QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Dixon, L. B. Articles by Potischman, N. Search for Related Content PubMed Articles by Dixon, L. B. Articles by Potischman, N.

---

Nutritional Epidemiology

Carotenoid and Tocopherol Estimates from the NCI Diet History Questionnaire Are Valid Compared with Multiple Recalls and Serum Biomarkers¹

² Department of Nutrition, Food Studies, and Public Health, New York University, New York City, NY; ³ Division of Cancer Prevention and Population Sciences, National Cancer Institute, NIH, Bethesda, MD; and ⁴ Information Management Services, Silver Spring, MD

^* To whom correspondence should be addressed. E-mail: beth.dixon{at}nyu.edu.

	ABSTRACT

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
To improve the measurement of usual dietary intake, the National Cancer Institute developed a cognitively based Diet History Questionnaire (DHQ), which has been validated against four 24-h dietary recalls (4 24-HR) for energy, macronutrients, and several vitamins and minerals. This analysis used data from The Eating at America's Table Study (EATS) to determine the validity of estimates for carotenoids and tocopherols from the DHQ. Over the course of a year, 163 participants provided 1 or 2 blood samples and completed the DHQ and 4 24-HR. For both the DHQ and the 4 24-HR, crude correlations between serum and diet were modest to strong for the provitamin A carotenoids (-carotene, ß-carotene, ß-cryptoxanthin), low to modest for lycopene, and very low for lutein. The individual dietary tocopherols were weakly correlated with the serum tocopherols, but vitamin E from food and dietary supplements was strongly and positively correlated with serum -tocopherol and strongly and inversely correlated with serum -tocopherol for both instruments. Adjustment for energy, BMI, smoking status, serum total cholesterol, and serum triacylglycerol did not appreciably change the correlations. Using the method of triads, validity coefficients for the DHQ were comparable to the 4 24-HR and were especially strong for -carotene, ß-cryptoxanthin, lutein + zeaxanthin, and total vitamin E in men and -tocopherol and total vitamin E in women. In this study, there was no advantage of 2 blood samples over 1, suggesting reasonably stable ranking of individuals for these biomarkers, which is important for large epidemiologic studies that typically obtain only 1 blood sample for biomarker status.

	Introduction

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

In an effort to improve the measure of usual dietary intake, investigators at the National Cancer Institute (NCI) developed the Diet History Questionnaire (DHQ) using cognitive-based methods described previously (3–6). The Eating at America's Table Study (EATS) was designed to compare nutrient intakes estimated from the DHQ with 4 24-HR collected over the course of 1 y and with serum biomarkers collected 1–2 times during that year (7). Previous analyses of EATS data showed that the NCI DHQ produced correlations for energy, macronutrients, and several vitamins and minerals, consistent with other widely used FFQs (7). In this analysis, we compared estimates of carotenoids and tocopherols from the DHQ and from 4 24-HR with serum carotenoid and tocopherol concentrations in EATS participants who provided at least 1 fasting blood sample. We also used Kaak's method of triads (8) to determine validity coefficients for each serum–diet correlation of carotenoid and tocopherol.

	Methods

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
    Sample and study design. In August 1997, nationally representative sampling and random-digit dialing techniques were used to obtain 12,615 telephone numbers. Nonworking, nonresidential, unanswered numbers, and participants deemed ineligible (i.e., not aged 20–70 y or on liquid diets) were excluded. Of the 3590 eligible persons identified, 1640 completed a baseline questionnaire. Between September 1997 and August 1998, 4 24-HR were administered, timed to occur through 1 calendar year with 1 recall per season. Based on the date of enrollment, blood was drawn from a subsample of participants twice, between mo 4–5 and mo 10–11. The subsample was recruited in periodic waves, leading to a set of blood samples collected across the course of the year. After the year in which the 4 24-HR and blood samples were collected, all participants completed the DHQ. This study was approved by the NCI Special Studies Internal Review Board.

    Dietary measures. The 4 24-HR were collected using the multiple-pass methodology developed for the 1994–1996 Continuing Survey of Intakes by Individuals (CSFII) (9). Trained interviewers collected and coded the data using the University of Texas Food Intake Analysis System (FIAS), version 3.0. The majority of participants used standard measuring guides (e.g., measuring cups and spoons, ruler, 2-dimensional pictures) to report intakes.

