Simultaneously improving diet quality and minimising dietary environmental impacts are central to the development of sustainable healthy diets. Altering dietary patterns to increase the proportion of plant-based foods and reduce animal-source foods has the potential to reduce dietary greenhouse gas emissions (GHGEs) and other environmental indicators(1). However, data evaluating the impacts of these dietary changes on micronutrient intakes are scarce(2).

The MyPlanetDiet randomised controlled trial (RCT) was conducted in healthy adults across the island of Ireland [NCT05253547]. Participants were randomly assigned to follow a diet with reduced GHGEs (intervention group), or a diet based on national dietary guidelines (control group), for 12 weeks. Otherwise, all study procedures were identical. Dietary intake was assessed at baseline and endpoint using three non-consecutive 24-hour recalls, collected remotely using the Foodbook24 web-based dietary assessment tool(3). Dietary GHGEs and micronutrient intakes were calculated from food sources only. Data were analysed using paired t-tests to assess for withingroup differences between baseline and week 12, and one-way analysis of covariance (ANCOVA) to test for differences between groups at week 12, whilst controlling for baseline intakes.

Here, we present data from one of three sites (University College Cork). Of the 127 participants enrolled, 108 completed the trial (n = 57, intervention group; n = 51, control group). The mean (SD) age of participants was 42.2 (11.9) years and 59% were female. Ninety-three percent of participants were white and 88% completed third-level education. At baseline, mean (SD) GHGEs were 7.3 (3.1) kg CO2 equivalents/day. GHGEs reduced significantly in the intervention group (p<0.001), and GHGEs were significantly lower in the intervention group compared to the control group at week 12 (4.6 (1.5) vs. 6.8 (2.9) kg CO2equivalents/day, p<0.001).

In the control group, intakes of carotene, vitamins B12 and C increased significantly from baseline (p<0.05), whereas vitamin E reduced significantly (p = 0.005). In the intervention group, intakes of retinol, riboflavin, niacin equivalents, vitamins B6 and B12, biotin, pantothenate, calcium, phosphorous, zinc, and potassium reduced significantly (p<0.05).

Compared to the control group at week 12, the intervention group had significantly higher intakes of vitamin E (p = 0.015) and significantly lower intakes of retinol, thiamin, riboflavin, niacin equivalents, vitamins B6 and B12, biotin, pantothenate, calcium, phosphorous, zinc and potassium (p<0.05). There were no differences in carotene, retinol equivalents, total folates, vitamin C, magnesium, iron, copper, or selenium.

Micronutrient intakes generally remained similar or improved in the healthy control group, whereas the low-GHGE diet resulted in reductions in some key micronutrients. Further analysis of dietary composition, the prevalence of inadequate intakes, and biomarkers of nutritional status will enable a more comprehensive understanding of the impacts of such a dietary transition on nutritional status.

Excluding all animal-sourced foods may be associated with increased risks of nutrient deficiencies. As indispensable amino acids (IAAs) cannot be stored or endogenously produced, consistent protein consumption throughout the day is important to improve protein quality for optimal metabolic function(1). Assessment of protein adequacy needs to be undertaken at the meal rather than daily intake level because food combinations within each meal can be complementary and influence the overall amino acid profile of the meal(2).

Outcomes of our previous review found that among plant-sourced foods, soy, legumes, nuts and seeds provide greater protein content and quality(3). We hypothesise that variation in protein intake will exist both between vegan individuals and between observation days for the same individuals. Previous investigations of the relationship between meals and nutrient intake based on specified time windows for eating may have been subject to researcher bias in the definition of these windows. The main outcome of this study is to utilise time series clustering to determine the impact of dietary patterns on protein distribution, across the day.

Intake data was obtained using a four-day food diary from a cross-sectional survey of 193 New Zealand vegans (Ethical approval: HDEC 2022 EXP 12312). The inclusion criteria required participants to have followed an exclusive vegan diet for at least two years. A kernel density contour estimation was used to visualise protein distribution across eating occasions for all participants over four days. Dynamic Time Warping (DTW) was then used to align two temporal sequences (time series) to compute an output of distance(4) which was used for hierarchical clustering using the Ward.D2 method. An optimal cluster of 3 was identified using silhouette coefficient and domain knowledge.

Participants had a mean age of 39.4 years (SD=12.3), with 90.1% having attained a tertiary-level education or higher. Overall, mean protein intake was 1.11 g/kg/d (SD=0.39), with 8.29% of participants below the Estimated Average Requirements (EAR) and 24.3% of participants below the Recommended Dietary Allowance (RDA) for adults. The mean Acceptable Macronutrient Distribution Range (AMDR) for protein is 15.5% (SD=4.16), with 96.9% of participants within the recommended AMDR range (10-35%). Peak protein consumption was observed at 1230 and 1900. Sequential colour scale representing density found higher distribution of data points representing protein intake of less than 10g per eating occasion. Time series similar in shape and amplitude were assigned to the same cluster. Preliminary findings identified three different protein intake profiles across the day.

A small percentage of participants has protein intake below the daily requirements for adults. More occasions with lower protein intake per eating moment was observed. This approach classifies dietary patterns objectively for analysis of daily protein and IAA intake.

Dietary intake assessment is an essential part of nutrition research and practice, with the use of digital technology now well established(1) and artificial intelligence (AI) in the form of image recognition readily available in research and commercial settings(2). Recent advances in large language models (LLMs), such as ChatGPT, allow computers to converse in a human-like way providing text responses to typed queries. No studies, however, have utilised both the LLM and image recognition components of ChatGPT-4 to evaluate its accuracy to estimate nutritional content of meals.

The aim of this study was to evaluate the accuracy of the ChatGPT-4 LLM and image recognition model in estimating the nutritional content of meals.

Thirty-eight meal photographs with known nutritional content (from McCance and Widdowson’s Composition of Foods) were uploaded to ChatGPT, and it was asked to provide point estimates for each of the meals for each of the following: energy (kcal), protein (g), total carbohydrate (g), dietary fibre (g), total sugar (g), total fat (g), saturated fat (g), monounsaturated fat (g), polyunsaturated fat (g), calcium (mg), iron (mg), sodium (mg), potassium (mg), vitamin D (mcg), folate (mcg), and vitamin C (mg). Comparisons were made between ChatGPT estimates and those from McCance and Widdowson using the Wilcoxon signed rank test, percent difference, Spearman’s correlation, and cross-classification of quartiles. Interpretation of statistical measures was based on Lombard et al.(3).

