ArticleDiabetology & metabolic syndrome2025
A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target.
Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Development and Validation of Nomogram-Based Predictive Models for Severe Hypoglycemia in Adults With Type 1 Diabetes Treated With Multiple Daily Injections: The SEHYPAN Study.Diabetes/metabolism research and reviews · 2026Observational
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9 authors.
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Abstract
aimThis study aims to predict risk factors for hypoglycemia in patients with type 2 diabetes mellitus (T2DM) using continuous glucose monitoring (CGM) and with time in range (TIR) > 70%.
methodsData from 111 patients with T2DM who underwent CGM with TIR > 70% were analyzed. A hypoglycemia episode was defined as CGM-detected glucose < 3.9mmol/L sustained for at least 5 min. Logistic regression analysis was performed to examine the relationship between hypoglycemia and mean blood glucose (MBG), glycemic variability (GV) metrics [including mean amplitude of glucose excursion (MAGE), largest amplitude of glycemic excursion (LAGE), mean of daily difference (MODD), coefficient of variation (CV), standard deviation (SD)], and low blood glucose index (LBGI). A nomogram model was constructed, and its diagnostic performance was assessed. Data were bootstrapped 1000 times for internal validation, and a calibration curve was drawn to evaluate the model's predictive ability. Decision curve analysis was performed to assess its clinical usefulness.
resultsAmong the 111 included patients, 53 experienced hypoglycemic event during wearing CGM (47.75%). GV metrics were higher in hypoglycemia group, while MBG was lower. The multivariable logistic regression analysis showed that the MBG, GV metrics, LBGI were independently associated with hypoglycemia. The receiver operating characteristics (ROC) analysis indicated that the area under the curve (AUC) for the MBG-SD-LBGI model was 0.93 (95% CI = 0.88-0.97). The calibration curve showed good consistency between the predicted and observed probabilities. Decision curve analysis demonstrated strong clinical applicability.
conclusionThis study demonstrates a significant correlation between CGM metrics and hypoglycemia in patients with T2DM who achieved TIR > 70%. These findings suggest that CGM metrics can predict the risk of hypoglycemia in T2DM patients with a TIR > 70%, and the nomogram developed from these metrics holds strong potential for clinical application.
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