The DHQ asked about frequency of intake over the past year for 124 food items and included portion sizes (10). Forty-four of the foods had embedded questions about seasonal intake, food type (e.g., low fat, diet, caffeine free), and fat uses or additions. The DHQ also included questions about the intake of low-fat foods and dietary supplements.

The 4 24-HR and the DHQ provided estimates for 6 individual dietary carotenoids (-carotene, ß-carotene, ß-cryptoxanthin, lutein + zeaxanthin, and lycopene) and 2 individual dietary tocopherols (-tocopherol and -tocopherol) from food. The 4 24-HR and the DHQ also provided estimates for total ß-carotene equivalents, which is a summary measure that includes the provitamin A carotenoids: -carotene, ß-carotene, and ß-cryptoxanthin from food and ß-carotene from dietary supplements. We derived an estimate for total vitamin E intake (-tocopherol from food and dietary supplements) for these 2 dietary instruments.

Carotenoid and tocopherol estimates for foods reported on the 4 24-HR and the DHQ were determined from similar food items in the University of Minnesota Nutrition Data System for Research (NDSR) (11) using methodology developed by Dixon et al. (12). ß-carotene and vitamin E from dietary supplements were determined from the sum of single-nutrient supplements and from multivitamin use. For single-nutrient supplements for which dose was not reported and for multivitamins, nutrient values were assigned that corresponded to defaults recommended in NHANES 1999–2002: 5000 µg ß-carotene for single supplement, 180 mg -tocopherol for vitamin E single supplement, 13.5 mg -tocopherol of vitamin E and 0 µg ß-carotene for one-a-day types without minerals, and 13.5 mg -tocopherol of vitamin E and 610 µg ß-carotene for one-a-day types with minerals. The mean dose of multivitamins per day was calculated by dividing the dose by the frequency of intake.

    Serum measures. A subsample of 240 participants was targeted for the blood collection in 4 U.S. metropolitan areas (Albany, New York; Tucson, Arizona; Indianapolis, Indiana; and Austin, Texas) participating from the larger study. Participants who provided blood specimens received a desktop calculator and $20 remuneration as incentives. Although 240 participants agreed to provide a blood sample, a total of 163 participants completed at least 1 blood draw (n = 155 at 4–5 mo, n = 144 at 10–11 mo, and n = 135 at both time points). Reasons for nonparticipation included refusal (n = 62), lost to follow-up for the main study (n = 14), or moved away (n = 1).

Participants were instructed to fast from midnight until the morning of the blood draw. Fasting status was confirmed by questionnaire at the time of the draw. Blood samples were allowed to clot at room temperature for 45 min and then centrifuged (2900 x g; 20 min), and serum was stored at –20°C or at –70°C until shipment to the main repository for long-term storage. All assays were conducted by the Medical Research Laboratories of Highland Heights, Kentucky. Serum carotenoids and tocopherols were measured by HPLC using the method of Kaplan et al. (13). Samples were run against standard references from the National Institute of Standards and Technology for quality control; values >2 SD were repeated. Serum cholesterol and triacylglycerol were measured by enzyme analysis using standard methods of the Lipid Research Clinics Program (14). Duplicate samples of in-house sera were run for quality control and unusual values of serum lipids were flagged and rechecked.

    Other measures. Data from the baseline questionnaire were used to determine age, race or ethnicity, highest level of education completed, smoking status (never, past, current), and self-reported BMI (kg/m²) of the participants.

    Statistical analysis. All analyses were stratified by sex. Frequencies of demographic and behavioral characteristics were calculated to describe the study sample. Serum and dietary carotenoids and tocopherols were log-transformed prior to calculating geometric means and 95% CI. Paired t tests were run to determine differences between serum values for Draw 1 and Draw 2. Spearman rank correlation coefficients were calculated between the serum nutrients at the first and second draws to determine the reproducibility of the serum data. Spearman rank correlation coefficients were also calculated between the respective serum and dietary nutrients and adjusted for energy, BMI, smoking status (never, former, current), serum total cholesterol, and serum triacylglycerol. Because underreporting of food intake may affect the association between serum and dietary nutrients, participants who likely underreported their energy intake were determined according to age- and sex-specific formulas derived for adults (15). Underreporters had a ratio of energy intake estimated from the mean of 4 24-HR to basal metabolic rate (BMR_est) that was <1.06, a cut-point that is appropriate for 4 dietary recalls (16). Analyses of correlations were compared by strata of various factors including energy intake (adequate vs. underreporting), BMI (<25 kg/m² or normal weight vs. BMI 25 kg/m² or overweight) and smoking status (never vs. former or current smoker).

