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Orientador(es)
Resumo(s)
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.
Descrição
Funding Information: This work was supported by ‘Fundação para a Ciência e a Tecnologia’—FCT to AP (PD/BD/136887/2018), MPM (PTDC/MEC‐MET/29314/2017 and PTDC/BIM‐MET/2115/2014), iNOVA4Health (UIDB/Multi/04462/2020); by the European Commission Marie Skłodowska‐Curie Action H2020 (grant agreement n. 734719); by FEDER and Lisboa2020 and Alentejo2020; and by the Sociedade Portuguesa de Diabetologia. Publisher Copyright: © 2022 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.
Palavras-chave
big data cluster analysis diabetes machine learning Biochemistry Clinical Biochemistry SDG 3 - Good Health and Well-being