Diabetes Technol Ther. 2024 Aug 8. doi: 10.1089/dia.2024.0226. Online ahead of print.
ABSTRACT
OBJECTIVE: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7% / 39 mmol/mol) using metrics derived from Continuous Glucose Monitoring (CGM). Additionally, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns.
METHODS: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The dataset was split 70/30 for training and testing two classification models (XGBoost / Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group.
RESULTS: The analysis included 836 participants (healthy: n=282; prediabetes: n=133; T2DM: n=432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (p<0.001). Statistically significant differences (p<0.01) were noted in mean glucose, Time Below Range, Time Above 140 mg/dl, Mmobility, Multiscale Complexity Index and Glycemic Risk Index when transitioning from health to prediabetes. The XGBoost models achieved the highest Receiver Operating Characteristic Area Under the Curve (ROC-AUC) values on the test dataset ranging from 0.91 [CI: 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (Dysglycemia identification).
CONCLUSION: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.
PMID:39115921 | DOI:10.1089/dia.2024.0226
