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Articles

Serum Concentrations of Retinol, -Tocopherol and the Carotenoids Are Influenced by Diet, Race and Obesity in a Sample of Healthy Adolescents^{1 ,2}

Marian L. Neuhouser^*3, Cheryl L. Rock, Alison L. Eldridge^**, Alan R. Kristal^*, Ruth E. Patterson^*, Dale A. Cooper^**, Dianne Neumark-Sztainer, Lawrence J. Cheskin and Mark D. Thornquist^*

^* Cancer Prevention Research Program, Fred Hutchinson Cancer Research Center, Seattle, WA 98109; Department of Family and Preventive Medicine, University of California, San Diego, La Jolla, CA 92093; ^** Nutrition Science Institute, Procter & Gamble Company, Cincinnati, OH 45224; Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, MN 55454; and Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD 21224

³To whom correspondence should be addressed. E-mail: mneuhous{at}fhcrc.org

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
An important part of understanding the functions of vitamin A, vitamin E and the carotenoids in nutritional status assessment, health promotion and disease prevention is knowledge of factors that influence their distribution in human tissues. Our objective was to examine serum concentrations of these nutrients and compounds in a sample of 285 healthy participants, 12–17 y old, from three U. S. cities. Pearson correlations between diet measured with a food frequency questionnaire and serum nutrient concentrations among these adolescents (adjusted for total serum cholesterol, age, sex, race and body mass index) were as follows: retinol, 0.23; -tocopherol, 0.16; -carotene, 0.31; ß-carotene, 0.15; ß-cryptoxanthin, 0.38; lycopene, 0.08; and lutein + zeaxanthin, 0.25. Multivariate linear regression modeled associations of demographic, dietary and physiologic variables with serum concentrations of these nutrients. African-American participants had significantly lower concentrations of serum retinol (P < 0.001), -tocopherol (P < 0.01) and -carotene (P < 0.02), but higher concentrations of lutein + zeaxanthin (P = 0.001) compared with Caucasians. Obese participants had serum nutrient concentrations that were 2–10% (P < 0.05) lower than normal weight participants. Dietary intake was a significant predictor of all serum analytes (P < 0.01) except lycopene. These models explained 20% of the variability in serum retinol, 28% of the variability in serum -tocopherol, and 14–24% of the variability in serum carotenoids.

KEY WORDS: • retinol • -tocopherol • carotenoids • humans • adolescents • dietary assessment

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Epidemiologic studies have consistently shown that higher intakes of vitamin A, vitamin E and the carotenoids are associated with reduced risk of several chronic diseases, including cardiovascular disease, age-related macular degeneration and some cancers (1 2 3 4) . These micronutrients exhibit multiple biological actions that may protect against disease. For example, vitamin E is a chain breaking antioxidant that protects cell membranes from damage caused by lipid peroxidation and also inhibits cell proliferation, platelet adhesion and formation of N-nitroso compounds (5) . Both the carotenoids and vitamin E stimulate cell-mediated and humoral immunity (6 ,7) . Retinol and its precursors (e.g., -carotene and ß-carotene) are essential for differentiation of epithelial cells and maintenance of cell signaling and communication. Other potential benefits of these nutrients have been reviewed previously (4 ,8 9 10 11 12 13) .

An important part of understanding the role of vitamin A, vitamin E and the carotenoids in nutritional status assessment and their function in disease prevention is knowledge of factors that influence their absorption and distribution in human tissues. Several published reports have identified dietary, demographic and lifestyle variables that affect serum concentrations of these nutrients in adults (14 15 16 17 18) . However, there are fewer data of this nature published from child and adolescent populations. Four reports have used National Health and Nutrition Examination Survey (NHANES)⁴ II and HHANES data to examine relationships of age or race/ethnicity with serum concentrations of vitamins A and E in children and adolescents (19 20 21 22) and one investigation using NHANES III data showed that obese, 6- to 19-y-old children had significantly lower serum concentrations of -tocopherol and ß-carotene than nonobese children (23) . Other studies have focused on the risk of inadequate vitamin E, vitamin A or carotenoid status among newborn infants (24) , children with chronic diseases (e.g., cystic fibrosis, malaria, renal disease) and low income or malnourished children (25 26 27) .

