Risk factors of gender, age, BMI, and behavioral aspects for Colorectal Cancer in European countries: A Systematic Review and Meta-analysis

Risk factors of gender, age, BMI, and behavioral aspects for Colorectal Cancer in European countries: A Systematic Review and Meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Risk factors of gender, age, BMI, and behavioral aspects for Colorectal Cancer in European countries: A Systematic Review and Meta-analysis Benedict Herhaus, Ileana Steffens, Gabrielle Oestreicher, Markus Moehler, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5655410/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Risk factors for the onset of colorectal cancer (CRC) risk vary significantly across populations and regions. This systematic review and meta-analysis examined gender, age, BMI, and behavioral risk factors associated with colorectal cancer (CRC) in European countries to identify and quantify their impact on CRC incidence and outcomes. Methods: A s ystematic literature review of observational studies with risk factors of behavioral factors, age, gender and BMI aspects was performed. The meta-analysis was carried out using log-risk ratio or standardized mean difference as the outcome measure and the random-effects model was fitted to the data. Results: From 3018 studies, nine studies were included in this meta-analysis. The risk factors of male sex, age, overweight/obese BMI, and alcohol consumption were all significantly associated with a higher risk of developing CRC in European countries. Smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits were suggestive but statistically non-significant factors. Conclusion: The results of this study provide robust evidence for the significant association of demographic factors and modifiable lifestyle factors with CRC risk in European populations. Our findings are consistent with previous research and highlight the importance of addressing obesity and alcohol consumption through targeted public health initiatives. colorectal cancer risk factors Europe prevention screening Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Background Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide and the second leading cause of cancer-related deaths [ 1 ]. There is a widespread increase in CRC incidence in Europe, with CRC estimated to account for 12.7% of all new cancer diagnoses and 12.4% of all canter deaths in EU-27 countries in 2020 [ 2 ]. The economic and social costs associated with CRC are significant [ 3 ], underscoring the need for targeted prevention and intervention strategies. Numerous studies have identified various risk factors for CRC, with demographic factors such as age and gender consistently associated with increased risk [ 4 , 5 ]. In addition, lifestyle factors such as diet, physical activity, alcohol consumption, and overweight/obesity play a significant role in influencing CRC incidence, highlighting the complex interplay between inherent and modifiable risks [ 6 , 7 ]. For example, there is evidence that red and processed meat intake is associated with an increased risk of CRC [ 8 ], whereas higher dietary fiber intake has a protective effect [ 9 ]. Similarly, alcohol consumption has been associated with an increased risk of CRC [ 8 ]. These dietary and behavioral factors not only directly influence CRC risk, but also contribute to weight gain and obesity, a well-established risk factor for CRC. There is evidence that weight gain and obesity are associated with an increased risk of CRC [ 10 , 11 ]. However, the contribution of these factors to CRC risk varies widely between populations and regions. National differences may be explained by differing levels of healthcare expenditure and the resulting quality of screening, diagnosis, and treatment [ 2 ]. Although numerous individual studies have investigated CRC risk factors, a comprehensive synthesis focusing on European populations is lacking. To our knowledge, no meta-analysis has systematically examined the combined impact of demographic and behavioral risk factors such as gender, age, BMI, and lifestyle behaviors on CRC incidence across European countries. Furthermore, through our meta-analysis, we categorized risk factors for colorectal cancer into 'significant' and 'suggestive' categories. Significant risk factors are those supported by robust statistical evidence and consistent findings across studies, while suggestive risk factors indicate preliminary associations that warrant further investigation. Such a distinction is crucial for tailoring prevention strategies to the unique epidemiological landscape of European countries. This systematic review and meta-analysis examines the influence of gender, age, BMI, and behavioral factors on CRC risk in European countries, addressing a crucial gap by evaluating the combined effects of demographic and lifestyle-related risk factors within this population. 2. Methods 2.1 Search Strategy The electronic databases PubMed (United States National Library of Medicine, Bethesda, MD, USA) and Web of Science (Clarivate, Philadelphia, Pennsylvanica, USA) were used for literature search from inception to August 1, 2024. A systematic search of literature was conducted with the following combination of keywords with Boolean operator (OR, AND): Colorectal* OR colorectal cancer OR CRC OR tumorigenesis OR tumor* OR carcinogen* OR Colonic adenocarcinoma OR colonic adenoma AND sex OR gender OR age OR smok* OR alcohol* OR dietary OR fruit OR vegetable OR meat OR BMI OR obesity OR obes* OR overweight In first round abstract and title screening was conducted by B.H. and I.S. In second round full-text screening was performed by B.H. and K.P. Meta-analyses and reviews were excluded, but were examined to relevant articles. Only English language publications involving adults of at least 18 years were included. 2.2 Selection Criteria and Review Process In order to be considered for inclusion, studies were required to report on findings from an observational study (prospective or retrospective cohort studies, case-control studies, or cross-sectional studies) that investigated the correlation between non-genetic factors and the increased risk of developing CRC. Eligible risk factors included behavioral factors, age, gender and BMI aspects. Because this systematic meta-analysis/review will focus on inequalities in risk factors within and between EU countries and regions, only studies investigated EU countries and regions were included. 2.3 Study Quality Assessment The quality of the study was assessed using a modified version of the Newcastle-Ottawa Scale (NOS; [ 12 ]) for case-control and cohort studies. The NOS is a scale that assesses the quality of studies based on three main categories: (1) the selection of study groups, (2) the comparability of the groups, and (3) the ascertainment of exposure and outcome. Since the NOS is specifically designed for evaluating the quality of case-control or cohort studies, we used the adapted version for cross-sectional studies by O’Sullivan et al. [ 13 ]. Thereby, the study quality based on the total score as follows: low (0–3), moderate (4–6), and high (7–9). Because this study focused on a wide range of eligible risk factors, we also used the modification of the ascertainment of exposure and outcome by O’Sullivan et al. [ 13 ] allowing scores from 1 to 3. A score of 3 indicates adjustment for the majority of established risk factors for CRC (≥ 75% of the risk factors), a score of 2 indicates adjustment for a few established risk factors for CRC (< 75% of the risk factors), and a score of 1 indicates adjustment for none of the established risk factors for CRC, except age and sex. To determine if variables were adjusted for, we used methods such as backwards elimination, stepwise selection, or change-in-estimate approaches, where variables were considered adjusted if they were eliminated from the final model. 