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4 Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, NY 11367 and 5 Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, NIH, Bethesda, MD 20892
* To whom correspondence should be addressed. E-mail: ashima.kant{at}qc.cuny.edu.
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ABSTRACT |
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 TOP ABSTRACT IntroductionMethodsResultsDiscussionLITERATURE CITED |
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0.01). Both education and income were independent inverse predictors of risk of marginal vitamin C concentration in men (P 0.003). Relative to income, ethnicity and education were stronger independent predictors of several outcomes. Ethnic differences in status of several micronutrients persisted after adjustment for education and income, suggesting the importance of ethnicity-specific nutrition interventions.
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Introduction |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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Understanding independent associations of ethnicity, education, and income with nutritional risk is important for the design and targeting of potential interventions. However, this risk appraisal is complicated by the fact that reporting bias (especially low energy reporting) may be more likely in ethnic minorities and in association with low income and education (5–7), thus making it difficult to determine whether the observed differences in dietary intakes are real or a reporting artifact. Also, a given nutrient database for computing nutrient intakes may inadequately represent the actual foods consumed by an ethnic group (8). Finally, summary estimates of dietary nutrient intake provide no information about the amount of bioavailable nutrient (9), which may be affected by cultural food practices. Therefore, serum analytes that are known to predict dietary intake and nutrient status (9,10) may be especially important for an assessment of modification of dietary risk associated with ethnicity, income, and education.
We note that several analyses of national survey data have examined population distributions and determinants of a number of biomarkers (11–23), but few have examined biomarkers and dietary intakes in relation to independent effects of ethnicity, education, and income using similar methods and potential confounders that are differentially distributed among ethnic and socioeconomic groups. To fill this gap, we examined socioeconomic position-adjusted ethnic differences in biomarkers of dietary micronutrient intake.
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Methods |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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Although the NHANES III is a relatively older survey, we chose this survey (along with the recent 1999–2002 surveys) for this study for the following reasons: 1) the more recent NHANES (1999–04) do not provide the array of nutrient biomarkers available in the NHANES III; 2) the 6-y NHANES III sample is large enough to provide reliable estimates for Non-Hispanic blacks and Mexican-Americans; and 3) potential covariates such as region of the country, urban/rural residence, and date of the MEC exam are not available in the public release data files for the newer NHANES. Because more of the minority groups may reside in particular regions of the country [non-Hispanic blacks live in the south and Mexican-Americans live in the west (8)] and season of the MEC exam may reflect seasonal effects on food availability, we considered this information to be important for understanding the ethnic and socioeconomic differentials (if any) in nutrient intake and status. Finally, estimates of association from the NHANES III data can serve as a baseline for comparisons with data collected in future surveys. In this study, use of the NHANES 1999–2002 data allows us to assess consistency of results observed in the NHANES III.
Ethnicity. All surveys mentioned above included a race-ethnicity variable derived from self-reported information on race and ethnicity obtained in the screener and the household interviews. Our analyses were categorized by non-Hispanic white, non-Hispanic black, and Mexican-American ethnicity per NHANES analytic guidelines (24). For other race/ethnic groups (e.g. other Hispanic, Asian/pacific Islander, and Native American), the sample size is not considered adequate to obtain reliable estimates.
Measures of socioeconomic position.
In this study, we used poverty income ratio (PIR) and years of formal education as measures of socioeconomic position. The PIR is a ratio of total family income to the poverty threshold for a family of given characteristics specific to each survey. Although not without limitations, the PIR is a normative construct, because it assesses income in relation to need, adjusting for inflation (25). PIR was categorized as <1, 1 to <2, and 
2, which corresponds to poor, near poor, and not poor, and education was categorized as <12, 12, and >12 y. We acknowledge that the highest category for both variables will include a wide range of incomes and years of education. However, because there are a relatively small number of non-Hispanic blacks and Mexican-Americans with high income and education, this grouping was a reasonable choice.
Biomarkers.
The biomarkers examined included serum analytes that are known to predict intake or status of micronutrients of public health significance (10,26–29) and included serum 25-OH-vitamin D (referred to as vitamin D hereafter), vitamins C and E, folate, red blood cell (RBC) folate, selenium, ferritin, and the carotenoids (
-carotene, ß-carotene, lutein + zeaxanthin, ß-cryptoxanthin, and lycopene) in the NHANES III; serum vitamin E in the NHANES 1999–2000; and serum and RBC folate, and ferritin concentrations in the NHANES 1999–2002. Other biomarkers listed above were not available in NHANES 1999–2002. We recognize that serum ferritin indicates storage iron and may not reflect dietary intakes (28) and serum vitamin D concentration reflects both dietary intakes and cutaneous synthesis (29). Each survey used standardized methods for collection of blood samples, their subsequent handling, and laboratory assays for quantifying the analytes (24,30).
