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American Journal of Clinical Nutrition, Vol. 87, No. 5, 1464-1471, May 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Ethnic and socioeconomic differences in variability in nutritional biomarkers1,2,3

Ashima K Kant and Barry I Graubard

1 From the Department of Family, Nutrition, and Exercise Sciences, Queens College of the City University of New York, Flushing, NY (AKK), and the Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD (BIG)

2 Supported by grant no. CA108274 from the National Institutes of Health (to AKK) and by the intramural research program of the Department of Health and Human Services, National Cancer Institute, National Institutes of Health (to BIG).

3 Reprints not available. Address correspondence to AK Kant, Department of Family, Nutrition, and Exercise Sciences, Remsen Hall, Room 306E, Queens College of the City University of New York, Flushing, NY 11367. E-mail: ashima.kant{at}qc.cuny.edu.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background: Several studies have reported ethnic, education, and income differentials in concentrations of selected nutritional biomarkers in the US population. Although biomarker measurements are not subject to biased self-reports, biologic variability due to individual characteristics and behaviors related to dietary exposures contributes to within-subject variability and measurement error.

Objective: We aimed to establish whether the magnitude of components of variance for nutritional biomarkers also differs in these high-risk groups.

Design: We used data from 2 replicate measurements of serum concentrations of vitamins A, C, D, and E; folate; carotenoids; ferritin; and selenium in the third National Health and Nutrition Examination Survey second examination subsample (n = 948) to examine the within-subject and between-subject components of variance. We used multivariate regression methods with log-transformed analyte concentrations as outcomes to estimate the ratios of the within-subject to between-subject components of variance by categories of ethnicity, income, and education.

Results: In non-Hispanic blacks, the within-subject to between-subject variance ratio for β-cryptoxanthin concentration was higher (0.23; 95% CI: 0.17, 0.29) relative to non-Hispanic whites (0.13; 0.11, 0.16) and Mexican Americans (0.11; 0.07, 0.14), and the lutein + zeaxanthin ratio was higher (0.29; 0.21, 0.38) relative to Mexican Americans (0.15; 0.10, 0.19). Higher income was associated with larger within-subject to between-subject variance ratios for serum vitamin C and red blood cell folate concentrations but smaller ratios for serum vitamin A. Overall, there were few consistent up- or down-trends in the direction of covariate-adjusted variability by ethnicity, income, or education.

Conclusion: Population groups at high risk of adverse nutritional profiles did not have larger variance ratios for most of the examined biomarkers.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Diet may be a mediator of the acknowledged ethnicity-, income-, and education-related differentials in health outcomes (1, 2). However, self-reported dietary intake is measured with error, and the likelihood of biased reporting is higher in ethnic minorities and persons of low socioeconomic status (SES) (3-6). Furthermore, summary estimates of nutrient intake provide little information about possible nutrient bioavailability differentials related to biologic or ethnic and SES differences in the selection, preparation, and combinations of foods consumed (7). Tissue concentrations of micronutrients depend on the amount of bioavailable nutrient and its subsequent metabolism in vivo (7, 8). Therefore, biomarkers that serve as measures of nutritional status of certain nutrients may be important adjuncts for understanding ethnic and SES differentials in self-reported nutrient intakes.

Several studies have reported ethnic, education, and income differentials in concentrations of selected nutritional biomarkers (9-18). Although biomarker measurements are not subject to biased self-reports, biologic variability due to individual characteristics and behaviors contributes to within-subject variability and measurement error (7, 19, 20). It is not known whether the magnitude of components of variance for nutritional biomarkers also differs in these high-risk groups (21). It is possible that food selection and food-consumption patterns in ethnic minority subgroups may be less variable than those in non-Hispanic whites because of cultural similarities in these behaviors, which may be expected to decrease the between-subject variability. Low income and education may also be associated with less-varied diets, which may decrease the within-subject variability. Alternatively, ethnicity, low income, and education may be associated with food procurement environments that may contribute to higher within- and between-subject variability. Because national surveys such as the National Health and Nutrition Examination Survey (NHANES) and many epidemiologic studies include a single measurement of biomarkers of key nutrients, differential variability by ethnicity, income, or education may affect the possible conclusions about biomarker and health associations among these high-risk population groups. In particular for linear regression analyses, groups with larger variance ratios will experience greater attenuation in estimates of association. For example, in the case of simple linear regression, when the ratio of within-subject to between-subject variance is 0.5, the attenuated slope is two-thirds the actual slope.

