A recently published international consensus report from the Precision Medicine in Diabetes Initiative (PMDI) is paving the way for advancements in precision medicine for diabetes. The report, which was published in Nature Medicine, summarizes systematic reviews and consensus among the PMDI consortium on precision medicine for various types of diabetes, including monogenic diabetes, gestational diabetes, type 1 diabetes, and type 2 diabetes.
Precision medicine is an innovative approach to evidence-based medicine that aims to improve the accuracy of health recommendations and medical judgments. In the context of diabetes, precision medicine examines the origins, clinical presentation, and pathophysiology of different types of diabetes to provide personalized diagnoses and treatment plans.
The report highlights the current knowledge gaps and potential applications of precision medicine in diabetes. While precision diabetes therapy shows promise in addressing the global diabetes epidemic, there are still gaps in information regarding cost-effectiveness, health equity, prediction accuracy, liability, and accessibility.
To integrate precision medicine into clinical practice, the report suggests revising criteria to reduce errors and improve the accuracy of medical decisions. Personalized medicine uses individual-level data to objectively evaluate the safety, efficacy, and tolerance of treatments. It also calls for studies specifically designed to evaluate precision medicine hypotheses to optimize treatment recommendations.
Precision medicine in diabetes requires further research on physiological processes identified by biomarkers. However, the complexity of these processes and proprietary considerations can hinder commercialization. Balancing accessibility and commercialization while considering economic, social, and ethical factors is crucial.
The report also delves into the specifics of precision medicine for different types of diabetes. For type 1 diabetes (T1D), which affects approximately two percent of all diabetes cases globally, the report emphasizes the role of autoantibodies in pancreatic islet cells as estimators of diabetes progression. It also discusses the effectiveness of novel technologies in improving glycated hemoglobin levels, continuous glucose monitoring, and diabetes-related outcomes.
Type 2 diabetes (T2D), which affects around 500 million individuals worldwide, requires precision prevention strategies to identify response predictors and individuals who are most likely to benefit from lifestyle changes. Machine learning technologies have helped identify reproducible subtypes of T2D.
Monogenic diabetes (MDM), a rare type of diabetes diagnosed in infancy or before the age of 45, accounts for up to 5% of all diabetes cases. Precision medicine can provide accurate diagnostic options, as MDM often presents with overlapping clinical symptoms of T1D or T2D. The report highlights the effectiveness of targeted genetic tests in diagnosing various types of diabetes.
Gestational diabetes mellitus (GDM), a metabolic disorder during pregnancy, poses serious health risks to both the mother and unborn child. The report recommends lifestyle improvements and interventions like metformin to reduce the incidence of GDM.
In conclusion, the consensus report underscores the need for specific diagnoses, biomarkers, and personalized approaches to prevention, treatment, and prognosis in diabetes. To achieve generalizability in precision diabetic management, future research should address the lack of data from non-European-ancestry individuals. Standardization, sharing of laboratory technology, and the use of computational algorithms and artificial intelligence can improve access, repeatability, and biomarker evaluation. Furthermore, systems for evaluating diabetes heterogeneity and revising diabetes categorization across the lifespan are necessary. A framework tailored to the target population is needed to translate precision diabetes research into clinical applications.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
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