COVID-19 and metabolic diseases: a heightened awareness of health inequities and a renewed focus for research priorities

Comparative Analysis of Dietary Habits and Obesity Prediction: Body Mass Index versus Body Fat Percentage Classification Using Bioelectrical Impedance Analysis

Background: Obesity remains a widely debated issue, often criticized for the limitations in its identification and classification. This study aims to compare two distinct systems for classifying obesity: body mass index (BMI) and body fat percentage (BFP) as assessed by bioelectrical impedance analysis (BIA). By examining these measures, the study seeks to clarify how different metrics of body composition influence the identification of obesity-related risk factors. Methods: The study enrolled 1255 adults, comprising 471 males and 784 females, with a mean age of 36 ± 12 years. Participants exhibited varying degrees of weight status, including optimal weight, overweight, and obesity. Body composition analysis was conducted using the TANITA Body Composition Analyzer BC-418 MA III device (T5896, Tokyo, Japan), evaluating the following parameters: current weight, basal metabolic rate (BMR), adipose tissue (%), muscle mass (%), and hydration status (%). Results: Age and psychological factors like cravings, fatigue, stress, and compulsive eating were significant predictors of obesity in the BMI model but not in the BFP model. Additionally, having a family history of diabetes was protective in the BMI model (OR: 0.33, 0.11-0.87) but increased risk in the BFP model (OR: 1.66, 1.01-2.76). The BMI model demonstrates exceptional predictive ability (AUC = 0.998). In contrast, the BFP model, while still performing well, exhibits a lower AUC (0.975), indicating slightly reduced discriminative power compared to the BMI model. Conclusions: BMI classification demonstrates superior predictive accuracy, specificity, and sensitivity. This suggests that BMI remains a more reliable measure for identifying obesity-related risk factors compared to the BFP model.

Registration with Primary Health Care and COVID-19 mortality: cohort of diabetics from five administrative health regions in the city of Rio de Janeiro, Brazil, 2020-2021

OBJECTIVE

The present study carried out an analysis of survival according to the status of registration with Primary Health Care (PHC) and of factors associated with death from COVID-19, in cases residing in Programmatic Area 3.1 (PA3.1) with a diagnosis of diabetes (in the notification form or in the electronic medical record), of the Municipality of Rio de Janeiro (RJ), Brazil, in 2020-2021.

METHODS

A probabilistic linkage of databases was performed based on information on cases notified as COVID-19 and data from the electronic medical records of people living with diabetes. A survival analysis was carried out, using the Cox regression model stratified by age group and adjusted for confounding variables.

RESULTS

Individuals registered with the PHC of PA3.1 had almost twice the risk of death from COVID-19 (adjusted hazard ratio [HRadj]=1.91) when compared to those unregistered. This association was stronger in individuals aged 18 to 59 years registered with the PHC (HRadj=2.82) than in individuals aged 60 years or over (HRadj=1.56).

CONCLUSION

Surveillance strategies for identifying and adequately monitoring higher-risk groups, among individuals living with diabetes, within the scope of Primary Health Care, can contribute to reducing mortality from COVID-19.

Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models

Purpose

The objective of this study was to employ machine learning (ML) models utilizing non-invasive factors to achieve early and low-cost identification of MetS in a large physical examination population.

Patients and Methods

The study enrolled 9171 participants who underwent physical examinations at Northern Jiangsu People's Hospital in 2009 and 2019, to determine MetS based on criteria established by the Chinese Diabetes Society. Non-invasive characteristics such as gender, age, body mass index (BMI), systolic blood pressure (SBP), and diastolic blood pressure (DBP) were collected and used as input variables to train and evaluate ML models for MetS identification. Several ML models were used for MetS identification, including logistic regression (LR), k-nearest neighbors algorithm (k-NN), naive bayesian (NB), decision tree (DT), random forest (RF), artificial neural network (ANN), and support vector machine (SVM).

Results

Our ML models all showed good performance in the 10-fold cross-validation except for the SVM model. In the external validation, the NB model exhibited the best performance with an AUC of 0.976, accuracy of 0.923, sensitivity of 98.32%, and specificity of 91.32%.

Conclusion

This study proposed a new non-invasive method for early and low-cost identification of MetS by using ML models. This approach has the potential to serve as a highly sensitive, convenient, and cost-effective tool for large-scale MetS screening.

