Reformulation of food products is part of the calorie reduction programme, proposed by the UK Government in the report ‘Childhood obesity: A Plan for Action’. The food industry was challenged to reduce calories in products by 20% in 2025. An evaluation of the food supply in 2021, one year after the guidelines were published showed very little progress(1). Our aim was to explore the extent of changes in energy and nutrient levels in specific food categories targeted by the calorie reduction programme using the myfood24 database.

We compared the myfood24 UK branded food composition database between 2021 and 2025. The data is from back of pack nutrient information. Differences in nutrients declared between these time points were explored as a potential indicator of reformulation. Five food groups matching the Calorie Reduction Programme food categories were explored for changes between 2021 and 2025. Also, foods from these groups with the same European Article Number (EAN) were matched. myfood24 food databases are regularly reviewed and updated taking account of recipe changes, new products and reformulation. During this time ~47,000 food items (37% of the database) had nutrient information updated. Nutrient composition of foods/100g were compared using t-tests and paired sample t-tests.

11,342 foods from 2021 and 26,908 from 2025 in the 5 food groups selected were identified. In 2025, these were 32% cakes and sweet bakery; 2% fried foods; 9% pies, pastries and quiches; 18% pizza, pasta and garlic bread; 40% prepared meals. Overall, in 2025 compared to 2021, mean nutrient content/100g food was 3.5kcal (95%CI 0.4, 6.4) lower. Food groups with lower energy content in 2025 v. 2021 were cakes and sweet bakery (-8kcal/100g); fried foods (-7kcal/100g); pizza/pasta/garlic bread (-11kcal/100g). Pies/pasties/quiches (5kcal/100g) and prepared meals (12kcal/100g) had higher energy values. 10,177 products with the same EANs in 2021 and 2025 were matched. There was very little change between 2021 and 2025. Energy content of pizza, pasta and garlic bread had reduced in 2025 by 0.6 (95%CI -1.1, -0.06) kcal/100g this was statistically significant but extremely small. Total sugars also saw a very small reduction in 2025, which was statistically significant in cakes and sweet bakery (g/100g difference 2025-2021: -0.1, 95%CI -0.2, -0.5. P <0.0001). Total fat g/100g had also reduced by a very small amount, which was statistically significant and driven by reductions in cakes and sweet bakery, fried foods, pies, pasties and quiches. Portion sizes declared had also not changed.

Very small changes in macronutrient levels in key food categories during the period 2021 to 2025 were seen. These changes are not sufficient to deliver the calorie reduction targets set in 2020. The UK government needs to explore other levers to make progress in this area.

Current use of the food classifications: High in Fat, Salt and Sugar (HFSS) and NOVA (a scheme for food processing) for public health policy is challenging. HFSS-based policies focus on measurable nutrient content. NOVA score 4, Ultra Processed Foods (UPF) does not completely overlap with the HFSS score making integration of the two scores problematic. Analysis of the National Diet and Nutrition Survey found only just over half of foods consumed were classified as both HFSS and UPF(1). Our objective was to explore the overlap in HFSS and NOVA scores in a large, branded UK food composition database from myfood24. Identifying food categories where there was agreement and disagreement between classifications.

The myfood24 branded food composition database uses back of pack nutrition information per 100g product to calculate HFSS and NOVA. HFSS classification used standard algorithms(2). NOVA classifications were defined from the NDNS source(3). Scores were allocated to each item based on their ingredient decks. Some assumptions were made across all categories such as items with preservatives were given NOVA score of 4. For items where ingredient decks are not published online, NOVA scores were assumed based on similar items in the database. Food categories and nutritional content of products were compared by both HFSS and NOVA score.

