Biochemical Validation of Food Frequency Questionnaire-Estimated Carotenoid, α-Tocopherol, and Folate Intakes Among African Americans and Non-Hispanic Whites in the Southern Community Cohort Study

Abstract

Few food frequency questionnaires (FFQs) have been developed specifically for use among African Americans, and reports of FFQ performance among African Americans or low-income groups assessed using biochemical indicators are scarce. The authors conducted a validation study within the Southern Community Cohort Study to evaluate FFQ-estimated intakes of α-carotene, β-carotene, β-cryptoxanthin, lutein/zeaxanthin, lycopene, folate, and α-tocopherol in relation to blood levels of these nutrients. Included were 255 nonsmoking participants (125 African Americans, 130 non-Hispanic whites) who provided a blood sample at the time of study enrollment and FFQ administration in 2002–2004. Levels of biochemical indicators of each micronutrient (α-tocopherol among women only) significantly increased with increasing FFQ-estimated intake (adjusted correlation coefficients: α-carotene, 0.35; β-carotene, 0.28; β-cryptoxanthin, 0.35; lutein/zeaxanthin, 0.28; lycopene, 0.15; folate, 0.26; α-tocopherol, 0.26 among women; all P's < 0.05). Subjects in the top decile of FFQ intake had blood levels that were 27% (lycopene) to 178% (β-cryptoxanthin) higher than those of subjects in the lowest decile. Satisfactory FFQ performance was noted even for participants with less than a high school education. Some variation was noted in the FFQ's ability to predict blood levels for subgroups defined by race, sex, and other characteristics, but overall the Southern Community Cohort Study FFQ appears to generate useful dietary exposure rankings in the cohort.

The ongoing study of nutrition and disease implicates fruit and vegetable intake as potentially protective for major public health concerns such as cancer and heart disease (1, 2). These foods contain a number of components that may help lower the risk of disease, including vitamin E (a potent antioxidant), carotenoids (which may exert health-promoting effects through in vivo conversion to vitamin A or by other means), and folate (a water-soluble vitamin that may modulate DNA methylation and decrease homocysteine levels) (3–6). The properties and activities of these compounds are varied and include the ability to protect cells from free radical damage, decrease inflammation, promote cellular differentiation, and mediate DNA stability, integrity, and repair (3–7). Racial disparities in disease risk may in part be related to protective dietary exposures that differ across racial/ethnic groups. It is important to test such hypotheses because diet is amenable to intervention and modification, and studies designed to better understand diet's role in the excess burden of chronic disease among various racial/ethnic groups, including African Americans, are under way (8, 9).

In large-scale epidemiologic studies, an efficient means of collecting data on general or long-term diet is the use of a food frequency questionnaire (FFQ). The measurement error associated with FFQs has been much discussed (10–12), although FFQs usually appear able to rank participants reasonably well according to their intake levels (13). Validation of FFQ performance to determine how well an FFQ estimates nutrient intake as compared with a “gold standard” is often accomplished by comparison with diet diaries or repeated 24-hour recalls, although the errors associated with these modes of evaluation are likely to be correlated with those from the FFQ, since biased self-reporting may track across research instruments (14). Thus, biochemical indicators measured in the blood (errors in which are assumed to be independent of self-reports (14)) can be a valuable means of validating FFQ intake for nutrients whose circulating levels are responsive to intake and are not homeostatically regulated (15).

Few FFQs have been developed specifically for use in African-American populations, and the body of literature on FFQ performance in this group is relatively small, though growing (16–18). Validation reports for the performance of FFQs among African Americans using biochemical indicators are fairly scarce (19–24) and are further limited by having restrictions based on sex, body size, or diseases or other conditions (e.g., cancer, pregnancy) that may affect the interpretation or generalizability of the findings (19, 21, 22, 24). Moreover, few reports in the literature include (more than incidentally) persons with low incomes or low levels of education (21), although the importance of nutritional studies in these groups (including those related to dietary instrument evaluation) is crucial given their disproportionate burden of disease (25). Our goal in the present study was to evaluate the performance of the Southern Community Cohort Study (SCCS) FFQ among African-American and non-Hispanic white participants using blood levels of nutrients known to be sensitive to intake and potentially important in studies of racial and socioeconomic health disparities (α-carotene, β-carotene, β-cryptoxanthin, lutein/zeaxanthin, lycopene, folate, and α-tocopherol).