2.4 Statistical Analysis The statistical analysis was carried out using the log risk ratio or the standardized mean difference as the outcome measure. A random-effects model was fitted to the data. The amount of heterogeneity (i.e., tau²), was estimated using the restricted maximum-likelihood estimator [ 14 ]. In addition to the estimate of tau², the Q-test for heterogeneity (Cochran 1954) and the I² statistic are reported. For overweight and obese BMI, effect estimates for body mass index (BMI) greater than 25 kg/m² were pooled and compared with referent category healthy weight (BMI < 25 kg/m²). Study by [ 15 ] that examined BMI classes was not included in the overweight and obese BMI meta-analysis because no information about BMI classification of healthy weight, overweight and obesity. For alcohol consumption, smoking, high intake of processed meat, and no/low intake of vegetables/fruits, effect estimates for the highest/lowest category were pooled and compared with no alcohol consumption, no smoking, low/no intake of processed meat, and high intake of vegetables/fruits. All analyses were performed by using the software program JAMOVI (Version 2.2) with the R package metafor [ 14 ]. 3. Results 3.1 Study Inclusion and Study Quality In total, 3018 articles were identified during the initial literature search, of which 30 articles underwent fulltext review. After full-text review, a total of 9 studies were included in the meta-analysis. The most common reasons for exclusion during the full-text review were wrong comparator (n = 4), inappropriate study population (n = 13), or incorrect outcome (n = 4) (see Fig. 1). Characteristics of the 9 retained studies [ 15 – 23 ] examining the risk of developing CRC associated with demographic characteristics, BMI, or behavioral factors are presented in Table 1 . The average sum score of the modified version of the Newcastle-Ottawa Scale was 7 and demonstrated a high study quality. Both cohort studies (n = 2) used an unrepresentative cohort and half of the case-control studies used hospital-based controls. Table 1 Characteristics of all included studies investigating risk factors for the development of colorectal cancer in the EU Study and location Study design (period) Early-Onset/ Late-Onset/ Mixed Sample size Sex (% female) Demographics BMI Classification Behavioral factors Alegria-Lertxundi et al. 2020 (Spain) Case-Control (2014–2016) Late-Onset (50–69) 612 33.8% Age Gender Healthy weight Overweight/Obesity Physical activity Smoking Alcohol consumption Dietary Aleksandrova et al. 2013 (10 European countries) Cohort (1992–2010) Early-Onset (20–50 years) 201,696 63.89% Age Gender None Physical activity Smoking Alcohol consumption Dietary Feng et al. 2021 (United Kingdom) Cohort (2006–2010) Mixed (40–69 years) 415,524 58.45% Age Gender None Physical activity Smoking Alcohol consumption Dietary Knudsen et al. 2016 (Norway) Case-Control (2012–2013) Late-Onset (50–74 years) 6,315 52.00% Age Gender Healthy weight Overweight/Obesity Physical activity Smoking Alcohol consumption Dietary Lewandowska et al. 2022 (Poland) Case-Control (2019–2020) Mix (34–85 years) 800 55.00% Age Gender Healthy weight Overweight Obesity Physical activity Smoking Alcohol consumption Dietary Negri et al. 1998 (Italy) Case-Control (1992–1996) Mixed (20–74 years) 5,379 44.67% Age Gender None None Rosato et al. 2013 (Italy and Switzerland) Case-Control (1985–2009) Early-Onset (19–45 years) 1,690 48.65% Age Gender Underweight Healthy Weight Overweight/Obesity Physical activity Smoking Alcohol consumption Dietary Shivappa et al. 2015 (Italy) Case-Control (1992–1996) Mixed (19–74 years) 6,107 47.63% Age Gender Healthy weight Overweight Obesity Physical activity Smoking Alcohol consumption Dietary Vulcan et al. 2018 (Sweden) Case-Control (1991–1996 Mixed (41–73 years) 27,931 56.50% Age Gender None Physical activity Smoking Alcohol consumption Dietary Figure 1 Table 1 3.2 Gender Eight studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 2. The observed log risk ratios for male gender ranged from − 0.2221 to 0.5538, with the majority of estimates being positive (88%). The estimated average log risk ratio for male gender based on the random-effects model was 0.3398 (95% CI: 0.1746 to 0.5049). Male gender was significantly associated with the development of CRC because the average outcome differed significantly from zero (z = 4.0327, p < 0.0001). According to the Q-test, the true outcomes appear to be heterogeneous (Q(7) = 85.1562, p < 0.0001, tau² = 0.0523, I² = 95.4471%). Figure 2 3.3 Age Five studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 3. The observed standardized mean differences for age ranged from 0.3145 to 0.6828, with the majority of estimates being positive (100%). The estimated average standardized mean for age difference based on the random-effects model was 0.4449 (95% CI: 0.3190 to 0.5708). Age was significantly associated with the development of CRC because the average outcome differed significantly from zero (z = 6.9269, p < 0.0001). According to the Q-test, the true outcomes appear to be heterogeneous (Q(4) = 49.8786, p < 0.0001, tau² = 0.0186, I² = 94.2442%). Figure 3 3.4 BMI Three studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 4. The observed log risk ratios for overweight and obese BMI ranged from 0.1108 to 0.1502, with the majority of estimates being positive (100%). The estimated average log risk ratio for overweight and obese BMI based on the random-effects model was 0.1224 (95% CI: 0.0822 to 0.1626). Overweight and obese BMI was significantly associated with the development of CRC because the average outcome differed significantly from zero (z = 5.9653, p < 0.0001). According to the Q-test, there was no significant amount of heterogeneity in the true outcomes (Q(2) = 0.5915, p = 0.7440, tau² = 0.0000, I² = 0.0000%). Figure 4 3.5 Alcohol consumption Five studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 5. The observed log risk ratios for alcohol consumption ranged from 0.0042 to 0.1272, with the majority of estimates being positive (100%). The estimated average log risk ratio for alcohol consumption based on the random-effects model was 0.0366 (95% CI: 0.0007 to 0.0725). Alcohol consumption was significantly associated with the development of CRC because the average outcome differed significantly from zero (z = 1.9973, p = 0.0458). According to the Q-test, the true outcomes appear to be heterogeneous (Q(4) = 22.9018, p = 0.0001, tau² = 0.0012, I² = 90.5562%). Figure 5 3.6 Smoking Five studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 6. The observed log risk ratios for smoking ranged from − 0.0485 to 1.0194, with the majority of estimates being positive (75%). The estimated average log risk ratio for smoking based on the random-effects model was 0.3409 (95% CI: -0.1137 to 0.7955). Smoking wasn´t significantly associated with the development of CRC because the average outcome did not differ significantly from zero (z = 1.4698, p = 0.1416). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3) = 69.8726, p < 0.0001, tau² = 0.2085, I² = 97.9245%). Figure 6 3.7 Low Physical Activity Four studies were included in the analysis and the results of the meta-analysis are displayed in Fig. 7. The observed log risk ratios low physical activity ranged from 0.0168 to 0.6258, with the majority of estimates being positive (100%). Low physical activity was associated with the development of CRC because the estimated average log risk based on the random-effects model was 0.1667 (95% CI: -0.0859 to 0.4193) but the average outcome did not differ significantly from zero (z = 1.2934, p = 0.1959). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3) = 17.5335, p = 0.0005, tau² = 0.0607, I² = 95.6932%). Figure 7 3.8 Dietary Four studies were included in the analysis of the association between high intake of processed meat and CRC (see Fig. 8). The observed log risk ratios ranged from 0.0290 to 0.6947, with the majority of estimates being positive (100%). High intake of processed meat was associated with the development of CRC because the estimated average log risk ratio based on the random-effects model was 0.2220 (95% CI: -0.0891 to 0.5330) but the average outcome did not differ significantly from zero (z = 1.3986, p = 0.1619). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3) = 121.2096, p < 0.0001, tau² = 0.0970, I² = 97.5441%). Four studies were included in the analysis of the association between no/low intake of vegetables/fruits and CRC (see Fig. 8). The observed log risk ratios ranged from − 1.9452 to 0.1888, with the majority of estimates being negative (50%). No/low intake of vegetables/fruits was associated with the development of CRC because the estimated average log risk ratio based on the random-effects model was − 0.4351 (95% CI: -1.4290 to 0.5588) but the average outcome did not differ significantly from zero (z = -0.8580, p = 0.3909). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3) = 1289.5265, p < 0.0001, tau² = 1.0236, I² = 99.7012%). Figure 8 4. Discussion In this systematic review and meta-analysis, male sex, age, overweight/obese BMI, and alcohol consumption were all significantly associated with a higher risk of developing CRC in European countries. Smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits were suggestive but statistically non-significant factors. With the exception of overweight/obese BMI, there was considerable heterogeneity among included studies. Our findings align with previous studies [ 24 ], confirming that advancing age and sex are significant risk factors for CRC development in European countries. In general, older and male adults have a higher risk of developing CRC due to combination of biological, behavioral, and environmental factors [ 25 ]. However, recent epidemiologic data highlight a significant increase in CRC incidence among younger populations over the past three decades, which cannot be fully explained by hereditary or familial factors [ 26 ]. Instead, behavioral, lifestyle, nutritional, microbial, and environmental factors appear to play a critical role in early-onset CRC cases [ 26 ]. These findings suggest a need for further research into the specific mechanisms underlying these nonhereditary etiologies and their interaction with host factors. The unique challenges faced by young CRC patients, such as treatment-related somatic morbidity and psychosocial impacts, also call for tailored management strategies. Moreover, the combination and interaction with other risk factors tends to have a stronger impact in early-onset cases [ 24 ]. For example, young individuals with family history of CRC have a stronger risk factor for CRC compared to middle age and elderly ones [ 18 ]. Sex differences in CRC highlight important disparities, with men showing higher incidence and earlier age distribution, while women more often present as emergency cases despite higher screening uptake under age 69 years [ 4 ]. These findings emphasize the importance of sex-specific strategies in CRC prevention and care. Concerning the BMI, our data provide further support that overweight and obese BMI is a risk factors for CRC in European countries. There are different theoretical models to explain the association between CRC and overweight/obesity. With regard to biological models, there is evidence that CRC in individuals with overweight and obesity is a result of chronic inflammation, increase levels of insulin-like growth factors, and gut microbiome alteration [ 27 ]. A further possible explanation in view of the association between CRC and overweight/obesity might the lifestyle mediators. Individuals with overweight and obesity often show dietary patterns (e.g. red and processed meat, low fiber) and physical inactivity, which themselves are independent risk factors for CRC [ 25 ]. In view of modifiable risk factors, our meta-analysis demonstrated that only alcohol consumption is a significant risk factor and smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits are suggestive but statistically non-significant factors in European countries. Since the analyses of the suggestive but statistically non-significant factors is based on four studies, it is recommended to include at least five studies, ideally more than ten, to strengthen the findings [ 28 ]. Additionally, only dichotomous outcomes could be compared across these risk factors, as the studies utilized different data formats. In general, there is evidence that smoking, low physical activity, and dietary patterns increase the risk for CRC [ 24 , 25 ]. This meta-analysis has several strengths, including the use of a comprehensive search strategy, a stringent selection process, and rigorous quality assessment using a modified Newcastle-Ottawa Scale. However, potential limitations must be considered. Because this systematic meta-analysis focused on inequalities in risk factors within EU countries and regions, only studies investigated EU countries and regions were included. Therefore, the number of studies included for the non-significant risk factors was inadequate for a robust meta-analysis and too small to draw statistically powerful conclusions [ 28 ]. Concerning the self-reported measures for lifestyle factors in some studies misclassification bias must be considered. This meta-analysis provides robust evidence for the significant association of demographic factors such as age and gender, as well as modifiable lifestyle factors, with CRC risk in European populations. Our results are consistent with prior research and highlight the importance of addressing obesity and alcohol consumption through targeted public health initiatives. Future research should prioritize longitudinal and interventional studies to clarify causal pathways and to design regionally tailored prevention strategies. Declarations Conflict of interests: The authors have no conflict of interest to disclose. Source of Funding: This study was funded by the European Union project ´ONCOSCREEN´ (Project-number: 101097036). Author contributions: BH researched and analyzed data, IS and GO researched data, MM and KP researched data and supervised the project. All authors contributed to discussion and approved the final version of the manuscript prior to submission. References WHO. Colorectal cancer. 2023. https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer. Accessed 15 Dec 2024. The European Commission Initiative on Colorectal Cancer (ECICC). Colorectal cancer burden in EU-27. https://ecis.jrc.ec.europa.eu/pdf/Colorectal_cancer_factsheet-Mar_2021.pdf. 2021. Jafari A, Hosseini FA, Jalali FS. A systematic review of the economic burden of colorectal cancer. Health Sci Rep. 2024;7. White A, Ironmonger L, Steele RJC, Ormiston-Smith N, Crawford C, Seims A. A review of sex-related differences in colorectal cancer incidence, screening uptake, routes to diagnosis, cancer stage and survival in the UK. BMC Cancer. 2018;18:906. Kolligs FT, Crispin A, Munte A, Wagner A, Mansmann U, Göke B. Risk of Advanced Colorectal Neoplasia According to Age and Gender. PLoS One. 2011;6:e20076. Yu J, Feng Q, Kim JH, Zhu Y. Combined Effect of Healthy Lifestyle Factors and Risks of Colorectal Adenoma, Colorectal Cancer, and Colorectal Cancer Mortality: Systematic Review and Meta-Analysis. Front Oncol. 2022;12. Goodarzi G, Mozaffari H, Raeisi T, Mehravar F, Razi B, Ghazi ML, et al. Metabolic phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies. BMC Cancer. 2022;22:89. Zhu J ‐Z., Wang Y ‐M., Zhou Q ‐Y., Zhu K ‐F., Yu C ‐H., Li Y ‐M. Systematic review with meta‐analysis: alcohol consumption and the risk of colorectal adenoma. Aliment Pharmacol Ther. 2014;40:325–37. Nucci D, Fatigoni C, Salvatori T, Nardi M, Realdon S, Gianfredi V. Association between Dietary Fibre Intake and Colorectal Adenoma: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2021;18:4168. Dong Y, Zhou J, Zhu Y, Luo L, He T, Hu H, et al. Abdominal obesity and colorectal cancer risk: systematic review and meta-analysis of prospective studies. Biosci Rep. 2017;37. Chen Q, Wang J, Yang J, Jin Z, Shi W, Qin Y, et al. Association between adult weight gain and colorectal cancer: A dose–response meta‐analysis of observational studies. Int J Cancer. 2015;136:2880–9. Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2014. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 5 Jun 2023. O’Sullivan DE, Sutherland RL, Town S, Chow K, Fan J, Forbes N, et al. Risk Factors for Early-Onset Colorectal Cancer: A Systematic Review and Meta-analysis. Clinical Gastroenterology and Hepatology. 2022;20:1229-1240.e5. Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36. Lewandowska A, Rudzki G, Lewandowski T, Stryjkowska-Góra A, Rudzki S. Risk Factors for the Diagnosis of Colorectal Cancer. Cancer Control. 2022;29:107327482110566. Vulcan A, Ericson U, Manjer J, Ohlsson B. A colorectal cancer diet quality index is inversely associated with colorectal cancer in the Malmö diet and cancer study. European Journal of Cancer Prevention. 