Changes in sugar-sweetened beverage consumption in the first two years (2018 - 2020) of San Francisco's tax: A prospective longitudinal study

BACKGROUND

Sugar sweetened beverage (SSB) taxes are a promising strategy to decrease SSB consumption, and their inequitable health impacts, while raising revenue to meet social objectives. In 2016, San Francisco passed a one cent per ounce tax on SSBs. This study compared SSB consumption in San Francisco to that in San José, before and after tax implementation in 2018.

METHODS & FINDINGS

A longitudinal panel of adults (n = 1,443) was surveyed from zip codes in San Francisco and San José, CA with higher densities of Black and Latino residents, racial/ethnic groups with higher SSB consumption in California. SSB consumption was measured at baseline (11/17-1/18), one- (11/18-1/19), and two-years (11/19-1/20) after the SSB tax was implemented in January 2018. Average daily SSB consumption (in ounces) was ascertained using the BevQ-15 instrument and modeled as both continuous and binary (high consumption: ≥6 oz (178 ml) versus low consumption: <6 oz) daily beverage intake measures. Weighted generalized linear models (GLMs) estimated difference-in-differences of SSB consumption between cities by including variables for year, city, and their interaction, adjusting for demographics and sampling source. In San Francisco, average SSB consumption in the sample declined by 34.1% (-3.68 oz, p = 0.004) from baseline to 2 years post-tax, versus San José which declined 16.5% by 2 years post-tax (-1.29 oz, p = 0.157), a non-significant difference-in-differences (-17.6%, adjusted AMR = 0.79, p = 0.224). The probability of high SSB intake in San Francisco declined significantly more than in San José from baseline to 2-years post-tax (AOR[interaction] = 0.49, p = 0.031). The difference-in-differences of odds of high consumption, examining the interaction between cities, time and poverty, was far greater (AOR[city*year 2*federal poverty level] = 0.12, p = 0.010) among those living below 200% of the federal poverty level 2-years post-tax.

CONCLUSIONS

Average SSB intake declined significantly in San Francisco post-tax, but the difference in differences between cities over time did not vary significantly. Likelihood of high SSB intake declined significantly more in San Francisco by year 2 and more so among low-income respondents.

Machine Learning Approach for Metabolic Syndrome Diagnosis Using Explainable Data-Augmentation-Based Classification

Metabolic syndrome (MetS) is a cluster of risk factors including hypertension, hyperglycemia, dyslipidemia, and abdominal obesity. Metabolism-related risk factors include diabetes and heart disease. MetS is also linked to numerous cancers and chronic kidney disease. All of these variables raise medical costs. Developing a prediction model that can quickly identify persons at high risk of MetS and offer them a treatment plan is crucial. Early prediction of metabolic syndrome will highly impact the quality of life of patients as it gives them a chance for making a change to the bad habit and preventing a serious illness in the future. In this paper, we aimed to assess the performance of various algorithms of machine learning in order to decrease the cost of predictive diagnoses of metabolic syndrome. We employed ten machine learning algorithms along with different metaheuristics for feature selection. Moreover, we examined the effects of data augmentation in the prediction accuracy. The statistics show that the augmentation of data after applying feature selection on the data highly improves the performance of the classifiers.

Dietary Patterns, Cardiometabolic and Lifestyle Variables in Greeks with Obesity and Metabolic Disorders

There is considerable evidence that some dietary patterns contribute to obesity and metabolic disorders but there is less data on diet's association with different health parameters. We investigated the interaction between different dietary patterns and anthropometric, biochemical, lifestyle, and psychological health parameters in a Greek population with obesity and metabolic disorders. To the best of our knowledge, this is the first study in Greece with a thorough and holistic approach in analyzing such relationships. For assessing food patterns, revealing underlying structures, and reducing the number of variables we applied exploratory factor analysis (EFA). Principal Component Analysis was chosen as the extraction method using Varimax rotation, and three regression sets were computed. The study involved 146 Greek metabolically unhealthy obese adults, both men and women. Our cohort was categorized into four dietary patterns: "Western type diet", "Mediterranean-like diet", "Healthy diet", and "Animal meat and sauces diet". Dietary patterns characterized by a high consumption of energy-dense and animal-derived foods were positively associated with anthropometric and biochemical parameters related to metabolic disorders. Plant-based, healthier dietary patterns, on the other hand, were associated with better biochemical and mental health profiles among metabolically unhealthy obese individuals.

Influence of Literacy, Self-Efficacy, and Social Support on Diabetes-Related Outcomes Following Hospital Discharge

Objective

To evaluate the relationship between health literacy, social support, and self-efficacy as predictors of change in A1c and readmission among hospitalized patients with type 2 diabetes (T2D).