MATERIALS AND METHODS

Study population

The SCCS is a prospective cohort investigation of cancer and other chronic diseases comprised primarily of African-American and non-Hispanic white residents of the southeastern United States (8). An explicit study aim is to investigate the potential role of dietary exposures in health disparities. Since 2002, more than 73,000 SCCS participants (males and females aged 40–79 years) have been recruited at community health centers across Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, and West Virginia. Because community health centers are located in underresourced areas, the communities they serve are generally low-income; the majority (62%) of SCCS participants enrolled at community health centers have annual household incomes less than $15,000. At the time of enrollment, participants completed a baseline in-person interview that included a dietary assessment via FFQ, and approximately 92% provided either a baseline blood sample (53%) or a buccal cell sample (39%).

Assessment of dietary intake

Average dietary intake (with reference to the year preceding enrollment) was assessed using an 89-item FFQ administered through a computer-assisted in-person interview conducted in the community health center. This FFQ has been described in detail elsewhere (26, 27) and was developed for the SCCS in order to capture all major foods and main sources of energy and key nutrient intake for African Americans and non-African Americans in the South. Of the 89 items, 9 are specific to fruits or fruit juices, 13 are specific to vegetables, and many other items (e.g., for mixed foods) could include fruit or vegetable components. Participants selected from 9 frequency categories for each FFQ item: “never,” “rarely,” “1/month,” “2–3/month,” “1/week,” “2–3/week,” “4–6/week,” “1/day,” and “2+/day.” The SCCS FFQ does not elicit information about portion size, but it assigns sex- and race-specific average portion size estimates (27). Information on the use of vitamin supplements, including multivitamins, vitamin E, and folic acid, was also collected.

Nutrient estimation was accomplished using sex- and race-specific nutrient databases developed for the SCCS that were based on dietary patterns in the southern United States (27). From the FFQ, we thus calculated estimates of average daily intake for the 5 major carotenoids (α-carotene (μg), β-carotene (μg), β-cryptoxanthin (μg), lutein and zeaxanthin combined (μg), and lycopene (μg)), folate (as dietary folate equivalents (μg)), and α-tocopherol (mg) to compare them with biochemical indicators. These values reflect only food sources of the nutrients, not supplement sources.

Sample selection and measurement of biochemical indicators

In early 2005, from participants who joined the SCCS between March 2002 and October 2004 and donated a baseline blood sample at enrollment (n = 12,162), 396 were randomly selected using a 2 × 2 × 3 × 3 factorial design, with 11 persons being selected within each of the 36 strata defined by race (African-American/white), sex, smoking status (current/former/never), and body mass index (weight (kg)/height (m)²; 18–24.99, 25–29.99, or 30–45). At the time of the baseline interview, these participants had provided a 20-mL nonfasting venous blood sample that was kept refrigerated and, generally within 24 hours, was centrifuged, separated into components, and stored at −86°C to await analysis. At the time of assay, the specimens had been stored for a period of up to 3.0 years (median, 1.6 years), and none had been previously thawed. Because of the known changes in blood carotenoid levels induced by active smoking (28, 29), we restricted the present analysis to never or former smokers (n = 264).

Plasma α-carotene, β-carotene, β-cryptoxanthin, lutein plus zeaxanthin, lycopene, and α-tocopherol levels were measured using an assay based on high performance liquid chromatography that has been described previously (30). The assay was a modification of the method of Bieri et al. (31), with calibration as described by Craft et al. (32) and sample handling as described by Gross et al. (33). Calibration was performed with pure compounds (Hoffmann-La Roche, Nutley, New Jersey; Sigma Chemical Company, St. Louis, Missouri). Quality-control procedures included routine analysis of plasma and serum control pools containing high and low concentrations of each analyte. In addition, the laboratory routinely analyzed National Institute of Standards and Technology reference sera and was a participant in the National Institute of Standards and Technology Fat-Soluble Vitamin Quality Assurance Group. The coefficients of variation were less than 10% for all analytes and control pools. The intraclass correlation coefficients for this method have been shown to be 0.93 for α-carotene, 0.98 for β-carotene, 0.73 for lutein plus zeaxanthin, 0.97 for β-cryptoxanthin, 0.73 for lycopene, and 0.93 for α-tocopherol (34). Serum folate levels were analyzed using a microbiologic assay as described previously (35, 36).

Statistical analysis

Among the 264 subjects sampled for this study, 9 had either more than 10 missing FFQ items, an estimated total energy intake outside the set allowable range (600–8,000 kcal/day, which excludes the approximate top and bottom 2.5% of all SCCS participants), or missing laboratory values for each nutrient and were thus excluded from analyses. Of the remaining 255 participants, nearly all (n = 249; 98%) had complete FFQ data, with 2% having missing answers for 1–10 food items. Missing data were not imputed and contributed zero nutrient values.

Mean blood levels of the 7 nutrients were calculated and compared across race groups using 2-sample t tests. For the purposes of calculating correlation coefficients, the FFQ and biochemical indicator values were either square root-transformed (plasma lycopene) or natural log-transformed (all other FFQ and blood nutrients) to improve normality. Pearson correlation coefficients assessing the linear association between the FFQ and blood values were calculated for the total study sample, as well as separately by race, sex, educational status, obesity status, and vitamin supplement usage. The Fisher z-transform method of comparing independent correlations was used to test whether correlation coefficients were statistically different across these strata. We calculated partial correlation coefficients to adjust the estimates for relevant confounders identified from the literature (15, 21, 37–40): age (years; continuous), sex, race (African-American/white), total energy intake (kcal/day; continuous), body mass index (continuous), use of vitamin supplements (yes/no; answer to the question, “During the past year, have you taken any vitamin, mineral, herbal, or other nutritional supplements at least once a month?”), physical activity (total activity in metabolic equivalent-hours/day; continuous), education (less than high school, high school, more than high school), total serum cholesterol (mg/dL; continuous), serum triglycerides (mg/dL; continuous), current use of cholesterol-lowering medication (yes/no), and alcohol consumption (drinks/week).

To further evaluate the associations between the biochemical indicators and FFQ-estimated intakes, we compared the mean blood values for each nutrient across quintiles of FFQ intake. To facilitate comparisons with earlier studies conducted in African Americans (19, 22), we also calculated the mean blood levels for the top and bottom deciles of FFQ intake and compared levels between these extremes of intake. We also used linear regression models to estimate the change in blood value as a function of FFQ intake, adjusted for the set of confounders noted above. Statistical analyses were performed using Stata, version 10 (Stata Corporation, College Station, Texas).

RESULTS

Characteristics of the sampled subjects, along with their average nutrient intake values from the FFQ and measured blood values, are presented in Table 1. More than half (55%) of the participants had a total household income less than $15,000 per year, and only 20% had a household income greater than or equal to $25,000 per year. Approximately one-third (31%) had less than a high school education, 40% a high school education or its equivalent, and 29% had some education beyond high school. Half (50%) of the subjects reported at least monthly use of some sort of nutritional supplement. With regard to specific vitamin supplementation likely to affect our analyses, 40% (n = 102) of the subjects reported taking a multivitamin at least once per week, 20% (n = 51) reported taking a vitamin E supplement at least once per week, and 7% (n = 18) reported taking a folic acid supplement at least once per week, with the majority (>80%) of these supplement users reporting daily use (not shown).

