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Articles

Dietary Intakes and Serum Nutrients Differ between Adults from Food-Insufficient and Food-Sufficient Families: Third National Health and Nutrition Examination Survey, 1988–1994¹

Lori Beth Dixon^*2, Marilyn A. Winkleby and Kathy L. Radimer^**

^* Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland; Stanford Center for Research in Disease Prevention, Stanford University School of Medicine, Stanford, California and ^** National Center for Health Statistics, Centers for Disease Control and Prevention, Hyattsville, Maryland

²To whom correspondence should be addressed at Applied Research Program, NCI, 6130 Executive Blvd., MSC 7344, EPN 4005, Bethesda, MD 20892. E-mail: ld120i{at}nih.gov

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Approximately 10.2 million persons in the United States sometimes or often do not have enough food to eat, a condition known as food insufficiency. Using cross-sectional data from the Third National Health and Nutrition Examination Survey (NHANES III), we examined whether dietary intakes and serum nutrients differed between adults from food-insufficient families (FIF) and adults from food-sufficient families (FSF). Results from analyses, stratified by age group and adjusted for family income and other important covariates, revealed several significant findings (P < 0.05). Compared with their food-sufficient counterparts, younger adults (aged 20–59 y) from FIF had lower intakes of calcium and were more likely to have calcium and vitamin E intakes below 50% of the recommended amounts on a given day. Younger adults from FIF also reported lower 1-mo frequency of consumption of milk/milk products, fruits/fruit juices and vegetables. In addition, younger adults from FIF had lower serum concentrations of total cholesterol, vitamin A and three carotenoids (-carotene, ß-cryptoxanthin and lutein/zeaxanthin). Older adults (aged 60 y) from FIF had lower intakes of energy, vitamin B-6, magnesium, iron and zinc and were more likely to have iron and zinc intakes below 50% of the recommended amount on a given day. Older adults from FIF also had lower serum concentrations of high-density lipoprotein cholesterol, albumin, vitamin A, ß-cryptoxanthin and vitamin E. Both younger and older adults from FIF were more likely to have very low serum albumin (<35 g/L) than were adults from FSF. Our findings show that adults from FIF have diets that may compromise their health.

KEY WORDS: • biomarker • dietary intake • food insecurity • food insufficiency • hunger • NHANES III

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Despite the wealth and abundance of food in the United States, findings from the Third National Health and Nutrition Examination Survey (NHANES III)³ indicate that 10.2 million persons in the United States experience food insufficiency, a condition in which persons sometimes or often do not have enough food to eat (Alaimo et al. 1998 ). Within this national scope, black and Mexican American families, families headed by single women and families reporting incomes below the poverty level are more likely to be food insufficient than the general population (Alaimo et al. 1998 ). These findings agree with those from more recent surveys (Bickel et al. 1999 ) that measured a similar concept, food insecurity with hunger, defined as "limited or uncertain availability of nutritionally adequate and safe foods or limited or uncertain ability to acquire acceptable foods in socially acceptable ways" accompanied by "the uneasy or painful sensation caused by lack of food" (Anderson 1990 ). This serious problem is acknowledged in Healthy People 2010, which includes as one of its public health objectives a decrease in the prevalence of food insecurity in the United States from 12 to 6% by 2010 (U.S. Department of Health and Human Services 2000 ).

Inadequate food intake may directly compromise nutritional status. Previous analyses of national data showed lower intakes of energy, protein, many vitamins and minerals and gram amounts of various food groups by children, women of child-bearing age and elderly members of food-insufficient households compared with their food-sufficient counterparts (Cristofar and Basiotis 1992 , Rose and Oliveira 1997 ). However, the data used by these studies did not include serum concentrations of nutrients, many of which reflect longer-term nutritional status and are less prone to measurement error than nutrient intakes from 24-h dietary recalls (Hunter 1998 ).

NHANES III is a national survey that provides a wealth of sociodemographic, dietary and biochemical data, creating an opportunity to compare dietary intakes and serum nutrients of adults from families who reported food insufficiency versus those who report food sufficiency. Previous research using NHANES III data examined the sociodemographic characteristics of persons from food-insufficient families (FIF) and found that although food insufficiency was primarily related to poverty status, it also occurred in families with incomes above the poverty threshold (Alaimo et al. 1998 ). In this study, we used NHANES III data to examine whether energy and nutrient intakes, frequencies of foods eaten and serum nutrient concentrations differed among younger adults, aged 20–59 y, and older adults, aged 60 y, by food insufficiency status after adjustment for family income and other important covariates. We also compared the percentages of adults from FIF with nutrient intakes that did not meet the current dietary recommendations and with serum nutrient concentrations outside of the recommended or normal ranges with corresponding percentages of adults from food-sufficient families (FSF).

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
NHANES III is a nationally representative, cross-sectional survey, conducted from 1988 through 1994, that used a multistage probability design [National Center for Health Statistics (NCHS) 1994 ]. Black and Mexican Americans, children aged 2 mo to 5 y and adults aged 60 y were oversampled to provide representative data from these population subgroups. Sociodemographic, health and nutrition data were collected via extensive questionnaires administered at home by health interviewers to 33,994 participants from 19,528 households throughout the United States. Additional health, nutrition and laboratory data were collected from 30,818 participants by health professionals during visits to NHANES mobile examination centers (MEC) at 89 sites. The survey procedures were approved by the NCHS Internal Review Board, and all participants signed informed consent forms (NCHS 1994 ).

Study sample.

The initial sample for our analyses included 10,768 adults, aged 20–59 y, and 5143 adults, aged 60 y, who were non-Hispanic whites, non-Hispanic blacks or Mexican American and who were interviewed at home and examined at the MEC. We selected the lower age cutpoint of 20 y because someone of this age is considered to be an adult according to the Analytic and Reporting Guidelines provided by NHANES III (NCHS 1996 ). We selected the upper age cutpoint of 60 y because dietary intakes and food insufficiency status may change with retirement and because although many adults retire after age 60, the Analytic and Reporting Guidelines of NHANES III encourage dividing groups at the start of each decade.

