Selected Physiologic Variables Are Weakly to Moderately Associated with 29 Biomarkers of Diet and Nutrition, NHANES 2003-2006

The physiologic status of an individual may influence biomarkers of nutritional status. To help researchers with planning studies and interpreting data, we assessed the associations between common physiologic variables (fasting, inflammation, renal function, and pregnancy) and 29 biomarkers of diet and nutrition measured in blood or urine in a representative sample of the adult U.S. population (aged >= 20 y; pregnancy variable and iron indicators limited to women aged 20-49 y) participating in NHANES 2003-2006. We compared simple linear regression (model 1) with multiple linear regression [model 2, controlling for age, sex, race-ethnicity, smoking, supplement use, and the physiologic factors (and urine creatinine for urine biomarkers)] and report significant findings from model 2. Not being fasted was positively associated with most water-soluble vitamins (WSVs) and related metabolites (RMs). Some WSV, fat-soluble vitamin (FSV) and micronutrient (MN), and phytoestrogen concentrations were lower in the presence of inflammation (C-reactive protein >= 5 mg/L), whereas fatty acids and most iron indicators were higher. Most WSVs and RMs were higher when renal function was impaired [estimated glomerular filtration rate <60 mL/(min.1.73 m(2))]. Most WSV, FSV and MN, and fatty acid concentrations were higher in pregnant compared with nonpregnant women, but vitamins A and B-12 and most iron indicators were lower, The estimated changes in biomarker concentrations with different physiologic status were mostly small to moderate (<= 25%) and generally similar between models; renal function, however, showed several large differences for WSV and RM concentrations. This descriptive analysis of associations between physiologic variables and a large number of nutritional biomarkers showed that controlling for demographic variables, smoking, and supplement use generally did not change the interpretation of bivariate results. The analysis serves as a useful basis for more complex future research.