Pattern analysis of vegan eating reveals healthy and unhealthy patterns within the vegan diet.

OBJECTIVE: This study aimed to identify the types of foods that constitute a vegan diet and establish patterns within the diet. Dietary pattern analysis, a key instrument for exploring the correlation between health and disease, was used to identify patterns within the vegan diet. DESIGN: A modified version of the EPIC-Norfolk FFQ was created and validated to include vegan foods and launched on social media. SETTING: UK participants, recruited online. PARTICIPANTS: A convenience sample of 129 vegans voluntarily completed the FFQ. Collected data were converted to reflect weekly consumption to enable factor and cluster analyses. RESULTS: Factor analysis identified four distinct dietary patterns including: (1) convenience (22 %); (2) health conscious (12 %); (3) unhealthy (9 %) and (4) traditional vegan (7 %). Whilst two healthy patterns were defined, the convenience pattern was the most identifiable pattern with a prominence of vegan convenience meals and snacks, vegan sweets and desserts, sauces, condiments and fats. Cluster analysis identified three clusters, cluster 1 'convenience' (26·8 %), cluster 2 'traditional' (22 %) and cluster 3 'health conscious' (51·2 %). Clusters 1 and 2 consisted of an array of ultraprocessed vegan food items. Together, both clusters represent almost half of the participants and yielding similar results to the predominant dietary pattern, strengthens the factor analysis. CONCLUSIONS: These novel results highlight the need for further dietary pattern studies with full nutrition and blood metabolite analysis in larger samples of vegans to enhance and ratify these results.