From this initial sample, we excluded 45 participants whose interviews were coded as unreliable, 281 participants who were pregnant, 18 participants who did not answer the question about food insufficiency and 6 families with members of two or more races/ethnicities. We followed the methodology used by previous analyses of food insufficiency in NHANES III (Alaimo et al. 1998 ) and randomly selected one adult per family because food insufficiency was a family variable. Also, dietary data from multiple participants from the same family are likely to be similar, which can potentially bias results (i.e., families with more members could influence the dietary data more strongly than families with fewer members). From this subset, we excluded an additional 1013 participants who did not provide family income data (a covariate in our models).

Our final sample included 6475 white, black or Mexican American adults, aged 20–59 y, and 3690 white, black or Mexican American adults, aged 60 y. For analyses using the 24-h dietary recall data, we followed the NHANES III recommendations (NCHS 1996 ) and excluded an additional 356 participants whose dietary recalls were not reliable and complete.

Definition of food insufficiency.

In the NHANES III household family questionnaire, participants were asked: "Which one of the following statements best describes the food eaten by you/your family? Do you have enough food to eat, sometimes not enough to eat, or often not enough to eat?" Adults who answered "sometimes not enough to eat" or "often not enough to eat" were considered to be from FIF. Adults who answered "enough food to eat" were considered to be from FSF. This question was pilot tested before NHANES III and was found to be a reliable measure of food insufficiency (Briefel and Woteki 1992 ).

Measurement of diet.

Dietary data were collected using two instruments: a single 24-h dietary recall and a 1-mo qualitative 60-item food frequency questionnaire (FFQ). The 24-h dietary recall was administered at the MEC using an automated, interactive interview and coding system that featured a standardized interview format and automated probes to obtain detailed information about foods, including brand names, food preparation methods and ingredients used in food preparation methods (NCHS 1994 ). Three-dimensional food models, measurement aids and food-specific units were used to estimate amounts consumed. Seasoning added to prepared foods at the table, nutrients from dietary supplements and medications were not included. Each individual’s intake of energy, fat, fiber, protein, vitamins and minerals was determined from their 24-h dietary recall. Dietary fat intake from each individual was compared with national dietary guidelines: total fat (30% of energy/d), saturated fat (<10% of energy/d) and cholesterol (300 mg/d) (U.S. Department of Agriculture and U.S. Department of Health and Human Services 2000 ). Dietary fiber intake from each individual was compared with the lower range of the recommended intake (<20 g/d) (Butrum et al. 1988 ). Intakes of protein, vitamins A, C, E, B-6, B-12 and folate and magnesium, iron, and zinc from each individual were compared with the Recommended Dietary Allowances (RDA) appropriate for each gender and life stage group (Institute of Medicine 1997 , 1998 , 2000 and 2001 , National Research Council 1989 ). Calcium intake from each individual was compared with the Adequate Intake (AI) appropriate for each gender and life stage group (Institute of Medicine 1997 ). We examined low nutrient intakes by creating dichotomous variables of protein, vitamin and mineral intakes below and above 50% of the RDA (or AI for calcium). As previously discussed (Rose and Oliveira 1997 ), these conservative cutpoints were chosen to compensate for the following: the RDA is designed to meet the nutrient requirements for nearly all (97–98%) healthy individuals, 24-h dietary recall data are biased due to underreporting of food intake and 1-d dietary recall data are highly variable within each individual.

The FFQ was administered during the household interview and asked the average number of times foods were eaten during the 1-mo period preceding the respondent’s interview date. Frequencies of specific types of foods from the following designated food groups and subgroups were ascertained: milk and milk products, meat and meat dishes, eggs and egg dishes, fruits and fruit juices (including citrus fruits and fruit juices), vegetables (including dark green leafy vegetables, deep orange and yellow vegetables and white potatoes), grains and legumes (including cereals, breads, legumes and salty snacks), desserts and sweets, beverages (including nonalcoholic and alcoholic beverages) and added fats. The FFQ used in NHANES III was designed to target food sources of calcium and vitamins A and C (Sempos et al. 1992 ) and included foods high in these nutrients that were reported by NHANES II participants and Mexican Americans in the Hispanic Health and Nutrition Examination Survey (NCHS 1994 ), making it more comprehensive than the FFQ used in previous NCHS surveys (McDowell et al. 1981 , Miller 1973 , NCHS 1985 ). Cognitive testing of the NHANES III FFQ among adolescents and adults and pretesting of the NHANES III FFQ in English and Spanish showed that the NHANES III FFQ performed reasonably well among adolescents, older adults, low income persons and black and Mexican Americans [Ronette Briefel, Mathematica Policy Institute (formerly at NCHS), personal communication, November 2000). Like previous versions, the NHANES III FFQ did not include information about portion size and cannot be used to estimate nutrient intakes. However, this method of dietary assessment is appropriate for comparing frequencies of food intakes between groups of individuals (Thompson and Byers 1994 ).

Measurement of serum nutrients.

Blood was collected from participants in the MEC through venipuncture using standard protocols. Several blood components were analyzed for NHANES III. Concentrations of serum lipids, serum albumin, serum carotenoids (-carotene, ß-carotene, ß-cryptoxanthin, lutein/zeaxanthin and lycopene), serum vitamins A, C, and E, serum and red blood cell folate, serum vitamin B-12, and serum ferritin are reported in this study. Serum nutrients, except serum LDL cholesterol and serum vitamin B-12, were determined for the entire sample of NHANES III participants. All participants were instructed to fast 8.5 h if examined in the morning or 6 h if examined in the afternoon (NCHS 1994 ). Values for serum LDL cholesterol were calculated by the Friedewald equation (Friedewald et al. 1972 ) only for examinees who fasted 9 h, who were examined in the morning and who were randomly assigned to the morning fasting sample group (NCHS 1996 ). Serum vitamin B-12 was determined only during the second phase of NHANES III (1991–1994). Detailed information about the procedures and quality control protocols used for the measurement of these serum nutrients is provided in the NHANES III documentation (NCHS 1994 , 1996 ) and in the Laboratory Procedures used for NHANES III (Gunter et al. 1996 ).

We used the National Cholesterol Education Program recommendations (U.S. Department of Health and Human Services 1990 ) to determine cutpoint values for serum lipids. We used laboratory values from The Merck Manual of Diagnosis and Therapy (Beers and Berkow 1999 ) to determine cutpoint values for other serum nutrients (values for these cutpoints are presented in Table 6 ).

