Temporal Shifts in the Age-Specific Burden and Risk Factors of Colorectal Cancer in China, 1990–2021

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Methods Data on the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of CRC in China from 1990 to 2021 were extracted from the Global Burden of Disease (GBD) 2021 database, along with corresponding age-standardized rates. Temporal trends were analyzed using Joinpoint regression, and risk factors were assessed via population attributable fraction (PAF), with stratified comparisons by age and sex. Results From 1990 to 2021, the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) of CRC in China increased by 65.13% and 141.23%, respectively, while the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) decreased by 11.94% and 15.08%, respectively. The average annual percentage changes (AAPCs) were as follows: ASIR 1.67%, ASPR 2.95%, ASMR − 0.45%, and ASDR − 0.54%. In 2021, metabolic risk factors (high BMI and high fasting plasma glucose) showed the largest increase in PAF (up 5.4% since 1990) in the 60–74 age group, with high BMI alone accounting for a PAF of 7.7%. The combined PAF for smoking and alcohol use was 19.2% in men versus only 2.0% in women, indicating substantial sex-based disparities in behavioral risks. Dietary factors remained the leading risk category, though their composition shifted: excessive red meat consumption accounted for a PAF of 16.0%, while inadequate calcium intake contributed 7.1–8.1%, with no marked sex differences. Conclusion From 1990 to 2021, men aged 60–74 bore the heaviest burden of CRC in China, driven collectively by metabolic factors (high BMI, high blood glucose), behavioral factors (smoking, alcohol use), and dietary factors (excessive red meat intake). This demographic should be prioritized for CRC screening and benefit from integrated interventions such as chronic disease management, smoking and alcohol cessation, and dietary modifications. Colorectal Cancer Disease Burden Risk Factors High-Risk Population Characteristics Precision Prevention and Control Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Colorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Due to its often insidious or nonspecific early symptoms, many patients are diagnosed at advanced stages, leading to generally poor prognosis [ 1 , 2 ] . Against the backdrop of socioeconomic development and a rapidly aging population, China has experienced significant shifts in its disease spectrum [ 5 ] , with CRC emerging as a particularly prominent public health challenge [ 3 , 4 ] . According to the latest epidemiological data from the National Cancer Center of China and the WHO, approximately 517,100 new CRC cases were reported in China in 2022, accounting for 26.8% of the global incidence, along with around 240,000 deaths, representing 26.5% of global CRC mortality. CRC ranks as the second most common cancer and the fourth leading cause of cancer-related deaths in China, posing substantial challenges for prevention and control [ 6 ] . In the context of population aging, the proportion of CRC incidence and mortality among older adults continues to rise. Elderly patients often present with multiple comorbidities, declined physical function, and reduced tolerance to treatments, which considerably complicate clinical management and further elevate the overall societal disease burden [ 7 , 8 , 9 ] . Previous studies on the disease burden of colorectal cancer (CRC) in China have certain limitations in terms of timeliness, comprehensiveness, and analysis of population heterogeneity [ 10 , 11 , 12 ] . The GBD 2021 (released in 2024) incorporates data from the COVID-19 pandemic for the first time, providing a more accurate reflection of recent changes in disease burden compared to GBD 2019 [ 13 ] .Based on the GBD 2021 database, this study employed standardized epidemiological analytical methods to systematically evaluate the trends in the burden of CRC in China from 1990 to 2021 across four dimensions: incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates. Age-period-cohort modeling was constructed, combined with Joinpoint regression analysis, to examine the magnitude of changes in CRC burden across different periods. Furthermore, the population attributable fraction (PAF) was applied to quantitatively assess the contribution of major risk factors and to analyze their temporal trends and population-specific differences. This study specifically focuses on the epidemiology and distribution of risk factors for CRC among the elderly within the context of population aging, aiming to provide an evidence-based foundation for developing tailored prevention and control strategies that address the risk profile of older adults and meet the requirements of precision healthcare. 1. Materials and Methods 1.1 Data Sources and Outcome Measures Data for this study were obtained from the Global Burden of Disease Study 2021 (GBD 2021) database, released in 2024 by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, USA. This database employs standardized epidemiological modeling methods to integrate data on health loss from 371 diseases and injuries and 88 risk factors across 204 countries and territories. It is recognized as one of the most authoritative and comprehensive global databases for burden of disease assessment [ 13 , 14 ] .Using the online GBD Results Tool, we extracted metrics including the number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs) attributable to colorectal cancer in China from 1990 to 2021, along with their corresponding age-standardized rates (ASRs) [ 15 ] . Statistical uncertainty is presented as 95% uncertainty intervals (95% UI). As this study utilizes anonymized aggregate data with no personally identifiable information, it qualifies for exemption from ethical approval in accordance with the Declaration of Helsinki [ 16 ] . 1.2 Statistical Methods The DisMod-MR 2.1 Bayesian hierarchical regression model [ 17 ] was employed to analyze, model, and estimate metrics including the number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs) attributable to colorectal cancer in China. Temporal trends were examined using the Joinpoint Regression Program (Version 5.1.0.0) to fit segmented trends for the burden of colorectal cancer from 1990 to 2021. The annual percentage change (APC) and average annual percentage change (AAPC) along with their corresponding 95% confidence intervals (95% CI) were calculated for each indicator over the 31-year period. The trend was classified as significantly increasing if the lower bound of the 95% CI for the AAPC was greater than zero, significantly decreasing if the upper bound was less than zero, and stable otherwise [ 18 ] . All statistical analyses were performed using R software (Version 4.5.0), with a two-sided significance level of α = 0.05. 