High protein sources especially animal protein is being used widely in people’s diet. Ensuring a healthy and sustainable diet should be a global priority. Compared to diets rich in animal products, plant-based diets are more sustainable because they have less environmental impact. Aim of this article is to review mushroom’s sustainability. Using meat analogues like mushrooms seems to be a good option because their taste and texture are alike meat and they are sustainable healthy foods as they are good environmental choice due to their less water and land footprint but they are not a cost-benefit food. Mushroom is a good nutritional and environmental meat substitute as it has less water and land footprint but not as a cost-benefit meat alternative. Therefore, the governments should make policies to use mushroom as an economical meat alternative and a source of protein for all consumers.

Promoting sustainability in food and nutrition systems is essential to address the various challenges and trade-offs within the current food system. This imperative is guided by key principles and actionable steps, including enhancing productivity and efficiency, reducing waste, adopting sustainable agricultural practices, improving economic growth and livelihoods, and enhancing resilience at various levels. However, in order to change the current food consumption patterns of the world and move toward sustainable diets, as well as increase productivity in the food production chain, it is necessary to employ the findings and achievements of other sciences. These include the use of artificial intelligence-based technologies. Presented here is a narrative review of possible applications of artificial intelligence in the food production chain that could increase productivity and sustainability. In this study, the most significant roles that artificial intelligence can play in enhancing the productivity and sustainability of the food and nutrition system have been examined in terms of production, processing, distribution, and food consumption. The research revealed that artificial intelligence, a branch of computer science that uses intelligent machines to perform tasks that require human intelligence, can significantly contribute to sustainable food security. Patterns of production, transportation, supply chain, marketing, and food-related applications can all benefit from artificial intelligence. As this review of successful experiences indicates, artificial intelligence, machine learning, and big data are a boon to the goal of sustainable food security as they enable us to achieve our goals more efficiently.

Objective: Sustainable diets are diets with low environmental impacts and high affordability which contribute to food and nutrition security. The present study aimed to develop a healthy, low-cost and environmental-friendly food basket for Iran based on current consumption.Design: The Households Income and Expenditure Survey data were used. Linear Programming was utilised to obtain the optimal diets, separately, for each goal of the sustainable food basket: (1) Diet with maximum Nutrient Rich Food (NRF) index, (2) Diet with minimum cost, (3) Diet with the minimum water footprint and (4) Diet with the minimum carbon footprint. Goal Programming techniques were used to optimise the sustainable food basket by considering all goals simultaneously.Setting: Iran.Participants: Households (n 100 500) in urban and rural areas of Iran, nationally representative.Results: In the ‘optimal model’, compared with the usual consumption, the amount of the ‘bread, cereal, rice, and pasta’, ‘meat, poultry, fish, eggs, legumes, and nuts’ and ‘fats, oils, sugars, and sweets’ groups was decreased. Inside those food groups, cereals, poultry and vegetable oil subgroups were increased. Also, dairy, fruits and vegetable groups were increased. In this model, there was a 14 % reduction in the total water footprint, a 14 % decrease in the total carbon footprint, a 23 % decrease in the cost and a 7 % increase in NRF of diet compared with the usual consumption.Conclusions: Increasing the consumption of dairy, fruits and vegetables and reducing the consumption of bread, rice, pasta, meat, fish, eggs, legumes, nuts, hydrogenated fats and sugars are required to achieve a sustainable food basket.

Objective: A sustainable diet is an affordable diet with low environmental impact, high food security and sufficient healthiness. The present study aimed to assess the correlation between the socio-economic status of households and a sustainable diet.Design: The food basket and socio-economic data of Iranian households were evaluated during 2016–2018. The households were classified based on the sustainability of their diet by determining the dietary carbon footprint, dietary water footprint, lower dietary costs of the household than the median and a higher dietary quality index than the median. Logistic regression was used with four models to calculate the OR of a more sustainable diet as the dependent variable regarding the different quartiles of socio-economic status (SES) as the independent variable.Setting: Iran.Participants: Iranian households (n 102 303), nationally representative, were studied.Results: Lower SES was associated with the higher OR of a sustainable diet (OR: 0·90; (95 % CI 0·87, 0·91)). Higher quartiles of SES compared with the lower SES group indicated the higher energy intake and consumption of more dairies, meat, poultry, fish, eggs, legumes, nuts and fruits, as well as the lower intake of bread, cereal, rice and pasta.Conclusion: In countries such as Iran, where nutrition transition occurs rapidly, better economic and social levels in the populations with a higher SES are associated with increased energy intake and higher consumption of animal-based foods, which decreases sustainable diets compared with the groups with a lower SES.

Previous studies have suggested a link between diet and mental health. However, there is a lack of evidence regarding the association between emerging diets such as the EAT-Lancet reference diet (ELD) and the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet, and mental health in different societies. This study aimed to determine the association between adherence to ELD and MIND diets and the risk of depression, anxiety, and stress. This research involved 4579 participants from the PERSIAN Organizational Cohort Study in Mashhad (POCM). To assess dietary intake, a comprehensive 118-item semi-quantitative food frequency questionnaire (FFQ) was employed. The Planetary Health Diet Index (PHDI) was used to assess adherence to the ELD. Mental health status was evaluated using the Depression, Anxiety, and Stress Scale-21 items (DASS-21) questionnaire. Binary logistic regression was utilized to examine the relationship between these scores and mental health indicators. In the adjusted model, the highest quartile of PHDI showed a 35% reduced risk of depression compared to those in the lowest quartile (OR: 0.653, 95% CI: 0.483–0.883; P = 0.008). However, compared to the reference quartile, participants in the highest quartile of MIND diet exhibited significantly lower risks of depression (OR: 0.611, 95% CI: 0.447–0.836; P = 0.005), anxiety (OR: 0.559, 95% CI: 0.418–0.746; P < 0.001), and stress (OR: 0.629, 95% CI: 0.419–0.944; P = 0.008). The ELD and MIND diet were both associated with reduced odds of depression. Additionally, MIND diet was associated with decreased likelihood of anxiety and stress. However, no connection was observed between ELD and anxiety or stress. Further large-scale interventions are required to confirm these findings. Association between EAT-Lancet reference diet (ELD) and MIND Diet with Stress, Anxiety, and Depression. PHDI: planetary health diet index, MIND diet: Mediterranean-DASH Intervention for Neurodegenerative Delay diet

The Planetary Health Diet Index (PHDI) was recently introduced to assess adherence to the EAT-Lance recommendations. In this study, we aimed to evaluate the relationship between PHDI and metabolic syndrome (MS). We used the data of 6465 participants from the PERSIAN cohort study at Mashhad University of Medical Sciences, Mashhad, Iran. Diet was assessed using a 130-item Food Frequency Questionnaire (FFQ). The PHDI comprises sixteen components and is scored between 0 and 150 points. We first assessed the validity and reliability of the PHDI for this population. We used regression logistic models to assess the relationships between PHDI and MS and its related indicators. The average PHDI score was 52.3 ± 9. The Cronbach’s alpha value was 0.53. After controlling for age and sex, the PHDI was positively related to the Diet Quality Index-International (DQI-I) and was negatively related to carbon and water footprints (p < 0.001). PHDI quartile was negatively associated with MS, hypo-HDL cholesterolemia, and abdominal obesity after controlling for confounders (P < 0.05). The validity and reliability of the PHDI were found to be satisfactory for the Iranian population we studied. Our results showed that a higher PHDI was potentially related to a reduced likelihood of MS, hypo-HDL cholesterolemia, and abdominal obesity.