Analyses of the relationship between food habits and health began by studying the role that specific nutrients may play in the etiopatogenesis of certain diseases. For instance, a folate deficiency was associated with a higher risk of neural tube defects. However, for certain diseases, the complexity of the relationships between dietary intake and the pathology cannot be attributed to a single nutrient but rather to multiple nutrients and foods. Thus, the correct exposure has to be measured to understand such a relationship, and not only nutrients but also foods, and the interaction between them, are of concern for this kind of evaluation. Food pattern analysis is then a key issue to investigate the linkages between nutrition and disease.
Diet scores or diet indices were the first methods used in nutritional epidemiology to assess the effect that a combination of nutrients or foods (not only a single nutrient) may exert on health. With that purpose two diet indices, the nutrient adequacy ratio (NAR) and the mean adequacy ratio (MAR), were defined to evaluate the overall dietary adequacy of individuals and population groups(Reference Madden and Yoder1). Diet indices, defined as a composite score of nutrients, foods or both, have been created based on previous nutrition knowledge for evaluating the adherence to pre-specified guidelines or recommendations(Reference Kant2–Reference Arvaniti and Panagiotakos6). These patterns, as they are hypothesis oriented, are known as a priori defined. Another approach, the so-called a posteriori approach, consists of defining food patterns once the dietary data are collected and using specific statistical analyses to identify the relevant actual food patterns of the study population. Such statistical analyses were first applied by Schwerin et al. (Reference Newby and Tucker7) (1982). From then on, several publications have used various statistical procedures, mainly factor analysis or cluster analysis to analyse dietary data and to empirically identify dietary patterns(Reference Kant3, Reference Moeller, Reedy and Millen5, Reference Newby and Tucker7–Reference Bailey, Gutschall and Mitchell11).
Both a priori hypothesis-oriented diet indices and a posteriori defined patterns have been related to the incidence of health outcomes (hard clinical end points) and biomarkers in epidemiological or clinical studies. Some of these dietary patterns have been related to nutrient adequacy. This approach parallels that of a validation study, based on the rationale that if the classification of participants according to their adherence to the dietary pattern is able to ascertain whether or not they fail to meet the optimal nutrient intake, the use of the dietary pattern is sufficiently valid. The purpose of the present study was to review which methods that evaluate dietary patterns have been tested for their validity in measuring micronutrient intake adequacy.
Material and methods
We searched PubMed up to April 2008. The search strategy included both keywords and MesH terms. The keywords included were: ‘diet quality’, ‘dietary pattern’, ‘food pattern’, ‘food groups’, ‘dietary diversity’, ‘dietary variety’, ‘diet score’, ‘diet index’, ‘nutrient adequate intake’, ‘nutrient inadequate intake’, ‘nutrient adequacy’ and ‘nutrient inadequacy’. And the MeSH terms were: ‘factor analysis’, ‘statistical’, ‘cluster analysis’, ‘statistical analysis’.
We selected studies that analysed the correlation between food patterns (either defined a priori or a posteriori) and nutrient intake adequacy (assessed by the methods of the probability approach, the MAR, a cut-off point of the recommended reference intakes or other methods), or biochemical markers of intake. We identified studies that assessed the validity of the dietary patterns, in other words, studies that used two different methods of diet analysis (food frequency questionnaire, diet record and 24 hour recall) to derive dietary patterns and the adequacy of nutrient intake.
We excluded studies based on food balance sheet data, and studies that compared the dietary pattern against nutrient intake values for certain nutrients using the same instrument (nutrient intake data obtained from the same diet measurement tool that had been used to assess the dietary pattern). Studies that correlated dietary patterns with the incidence of health outcomes (hard clinical end points) were also excluded from the present analysis.
Articles were selected by reading the title and the abstract. The access to the full text of the article permitted a second stage of selection. Reference articles were also reviewed for potential inclusion.
Results
The initial Pubmed search retrieved 1504 articles. After applying the first selection criteria (title and abstract evaluation), fifty articles were obtained. The specific inclusion and exclusion criteria were applied and seventeen articles were finally included for the review. Thirteen more articles were identified by reviewing the references, thus obtaining a final selection of thirty articles.