In 2000, the Panel on Dietary Antioxidants and Related Compounds (National Academy of Sciences, Institute of Medicine) published NHANES III (1998–1994) data, which provided distributions of serum vitamin E and the carotenoids among a representative sample of the U. S. population, including adolescents (9) . The Panel on Micronutrients released similar data for serum vitamin A (retinol) in 2001 (13) . These NHANES III data are important because they provide current reference values for this group of important nutrients, but they do not include any information on factors that may influence these distributions, such as diet or other physiologic or lifestyle variables. There are at least two reasons that identification of determinants of these serum nutrient concentrations in adolescents would be a useful addition to currently published data. First, this information would provide additional details about nutritional status in this population subgroup and would identify health or lifestyle factors (e.g., obesity) that might place individuals at risk of nutrient inadequacy. Second, scientists conducting research to investigate associations of serum vitamin A, vitamin E and the carotenoids with growth, development and other health outcomes among adolescents must be aware of these potentially confounding variables. These influencing factors should be carefully considered during analysis and interpretation of data and subsequent conclusions about diet/health relationships. To investigate these issues, we conducted a comprehensive examination of serum concentrations of retinol, -tocopherol and the carotenoids among a group of healthy U. S. adolescents who were participants in a study of diet and health. Specifically, we examined associations of age, sex, race, body mass index (BMI) and other physiologic and lifestyle variables, together with usual dietary intake of vitamin A, vitamin E, -carotene, ß-carotene, ß-cryptoxanthin, lycopene and lutein + zeaxanthin with their respective serum concentrations.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study design and subjects.

Data are from the Olestra Post-Marketing Surveillance Study (OPMSS); this project was designed to monitor the adoption of olestra-containing foods and to examine associations of olestra consumption with serum concentrations of fat-soluble vitamins and carotenoids in representative samples of the U. S. population. The design of OPMSS offers a unique opportunity to examine a large number of serum nutrients and their correlates in the diets of population subgroups, such as adolescents. Details of the design of OPMSS and baseline results of adults in the study have been reported previously (15 ,28 ,29) . Briefly, the first phase of OPMSS is a list-assisted random-digit-dial telephone survey conducted by WESTAT, Inc. (Rockville, MD). Adults 18 y of age and older in four U. S. cities (Indianapolis, Baltimore, Minneapolis and San Diego) and their surrounding suburbs and unincorporated areas were recruited to complete a telephone survey with a focus on beliefs and attitudes about health and usual dietary intake of fruit, vegetables and savory snacks. A random sample of participants who completed the telephone survey was invited to attend a clinic visit. If the household contained a child 7–17 y old, then the child was invited to join the study. In households with more than one child, the one with the closest birthday to the phone call date was selected as the participant. The participation rate for the data presented in this report (number of participants 12–17 y old who completed clinic visits divided by the number households with completed telephone interviews and at least one adolescent child) was 63.8%. Individuals with medical conditions (e.g., cystic fibrosis, kidney disease requiring dialysis, short bowel syndrome) that would interfere with accurate measurements of the serum analytes under investigation were excluded (30) . Clinic visits were conducted between October 1997 and April 1998, before the introduction of olestra products in Baltimore, Minneapolis and San Diego. Because slightly different data collection instruments were used at the Indianapolis clinic, those results are not included in this report. The institutional review boards of all the participating institutions approved procedures for this study, and written informed consent was obtained from all participants and a consenting adult.

Measures.

