Epidemiological Studies on Type 2 Diabetes Using the German Disease Analyser Database: A Narrative Review

Résumé fourni par la source

BACKGROUND: The German IQVIA Disease Analyser (DA) database provides longitudinal, anonymised outpatient data from a large network of German general practices and specialist offices, including ICD-10 diagnoses, ATC-coded prescriptions, consultation dates and selected clinical measurements (HbA1c, BMI). AIMS: This narrative review synthesises DA-based research in patients with type 2 diabetes (T2D) published between 2020 and 2025, with a focus on studies examining glucose-lowering drug effectiveness, real-world prescribing patterns, pharmacoepidemiological associations and relevant comorbidities. METHODS: A structured PubMed search was used to identify eligible studies, and methodological limitations of the included observational studies are discussed. RESULTS: Key findings include observational associations between SGLT2 inhibitor therapy and lower incidence of iron deficiency anaemia and dementia in patients with T2D, a decline in cardiovascular event incidence among patients with newly diagnosed T2D over a 17-years period, a gap between guideline recommendations and real-world use of GLP-1 receptor agonists and SGLT2 inhibitors in patients with established cardiovascular disease, and associations between glucose-lowering drug classes and cerebrovascular outcomes. DISCUSSION: Given the observational design of all included studies, these associations should be regarded as hypothesis-generating rather than causal. The DA database complements randomised trial evidence by capturing large, heterogeneous populations in routine care. Limitations include underestimation of hypoglycaemia, absence of lifestyle and socioeconomic variables, and incomplete inpatient data, as well as reliance on a single data source to which several included studies and authors of this review have contributed. CONCLUSION: DA-based pharmacoepidemiological evidence is a valuable, albeit methodologically constrained, addition to the evidence base for contemporary diabetes management.

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