Abstract Objectives To evaluate the relative contribution of basal hyperglycemia (BHG) and postprandial hyperglycemia (PHG) to the time in range (TIR) categories and adverse pregnancy outcomes in ...pregnant women with type 1 diabetes mellitus (T1DM). Materials and Methods This observational study included 112 pregnancies with T1DM from the CARNATION study who wore continuous glucose monitoring (CGM) devices during pregnancy. The data from CGM were analyzed for TIR (range, 3.5-7.8 mmol/L), areas under the curve of PHG, area under the curve of BHG, and BHG and PHG contribution rates. The contribution rates of BHG and PHG to the different levels of TIR (<60%, 60-78%, ≥78%) and adverse pregnancy outcomes were analyzed. Results The participants’ average age was 28.8 ± 3.9 years with a diabetes duration of 8.4 ± 6.2 years. All women experienced a mean TIR of 75.6 ± 19.0% and a mean glycated hemoglobin of 6.2 ± 1.1% during pregnancy. The BHG contribution accounted for 74.9% (36.8, 100), 69.2% (13.4, 100), and 66.5% (10.0, 100) (P < .001) and PHG accounted for 25.1% (0, 63.2), 30.8% (0, 86.6), and 33.5% (0, 90.0) (P < .001) when participants experienced the TIR<60%, 60%-78%, and ≥78%, respectively. Participants with higher BHG contribution rates tended to have more adverse pregnancy outcomes. Conclusion Basal hyperglycemia was the major contributor to TIR during pregnancy. Along with controlling PHG, pregnant women with T1DM who did not reach the target of TIR may benefit more from the optimization of insulin regimens focusing on reducing basal glucose.
Aim
To assess the efficacy of artificial pancreas systems (APS) use among pregnant women with type 1 diabetes mellitus (T1DM) by conducting a meta‐analysis.
Methods
We searched five databases, ...including EMBASE, Web of Science, PubMed, Cochrane Library and SCOPUS, for literature on APS use among pregnant women with T1DM before October 9, 2023. The primary endpoint was 24‐hour time in range (TIR; 3.5‐7.8 mmol/L). Secondary endpoints included glycaemic metrics for 24‐hour (mean blood glucose MBG, time above range TAR, time below range TBR), and overnight TIR and TBR.
Results
We identified four randomized controlled trials involving 164 participants; one study with 16 participants focused on overnight APS use, and the other three focused on 24‐hour APS use. Compared with standard care, APS exhibited a favourable effect on 24‐hour TIR (standard mean difference SMD = 0.53, 95% confidence interval CI 0.25, 0.80, P < 0.001), overnight TIR (SMD = 0.67, 95% CI 0.39, 0.95, P < 0.001), and overnight TBR (<3.5 mmol/L; SMD = −0.49, 95% CI −0.77, −0.21 P < 0.001), while there was no significant difference in 24‐hour TAR, 24‐hour TBR, or MBG between the two groups. We further conducted subgroup analyses after removing the trial focused on overnight APS use and showed that 24‐hour APS use reduced not only the 24‐hour TIR (SMD = 0.41, 95% CI 0.12, 0.71; P = 0.007) but also the 24‐hour TBR (<2.8 mmol/L; SMD = −0.77, 95% CI −1.32, −0.23, P = 0.006).
Conclusion
Our findings suggest that APS might improve 24‐hour TIR and overnight glycaemic control, and 24‐hour APS use also significantly reduced 24‐hour TBR (2.8 mmol/L) among pregnant women with T1DM.
Glucose management indicator (GMI) is a core metric derived from continuous glucose monitoring (CGM) and is widely used to evaluate glucose control in patients with diabetes. No study has explored ...the pregnancy-specific GMI. This study aimed to derive a best-fitting model to calculate GMI from mean blood glucose (MBG) obtained from CGM among pregnant women with type 1 diabetes mellitus (T1DM).
A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analysed in this study. Continuous glucose monitoring data were collected to calculate MBG, time-in-range (TIR), and glycaemic variability parameters. The relationships between the MBG and HbA1c during pregnancy and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM.
The pregnant women had a mean age of 28.9 ± 3.8 years, with a diabetes duration of 8.8 ± 6.2 years and a mean body mass index (BMI) of 21.1 ± 2.5 kg/m
. The HbA1c levels were 6.1 ± 1.0% and 6.4 ± 1.0% during pregnancy and at postpartum (p = 0.024). The MBG levels were lower during pregnancy than those at postpartum (6.5 ± 1.1 mmol/L vs. 7.1 ± 1.5 mmol/L, p = 0.008). After adjusting the confounders of haemoglobin (Hb), BMI, trimesters, disease duration, mean amplitude of glycaemic excursions and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* Trimester + 0.08 * BMI in kg/m
+ 0.01 * Hb in g/mL + 0.50 * MBG in mmol/L.
We derived a pregnancy-specific GMI equation, which should be recommended for antenatal clinical care.
ChiCTR1900025955.
Obesity-associated chronic inflammation in adipose tissue is partly attributed to hypoxia with insufficient microcirculation. Previous studies have shown that exenatide, a glucagon-like peptide 1 ...(GLP-1) receptor agonist, plays an anti-inflammatory role. Here, we investigate its effects on inflammation, hypoxia and microcirculation in white adipose tissue of diet-induced obese (DIO) mice. DIO mice were injected intraperitoneally with exenatide or normal saline for 4 weeks, while mice on chow diet were used as normal controls. The mRNA and protein levels of pro-inflammatory cytokines, hypoxia-induced genes and angiogenic factors were detected. Capillary density was measured by laser confocal microscopy and immunochemistry staining. After 4-week exenatide administration, the dramatically elevated pro-inflammatory cytokines in serum and adipose tissue and macrophage infiltration in adipose tissue of DIO mice were significantly reduced. Exenatide also ameliorated expressions of hypoxia-related genes in obese fat tissue. Protein levels of endothelial markers and pro-angiogenic factors including vascular endothelial growth factor and its receptor 2 were augmented in accordance with increased capillary density by exenatide in DIO mice. Our results indicate that inflammation and hypoxia in adipose tissue can be mitigated by GLP-1 receptor agonist potentially via improved angiogenesis and microcirculation in obesity.
