Key result
A non-invasive health checkup model moderately predicts T2DM risk with an AUC of ~0.68.
Why the study?
This study aimed to develop a predictive model for type 2 diabetes mellitus using only non-invasive measurements and lifestyle information from specific health checkups and to identify key predictors.
Can a predictive model using only non-invasive measurements from health checkups accurately identify individuals at risk for Type 2 Diabetes Mellitus?
Observational (n=1,363)
No
Can a predictive model using only non-invasive measurements from health checkups accurately identify individuals at risk for Type 2 Diabetes Mellitus?
Effect estimate: AUC 0.680 (ages 40-74), AUC 0.665 (ages ≥75)
A prediction model based solely on non-invasive health checkup measurements can moderately identify individuals at risk for Type 2 diabetes, potentially supporting early targeted preventive strategies.
May support targeted prevention via routine checkups; leaves open need for prospective validation before adoption.
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.
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Shimai et al. (2026) conducted an observational in Type 2 diabetes mellitus (n=1,363). Non-invasive predictive model was evaluated on T2DM diagnosis (AUC 0.680 (ages 40-74), AUC 0.665 (ages ≥75)). A predictive model based solely on non-invasive health checkup measurements moderately identified individuals at risk for T2DM, achieving an AUC of 0.680 for ages 40-74 and 0.665 for ages ≥75.
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