Dietary nutrient intake. In all surveys examined in this study, a 24-h dietary recall was collected by a trained dietary interviewer in an MEC interview using an automated, microcomputer-based interview and coding system (24). The dietary nutrients examined in this study were those that may correspond to the available biomarkers and included vitamins C, D, and E; folate; selenium; iron; and total carotene.
Analytic samples in the NHANES III and the NHANES 1999–2002. In each survey, all adults aged 25 y and over with an MEC exam and measured analyte concentration were eligible for inclusion in this study. We excluded pregnant or nursing women and those missing information on income and education, resulting in an analytic sample of 13,113 in the NHANES III, 3273 in the NHANES 1999–2000, and 3973 in the NHANES 2001–2002.
Statistical analyses. The independent association of ethnicity, education, and income with each biomarker as a continuous outcome was examined using sex-specific multiple linear regression models to adjust for a number of covariates. The covariates included were decided a priori based on putative associations of sociodemographic, lifestyle, and health factors with our principal exposures (ethnicity, income, and education) and each outcome. These included: age, smoking status, BMI, alcohol use, hours of fasting before phlebotomy, supplement use in the 24 h before phlebotomy, supplement use in the past month before the MEC exam, region of the country (NHANES III), season of MEC exam (NHANES III), metropolitan area of residence (NHANES III), serum cholesterol and triglycerides (for vitamin E and the carotenoids), C-reactive protein (for ferritin), and presence of self-reported chronic diseases (diabetes, high blood pressure, stroke, and heart disease) or elevated serum concentrations of alanine aminotransferase and aspartate aminotransferase or creatinine (as indicators of liver and kidney function).
Logistic regression analyses were used to model serum analytes as less than or more than the marginal concentrations of biomarkers. The following concentrations were considered marginal: vitamin C (<11 µmol/L), vitamin D (<37.5 nmol/L), vitamin E (<16 µmol/L), serum folate (<7 nmol/L), RBC folate (<305 nmol/L), ferritin (<12 µg/L), and selenium (<0.8 µmol/L) (26–29). Dietary nutrients were modeled as continuous outcomes with covariates restricted to those associated with dietary intakes. The adjusted mean estimates presented for each outcome (both continuous and binary outcomes), which are also referred to as predicted margins (31,32), were obtained from covariate-adjusted (the same covariates as in the multiple linear regression analysis), gender-specific models by race/ethnicity categories.
We decided not to transform the serum analytes (e.g. logarithmic transformation), because the linearization method used for estimating SE and the Wald test statistics that were used for determining P-values do not require assumptions about the normality and homogeneity of variances of the data (31). Tests of trend for education and income variables were determined by significance of the regression coefficient for these variables with categories of each as an independent trend variable in the multiple regression models.
All statistical analyses were performed using SAS (version 8.1, SAS Institute) and software designed for analysis of survey data (SAS-callable SUDAAN) (32). This software computes variance estimates that are corrected for multi-stage, stratified, cluster probability design of complex surveys. Sample weights provided by the National Center for Health Statistics to correct for differential probabilities of selection, noncoverage, and nonresponse were used in all analyses to obtain point estimates. Although we present all P-values in the results, because of multiple tests of association in this study, we considered the more stringent 2-sided P-value of 0.01 as significant. In Tables 2–4, the P-values for race/ethnicity represent the multivariate-adjusted significance of global tests of differences among the ethnic groups; and the P-values for education and PIR reflect the significance of the general upward or downward trend in the independent association of these variables with each outcome. Non-Hispanic whites were the reference group in all comparisons across ethnic groups, presented in the Results.
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Results |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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In both the NHANES III (Table 1) and the NHANES 1999–2002 (Supplemental Table 1), the proportion of non-Hispanic whites reporting PIR of 2 or >12 y of education, age 60 y, and supplement use was higher relative to other ethnic groups. In all surveys, compared with other ethnic groups, Mexican-Americans had a higher proportion of men, more were aged 25–39 y, and were least likely to have education of >12 y or PIR of 2. In all surveys, non-Hispanic blacks were more likely than other ethnic groups to be current smokers and have a chronic disease condition.
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Relative to non-Hispanic whites, the nonwhite ethnicity-gender groups had higher serum concentrations of ß-cryptoxanthin and lutein + zeaxanthin (P < 0.001) but lower serum and RBC folate, and vitamin D (P < 0.0001) (Table 2). Serum vitamin C and selenium concentrations in non-Hispanic blacks were lower (P 0.01) relative to non-Hispanic whites. Black men had lower serum vitamin E concentrations than non-Hispanic white men (P < 0.0001). Mexican-American women had higher mean serum -carotene concentrations than non-Hispanic white women but lower serum ferritin (P 0.01).