Validity coefficients between true intakes of carotenoids and tocopherols and estimated intakes from the DHQ, the 4 24-HR, and serum biomarkers were estimated using the method of triads (8). This method assumes that each of the 3 nutrient measures is linearly related to a latent factor corresponding to the true intake of the nutrient. Each validity coefficient is calculated from the correlations between each pair of methods and represents the square root of the proportion of variation in each nutrient measure that is related to true intake and also to the variation in the other 2 nutrient measures. Although the method of triads assumes that measurement errors from each instrument are mutually independent, random errors associated with a FFQ and 4 24-HR are likely related. Therefore, the correlations between nutrients estimated from both dietary instruments will be overestimated, and the validity coefficients are considered to be the upper limits of the true validity coefficients, whereas the correlations between nutrients from each dietary instrument and serum are considered to be the lower limits of the validity coefficients. For this study, we followed this reasoning to calculate the range for each validity coefficient.

To maximize sample size, the analytic sample in this study included 130 EATS participants (86 women, 44 men) who completed the DHQ and the 4 24-HR and had blood drawn at the first time period. For comparison, all results were repeated for participants who had their blood drawn at the second time period (82 women, 44 men) or at both time periods (77 women, 42 men). Analyses were conducted using SAS, version 8.2 (SAS Institute). For all analyses, < 0.05 was used to determine significance.

	Results

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
The majority of EATS participants were Caucasian women between the ages of 40 and 59 y who completed >12 y of education, did not smoke, and were normal or overweight (Table 1). About one-third of EATS participants reported taking a daily multivitamin. About 24% of women and 14% of men took a daily vitamin E supplement. Few participants reported ever taking a single ß-carotene supplement.

With the exception of the dietary tocopherols, adjustment for energy, BMI, smoking status, serum cholesterol, and serum triacylglycerol did not appreciably change the respective values or the significance of the correlations. We also evaluated the serum–diet correlations within strata of energy intake, BMI, and smoking status. For the DHQ, serum–diet correlations for the carotenoids and tocopherols differed the most between never and former or current smokers and, in some cases, the correlations were higher in women who were former or current smokers. For the 4 24-HR, serum–diet correlations for the carotenoids tended to be lower in women who underreported energy, were overweight, or smoked. In these women, serum–diet correlations for the tocopherols tended to be higher than in women with adequate energy intake, normal weight, or who never smoked; but correlations for total vitamin E were similar between all comparison groups. The effects of energy intake, weight, or smoking on serum–diet correlations were not consistent for either instrument in men.

Most validity coefficients for carotenoids and tocopherols estimated from the DHQ, the 4 24-HR, and serum were strong (Table 5). In men, the validity coefficients for estimating carotenoids from the DHQ were all >0.5, with the exception of lycopene, and were highest for -carotene, ß-cryptoxanthin, and lutein + zeaxanthin compared with values for the 4 24-HR or serum. Similarly, in women, most validity coefficients for carotenoids estimated from the DHQ were >0.5, although they tended to be lower than the respective values in men. To note, the validity coefficient for lutein + zeaxanthin from the DHQ was lowest in women but highest in men compared with the 4 24-HR and serum. In both women and men, validity coefficients for the individual tocopherols and total vitamin E tended to be highest for the DHQ. The ranges of most validity coefficients were wide, but most coefficients had upper limits >0.5.

	Discussion

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 
In this sample of EATS participants, mean intakes of carotenoids and tocopherols from the DHQ were similar to those from the 4 24-HR collected over the course of 1 y. Mean intakes were also comparable to national data (17,18). Mean concentrations of serum carotenoids and tocopherols of EATS participants were also comparable to national data, although EATS participants had lower serum ß-cryptoxanthin and higher serum lycopene concentrations than adult participants in NHANES III (mean cryptoxanthin was 0.17 µmol/L and lycopene was 0.44 µmol/L (19). These differences may be related to the testing laboratory, different isomers measured, or to inherent differences between the NHANES and EATS participants.

