Neuhouser Marian L, Tinker Lesley, Shaw Pamela A, Schoeller Dale, Bingham Sheila A, Horn Linda Van, Beresford Shirley A A, Caan Bette, Thomson Cynthia, Satterfield Suzanne, Kuller Lew, Heiss Gerardo, Smit Ellen, Sarto Gloria, Ockene Judith, Stefanick Marcia L, Assaf Annlouise, Runswick Shirley, Prentice Ross L
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109-1024, USA.
Am J Epidemiol. 2008 May 15;167(10):1247-59. doi: 10.1093/aje/kwn026. Epub 2008 Mar 15.
Underreporting of energy consumption by self-report is well-recognized, but previous studies using recovery biomarkers have not been sufficiently large to establish whether participant characteristics predict misreporting. In 2004-2005, 544 participants in the Women's Health Initiative Dietary Modification Trial completed a doubly labeled water protocol (energy biomarker), 24-hour urine collection (protein biomarker), and self-reports of diet (assessed by food frequency questionnaire (FFQ)), exercise, and lifestyle habits; 111 women repeated all procedures after 6 months. Using linear regression, the authors estimated associations of participant characteristics with misreporting, defined as the extent to which the log ratio (self-reported FFQ/nutritional biomarker) was less than zero. Intervention women in the trial underreported energy intake by 32% (vs. 27% in the comparison arm) and protein intake by 15% (vs. 10%). Younger women had more underreporting of energy (p = 0.02) and protein (p = 0.001), while increasing body mass index predicted increased underreporting of energy and overreporting of percentage of energy derived from protein (p = 0.001 and p = 0.004, respectively). Blacks and Hispanics underreported more than did Caucasians. Correlations of initial measures with repeat measures (n = 111) were 0.72, 0.70, 0.46, and 0.64 for biomarker energy, FFQ energy, biomarker protein, and FFQ protein, respectively. Recovery biomarker data were used in regression equations to calibrate self-reports; the potential application of these equations to disease risk modeling is presented. The authors confirm the existence of systematic bias in dietary self-reports and provide methods of correcting for measurement error.
通过自我报告来记录能量消耗存在漏报的情况，这是广为人知的，但以往使用恢复生物标志物的研究规模还不够大，无法确定参与者的特征是否能预测漏报情况。在2004年至2005年期间，女性健康倡议饮食调整试验中的544名参与者完成了双标水试验（能量生物标志物）、24小时尿液收集（蛋白质生物标志物）以及饮食自我报告（通过食物频率问卷（FFQ）评估）、运动和生活方式习惯；111名女性在6个月后重复了所有程序。作者使用线性回归估计了参与者特征与漏报之间的关联，漏报定义为对数比率（自我报告的FFQ/营养生物标志物）小于零的程度。试验中的干预组女性能量摄入量漏报了32%（相比之下，对照组为27%），蛋白质摄入量漏报了15%（相比之下，对照组为10%）。年轻女性的能量（p = 0.02）和蛋白质（p = 0.001）漏报情况更严重，而体重指数增加则预示着能量漏报增加以及来自蛋白质的能量百分比多报（分别为p = 0.001和p = 0.004）。黑人和西班牙裔的漏报情况比白种人更严重。生物标志物能量、FFQ能量、生物标志物蛋白质和FFQ蛋白质的初始测量值与重复测量值（n = 111）的相关性分别为0.72、0.70、0.46和0.64。恢复生物标志物数据被用于回归方程以校准自我报告；还介绍了这些方程在疾病风险建模中的潜在应用。作者证实了饮食自我报告中存在系统偏差，并提供了校正测量误差的方法。
