Regression calibration in nutritional epidemiology: example of fat density and total energy in relationship to postmenopausal breast cancer.

Regression calibration using biomarkers provides an attractive approach to strengthening nutritional epidemiology. We consider this approach to assessing the relationship of fat and total energy consumption with postmenopausal breast cancer. In analyses that included fat density data, biomarker-calibrated total energy was positively associated with postmenopausal breast cancer incidence in cohorts of the US Women's Health Initiative from 1994-2010. The estimated hazard ratio for a 20% increment in calibrated food frequency questionnaire (FFQ) energy was 1.22 (95% confidence interval (CI): 1.15, 1.30). This association was not evident without biomarker calibration, and it ceased to be apparent following control for body mass index (weight (kg)/height (m)(2)), suggesting that the association is mediated by body fat deposition over time. The hazard ratio for a corresponding 40% increment in FFQ fat density was 1.05 (95% CI: 1.00, 1.09). A stronger fat density association, with a hazard ratio of 1.19 (95% CI: 1.00, 1.41), emerged from analyses that used 4-day food records for dietary assessment. FFQ-based analyses were also carried out by using a second dietary assessment in place of the biomarker for calibration. This type of calibration did not correct for systematic bias in energy assessment, but may be able to accommodate the "noise" component of dietary measurement error. Implications for epidemiologic applications more generally are described.