89,496 branded foods had both HFSS and NOVA scores calculated. 40,032 (45%) foods were classified as being both HFSS and NOVA4. 36,256 (41%) of foods were classified as non-HFSS but NOVA4. 8,130 (9%) of foods were non-HFSS and NOVA1 (minimally processed) and 1,189 (1%) of foods were HFSS but NOVA1. Food categories with the largest disagreements between classification as non-HFSS but NOVA4 were canned/tinned foods (28%, n=192); fish/seafood (16%, n=173); fruit juices/smoothies (78%, n=406); nuts/seeds (18%, n=157); pulses (18%, n=73). Food categories with majority agreement between scores, non-HFSS and NOVA1 were fruits, pulses, rice/pasta, vegetables/potatoes. Food groups with both HFSS and NOVA4 as the majority classification were bread, breakfast cereals, cakes/sweet bakery, canned/tinned foods, confectionary, dairy/eggs, fried food, jams/honey, meat/poultry, pies/pasties, pizza/pasta, prepared meals, savoury snacks, soft drinks/squash, soups/salad/sandwich. Nutritional composition varied between classification group. HFSS and NOVA4 had nutrients/100g which were all higher than non-HFSS and NOVA4. Mean energy of 348 v. 144kcal/100g, protein 9 v. 7g/100g, carbohydrates 37 v. 17g/100g, total sugar 19g/100g; fat 18 v. 5g/100g respectively.

HFSS and NOVA4 both tend to classify foods with high energy density. But some non- HFSS food categories, with very differing nutritional compositions are classified as NOVA4. Understanding where the differences and similarities lie between the scores can help interpret epidemiological research and develop public health policy to improve the quality of the UK diet.

This abstract was awarded the student prize for best poster presentation.

Diet represents one of the most influential modifiable factors shaping gut microbial composition and metabolism. Prebiotics, polyphenols, and proteins each affect the microbiome through distinct mechanisms. Prebiotics, such as the dietary fiber Inulin, promote growth of saccharolytic bacteria and SCFA production(1). Bioactive plant polyphenols, such as grapeseed extract, can modulate microbial metabolism, exert antimicrobial properties against select pathogens, and generate bioactive metabolites involved in host inflammatory processes(1). Whey protein serves as a substrate for proteolytic bacteria and microbial amino acid bio-transformations, affecting microbial composition and metabolite profiles(2). While these components have established individual effects, their combined influence remains underexplored. This study examined the individual and combined effects of pre-digested inulin, grapeseed extract, and whey protein isolate on gut microbial composition and metabolism using the MiGut in vitro colon model.

The novel MiGut platform is a miniaturised triple-stage continuous flow in vitro gut model(3) that simulates proximal to distal human colon environments. We used MiGut to undertake a 6-week experiment using eight MiGut reactors (one per treatment condition for each of two healthy human donors). Donor faecal inocula were introduced, followed by a 2-week equilibration period establishing steady-state conditions. Experimental treatments (inulin, grapeseed extract, whey protein isolate, or a combination of all three) were added daily at doses equivalent to dietary recommendations for 2 weeks. All treatments underwent pre-digestion using the INFOGEST in vitro digestion protocol prior to addition(4). This was followed by a 2-week recovery phase without supplementation to assess microbiome elasticity. Samples were collected at each phase from all colon regions.Statistical analysis used a linear mixed-effects model, with Treatment, State, and Vessel as fixed effects, and Donor as a random effect. Significance was assessed using Type III ANOVA with p < 0.05.

PCR analysis revealed distinctive treatment effects relative to baseline. All fold change values reflect differences in microbial abundance compared to post-equilibration levels. Significant treatment effects were observed for Akkermansia, Bacteroides, Bifidobacterium, and Enterococcus (all p<0.05). Inulin increased Bifidobacterium 1.9-fold while reducing Enterobacteriaceae. Whey protein increased Lactobacillus 3.8-fold; grapeseed extract increased Bacteroides by 9.3-fold (p<0.05). The combination treatment demonstrated distinct effects from individual components, enhancing Bifidobacterium populations (2.4-fold increase, p<0.05), but unexpectedly reduced beneficial butyrate-producing bacteria like Roseburia, unlike the individual treatments. Temporal and spatial variations were detected for multiple bacterial targets. Upcoming 16S rRNA sequencing and SCFA analysis will provide deeper taxonomic and functional context.

Findings confirm the importance of assessing individual dietary components and their interactions in microbiome research. The MiGut platform was effective in modelling microbial responses to pre-digested nutrients. Microbiome profiling and metabolomics will provide deeper insights into functional outcomes. This work advances understanding of how multi-nutrient approaches may optimise microbiome-targeted interventions for gut health.

