Switches in food and beverage product purchases can reduce greenhouse gas emissions in Australia.

Switching between similar food and beverage products may reduce greenhouse gas emissions (GHGe). Here, using consumer data linked to 23,550 product-specific GHGe values, we estimated annual GHGe attributable to product purchases consumed at home in Australia and calculated reductions from specific switches. Potential changes to mean Health Star Rating, mean energy density and the proportion of ultraprocessed foods purchased were assessed. Approximately 31 million tonnes of GHGe were attributable to products consumed at home in 2019, the three highest contributors of GHGe being 'meat and meat products' (49%), 'dairy' (17%) and 'non-alcoholic beverages' (16%). Switching higher-emission products for 'very similar' lower-emission products could reduce total emissions by 26%. Switches to 'less similar' lower-emission products could lead to a 71% reduction. Switches had little impact on the average Health Star Rating, energy density of purchases and proportion of ultraprocessed foods purchased. Directing manufacturing and marketing towards lower-environmental-impact products and signposting such options to consumers are key.

Extent of alignment between the Australian Dietary Guidelines and the NOVA classification system across the Australian packaged food supply.

AIMS: The Australian Dietary Guidelines are currently being revised and ultra-processed foods have been identified as a high priority action area. To better understand how well the current Dietary Guidelines align with level of processing classifications, the aim of this study was to assess the alignment between the Australian Dietary Guidelines and the NOVA classification system for classifying the healthiness of packaged foods. METHODS: Data were sourced from the Australian FoodSwitch dataset, which included 28 071 packaged food and beverage products available in major Australian supermarkets in 2022. Products were classified as (i) core or discretionary (Australian Dietary Guidelines) and (ii) non-ultra-processed or ultra-processed (NOVA). Agreement between the two systems (core vs. non-ultra-processed and discretionary vs. ultra-processed) was evaluated using the kappa statistic. RESULTS: There was 'moderate' agreement (κ = 0.41, 95% CI: 0.40-0.42) between the Australian Dietary Guidelines and the NOVA system, with 69.8% of products aligned across the two systems. Alignment was more common for discretionary foods (80.6% were ultra-processed) than core foods (59.9% aligned were not-ultra-processed). Food categories exhibiting the strongest levels of alignment included confectionary, foods for specific dietary use, and egg and egg products. Discordance was most common for convenience foods, sugars, honey and related products, and cereal and grain products. CONCLUSIONS: Despite moderate alignment between the Australian Dietary Guidelines and NOVA, the discordance observed for almost one-third of products highlights the opportunity to develop recommendations for ultra-processed foods within the guidelines to advise Australians how these foods should be considered as part of a healthy diet.

Criterion validation of nutrient profiling systems: a systematic review and meta-analysis.

BACKGROUND: Nutrient profiling systems (NPSs) use algorithms to evaluate the nutritional quality of foods and beverages. Criterion validation, which assesses the relationship between consuming foods rated as healthier by the NPS and objective measures of health, is essential to ensure the accuracy of NPSs. OBJECTIVE: We examined and compared NPSs that have undergone criterion validity testing in relation to diet-related disease risk and risk markers. METHODS: Academic databases were searched for prospective cohort and cross-sectional studies published before November, 2022. NPSs were eligible if they incorporated multiple nutrients or food components using an algorithm to determine an overall summary indicator (e.g., a score or rank) for individual foods. Studies were included if they assessed the criterion validity of an eligible NPS. Validation evidence was first summarized in narrative form by NPS, with random effects meta-analysis where ≥2 prospective cohort studies assessed the same NPS and outcomes. RESULTS: Of 4519 publications identified, 29 describing 9 NPSs were included in the review. The Nutri-Score NPS was assessed as having substantial criterion validation evidence. Highest compared with lowest diet quality as defined by the Nutri-Score was associated with significantly lower risk of cardiovascular disease (hazard ratio [HR]: 0.74; 95% confidence interval [CI]: 0.59, 0.93; n = 6), cancer (HR: 0.75; 95% CI: 0.59, 0.94; n = 5), all-cause mortality (HR: 0.74; 95% CI; 0.59, 0.91; n = 4) and change in body mass index (HR: 0.68; 95% CI: 0.50, 0.92; n = 3). The Food Standards Agency NPS, Health Star Rating, Nutrient Profiling Scoring Criterion, Food Compass, Overall Nutrition Quality Index, and the Nutrient-Rich Food Index were determined as having intermediate criterion validation evidence. Two other NPSs were determined as having limited criterion validation evidence. CONCLUSIONS: We found limited criterion validation studies compared with the number of NPSs estimated to exist. Greater emphasis on conducting and reporting on criterion validation studies across varied contexts may improve the confidence in existing NPSs.

