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Diabetes: Management and Pharmacotherapy01:15

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The therapy for diabetes aims to alleviate hyperglycemia-related symptoms, prevent acute metabolic decompensation, and reduce chronic end-organ complications. Glycemic control is evaluated through short-term (self-monitoring, continuous glucose monitoring) and long-term (A1c, fructosamine) metrics, enabling near real-time tracking of blood glucose levels and reflecting glycemic control over specific time frames.
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Big data and machine learning to tackle diabetes management.

Ana F Pina1,2, Maria João Meneses1,3,4, Inês Sousa-Lima1

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Cluster analysis can reveal Type 2 Diabetes (T2D) heterogeneity by integrating diverse patient data. This approach enables personalized prevention and treatment strategies for better diabetes management and complication avoidance.

Keywords:
big datacluster analysisdiabetesmachine learning

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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.