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Food patterns derived with multivariate statistical methods and their association with chronic disease in a multi-country setting: the European Prospective Investigation into Cancer and Nutrition

Food patterns derived with multivariate statistical methods and their association with chronic disease in a multi-country setting: the European Prospective Investigation into Cancer and Nutrition
通过多变量统计方法得出的食物模式及其与多国环境中慢性疾病的关联:欧洲癌症和营养前瞻性调查
批准号:
257392992
负责人:
Dr. Brian Buijsse, Ph.D.
金额:
$0.0万
依托单位国家:
德国
项目类别:
Research Grants
财政年份:
2014
资助国家:
德国
项目状态:
已结题
起止时间:
2013-12-31 至 2014-12-31

项目摘要

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中文摘要
翻译
研究整体饮食模式是营养流行病学中一个相对较新的方向。除了“先验的”饮食质量评分,到目前为止,几乎所有的研究都使用了所谓的非监督方法,包括主成分分析。这些方法提取饮食模式,而不考虑疾病结果。因此,这种饮食模式可能与疾病无关(即,预测性)。因此,需要测试其他多变量统计方法,以确定具有更大预防疾病潜力的饮食模式。可使用可利用先前存在的慢性病风险知识的监督方法来实现这一目的。以发生的结直肠癌为例,这项研究将检验在预测慢性病风险方面,用监督方法提取的食物模式是否与非监督方法提取的食物模式一样好或更好,或者比以前提出的基于食物的先验饮食质量评分好还是更好。有监督的方法有随机生存森林分析和支持向量机学习,无监督的方法有主成分分析和聚类分析。饮食质量分数将根据被认为是健康或不健康的食物类别的四分位数排名来构建。这项研究将在欧洲癌症和营养前瞻性调查(EPIC)中进行,研究开始时(1992-2000年)有来自10个国家和地区的478,000名没有患结直肠癌的男性和女性,他们在平均11年的随访期内发生了大约4517例结直肠癌病例。这项研究将阐明,就与慢性病的关联而言,用监督方法确定的食物模式是否可以比现有方法确定的食物模式更好。如果是这样的话,将努力找出背后的原因。
英文摘要
Studying overall dietary patterns is a relatively new direction in nutritional epidemiology. In addition to 'a priori' diet quality scores, virtually all studies so far have used so-called unsupervised methods, including principal component analysis. These methods extract dietary patterns without taking the disease outcome into account. Consequently, such dietary patterns may not be relevant (that is, predictive) for a disease. Thus, other multivariate statistical methods to identify dietary patterns with greater potential for disease prevention need to be tested. Supervised methods, which can make use of pre-existing knowledge on chronic disease risk, may be used for this aim. Using incident colorectal cancer as an example, this study will examine whether food patterns extracted with supervised methods are as good or better than food patterns from unsupervised methods and a previously proposed food-based 'a priori' diet quality score in terms of predicting chronic disease risk. The supervised methods to test are random survival forest analysis and support vector machine learning, and the unsupervised methods are principal component analysis and cluster analysis. The diet quality score will be constructed based on quartile rankings for food groups considered to be either healthy or unhealthy. This study will be conducted in the European Prospective Investigation into Cancer and Nutrition (EPIC), with 478,000 men and women from 10 countries without colorectal cancer at study inception (1992-2000) and in whom about 4517 incident cases of colorectal cancer occurred during a mean follow-up of 11 years. This study will clarify whether food patterns identified with supervised methods can outperform food patterns from established methods in terms of their association with chronic disease. If so, it will be tried to find out the underlying reasons.
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