Metabolic and lifestyle determinants of early-onset colorectal cancer in Korea: lessons from a nationwide nested case-control study

Early-onset colorectal cancer (EoCRC), conventionally defined as colorectal cancer diagnosed before age 50 years, has become a major concern in contemporary cancer epidemiology. Population-based registry studies have reported sustained increases in colorectal cancer incidence among adults younger than 50 years across North America, Europe, and Asia, even as incidence has stabilized or declined among older adults in many high-income settings. In several settings, the most rapid relative increases have been observed in younger age strata, including adults in their 20s and 30s, although absolute rates remain highest in older populations. These patterns have intensified debate about whether EoCRC reflects a shift in the age distribution of conventional colorectal cancer or whether it represents partially distinct etiologic pathways shaped by life-course exposures, metabolic dysfunction, and changing behaviors (1-4).

Rising EoCRC incidence has also reshaped screening discussions. For decades, many guidelines initiated average-risk colorectal cancer screening at age 50 years, reflecting the historic age distribution of disease. In response to emerging incidence patterns and modeling of benefits and harms, several organizations have lowered the recommended starting age for average-risk screening to 45 years, including the U.S. Preventive Services Task Force in 2021 (5). Earlier screening is an important step, but it does not fully address heterogeneity of risk within younger adults. In particular, risk factor profiles and the relative contributions of modifiable exposures differ by sex and by survivorship history, which supports complementary risk-stratified approaches that leverage routinely collected clinical and behavioral data.

In this context, Baek et al. report an important analysis of modifiable risk factors for EoCRC using the Korean National Health Insurance Service-Health Screening Cohort (NHIS-HEALS) (6). Their nested case-control study quantifies both relative risks and population attributable fractions (PAFs) for metabolic and lifestyle exposures, comparing adults diagnosed at ages 40–49 years with those diagnosed at age 50 years or older. The work provides rare, large-scale, population-based data from Asia on determinants of EoCRC and highlights sex-specific differences that are directly relevant to prevention and risk-stratified screening.

Baek et al. identified 10,231 colorectal cancer cases within 514,866 NHIS-HEALS participants aged 40–79 years who underwent health screening in 2002–2003 (6). After a wash-out period, 9,602 individuals remained eligible: 285 (3.0%) were classified as EoCRC (age 40–49 years at diagnosis) and 9,317 (97.0%) as late-onset colorectal cancer (LoCRC; 50 years or older). Each case was matched to up to 15 controls on birth year, sex, and screening year, yielding 4,275 controls for EoCRC and 139,755 controls for LoCRC. Baseline screening and claims data provided information on body mass index (BMI), waist circumference, smoking, alcohol use, physical activity, diabetes, cardiovascular and cerebrovascular disease, dyslipidemia, and history of cancers other than colorectal cancer (6).

Using conditional logistic regression, the authors estimated adjusted odds ratios (aORs) for each exposure. They then applied standard formulas to compute PAFs, approximating relative risks with aORs and using exposure prevalences from the 2011 NHIS-HEALS population as a mid-period reference (6). To better characterize the uncertainty around PAFs, which is particularly important given the modest number of EoCRC cases, they implemented 10,000-iteration Monte Carlo simulations that drew from log-normal distributions defined by the regression estimates (6). This design, nested within a well-characterized cohort, combines the temporal advantages of prospective exposure assessment with the efficiency of a case-control analysis.

The main results are striking in their simplicity for EoCRC. In fully adjusted models, only two factors were significantly associated with EoCRC: BMI at least 25 kg/m² [aOR =1.32; 95% confidence interval (CI): 1.02–1.70] and a history of cancers other than colorectal cancer (aOR =5.68; 95% CI: 3.82–8.45) (6). In contrast, LoCRC showed a broader array of modest risk elevations. Elevated BMI, long-duration smoking of at least 20 years, frequent alcohol intake at least three times per week, physical inactivity, diabetes, and prior cancers were all associated with increased odds of LoCRC (6). The forest plots in Fig. 2 of Baek et al. visually emphasize the narrower set of risk factors for EoCRC relative to LoCRC (6).