The increasing demand for sustainable and nutritious protein sources has elevated interest in plant-based alternatives like pea protein (Pisum sativum), which offers good bioavailability, low allergenicity, high nutritional value, and affordable production costs(1).

While plant proteins contain microbiota-relevant substrates such as lysine and tryptophan, limited research exists on their specific effects on gut microbiota compared to animal- derived proteins(2).

We utilized the MiGut platform, a miniaturized triple-stage in vitro model simulating proximal, medial, and distal regions of the human colon(3). MiGut’s scalability enabled direct comparison of pea versus whey protein supplementation on microbiota from three human donors under identical conditions. Our design included six models to evaluate both protein types with each donor’s microbiota. Following two-week equilibration, models were dosed daily with pre-digested protein for two weeks (pre-digestion following the INFOGEST protocol), followed by a two-week recovery period. Samples were collected after each phase. Significance between groups was assessed using a paired t-test.

Quantitative PCR analysis revealed key differences between protein interventions. In proximal colon, pea protein maintained Bifidobacterium levels (+0.31 log10copies/μL) while whey caused notable decreases (-2.36 log). Both reduced Bacteroides, though less with pea (-0.88 vs -2.16 log). Whey more reduced potential pathobionts, decreasing Enterobacteriaceae (-2.9 vs -0.42 log, p<0.05). Prevotella, Faecalibacterium, and C. leptum remained stable with both supplements. Distal colon showed similar but less pronounced trends. 16S rRNA sequencing confirmed higher diversity in distal colon (Shannon index: 2.58±0.21) versus proximal (1.82±0.6), consistent with in vivo patterns. Pea protein caused greater shifts in microbial ecology than whey (Bray-Curtis 0.71 vs 0.45, p<0.05). SCFA analysis showed pea protein significantly increased butyrate and propionate (+590 and +727 μM, p<0.05) with minimal acetate changes, while whey produced no significant SCFA alterations. This data gives an initial overview of the impact of pea and whey protein, and we plan to perform further analysis such as amino acid quantification and tryptophan metabolite profiling.

This study demonstrates both the utility of the MiGut platform for nutrition research and the potential benefits of pea protein for gut health. The bacterial changes in the proximal colon and favourable SCFA profile changes suggest plant protein may offer advantages beyond sustainability benefits. The MiGut technology enabled this direct comparison through simultaneous experiments with multiple biological replicates, providing greater statistical power than previously possible with gut model systems. As plant-based proteins become increasingly prevalent in diets, characterising their specific effects on gut health will be crucial for developing evidence-based dietary recommendations that consider both environmental sustainability and human health outcomes.

Ethical approval was granted by the University of Leeds Ethics Committee (reference 0624).

Many older adults are at risk of malnutrition, but older adults often reject oral nutritional supplements, which is the recommended treatment for malnutrition. The rejection of these unfamiliar foods can be ascribed to food neophobia – a reluctance to try unfamiliar foods. Food neophobia has been found to increase with older age. Food neophobia is commonly measured with the Food Neophobia Scale (FNS)(1). The scale has been validated, but it is unknown whether the FNS can adequately distinguish food neophobia from dietary restrictions. Many older adults have chronic medical conditions that require elimination of specific foods from the diet. Therefore, older adults may score higher on the FNS because they are reluctant to eat foods if it is uncertain whether the food meets their dietary requirements, while not actually being food neophobic. Younger adults with specific dietary restrictions score high on the FNS (e.g. people on a vegan/vegetarian diet, gluten free diet, or with food allergies), but to our knowledge no studies have focused on exploring this in older adults. We aimed to explore whether older adults with dietary restrictions score higher on the FNS than those without dietary restrictions, and whether poorer health status is related to FNS scores in older adults.

Health status was measured as physical- and mental health-related quality of life (QoL, SF- 12)(2), risk of sarcopenia (SARC-F)(3), and appetite (SNAQ)(4). Participants were recruited using convenience sampling, with help from local and national organizations with older adult members.

