By using text mining techniques, this study identifies the topics of sustainable consumption that are important during the COVID-19 pandemic. An Application Programming Interface (API) streaming method was used to extract the data from Twitter. A total of 14,591 tweets were collected using Twitter streaming API. However, after data cleaning, 13,635 tweets were considered for analysis. The objectives of the study are to identify (1) the topics users tweet about sustainable consumption and (2) to detect the emotion-based sentiments in the tweets. The study used Latent Dirichlet Allocation (LDA) algorithm for topic modeling and the Louvain algorithm for semantic network clustering. NRC emotion lexicon was used for sentiment analysis. The LDA model discovers six topics: organic food consumption, food waste, vegan food, sustainable tourism, sustainable transport, and sustainable energy consumption. While the Louvain algorithm detects four clusters—lifestyle and climate change, responsible consumption, energy consumption, and renewable energy, sentiment analysis results show more positive emotions among the users than the negative ones. The study contributes to existing literature by providing a fresh perspective on various interconnected topics of sustainable consumption that bring global consumption to a sustainable level.

Sustainable Consumption in Consumer Behavior in the Time of COVID-19: Topic Modeling on Twitter Data Using LDA

Addressing factors modulating COVID-19 is crucial since abundant clinical evidence shows that outcomes are markedly heterogeneous between patients. This requires identifying the factors and understanding how they mechanistically influence COVID-19. Here, we describe how eleven selected factors (age, sex, genetic factors, lipid disorders, heart failure, gut dysbiosis, diet, vitamin D deficiency, air pollution and exposure to chemicals) influence COVID-19 by applying the Adverse Outcome Pathway (AOP), which is well-established in regulatory toxicology. This framework aims to model the sequence of events leading to an adverse health outcome. Several linear AOPs depicting pathways from the binding of the virus to ACE2 up to clinical outcomes observed in COVID-19 have been developed and integrated into a network offering a unique overview of the mechanisms underlying the disease. As SARS-CoV-2 infectibility and ACE2 activity are the major starting points and inflammatory response is central in the development of COVID-19, we evaluated how those eleven intrinsic and extrinsic factors modulate those processes impacting clinical outcomes. Applying this AOP-aligned approach enables the identification of current knowledge gaps orientating for further research and allows to propose biomarkers to identify of high-risk patients. This approach also facilitates expertise synergy from different disciplines to address public health issues.

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Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework

Many studies have shown that the immune system requires adequate nutrition to work at an optimal level. Not only do optimized nutritional strategies support the immune system, but they also reduce chronic inflammation. Nutritional supplements that are recommended for patients with critical illnesses are thought to also be effective for the coronavirus disease 2019 (COVID-19) patients in the intensive care unit. Some studies have recommended fresh fruits and vegetables, soy, nuts, and antioxidants, such as omega-3 fatty acids, to improve immune system activity. Although nutritional status is considered to be an important prognostic factor for patients with COVID-19, there is to date no sufficient evidence that optimal nutritional therapies can be beneficial for these patients. Some have argued that the COVID-19 pandemic is a good opportunity to test the effectiveness of nutritional intervention for infectious diseases. Many researchers have suggested that testing the proposed nutritional approaches for infectious diseases in the context of a pandemic would be highly informative. The authors of other review papers concluded that it is important to have a diet based on fresh foods, such as fruits, vegetables, whole grains, low-fat dairy products, and healthy fats (i.e., olive oil and fish oil), and to limit the intake of sugary drinks as well as high-calorie and high-salt foods. In this review, we discuss the clinical significance of functional food ingredients as complementary therapies potentially beneficial for the prevention or treatment of COVID-19. We believe that our review will be helpful to plan and deploy future studies to conclude these potentials against COVID-19, but also to new infectious diseases that may arise in the future.