Dietary and serum data vary by sociodemographic and behavioral characteristics. To determine whether food insufficiency was independently associated with differences in dietary intakes or serum nutrient concentrations, we adjusted for several potential confounding variables in our analyses (Alaimo et al. 1998 , Hunter 1998 ). Self-reported information regarding each individual’s gender, age and race/ethnicity was collected during the household interview. Total family income was reported by a responsible adult in the household and assigned to an income category. The midpoint of the income category was the numerator, and the poverty threshold, age of the family reference person and the calendar year in which the family was interviewed determined the denominator of the poverty income ratio (PIR). Region of the United States was defined as one of the four U.S. Census regions.

Behaviors, including cigarette smoking, alcohol consumption and taking a dietary supplement, were determined from questions asked during the household interview or at the MEC. Respondents were asked if they smoked 100 cigarettes during their entire life, if they smoked cigarettes now and about how many cigarettes they smoked per day. If respondents smoked <100 cigarettes during their entire life or did not currently smoke, they were considered nonsmokers and coded as smoking 0 cigarettes/d. Alcohol consumption was estimated from the 1-mo FFQ and from the 24-h dietary recall. Respondents were also asked several questions about current and past alcohol consumption in a private interview at the MEC, but at least half of the respondents chose not to answer these questions. Because FFQ are thought to be better representative of "usual" alcohol intake than a single 24-h dietary recall (Sempos et al. 1992 ), we used the sum of the 1-mo frequencies of beer, wine and hard liquor from the FFQ in our analyses. During the household interview, respondents were also asked if they had taken any vitamins or minerals in the past month. Percentages of adults who reported taking dietary supplements, including multivitamin and mineral supplements, in the past month were determined, and a dichotomous variable for taking/not taking dietary supplements in the past month was created.

Statistical methods.

Separate analyses were conducted for adults aged 20–59 y (referred to as "younger adults") and adults aged 60 y (referred to as "older adults") from FIF and FSF. Our analyses of energy and nutrient intakes included all adults with reliable and complete 24-h dietary recalls; our analyses of food frequencies included all adults with complete FFQ. In the multivariate linear and logistic regression analyses, dependent variables included energy, nutrients (absolute intakes, as percentages of the RDA or AI, or as dichotomous variables below or above a specific cutpoint) and frequencies of foods; independent variables included the food insufficiency variable and gender, age (centered at the sample mean for each gender), race/ethnicity (non-Hispanic white, non-Hispanic black or Mexican American), family income in relation to the PIR and region of the United States as covariates.

Our analyses of serum nutrients included all adults regardless of fasting status, except for analyses of serum LDL cholesterol and serum triglycerides. Although serum triglycerides were measured on all respondents regardless of fasting status, per NHANES III recommendations (NCHS 1996 ), we compared serum triglycerides only for those individuals for whom serum LDL cholesterol was calculated. In the multivariate linear or logistic regression analyses, dependent variables included serum nutrient concentrations (absolute values or as dichotomous variables below or above a specific cutpoint); independent variables included the food insufficiency variable and gender, age (centered at the sample mean for each gender), race/ethnicity (non-Hispanic white, non-Hispanic black or Mexican American), family income in relation to the PIR, region of the United States, number of cigarettes smoked per day, frequency of alcohol intake reported in the 1-mo FFQ, if a dietary supplement was taken in the past month and hours of fasting as covariates. Hours of fasting were calculated from the time of venipuncture and the time the examinee last ate food or drank liquids (other than water). Although the laboratory procedures used for NHANES III (Gunter et al. 1996 ) suggested that analyses of several serum nutrients be conducted on samples from individuals who reported fasting (12 h for serum lipids and unspecified lengths of time for other serum nutrients), our analyses showed that mean serum nutrient concentrations did not differ between adults from FIF and FSF by fasting status (i.e., <12 h versus 12 h). However, hours of fasting were associated with certain serum nutrients, so we included this term as a continuous variable in our models. Grams of total serum lipid have been shown to influence serum concentrations of fat-soluble vitamins (Hunter 1998 ). In our analyses of serum vitamin A, serum carotenoids and serum vitamin E, we also adjusted for grams of total serum lipid calculated from the sum of grams of serum total cholesterol and serum triglycerides (Winbauer et al. 1999 ).

We conducted our multivariate linear regression analyses using transformed dependent variables, because dietary and serum data are not normally distributed. Several power transformations of each dietary and serum variable were performed (e.g., square root, cube root, fourth root, fifth root and natural logarithm), and the transformation that best approximated normality, as evidenced by a skewness value closest to 0, was selected for each variable. Although the standard transformation applied to analyses of dietary and serum data are the natural logarithm, the natural logarithm generally produced highly skewed distributions in the opposite direction of the original data, whereas other power transformations produced more normal distributions for most variables.

We conducted Spearman rank correlation analyses, appropriate for skewed data, between energy and nutrient intakes from 24-h dietary recalls, frequencies of foods from the FFQ and serum nutrients to determine the degree to which energy and nutrient intakes correlated with food sources of those nutrients and whether dietary variables correlated with serum variables.

We conducted ² analyses to determine whether adults with and without reliable and complete 24-h dietary recalls or adults with and without family income data differed by food insufficiency status. In addition, we reran all multivariate linear and logistic regression analyses including the 512 younger adults and the 501 older adults with missing family income data to determine whether the exclusion of such a large number of participants influenced any of our dietary and serum results. In these analyses, we included a dichotomous variable for family income (i.e., missing versus provided family income data).

We also considered the possibility that differences in dietary intakes of adults from FIF may reflect differences in underreporting of food intakes. We tested this empirically by calculating the ratio of energy intake from the 24-h dietary recalls to estimated basal metabolic rate (EI/BMR_est) using age- and gender-specific formulas derived for adults (Schofield 1985 ). Cutpoint values for EI/BMR_est test whether reported energy intakes are representative of food intake during the measurement period (Goldberg et al. 1991 ). These values vary according to the sample size and the number of days of intake. A cutpoint of 0.9 is appropriate for individuals with single 24-h dietary recalls and has been used in previous NHANES III analyses (Briefel et al. 1997 ). We determined the percentages of younger and older adults from FIF and FSF with EI/BMR_est <0.9 (underreporters) and EI/BMR_est 0.9 (adequate reporters), and we compared these percentages by ² analyses and multivariate logistic regression analyses adjusted for covariates used in analyses of dietary data.