Table 1.

Sociodemographic and Dietary Characteristics of Participants and Mean Blood Levels of Selected Nutrients, by Race and Sex, Southern Community Cohort Study, 2002–2004

Characteristic	African Americans (n = 125)						Whites (n = 130)
	Females (n = 63)			Males (n = 62)			Females (n = 64)			Males (n = 66)
	No.	%	Mean (SD)	No.	%	Mean (SD)	No.	%	Mean (SD)	No.	%	Mean (SD)
Age, years
40–49	25	39.7		32	51.6		18	28.1		23	34.8
50–59	20	31.7		17	27.4		22	34.4		20	30.3
≥60	18	28.6		13	21.0		24	37.5		23	34.8
Education
Less than high school	17	27.0		25	40.3		16	25.0		20	30.3
High school or equivalent	33	52.4		19	30.7		30	46.9		20	30.3
More than high school	13	20.6		18	29.0		18	28.1		26	39.4
Annual household income
<$15,000	34	54.0		36	60.0		38	60.3		32	48.5
$15,000–$24,999	15	23.8		15	25.0		13	20.6		17	25.8
≥$25,000	14	22.2		9	15.0		12	19.1		17	25.8
Obesea		33			34			33			33
Taking a vitamin supplementb		51			44			53			52
No. of alcoholic drinks consumed per week			1.0 (4.7)			12.2 (29.8)			0.8 (2.6)			6.7 (18.5)
Serum total cholesterol levelc, mg/dL			203.6 (41.3)			195.2 (41.7)			223.8 (48.6)			203.6 (38.4)
Serum triglyceride levelc, mg/dL			100.5 (38.9)			149.6 (108.3)			215.6 (175.7)			228.3 (150.1)
Current use of cholesterol-lowering medication		8			21			20			23
Current use of hormone replacement therapy		13						25
FFQ-estimated dietary intaked
α-Carotene, μg/day			686.0* (787.5)			556.2 (533.1)			419.0* (364.3)			572.2 (483.9)
β-Carotene, μg/day			5,820.8* (5,119.8)			6,212.2* (5,750.7)			3,203.4* (1,952.5)			3,617.0* (2,656.1)
β-Cryptoxanthin, μg/day			264.1* (213.5)			298.6* (263.4)			160.5* (164.0)			175.7* (150.6)
Lutein and zeaxanthin, μg/day			5,025.2* (4,934.4)			5,497.4* (5,769.8)			2,223.4* (1,375.1)			2,583.7* (2,199.7)
Lycopene, μg/day			4,892.1 (5,301.7)			6,994.7 (5,098.4)			4,050.9 (2,979.3)			6,949.9 (5,299.3)
Folate (dietary folate equivalents), μg/day			565.2 (293.8)			826.5 (450.8)			529.3 (223.1)			693.2 (403.8)
α-Tocopherol, mg/day			7.9 (4.5)			10.6 (5.7)			6.9 (2.4)			9.1 (5.2)
Blood levels of biochemical indicators
Plasma α-carotene, μg/dL			4.4* (4.8)			2.7 (2.6)			2.7* (2.0)			3.7 (4.8)
Plasma β-carotene, μg/dL			21.3* (20.6)			13.1 (11.1)			13.8* (12.6)			11.2 (9.9)
Plasma β-cryptoxanthin, μg/dL			10.7* (6.7)			8.2 (5.6)			6.4* (4.3)			6.9 (4.1)
Plasma lutein and zeaxanthin, μg/dL			21.8* (10.4)			20.9* (10.0)			14.3* (6.3)			15.3* (7.0)
Plasma lycopene, μg/dL			28.5 (12.7)			33.4 (17.2)			31.1 (13.6)			33.8 (14.8)
Serum folate, ng/mL			16.0 (10.0)			13.9 (6.7)			17.1 (10.4)			15.7 (10.0)
Plasma α-tocopherol, mg/dL			1.2 (0.6)			1.1* (0.3)			1.5 (0.8)			1.4* (0.7)

Abbreviations: FFQ, food frequency questionnaire; SD, standard deviation.

