R. MacInnis, M. Jenkins, J. Hopper, L. Cannon-Albright
{"title":"摘要B06:犹他州家族性结直肠癌风险模型","authors":"R. MacInnis, M. Jenkins, J. Hopper, L. Cannon-Albright","doi":"10.1158/1538-7755.CARISK16-B06","DOIUrl":null,"url":null,"abstract":"Background: Family history is an important risk factor for CRC, but there is still confusion about the appropriate guidelines councilors should recommend to people depending on the specifics of their family history. Most previous studies that have estimated familial relative risk (FRR) of CRC have based this on first-degree relatives (FDRs), whereas information on second- or third-degree relatives (SDRs or TDRs) has been of poor quality or non-existent. The most notable exception to this is a publication by Taylor et al that utilized the Utah Population Database (UPDB), a population-based resource with a computerized genealogy linked to statewide cancer registry records.[1] They reported FRRs of CRC for probands by selected combinations of affected relatives, extending to third-degree. The aim of this study was to extend this work by developing a simple and clinically-useful model of familial CRC risk. Methods: We restricted the analysis to people aged 30 years or older born between 1930 and 1985 (probands) from the UPDB. Data were collected on the proband9s age, sex and history of CRC for FDRs, SDRs and TDRs. Unconditional multiple linear logistic regression was used to model the familial CRC risk for probands as a function of their family history measures. Various combinations of CRC status of relatives were considered, including categorizations by ages at diagnoses ( Results: A total of 591,535 probands were extracted of whom 2,115 probands were identified as having a primary diagnosis of CRC. The best-fitting model for CRC was FRR = exp(SUM/5)*0.8, where SUM equals: 4 points for each parent diagnosed with CRC 6 points for each sibling diagnosed with CRC 12 points for each child diagnosed with CRC 2 points for each SDR diagnosed with CRC 1 point for each TDR diagnosed with CRC. Therefore, a doubling of risk would be 5 points, a tripling of risk would be 7 points, while a 5-fold increased risk would be 10 points. The model had good internal consistency. Additional information on ages at diagnoses of affected FDRs, SDRs or TDRs or whether diagnoses were confined to a particular side of the family did not improve the model fit. Conclusions: This simple algorithm shows that knowing the total number of affected parents, siblings, children, SDRs and TDRs, irrespective of the age at diagnosis, is sufficient for accurate estimation of FRR. This model could be useful in the clinical and genetic counseling setting. 1. Taylor DP, et al. Population-based family history-specific risks for colorectal cancer: a constellation approach. Gastroenterology. 2010 Mar;138(3):877-85. This abstract is also being presented as PosterB06. Citation Format: Robert J. MacInnis, Mark A. Jenkins, John L. Hopper, Lisa A. Cannon-Albright. Utah familial colorectal cancer risk model. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR11.","PeriodicalId":9487,"journal":{"name":"Cancer Epidemiology and Prevention Biomarkers","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Abstract B06: Utah familial colorectal cancer risk model\",\"authors\":\"R. MacInnis, M. Jenkins, J. Hopper, L. Cannon-Albright\",\"doi\":\"10.1158/1538-7755.CARISK16-B06\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: Family history is an important risk factor for CRC, but there is still confusion about the appropriate guidelines councilors should recommend to people depending on the specifics of their family history. Most previous studies that have estimated familial relative risk (FRR) of CRC have based this on first-degree relatives (FDRs), whereas information on second- or third-degree relatives (SDRs or TDRs) has been of poor quality or non-existent. The most notable exception to this is a publication by Taylor et al that utilized the Utah Population Database (UPDB), a population-based resource with a computerized genealogy linked to statewide cancer registry records.[1] They reported FRRs of CRC for probands by selected combinations of affected relatives, extending to third-degree. The aim of this study was to extend this work by developing a simple and clinically-useful model of familial CRC risk. Methods: We restricted the analysis to people aged 30 years or older born between 1930 and 1985 (probands) from the UPDB. Data were collected on the proband9s age, sex and history of CRC for FDRs, SDRs and TDRs. Unconditional multiple linear logistic regression was used to model the familial CRC risk for probands as a function of their family history measures. Various combinations of CRC status of relatives were considered, including categorizations by ages at diagnoses ( Results: A total of 591,535 probands were extracted of whom 2,115 probands were identified as having a primary diagnosis of CRC. The best-fitting model for CRC was FRR = exp(SUM/5)*0.8, where SUM equals: 4 points for each parent diagnosed with CRC 6 points for each sibling diagnosed with CRC 12 points for each child diagnosed with CRC 2 points for each SDR diagnosed with CRC 1 point for each TDR diagnosed with CRC. Therefore, a doubling of risk would be 5 points, a tripling of risk would be 7 points, while a 5-fold increased risk would be 10 points. The model had good internal consistency. Additional information on ages at diagnoses of affected FDRs, SDRs or TDRs or whether diagnoses were confined to a particular side of the family did not improve the model fit. Conclusions: This simple algorithm shows that knowing the total number of affected parents, siblings, children, SDRs and TDRs, irrespective of the age at diagnosis, is sufficient for accurate estimation of FRR. This model could be useful in the clinical and genetic counseling setting. 