Younger age at colorectal cancer diagnosis in hereditary gastrointestinal cancer predisposition syndromes and Lynch syndrome: a nationwide analysis

Introduction

Many hereditary gastrointestinal cancer predisposition syndromes (HGCPS) are characterized by an increased risk for gastrointestinal (GI) cancers, including colorectal cancer (CRC), at younger ages. Lynch syndrome (LS) is the most common hereditary CRC predisposition syndrome, traditionally presenting with early-onset CRC at a median age of 44 years in the absence of surveillance, with nearly 40% of cases occurring before age 40 years (1). In contrast, sporadic CRC has historically presented later in life with a median diagnosis of 66 years (2). In recent years, multiple reports have highlighted a younger age distribution for CRC in the general population, with the overall median age at CRC diagnosis declining by 5–6 years over the past two decades (3,4). For example, the median age decreased from 72 in the early 2000s, to 66 years by 2019 (5,6). Incidence rates among adults younger than 50 also increased (7). Since 2010, CRC incidence increased by 2–3% annually among adults <50 years, while stabilizing or declining among adults ≥65 years (7,8). This concerning rise in young individuals has prompted the U.S. screening age to be lowered to 45 for average-risk individuals (6). Despite clear recognition of these trends in the general population, temporal shifts in the age at CRC onset among individuals with hereditary GI cancer susceptibility remain poorly characterized. Understanding whether these hereditary-risk populations are experiencing similar shifts in age at CRC onset is of scientific and clinical importance. First, clarifying these patterns directly informs the appropriate timing of current CRC screening and gene-specific surveillance strategies for HGCPS/LS. Additionally, it offers insights into the interplay between germline susceptibility and contemporary environmental and lifestyle exposures that have been implicated in the rising incidence of early-onset CRC in the general population. To address this gap, we conducted a large, multicenter, real-world analysis to evaluate temporal changes in the age distribution of CRC diagnoses among patients with HGCPS/LS compared with average-risk individuals from 2010 through 2024. We present this article in accordance with the STROBE reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0167/rc).

Methods

This retrospective cohort study analyzed data from the TriNetX U.S. Analytics Network Platform, a multicenter database that includes de-identified electronic health records (EHRs) of 71 health care organizations (HCOs) with over 108 million patients across the U.S. HCOs included in the TriNetX dataset are academic, community medical centers, and outpatient clinics. A rigorous quality assurance process ensures data integrity during the extraction from EHRs. Patients’ records were identified using International Classification of Diseases, Ninth or Tenth Edition, Clinical Modification (ICD-10-CM) codes for patients’ diagnoses. For patients included before 2015, ICD-9 codes were automatically mapped into corresponding ICD-10 codes using TriNetX General Equivalence Mappings (GEMs). The de-identification process, as stipulated by the Health Insurance Portability and Accountability Act Privacy Rule (HIPAA), is meticulously executed at the network level by a qualified expert within the TriNetX framework.

We conducted our retrospective study from 2010 to 2024. Patients with familial adenomatous polyposis (D13.91), Crohn’s disease (K50), or ulcerative colitis (K51) were excluded. Adults ≥18 years who attended outpatient clinics were categorized into two cohorts. Cohort 1 (hereditary-risk cohort) included patients with HGCPS/LS identified using ICD-10-CM codes Z15.09 (genetic susceptibility to other malignant neoplasm) or Z14.8 (carrier of other genetic disorders) (9). In addition, patients were examined to determine age at HGCPS/LS diagnosis. Cohort 2 (non-coded hereditary susceptibility cohort) included adults without hereditary cancer susceptibility codes (ICD-10-CM codes: Z15.09 or Z14.8), and without high-risk conditions to approximate average-risk individuals. The primary outcome was the mean age of CRC diagnosis in each cohort (hereditary-risk cohort vs. non-coded hereditary average risk cohort) using means and predefined age categories (<20, 20–34, 35–44, 45–54, 55–64, 65–74, ≥75 years). CRC diagnoses were identified using ICD-10-CM codes: C18, C19, C20.

Statistical analysis

All analyses were conducted within the TriNetX Advanced Analytics Platform, which applies R for statistical computation. Continuous variables were summarized as mean ± standard deviation (SD) and categorical variables as frequencies and percentages. Age at CRC diagnosis was examined both as a continuous measure and across predefined age categories (<20, 20–34, 35–44, 45–54, 55–64, 65–74, and ≥75 years). Differences in the overall distribution of age at CRC diagnosis between the HGCPS/LS and average-risk cohorts were assessed using the two-sample Kolmogorov-Smirnov test. The difference in median age at CRC diagnosis between cohorts was estimated using median quantile regression, reported as the regression coefficient (β) with 95% confidence interval (CI). Categorical characteristics, including sex, obesity, and tobacco use, were compared between cohorts using the chi-squared test. A two-sided P value <0.05 was considered statistically significant.

