The association between healthy beverage index and sarcopenic obesity among women with overweight and obesity: a cross-sectional study.

INTRODUCTION: Sarcopenic obesity is related to changes in body composition, loss of muscle mass, and raised adipose tissue. Beverage patterns are effective with changes in health status. Therefore, the aim of this study was to investigate the association between sarcopenic obesity (SO) and the healthy beverage index (HBI) in women with overweight and obesity. METHODS: This cross-sectional study conducted on 210 overweight and obese (BMI ≥25 kg/m2) women aged 18-56 years. The measurement of skeletal muscle mass (SMM) and fat mass (FM) done by bioelectric impedance analyzer (BIA) (Inbody Co., Seoul, Korea) based on guidelines. The two lowest quintiles SMM and the two highest quintiles FM and body mass index (BMI) ≥30 are considered sarcopenic obesity in women. A validated and reliable semi-quantitative food-frequency questionnaire (FFQ) was used to evaluate the beverage dietary data. and RFS and NRFS was calculated. Biochemical assessments were quantified by standard approaches, and physical activity were evaluated by international physical activity questionnaire (IPAQ). RESULT: In this cross-sectional study, 210 overweight and obese females took part (18-56) years old). The studies were carried out using binary logistic regression. After controlling for a wide variety of confounding variables such as age, energy intake, physical activity, education, and economic status, we found a negative association between HBI and risk of SO (OR = 0.29, 95% CI = 0.35 to 1.01, P = 0.05). CONCLUSION: We observed that the odds of SO was reduced by 69% in participants with higher HBI score. More well-designed studies need to confirm our findings.

The relationship between dietary lipophilic index and load with depression, anxiety, and stress symptoms.

BACKGROUND: Dietary fatty acids can affect brain health by modifying neuronal membrane fluidity. Dietary lipophilic index (LI) and load (LL) may be related to cell membrane fluidity. This study aimed to determine the relationship between dietary LI and LL with symptoms of depression, anxiety and stress. METHODS: In this cross-sectional study, taken from the YaHS (Yazd Health Study) population-based cohort, the data of 2,982 individuals was extracted. Several questionnaires- a 178-item Food Frequency Questionnaire (FFQ), Depression, Anxiety and Stress Scale 21 (DASS 21), and International Physical Activity Questionnaire (IPAQ)- were used to obtain information on dietary intake, mental status, and physical activity, respectively. LI and LL were calculated using dietary intake and the melting point of each fatty acid. RESULTS: The analysis was performed on 2982 individuals. The odds ratio of depression in the second tertile of dietary LI compared to the first tertile was 0.815 (95% CI 0.66-1.00, P = 0.051, Ptrend = 0.017) and after adjusting confounders was 0.793 (95% CI 0.63-0.99, P = 0.043, Ptrend = 0.011). Also, LL was related inversely with anxiety (0.771, 95% CI 0.63-0.93, P = 0.003) that after multiple regression, OR of anxiety was 0.762 (95% CI 0.53-1.07, P = 0.045). The odds of stress in the third tertile of LL was 1.064 but not statistically significant (95% CI 0.88-1.28, P = 0.729). CONCLUSION: This study showed an inverse association between dietary LI and depression symptoms. Anxiety and stress did not show a significant relationship with LI or LL.

Investigation of the interaction between Genetic Risk Score (GRS) and fatty acid quality indices on mental health among overweight and obese women.

