Article Text

Download PDFPDF

Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis
  1. Carlo Bruno Giorda1,
  2. Federico Pisani2,
  3. Alberto De Micheli3,
  4. Paola Ponzani4,
  5. Giuseppina Russo5,
  6. Giacomo Guaita6,
  7. Rita Zilich7,
  8. Nicoletta Musacchio8
  9. on behalf of the Associazione Medici Diabetologi (AMD) Annals Study Group
  1. 1Diabetes and Endocrinology Unit, ASL TO5, Chieri, Turin, Italy
  2. 2Freelance Artificial Intelligence Expert, Ivrea (TO), Ivrea, Italy
  3. 3ACISMOM, Genova, Italy
  4. 4Operative Unit of Diabetology, La Colletta Hospital, ASL 3, Genova, Italy
  5. 5Internal Medicine, University of Messina, Messina, Italy
  6. 6Diabetology, Endocrinology and Metabolic Diseases Service, ATS Sardegna-ASSL, Carbonia, Italy
  7. 7Mix-x Partner, Milano, Italy
  8. 8Associazione Medici Diabetologi (AMD), “Fondazione AMD”, Roma, Italy
  1. Correspondence to Dr Carlo Bruno Giorda; carlogiordaposta{at}gmail.com

Abstract

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.

  • disease management
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/.

View Full Text

Statistics from Altmetric.com

Footnotes

  • Collaborators AMD Annals participating centre list is available at

    https://aemmedi.it/files/ANNALI-AMD/Autori_Annali_2012.pdf

  • Contributors CBG: literature search, study design, data interpretation, writing. FP: data interpretation. ADM: literature search, study design, data interpretation. PP: literature search, study design, data interpretation. GR: literature search, study design, data interpretation. GG: literature search, study design, data interpretation. RZ: literature search, data interpretation. NM: literature search, study design, data interpretation, writing. All authors approved the final version.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Ethics approval According to the Italian Law 211/2003, no approval is required for epidemiological analysis regarding anonymous data.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data are available on reasonable request by writing an email to the corresponding author.

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.