We used SAS for Windows, version 6.12 (SAS Institute, Cary, NC) to form the datasets for our analyses, to modify the sampling weights of the respondents randomly selected from each family by averaging the sampling weights of all individuals in that family (Alaimo et al. 1998 ) and to conduct the correlation analyses. For our descriptive and multivariate regression analyses, we used SUDAAN, version 7.5 (Research Triangle Institute, Research Triangle Park, NC), to account for the complex sampling design in addition to the sampling weights. Two-tailed P-values <0.05 were considered significant.

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
Baseline characteristics of covariates used in our analyses of adults from FIF and FSF are shown in Table 1 . Younger and older adults from FIF were more likely to be black or Mexican American, to earn incomes below the poverty threshold and to have <12 y of education compared with adults from FSF. Younger adults from FIF were also more likely to smoke cigarettes and less likely to take a dietary supplement in the past month. Older adults from FIF were less likely to drink alcohol in the past month.

Results from our multivariate analyses of the 1-mo FFQ data are presented in Table 4 . Younger adults from FIF reported significantly fewer milk/milk products, fruits/fruit juices (in particular, less citrus fruits and juices), vegetables (in particular, dark green leafy vegetables), salty snacks and desserts/sweets than younger adults from FSF. Older adults from FIF reported significantly fewer cereals, salty snacks and nonalcoholic beverages than older adults from FSF.

Results from correlation analyses of the dietary and serum variables are presented in the Appendix. Although adults from FIF generally had lower intakes and lower serum concentrations of many nutrients and lower intakes of related food groups, the dietary intakes were weakly correlated with the serum nutrients (Spearman rank correlation coefficients <0.2 for most comparisons) (Table A). In both age groups, correlation coefficients of 0.2–0.4 were observed only for certain serum nutrients (i.e., carotenoids, vitamin C and folate) and corresponding dietary nutrients (i.e., vitamins A and C and folate) and food sources of those nutrients (i.e., fruits and vegetables). Energy, nutrient and food intakes were also weakly correlated, with the strongest correlation coefficients observed for calcium and milk/milk products and for vitamin C and fruits/fruit juices (Table B).

Results from ² analyses showed that adults with and without reliable and complete 24-h dietary recalls or with and without family income data did not differ by food insufficiency status. Results from multivariate linear and logistic regression analyses of dietary and serum data that included adults with missing family income data agreed with our findings from analyses that included only adults who provided family income data. In addition, these analyses yielded the following significant findings (P < 0.05). Younger adults from FIF were more likely to have protein, vitamins C, B-6, and folate, magnesium, iron, and zinc intakes below 50% of the RDA and lower 1-mo frequencies of deep orange/yellow vegetables and cereals than younger adults from FSF. Younger adults from FIF also had significantly lower serum concentrations of ß-carotene, lycopene and vitamins C and E, with significantly higher percentages having very low serum total carotene and very low serum vitamin C concentrations. Older adults from FIF had significantly lower intakes of protein and vitamin E, and were more likely to have protein intakes below 50% of the RDA than older adults from FSF. Older adults from FIF had significantly higher 1-mo frequencies of lugumes and lower 1-mo frequencies of desserts/sweets and added fats. Older adults from FIF also had significantly lower serum vitamin C, with higher percentages having very low serum vitamin C concentrations.

The mean EI/BMR_est of younger adults from FIF was 1.43 ± 0.07 and did not differ from that of younger adults from FSF (1.43 ± 0.01, P = 0.9291). Using the cutpoint value of 0.9, we estimated that 27% of younger adults from FIF underreported their food intakes compared with 20% of younger adults from FSF (P = 0.0638). Mean EI/BMR_est of older adults from FIF was 1.02 ± 0.08, which was significantly less than that of older adults from FSF (1.24 ± 0.02, P = 0.0352). Higher percentages of older adults from FIF were estimated to underreport their food intakes compared with older adults from FSF (51% versus 30%, P = 0.0080). However, after adjustment for covariates used in analyses of dietary data, odds ratios generated from multivariate logistic regression analyses were not significant for younger or older adults.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 
We found that adults from FIF were more likely to have diets that may compromise their health than were adults from FSF. After adjustment for family income and other covariates, younger adults from FIF had significantly lower intakes of calcium; older adults from FIF had significantly lower intakes of energy, vitamin B-6, magnesium, iron and zinc. Although not significantly different than their food-sufficient counterparts, more than one third of younger adults from FIF also had low dietary intakes of vitamins A, C and folate. More than one third of older adults from FIF had low dietary intakes of vitamins A, C, E and folate, calcium and magnesium. Younger adults from FIF reported significantly lower 1-mo frequencies of milk/milk products, major food sources of calcium, and fruits/fruit juices and vegetables (in particular, citrus fruits and juices and dark green leafy vegetables), major food sources of vitamins A, C and folate. Older adults from FIF reported significantly lower 1-mo frequencies of cereals, a major food source of vitamins and minerals, and salty snacks and nonalcoholic beverages, food sources of energy.

Comparison with other studies.

Our dietary results agree in part with two previous studies of dietary intakes of food-insufficient adults using national data. Rose and Oliveira (1997 ) analyzed the diets of adult women aged 19–50 y and adults aged 65 y by food insufficiency status using single 24-h dietary recall data from the 1989–1991 Continuing Survey of Food Intakes by Individuals (CSFII). In that study, adults who reported food insufficiency had significantly lower intakes of several nutrients, including lower calcium intake among adult women and lower energy and lower vitamin B-6 and zinc intakes among elderly adults. Similarly, Cristofar and Basiotis (1992 ) analyzed single 24-h dietary recall data from the 1985–1986 CSFII and reported lower intakes of these nutrients by low-income food-insufficient women compared with low-income food-sufficient women. In addition, Cristofar and Basiotis (1992 ) reported lower gram amounts of milk/milk products and vegetables and fruits among low-income food-insufficient women.

Smaller regional studies have also examined the diets of women who reported food insecurity with hunger, a measure similar to food insufficiency. Kendall et al. (1996 ) used the Radimer/Cornell measures of food insecurity to evaluate the diets of 193 women, aged 15–40 y and living in a rural county of New York, and found lower intakes of fruits and vegetables among food-insecure women. Tarasuk and Beaton (1999 ) used a modified version of the 30-d scale items from the U.S. Department of Agriculture Food Security Measurement Project (Hamilton et al. 1997 ) to evaluate the diets of 153 women, aged 19–49 y, in families who received emergency food assistance in Toronto, Canada. They reported significantly lower intakes of energy and several nutrients (but not calcium) among women who reported food insecurity with hunger compared with women who reported food security.