2019;28:463–71. Shivappa N, Zucchetto A, Montella M, Serraino D, Steck SE, La Vecchia C, et al. Inflammatory potential of diet and risk of colorectal cancer: a case–control study from Italy. British Journal of Nutrition. 2015;114:152–8. Rosato V, Bosetti C, Levi F, Polesel J, Zucchetto A, Negri E, et al. Risk factors for young-onset colorectal cancer. Cancer Causes & Control. 2013;24:335–41. Negri E, Braga C, La Vecchia C, Franceschi S, Filiberti R, Montella M, et al. Family history of cancer and risk of colorectal cancer in Italy. Br J Cancer. 1998;77:174–9. Feng Q, Wong SH, Zheng J, Yang Q, Sung JJY, Tsoi KKF. Intake of processed meat, but not sodium, is associated with risk of colorectal cancer: Evidence from a large prospective cohort and two-sample Mendelian randomization. Clinical Nutrition. 2021;40:4551–9. Knudsen MD, de Lange T, Botteri E, Nguyen D-H, Evensen H, Steen CB, et al. Favorable lifestyle before diagnosis associated with lower risk of screen-detected advanced colorectal neoplasia. World J Gastroenterol. 2016;22:6276. Aleksandrova K, Pischon T, Buijsse B, May AM, Peeters PH, Bueno-de-Mesquita HBas, et al. Adult weight change and risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition. Eur J Cancer. 2013;49:3526–36. Alegria-Lertxundi I, Aguirre C, Bujanda L, Fernández FJ, Polo F, Ordovás JM, et al. Gene–Diet Interactions in Colorectal Cancer: Survey Design, Instruments, Participants and Descriptive Data of a Case–Control Study in the Basque Country. Nutrients. 2020;12:2362. Roshandel G, Ghasemi-Kebria F, Malekzadeh R. Colorectal Cancer: Epidemiology, Risk Factors, and Prevention. Cancers (Basel). 2024;16:1530. Sawicki T, Ruszkowska M, Danielewicz A, Niedźwiedzka E, Arłukowicz T, Przybyłowicz KE. A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers (Basel). 2021;13:2025. Ben-Aharon I, van Laarhoven HWM, Fontana E, Obermannova R, Nilsson M, Lordick F. Early-Onset Cancer in the Gastrointestinal Tract Is on the Rise—Evidence and Implications. Cancer Discov. 2023;13:538–51. Ye P, Xi Y, Huang Z, Xu P. Linking Obesity with Colorectal Cancer: Epidemiology and Mechanistic Insights. Cancers (Basel). 2020;12:1408. Myung S-K. How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication). J Educ Eval Health Prof. 2023;20:24. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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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Herhaus","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACAwkGBmYQgx+IDwAxD4gjQZQWyQaStRgcQBLFq8VcuvnY54Kaw/bGN3IPHmDMsZMx5z/AeOMDHi2Wc44lz55x7HDitht5CQcYtyXzWDYcYLacgc9hN3KMmXnYDieY3cgxOPx3GzOPwcEGNmkevFryPzPz/AM6bEaOAdCWeh6Dwwxs0n/w28LMzNt2mHGDBFjLYR6DY0At+LxvOSPNmJm3Lz1xxpk3IC3HeQzOMDZb9uDRYi6R/JiZ55u1PX97jvEHxm3V9gbnDx+88QOfNVgAYwOJGkbBKBgFo2AUoAMA35pKnLZv0FMAAAAASUVORK5CYII=","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University Mainz","correspondingAuthor":true,"prefix":"","firstName":"Benedict","middleName":"","lastName":"Herhaus","suffix":""},{"id":404276946,"identity":"862cb00d-c632-4366-9169-fdf8f055d69e","order_by":1,"name":"Ileana Steffens","email":"","orcid":"","institution":"University Medical Center of the Johannes Gutenberg University 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Mainz","correspondingAuthor":false,"prefix":"","firstName":"Katja","middleName":"","lastName":"Petrowski","suffix":""}],"badges":[],"createdAt":"2024-12-16 16:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5655410/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5655410/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83798903,"identity":"53b3feea-b89b-43b4-a13f-388ab24250f2","added_by":"auto","created_at":"2025-06-03 02:06:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":262587,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/1442781d9076b5efce65529a.png"},{"id":83799289,"identity":"bae2b697-7c37-4498-8158-5a5d2b97ecd5","added_by":"auto","created_at":"2025-06-03 02:14:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":174752,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/ea3aed691ae936af937b010c.png"},{"id":83799290,"identity":"f3807047-2051-4dbe-a42e-a3129c98427d","added_by":"auto","created_at":"2025-06-03 02:14:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":171450,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/9f0e765b682899e22c1453e6.png"},{"id":83798907,"identity":"422597bb-026f-495e-96ab-bcb07ac4881b","added_by":"auto","created_at":"2025-06-03 02:06:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160635,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/ceab7f6d4ea9fcbee6f2c6d8.png"},{"id":83799418,"identity":"ee8d20f6-fcac-4a47-9fea-6ce383c419db","added_by":"auto","created_at":"2025-06-03 02:22:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":187400,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/8bc63f2c7900348551ad5818.png"},{"id":83798911,"identity":"cceb908f-9317-46e9-8acf-58008821f5b9","added_by":"auto","created_at":"2025-06-03 02:06:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":172462,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/702a46fd12d7e33b0849e28e.png"},{"id":83798916,"identity":"5a0fee5e-3208-4c66-9385-0c5ef8b5f2c3","added_by":"auto","created_at":"2025-06-03 02:06:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156453,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/b14289c8e9de46bcce400baf.png"},{"id":83799298,"identity":"652829a5-de36-4a65-9dcb-910f56ac7876","added_by":"auto","created_at":"2025-06-03 02:14:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":318053,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/e63f984ad3bcc18834be1569.png"},{"id":83799919,"identity":"3a92a722-df98-4d17-a15a-225ce447dc56","added_by":"auto","created_at":"2025-06-03 02:38:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2321978,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5655410/v1/2f985d20-7a61-4972-96df-174676684d70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk factors of gender, age, BMI, and behavioral aspects for Colorectal Cancer in European countries: A Systematic Review and Meta-analysis","fulltext":[{"header":"1. Background","content":"\u003cp\u003eColorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide and the second leading cause of cancer-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. There is a widespread increase in CRC incidence in Europe, with CRC estimated to account for 12.7% of all new cancer diagnoses and 12.4% of all canter deaths in EU-27 countries in 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The economic and social costs associated with CRC are significant [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], underscoring the need for targeted prevention and intervention strategies.\u003c/p\u003e \u003cp\u003eNumerous studies have identified various risk factors for CRC, with demographic factors such as age and gender consistently associated with increased risk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, lifestyle factors such as diet, physical activity, alcohol consumption, and overweight/obesity play a significant role in influencing CRC incidence, highlighting the complex interplay between inherent and modifiable risks [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For example, there is evidence that red and processed meat intake is associated with an increased risk of CRC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], whereas higher dietary fiber intake has a protective effect [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Similarly, alcohol consumption has been associated with an increased risk of CRC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These dietary and behavioral factors not only directly influence CRC risk, but also contribute to weight gain and obesity, a well-established risk factor for CRC. There is evidence that weight gain and obesity are associated with an increased risk of CRC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the contribution of these factors to CRC risk varies widely between populations and regions. National differences may be explained by differing levels of healthcare expenditure and the resulting quality of screening, diagnosis, and treatment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough numerous individual studies have investigated CRC risk factors, a comprehensive synthesis focusing on European populations is lacking. To our knowledge, no meta-analysis has systematically examined the combined impact of demographic and behavioral risk factors such as gender, age, BMI, and lifestyle behaviors on CRC incidence across European countries. Furthermore, through our meta-analysis, we categorized risk factors for colorectal cancer into 'significant' and 'suggestive' categories. Significant risk factors are those supported by robust statistical evidence and consistent findings across studies, while suggestive risk factors indicate preliminary associations that warrant further investigation. Such a distinction is crucial for tailoring prevention strategies to the unique epidemiological landscape of European countries.