# Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors

## TL;DR

This study developed a model to predict type 2 diabetes using non-invasive health checkup data and identified key risk factors in a Japanese population.

## Contribution

The study introduces a non-invasive predictive model for type 2 diabetes using health checkup data and identifies specific risk factors.

## Key findings

- The model achieved moderate discrimination with an AUC of 0.680 for those aged 40–74 years and 0.665 for those aged ≥75 years.
- Key predictors included male sex, slower walking speed, and not eating within 2 hours before bedtime.
- Use of antihypertensive drugs was positively associated with T2DM diagnosis.

## Abstract

Background: Specific health checkups in Japan aim to prevent and detect non-communicable diseases (NCDs). Lifestyle information and non-invasive measurements obtained during these checkups are valuable for population health monitoring. This study aimed to develop a predictive model for type 2 diabetes mellitus (T2DM) using only non-invasive measurements and to identify key predictors. Methods: A retrospective observational study was conducted using linked health checkup records and medical claims from a city in Japan. Logistic regression was performed to predict a T2DM diagnosis. Results: A total of 409 of the 1363 participants were diagnosed with T2DM, including 285 of the 950 participants aged 40–74 years and 124 of the 413 participants aged ≥75 years. The model achieved an area under the receiver operating characteristic curve of 0.680 for those aged 40–74 years and 0.665 for those aged ≥75 years, indicating moderate discrimination. Key predictors included male sex, use of antihypertensive drugs, walking speed, and eating habits within 2 h before bedtime. In particular, male sex, having a slower walking speed, and not eating within 2 h before bedtime were positively associated with T2DM diagnosis. Conversely, the absence of antihypertensive or lipid-lowering medications was negatively associated with T2DM diagnosis. Conclusion: A model based solely on non-invasive measurements moderately identified individuals at risk for T2DM in this community-based Japanese population. Routinely collected health checkup data may support early identification and targeted preventive strategies.