TY - JOUR
T1 - Stratification of type 2 diabetes based on routine clinical markers
AU - Safai, Narges
AU - Ali, Ashfaq
AU - Rossing, Peter
AU - Ridderstråle, Martin
PY - 2018
Y1 - 2018
N2 - Aims: We hypothesized that patients with dysregulated type 2 diabetes may be stratified based on routine clinical markers. Methods: In this retrospective cohort study, diabetes related clinical measures including age at onset, diabetes duration, HbA1c, BMI, HOMA2-β, HOMA2-IR and GAD65 autoantibodies, were used for sub-grouping patients by K-means clustering and for adjusting. Probability of diabetes complications (95% confidence interval), were calculated using logistic regression. Results: Based on baseline data from patients with type 2 diabetes (n = 2290), the cluster analysis suggested up to five sub-groups. These were primarily characterized by autoimmune β-cell failure (3%), insulin resistance with short disease duration (21%), non-autoimmune β-cell failure (22%), insulin resistance with long disease duration (32%), and presence of metabolic syndrome (22%), respectively. Retinopathy was more common in the sub-group characterized by non-autoimmune β-cell failure (52% (47.7–56.8)) compared to other sub-groups (22% (20.1–24.1)), adj. p < 0.001. The prevalence of cardiovascular disease, nephropathy and neuropathy also differed between sub-groups, but significance was lost after adjustment. Conclusions: Patients with type 2 diabetes cluster into clinically relevant sub-groups based on routine clinical markers. The prevalence of diabetes complications seems to be sub-group specific. Our data suggests the need for a tailored strategy for the treatment of type 2 diabetes.
AB - Aims: We hypothesized that patients with dysregulated type 2 diabetes may be stratified based on routine clinical markers. Methods: In this retrospective cohort study, diabetes related clinical measures including age at onset, diabetes duration, HbA1c, BMI, HOMA2-β, HOMA2-IR and GAD65 autoantibodies, were used for sub-grouping patients by K-means clustering and for adjusting. Probability of diabetes complications (95% confidence interval), were calculated using logistic regression. Results: Based on baseline data from patients with type 2 diabetes (n = 2290), the cluster analysis suggested up to five sub-groups. These were primarily characterized by autoimmune β-cell failure (3%), insulin resistance with short disease duration (21%), non-autoimmune β-cell failure (22%), insulin resistance with long disease duration (32%), and presence of metabolic syndrome (22%), respectively. Retinopathy was more common in the sub-group characterized by non-autoimmune β-cell failure (52% (47.7–56.8)) compared to other sub-groups (22% (20.1–24.1)), adj. p < 0.001. The prevalence of cardiovascular disease, nephropathy and neuropathy also differed between sub-groups, but significance was lost after adjustment. Conclusions: Patients with type 2 diabetes cluster into clinically relevant sub-groups based on routine clinical markers. The prevalence of diabetes complications seems to be sub-group specific. Our data suggests the need for a tailored strategy for the treatment of type 2 diabetes.
KW - Clusters
KW - Heterogeneity
KW - Personalized medicine
KW - Sub-group
KW - Type 2 diabetes
U2 - 10.1016/j.diabres.2018.05.014
DO - 10.1016/j.diabres.2018.05.014
M3 - Journal article
C2 - 29782936
AN - SCOPUS:85047653146
SN - 0168-8227
VL - 141
SP - 275
EP - 283
JO - Diabetes Research and Clinical Practice
JF - Diabetes Research and Clinical Practice
ER -