A total of 283 older adults took part in an online survey, of which 97 indicated to restrict their diet. The total sample contained 199 males and had a mean age of 74 ± 5 years old. FNS scores of older adults with dietary restrictions (31 ± 11) were not significantly higher than the FNS scores of older adults with no dietary restrictions (28 ± 9) when compared with an independent samples t-test. Regarding the relations between health status and FNS, Pearson correlations were used. We found that FNS scores were significantly correlated with poorer appetite (r = -.171, P = .004), but were not with physical or mental health-related QoL, or risk of sarcopenia. FNS scores were correlated with higher age (r = .212, P < .001), and lower education level (r = -.160, P = .007), but not with living status (alone or with others).

We conclude that the FNS scores are not different for older adults with or without dietary restrictions. The FNS scores may be higher for older adults with poorer appetite, but not for those with risk of sarcopenia or lower health related QoL. The FNS can be used to measure food neophobia in the older population despite the high prevalence of dietary restrictions.

The term ‘ultra-processed food’ (UPF), as characterised by the NOVA system, is now synonymous with poor quality industrially prepared foods. UPFs make up a substantial part of the UK diet, including in children, and concerns surround their convenience, ingredients (reliance on additives and non-household ingredients rather than whole foods), and food format (soft texture).(1,2) However, NOVA was not designed with commercial baby foods in mind, which are typically distinct in form and ingredients from many general products. No prior study has examined how NOVA aligns with overall baby food quality as evaluated using the WHO Nutrient & Promotion Profile Model (NPPM) baby foods standards.(3) This work therefore aimed to classify contemporary baby foods using NOVA and cross-examine UPF classification with baby food quality as evaluated with the NPPM.

Baby foods listed on websites of the 5 largest UK grocery retailers (Tesco, ASDA, Sainsbury’s, Morrisons, Aldi) in June-August 2024 were included. The NPPM was used to evaluate nutritional quality and marketing practices. NOVA was used to classify the degree of industrial processing. NPPM metrics were evaluated in UPF and non-UPF products to characterise gaps and overlaps in the two approaches.

632 baby foods were included, with 196 (31%) classed as UPFs. These were snacks (n112), dry cereals containing non-household ingredients (e.g. hydrolysed flour, inulin, maltodextrin, whey powder) (n46), meals (n17), and confectionery (n21). 85% (n112/132) of snacks were UPF, due to i) non-household ingredients (n29), ii) produced by industrial techniques with no household equivalent (i.e. melty/puff snacks, moulded fruit shapes, or fruit-filled cake bars) (n78), or iii) both reasons (n5).

While poor marketing practices were universal, NPPM nutritional assessments varied by category. Of 46 UPF cereals, many were flagged by the NPPM because of added sugars (n11), marketed from ‘4+ months’ (n7), misleading names (n7), or excess fruit content (n3). UPF puff snacks (n67) are not generally flagged as problematic by the NPPM but of these, 21% were flagged for excess serving size and 19% contained added sugars.

NOVA does not include nutritional criteria and so did not identify non-UPF products containing added sugar as problematic (n33, 5% of sample) or fruit-based products with high sugar levels (22% of sample). The NPPM identifies these as major issues, with 89% of fruit products being so high in total sugar (61% of calories from sugar on average) that the NPPM recommends a front-of pack high-sugar warning. Each system differently flags textural issues (smooth purees by the NPPM and ‘melty’ snacks by NOVA).

While NOVA may seem a convenient framework for product evaluation, the nutrition and marketing standards within the NPPM offer a more comprehensive and nuanced understanding of issues beyond processing methods and provide a framework for reformulation and regulation.

Previous studies suggest a link between protein intake and diabetes,(1,2) but little is known about whether protein intake timing across the day (i.e. temporal protein patterns) is associated with diabetes. This study aimed to examine temporal protein patterns in American adults and their associations with diabetes, BMI and glycemic measures.

