Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.
Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States.
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DOI：
10.1001/jamaoncol.2016.1025
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发表时间：
2016-10-01
期刊：
影响因子：
28.4
通讯作者：
Chatterjee N
中科院分区：
文献类型：
--
作者：
Maas P;Barrdahl M;Joshi AD;Auer PL;Gaudet MM;Milne RL;Schumacher FR;Anderson WF;Check D;Chattopadhyay S;Baglietto L;Berg CD;Chanock SJ;Cox DG;Figueroa JD;Gail MH;Graubard BI;Haiman CA;Hankinson SE;Hoover RN;Isaacs C;Kolonel LN;Le Marchand L;Lee IM;Lindström S;Overvad K;Romieu I;Sanchez MJ;Southey MC;Stram DO;Tumino R;VanderWeele TJ;Willett WC;Zhang S;Buring JE;Canzian F;Gapstur SM;Henderson BE;Hunter DJ;Giles GG;Prentice RL;Ziegler RG;Kraft P;Garcia-Closas M;Chatterjee N
改进的风险分层模型可用于指导预防乳腺癌的公共卫生策略。评价常见低穿透性单核苷酸多态(SNPs)和流行病学危险因素的联合危险分层效用。使用从乳腺癌和前列腺癌队列联盟(BPC3)和参加2010年全国健康访谈调查的5879名妇女总共抽样的17 171例和19862例对照，建立了一个预测乳腺癌绝对风险的模型，该模型结合了关于流行病学风险因素的个人水平数据和来自前瞻性队列研究的24个分型SNP的信息，另外68个SNPs的优势比已公布的估计，来自国家癌症研究所-监测，流行病学和最终结果计划癌症登记的人口发病率，以及来自全国代表性健康调查的风险因素分布的数据。该模型被用来预测在对相互竞争的死亡原因进行调整后，美国白人女性人口的绝对风险分布。单核苷酸多态、家族史、人体测量因素、月经和/或生殖因素以及生活方式因素。由于不可修改因素(SNPs、家族史、身高以及月经和/或生育史的某些组成部分)和可修改因素(体重指数[BMI；体重(公斤)除以身高(米)平方]、更年期激素治疗[MHT]、饮酒和吸烟)导致的绝对风险分层程度。在美国，30岁的白人女性在80岁之前患浸润性乳腺癌的平均绝对风险为11.3%。一个包含所有风险因素的模型分别为处于风险分布底部和顶部的女性提供了从4.4%到23.5%的平均绝对风险范围。对于处于不可改变风险的最低和最高分位数的女性，与4个可改变因素相关的风险分布的第5和第95百分位数范围分别为2.9%~5.0%和15.5%~25.0%。对于由于不可改变的因素而处于风险最高十分之一的女性来说，那些体重指数较低、不喝酒或吸烟、不使用MHT的女性的风险与一般人群中的平均女性相当。这个包括SNPs在内的乳腺癌绝对风险模型可以为美国白人女性人口提供分层。该模型还可以确定风险较高的人群中的子集，这些子集将从基于改变可修改因素的风险降低策略中受益最大。这一模型对个人风险沟通的有效性有待进一步研究。
An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention. To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors. Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality. Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors. Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking). The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population. This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.