The PAF analysis provides a complementary view of population impact. Overall, the combined PAF for the included metabolic and lifestyle factors was estimated at 18.0% for EoCRC and 16.6% for LoCRC (6). BMI alone accounted for about half of the attributable burden in EoCRC (mean PAF 9.4%), compared with 1.7% in LoCRC, and a history of other cancers contributed an additional 5.2% of EoCRC and 0.7% of LoCRC (6). Long-term smoking, frequent alcohol intake, and physical inactivity contributed more to LoCRC than to EoCRC. At least in this cohort of adults over 40 years, EoCRC therefore appears to be more tightly linked to excess adiposity and prior malignancy than to the broader constellation of behavioral exposures that characterize LoCRC.

Sex-stratified PAFs reveal further heterogeneity. Among men, overall PAFs were similar for EoCRC and LoCRC (20.8% vs. 21.7%). In male EoCRC, high BMI (10.5%), long-duration smoking (7.4%), and prior cancers (3.1%) were the dominant contributors (6). Among women, the overall PAF for EoCRC was three times higher than for LoCRC (34.2% vs. 11.2%). In female EoCRC, high BMI (9.5%), prior cancers (9.5%), physical inactivity (8.4%), and frequent alcohol use (6.9%) all contributed substantially, with smaller contributions from diabetes and dyslipidemia (6). These patterns suggest that, in Korean women approaching age 50 years, a relatively large share of EoCRC may be linked to modifiable lifestyle and metabolic factors that are routinely captured in health screening programs.

These sex-specific patterns also have direct implications for risk-stratified colorectal cancer screening. Risk prediction approaches that treat sex only as an adjustment variable may miss important differences in how modifiable exposures map to risk. In men, the modifiable burden of EoCRC appears most influenced by BMI, long-duration smoking, and prior cancer history, whereas in women, BMI and prior cancers remain important but physical inactivity and alcohol consumption contribute disproportionately to the attributable burden (6). Incorporating sex-specific coefficients or explicit interaction terms may improve model calibration and clinical usefulness, and it may better identify younger adults who could benefit from earlier diagnostic evaluation or intensified screening beyond age-based thresholds alone.

The central role of excess adiposity in this study aligns with a growing literature that implicates metabolic dysfunction as a driver of EoCRC. Large-scale syntheses and observational studies have consistently linked obesity and related metabolic disturbances with increased EoCRC risk (4,7-9). Within this framework, the contribution of BMI in Baek et al. highlights that excess adiposity may account for a meaningful share of EoCRC even in populations with structured national health screening programs (6).

The sex-specific findings invite further etiologic exploration. In men, the risk architecture of EoCRC in this cohort resembles that of traditional LoCRC, with excess BMI and long-duration smoking functioning as the main modifiable contributors. In women, the relatively larger PAFs attributed to physical inactivity and alcohol use, alongside adiposity and prior cancers, suggest different patterns of behavioral clustering and metabolic vulnerability (6). Prior studies have described sex- and gender-related differences in colorectal cancer risk, including differences in adiposity distribution, insulin resistance, and hormonal exposures that may interact with lifestyle factors (10). Baek et al. did not directly examine mechanistic pathways, but their sex-stratified analyses underscore the importance of considering sex as an effect modifier in EoCRC research rather than treating it solely as a covariate to be adjusted (6).

The strong association between a history of cancers other than colorectal cancer and EoCRC is perhaps the most provocative finding. The aOR of 5.68 for EoCRC, compared with 1.50 for LoCRC, indicates that prior malignancy is a powerful marker of susceptibility to EoCRC in this cohort (6). Multiple explanations are plausible. Shared genetic susceptibility, ranging from high-penetrance hereditary syndromes to polygenic risk, may predispose some individuals to multiple primary cancers at younger ages. Treatment-related exposures, such as radiation and specific systemic therapies, may promote colorectal carcinogenesis through DNA damage, chronic inflammation, or microbiome disruption. Survivorship care usually includes more frequent imaging and clinical contact, which can increase the likelihood of early colorectal cancer detection and thus inflate apparent relative risks through surveillance bias. The claims-based data also do not distinguish multiple primaries from metastases, so some proportion of secondary colorectal diagnoses may represent metastatic spread rather than new primaries.