Total protein intake at eating occasions was estimated from the National Health and Nutrition Examination Survey 2017-2020 dietary recall data (≥20 y; n=7625). Latent variable mixture models were used to identify temporal patterns based on hourly protein intake (g). Adults with fasting glucose ≥7.0 mmol/L, poor glycemic control (HbA1c ≥6.5%), taking diabetic medications, or previous diabetes diagnosis were classified as having diabetes. Insulin ≥72 pmol/L and HOMA-IR ≥2.5 were used to determine hyperinsulinemia and insulin-resistance, respectively. Regression models were used to examine associations for temporal protein patterns with diabetes prevalence, BMI and glycemic measures (fasting glucose, HbA1c, insulin, Homeostatic Model Assessment of Insulin Resistance), adjusted for multiple confounders.

Three latent classes for men’s and women’s temporal protein patterns were identified. Class 1 had higher conditional probabilities of eating protein at 18:00h, while Class 2 tended to eat protein an hour later than Class 1 and had higher overall protein intake (p<0.001). Class 3 was characterised by variable protein intake timing and had lower overall protein intake than the other classes (p<0.001). Men’s Class 1 had lower probabilities for hyperinsulinemia (32.5%) and poor glycemic control (5.9%), but associations were attenuated after adjusting for BMI. There were no associations for temporal protein patterns with diabetes, BMI and other glycemic measures.

Temporal protein patterns were not associated with diabetes. The inverse association with several glycemic measures observed for men in Class 1 may be mediated by BMI. Future prospective studies may better examine the effects of temporal protein patterns on diabetes by considering different protein food sources.

The percentage of the global population aged over 65 is expected to rise to 16% by 2050(1). Community-dwelling older adults experience a high prevalence of malnutrition(2), which negatively impacts quality of life and increases mortality risk(3,4). Home-delivered meal programmes may improve the physical and mental well-being of older adults living independently. However, the effects of such programmes have not been comprehensively quantified. This systematic review and meta-analysis aimed to evaluate the impact of home-delivered meals on health-related outcomes among adults aged 60 and older residing in the community.

A systematic search of 6 databases was conducted to identify randomised controlled trials (RCTs) and non-RCTs reporting pre- and post-intervention changes in health-related outcomes. Only studies delivering meals were included; interventions involving snacks, oral supplements, or diet-specific meals were excluded. Studies were limited to participants living independently in the community; those in hospitals, care homes, or residential care units were excluded. Primary outcomes included body weight, energy intake, protein intake, nutrient status assessed by Mini Nutritional Assessment Scale, handgrip strength, and social loneliness. Standardised mean difference (Cohen’s d) estimated effect size.

Heterogeneity between trials was assessed using the I2 statistic. n denotes number of studies included.

A total of 15 studies out of 4414 were included (n = 15), including 5 RCTs and 10 non-RCTs and comprising 1206 and 1606 participants, respectively (mean pooled age: 77.3 ± 8.7 yrs). Meal delivery interventions varied in frequency (from one hot meal per day to five frozen meals delivered weekly) and duration (3 weeks to 24 months). No significant changes were identified between RCTs and non-RCTs for each outcome (P ≥ 0.42); pooled analyses are therefore presented. The meta-analysis showed no changes in body weight (d = -0.07; 95% CI: -0.20 to 0.05; P = 0.27; I2 = 0%; n = 5), energy intake (d = 0.16; 95% CI: -0.04 to 0.37, P = 0.12; I2 = 63%; n = 8), or protein intake (d = 0.11; 95% CI: -0.17 to 0.39, P = 0.44; I2 = 87%; n = 8). However, an improvement was observed in nutrient status (d = 0.57; 95% CI: 0.41 to 0.73; P < 0.01; I2 = 30%; n =6). No effects were detected for handgrip strength (d = 0.11; 95% CI: -0.12 to 0.35; P = 0.35; I2 = 0%; n =2). Social loneliness showed a small decrease (d = -0.22; 95% CI: -0.34 to -0.10; P < 0.01; I2 = 0%; n = 4).

Home-delivered meal interventions improve nutrient status and reduce social loneliness in community-dwelling older adults, though they show no significant effect on body weight or macronutrient intake. Further high-quality research is needed to explore broader health- related impacts and strengthen the evidence base.

