Big data and machine learning to tackle diabetes management.

Area of Science:

• Metabolic Diseases
• Biostatistics
• Personalized Medicine

Background:

• Type 2 Diabetes (T2D) diagnosis relies solely on glycaemia, overlooking its complex dysmetabolic pathways.
• The heterogeneity of T2D presents challenges for real-world management, necessitating methods to dissect its complexity.
• Cluster analysis offers a promising approach to identify natural groupings within multidimensional data for understanding diabetes complexity.

Purpose of the Study:

• To review and integrate existing research on cluster analysis applications in Type 2 Diabetes (T2D).
• To explore how cluster analysis can unravel the heterogeneity of T2D.
• To propose a framework for a more holistic interpretation of individual diabetes pathology.

Main Methods:

• Systematic review and integration of studies on cluster analysis and T2D.
• Analysis of parameters used in existing cluster analysis for T2D stratification.
• Development of a theoretical Integrative Model for T2D profiling.

Main Results:

• Cluster analysis requires more parameters than traditional ones (e.g., etiological factors, pathophysiological mechanisms, comorbidities, biochemical milieu) for accurate subject stratification.
• These additional factors significantly impact diabetes progression and complications.
• The proposed Integrative Model categorizes factors into etiological, mechanistic, and milieu components for holistic interpretation.

Conclusions:

• Comprehensive individual profiling, including genomic, environmental, and temporal exposure factors, is crucial for advancing precision medicine in diabetes.
• This holistic approach will drive the prevention of diabetes complications.
• Personalized prevention and therapeutic strategies are essential for effective diabetes management.