^*P < 0.05 (2-sample t test comparing mean values by race within each sex).

^aObesity was defined as a body mass index (weight (kg)/height (m)²) ≥30 (sampling variable; see Materials and Methods).

^bDefined as a positive answer to the question, “During the past year, have you taken any vitamin, mineral, herbal, or other nutritional supplements at least once a month?”

^cNonfasting.

^dCalculated only among participants with nonmissing blood measurements for the corresponding blood marker.

With the exception of α-tocopherol, the crude correlation coefficients indicated significant positive associations between FFQ-estimated nutrient values and the biochemical indicators, ranging from 0.18 (lycopene) to 0.37 (β-cryptoxanthin) (Table 2). Covariate adjustment resulted in minor changes in the correlations, with the strongest adjusted associations being observed for α-carotene (0.35) and β-cryptoxanthin (0.35). Further adjustment for total fat intake (FFQ-estimated) and frozen blood storage time did not alter the estimates (not shown). After excluding persons who used multivitamins at least once per week and after excluding users of vitamin E and folic acid from the α-tocopherol and folate analyses, respectively, the adjusted correlation coefficients were not appreciably changed (0.32 for α-carotene, 0.27 for β-carotene, 0.31 for β-cryptoxanthin, 0.26 for lutein/zeaxanthin, 0.08 for lycopene, 0.27 for folate, and 0.06 for α-tocopherol); therefore, this restriction was not made for the remaining analyses.

Table 2.

Partial Correlation Coefficients^a Estimating the Strength of the Linear Association Between Food Frequency Questionnaire-Estimated Dietary Intakes and Measurements of Blood Levels of Selected Nutrients, by Participant Characteristics, Southern Community Cohort Study, 2002–2004

	α-Carotene	β-Carotene	β-Cryptoxanthin	Lutein/Zeaxanthin	Lycopene	Folate	α-Tocopherol
Total study group
Crude correlation coefficient	0.32***	0.25***	0.37***	0.35***	0.18**	0.19**	−0.05
Adjusteda correlation coefficient	0.35***	0.28***	0.35***	0.28***	0.15*	0.26***	0.09
Characteristic (adjusteda correlation coefficient)
Race
African-American	0.44***	0.35***	0.41***	0.23*	0.06b	0.16	0.09
White	0.23*	0.23*	0.29**	0.32***	0.31b,**	0.32***	0.09
Sex
Male	0.23b,*	0.17	0.34***	0.26**	0.23*	0.28**	−0.04b
Femalec	0.47b,***	0.41***	0.37***	0.35***	0.11	0.22*	0.26b,**
Education
Less than high school	0.39**	0.33**	0.50***	0.04b	0.04	0.39b,**	0.16
High school graduation	0.34**	0.24*	0.23*	0.40b,***	0.18	0.01b	0.07
More than high school	0.39**	0.48***	0.37**	0.44b,**	0.22	0.51b,***	0.01
Obesity status
Obesed	0.31*	0.24	0.39**	0.45***	0.32*	0.24*	0.00
Nonobese	0.38***	0.33***	0.42***	0.21**	0.12	0.29***	0.15
Vitamin supplement use
Usere	0.37***	0.35***	0.41***	0.26**	0.21*	0.34***	0.12
Nonuser	0.31**	0.24*	0.29**	0.26**	0.08	0.15	0.04

* P < 0.05; **P < 0.01; ***P < 0.001.

^aAdjusted for age, sex, race, total energy intake, body mass index, use of vitamin supplements, physical activity, education, serum cholesterol level, serum triglyceride level, use of cholesterol-lowering medication, and alcohol consumption.