Several reasons may explain specific differences in the dietary findings of our study compared with those of previous studies, including differences in populations sampled (e.g., both genders versus women only, different age ranges, all-income versus low-income only, national samples versus regional samples), differences in assessment of food insufficiency or food insecurity (e.g., three possible responses to a single food insufficiency question in NHANES compared with four possible responses to a single food insufficiency question in CSFII, one question used to determine food insufficiency compared with multiple questions used to determine food insecurity), differences in dietary assessment (e.g., single versus multiple 24-h dietary recalls) and differences in analytic methods (e.g., covariates included in statistical models, sampling weights used). Nonetheless, results from our study and previous studies indicate that adults who live in families without enough food to eat are more likely to have lower intakes of several nutrients and foods.

Serum data.

A unique contribution to the literature is our analysis of serum nutrient concentrations of adults from FIF and FSF. Our analyses revealed significantly lower concentrations of several serum nutrients among younger and older adults from FIF compared with their food-sufficient counterparts. Mean serum concentrations of all nutrients were within the normal ranges for both age groups of adults from FIF, suggesting that the lower mean values are unlikely to be clinically meaningful.

It is striking, however, that more than one third of younger and older adults from FIF had very low serum total carotene. Serum carotenoids are entirely dependent on diet and have half-lives of 2 wk (Olson 1999 ). Unlike serum vitamin A, which is rarely low unless liver stores are nearly depleted, low serum carotenoids may be long-term markers of low vegetable and fruit intake (Ito et al. 1999 , McEligot et al. 1999 ). Younger adults from FIF reported significantly lower 1-mo frequencies of vegetables and fruits, major food sources of carotenoids. Correlation coefficients between these FFQ food groups and the serum carotenoids, except lycopene, were between 0.13–0.35.

Serum vitamin C is also of concern. Mean concentrations of serum vitamin C were noticeably lower among younger and older adults from FIF and reached statistical significance when adults without family income data were included in the analyses. More than one fourth of adults from FIF reported very low serum vitamin C concentrations. Such low concentrations of serum vitamin C may reflect chronically low vitamin C intake (Loria et al. 1998 ). Results from the FFQ showed that younger adults from FIF had lower frequencies of citrus fruits and juices and dark green leafy vegetables, major food sources of vitamin C, in the previous month. Correlations between these FFQ food groups and serum vitamin C of 0.15–0.36 were observed. It is possible, however, that factors in addition to diet, including smoking, stress from cold temperatures, surgery, trauma, chronic inflammatory diseases and infection, could explain lower serum vitamin C concentrations in this population (Gibson 1993 ). We adjusted for differences in cigarette smoking in our analyses but did not have data to determine whether the other factors differed by food insufficiency status.

Although significantly higher percentages of younger adults from FIF had very low concentrations of serum albumin and serum vitamin A, this affected <4%. However, 10% of older adults from FIF had very low serum albumin concentrations. Like serum vitamin C, factors other than diet, including stress, trauma, chronic infection and strenuous exercise, could account for lower serum albumin concentrations in this population (Gibson 1993 ). However, serum albumin has a long half-life of 14–20 d and could also result from long-term inadequate energy intake and deficiencies in electrolytes, trace elements (e.g., iron, zinc) and vitamins (e.g., vitamin A).

It is important to note that our cutpoint values were based on acute manifestations of deficiency (Beers and Berkow 1999 ) rather than the prevention of chronic disease. For example, serum vitamin E concentrations of <20 µmol/L have been associated with an increased risk of cardiovascular disease (Ford and Sowell 1999 ). With this value, 46% of younger adults from FIF compared with 30% of younger adults from FSF had an increased risk of cardiovascular disease. Approximately 17% of older adults from FIF compared with 10% of older adults from FSF had an increased risk. A more accurate assessment of vitamin E status is a ratio of serum vitamin E to the sum of serum cholesterol and serum triglycerides (Winbauer et al. 1999 ). Although older adults from FIF had a significantly lower ratio compared with older adults from FSF (72.1 versus 86.9 µmol/g, P = 0.0047), <1% of older adults from FIF had marginal serum vitamin E concentrations (i.e., <33 µmol/g). No differences in this ratio were observed between younger adults by food insufficiency status.

Others have also suggested using less conservative cutpoint values for serum and red blood cell folate as markers of deficiency (Selhub and Rosenberg 1996 ). In our study, 28% of younger adults and 14% of older adults from FIF had serum folate concentrations of <6.7 nmol/L compared with 20% of younger adults and 8% of older adults from FSF; 48% of younger adults and 13% of older adults from FIF had red blood cell folate concentrations of <315 nmol/L compared with 33% of younger adults and 19% of older adults from FSF. However, regardless of the cutpoint values used, odds ratios from multivariate logistic regression analyses were not significant for serum vitamin E, serum folate or red blood cell folate.

Although adults from FIF generally had lower intakes of energy, nutrients and foods and lower serum nutrient concentrations, the correlations were weak. We offer several explanations: 1) some serum nutrients (e.g., serum vitamin A) are tightly regulated and not affected by recent dietary intake, 2) serum concentrations of some nutrients reflect longer-term intakes than the previous 24 h, and the 1-mo FFQ in NHANES III was not designed to quantify nutrient intakes, 3) single measures of dietary nutrients are highly variable within individuals and 4) FFQ are imprecise measures of food intake and are prone to measurement error, especially among older adults. Nevertheless, correlation coefficients were largest for the expected associations (e.g., dietary calcium with milk/milk products, dietary vitamin C with fruits/fruit juices and serum vitamin C, dietary folate with fruits/fruit juices and serum folate, and fruits/fruit juices and vegetables with serum carotenoids), and the absolute values of the correlation coefficients concur with those of previous studies (Anderson et al. 1999 , Kardinaal et al. 1995 ).