Eight of the articles were reviews and three of them were methodological. Nineteen studies investigated the value of the dietary patterns assessed by diet index (thirteen studies), factor analysis (four studies) or cluster analysis (two studies). The methods used to evaluate nutrient intake adequacy were the probability approach in four studies, the MAR in five studies, a cut-off point of the reference intake values in one study and biomarkers of intake in five studies. Nine articles assessed validity comparing nutrient intake and the defined dietary pattern, both obtained from two different assessment tools (diet records were used as the standard to compare the dietary pattern).
Table 1 shows the results found for the studies in the search and Table 2 shows detailed information for micronutrient intake adequacy related to each dietary pattern, when the information was available.
Table 1 Studies assessing the validity of diet pattern to test nutrient intake adequacy
24 HR, 24 hour recall; DDS, dietary diversity score; MPA, mean probability approach; FVS, food variety score; MAR, mean adequacy ratio; HEI, healthy eating index; RBCF, red blood cell folate; FFQ, Food Frequency Questionnaire; DQI-R, Diet Quality Index Revised; DR, dietary record; RDI, Recommended Dietary Intake; WR, weighed record; HDI, healthy diet indicator; HP, healthy pattern; WP, western pattern; DP, drinker pattern; SP, sweets patterns; FA, factor analysis; PP, prudent pattern; HEDP, health energy density pattern; TP, traditional pattern; H-CP, health-conscious pattern; GS, global score; NN, number of nutrients for which intake is equal to or better than recommended levels; NN66, number of nutrients for which intake is equal to or better than 66 %.
Table 2 Correlation coefficient between nutrient adequacy and diet pattern methods
DDS, diet diversity score; HEI, healthy eating index; DQI-R, diet quality index revised; FVS, food variety score.
* Biochemical measurement.
† r = 0·54 when comparing DQI-R and prevalence of adequacy.
‡ r = 0·06 when comparing DQI-R and prevalence of adequacy.
§ Comparison against nutrient intake.
∥ Correlation shown for the prudent/healthy pattern derived from data from FFQ and biochemical data.
Diet indices
Only five studies were identified as being a validation study and using two different methods to assess diet pattern and nutrient intake adequacy.
Newby(Reference Newby, Hu and Rimm12) studied a subsample of 127 men from the Health Professionals Follow-up study, to test the validity and reproducibility of the Diet Quality Index Revised (DQI-R). The individuals completed two FFQ, 1 year apart, and two 1-week diet records. They also evaluated biochemical parameters to validate the index. The validity correlations for the DQI-R between each FFQ and the diet record (the effect of week to week variation in diet records was reduced statistically) were r = 0·66 (FFQ1) and r = 0·72 (FFQ2). The fruit score was the most strongly correlated component between the FFQ2 and the diet records (r = 0·71). The association of Ca intake using the FFQ2 showed a correlation coefficient of r = 0·35 against the DQI-R. The DQI-R was also compared with nutrient intake estimated by the diet records. The intake of folic acid, Mg, Fe, Ca, vitamin A, carotene, vitamins B6 and C was directly related to DQI-R scores. The correlation coefficient between the score for DQR-I from FFQ1 and FFQ2 was r = 0·72. They concluded that the DQR-I was reasonably reproducible over time and reasonably valid compared with plasma biomarkers and compared with food record-derived nutrient intakes.
Torheim et al. (Reference Torheim, Barikmo and Parr13) evaluated the validity of three indices of diet quality calculated from a FFQ in two different population groups (seventy-five and seventy individuals, respectively) from Western Mali. They used a 2-d weighed record as a validation tool. The indices were the Food Variety Score (FVS), the Diet Diversity Score (DDS) and the MAR. They found Spearman correlation coefficients of r = 0·3 and 0·15 (population group A and population group B) between FVS from the FFQ and the two weighed records; r = 0·20 (for both population groups) between the DDS from the FFQ and the two weighed records; and r = 0·4 (population group A) and r = 0·49 (population group B) between the MAR from FFQ and two weighed records. They also compared the FVS and the DDS against the adequacy of the intake evaluated by the MAR from the two weighed records. They found correlation coefficients of r = 0·36 and 0·24 (group A and B) between FVS and MAR, and r = 0·35 (study A) and r = 0·29 (study B) between DDS and MAR. They found different correlation coefficients for males and females, and they concluded that both indices were relatively good indicators for nutrient adequacy among males.