The EATS data showed modest to strong serum–diet correlations for the carotenoids with provitamin A activity (i.e., -carotene, ß-carotene, ß-cryptoxanthin), low to modest correlations for lycopene, and very low correlations for lutein. The provitamin A carotenoids and lycopene are found primarily in a few foods included on the DHQ (i.e., carrots and spinach for - and ß-carotene, orange juice and oranges for ß-cryptoxanthin, tomatoes and tomato products for lycopene). Lutein and zeaxanthin are found primarily in green leafy vegetables but also in a variety of other foods. Intake of lutein and zeaxanthin is more difficult to quantify because of known limitations of food composition tables for these carotenoids (20). Other studies have also reported similar serum–diet correlations for lutein (21–24).

The EATS data showed weak serum–diet correlations for the individual tocopherols, which may be due to measurement error from: 1) underreporting of energy and fat intake, 2) poor assessment of fats and oils added during food preparation, 3) uncertainty regarding the types of fats and oils consumed, and 4) high variability of vitamin E content in the food composition databases (19). In contrast, the EATS data showed strong positive correlations for total vitamin E from both instruments with serum -tocopherol but strong inverse correlations with serum -tocopherol. Like other studies (19,25), these results are consistent with the knowledge that supplemental vitamin E is in the form of -tocopherol, which overwhelms amounts in the typical diet (400 mg vs. 8 mg/d from food on average), and that after absorption, -tocopherol is preferentially resecreted by the liver into circulation, whereas -tocopherol and other tocopherols are excreted.

In theory, a FFQ should better estimate typical intake, especially for foods infrequently consumed that may be rich sources of a single nutrient (e.g., yams for ß-carotene), compared with single or multiple days of dietary intake when consumption might be missed on recording days. The findings from EATS support this theory to some degree but seem to be nutrient dependent. The DHQ had higher correlations for serum -carotene, ß-cryptoxanthin (men only), lutein (men only), and -tocopherol, whereas the 4 24-HR had higher correlations for ß-carotene, total ß-carotene equivalents, lycopene (men only), and -tocopherol. The magnitude of the correlations was often comparable between instruments. We observed no clear advantage of 4 24-HR, suggesting that the DHQ is adequate for large epidemiologic studies that may not be able to include multiple recalls.

For comparison, we identified 7 studies that reported serum–diet correlations for carotenoids and/or tocopherols with at least 1 FFQ and multiple recalls (26–32) and another 6 studies with multiple food records (21,33–37). The serum–diet correlations reported in these studies varied, and no particular dietary method produced consistently stronger correlations for individual carotenoids or tocopherols. Some research (38) suggests that weighed food records or diet diaries may produce more valid nutrient estimates than FFQ or multiple recalls. However, the serum–diet correlations for the provitamin A carotenoids and total vitamin E from the DHQ and from the 4 24-HR were as strong as those reported in studies that used multiple weighed food records or diet diaries (21,33–37). In EATS and other studies, the variability in the serum–diet correlations by instrument likely depends on the population group, their particular dietary intakes, the proximity of blood collection to administration of the 4 24-HR, and other unknown factors. Combining both instruments may provide better estimates of usual intake than using either instrument alone (32,39).

Like other studies (40,41), the EATS data showed strong reproducibility for the individual carotenoids and tocopherols between the first and second draws, a time period of 6 mo. In EATS, the serum–diet correlations were comparable whether serum data were from the first, second, or the mean of both draws. They were also comparable to those reported in the literature from 2 blood draws (21,31,33,34). These findings suggest that 1 blood collection within a year is likely to be representative of usual nutritional status for these nutrients, and that there is no advantage to collecting >1 blood sample for epidemiologic studies, especially because of cost and burden to the participants.

Adjustment for energy, smoking, body weight, and serum lipids, did not appreciably improve the crude serum–diet correlations for the DHQ or the 4 24-HR, except for -tocopherol for both dietary instruments in men. Like EATS, some studies (21,28,33) showed no improvement with adjustment for energy, lipids, and covariates, whereas other studies (27,44) showed improvement for -tocopherol but less change for individual carotenoids. In our study, underreporting appeared to only affect the serum–diet correlations for the 4 24-HR in women, resulting in lower correlations compared with women who reported adequate energy intake. For the DHQ in women and for both instruments in men, the serum–diet correlations were comparable between participants with adequate energy intake and those who may have underreported, suggesting that the DHQ ranks well even among underreporters.

