A LATENT VARIABLE APPROACH TO DIETARY PATTERNS RESEARCH

INTRODUCTION The dietary pattern approach is useful to study the effect of the overall diet on health outcomes, through considering the network of complex interactions between foods or nutrients. The main methods traditionally used to identify dietary patterns are principal components analysis, factor analysis, principal components factor analysis and cluster analysis. Latent class analysis (LCA) is a latent variable approach, that has some advantages in comparison to the previous methods. Unlike principal component, factor and principal component factor analysis, it can be used to classify individuals into mutually exclusive groups conceived as dietary patterns and differently from cluster analysis, which has the same aim of grouping subjects, it permits quantification of the uncertainty of class membership, and assessment of goodness of fit. Moreover, it allows for adjustment for covariates directly in the pattern identification. OBJECTIVES As latent class analysis has rarely been applied in dietary pattern studies, the aim of this research is to apply the recent developments of the techniques to this area of research. We targeted to address the issue of dietary pattern identification in the case-control setting using latent class analysis and latent class trees. We provided estimation of pattern sizes and their characterization, taking into account correlations between dietary variables (local dependencies), and covariate adjustment. We also evaluated the robustness of the identified dietary patterns to total non-alcoholic energy intake adjustment, for different types of correction. Finally, we illustrated the method’s properties in the assessment of the relation between the identified dietary patterns and selected health outcomes, given the all the above. DIETARY PATTERNS AND THE RISK OF ORAL AND PHARYNGEAL CANCER We analyzed data from a multicentric case-control study on oral and pharyngeal cancer (OPC) carried out between 1992 and 2009, including 946 cases and 2492 hospital controls. Information on diet was collected through a food frequency questionnaire (FFQ). Using LCA, we identified 4 dietary patterns, conceived as mutually exclusive groups of people who shared a common dietary behaviour within groups. The first pattern, labelled ‘Prudent pattern’, showed higher probability of consuming more leafy and fruiting vegetables, citrus fruit and all other kinds of fruits, tea while showing lower probability of consuming red meat. The second pattern, that we named ‘Western pattern’, reported higher consumption of red meat and lower consumption of fruits, cruciferous and fruiting vegetables. We termed the third pattern ‘Lower consumers-combination pattern’ as people in it were less likely to eat fruits, leafy and fruiting vegetables, pulses, potatoes, fish, white and red meat, bread and tea/decaffeinated coffee. The last pattern had higher probability to eating fruiting, leafy and other vegetables, white and red meat and bread, while showed a lower probability to consume coffee, tea, processed meat, cheese, fish, sugary drinks and desserts. We called this last pattern ‘Higher consumers-combination pattern’. Dietary patterns were adjusted for total non-alcoholic energy intake and correlation between certain foods item (sugar-coffee, soups-pulses) was allowed during classes identification. Compared to the Prudent pattern, the Western and the Lower consumers-combination ones were positively related to the risk of OPC (OR=2.56, 95% CI: 1.90 – 3.45 and OR=2.23, 95% CI: 1.64 – 3.02). Higher consumers-combination pattern didn’t differ significantly from the Prudent pattern (OR=1.28, 95% CI: 0.92 – 1.77). ENERGY INTAKE ADJUSTMENT IN DIETARY PATTERN RESEARCH USING LATENT CLASS ANALYSIS Using data from the multicentric case-control study on OPC (Italy, 1992-2009), we identified and compared dietary patterns adjusting or not for total non-alcoholic energy intake in the classes identification phase of the analysis. Three possible ways to correct for total energy intake in class identification were presented, corresponding to different hypothesis on the effect of this variable. In general unadjusted and adjusted solutions were comparable. The main difference was related to the patterns that showed highest/lowest non-alcoholic energy intake, that resulted in a variation of number of classes (4/5/7 patterns for the different adjusted solutions and 5 patterns for the unadjusted one). Then, to determine the effect of adjustment in predicting an health outcome, we compared the effect of unadjusted dietary patterns, unadjusted dietary patterns with non-alcoholic energy intake variable also included in the model as a confounder, and adjusted dietary patterns on the risk of OPC . Differences in the estimations for the distinct solutions were found when Odds Ratios (ORs) were not corrected for known/potential risk factors. In general, adjustments for non-alcoholic energy intake results in a mitigation of the effects, thus remaining in the same order. When adjusting for known/potential risk factors, estimations of ORs and related confidence intervals (CIs) remained consistent in all the models we fitted. In the end, specific suggestions on how to perform energy correction in dietary patterns research using LCA were delivered, basing on the results of the current analysis. DIETARY PATTERNS INSPECTION THROUGH LATENT CLASS TREE We analyzed data from two Italian case–control studies, the first included 946 cases with OPC and 2492 hospital controls, and the second included 304 cases with squamous cell carcinoma of the esophagus (ESCC) and 743 hospital controls. In our application of latent class analysis on the combined dataset of the two case-control studies (Italy, 1992-2009), we found the best fit for a solution that was difficult to interpret and included minor differences between clusters. To address these issues, the Latent Class Tree method was proposed. Three fit statistics (AIC, AIC3, BIC) were used for their different level of penalty that resulted in different lengths of the tree and consequently, different granularity in the analysis. For the first split we allowed for a 4-class solution which identified a pattern characterized by high intake of leafy and fruiting vegetable and fruits (‘Prudent pattern’), a pattern with a high intake of red meat and low intake of certain fruits and vegetables (‘Western pattern’) and two patterns which showed a combination-type of diet. The first ‘combination’ pattern showed a low intake of the majority of foods (‘Lower consumers-combination pattern’), and the other one high intake of various foods (‘Higher consumers-combination pattern’). Compared to the Prudent pattern, the Western one was positively related to OPC (OR=1.91, 95% CI: 1.41-2.58) and to ESCC (OR=3.22, 95% CI: 1.78 – 5.82). The Lower consumers-combination pattern was positively associated to OPC (OR=2.14, 95% CI: 1.58-2.91) and to ESCC (OR=2.85, 95% CI: 1.47-5.55). No significant association was found between the Higher consumers-combination pattern and OPC (1.04, 95% CI: 0.74-1.46) and ESCC (OR=0.89, 95% CI: 0.39-1.99). In the ‘Prudent pattern’ branch of the tree, at the third level, we found two classes that differed in the risk of both cancer types. These two classes differed mainly for the intake of citrus fruit, showing respectively, OR=1.85, 95% CI:1.07-3.19 for OPC and OR=5.37, 95% CI: 1.48-19.44 for ESCC for the class that reported low intake of citrus fruit with respect to the class which exhibit a high intake of citrus fruit. No other significant differences were found between the other pairs of classes at any other level of the tree. CONCLUSION We presented latent class methods as powerful tools to determine dietary patterns conceived as mutually exclusive homogeneous groups of subjects which shared common dietary habits. These methods exhibit some advantages, with respect to classical approaches, that can address important issues in dietary pattern research. For example, it is possible to obtain estimation for pattern prevalence in the population, and to perform energy intake adjustment in the pattern identification phase of the analysis. Moreover, class formation inspection, comparison between different solutions and the analysis of subgroups that may be relevant for the research at hand are features offered by the newly developed latent class tree approach.