Methods

This is a secondary analysis of patients with T2D (A1c >8.5%) enrolled in a randomized trial in which health literacy (Newest Vital Sign), social support (Multidimensional Scale of Perceived Social Support), and empowerment (Diabetes Empowerment Scale-Short Form) was assessed at baseline. Multivariable models evaluated whether these concepts were associated with A1c reduction at 12 weeks (absolute change, % with >1% reduction, % reaching individualized target) and readmission (14 and 30 days).

Results

A1c (N=108) decreased >1% in 60%, while individualized A1c target was achieved in 31%. After adjustment for baseline A1c and potential confounders, health literacy was associated with significant reduction in A1c (Estimate -0.21, 95% CI -0.40, -0.01, p=0.041) and >1% decrease in A1c (OR 1.37, 95% CI 1.08, 1.73, p=0.009). However, higher social support was associated with greater adjusted odds of reaching the individualized A1c target (OR 1.63, 95% CI 1.04, 2.55, p=0.32). Both higher empowerment (OR 0.23, 95% CI 0.08, 0.64, p=0.005) and social support (OR 0.57, 95% CI 0.36, 0.91, p=0.018) were associated with fewer readmissions by 14 days, but not 30 days.

Conclusion

The study indicates that health literacy and social support may be important predictors of A1c reduction post-discharge among hospitalized patients with T2D. Social support and diabetes self-management skills should be addressed and early follow-up may be critical for avoiding readmissions.

Clinical Trial

NCT03455985.

Health awareness of rural households towards COVID-19 pandemic in India: Evidence from Rural Impact Survey of the World Bank

This paper aims at analysing the level of awareness of the symptoms and the methods of protection from COVID-19 based on the Rural Impact Survey of the World Bank, collected from 5200 households belonging to six states in India that is, Andhra Pradesh, Bihar, Jharkhand, Madhya Pradesh, Rajasthan, and Uttar Pradesh. Data has been analysed using chi-square test and regression analysis. Results of the analysis indicate that about 70.8% rural households are aware of the symptom of coronavirus, and 81.9% are aware of the preventive measures for controlling the spread of COVID-19. Analysis indicates a significant association between awareness level on symptoms and prevention of COVID-19 and socio-demographics and location. The study further analyses the key determinants of awareness of COVID-19 symptoms and preventive measures using the logistics regression model, indicating that age, gender, education, income, poverty status, access to information, cash relief and medical services are the determining factors of health awareness on COVID-19 pandemic among rural households in India. Considering the importance of self-protecting measures in fighting the pandemic, this paper highlights the importance of strengthening public awareness for containing the spread of the COVID-19 pandemic.

High-Fat High-Sugar Diet-Induced Changes in the Lipid Metabolism Are Associated with Mildly Increased COVID-19 Severity and Delayed Recovery in the Syrian Hamster

Pre-existing comorbidities such as obesity or metabolic diseases can adversely affect the clinical outcome of COVID-19. Chronic metabolic disorders are globally on the rise and often a consequence of an unhealthy diet, referred to as a Western Diet. For the first time in the Syrian hamster model, we demonstrate the detrimental impact of a continuous high-fat high-sugar diet on COVID-19 outcome. We observed increased weight loss and lung pathology, such as exudate, vasculitis, hemorrhage, fibrin, and edema, delayed viral clearance and functional lung recovery, and prolonged viral shedding. This was accompanied by an altered, but not significantly different, systemic IL-10 and IL-6 profile, as well as a dysregulated serum lipid response dominated by polyunsaturated fatty acid-containing phosphatidylethanolamine, partially recapitulating cytokine and lipid responses associated with severe human COVID-19. Our data support the hamster model for testing restrictive or targeted diets and immunomodulatory therapies to mediate the adverse effects of metabolic disease on COVID-19.

Climate Change and Obesity

Global warming and the rising prevalence of obesity are well described challenges of current mankind. Most recently, the COVID-19 pandemic arose as a new challenge. We here attempt to delineate their relationship with each other from our perspective. Global greenhouse gas emissions from the burning of fossil fuels have exponentially increased since 1950. The main contributors to such greenhouse gas emissions are manufacturing and construction, transport, residential, commercial, agriculture, and land use change and forestry, combined with an increasing global population growth from 1 billion in 1800 to 7.8 billion in 2020 along with rising obesity rates since the 1980s. The current Covid-19 pandemic has caused some decline in greenhouse gas emissions by limiting mobility globally via repetitive lockdowns. Following multiple lockdowns, there was further increase in obesity in wealthier populations, malnutrition from hunger in poor populations and death from severe infection with Covid-19 and its virus variants. There is a bidirectional relationship between adiposity and global warming. With rising atmospheric air temperatures, people typically will have less adaptive thermogenesis and become less physically active, while they are producing a higher carbon footprint. To reduce obesity rates, one should be willing to learn more about the environmental impact, how to minimize consumption of energy generating carbon dioxide and other greenhouse gas emissions, and to reduce food waste. Diets lower in meat such as a Mediterranean diet, have been estimated to reduce greenhouse gas emissions by 72%, land use by 58%, and energy consumption by 52%.

