Multilevel clustering approach driven by continuous glucose monitoring data for further classification of type 2 diabetes

Abstract

Introduction: Mining knowledge from continuous glucose monitoring (CGM) data to classify highly heterogeneous patients with type 2 diabetes according to their characteristics remains unaddressed. A refined clustering method that retrieves hidden information from CGM data could provide a viable method to identify patients with different degrees of dysglycemia and clinical phenotypes.

Research design and methods: From Shanghai Jiao Tong University Affiliated Sixth People's Hospital, we selected 908 patients with type 2 diabetes (18-83 years) who wore blinded CGM sensors (iPro2, Medtronic, California, USA). Participants were clustered based on CGM data during a 24-hour period by our method. The first level extracted the knowledge-based and statistics-based features to describe CGM signals from multiple perspectives. The Fisher score and variables cluster analysis were applied to fuse features into low dimensions at the second level. The third level divided subjects into subgroups with different clinical phenotypes. The four subgroups of patients were determined by clinical phenotypes.

Results: Four subgroups of patients with type 2 diabetes with significantly different statistical features and clinical phenotypes were identified by our method. In particular, individuals in cluster 1 were characterized by the lowest glucose level factor and glucose fluctuation factor, and the highest negative glucose factor and C peptide index. By contrast, cluster 2 had the highest glucose level factor and the lowest C peptide index. Cluster 4 was characterized by the greatest degree of glucose fluctuation factor, was the most insulin-sensitive, and had the lowest insulin resistance. Cluster 3 ranked in the middle concerning the CGM-derived metrics and clinical phenotypes compared with those of the other three groups.

Conclusion: A novel multilevel clustering approach for knowledge mining from CGM data in type 2 diabetes is presented. The results demonstrate that subgroups are adequately distinguished with notable statistical and clinical differences.

Keywords: classification; diabetes mellitus; disease management; type 2 diabetes.

Figure 1

Dendrogram of hierarchical cluster analysis of continuous glucose monitoring variables. Height indicates the distance of correlation method between substructures. ADRR, average daily risk range; CV, coefficient of variation; GRADE, glycemic risk assessment diabetes equation score; HBGI, high blood glucose indices; J, J index; LAGE, largest amplitude of glycemic excursions; LI, liability index; M, M value; MAGE, mean amplitude of glycemic excursions; MPSG, mean postprandial sensor glucose; MSG, mean sensor glucose; SDSG, SD of the sensor glucose; TIR, percentages of values within the target range (3.9–10 mmol/L); TOR, percentages of values out of the target range (<3.9 mmol/L or >10 mmol/L).

Figure 2

Continuous glucose monitoring curve of each subgroup: (A) cluster 1, (B) cluster 2, (C) cluster 3 and (D) cluster 4. The solid line is the median. The dark blue bar means the 25th–75th percentiles and the light blue bar means the 10th–90th percentiles. The mean sensor glucose is given at the top left corner.

Figure 3

Box plot of cluster factors in patients. Adjusted p value was used. **Adjusted p values were under 0.01; ***adjusted p values were under 0.001. HLHFD, high-level and high-fluctuation diabetes; LLLFD, low-level and low-fluctuation diabetes; MLHFD, moderate-level and high-fluctuation diabetes; MLMFD, moderate-level and moderate-fluctuation diabetes.