In the NHANES 1999–2002, the mean serum and RBC folate, and in 1999–2000, the mean serum vitamin E concentrations were higher (relative to NHANES III) in all race/ethnic groups. However, the black-white differential in RBC folate and ferritin concentrations remained significant in both genders (Supplemental Table 2).
Compared with non-Hispanic whites, marginal RBC and serum folate (men only) concentrations were more likely (P < 0.01) in non-Hispanic blacks in the NHANES III (Table 3). Marginal ascorbate was less likely (P < 0.001) in Mexican-American men than in non-Hispanic white men. Marginal concentrations of vitamin D were more likely among non-Hispanic blacks and Mexican-Americans than non-Hispanic whites. Mexican-American women were more likely than non-Hispanic white women to have marginal serum ferritin concentration (P < 0.01) in both surveys. In the NHANES 1999–2002, the prevalence of marginal concentration of RBC folate was lower than the NHANES III in all ethnicity-gender groups; however, the black-white differential persisted in both genders (Supplemental Table 3).
Education trends in biomarker concentration
In the NHANES III, serum -carotene, ß-cryptoxanthin, lutein + zeaxanthin, and vitamin C concentrations increased with increasing level of education in both men and women (Table 2). The serum concentrations of folate, ferritin, and selenium were unrelated with education in both men and women. Serum concentrations of lycopene increased (P = 0.01), but vitamin D decreased (P = 0.0003) with increasing education in men only. In women, serum ß-carotene and vitamin E increased with education level (P 0.003) (Table 2). Education was inversely associated with the likelihood of marginal concentrations of RBC folate (P = 0.01) and vitamin C (P = 0.003) in men (Table 3).
In the NHANES 1999–2002, the inverse association of education and serum ferritin was significant (P 0.005) in both genders and a positive association of education with serum and RBC folate was significant (P 0.01) in men only (Supplemental Table 2). However, the likelihood of marginal concentrations of examined biomarkers was not associated with education or PIR in these surveys (Supplemental Table 3).
PIR trends in biomarker concentration
Serum concentrations of lycopene and PIR were positively related in both men and women (P 0.005) (Table 3). In women only, the serum concentrations of -carotene and vitamin C increased with increasing PIR (P = 0.007) (Table 3). In men, serum concentrations of ß-cryptoxanthin and vitamin E increased with PIR (P < 0.001). Higher income was associated with lower likelihood of marginal vitamin C concentrations in men (P 0.004) (Table 3). In women, marginal serum vitamin E concentration was more likely with lower PIR (P < 0.0001); however, marginal vitamin D concentration was more likely with higher PIR (P < 0.001).
Dietary nutrient intakes
Ethnic differences in self-reported 24-h dietary nutrient intake.
In the NHANES III, dietary vitamin E, iron, and folate (also in 1999–2002) intakes were lower in non-Hispanic-blacks than in non-Hispanic whites (Table 4; Supplemental Table 4). Relative to non-Hispanic-whites, Mexican-Americans reported higher intakes of vitamin C and total carotenes (P 0.01) but lower dietary iron (men only). In all nonwhite ethnicity-gender groups, mean dietary vitamin D intake was lower than non-Hispanic whites (P < 0.0001).
Education and PIR trends in dietary nutrient intake. In the NHANES III, dietary vitamin C, carotene, vitamin E, folate (also in 1999–2002), and iron intakes increased in both genders with increasing education (Table 4; Supplemental Table 4). Dietary vitamin D intakes increased with increasing education in women only (P = 0.006). Except dietary selenium in women, PIR was not a predictor of dietary intake of any of the examined micronutrients (P > 0.01). The observed associations of biomarkers with ethnicity, education, and PIR remained unchanged after addition of the 24-h dietary intake of the relevant nutrient to regression models (data not shown).
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Discussion |
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TOPABSTRACTIntroductionMethodsResultsDiscussionLITERATURE CITED |
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Ethnicity was a strong independent predictor of several outcomes examined in this study (Tables 2–4). Relative to non-Hispanic whites, the education- and income-adjusted concentrations of several protective nutrients (vitamin C, D, folate, and selenium) were lower in non-Hispanic blacks. Mexican-Americans also had lower serum vitamin D concentrations, but in general their biomarker profiles were similar to or better than that of non-Hispanic whites. Although the ethnic differentials in biomarkers are generally consistent with higher risk for leading causes of mortality (cardiovascular disease and certain cancers) in non-Hispanic blacks in the US (1,2), the extent to which these ethnic differentials contribute to race-ethnic differentials in health cannot be examined with the cross-sectional NHANES data.
Lack of adjustment for socioeconomic position is an acknowledged source of bias in understanding race/ethnic differentials in health (33). Because different measures of socioeconomic position may differ in the potential pathways through which they relate to health (or diet in the present study), at least 2 different measures have been recommended (33). Our results of ethnicity-independent associations of education and income with biomarkers confirm the value of the above recommendation. To our knowledge, few published studies (21) have used an analytic approach similar to ours to permit valid comparisons. We highlight some of the differences of our results from published results below.
