Suresh Rama Chandran, Ming Ming Teh, Hong Chang Tan, May Zin Oo, Alcey Ang Li Chang, Daphne Gardner
{"title":"在低连续血糖监测中使用非血糖因子识别高血糖变异性。","authors":"Suresh Rama Chandran, Ming Ming Teh, Hong Chang Tan, May Zin Oo, Alcey Ang Li Chang, Daphne Gardner","doi":"10.1016/j.pcd.2025.08.008","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Identifying non-glycemic factors associated with high Glucose variability (GV).</p><p><strong>Methods: </strong>A cross-sectional observational study recruited people with type 2 diabetes, who wore a Freestyle Libre Pro CGM.</p><p><strong>Independent variables: </strong>Age, sex, BMI, diabetes medication, diabetes duration, HbA1c and estimated glomerular filtration rate (eGFR). CGM-derived variables calculated included Time-in-Range (TIR, 70-180 mg/dl), below-range 1 (TBR1, <70 mg/dl), -below-range 2 (TBR2, <54 mg/dl) and -above-range (TAR, >180 mg/dl), coefficient of variation (%CV). A logistic regression model examined independent variables associated with high GV (CV ≥36 %). All analysis was done on R version 4.3.1 RESULTS: T2D cohort (n = 403), 46 % women, had median age of 61 y, BMI of 26.5 kg/m<sup>2</sup>, diabetes duration 14 y, HbA1c 7.8 %(62 mmol/mol) and creatinine of 75 µmol/L. Using sulphonylurea, premixed or basal-bolus insulin had an odds ratio (OR) of 4.7 - 5.2 for CV ≥ 36 %. Longer diabetes duration [OR 1.2], and lower eGFR [OR 1.2] were associated with higher odds and older age [OR 0.8]and higher BMI [0.8] were associated with lower odds of CV≥ 36 %. Sex and HbA1c had no association with high GV.</p><p><strong>Conclusion: </strong>Nonglycemic-factors like medication type, diabetes duration and eGFR can aid in identification of high GV even in low-CGM use settings.</p>","PeriodicalId":94177,"journal":{"name":"Primary care diabetes","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying high glucose variability using non-glycemic factors in low continuous glucose monitoring use settings.\",\"authors\":\"Suresh Rama Chandran, Ming Ming Teh, Hong Chang Tan, May Zin Oo, Alcey Ang Li Chang, Daphne Gardner\",\"doi\":\"10.1016/j.pcd.2025.08.008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aims: </strong>Identifying non-glycemic factors associated with high Glucose variability (GV).</p><p><strong>Methods: </strong>A cross-sectional observational study recruited people with type 2 diabetes, who wore a Freestyle Libre Pro CGM.</p><p><strong>Independent variables: </strong>Age, sex, BMI, diabetes medication, diabetes duration, HbA1c and estimated glomerular filtration rate (eGFR). CGM-derived variables calculated included Time-in-Range (TIR, 70-180 mg/dl), below-range 1 (TBR1, <70 mg/dl), -below-range 2 (TBR2, <54 mg/dl) and -above-range (TAR, >180 mg/dl), coefficient of variation (%CV). A logistic regression model examined independent variables associated with high GV (CV ≥36 %). All analysis was done on R version 4.3.1 RESULTS: T2D cohort (n = 403), 46 % women, had median age of 61 y, BMI of 26.5 kg/m<sup>2</sup>, diabetes duration 14 y, HbA1c 7.8 %(62 mmol/mol) and creatinine of 75 µmol/L. Using sulphonylurea, premixed or basal-bolus insulin had an odds ratio (OR) of 4.7 - 5.2 for CV ≥ 36 %. Longer diabetes duration [OR 1.2], and lower eGFR [OR 1.2] were associated with higher odds and older age [OR 0.8]and higher BMI [0.8] were associated with lower odds of CV≥ 36 %. Sex and HbA1c had no association with high GV.</p><p><strong>Conclusion: </strong>Nonglycemic-factors like medication type, diabetes duration and eGFR can aid in identification of high GV even in low-CGM use settings.</p>\",\"PeriodicalId\":94177,\"journal\":{\"name\":\"Primary care diabetes\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-08-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Primary care diabetes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1016/j.pcd.2025.08.008\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Primary care diabetes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.pcd.2025.08.008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying high glucose variability using non-glycemic factors in low continuous glucose monitoring use settings.
Aims: Identifying non-glycemic factors associated with high Glucose variability (GV).
Methods: A cross-sectional observational study recruited people with type 2 diabetes, who wore a Freestyle Libre Pro CGM.
Independent variables: Age, sex, BMI, diabetes medication, diabetes duration, HbA1c and estimated glomerular filtration rate (eGFR). CGM-derived variables calculated included Time-in-Range (TIR, 70-180 mg/dl), below-range 1 (TBR1, <70 mg/dl), -below-range 2 (TBR2, <54 mg/dl) and -above-range (TAR, >180 mg/dl), coefficient of variation (%CV). A logistic regression model examined independent variables associated with high GV (CV ≥36 %). All analysis was done on R version 4.3.1 RESULTS: T2D cohort (n = 403), 46 % women, had median age of 61 y, BMI of 26.5 kg/m2, diabetes duration 14 y, HbA1c 7.8 %(62 mmol/mol) and creatinine of 75 µmol/L. Using sulphonylurea, premixed or basal-bolus insulin had an odds ratio (OR) of 4.7 - 5.2 for CV ≥ 36 %. Longer diabetes duration [OR 1.2], and lower eGFR [OR 1.2] were associated with higher odds and older age [OR 0.8]and higher BMI [0.8] were associated with lower odds of CV≥ 36 %. Sex and HbA1c had no association with high GV.
Conclusion: Nonglycemic-factors like medication type, diabetes duration and eGFR can aid in identification of high GV even in low-CGM use settings.