Current UK baby food regulation is outdated, with no guidelines on added or total sugar levels.(1) Political inaction poses a threat to the nutrition and health of our children. Many products are high in sugar and are misleadingly marketed, appearing healthier than they are which confuses parents. Increased financial pressures on families has highlighted inequalities, with more healthy foods being over twice as expensive per calorie as less healthy foods.(2) We have no contemporary data on baby food quality in relation to cost. The study aimed to characterise UK commercial baby food quality and price and secondly to drive evidence-based change in UK food policy through broad and targeted dissemination and impact.

All commercially available baby and toddler foods (for children under age 3) listed on websites of the 5 largest UK grocery retailers (Tesco, Sainsbury’s, Morrisons, Aldi and ASDA) were included in the sample. Websites were viewed between June-August 2024 to identify unique products. Nutritional, ingredient, and packet marketing details were collected along with price. Nutritional quality and marketing practices were evaluated using the WHO Nutrient & Promotion Profile Model (NPPM).(1) A dissemination and impact plan was drafted and delivered, drawing on sector-wide support to target policy-makers, retailers and manufacturers.

632 products were analysed. All products are marketed inappropriately and many fall short of nutritional standards, in particular being very high in sugar. 28% of products were pouches with spouts, half of which had no warning not to drink via the spout. 25% of all products would require a front-of-pack ‘high-sugar’ indicator according to WHO standards and 55% of snacks contained added sugars. Of fruit-based purees sold as being suitable for early weaning (‘4+’ or ‘6+’ months) n47 (36%) were too low in energy (watery) whilst deriving an average of 71% (SD17) of their total energy from sugar. Cost analysis revealed that cheaper pouches tended to have lower energy density (higher water content) and cheaper fruit pouches derived a greater proportion of energy from sugar. Cheaper snacks were also higher in sugar.

Dissemination and impact activities included targeted media and social media coverage, engaging with members of parliament, and a cross-sector webinar for stakeholders.

This evidence highlights unacceptable issues with commercial baby foods. Legislative gaps have allowed poor quality products, with misleading and inappropriate marketing, to become mainstream. Understanding the role of price in product quality demonstrates how families shopping on a budget are more likely to take poorer quality products home, potentially widening social and health inequalities. Targeting all stakeholders including civil society (non-government organisations and families), baby food manufacturers/retailers and government policy makers should increase pressure for meaningful change throughout the food system and drive legislative reform to enable improved early years nutrition.

Dietary intake is a major determinant of later life course outcomes, with poor dietary intake linked with increased incidence of non-communicable diseases. Protein under-nutrition is a contributing factor to declining health in community dwelling older adults. Mycoprotein is a food ingredient produced from the fermentation of Fusarium venenatum, that is high in protein and fibre, has a complete amino acid profile as well as being low in fat(1). The aim of this study was to utilise in-vitro gastrointestinal digestion models to determine the extent of variability in the digestibility profile of mycoprotein when comparing a standard adult model, to the older adult model.

Freezed dried Mycoprotein paste was subjected to in-vitro gastrointestinal digestion, using a computer controlled bioreactor system(2), utilising optimised conditions which simulated both adult and older adult gastrointestinal conditions(3). Following digestion, absorbable and non-absorbable protein fractions were separated and the protein content determined determined using total nitrogen analysis (Rapid Max N exceed, Elementar UK) and total amino acid analysis via ion exchange chromatography with post-column ninhydrin derivitisation (Hitachi LA8080, Avantor UK). All values were corrected using blank (protein free) digestions containing only digestive enzymes.

After mass balance correction (for protein content of digestive enzymes and non-protein nitrogen), total in-vitro protein digestibility of mycoprotein was 65.0 % (SD 0.03 %) using standard digestion conditions in triplicate. For conditions that simulated older adult digestion, total protein digestibility was reduced by 3.4 %. Digestibility of indispensable amino acids between the two models, were non-significant (p > 0.05). Previously published trials have shown that for plant based feedstocks, protein digestibility reduced by up to 20 % using older adult conditions.

As the global food system evolves to meet the nutritional needs of a growing population and challenges of Net Zero, there is potential for high protein plant based alternatives to meet the nutritional needs of an ageing population. This work has demonstrated that the digestion of mycoprotein is largely unaffected by the changing gastrointestinal conditions of older adults and therefore could play an important role in future nutritional support strategies for older adults.