WC and CRC risk according to tumour mutation status (41–43) with our results being in line with the previous limited literature. A pooled analysis of two studies reported a statistically significant positive association for WC and both BRAF wild-type and mutated tumours compared to controls (41). The same study also found that the association between WC and CRC was similar according to MSI status but reached statistical significance only for MSI-H, probably due to the low number of cases (41). A small study (n=494 incident CRC cases) observed a positive association between WC and risk of KRAS-mutated and BRAF wild-type tumours; however, without evidence for a difference within the BRAF and KRAS mutation status (43). A recent Mendelian randomisation study reported that genetically predicted WC is associated with all individual subtypes without any difference within each molecular subtype (42). There is no previous literature examining the impact of ABSI on individual molecular subtypes of CRC.

Our study did not find any heterogeneity across molecular subtypes defined by Jass. Compared to controls, a positive association was observed for WC and Jass type 3 CRC, the alternate pathway characterised by KRAS mutation and non-MSI-high or CIMP-low or negative in our study. This is consistent with a Mendelian randomisation study that reported similar findings (42). A recent pooled observational analysis also found a positive association between higher BMI and Jass type 3 CRC compared to controls (11). We also observed a positive association between WC and the Jass type 4 CRC, the traditional adenoma-carcinoma pathway, defined by non-MSI-high and CIMP-low or negative, and no mutations in BRAF or KRAS. This was of similar magnitude to the findings of a previous study examining the impact of WC on molecular subtypes, but their finding did not reach statistical significance (42). The effect estimate of the Jass type 4 CRC was also similar to the result from a recent study investigating whether BMI affects molecular subtypes of CRC (11). Together, these results support a positive association between obesity and CRC development through the traditional adenoma-carcinoma (Jass type 4) and the alternate pathway (Jass type 3). Prior investigations of body size-related traits failed to find associations for Jass type 5 (Lynch syndrome), suggesting that obesity might not be a risk factor for the development of CRC in individuals with an inherited predisposition to Lynch syndrome (11,42). In our primary analysis, we were not able to examine rare Jass types, including Jass type 5, due to the limited sample size (<50 cases); however, when we imputed for missing values of WC and covariates, we observed non-statistically significant associations, which were similar in magnitude to the other CRC Jass types.

Our results suggest that increased WC can affect CRC tumorigenesis through the traditional adenoma-to-cancer pathway (Jass type 4) and the alternate pathway (Jass type 3). However, our analysis used broad categories to define molecular subtypes. Next-generation sequencing of tumour samples will allow a deeper classification of tumour subtypes by identifying somatically mutated genes. Examining associations between WC and newly identified CRC subtypes could provide important insights into the molecular mechanisms underlying the WC and CRC positive association.

Our study used WC and ABSI, which are more rarely studied. Assessing these anthropometric factors alongside other measures of obesity, such as BMI, may provide additional information for evaluating CRC risk. However, further research is needed to determine the optimal cut-off points of WC and ABSI for risk assessment. Future research should also explore other traditional and allometric indices (e.g., hip circumference, waist-to-hip ratio) focusing on differential body composition assessment and whether they affect CRC molecular subtypes.

A major strength of our study is the ability to pool data from four cohort studies with available information on WC and ABSI measurements. Due to harmonisation and consistent analysis, our results are less prone to between-study heterogeneity. The large sample size allowed an examination of the association between WC and the major Jass types, providing insights into how excess WC and ABSI are associated with different pathways of tumorigenesis. Some limitations should be considered when interpreting the findings. Due to the limited sample size, it was not possible to investigate all 16 Jass types of CRC, and the conclusions could not be inferred for the rarer subtypes. Nevertheless, for the rarer Jass types that were included in the imputed model, we did not observe any differential effect. Despite the adjustment for a number of potential confounders, we cannot rule out the possibility of unmeasured and residual confounding, although further adjustments in the sensitivity analysis did not affect the results. Finally, this study included predominantly white populations from the USA and Australia; therefore, the findings may not be generalisable to other racial and ethnic groups.

In conclusion, this study confirms the association between WC, ABSI, and CRC risk and suggests that WC and ABSI do not differentially influence the four major molecular mutations involved in colorectal carcinogenesis. Maintaining a healthy body weight can be crucial in CRC prevention.

Supplementary Material

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Acknowledgements

CPS-II: The authors express sincere appreciation to all Cancer Prevention Study-II participants, and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries and cancer registries supported by the National Cancer Institute’s Surveillance Epidemiology and End Results Program. The authors assume full responsibility for all analyses and interpretation of results. The views expressed here are those of the authors and do not necessarily represent the American Cancer Society or the American Cancer Society – Cancer Action Network.