1. Taylor DP, et al. Population-based family history-specific risks for colorectal cancer: a constellation approach. Gastroenterology. 2010 Mar;138(3):877-85. This abstract is also being presented as PosterB06. Citation Format: Robert J. MacInnis, Mark A. Jenkins, John L. Hopper, Lisa A. Cannon-Albright. Utah familial colorectal cancer risk model. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. 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引用次数: 0
摘要
背景:家族史是结直肠癌的一个重要危险因素,但对于咨询师应根据其家族史的具体情况向人们推荐适当的指导方针仍然存在困惑。先前大多数估计结直肠癌家族相对风险(FRR)的研究都是基于一级亲属(FDRs),而关于二度或三度亲属(SDRs或TDRs)的信息质量较差或不存在。最值得注意的例外是Taylor等人利用犹他州人口数据库(UPDB)发表的一篇文章,UPDB是一种基于人口的资源,具有与全州癌症登记记录相关联的计算机家谱他们通过选择受影响亲属的组合报告了先证者的CRC frr,延伸到三度。本研究的目的是通过建立一种简单且临床有用的家族性结直肠癌风险模型来扩展这项工作。方法:我们将分析限制在1930年至1985年出生的30岁或以上的人(先证者)。收集fdr、sdr和tdr的年龄、性别和结直肠癌病史。使用无条件多元线性逻辑回归对先证者的家族性CRC风险作为其家族史测量的函数进行建模。考虑了亲属结直肠癌状况的各种组合,包括诊断时年龄的分类(结果:共提取了591,535个先证者,其中2,115个先证者被确定为初步诊断为结直肠癌。CRC的最佳拟合模型为FRR = exp(SUM/5)*0.8,其中SUM等于:诊断为CRC的父母每名4分诊断为CRC的兄弟姐妹每名6分诊断为CRC的儿童每名12分诊断为CRC的SDR每名2分诊断为CRC的TDR每名1分。因此,风险增加一倍为5分,风险增加三倍为7分,而风险增加五倍为10分。模型具有良好的内部一致性。关于诊断受影响的fdr、sdr或tdr的年龄或诊断是否局限于家庭的某一方的额外信息并没有改善模型拟合。结论:这一简单的算法表明,无论诊断年龄如何,只要知道患病父母、兄弟姐妹、子女、sdr和tdr的总数,就足以准确估计FRR。该模型可用于临床和遗传咨询设置。1. Taylor DP,等。以人群为基础的结直肠癌家族史特异性风险:星座方法胃肠病学杂志,2010;38(3):877-85。此摘要也以PosterB06的形式呈现。引文格式:Robert J. MacInnis, Mark A. Jenkins, John L. Hopper, Lisa A. Cannon-Albright。犹他州家族性结直肠癌风险模型。[摘要]。摘自:AACR特别会议论文集:改进癌症风险预测以预防和早期发现;2016年11月16日至19日;费城(PA): AACR;Cancer epidemiology Biomarkers pre2017;26(5增刊):摘要nr PR11。
Abstract B06: Utah familial colorectal cancer risk model
Background: Family history is an important risk factor for CRC, but there is still confusion about the appropriate guidelines councilors should recommend to people depending on the specifics of their family history. Most previous studies that have estimated familial relative risk (FRR) of CRC have based this on first-degree relatives (FDRs), whereas information on second- or third-degree relatives (SDRs or TDRs) has been of poor quality or non-existent. The most notable exception to this is a publication by Taylor et al that utilized the Utah Population Database (UPDB), a population-based resource with a computerized genealogy linked to statewide cancer registry records.[1] They reported FRRs of CRC for probands by selected combinations of affected relatives, extending to third-degree. The aim of this study was to extend this work by developing a simple and clinically-useful model of familial CRC risk. Methods: We restricted the analysis to people aged 30 years or older born between 1930 and 1985 (probands) from the UPDB. Data were collected on the proband9s age, sex and history of CRC for FDRs, SDRs and TDRs. Unconditional multiple linear logistic regression was used to model the familial CRC risk for probands as a function of their family history measures. Various combinations of CRC status of relatives were considered, including categorizations by ages at diagnoses ( Results: A total of 591,535 probands were extracted of whom 2,115 probands were identified as having a primary diagnosis of CRC. The best-fitting model for CRC was FRR = exp(SUM/5)*0.8, where SUM equals: 4 points for each parent diagnosed with CRC 6 points for each sibling diagnosed with CRC 12 points for each child diagnosed with CRC 2 points for each SDR diagnosed with CRC 1 point for each TDR diagnosed with CRC. Therefore, a doubling of risk would be 5 points, a tripling of risk would be 7 points, while a 5-fold increased risk would be 10 points. The model had good internal consistency. Additional information on ages at diagnoses of affected FDRs, SDRs or TDRs or whether diagnoses were confined to a particular side of the family did not improve the model fit. Conclusions: This simple algorithm shows that knowing the total number of affected parents, siblings, children, SDRs and TDRs, irrespective of the age at diagnosis, is sufficient for accurate estimation of FRR. This model could be useful in the clinical and genetic counseling setting. 1. Taylor DP, et al. Population-based family history-specific risks for colorectal cancer: a constellation approach. Gastroenterology. 2010 Mar;138(3):877-85. This abstract is also being presented as PosterB06. Citation Format: Robert J. MacInnis, Mark A. Jenkins, John L. Hopper, Lisa A. Cannon-Albright. Utah familial colorectal cancer risk model. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr PR11.