Precision Food Composition Data as a Tool to Decipher the Riddle of Ultra-Processed Foods and Nutritional Quality

Abstract

Background: Epidemiology supports a link between ultra-processed foods (UPFs) and health, mediated mainly through the clustering of foods with suboptimal nutrient profiles within UPFs. However, successful NOVA categorization requires access to a food’s ingredient list, which we hypothesized can impact both UPF identification and the link between processing and composition. Methods: Foods (n = 4851) in the HelTH branded food composition database were classified as NOVA1-4, with or without using the ingredient lists (generic and branded approach, respectively), to identify differences in NOVA classification (chi-square test) and the estimated average nutritional composition of each NOVA group (Kruskal–Willis U test). Results: Using the ingredients list increased UPF identification by 30%. More than 30% of foods commonly assumed to be minimally processed (NOVA1-plain dairy, frozen vegetables, etc.) were reclassified as UPFs when using ingredient lists. These reclassified foods, however, had nutritional compositions comparable to NOVA1 foods and better than UPFs for energy, fat, sugars, and sodium (p < 0.001). In fact, UPFs did not show a uniform nutritional composition covering foods from Nutri-Score A (~10%) to Nutri-Score E (~20%). Conclusions: The assumption that all UPFs have the same unfavorable nutritional composition is challenged when NOVA is applied using the appropriate branded food composition database.

Keywords

• ultra-processed foods
• NOVA
• formulation
• composition
• ingredients
• food composition database