2. Results 2.1 Temporal Trends in the CRC Burden in China, 1990–2021 From 1990 to 2021, the burden of colorectal cancer in China showed a significant increasing trend (Table 1 ). The number of incident cases and prevalent cases increased by 315.59% and 467.28%, respectively, while the number of deaths and disability-adjusted life years (DALYs) rose by 130.61% and 92.09%, respectively. After age standardization, the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) in 2021 increased by 65.13% and 141.23%, respectively, compared with 1990. In contrast, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) decreased by 11.94% and 15.08%, respectively. These findings indicate that although the absolute burden has risen, the standardized rates of mortality and DALYs have declined, suggesting possible improvements in prevention and treatment measures in recent years. Detailed data for all metrics are provided in Supplementary Table 1. Table 1 Comparison of crude and age-standardized rates for incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of colorectal cancer in China both 1990 and 2021 (with 95% uncertainty intervals) Measure Sex Crude Rate 1990 (Counts) Age-Standardized Rate 1990 (/100,000) Crude Rate 2021 (Counts) Age-Standardized Rate 2021 (/100,000) Average Annual Percentage Change (AAPC) n(95% UI) n(95% UI) n(95% UI) n(95% UI) Incidence Both 158389(135419,182577) 19.04(16.46,21.81) 658321(531995,798063) 31.44(25.53,37.97) 1.67% Male 88370(69951, 107467) 22.31(17.92,26.82) 419012(319825,541668) 42.24(32.56,54.26) Female 70019(55493, 85557) 16.43(13.18,19.95) 239309(181750,305839) 21.87(16.58,27.92) Prevalence Both 635609(548090,729557) 69.9(60.62, 79.84) 3605686(2912081, 4349689) 168.62(136.58, 203.05) 2.95% Male 357598(286535,430343) 79.52(65.19,94.84) 2283712(1753655, 2945054) 219.3(169.59, 280.99) Female 278012(224599,338304) 61.15(49.93,73.74) 1321974(1018073, 1687271) 120.03(92.27, 153.21) Mortality Both 119303(102706,137153) 15.49(13.43,17.7) 275129(223379,330960) 13.64(11.09,16.31) −0.45% Male 66235(52779,80322) 18.56(15.05,22.07) 174400(133842,226280) 18.95(14.65,24.34) Female 53068(42217,64519) 13.23(10.7,16.02) 100729(76598,128091) 9.34(7.1,11.88 ) Disability-Adjusted Life Years (DALYs) Both 3565196(3027610,4106701) 390.63(333.24,448.92) 6848390(5513407,8284228) 331.73(267.78,400.7) −0.54% Male 2039861(1602505,2481120) 454.1(360.34,547.99) 4488271(3427062,5852471) 452.83(349.19,585.28) Female 1525334(1200993,1884418) 334.19(263.37,410.15) 2360118(1798152,3027363) 220.01(167.51,282.02) 2.2 Age-Specific Trends in the CRC Burden in China, 1990–2021 Figure 1 presents the trends in case numbers and crude rates of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) across different age groups for colorectal cancer in China in 1990 and 2021. From 1990 to 2021, the peak age for both incidence and prevalence of colorectal cancer shifted toward older ages. The peak incidence consistently occurred in the 65–69 age group, while the peak prevalence moved from the 55–59 to the 65–69 age group. Similar shifts toward older age groups were observed for mortality and DALYs. The 60–74 age group showed the most significant increase in both incidence and prevalence (P < 0.05), emerging as the most prominently affected group in terms of growing disease burden. The crude mortality rate (CMR) and crude DALY rate (CDR) remained the highest in the population aged 85 years and above. Comprehensive analysis indicates that the 60–74 age group should be the primary target for prevention and control strategies due to its marked increase in incidence and prevalence of colorectal cancer, while those aged 85 and above represent a relatively high-risk population. 2.3 Age and Sex Distribution Characteristics of the CRC Burden in China, 1990–2021 Figure 2 compares the burden of colorectal cancer in China between 1990 and 2021 across different age groups by sex, in terms of number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs). Overall, the peak age of burden for all metrics shifted later in both sexes. The peak age of incidence shifted from 60–64 to 65–69 years in males, and from 65–69 to 70–74 years in females (Figs. 2 A– 2 B). In 1990, the peak number of prevalent cases occurred in the 55–59 age group for males and 60–64 for females. By 2021, the peak had shifted to the 65–69 age group in both sexes, with the number of prevalent cases declining after age 70 (Figs. 2 C– 2 D). For deaths, the peak was observed in the 65–69 age group for males and 70–74 for females in 1990. In 2021, the peak age for deaths concentrated in the 70–74 age group in both sexes (Figs. 2 E– 2 F). The peak DALYs occurred in the 55–59 age group for both sexes in 1990 and shifted to the 65–69 age group by 2021 (Figs. 2 G– 2 H). The burden of colorectal cancer showed clear age-specific patterns. In 2021, approximately 68.17% of new cases (448.7 thousand), 61.82% of prevalent cases (2.2289 million), and 75.79% of deaths (208.5 thousand) in China occurred in individuals aged 60 years and older. A more detailed age stratification revealed that the 60–74 year-old group carried the most substantial disease burden, with higher numbers of incident cases (280.2 thousand), prevalent cases (1.6164 million), and deaths (106.9 thousand) compared to the ≥ 75 age group (168.5 thousand incident cases, 612.4 thousand prevalent cases, and 101.6 thousand deaths). These results indicate that within the 60–74 age group in 2021, males bore a significantly higher burden in incidence, prevalence, and mortality compared to females, suggesting elevated colorectal cancer risks for men in this age range. 2.4 Joinpoint Regression Analysis of the CRC Burden in China, 1990–2021 Joinpoint regression analysis of the colorectal cancer burden in China from 1990 to 2021 revealed that the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) showed a consistent upward trend overall. The most rapid increase in ASIR occurred between 2002 and 2005, while ASPR rose most markedly during 2016–2021. In contrast, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) exhibited a generally fluctuating downward trend, with the most significant decline observed between 2004 and 2007. Although a slight rebound followed a modest decrease during 2011–2014, the long-term trend remained downward. The concurrent rise in ASIR and ASPR alongside the decline in ASMR and ASDR suggests that improved screening uptake and advances in treatment have enhanced patient outcomes. Nevertheless, the growing burden of incidence highlights that primary prevention strategies within the current healthcare system still require strengthening. 