Machine-Learning Models Utilizing Cyp3A4*1G Show Improved Prediction of Hypoglycemic Medication in Type 2 Diabetes

Abstract

The effectiveness and side effects of Type 2 diabetes (T2D) medication are related to individual genetic background. SNPs CYP3A4 and CYP2C19 were introduced to machine-learning models to improve the performance of T2D medication prediction. Two multilabel classification models, ML-KNN and WRank-SVM, trained with clinical data and CYP3A4/CYP2C19 SNPs were evaluated. Prediction performance was evaluated with Hamming loss, one-error, coverage, ranking loss and average precision. The average precision of ML-KNN and WRank-SVM using clinical data was 92.74% and 92.9%, respectively. Combined with CYP2C19*2*3, the average precision dropped to 88.84% and 89.93%, respectively. While combined with CYP3A4*1G, the average precision was enhanced to 97.96% and 97.82%, respectively. Results suggest that CYP3A4*1G can improve the performance of ML-KNN and WRank-SVM models in predicting T2D medication performance.

Plain language summary

About 10% of adults around the world are living with Type 2 Diabetes (T2D). Due to the huge number of patients and the complexity of individual makeup, it is a challenge for doctors to prescribe appropriate hypoglycemic drugs. To aid prescribing, machine-learning models were developed to predict medication schemes based on patients’ demographic information and laboratory test results. These models treat prediction as a multilabel classification problem, with each class of medication as a label. This work was designed to determine whether the introduction of genetic information would improve prediction performance. The machine-learning models were trained using datasets with and without genetic information and their performance was compared. The performance of the machine-learning models was improved by incorporating the SNP CYP3A4*1G into the datasets. Thus, this work demonstrates a novel strategy to improve the prediction of T2D hypoglycemic medication performance and provides new ideas for how to support the T2D health system with machine-learning techniques.

Keywords::

• hypoglycemic medication
• machine learning
• prediction
• single nucleotide polymorphisms
• SNP
• Type 2 diabetes

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

No writing assistance was utilized in the production of this manuscript.

Data collection was approved by the review board of Shanghai Changzheng Hospital.