Soy Consumption and Obesity

Obesity is now present worldwide, including China, India and developing countries. It now seems no longer acceptable to argue that obesity can simply be explained in terms of caloric consumption only using simple concept of energy in and energy out. There may be specific causes of altered metabolism that produce nutritional imbalances. Individual variation in response to food intake may also be considered. Specific substances in the food chain can influence meta-bolism towards an increase in fat deposits. Xenoestrogens have been suggested to have such an influence. Soy contains phytoestrogens plus phytates, protease inhibitors and other anti-nutrients which block or compromise the body’s uptake of essential vitamins and minerals. This may contribute to nutritional anomalies. We analyzed data from WHO and FAO for 167 countries. These contained percentage of obese individuals (BMI > 30 kg/m2), GDP, caloric consump-tion per capita, and sugar and soy consumption per capita. Regressions and partial correlations were used. Soy con-sumption correlates significantly with levels of obesity, irrespective of GDP and caloric intake. For instance, poor Latin America with soy consumption of 28.9 kg/person/year has more obesity (18.4%) than better off European Union (14.1%) consuming 16.1 kg/person/year of soy. Soy consumption seems to contribute approximately 10% - 21% to the worldwide variation in obesity, depending on the method of statistical analysis. The ubiquitous presence of unfermented soy products in mass produced foods seems to be an important contributor to the obesity epidemic.

Ecological Analysis of the Influence of ACEIs and ARBs on the COVID-19 Prevalence and Death from COVID-19

Background: Angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin-II receptor blockers (ARBs) have been an arguable risk factor for COVID-19 diseases because they could upregulate Angiotensin Converting Enzyme-2 (ACE2) expression, facilitating SARS-CoV2 entry to the lungs. Several retrospective clinical studies, however, found no such effect. Here, we explore how the use of ACEIs and ARBs links to COVID-19 across all countries of the world. Methods: Data on the availability of ACEIs and ARBs for 200 countries and on the number of cases and number of deaths per country by 28 December 2020 were extracted from WHO and Worldometer website, respectively. Data on life expectancy at age 65 years as a measure of ageing were from WHO and on Gross Domestic Product Per Capita (GDP PPP) and the percentage of urbanization were from the World Bank. Excel and SPSS v 26 software were used for statistical analyses. Results: In linear regression and logistic conditional regression analysis, GDP correlates with COVID-19 prevalence (rho = 0.66, p > 0.001) and deaths from COVID-19 (rho = 0.55, p < 0.001) while urbanization and life expectancy do not when GDP influence is controlled for. After statistically removing the effects of GDP on the prevalence and mortality from COVID-19, we found that countries without ACEI and ARB availability had lower COVID-19 cases and deaths (p < 0.02). Conclusions: Our study based on the global data contradicts findings of most published clinical studies at regional levels. We found that GDP positively correlates with prevalence of and mortality related to COVID-19. ACEI and ARB use increases COVID-19 infectivity and mortality.