Harvard cohorts: 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 acknowledge Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital as home of the NHS. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention’s National Program of Cancer Registries (NPCR) and/or the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program. Central registries may also be supported by state agencies, universities, and cancer centers. Participating central cancer registries include the following: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, Wyoming. The authors assume full responsibility for analyses and interpretation of these data.

ISACC: The authors would like to thank all those at the ISACC Coordinating Center for helping bring together the data and people that made this project possible.

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) 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.

CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study-II (CPS-II) cohort. The study protocol was approved by the institutional review boards of Emory University, and those of participating registries as required.

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

S.O. was supported by the American Cancer Society Clinical Research Professor Award (grant number, CRP-24-1185864-01-PROF).

MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 509348, 209057, 251553 and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. BMLynch was supported by MCRF18005 from the Victorian Cancer Agency.

This work was supported by a Cancer Research UK (CRUK) grant awarded to KKT (PPRCPJT\100005)

Footnotes

Conflict of interest

U. Peters was a consultant with AbbVie, and her husband holds individual stocks in the following companies: BioNTech SE—ADR, Amazon, CureVac BV, NanoString Technologies, Google/Alphabet Inc Class C, NVIDIA Corp, and Microsoft Corp.

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20.Andreasson A, Hagström H, Sköldberg F, Önnerhag K, Carlsson AC, Schmidt PT, et al. The prediction of colorectal cancer using anthropometric measures: A Swedish population-based cohort study with 22 years of follow-up. United Eur Gastroenterol J. 2019. Nov;7(9):1250–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Calle EE, Rodriguez C, Jacobs EJ, Almon ML, Chao A, McCullough ML, et al. The American Cancer Society Cancer Prevention Study II Nutrition Cohort: Rationale, study design, and baseline characteristics. Cancer. 2002. Jan 15;94(2):500–11. [DOI] [PubMed] [Google Scholar]
22.Campbell PT, Deka A, Briggs P, Cicek M, Farris AB, Gaudet MM, et al. Establishment of the Cancer Prevention Study II Nutrition Cohort Colorectal Tissue Repository. Cancer Epidemiol Biomarkers Prev. 2014. Dec 1;23(12):2694–702. [DOI] [PubMed] [Google Scholar]
23.Milne RL, Fletcher AS, MacInnis RJ, Hodge AM, Hopkins AH, Bassett JK, et al. Cohort Profile: The Melbourne Collaborative Cohort Study (Health 2020). Int J Epidemiol. 2017. Dec 1;46(6):1757–1757i. [DOI] [PubMed] [Google Scholar]
24.Belanger CF, Hennekens CH, Rosner B, Speizer FE. The Nurses’ Health Study. Am J Nurs. 1978. Jun;78(6):1039. [PubMed] [Google Scholar]
25.Ogino S, Brahmandam M, Cantor M, Namgyal C, Kawasaki T, Kirkner G, et al. Distinct molecular features of colorectal carcinoma with signet ring cell component and colorectal carcinoma with mucinous component. Mod Pathol. 2006. Jan;19(1):59–68. [DOI] [PubMed] [Google Scholar]
26.Hoffmeister M, Bläker H, Kloor M, Roth W, Toth C, Herpel E, et al. Body Mass Index and Microsatellite Instability in Colorectal Cancer: A Population-based Study. Cancer Epidemiol Biomarkers Prev. 2013. Dec 1;22(12):2303–11. [DOI] [PubMed] [Google Scholar]
27.Findeisen P, Kloor M, Merx S, Sutter C, Woerner SM, Dostmann N, et al. T25 Repeat in the 3′ Untranslated Region of the CASP2 Gene: A Sensitive and Specific Marker for Microsatellite Instability in Colorectal Cancer. Cancer Res. 2005. Sep 15;65(18):8072–8. [DOI] [PubMed] [Google Scholar]