For estimating the content of meals, differences (p < 0.05) existed between the methods for 11 of the 16 nutrients, and 12 nutrients had a percent difference of >10%, indicating poor agreement for most nutrients. ChatGPT underestimated 15 of the 16 nutrients. Conversely, when considering the ranking of meals, all nutrients had correlation coefficients which indicated good (rs ≥ 0.50)(11 of 16) or acceptable (0.20 < rs < 0.49)(5 of 16) agreement. In the cross-classification of quartiles, ≥50% of meals were classified into the same quartile by both methods for 9 nutrients and 10% of meals were classified into opposite quartiles for 14 nutrients, indicating good agreement. ChatGPT also provided caveats regarding its estimations such as “the caloric estimate assumes the butter is spread thinly” and “cornflakes can often be fortified with vitamins and minerals […] and exact content could also vary based on the brand of cornflakes”.

ChatGPT showed poor agreement for estimating the nutritional composition of meal photographs for most nutrients, but the correlation and cross-classification of quartiles indicate good ability to rank meal photographs according to nutritional composition. Further research is required with a wider range of meals and in real-world settings. Future work should consider the training of language models using high-quality nutrition data to improve accuracy and maximise the potential for their use in dietary assessment.

Extended reality (XR), which includes Virtual Reality (VR) as well as Augmented Reality (AR) technologies, has emerged as an innovative tools with potential applications in both the health and education sectors. For nutrition studies, XR technology offers alternative approaches to addressing challenges related to the prevention and management of chronic conditions such as overweight, obesity, diabetes, and cardiovascular diseases (CVD)(1). By simulating immersive environments and interactive experiences, XR technology presents the opportunity to influence dietary behaviours, modify eating habits, to improve health by reducing weight and body mass index (BMI). This review aims to explore the application of XR technology in nutrition studies, particularly focusing on dietrelated non-communicable diseases (obesity, diabetes, and cardiovascular diseases), with an emphasis on managing relevant health outcomes.

A comprehensive search utilising multiple databases including PubMed, Cochrane, Web of Science, Scopus, CINAHL, Medline, and ProQuest was conducted (December to March 2024). Key search terms encompassed XR technology (e.g., virtual reality, augmented reality, Oculus, mobile app, immersive) in conjunction with nutrition-preventable terms (obesity, overweight, diabetes, CVD and health outcomes BMI). Following the search, duplicates were removed, and articles were screened against predefined inclusion criteria. Data extraction focused on study details, participant characteristics, interventions, outcomes, and results.

845 articles were identified, and five met the inclusion criteria(3,4,5,6,7). These included one randomised cross-over study(4) and four randomised controlled trials(1–3,5). Of these articles, four studies explored VR-based approaches(2–5), while one study used AR technology(1). The primary outcomes assessed across these studies focussed on the efficacy of VR and AR interventions in various domains, including portion size reduction, hunger, eating behaviour, food preferences, and weight management. Two studies showed improved portion size self-efficacy with VR interventions(2,5). One study reported that even though eating a virtual meal does not appear to significantly reduce hunger in healthy individuals, meal duration was significantly shorter in the virtual meal, than in the actual or real meal, which led to a higher eating rate(4). The use of XR interventions also showed the potential to support optimal portion size selection and reduction in implicit food preferences(1,3).

XR interventions may be effective in addressing various aspects of eating behaviour and portion control. These findings suggest potential applications in nutrition education and obesity management, especially for those for whom technology usage is the norm, by offering innovative approaches for intervention and behaviour change. Further research in this domain is needed to elucidate the efficacy, feasibility, and long-term impact of XR/VR-based interventions in the prevention and management of chronic conditions.

Poor diet quality among young adults contributes to increased rates of overweight and obesity(1). Improving diet quality requires small and achievable changes in eating behaviours(2). Personalised nutrition interventions offer a promising strategy to modify behaviour and subsequently enhance diet quality but require input data on individuals past behaviour and their environmental contexts to ensure advice is relevant and effective(3). Machine learning (ML) is a useful tool for predicting behaviours, but few studies have explored the integration of ML capabilities into precision nutrition applications(4–6). Therefore, this study used ML to investigate whether contextual factors occurring at eating occasions (EO) predict food consumption and, consequently, overall daily diet quality.

Analyses were conducted on cross-sectional data from the Measuring Eating in Everyday Life Study (MEALS) (7–8). Participants (aged 18-30 years, n=675) recorded dietary intakes at EO (i.e. meals and snacks) in near-real time (3-4 non-consecutive days) using a Smartphone food diary app. Contextual factors for each EO were recorded via the app and categorised as socialenvironmental factors (e.g. activity, persons present while eating) and physical-environmental factors (e.g. consumption location, purchase location). Person level factors describing participant characteristics were collected during an online survey. Intake (servings per EO) of vegetables, fruits, grains, meat, dairy, and discretionary foods were estimated, as per Australian Dietary Guidelines. Gradient boost decision tree(9) and random forest models(10) were chosen a priori ; decision tree provide explainable ML, while random forest improves accuracy(11). Their performance was evaluated using 10-fold cross-validation, comparing mean absolute error (MAE), root mean square error (RMSE), and R squared. Feature importance analysis was performed to understand important variables for predicting food consumption. All analysis was performed using R.

Results indicate that ML can predict most food groups at EO using contextual factors, with an acceptable range in differences between actual consumption and predicted consumption (<1 serving per EO). For instance, MEA values for fruits, dairy, and meat were 0.35, 0.34, and 0.56 servings, respectively. This suggests that, on average, models’ predictions are off by 0.35 servings of fruits per EO using contextual factors (RMSE values for fruit, dairy, and meat were 0.61, 0.50, and 0.80 servings, respectively). Notably, when investigating the influences of different contextual factors on models’ predictions, feature importance analysis indicated that person level factors such as self-efficacy and age were considered highly important, while person presence and purchase locations ranked highly in importance within eating occasion-level factors across most food groups.

ML can offer valuable insights into the interplay between contextual factors and food consumption. Future research should investigate which contextual factors, when modified, lead to favourable dietary behaviours, and incorporate these findings into precision nutrition interventions.

Measuring dietary intake accurately is essential in establishing relationships between food consumption patterns and non-communicable diseases. Researchers are now using online tools to replace traditional methods1. New technologies provide a more flexible approach and are generally preferred by study participants. However, it is not known whether the underlying databases are reliable. Weight loss app energy calculations vary considerably compared to weighed food records2. The aim of this work was to review the quality and compare the content of online food composition tables including those linked to assessment tools.