Biochemical data must be interpreted with caution because factors other than diet may influence concentrations (Kaaks et al. 1997 ). For example, we were unable to control differences in individual metabolism. However, our analyses did attempt to control for key factors (e.g., sociodemographic and behavioral characteristics shown to affect serum nutrients, hours of fasting, amount of circulating plasma lipids that transport fat-soluble vitamins), suggesting that the lower concentrations of several serum nutrients among adults from FIF reflect lower long-term intakes of foods that provide those nutrients.

Our estimates of underreporting suggest that younger adults from FIF were no more likely to underreport their food intakes than were younger adults from FSF. In contrast, a higher percentage of older adults from FIF were estimated to underreport their food intakes compared with older adults from FSF, but this finding was no longer significant after adjustment for covariates. Although it is possible that underreporting did contribute to differences noted between adults by food insufficiency status, we believe that our findings for adults from FIF are not simply due to underreporting. As discussed by Tarasuk and Beaton (1998), the methodology used assumes that individuals are in energy balance during the period of assessment and that usual intake is being assessed. Neither of these assumptions may be valid in individuals who report sometimes or often not having enough food to eat. For example, in NHANES III, many adults from FIF reported behaviors related to lower food intakes (e.g., cutting the size of their meals because of lack of money or food, skipping meals because of lack of money or food).

Policy implications.

Low intakes and concentrations of many serum nutrients may compromise immune function and increase the risk of developing major chronic diseases, including cardiovascular disease, certain cancers, osteoporosis, macular degeneration and cataracts (Carr and Frei 1999 , Cooper et al. 1999 , Miller and Anderson 1999 , Selhub and Rosenberg 1996 , Sokol 1996 ). Some may conclude that adults who experience food insufficiency should take dietary supplements that contain the recommended amounts of vitamins and minerals, especially because the recommended amounts for several vitamins and minerals have increased (Institute of Medicine 1997 , 1998 , 2000 and 2001 ). Also, many multivitamin and mineral supplements are inexpensive. A. C. Nielson data collected from supermarkets and drugstores in 1996–1997 showed that average tablet cost <10 cents (Levedahl 1999 ). In our analyses, 15% of younger adults from FIF and 18% of older adults from FIF reported taking a dietary supplement containing multiple vitamins and minerals at least one time in the month preceding the interview. However, dietary supplements do not provide energy, which is especially important for older adults. In addition, several health organizations, including the World Health Organization’s International Agency for Research on Cancer, the American Institute for Cancer Research and the American Heart Association, promote the consumption of healthful foods (e.g., fruits and vegetables) rather than dietary supplements (Cooper et al. 1999 ).

For many Americans, increasing the intake of some nutrients from food sources may not be as much of a concern today because many foods are now fortified. For example, as of January 1, 1998, folate has been added to enriched grain products (U.S. Department of Health and Human Services and Food and Drug Administration 1996 ), and an increasing number of foods are fortified with vitamin C and calcium. However, low-income adults, who are more likely to be food insufficient, often live in inner cities where grocery store prices of these foods (e.g., fruit juices fortified with vitamin C or calcium) may be higher (Kaufman et al. 1997 ). In addition, consumer retail prices of fruits and vegetables, which are natural food sources of many nutrients (e.g., vitamins A and C), have increased more than the prices of other types of foods in the past 20 y (Putnam and Gerrior 1999 ). Financial assistance is needed to assist low-income adults from FIF with the purchase of these foods. This could be in the form of store or farmers’ market vouchers, increased food stamp allotments and the inclusion of fruits and vegetables at commodity food distributions. Adults in FIF may also need nutrition education to help them choose healthy foods within their limited financial means.

General strengths and limitations.

NHANES III is a nationally representative survey of 33,994 persons in the United States that oversampled Mexican Americans, low-income and older adults, allowing for adequate sample sizes when controlling for important covariates in our analyses. A unique strength of this particular survey is the inclusion of multiple measures of dietary intake and measures of several serum nutrients. However, measures of nutritional status have inherent limitations (Thompson and Byers 1994 ). FFQ are imprecise measures of intake, and a single 24-h dietary recall does not capture usual intake. In NHANES III, estimates of percentages of adults from FIF with low nutrient intakes were determined from the distributions of nutrient intakes on a single day as opposed to several days.

Although we acknowledge the limitations associated with nutritional assessment, we believe that our results most likely understate the poor nutritional status of adults from FIF for the following reasons. The NHANES III food insufficiency question inquires about food availability in the family, and our measures of nutritional status refer to individual adults who were randomly selected from those families. Because food insufficiency is a dynamic process, different persons in the family may experience food insufficiency at different times (e.g., a mother may limit her food intake to feed her family between paychecks). Therefore, not all adults who were randomly selected from FIF may have experienced food insufficiency themselves. Also, the food insufficiency question in NHANES III did not specify a time frame. It followed questions that referred to the past year but was followed by questions related to food insufficiency that referred to the past month, so not all participants may have answered the question with the same time period in mind. For those randomly selected adults who did experience food insufficiency, the dietary measures (i.e., single 24-h dietary recall, 1-mo FFQ) or serum measures may not have reflected a time period when food intake was limited. In addition, Frongillo et al. (1997 ) have shown that a single question about food insufficiency, such as that used in NHANES III, underestimates the prevalence of food insufficiency compared with the more comprehensive indices developed by Radimer et al. (1992 ) or the Community Childhood Hunger Identification Project (Wehler et al. 1992 ). Last, our results from analyses that included adults without family income data suggest that our findings from the main analyses that included only adults who reported family income are conservative. Adults from FIF without family income data had worse nutritional status, as demonstrated by significantly lower intakes of more nutrients and foods and significantly lower concentrations of more serum nutrients.

Hunger is a chronic societal problem as reflected by national policies beginning in the 1930s and continuing through the present (Nestle 1999 ). Despite a booming economy and an ample supply of food, recent surveys show that the prevalence of food insecurity has remained at 12% and that of hunger has remained at 4% since 1995 (Bickel et al. 1999 ). In our study, higher percentages of adults from FIF had low intakes of several nutrients and very low concentrations of serum nutrients compared with adults from FSF. Our findings generally agree with those of previous studies and suggest that millions of Americans who experience food insufficiency are likely to have poor diets that may compromise their health.

	APPENDIX

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 

	ACKNOWLEDGMENTS

	FOOTNOTES

 
¹ This work was completed while Dr. Dixon was a Cancer Prevention Fellow at the National Cancer Institute.