Using Kaaks' method of triads (8), validity coefficients for the carotenoids and tocopherols for the DHQ were comparable to those observed for the 4 24-HR and the serum and were notably strong (>0.8) for -carotene, ß-cryptoxanthin, lutein + zeaxanthin, and total vitamin E in men and for -tocopherol and total vitamin E in women. In other studies (26,29,32,35,37), the validity coefficients were comparable in magnitude to the values observed in EATS. Ranges of validity coefficients in EATS were wide, leading us to conclude, similarly, that only strong associations between many of these dietary factors and disease will be detected (37).

EATS is one of the few studies that compares multiple methods of dietary assessment to a spectrum of serum carotenoids and tocopherols. The database (11) and methodology used (12) to estimate intake of individual carotenoids and tocopherols from the DHQ and the 4 24-HR were more comprehensive than previous studies (21,43,44). However, there are a variety of factors that impact direct correlations between intake and serum estimates, many of which cannot be addressed with current data. The most troubling limitation of all dietary assessment methods is measurement error, especially due to the underreporting of energy intake (45). We do not know to what degree such error impacts the measurement of individual carotenoids and tocopherols. In addition, correlated errors between a FFQ and reference method (multiple recalls or records) are likely to be much greater than observed (46), making the validity coefficients from the method of triads overestimated. An alternative approach using multiple latent variables, such as 2 biomarkers, may better satisfy assumptions about correlated errors in assessment methods (47), but these models need further exploration (48).

Even with these limitations, findings from EATS show that the DHQ performed generally as well as multiple recalls when compared with serum concentrations of carotenoids and tocopherols. The NCI DHQ produced reliable and valid estimates for dietary carotenoids and tocopherols and diet–serum correlations with strong validity coefficients, demonstrating its comparability to other FFQs for use in large epidemiologic studies of diet and health.

	ACKNOWLEDGMENTS

 
We appreciate the extensive database work by Thea Zimmerman and the assistance of Temitope Erinosho with making data tables.

	FOOTNOTES

 
¹ This work was supported in part by an independent contract from the Division of Cancer Control and Population Sciences, National Cancer Institute, NIH (L.B.D.). None of the authors have conflicts of interest.

⁵ Abbreviations used: DHQ, diet history questionnaire; EATS, Eating at America's Table Study; FFQ, food frequency questionnaire; 4 24-HR, four 24-h dietary recalls.

Manuscript received 21 May 2006. Initial review completed 19 June 2006. Revision accepted 21 September 2006.

	LITERATURE CITED

TOP ABSTRACT Introduction Methods Results Discussion LITERATURE CITED

 

1. Thompson FE, Subar AF. Dietary assessment methodology. In: Coulston AM, Rock CL, Monsen ER, editors. Nutrition in the prevention and treatment of disease. San Diego (CA): Academic Press, 2001.

2. Potischman N, Freudenheim JL. Biomarkers of nutritional exposure and nutritional status: an overview. J Nutr. 2003;133: Suppl:873S–973S.[Free Full Text]

3. The National Cancer Institute. Diet history questionnaire (DHQ). [cited 2006 Oct]. Available from: www.riskfactor.cancer.gov/DHQ

4. Subar AF, Thompson FE, Smith AF, Jobe JB, Ziegler RG, Potischman N, Schatzkin A, Hartman A, Swanson C, Kruse L. Improving food frequency questionnaires: a qualitative approach using cognitive interviewing. J Am Diet Assoc. 1995;95:781–8.[Medline]

5. Subar AF, Ziegler RG, Thompson FE, Johnson CC, Weissfeld JL, Reding D, Karounis KH, Hayes RB. Is short always better? Relative importance of questionnaire length and cognitive ease on response rates and data quality for 2 dietary questionnaires. Am J Epidemiol. 2001;153:404–9.[Abstract/Free Full Text]

6. Thompson FE, Subar AF, Brown CC, Smith AF, Sharbaugh CO, Jobe JB, Mittl B, Gibson JT, Ziegler RG. Cognitive research enhances accuracy of food frequency questionnaire reports: results of an experimental validation study. J Am Diet Assoc. 2002;102:212–25.[Medline]