We undertook a Google search supplemented by expert knowledge to identify online food composition tables in the English language. Databases were either stand-alone online food and drink databases or associated with dietary assessment tools. Databases found were accessed; and we conducted a review of their features. Information on numbers and types of foods, their origin, nutrients and completeness of datasets were extracted using a standardised approach.

Twenty-two online food composition databases were reviewed from around the world. 12 (55%) of the databases were associated with dietary assessment software and 9 (41%) solely used an API to link with other systems. Numbers of food items included in the databases ranged from 18 million (‘myfitnesspal’) to 600 (‘FoodDB’). The largest databases generally used crowd sourced data.

‘myfood24’ had the largest total number of variables (n = 326) and ‘Carbs and Cals’ and ‘Nutracheck’ only had 8 nutrient variables. The source of food composition data is not clear for all of the datasets. 7 of the databases explicitly include the UK and 3 the US generic food tables, often in addition to branded data. Most databases do not state how they are checked for accuracy or completeness. Where databases state how missing data is infilled only ‘myfood24’ considers both macro and micronutrients. Eight of the databases were only relevant for one country, and 13 tools reported having food data for a range of countries. In addition to nutrients some databases also include non-nutrient phytochemicals, allergens or sustainability metrics. Food images were often used to assist with portion size estimation, although some tools only used general pictures or pictures that did not reflect the food. Searching for food items varied in accuracy and some systems allowed barcode scanning.

Online food composition databases allow for much greater differentiation of foods, including branded items compared to traditional tables. They range considerably in size and quality. Care should be taken to ensure databases underlying online assessment tools are reliable with limited missing data to ensure the application of such databases to research does not produce misleading results.

Food composition databases (FCDs) play a vital role in nutrition research, providing essential data on the nutritional content of food and drinks, typically obtained through chemical analyses of representative samples. FCDs are used in dietary surveys, clinical practice, research, and policy development(1). Public Health England funds and systematically publishes a UK FCD McCance Widdowson’s The Composition of Foods Integrated Dataset (CoFID). Some nutrients that are not available in the CoFID database including Omega-3 and 6, Folates, Amino Acids, and Vitamin D(2) but are available in sources including the Quadram Institute Food Labelling 2021 dataset(3) and National FCDs(1). To address this limitation, this study aims to enrich the CoFID database with additional nutrients and create a new database known as “GB23”.

A reference hierarchy was established, including the CoFID 2021 dataset, the Quadram Institute Food Labelling Dataset 2021, and National Food Composition Databases. Nutrients were gap-filled where (a) data was represented as ‘N’ i.e. the nutrient is present in significant quantities, but the specific amount is unknown or not reliably documented or (b) the nutrient may be present in significant quantities, but the value was not reported. Nutrients were gap-filled, and the GB23 database was cross-checked against the previous CoFID database (GB15), and any outliers were identified. Aggregate nutrient values, nutrient breakdown and variability was analysed to ensure accuracy and consistency. Analysis was completed using Excel version V16.69.1.

2,887 foods and 34 unique nutrients were updated. 7 nutrients were gap-filled due to criteria (a) and 27 were updated due to criteria (b). 43,067 micronutrients values were gap-filled, including Omega 3 (n 84), Omega 6 (n 74), Folate (n 28), folateDFE (n 19), folicAcidFortified (n 19), folateFood (n 6), 21 Amino Acids (n 42,837), and Vitamin D (n 0). 5,553 macronutrients were updated based on re-calculated values from the Food Labelling 2021 dataset on Carbohydrate, Sugar, Fat, Saturated Fat, Fibre, Protein, Salt. Folic acid values (n 19) were taken from manufacturer and supermarket websites, and representative values calculated. 5 FCDs (2006 Norwegian FCD, 2014 German Nutrient DB, 2015 Canadian Nutrient File, 2014 NEVO online, and 2016 New Zealand FoodFiles) were identified as sources for Vitamin D values, but additional quality checks are required. 75 photos were added to new CoFID foods. Correlation between missing nutrients, foods, and food categories was analysed, e.g. 36 products were identified as sources of missing Omega-3 and 6 values.

This study enhances the CoFID database and identifies areas for improvement, including limited Vitamin D data, and increases transparency on the Nutritics GB23 database. As FCDs are and will remain central to nutrition, analysis on their limitations and areas for enhancements are key to ensuring robust nutrition analysis, research, and policy development.

Overweight/obesity and iron deficiency are highly prevalent, particularly in women of reproductive age (WRA)(1). Both can seriously affect women’s health and cause severe complications in pregnancy(1). Obesity is associated with nutritional deficiencies but its influence on iron deficiency is unclear. This study aimed to investigate the influence of adiposity on the prevalence of iron deficiency in UK women of reproductive age.

Data from NDNS (years 2008-2019) was used including general characteristics, anthropometry, iron biomarkers; haemoglobin (Hb), ferritin, and C-reactive protein (CRP) as a marker of inflammation. WRA aged 18-49 years with BMI ≥18.5kg/m2 were included. Data were categorised as follows: Anaemia: Hb<120 g/L, Iron Deficiency Anaemia (IDA): Hb<120g/L and ferritin <30 μg/L, Iron Deficiency (ID): ferritin <30 μg/L, Iron Deficiency Without Anaemia (IDWA): Ferritin <30 μg/L and Hb>120 g/L(2). Ferritin was adjusted for CRP (aFerritin)(3). WRA were divided into two adiposity groups: BMI <25 kg/m2 and ≥25 kg/m2, waist circumference (WC) <80cm and ≥80cm, waist-toheight (WHtR) <0.50 and ≥0.50, and waist-to-hip ratio (WHR)<0.85 and ≥0.85 according to guidelines(4,5). Weighting was applied to account for response differences between the surveys. Chi-square was performed to compare iron deficiency prevalence between adiposity groups and multiple logistic regressions adjusting for relevant covariates to evaluate the associations between adiposity and anaemia, IDA, ID and IDWA. P-value <0.05 considered significant.

n = 1098 WRA were included, n = 496 with normal weight and n = 602 with overweight/obesity (Ow/Ob). The overall anaemia, IDA, ID, and IDWA prevalence was 9.2%, 5.3%, 49.7%, and 42.9% respectively. Anaemia prevalence was greater in those with higher WHtR and WHR (11.9% vs 5.9% and 16.7% vs 6.5% respectively; both p<0.001). Greater IDA prevalence was observed in those with higher WC, WHtR and WHR (8.5% vs 4.3%, p = 0.005; 9.4% vs 3.3%, p<0.001; and 12.1% vs 4.9%, p<0.001). Overall prevalence of ID was 43% using unadjusted ferritin and 49.7% using aFerritin, both with similar prevalence observed between adiposity groups. IDWA prevalence was higher in the low WC compared to the high WC group (46.5% vs 40.1%, p = 0.030), however, no association was found between ID or IDWA with any adiposity group. Logistic regression analyses showed that higher WHtR and WHR predicted anaemia and IDA (anaemia: WHtR aOR 1.74 p = 0.045, WHR aOR 4.05 p<0.001; IDA: WHtR aOR 2.45 p = 0.011, WHR aOR 3.82 p<0.001).