In another study in a sample of 340 women, Hann et al. (Reference Hann, Rock and King14) showed data for validating the Healthy Eating Index (HEI) score against plasma biomarkers. Dietary data was assessed using a 3-d food record. They showed that higher HEI scores were associated with higher plasma concentrations of certain carotenoids (α-carotene, r = 0·41; β-carotene r = 0·30; β-cryptoxanthin, r = 0·40; and lutein, r = 0·24) and vitamin C (r = 0·33)
Data from the Third National Health and Nutrition Examination Survey were used to relate nutritional biomarkers with the HEI(Reference Weinstein, Vogt and Gerrior15). The results showed a positive correlation between HEI and serum folate (r = 0·25) and red blood cell folate (r = 0·27), serum vitamins C (r = 0·30), E (r = 0·21), α-carotene (r = 0·27), β-carotene (r = 0·21), β-cryptoxanthin (r = 0·24) and lutein (r = 0·17). No correlations were found between the HEI score and cholesterol, triglyceride, vitamin D, ferritin, Se or total Ca level.
Other studies have associated diet indices with plasma biomarkers. Gerber et al. (Reference Gerber, Scali and Michaud16) found that an adaptation of DQI to French dietary habits was positively related to omega 3 fatty acids (EPA and DHA) and inversely associated to cholesterol in a sample of 147 volunteers from southern France. Nutrition data were obtained with a FFQ.
The following studies do not use a validation tool, but correlated the diet patterns under study against the measurement of nutrient intake adequacy using the same dietary data collection method.
In a group of Filipino children, Kennedy et al. (Reference Kennedy, Pedro and Seghieri17) showed that the DDS was a valid tool to predict adequate intake. The probability approach was used to calculate the adequacy of intake for eleven nutrients and a mean probability of adequate nutrient intake (MPA) was calculated. The Pearson's correlation between DDS and MPA was significant (r = 0·36) and improved to 0·44 when they calculated a DDS applying a 10 g minimum intake for all food groups (except fats and oils). The correlation between the DDS and the MPA was significant for nine nutrients (vitamin A, vitamin C, thiamin, riboflavin, niacin, vitamin B6, folic acid, absorbed Zn and absorbed Fe).
Mirmiran(Reference Mirmiran, Azadbakht and Azizi18) studied the appropriateness of DDS to assess nutrient adequacy in a population of Iranian women. They calculated DDS with a set of five groups. The probability of adequacy for fourteen nutrients and MPA was calculated. The cereals diversity score correlated with the probability of adequacy for vitamin B2. The vegetable diversity score correlated with intake adequacy for vitamins A, C and K. The fruit diversity score correlated with the probability of adequacy for vitamins A, C and K. The dairy diversity score correlated with Ca, P and vitamin B2 and Zn adequacies. The meat diversity score correlated with the probability of adequacy for vitamins B6 and B12, Fe and P. The correlation between DDS and the MPA was r = 0·6 (r = 0·43 when adjusted for energy intake).
Steyn et al. (Reference Steyn, Nel and Nantel19)assessed whether the FVS and the DDS were good indicators of nutrient intake adequacy among a sample of 2200 children aged 1–8 years old. Dietary data were obtained with a 24 hour recall and nutrient intake adequacy was assessed calculating the MAR for eleven nutrients. They obtained a high correlation between FVS and MAR (r = 0·726) and DDS and MAR (r = 0·657). Both the FVS and the DDS were correlated with the adequacy of vitamins A, B6, B12, C, and Ca, folic acid, Fe, niacin, riboflavin, thiamin and Zn for all subjects.