Previous studies examined components of variance for biomarkers of fat intake, cardiovascular disease, markers of oxidative stress, and antioxidants (21-29); however, to our knowledge, none examined the differential variability in nutritional biomarkers due to race-ethnicity and SES. To fill this gap, we examined the ratio of intraindividual (within-subject) to interindividual (between-subjects) components of variance for a number of key nutritional biomarkers in different ethnic, income, and education groups.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
We used data from the third NHANES (NHANES III) second examination subsample for this study (30, 31). A representative sample of the civilian, noninstitutionalized US population was examined in NHANES III, which lasted from 1988 to 1994 (30). The survey consisted of a home interview and an examination in the mobile examination center (MEC). The MEC examination included a dietary interview, physical and dental examinations, measurements of various types, and collection of urine and blood samples for assessment of biochemical analytes.

The NHANES III second examination subsample
A nonrandom 5% of the MEC-examined primary sample was invited back for a second MEC visit (31). The second examination sample included adults with the goals of half of the sample being aged 20–40 y and half being aged >40 y and of equal sex distribution. All MEC assessments were repeated during the second visit. The protocols followed were similar to those during the primary examination (31).

Biomarkers
The biomarkers examined included serum analytes that are known to predict dietary exposure to or status of micronutrients of putative public health importance and included serum selenium; ferritin; vitamins A (as retinol), C, D (as 25-hydroxyvitamin D), and E (as {alpha}-tocopherol); folate; and the carotenoids—{alpha}-carotene, β-carotene, lutein + zeaxanthin, β-cryptoxanthin, and lycopene (30, 32-35). The methods used for collection of blood samples, their subsequent handling, and laboratory assay methods for the analytes mentioned above have been described (36). Briefly, serum vitamins A, C, and E and carotenoids were measured by using isocratic HPLC; serum and red blood cell (RBC) folate were measured by using a radioassay; ferritin was measured by using an immunoradiometric assay; vitamin D was measured by using a radioimmunoassay; and selenium was measured by using atomic absorption spectrometry (36).

Analytic sample
All non-Hispanic white, non-Hispanic black, and Mexican American respondents aged ≥25 y with information on education and income and with measurements of a serum analyte at both the primary and the second examination visit were eligible for inclusion in our study (n = 1590). We excluded respondents with missing values for hours of fasting before phlebotomy at either visit or missing information on the use of vitamin and mineral supplements in the 24 h before phlebotomy at either visit, which left an analytic sample of 948 respondents. The number of respondents was different for different serum analytes.

Race-ethnicity and socioeconomic status
Race-ethnicity information was self-reported by respondents. We used poverty income ratio (PIR) and years of formal education as measures of SES. The PIR assesses income in relation to need, after adjustment for inflation, and is a ratio of total family income to the poverty threshold for a family of given characteristics. Our analyses were categorized by race-ethnicity (non-Hispanic white, non-Hispanic black, or Mexican American), PIR (<1, 1 to <2, or ≥2, corresponding to poor, near-poor, and not poor), and years of education (<12, 12, or >12 y).

Covariates
Because this study aims to examine variability in serum biomarker concentrations for different ethnic, education, and income groups, we adjusted for a number of subject characteristics that may be associated with ethnicity, income, and education and the biomarkers. These covariates included age, sex, smoking status (assessed via serum cotinine concentration), season of MEC examination, the geographic region of residence, rural or urban residence, hours of fasting, supplement use in the month before phlebotomy, supplement use in the 24 h before phlebotomy, interval between the first and second examinations (in d), body mass index (BMI; in kg/m2), alcohol use, and history of any self-reported chronic disease (eg, diabetes, heart failure, heart attack, stroke, or high blood pressure) or elevated liver and kidney function tests (elevated serum concentration of alanine aminotransferase, aspartate aminotransferase, or creatinine). For serum vitamins A and E and the carotenoids, serum total cholesterol was an additional covariate, and serum ferritin analyses also included C-reactive protein concentrations.

Statistical analysis
All biomarkers were log transformed to approximate normality and homoschedasticity for purposes of using analysis of variance and mixed-effects linear modeling in our analyses. Components of variance without adjustment for covariates were estimated by using the PROC GLM procedure in SAS software (version 9; SAS Institute, Cary, NC). Components of variance with adjustment for covariates were estimated by using PROC MIXED regression models in SAS by ethnicity, PIR, and education categories. Both procedures use the restricted maximum likelihood estimation method for components of variance. The results presented in the tables include the ratio of within-subject to between-subject variance. We computed the 95% CIs for the unadjusted ratios on the basis of F distribution (37) and for the multivariate-adjusted ratios on the basis of the delta method and large sample normal approximation for each analyte by ethnicity, PIR, and education categories (38).

