Abstract
Aims/hypothesis
The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.
Methods
Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost’s importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.
Results
For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual’s hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.
Conclusions/interpretation
Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.
The objective of the Hypoglycaemia REdefining SOLutions for better liVES (Hypo-RESOLVE) project is to use a dataset of pooled clinical trials across pharmaceutical and device companies in people with type 1 or type 2 diabetes to examine factors associated with incident hypoglycaemia events and to quantify the prediction of these events.
Methods
Data from 90 trials with 46,254 participants were pooled. Analyses were done for type 1 and type 2 diabetes separately. Poisson mixed models, adjusted for age, sex, diabetes duration and trial identifier were fitted to assess the association of clinical variables with hypoglycaemia event counts. Tree-based gradient-boosting algorithms (XGBoost) were fitted using training data and their predictive performance in terms of area under the receiver operating characteristic curve (AUC) evaluated on test data. Baseline models including age, sex and diabetes duration were compared with models that further included a score of hypoglycaemia in the first 6 weeks from study entry, and full models that included further clinical variables. The relative predictive importance of each covariate was assessed using XGBoost’s importance procedure. Prediction across the entire trial duration for each trial (mean of 34.8 weeks for type 1 diabetes and 25.3 weeks for type 2 diabetes) was assessed.
Results
For both type 1 and type 2 diabetes, variables associated with more frequent hypoglycaemia included female sex, white ethnicity, longer diabetes duration, treatment with human as opposed to analogue-only insulin, higher glucose variability, higher score for hypoglycaemia across the 6 week baseline period, lower BP, lower lipid levels and treatment with psychoactive drugs. Prediction of any hypoglycaemia event of any severity was greater than prediction of hypoglycaemia requiring assistance (level 3 hypoglycaemia), for which events were sparser. For prediction of level 1 or worse hypoglycaemia during the whole follow-up period, the AUC was 0.835 (95% CI 0.826, 0.844) in type 1 diabetes and 0.840 (95% CI 0.831, 0.848) in type 2 diabetes. For level 3 hypoglycaemia, the AUC was lower at 0.689 (95% CI 0.667, 0.712) for type 1 diabetes and 0.705 (95% CI 0.662, 0.748) for type 2 diabetes. Compared with the baseline models, almost all the improvement in prediction could be captured by the individual’s hypoglycaemia history, glucose variability and blood glucose over a 6 week baseline period.
Conclusions/interpretation
Although hypoglycaemia rates show large variation according to sociodemographic and clinical characteristics and treatment history, looking at a 6 week period of hypoglycaemia events and glucose measurements predicts future hypoglycaemia risk.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Diabetologia |
| Vol/bind | 67 |
| Udgave nummer | 8 |
| Sider (fra-til) | 1588-1601 |
| Antal sider | 14 |
| ISSN | 0012-186X |
| DOI | |
| Status | Udgivet - 2024 |
Bibliografisk note
Funding Information:HMC has research grants from JDRF and Diabetes UK. She has participated in a speakers bureau, on an advisory board for Novo Nordisk, on an advisory panel for Bayer AG, and is a shareholder of Roche Pharmaceuticals and Bayer AG. SH has participated on advisory boards for Eli Lilly and Novo Nordisk and on a speakers bureau for Novo Nordisk and has received honorarium via his institution from Medtronic. He has consulted for Vertex Pharma, Zucara Pharmaceuticals and Zealand Pharma. AS is an employee of, and holds shares in, Novo Nordisk. UP-B received personal honorarium for lectures, presentations and expert testimony from Sanofi and Novo Nordisk and is a member of the editorial board of Diabetologia. BEdG is a member of the editorial board of Diabetologia. RJM received personal honorarium for lectures, and presentations from Sanofi and Novo Nordisk, and support for travel to lecture at an educational event from Sanofi. He is a non-executive member of NHS Tayside Health Board. MR is an employee of, and holds shares in, Eli Lilly and Company. SAA has served on Advisory Boards for Vertex Pharmaceuticals and spoken at educational events sponsored by Sanofi and Novo Nordisk. M-AG is an employee of, and holds shares in, Novo Nordisk. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.
Funding Information:
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement no. 777460. The JU receives support from the European Union\u2019s Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations and T1DExchange, JDRF, International Diabetes Federation and Helmsley Charitable Trust. The industry partners supporting the JU include Abbott Diabetes Care, Eli Lilly, Medtronic, Novo Nordisk and Sanofi-Aventis. Results reflects only the authors\u2019 views and JU is not responsible for any use that may be made of the information it contains. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Publisher Copyright:
© The Author(s) 2024.