28.Buchanan DD, Sweet K, Drini M, Jenkins MA, Win AK, English DR, et al. Risk Factors for Colorectal Cancer in Patients with Multiple Serrated Polyps: A Cross-Sectional Case Series from Genetics Clinics. Aziz SA, editor. PLoS ONE. 2010. Jul 16;5(7):e11636. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Rosty C, Pearson SA, Clendenning M, Win AK, Frankel WL, Goldblatt J, et al. Phenotype and Polyp Landscape in Serrated Polyposis Syndrome: A Series of 100 Patients From Genetics Clinics. Am J Surg Pathol. 2012;36(6). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Ogino S, Kawasaki T, Brahmandam M, Yan L, Cantor M, Namgyal C, et al. Sensitive Sequencing Method for KRAS Mutation Detection by Pyrosequencing. J Mol Diagn. 2005. Aug;7(3):413–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Ogino S, Kawasaki T, Kirkner GJ, Loda M, Fuchs CS. CpG Island Methylator Phenotype-Low (CIMP-Low) in Colorectal Cancer: Possible Associations with Male Sex and KRAS Mutations. J Mol Diagn. 2006. Nov;8(5):582–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Ogino S, Meyerhardt JA, Cantor M, Brahmandam M, Clark JW, Namgyal C, et al. Molecular Alterations in Tumors and Response to Combination Chemotherapy with Gefitinib for Advanced Colorectal Cancer. Clin Cancer Res. 2005. Sep 15;11(18):6650–6. [DOI] [PubMed] [Google Scholar]
33.Imamura Y, Lochhead P, Yamauchi M, Kuchiba A, Qian ZR, Liao X, et al. Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review. Mol Cancer. 2014. Dec;13(1):135. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Ogino S, Kawasaki T, Kirkner GJ, Kraft P, Loda M, Fuchs CS. Evaluation of Markers for CpG Island Methylator Phenotype (CIMP) in Colorectal Cancer by a Large Population-Based Sample. J Mol Diagn. 2007. Jul;9(3):305–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.English DR, Young JP, Simpson JA, Jenkins MA, Southey MC, Walsh MD, et al. Ethnicity and Risk for Colorectal Cancers Showing Somatic BRAF V600E Mutation or CpG Island Methylator Phenotype. Cancer Epidemiol Biomarkers Prev. 2008. Jul 1;17(7):1774–80. [DOI] [PubMed] [Google Scholar]
36.Eads CA. MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res. 2000. Apr 15;28(8):32e–0. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011. Feb 20;30(4):377–99. [DOI] [PubMed] [Google Scholar]
38.Carr PR, Alwers E, Bienert S, Weberpals J, Kloor M, Brenner H, et al. Lifestyle factors and risk of sporadic colorectal cancer by microsatellite instability status: a systematic review and meta-analyses. Ann Oncol. 2018. Apr;29(4):825–34. [DOI] [PubMed] [Google Scholar]
39.Weisenberger DJ, Levine AJ, Long TI, Buchanan DD, Walters R, Clendenning M, et al. Association of the Colorectal CpG Island Methylator Phenotype with Molecular Features, Risk Factors, and Family History. Cancer Epidemiol Biomarkers Prev. 2015. Mar 1;24(3):512–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Carr PR, Amitay EL, Jansen L, Alwers E, Roth W, Herpel E, et al. Association of BMI and major molecular pathological markers of colorectal cancer in men and women. Am J Clin Nutr. 2020. Mar;111(3):562–9. [DOI] [PubMed] [Google Scholar]
41.Hughes LA, Williamson EJ, Van Engeland M, Jenkins MA, Giles GG, Hopper JL, et al. Body size and risk for colorectal cancers showing BRAF mutations or microsatellite instability: a pooled analysis. Int J Epidemiol. 2012. Aug 1;41(4):1060–72. [DOI] [PubMed] [Google Scholar]
42.Papadimitriou N, Qu C, Harrison TA, Bever AM, Martin RM, Tsilidis KK, et al. Body size and risk of colorectal cancer molecular defined subtypes and pathways: Mendelian randomization analyses. eBioMedicine. 2024. Mar;101:105010. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Brändstedt J, Wangefjord S, Nodin B, Eberhard J, Sundström M, Manjer J, et al. Associations of Anthropometric Factors with KRAS and BRAF Mutation Status of Primary Colorectal Cancer in Men and Women: A Cohort Study. Moschetta A, editor. PLoS ONE. 2014. Jun 11;9(6):e98964. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