All study participants completed a self-administered 122-item food frequency questionnaire (FFQ) at home, which was reviewed for completeness by staff during the clinic visit. The reference period for the FFQ was in the past month. This FFQ is divided into three sections: 1) adjustment questions; 2) foods and food groups; and 3) summary questions. The 19 adjustment questions permit refined analysis of fat intake by asking detailed questions about foods preparation practices and fats added in cooking and at the table. The main section of the FFQ is 122 foods or food groups, with questions on the usual frequency of intake (from "never or less than once a month" to "2+ per day" for foods and "6+ per day" for beverages and portion size (small, medium or large compared with the stated medium portion size). These line items include 13 fruit and fruit juice line items, 19 vegetable and vegetable juice line items and 12 mixed foods with vegetables (e.g., pizza, stew) line items. Finally, the four summary questions ask about usual intake of fruits, vegetables and fat added to foods and used in cooking (31) . The nutrient database for the FFQ was derived from the University of Minnesota Nutrition Coordinating Center (NCC) nutrient database (32) and included the most recent U. S. Department of Agriculture-NCC Carotenoid Database for U. S. Foods (33) . This carotenoid database is an important resource for investigators conducting research on carotenoids because it contains carotenoid content for 215 foods, including mixed dishes (e.g., pizza and stew) (33) . Our approach to analyzing food frequency questionnaires and the algorithms for analysis are described in detail elsewhere (34) .

Data on vitamin supplement use over the past month were obtained from all participants, using a validated inventory procedure (35) that was modified to collect detailed dosage information on vitamin A, vitamin E and ß-carotene (the only carotenoid available in supplements at the time). Total micronutrient intakes used in analyses included sources from all supplements plus food. Trained staff measured height and weight of all participants using a standardized protocol and BMI was calculated as weight (kg)/height (m²). Staff members also collected information on medical history, age, sex, race/ethnicity, household income and alcohol and tobacco use.

Blood collection and processing.

Phlebotomists collected nonfasting blood samples by venipuncture into 13-mL serum separating tubes, which were protected from heat and light throughout handling and processing. Serum was stored at -20°C for no longer than four days, shipped to the study’s Coordinating Center on dry ice and then stored at -70°C until analysis. All assays were conducted at Quintiles Laboratories (Atlanta, GA). Details on laboratory analysis and procedures are given elsewhere (15) . Serum retinol, -tocopherol and the carotenoids were analyzed using reversed-phase HPLC methodology. The interassay coefficients of variation for individual analytes ranged from 1.9% to 9.8%. Total serum cholesterol was analyzed using enzymatic methods. Precision was evaluated using packaged reagents, pooled human serums and control serums; both interassay precision and bias were <3%.

Statistical analysis.

We excluded from analyses participants who were pregnant (n = 2) at the time of the clinic visit because of the profound changes in serum nutrient concentrations that can occur during pregnancy (36) . For participants whose serum values were undetectable by laboratory methods (10% undetectable for -carotene, <1% undetectable for all other carotenoids and -tocopherol), we replaced the missing values with the midpoint between zero and the laboratory’s minimum detectable value. The minimum detectable concentrations were as follows (µmol/L): -tocopherol, 1.163; -carotene, 0.005; ß-carotene, ß-cryptoxanthin and lycopene, 0.011; lutein, 0.004; and zeaxanthin, 0.01. The interpretation of data and our conclusions were not changed by these analytic decisions. We excluded from analysis data from 34 (10.6%) FFQ because the energy intakes were outside the range considered acceptable and reliable [<3347 kJ/d (800 kcal/d) or >20.92 MJ/d (5000 kcal/d) for males or < 2510 kJ/d (600 kcal/d) or > 16.74 MJ/d (4000 kcal/d) for females] (37) . For quality control purposes, all adjustment questions, 90% of line items and all summary questions had to be completed to be included in the dataset. These procedures worked well because <0.01% of all FFQ in the OPMSS did not meet these standards. Eleven participants (3%) did not complete an FFQ or submitted an incomplete questionnaire that did not meet quality control standards, leaving 285 for analysis.

