Artificial Intelligence Model Successfully Assesses T2D Risk

June 10, 2024

22 2 minutes read

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TOPLINE:

An artificial intelligence–based model that incorporates polygenic and multi-image risk scores with key demographic variables effectively identifies individuals at a high risk for type 2 diabetes (T2D), suggests a Taiwanese study.

METHODOLOGY:

Early detection and risk assessment of T2D are critical for effective health management, as diabetes imposes a significant mortality and economic burden on patients.
Researchers used an artificial intelligence machine learning approach (eXtreme Gradient Boosting) to design various risk assessment models for T2D by integrating genome-wide single-nucleotide polymorphisms, multimodality imaging data, and demographic information from 68,911 participants from the Taiwan Biobank.
Taiwan Biobank collected baseline and follow-up questionnaires; blood and urine samples; biomarker measurements; and medical imaging data, including abdominal ultrasonography (ABD), carotid artery ultrasonography (CAU), bone mineral density (BMD), electrocardiography, and thyroid ultrasonography.
In the genetic-centric analysis, 50,984 participants were included, 2531 of whom were self-reported patients with T2D and 48,453 were self-reported control individuals without T2D.
In the genetic imaging integrative analysis, 17,785 participants whose genetic and medical imaging data were available were analyzed, 1366 of whom were self-reported patients with T2D and 16,419 were self-reported control individuals without T2D.

TAKEAWAY:

The model that used polygenic risk scores (PRS) along with demographic variables such as age, sex, and family history of T2D showed a good accuracy in predicting the risk for T2D, with an area under the receiver operating curve (AUC) of 0.915.
Integrating the image features with genetic information and demographic factors further increased the AUC to 0.949.
A simplified version incorporating only eight key variables (family history, age, fatty liver from the ABD image, spine thickness from the BMD image, PRS, end-diastolic velocities in the right and left common carotid arteries from the CAU images, and RR interval from the ECG images) showed an AUC of 0.939.
Lastly, the performance of this model was validated in a second independent dataset, which yielded an AUC of 0.905.

IN PRACTICE:

“We successfully developed artificial intelligence models that effectively combined genetic markers, medical imaging features, and demographic variables for early detection and risk assessment of T2D,” the authors commented.

SOURCE:

Yi-Jia Huang, Institute of Public Health, National Yang-Ming Chiao-Tung University, Taipei, Taiwan, led this study, which was published online in Nature Communications.

LIMITATIONS:

The model needs to be validated in external cohorts for improved generalizability. Owing to the limited follow-up time of this study, only a few participants reported a change in their T2D status from baseline to follow-up.