National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA, USA.
J Nutr. 2013 Jun;143(6):948S-56S. doi: 10.3945/jn.112.172957. Epub 2013 Apr 17.
The collection of articles in this supplement issue provides insight into the association of various covariates with concentrations of biochemical indicators of diet and nutrition (biomarkers), beyond age, race, and sex, using linear regression. We studied 10 specific sociodemographic and lifestyle covariates in combination with 29 biomarkers from NHANES 2003-2006 for persons aged ≥ 20 y. The covariates were organized into 2 sets or "chunks": sociodemographic (age, sex, race-ethnicity, education, and income) and lifestyle (dietary supplement use, smoking, alcohol consumption, BMI, and physical activity) and fit in hierarchical fashion by using each category or set of related variables to determine how covariates, jointly, are related to biomarker concentrations. In contrast to many regression modeling applications, all variables were retained in a full regression model regardless of significance to preserve the interpretation of the statistical properties of β coefficients, P values, and CIs and to keep the interpretation consistent across a set of biomarkers. The variables were preselected before data analysis, and the data analysis plan was designed at the outset to minimize the reporting of false-positive findings by limiting the amount of preliminary hypothesis testing. Although we generally found that demographic differences seen in biomarkers were over- or underestimated when ignoring other key covariates, the demographic differences generally remained significant after adjusting for sociodemographic and lifestyle variables. These articles are intended to provide a foundation to researchers to help them generate hypotheses for future studies or data analyses and/or develop predictive regression models using the wealth of NHANES data.
本增刊中的文章通过线性回归，深入探讨了除年龄、种族和性别以外，各种协变量与饮食和营养的生化指标（生物标志物）浓度之间的关系。我们研究了 10 种特定的社会人口统计学和生活方式协变量，以及 2003-2006 年 NHANES 中 29 种生物标志物，研究对象为年龄≥20 岁的人群。这些协变量分为 2 组或“块”：社会人口统计学（年龄、性别、种族-民族、教育程度和收入）和生活方式（膳食补充剂使用、吸烟、饮酒、BMI 和身体活动），并采用分层方式，使用每个类别或相关变量组来确定协变量如何共同影响生物标志物浓度。与许多回归模型应用不同，所有变量都保留在全回归模型中，无论其对保留 β系数、P 值和 CI 的统计特性的解释是否重要，以保持一组生物标志物之间的解释一致。在数据分析之前，对变量进行了预选，并且在开始时设计了数据分析计划，通过限制初步假设检验的数量，尽量减少假阳性发现的报告。尽管我们通常发现，在忽略其他关键协变量时，生物标志物中存在的人口统计学差异被高估或低估，但在调整社会人口统计学和生活方式变量后，这些差异通常仍然显著。这些文章旨在为研究人员提供基础，帮助他们生成未来研究或数据分析的假设，或使用 NHANES 数据开发预测回归模型。