Pearson partial correlations were used to assess associations of dietary intake of preformed vitamin A, vitamin E and five carotenoids with their respective serum concentrations. Multivariate linear regression was used to model associations between the dependent variable (serum retinol, -tocopherol and individual carotenoids) and the predictor variables. All models included age, sex, race and serum cholesterol concentrations. Additional variables, including energy intake, total dietary intake of the nutrient being modeled (from food plus supplements), percentage of energy from fat, mean daily servings of fruit and vegetables, income and BMI were added to the model if the P value for the variable in the model was <0.10, using stepwise regression. Serum triglycerides, exercise, household income, smoking and alcohol use were examined but did not enter any models. All dependent variables (i.e., serum nutrient concentrations) were log transformed before analyses to improve normality. Thus, after appropriate back transformation all regression coefficients are interpreted as the percentage of change in the serum nutrient associated with change in each independent variable. Dietary intake measures, with the exception of percentage of energy from fat, were log transformed. The logarithms of dietary intake variables and serum cholesterol concentration were divided by the logarithm of 1.10 and percentage of energy from fat was divided by 5.0. Thus, after appropriate back transformation when required, these regression coefficients are interpreted as the effects of increasing exposure by 10% and 5% per day, respectively. Dummy variables were used to code race and BMI so that the regression coefficients represent percentage of change in the analyte compared with the reference group (e.g., Caucasian). All analyses were performed with SAS, Version 6.12 (Cary, NC).

	RESULTS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Table 1 gives the demographic characteristics of the study population. Fifty-four percent of participants were male and the mean age was 143/4 y of age. Twenty-three percent of participants were African-American, nearly 12% were Hispanic, and 7.8% were Asian-American or of mixed-race/ethnicity. The mean BMI was 22.7 (SD ± 4.5); 19.6% of participants were overweight and nearly 14% were obese, which we defined as the 85th and 95th percentiles, respectively, of the NHANES II distribution (38 ,39) . Twenty-four percent of participants used a dietary supplement, such as multivitamins, at least three times per week. Eight percent were smokers and 20% reported use of alcohol within the previous month (data not shown). On average, these adolescents ate two servings of fruit and vegetables per day. The estimated mean intake of vitamin A was 960 (SD ± 603) RE/d for males and 752 (SD ± 517) RE/d for females, and estimated mean vitamin E intakes were 10 (SD ± 7) and 8 (SD ± 7) mg/d, for males and females, respectively (data not shown). These estimated dietary intakes are very close to the RDA for vitamin A (13) , but 50–67% below the RDA for vitamin E (9) . We note the wide variability in the estimated intakes of these vitamins, indicating that some participants in our study may have had marginal nutrient intakes.

View this table: [in this window] [in a new window]   Table 3. Pearson correlation coefficients between serum retinol, -tocopherol and carotenoid concentrations and respective dietary intakes in healthy adolescents aged 12–17 y (n = 285)

View this table: [in this window] [in a new window]   Table 4. Predictors of serum retinol, -tocopherol and carotenoid concentrations in multivariate analyses in 285 adolescents aged 12–17 y

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
In this study of healthy adolescents from three U. S. cities, the distributions of serum retinol, -tocopherol and the carotenoids were very similar to results from NHANES III, a large nationally representative sample (9 ,13) . The mean concentrations of serum retinol in our sample were slightly lower than those for males and females aged 14–18 y in NHANES III. We note, however, that serum retinol for NHANES III participants aged 9–13 y was 1.43 µmol/L for males and 1.40 µmol/L for females; the lower portion of our age distribution overlaps with the NHANES III 9- to 13-y-old group (13) , which may explain the slightly lower mean values for our entire sample. Similar age-related changes in serum retinol concentrations were recognized in NHANES II (19) . Mean serum ß-carotene and all other carotenoid concentrations in our sample were very similar to those for males and females aged 14–18 y in NHANES III. Overall, we found that the predominant carotenoids in our sample of healthy adolescents were serum lycopene and ß-carotene. Apgar and colleagues (40) observed that the predominant circulating carotenoids were lutein and ß-carotene among 493 healthy children in Belize. Variations in dietary patterns, such as higher consumption of lycopene-rich condiments and pizza, among American children, may explain these differences.