\u003c/p\u003e \u003cp\u003eThis systematic review and meta-analysis examines the influence of gender, age, BMI, and behavioral factors on CRC risk in European countries, addressing a crucial gap by evaluating the combined effects of demographic and lifestyle-related risk factors within this population.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Search Strategy\u003c/h2\u003e \u003cp\u003eThe electronic databases PubMed (United States National Library of Medicine, Bethesda, MD, USA) and Web of Science (Clarivate, Philadelphia, Pennsylvanica, USA) were used for literature search from inception to August 1, 2024. A systematic search of literature was conducted with the following combination of keywords with Boolean operator (OR, AND):\u003c/p\u003e \u003cp\u003eColorectal* OR colorectal cancer OR CRC OR tumorigenesis OR tumor* OR carcinogen* OR Colonic adenocarcinoma OR colonic adenoma AND sex OR gender OR age OR smok* OR alcohol* OR dietary OR fruit OR vegetable OR meat OR BMI OR obesity OR obes* OR overweight\u003c/p\u003e \u003cp\u003eIn first round abstract and title screening was conducted by B.H. and I.S. In second round full-text screening was performed by B.H. and K.P. Meta-analyses and reviews were excluded, but were examined to relevant articles. Only English language publications involving adults of at least 18 years were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Selection Criteria and Review Process\u003c/h2\u003e \u003cp\u003eIn order to be considered for inclusion, studies were required to report on findings from an observational study (prospective or retrospective cohort studies, case-control studies, or cross-sectional studies) that investigated the correlation between non-genetic factors and the increased risk of developing CRC. Eligible risk factors included behavioral factors, age, gender and BMI aspects. Because this systematic meta-analysis/review will focus on inequalities in risk factors within and between EU countries and regions, only studies investigated EU countries and regions were included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Study Quality Assessment\u003c/h2\u003e \u003cp\u003eThe quality of the study was assessed using a modified version of the Newcastle-Ottawa Scale (NOS; [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]) for case-control and cohort studies. The NOS is a scale that assesses the quality of studies based on three main categories: (1) the selection of study groups, (2) the comparability of the groups, and (3) the ascertainment of exposure and outcome. Since the NOS is specifically designed for evaluating the quality of case-control or cohort studies, we used the adapted version for cross-sectional studies by O\u0026rsquo;Sullivan et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thereby, the study quality based on the total score as follows: low (0\u0026ndash;3), moderate (4\u0026ndash;6), and high (7\u0026ndash;9). Because this study focused on a wide range of eligible risk factors, we also used the modification of the ascertainment of exposure and outcome by O\u0026rsquo;Sullivan et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] allowing scores from 1 to 3. A score of 3 indicates adjustment for the majority of established risk factors for CRC (\u0026ge;\u0026thinsp;75% of the risk factors), a score of 2 indicates adjustment for a few established risk factors for CRC (\u0026lt;\u0026thinsp;75% of the risk factors), and a score of 1 indicates adjustment for none of the established risk factors for CRC, except age and sex. To determine if variables were adjusted for, we used methods such as backwards elimination, stepwise selection, or change-in-estimate approaches, where variables were considered adjusted if they were eliminated from the final model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was carried out using the log risk ratio or the standardized mean difference as the outcome measure. A random-effects model was fitted to the data. The amount of heterogeneity (i.e., tau\u0026sup2;), was estimated using the restricted maximum-likelihood estimator [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition to the estimate of tau\u0026sup2;, the Q-test for heterogeneity (Cochran 1954) and the I\u0026sup2; statistic are reported. For overweight and obese BMI, effect estimates for body mass index (BMI) greater than 25 kg/m\u0026sup2; were pooled and compared with referent category healthy weight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u0026sup2;). Study by [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] that examined BMI classes was not included in the overweight and obese BMI meta-analysis because no information about BMI classification of healthy weight, overweight and obesity. For alcohol consumption, smoking, high intake of processed meat, and no/low intake of vegetables/fruits, effect estimates for the highest/lowest category were pooled and compared with no alcohol consumption, no smoking, low/no intake of processed meat, and high intake of vegetables/fruits. All analyses were performed by using the software program JAMOVI (Version 2.2) with the R package metafor [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Inclusion and Study Quality\u003c/h2\u003e \u003cp\u003eIn total, 3018 articles were identified during the initial literature search, of which 30 articles underwent fulltext review. After full-text review, a total of 9 studies were included in the meta-analysis. The most common reasons for exclusion during the full-text review were wrong comparator (n\u0026thinsp;=\u0026thinsp;4), inappropriate study population (n\u0026thinsp;=\u0026thinsp;13), or incorrect outcome (n\u0026thinsp;=\u0026thinsp;4) (see Fig.\u0026nbsp;1). Characteristics of the 9 retained studies [\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] examining the risk of developing CRC associated with demographic characteristics, BMI, or behavioral factors are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The average sum score of the modified version of the Newcastle-Ottawa Scale was 7 and demonstrated a high study quality. Both cohort studies (n\u0026thinsp;=\u0026thinsp;2) used an unrepresentative cohort and half of the case-control studies used hospital-based controls.\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\u003eCharacteristics of all included studies investigating risk factors for the development of colorectal cancer in the EU\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy and location\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy design (period)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly-Onset/ Late-Onset/ Mixed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSex (% female)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBMI Classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBehavioral factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlegria-Lertxundi et al. 2020 (Spain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control (2014\u0026ndash;2016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLate-Onset\u003c/p\u003e \u003cp\u003e(50\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHealthy weight\u003c/p\u003e \u003cp\u003eOverweight/Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAleksandrova et al. 2013\u003c/p\u003e \u003cp\u003e(10 European countries)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003cp\u003e(1992\u0026ndash;2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly-Onset (20\u0026ndash;50 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e201,696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e63.