Predictive ability of visit-to-visit glucose variability on diabetes complications
Issued Date
2025-12-01
Resource Type
eISSN
14726947
Scopus ID
2-s2.0-105000416043
Pubmed ID
40098113
Journal Title
BMC Medical Informatics and Decision Making
Volume
25
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Medical Informatics and Decision Making Vol.25 No.1 (2025)
Suggested Citation
Teh X.R., Looareesuwan P., Pattanaprateep O., Pattanateepapon A., Attia J., Thakkinstian A. Predictive ability of visit-to-visit glucose variability on diabetes complications. BMC Medical Informatics and Decision Making Vol.25 No.1 (2025). doi:10.1186/s12911-025-02964-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/108510
Title
Predictive ability of visit-to-visit glucose variability on diabetes complications
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Identification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared. Methods: A retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models. Results: A total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748–0.758 and 0.774–0.787) and CKD models (C-indices: 0.734–0.750 and 0.724–0.740) had modestly better performance than CVD models (C-indices: 0.703–0.730 and 0.698–0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance. Conclusions: We found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.