Dataset Information

Selected physiologic variables are weakly to moderately associated with 29 biomarkers of diet and nutrition, NHANES 2003-2006.

ABSTRACT: 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.

SUBMITTER: Haynes BM

PROVIDER: S-EPMC4811331 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Selected physiologic variables are weakly to moderately associated with 29 biomarkers of diet and nutrition, NHANES 2003-2006.

Haynes Bridgette M H BM Pfeiffer Christine M CM Sternberg Maya R MR Schleicher Rosemary L RL

The Journal of nutrition 20130417 6

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 NH ...[more]

PMID: 23596168

Similar Datasets

Project description:BACKGROUND:We propose a method for estimating the timing of in-bed intervals using objective data in a large representative US sample, and quantify the association between these intervals and age, sex, and day of the week. METHODS:The study included 11,951 participants 6 years and older from the National Health and Nutrition Examination Survey (NHANES) 2003-2006, who wore accelerometers to measure physical activity for seven consecutive days. Participants were instructed to remove the device just before the nighttime sleep period and put it back on immediately after. This nighttime period of non-wear was defined in this paper as the objective bedtime (OBT), an objectively estimated record of the in-bed interval. For each night of the week, we estimated two measures: the duration of the OBT (OBT-D) and, as a measure of the chronotype, the midpoint of the OBT (OBT-M). We estimated day-of-the-week-specific OBT-D and OBT-M using gender-specific population percentile curves. Differences in OBT-M (chronotype) and OBT-D (the amount of time spent in bed) by age and sex were estimated using regression models. RESULTS:The estimates of OBT-M and their differences among age groups were consistent with the estimates of chronotype obtained via self-report in European populations. The average OBT-M varied significantly by age, while OBT-D was less variable with age. In the reference group (females, aged 17-22 years), the average OBT-M across 7 days was 4:19 AM (SD = 30 min) and the average OBT-D was 9 h 19 min (SD = 12 min). In the same age group the average OBT-D was 18 minutes shorter for males than for females, while the average OBT-M was not significantly different between males and females. The most pronounced differences were observed between OBT-M of weekday and weekend nights. In the reference group, compared to the average OBT-M of 3:50 am on Monday through Thursday nights, there was a 57-minute delay in OBT-M on Friday nights (entering the weekend), a 69-minute delay on Saturday nights (staying in the weekend), and a 23-minute delay on Sunday night (leaving the weekend). For both OBT-M and OBT-D, in most age groups and for most days of the week, there were no statistically significant differences between males and females, except for OBT-D on Wednesdays and Thursdays, with males having 31 (p-value < 0.05) and 45 (p-value < 0.05) minutes shorter OBT-D, respectively. CONCLUSIONS:The proposed measures, OBT-D and OBT-M, provide useful information of time in bed and chronotype in NHANES 2003-2006. They identify within-week patterns of bedtime and can be used to study associations between the bedtime and the large number of health outcomes collected in NHANES 2003-2006.

Dataset Information

Selected physiologic variables are weakly to moderately associated with 29 biomarkers of diet and nutrition, NHANES 2003-2006.

Publications

Selected physiologic variables are weakly to moderately associated with 29 biomarkers of diet and nutrition, NHANES 2003-2006.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets