182 Am J C/in Nzilr 1993;57:182-9. Printed in USA. © 1993 American Society for Clinical Nutrition

Comparison of micronutrient intake measured by a dietaryquestionnaire and biochemical indicators ofmicronutrient status13

Paul F Jacques, Sandra I 514/sky, James A Sadowski, Judy CC Phillips,

David Rush, and Walter C Willett

ABSTRACT We compared the intake of 12 micronutrients

as reported on a semiquantitative food frequency questionnaire

with corresponding biochemical indicators of nutrient status in

a sample of 57 males and 82 females aged 40-83 y. Age-, sex-

and energy-adjusted correlation coefficients ranged from near

zero for thiamin, vitamin A, and zinc to 0.63 for folate. Cor-

relation coefficients between intake and the biochemical mea-

sures were > 0.30 for carotenoids, vitamin D, vitamin E, vitamin

B- 12, folate, and vitamin C. Differences of 50% or more were

observed between extreme quartiles of intake for mean plasma

concentrations offolate, vitamin B-12, and vitamin C. Excluding

nutrient supplement users generally reduced the correlations.

These data demonstrate that food frequency questionnaires can

provide valid information on intake for a number of

micronutrients. Am J C/in Nutr 1993;57: 182-9.

KEY WORDS Blood, carotenoids, diet, minerals, nutrition

surveys, vitamins

Introduction

Micronutrient intake may play an important role in the etiol-

ogy of major diseases such as cancer and heart disease (1-7).

The large-scale studies that have suggested such relationships

rely on either reported dietary intake or simple biochemical

measures to assess nutrient intake or status. Because of the cx-

pense and difficulty of collecting and analyzing either blood

samples or multiple-day diet records in large numbers of free-

living subjects, investigators have come to depend more heavily

on food frequency questionnaires as a measure of nutrient intake

( 1 , 8- 1 2). Food frequency questionnaires are inexpensive and

fairly easy to administer but cost and ease of use are inconse-

quential if the method is not valid.

The validation of food frequency questionnaires is complicated

by the lack of an ideal standard. Diet records obtained over

many days are typically considered to be the referent standard

for many nutrients (9-12), but diet records may substantially

underestimate usual intake (1 3, 14) and recording diet may cause

individuals to alter intake (1 5). In spite of these shortcomings,

nutrient data derived from diet records and food frequency

questionnaires are usually reasonably correlated (9- 1 2), but

comparison of different methods of measuring dietary intake

may be complicated by correlated errors. For example, a ten-

dency to misreport food intake may be similar for different

methods of dietary assessment. Also, nutrient intake calculated

from different dietary methods are still based on similar under-

lying food composition data.

In contrast to the potential for correlated errors associated

with the different dietary assessment methods, errors in the es-

timation of nutrient status from dietary and biochemical mea-

sures are much more likely to be independent. Comparing such

dissimilar methods does not allow for direct validation: one

method measures intake: the other measures circulating con-

centrations that are influenced not only by intake but also by a

number of physiological and environmental factors. However,

for nutrients with biochemical markers that are responsive to

intake, a correlation between a reported dietary intake and an

objective biochemical measure can be interpreted as the lower

bound ofthe true questionnaire validity. Data relating nutrient

intake assessed by food frequency questionnaires to biochemical

measures of nutrient status are presently limited (1).

In this report, we compare the intakes of 1 2 micronutrients

estimated by a semiquantitative food frequency questionnaire

(9) with single measures of corresponding biochemical indicators

of nutrient status in 1 39 adults.

Methods

Subjects

Subjects were participants in a case-control study of nutritional

status and age-related cataracts ( 1 6). Blood samples and food

frequency questionnaires were available for 1 39 subjects. The

Human Investigations Review Committee at the New England

I From the US Department ofAgriculture Human Nutrition Centeron Aging at Tufts University, Boston.

2 Supported in part by federal funds from the US Department of Agri-

culture Agricultural Research Service under contract number 53-3K06-

5-10 and a grant from Hoffmann-LaRoche, Inc. The contents of this

publication do not necessarily reflect the views or policies of the USDepartment of Agriculture.

3 Address reprint requests to PF Jacques, USDA Human Nutrition

Research Center on Aging, Tufts University, 7 1 1 Washington Street,

Boston, MA 02111.

Received April 17, 1992.

Accepted for publication September 22, 1992.

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0 TE, cs-tocopherol equivalents.

t RBC, red blood cell: AC, activity coefficient.

NUTRIENT INTAKE AND BIOCHEMICAL MARKERS I 83

Medical Center and the Committee for the Protection of Human

Subjects from Research Risk at the Brigham and Women’s Hos-

pital approved all study procedures.

Nutritional asscssineizt

We used a 1 16-item semiquantitative food frequency ques-

tionnaire developed by Willett et al (9, 17) to assess usual dietary

intake for the previous year. Although it was designed to be self-

administered, a registered dietitian administered the food fre-

quency questionnaire in our study. A detailed history of nutrient

supplement use replaced the supplement information from the

food frequency questionnaire and included product name, fre-

quency and duration of use, dosage, and time since last use.

From this information we estimated average daily intake for the

year before the interview. In this report, we defined nutrient

intake as the sum of dietary and supplemental intake. We des-

ignated subjects as supplement users for a specific nutrient if,

over the past year, their average daily intake from supplements

was equal to or greater than one-third ofthe recommended di-

etary allowances ( 1 8) for that nutrient and the supplement was

taken within the past 2 wk. The food frequency questionnaire

estimated carotene intake as the total vitamin A activity from

plant sources and one-third the total vitamin A activity in dairy

fat ( 19), thus representing primarily fl-carotene and cs-carotene

intake.

Biochemical indicators ofnutrient status included plasma total

carotenoids (19), vitamin A (retinol) (20), vitamin E (a-tocoph-

erol) (20), vitamin D (25-hydroxycholecalciferol) (2 1 ), vitamin

C (total ascorbic acid) (22, 23), folate (Ciba-Corning Magnetic

Immunochemistries. Inc Medfield, MA), and vitamin B-12

(Ciba-Corning Magnetic Immunochemistries, Inc); red blood

cell (RBC) thiamin, riboflavin, and vitamin B-6 based on en-

zyme-activation tests (24); and serum magnesium (25) and

zinc (26).

Table 1 lists the nutrients measured in this study with the

corresponding biochemical indicators. Plasma concentrations

of the fat-soluble nutrients are adjusted for total cholesterol by

using residuals calculated by regressing plasma nutrients on

cholesterol concentrations to account for extraneous variation

in the measurement of fat-soluble nutrients ( 1 ). Cholesterol was

measured enzymatically by spectrophotometric determination

(27). Based on the observations ofKrall et al (28), we examined

the relationship between vitamin D intake and plasma vitamin

D concentrations in a subset ofindividuals who were seen during

the late winter and early spring (March to May) to adjust for

the effect of sunlight exposure on plasma vitamin D concentra-

tions.

Statistical anal vses

We classified values ofnutrient intakes and biochemical mea-

sures lying more than three interquartile ranges above the 95th

percentile (or below the fifth percentile) as extreme and poten-

tially influential outliers, which were not used in any analyses

(Tables 4 and 5). All nutrient intakes and the following bio-

chemical measures were extremely positively skewed, requiring

a logarithmic transformation: retinol, riboflavin, folate, and zinc.

Plasma carotenoids, vitamin E (for the entire sample), and vi-

tamin B-l2 displayed moderate positive skewing that was nor-

malized by a square root transformation. Plasma vitamin E con-

centrations were not skewed after exclusion of vitamin E sup-

TABLE 1Measures of nutrient intake and corresponding biochemical indicators

of nutrient status

Measures ofnutrient intake

Biochemical indicatorsof nutrient status

Carotene (IU) Plasma total carotenoids (�imol/L)Vitamin A (IU) Plasma retinol (zmol/L)Vitamin E (mg TE0) Plasma cs-tocopherol (jmol/L)Vitamin D (IU) Plasma 25-OH vitamin D (nmol/L)

Thiamin (mg) RBC thiamin (AC)tRiboflavin (mg) RBC riboflavin (AC)t

Vitamin B-6 (mg) RBC vitamin B-6 (AC)t

Vitamin B-12 (pg) Plasma vitamin B-12 (pmol/L)

Folate (/)g) Plasma folate (nmol/L)Vitamin C (mg) Plasma total vitamin C (�mol/L)

Magnesium (mg) Serum magnesium (mmol/L)Zinc (mg) Serum zinc (�mol/L)

plement users; thus, plasma vitamin E concentrations were not

transformed for analyses excluding supplement users.

For each nutrient, we estimated the crude correlation coeffi-

cients and correlation coefficients adjusted for age, sex, and en-

ergy intake. We tested each relationship for age and sex inter-

actions, and examined the appropriateness of the assumption

of linear association by using second-order polynomial (qua-

dratic) models. When interactions were identified, we calculated

the correlation coefficients separately by sex or age group. If a

quadratic term significantly improved the fit of the model (P< 0.05), we considered nonlinear models. Age-, sex-, and energy-

adjusted mean biochemical nutrient concentrations and tests of

linear trend are also presented across quartiles of nutrient intake.

When interactions with age or sex were significant (P < 0.05),

we calculated means by subgroup. We performed analyses with

the SPSS REGRESSION and MANOVA procedures (29). Un-

less otherwise noted, statistical significance refers to P < 0.05.

Results

Subjects ranged in age from 40 to 83 y, and mean age was 61

y. Seventeen percent of the subjects were nonwhite. Reported

mean (SD) energy intakes for the males and females were 12.8

(3.7) and 9.7 (3.2) MJ/d, respectively. Table 2 and Table 3 present

the untransformed means and SDs for the nutrient intakes and

biochemical measures, respectively. Table 3 also indicates the

numbers of subjects with complete data for each nutrient. The

means and SDs shown in these tables were based on untrans-

formed data and show the effect ofpositive skewing and extreme

outliers.

Table 4 displays the crude and adjusted correlation coefficients

between the nutrient intakes and the biochemical measures. Ad-

justment for age, sex, and energy intake reduced the correlation

coefficients for riboflavin and vitamin B-6 and increased the

correlation coefficient for folate. The adjusted correlations ranged

from near zero for retinol, thiamin, and zinc to 0.63 for folate.

The negative correlations for riboflavin and vitamin B-6 reflect

the expected inverse relationships between activity coefficients

resulting from enzyme-activation assays and nutrient status.

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184 JACQUES ET AL

The correlations for all nutrients were generally weaker when

supplement users were excluded. This was most striking for vi-

tamin E, vitamin B-l2, and magnesium. Riboflavin did not fol-

low this pattern because the association between riboflavin intake

and the RBC riboflavin-activity coefficient for the entire sample

was nonlinear: the correlation coefficient for riboflavin intakes

< 5 mg/d was -0.29 (P < 0.01), whereas > S mg/d the corre-

lation was 0.01.

A significant sex interaction was observed for the carotene

intake-plasma carotenoid association. Carotene intake was sig-

nificantly associated with carotenoid concentrations in females,

but there was little or no association in males. The correlation

coefficients were 0.49 (P < 0.001) and 0. 19 (not significant) for

females and males, respectively. Season appeared to influence

the correlation between intake and plasma concentrations of

vitamin D. The correlation among subjects (10 males and 15

females) who were seen during late winter and early spring was

0.58 (P < 0.001); the correlation for subjects seen during the

remainder of the year was 0.28 (P < 0.01).

Table 5 shows the age-. sex-, and energy-adjusted mean bio-

chemical indicators by quartile ofintake. The differences in the

mean biochemical indicator concentrations across quartiles of

intake correspond closely to the correlation analyses. Mean con-

centrations of plasma retinol, RBC thiamin, and serum zinc

changed little across intake quartiles. Among all subjects, plasma

folate was twice as high in the highest quartile of folate intake

than in the lowest quartile ofintake. Vitamins B-12 and C showed

differences � 50% between extreme quartiles; vitamins D and

E exhibited interquartile differences of 48% and 42%, respec-

tively. Although the magnitude ofthe differences were generally

reduced, results were comparable when supplement users were

excluded: folate had the largest relative difference (60%) across

quartiles of intake; the vitamin C and vitamin D differences

were 47% and 44%, respectively. Exclusion ofsupplement users

reduced the vitamin B- 12 and E differences across quartiles to

32% and 20%, respectively. This analysis again demonstrated

that riboflavin-activity coefficient levels were nonlinearly related

to riboflavin intake with the P value for quadratic trend equal

to 0.04 for all subjects and 0.28 after excluding users of riboflavin

supplements.

The sex difference observed for the carotene intake-plasma

carotenoid correlation was also seen for mean plasma carotenoid

concentrations across carotene-intake quartiles. The relative dif-

ferences in plasma carotenoids across quartiles was 65% (2.0 vs

3.3 j�smol/L)) for females (P < 0.0.01) and 17% (2.3 vs 2.7) for

males (not significant). Excluding supplement users had little

influence on this association. We did not present the mean

plasma vitamin D concentrations by quartile ofvitamin D intake

for the subjects seen during late winter and early spring because

of the small numbers of subjects within quartiles.

Discussion

The purpose of this study was to validate the food frequency

questionnaire for assessing nutrient intake by using biochemical

indicators of nutrient status. Although this study was not a direct

validation ofthe food frequency questionnaire, the true validity

of the questionnaire must be stronger than observed because

the errors in reporting nutrient intake from this questionnaire

and errors in determining nutrient status by biochemical mdi-

cators are unlikely to be correlated. Thus, the observation of a

correlation between these independent methods of assessment

argues for the validity of the food frequency questionnaire as a

measure ofnutrient intake. A number ofthe nutrients examined

in this report displayed such correlations, supporting the validity

of this dietary assessment method for these nutrients.

The use of biochemical indicators to validate a measure of

intake is based on the assumption that the biochemical indicators

are responsive to intake in a dose-dependent manner ( 1 ). No

biochemical indicator will provide a perfect measure of intake

because many factors other than diet influence circulating nu-

trient concentrations ( 1 ). Complex mechanisms that regulate or

enhance absorption may affect circulating concentrations for

certain nutrients, whereas circulating concentrations of some

nutrients are under direct homeostatic control. Age, sex, smok-

ing, or disease status may also alter circulating concentrations

of some nutrients. Control of circulating nutrient concentrations

by factors other than diet tends to reduce correlations between

intake and the biochemical indicators of nutrient status. The

observed associations, therefore, reflect the lower bound of

questionnaire validity. The assumption that biochemical mdi-

cators are responsive to intake is considered below for each nu-

trient examined in this report.

Plasma carotenoids are responsive to intake (30-32) and

clearly discriminate over a wide range ofintakes (33). The mag-

nitude of the correlation coefficient we observed is similar to

that previously reported by Willett et al (34) using the same

nutrient assessment methods in a mixed sample of men and

women. We observed a 36% increase in mean carotenoid con-

centrations across the quintiles of intake. By the nature of our

comparison (provitamin A intake vs total plasma carotenoids),

we have probably substantially underestimated the true rela-

tionship between specific carotenoid intake and plasma concen-

trations ofspecific carotenoids. Published data on concentrations

of carotenoids other than carotenes are not yet available for

incorporation into food composition data, and plasma fl-carotene

constitutes on average < 20% of the total plasma carotenoids

(32). Thus, carotene intake is likely to be a much better indicator

of carotene status than was indicated by our analyses. The fact

that fl-carotene constitutes a higher percentage of plasma ca-

rotenoids for females than for males (35, 36) is the most rca-

sonable explanation for the sex interaction observed for dietary

carotene-plasma carotenoid association. The association between

carotene intake and plasma carotenoids in females was one of

the strongest that we observed. Plasma carotenoid concentration

increased 65% across carotene intake quartiles for females.

Plasma retinol concentrations are only responsive to vitamin

A intake in individuals with inadequate vitamin A status (30,

3 1). We observed no correlation between retinol and vitamin A

intake. The food frequency questionnaire measured vitamin A

intake in international units rather than retinol equivalents, al-

though retinol equivalents provide a better measure of vitamin

A activity. The use ofinternational units could potentially reduce

the correlation between plasma retinol and vitamin A intake,

but this is an unlikely explanation for the absence ofa correlation.

The more likely explanation is that our subjects were reasonably

well-nourished and we would expect no correlation.

We estimated thiamin, riboflavin, and vitamin B-6 status by

using enzyme-activation (stimulation) tests. Depletion studies

provide much of the information relating vitamin intake to ac-

tivity coefficients, and few data exist on the relationships between

activation tests and intakes within the range of the general US

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NUTRIENT INTAKE AND BIOCHEMICAL MARKERS 185

TABLE 2Nutrient intake

Nutrient

All males o r females Excluding supp lement users

Supplement

users0 (n)Absolute intake Intake/4.2 MI Absolute intake lntake/4.2 MI

Males (n = 57)

Carotenes(IU) 13668± 12530t 4682±4572 12276± 10326 4311 ±4202 2

VitaminA(IU) 18270± 12 185 6258±4665 17334± 12027 5994±4800 13

Vitamin E (mg TEf) 71.9 ± 164.9 28.8 ± 72.4 15.8 ± 16.6 4.8 ± 3.1 14

VitaminD(ILJ) 374±268 125±85 283± 164 94±46 13

Thiamin (mg) 6.5 ± 1 1.6 2.2 ± 4.2 2.1 ± 1.3 0.7 ± 0.2 14

Riboflavin (mg) 7.6 ± 1 1.9 2.6 ± 4.2 3.1 ± 2.0 1.0 ± 0.4 14

Vitamin B-6 (mg) 9.3 ± 23.9 3.2 ± 13.8 2.9 ± 1.7 0.9 ± 0.4 14

Vitamin B-12 (;2g) 15.8 ± 20.4 5.2 ± 5.9 9.5 ± 6.4 3.2 ± 1.6 14Folate (;zg) 557 ± 412 181 ± 103 481 ± 338 154 ± 77 1 1

VitaminC(mg) 352±278 119±97 256± 154 86±50 16Magnesium(mg) 426±214 146±94 358± 142 118±34 17

Zinc (mg) 18.8 ± 13.3 6.2 ± 4.0 15.3 ± 7.7 4.9 ± 1.9 12Females (n = 82)

Carotenes(IU) 15309± 17220 7239± 10435 13677±8899 6164±3797 1

VitaminA(IU) 18644±11258 8279±4427 17189±9240 7669±3799 11

Vitamin E (mg TE�) 26.2 ± 51.2 10.9 ± 18.6 14.2 ± 17.8 5.8 ± 5.8 12Vitamin D (IU) 354 ± 297 152 ± 107 296 ± 227 125 ± 70 14

Thiamin (mg) 4.2 ± 10.6 1.8 ± 5.1 1.7 ± 1.8 0.7 ± 0.4 15Riboflavin (mg) 4.9 ± 10.6 2.2 ± 5.1 2.5 ± 1.7 1.1 ± 0.5 16

Vitamin B-6 (mg) 5.4 ± 14.1 2.3 ± 5.8 2.3 ± 1.2 1.0 ± 0.4 16Vitamin B-l2 (;.Lg) 1 1.9 ± 13.3 5.1 ± 5.8 9.2 ± 8.3 3.9 ± 2.8 14Folate(�og) 446±297 196± 109 380± 189 166±69 11

VitaminC(mg) 322±347 139± 132 221 ± 154 95±52 20

Magnesium (mg) 330 ± 199 144 ± 64 289 ± 146 128 ± 44 12

Zinc (mg) 12.4 ± 6.9 5.4 ± 2.5 1 1.7 ± 6.4 5.1 ± 2.2 6

0 Supplement use is defined individually for each nutrient.

t I ± SD.:1:TE, cs-tocopherol equivalents.

population (1). Bayoumi and Rosalki (37) demonstrated signif-

icant reduction in all three activity coefficients when subjects

with apparently normal concentrations of these nutrients were

supplemented with amounts substantially greater than the rec-

ommended dietary allowances (18).

We observed no correlation between thiamin intake and the

thiamin-activation test. Because of the limited data on the sen-

sitivity of this test to thiamin intake in normal individuals, we

cannot evaluate the usefulness ofthe food-frequency assessment

for thiamin. The nonlinear relationship between riboflavin intake

and riboflavin activity coefficients suggests that the activation

test fails to reflect riboflavin intake over the entire range of in-

takes. The activation test does appear to reflect reported intake

at nonsupplemental amounts. Although we observed a crude

association between vitamin B-6 intake and the vitamin B-6-

activation test, it disappeared after adjustment for energy intake.

Prior analyses demonstrated a correlation between vitamin B-6

intake based on the same food frequency questionnaire and

plasma pyridoxal phosphate (1). Again, the enzyme-activation

test employed in the present analyses will not reflect vitamin B-

6 intake if the enzyme is saturated over most of the range of

intake in our sample.

We could not identify any experimental data that assessed

the relationship between vitamin B-12 intake and plasma vitamin

B-12 in individuals with apparently adequate intake. The cor-

relation we observed between intake and plasma concentrations

for the sample as a whole suggests a responsiveness of plasma

concentrations to intake, but the association appears to result

largely, although not entirely, from the inclusion of supplement

users in the analyses. We observed a 57% increase in plasma

vitamin B- 12 concentrations across quartiles of intake for the

entire sample, but only a marginally significant 29% increase

among subjects who did not consume vitamin B- 12 supplements.

The limited data relating folate intake with plasma folate con-

centrations suggest that plasma folate is sensitive to recent folate

intake, but may not reflect body stores of folate (1, 31). Of the

nutrients we considered, folate displayed the highest correlation

and the highest relative difference across quartiles. Plasma folate

concentrations doubled across the quartiles of folate intake for the

entire sample. The association remained strong after excluding users

of folate supplements. Our data suggest that the food frequency

questionnaire provides a valid measure of folate intake.

Plasma vitamin C concentrations are known to respond to

vitamin C intake (1), even in well-nourished subjects (38). The

moderate correlations and relatively large differences across

quartiles that we observed suggest that vitamin C assessment

using the food frequency questionnaire is valid. Plasma vitamin

C concentrations increased �50% across quartiles ofvitamin C

intake for the entire sample and for subjects not consuming

vitamin C supplements.

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186 JACQUES ET AL

TABLE 3

Biochemical indicators of nutrient status

Males Females

Excluding ExcludingBiochemical indicators All supplement users All supplement users

Carotenoids (�mol/L) 2.4 ± 0.9 [55]0 2.4 ± 0.9 [53] 2.7 ± 1.1 [82] 2.7 ± 1.1 [81]

Retinol (�zmol/L) 2.08 ± 0.62 [55] 1.98 ± 0.54 [42] 1.95 ± 0.61 [82] 1.97 ± 0.65 [71]

Vitamin E (�mol/L) 30 ± 12 [55] 26 ± 8 [41] 28 ± I 1 [82] 26 ± 7.3 [70]Vitamin D (nmol/L) 83 ± 32 [55] 79 ± 34 [42] 73 ± 31 [80] 70 ± 31 [66]

Thiamin (AC)t 1.10 ± 0.1 1 [56] 1.10 ± 0.12 [42] 1.08 ± 0.10 [82] 1.08 ± 0.1 1 [67]

Ribofivain (AC)t 1.10 ± 0.13 [55] 1.10 ± 0.14 [42] 1.14 ± 0.14 [82] 1.16 ± 0.14 [66]

Vitamin B-6 (AC)t 1.67 ± 0.31 [56] 1.72 ± 0.30 [42] 1.77 ± 0.26 [82] 1.80 ± 0.26 [66]

Vitamin B-12 (pmol/L) 280 ± 151 [54] 244 ± 132 [40] 294 ± 150 [78] 276 ± 137 [65]

Folate (nmol/L) 14 ± 5 [54] 13 ± 4 [43] 16 ± 7 [78] 14 ± 5 [67]

Vitamin C (�.smol/L) 67 ± 25 [55] 62 ± 25 [39] 7 1 ± 28 [80] 65 ± 27 [60]

Magnesium (mmol/L) 0.85 ± 0.08 [56] 0.83 ± 0.08 [39] 0.82 ± 0.09 [82] 0.82 ± 0.09 [70]

Zinc (�smol/L) 17.0 ± 2.4 [56] 16.9 ± 2.5 [44] 16.9 ± 4.1 [82] 16.9 ± 4.2 [76]

0 j: � SD En].

t AC, red blood cell-activity coefficient.

Because plasma vitamin D concentrations are influenced by Because we have no information on sunlight exposure for this

both diet and sunlight exposure (2 1), plasma vitamin D would sample, we would underestimate the true relationship between

appear to be a reasonable indicator of vitamin D intake only diet and plasma concentrations even ifwe measured vitamin D

among individuals with low sunlight exposure (1). Using a food intake and plasma vitamin D without error. In spite of this,

frequency questionnaire, Krall et al (28) observed a correlation plasma vitamin D displayed moderate correlations with intake

of0.29 between vitamin D intake and plasma vitamin D. When and vitamin D increased nearly 50% across quartiles for all sub-

these authors confined their analyses to subjects measured in jects and after exclusion ofusers ofvitamin D supplements. The

the late winter or early spring, the correlation increased to 0.65, magnitude ofthis correlation was more than doubled for subjects

demonstrating both the responsiveness of plasma vitamin D seen in late winter and early spring compared with subjects seen

concentrations to diet and the influence ofsunlight exposure on during the remainder of the year, mirroring the results of Krall

that relationship. et al (28). The food frequency questionnaire appears to be a

TABLE 4

Correlations (r) between nutrient intake and biochemical indicators of nutrient status

Excluding supplement users

Adjusted for Adjusted for

Nutrient0 Crude Energy Age, sex, energy Crude Energy Age, sex, energy

Carotenoidstt 0.35� 0.37� 0.37� 0.27� 0.29� 0.29�Retinol 0.06 0.06 0.07 0.00 0.01 0.02Vitamin Ej 0.5l� 0.52� 0.53� 0.28� 0.32� 0.35�Vitamin D� 0.34� 0.33� 0.35� 0.2111 0.22� 0.25�

Thiamin 0.00 0.02 0.02 -0.01 0.00 0.01

Riboflavin -0.23� -0.13 -0.13 -0.32� -0.2011 -0.2111

Vitamin B-6 -0.24� -0.1611 -0.15 -0.06 0.04 0.05

Vitamin B-12 0.30� 0.34� 0.35� 0.16 0.2011 0.19

Folate 0.49� 0.62� 0.63� 0.34� 0.5l� 0.6l�Vitamin C 0.39� 0.44� 0.43� 0.34� 0.40� 0.38�Magnesium 0.27� 0.29� 0.27� 0.1 1 0.17 0.15ZinciT 0.06 0.1 1 0.1 1 0.02 0.09 0.10

0 See Table 1 for corresponding intake and biochemical measures.

t Sex-intake interaction, P < 0.05.f Plasma fat-soluble nutrients adjusted for plasma cholesterol concentration.§ Significantly different from 0, P < 0.01.

II Significantly different from 0, P < 0.05.#{182}Serum zinc also adjusted for serum albumin concentration.

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NUTRIENT INTAKE AND BIOCHEMICAL MARKERS 187

TABLE 5Adjusted mean concentrations of biochemical indicators of nutrient status by quartile of total nutrient intake0

Biochemical measurest

Nutrient intake quartiles

P

Excluding supplemen t users

Nutrient intake quartiles

1 2 3 4 1 2 3 4 P

Carotenoids (Mmol/L4

Retinol (�moI/L)

Vitamin E (j.smol/L)�

Vitamin D (nmol/L%Thiamin (AC)�

Riboflavin (AC)�Vitamin B-6 (AC)�

Vitamin B-12 (pmol/L)

Folate (nmol/L)Vitamin C (�mol/L)Magnesium (mmol/L)

Zinc (�zmol/L)II

2.2

1.81

24

631.12

1.161.73

227

1054

0.80

16.3

2.4 2.5

1.93 2.09

23 29

79 76

1.06 1.09

1.1 1 1.081.79 1.76

240 250

12 1567 76

0.83 0.83

16.4 16.9

3.0

1.86

34

93

1.08

1.121.68

3562081

0.88

16.6

<0.001

0.47

<0.001

<0.001

0.54

0.150.42

<0.001

<0.001<0.001

<0.0010.48

2.2

1.80

25

60

1.13

1.191.71

2211047

0.7916.2

2.4 2.4

1.98 1.92

24 26

79 681.07 1.07

1.12 1.101.82 1.68

226 25512 1458 79

0.82 0.82

16.3 16.7

3.0

1.83

30

851.09

1.091.87

291

1669

0.86

16.6

0.002

0.96

0.02

0.03

0.35

0.050.19

0.07

<0.001<0.001

0.01

0.50

0 Adjusted for age, sex, and energy intake.

t See Table I for corresponding intake and biochemical indicators.

1:Plasma fat-soluble nutrients adjusted for plasma cholesterol concentration.§ AC, red blood cell-activity coefficient.

II Serum zinc also adjusted for serum albumin concentration.

valid method for assessing vitamin D intake in our sample. Al-

though it would be more difficult to use plasma vitamin D con-

centrations to assess the validity ofvitamin D intake in individ-

uals with more sunlight exposure, there is no reason to suspect

that the questionnaire would not provide a valid assessment in

other populations.

A single measurement ofplasma vitamin E has been reported

to represent long-term vitamin E intake ( 1) and plasma vitamin

E is responsive to vitamin E intake in well-nourished subjects

(30). However, plasma concentrations may not reflect vitamin

E intake at intakes above 440 mg/d (39). We observed a strong

correlation between plasma vitamin E and vitamin E intake

when we included vitamin E supplement users in the analyses.

Plasma vitamin E concentrations increased by 42% across quar-

tiles of vitamin E intake. Exclusion of vitamin E supplement

users reduced the strength of this association, but it remained

substantial and statistically significant. The food frequency

questionnaire, therefore, appears to be useful for assessing vi-

tamin E intake.

Because serum magnesium is under physiological control, the

utility of serum magnesium as an indicator of magnesium intake

is questionable ( 1 , 3 1 ). However, our results and previously

published data argue that serum magnesium may respond to

magnesium intake. Lakshmanan et al (40) reported a nonsig-

nificant correlation of0.28 between plasma magnesium and long-

term magnesium intake in a small sample of men and women.

This is quite similar to the correlation of0.27 that we observed

between these measures. This correlation was reduced among

nonusers of magnesium supplements suggesting that the ques-

tionnaire did not adequately reflect dietary intake, but that serum

magnesium was somewhat responsive to intake as measured by

supplement use. In addition, we observed a significant correlation

of 0.33 (P < 0.0 1) between serum magnesium and intake for

nonusers of magnesium supplements among a subset of our

sample who had completed 3-d diet records. The nutrient com-

position data used for the diet records (GRAND, Release 8606,

USDA-ARS Grand Forks Human Nutrition Research Center,

Grand Forks, ND) differed from that used for the food frequency

questionnaire, and is more complete for magnesium. Taken to-

gether these data suggest that serum magnesium may be re-

sponsive to diet, but that the database used for the food frequency

questionnaire in this study did not appear to estimate intake

adequately. The lack ofa correlation could also be due to timing,

because the questionnaire assesses long-term intake and the

serum concentration may reflect short-term intake.

Available data suggest that serum zinc concentrations are in-

adequate to assess zinc intake ( 1 , 3 1 ). We observed weak non-

significant correlations, but these data do not allow us to assess

the validity ofthe food-frequency questionnaire for zinc intake.

The weak relationship could result ifserum zinc did not respond

to zinc intake or ifthe food frequency questionnaire was not an

adequate measure ofzinc intake.

Inclusion of supplement users strengthened most associations

between the food frequency questionnaire and biochemical

measures. Because supplement use substantially extends the

range of nutrient intake and is easily quantified, supplement

users are more accurately classified according to intake. In ad-

dition, including supplement users expands the range of many

of the biochemical measures. These factors improve the corre-

lations between reported intake and biochemical measures. Any

dietary assessment method used in the United States must in-

corporate supplement use because they are an important source

of nutrients. Forty-three percent of our sample used some form

of nutrient supplements, which is comparable to that in similar

US populations (41-43).

We must provide two notes of caution regarding our use of

the food frequency questionnaire. First, although this question-

naire was designed to be self-administered, we used it as an in-

terviewer-administered instrument. Other investigators (44) have

demonstrated substantial differences in data quality when a

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188 JACQUES ET AL

questionnaire was self-administered or interviewer-administered.

We have no evidence that this occurs for the questionnaire that

we used. Second, even though the questionnaire is designed to

elicit information on nutrient supplement use, we obtained our

information on nutrient supplement use from a more detailed

supplement-use history. However, the nutrient-supplement data

that we used to determine usual intake for these analyses was

very similar to that usually provided by the food frequency

questionnaire. Thus, the fact that we derived nutrient-supple-

ment data from another source should have minimal impact on

the observed associations.

Our results indicate that the food frequency questionnaire

provides useful and valid information on micronutrient intake

for folate, vitamin C, vitamin D, vitamin B-l2, and vitamin E

for both sexes, and carotene intake for females. Reasons for the

low correlation between nonsupplemental magnesium intake

and serum concentrations, such as adequacy of the nutrient-

composition database, should be explored and could lead to

improved estimation of intakes. Although we were unable to

demonstrate meaningful linear relationships between the bio-

chemical and dietary assessment methods for thiamin, riboflavin,

and vitamin B-6, this is probably a limitation of the enzyme-

activation assays. Similarly, the lack ofcorrelation for zinc and

preformed vitamin A is probably because ofthe insensitivity of

plasma concentrations to intake of these nutrients. B

We thank Leo T Chylack, Jr. who was largely responsible for the studyfrom which we obtained these data. The authors gratefully acknowledge

the contributions of Frank Morrow, Saul Tannenbaum, Lorry Scura,and the entire staff of the Nutrition Evaluation Laboratory and the Di-vision of Scientific Computing at the US Department of AgricultureHuman Nutrition Research Center on Aging at Tufts University.

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