Promoting Sustainable Lifestyle Habits: “Real Food” and Social Media in Spain

Obesity and state of being overweight are beginning to be treated as global epidemics. In this context, health professionals are increasingly acting as expert opinion leaders that use social media to connect with the public, in order to promote healthy lifestyles and provide specific recommendations for different product categories, including fresh, processed, and ultra-processed meat products. This study investigates how exposure to content created by health professionals, and posted on social media, influences consumers’ attitudes. For this purpose, the collaboration of one relevant nutritionist influencer in Spain has been obtained. The online survey created has provided 4.584 responses, received from followers (from May to June 2019). After applying a partial least squares path modeling approach, the results suggest that trust in the content shared, the perceived credibility of the professional sharing the information and the informative value, determine the strength with which consumers acquire more knowledge about endorsed products, develop a favorable predisposition towards them, prefer them over their options, and modify their behaviour by purchasing them, instead of their usual foods. The link is stronger, in the case of trust and influencer’s credibility, than for informative value. However, the latter has an indirect effect on the attitude phases through the former.

Exploring culturally acceptable, nutritious, affordable, and low climatic impact diet for Japanese diets: Proof of concept of applying a new modelling approach using Data Envelopment Analysis

A future sustainable dietary pattern for Japanese is yet undefined. This study aimed to explore more sustainable Japanese diets, that are nutritious, affordable, and with low greenhouse gas emissions (GHGE) and particular emphasis on cultural acceptability. A newly developed Data Envelopment Analysis diet model was applied to 4-d dietary record data among 184 healthy Japanese men and 185 women volunteers aged 21–69 y. Alternative diets were calculated as the linear combinations of observed diets. Firstly, for each individual, four modelled diets were calculated that maximised cultural acceptability (i.e. minimize dietary change from observed diet), maximised nutritional quality assessed by the Nutrient-Rich Food Index (NRF), minimized monetary diet costs, or minimized diet-related GHGE. The final modelled diet combined all four indicators. In the first four models, the largest improvement was obtained for each targeted indicator separately, while relatively small improvements or unwanted changes were observed for other indicator. When all indicators were aimed to optimize, the NRF score and diet-related GHGE was improved by 8–13% with the lower monetary cost than observed diets, although the percentage improvement was a bit smaller than the separate models. The final modelled diets demanded increased intakes for whole grains, fruits, milk/cream/yoghurt, legumes/nuts, and decreased intakes for red and processed meat, sugar/confectionaries, alcoholic and sweetened beverages, and seasonings in both sexes. In conclusion, more sustainable dietary patterns considering several indicators are possible for Japanese while total improvement is moderate due to trade-offs between indicators and methodological limitation of DEA diet model.

Bacon causes cancer: Do consumers care?

In October 2015, the International Agency for Research on Cancer (IARC) released a report classifying processed meat as a type 1 carcinogen. The report prompted headlines and attracted immediate public attention, but the economic impacts remain unknown. In this paper, we investigate the impacts of the IARC report on selected processed meat prices and purchases using retail scanner data from US grocery stores. We compare changes in prices and sales of selected processed meat products to a constructed synthetic control group (using a convex combination of nonmeat food products). We find a significant decrease in bacon prices in the wake of the IARC report release, but we find no evidence of a sales reduction. We find no significant changes in price and sales for ham and sausage. The pattern of price and quantity changes are consistent with downward shifts in demand and outward shifts in supply for bacon and sausage following the release of the IARC report.

The Isocaloric Substitution of Plant-Based and Animal-Based Protein in Relation to Aging-Related Health Outcomes: A Systematic Review

Plant-based and animal-based protein intake have differential effects on various aging-related health outcomes, but less is known about the health effect of isocaloric substitution of plant-based and animal-based protein. This systematic review summarized current evidence of the isocaloric substitutional effect of plant-based and animal-based protein on aging-related health outcomes. PubMed and Embase databases were searched for epidemiologic observational studies published in English up to 15 March 2021. Studies that included adults ≥18 years old; use of a nutritional substitution model to define isocaloric substitution of plant protein and animal protein; health outcomes covering mortality, aging-related diseases or indices; and reported association estimates with corresponding 95% confidence intervals were included. Nine cohort studies and 3 cross-sectional studies were identified, with a total of 1,450,178 subjects included in this review. Consistent and significant inverse association of substituting plant protein for various animal proteins on all-cause mortality was observed among 4 out of 5 studies with relative risks (RRs) from 0.54 to 0.95 and on cardiovascular disease (CVD) mortality among all 4 studies with RRs from 0.58 to 0.91. Among specific animal proteins, the strongest inverse association on all-cause and CVD mortality was identified when substituting plant protein for red and/or processed meat protein, with the effect mainly limited to bread, cereal, and pasta protein when replacing red meat protein. Isocaloric substitution of plant-based protein for animal-based protein might prevent all-cause and CVD-specific mortality. More studies are needed on this topic, particularly for cancer incidence and other specific aging-related diseases.

