Objective: Prevalence of type 1 diabetes (T1D) disease is increasing worldwide. We aim to test correlation of T1D prevalence to the reduced natural selection measured by Biological State Index (I_bs). Research design and methods: Country-specific estimates of T1D prevalence, life expectancy, obesity prevalence rate, urbanization rates, per capita sugars consumption and per capita gross domestic product (GDP) were obtained. I_bs and country-specific longevity (e₅₀) increase for each country were self-calculated. These data were then matched to T1D prevalence by country for our ecological study among 118 countries. Countries were also grouped to study the associations in different regions. SPSS V.22 was used for correlation analysis. Results: Worldwide, both I_bs and life expectancy at birth (I_bs proxy) were significantly correlated to T1D prevalence in Pearson r (r=0.713, p<0.001 and r=0.722, p<0.001, respectively) and Spearman's r (r=0.724, p<0.001 and r=0.689, p<0.001, respectively). T1D prevalence was not correlated to longevity increase measured as life expectancy at 50 years old. T1D prevalence was significantly associated with I _bs (r=0.307, p<0.001) and newborn life expectancy (r=0.349, p<0.001) independent of per capita total sugar consumption, per capita GDP, urbanization and obesity prevalence in partial correlation. Globally, both life expectancy at birth and I_bs exponentially correlated to T1D prevalence. Pearson correlations generally existed in different country categorizations by geographic region, culture background and economic status. Conclusions: Reduced natural selection may have contributed to the increasing T1D prevalence worldwide. T1D epidemiology study in total population may be the practical solution to identify the causes of increasing T1D prevalence.

Cancer incidence increase has multiple aetiologies. Mutant alleles accumulation in populations may be one of them due to strong heritability of many cancers. The opportunity for the operation of natural selection has decreased in the past ~150 years because of reduction in mortality and fertility. Mutation‐selection balance may have been disturbed in this process and genes providing background for some cancers may have been accumulating in human gene pools. Worldwide, based on theWHOstatistics for 173 countries the index of the opportunity for selection is strongly inversely correlated with cancer incidence in peoples aged 0–49 years and in people of all ages. This relationship remains significant when gross domestic product per capita (GDP), life expectancy of older people (e₅₀), obesity, physical inactivity, smoking and urbanization are kept statistically constant for fifteen (15) of twenty‐seven (27) individual cancers incidence rates. Twelve (12) cancers which are not correlated with relaxed natural selection after considering the six potential confounders are largely attributable to external causes like viruses and toxins. Ratios of the average cancer incidence rates of the 10 countries with lowest opportunities for selection to the average cancer incidence rates of the 10 countries with highest opportunities for selection are 2.3 (all cancers at all ages), 2.4 (all cancers in 0–49 years age group), 5.7 (average ratios of strongly genetically based cancers) and 2.1 (average ratios of cancers with less genetic background).

Excessive energy intake has been identified as a major contributor to the global obesity epidemic. However, it is not clear whether dietary patterns varying in their composition of food groups contribute. This study aims to determine whether differences in per capita availability of the major food groups could explain differences in global obesity prevalence.Country-specific Body Mass Index (BMI) estimates (mean, prevalence of obesity and overweight) were obtained. BMI estimates were then matched to mean of three year-and country-specific availability of total kilocalories per capita per day, major food groups (meat, starch, fibers, fats and fruits). The per capita Gross Domestic Product (GDP) and prevalence of physical inactivity for each country were also obtained. SPSS was used for log-transformed data analysis.Spearman analyses of the different major food groups shows that meat availability is most highly correlated with prevalence of obesity (r = 0.666, p < 0.001) and overweight (r = 0.800, p < 0.001) and mean BMI (r = 0.656, p < 0.001) and that these relationships remain when total caloric availability, prevalence of physical inactivity and GDP are controlled in partial correlation analysis. Stepwise multiple linear regression analysis indicates that meat availability is the most significant predictors of prevalence of obesity and overweight and mean BMI among the food groups. Scatter plot diagrams show meat and GDP adjusted meat are strongly correlated to obesity prevalence.High meat availability is correlated to increased prevalence of obesity. Effective strategies to reduce meat consumption may have differential effects in countries at different stages of the nutrition transition.