2.5 Risk Factor Analysis for the CRC Burden in China, 1990–2021 This study compared the population attributable fractions (PAF) of disability-adjusted life years (DALYs) related to colorectal cancer across three age groups (< 60, 60–74, and ≥ 75 years) between 1990 and 2021. During this period, the structure of risk factors contributing to the colorectal cancer burden in China underwent a remarkable transition, marked most notably by metabolic factors displacing dietary factors as the primary driver of increasing burden—particularly in the 60–74 age group. Within this group, the total PAF of metabolic factors rose from 9.2–14.6%, an increase of 5.4 percentage points since 1990.At the same time, behavioral factors exhibited substantial sex-based disparities: in 2021, the combined PAF for smoking and alcohol use reached 19.2% among men, compared to only 2.0% among women. Although dietary factors remained dominant overall, their composition changed significantly: the PAF for excessive red meat consumption increased to 16.0%, while that for inadequate calcium intake declined noticeably (to 7.1–8.1%). Moreover, dietary risks were sex-specific, with women being more susceptible to low dairy and calcium intake.The analysis indicates that men aged 60–74 experienced the largest increase in metabolic risks while continuing to carry significant dietary and behavioral risks, identifying them as a high-risk subgroup facing multiple overlapping risk factors for colorectal cancer. These findings highlight the limitation of a conventional “one-size-fits-all” prevention strategy and underscore the need for risk-stratified, mechanism-oriented, and tailored interventions in this target population—including better management of metabolic syndrome, tobacco and alcohol cessation support, and dietary restructuring. 3. Discussion Building upon previous research, this study conducted a refined age- and sex-stratified analysis of risk factors for colorectal cancer (CRC), thereby offering deeper insights into the distribution and evolving patterns of the CRC burden across different subpopulations in China. This approach provides critical evidence for identifying high-risk groups and informing targeted prevention and control strategies [ 12 , 19 ] .The results reveal a divergent trend in the CRC disease burden in China from 1990 to 2021, characterized by rising incidence and prevalence rates alongside declining mortality and DALY rates. This pattern suggests that the widespread adoption of early screening and advances in treatment have contributed to improved patient outcomes. However, the persistent increase in CRC incidence is likely closely linked to population aging: a growing proportion of elderly individuals has expanded the population exposed to CRC-related risk factors, thereby elevating the overall risk of developing the disease. Joinpoint regression analysis further indicates a deceleration in the decline of mortality after 2014, a period coinciding with China's rapid economic development. This suggests that the previous gains in control are being offset by the compounded risks posed by an aging population and changing lifestyles [ 20 , 21 ] . It underscores an urgent need to systematically optimize prevention and control strategies through source control of risk factors, refined screening protocols, and improved management of comorbidities in the elderly. Current CRC screening guidelines typically target individuals aged 50 and above [ 22 ] . Our findings further refine this by identifying men aged 60–74 as bearing the heaviest CRC burden in China. In 2021, this demographic accounted for 26.72% of all new cases, and the combined Population Attributable Fraction (PAF) for behavioral and metabolic risk factors reached 36.6%, significantly higher than in other age groups (P < 0.05). Specifically, the combined PAF for smoking and alcohol use in this group was 19.2%, markedly higher than that in women (2.0%). The PAF for metabolic risk factors was also substantial at 13.7%. These results not only highlight the rapid rise of metabolic risk factors in China and their interaction with population aging—forming a new disease pattern—but also point to new directions for precision prevention. This includes developing more comprehensive risk assessment systems by integrating inflammatory markers, metabolic functional data, and genetic risk scores, thereby enabling early interventions (e.g., dietary modifications or pharmacological prevention targeting insulin and lipid metabolic abnormalities) in high-risk individuals. Furthermore, integrating smoking cessation and alcohol moderation initiatives with the management of metabolic diseases, coupled with long-term follow-up and health management, could more precisely mitigate CRC risk.The study also identified notable sex differences in dietary risks: excessive red meat intake was predominant in men, whereas inadequate calcium and dairy product intake was more prominent among women. Based on these findings, we recommend that men aged 60–74 be considered the core target for precision prevention strategies. Interventions should include integrated risk-stratified approaches focusing on smoking cessation, alcohol reduction, weight management, and dietary adjustments. Simultaneously, it is necessary to move the intervention window forward to the 45–59 age group, implementing systematic metabolic health monitoring and dietary structure modifications during this stage to counter the lagged effect of CRC development, thereby enhancing the timeliness and targeting of prevention strategies [ 23 ] . Due to inherent limitations of the GBD 2021 data, which did not fully account for urban-rural disparities and regional distribution characteristics, the precise identification of high-risk populations might be affected [ 10 ] . Future studies should integrate multi-source data, including clinical records, cancer registry systems, and environmental exposure information, to build more robust risk prediction models. Concurrently, intervention measures for high-risk populations should be prioritized, encompassing the management of metabolic syndrome and lifestyle interventions, alongside the formulation of differentiated regional prevention and control strategies [ 24 ] , to alleviate the public health burden attributable to colorectal cancer. Conclusion This study reveals divergent trends in China's colorectal cancer burden from 1990 to 2021: age-standardized incidence and prevalence rates demonstrated sustained increases, while mortality and DALY rates showed overall declines. The population aged 60–74 years constituted the epicenter of disease burden, with the peak burden age shifting upward in parallel with population aging. Males faced a disproportionately high burden, exhibiting a 1.93-fold higher incidence rate than females, with a combined smoking and alcohol consumption risk 9.6 times greater than in females. A paradigm shift in risk factor structure was observed: metabolic factors emerged as the predominant concern, while excessive red meat intake superseded calcium deficiency as the primary dietary risk, with significant gender-based divergence. These findings highlight the urgent need for intensified metabolic and behavioral interventions targeting males aged 60–74 years, alongside reinforced early metabolic interventions and dietary guidance for the 45-59-year age group to effectively mitigate cumulative risks. Declarations Acknowledgements We are indebted to individuals who have participated in this study or have helped with this article. Funding This work was supported by a grant from the Hospital Management Innovation Research Project of Jiangsu Provincial Hospital Association (Grant No. JSYGY-3-2024-126). Availability of data and materials The data supporting this