ID prevalence in a representative sample of UK WRA is high, at 43%, with higher rates of 50% observed when ferritin is adjusted for inflammation, indicating the increased sensitivity of aFerritin in ID detection. A greater central adiposity in WRA is a risk factor for IDA. Public health strategies are needed to address the high prevalence of ID in UK WRA, particularly those living with Ow/Ob.

Dietary intake can influence immune function indirectly by affecting the gut microbiota composition and metabolism(1). Fish consumption has been shown to positively regulate the gut microbiota in humans(2,3);albeit in those studies fish was consumed in high amounts (500-750g/week) and immune function was not investigated. This study investigated the effect of consuming the UK dietary recommendation for fish(4) (2 portions [140-280g/week], one of which is oily) on the gut microbiota alpha diversity. Further, we examined if changes (pre- to post-intervention) in the gut microbiota composition were associated with changes in immune cytokine concentrations.

An 8-week randomised controlled trial in low fish consuming women of childbearing age (n = 41; median age 23y) investigated the effect of consuming 1 or 2 portions of fish (tuna or sardines)/week compared to not consuming fish. A blood sample was collected to measure inflammatory cytokines (tumour necrosis factor-a, interleukin [IL]-1b, IL-5, IL-6, IL-17A and IL-22) pre-and post-intervention. Faecal samples were collected at both timepoints and extracted DNA was used to determine gut microbiota compositional profiles using 16S metagenomic sequencing (Illumina, USA). Statistical analysis investigated significant differences in changes in gut microbiota alpha diversity and compositional relative abundances between fish intervention (N = 26) and control (N = 15), then secondary analysis stratified by portion size (1 vs 2 portions) and type of fish (tuna vs sardines). Differences in cytokines between fish intervention and control were assessed by Mann-Whitney U. Spearman rank coefficient assessed associations between the changes in gut microbiota relative abundances with cytokine changes in fish and control groups.

Fish consumption increased gut microbiota alpha diversity indices (Chao1 [7.37±41.23], Simpson [0.003±0.163], Shannon [0.07±0.33], phylogenetic diversity [0.35±2.59], observed species [9.00±40.06]), albeit this was not significant compared to the control group (p>0.05). Consumption of fish, specifically sardines, for 8 weeks significantly reduced Bacteroidetes (-4.77±4.88%) when compared to control (+4.15±7.58%) (p<0.01). No significant differences were observed between the change in relative abundances of gut microbiota at genus-level taxa or inflammatory cytokines between the fish intervention and control. In the fish intervention group, increases in IL-17A, IL-22 and IL-6 concentrations were positively correlated with changes in Alistipes, Rhodococcus, Haemophilus, Barnesiella and Akkermansia relative abundances (p<0.05).

Although not statistically significant, consistent findings suggest that fish intake, in line with dietary guidelines, may have favourable impact on gut microbiota. Sardines, oily fish rich in n-3 polyunsaturated fatty acids, may have health benefits in disease states where Bacteroidetes is elevated; nevertheless, further research is required in a larger cohort over a longer period.

Approximately 25% of deaths in the UK are caused by cancer(1). Studies have reported a lower risk of all site cancers, and specifically kidney, stomach, oesophagus, colorectal, oropharyngeal, and upper aerodigestive cancers in higher consumers of pulses compared to low consumers(2). However, findings are inconsistent with others showing no association(3). In view of the limited studies conducted to date, the objective of this data analysis was to assess the association of pulse-rich diets (beans, chickpeas, and lentils) with cancer incidence and mortality in UK adults.

This prospective cohort study of the UK Biobank included 115,011 participants (55%, n = 62,977 females) aged 40-70 years at recruitment (2006 and 2010) who had undertaken at least two complete 24-hour dietary recalls and were followed up until 2022. Participants with cancer at baseline, extreme dietary energy intakes, or pregnant were also excluded from the analysis. Pulse consumption was split as non-consumers (NC,0g/day, n = 73,266), and three tertiles (T) according to the level of pulse intake (T1:1-20.4g/day, n = 14,035; T2:20.5-40.4g/day, n = 13,509; and T3:40.5360g/day, n = 14,161). Using Cox proportional hazard models, the hazard ratios (HR) of the effect of pulse consumption on total and multi-site cancer incidence and mortality was conducted with sex, age, dietary energy, region, ethnicity, income, employment, smoking status, education, exercise level, body mass index (BMI), supplement use, alcohol intake, family history of cancer, intake of fruits and vegetables, processed meat, and dietary fibre as covariates in the fully adjusted model as appropriate for the cancer site.

The cohort had a mean ± SD age of 55.9 ± 7.0 years, BMI of 26.7 ± 4.6 kg/m2 and pulse intake of 13.3 ± 20.5g/day. White people (n = 110,890, 96.4%) had the lowest mean ± SD pulse intake compared to all other ethnicities (13.2 ± 24.4g/day vs 17.0 ± 27.7g/day, p<0.001 respectively). After a mean follow-up of 12.4 years, there were 3281 cancer deaths and 17,415 incidences of cancer. There were inverse associations of pulse intake with the incidence of liver cancer (HR 0.39: 95% CI:0.17, 0.90) and thyroid cancer (HR 0.40: 95% CI: 0.16, 0.99) in T2 compared to NC for both sexes and in NC compared with T1 for breast cancer in females (HR 0.55: 95% CI: 0.36, 0.86). No associations were observed for other cancer sites or for cancer mortality.