BACKGROUND & AIMS: Mental disorders are associated with dietary fatty acids and genome-wide association studies have found multiple risk loci robustly related to depression, anxiety, and stress. The aim of this study is to investigate the interaction of genetic risk score (GRS) and dietary fat quality indices on mental health. METHODS: This cross-sectional study included 279 overweight and obese women for N6/N3 ratio and 378 overweight and obese women for CSI aged 18-68 years. Using reliable and verified standard protocols, body composition, anthropometric indices, blood pressure, physical activity, and dietary fat quality were measured. Serum samples were used to determine biochemical tests. A genetic risk score (GRS) was calculated using the risk alleles of the three SNPs. A generalized linear model (GLM) was applied to assess the interactions between GRS and fat quality indices. Mental health was evaluated using Depression Anxiety Stress Scales (DASS-21). RESULTS: The mean (± SD) age and BMI of our participants were 36.48 (8.45) and 30.73 (3.72) kg/m2 respectively. There was a marginally significant mean difference among tertiles of the CSI in terms of stress (P = 0.051), DASS-21 (P = 0.078) in the crude model. After adjusting for age, energy intake, physical activity and BMI in model 1, there was a positive interaction between GRS and T3 of N6/N3 ratio on anxiety (ß = 0.91, CI = 0.08,1.75, P = 0.031), depression (ß = 1.05, CI = 0.06,2.04, P = 0.037), DASS-21 (ß = 2.22, CI= -0.31,4.75, P = 0.086). CONCLUSION: Our findings indicate that higher ratio of N-6 to N-3 considering genetics were predictive of mental disorder in our population.

Changes in Fat Mass Index and Fat-Free Mass Index Vary Significantly by Age and Sex of the Patient, and by Type of Bariatric Surgery.

BACKGROUND: The loss of fat-free mass after rapid weight loss following bariatric surgery shows BMI is inadequate for reporting weight loss, emphasizing the need for a more accurate body composition metric. This study assessed changes in fat mass index (FMI), fat-free mass index (FFMI), and dietary intake over 6 months after one-anastomosis gastric bypass (OAGB), Roux-en-Y gastric bypass (RYGB), and sleeve gastrectomy (SG) across age and sex groups. MATERIALS AND METHODS: This prospective observational study included 151 bariatric surgery candidates. Data on demographics, anthropometric, body composition, and food intake data were collected before and 6 months after surgery. SPSS version 22 was used for analysis, employing ANOVA and t-tests to assess group differences. RESULTS: Younger patients (≤ 35 years) had higher percent total and excess weight loss (%TWL and %EWL) across all bariatric surgeries, and those undergoing SG experienced more favorable changes in fat-free mass (FFM) and visceral fat compared to other procedures (P = 0.002). Males had higher percent change in fat mass index (%∆ FMI) and visceral fat than females with OAGB and RYGB (P < 0.05). Women undergoing SG and RYGB had a more significant decrease in energy intake compared to OAGB (P < 0.001), and the ≤ 35 and 35-44 age groups demonstrated lower reductions in energy intake following OAGB. CONCLUSION: Age, sex, and surgery type significantly affect bariatric surgery outcomes. Younger patients achieved greater weight-related improvements, while men undergoing RYGB experienced more substantial fat mass changes than women.

Implementing micronutrient fortification programs as a potential practical contribution to achieving sustainable diets.

Due to sustainability concerns related to current diets and environmental challenges, it is crucial to have sound policies to protect human and planetary health. It is proposed that sustainable diets will improve public health and food security and decrease the food system's effect on the environment. Micronutrient deficiencies are a well-known major public health concern. One-third to half of the world's population suffers from nutrient deficiencies, which have a negative impact on society in terms of unrealised potential and lost economic productivity. Large-scale fortification with different micronutrients has been found to be a useful strategy to improve public health. As a cost-effective strategy to improve micronutrient deficiency, this review explores the role of micronutrient fortification programmes in ensuring the nutritional quality (and affordability) of diets that are adjusted to help ensure environmental sustainability in the face of climate change, for example by replacing some animal-sourced foods with nutrient-dense, plant-sourced foods fortified with the micronutrients commonly supplied by animal-sourced foods. Additionally, micronutrient fortification considers food preferences based on the dimensions of a culturally sustainable diet. Thus, we conclude that investing in micronutrient fortification could play a significant role in preventing and controlling micronutrient deficiencies, improving diets and being environmentally, culturally and economically sustainable.

Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications.

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.