An omics-based machine learning approach to predict diabetes progression

An omics-based machine learning approach to predict diabetes progression: a RHAPSODY study

Slieker, Roderick C. (Vrije Universiteit Amsterdam; Amsterdam Public Health; Amsterdam Cardiovascular Sciences; Leiden University Medical Center)
Münch, Magnus (Vrije Universiteit Amsterdam)
Donnelly, Louise A. (University of Dundee)
Bouland, G.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Dragan, Iulian (SIB Swiss Institute of Bioinformatics)
Kuznetsov, Dmitry (SIB Swiss Institute of Bioinformatics)
Elders, Petra J.M. (Amsterdam Public Health; Amsterdam Cardiovascular Sciences; Amsterdam UMC)
Rutter, Guy A. (Nanyang Technological University; Université de Montréal)
Ibberson, Mark (SIB Swiss Institute of Bioinformatics)

2024

Aims/hypothesis: People with type 2 diabetes are heterogeneous in their disease trajectory, with some progressing more quickly to insulin initiation than others. Although classical biomarkers such as age, HbA_1c and diabetes duration are associated with glycaemic progression, it is unclear how well such variables predict insulin initiation or requirement and whether newly identified markers have added predictive value. Methods: In two prospective cohort studies as part of IMI-RHAPSODY, we investigated whether clinical variables and three types of molecular markers (metabolites, lipids, proteins) can predict time to insulin requirement using different machine learning approaches (lasso, ridge, GRridge, random forest). Clinical variables included age, sex, HbA_1c, HDL-cholesterol and C-peptide. Models were run with unpenalised clinical variables (i.e. always included in the model without weights) or penalised clinical variables, or without clinical variables. Model development was performed in one cohort and the model was applied in a second cohort. Model performance was evaluated using Harrel’s C statistic. Results: Of the 585 individuals from the Hoorn Diabetes Care System (DCS) cohort, 69 required insulin during follow-up (1.0–11.4 years); of the 571 individuals in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) cohort, 175 required insulin during follow-up (0.3–11.8 years). Overall, the clinical variables and proteins were selected in the different models most often, followed by the metabolites. The most frequently selected clinical variables were HbA_1c (18 of the 36 models, 50%), age (15 models, 41.2%) and C-peptide (15 models, 41.2%). Base models (age, sex, BMI, HbA_1c) including only clinical variables performed moderately in both the DCS discovery cohort (C statistic 0.71 [95% CI 0.64, 0.79]) and the GoDARTS replication cohort (C 0.71 [95% CI 0.69, 0.75]). A more extensive model including HDL-cholesterol and C-peptide performed better in both cohorts (DCS, C 0.74 [95% CI 0.67, 0.81]; GoDARTS, C 0.73 [95% CI 0.69, 0.77]). Two proteins, lactadherin and proto-oncogene tyrosine-protein kinase receptor, were most consistently selected and slightly improved model performance. Conclusions/interpretation: Using machine learning approaches, we show that insulin requirement risk can be modestly well predicted by predominantly clinical variables. Inclusion of molecular markers improves the prognostic performance beyond that of clinical variables by up to 5%. Such prognostic models could be useful for identifying people with diabetes at high risk of progressing quickly to treatment intensification. Data availability: Summary statistics of lipidomic, proteomic and metabolomic data are available from a Shiny dashboard at https://rhapdata-app.vital-it.ch. Graphical Abstract: (Figure presented.).

Machine learning
Prediction model
Progression
Type 2 diabetes

http://resolver.tudelft.nl/uuid:2195e3ab-bc08-4b5d-ada8-a864817aecfd

https://doi.org/10.1007/s00125-024-06105-8

0012-186X

Diabetologia, 67 (5), 885-894

Institutional Repository

journal article

© 2024 Roderick C. Slieker, Magnus Münch, Louise A. Donnelly, G.A. Bouland, Iulian Dragan, Dmitry Kuznetsov, Petra J.M. Elders, Guy A. Rutter, Mark Ibberson, More Authors