Increased Glycemic Variability Evaluated by Continuous Glucose Monitoring is Associated with Osteoporosis in Type 2 Diabetic Patients

Huang, Rong; Wang, Huiying; Zheng, Shen; Cai, Tingting; Zhou, Yunting; Wang, Yuming; Xia, Wenqing; Ding, Bo; Yan, Rengna; Li, Huiqin; Wu, Jindan; Ma, Jianhua

doi:10.3389/fendo.2022.861131

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…A recent study demonstrated that MAGE is an independent predictive factor of osteoporosis in patients with T2DM. 19 In the present study, MAGE was demonstrated to be a vital influencing factor of bone turnover, as shown in Figure 2. Furthermore, β-CTX was found to be independently negatively correlated with glycemic variability; however, with respect to P1NP, the relationship was weakened after adjustment.…”

Section: Discussionsupporting

confidence: 56%

Correlation Between Mean Amplitude of Glycemic Excursion and Bone Turnover Markers in Patients with Type 2 Diabetes: A Cross-Sectional Study

Yang¹,

Chen²,

Wang³

et al. 2023

DMSO

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Objective The present study explores the relationship between glycemic excursion and bone turnover markers. Methods A total of 250 patients with type 2 diabetes mellitus (T2DM) (142 female and 108 male patients) were enrolled in this study. All participants underwent 72 hours of continuous glycemic monitoring to evaluate the mean amplitude of glycemic excursions (MAGE) of each person. Bone turnover markers and other biochemical data were measured for each patient. Linear regression was performed to explore the relationship between bone turnover markers and glycemic excursion. A value of P < 0.05 was considered statistically significant. Results MAGE was negatively correlated to N-terminal propeptide of type 1 collagen (P1NP) female: [odds ratios (95% confidence interval) (OR (95% CI)), −2.516 (−5.389, 0.356)]; male: [−2.895, (−6.521, −0.731)] and C-terminal telopeptide fragments of type-I collagen (β-CTX) female [−0.025, (−0.036, 0.005)]; male [−0.043, (−0.082, 0.003)]. MAGE was still negatively correlated with β-CTX female [−0.036, (−0.198, −0.030)]; male [−0.048, (−0.089, −0.007)] after adjusting for clinical data and biochemical indices. Conclusion An independent negative relationship between glycemic excursion and bone turnover markers in patients with T2DM was identified in this study.

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Section: Discussionsupporting

confidence: 56%

Correlation Between Mean Amplitude of Glycemic Excursion and Bone Turnover Markers in Patients with Type 2 Diabetes: A Cross-Sectional Study

Yang¹,

Chen²,

Wang³

et al. 2023

DMSO

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“…Mean blood glucose (MBG) and coefficient of variation (CV) have become significant indicators of glycemic control levels 11 . Compared with single BG, the MBG offers a better reflection for the general status of BG, while the CV provides a clearer depiction of the degree of BG fluctuations 12 , 13 . Studies have shown that both early hypoglycemia and increased glycemic variability are independent predictors of mortality risk in critically ill patients 14 – 16 .…”

Section: Introductionmentioning

confidence: 99%

Association between glycemic variability and short-term mortality in patients with acute kidney injury: a retrospective cohort study of the MIMIC-IV database

Guo,

Qiu,

Xue

et al. 2024

Sci Rep

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Acute kidney injury (AKI) represents a significant challenge to global public health problem and is associated with poor outcomes. There is still considerable debate about the effect of mean blood glucose (MBG) and coefficient of variation (CV) of blood glucose on the short-term mortality of AKI patients. This retrospective cohort study aimed to explore the association between glycemic variability and short-term mortality in patients with AKI. Data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed, including 6,777 adult AKI patients. MBG and CV on the first day of ICU admission were calculated to represent the overall glycemic status and variability during the ICU stay in AKI patients. The primary outcome indicator was ICU 30-day mortality of AKI patients. Multivariate Cox regression analysis and smoothed curve fitting were used to assess the relationship between blood glucose levels and mortality. Eventually, the ICU 30-day mortality rate of AKI patients was 23.5%. The increased MBG and CV were significantly correlated with ICU 30-day mortality (hazards ratio (HR) = 1.20, 95% confidence interval (CI) 1.14–1.27; HR = 1.08, 95% CI 1.03–1.13). The smoothed curve fitting showed a U-shaped relationship between MBG on the first day of ICU admission and ICU 30-day mortality (inflection point = 111.3 mg/dl), while CV had a linear relationship with 30-day ICU mortality. Thus, we conclude that MBG and CV were significantly associated with short-term mortality in intensive care patients with AKI. Tighter glycemic control may be an effective measure to improve the prognosis of patients with AKI.

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Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels

et al. 2023

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Introduction Time in range (TIR) as assessed by continuous glucose monitoring (CGM) measures an individual’s glucose fluctuations within set limits in a time period and is increasingly used together with HbA1c in patients with diabetes. HbA1c indicates the average glucose concentration but provides no information on glucose fluctuation. However, before CGM becomes available for patients with type 2 diabetes (T2D) worldwide, especially in developing nations, fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) are still the common biomarkers used for monitoring diabetes conditions. We investigated the importance of FPG and PPG to glucose fluctuation in patients with T2D. We used machine learning to provide a new estimate of TIR based on the HbA1c, together with FPG and PPG. Methods This study included 399 patients with T2D. (1) Univariate and (2) multivariate linear regression models and (3) random forest regression models were developed to predict the TIR. Subgroup analysis was performed in the newly diagnosed T2D population to explore and optimize the prediction model for patients with different disease history. Results Regression analysis suggests that FPG was strongly linked to minimum glucose, while PPG was strongly correlated with maximum glucose. After FPG and PPG were incorporated into the multivariate linear regression model, the prediction performance of TIR was improved compared with the univariate correlation between HbA1c and TIR, and the correlation coefficient (95% CI) increased from 0.62 (0.59, 0.65) to 0.73 (0.72, 0.75) ( p < 0.001). The random forest model significantly outperformed the linear model ( p < 0.001) in predicting TIR through FPG, PPG and HbA1c, with a stronger correlation coefficient 0.79 (0.79, 0.80). Conclusions The results offered a comprehensive understanding of glucose fluctuations through FPG and PPG compared to HbA1c alone. Our novel TIR prediction model based on random forest regression with FPG, PPG, and HbA1c provides a better prediction performance than the univariate model with solely HbA1c. The results indicate a nonlinear relationship between TIR and glycaemic parameters. Our results suggest that machine learning may have the potential to be used in developing better models for understanding patients’ disease status and providing necessary interventions for glycaemic control. Supplementary Information The online version contains supplementary material available at 10.1007/s13300-023-01432-2.

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Increased Glycemic Variability Evaluated by Continuous Glucose Monitoring is Associated with Osteoporosis in Type 2 Diabetic Patients

Cited by 5 publications

References 52 publications

Correlation Between Mean Amplitude of Glycemic Excursion and Bone Turnover Markers in Patients with Type 2 Diabetes: A Cross-Sectional Study

Correlation Between Mean Amplitude of Glycemic Excursion and Bone Turnover Markers in Patients with Type 2 Diabetes: A Cross-Sectional Study

Association between glycemic variability and short-term mortality in patients with acute kidney injury: a retrospective cohort study of the MIMIC-IV database

Time in Range Estimation in Patients with Type 2 Diabetes is Improved by Incorporating Fasting and Postprandial Glucose Levels

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