In addition, Mirmiran et al. (Reference Mirmiran, Azadbakht and Esmaillzadeh20) evaluated the DDS as an indicator of nutritional adequacy among 304 adolescents from Tehran. They used the MAR to assess nutrient intake adequacy and found a correlation coefficient between DDS and MAR of r = 0·42. The MAR reflected the nutrient adequacy ratio of twelve nutrients (vitamin A, riboflavin, thiamin, vitamin C, Ca, Fe, Zn, P, Mg, protein, K and fat). The correlation between DDS and the Nutrient Adequacy Ratio was significant for vitamin A, riboflavin, Zn, Ca, K, P and Mg.
Foote et al. (Reference Foote, Murphy and Wilkens21) assessed the association between diet variety, as measured with a commodity-based definition similar to the HEI and nutrient adequacy. The sample came from the Continuing Survey of Food Intakes by Individuals (94–6). They calculated the probability of adequacy for fifteen nutrients and created an MPA. The results showed that total dietary variety was correlated with the mean probability of adequacy in both men (r = 0·44) and women (r = 0·46), after adjusting for energy intake. Dairy diversity score correlated with Ca and vitamin A. The cereals diversity score correlated with the probability of adequacy for folate and Mg. The fruit diversity score correlated with the probability of adequacy for vitamins A and C.
Serra-Majem et al. (Reference Serra-Majem, Ribas and García22) compared the nutritional status of Spanish children against a Mediterranean diet score (KIDMED index). The nutrient intake adequacy was assessed as the percentage of population with intakes below two-thirds of the recommended nutrient intakes. The higher the KIDMED index score, the lower the prevalence of inadequacy for Ca, Fe (in females), Mg, vitamin B6 (excluding males aged 6–14) and vitamins C and A (in females).
Dubois(Reference Dubois, Girard and Bergeron23) assessed the appropriateness of three methods of measuring diet quality (DQI, HEI and Healthy Diet Indicator = HDI) to evaluate adherence to the Canadian recommendations. The study was developed with data from the Québec Nutrition Survey (2103 individuals that completed a 24 hour recall). The results showed that the HEI was the method having a higher correlation coefficient when compared with the MAR (r = 0·287).
Hatløy et al. (Reference Hatløy, Torheim and Oshaug24) also used the MAR as an indicator of nutrient adequacy to validate FVS and DDS in a sample of 77 children, 13–58 months of age in Mali. The diet was assessed with a 2 or 3-d weighed record. They found positive correlation coefficients between FVS and MAR (r = 0·33) and DDS and MAR (r = 0·39). Both the FVS and the DDS correlated with the nutrient adequacy ratio of vitamin C and vitamin A.
Factor analysis
There is scarce data on the validity and reproducibility of the factor analysis method. Only two studies reported validation data related to diet patterns derived from factor analysis.
Hu et al. (Reference Hu, Rimm and Smith-Warner25) analysed the reproducibility and validity of two dietary patterns defined by factor analysis, the Prudent diet and the Western diet, in a group of 157 men participating in the Health Professionals' Follow-up study. They compared the consistency of the dietary patterns derived from the two FFQ, obtained in a 1 year interval, and diet records (two 7-d diet records). Good correlation coefficients were observed (a coefficient of r = 0·7 for the prudent pattern and r = 0·67 for the Western pattern when assessed by the two FFQ). When they compared the dietary pattern defined from the two FFQ against that defined by diet records, they found correlation coefficients of r = 0·45 for the Prudent pattern and r = 0·74 for the Western pattern. They also assessed the validity of the defined dietary patterns by comparing the dietary patterns with computed nutrients obtained from the diet record and with plasma biochemical concentrations. The results showed moderate correlation values between the dietary patterns and certain nutrient intakes (fibre, Mg, K, folic acid, vitamin B6 and carotenes for the Prudent pattern, and total fat and saturated fat for the Western diet). The Pearson correlation coefficient between the Prudent pattern and plasma concentration of biomarkers ranged from r = 0·29 (FFQ1) to r = 0·39 (FFQ2) for α-carotene; from r = 0·23 (FFQ1) to r = 0·37 (FFQ2) for β-carotene; from r = 0·28 (FFQ1) to r = 0·31 (FFQ2) for lycopene; and from r = 0·33 (FFQ1) to r = 0·33 (FFQ2) for lutein. The dietary pattern defined from the diet record showed better correlation values with the blood measurements than the ones defined from the FFQ.
