背景：早发性结直肠癌（EOCRC）的发病率在1990年代中期突然上升，继续增加，现在已经在许多国家注意到了。到2030年，25%的美国直肠癌患者将在49岁或更年轻。大多数EOCRC病例未在具有种系癌症易感性突变的患者中发现（例如，林奇综合征）或炎症性肠病。因此，环境或生活方式因素是可疑的司机。肥胖,久坐的生活方式，糖尿病,吸烟,酒精,或抗生素影响肠道微生物组已经提出。然而,这些因素,自1950年代以来一直存在，尚未确定与EOCRC的突然增加有关。急剧增加表明年轻人引入了新的风险因素。我们假设驱动因素可能是药剂的脱靶效应(即，一种在普通人群中使用前需要获得监管批准的药物，或者在年轻人的遗传易感亚组中使用以前批准的药物）。如果药物是EOCRC的驱动因素，可以使用监管风险缓解策略。
目的：我们旨在评估药物作为EOCRC危险因素的可能性。
方法：我们进行了病例对照研究。数据包括人口统计，合并症,并从马卡比医疗服务的电子病历数据库获得完整的配药史，国家授权的医疗服务提供者，覆盖了26％的以色列人口。参与者包括2001-2019年期间诊断为EOCRC（≤50岁）的941例患者，其密度以1：10的比例匹配9410例对照患者。炎症性肠病和已知遗传性癌症易感综合征的患者被排除在外。使用基于梯度增强决策树的高级机器学习算法，结合贝叶斯模型优化和重复数据采样，对非常高维的药物分配数据进行排序，以识别与EOCRC始终相关的特定药物组，同时允许药物之间的协同或拮抗相互作用。从条件逻辑回归模型获得已识别药物类别的赔率比。
结果：在800多个药物类别中，我们在独立训练的模型中确定了几个与EOCRC风险一致相关的类别.药物组之间的相互作用似乎对风险没有实质性影响。在我们的分析中,与EOCRC始终呈正相关的药物组包括β受体阻滞剂和缬草(缬草).抗生素与EOCRC风险并不一致。
结论：我们的分析表明，EOCRC的发展可能与先前使用特定药物有关。应使用其他分析来验证结果。然后需要确定通过候选药剂诱导EOCRC的作用机制。
BACKGROUND: The incidence of early-onset colorectal cancer (EOCRC) rose abruptly in the mid 1990s, is continuing to increase, and has now been noted in many countries. By 2030, 25% of American patients diagnosed with rectal cancer will be 49 years or younger. The large majority of EOCRC cases are not found in patients with germline cancer susceptibility mutations (eg, Lynch syndrome) or inflammatory bowel disease. Thus, environmental or lifestyle factors are suspected drivers. Obesity, sedentary lifestyle, diabetes mellitus, smoking, alcohol, or antibiotics affecting the gut microbiome have been proposed. However, these factors, which have been present since the 1950s, have not yet been conclusively linked to the abrupt increase in EOCRC. The sharp increase suggests the introduction of a new risk factor for young people. We hypothesized that the driver may be an off-target effect of a pharmaceutical agent (ie, one requiring regulatory approval before its use in the general population or an off-label use of a previously approved agent) in a genetically susceptible subgroup of young adults. If a pharmaceutical agent is an EOCRC driving factor, regulatory risk mitigation strategies could be used.
OBJECTIVE: We aimed to evaluate the possibility that pharmaceutical agents serve as risk factors for EOCRC.
METHODS: We conducted a case-control
study. Data including demographics, comorbidities, and complete medication dispensing history were obtained from the electronic medical records database of Maccabi Healthcare Services, a state-mandated health provider covering 26% of the Israeli population. The participants included 941 patients with EOCRC (≤50 years of age) diagnosed during 2001-2019 who were density matched at a ratio of 1:10 with 9410 control patients. Patients with inflammatory bowel disease and those with a known inherited cancer susceptibility syndrome were excluded. An advanced machine learning algorithm based on gradient boosted decision trees coupled with Bayesian model optimization and repeated data sampling was used to sort through the very high-dimensional drug dispensing data to identify specific medication groups that were consistently linked with EOCRC while allowing for synergistic or antagonistic interactions between medications. Odds ratios for the identified medication classes were obtained from a conditional logistic regression model.
RESULTS: Out of more than 800 medication classes, we identified several classes that were consistently associated with EOCRC risk across independently trained models. Interactions between medication groups did not seem to substantially affect the risk. In our analysis, drug groups that were consistently positively associated with EOCRC included beta blockers and valerian (Valeriana officinalis). Antibiotics were not consistently associated with EOCRC risk.
CONCLUSIONS: Our analysis suggests that the development of EOCRC may be correlated with prior use of specific medications. Additional analyses should be used to validate the results. The mechanism of action inducing EOCRC by candidate pharmaceutical agents will then need to be determined.
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