Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes.
Keywords: biomarkers; diet; measurement error; prediction; regression calibration; study design.
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