Khani et al. (Reference Khani, Ye and Terry26) studied the validity and reproducibility of major dietary patterns in Swedish women from the Swedish Mammography Cohort. They defined three different dietary patterns by factor analysis (Healthy, Western and Drinker) and tested their reproducibility. Patterns were compared, as defined from two FFQ administered 1 year apart. They obtained coefficients of reproducibility of r = 0·63 (Healthy pattern), r = 0·68 (Western pattern) and r = 0·73 (Drinker pattern). The information obtained from four 7-d diet records was used as a gold standard to validate the dietary patterns. Correlation coefficients of r = 0·47 for the Healthy pattern, r = 0·41 for the Western and r = 0·73 for the Drinker profile were obtained.
The following studies correlated dietary patterns based on factor analysis against nutrient intake adequacy using dietary data obtained from the same instrument.
Beaudry et al. (Reference Beaudry, Galibois and Chaumette27) evaluated the dietary pattern of 2118 adults participating in the Canadian Provincial Nutrition Survey. They defined three patterns (high-energy density, traditional and health conscious) and correlated it with three scores (global score, energy score, number of nutrients (NN) for which intake was equal to or better than the recommended level and the NN for which intake was equal to or better than 66 % of recommended level (NN66)). These have been defined previously by the same authors to evaluate nutrient adequacy(Reference Beaudry, Galibois and Chaumette28). The Health-conscious pattern was positively correlated with each of the nutrient adequacy scores defined, with Kendall's τ-b correlation coefficient of 0·33 against global score, 0·22 against energy score and 0·34 and 0·30 against NN and NN66, respectively. The Traditional pattern also correlated with the nutrient adequacy scores (0·40 between the pattern and the global score, 0·42 between the pattern and NN and 0·37 between the pattern and NN66).
Newby(Reference Newby, Weismayer and Akesson29) tested the long-term stability and reproducibility of dietary patterns defined by confirmatory factor analysis among a sample of Swedish women. Correlation values for food group intake across 10 year intervals were moderate and ranged from r = 0·27 to 0·54 for the four patterns defined. The Healthy pattern was strongly correlated with nutritional intakes of vitamin B6, folic acid, vitamin C and β-carotene.
In another study that examined the stability of dietary patterns in a group of ninety-four women from the Southampton Women's Survey, Borland et al. (Reference Borland, Robinson and Crozier30) assessed the reproducibility of dietary scores that were analysed 2 years apart. A Bland–Altman plot of the differences between scores obtained on the two occasions was also calculated. Two dietary patterns were defined using principal component analysis calculated from the dietary data derived from a FFQ, the ‘Prudent’ and ‘High-energy patterns’. They showed Spearman correlation coefficients of 0·81 and 0·64 between the initial and repeated score for the prudent and high energy patterns, respectively.
Cluster analysis
A validation study for dietary pattern as defined by cluster analysis was published by Millen et al. (Reference Millen, Quatromoni and Copenhafer31) and Quatromoni et al. (Reference Quatromoni, Copenhafer and Demissie32). The sample came from 1828 women participating in the Framingham Offspring–Spouse study. They correlated the five dietary patterns derived from dietary data obtained from a FFQ (Heart Healthy, Light eating, Wine and Moderate eating, High fat and Empty energy) against a nutrient rank risk. The nutrient rank risk was calculated from nutrient intake data obtained from a 3-d dietary record, and it represented the independent criteria for which to assess the internal validity of dietary patterns. They ranked nutrient intake among all women according to a desirable nutrient intake level as protective or risky in terms of chronic disease prevention. A mean rank was calculated as the average of rankings from nineteen individual nutrient variables for each woman. The results showed that dietary patterns were associated with differences in nutrient intake profiles. For instance, the Heart-healthy pattern was associated with the most desirable overall rank, and with the lowest ranks of intake for protein, total, saturated and monounsaturated fat, carbohydrate, fibre, Ca, vitamins C, B6 and E, folic acid and β-carotene. Women in the Empty energy cluster consumed low-nutrient quality diets, with a higher intake of total fat and energy, and lower intakes of vitamins, fibre, protein and alcohol.
Discussion
Two approaches can be used to define dietary patterns: one is an a priori or theoretical approach that consists of the definition of certain scores or indices(Reference Kant2–Reference Arvaniti and Panagiotakos6, Reference Ruel33). The second comprises an empirical or an a posteriori definition; it is defined after dietary data collection and is based on statistical calculations(Reference Kant3, Reference Moeller, Reedy and Millen5, Reference Newby and Tucker7–Reference van Dam9).
The a priori approach. Defined by Waijers et al. (Reference Waijers, Feskens and Ocké4) as ‘Nutritional variables considered to be important to health that are quantified and summed to provide an overall measure of diet quality’. These score-based approaches are mostly constructed following certain dietary recommendations, and the inclusion of the selected nutrients or foods depends on their presence in the guidelines utilised to define the score (and to a certain degree, it also depends on the investigator's judgement).
The construction of dietary indices may be based on nutrients only, foods or food groups and a combination of nutrients and foods(Reference Kant2). The data are grouped according to predefined cut-off points. The newly defined groups are assigned values, which are then summed up to obtain a measure of diet quality or a diet score. Recent reviews of Waijers et al. (Reference Waijers, Feskens and Ocké4) and Arvaniti et al. (Reference Arvaniti and Panagiotakos6) have reported more than twenty currently existing indices.
The main advantage of the a priori approach is its generalisability, i.e. that it can be applied to multiple populations. On the other hand, several inconveniencies arise when analysing data: their definition might rely on the investigator's subjectivity, and they are based on dietary recommendations, which have been defined for certain populations and may not be applicable to others(Reference Kant2, Reference Waijers, Feskens and Ocké4, Reference Arvaniti and Panagiotakos6).
The a posteriori approach or exploratory approach. Data reduction techniques use between-food group correlations to reduce dietary data collected from FFQ, 24 hour recalls or diet records into smaller sets of variables to define dietary patterns. Three techniques are used for this purpose: factor analysis, cluster analysis and reduced-rank regression.
Factor analysis. Factor analysis is a procedure that aggregates and reduces dietary data into food groups according to an intercorrelation between dietary items(Reference Martínez, Marshall and Sechrest34). It includes both principal component analysis and confirmatory factor analysis.
As Martínez et al. (Reference Martínez, Marshall and Sechrest34) argue, the application of factor analysis to dietary data, although being a mathematical model, is pragmatic and atheoretical. They cited several subjective decisions that must be made when defining the dietary pattern: which variables will be included in the analysis to construct factors, what level will be used to decide whether a variable may contribute to a factor and finally, what label to apply to the given factor. Such decisions can lead to erroneous conclusions. For instance, when choosing the variables to analyse, unrelated variables may be included in the analysis when other variables were excluded so as to simplify the analysis.
Exploratory factor analysis is used when an a priori hypothesis exists about the factor structure. As such, its advantage is(Reference Moeller, Reedy and Millen5, Reference Newby, Weismayer and Akesson29) that it reduces some of the subjectivity associated with the exploratory procedure and can be applied in different population samples(Reference Schulze, Hoffmann and Kroke35).
Cluster analysis. The cluster analysis is a multivariate statistical approach that unifies individuals (cluster) that share similar observations for a certain number of variables. Individuals in a cluster have similarities between them (i.e. similar diets) and disparities with individuals from another cluster.
This method also includes a certain amount of subjectivity: the choice of which variables will be used, or what level of significance (variables to include in the analysis and number of factors to be included) will be applied(Reference Moeller, Reedy and Millen5).
