Secondary analyses of global datasets: do obesity and physical activity explain variation in diabetes risk across populations?

Abstract

Type 2 diabetes rates vary significantly across geographic regions. These differences are sometimes assumed to be entirely driven by differential distribution of environmental triggers, including obesity and insufficient physical activity (IPA). In this review, we discuss data which conflicts with this supposition. We carried out a secondary analysis of publicly available data to unravel the relative contribution of obesity and IPA towards diabetes risk across different populations. We used sex-specific, age-standardized estimates from Non-Communicable Disease Risk Factor Collaboration (NCD-RisC) on diabetes (1980-2014) and obesity (1975-2016) rates, in 200 countries, and from WHO on IPA rates in 168 countries in the year 2016. NCD-RisC and WHO organized countries into nine super-regions. All analyses were region- and sex-specific. Although obesity has been increasing since 1975 in every part of the world, this was not reflected in a proportional increase in diabetes rates in several regions, including Central and Eastern Europe, and High-income western countries region. Similarly, the association of physical inactivity with diabetes is not homogeneous across regions. Countries from different regions across the world could have very similar rates of diabetes, despite falling on opposite ends of IPA rate spectrum. The combined effect of obesity and IPA on diabetes risk was analyzed at the worldwide and country level. The overall findings highlighted the larger impact of obesity on disease risk; low IPA rates do not seem to be protective of diabetes, when obesity rates are high. Despite that, some countries deviate from this overall observation. Sex differences were observed across all our analyses. Overall, data presented in this review indicate that different populations, while experiencing similar environmental shifts, are apparently differentially subject to diabetes risk. Sex-related differences observed suggest that males and females are either subject to different risk factor exposures or have different responses to them.

Fig. 1. Region and sex-specifc trends of diabetes and obesity in the last 35-40 years.

A Age-adjusted diabetes prevalence (1980–2014) in males, B Age-adjusted diabetes prevalence (1980–2014) in females, C Age-adjusted obesity prevalence (1975–2016) in males, and D Age-adjusted obesity prevalence (1975–2016) in females. Age-standardized diabetes and obesity prevalence rates as estimated by Non-Communicable Disease risk Collaboration (NCD-RisC) group, for each year from 1980 to 2014, and 1975 to 2016, respectively. NCD-RisC group reported diabetes and obesity prevalence estimates in 200 countries, which were categorized into nine super-regions, including Central and Eastern Europe, Central Asia Middle East and North Africa, East and South East Asia, High-income Asia Pacific, High-income Western countries, Latin America and Caribbean, Oceania, South Asia, and Sub-Saharan Africa.

Fig. 2. Region- and sex-specific correlation of obesity with diabetes.

A Males and B Females. The data presented was based on Non-Communicable Disease risk Collaboration (NCD-RisC) group estimates of diabetes and obesity, in 2014 and 2016, respectively. Scattergraphs A and B represent the age-standardized prevalence rates of diabetes in 2014 (x-axis) against obesity in 2016 (y-axis), across 200 countries, in males and females, respectively. For each graph, the vertical dotted line represents the worldwide age-standardized prevalence rate of diabetes in 2014, and the horizontal line represents the worldwide age-standardized prevalence rate of obesity in 2016. The 200 countries were categorized into nine super-regions and color-coded in the figure, which include Central and Eastern Europe, Central Asia Middle East and North Africa, East and South East Asia, High-income Asia Pacific, High-income Western countries, Latin America and Caribbean, Oceania, South Asia, and Sub-Saharan Africa.

Fig. 3. Region- and sex-specific correlation of insufficient physical activity with diabetes.

A Males and B Females. The data presented was based on diabetes prevalence rates estimates by Non-Communicable Disease risk Collaboration (NCD-RisC) group in 2014, and insufficient physical activity prevalence rates estimates by World Health Organization (WHO) in 2016. Scattergraphs A and B represent the age-standardized prevalence rates of diabetes in 2014 (x-axis) against insufficient physical activity 2016 (y-axis), across 163 countries, in males and females, respectively. For each graph, the vertical dotted line represents the worldwide age-standardized prevalence rate of diabetes in 2014, and the horizontal line represents the worldwide age-standardized prevalence rate of insufficient physical activity in 2016. The 163 countries were categorized into nine super-regions and color-coded in the figure, which include Central and Eastern Europe, Central Asia Middle East and North Africa, East and South East Asia, High-income Asia Pacific, High-income Western countries, Latin America and Caribbean, Oceania, South Asia, and Sub-Saharan Africa.

Fig. 4. Joint effects of obesity and insufficient physical activity levels with diabetes risk.

A Males and B Females. The diabetes and obesity rates were estimated by Non-Communicable Disease Risk Collaboration (NCD-RisC) group in 2014 and 2016, respectively. Insufficient physical activity rates were estimated by World Health Organization (WHO) in 2016. Scattergraphs represent distribution of age-standardized prevalence rates of obesity (x-axis) against insufficient physical activity rates in 163 countries. Estimates of obesity, insufficient physical actiivty, and diabetes were grouped into tertiles. For diabetes rates, countries that fell in the first tertile (with the lowest rates of diabetes) were coded as low risk (green), and those that fell in the second and third tertiles, were coded as moderate (yellow) and high (red) risk, respectively. Dotted vertical and horizontal lines represent the first and second tertiles of obesity and insufficient physical activity rates, respectively.