Big data and machine learning to tackle diabetes management

Background Type 2 Diabetes (T2D) diagnosis is based solely on glycaemia, even though it is an endpoint of numerous dysmetabolic pathways. Type 2 Diabetes complexity is challenging in a real-world scenario; thus, dissecting T2D heterogeneity is a priority. Cluster analysis, which identifies natural clusters within multidimensional data based on similarity measures, poses a promising tool to unravel Diabetes complexity. Methods In this review, we scrutinize and integrate the results obtained in most of the works up to date on cluster analysis and T2D. Results To correctly stratify subjects and to differentiate and individualize a preventive or therapeutic approach to Diabetes management, cluster analysis should be informed with more parameters than the traditional ones, such as etiological factors, pathophysiological mechanisms, other dysmetabolic co-morbidities, and biochemical factors, that is the millieu. Ultimately, the above-mentioned factors may impact on Diabetes and its complications. Lastly, we propose another theoretical model, which we named the Integrative Model. We differentiate three types of components: etiological factors, mechanisms and millieu. Each component encompasses several factors to be projected in separate 2D planes allowing an holistic interpretation of the individual pathology. Conclusion Fully profiling the individuals, considering genomic and environmental factors, and exposure time, will allow the drive to precision medicine and prevention of complications.

Automated summary

This review examines the relationship between cluster analysis and T2D and highlights the complexity and heterogeneity of diabetes. It proposes an integrative model for understanding individual pathology. To achieve precision medicine and prevent complications, more factors such as etiological factors, pathophysiological mechanisms, and environmental factors should be considered.