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeng et al. 2021\u003c/p\u003e \u003cp\u003e(United Kingdom)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003cp\u003e(2006\u0026ndash;2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(40\u0026ndash;69 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e415,524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKnudsen et al. 2016\u003c/p\u003e \u003cp\u003e(Norway)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control (2012\u0026ndash;2013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLate-Onset\u003c/p\u003e \u003cp\u003e(50\u0026ndash;74 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e52.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHealthy weight\u003c/p\u003e \u003cp\u003eOverweight/Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLewandowska et al. 2022\u003c/p\u003e \u003cp\u003e(Poland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control\u003c/p\u003e \u003cp\u003e(2019\u0026ndash;2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMix\u003c/p\u003e \u003cp\u003e(34\u0026ndash;85 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHealthy weight\u003c/p\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegri et al. 1998\u003c/p\u003e \u003cp\u003e(Italy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control\u003c/p\u003e \u003cp\u003e(1992\u0026ndash;1996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(20\u0026ndash;74 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44.67%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRosato et al.\u003c/p\u003e \u003cp\u003e2013\u003c/p\u003e \u003cp\u003e(Italy and Switzerland)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control\u003c/p\u003e \u003cp\u003e(1985\u0026ndash;2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly-Onset\u003c/p\u003e \u003cp\u003e(19\u0026ndash;45 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003cp\u003eHealthy Weight\u003c/p\u003e \u003cp\u003eOverweight/Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShivappa et al. 2015\u003c/p\u003e \u003cp\u003e(Italy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control\u003c/p\u003e \u003cp\u003e(1992\u0026ndash;1996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(19\u0026ndash;74 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHealthy weight\u003c/p\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVulcan et al. 2018\u003c/p\u003e \u003cp\u003e(Sweden)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCase-Control\u003c/p\u003e \u003cp\u003e(1991\u0026ndash;1996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003cp\u003e(41\u0026ndash;73 years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePhysical activity\u003c/p\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003cp\u003eAlcohol consumption\u003c/p\u003e \u003cp\u003eDietary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure 1\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Gender\u003c/h2\u003e \u003cp\u003eEight studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;2. The observed log risk ratios for male gender ranged from \u0026minus;\u0026thinsp;0.2221 to 0.5538, with the majority of estimates being positive (88%).\u003c/p\u003e \u003cp\u003eThe estimated average log risk ratio for male gender based on the random-effects model was 0.3398 (95% CI: 0.1746 to 0.5049). Male gender was significantly associated with the development of CRC because the average outcome differed significantly from zero (z\u0026thinsp;=\u0026thinsp;4.0327, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). According to the Q-test, the true outcomes appear to be heterogeneous (Q(7)\u0026thinsp;=\u0026thinsp;85.1562, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, tau\u0026sup2; = 0.0523, I\u0026sup2; = 95.4471%).\u003c/p\u003e \u003cp\u003eFigure 2\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Age\u003c/h2\u003e \u003cp\u003eFive studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;3. The observed standardized mean differences for age ranged from 0.3145 to 0.6828, with the majority of estimates being positive (100%). The estimated average standardized mean for age difference based on the random-effects model was 0.4449 (95% CI: 0.3190 to 0.5708). Age was significantly associated with the development of CRC because the average outcome differed significantly from zero (z\u0026thinsp;=\u0026thinsp;6.9269, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). According to the Q-test, the true outcomes appear to be heterogeneous (Q(4)\u0026thinsp;=\u0026thinsp;49.8786, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, tau\u0026sup2; = 0.0186, I\u0026sup2; = 94.2442%).\u003c/p\u003e \u003cp\u003eFigure 3\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 BMI\u003c/h2\u003e \u003cp\u003eThree studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;4. The observed log risk ratios for overweight and obese BMI ranged from 0.1108 to 0.1502, with the majority of estimates being positive (100%). The estimated average log risk ratio for overweight and obese BMI based on the random-effects model was 0.1224 (95% CI: 0.0822 to 0.1626). Overweight and obese BMI was significantly associated with the development of CRC because the average outcome differed significantly from zero (z\u0026thinsp;=\u0026thinsp;5.9653, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). According to the Q-test, there was no significant amount of heterogeneity in the true outcomes (Q(2)\u0026thinsp;=\u0026thinsp;0.5915, p\u0026thinsp;=\u0026thinsp;0.7440, tau\u0026sup2; = 0.0000, I\u0026sup2; = 0.0000%).\u003c/p\u003e \u003cp\u003eFigure 4\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Alcohol consumption\u003c/h2\u003e \u003cp\u003eFive studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;5. The observed log risk ratios for alcohol consumption ranged from 0.0042 to 0.1272, with the majority of estimates being positive (100%).\u003c/p\u003e \u003cp\u003eThe estimated average log risk ratio for alcohol consumption based on the random-effects model was 0.0366 (95% CI: 0.0007 to 0.0725). Alcohol consumption was significantly associated with the development of CRC because the average outcome differed significantly from zero (z\u0026thinsp;=\u0026thinsp;1.9973, p\u0026thinsp;=\u0026thinsp;0.0458). According to the Q-test, the true outcomes appear to be heterogeneous (Q(4)\u0026thinsp;=\u0026thinsp;22.9018, p\u0026thinsp;=\u0026thinsp;0.0001, tau\u0026sup2; = 0.0012, I\u0026sup2; = 90.5562%).\u003c/p\u003e \u003cp\u003eFigure 5\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Smoking\u003c/h2\u003e \u003cp\u003eFive studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;6. The observed log risk ratios for smoking ranged from \u0026minus;\u0026thinsp;0.0485 to 1.0194, with the majority of estimates being positive (75%). The estimated average log risk ratio for smoking based on the random-effects model was 0.3409 (95% CI: -0.1137 to 0.7955). Smoking wasn\u0026acute;t significantly associated with the development of CRC because the average outcome did not differ significantly from zero (z\u0026thinsp;=\u0026thinsp;1.4698, p\u0026thinsp;=\u0026thinsp;0.1416). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3)\u0026thinsp;=\u0026thinsp;69.8726, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, tau\u0026sup2; = 0.2085, I\u0026sup2; = 97.9245%).\u003c/p\u003e \u003cp\u003eFigure 6\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Low Physical Activity\u003c/h2\u003e \u003cp\u003eFour studies were included in the analysis and the results of the meta-analysis are displayed in Fig.\u0026nbsp;7. The observed log risk ratios low physical activity ranged from 0.0168 to 0.6258, with the majority of estimates being positive (100%). Low physical activity was associated with the development of CRC because the estimated average log risk based on the random-effects model was 0.1667 (95% CI: -0.0859 to 0.4193) but the average outcome did not differ significantly from zero (z\u0026thinsp;=\u0026thinsp;1.2934, p\u0026thinsp;=\u0026thinsp;0.1959). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3)\u0026thinsp;=\u0026thinsp;17.5335, p\u0026thinsp;=\u0026thinsp;0.0005, tau\u0026sup2; = 0.0607, I\u0026sup2; = 95.6932%).\u003c/p\u003e \u003cp\u003eFigure 7\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Dietary\u003c/h2\u003e \u003cp\u003eFour studies were included in the analysis of the association between high intake of processed meat and CRC (see Fig.\u0026nbsp;8). The observed log risk ratios ranged from 0.0290 to 0.6947, with the majority of estimates being positive (100%). High intake of processed meat was associated with the development of CRC because the estimated average log risk ratio based on the random-effects model was 0.2220 (95% CI: -0.0891 to 0.5330) but the average outcome did not differ significantly from zero (z\u0026thinsp;=\u0026thinsp;1.3986, p\u0026thinsp;=\u0026thinsp;0.1619). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3)\u0026thinsp;=\u0026thinsp;121.2096, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, tau\u0026sup2; = 0.0970, I\u0026sup2; = 97.5441%). Four studies were included in the analysis of the association between no/low intake of vegetables/fruits and CRC (see Fig.\u0026nbsp;8). The observed log risk ratios ranged from \u0026minus;\u0026thinsp;1.9452 to 0.1888, with the majority of estimates being negative (50%). No/low intake of vegetables/fruits was associated with the development of CRC because the estimated average log risk ratio based on the random-effects model was \u0026minus;\u0026thinsp;0.4351 (95% CI: -1.4290 to 0.5588) but the average outcome did not differ significantly from zero (z = -0.8580, p\u0026thinsp;=\u0026thinsp;0.3909). According to the Q-test, the true outcomes appear to be heterogeneous (Q(3)\u0026thinsp;=\u0026thinsp;1289.5265, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, tau\u0026sup2; = 1.0236, I\u0026sup2; = 99.7012%).\u003c/p\u003e \u003cp\u003eFigure 8\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this systematic review and meta-analysis, male sex, age, overweight/obese BMI, and alcohol consumption were all significantly associated with a higher risk of developing CRC in European countries. Smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits were suggestive but statistically non-significant factors. With the exception of overweight/obese BMI, there was considerable heterogeneity among included studies.\u003c/p\u003e \u003cp\u003eOur findings align with previous studies [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], confirming that advancing age and sex are significant risk factors for CRC development in European countries. In general, older and male adults have a higher risk of developing CRC due to combination of biological, behavioral, and environmental factors [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, recent epidemiologic data highlight a significant increase in CRC incidence among younger populations over the past three decades, which cannot be fully explained by hereditary or familial factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Instead, behavioral, lifestyle, nutritional, microbial, and environmental factors appear to play a critical role in early-onset CRC cases [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These findings suggest a need for further research into the specific mechanisms underlying these nonhereditary etiologies and their interaction with host factors. The unique challenges faced by young CRC patients, such as treatment-related somatic morbidity and psychosocial impacts, also call for tailored management strategies. Moreover, the combination and interaction with other risk factors tends to have a stronger impact in early-onset cases [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For example, young individuals with family history of CRC have a stronger risk factor for CRC compared to middle age and elderly ones [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSex differences in CRC highlight important disparities, with men showing higher incidence and earlier age distribution, while women more often present as emergency cases despite higher screening uptake under age 69 years [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These findings emphasize the importance of sex-specific strategies in CRC prevention and care.\u003c/p\u003e \u003cp\u003eConcerning the BMI, our data provide further support that overweight and obese BMI is a risk factors for CRC in European countries. There are different theoretical models to explain the association between CRC and overweight/obesity. With regard to biological models, there is evidence that CRC in individuals with overweight and obesity is a result of chronic inflammation, increase levels of insulin-like growth factors, and gut microbiome alteration [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A further possible explanation in view of the association between CRC and overweight/obesity might the lifestyle mediators. Individuals with overweight and obesity often show dietary patterns (e.g. red and processed meat, low fiber) and physical inactivity, which themselves are independent risk factors for CRC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn view of modifiable risk factors, our meta-analysis demonstrated that only alcohol consumption is a significant risk factor and smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits are suggestive but statistically non-significant factors in European countries. Since the analyses of the suggestive but statistically non-significant factors is based on four studies, it is recommended to include at least five studies, ideally more than ten, to strengthen the findings [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, only dichotomous outcomes could be compared across these risk factors, as the studies utilized different data formats. In general, there is evidence that smoking, low physical activity, and dietary patterns increase the risk for CRC [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis meta-analysis has several strengths, including the use of a comprehensive search strategy, a stringent selection process, and rigorous quality assessment using a modified Newcastle-Ottawa Scale. However, potential limitations must be considered. Because this systematic meta-analysis focused on inequalities in risk factors within EU countries and regions, only studies investigated EU countries and regions were included. Therefore, the number of studies included for the non-significant risk factors was inadequate for a robust meta-analysis and too small to draw statistically powerful conclusions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Concerning the self-reported measures for lifestyle factors in some studies misclassification bias must be considered.\u003c/p\u003e \u003cp\u003eThis meta-analysis provides robust evidence for the significant association of demographic factors such as age and gender, as well as modifiable lifestyle factors, with CRC risk in European populations. Our results are consistent with prior research and highlight the importance of addressing obesity and alcohol consumption through targeted public health initiatives. Future research should prioritize longitudinal and interventional studies to clarify causal pathways and to design regionally tailored prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Funding:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the European Union project \u0026acute;ONCOSCREEN\u0026acute; (Project-number: 101097036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBH researched and analyzed data, IS and GO researched data, MM and KP researched data and supervised the project. All authors contributed to discussion and approved the final version of the manuscript prior to submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWHO. Colorectal cancer. 2023. https://www.who.int/news-room/fact-sheets/detail/colorectal-cancer. Accessed 15 Dec 2024.\u003c/li\u003e\n\u003cli\u003eThe European Commission Initiative on Colorectal Cancer (ECICC). Colorectal cancer burden in EU-27. https://ecis.jrc.ec.europa.eu/pdf/Colorectal_cancer_factsheet-Mar_2021.pdf. 2021.\u003c/li\u003e\n\u003cli\u003eJafari A, Hosseini FA, Jalali FS. A systematic review of the economic burden of colorectal cancer. Health Sci Rep. 2024;7.