NIHMS2056041-supplement-1.docx^{(16.4KB, docx)}

NIHMS2056041-supplement-2.docx^{(16.6KB, docx)}

NIHMS2056041-supplement-3.docx^{(16.6KB, docx)}

NIHMS2056041-supplement-4.docx^{(569.5KB, docx)}

NIHMS2056041-supplement-5.docx^{(256.4KB, docx)}

NIHMS2056041-supplement-6.docx^{(220.1KB, docx)}

NIHMS2056041-supplement-7.docx^{(22.6KB, docx)}

NIHMS2056041-supplement-8.docx^{(22.5KB, docx)}

NIHMS2056041-supplement-9.docx^{(22.8KB, docx)}

NIHMS2056041-supplement-10.docx^{(22.6KB, docx)}

NIHMS2056041-supplement-11.docx^{(22.9KB, docx)}

NIHMS2056041-supplement-12.docx^{(22.7KB, docx)}

NIHMS2056041-supplement-13.docx^{(21.3KB, docx)}

NIHMS2056041-supplement-14.docx^{(21.2KB, docx)}

NIHMS2056041-supplement-15.docx^{(22KB, docx)}

NIHMS2056041-supplement-16.docx^{(21.9KB, docx)}

NIHMS2056041-supplement-17.docx^{(21.2KB, docx)}

NIHMS2056041-supplement-18.docx^{(207.7KB, docx)}

NIHMS2056041-supplement-19.docx^{(24KB, docx)}

Data Availability Statement

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[R22] 22.Campbell PT, Deka A, Briggs P, Cicek M, Farris AB, Gaudet MM, et al. Establishment of the Cancer Prevention Study II Nutrition Cohort Colorectal Tissue Repository. Cancer Epidemiol Biomarkers Prev. 2014. Dec 1;23(12):2694–702. [DOI] [PubMed] [Google Scholar]

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[R25] 25.Ogino S, Brahmandam M, Cantor M, Namgyal C, Kawasaki T, Kirkner G, et al. Distinct molecular features of colorectal carcinoma with signet ring cell component and colorectal carcinoma with mucinous component. Mod Pathol. 2006. Jan;19(1):59–68. [DOI] [PubMed] [Google Scholar]

[R26] 26.Hoffmeister M, Bläker H, Kloor M, Roth W, Toth C, Herpel E, et al. Body Mass Index and Microsatellite Instability in Colorectal Cancer: A Population-based Study. Cancer Epidemiol Biomarkers Prev. 2013. Dec 1;22(12):2303–11. [DOI] [PubMed] [Google Scholar]

[R27] 27.Findeisen P, Kloor M, Merx S, Sutter C, Woerner SM, Dostmann N, et al. T25 Repeat in the 3′ Untranslated Region of the CASP2 Gene: A Sensitive and Specific Marker for Microsatellite Instability in Colorectal Cancer. Cancer Res. 2005. Sep 15;65(18):8072–8. [DOI] [PubMed] [Google Scholar]

[R28] 28.Buchanan DD, Sweet K, Drini M, Jenkins MA, Win AK, English DR, et al. Risk Factors for Colorectal Cancer in Patients with Multiple Serrated Polyps: A Cross-Sectional Case Series from Genetics Clinics. Aziz SA, editor. PLoS ONE. 2010. Jul 16;5(7):e11636. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Rosty C, Pearson SA, Clendenning M, Win AK, Frankel WL, Goldblatt J, et al. Phenotype and Polyp Landscape in Serrated Polyposis Syndrome: A Series of 100 Patients From Genetics Clinics. Am J Surg Pathol. 2012;36(6). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Ogino S, Kawasaki T, Brahmandam M, Yan L, Cantor M, Namgyal C, et al. Sensitive Sequencing Method for KRAS Mutation Detection by Pyrosequencing. J Mol Diagn. 2005. Aug;7(3):413–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Ogino S, Kawasaki T, Kirkner GJ, Loda M, Fuchs CS. CpG Island Methylator Phenotype-Low (CIMP-Low) in Colorectal Cancer: Possible Associations with Male Sex and KRAS Mutations. J Mol Diagn. 2006. Nov;8(5):582–8. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Ogino S, Meyerhardt JA, Cantor M, Brahmandam M, Clark JW, Namgyal C, et al. Molecular Alterations in Tumors and Response to Combination Chemotherapy with Gefitinib for Advanced Colorectal Cancer. Clin Cancer Res. 2005. Sep 15;11(18):6650–6. [DOI] [PubMed] [Google Scholar]

