A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target
Abstract Aim This 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%. Methods Data from 111 patients with T2DM who underwent CGM with TIR > 70% were analyzed. A hypogl...
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BMC
2025-05-01
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| Series: | Diabetology & Metabolic Syndrome |
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| Online Access: | https://doi.org/10.1186/s13098-025-01713-9 |
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| author | Jianwen Lu Danrui Chen Beisi Lin Zhigu Liu Yanling Yang Ling He Jinhua Yan Daizhi Yang Wen Xu |
| author_facet | Jianwen Lu Danrui Chen Beisi Lin Zhigu Liu Yanling Yang Ling He Jinhua Yan Daizhi Yang Wen Xu |
| author_sort | Jianwen Lu |
| collection | DOAJ |
| description | Abstract Aim This 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%. Methods Data 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. Results Among 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. Conclusion This 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. |
| format | Article |
| id | doaj-art-50bd8ae8ccfc49f7931d3d2d38ebf006 |
| institution | OA Journals |
| issn | 1758-5996 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
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| series | Diabetology & Metabolic Syndrome |
| spelling | doaj-art-50bd8ae8ccfc49f7931d3d2d38ebf0062025-08-20T01:53:25ZengBMCDiabetology & Metabolic Syndrome1758-59962025-05-0117111010.1186/s13098-025-01713-9A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in targetJianwen Lu0Danrui Chen1Beisi Lin2Zhigu Liu3Yanling Yang4Ling He5Jinhua Yan6Daizhi Yang7Wen Xu8Department of Metabolism and Endocrinology, Guangzhou First People’s Hospital, South China University of TechnologyDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Metabolism and Endocrinology, Guangzhou First People’s Hospital, South China University of TechnologyDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityDepartment of Endocrinology and Metabolism, The Third Affiliated Hospital of Sun Yat-sen UniversityAbstract Aim This 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%. Methods Data 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. Results Among 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. Conclusion This 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.https://doi.org/10.1186/s13098-025-01713-9HypoglycemiaType 2 diabetesContinuous glucose monitoringTime in rangeNomogram |
| spellingShingle | Jianwen Lu Danrui Chen Beisi Lin Zhigu Liu Yanling Yang Ling He Jinhua Yan Daizhi Yang Wen Xu A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target Diabetology & Metabolic Syndrome Hypoglycemia Type 2 diabetes Continuous glucose monitoring Time in range Nomogram |
| title | A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target |
| title_full | A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target |
| title_fullStr | A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target |
| title_full_unstemmed | A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target |
| title_short | A CGM-Based model for predicting hypoglycemia in type 2 diabetes patients with TIR in target |
| title_sort | cgm based model for predicting hypoglycemia in type 2 diabetes patients with tir in target |
| topic | Hypoglycemia Type 2 diabetes Continuous glucose monitoring Time in range Nomogram |
| url | https://doi.org/10.1186/s13098-025-01713-9 |
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