Rather than suggesting that one approach is superior, our results demonstrate that findings can vary depending on the methods used to elucidate dietary patterns, because each method is designed to answer a different question. Cluster analysis and factor analysis ask what accounts for the variation in intakes and how well those variances relate to risk, whereas index analysis asks whether variation from a predefined diet relates to risk. Nonetheless, similarities were seen across methods, suggesting some basic qualities of healthy diets.
Overall, we can summarize the evidence regarding dietary patterns and risk as follows: For men, cluster analysis, factor analysis, and index analysis come together to help us understand patterns that can reduce risk—diets rich in fruits and vegetables and diets including lower fat foods—and the evidence for patterns (based on factor analysis and index analysis) that can increase risk—diets defined by a meat and potatoes pattern. For women, the results were less consistent, as only one factor revealed increased risk (meat and potatoes factor) and one index pattern showed decreased risk (HEI-2005).
The different findings between men and women could be due to the greater heterogeneity in women's diets (9
), biologic differences, increased measurement error among women (21
), differences in how men and women completed the food frequency questionnaire, or other reasons. Additionally, though, we found differences in the health behavior characteristics of men and women in similar-looking patterns that might help to explain why these patterns produced different results. The women in the diet food pattern groups (defined as diet foods/lean meats cluster and fat-reduced/diet foods factor) look like “dieters,” women who are in poorer health/overweight, trying to change their behaviors, or at least report a “good” diet. On the other hand, the men in the diet food pattern groups (defined as fat-reduced foods cluster and fat-reduced/diet foods factor) look “health-conscious.” Thus, the women and men in the diet food pattern groups had dissimilar health behavior characteristics. The “women dieters” had profiles most similar with those individuals in the meat and potatoes factor, and the “health-conscious men” looked more like those in the fruits and vegetables pattern groups and all indexes.
Among men, however, we also saw differences in cancer risk. We did not see the same association with colorectal cancer for men in the fatty meats cluster and the meat and potatoes factor. This may be due to differences in group size, but it also reflects that, even when the health characteristics and nutrient profiles are similar, the actual foods and/or people that make up these patterns differ because they are defined by using different statistical procedures.
In cluster analysis and factor analysis, labels such as “fruits and vegetable factor” are commonly attached to factors and clusters that emerge analytically. However, similar labels can represent meaningfully different patterns. In the analyses presented by Wirfält et al. (5
) and Flood et al. (6
), the clusters and factors were derived separately for men and women and, despite the similar names, they are not defined by exactly the same foods, nor are they the same as clusters and factors similarly named in other studies. These methods are data driven and dependent on the intake within the population from which they are drawn. Labels help to clarify the discussion of the findings; indeed, we have used labels here regarding “fruits and vegetables,” “diet food,” and “meat” pattern groups. Using labels makes for easier presentation to an audience, but it makes less clear the fact that clusters or factors with similar or identical names may be quite different.
Other comparative work with cluster analysis and factor analysis has focused on the stability and reproducibility of clusters and factors and, to a lesser extent, on the general picture provided by the methods. Research that has compared different methods with a biomarker or health outcome includes comparisons of cluster analysis and factor analysis with plasma lipid biomarkers (22
), factor analysis and reduced rank regression with biomarkers of subclinical atherosclerosis (23
), and factor analysis and index analysis with plasma sex hormone concentrations (24
), mortality (25
), and hypertension (26
). In related analyses of cluster analysis and factor analysis, Newby et al. (2
) also found that some associations were significant for men and not for women (white bread cluster and lower high density lipoprotein cholesterol), some were significant when using factor analysis but not cluster analysis (sweets factor and lower high density lipoprotein cholesterol), and some were similar with cluster analysis and factor analysis (healthy pattern and lower plasma triacylglycerols). Nettleton et al. (4
) found that prior information about inflammation included with reduced rank regression strengthened the ability to detect an association (no association was found for factor analysis). Although differences were found in the foods in the patterns, this did not entirely account for the lack of association when using factor analysis. This reinforces the unique information provided by different pattern analysis methods (4
There have been 3 analyses that have compared index analysis and factor analysis by using different outcomes: Fung et al. (22
) found an association with index analysis (higher AHEI score and lower levels of free estradiol) but not factor analysis for plasma sex hormone concentrations; Osler et al. (23
) found an association with factor analysis (prudent pattern and all-cause and cardiovascular morality) but not index analysis; and Schulze et al. (24
) found no associations with either index analysis or factor analysis for hypertension (although the third of 4 quintiles measured with a Dietary Approaches to Stop Hypertension (DASH) Index was associated with a reduced risk). Although Fung et al. (22
) and Schulze et al. (24
) postulate that index analysis may provide a stronger ability to find more significant effects on disease risk than factor analysis, this is likely because of the inclusion of relevant, evidence-based components within a given index (22
). For example, Fung et al. (22
) suggest that the reason they found a relation with the AHEI and not with factor analysis may be due to the emphasis on soy in the index used. However, although an index may include a critical component, it may suffer from dilution if some dietary components are not relevant (24
Comparisons across the methods are somewhat limited here by our decisions to define our initial food variables. Index analysis used aggregated food groups as used in food-based recommendations. However, both cluster analysis and factor analysis used single foods or minimally aggregated food groups.
Regardless of the food grouping strategy selected, we recommend using energy-adjusted variables—as we did—to account for the energy compositions of the diet rather than using variables that are derived from absolute dietary intakes. This adjustment is suggested because energy needs are determined by body size, age, physical activity, and other factors and also because diet quality is of greater interest rather than absolute intakes. Energy adjustment may also help to reduce measurement error (21
), although future work is needed in this area.
The goal with dietary pattern analyses is to examine the multiple dimensions of the diet simultaneously relative to a given outcome. Thus, we consider the best way to operationalize and model the multidimensionality of the total diet. Although cluster analysis, factor analysis, and index analysis are useful and answer different questions, perhaps we should not limit ourselves to these common approaches (25
). Other methods hold promise for new ways to explain the complexity of dietary data and would allow us to ask other questions: What combination of foods explains the variation in a set of intermediate health markers (reduced rank regression) (26
)? What combination of foods minimizes cancer risk (neural networks) (27
)? What features of the diet are most strongly associated with a reduced risk of cancer (classification and regression trees) (28
Dietary pattern analyses play a unique role in assessing the relations between diet and disease. Although most research with dietary patterns has been shown to be more strongly related to risk of disease than individual parts of the diet (29
), the World Cancer Research Fund Panel stated that there was insufficient evidence to make judgments regarding dietary patterns and cancer risk (30
). Our results are consistent with their summaries for specific foods and dietary components and reinforce the Panel's recommendation that additional research be done investigating dietary patterns.