ArticleBMJ open2020
Strategies to identify individuals with monogenic diabetes: results of an economic evaluation.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01238380 (Using Pharmacogenetics to Improve Treatment in Early-onset Diabetes), which is not on this map. Cited by 7 papers.
What it found
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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.
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.
Using Pharmacogenetics to Improve Treatment in Early-onset Diabetes
Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Actionability of Genetic Variants in Diabetes: Core Aspects and Applied Examples.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Two siblings with a rare type of maturity-onset diabetes of the young (MODY).BMJ case reports · 2023Article
- A roadmap to achieve pharmacological precision medicine in diabetes.Diabetologia · 2022Review
- Search for a time- and cost-saving genetic testing strategy for maturity-onset diabetes of the young.Acta diabetologica · 2022Article
- Monogenic Diabetes in Youth With Presumed Type 2 Diabetes: Results From the Progress in Diabetes Genetics in Youth (ProDiGY) Collaboration.Diabetes care · 2021Article
- Will better evidence on clinical utility bring about greater use of (genetic) tests?NPJ genomic medicine · 2021Article
- Monogenic Diabetes: From Genetic Insights to Population-Based Precision in Care. Reflections From aDiabetes care · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
objectivesTo evaluate and compare the lifetime costs associated with strategies to identify individuals with monogenic diabetes and change their treatment to more appropriate therapy.
designA decision analytical model from the perspective of the National Health Service (NHS) in England and Wales was developed and analysed. The model was informed by the literature, routinely collected data and a clinical study conducted in parallel with the modelling.
settingSecondary care in the UK.
participantsSimulations based on characteristics of patients diagnosed with diabetes <30 years old.
interventionsFour test-treatment strategies to identify individuals with monogenic diabetes in a prevalent cohort of diabetics diagnosed under the age of 30 years were modelled: clinician-based genetic test referral, targeted genetic testing based on clinical prediction models, targeted genetic testing based on biomarkers, and blanket genetic testing. The results of the test-treatment strategies were compared with a strategy of no genetic testing. PRIMARY AND SECONDARY OUTCOME MEASURES: Discounted lifetime costs, proportion of cases of monogenic diabetes identified.
resultsBased on current evidence, strategies using clinical characteristics or biomarkers were estimated to save approximately £100-£200 per person with diabetes over a lifetime compared with no testing. Sensitivity analyses indicated that the prevalence of monogenic diabetes, the uptake of testing, and the frequency of home blood glucose monitoring had the largest impact on the results (ranging from savings of £400-£50 per person), but did not change the overall findings. The model is limited by many model inputs being based on very few individuals, and some long-term data informed by clinical opinion.
conclusionsCosts to the NHS could be saved with targeted genetic testing based on clinical characteristics or biomarkers. More research should focus on the economic case for the use of such strategies closer to the time of diabetes diagnosis. TRIAL REGISTRATION NUMBER: NCT01238380.
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