Compared with NC, our findings suggest that diets with an intake of pulses up to 40.4g/day are associated with lower incidence of liver, thyroid, and female breast cancer but not other cancers or mortality. The lack of association between diets containing higher pulse intake with cancer incidence and mortality was unexpected and suggestive of effects of other dietary components, studies on the dose dependent effects and types of pulse intake on cancer incidence and mortality are needed.

Funding received from BBSRC (BB/W017946).

Gut microbiome fermentation of prebiotic fibre increases short-chain fatty acids (SCFA), which can stimulate mucosal B-cells to express secretory IgA in distant tissues(1,2), and can support gut barrier function. Secretory IgA levels have been associated with reduced upper-respiratory tract infections incidence in immune-compromised populations(3,4). However, it is not known if prebiotic supplementation would be effective in healthy adults. This study investigated whether inulin can increase mucosal immunity (sIgA) and intestinal permeability markers, alongside acceptability of intervention.

Healthy adults (n = 23, BMI: 23.85 ± 3.2 kg.m-2, age: 33.4 ± 10.1 years) took part in this randomisedcontrolled cross-over trial. Participants were randomly allocated to inulin syrup (12g/day) or placebo syrup (20% diluted honey) for 2-weeks. This was followed by 2-weeks wash-out, and then conditions were switched for a final 2-week supplementation. Before and after each supplementation, faecal pH was measured with glass probe, and salivary sIgA and plasma lipopolysaccharide binding protein (LBP, marker of intestinal permeability) were measured with ELISA kits. Time to provide 2ml of saliva through passive drooling was recorded, and used to calculate saliva flow (ml/min) and thus IgA secretion rate (μg/min). Participants were asked to report on their diet intake (4-day food diary) before and after each supplementation phase, as well as reporting on any respiratory and gastrointestinal symptoms using a validated questionnaire. Differences between groups were analysed with one-way ANOVA on post outcome values, adjusted for outcome baseline values, and chi-squared for categorical data (SPSS software).

There was a significant reduction in LBP levels in inulin compared to placebo condition (-3.15, 95%CI -5.18, -1.12; p =0.03). Mean differences between groups (inulin vs placebo) were not significant (p> 0.10) for pH (0.43 95%CI −0.32,0.41) and IgA secretion rate (31.80 μg/min 95%CI 150.90, 87.29). Gastrointestinal symptoms were not different between groups (p>0.05), except for mild bloating/rumbling (reported in 70.8% inulin vs 44.4% placebo, p = 0.01). Respiratory symptoms were lower in inulin compared to placebo (runny nose 19.2% inulin vs 38.2%, p =0.03). Inulin condition resulted in higher fibre intake compared to placebo, with no other differences in dietary intake (+5.43 g/day 95%CI 0.09, 10.86 p =0.05). 90% of participants reported the intervention as acceptable and easy to follow.

Inulin was well tolerated with minimal side effects of gastrointestinal bloating and rumbling. While markers of mucosal immunity did not change, intestinal permeability markers improved suggesting that in healthy populations prebiotic supplements can support gastrointestinal health but not necessarily improve mucosal immunity.

The World Health Organisation currently recommends a global reduction of free sugar intakes to reduce diet-related ill-health(1). One of the proposed strategies to achieve this reduction is to limit the consumption of all sweet-tasting foods and beverages, based on a rationale that regular exposure to dietary sweet taste increases preferences for all sweet-tasting foods and beverages, including those which contain free sugars(2). Limited studies, however, have directly examined the effects of repeated dietary sweet taste exposure on subsequent preferences and intakes of sweettasting foods and beverages, with the existing literature exhibiting contradictions and failing to investigate the effects of whole-diet-sweet taste exposure on the outcomes of interest(3).

This randomized controlled trial aimed to assess the effects of a whole-diet, six-day dietary sweet food and beverage intervention on sweet taste perceptions and intakes of sweet-tasting foods and beverages. A total of 104 adults were recruited and randomized to either: a) increase their sweet food intake (n=40); b) decrease their sweet food intake (n=43); or c) make no dietary change (n=21); for six consecutive days. On day 0 (baseline) and day 7 (end), pleasantness, desire to eat, and sweet taste intensity were rated for six dietary items, and sweet food intake was measured in a buffet meal, as percentage weight and percentage energy consumed from sweet foods and beverages, weight of sugar consumed and percentage energy consumed from sugars.

Intention-to-treat analyses found no statistically-significant dietary exposure x time interactions for perceived pleasantness (F(2, 101)=2.04, p=.14, np2=.04), desire to eat (F(2, 101)=1.49, p=.23, np2=.03) or any measure of sweet food intake (largest F=2.53, p=.09). A significant dietary exposure x time interaction was detected for sweet taste intensity (F(2, 101)=4.10, p=.02, np2=.08), which showed that for participants in the decrease sweet food consumption group, sweet foods tasted sweeter post-intervention compared to baseline (t(42)=3.36, p < .01, Mdiff=6, SE=2). No effects were found in the increase sweet food consumption or no diet change groups (largest t(39)=.38, p=.70).

Our findings contradict the predictions made by public health institutions that dietary sweet taste exposure influences the intake of sweet-tasting foods and beverages, and that reduced exposure leads to reduced intakes(2). They also suggest that, whilst the extent of dietary exposure to sweettasting foods and beverages affects sweet taste intensity, it does not affect the perceived pleasantness of or desire to eat other sweet-tasting foods.

Food systems significantly influence both human health and the environment. In 2019, the EATLancet Commission introduced a universal, healthy reference diet aimed at enhancing environmental sustainability and preventing non-communicable diseases.(1) This diet, known as the EAT-Lancet reference diet (ELR-diet), has been associated with reduced risks of cardiometabolic diseases and mortality,(2,3) as well as a diminished environmental impact.(4) However, the distinctions between the ELR-diet and other dietary patterns—traditionally defined solely by their health benefits—remain less examined, particularly in the context of both health outcomes and environmental sustainability. This study evaluates hypothesis-driven diet quality scores—Alternate Healthy Eating Index (AHEI), Mediterranean-style Diet Score (MDS), Dietary Approaches to Stop Hypertension (DASH), Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet, Nordic Diet Score, healthful Plant-Based Diet Index (hPDI), and the ELR-diet—across their effects on cardiometabolic health, cognitive function, and ecological sustainability.