Results

Between 2010 and 2024, we identified 136,394 patients with HGCPS/LS, including 81,836 patients (60%) identified using ICD-10-CM code Z15.09 and 54,558 patients (40%) using code Z14.8 and 106,114,751 in the non-coded hereditary susceptibility cohort. A total of 6,561 (4.8%) new CRC cases were diagnosed among HGCPS/LS patients, while the non-coded hereditary susceptibility cohort recorded a total of 364,189 (0.34%) CRC diagnoses.

Temporal shifts in age at diagnosis

Analysis of age at HGCPS/LS diagnosis demonstrated a modest shift toward younger age in the more recent cohort, with a higher proportion of patients diagnosed before 45 years (Figure S1). The mean age at HGCPS/LS diagnosis decreased from approximately 49 years in 2010–2017 to 43 years in 2018–2024.

During 2010–2017, CRC diagnoses among HGCPS/LS patients mostly occurred between ages 45–54 years, whereas in the non-coded hereditary susceptibility cohort, the peak was between 55–64 years (Figure 1A,1B). By contrast, in the 2018–2024 period, the peak age shifted earlier in both groups: among HGCPS/LS patients, the highest frequency of CRC diagnoses occurred between ages 35–44 years, while in the non-coded hereditary susceptibility cohort the peak shifted to ages 45–54 years.

Figure 1 Age distribution of colorectal cancer diagnoses in HGCPS/LS vs. non-coded hereditary susceptibility/average risk cohorts. (A) HGCPS/LS comparing two time frames: 2010–2017 and 2018–2024. (B) Non-coded hereditary susceptibility/average risk cohort, 2010–2017 vs. 2018–2024. (C) Direct comparison in 2018–2024: HGCPS/LS vs. non-coded hereditary susceptibility/average risk. CRC, colorectal cancer; HGCPS, hereditary gastrointestinal cancer predisposition syndromes; LS, Lynch syndrome.

Among HGCPS/LS patients, the mean age at CRC diagnosis declined from 51±17 years in 2010–2017 to 47±18 years in 2018–2024 (Figure 1A). The mean age at CRC diagnosis during 2018–2024 was 47±18 years in HGCPS/LS patients, compared to 58±14 years in the average risk cohort (Figure 1C). The HGCPS/LS cohort demonstrated a significant leftward shift in age at CRC diagnosis compared with the non-coded hereditary susceptibility cohort. In the HGCPS/LS cohort, 55.4% of CRC diagnoses occurred by age 49 years, compared with 13.8% of patients in the control cohort. The Kolmogorov-Smirnov test demonstrated a significant difference in the overall age-at-diagnosis distribution (D=0.416, P<0.001). In median quantile regression, patients in the HGCPS/LS cohort were diagnosed at a median age 20.0 years younger than patients in the non-coded hereditary susceptibility cohort (β=−20.0 years; 95% CI: −21.3 to −18.7; P<0.001).

Additionally, stratified analyses by coding definition (Z15.09 vs. Z14.8) showed consistent age distributions and temporal trends at both time intervals (Figure S2).

Proportion of early-onset CRC

The proportion of CRC cases diagnosed at younger ages increased substantially over time. Among HGCPS/LS patients, 3,213 (51%) CRC cases occurred by age 44 in 2018–2024, compared with 2,205 (35%) during 2010–2017. Similarly, in the average risk cohort, 123,824 (34%) of CRC cases occurred by age 54 in 2010–2017, rising to 149,317 (41%) in 2018–2024 (Figure 1A,1B).

Demographic and clinical characteristics

Compared with the non-coded hereditary susceptibility cohort, patients with HGCPS/LS-associated CRC were more likely to be female (56% vs. 46%, P<0.01). Racial and ethnic distribution among HGCPS/LS CRC patients was predominantly non-Hispanic White (75.4%), followed by Hispanic and Black (7.3% and 7.2%), Asian (4.6%), American Indian/Alaska Native (0.8%), and Native Hawaiian or Other Pacific Islander (0.6%). HGCPS/LS CRC patients had a higher prevalence of obesity compared with non-coded hereditary susceptibility CRC patients (31% vs. 22%, P<0.01) and a higher proportion of documented tobacco use (18% vs. 15%, P<0.01) (Figure S3).

Discussion

In this real-world analysis, the age distribution of CRC diagnoses among patients with HGCPS/LS shifted toward younger ages in 2018–2024 compared with 2010–2017. At both time periods, hereditary-risk patients were younger at CRC diagnosis than average risk cohort patients. These observations suggest that the left shift in CRC age seen in the general population also characterizes hereditary-risk populations.

