Objective Factors that lead to metabolic dysregulation are associated with increased risk of early-onset colorectal cancer (CRC diagnosed under age 50). However, the association between metabolic syndrome (MetS) and early-onset CRC remains unexamined.
Design We conducted a nested case–control study among participants aged 18–64 in the IBM MarketScan Commercial Database (2006–2015). Incident CRC was identified using pathologist-coded International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes, and controls were frequency matched. MetS was defined as presence of ≥3 conditions among obesity, hypertension, hyperlipidaemia and hyperglycaemia/type 2 diabetes, based on ICD-9-CM and use of medications. Multivariable logistic regressions were used to estimate ORs and 95% CIs.
Results MetS was associated with increased risk of early-onset CRC (n=4673; multivariable adjusted OR 1.25; 95% CI 1.09 to 1.43), similar to CRC diagnosed at age 50–64 (n=14 928; OR 1.21; 95% CI 1.15 to 1.27). Compared with individuals without a metabolic comorbid condition, those with 1, 2 or ≥3 conditions had a 9% (1.09; 95% CI 1.00 to 1.17), 12% (1.12; 95% CI 1.01 to 1.24) and 31% (1.31; 95% CI 1.13 to 1.51) higher risk of early-onset CRC (ptrend <0.001). No associations were observed for one or two metabolic comorbid conditions and CRC diagnosed at age 50–64. These positive associations were driven by proximal (OR per condition 1.14; 95% CI 1.06 to 1.23) and distal colon cancer (OR 1.09; 95% CI 1.00 to 1.18), but not rectal cancer (OR 1.03; 95% CI 0.97 to 1.09).
Conclusions Metabolic dysregulation was associated with increased risk of early-onset CRC, driven by proximal and distal colon cancer, thus at least in part contribute to the rising incidence of early-onset CRC.
- colorectal cancer
- cancer epidemiology
Data availability statement
Data may be obtained from a third party and are not publicly available.
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HC, XBZ and XYZ contributed equally.
Contributors HC and YC had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. HC, XBZ, ELG, MAO, and YC helped in study concept and design. KBN, AT, MAO and YC helped in acquisition of data. All coauthors performed analysis and interpretation of data. Drafting of the manuscript was done by HC, XBZ, XYZ, ZL, NL and YC. Critical revision of the manuscript for important intellectual content was done by all coauthors. HC, XYZ, ZL and YC helped in statistical analysis. YC helped in obtaining funding. Administrative, technical or material support was provided by MAO and YC. Study supervision was done by YC.
Funding This work was supported by US National Institutes of Health (NIH) grant P30CA091842. The Center for Administrative Data Research is supported in part by the Washington University Institute of Clinical and Translational Sciences grant UL1 TR002345 from the National Center for Advancing Translational Sciences (NCATS) of the NIH and Grant Number R24 HS19455 through the Agency for Healthcare Research and Quality (AHRQ). HC is a self-funded visiting scholar without financial support from the First Affiliated Hospital of China Medical University. XBZ was supported by the International Program for PhD Candidates, Sun Yat-sen University (grant/award number: NA). ZL is a self-funded visiting scholar without financial support from National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. CDF was supported by T32 DK007130.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
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