Evidence map›Paper›PMID 40900398›Full record

ArticleDiabetes therapy : research, treatment and education of diabetes and related disorders2025

Nationwide Trends in Type 1 and Type 2 Diabetes in France (2010-2019): A Population-Based Study Using a Machine Learning Classification Algorithm.

Guy Fagherazzi, Pierre Serusclat, Barbara Roux, Oriane Bretin, Emilie Casarotto, Pascaline Rabiéga, Yolaine Rabat, Cécile Berteau, Antoine Pouyet, Michael Joubert

Abstract read
In one paragraph

Article in Diabetes therapy : research, treatment and education of diabetes and related disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Economic Burden of Type 1 Diabetes in France.Diabetes, obesity & metabolism · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Guy FagherazziDeep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, 1A-B, Rue Thomas Edison, 1445, Strassen, Luxembourg.ORCID http://orcid.org/0000-0001-5033-5966
Pierre SerusclatEndocrinology, Diabetology and Nutrition, Clinique Portes du Sud, Venissieux, France.
Barbara RouxIQVIA, RWS, La Défense, France.ORCID http://orcid.org/0000-0002-0303-1246
Oriane BretinIQVIA, RWS, La Défense, France.
Emilie CasarottoIQVIA, RWS, La Défense, France.ORCID http://orcid.org/0000-0003-1891-417X
Pascaline RabiégaIQVIA, RWS, La Défense, France.
Yolaine RabatIQVIA, RWS, La Défense, France.ORCID http://orcid.org/0000-0002-1244-0535
Cécile BerteauRoche Diagnostics, Melun, France.
Antoine PouyetTimkl, Montbonnot-Saint-Martin, France.
Michael JoubertDiabetes Care Unit, Caen University Hospital, UNICAEN, 14033, Caen Cedex 09, France. joubert-m@chu-caen.fr.ORCID http://orcid.org/0000-0002-8731-7355

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDiabetes represents an increasing public health challenge in France, yet national data distinguishing type 1 from type 2 diabetes and insulin use remain limited. This study aimed to describe trends in the epidemiology, care pathways and health outcomes of adult individuals living with type 1 or type 2 diabetes in France from 2010 to 2019. It focused on individuals treated or not with insulin and applied a predictive classification algorithm to accurately distinguish between diabetes types using real-world data.

methodsA 10-year retrospective population-based cohort study was conducted from a representative one-tenth sample of the French national healthcare database (i.e. SNDS, Système National des données de Santé), covering nearly the entire French population. Adults (≥ 18 years) affiliated with the general insurance scheme were included. A machine learning algorithm, trained on clinical data from general practitioners, was applied to classify diabetes type. Annual trends in prevalence, incidence, comorbidities, treatments, outpatient care, complications and mortality were assessed.

resultsAmong an extrapolated 5.5 million individuals with diabetes in 2019, 3.5% had type 1 diabetes and 96.5% had type 2 diabetes. The prevalence of type 2 diabetes increased from 6.2% in 2010 to 8.0% in 2019, while type 1 diabetes remained stable. Comorbidity rates were high and increasing in insulin-treated individuals with type 2 diabetes. In 2019, 15.3% of insulin-treated individuals with type 2 diabetes had at least one complication-related hospitalisation. Specialist consultations were underused, especially in type 2 diabetes. The mortality rate in individuals with type 1 diabetes declined from 2.6% to 1.5%, with an increase in mean age at death.

conclusionThis national study provides updated insights into diabetes in France and highlights the need to improve access to specialised care and reinforce long-term surveillance strategies.

Indexed as

EpidemiologyFranceHealth outcomesMachine learning algorithmType 1 diabetesType 2 diabetes

Identifiers

PMID40900398
PMCPMC12474798

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.