^bCorrelation coefficients were statistically significantly different across strata at the P < 0.05 level.

^cCorrelation coefficients for women were further adjusted for current use of hormone replacement therapy.

^dObesity was defined as a body mass index (weight (kg)/height (m)²) ≥30.

^eDefined as a positive answer to the question, “During the past year, have you taken any vitamin, mineral, herbal, or other nutritional supplements at least once a month?”

When the correlation coefficients were stratified by participant characteristics, some variation was evident, although differences tended not to be large or significant (Table 2). Across strata of race, correlations were somewhat higher among African Americans for α-carotene, β-carotene, and β-cryptoxanthin but higher among whites for lutein/zeaxanthin, lycopene, and folate. The only racial difference of statistical significance was for lycopene, where the lack of correlation among African Americans (r = 0.06) was primarily due to a null association among African-American women (r = 0.00, P = 0.98), since we observed a nonsignificant positive association for African-American men (r = 0.19, P = 0.20). Overall, the lack of association between FFQ and plasma α-tocopherol was restricted to men (r = 0.26 for women, −0.04 for men). Except for lycopene and lutein/zeaxanthin, we did not observe a monotonic trend with correlations increasing across higher levels of education. While it was true that for most of the nutrients (α-carotene, β-carotene, lutein/zeaxanthin, lycopene, and folate), the highest correlations were observed for subjects with more than a high school education, associations among those with less than a high school education were also generally strong and significant, sometimes more so than among subjects with a high school education. No significant heterogeneity of the correlations was observed across strata of obesity or vitamin supplement usage.

For each FFQ-determined quintile of intake, we computed and plotted both the average FFQ-estimated intake and the average biochemical indicator level to graphically display the relation between the 2 values (Figure 1). With the exception of α-tocopherol, these graphs display clear positive associations between the FFQ and blood values, with relatively linear associations for α-carotene, β-carotene, β-cryptoxanthin, and lutein/zeaxanthin. For lycopene and folate, the increases in blood levels appeared to level off after the third or fourth quintile of FFQ-estimated intake. We also calculated the mean blood nutrient levels at the extreme deciles of FFQ-estimated intake (Table 3), noting statistically significant differences for all nutrients except α-tocopherol. Blood levels (of α-carotene, β-carotene, β-cryptoxanthin, lutein/zeaxanthin, lycopene, and folate) for subjects in the top decile of FFQ intake were 27%–178% higher than those for subjects in the lowest decile.

Figure 1.

Plots of the average levels of selected biochemical indicators (y-axis) across quintiles of food frequency questionnaire (FFQ)-estimated dietary intake (x-axis), Southern Community Cohort Study, 2002–2004. Points represent the average FFQ-estimated intake and the average biochemical indicator level for each FFQ-determined quintile of intake. Superimposed is the predicted line from a univariate linear regression model with FFQ-estimated intake as the independent variable and the biochemical indicator as the dependent variable.

Table 3.

Mean Blood Nutrient Levels for Subjects in the Top and Bottom Deciles of Food Frequency Questionnaire-Estimated Dietary Intake of Selected Nutrients, Southern Community Cohort Study, 2002–2004

Nutrient	Mean Blooda Nutrient Level		Increase From Bottom Decile to Top Decile, %
	Top Decile of FFQ Intake	Bottom Decile of FFQ Intake
α-Carotene, μg/dL	4.3 (5.4)b	1.8 (1.9)	136
β-Carotene, μg/dL	18.0 (19.8)	10.9 (11.9)	65
β-Cryptoxanthin, μg/dL	12.6 (8.6)	4.5 (2.2)	178
Lutein/zeaxanthin, μg/dL	23.4 (11.3)	12.2 (6.0)	92
Lycopene, μg/dL	33.2 (16.3)	26.2 (13.8)	27
Folate, ng/mL	16.7 (7.5)	11.4 (6.8)	46
α-Tocopherol, mg/dL	1.3 (0.8)	1.2 (0.5)	7c

Abbreviation: FFQ, food frequency questionnaire.

