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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...

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Autores principales: Pina, Ana F., Meneses, Maria João, Sousa‐Lima, Inês, Henriques, Roberto, Raposo, João F., Macedo, Maria Paula
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078354/
https://www.ncbi.nlm.nih.gov/pubmed/36254106
http://dx.doi.org/10.1111/eci.13890
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author Pina, Ana F.
Meneses, Maria João
Sousa‐Lima, Inês
Henriques, Roberto
Raposo, João F.
Macedo, Maria Paula
author_facet Pina, Ana F.
Meneses, Maria João
Sousa‐Lima, Inês
Henriques, Roberto
Raposo, João F.
Macedo, Maria Paula
author_sort Pina, Ana F.
collection PubMed
description 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.
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spelling pubmed-100783542023-04-07 Big data and machine learning to tackle diabetes management Pina, Ana F. Meneses, Maria João Sousa‐Lima, Inês Henriques, Roberto Raposo, João F. Macedo, Maria Paula Eur J Clin Invest Narrative Reviews 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. John Wiley and Sons Inc. 2022-11-05 2023-01 /pmc/articles/PMC10078354/ /pubmed/36254106 http://dx.doi.org/10.1111/eci.13890 Text en © 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. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Narrative Reviews
Pina, Ana F.
Meneses, Maria João
Sousa‐Lima, Inês
Henriques, Roberto
Raposo, João F.
Macedo, Maria Paula
Big data and machine learning to tackle diabetes management
title Big data and machine learning to tackle diabetes management
title_full Big data and machine learning to tackle diabetes management
title_fullStr Big data and machine learning to tackle diabetes management
title_full_unstemmed Big data and machine learning to tackle diabetes management
title_short Big data and machine learning to tackle diabetes management
title_sort big data and machine learning to tackle diabetes management
topic Narrative Reviews
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10078354/
https://www.ncbi.nlm.nih.gov/pubmed/36254106
http://dx.doi.org/10.1111/eci.13890
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