营养基因组学研究中出现的2型糖尿病数据集的复杂性：降维的必要性？
Complexity of type 2 diabetes mellitus data sets emerging from nutrigenomic research: a case for dimensionality reduction?
作者信息
Kaput Jim, Dawson Kevin
机构信息
Center of Excellence in Nutritional Genomics, University of California at Davis, Davis, CA 95616, USA.
出版信息
Mutat Res. 2007 Sep 1;622(1-2):19-32. doi: 10.1016/j.mrfmmm.2007.02.033. Epub 2007 May 5.
Nutrigenomics promises personalized nutrition and an improvement in preventing, delaying, and reducing the symptoms of chronic diseases such as diabetes. Nutritional genomics is the study of how foods affect the expression of genetic information in an individual and how an individual's genetic makeup affects the metabolism and response to nutrients and other bioactive components in food. The path to those promises has significant challenges, from experimental designs that include analysis of genetic heterogeneity to the complexities of food and environmental factors. One of the more significant complications in developing the knowledge base and potential applications is how to analyze high-dimensional datasets of genetic, nutrient, metabolomic (clinical), and other variables influencing health and disease processes. Type 2 diabetes mellitus (T2DM) is used as an illustration of the challenges in studying complex phenotypes with nutrigenomics concepts and approaches.
营养基因组学有望实现个性化营养，并改善诸如糖尿病等慢性疾病的预防、延缓和症状减轻。营养基因组学研究食物如何影响个体遗传信息的表达，以及个体的基因构成如何影响对食物中营养物质和其他生物活性成分的代谢及反应。从包含基因异质性分析的实验设计到食物和环境因素的复杂性，实现这些承诺的道路面临重大挑战。在发展知识库和潜在应用方面，更显著的复杂问题之一是如何分析影响健康和疾病进程的基因、营养、代谢组学（临床）及其他变量的高维数据集。2型糖尿病（T2DM）被用作一个例子，来说明运用营养基因组学概念和方法研究复杂表型所面临的挑战。
Zotero 插件