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影响因子：
5
作者：
Joshi, Amit D.;Lindstrom, Sara;Kraft, Peter
通讯作者：
Kraft, Peter
DOI：
10.1093/jnci/87.22.1681
发表时间：
1995-11-15
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
MADIGAN, MP;ZIEGLER, RG;HOOVER, RN
通讯作者：
HOOVER, RN
影响因子：
30.8
作者：
Michailidou K;Beesley J;Lindstrom S;Canisius S;Dennis J;Lush MJ;Maranian MJ;Bolla MK;Wang Q;Shah M;Perkins BJ;Czene K;Eriksson M;Darabi H;Brand JS;Bojesen SE;Nordestgaard BG;Flyger H;Nielsen SF;Rahman N;Turnbull C;BOCS;Fletcher O;Peto J;Gibson L;dos-Santos-Silva I;Chang-Claude J;Flesch-Janys D;Rudolph A;Eilber U;Behrens S;Nevanlinna H;Muranen TA;Aittomäki K;Blomqvist C;Khan S;Aaltonen K;Ahsan H;Kibriya MG;Whittemore AS;John EM;Malone KE;Gammon MD;Santella RM;Ursin G;Makalic E;Schmidt DF;Casey G;Hunter DJ;Gapstur SM;Gaudet MM;Diver WR;Haiman CA;Schumacher F;Henderson BE;Le Marchand L;Berg CD;Chanock SJ;Figueroa J;Hoover RN;Lambrechts D;Neven P;Wildiers H;van Limbergen E;Schmidt MK;Broeks A;Verhoef S;Cornelissen S;Couch FJ;Olson JE;Hallberg E;Vachon C;Waisfisz Q;Meijers-Heijboer H;Adank MA;van der Luijt RB;Li J;Liu J;Humphreys K;Kang D;Choi JY;Park SK;Yoo KY;Matsuo K;Ito H;Iwata H;Tajima K;Guénel P;Truong T;Mulot C;Sanchez M;Burwinkel B;Marme F;Surowy H;Sohn C;Wu AH;Tseng CC;Van Den Berg D;Stram DO;González-Neira A;Benitez J;Zamora MP;Perez JI;Shu XO;Lu W;Gao YT;Cai H;Cox A;Cross SS;Reed MW;Andrulis IL;Knight JA;Glendon G;Mulligan AM;Sawyer EJ;Tomlinson I;Kerin MJ;Miller N;kConFab Investigators;AOCS Group;Lindblom A;Margolin S;Teo SH;Yip CH;Taib NA;Tan GH;Hooning MJ;Hollestelle A;Martens JW;Collée JM;Blot W;Signorello LB;Cai Q;Hopper JL;Southey MC;Tsimiklis H;Apicella C;Shen CY;Hsiung CN;Wu PE;Hou MF;Kristensen VN;Nord S;Alnaes GI;NBCS;Giles GG;Milne RL;McLean C;Canzian F;Trichopoulos D;Peeters P;Lund E;Sund M;Khaw KT;Gunter MJ;Palli D;Mortensen LM;Dossus L;Huerta JM;Meindl A;Schmutzler RK;Sutter C;Yang R;Muir K;Lophatananon A;Stewart-Brown S;Siriwanarangsan P;Hartman M;Miao H;Chia KS;Chan CW;Fasching PA;Hein A;Beckmann MW;Haeberle L;Brenner H;Dieffenbach AK;Arndt V;Stegmaier C;Ashworth A;Orr N;Schoemaker MJ;Swerdlow AJ;Brinton L;Garcia-Closas M;Zheng W;Halverson SL;Shrubsole M;Long J;Goldberg MS;Labrèche F;Dumont M;Winqvist R;Pylkäs K;Jukkola-Vuorinen A;Grip M;Brauch H;Hamann U;Brüning T;GENICA Network;Radice P;Peterlongo P;Manoukian S;Bernard L;Bogdanova NV;Dörk T;Mannermaa A;Kataja V;Kosma VM;Hartikainen JM;Devilee P;Tollenaar RA;Seynaeve C;Van Asperen CJ;Jakubowska A;Lubinski J;Jaworska K;Huzarski T;Sangrajrang S;Gaborieau V;Brennan P;McKay J;Slager S;Toland AE;Ambrosone CB;Yannoukakos D;Kabisch M;Torres D;Neuhausen SL;Anton-Culver H;Luccarini C;Baynes C;Ahmed S;Healey CS;Tessier DC;Vincent D;Bacot F;Pita G;Alonso MR;Álvarez N;Herrero D;Simard J;Pharoah PP;Kraft P;Dunning AM;Chenevix-Trench G;Hall P;Easton DF
通讯作者：
Easton DF
影响因子：
6.4
作者：
Bray, Freddie;Ren, Jian-Song;Ferlay, Jacques
通讯作者：
Ferlay, Jacques
DOI：
10.1093/jnci/djn180
发表时间：
2008-07-16
期刊：
Journal of the National Cancer Institute
影响因子：
--
作者：
Gail MH
通讯作者：
Gail MH