10种饮食习惯与乳腺癌的关联:孟德尔的随机化研究
Xuyutian Wang1, Lanlan Chen2, Runxiang Cao1
1Breast Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, China.
Frontiers in nutrition
|December 11, 2023
概括
更高的干果和油性鱼的摄入量可能会降低乳腺癌的风险. 这项遗传研究表明,这些食物对整体,ER+和ER-乳腺癌具有保护作用. 需要进一步的研究.
科学领域:
- 营养流行病学 营养流行病学
- 遗传学 是一个遗传学.
- 在瘤学瘤学.
背景情况:
- 流行病学研究表明,饮食习惯与乳腺癌风险之间存在相关性.
- 特定食物消费与乳腺癌之间的因果关系需要进一步研究.
研究的目的:
- 使用孟德尔随机化研究十种饮食习惯对乳腺癌风险的因果关系.
- 分析饮食模式对雌激素受体阳性 (ER+) 和雌激素受体阴性 (ER-) 乳腺癌亚型的影响.
主要方法:
- 孟德尔的随机化分析被用来评估因果关系.
- 对饮食模式 (n=9,851,867个SNP) 和乳腺癌 (n=10,680,257个SNP) 的全基因组关联研究 (GWAS) 数据使用了IEU OpenGWAS.
- 进行了敏感性分析,以确保调查结果的可靠性和可信性.
主要成果:
- 对干果摄入量较高的遗传倾向与整体乳腺癌 (OR=0.55,p=1.75×10−6),ER+乳腺癌 (OR=0.62,p=8.96×10−4) 和ER-乳腺癌 (OR=0.48,p=3.18×10−5) 的风险降低有关.
- 增加油性鱼类摄入量的遗传倾向显示出与较低ER+乳腺癌风险的潜在联系 (OR=0.73,p=0.04).
结论:
- 对食用干果的遗传倾向可能会对乳腺癌产生保护作用.
- 油性鱼类的消费也可能与ER+乳腺癌的风险降低有关.
- 需要进一步的研究来证实这些发现,并阐明潜在的机制.
更多相关视频
08:15gDNA Enrichment by a Transposase-based Technology for NGS Analysis of the Whole Sequence of BRCA1, BRCA2, and 9 Genes Involved in DNA Damage Repair
Published on: October 6, 2014
12.3K
09:10Palatable Western-style Cafeteria Diet as a Reliable Method for Modeling Diet-induced Obesity in Rodents
Published on: November 1, 2019
10.8K
相关概念视频
Cancer Prevention
6.2K
Several factors can increase the risk of cancer in an individual. About 50% of cancer cases can be prevented by adopting a healthy lifestyle, regular exercise, eating healthy, and following a modest cancer prevention diet. Epidemiological studies have consistently shown that populations with vegetable and fruit-rich diets have reduced the incidence of cancer. On the other hand, populations who have a diet rich in animal fat, red meat, junk food, or high calories are predisposed to cancer.
Some...
Some...
6.2K
Hazard Ratio
130
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
130
Causality in Epidemiology
424
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
424
Study Designs in Epidemiology
225
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and...
225
Statistical Methods for Analyzing Epidemiological Data
371
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
371
Cause and Effect
10.9K
While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
10.9K