The relation between the social and the biological and COVID-19

Social factors have been linked to disease severity and mortality in COVID-19. These social factors are ethnicity, social disadvantage, age, gender and occupation. Pre-existing medical conditions have also been identified as an increasing risk. This paper explores the relationship between these social and biological factors using a syndemic frame of reference. The paper argues that although the associations have been very well documented, the mechanisms linking the social factors and disease outcomes are not well understood. An approach that seeks to find commensurability between the social and the biological, is suggested.

Western diet increases COVID-19 disease severity in the Syrian hamster

Pre-existing comorbidities such as obesity or metabolic diseases can adversely affect the clinical outcome of COVID-19. Chronic metabolic disorders are globally on the rise and often a consequence of an unhealthy diet, referred to as a Western Diet. For the first time in the Syrian hamster model, we demonstrate the detrimental impact of a continuous high-fat high-sugar diet on COVID-19 outcome. We observed increased weight loss and lung pathology, such as exudate, vasculitis, hemorrhage, fibrin, and edema, delayed viral clearance and functional lung recovery, and prolonged viral shedding. This was accompanied by an increased trend of systemic IL-10 and IL-6, as well as a dysregulated serum lipid response dominated by polyunsaturated fatty acid-containing phosphatidylethanolamine, recapitulating cytokine and lipid responses associated with severe human COVID-19. Our data support the hamster model for testing restrictive or targeted diets and immunomodulatory therapies to mediate the adverse effects of metabolic disease on COVID-19.

Advances in Pancreatic Islet Transplantation Sites for the Treatment of Diabetes

Diabetes is a complex disease that affects over 400 million people worldwide. The life-long insulin injections and continuous blood glucose monitoring required in type 1 diabetes (T1D) represent a tremendous clinical and economic burdens that urges the need for a medical solution. Pancreatic islet transplantation holds great promise in the treatment of T1D; however, the difficulty in regulating post-transplantation immune reactions to avoid both allogenic and autoimmune graft rejection represent a bottleneck in the field of islet transplantation. Cell replacement strategies have been performed in hepatic, intramuscular, omentum, and subcutaneous sites, and have been performed in both animal models and human patients. However more optimal transplantation sites and methods of improving islet graft survival are needed to successfully translate these studies to a clinical relevant therapy. In this review, we summarize the current progress in the field as well as methods and sites of islet transplantation, including stem cell-derived functional human islets. We also discuss the contribution of immune cells, vessel formation, extracellular matrix, and nutritional supply on islet graft survival. Developing new transplantation sites with emerging technologies to improve islet graft survival and simplify immune regulation will greatly benefit the future success of islet cell therapy in the treatment of diabetes.

Immune Protection of Stem Cell-Derived Islet Cell Therapy for Treating Diabetes

Insulin injection is currently the main therapy for type 1 diabetes (T1D) or late stage of severe type 2 diabetes (T2D). Human pancreatic islet transplantation confers a significant improvement in glycemic control and prevents life-threatening severe hypoglycemia in T1D patients. However, the shortage of cadaveric human islets limits their therapeutic potential. In addition, chronic immunosuppression, which is required to avoid rejection of transplanted islets, is associated with severe complications, such as an increased risk of malignancies and infections. Thus, there is a significant need for novel approaches to the large-scale generation of functional human islets protected from autoimmune rejection in order to ensure durable graft acceptance without immunosuppression. An important step in addressing this need is to strengthen our understanding of transplant immune tolerance mechanisms for both graft rejection and autoimmune rejection. Engineering of functional human pancreatic islets that can avoid attacks from host immune cells would provide an alternative safe resource for transplantation therapy. Human pluripotent stem cells (hPSCs) offer a potentially limitless supply of cells because of their self-renewal ability and pluripotency. Therefore, studying immune tolerance induction in hPSC-derived human pancreatic islets will directly contribute toward the goal of generating a functional cure for insulin-dependent diabetes. In this review, we will discuss the current progress in the immune protection of stem cell-derived islet cell therapy for treating diabetes.