study are publicly available from the Global Burden of Disease Study 2021 (GBD 2021) repository (http://ghdx.healthdata.org/gbd-2021). All analytical methods are described in the manuscript. Ethical oversight and participant consent This analysis utilizes anonymized, population-level data from the publicly accessible Global Burden of Disease (GBD) database. As no individual-level information is included, ethical approval and informed consent were not required. Data access adhered to all applicable guidelines and use agreements established by the Institute for Health Metrics and Evaluation (IHME). Consent to participate Not applicable. Competing interests The authors declare no competing interests. Author Contribution Yujie Han：conceptualization, data management, formal analysis, manuscript writing.Xiangkun Huan：conceptualisation, methodology, writing, review, and editing.Rui Zhu：visualization and data management.Xinping Wang：data curation and investigation.Chao Jiang：conceptualization, supervision and funding acquisition. References Wang Y, Wu ZL, Wang YG, Wang H, Jia XY. Early colorectal cancer screening-no time to lose. World J Gastroenterol. 2024;30(23):2959-2963. doi:10.3748/wjg.v30.i23.2959 Chen K, Collins G, Wang H, Toh JWT. Pathological Features and Prognostication in Colorectal Cancer. Curr Oncol. 2021;28(6):5356-5383. Published 2021 Dec 13. doi:10.3390/curroncol28060447 Shao B, Zhu M, Shen K, et al. Disease Burden of Total and Early-Onset Colorectal Cancer in China from 1990 to 2019 and Predictions of Cancer Incidence and Mortality. Clin Epidemiol. 2023;15:151-163. Published 2023 Feb 1. doi:10.2147/CLEP.S391058 Morgan E, Arnold M, Gini A, et al. 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Cite Share Download PDF Status: Published Journal Publication published 09 Jan, 2026 Read the published version in Journal of Gastrointestinal Cancer → Version 1 posted Editorial decision: Revision requested 12 Oct, 2025 Reviews received at journal 12 Oct, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 15 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 08 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":159193,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of incidence cases, prevalence cases, death cases, DALY numbers and their crude rates among different age groups for colorectal cancer in China between 1990 and 2021.\u003c/p\u003e\n\u003cp\u003eNote:A. Incident cases and crude incidence rate (CIR);B. Prevalent cases and crude prevalence rate (CPR); C. Deaths and crude mortality rate (CMR); D. DALYs and crude DALY rate (CDR). Bar charts represent counts; line graphs represent crude rates.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560356/v1/7ea5824c5e52fd330d93428b.jpg"},{"id":92066278,"identity":"7cc3959a-716d-4160-9b18-0bad72502f98","added_by":"auto","created_at":"2025-09-24 09:03:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":148570,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of colorectal cancer incidence, prevalence, mortality by age and sex in China between 1990 and 2021\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560356/v1/ba27cb9d0da35076f8a88d1d.jpg"},{"id":92067424,"identity":"546def8b-19fa-45e3-a47f-655b4ed144ce","added_by":"auto","created_at":"2025-09-24 09:11:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101432,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint analysis of colorectal cancer burden trends in China between 1990 and 2021\u003c/p\u003e\n\u003cp\u003eNote: A. Age-standardized incidence rate (ASIR); B. Age-standardized prevalence rate (ASPR); C. Age-standardized mortality rate (ASMR); D. Age-standardized disability-adjusted life year rate (ASDR).\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560356/v1/662a52446d6502791317f4a1.jpg"},{"id":92066282,"identity":"45d1a369-600d-4a4f-97b1-da03655bfb11","added_by":"auto","created_at":"2025-09-24 09:03:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157429,"visible":true,"origin":"","legend":"\u003cp\u003eA-F: Attribution analysis of risk factors for colorectal cancer burden in populations aged \u0026lt;60 years, 60-74 years, and ≥75 years in 1990 and 2021.\u003cbr\u003e\nG-H: Risk factor attribution analysis for colorectal cancer burden among Chinese males and females aged 60-74 years in 2021.\u003c/p\u003e\n\u003cp\u003eNote: Attributable fractions of major risk factors are ranked in descending order.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7560356/v1/cefcb8bdfefec4b9657079cf.jpg"},{"id":100069818,"identity":"9bd161ba-372b-49ac-9553-f5022e5bb4c1","added_by":"auto","created_at":"2026-01-12 16:15:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1328089,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7560356/v1/4c7254c9-a8f3-48bd-be2f-e11da2a3315a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Temporal Shifts in the Age-Specific Burden and Risk Factors of Colorectal Cancer in China, 1990–2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed malignancy worldwide. Due to its often insidious or nonspecific early symptoms, many patients are diagnosed at advanced stages, leading to generally poor prognosis \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Against the backdrop of socioeconomic development and a rapidly aging population, China has experienced significant shifts in its disease spectrum \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e, with CRC emerging as a particularly prominent public health challenge \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. According to the latest epidemiological data from the National Cancer Center of China and the WHO, approximately 517,100 new CRC cases were reported in China in 2022, accounting for 26.8% of the global incidence, along with around 240,000 deaths, representing 26.5% of global CRC mortality. CRC ranks as the second most common cancer and the fourth leading cause of cancer-related deaths in China, posing substantial challenges for prevention and control \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In the context of population aging, the proportion of CRC incidence and mortality among older adults continues to rise. Elderly patients often present with multiple comorbidities, declined physical function, and reduced tolerance to treatments, which considerably complicate clinical management and further elevate the overall societal disease burden \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious studies on the disease burden of colorectal cancer (CRC) in China have certain limitations in terms of timeliness, comprehensiveness, and analysis of population heterogeneity \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. The GBD 2021 (released in 2024) incorporates data from the COVID-19 pandemic for the first time, providing a more accurate reflection of recent changes in disease burden compared to GBD 2019 \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.Based on the GBD 2021 database, this study employed standardized epidemiological analytical methods to systematically evaluate the trends in the burden of CRC in China from 1990 to 2021 across four dimensions: incidence, prevalence, mortality, and disability-adjusted life years (DALYs) rates. Age-period-cohort modeling was constructed, combined with Joinpoint regression analysis, to examine the magnitude of changes in CRC burden across different periods. Furthermore, the population attributable fraction (PAF) was applied to quantitatively assess the contribution of major risk factors and to analyze their temporal trends and population-specific differences. This study specifically focuses on the epidemiology and distribution of risk factors for CRC among the elderly within the context of population aging, aiming to provide an evidence-based foundation for developing tailored prevention and control strategies that address the risk profile of older adults and meet the requirements of precision healthcare.