Current trends in diabetes mellitus database research in Japan

Abstract

With the widespread use of electronic medical records and administrative claims databases, analytic results from so‐called real‐world data have become increasingly important in healthcare decision‐making. Diabetes mellitus is a heterogeneous condition that involves a broad spectrum of patients. Real‐world database studies have been recognised as a powerful tool to understand the impact of current practices on clinical courses and outcomes, such as long‐term glucose control, development of microvascular or macro‐vascular diseases, and mortality. Diabetes is also a major global health issue and poses a significant social and economic burden worldwide. Therefore, it is critical to understand the epidemiology, clinical course, treatment reality, and long‐term outcomes of diabetes to determine realistic solutions to a variety of disease‐related issues that we are facing. In the present review, we summarise the healthcare system and large‐scale databases currently available in Japan, introduce the results from recent database studies involving Japanese patients with diabetes, and discuss future opportunities and challenges for the use of databases in the management of diabetes.

Burden, trends, and risk factors for breast cancer in China from 1990 to 2019 and its predictions until 2034: an up-to-date overview and comparison with those in Japan and South Korea

“…Similarly, an increasing contribution of high FPG was observed, which was in line with the rise in diabetes prevalence in the three countries [58][59][60]. Major established risk factors for diabetes included obesity, family history and westernized lifestyle [58][59][60]. The current research has demonstrated that the changes in the expression and secretory spectrum of inflammatory mediators of adipocytes and the increase in other inflammatory factors promotes the proliferation and invasion of tumor cells and the formation of neovascularization, especially in obese individuals [61].…”

“…Different from that high FPG was the leading cause of global BC deaths [8], high FPG was the second or third cause of BC deaths and DALYs for Chinese, Japanese and South Korean females. Similarly, an increasing contribution of high FPG was observed, which was in line with the rise in diabetes prevalence in the three countries [58][59][60]. Major established risk factors for diabetes included obesity, family history and westernized lifestyle [58][59][60].…”

Real world data and data science in medical research: present and future

“…The largest medical database in Japan is the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), which covers approximately 99% of health insurance claims in Japan. Although its use was limited to certain organizations for public welfare and academic institutions, NDB has been open to private companies for research of public health since October 2020 (Kaneyama et al, 2017 ; Kohsaka et al, 2021 ).…”

“Big Data” Approaches for Prevention of the Metabolic Syndrome

“…The purpose of data mining in itself is to discover patterns in large data sets to generate new knowledge. For this purpose, a group of tools from multidisciplinary fields are available, including database technology providing a source of data, such as the Swedish Longitudinal Integrated Database for Health Insurance and Labour Market Studies (LISA, Longitudinell Integrationsdatabas för Sjukförsäkrings-och Arbetsmarknadsstudier) and diabetes mellitus database ( Ludvigsson et al, 2019 ; Kohsaka et al, 2021 ), statistics as the mathematical basis, such as the TREAT model, MAYO Clinic model, and Liverpool Lung Project model ( Shipe et al, 2019 ), and machine learning, unsupervised learning, unsupervised learning, and deep learning as analytic tools ( Handelman et al, 2018 ; Rauschert et al, 2020 ). For MetS preventions on different levels, the current research advance has used different analytical algorithms for various data sources.…”

“…Machine learning has been used effectively for diabetes research, demonstrating its potential for advancing the management of various diabetes phenotypes across their natural histories ( Türkoglu et al, 2003 ; Lorenzo et al, 2007 ; Després et al, 2008 ). With the advent of natural language processing—a branch of artificial intelligence amenable to unstructured text data—clinical text mining is increasingly used in various health domains ( Lan et al, 2018 ; Ludvigsson et al, 2019 ; Kohsaka et al, 2021 ). In diabetes research, it has been used in areas such as the analysis of protein–protein interactions and early drug discovery ( Shipe et al, 2019 ; Rauschert et al, 2020 ).…”