ArticleBioengineering (Basel, Switzerland)2026
Development of a Type 2 Diabetes Prediction Model Using Specific Health Checkup Data and Extraction of Predictive Factors.
Article in Bioengineering (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSpecific 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.
methodsA 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.
resultsA 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.
conclusionA 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.
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