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Background  The patterning of food intake at eating occasions is a poorly understood, albeit important, step towards achieving a healthy dietary pattern. However, to capture the many permutations of food combinations at eating occasions, novel analytic approaches are required. We applied a latent variable mixture modelling (LVMM) approach to understand how foods are consumed in relation to each other at breakfast.    Methods  Dietary intake at breakfast ( n  = 8145 occasions) was assessed via 24-h recall during the 2011–12 Australian National Nutrition and Physical Activity Survey ( n  = 3545 men and  n  = 4127 women, ⩾19 y). LVMM was used to determine breakfast food profiles based on 35 food group variables, reflecting compliance with Australian Dietary Guidelines. F and adjusted-chi2 tests assessed differences in timing of consumption and participant characteristics between the breakfast profiles. Regression models, adjusted for covariates, were used to examine associations between breakfast food profiles and objective adiposity measures (BMI and waist circumference).    Results  Five distinct profiles were found. Three were similar for men and women. These were labelled: “Wholegrain cereals and milks” (men: 16%, women: 17%), “Protein-foods” (men and women: 11%) and “Mixed cereals and milks” (men: 33%, women: 37%). Two “Breads and spreads” profiles were also found that were differentiated by their accompanying beverages (men) or type of grain (women). Profiles were found to vary by timing of consumption, participant characteristics and adiposity indicators. For example, the “Protein-foods” profile occurred more frequently on weekends and after 9 am. Men with a “Bread and spreads (plus tea/coffee)” profile were older ( P  < 0.001) and had lower income and education levels ( P  < 0.05), when compared to the other profiles. Women with a “Protein-foods” profile were younger (P < 0.001) and less likely to be married ( P  < 0.01). Both men and women with a “Wholegrain cereals and milks” profile had the most favourable adiposity estimates ( P  < 0.05).    Conclusions  We identified five breakfast food profiles in adults that varied by timing of consumption, participant characteristics and adiposity indicators. LVMM was a useful approach for capturing the complexity of food combinations at breakfast. Future research could collect contextual information about eating occasions to understand the complex factors that influence food choices.    Supplementary Information  The online version contains supplementary material available at 10.1186/s12966-021-01115-w.

What do Australian adults eat for breakfast? A latent variable mixture modelling approach for understanding combinations of foods at eating occasions