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Modifiable Host Factors for the Prevention and Treatment of COVID-19: Diet and Lifestyle/Diet and Lifestyle Factors in the Prevention of COVID-19

The current global pandemic, Covid-19, is a severe threat to human health and existence especially when it is mutating very frequently. Being a novel disease, Covid-19 is impacting the patients with comorbidities and is predicted to have long-term consequences, even for those who have recovered from it. To clearly recognize its impact, it is important to comprehend the complex relationship between Covid-19 and other diseases. It is also being observed that people with good immune system are less susceptible to the disease. It is perceived that if a correlation between Covid-19, other diseases, and diet is realized, then caregivers would be able to enhance their further course of medical action and recommendations. Network Analysis is one such technique that can bring forth such complex interdependencies and associations. In this paper, a Network Analysis-based approach has been proposed for analyzing the interplay of diets/foods along with Covid-19 and other diseases. Relationships between Covid-19, diabetes mellitus type 2 (T2DM), non-alcoholic fatty liver disease (NAFLD), and diets have been curated, visualized, and further analyzed in this study so as to predict unknown associations. Network algorithms including Louvain graph algorithm (LA), K nearest neighbors (KNN), and Page rank algorithms (PR) have been employed for predicting a total of 60 disease-diet associations, out of which 46 have been found to be either significant in disease risk prevention/mitigation or in its progression as validated using PubMed literature. A precision of 76.7% has been achieved which is significant considering the involvement of a novel disease like Covid-19. The generated interdependencies can be further explored by medical professionals and caregivers in order to plan healthy eating patterns for Covid-19 patients. The proposed approach can also be utilized for finding beneficial diets for different combinations of comorbidities with Covid-19 as per the underlying health conditions of a patient. Graphical abstract. 

https://link.springer.com/content/pdf/10.1007/s11517-022-02505-3.pdf

Exploring diet associations with Covid-19 and other diseases: a Network Analysis–based approach

Over the past few months, numerous studies harnessed in silico methods such as molecular docking to evaluate food compounds for inhibitory activity against coronavirus infection and replication. These studies capitalize on the efficiency of computational methods to quickly guide subsequent research and examine diet-disease relationships, and their sudden widespread utility may signal new opportunities for future antiviral and bioactive food research. Using Coronavirus Disease 2019 (COVID-19) research as a case study, we herein provide an overview of findings from studies using molecular docking to study food compounds as potential inhibitors of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), explore considerations for the critical interpretation of study findings, and discuss how these studies help shape larger conversations of diet and disease. 

https://doi.org/10.1016/j.cofs.2021.12.013

Predicting global diet-disease relationships at the atomic level: a COVID-19 case study

Since the outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the coronavirus disease-19 (COVID-19), in December 2019, the infection has spread around the globe. Some of the risk factors include social distancing, mask wearing, hand washing with soap, obesity, diabetes, hypertension, asthma, cardiovascular disease, and dysbiosis. Evidence has shown the incidence of total infection and death rates to be lower in sub-Saharan Africa when compared with North Africa, Europe and North America and many other parts of the world. The higher the metabolic syndrome rate, the higher the risk of SARS-CoV-2 infection. Africa has a lower rate of metabolic syndrome risk than many other continents. This paradox has puzzled several in the biomedical and scientific communities. Published results of research have demonstrated the exciting correlation that the combination of young age of the population coupled with their native plant-based diet has lowered their risk factors. The plant-based diet include whole grains (millet, sorghum), legumes (black-eye peas, dry beans, soybean), vegetables, potato, sweet potato, yams, squash, banana, pumpkin seeds, and moringa leaves, and lower consumption of meat. The plant-based diet results in a different gut microbiota than of most of the rest of the world. This has a significant impact on the survival rate of other populations. The "plant-based diet" results in lower rates of obesity, diabetes and dysbiosis, which could contribute to lower and less severe infections. However, these hypotheses need to be supported by more clinical and biostatistics data.

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Merry Jean N. Losso

Papers

The Young Age and Plant-Based Diet Hypothesis for Low SARS-CoV-2 Infection and COVID-19 Pandemic in Sub-Saharan Africa.