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Precision medicine in diabetes care
Si Hua Clara Tan1, Wann Jia Loh2,3,4, Su Chi Lim1,5,6,7
1Clinical Research Unit, Khoo Teck Puat Hospital.
Current Opinion in Endocrinology, Diabetes, and Obesity
|November 20, 2024
Summary
Precision diabetes care is expanding beyond monogenic diabetes to type 2 diabetes (T2D). Advanced methods like multiomics and machine learning are key for personalized T2D treatment strategies.
Area of Science:
- Endocrinology and Metabolism
- Genetics and Genomics
- Computational Biology
Background:
- Precision diabetes care, initially focused on monogenic diabetes, is evolving.
- Increasing prevalence of diabetes in adolescents and young adults presents diagnostic challenges.
Purpose of the Study:
- To review emerging evidence for precision diabetes care, including monogenic diabetes and type 2 diabetes (T2D).
- To discuss opportunities, challenges, and limitations for clinical adoption of precision diabetes care.
Main Methods:
- Review of current literature on precision diabetes care strategies.
- Exploration of multiomics and machine learning for T2D risk prediction and sub-phenotyping.
Main Results:
- Monogenic diabetes serves as a prime example for precision care.
- Multiomics and machine learning enhance T2D risk prediction and identify distinct patient subgroups.
- Sub-phenotyping T2D using these methods shows potential for informing clinical decisions.
Conclusions:
- Precision diabetes care now encompasses T2D, requiring sophisticated multiomics and machine learning approaches.
- Clinical translation necessitates further evidence generation and stakeholder collaboration.
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