\u003c/p\u003e"},{"header":"1. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e1.1 Data Sources and Outcome Measures\u003c/h2\u003e\u003cp\u003eData for this study were obtained from the Global Burden of Disease Study 2021 (GBD 2021) database, released in 2024 by the Institute for Health Metrics and Evaluation (IHME) at the University of Washington, USA. This database employs standardized epidemiological modeling methods to integrate data on health loss from 371 diseases and injuries and 88 risk factors across 204 countries and territories. It is recognized as one of the most authoritative and comprehensive global databases for burden of disease assessment \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.Using the online GBD Results Tool, we extracted metrics including the number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs) attributable to colorectal cancer in China from 1990 to 2021, along with their corresponding age-standardized rates (ASRs) \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Statistical uncertainty is presented as 95% uncertainty intervals (95% UI). As this study utilizes anonymized aggregate data with no personally identifiable information, it qualifies for exemption from ethical approval in accordance with the Declaration of Helsinki \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e1.2 Statistical Methods\u003c/h2\u003e\u003cp\u003eThe DisMod-MR 2.1 Bayesian hierarchical regression model\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e was employed to analyze, model, and estimate metrics including the number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs) attributable to colorectal cancer in China. Temporal trends were examined using the Joinpoint Regression Program (Version 5.1.0.0) to fit segmented trends for the burden of colorectal cancer from 1990 to 2021. The annual percentage change (APC) and average annual percentage change (AAPC) along with their corresponding 95% confidence intervals (95% CI) were calculated for each indicator over the 31-year period. The trend was classified as significantly increasing if the lower bound of the 95% CI for the AAPC was greater than zero, significantly decreasing if the upper bound was less than zero, and stable otherwise\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. All statistical analyses were performed using R software (Version 4.5.0), with a two-sided significance level of α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Temporal Trends in the CRC Burden in China, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, the burden of colorectal cancer in China showed a significant increasing trend (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of incident cases and prevalent cases increased by 315.59% and 467.28%, respectively, while the number of deaths and disability-adjusted life years (DALYs) rose by 130.61% and 92.09%, respectively. After age standardization, the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) in 2021 increased by 65.13% and 141.23%, respectively, compared with 1990. In contrast, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) decreased by 11.94% and 15.08%, respectively. These findings indicate that although the absolute burden has risen, the standardized rates of mortality and DALYs have declined, suggesting possible improvements in prevention and treatment measures in recent years. Detailed data for all metrics are provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of crude and age-standardized rates for incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of colorectal cancer in China both 1990 and 2021 (with 95% uncertainty intervals)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrude Rate 1990 (Counts)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge-Standardized Rate 1990 (/100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCrude Rate 2021 (Counts)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAge-Standardized Rate 2021 (/100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage Annual Percentage Change (AAPC)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003en(95% UI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eIncidence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158389(135419,182577)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19.04(16.46,21.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e658321(531995,798063)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.44(25.53,37.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e1.67%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e88370(69951, 107467)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.31(17.92,26.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e419012(319825,541668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.24(32.56,54.26)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70019(55493, 85557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16.43(13.18,19.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e239309(181750,305839)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.87(16.58,27.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003ePrevalence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e635609(548090,729557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69.9(60.62, 79.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3605686(2912081, 4349689)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e168.62(136.58, 203.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e2.95%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e357598(286535,430343)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e79.52(65.19,94.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2283712(1753655, 2945054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e219.3(169.59, 280.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e278012(224599,338304)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e61.15(49.93,73.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1321974(1018073, 1687271)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e120.03(92.27, 153.21)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119303(102706,137153)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.49(13.43,17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e275129(223379,330960)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.64(11.09,16.