Several lifestyle and environmental factors, including unhealthy obesogenic diet, physical inactivity, obesity, alcohol use, and smoking, may contribute to the rise in early-onset CRC. For HGCPS/LS carriers, these exposures likely amplify the effect of underlying pathogenic germline variants in mismatch-repair genes, leading to earlier cancer onset in the modern cohorts of HGCPS/LS, compared with previous generations (10). Additionally, the advances in genetic testing and surveillance have increased the detection of cancers at younger ages. Importantly, improvements in genetic testing, broader implementation of multigene panel testing, and increased awareness of hereditary cancer susceptibility have likely led to earlier identification and coding of at-risk individuals in contemporary practice (11). As a result, part of the observed shift toward younger age at diagnosis may reflect earlier ascertainment and detection rather than a true biological change in disease onset. This distinction is particularly relevant in EHR-based studies, where the timing of diagnosis may be influenced by evolving clinical practices and documentation patterns. The progressive left-shift supports continuing individualized, gene and family history-based surveillance rather than uniform age thresholds (12).

Several limitations are present in our study. Identification of HGCPS/LS relies on administrative codes to identify hereditary CRC susceptibility, which may be subject to errors in documentation, and potentially may lead to misclassification. Notably, prior EHR studies demonstrated that hereditary cancer syndromes are frequently under-documented (12). This limitation likely underestimates both the true prevalence of hereditary syndromes and the magnitude of any observed left shift in CRC age. Additionally, the widespread implementation of multigene panel testing after 2018 has markedly improved identification of hereditary cases, which may artificially lower the recorded age at diagnosis in more recent years due to earlier ascertainment rather than biological change. Furthermore, the comparator group, defined as patients without coded hereditary cancer susceptibility or selected high-risk conditions, represents an approximation of individuals without documented hereditary cancer susceptibility, rather than a true average-risk population. In addition, our patients were predominantly White which might limit generalizability. Furthermore, residual confounding by unmeasured or incompletely captured variables remains possible. Although we evaluated selected clinical characteristics such as obesity and tobacco use, detailed data on diet, physical activity, alcohol consumption, medication exposures, and socioeconomic factors were not available. However, there are multiple strengths to our study. First, the use of a large, national, multicenter EHR database enabled a substantial sample size and geographic diversity of participating HCOs, enhancing statistical power and improving the generalizability of observed temporal trends beyond single-center or registry-based cohorts. Second, the long study period spanning 2010 to 2024 allowed assessment of secular changes in CRC age at diagnosis across distinct clinical eras. The use of distribution-based statistical methods allowed evaluation of shifts across the full age spectrum rather than reliance on summary measures alone.

Conclusions

In summary, our study demonstrates a shift toward earlier onset CRC in patients with hereditary GI-related susceptibility, paralleling trends observed in the average risk cohort. This shift might be attributed to environmental exposures, lifestyle patterns, or increased genetic testing with earlier initiation of colonic surveillance.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0167/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0167/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-1-0167/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are 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. Zinkeng A, Taylor FL, Cheong SH, et al. Early Onset Colorectal Cancer: Molecular Underpinnings Accelerating Occurrence. Cell Mol Gastroenterol Hepatol 2025;19:101425. [Crossref] [PubMed]
2. Sung H, Siegel RL, Laversanne M, et al. Colorectal cancer incidence trends in younger versus older adults: an analysis of population-based cancer registry data. Lancet Oncol 2025;26:51-63. [Crossref] [PubMed]
3. Hofseth LJ, Hebert JR, Chanda A, et al. Early-onset colorectal cancer: initial clues and current views. Nat Rev Gastroenterol Hepatol 2020;17:352-64. [Crossref] [PubMed]
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. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024;74:12-49. [Crossref] [PubMed]
6. Siegel RL, Miller KD, Goding Sauer A, et al. Colorectal cancer statistics, 2020. CA Cancer J Clin 2020;70:145-64. [Crossref] [PubMed]
7. Saraiva MR, Rosa I, Claro I. Early-onset colorectal cancer: A review of current knowledge. World J Gastroenterol 2023;29:1289-303. [Crossref] [PubMed]
8. Saad El Din K, Loree JM, Sayre EC, et al. Trends in the epidemiology of young-onset colorectal cancer: a worldwide systematic review. BMC Cancer 2020;20:288. [Crossref] [PubMed]
9. Mittendorf KF, Hunter JE, Schneider JL, et al. Recommended care and care adherence following a diagnosis of Lynch syndrome: a mixed-methods study. Hered Cancer Clin Pract 2019;17:31. [Crossref] [PubMed]
10. Pandey AS, Drogan C, Huo D, et al. Anticipation in families with MLH1-associated Lynch syndrome. Cancer 2025;131:e35589. [Crossref] [PubMed]
11. Castillo-Iturra J, Sánchez A, Balaguer F. Colonoscopic surveillance in Lynch syndrome: guidelines in perspective. Fam Cancer 2024;23:459-68. [Crossref] [PubMed]
12. Elhanan G, Kiser D, Neveux I, et al. Incomplete Penetrance of Population-Based Genetic Screening Results in Electronic Health Record. Front Genet 2022;13:866169. [Crossref] [PubMed]