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 food frequency questionnaire (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 was 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 one ‘convenience’ (26.8%), cluster two, ‘traditional’ (22%) and cluster 3 ‘health conscious’ (51.2%). Clusters one and two consisted of an array of ultra-processed vegan food items. Together, both clusters represent almost half of participants and yielding similar results to the predominant dietary pattern, strengthens the factor analysis. Conclusions: These novel results highlight a need for further dietary pattern studies with full nutrition and blood metabolite analysis in larger samples of vegans to enhance and ratify these results.