³ Abbreviations used: AI, adequate intake; CSFII, Continuing Survey of Food Intakes by Individuals; EI/BMR_est, energy intake to estimated basal metabolic rate; FFQ, food frequency questionnaire; FIF, food-insufficient families; FSF, food-sufficient families; MEC, mobile examination center; NCHS, National Center for Health Statistics; NHANES III, Third National Health and Nutrition Examination Survey; PIR, poverty income ratio; RDA, Recommended Dietary Allowance.

Manuscript received June 13, 2000. Initial review completed July 24, 2000. Revision accepted January 2, 2001.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION APPENDIX REFERENCES

 

1. Alaimo K., Briefel R. R., Frongillo E. A., Olson C. M. Food insufficiency exists in the United States: results from the Third National Health and Nutrition Examination Survey (NHANES III). Am. J. Public Health 1998;88:419-426[Abstract/Free Full Text]

2. Anderson L. F., Solvoll K., Johansson L.R.K., Salminen I., Aro A., Drevon C. A. Evaluation of a food frequency questionnaire with weighed records, fatty acids, and alpha-tocopherol in adipose tissue and serum. Am. J. Epidemiol. 1999;150:75-87[Abstract/Free Full Text]

3. Anderson S. A. Core indicators of nutritional state for difficult-to-sample populations. J. Nutr. 1990;120:1557S-1600S

4. Beers M. H., Berkow R. Normal laboratory values. Beers M. H. Berkow R. eds. The Merck Manual of Diagnosis and Therapy 1999:2526-2543 Merck Research Laboratories Whitehouse Station, NJ.

5. Bickel A., Carlson S., Nord M. Household Food Security in the United States 1999:1995-1998 Advance Report. U.S. Department of Agriculture Food and Nutrition Service, Washington, D.C.

6. Briefel R. R., Woteki C. E. Development of food sufficiency questions for the Third National Health and Nutrition Examination Survey. J. Nutr. Educ. 1992;24:24S-28S

7. Briefel R. R., Sempos C. T., McDowell M. A., Chien S., Alaimo K. Dietary methods research in the Third National Health and Nutrition Examination Survey: underreporting of energy intake. Am. J. Clin. Nutr. 1997;65:1203S-1209S[Abstract/Free Full Text]

8. Butrum R. R., Clifford C. K., Lanza E. NCI dietary guidelines: rationale. Am. J. Clin. Nutr. 1988;48:888-895[Abstract/Free Full Text]

9. Carr A. C., Frei B. Toward a new recommended dietary allowance for vitamin C based on antioxidant and health effects in humans. Am. J. Clin. Nutr. 1999;69:1086-1107[Abstract/Free Full Text]

10. Cooper D. A., Eldridge A. L., Peters J. C. Dietary carotenoids and certain cancers, heart disease, and age-related macular degeneration: a review of recent research. Nutr. Rev. 1999;57:201-214[Medline]

11. Cristofar S. P., Basiotis P. P. Dietary intakes and selected characteristics of women ages 19–50 years and their children ages 1–5 years by reported perception of food sufficiency. J. Nutr. Educ. 1992;24:53-58

12. Ford E. S., Sowell A. Serum alpha-tocopherol status in the United States population: findings from the Third National Health and Nutrition Survey. Am. J. Epidemiol. 1999;150:290-300[Abstract/Free Full Text]

13. Friedewald W. T., Levy R. I., Fredrickson D. S. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin. Chem. 1972;18:499-502[Abstract]

14. Frongillo E. A., Rauschenbach B. S., Olson C. M., Kendall A., Colmenares A. G. Questionnaire-based measures are valid for the identification of rural households with hunger and food insecurity. J. Nutr. 1997;127:699-705[Abstract/Free Full Text]

15. Gibson R. S. Nutritional Assessment: A Laboratory Manual 1993 Oxford University Press New York, NY.

16. Goldberg G. R., Black A.E.J.S.A., Cole T. J., Murgatroyd P. R., Coward W. A., Prentice A. M. 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;45:569-581[Medline]

17. Gunter E. W., Lewis B. G., Koncikowski S. M. Laboratory procedures used for the Third National Health and Nutrition Examination Survey (NHANES III), 1988–1994 1996 U.S. Department of Health and Human Services Hyattsville, MD.

18. Hamilton W. L., Cook J. T., Thompson W. W., Buron L. F., Frongillo E. A., Olson C. M., Wehler C. A. Household Food Security in the United States: Summary Report of the Food Security Measurement Project 1997 U.S. Department of Agriculture Food and Consumer Service, Alexandria, VA.

19. Hunter D. Biochemical indicators of dietary intake. Willett W. eds. Nutritional Epidemiology 1998:174-243 Oxford University Press New York.

20. Institute of Medicine, Food and Nutrition Board Dietary Reference Intakes for Calcium, Phosphorus, Magnesium, Vitamin D, and Fluoride 1997 National Academy Press Washington, D.C.

21. Institute of Medicine, Food and Nutrition Board Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline 1998 National Academy Press Washington, D.C.

22. Institute of Medicine, Food and Nutrition Board Dietary Reference Intakes fro Vitamin A, Vitamin K, Arsenic, Boron, Chromium, Copper, Iodine, Iron, Manganese, Molybdenum, Nickel, Silicon, Vanadium, and Zinc 2001 National Academy Press Washington, D.C.

23. Institute of Medicine, Food and Nutrition Board Dietary Reference Intakes for Vitamin C, Vitamin E, Selenium, and Carotenoids 2000 National Academy Press Washington, D.C.

24. Ito Y., Shimizu H., Yoshimura T., Ross R. K., Kabuto M., Takatsuka N., Tokui N., Suzuki K., Shinohara R. Serum concentrations of carotenoids, alpha-tocopherol, fatty acids, and lipid peroxides among Japanese in Japan, and Japanese and Caucasians in the U.S. Int. J. Vitamin. Nutr. Res. 1999;69:385-395

25. Kaaks R., Riboli E., Sinha R. Biochemical markers of dietary intake. Toniolo P. Boffetta P. Shuker D. E. G. Hulka B. Pearce N. eds. Application of Biomarkers in Cancer Epidemiology 1997 International Agency for Research on Cancer Lyon, France.

