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

Kenichiro Shimai1, Kazuki Ohashi2, Teppei Suzuki2,3

  • 1Faculty of Health Care Sciences, Department of Clinical Engineering, Jikei University of Health Care Sciences, Yodogawa-ku, Osaka 532-0003, Japan.

Bioengineering (Basel, Switzerland)
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Summary
This summary is machine-generated.

A predictive model using non-invasive health checkup data moderately identified individuals at risk for type 2 diabetes mellitus (T2DM) in Japan. Key predictors included male sex, walking speed, and eating habits.

Keywords:
claims datahealth-checkupslogistic regression analysispredictive modeltype 2 diabetes mellitus

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Area of Science:

  • Public Health
  • Epidemiology
  • Preventive Medicine

Background:

  • Japanese health checkups aim to prevent and detect non-communicable diseases (NCDs).
  • Non-invasive measurements and lifestyle data from checkups are valuable for population health monitoring.
  • Type 2 Diabetes Mellitus (T2DM) is a significant NCD requiring effective early detection strategies.

Purpose of the Study:

  • To develop a predictive model for T2DM using only non-invasive measurements from health checkups.
  • To identify key non-invasive predictors associated with T2DM risk in a Japanese population.
  • To assess the model's predictive performance in different age groups.

Main Methods:

  • A retrospective observational study utilized linked health checkup records and medical claims from a Japanese city.
  • Logistic regression analysis was employed to predict T2DM diagnosis.
  • The study included participants aged 40-74 years and those aged ≥75 years.

Main Results:

  • The predictive model demonstrated moderate discrimination for T2DM, with an AUC of 0.680 (40-74 years) and 0.665 (≥75 years).
  • Key predictors for T2DM included male sex, slower walking speed, and eating habits within 2 hours before bedtime.
  • Absence of antihypertensive or lipid-lowering medications was negatively associated with T2DM diagnosis.

Conclusions:

  • A predictive model based solely on non-invasive health checkup data can moderately identify individuals at risk for T2DM.
  • Routinely collected health checkup data offers potential for early T2DM identification and targeted prevention.
  • This approach supports community-based public health initiatives for NCD management.