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We recently evaluated associations of biomarker-calibrated protein intake, protein density, carbohydrate intake, and carbohydrate density with the incidence of cardiovascular disease, cancer, and diabetes among postmenopausal women in the Women's Health Initiative (1993–present, 40 US clinical centers). The biomarkers relied on serum and urine metabolomics profiles, and biomarker calibration used regression of biomarkers on food frequency questionnaires. Here we develop corresponding calibration equations using food records and dietary recalls. In addition, we use calibrated intakes based on food records in disease association estimation in a cohort subset (n  = 29,294) having food records. In this analysis, more biomarker variation was explained by food records than by FFQs for absolute macronutrient intake, with 24-hour recalls being intermediate. However, the percentage of biomarker variation explained was similar for each assessment approach for macronutrient densities. Invasive breast cancer risk was related inversely to carbohydrate and protein densities using food records, in analyses that included (calibrated) total energy intake and body mass index. Corresponding analyses for absolute intakes did not differ from the null, nor did absolute or relative intakes associate significantly with colorectal cancer or coronary heart disease. These analyses do not suggest major advantages for food records or dietary recalls in comparison with less costly and logistically simpler food frequency questionnaires for these nutritional variables. [ABSTRACT FROM AUTHOR]

Background Biomarkers provide potential to objectively measure the intake of nutrients and foods, and thereby to strengthen nutritional epidemiology association studies. However, there are only a few established intake biomarkers, mostly based on recovery of nutrients or their metabolites in urine. Blood concentration measures provide a potential biomarker source for many additional nutritional variables, but their use in disease-association studies requires further development. Objective The aim of this study was to apply recently proposed serum-based carotenoid and tocopherol intake biomarkers and to examine their association with the incidence of major cardiovascular diseases, cancers, and diabetes in a subset of Women's Health Initiative (WHI) cohorts. Methods Serum concentrations of α- and β-carotene, lutein plus zeaxanthin (L + Z), and α-tocopherol were routinely measured at baseline in a subset of 5488 enrollees in WHI cohorts. Intake biomarkers for these 4 micronutrients, obtained by combining serum concentrations with participant characteristics, were recently proposed using a 153-woman feeding study within WHI. These biomarker equations are augmented here to include pertinent disease risk factors and are associated with subsequent chronic disease incidence in this WHI subset. Results HRs for a doubling of micronutrient intake differed only moderately from the null for the outcomes considered. However, somewhat lower risks of specific cardiovascular outcomes, breast cancer, and diabetes were associated with a higher intake of α- and β-carotene, lower risk of diabetes was associated with higher L + Z intake, and elevated risks of certain cardiovascular outcomes were associated with a higher intake of α-tocopherol. These patterns remained following the exclusion of baseline users of dietary supplements. Conclusions Concentration biomarkers can be calculated from blood specimens obtained in large epidemiologic cohorts and applied directly in disease-association analyses, without relying on self-reported dietary data. Observed associations between carotenoid and tocopherol biomarkers and chronic disease risk could be usefully evaluated further using stored serum specimens on the entire WHI cohort. This study was registered at www.clinicaltrials.gov as NCT00000611. [ABSTRACT FROM AUTHOR]

Background: The extent to which psychosocial and diet behavior factors affect dietary self-report remains unclear. We examine the contribution of these factors to measurement error of self-report.Methods: In 450 postmenopausal women in the Women's Health Initiative Observational Study doubly labeled water and urinary nitrogen were used as biomarkers of objective measures of total energy expenditure and protein. Self-report was captured from food frequency questionnaire (FFQ), four day food record (4DFR) and 24 hr. dietary recall (24HR). Using regression calibration we estimated bias of self-reported dietary instruments including psychosocial factors from the Stunkard-Sorenson Body Silhouettes for body image perception, the Crowne-Marlowe Social Desirability Scale, and the Three Factor Eating Questionnaire (R-18) for cognitive restraint for eating, uncontrolled eating, and emotional eating. We included a diet behavior factor on number of meals eaten at home using the 4DFR.Results: Three categories were defined for each of the six psychosocial and diet behavior variables (low, medium, high). Participants with high social desirability scores were more likely to under-report on the FFQ for energy (β = -0.174, SE = 0.054, p < 0.05) and protein intake (β = -0.142, SE = 0.062, p < 0.05) compared to participants with low social desirability scores. Participants consuming a high percentage of meals at home were less likely to under-report on the FFQ for energy (β = 0.181, SE = 0.053, p < 0.05) and protein (β = 0.127, SE = 0.06, p < 0.05) compared to participants consuming a low percentage of meals at home. In the calibration equations combining FFQ, 4DFR, 24HR with age, body mass index, race, and the psychosocial and diet behavior variables, the six psychosocial and diet variables explained 1.98%, 2.24%, and 2.15% of biomarker variation for energy, protein, and protein density respectively. The variations explained are significantly different between the calibration equations with or without the six psychosocial and diet variables for protein density (p = 0.02), but not for energy (p = 0.119) or protein intake (p = 0.077).Conclusions: The addition of psychosocial and diet behavior factors to calibration equations significantly increases the amount of total variance explained for protein density and their inclusion would be expected to strengthen the precision of calibration equations correcting self-report for measurement error.Trial Registration: ClinicalTrials.gov identifier: NCT00000611. [ABSTRACT FROM AUTHOR]