The correlations between dietary intake of vitamin A, vitamin E and the carotenoids, as measured by the food frequency questionnaire, and the serum nutrient concentrations, differed somewhat from previously published reports in adults. For example, in the Framingham Study, adjusted correlations of diet and serum carotenoids ranged from 0.14 to 0.45 (16) . The Nurse’s Health Study and the Health Professionals’ Follow-Up Study reported diet-serum carotenoid correlations of 0.21–0.48 for women and 0.35–0.47 for men (41) , compared with our reported range of 0.08–0.38. We found a very weak correlation between diet and serum lycopene (0.08), which is similar to results from Campbell et al. (42) , who found a diet-serum lycopene correlation of 0.11, but is lower than the 0.20 reported by Casso et al. (43) . We propose two reasons for this weak dietary lycopene-serum association. First, the primary sources of dietary lycopene are tomatoes and tomato products, such as catsup, tomato sauce and salsa. Because catsup may be a substantial source of lycopene in the adolescent diet, and there is no specific line item for catsup on the food frequency questionnaire, dietary intake of lycopene is likely measured with error, thus reducing the ability to explain variance in serum lycopene. Second, reliable estimates of lycopene content of foods are limited. Although the U. S. Department of Agriculture-NCC Carotenoid Database for U. S. Foods contains recent food carotenoid data for 215 foods (including mixed foods), only 2% of the foods analyzed have been given a confidence code of A, meaning the user can have considerable confidence in the mean carotenoid estimates for that food (33) . Moreover, data for many of the carotenoids are incomplete; there are lycopene values for 79 foods, and zeaxanthin values for only 22 foods (33) . Correlates of dietary and serum carotenoids will improve as the U. S. Department of Agriculture’s Nutrient Data Laboratory continues their extensive and ongoing research program and adds new and improved values to the dietary database. Results from the Women’s Health Initiative were very similar to our findings for serum -tocopherol. The partial correlation for diet and serum -tocopherol was only 0.11 among nonsupplement users in a sample of 1047 women drawn from this large study of diet and health (44) , which is similar in magnitude to our adjusted correlation of 0.16. These weak correlations are likely due to the fact that only 24% of the adolescents in our study used vitamin E-containing multivitamins and none used single supplements; these dietary supplements are very strong predictors of circulating -tocopherol (15 ,44) .

The value of using serum analytes as nutritional biomarkers depends in part on an understanding of physiologic and lifestyle factors that influence their circulating concentrations (15) . An interesting finding from this study was that the determinants of serum retinol, -tocopherol and the carotenoids among adolescents were very similar to the factors that influence serum concentrations of these nutrients in adults. The strongest and most consistent predictor of all serum fat-soluble nutrients was serum cholesterol, a finding that agrees with results from studies conducted in adults (15 ,17 ,44) and one study of 509 French children aged 10–15 y (45) . Because the carotenoids and vitamin E are carried by the cholesterol-rich lipoproteins, this consistent physiologic association is expected. Dietary intake, when measured as a specific intake variable, was a statistically significant predictor of all analytes except serum lycopene. These results are in agreement with studies conducted in adult populations, which have shown that intakes of fat-soluble vitamins and individual carotenoids are important predictors of plasma carotenoids (15 ,16 ,41 ,42) . Similar to findings in adults, we found an inverse association of both energy intake and percentage of energy from fat with serum concentrations of most fat-soluble nutrients (15 ,46) . Although vitamin A, vitamin E and the carotenoids are fat-soluble and require some dietary fat for absorption, the amount required is small (3–5 g/meal) and excessive fat intake does not further increase bioavailability (47) . In addition, it has been noted in many studies that dietary patterns that are high in fat and energy are frequently low in fruits and vegetables (48) , which could explain the inverse relationships between fat and energy and most of the serum nutrients examined.