We based our analyses on participants from the Rhineland study with available nutritional assessment, cardiometabolic markers, and cognitive function data at baseline (n = 6473, age 56.1 ± 13.4 years, 56% women). Based on dietary data collected through a semi-quantitative food frequency questionnaire, we estimated and compared adherence to seven diet quality scores, including the ELR-diet. Cardiometabolic risk markers included adiposity indicators, lipoprotein levels, and blood pressure. Cognitive performance was measured in multiple domains and averaged into a global cognitive function score. Diet-related greenhouse-gas emissions (GHGEs) and land use (LU) were calculated per participant’s food intake, expressed as kgCO2 equivalents and m2×year, respectively.(5) Correlations between diet quality scores were quantified using Spearman correlation, and associations with outcomes were analyzed using multivariate linear models.

All diet quality scores were positively correlated with each other. The EAT-Lancet diet was most strongly correlated with the hPDI (r= 0.43; p<0.001), and least with the Nordic diet (r= 0.16; p<0.001). Higher ELR-diet adherence was associated with reductions in various adiposity markers, lipoprotein levels, and blood pressure although these reductions were generally smaller than those observed with the DASH and AHEI diets. ELR-diet adherence was also associated with better global cognitive performance (ß =0.03, 95%CI =0.02;0.05), closely matching the MIND diet (ß =0.04, 95%CI =0.03;0.05). Moreover, the ELR-diet was inversely associated with GHGE (ß =−0.3, 95%CI =−0.33;−0.27) and LU (ß =−0.37, 95%CI =−0.41;−0.33), yet the hPDI diet, was associated with the greatest decrease in both markers (GHGE ß =−0.48, 95%CI =−0.51;−0.45; LU ß =−0.58, 95%CI =−0.62;−0.54). Conversely, the Nordic diet was associated with increases in both GHGE and LU.

Our study provides evidence that higher diet quality, measured though different scores, is associated with favorable cardiometabolic health, better cognitive performance and reduced environmental impact. These findings support the generalizability of the beneficial effects of adherence to a healthy diet across both health-related and sustainability outcomes.

There is evidence that human health and environmental sustainability are linked(1) and a shift to more sustainable diets would be beneficial for both human and planetary health(2). Greenhouse gas emissions (GHGe) are an environmental metric that can be used to assess the environmental impact of diets(3). Research indicates that a nutritionally adequate diet low in GHGe can be achieved, but that a healthy diet will not always result in lower GHGe(4). To date, there is no information on the food choices driving GHGe in Northern Ireland. This study examined the food groups and derived nutrient intakes driving GHGe for Northern Ireland.

Analyses were performed on the Northern Ireland sub cohort of the UK National Dietary Nutrition

Survey (NDNS 2016-2019)(5) on adults aged 18-64 years old (n = 210). GHGe were sourced from Scheelbeek et al. 2020(6). Mean daily intakes (MDI) for nutrients and GHGe for the total population were calculated and the population was divided into tertile groups based on low (T1), medium (T2) and high (T3) GHGe. Differences in population characteristics, MDI of energy and key nutrients (%TE or per 10MJ) and contributing food sources to GHGe were examined across these tertiles, using chi-square and one-way ANOVA with covariates (blue water use, sex and social class) and correcting for multiple comparisons as appropriate (P<0.0011 for food groups and P<0.002 for nutrients).

Mean GHGe for adults were 4.3±1.9 kgC02eq/d and 7% of the population fell below the GHGe planetary boundary of 1.87 kgC02eq/d(7). The food groups ‘tea, coffee and water’ (0.80±0.56 kgC02eq/d), ‘burgers, sausages, kebabs, meat pies and other meat and meat products’ (0.39±0.64 kgC02eq/d) and ‘alcoholic beverages’ (0.39±1.02 kgC02eq/d) were the major contributors to overall GHGe. Across the tertiles, the difference of GHGe between T3 and T1 was 3.6 kgC02eq/d (6.3 kgC02eq/d vs 2.7 kgC02eq/d). The contribution of ‘bacon, ham and dishes’, ‘beef, veal and dishes’, ‘lamb and lamb dishes’, ‘tea, coffee and water’ and ‘alcoholic beverages’ were higher (P<0.001) in T3 versus T1. MDI of energy (kcal) was higher in T3 versus T2 and T1 (2141.3kcal, 1791.2kcal and 1368.9kcal, P<0.001). Significantly lower carbohydrate (%TE) intakes were reported in T3 compared to T1 (42.7%TE vs 47.5%TE, P<0.001). Protein and fat (%TE) were higher in T3 in comparison to T2 and T1, with protein at 17.4%, 17.1% and 16.7%TE respectively, and fat at 35.4%, 35.3% and 33.7%TE respectively.

In summary, the main food groups driving GHGe in NI were beverage and animal based.

Differences in energy and carbohydrate intake were observed across the tertiles. These results will aid in the development of strategies to diversify foods within Northern Ireland to combat high GHGe.

Plant-based meat alternatives (PBMAs) are products made from manufactured ingredients such as protein isolates, to replicate the organoleptic and functional properties of meat(1). Traditional plant proteins (TPPs) such as tofu, tempeh and legume-based dishes are whole plant foods and have been longstanding components of the protein group of food-based dietary guidelines (FBDGs)(2). PBMAs have been added to some recently revised FBDGs(3,4), however, with considerable differences in ingredient composition, the objective of this study was to evaluate if these products are similar in terms of energy and macronutrients content.

An online audit of PBMAs available in Tesco and Sainsbury’s was completed (November 2023January 2024). On-pack information was extracted, and similar products were grouped. Within eligible categories (burgers, beef-style dishes and seafood), products were further grouped according to their classification as a PBMA or TPP. Categories such as tofu and tempeh were combined and compared with PBMA chicken fillets/chunks and beef/pork products separately. Mean energy and nutrient contents were compared using independent sample t-tests, with P values ≤ 0.05 considered statistically significant. A-scores from the UK’s Nutrient Profiling Model(5) were calculated and the EU threshold for protein claims (≥12% energy from protein)(6) was applied to determine the proportion of products within each category considered a protein source.

Within the burger category, PBMAs (n = 43) had a significantly higher mean energy (206.8 vs. 197.4 kcal/100g, P = 0.008), total fat (11.2 vs. 8.4 g/100g, P = 0.05), and saturated fat content (2.5 vs. 0.8 g/100g, P = 0.02), and significantly lower carbohydrate content (10.4 vs. 23.5 g/100g, P = 0.002) than TPPs (n = 14). Within the seafood category, PBMAs (n = 11) had a significantly lower total fat content (9.9 vs. 11.4 g/100g, P = 0.001) and significantly higher protein (6.6 vs. 4.5 g/100g, P = 0.04) and salt content (1.0 vs. 0.8 g/100g, P = 0.03) than TPPs (n = 5). PBMAs within the beef-style dishes category (n = 19) were significantly higher in energy (174.2 vs. 111.5 kcal/100g, P = 0.003) than TPPs (n = 20).