Ford et al. found ethnicity to be a significant predictor of serum vitamin E concentration in the NHANES 1999–2000 (23). Those results differ from essentially null findings for 1999–2000 in our study, possibly due to confounding in unadjusted analyses of Ford et al. (23). In another report using the NHANES III data, ethnicity predicted serum lycopene concentration after adjusting for multiple covariates that included education but not income (13). In our study, income assessed as PIR was a stronger predictor of serum lycopene concentration relative to ethnicity or education and ethnic differences were attenuated and were no longer significant.
Ethnic differences in serum vitamin D concentration have been acknowledged previously and may be related to skin pigmentation (34); however, associations with education and PIR differ from previous studies due to multivariate adjustment. For example, Zadshir et al. (19) reported that education was unrelated with serum vitamin D concentration in men in the NHANES III but predicted higher vitamin D concentration and lower risk of hypovitaminosis D in women. These analyses did not adjust for ethnicity, income, or other covariates. With multivariate adjustment, we found that education was an independent inverse predictor of vitamin D concentration in men but was unrelated in women; however, higher income predicted higher risk of marginal vitamin D concentration in women. The inverse associations of education and income with serum vitamin D (after adjustment for ethnicity) may to some extent reflect the nature of occupations with lower cutaneous exposure to the sun or greater sunscreen use with increasing income and education. In primarily univariate analyses, Niskar et al. (17) reported that serum selenium increased with increasing PIR in the NHANES III. However, our multivariate analyses did not find PIR to be an independent predictor of serum selenium.
The strength of the association of ethnicity, education, and income with several outcomes examined appeared to differ between men and women. Risk of marginal serum concentrations of folate and vitamin C differed by ethnicity and education in men but not women. Income predicted lower likelihood of marginal vitamin E concentration and higher likelihood of marginal vitamin D concentration in women but not men. These findings suggest differential importance of ethnicity, education, and income in determining nutritional risk and subsequent targeting of relevant interventions in men and women.
Given the possible limitations of self-reported summary intakes of dietary nutrients for predicting tissue nutrient concentrations (outlined in the introduction), we found a number of biomarkers where black-white differences in serum concentrations of biomarkers paralleled those observed for diet. For vitamins E, D, and folate (but not vitamin C or selenium), the black-white differences in serum concentrations paralleled these differences in dietary intakes (compare results in Tables 2 and 3 with Table 4). Education trends in total dietary carotene and vitamin C intakes, but not folate, were paralleled by similar associations in serum vitamin C and 3 of the 5 carotenoids in both genders. Relative to ethnicity and education, the results for PIR associations with serum biomarkers or risk of marginal concentrations were the most discrepant from PIR associations with diet, because PIR predicted virtually no dietary outcomes (except selenium in women). Therefore, the apparent usefulness of nutrient intakes estimated from a 24-h dietary recall for estimating nutritional risk associated with ethnicity, education, and income differed for the various biomarkers. Further research on the associations of ethnicity, income, and education with longer term dietary intakes is indicated.
Although serum analytes are not subject to reporting errors, biomarkers are not without limitations and tend to vary within subjects (35,36). With some exceptions (37,38), relatively little is known about the components of variance for most of the examined biomarkers. Even less is known about whether variability differs by ethnicity, income, and education. With increasing use of dietary biomarkers for determination of nutritional risk, it is important that sources of variability in biomarkers be given as much attention as sources of variability in dietary intakes.
We note that our regression models were adjusted for a number of factors that could relate to intake, absorption, and metabolism of nutrients and to ethnicity, education, or income. Nevertheless, the possibility of residual confounding by poorly measured or unknown confounders cannot be ruled out. This study focused on main effects of ethnicity, education, and income; interactions among these exposures may modify outcomes and warrant further study.
Our results suggest that targeting nutrition interventions based solely on income and education may be of limited value for eliminating the ethnic differentials in risk for marginal micronutrient status. Instead, culture-specific interventions that may need to target at-risk ethnic groups to promote dietary changes to improve micronutrient intake are indicated. To enable such targeted interventions, greater understanding of culture-specific food selection and intake patterns ranging from food purchasing, sources, preparation, and food combinations, of different ethnic groups is needed.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Author disclosures: A. K. Kant and B. I. Graubard, no conflicts of interest.
3 Supplemental Tables 1–4 are available with the online posting of this paper at jn.nutrition.org.
6 Abbreviations used: MEC, mobile examination center; PIR, poverty income ratio; RBC, red blood cell.
Manuscript received 3 May 2007. Initial review completed 13 June 2007. Revision accepted 20 August 2007.
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LITERATURE CITED |
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