Different statistical methods identify similar population-specific dietary patterns: an analysis of Longitudinal Study of Adult Health (ELSA-Brasil).

In recent decades, different data-driven approaches have emerged to identify dietary patterns (DP) and little is discussed about how these methods are able to capture diet complexity within the same population. This study aimed to apply three statistical methods to identify the DP of the Longitudinal Study of Adult Health (ELSA-Brasil) population and evaluate the similarities and differences between them. Dietary data were assessed at baseline in the ELSA-Brasil study using a FFQ. DP were identified by applying three statistical methods: (1) factor analysis (FA), (2) treelet transform (TT) and (3) reduced rank regression (RRR). The characteristics of individuals classified in the last tertile of each DP were compared. Cross-classification and Pearson's correlation coefficients were assessed to evaluate the agreement between individuals' adherence to DP of the three methods. A similar convenience DP was identified for all three methods. FA and TT also identified a similar prudent DP and a DP highly loaded for the food groups rice and beans. Individuals classified in the third tertile of similar DP of each method presented similar socio-demographic and nutrient intake characteristics. Regarding the cross-classification, prudent DP from FA and TT presented a higher level of agreement (75 %), while convenience DP from TT and RRR presented the lowest agreement (44·8 %). The different statistical methods were able to capture the populations' DP in a similar way while highlighting the particularities of each method.