^aAll biochemical indicators were measured in plasma, except for folate, which was measured in serum.

^bNumbers in parentheses, standard deviation.

^c38% for women; −6% for men.

Multivariate linear regression models predicted increases in blood levels of 3.02 μg/dL (P ≤ 0.001) for α-carotene, 13.9 μg/dL for β-carotene (P < 0.001), 5.5 μg/dL for β-cryptoxanthin (P ≤ 0.001), 8.2 μg/dL for lutein/zeaxanthin (P < 0.001), 5.6 μg/dL for lycopene (P = 0.16), 9.3 ng/mL for folate (P = 0.001), and 0.3 mg/dL for α-tocopherol (P = 0.15) for the top quintile of FFQ intake versus the bottom quintile for these nutrients.

DISCUSSION

In this stratified random sample of SCCS participants, we found that biochemical indicators of each of the 5 major carotenoids, folate, and α-tocopherol (the latter only among women) significantly increased with increasing FFQ-estimated intake. The correlations we observed were within the ranges noted by other investigators using different FFQs administered in populations across the United States and other countries (19–24, 38–47). Although modest in magnitude (all correlation coefficients presented were less than or equal to 0.51), the positive associations were clear and demonstrable across quantiles of FFQ-estimated intake, providing evidence that the SCCS FFQ generates useful information about the relative intake of these nutrients and would allow reasonable exposure ranking in our cohort. The correlations we observed were probably affected (underestimated) by the fact that FFQ estimates were based on food sources only, whereas biochemical indicators would also have reflected dietary supplement exposure. However, as we noted above, analyses restricted to subjects not reporting supplement use were similar to our overall analyses. Subgroup analyses raised the possibility that FFQ estimates were not predictive of blood levels of α-tocopherol for African-American and white men or of lycopene for African-American women, suggesting a cautious approach to the use of these specific data for these subgroups. However, the multiple testing, the smaller subgroup numbers, and the likelihood of extraneous factors’ influencing blood levels and diminishing our ability to detect correlations (see discussion below) limits any conclusive inferences in this regard.

A relative strength of the current study was the inclusion of both African-American and white participants, which allowed evaluation of the FFQ in both groups. To our knowledge, very few studies have contrasted FFQ performances between African Americans and whites using biochemical indicators (22, 24). Our study supports these earlier studies in finding little evidence for systematic variation in FFQ performance by race. Our findings should also be generalizable to the entire SCCS, because 1) we employed a randomly selected group as opposed to calibration study volunteers, who may pay enhanced attention to the detail of their FFQ reporting, and 2) within race and sex groups, SCCS participants who provided blood samples were similar to those who did not.

The relatively high performance of the FFQ among participants with less than a high school education (one-third of whom had fewer than 9 years of education) was somewhat unexpected. The FFQ has been criticized because it can require complex cognitive estimation with regard to combined food items or mathematical calculations in order to arrive at reports of “average” intake. Although this is speculation, it may be that the diets of low-income persons with this level of education are more restricted, with the resulting lack of variation resulting in simpler FFQ reporting. A review of FFQ responses by educational level did reveal that of the 89 FFQ items, subjects with less than a high school education answered “never” for an average of 20.3 items, as compared with a significantly (P < 0.001) lower average of 14.4 items for subjects with a high school education or greater. The in-person, as opposed to self-administered, nature of the dietary interview may also have improved the accuracy of the collected data.