Comparing product healthiness according to the Health Star Rating and the NOVA classification system and implications for food labelling systems: An analysis of 25 486 products in Australia.

We investigated the extent of alignment between 'healthiness' defined by a food classification system that classifies foods and beverages primarily by their nutrient composition, the Health Star Rating (HSR) and a system that considers only the degree of processing of the product, the NOVA classification system. We used data for 25 486 products contained within the George Institute for Global Health's Australian 2022 FoodSwitch Dataset. Agreement between the two systems in the proportion of products classified as 'healthier' (HSR ≥3.5 or NOVA group 1-3) or 'less healthy' (HSR <3.5 or NOVA group 4) was assessed using the κ statistic. There was 'fair' agreement (κ = 0.30, 95%CI: 0.29-0.31) between both systems in the proportion of all products classified as healthier or less healthy. Approximately one-third (n = 8729) of all products were defined as 'discordant', including 34.3% (n = 5620) of NOVA group 4 products with HSR ≥3.5 (commonly convenience foods, sports/diet foods, meat alternatives, as well as products containing non-sugar sweeteners) and 34.1% (n = 3109) of NOVA group 1-3 products with HSR <3.5 (commonly single-ingredient foods such as sugars/syrups, full-fat dairy and products specially produced to contain no ultra-processed ingredients). Our analysis strengthens the evidence for the similarities and differences in product healthiness according to a nutrient-based classification system and a processing-based classification system. Although the systems' classifications align for the majority of food and beverage products, the discordance found for some product categories indicates potential for confusion if systems are deployed alongside each other within food policies.

The effect of mycoprotein intake on biomarkers of human health: a systematic review and meta-analysis.

BACKGROUND: Mycoprotein is a fungal source of protein that is increasingly consumed as an ingredient in meat analogs. OBJECTIVES: This study aimed to systematically review and meta-analyze the effects of mycoprotein intake on selected biomarkers of human health. METHODS: This study was registered in PROSPERO (CRD42022308980). We searched the PubMed, Scopus, and Embase databases to identify randomized control trials in any language until 16 August, 2022. Trials were included if they administered a mycoprotein intervention against a nonmycoprotein control arm and if reported outcomes included blood lipids, blood glucose, insulin, blood pressure, or body weight. Eligible trials were assessed for risk of bias using the Cochrane risk-of-bias tool for randomized trials. An inverse-variance-weighted, random-effects meta-analysis model was used to assess the effects of intake across each biomarker. RESULTS: Nine trials that included 178 participants with a mean follow-up of 13 d were included, with 4 reporting on blood lipids and 5 reporting on postprandial blood glucose or insulin. The overall reduction of total cholesterol was -0.55 mmol/L (95% CI: -0.85 to -0.26; P < 0.001) in the mycoprotein group compared to control, but no clear effects on HDL cholesterol, LDL cholesterol, or TGs were found (all P > 0.05). There were no reductions in postprandial blood glucose concentrations at 30, 60, 90 or 120 min. Postprandial blood insulin concentration was reduced by -76.51 pmol/L (95% CI: -150.75 to -2.28; P = 0.043) at 30 min, with no detectable effects at 60, 90, or 120 min. CONCLUSIONS: Mycoprotein intake may have important effects on blood lipids, but the evidence base is limited by the small sample sizes and short intervention periods of the contributing trials. The protocol for this systematic review has been registered in PROSPERO as CRD42022308980.

The nutritional profile of plant-based meat analogues available for sale in Australia.

