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AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes

Author

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  • Yi-Jia Huang

    (National Yang-Ming Chiao-Tung University
    Academia Sinica)

  • Chun-houh Chen

    (Academia Sinica)

  • Hsin-Chou Yang

    (National Yang-Ming Chiao-Tung University
    Academia Sinica
    Academia Sinica
    National Cheng Kung University)

Abstract

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

Suggested Citation

  • Yi-Jia Huang & Chun-houh Chen & Hsin-Chou Yang, 2024. "AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-48618-1
    DOI: 10.1038/s41467-024-48618-1
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    References listed on IDEAS

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    1. Tian Ge & Chia-Yen Chen & Yang Ni & Yen-Chen Anne Feng & Jordan W. Smoller, 2019. "Polygenic prediction via Bayesian regression and continuous shrinkage priors," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    2. Angli Xue & Yang Wu & Zhihong Zhu & Futao Zhang & Kathryn E. Kemper & Zhili Zheng & Loic Yengo & Luke R. Lloyd-Jones & Julia Sidorenko & Yeda Wu & Allan F. McRae & Peter M. Visscher & Jian Zeng & Jian, 2018. "Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes," Nature Communications, Nature, vol. 9(1), pages 1-14, December.
    3. Cassandra N. Spracklen & Momoko Horikoshi & Young Jin Kim & Kuang Lin & Fiona Bragg & Sanghoon Moon & Ken Suzuki & Claudia H. T. Tam & Yasuharu Tabara & Soo-Heon Kwak & Fumihiko Takeuchi & Jirong Long, 2020. "Identification of type 2 diabetes loci in 433,540 East Asian individuals," Nature, Nature, vol. 582(7811), pages 240-245, June.
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