结直肠癌 (CRC) 是欧洲第二大常见癌症，其中五分之一的病例归因于不健康的生活方式。用于量化 CRC 风险和识别高风险群体的风险预测模型已在欧洲人群中开发或验证，其中一些将生活方式视为预测因素。识别适用于欧洲人群的现有风险预测模型中考虑的生活方式预测因素，并表征其相应的参数值更好地了解它们对不同模型预测的相对贡献。2000 年 1 月至 2021 年 8 月期间，PubMed 和 Web of Science 进行了系统评价。风险预测模型包括以下条件：(1) 在成人无症状欧洲人中开发和/或验证人口，(2) 基于非侵入性测量的预测因素，(3) 报告的预测因素的平均估计值和不确定性。为了便于比较，使用森林图可视化特定模型的生活方式预测因子。总共 21 个 CRC 风险预测模型（在 16 项研究中报告）符合资格，其中 11 个在欧洲成年人群中得到验证，但在其他地方（主要是美国）开发。除两个模型外，所有模型都报告了至少一种生活方式因素作为预测因素。在生活方式因素中，最常见的预测因素是体重指数 (BMI) 和吸烟（均出现在 13 个模型中），其次是酒精 (11) 和体力活动 (7)，而饮食相关因素则较少考虑。最常见的是肉类 (9)、蔬菜 (5) 或乳制品 (2)。尽管 BMI、吸烟和饮酒的效应大小估计存在明显差异，但当收集得更详细时，独立预测贡献通常更大。根据生活方式数据早期识别高风险群体有可能鼓励参与生活方式改变和筛查计划，从而减轻 CRC 负担。我们建议将共同的生活方式预测因子进一步用于公共卫生预测模型，以提高模型的采用率。© 作者（或其雇主）2024。CC BY-NC 允许重复使用。不得商业再利用。请参阅权利和权限。英国医学杂志出版。

Colorectal cancer (CRC) is the second most prevalent cancer in Europe, with one-fifth of cases attributable to unhealthy lifestyles. Risk prediction models for quantifying CRC risk and identifying high-risk groups have been developed or validated across European populations, some considering lifestyle as a predictor.To identify lifestyle predictors considered in existing risk prediction models applicable for European populations and characterise their corresponding parameter values for an improved understanding of their relative contribution to prediction across different models.A systematic review was conducted in PubMed and Web of Science from January 2000 to August 2021. Risk prediction models were included if (1) developed and/or validated in an adult asymptomatic European population, (2) based on non-invasively measured predictors and (3) reported mean estimates and uncertainty for predictors included. To facilitate comparison, model-specific lifestyle predictors were visualised using forest plots.A total of 21 risk prediction models for CRC (reported in 16 studies) were eligible, of which 11 were validated in a European adult population but developed elsewhere, mostly USA. All models but two reported at least one lifestyle factor as predictor. Of the lifestyle factors, the most common predictors were body mass index (BMI) and smoking (each present in 13 models), followed by alcohol (11), and physical activity (7), while diet-related factors were less considered with the most commonly present meat (9), vegetables (5) or dairy (2). The independent predictive contribution was generally greater when they were collected with greater detail, although a noticeable variation in effect size estimates for BMI, smoking and alcohol.Early identification of high-risk groups based on lifestyle data offers the potential to encourage participation in lifestyle change and screening programmes, hence reduce CRC burden. We propose the commonly shared lifestyle predictors to be further used in public health prediction modelling for improved uptake of the model.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.