Mediterranean Diet and Fatty Liver Risk in a Population of Overweight Older Italians: A Propensity Score-Matched Case-Cohort Study

Hepatic steatosis, often known as fatty liver, is the most common hepatic disease in Western countries. The latest guidelines for the treatment of nonalcoholic fatty liver disease emphasize lifestyle measures, such as changing unhealthy eating patterns. Using a propensity score-matching approach, this study investigated the effect of adhering to a Mediterranean diet (MedDiet) on fatty liver risk in an older population (≥65 years) from Southern Italy. We recruited 1.403 subjects (53.6% men, ≥65 years) who completed a food frequency questionnaire (FFQ) and underwent clinical assessment between 2015 and 2018. For the assessment of the liver fat content, we applied the Fatty Liver Index (FLI). To evaluate the treatment effect of the MedDiet, propensity score matching was performed on patients with and without FLI > 60. After propensity score-matching with the MedDiet pattern as treatment, we found a higher consumption of red meat (p = 0.04) and wine (p = 0.04) in subjects with FLI > 60. Based on the FLI, the inverse association shown between adherence to the MedDiet and the risk of hepatic steatosis shows that the MedDiet can help to prevent hepatic steatosis. Consuming less red and processed meat, as well as alcoholic beverages, may be part of these healthy lifestyle recommendations.

The Dietary Approach to Stop Hypertension (DASH) diet is associated with a lower risk of heart failure: A cohort study

Aims Trials demonstrate that following the DASH diet lowers blood pressure, which may prevent development of heart failure (HF). We investigated the association between long-term adherence to the DASH diet and food substitutions within the DASH diet on the risk of HF. Methods Men and women aged 45-83 years without previous HF, ischemic heart disease or cancer at baseline in 1998 from the Cohort of Swedish Men (n = 41,118) and the Swedish Mammography Cohort (n = 35,004) were studied. The DASH diet emphasizes intake of fruit, vegetables, whole grains, nuts and legumes and low-fat dairy and deemphasizes red and processed meat, sugar-sweetened beverages and sodium. DASH diet scores were calculated based on diet assessed by food frequency questionnaires in late 1997 and 2009. Incidence of HF was ascertained using the Swedish Patient Register. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HR) with 95% confidence intervals (CI). Results During the median 22 years of follow-up (1998-2019) 12,164 participants developed HF. Those with the greatest adherence to the DASH diet had a lower risk of HF compared to those with the lowest adherence (HR 0.85, 95% CI 0.80, 0.91 for baseline diet and HR 0.83, 95% CI 0.78, 0.89 for long-term diet, comparing quintiles). Replacing 1 serving/day of red and processed meat with emphasized DASH diet foods was associated with an 8-12% lower risk of HF. Conclusion Long-term adherence to the DASH diet and relevant food substitutions within the DASH diet were associated with a lower risk of HF.

Using Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene–Gene and Gene–Diet Interactions

Obesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual’s risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants’ obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity.Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].

Novel Techniques for Microbiological Safety in Meat and Fish Industries

The consumer tendency towards convenient, minimally processed meat items has placed extreme pressure on processors to certify the safety of meat or meat products without compromising the quality of product and to meet consumer’s demand. This has prompted difficulties in creating and carrying out novel processing advancements, as the utilization of more up-to-date innovations may influence customer decisions and assessments of meat and meat products. Novel advances received by the fish and meat industries for controlling food-borne microbes of huge potential general wellbeing concern, gaps in the advancements, and the requirement for improving technologies that have been demonstrated to be effective in research settings or at the pilot scale shall be discussed. Novel preparing advancements in the meat industries warrant microbiological approval before being named as industrially suitable alternatives and authorizing infra-structural changes. This miniature review presents the novel techniques for the microbiological safety of meat products, including both thermal and non-thermal methods. These technologies are being successfully implemented and rationalized in subsisting processing surroundings.

Upconversion Nanoparticles-Based Fluorescence Immunoassay for the Sensitive Detection of 2-Amino-3-methylimidazo [4,5-f] Quinoline (IQ) in Heat Processed Meat

A competitive fluorescence immunoassay for the quantitative detection of 2-amino-3-methylimidazo [4, 5-f] quinoline (IQ) in pan-fried meat patties was developed, using magnetic nanoparticles coupled with coating antigen as the capture probe and anti-IQ antibody coupled with NaYF4: Yb, Er upconversion nanoparticles as the signal probe. Under optimal conditionals, the wide detection range for IQ in phosphate buffer saline is from 0.01 to 100 μg·L−1 (R2 = 0.991) with a detection limit of 0.007 μg·L−1. This proposed method has been applied to detect IQ in two different types of pan-fried meat patties at varying frying times, and the IQ content in chicken patties and fish patties are 2.11–3.47 μg·kg−1 and 1.35–2.85 μg·kg−1, respectively. These results are consistent with that of the ultraperformance liquid chromatography-tandem mass spectrometry. In summary, this method can serve as a sensitive and specific test tool for the determination of IQ in processed meat.