Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes
Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. M...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer
2024
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| _version_ | 1811139531581161472 |
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| author | Li, X Donnelly, LA Slieker, RC Beulens, JWJ ‘t Hart, LM Elders, PJM Pearson, ER van Giessen, A Leal, J Feenstra, T |
| author_facet | Li, X Donnelly, LA Slieker, RC Beulens, JWJ ‘t Hart, LM Elders, PJM Pearson, ER van Giessen, A Leal, J Feenstra, T |
| author_sort | Li, X |
| collection | OXFORD |
| description | Aims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. Methods: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Results: Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators. Graphical Abstract: |
| first_indexed | 2024-09-25T04:07:34Z |
| format | Journal article |
| id | oxford-uuid:ad0b08ec-a793-44ca-8913-29de39e62a21 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:07:34Z |
| publishDate | 2024 |
| publisher | Springer |
| record_format | dspace |
| spelling | oxford-uuid:ad0b08ec-a793-44ca-8913-29de39e62a212024-06-05T20:15:22ZTrajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ad0b08ec-a793-44ca-8913-29de39e62a21EnglishJisc Publications RouterSpringer2024Li, XDonnelly, LASlieker, RCBeulens, JWJ‘t Hart, LMElders, PJMPearson, ERvan Giessen, ALeal, JFeenstra, TAims/hypothesis: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist’s novel diabetes subgroups and previously analysed by Slieker et al. Methods: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. Results: Subgroups’ risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. Conclusions/interpretation: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators. Graphical Abstract: |
| spellingShingle | Li, X Donnelly, LA Slieker, RC Beulens, JWJ ‘t Hart, LM Elders, PJM Pearson, ER van Giessen, A Leal, J Feenstra, T Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title | Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title_full | Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title_fullStr | Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title_full_unstemmed | Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title_short | Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes |
| title_sort | trajectories of clinical characteristics complications and treatment choices in data driven subgroups of type 2 diabetes |
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