When PBMA chicken (n = 81) and beef/pork (n = 20) were compared with tofu, tempeh and jackfruit (n = 17 and 20 respectively), PBMA chicken had a significantly higher carbohydrate content (12.2 vs. 2.6 g/100g, P<0.001) and PBMA beef/pork had a significantly higher protein content (17.2 vs. 13.1 g/100g, P =0.005). PBMAs had higher mean A-scores (6.4 - 8.5) than TPPs (3.2 - 6.6) in 4/5 categories indicating they are ‘less healthy.’ Very few TPPs within the burger and seafood categories met the protein threshold compared to PBMAs.

Differences in energy, macronutrient content and A-scores, mean PBMAs and TPPs cannot be considered nutritionally equal.

The food industry is intertwined in every continent and culture, shaping dietary habits and economies worldwide(1). Sustainability has become an increasingly important and widely discussed topic in the food industry influencing the decisions and actions of food industry companies and stakeholders worldwide. In an effort to become environmentally conscious and protect the earth for future generations, sustainability practices have been developed and integrated into food industry actions and policies which aim to meet present needs without compromising the ability of future generations to meet their own needs(2). This study was part of a joint Erasmus KA2 European-funded project (2022-1-IE01-KA220-VET-000087508 Digitalisation of Sustainable Health Education) and aimed to explore current food sustainability priorities across the food industry in Europe and how initiatives are developed, implemented and evaluated to achieve food sustainability targets.

In-depth semi-structured, audio-recorded interviews were conducted with 21 employees in the Food Industry sector across Ireland, Poland, Lithuania and Cyprus. These participants were selected based on their knowledge and oversight of their company’s sustainability practices. Interviews were transcribed and thematically analysed(3,4), whereby the data were coded, themes identified and discussed by all authors.

Three themes were identified in the data: 1. sustainable practice challenges, 2. factors for consideration, and 3. thinking to the future. Sustainable practices were already implemented in all but one of the 21 participating companies. The main drivers reported behind development of sustainable initiatives included complying to the Science-Based Targets initiative (SBTi) and setting emissions targets such as being net zero by 2050 and minimising waste generation. Participants reported cost, time required for monitoring and evaluation, retaining quality of products, and employee engagement as the main challenges for initiative development and implementation. When developing a sustainability initiative, participants felt that employee and stakeholder understanding and acceptance were important to consider for the success of the initiative. Future goals and initiatives were positively discussed as expanding on current priorities to reduce emissions and implement sustainable procurement and production practices, and gaps in research were identified as the proliferation of environmental labels and exploration of greater inter-collaboration between companies to share sustainability data.

A focus on becoming more sustainable was at the centre of innovation development and future proofing for all participating companies, with plans to develop new or further enhance current policies to continue producing quality food in an environmentally sustainable way. Educating employees and stakeholders, including nutrition educators and graduates, on future initiatives and potential impacts is essential for raising awareness, strengthening implementation and ensuring long-term maintenance and success of food sustainability practices within industry. Future research into a policy for sharing of sustainability data, such as raw sustainability metrics between companies could strengthen initiative outcomes and inform nutrition education and research to meet market needs.

People with lower socio-demographic status (SES) tend to have less healthy diets, partly through access and affordability(1). As people are encouraged to reduce their meat consumption as part of a sustainable diet(2), plant-based alternatives to meat are increasingly available. We hypothesised that lower SES populations may not be able to access plant-based meat alternatives (PBMAs) from nearby shops.

A cross-sectional survey of food retailers in Aberdeen, a city of approximately 260,000 people with a wide range of SES, was conducted in 2023. For each decile of the nationally representative Scottish Index of Multiple Deprivation (SMID)(3), a postcode was randomly selected around the centroid of which a 0.5 mile area was identified and all food vendors were visited. All PBMAs (e.g., plant-based sausages, cold cuts and chicken nugget replacements both frozen and chilled) were recorded along with descriptive characteristics of the shop (e.g., size, whether in a chain, proximity to public transport). Separately, to approximate the shopping habits of people in each SIMD decile, Kantar World Panel purchase data for the whole of Scotland (2017-2023) was used to model the proportion of monthly household visits to and spending in different food shops by household SIMD decile using a beta regression with mixed effects for repeated measures (n=3054 households with median 30, IQR 1, 33 monthly observations).

Forty-one retailers were visited and a total of 267 different PBMAs products identified. There was no association between the SIMD decile where a shop was located and the availability of PBMAs. The only statistically significant predictor of PBMAs sale was supermarket size (Chi-squared test, p=0.002) and PBMAs were only available in large or very large supermarkets (n = 12/41). Larger supermarkets tended to be positioned in accessible locations with middling SIMD deciles (4th and 6th), albeit access may be more limited for vulnerable groups who may be less likely to own a car From the household purchase data, there was no statistical difference in the proportion of monthly household spending on food in large supermarkets between SIMD deciles (0.01, 95%CI 0.003,0.022, p = 0.14) and the proportion of visits to large supermarkets was not meaningfully different by SIMD (range 0.33 to 0.39), from which we infer that people from all SIMD deciles could access large supermarkets which stock PMBAs.

We found no evidence to support our hypothesis that access to PBMA was driven by SIMD. Access was limited to large food shops, however, all SIMD deciles use large food retailers and could, in principle buy PBMAs. We do not know, from these data, whether supermarkets are equally used by, or convenient for, consumers from different SIMDs, however,access to PBMAs may not be one of the major barriers for consumers reducing meat consumption

Current dietary patterns are suboptimal for both human and planetary health(1,2). With growing consumer and business concerns around food sustainability, estimating the environmental footprint of foods and diets is pertinent. In many countries, supermarkets are the primary provider of foods and beverages; therefore, supermarket purchasing records represent a novel source of population dietary data that offers advantages over traditional methods(3). We developed a method for mapping greenhouse gas emissions (GHGE) to food and beverage products from a high-street retailer’s portfolio, to enable the estimation of the environmental footprint of population diets when linked with sales information.