Relaxed natural selection, measured by Biological State Index (I_bs), results in unfavourable genes/mutations accumulation in population. Obesity is partly heritable. We aim to examine and compare the effects of relaxed natural selection on male and female obesity prevalence. Data for 191 countries of the world were captured for this ecological study. Curvilinear regressions, bivariate and partial correlations, linear mixed models and multivariate linear regression analyses were used to examine the relationship between I_bs and sex-specific obesity prevalence. Per capita GDP, urbanization and caloric intake were controlled for as the confounding factors. Fisher r-to-z transformation, R² increment in multivariate regression and F-test were used to compare the correlations. Curvilinear regressions, bivariate and partial correlations (controlled for GDP, urbanization and calories) revealed that I_bs was significantly correlated to obesity prevalence of both sexes, but significantly stronger to male than to female obesity prevalence. Curvilinear regression models also showed strong correlations. Mixed linear models, with effects of GDP, urbanisation and caloric intake controlled for, showed that male and female average obesity prevalence rates were significantly higher in countries with greater I_bs value than their equivalents in countries with lower I_bs. Between higher and lower I_bs countries, the gap of male obesity prevalence is 60% greater than the gap of female obesity prevalence. Stepwise multiple regression identified that I_bs was a significant predictor of obesity prevalence of both sexes. Multivariate regression showed that, adding I_bs as an obesity predictor, R² increment in male model was significantly greater than in female model. Relaxed natural selection may drive males and females to accumulate metabolic faulty genes equally. Probably due to greater environmental, personal intervention in regulating female body mass, relaxed natural selection shows less contributing effects to female obesity prevalence than to male obesity prevalence. Gene therapy to prevent obesity may need to be also taken into account.

Background: Large households/families may create more happiness and offer more comprehensive healthcare among the members. We correlated household size to dementia mortality rate at population level for analysing its protecting role against dementia mortality. Methods: This is a retrospective cross-sectional study. Dementia specific mortality rates of the 183 member states of World Health Organization were calculated and matched with the respective country data on household size, Gross Domestic Product (GDP), urban population and ageing. Scatter plots were produced to explore and visualize the correlations between household size and dementia mortality rates. Pearson’s and nonparametric correlations were used to evaluate the strength and direction of the associations between household size and all other variables. Partial correlation of Pearson’s approach was used to identify that household size protects against dementia regardless of the competing effects from ageing, GDP and urbanization. Multiple regression was used to identify significant predictors of dementia mortality. Results: Household size was in a negative and moderately strong correlation (r = -0.6034, p < 0.001) with dementia mortality. This relationship was confirmed in both Pearson r (r = - 0.524, p<0.001) and nonparametric (rho = -0.579, p < 0.001) analyses. When we controlled for the contribution of ageing, socio-economic status and urban lifestyle in partial correlation analysis, large household was still in inverse and significant correlation to dementia mortality (r = −0.331, p <0.001). This suggested that, statistically, large household protect against dementia mortality regardless of the contributing effects of ageing, socio-economic status and urban lifestyle. Stepwise multiple regression analysis selected large household as the variable having the greatest contribution to dementia mortality with R² = 0.263 while ageing was placed second increasing R² to 0.259. GDP and urbanization were removed as having no statistically significant influence on dementia mortality. Conclusions: While acknowledging ageing, urban lifestyle and greater GDP associated with dementia mortality, this study suggested that, at population level, household size was another risk factor for dementia mortality. As part of dementia prevention, healthcare practitioners should encourage people to increase their positive interactions with persons from their neighbourhood or other fields where large household/family size is hard to achieve.

Ageing and genetic traits can only explain the increasing dementia incidence partially. Advanced healthcare services allow dementia patients to survive natural selection and pass their genes onto the next generation. Country-specific estimates of dementia incidence rates (all ages and 15–49 years old), Biological State Index expressing reduced natural selection (I_s), ageing indexed by life expectancy e₍₆₅₎, GDP PPP and urbanization were obtained for analysing the global and regional correlations between reduced natural selection and dementia incidence with SPSS v. 27. Worldwide, I_s significantly, but inversely, correlates with dementia incidence rates for both all ages and 15–49 years old in bivariate correlations. These relationships remain inversely correlated regardless of the competing contributing effects from ageing, GDP and urbanization in partial correlation model. Results of multiple linear regression (enter) have shown that I_s is the significant predictor of dementia incidence among all ages and 15–49 years old. Subsequently, I_s was selected as the variable having the greatest influence on dementia incidence in stepwise multiple linear regression. The I_s correlated with dementia incidence more strongly in developed population groupings. Worldwide, reduced natural selection may be yet another significant contributor to dementia incidence with special regard to developed populations.