Among the carotenoids, the weakest overall association we observed between FFQ and plasma values was for lycopene, due to the lack of association (r = 0.00) among African-American women (correlations among white men, white women, and African-American men were 0.39, 0.25, and 0.19, respectively). This variation may have been due to chance, although in previous studies of African Americans, investigators have also reported poor correspondence between blood and FFQ-measured lycopene values (19–23), while in studies of predominantly Caucasian populations, investigators have reported correlations in the range of approximately 0.15–0.45 (38, 40, 41, 43). Lycopene is derived primarily from tomatoes and tomato products, with bioavailability being greatest for tomatoes that have been heat-processed (e.g., tomato sauce, ketchup) (48). There is some evidence that circulating lycopene is less responsive to dietary intake than other carotenoids (48), which could help explain its poor concordance with FFQ measurements, though not the suggested racial differences in findings.

We detected no linear correlation between FFQ-estimated and plasma α-tocopherol levels for men, nor did we observe a positive difference in mean plasma levels when comparing the top decile of FFQ-estimated intake with the bottom decile. This is in contrast to a recent report including adult African-American men, where participants in the top decile of FFQ intake for α-tocopherol had plasma α-tocopherol levels 100% higher than those of participants in the lowest decile (although plasma levels did not appreciably rise until the ninth/10th deciles) (19). Some earlier studies (37–39), but not all (44, 46), documented positive correlations between FFQ-estimated and plasma α-tocopherol levels in (non-African-American) men. In a report from the European Prospective Investigation into Cancer and Nutrition, correlations with plasma α-tocopherol varied widely and, for 5 of the 6 countries included, were low (<0.20) for men, women, or both (49). Nuts, seeds, and vegetable oils are primary sources of α-tocopherol, with substantial variation in content across similar foods (e.g., sunflower oil contains 3 times as much as corn oil, and dry-roasted almonds contain more than 3 times as much as dry-roasted peanuts (50)), and the SCCS FFQ combines peanuts with “other nuts” in 1 FFQ item and does not distinguish between types of cooking oils. We therefore expected some level of measurement error, though not the strong sex-based difference we observed. It is unlikely that our vitamin E findings stemmed from sex differences in supplement use, since more women (24%) than men (16%) reported vitamin E supplement use, and removing users of multivitamin and vitamin E supplements from the analysis did not affect the sex difference (correlation coefficients of −0.06 for men and 0.21 for women).

Blood levels of carotenoids (7, 51–54), folate (55, 56), and α-tocopherol (51, 54, 57) are responsive to intake, but circulating levels probably reflect more recent intake as opposed to long-term intake (37, 53), which our FFQ queried about (past year). Numerous exogenous factors (e.g., tobacco use, alcohol intake, medication use, other components of the diet) can also affect blood levels of our studied nutrients and interfere with the desired comparisons with FFQ values (15), although we were able to account for some of these factors by restriction or statistical adjustment. Nutrient absorption and metabolism can also vary substantially in normal subjects (52, 58). We would expect the net effect of these measurement issues (as well as random laboratory measurement error) to be attenuation of the correlations between biochemical indicators and the FFQ-estimated values (14). We thus consider the observed correlations to be lower-bound estimates of the ability of the SCCS FFQ to measure these particular nutrients.

A validation study should compare the measures of interest (in this case, FFQ-estimated intake) with a “gold standard” objective true value, or a close proxy. Although biochemical indicators serve a useful purpose in avoiding correlated errors between FFQs and other self-reported measures (such as 24-hour recalls), a single nonfasting blood sample has limitations as an ideal gold standard, although it is an acceptable surrogate (15). A recent report by Dixon et al. (38) also offers some indication that multiple blood samples would not necessarily add benefit to these types of validation analyses. Overall, with a few subgroup exceptions, we were successful in using biochemical indicators to demonstrate the utility of the SCCS FFQ for ranking dietary intakes of several nutrients of interest in this racially diverse, low-income population. These findings will be useful in proceeding to SCCS studies of diet and disease, as well as in understanding the potential differences in FFQ performance by race, sex, education, and other subject characteristics.

Glossary

Abbreviations

FFQ

food frequency questionnaire

SCCS

Southern Community Cohort Study