\u003c/li\u003e\n\u003cli\u003eWhite A, Ironmonger L, Steele RJC, Ormiston-Smith N, Crawford C, Seims A. A review of sex-related differences in colorectal cancer incidence, screening uptake, routes to diagnosis, cancer stage and survival in the UK. BMC Cancer. 2018;18:906.\u003c/li\u003e\n\u003cli\u003eKolligs FT, Crispin A, Munte A, Wagner A, Mansmann U, G\u0026ouml;ke B. Risk of Advanced Colorectal Neoplasia According to Age and Gender. PLoS One. 2011;6:e20076.\u003c/li\u003e\n\u003cli\u003eYu J, Feng Q, Kim JH, Zhu Y. Combined Effect of Healthy Lifestyle Factors and Risks of Colorectal Adenoma, Colorectal Cancer, and Colorectal Cancer Mortality: Systematic Review and Meta-Analysis. Front Oncol. 2022;12.\u003c/li\u003e\n\u003cli\u003eGoodarzi G, Mozaffari H, Raeisi T, Mehravar F, Razi B, Ghazi ML, et al. Metabolic phenotypes and risk of colorectal cancer: a systematic review and meta-analysis of cohort studies. BMC Cancer. 2022;22:89.\u003c/li\u003e\n\u003cli\u003eZhu J ‐Z., Wang Y ‐M., Zhou Q ‐Y., Zhu K ‐F., Yu C ‐H., Li Y ‐M. Systematic review with meta‐analysis: alcohol consumption and the risk of colorectal adenoma. Aliment Pharmacol Ther. 2014;40:325\u0026ndash;37.\u003c/li\u003e\n\u003cli\u003eNucci D, Fatigoni C, Salvatori T, Nardi M, Realdon S, Gianfredi V. Association between Dietary Fibre Intake and Colorectal Adenoma: A Systematic Review and Meta-Analysis. Int J Environ Res Public Health. 2021;18:4168.\u003c/li\u003e\n\u003cli\u003eDong Y, Zhou J, Zhu Y, Luo L, He T, Hu H, et al. Abdominal obesity and colorectal cancer risk: systematic review and meta-analysis of prospective studies. Biosci Rep. 2017;37.\u003c/li\u003e\n\u003cli\u003eChen Q, Wang J, Yang J, Jin Z, Shi W, Qin Y, et al. Association between adult weight gain and colorectal cancer: A dose\u0026ndash;response meta‐analysis of observational studies. Int J Cancer. 2015;136:2880\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eWells GA, Shea B, O\u0026rsquo;Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. 2014. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp. Accessed 5 Jun 2023.\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Sullivan DE, Sutherland RL, Town S, Chow K, Fan J, Forbes N, et al. Risk Factors for Early-Onset Colorectal Cancer: A Systematic Review and Meta-analysis. Clinical Gastroenterology and Hepatology. 2022;20:1229-1240.e5.\u003c/li\u003e\n\u003cli\u003eViechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36.\u003c/li\u003e\n\u003cli\u003eLewandowska A, Rudzki G, Lewandowski T, Stryjkowska-G\u0026oacute;ra A, Rudzki S. Risk Factors for the Diagnosis of Colorectal Cancer. Cancer Control. 2022;29:107327482110566.\u003c/li\u003e\n\u003cli\u003eVulcan A, Ericson U, Manjer J, Ohlsson B. A colorectal cancer diet quality index is inversely associated with colorectal cancer in the Malm\u0026ouml; diet and cancer study. European Journal of Cancer Prevention. 2019;28:463\u0026ndash;71.\u003c/li\u003e\n\u003cli\u003eShivappa N, Zucchetto A, Montella M, Serraino D, Steck SE, La Vecchia C, et al. Inflammatory potential of diet and risk of colorectal cancer: a case\u0026ndash;control study from Italy. British Journal of Nutrition. 2015;114:152\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eRosato V, Bosetti C, Levi F, Polesel J, Zucchetto A, Negri E, et al. Risk factors for young-onset colorectal cancer. Cancer Causes \u0026amp; Control. 2013;24:335\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eNegri E, Braga C, La Vecchia C, Franceschi S, Filiberti R, Montella M, et al. Family history of cancer and risk of colorectal cancer in Italy. Br J Cancer. 1998;77:174\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eFeng Q, Wong SH, Zheng J, Yang Q, Sung JJY, Tsoi KKF. Intake of processed meat, but not sodium, is associated with risk of colorectal cancer: Evidence from a large prospective cohort and two-sample Mendelian randomization. Clinical Nutrition. 2021;40:4551\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eKnudsen MD, de Lange T, Botteri E, Nguyen D-H, Evensen H, Steen CB, et al. Favorable lifestyle before diagnosis associated with lower risk of screen-detected advanced colorectal neoplasia. World J Gastroenterol. 2016;22:6276.\u003c/li\u003e\n\u003cli\u003eAleksandrova K, Pischon T, Buijsse B, May AM, Peeters PH, Bueno-de-Mesquita HBas, et al. Adult weight change and risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition. Eur J Cancer. 2013;49:3526\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eAlegria-Lertxundi I, Aguirre C, Bujanda L, Fern\u0026aacute;ndez FJ, Polo F, Ordov\u0026aacute;s JM, et al. Gene\u0026ndash;Diet Interactions in Colorectal Cancer: Survey Design, Instruments, Participants and Descriptive Data of a Case\u0026ndash;Control Study in the Basque Country. Nutrients. 2020;12:2362.\u003c/li\u003e\n\u003cli\u003eRoshandel G, Ghasemi-Kebria F, Malekzadeh R. Colorectal Cancer: Epidemiology, Risk Factors, and Prevention. Cancers (Basel). 2024;16:1530.\u003c/li\u003e\n\u003cli\u003eSawicki T, Ruszkowska M, Danielewicz A, Niedźwiedzka E, Arłukowicz T, Przybyłowicz KE. A Review of Colorectal Cancer in Terms of Epidemiology, Risk Factors, Development, Symptoms and Diagnosis. Cancers (Basel). 2021;13:2025.\u003c/li\u003e\n\u003cli\u003eBen-Aharon I, van Laarhoven HWM, Fontana E, Obermannova R, Nilsson M, Lordick F. Early-Onset Cancer in the Gastrointestinal Tract Is on the Rise\u0026mdash;Evidence and Implications. Cancer Discov. 2023;13:538\u0026ndash;51.\u003c/li\u003e\n\u003cli\u003eYe P, Xi Y, Huang Z, Xu P. Linking Obesity with Colorectal Cancer: Epidemiology and Mechanistic Insights. Cancers (Basel). 2020;12:1408.\u003c/li\u003e\n\u003cli\u003eMyung S-K. How to review and assess a systematic review and meta-analysis article: a methodological study (secondary publication). J Educ Eval Health Prof. 2023;20:24.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"colorectal cancer, risk factors, Europe, prevention, screening","lastPublishedDoi":"10.21203/rs.3.rs-5655410/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5655410/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Risk factors for the onset of colorectal cancer (CRC) risk vary significantly across populations and regions. This systematic review and meta-analysis examined gender, age, BMI, and behavioral risk factors associated with colorectal cancer (CRC) in European countries to identify and quantify their impact on CRC incidence and outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA \u003cstrong\u003es\u003c/strong\u003eystematic literature review of observational studies with risk factors of behavioral factors, age, gender and BMI aspects was performed. The meta-analysis was carried out using log-risk ratio or standardized mean difference as the outcome measure and the random-effects model was fitted to the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e From 3018 studies, nine studies were included in this meta-analysis. The risk factors of male sex, age, overweight/obese BMI, and alcohol consumption were all significantly associated with a higher risk of developing CRC in European countries. Smoking, low physical activity, high intake of processed meat, and low/no intake of vegetables/fruits were suggestive but statistically non-significant factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe results of this study provide robust evidence for the significant association of demographic factors and modifiable lifestyle factors with CRC risk in European populations. Our findings are consistent with previous research and highlight the importance of addressing obesity and alcohol consumption through targeted public health initiatives.\u003c/p\u003e","manuscriptTitle":"Risk factors of gender, age, BMI, and behavioral aspects for Colorectal Cancer in European countries: A Systematic Review and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 02:06:01","doi":"10.21203/rs.3.rs-5655410/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2a0195c8-efbe-439a-8a88-db39047e15b9","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T02:06:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 02:06:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5655410","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5655410","identity":"rs-5655410","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}