Prevalence of type 2 diabetes mellitus in a representative sample of Greek adults and its association with modifiable risk factors: results from the Hellenic National Nutrition and Health Survey

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

Purpose

Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen's Survey of Living Conditions were analyzed using interpretable machine learning methods.

Methods

Seven interpretable machine learning methods were used to analysis Japan citizens' census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi's quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors.

Results

Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age.

Conclusions

New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

Alterations in epidermal function in type 2 diabetes: Implications for the management of this disease

Epidermal function is regulated by numerous exogenous and endogenous factors, including age, psychological stress, certain skin disorders, ultraviolet irradiation and pollution, and epidermal function itself can regulate cutaneous and extracutaneous functions. The biophysical properties of the stratum corneum reflect the status of both epidermal function and systemic conditions. Type 2 diabetes in both murine models and humans displays alterations in epidermal functions, including reduced levels of stratum corneum hydration and increased epidermal permeability as well as delayed permeability barrier recovery, which can all provoke and exacerbate cutaneous inflammation. Because inflammation plays a pathogenic role in type 2 diabetes, a therapy that improves epidermal functions could be an alternative approach to mitigating type 2 diabetes and its associated cutaneous disorders.