Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women's Health Initiative.

Publication Type:

Journal Article

Source:

American journal of epidemiology, Volume 167, Issue 10, p.1247-59 (2008)

Keywords:

2008, Aged, Biological Markers, Body Mass Index, Center-Authored Paper, Diet Records, Dietary Proteins, Energy Intake, Female, Food Habits, Humans, Linear Models, Middle Aged, Nutrition Assessment Core Facility, Postmenopause, Questionnaires, Women's Health

Abstract:

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.