AIM: To assess the nutritional quality of plant-based meat analogues in Australia, compared to equivalent meat products, and to assess levels of micronutrient fortification in meat analogues. METHODS: This cross-sectional study used nutrition composition data for products collected in 2021 from major supermarkets in Australia. Nutritional quality was assessed using the Health Star Rating, energy (kJ), protein (g), saturated fat (g), sodium (mg), total sugars (g), and fibre content (g) per 100 g, and level of food processing using the NOVA classification. Proportion of products fortified with iron, vitamin B12 and zinc were reported. Differences in health star rating and nutrients between food categories were assessed using independent t-tests. RESULTS: Seven hundred ninety products (n = 132 plant-based and n = 658 meat) across eight food categories were analysed. Meat analogues had a higher health star rating (mean 1.2 stars, [95% CI: 1.0-1.4 stars], p < 0.001), lower mean saturated fat (-2.4 g/100 g, [-2.9 to -1.8 g/100 g], p < 0.001) and sodium content (-132 mg/100 g, [-186 to -79 mg/100 g], p < 0.001), but higher total sugar content (0.7 g/100 g, [0.4-1.1 g/100 g], p < 0.001). Meat analogues and meat products had a similar proportion of ultra-processed products (84% and 89%, respectively). 12.1% of meat analogues were fortified with iron, vitamin B12 and zinc. CONCLUSION: Meat analogues generally had a higher health star rating compared with meat equivalents, however, the nutrient content varied. Most meat analogues were also ultra-processed and few are fortified with key micronutrients found in meat. More research is needed to understand the health impact of these foods.

Socio-economic difference in purchases of ultra-processed foods in Australia: an analysis of a nationally representative household grocery purchasing panel.

BACKGROUND: Consumption of ultra-processed foods is associated with increased risk of obesity and non-communicable diseases. Little is known about current patterns of ultra-processed foods intake in Australia. The aim of this study was to examine the amount and type of ultra-processed foods purchased by Australian households in 2019 and determine whether purchases differed by socio-economic status (SES). We also assessed whether purchases of ultra-processed foods changed between 2015 and 2019.  METHODS: We used grocery purchase data from a nationally representative consumer panel in Australia to assess packaged and unpackaged grocery purchases that were brought home between 2015 to 2019. Ultra-processed foods were identified according to the NOVA system, which classifies foods according to the nature, extent and purpose of industrial food processing. Purchases of ultra-processed foods were calculated per capita, using two outcomes: grams/day and percent of total energy. The top food categories contributing to purchases of ultra-processed foods in 2019 were identified, and differences in ultra-processed food purchases by SES (Index of Relative Social Advantage and Disadvantage) were assessed using survey-weighted linear regression. Changes in purchases of ultra-processed foods between 2015 to 2019 were examined overall and by SES using mixed linear models. RESULTS: In 2019, the mean ± SD total grocery purchases made by Australian households was 881.1 ± 511.9 g/d per capita. Of this, 424.2 ± 319.0 g/d per capita was attributable to purchases of ultra-processed foods, which represented 56.4% of total energy purchased. The largest food categories contributing to total energy purchased included mass-produced, packaged breads (8.2% of total energy purchased), chocolate and sweets (5.7%), biscuits and crackers (5.7%) and ice-cream and edible ices (4.3%). In 2019, purchases of ultra-processed foods were significantly higher for the lowest SES households compared to all other SES quintiles (P < 0.001). There were no major changes in purchases of ultra-processed foods overall or by SES over the five-year period. CONCLUSIONS: Between 2015 and 2019, ultra-processed foods have consistently made up the majority of groceries purchased by Australians, particularly for the lowest SES households. Policies that reduce ultra-processed food consumption may reduce diet-related health inequalities.

Deconstructing the Supermarket: Systematic Ingredient Disaggregation and the Association between Ingredient Usage and Product Health Indicators for 24,229 Australian Foods and Beverages.

Unhealthy diets are underpinned by the over-consumption of packaged products. Data describing the ingredient composition of these products is limited. We sought to define the ingredients used in Australian packaged foods and beverages and assess associations between the number of ingredients and existing health indicators. Statements of ingredients were disaggregated, creating separate fields for each ingredient and sub-ingredient. Ingredients were categorised and the average number of ingredients per product was calculated. Associations between number of ingredients and both the nutrient-based Health Star Rating (HSR) and the NOVA level-of-processing classification were assessed. A total of 24,229 products, listing 233,113 ingredients, were included. Products had between 1 and 62 ingredients (median (Interquartile range (IQR)): 8 (3-14)). We identified 915 unique ingredients, which we organised into 17 major and 138 minor categories. 'Additives' were contained in the largest proportion of products (64.6%, (15,652/24,229)). The median number of ingredients per product was significantly lower in products with the optimum 5-star HSR (when compared to all other HSR score groups, p-value < 0.001) and significantly higher in products classified as ultra-processed (when compared to all other NOVA classification groups, p-value < 0.001). There is a strong relationship between the number of ingredients in a product and indicators of nutritional quality and level of processing.