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Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis
INTRODUCTION: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes. RESEARCH DESIGN AND METHODS: Overall, 5.5 million of Hab1c and corresponding w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BMJ Publishing Group
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490948/ https://www.ncbi.nlm.nih.gov/pubmed/32928790 http://dx.doi.org/10.1136/bmjdrc-2020-001362 |
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author | Giorda, Carlo Bruno Pisani, Federico De Micheli, Alberto Ponzani, Paola Russo, Giuseppina Guaita, Giacomo Zilich, Rita Musacchio, Nicoletta |
author_facet | Giorda, Carlo Bruno Pisani, Federico De Micheli, Alberto Ponzani, Paola Russo, Giuseppina Guaita, Giacomo Zilich, Rita Musacchio, Nicoletta |
author_sort | Giorda, Carlo Bruno |
collection | PubMed |
description | INTRODUCTION: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes. RESEARCH DESIGN AND METHODS: Overall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005–2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain. RESULTS: The combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control. CONCLUSIONS: Treating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention. |
format | Online Article Text |
id | pubmed-7490948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-74909482020-09-25 Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis Giorda, Carlo Bruno Pisani, Federico De Micheli, Alberto Ponzani, Paola Russo, Giuseppina Guaita, Giacomo Zilich, Rita Musacchio, Nicoletta BMJ Open Diabetes Res Care Epidemiology/Health services research INTRODUCTION: The aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes. RESEARCH DESIGN AND METHODS: Overall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005–2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain. RESULTS: The combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control. CONCLUSIONS: Treating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention. BMJ Publishing Group 2020-09-14 /pmc/articles/PMC7490948/ /pubmed/32928790 http://dx.doi.org/10.1136/bmjdrc-2020-001362 Text en © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Epidemiology/Health services research Giorda, Carlo Bruno Pisani, Federico De Micheli, Alberto Ponzani, Paola Russo, Giuseppina Guaita, Giacomo Zilich, Rita Musacchio, Nicoletta Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title | Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title_full | Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title_fullStr | Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title_full_unstemmed | Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title_short | Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
title_sort | determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis |
topic | Epidemiology/Health services research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490948/ https://www.ncbi.nlm.nih.gov/pubmed/32928790 http://dx.doi.org/10.1136/bmjdrc-2020-001362 |
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