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026minus;0.45%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66235(52779,80322)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.56(15.05,22.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e174400(133842,226280)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.95(14.65,24.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53068(42217,64519)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.23(10.7,16.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100729(76598,128091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.34(7.1,11.88\u0026nbsp;)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eDisability-Adjusted Life Years (DALYs)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBoth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3565196(3027610,4106701)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e390.63(333.24,448.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6848390(5513407,8284228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e331.73(267.78,400.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026minus;0.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2039861(1602505,2481120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e454.1(360.34,547.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4488271(3427062,5852471)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e452.83(349.19,585.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1525334(1200993,1884418)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e334.19(263.37,410.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2360118(1798152,3027363)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e220.01(167.51,282.02)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Age-Specific Trends in the CRC Burden in China, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the trends in case numbers and crude rates of incidence, prevalence, mortality, and disability-adjusted life years (DALYs) across different age groups for colorectal cancer in China in 1990 and 2021. From 1990 to 2021, the peak age for both incidence and prevalence of colorectal cancer shifted toward older ages. The peak incidence consistently occurred in the 65\u0026ndash;69 age group, while the peak prevalence moved from the 55\u0026ndash;59 to the 65\u0026ndash;69 age group. Similar shifts toward older age groups were observed for mortality and DALYs. The 60\u0026ndash;74 age group showed the most significant increase in both incidence and prevalence (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), emerging as the most prominently affected group in terms of growing disease burden. The crude mortality rate (CMR) and crude DALY rate (CDR) remained the highest in the population aged 85 years and above. Comprehensive analysis indicates that the 60\u0026ndash;74 age group should be the primary target for prevention and control strategies due to its marked increase in incidence and prevalence of colorectal cancer, while those aged 85 and above represent a relatively high-risk population.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Age and Sex Distribution Characteristics of the CRC Burden in China, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e compares the burden of colorectal cancer in China between 1990 and 2021 across different age groups by sex, in terms of number of incident cases, prevalent cases, deaths, and disability-adjusted life years (DALYs). Overall, the peak age of burden for all metrics shifted later in both sexes. The peak age of incidence shifted from 60\u0026ndash;64 to 65\u0026ndash;69 years in males, and from 65\u0026ndash;69 to 70\u0026ndash;74 years in females (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In 1990, the peak number of prevalent cases occurred in the 55\u0026ndash;59 age group for males and 60\u0026ndash;64 for females. By 2021, the peak had shifted to the 65\u0026ndash;69 age group in both sexes, with the number of prevalent cases declining after age 70 (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). For deaths, the peak was observed in the 65\u0026ndash;69 age group for males and 70\u0026ndash;74 for females in 1990. In 2021, the peak age for deaths concentrated in the 70\u0026ndash;74 age group in both sexes (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). The peak DALYs occurred in the 55\u0026ndash;59 age group for both sexes in 1990 and shifted to the 65\u0026ndash;69 age group by 2021 (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003eThe burden of colorectal cancer showed clear age-specific patterns. In 2021, approximately 68.17% of new cases (448.7 thousand), 61.82% of prevalent cases (2.2289\u0026nbsp;million), and 75.79% of deaths (208.5 thousand) in China occurred in individuals aged 60 years and older. A more detailed age stratification revealed that the 60\u0026ndash;74 year-old group carried the most substantial disease burden, with higher numbers of incident cases (280.2 thousand), prevalent cases (1.6164\u0026nbsp;million), and deaths (106.9 thousand) compared to the \u0026ge;\u0026thinsp;75 age group (168.5 thousand incident cases, 612.4 thousand prevalent cases, and 101.6 thousand deaths). These results indicate that within the 60\u0026ndash;74 age group in 2021, males bore a significantly higher burden in incidence, prevalence, and mortality compared to females, suggesting elevated colorectal cancer risks for men in this age range.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Joinpoint Regression Analysis of the CRC Burden in China, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eJoinpoint regression analysis of the colorectal cancer burden in China from 1990 to 2021 revealed that the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) showed a consistent upward trend overall. The most rapid increase in ASIR occurred between 2002 and 2005, while ASPR rose most markedly during 2016\u0026ndash;2021. In contrast, the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) exhibited a generally fluctuating downward trend, with the most significant decline observed between 2004 and 2007. Although a slight rebound followed a modest decrease during 2011\u0026ndash;2014, the long-term trend remained downward. The concurrent rise in ASIR and ASPR alongside the decline in ASMR and ASDR suggests that improved screening uptake and advances in treatment have enhanced patient outcomes. Nevertheless, the growing burden of incidence highlights that primary prevention strategies within the current healthcare system still require strengthening.