26. Kardinaal A.F.M., van’t Veer P., Brandts H.A.M., van den Berg H., van Schoonhoven J., Hermus R.J.J. Relations between antioxidant vitamins in adipose tissue, plasma, and diet. Am. J. Epidemiol. 1995;141:440-445[Abstract/Free Full Text]

27. Kaufman P. R., MacDonald J. M., Lutz S. M., Smallwood D. M. Do the Poor Pay More for Food? Item Selection and Price Differences Affect Low-Income Household Food Costs 1997 Agricultural Economic Report No. 759, U.S. Department of Agriculture Washington, D.C.

28. Kendall A., Olson C. M., Frongillo E. A. Relationship of hunger and food insecurity to food availability and consumption. J. Am. Diet. Assoc. 1996;96:1019-1024[Medline]

29. Levedahl W. Retail costs of vitamin and mineral supplements. The Use of Food Stamps to Purchase Vitamin and Mineral Supplements 1999 U.S. Department of Agriculture Food and Nutrition Service, Washington, D.C.

30. Loria C. M., Whelton P. K., Caulfield L. E., Klag M. J. Agreement among indicators of vitamin C status. Am. J. Epidemiol. 1998;147:587-596[Abstract/Free Full Text]

31. McDowell, A., Engel, A., Massey, J. T. & Maurer, K. (1981) Plan and operation of the Second National Health and Nutrition Examination Survey, 1976–80. National Center for Health Statistics. Vital Health Stat. 1(15).

32. McEligot A. J., Rock C. L., Flatt S. W., Newman V., Faerber S., Pierce J. P. Plasma carotenoids are biomarkers of long-term high vegetable intake in women with breast cancer. J. Nutr. 1999;129:2258-2263[Abstract/Free Full Text]

33. Miller G. D., Anderson J. J. The role of calcium in prevention of chronic diseases. J. Am. Coll. Nutr. 1999;18:371S-372S[Free Full Text]

34. Miller, H. W. (1973) Plan and Operation of the Health and Nutrition Examination Survey, United States, 1971–1973. National Center for Health Statistics. Vital Health Stat. 1(10a) and (10b).

35. National Center for Health Statistics Plan and Operation of the Hispanic Health and Nutrition Examination Survey, 1982–84. Vital Health Stat 1985;1:19

36. National Center for Health Statistics Plan and Operation of the Third National Health and Nutrition Examination Survey. Vital Health Stat 1994;1:1-32

37. National Center for Health Statistics (1996) Third National Health and Nutrition Examination Survey, 1988–1994, NHANES III Laboratory Data File (CD-ROM). Public Use Data File Documentation Number 76200: Centers for Disease Control and Prevention, Hyattsville, MD.

38. National Research Council Recommended Dietary Allowances 1989 National Academy Press Washington, D.C.

39. Nestle M. Hunger in America: a matter of policy. Soc. Res. 1999;66:257-282

40. Olson J. A. Carotenoids. Shils M. E. Olson J. A. Shike M. Ross A. C. eds. Modern Nutrition in Health and Disease 1999:525-541 Williams & Wilkins Baltimore, MD.

41. Putnam J., Gerrior S. Trends in the U.S. food supply, 1970–97. Frazao E. eds. America’s Eating Habits: Changes and Consequences 1999:133-160 U.S. Department of Agriculture Economic Research Service, Washington, D.C.

42. Radimer K., Olson C. M., Greene J. C., Campbell C. C., Habicht J. P. Understanding hunger and developing indicators to assess it in women and children. J. Nutr. Educ. 1992;24:36S-45S

43. Rose D., Oliveira V. Nutrient intakes of individuals from food-sufficient households in the United States. Am. J. Public Health 1997;87:1956-1961[Abstract/Free Full Text]

44. Schofield W. N. Predicting basal metabolic rate: new standards and review of previous work. Hum. Nutr. Clin. Nutr. 1985;39C:1203S-1209S

45. Selhub J., Rosenberg I. H. Folic acid. Ziegler E. E. Filer L. J. eds. Present Knowledge in Nutrition 1996 ILSI Press Washington, D.C.

46. Sempos, C.T., Briefel, R.R., Johnson, C. & Woteki, C. E. (1992) Process and rationale for selecting dietary methods for NHANES III. In: Dietary Methodology Workshop for the Third National Health and Nutrition Examination Survey, National Center for Health Statistics (Briefel, R. R. & Sempos, C. T., eds.). Vital Health Stat. 4: 85–90.

47. Sokol R. J. Vitamin E. Ziegler E. E. Filer L. J. eds. Present Knowledge in Nutrition 1996 ILSI Press Washington, D.C.

48. Tarasuk V. S., Beaton G. H. Women’s dietary intakes in the context of household food insecurity. J. Nutr. 1999;129:672-679[Abstract/Free Full Text]

49. Thompson F. E., Byers T. Dietary assessment resource manual. J. Nutr. 1994;124:2245S-2317S

50. U.S. Department of Agriculture and U.S. Department of Health and Human Services (2000) Nutrition and Your Health: Dietary Guidelines for Americans, 5th ed., Home and Garden Bulletin No. 232. Washington, D.C.

51. U.S. Department of Health and Human Services (2000) Healthy People 2010 (conference edition, in two volumes), Washington, D.C. http://www.health.gov/healthypeople/Publications/default.htm (accessed November 2000).

52. U.S. Department of Health and Human Services (1990) National Cholesterol Education Program Report of the Expert Panel on Population Strategies for Blood Cholesterol Reduction. NIH Publication No. 90-3046, Washington, D.C.

53. U.S. Department of Health and Human Services and Food and Drug Administration (1996) Food Standards: Amendment of Standards of Identity for Enriched Grain Products to Require Addition of Folic Acid. Final rule. Code of Federal Regulations, Title 21: Parts 136, 137 and 139. Federal Register 61 (44-March 5, 1996): 8781–8797.

54. Wehler C. A., Scott R. I., Anderson J. J. The Community Childhood Hunger Identification Project: a model of domestic hunger-demonstration project in Seattle, Washington. J. Nutr. Educ. 1992;24:29S-35S

55. Winbauer A. N., Pingree S. S., Nuttall K. L. Evaluating serum alpha-tocopherol (vitamin E) in terms of a lipid ratio. Ann. Clin. Lab. Sci. 1999;29:185-191[Abstract]

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