7. Subar AF, Thompson FE, Kipnis V, Midthune D, Hurwitz P, McNutt S, McIntosh A, Rosenfeld S. Comparative validation of the Block, Willett, and National Cancer Institute Food Frequency Questionnaires: the Eating at America's Table Study (EATS). Am J Epidemiol. 2001;154:1089–99.[Abstract/Free Full Text]

8. Kaaks RJ. Biochemical markers as additional measurements in studies of the accuracy of dietary questionnaire measurements: conceptual issues. Am J Clin Nutr. 1997;65:1232S–9S.[Medline]

9. Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA's dietary intake data system. J Food Compos Anal. 2004;17:545–55.

10. Subar AF, Midthune D, Kulldorff M, Brown CC, Thompson FE, Kipnis V, Schatzkin A. Evaluation of alternative approaches to assign nutrient values to food groups in food frequency questionnaires. Am J Epidemiol. 2000;152:279–86.[Abstract/Free Full Text]

11. University of Minnesota Nutrition Coordinating Center [database on the internet]. Nutrition data system for research (NDSR). [cited 2006 Oct]. Available from: www.ncc.umn.edu.

12. Dixon LB, Zimmerman TP, Kahle LL, Subar AF. Adding carotenoids to the NCI diet history questionnaire database. J Food Compos Anal. 2003;16:269–80.

13. Kaplan LA, Miller JA, Stein EA. Simulataneous measurement of serum retinol, tocopherols, carotenes, and carotenoids by high performance liquid chromatography. Methods Enzymol. 1990;189:155–67.[Medline]

14. Lipid Research Clinic's Program. Manual of Laboratory Operations, Lipid Research Clinic's Program, Vol.1: Lipid and lipoprotein analysis. DHEW Publication no. (NIH) 75628. Washington, DC: U.S. Government Printing Office, 1974.

15. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39C:5–41.[Medline]

16. Goldberg GR, Black AEJSA, Cole TJ, Murgatroyd PR, Coward WA, Prentice AM. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur J Clin Nutr. 1991;45:569–81.[Medline]

17. National Center for Health Statistics, Centers for Disease Control and Prevention. Third national health and nutrition examination survey, 1988–1994, NHANES III laboratory data file [CD-ROM]. Hyattsville (MD): U.S. Government Printing Office, 1996.

18. Millen AE, Dodd KW, Subar AF. Use of vitamin, mineral, nonvitamin, and nonmineral supplements in the United States: the 1987, 1992, and 2000 national health interview survey results. J Am Diet Assoc. 2004;104:942–50.[Medline]

19. Food and Nutrition Board, Institute of Medicine. Dietary reference intakes for vitamin C, vitamin E, selenium, and carotenoids. Washington (DC): National Academies Press, 2000.

20. Holden JM, Eldridge Al, Beecher GR, Buzzard M, Bhagwat S, Davis DS, Douglass LW, Gebhardt S, Haytowitz D, Schakel S. Carotenoid content of U.S. foods: an update of the database. J Food Comp Anal. 1999;12:169–96.

21. Forman MR, Lanza E, Yong LC, Holden JM, Graubard BI, Beecher GR, Meltiz M, Brown ED, Smith JC. The correlation between two dietary assessments of carotenoid intake and plasma carotenoid concentrations: application of a carotenoid food-composition database. Am J Clin Nutr. 1993;58:519–24.[Abstract/Free Full Text]

22. Tucker KL, Chen H, Vogel S, Wilson PW, Schaefer EJ, Lammi-Keefe CJ. Carotenoid intakes, assessed by dietary questionnaire, are associated with plasma carotenoid concentrations in an elderly population. J Nutr. 1999;129:438–45.[Abstract/Free Full Text]

23. Curran-Celentano J, Hammond BR, Jr., Ciulla TA, Cooper DA, Pratt LM, Danis RB. Relation between dietary intake, serum concentrations, and retinal concentrations of lutein and zeaxanthin in adults in a midwest population. Am J Clin Nutr. 2001;74:796–802.[Abstract/Free Full Text]