We used data from the food and beverage portfolio of a high-street retailer in the United Kingdom (UK), including product name/description, categorisation, ingredients, and weight. We mapped these products to GHGE (kg CO2-eq/kg) using a global database on the average environmental footprint of food commodities(4). This mapping process involved three stages utilising different mapping approaches, guided by product sales data, which we extracted from the retailer’s loyaltycard transactions for Yorkshire and the Humber (UK) region during 2022. Stage 1 involved categorising the products into Living Costs and Food Survey food categories and mapping each category to GHGE, where possible (food-category approach). Stage 2 involved splitting selected food categories (based on complexity, necessity of a better mapping, and sales) and creating a sub-category-specific mapping based on an indicator product, which was selected as most popular using sales data (food-sub-category approach). The indicator-product mapping represented a weighted average GHGE value calculated using information on product ingredients and their estimated proportions (ingredient approach). Stage 3 utilised word-searches in product descriptions to distinguish further between product types within selected prioritised subcategories. We used the estimated product-level GHGE (mapped GHGE × product weight) and sales data to estimate food-category contributions to total GHGE and assess how these estimations change by mapping stage.

Of >28,000 products, 77.7%, 98.0% and 98.6% were mapped to GHGE at the end of stages 1, 2 and 3, respectively. Of the final product mappings, 40% were at a food-category level and 60% at least at a sub-category level. We calculated 153 product-specific GHGE using ingredients information for prioritised indicator products. When using mappings from stage 3 vs 1, the contributions of ‘savoury snacks’ and ‘chocolate’ to total GHGE were approximately four and two times higher respectively, due largely to improved mapping that accounted for product sub-category and ingredients.

Mapping environmental sustainability metrics to a retail product dataset is feasible when using a staged approach, guided and prioritised by sales data. However, mapping approach and the estimations’ variability should be considered. This method could be used for estimating the environmental footprint from food purchasing data, helping to inform responses towards promoting healthier and more sustainable diets.

The food industry is experiencing a profound shift toward sustainability, defined by a growing emphasis on responsible sourcing, reducing environmental impact, and promoting social and economic well-being(1). This shift impacts food and nutrition graduate employability and the diverse skillsets required for success in this evolving industry. Skills including digital literacy and the ability to leverage technology for sustainable practices are increasingly valuable in the food industry. Food companies need graduates with the knowledge, abilities, and mindset to support these objectives as they work to incorporate sustainable practices into their operations. Graduates entering the food industry job market must be equipped with a solid understanding of sustainability principles and the ability to apply them in practical contexts(2). The aim of this study was to investigate the digital competencies and food sustainability knowledge, employers require of graduates in the food industry.

This study was part of a joint Erasmus KA2 European-funded project (2022-1-IE01-KA220-VET000087508 Digitalisation of Sustainable Health Education). In-depth semi-structured, audiorecorded focus groups were conducted with 24 employers in the food industry across project partner countries in Poland (n=10), Ireland (n=6), and Lithuania (n=8). Interviews were transcribed and thematically analysed, using the Braun and Clarke method(3,4)whereby the data were coded, and themes identified.

Three main themes were identified across the compiled data, these included: (1) perception of graduate preparedness for employability, (2) digital competence of graduates, and (3) food sustainability. The first theme highlighted that graduates from food and nutrition programmes are generally well prepared for employment in the food industry. There are a diverse array of industry roles on offer for graduates with employers reporting ‘on-the-job’ training as an integral part of graduates onboarding experience and career progression. The vast majority of participants felt graduates are currently meeting digital competency expectations when first entering the workforce however as digital competency is ever evolving it is essential to stay up to date. All employers highlighted sustainability as a key priority of their business however, there was no single common understanding for the term sustainability identified within the data. This finding emphasises the complexity of developing food and nutrition curricula that align to industry needs.

This study has highlighted the complex nature of the food industry and the need for graduates to possess a diverse skill set. It is evident that employment in the food industry requires a combination of technical expertise, digital competencies, and an understanding of sustainability practices. To ensure that food and nutrition graduates have the skills needed to succeed in this changing sector, collaboration between educational institutions and industry stakeholders, is a priority to ensure sector specific training and continuous professional development.

The global food system is the primary driver of biodiversity loss(1) and the source of a third of greenhouse gas emissions(2). Physical and psychological connectedness to nature are associated with improved health and sustainability-related outcomes, including increased pro-environmental behaviours and values(3). Nature connectedness refers to a person’s belief about the extent to which they are part of nature and include nature as part of their identity(3). Higher levels of nature connectedness have been associated with increased fruit and vegetable intake and the consumption of a more diverse diet(4).

This study aimed to assess the relationship between nature connectedness and food-related behaviours amongst Irish consumers.

A cross-sectional online survey was created to assess the attitudes, awareness, and behaviours of Irish consumers regarding the link between nature and food. Nature connectedness was assessed using the validated Nature Connection Index (NCI) scale [6]. The online survey assessed foodrelated behaviours regarding food waste, the purchasing of local, seasonal, and organic food, and the frequency of consumption of a variety of food groups. Covariate-adjusted general linear models and regression analyses were used to assess relationships between NCI scores and food-related behaviours. Analyses were adjusted for age, level of educational attainment, and employment status.

The survey was completed by 400 participants (74% female, age range = 18-92 years, Median age = 46 ± 31). The median NCI score for the total population was 86 (IQR± 27). NCI score was positively associated with increased purchasing of organic foods (B = 0.312, p<0.001), seasonal foods (B = 0.155, p = 0.021) and foods produced in Ireland (B = 0.158, p = 0.012). NCI score displayed an inverse relationship with the frequency of consuming beef (β =−0.136, p = 0.010), pork (β =−0.118, p = 0.027) and lamb/mutton (β =−0.133, p = 0.011). There were positive associations between NCI score and frequency of consuming legumes (β = 0.147, p = 0.004), green leafy vegetables (β = 0.195, p<0.001) and fruits of the Solanaceae family (tomatoes, peppers, aubergine) (β = 0.121, p = 0.022). NCI score also displayed a positive relationship with the frequency of reducing household food waste (β = 0.218, p<0.001) and choosing food with minimal packaging (β = 0.238, p<0.001).

This study highlights a positive relationship between nature-connectedness and more sustainable, nature-positive food behaviours amongst Irish consumers. The results of this study indicate that increasing the public’s nature connectedness and time spent in nature may help to foster more healthy and sustainable food-related behaviours to contribute to reducing the detrimental impact of the current food system on nature and the climate.