[R33] 33.Imamura Y, Lochhead P, Yamauchi M, Kuchiba A, Qian ZR, Liao X, et al. Analyses of clinicopathological, molecular, and prognostic associations of KRAS codon 61 and codon 146 mutations in colorectal cancer: cohort study and literature review. Mol Cancer. 2014. Dec;13(1):135. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Ogino S, Kawasaki T, Kirkner GJ, Kraft P, Loda M, Fuchs CS. Evaluation of Markers for CpG Island Methylator Phenotype (CIMP) in Colorectal Cancer by a Large Population-Based Sample. J Mol Diagn. 2007. Jul;9(3):305–14. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.English DR, Young JP, Simpson JA, Jenkins MA, Southey MC, Walsh MD, et al. Ethnicity and Risk for Colorectal Cancers Showing Somatic BRAF V600E Mutation or CpG Island Methylator Phenotype. Cancer Epidemiol Biomarkers Prev. 2008. Jul 1;17(7):1774–80. [DOI] [PubMed] [Google Scholar]

[R36] 36.Eads CA. MethyLight: a high-throughput assay to measure DNA methylation. Nucleic Acids Res. 2000. Apr 15;28(8):32e–0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.White IR, Royston P, Wood AM. Multiple imputation using chained equations: Issues and guidance for practice. Stat Med. 2011. Feb 20;30(4):377–99. [DOI] [PubMed] [Google Scholar]

[R38] 38.Carr PR, Alwers E, Bienert S, Weberpals J, Kloor M, Brenner H, et al. Lifestyle factors and risk of sporadic colorectal cancer by microsatellite instability status: a systematic review and meta-analyses. Ann Oncol. 2018. Apr;29(4):825–34. [DOI] [PubMed] [Google Scholar]

[R39] 39.Weisenberger DJ, Levine AJ, Long TI, Buchanan DD, Walters R, Clendenning M, et al. Association of the Colorectal CpG Island Methylator Phenotype with Molecular Features, Risk Factors, and Family History. Cancer Epidemiol Biomarkers Prev. 2015. Mar 1;24(3):512–9. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.Carr PR, Amitay EL, Jansen L, Alwers E, Roth W, Herpel E, et al. Association of BMI and major molecular pathological markers of colorectal cancer in men and women. Am J Clin Nutr. 2020. Mar;111(3):562–9. [DOI] [PubMed] [Google Scholar]

[R41] 41.Hughes LA, Williamson EJ, Van Engeland M, Jenkins MA, Giles GG, Hopper JL, et al. Body size and risk for colorectal cancers showing BRAF mutations or microsatellite instability: a pooled analysis. Int J Epidemiol. 2012. Aug 1;41(4):1060–72. [DOI] [PubMed] [Google Scholar]

[R42] 42.Papadimitriou N, Qu C, Harrison TA, Bever AM, Martin RM, Tsilidis KK, et al. Body size and risk of colorectal cancer molecular defined subtypes and pathways: Mendelian randomization analyses. eBioMedicine. 2024. Mar;101:105010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Brändstedt J, Wangefjord S, Nodin B, Eberhard J, Sundström M, Manjer J, et al. Associations of Anthropometric Factors with KRAS and BRAF Mutation Status of Primary Colorectal Cancer in Men and Women: A Cohort Study. Moschetta A, editor. PLoS ONE. 2014. Jun 11;9(6):e98964. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Waist circumference, a body shape index, and molecular subtypes of colorectal cancer: A pooled analysis of four cohort studies

Christos V Chalitsios

Georgios Markozannes

Christos Papagiannopoulos

Elom K Aglago

Sonja I Berndt

Daniel D Buchanan

Peter T Campbell

Yin Cao

Andrew T Chan

Niki Dimou

David A Drew

Amy J French

Peter Georgeson

Marios Giannakis

Stephen B Gruber

Marc J Gunter

Tabitha A Harrison

Michael Hoffmeister

Li Hsu

Wen-Yi Huang

Meredith AJ Hullar

Jeroen R Huyghe

Brigid M Lynch

Victor Moreno

Christina C Newton

Jonathan A Nowak

Mireia Obón-Santacana

Shuji Ogino

Conghui Qu

Stephanie L Schmit

Robert S Steinfelder

Wei Sun

Claire E Thomas

Amanda E Toland

Quang M Trinh

Tomotaka Ugai

Caroline Y Um

Bethany Van Guelpen

Syed H Zaidi

Neil Murphy

Ulrike Peters

Amanda I Phipps

Konstantinos K Tsilidis

Abstract

Background

Methods

Results

Conclusions

Impact

INTRODUCTION

MATERIALS and METHODS

Study population

Outcomes assessment

Exposure and confounder assessment

Statistical analysis

Table 1.

Data availability

RESULTS

Study population characteristics

Waist circumference and individual molecular subtypes

Table 2.

Table 3.

Waist circumference, ABSI, and combined molecular subtypes

Figure 1.

DISCUSSION

Supplementary Material

Acknowledgements

Footnotes

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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