\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Risk Factor Analysis for the CRC Burden in China, 1990\u0026ndash;2021\u003c/h2\u003e\u003cp\u003eThis study compared the population attributable fractions (PAF) of disability-adjusted life years (DALYs) related to colorectal cancer across three age groups (\u0026lt;\u0026thinsp;60, 60\u0026ndash;74, and \u0026ge;\u0026thinsp;75 years) between 1990 and 2021. During this period, the structure of risk factors contributing to the colorectal cancer burden in China underwent a remarkable transition, marked most notably by metabolic factors displacing dietary factors as the primary driver of increasing burden\u0026mdash;particularly in the 60\u0026ndash;74 age group. Within this group, the total PAF of metabolic factors rose from 9.2\u0026ndash;14.6%, an increase of 5.4 percentage points since 1990.At the same time, behavioral factors exhibited substantial sex-based disparities: in 2021, the combined PAF for smoking and alcohol use reached 19.2% among men, compared to only 2.0% among women. Although dietary factors remained dominant overall, their composition changed significantly: the PAF for excessive red meat consumption increased to 16.0%, while that for inadequate calcium intake declined noticeably (to 7.1\u0026ndash;8.1%). Moreover, dietary risks were sex-specific, with women being more susceptible to low dairy and calcium intake.The analysis indicates that men aged 60\u0026ndash;74 experienced the largest increase in metabolic risks while continuing to carry significant dietary and behavioral risks, identifying them as a high-risk subgroup facing multiple overlapping risk factors for colorectal cancer. These findings highlight the limitation of a conventional \u0026ldquo;one-size-fits-all\u0026rdquo; prevention strategy and underscore the need for risk-stratified, mechanism-oriented, and tailored interventions in this target population\u0026mdash;including better management of metabolic syndrome, tobacco and alcohol cessation support, and dietary restructuring.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eBuilding upon previous research, this study conducted a refined age- and sex-stratified analysis of risk factors for colorectal cancer (CRC), thereby offering deeper insights into the distribution and evolving patterns of the CRC burden across different subpopulations in China. This approach provides critical evidence for identifying high-risk groups and informing targeted prevention and control strategies \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.The results reveal a divergent trend in the CRC disease burden in China from 1990 to 2021, characterized by rising incidence and prevalence rates alongside declining mortality and DALY rates. This pattern suggests that the widespread adoption of early screening and advances in treatment have contributed to improved patient outcomes. However, the persistent increase in CRC incidence is likely closely linked to population aging: a growing proportion of elderly individuals has expanded the population exposed to CRC-related risk factors, thereby elevating the overall risk of developing the disease. Joinpoint regression analysis further indicates a deceleration in the decline of mortality after 2014, a period coinciding with China's rapid economic development. This suggests that the previous gains in control are being offset by the compounded risks posed by an aging population and changing lifestyles \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. It underscores an urgent need to systematically optimize prevention and control strategies through source control of risk factors, refined screening protocols, and improved management of comorbidities in the elderly.\u003c/p\u003e\u003cp\u003eCurrent CRC screening guidelines typically target individuals aged 50 and above \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Our findings further refine this by identifying men aged 60\u0026ndash;74 as bearing the heaviest CRC burden in China. In 2021, this demographic accounted for 26.72% of all new cases, and the combined Population Attributable Fraction (PAF) for behavioral and metabolic risk factors reached 36.6%, significantly higher than in other age groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, the combined PAF for smoking and alcohol use in this group was 19.2%, markedly higher than that in women (2.0%). The PAF for metabolic risk factors was also substantial at 13.7%. These results not only highlight the rapid rise of metabolic risk factors in China and their interaction with population aging\u0026mdash;forming a new disease pattern\u0026mdash;but also point to new directions for precision prevention. This includes developing more comprehensive risk assessment systems by integrating inflammatory markers, metabolic functional data, and genetic risk scores, thereby enabling early interventions (e.g., dietary modifications or pharmacological prevention targeting insulin and lipid metabolic abnormalities) in high-risk individuals. Furthermore, integrating smoking cessation and alcohol moderation initiatives with the management of metabolic diseases, coupled with long-term follow-up and health management, could more precisely mitigate CRC risk.The study also identified notable sex differences in dietary risks: excessive red meat intake was predominant in men, whereas inadequate calcium and dairy product intake was more prominent among women. Based on these findings, we recommend that men aged 60\u0026ndash;74 be considered the core target for precision prevention strategies. Interventions should include integrated risk-stratified approaches focusing on smoking cessation, alcohol reduction, weight management, and dietary adjustments. Simultaneously, it is necessary to move the intervention window forward to the 45\u0026ndash;59 age group, implementing systematic metabolic health monitoring and dietary structure modifications during this stage to counter the lagged effect of CRC development, thereby enhancing the timeliness and targeting of prevention strategies \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDue to inherent limitations of the GBD 2021 data, which did not fully account for urban-rural disparities and regional distribution characteristics, the precise identification of high-risk populations might be affected \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Future studies should integrate multi-source data, including clinical records, cancer registry systems, and environmental exposure information, to build more robust risk prediction models. Concurrently, intervention measures for high-risk populations should be prioritized, encompassing the management of metabolic syndrome and lifestyle interventions, alongside the formulation of differentiated regional prevention and control strategies \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, to alleviate the public health burden attributable to colorectal cancer.