Reduced-rank regression or maximum redundancy analysis. Hoffman et al. (Reference Hoffman, Schulze and Boeing36) introduced this statistical method to analyse dietary patterns. It is a statistical reduction technique that works with two different sets of variables: predictors and responses. Reduced-rank regression identifies linear functions of predictors that explain as much response variation as possible. The approach ensures that a change in reduced-rank regression pattern score results in a change in the related variable (biomarker or health outcome), which is generally associated with a decrease or increase in the health risk under study. As with other a posteriori approaches, it is difficult to assess whether the dietary pattern can be applied in two different population samples. Theoretically, if the weights and food variables used to calculate reduced-rank regression and to define a dietary pattern can be applied to another population sample, the results would then be comparable(Reference van Dam37).
Validity and reproducibility of dietary patterns
The usefulness of any method will depend upon its validity and reproducibility. Validity is defined as the degree to which a measurement is a true and accurate measure of what it pretends to measure. Dietary patterns are defined mainly for assessing eating behaviour and for relating intake to disease or health outcomes. The measurement of the validity of a method that assesses dietary patterns should take into account the purpose for which it was constructed. If the method was not developed to assess nutrient intake adequacy, its capacity to discriminate to what extent individuals' intakes are meeting their requirements will be an added value of the method. We have focused our search on the validity of the methods used for defining dietary patterns to assess micronutrient intake adequacy, i.e. the diet index under study or the dietary pattern defined as the healthiest one represents the most adequate in terms of nutrient intake. For this purpose, and to avoid correlation errors, only studies using two different methods to assess dietary data should be taken into account(Reference Willett38). Few studies meeting this criterion were found, as most of them studied the association between dietary patterns and nutrient intake adequacy assessed with the same dietary data collection method.
From the results shown, diet indices are valid tools to evaluate intake adequacy for certain micronutrients. A revised DQI showed to be a valid tool for measuring the adequacy of intake of carotene, vitamin C, vitamin B6, Ca, folic acid, Fe and Mg in an adult population(Reference Newby, Hu and Rimm12). Biomarkers of intake showed that the HEI was valid to assess the adequacy of α-carotene, β-carotene, β-cryptoxanthin, vitamin C and Ca(Reference Hann, Rock and King14, Reference Weinstein, Vogt and Gerrior15).
The correlation studies showed that the DDS had fair to moderate correlation coefficients for assessing adequacy for vitamin A, vitamin C, riboflavin, Ca, Zn, K and P, but only among children and adult women(Reference Mirmiran, Azadbakht and Azizi18, Reference Mirmiran, Azadbakht and Esmaillzadeh20). The FVS correlated with the adequacy of vitamin A, thiamin, riboflavin, niacin, vitamin B6, Ca, folic acid, Zn and Fe in two samples of children(Reference Steyn, Nel and Nantel19). The HEI has been demonstrated to be a good measure of Ca intake adequacy (moderate for vitamin A) in an adult population(Reference Foote, Murphy and Wilkens21).
Referring to factor analysis, the studies evaluating nutrient intake adequacy associated with dietary patterns showed that among men, the prudent pattern was valid to assess the intake adequacy of α-carotene, lycopene and lutein(Reference Hu, Rimm and Smith-Warner25). For women, this methodology was valid for assessing the adequacy of β-carotene, vitamin C, vitamin B6 and folic acid(Reference Newby, Weismayer and Akesson29).
According to the results from this review, vitamin B12 and vitamin E are the micronutrients with less probability of being adequately assessed in the application of dietary patterns, regardless of whether these are defined by a priori or a posteriori methods.
Acknowledgements
The studies reported herein have been carried out within the EURRECA Network of Excellence (www.eurreca.org), financially supported by the Commission of the European Communities, specific Research, Technology and Development (RTD) Programme Quality of Life and Management of Living Resources, within the Sixth Framework Programme, contract no. 036196. The present report does not necessarily reflect the Commission's views or its future policy in this area.
B. R.-V. screened the literature, undertook analysis and wrote the paper. L. R.-B. contributed to the literature search strategy, screened part of the literature and commented on drafts of the paper. J. N. commented on drafts of the paper. M. A. M.-G provided expert advice on data interpretation and the writing of the paper. T. M. A. W. commented on drafts of the paper. L.S.-M was supervisor of the study and revised the paper providing expert advice on data interpretation and discussion of the paper.
The authors have no conflict of interests to report.
