Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Li, Xinyu; Giessen, Anoukh van; Altunkaya, James; Slieker, Roderick C.; Beulens, Joline W.J.; Hart, Leen M ’t; Pearson, Ewan R.; Elders, Petra; Feenstra, Talitha; Leal, José

doi:10.2337/dc22-2170

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…Given that the data originated from routine care, some clinical parameters were slightly modified due to their availability [ 9 ]. Replication analyses showed good resemblance between cohorts and also compared with the original Swedish subgroups (developed by Ahlqvist et al [ 1 ]) [ 9 , 10 ], except for the refinement of the original MARD cluster into two new clusters, the mild diabetes subgroup developed by RHAPSODY (RHAP-MD) and the mild diabetes with high HDL-cholesterol subgroup developed by RHAPSODY (RHAP-MDH), following the addition of HDL-cholesterol. Both RHAP-MD and RHAP-MDH exhibited slow glycaemic deterioration, but they showed significantly different molecular signatures [ 8 ].…”

Section: Introductionmentioning

confidence: 81%

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Li,

Donnelly,

Slieker

et al. 2024

Diabetologia

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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

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Section: Introductionmentioning

confidence: 81%

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Li,

Donnelly,

Slieker

et al. 2024

Diabetologia

Self Cite

View full text Add to dashboard Cite

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Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

Salvatori,

Wegener,

Kotzaeridi

et al. 2024

Diabetologia

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Aims/hypothesis Gestational diabetes mellitus (GDM) is a heterogeneous condition. Given such variability among patients, the ability to recognise distinct GDM subgroups using routine clinical variables may guide more personalised treatments. Our main aim was to identify distinct GDM subtypes through cluster analysis using routine clinical variables, and analyse treatment needs and pregnancy outcomes across these subgroups. Methods In this cohort study, we analysed datasets from a total of 2682 women with GDM treated at two central European hospitals (1865 participants from Charité University Hospital in Berlin and 817 participants from the Medical University of Vienna), collected between 2015 and 2022. We evaluated various clustering models, including k-means, k-medoids and agglomerative hierarchical clustering. Internal validation techniques were used to guide best model selection, while external validation on independent test sets was used to assess model generalisability. Clinical outcomes such as specific treatment needs and maternal and fetal complications were analysed across the identified clusters. Results Our optimal model identified three clusters from routinely available variables, i.e. maternal age, pre-pregnancy BMI (BMIPG) and glucose levels at fasting and 60 and 120 min after the diagnostic OGTT (OGTT0, OGTT60 and OGTT120, respectively). Cluster 1 was characterised by the highest OGTT values and obesity prevalence. Cluster 2 displayed intermediate BMIPG and elevated OGTT0, while cluster 3 consisted mainly of participants with normal BMIPG and high values for OGTT60 and OGTT120. Treatment modalities and clinical outcomes varied among clusters. In particular, cluster 1 participants showed a much higher need for glucose-lowering medications (39.6% of participants, compared with 12.9% and 10.0% in clusters 2 and 3, respectively, p<0.0001). Cluster 1 participants were also at higher risk of delivering large-for-gestational-age infants. Differences in the type of insulin-based treatment between cluster 2 and cluster 3 were observed in the external validation cohort. Conclusions/interpretation Our findings confirm the heterogeneity of GDM. The identification of subgroups (clusters) has the potential to help clinicians define more tailored treatment approaches for improved maternal and neonatal outcomes. Graphical Abstract

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Potential Value of Identifying Type 2 Diabetes Subgroups for Guiding Intensive Treatment: A Comparison of Novel Data-Driven Clustering With Risk-Driven Subgroups

Cited by 2 publications

References 37 publications

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes

Identification and validation of gestational diabetes subgroups by data-driven cluster analysis

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