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study reveals divergent trends in China's colorectal cancer burden from 1990 to 2021: age-standardized incidence and prevalence rates demonstrated sustained increases, while mortality and DALY rates showed overall declines. The population aged 60\u0026ndash;74 years constituted the epicenter of disease burden, with the peak burden age shifting upward in parallel with population aging. Males faced a disproportionately high burden, exhibiting a 1.93-fold higher incidence rate than females, with a combined smoking and alcohol consumption risk 9.6 times greater than in females. A paradigm shift in risk factor structure was observed: metabolic factors emerged as the predominant concern, while excessive red meat intake superseded calcium deficiency as the primary dietary risk, with significant gender-based divergence. These findings highlight the urgent need for intensified metabolic and behavioral interventions targeting males aged 60\u0026ndash;74 years, alongside reinforced early metabolic interventions and dietary guidance for the 45-59-year age group to effectively mitigate cumulative risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted to individuals who have participated in this study or have helped with this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Hospital Management Innovation Research Project of Jiangsu Provincial Hospital Association (Grant No. JSYGY-3-2024-126).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study are publicly available from the Global Burden of Disease Study 2021 (GBD 2021) repository (http://ghdx.healthdata.org/gbd-2021). All analytical methods are described in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical oversight and participant consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis analysis utilizes anonymized, population-level data from the publicly accessible Global Burden of Disease (GBD) database. As no individual-level information is included, ethical approval and informed consent were not required. Data access adhered to all applicable guidelines and use agreements established by the Institute for Health Metrics and Evaluation (IHME).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYujie Han：conceptualization, data management, formal analysis, manuscript writing.Xiangkun Huan：conceptualisation, methodology, writing, review, and editing.Rui Zhu：visualization and data management.Xinping Wang：data curation and investigation.Chao Jiang：conceptualization, supervision and funding acquisition.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWang Y, Wu ZL, Wang YG, Wang H, Jia XY. Early colorectal cancer screening-no time to lose. 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(In Chinese with English abstract)\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Colorectal Cancer, Disease Burden, Risk Factors, High-Risk Population Characteristics, Precision Prevention and Control","lastPublishedDoi":"10.21203/rs.3.rs-7560356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7560356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eTo analyze the long-term trends, age- and sex-specific patterns, and attributable risk factors of the colorectal cancer (CRC) burden in the Chinese population aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years from 1990 to 2021, so as to provide a scientific basis for targeted prevention and control in this aging population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData on the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of CRC in China from 1990 to 2021 were extracted from the Global Burden of Disease (GBD) 2021 database, along with corresponding age-standardized rates. Temporal trends were analyzed using Joinpoint regression, and risk factors were assessed via population attributable fraction (PAF), with stratified comparisons by age and sex.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, the age-standardized incidence rate (ASIR) and age-standardized prevalence rate (ASPR) of CRC in China increased by 65.13% and 141.23%, respectively, while the age-standardized mortality rate (ASMR) and age-standardized DALY rate (ASDR) decreased by 11.94% and 15.08%, respectively. The average annual percentage changes (AAPCs) were as follows: ASIR 1.67%, ASPR 2.95%, ASMR \u0026minus;\u0026thinsp;0.45%, and ASDR \u0026minus;\u0026thinsp;0.54%. In 2021, metabolic risk factors (high BMI and high fasting plasma glucose) showed the largest increase in PAF (up 5.4% since 1990) in the 60\u0026ndash;74 age group, with high BMI alone accounting for a PAF of 7.7%. The combined PAF for smoking and alcohol use was 19.2% in men versus only 2.0% in women, indicating substantial sex-based disparities in behavioral risks. Dietary factors remained the leading risk category, though their composition shifted: excessive red meat consumption accounted for a PAF of 16.0%, while inadequate calcium intake contributed 7.1\u0026ndash;8.1%, with no marked sex differences.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eFrom 1990 to 2021, men aged 60\u0026ndash;74 bore the heaviest burden of CRC in China, driven collectively by metabolic factors (high BMI, high blood glucose), behavioral factors (smoking, alcohol use), and dietary factors (excessive red meat intake). This demographic should be prioritized for CRC screening and benefit from integrated interventions such as chronic disease management, smoking and alcohol cessation, and dietary modifications.\u003c/p\u003e","manuscriptTitle":"Temporal Shifts in the Age-Specific Burden and Risk Factors of Colorectal Cancer in China, 1990–2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 08:55:23","doi":"10.21203/rs.3.rs-7560356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T00:38:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-12T10:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6528847981526481869581255674071963706","date":"2025-09-22T09:37:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T01:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109823090770344750678228921923320163595","date":"2025-09-18T23:01:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T00:48:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T00:43:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-09T05:12:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Gastrointestinal Cancer","date":"2025-09-08T05:43:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-gastrointestinal-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijgc","sideBox":"Learn more about [Journal of Gastrointestinal Cancer](https://www.springer.com/journal/12029)","snPcode":"12029","submissionUrl":"https://submission.nature.com/new-submission/12029/3","title":"Journal of Gastrointestinal Cancer","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"521a357b-0421-4fa2-a7ef-6df8bd97cf61","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:08:13+00:00","versionOfRecord":{"articleIdentity":"rs-7560356","link":"https://doi.org/10.1007/s12029-025-01362-1","journal":{"identity":"journal-of-gastrointestinal-cancer","isVorOnly":false,"title":"Journal of Gastrointestinal Cancer"},"publishedOn":"2026-01-09 15:58:02","publishedOnDateReadable":"January 9th, 2026"},"versionCreatedAt":"2025-09-24 08:55:23","video":"","vorDoi":"10.1007/s12029-025-01362-1","vorDoiUrl":"https://doi.org/10.1007/s12029-025-01362-1","workflowStages":[]},"version":"v1","identity":"rs-7560356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7560356","identity":"rs-7560356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}