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Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth.

ABSTRACT: 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 (Ibs).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. Ibs 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.Worldwide, both Ibs and life expectancy at birth (Ibs 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 Ibs exponentially correlated to T1D prevalence. Pearson correlations generally existed in different country categorizations by geographic region, culture background and economic status.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.

SUBMITTER: You WP

PROVIDER: S-EPMC4780042 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Project description:Aims/hypothesisTables reporting life expectancies by common risk factors are available for individuals with type 2 diabetes; however, there is currently no published equivalent for individuals with type 1 diabetes. We aimed to develop a life expectancy table using a recently published simulation model for individuals with type 1 diabetes.MethodsThe simulation model was developed using data from a real-world population of patients with type 1 diabetes selected from the Swedish National Diabetes Register. The following six important risk factors were included in the life table: sex; age; current smoking status; BMI; eGFR; and HbA1c. For each of 1024 cells in the life expectancy table, a synthetic cohort containing 1000 individuals was created, with other risk factors assigned values representative of the real-world population. The simulations were executed for all synthetic cohorts and life expectancy for each cell was calculated as mean survival time of the individuals in the respective cohort.ResultsThere was a substantial variation in life expectancy across patients with different risk factor levels. Life expectancy of 20-year-old men varied from 29.3 years to 50.6 years, constituting a gap of 21.3 years between those with worst and best risk factor levels. In 20-year-old women, this gap was 18.9 years (life expectancy range 35.0-53.9 years). The variation in life expectancy was a function of the combination of risk factor values, with HbA1c and eGFR consistently showing a negative and positive correlation, respectively, with life expectancy at any level combination of other risk factors. Individuals with the lowest level (20 kg/m2) and highest level of BMI (35 kg/m2) had a lower life expectancy compared with those with a BMI of 25 kg/m2. Non-smokers and women had a higher life expectancy than smokers and men, respectively, with the difference in life expectancy ranging from 0.4 years to 2.7 years between non-smokers and smokers, and from 1.9 years to 5.9 years between women and men, depending on levels of other risk factors.Conclusions/interpretationThe life expectancy table generated in this study shows a substantial variation in life expectancy across individuals with different modifiable risk factors. The table allows for rapid communications of risk in an easily understood format between healthcare professionals, health economists, researchers, policy makers and patients. Particularly, it supports clinicians in their discussion with patients about the benefits of improving risk factors.

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Type 1 diabetes prevalence increasing globally and regionally: the role of natural selection and life expectancy at birth.

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