24. Neuhouser ML, Rock CL, Eldridge AL, Kristal AR, Patterson RE, Cooper DA, Neumark-Sztainer D, Cheskin LJ, Thornquist MD. Serum concentrations of retinol, alpha-tocopherol and the carotenoids are influenced by diet, race and obesity in a sample of healthy adolescents. J Nutr. 2001;131:2184–91.[Abstract/Free Full Text]

25. White E, Kristal AR, Shikany JM, Wilson AC, Chen C, Mares-Perlman JA, Masaki KH, Caan BJ. Correlates of serum alpha- and gamm-tocopherol in the Women's Health Initiative. Ann Epidemiol. 2001;11:136–44.[Medline]

26. Ocke MC, Kaaks RJ. Biochemical markers as additional measurements in dietary validity studies: application of the method of triads with examples from the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr. 1997;65:1240S–5S.[Medline]

27. EPIC Group of Spain. European Prospective Investigation into Cancer and Nutrition. Relative validity and reproducibility of a diet history questionnaire in Spain. III. Biochemical markers. Int J Epidemiol. 1997;26: Suppl 1:S110–7.[Abstract/Free Full Text]

28. Resnicow K, Odom E, Wang T, Dudley WN, Mitchell D, Vaughan R, Jackson A, Baranowski T. Validation of three food frequency questionnaires and 24-hour recalls with serum carotenoid levels in a sample of African American adults. Am J Epidemiol. 2000;152:1072–80.[Abstract/Free Full Text]

29. Kabagambe EK, Baylin A, Allan DA, Siles X, Spiegelman D, Campos H. Application of the method of triads to evaluate the performance of food frequency questionnaires and biomarkers as indicators of long-term dietary intake. Am J Epidemiol. 2001;154:1126–35.[Abstract/Free Full Text]

30. Tangney CC, Bienias JL, Evans DA, Morris MC. Reasonable estimates of serum vitamin E, vitamin C, and beta-cryptoxanthin are obtained with a food frequency questionnaire in older black and white adults. J Nutr. 2004;134:927–34.[Abstract/Free Full Text]

31. Shai I, Rosner BA, Shahar DR, Vardi H, Azrad AB, Kanfi A, Schwarzfuchs D, Fraser D. DEARR study. Dietary evaluation and attenuation of relative risk: multiple comparisons between blood and urinary biomarkers, food frequency, and 24-hour recall questionnaires: the DEARR study. J Nutr. 2005;135:573–9.[Abstract/Free Full Text]

32. Natarajan L, Flatt SW, Sun X, Gamst AC, Major JM, Rock CL, Al-Delaimy W, Thomson CA, Newman VA, et al. Validity and systematic error in measuring carotenoid consumption with dietary self-report instruments. Am J Epidemiol. 2006;163:770–8.[Abstract/Free Full Text]

33. Yong LC, Forman MR, Beecher GR, Graubard BI, Campbell WS, Reichman ME, Taylor PR, Lanza E, Holden JM, Judd JT. Relationship between dietary intake and plasma concentrations of carotenoids in premenopausal women: application of the USDA-NCI carotenoid food-composition database. Am J Clin Nutr. 1994;60:223–30.[Abstract/Free Full Text]

34. Bingham SA, Gill C, Welch A, Cassidy A, Runswick SA, Oakes S, Lubin R, Thurnham DI, Key TJ, et al. Validation of dietary assessment methods in the UK arm of EPIC using weighed records, and 24-hour urinary nitrogen and potassium and serum vitamin C and carotenoids as biomarkers. Int J Epidemiol. 1997;26:S137–51.[Abstract/Free Full Text]

35. Daures JP, Gerber M, Scali J, Astre C, Bonifacj C, Kaaks R. Validation of a food-frequency questionnaire using multiple-day records and biochemical markers: application of the triads method. J Epidemiol Biostat. 2000;5:109–15.[Medline]

36. Brunner E, Stallone D, Juneja M, Bingham S, Marmot M. Dietary assessment in Whitehall II: comparison of 7 d diet diary and food-frequency questionnaire and validity against biomarkers. Br J Nutr. 2001;86:405–14.[Medline]

37. McNaughton SA, Marks GC, Gaffney P, Williams G, Green A. Validation of a food-frequency questionnaire assessment of carotenoid and vitamin E intake using weighed food records and plasma biomarkers: the method of triads model. Eur J Clin Nutr. 2005;59:211–8.[Medline]

38. Bingham SA, Luben R, Welch A, Wareham N, Khaw KT, Day N. Are imprecise methods obscuring a relation between fat and breast cancer? Lancet. 2003;362:212–4.[Medline]

39. Tooze JA, Midthune D, Dodd KW, Freedman LS, Krebs-Smith SM, Subar AF, Guenther PM, Kipnis V. A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc, in press.