Mechanistic uncertainty notwithstanding, the clinical message is straightforward. Cancer survivorship should be recognized as a risk-enhancing condition for EoCRC. Survivors of non-colorectal malignancies may warrant more proactive counseling about colorectal symptoms and lower thresholds for diagnostic colonoscopy. In settings with adequate resources, earlier initiation of routine colorectal cancer screening may be appropriate for selected survivor groups, particularly those who received abdominal or pelvic radiation, have known hereditary syndromes, or accumulate multiple metabolic and lifestyle risk factors (6). Clarifying which survivor subgroups carry sufficiently elevated risks to justify altered screening pathways will require linkage of claims data with cancer registries, genomic information, and detailed treatment histories.

Baek et al.’s study has several notable strengths. The use of a nationwide, standardized screening cohort provides a population-based context that is seldom available in EoCRC research. Because risk factor information was collected before diagnosis as part of routine health examinations, recall bias is minimized. Incidence density sampling and matching of cases and controls on birth year, sex, and screening year improve internal validity by controlling for age, sex, and secular cohort effects. The PAF framework, combined with Monte Carlo simulation, yields an interpretable measure of potential preventive impact while explicitly conveying the uncertainty around those estimates (6).

Several limitations should be noted. First, the operational definition of EoCRC was restricted to diagnoses at ages 40–49 years because the NHIS-HEALS cohort includes only individuals aged 40–79 years at baseline (6). The most dramatic relative increases in EoCRC incidence in many high-income countries have been observed among individuals in their 20s and 30s, whose exposure histories and risk factor profiles may differ substantially from those in their 40s (1-4). In Korea and elsewhere, adults in their 40s still account for a substantial proportion of EoCRC diagnoses; however, these results should not be assumed to generalize to the youngest patients.

Second, both cases and controls were drawn from individuals who participated in the National Health Screening Program in 2002–2003. These participants are often healthier and more engaged with preventive care than non-participants, which could attenuate associations between adverse lifestyle factors and colorectal cancer risk (6). Third, key exposures, including BMI, smoking, alcohol intake, and physical activity, were measured at a limited number of time points, primarily in midlife. Smoking status, alcohol use, activity patterns, and metabolic status can change over time, and reliance on baseline measures may introduce exposure misclassification that can attenuate associations. In addition, Baek et al. did not include dietary factors, which limits inference regarding nutritional determinants that may be relevant to EoCRC etiology (6,7).

The reliance on BMI as the main index of adiposity and the incomplete waist circumference data also constrain interpretation. BMI does not distinguish between lean and fat mass or between central and peripheral adiposity. Evidence from Korea indicates that lipid abnormalities such as persistent hypertriglyceridemia may carry additional EoCRC risk beyond BMI alone (9). Baek et al. report on abdominal obesity where data are available, but the high proportion of missing measurements and categorical definitions limit more granular assessment of dose-response relationships (6).

From a methodological standpoint, PAF calculations assume causal relationships and independence of risk factors when combined multiplicatively. Lifestyle and metabolic exposures cluster strongly within individuals. For that reason, the overall PAF values reported by Baek et al. are best viewed as approximate upper bounds on the preventable fraction under idealized conditions, rather than precise forecasts of what real-world interventions could achieve (6).

Despite these limitations, the study carries clear implications for prevention and policy. First, the sex-stratified findings can be translated into pragmatic prevention priorities. For men, public health interventions may place particular emphasis on smoking cessation and metabolic weight management, consistent with the dominant contributions of long-duration smoking and BMI to male EoCRC burden (6). For women, clinical and public health guidance should prioritize increasing physical activity and moderating alcohol intake, in addition to weight optimization, given the larger attributable fractions observed for these exposures in female EoCRC (6). These recommendations are anchored to the exposures measured in NHIS-HEALS and should be evaluated alongside other determinants not captured in this dataset, including diet.

