Large-scale analysis to identify risk factors for ovarian cancer

医学 卵巢癌 孟德尔随机化 肿瘤科 内科学 逻辑回归 妇科 癌症 基因型 基因 生物化学 化学 遗传变异
作者
Iqbal Madakkatel,Amanda L. Lumsden,Anwar Mulugeta,Johanna Mäenpää,Martin K. Oehler,Elina Hyppönen
出处
期刊:International Journal of Gynecological Cancer [BMJ]
卷期号:: ijgc-005424
标识
DOI:10.1136/ijgc-2024-005424
摘要

Objective Ovarian cancer is characterized by late-stage diagnoses and poor prognosis. We aimed to identify factors that can inform prevention and early detection of ovarian cancer. Methods We used a data-driven machine learning approach to identify predictors of epithelial ovarian cancer from 2920 input features measured 12.6 years (IQR 11.9 to 13.3 years) before diagnoses. Analyses included 221 732 female participants in the UK Biobank without a history of cancer. During the follow-up 1441 women developed ovarian cancer. For factors that contributed to model prediction, we used multivariate logistic regression to evaluate the association with ovarian cancer, with evidence for causality tested by Mendelian randomization (MR) analyses in the Ovarian Cancer Genetics Consortium (25 509 cases). Results Greater parity and ever-use of oral contraception were associated with lower ovarian cancer risk (ever vs never OR 0.74, 95% CI 0.66 to 0.84). After adjustment for established risk factors, greater height, weight, and greater red blood cell distribution width were associated with increased ovarian cancer risk, while higher aspartate aminotransferase levels and mean corpuscular volume were associated with lower risk. MR analyses confirmed observational associations with anthropometric/adiposity traits (eg, body fat percentage per standard deviation (SD); OR inverse-variance weighted (OR IVW ) 1.28, 95% CI 1.13 to 1.46) and aspartate aminotransferase (OR IVW 0.87, 95% CI 0.78 to 0.98). MR also provided genetic evidence for a protective association of higher total serum protein on ovarian cancer, higher lymphocyte count on serous and endometrioid ovarian cancer, and greater forced expiratory volume in 1 s on serous ovarian cancer among other findings. Conclusions This study shows that certain risk factors for ovarian cancer are modifiable, suggesting that weight reduction and interventions to reduce the number of ovulations may provide potential for future prevention. We also identified blood biomarkers associated with ovarian cancer years before diagnoses, warranting further investigation.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小喵完成签到 ,获得积分10
1秒前
qiancib202完成签到,获得积分10
4秒前
dwgwushan完成签到 ,获得积分10
5秒前
Cold-Drink-Shop完成签到,获得积分10
10秒前
香蕉觅云应助Wang采纳,获得10
12秒前
14秒前
流星雨发布了新的文献求助10
17秒前
forest发布了新的文献求助10
20秒前
Jeffery426完成签到,获得积分10
24秒前
WY完成签到 ,获得积分10
25秒前
baobeikk完成签到 ,获得积分10
29秒前
我就想看看文献完成签到 ,获得积分10
31秒前
Zhou发布了新的文献求助10
32秒前
ylyao完成签到 ,获得积分10
40秒前
林谷雨完成签到 ,获得积分10
45秒前
不吃芹菜完成签到,获得积分10
47秒前
陈炳蓉完成签到,获得积分10
47秒前
xiaowuge完成签到 ,获得积分10
55秒前
刘丰完成签到 ,获得积分10
1分钟前
现实的大白完成签到 ,获得积分10
1分钟前
cq_2完成签到,获得积分10
1分钟前
orixero应助科研通管家采纳,获得10
1分钟前
霁昕完成签到 ,获得积分10
1分钟前
光亮若翠完成签到,获得积分10
1分钟前
onevip完成签到,获得积分10
1分钟前
石子完成签到 ,获得积分10
1分钟前
流浪的鲨鱼完成签到,获得积分20
1分钟前
Jasmineyfz完成签到 ,获得积分10
1分钟前
1分钟前
个性的孤风完成签到,获得积分20
1分钟前
ramsey33完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
1分钟前
1分钟前
helinahs完成签到 ,获得积分10
1分钟前
xingxing完成签到 ,获得积分10
1分钟前
YU发布了新的文献求助30
1分钟前
白昼の月完成签到 ,获得积分0
2分钟前
丰富的归尘完成签到 ,获得积分10
2分钟前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Les Mantodea de Guyane Insecta, Polyneoptera 1000
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 820
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Typology of Conditional Constructions 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3571332
求助须知:如何正确求助?哪些是违规求助? 3141926
关于积分的说明 9444874
捐赠科研通 2843331
什么是DOI,文献DOI怎么找? 1562830
邀请新用户注册赠送积分活动 731326
科研通“疑难数据库(出版商)”最低求助积分说明 718524