Application of a new statistical method to derive dietary patterns in nutritional epidemiology
Application of a new statistical method to derive dietary patterns in nutritional epidemiology
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DOI：
10.1093/aje/kwh134
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发表时间：
2004-05-15
影响因子：
5
通讯作者：
Boeing, H
中科院分区：
文献类型：
--
作者：
Hoffmann, K;Schulze, MB;Boeing, H
由于食物是混合食用的，因此在观察性研究中很难区分单一食物对疾病发展的影响。检查食物摄入量的综合影响的一种可能方法是通过使用适当的统计方法得出饮食模式。本研究的目的是应用一种新的统计方法，降秩回归（RRR），这是更灵活和强大的比经典的主成分分析。RRR可以有效地用于营养流行病学，通过选择疾病特异性的反应变量，并确定食物摄入量的组合，解释尽可能多的反应变化。作者应用RRR从49个食物组中提取饮食模式，指定四种糖尿病相关营养素和营养素比例作为响应。数据来源于欧洲癌症和营养前瞻性调查-波茨坦研究中的一项嵌套式德国病例对照研究，该研究包括193例2型糖尿病病例和385例对照。RRR提取的4个因子解释了93.1%的响应变异，而主成分分析得到的前4个因子只解释了41.9%。与主成分分析和其他方法相比，新的RRR方法提取了一个显着的糖尿病危险因素。
Because foods are consumed in combination, it is difficult in observational studies to separate the effects of single foods on the development of diseases. A possible way to examine the combined effect of food intakes is to derive dietary patterns by using appropriate statistical methods. The objective of this study was to apply a new statistical method, reduced rank regression (RRR), that is more flexible and powerful than the classic principal component analysis. RRR can be used efficiently in nutritional epidemiology by choosing disease-specific response variables and determining combinations of food intake that explain as much response variation as possible. The authors applied RRR to extract dietary patterns from 49 food groups, specifying four diabetes-related nutrients and nutrient ratios as responses. Data were derived from a nested German case-control study within the European Prospective Investigation into Cancer and Nutrition-Potsdam study consisting of 193 cases with incident type 2 diabetes identified until 2001 and 385 controls. The four factors extracted by RRR explained 93.1% of response variation, whereas the first four factors obtained by principal component analysis accounted for only 41.9%. In contrast to principal component analysis and other methods, the new RRR method extracted a significant risk factor for diabetes.