40. van Kappel AL, Steghens JP, Zeleniuch-Jacquotte A, Chajes V, Toniolo P, Riboli E. Serum carotenoids as biomarkers of fruit and vegetable consumption in the New York Women's Health Study. Public Health Nutr. 2001;4:829–35.[Medline]

41. Rock CL, Thornquist MD, Neuhouser ML, Kristal AR, Neumark-Sztainer D, Cooper DA, Patterson RE, Cheskin LJ. Diet and lifestyle correlates of lutein in the blood and diet. J Nutr. 2002;132:525S–30S.[Abstract/Free Full Text]

42. Willett WC, Stampfer MJ, Underwood BA, Speizer FE, Rosner B, Hennekens CH. Validation of a dietary questionnaire with plasma carotenoid and alpha-tocopherol levels. Am J Clin Nutr. 1983;38:631–9.[Abstract/Free Full Text]

43. Ritenbaugh C, Peng YM, Aickin M, Graver E, Branch M, Alberts DS. New carotenoid values for foods improve relationship of food frequency questionnaire intake estimates to plasma values. Cancer Epidemiol Biomarkers Prev. 1996;5:907–12.[Abstract]

44. Michaud DS, Giovannucci EL, Ascherio A, Rimm EB, Forman MR, Sampson L, Willett WC. Associations of plasma carotenoid concentrations and dietary intake of specific carotenoids in samples of 2 prospective cohort studies using a new carotenoid database. Cancer Epidemiol Biomarkers Prev. 1998;7:283–90.[Abstract]

45. Subar AF, Kipnis V, Troiano RP, Midthune D, Schoeller DA, Bingham S, Sharbaugh CO, Trabulsi J, Runswick S, et al. Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN study. Am J Epidemiol. 2003;158:1–13.[Abstract/Free Full Text]

46. Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP, Bingham S, Schoeller DA, Schatzkin A, Carroll RJ. Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol. 2003;158:14–21.[Abstract/Free Full Text]

47. Fraser GE, Butler TL, Shavlik D. Correlations between estimated and true dietary intakes: using two instrumental variables. Ann Epidemiol. 2005;15:509–18.[Medline]

48. Kaaks R, Ferrari P. Dietary intake assessments in epidemiology: can we know what we are measuring? Ann Epidemiol. 2006;16:377–80.[Medline]

This article has been cited by other articles:

				L. C. Vinikoor, J. C. Schroeder, R. C. Millikan, J. A. Satia, C. F. Martin, J. Ibrahim, J. A. Galanko, and R. S. Sandler Consumption of trans-Fatty Acid and Its Association with Colorectal Adenomas Am. J. Epidemiol., August 1, 2008; 168(3): 289 - 297. [Abstract] [Full Text] [PDF]

				A. R. Kristal, K. B. Arnold, J. M. Schenk, M. L. Neuhouser, P. Goodman, D. F. Penson, and I. M. Thompson Dietary Patterns, Supplement Use, and the Risk of Symptomatic Benign Prostatic Hyperplasia: Results from the Prostate Cancer Prevention Trial Am. J. Epidemiol., April 15, 2008; 167(8): 925 - 934. [Abstract] [Full Text] [PDF]

				S. A. Talegawkar, E. J. Johnson, T. Carithers, H. A. Taylor Jr., M. L. Bogle, and K. L. Tucker Total {alpha}-Tocopherol Intakes Are Associated with Serum {alpha}-Tocopherol Concentrations in African American Adults J. Nutr., October 1, 2007; 137(10): 2297 - 2303. [Abstract] [Full Text] [PDF]

This Article Abstract Full Text (PDF) Purchase Article View Shopping Cart Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Dixon, L. B. Articles by Potischman, N. Search for Related Content PubMed Articles by Dixon, L. B. Articles by Potischman, N.