Our approach to testing whether the within-subect to between-subject variance ratios differed among categories of ethnicity, PIR, and education was to examine the CIs for overlapping. This is a conservative but reasonable approach, given the multiplicity of possible comparisons in our study. Because the second examination subsample that was used in this analysis is a nonrandom sample without any sample weights, all analyses were unweighted.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Nonwhite ethnic groups were younger, had lower income and education, and were more likely to be current smokers, but they were less likely to use vitamin and mineral supplements (Table 1Go). The greatest proportion of the Mexican American subjects resided in the South and the West and underwent the MEC exam in the winter. The correlation of first and second measurements of serum concentration was ≥0.9 for folate, vitamins A and E, {alpha}-carotene, β-carotene, and ferritin (Table 2Go). For the remaining biomarkers, the correlations ranged from 0.73 to 0.89. Adjustment for covariates decreased the between-subject variability in the examined biomarkers (Appendix A). The variance ratios for all of the biomarkers by ethnicity, PIR, and education categories increased after adjustment for covariates. The variance ratios were the smallest for RBC folate and ferritin and largest for serum selenium and vitamin C in all ethnic and SES subgroups (Table 3Go, Table 4Go, and Table 5Go).


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  		TABLE 1. Characteristics of the second exam subsample from the third National Health and Nutrition Examination Survey

 

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  		TABLE 2. Serum biomarker concentration for the first and the second measurements and Pearson's correlation (r) between the first and the second measurements: second exam substudy from the third National Health and Nutrition Examination Survey

 

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  		TABLE 3. Ratio of within-subject to between-subject variance in log-transformed serum biomarker concentrations by race-ethnicity: second exam substudy from the third National Health and Nutrition Examination Survey1

 

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  		TABLE 4. Ratio of within-subject to between-subject variance in log-transformed serum biomarker concentrations by poverty income ratio (PIR): second exam substudy from the third National Health and Nutrition Examination Survey1

 

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  		TABLE 5. Ratio of within-subject to between-subject variance in log-transformed serum biomarker concentrations by categories of years of education: second exam substudy from the third National Health and Nutrition Examination Survey1

 
Ethnicity-associated variability in serum biomarkers
The multivariate-adjusted ratio of within-subject to between-subject variance in β-cryptoxanthin concentration was significantly higher in non-Hispanic blacks than in non-Hispanic whites or Mexican Americans. The ratio for lutein + zeaxanthin also was higher in blacks than in Mexican Americans (Table 3Go).

Poverty income ratio–associated variability in serum biomarkers
The multivariate-adjusted ratio of within-subject to between-subject variance for serum concentration of vitamin C was larger in the highest than in the lowest PIR category, and that for RBC folate was significantly higher in PIR categories of ≥1 than in those of <1 (Table 4Go). However, for serum vitamin A, relative to a PIR of <1, a smaller variance ratio was associated with a PIR ≥ 2.

Education-associated variability in serum biomarkers
The within-subject to between-subject variance ratios did not differ by categories of education for any of the examined biomarkers (Table 5Go). We also examined changes in variance ratios after adjustment for dietary intake of the relevant nutrient for some of the biomarkers. Although dietary intakes of vitamins C and E, folate, and the carotenoids were significant predictors, respectively, of serum vitamin C, serum vitamin E, serum folate, and all serum carotenoids (except lycopene), adjustment for these dietary variables had little effect on the ratios of within-subject and between-subject components of variance for these biomarkers (data not shown; available from authors).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
The results of our study indicate the following. First, of the 3 measures of SES examined in this study, PIR was the strongest predictor of differential variability for the examined biomarkers, and the findings for education were essentially null. Second, there were no consistent up- or down-trends in the direction of variability by ethnicity, PIR, or education. Third, the within-subject to between-subject variance ratios for the serum concentration of all examined biomarkers were <1, which suggested that variability among subjects was greater than that within subjects. Fourth, the variance ratios for all examined biomarkers increased with multivariate adjustment. Fifth, the highest unadjusted variability was noted for serum selenium and vitamin C concentrations, and the lowest was for RBC folate and serum ferritin.

As mentioned in the Introduction, in approaching this study, we had considered several hypothetical scenarios for ethnic, income, and education differentials in variability. The results of the study, however, show no consistent up- or down-trends in variance ratios for biomarkers among categories of ethnicity, income, and education. Relative to ethnicity and education, income showed larger differences in the ratio of variance components for biomarkers examined. Non-Hispanic blacks had larger variance ratios for serum β-cryptoxanthin and lutein or zeaxanthin concentrations; higher income was associated with larger ratios for vitamin C and RBC folate but with smaller ratios for vitamin A; and there were no education-related differences in variability for any of the examined biomarkers. We did not examine the possible interactions of ethnicity, income, and education in predicting variability in biomarkers in our study; such an examination may yield additional information on this topic.