Second, the findings support ongoing efforts to develop risk-stratified screening strategies. Many health systems face constraints in colonoscopy capacity and seek to target screening to those at highest risk. Risk models that incorporate age, sex, BMI, smoking, diabetes, and history of prior malignancy, built on routinely collected administrative and screening data, could help identify subsets of adults in their 40s, particularly women with clustered metabolic and behavioral risk factors and survivors of other cancers, for earlier or more intensive colorectal evaluation (6,8). The decision by the U.S. Preventive Services Task Force to lower the recommended age for average-risk colorectal cancer screening from 50 to 45 years reflects this broader shift toward acknowledging risk in younger adults (5). Datasets such as NHIS-HEALS provide ideal platforms for building and periodically recalibrating such tools (6).

Third, Baek et al. show that a substantial share of EoCRC remains unexplained by the measured factors, which underscores the need to broaden etiologic research. Diet, microbiome composition, antibiotic exposure, sleep and circadian disruption, endocrine-disrupting chemicals, and early-life socioeconomic and environmental conditions are all plausible contributors that were not captured in NHIS-HEALS. Integration of administrative data with biospecimens, detailed dietary assessment, microbiome profiling, and geocoded environmental metrics will be necessary to construct a more complete causal framework for EoCRC (3,7).

In summary, Baek et al. use a robust national dataset to show that metabolic and lifestyle factors, especially excess adiposity, account for a meaningful proportion of EoCRCs and that sex-specific differences are especially pronounced among women (6). The particularly high attributable fractions among women and among individuals with a history of another cancer highlight opportunities for targeted prevention and risk-adapted screening. Their work reinforces the view that EoCRC is not solely the product of immutable biology or chance; it is, at least in part, a preventable disease shaped by modifiable exposures and survivorship trajectories (3,6,7).

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Annals of Cancer Epidemiology. The article has undergone external peer review.

Peer Review File: Available at https://ace.amegroups.com/article/view/10.21037/ace-2025-1-16/prf

Funding: None.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at https://ace.amegroups.com/article/view/10.21037/ace-2025-1-16/coif). The author has no conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Lui RN, Tsoi KKF, Ho JMW, et al. Global Increasing Incidence of Young-Onset Colorectal Cancer Across 5 Continents: A Joinpoint Regression Analysis of 1,922,167 Cases. Cancer Epidemiol Biomarkers Prev 2019;28:1275-82. [Crossref] [PubMed]
2. Shah RR, Millien VO, da Costa WL Jr, et al. Trends in the incidence of early-onset colorectal cancer in all 50 United States from 2001 through 2017. Cancer 2022;128:299-310. [Crossref] [PubMed]
3. Ugai T, Sasamoto N, Lee HY, et al. Is early-onset cancer an emerging global epidemic? Current evidence and future implications. Nat Rev Clin Oncol 2022;19:656-73.
4. Mauri G, Sartore-Bianchi A, Russo AG, et al. Early-onset colorectal cancer in young individuals. Mol Oncol 2019;13:109-31. [Crossref] [PubMed]
5. US Preventive Services Task Force. Screening for Colorectal Cancer: US Preventive Services Task Force Recommendation Statement. JAMA 2021;325:1965-77. [Crossref] [PubMed]
6. Baek JY, Jeong SY, Shin A. Risk Factors for Early-Onset Colorectal Cancer: A Nested Case‒Control Study within the Korean National Health Insurance Service‒Health Screening Cohort. Cancer Epidemiol Biomarkers Prev 2026;35:647-54. [Crossref] [PubMed]
7. O'Sullivan DE, Sutherland RL, Town S, et al. Risk Factors for Early-Onset Colorectal Cancer: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol 2022;20:1229-1240.e5. [Crossref] [PubMed]
8. Gausman V, Dornblaser D, Anand S, et al. Risk Factors Associated With Early-Onset Colorectal Cancer. Clin Gastroenterol Hepatol 2020;18:2752-2759.e2. [Crossref] [PubMed]
9. Chang YH, Shin CM, Han K, et al. The Persistence of Hypertriglyceridemia and the Risk of Early Onset Colorectal Cancer According to Tumor Subsites: A Nationwide Population-Based Study. Cancer Res Treat 2024;56:825-37. [Crossref] [PubMed]
10. Kim SE, Paik HY, Yoon H, et al. Sex- and gender-specific disparities in colorectal cancer risk. World J Gastroenterol 2015;21:5167-75. [Crossref] [PubMed]