Combining biomarker and food intake data: calibration equations for citrus intake

Bingham, 2002, Biomarkers in nutritional epidemiology, Public Health Nutr., 5, 821, 10.1079/PHN2002368 Kipnis, 2002, Bias in dietary-report instruments and its implications for nutritional epidemiology, Public Health Nutr., 5, 915, 10.1079/PHN2002383 Dhurandhar, 2015, Energy balance measurement: when something is not better than nothing, Int J Obes., 39, 1109, 10.1038/ijo.2014.199 Tinker, 2011, Biomarker-calibrated dietary energy and protein intake associations with diabetes risk among postmenopausal women from the Women’s Health Initiative, Am J Clin Nutr., 94, 1600, 10.3945/ajcn.111.018648 Prentice, 2011, Biomarker-calibrated energy and protein consumption and cardiovascular disease risk among postmenopausal women, Epidemiology., 22, 170, 10.1097/EDE.0b013e31820839bc Lampe, 2017, Dietary biomarker evaluation in a controlled feeding study in women from the Women’s Health Initiative cohort, Am J Clin Nutr., 105, 466, 10.3945/ajcn.116.144840 Gibbons, 2017, Demonstration of the utility of biomarkers for dietary intake assessment; proline betaine as an example, Mol Nutr Food Res., 61, 10.1002/mnfr.201700037 Garcia-Perez, 2016, An analytical pipeline for quantitative characterization of dietary intake: application to assess grape intake, J Agric Food Chem., 64, 2423, 10.1021/acs.jafc.5b05878 Garcia-Perez, 2017, Objective assessment of dietary patterns by use of metabolic phenotyping: a randomised, controlled, crossover trial, Lancet Diabetes Endocrinol., 5, 184, 10.1016/S2213-8587(16)30419-3 Pujos-Guillot, 2013, Mass spectrometry-based metabolomics for the discovery of biomarkers of fruit and vegetable intake: citrus fruit as a case study, J Proteome Res., 12, 1645, 10.1021/pr300997c Heinzmann, 2010, Metabolic profiling strategy for discovery of nutritional biomarkers: proline betaine as a marker of citrus consumption, Am J Clin Nutr., 92, 436, 10.3945/ajcn.2010.29672 Lloyd, 2011, Proline betaine and its biotransformation products in fasting urine samples are potential biomarkers of habitual citrus fruit consumption, Br J Nutr., 106, 812, 10.1017/S0007114511001164 Gormley IC, Bai Y, Brennan L. Combining biomarker and self-report dietary intake data: a review of the state of the art and an overview of concepts. Stat Methods Med Res. In press. doi: 0.1177/0962280219837698. Zhang, 2011, A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment, Annals Appl Stat., 5, 1456, 10.1214/10-AOAS446 Prentice, 2011, Evaluation and comparison of food records, recalls, and frequencies for energy and protein assessment by using recovery biomarkers, Am J Epidemiol., 174, 591, 10.1093/aje/kwr140 Kipnis, 2009, Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes, Biometrics., 65, 1003, 10.1111/j.1541-0420.2009.01223.x Keogh, 2014, A toolkit for measurement error correction, with a focus on nutritional epidemiology, Stat Med., 33, 2137, 10.1002/sim.6095 Prentice, 2013, Regression calibration in nutritional epidemiology: example of fat density and total energy in relationship to postmenopausal breast cancer, Am J Epidemiol., 178, 1663, 10.1093/aje/kwt198 Prentice, 2009, Biomarker-calibrated energy and protein consumption and increased cancer risk among postmenopausal women, Am J Epidemiol., 169, 977, 10.1093/aje/kwp008 Zheng, 2014, Simultaneous association of total energy consumption and activity-related energy expenditure with risks of cardiovascular disease, cancer, and diabetes among postmenopausal women, Am J Epidemiol., 180, 526, 10.1093/aje/kwu152 Neuhouser, 2008, Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women’s Health Initiative, Am J Epidemiol., 167, 1247, 10.1093/aje/kwn026