The finding of higher within-subject to between-subject variance ratios after multivariate adjustment is consistent with our expectations. Multivariate adjustment resulted in lower between-subject variability because the covariates used in the adjustment were subject-level covariates that explained some proportion of the differences between subjects in biomarkers. Consequently, the adjustment produced higher within-subject to between-subject ratios for components of variance for the examined biomarkers than did unadjusted results. Nevertheless, the within-subject to between-subject ratios remained <1, which compares favorably with variance ratios in the range of 1.5 to >10 for dietary micronutrient intake (39).

Few previous studies examined variability in the biomarkers studied in this study by using comparable methods. Tangney et al (22) were among the first to explore the topic of variability in nutritional biomarkers. In 24 men aged 21–58 y, the ratios of within-subject to between-subject variance for 4 replicate measurements (collected weekly over 4 consecutive weeks) of plasma β-carotene, retinol, and {alpha}-tocopherol were much higher than those reported in the present study. A comparison of estimates of components of variance and their ratios for ascorbate, vitamin A, vitamin E, and the carotenoids from the current study with those published recently by Block et al (28) also shows widely differing results. With few exceptions, the estimates of within-subject variability published by Block et al are much larger than the NHANES III estimates. Within-subject variability in the serum concentration of a biomarker is a function of preanalytic variability related to specimen collection, analytic variability due to laboratory assay procedures, and biologic or physiologic day-to-day variability within a person (19, 20). With strict quality-control procedures, the preanalytic and analytic variability in laboratory assays can be minimized, and the NHANES III laboratory procedures have such quality controls in place (36). The biologic variability within a person may reflect differences in day-to-day exposure to the nutrient and its subsequent metabolism. When the time between replicate measurements is short, this variability may be smaller than that in replicates that span the seasons. Because the interval between replicate measurements in the study by Block et al is comparable to the interval in the present study, the reasons for these different results are not clear. Possible explanations include differences in analytic procedures and populations studied. The study of Block et al included 206 subjects, 65% of whom were smokers. The present study, however, included >900 subjects, and the sample was diverse, because it was drawn from a representative sample in NHANES III. Homogenous samples would result in lower variability among subjects, which would tend to increase the ratio of within-subject to between-subject components.

Our estimates of within-subject and between-subject components of variance are more in accord with those published by Lacher et al, which were also derived from the NHANES III data (21). Although there are several methodologic differences in estimation of biologic variability between the study of Lacher et al and the approach in the present study, the general magnitude of both the within-subject and between-subject variations for the analytes examined in both studies is comparable. Attenuation factors derived from studies with homogenous samples may be limited in their applicability to populations with different characteristics. We submit that investigators interested in correcting for attenuation due to measurement error in biomarkers may find our unadjusted estimates of variance ratios particularly useful. Multivariate-adjusted methods were used in the present study to enable conclusions about differences in ratios of variances related to ethnicity, income, and education.

We acknowledge the following limitations of our study. The NHANES III second examination substudy included only 2 replicates per biomarker, and that number may not be adequate to provide complete information on within-subject variability. Second, the short interval (mean: 17 d) between the repeat biomarker measurements would not be able to reflect within-subject variability related to seasonal differences in exposure to food sources of nutrients. Seasonal differences in biomarker concentrations have been reported for a number of nutrients (40, 41). However, serum selenium; vitamins C, D, and E; folate; and the carotenoids are responsive to short-term changes in nutrient ingestion (42-49). We did not expect such responses for serum retinol and ferritin, because serum retinol concentrations are known to respond to dietary intake only in states of vitamin A deficiency, and serum ferritin is considered an indicator of storage iron (35). Third, in an assessment of ethnic differences in variability, the adjustment for season of examination and area of residence is likely to be inadequate because of the concentration of Mexican Americans in the South and the West, where examinations were scheduled in the winter (50).

In conclusion, the population groups (non-Hispanic blacks, low income, and low education) at high risk of marginal self-reported intakes of several micronutrients (2, 51, 52) and biomarkers (9-18) did not have greater variability for most of the examined biomarkers. Therefore, our results do not support the need for a higher replication of biomarkers in these population groups.


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  		Appendix A. Unadjusted and multivariate-adjusted components of variance for log-transformed serum biomarker concentration: second exam substudy from the third National Health and Nutrition Examination Survey

 

   ACKNOWLEDGMENTS
 
We thank Lisa Licitra Kahle for expert support with respect to SAS programming.

The authors' responsibilities were as follows—AK: conceptualization of the study question, study design, data analysis, data interpretation, and preparation of the manuscript; and BG: input on study design, analytic strategy, data interpretation, and preparation of the manuscript. Neither of the authors had a personal or financial conflict of interest.


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 DISCUSSION
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Received for publication September 6, 2007. Accepted for publication January 8, 2008.





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