Predicting incident type 2 diabetes in a Japanese cohort: an 8-year analysis of the NAGALA database
Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: Using data from the NAGALA database, this retrospective cohort study set out to identify the predictive markers for incident Type 2 Diabetes Mellitus (T2DM), with a particular focus on the non-diabetic Japanese population. Methods: We examined the data from a cohort of 15,464 individuals (with a male representation of 54.5% and an average age of (43.71 ± 8.90 years) sourced from the NAGALA (NAfld in the Gifu Area Longitudinal Analysis) research which was a longitudinal study at Medical Health Checkup Center of Murakami Memorial Hospital. The analysis focused on the incidence of T2DM from 2004 to 2012. Baseline demographic, anthropometric, biochemical, and lifestyle data were collected. All participants were not type 2 diabetic at baseline. The diagnosis of T2DM was confirmed by HbA1c >= 48mmol/mol, fasting plasma glucose >= 126mg/dL, or diabetes reported by oneself. Multivariate analysis was performed after univariate Cox regression analysis was used to find early determinants of T2DM incidence. The ability of individual components and a composite risk score to discriminate was assessed using Receiver Operating Characteristic (ROC) curves. Results: < 0.001), signifying robust discriminatory ability. At a predictive probability threshold of >0.017, the model exhibited sensitivity and specificity of 0.863 and 0.828, respectively. Conclusion: Current research has underscored the significance of a multifaceted approach to the prevention of T2DM, which includes early intervention targeting modifiable risk factors such as obesity, unhealthy alcohol use, and smoking, in conjunction with the monitoring of key metabolic markers like HbA1c and liver enzymes.
Source: Frontiers in Endocrinology
Authors: Yangchun Wang, Fei Liu, Ruixiang Tong, Zhonghua He, Qin Fang, Jie Feng, Hongliang An, Junjun Liu
Language: English
Subfield: Endocrinology, Diabetes and Metabolism
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4410417852