Associations of race with fat-soluble nutrients varied. We speculate that the variability in these serum nutrient concentrations across race was due to differences in dietary intake that the FFQ cannot measure with precision or other potentially confounding factors, which are difficult to assess (e.g., exercise and growth). Finally, although we did not find any association of obesity with serum retinol concentration, in contrast to results from NHANES III (23) , we did show that obesity had an inverse association with the other nutrient analytes, except lutein + zeaxanthin. Our finding of an inverse association of serum -tocopherol and most of the carotenoids with BMI agrees with results from NHANES III (23) , a small study conducted in Hungary (49) and investigations conducted among adults (15 ,17 ,42 ,44) . The basis for lower serum concentrations of nutrients in obese people compared with nonobese people remains speculative, but it has been suggested that dietary differences (23) and variability in body compartment size (17) are likely explanations.

There are several strengths of this study. First, we used an effective recruitment strategy and we were able to recruit minorities and persons of lower socioeconomic status. Second, we collected detailed information about health, lifestyle and demographics; all data were collected in a uniform manner by centrally trained staff. Third, our dietary assessment tool, the FFQ, gives a more reliable estimate of usual dietary intake (37) than one 24-h recall, the dietary assessment method used in NHANES and components of other large national surveys (50) . Usual dietary intake assessed over the past month is especially important when estimating carotenoid intake due to the high day-to-day variability in intake of these compounds. There are also limitations that should be mentioned. First, our sample size of 285 is modest in comparison to the large, nationally representative NHANES III sample, which limits the generalizability of our conclusions. Second, although this FFQ has been validated in a sample of older women (31) , we have no data on its measurement characteristics among adolescents. Third, although FFQ have been used with success in many large studies of diet and health, there are many sources of error, such as the restrictions imposed by a fixed list of foods, portion size estimation, the cognitive challenge of reporting foods consumed over a broad range of time such as the past month (37) and the limited ability to differentiate between cooked and raw vegetables, which affects carotenoid bioavailability (14) . In addition, all self-reported dietary assessment instruments are subject to random and systematic bias (37) . A final limitation is that factors, such as smoking and alcohol intake, which have been shown to be predictors of retinol, -tocopherol and the carotenoids in adults (15 ,44) , did not enter any of the models in our study. Less than 10% of the adolescent participants reported tobacco use, which is substantially less than nationally reported estimates of 35–50% (51) , and 20% reported alcohol use. Although parents were not present with this age group during the clinic interview, many adolescents may still hesitate to report use of alcohol or tobacco.

One of the conclusions in the report from the Panel on Dietary Antioxidants and Related Compounds (National Academy of Sciences, Institute of Medicine) was that there has been insufficient nutrition-related research conducted among children and adolescents (9) . For this reason, the recommended levels of intakes for vitamin A and vitamin E in these life stage groups are extrapolated from adults, instead of being based on experimental data. Although the Panel did not propose a recommended intake of ß-carotene or other carotenoids for any life stage or sex group, they did recommend that Americans eat foods rich in these nutrients (9) . The data we have presented in this report suggest that serum concentrations of vitamin A, vitamin E and the carotenoids are influenced by similar physiologic and lifestyle factors as adults, namely serum cholesterol, diet, race and obesity. A critical need remains for continued research among children and adolescents to establish quantitative nutrient recommendations.

	FOOTNOTES

 
¹ Presented in part at the Experimental Biology 2000 Meeting, San Diego, CA (Neuhouser, M. L., Thornquist, M., Rock, C. L., Patterson, R. E., Kristal, A. R., Neumark-Sztainer, D. & Cheskin, L. Serum carotenoids, retinol, and -tocopherol in healthy US children and adolescents. FASEB J. 14: A516).

² Supported by the Procter & Gamble Company, Cincinnati, OH.

⁴ Abbreviations used: BMI, body mass index; FFQ, food frequency questionnaire; NCC, Nutrition Coordinating Center; NHANES, National Health and Nutrition Examination Survey; OPMSS, Olestra Post-Marketing Surveillance Study.

Manuscript received February 12, 2001. Initial review completed April 12, 2001. Revision accepted May 28, 2001.

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