Objective: To investigate the relationship between time in range (TIR, 3.9-10.0mmol/L) and glucose management indicator (GMI) in adolescents and children with type 1 diabetes mellitus (T1DM) and ...explore the impact of coefficient of variation (CV) on their relationship.
Methods: Data derived from continuous glucose monitoring (CGM) and other clinical data (including age, duration of T1DM, and laboratory-measured HbA1c) were obtained from the annual follow-up of the Guangdong T1DM Translational Medicine Study. The patients under 18 years old were included. The patients were divided into CV≤36% group and CV>36% group by the attainment of CV. The relationship between TIR and GMI of both groups was assessed with correlation coefficient. Further, patients were divided into 4 groups by the interquartile range of CV. The linear regression model was used to calculate the TIR predicted value corresponding to the same GMI in 4 groups.
Results: The 56 eligible datasets collected from May 2014 to August 2021 were included. The median age, duration of T1DM, and laboratory-measured HbA1c were 14.00 (12.00, 16.00) years, 4.15 (1.66, 5.29) years, and 9.00 (7.65, 11.23) %, respectively. The median TIR, GMI, CV, and valid number of days the CGM device was worn were 60.73 (42.59, 77.72) %, 7.37 (6.78, 8.56) %, 30.38 (24.87, 34.94) %, and 3.39 (2.97, 4.84) days, respectively. TIR and GMI were highly linear correlated (R2=0.89, p<0.001), and a significantly higher Spearman’s correlation coefficient was observed in the CV≤36% group than in CV>36% group ((R2=0.92, p<0.001) vs. (R2=0.63, p=0.004)). When the GMI was 7%, the corresponding TIR predicted values gradually decreased with the increase of CV, which were 75.66% (CV≤24.87%), 73.48% (24.87%<cv≤30.38%), 69.16% (30.38%34.94%).
Conclusions: TIR and GMI were highly linear correlated in adolescents and children with T1DM. With constant GMI, the less the glycemic fluctuation, the higher the TIR.</cv≤30.38%), 69.16% (30.38%
Disclosure
M.Lei: None. J.Lv: None. X.Mai: None. H.Deng: None. C.Wang: None. D.Yang: None. X.Yang: None. W.Xu: None. J.Yan: None.
Funding
Science and Technology Planning Project of Guangzhou (202102010154)
Objective: The relationship between glycated hemoglobin (HbA1c) and glucose concentrations was widely explored in patients with diabetes, but all of them excluded pregnancies. The purpose of this ...study was to derive a best-fitting model to calculate glucose management indicator (GMI) from mean blood glucose (MBG) obtained from continuous glucose monitoring (CGM) among pregnant women with type 1 diabetes mellitus (T1DM).
Methods: A total of 272 CGM data and corresponding laboratory HbA1c from 98 pregnant women with T1DM in the CARNATION study were analyzed in this study. CGM data were collected to calculate MBG, time-in-range (TIR), and glycemic variability parameters. The relationships between the MBG and HbA1c during pregnancy, and postpartum were explored. Mix-effect regression analysis with polynomial terms and cross-validation method was conducted to investigate the best-fitting model to calculate GMI from MBG obtained by CGM.
Results: The pregnant women had a mean age of 28.91±3.78 years, with a diabetes duration of 8.79±6.18 years and a mean BMI of 21.07±2.48 kg/m2. The HbA1c levels were 6.13±1.02% and 6.41±1.00% during pregnancy and at postpartum (P=0.024). The MBG levels were lower during pregnancy than those at postpartum (6.49±1.11 mmol/L vs 7.11±1.46 mmol/L, P= 0.008). After adjusting the confounders of hemoglobin (Hb), BMI, trimesters, disease duration, MAGE and CV%, we developed a pregnancy-specific GMI-MBG equation: GMI for pregnancy (%) = 0.84-0.28* Trimester+0.08 * BMI in kg/m2 +0.01* Hb in g/mL+ 0.50 * MBG in mmol/L.
Conclusion: We derived a pregnancy-specific GMI-MBG equation, which should be recommended for antenatal clinical care.
Disclosure
P.Ling: None. J.Yan: None. J.Weng: None. J.Lv: None. D.Yang: None. C.Wang: None. X.Zheng: None. S.Luo: None. X.Yang: None. H.Deng: None. W.Xu: None.
Funding
Science and Technology Planning Project of Guangzhou (202102010154); Shanghai Medical and Health Development Foundation (DMRFP_II_14); National Natural Science Foundation of China (81941022)
Objective: Acarbose is proved to improve glucose control and glucose variability in T2DM. However, few studies focused on the effect of acarbose in T1DM. We hereby evaluated the efficacy and safety ...of adding acarbose to insulin in T1DM.
Methods: A literature search on PubMed/Medline, Web of Science, Cochrane Library, Embase, Clinicaltrails.gov, CNKI, Wanfang Database and VIP Database for articles published before November 30, 2020 was performed to identify randomized controlled trials (RCTs) that investigated the efficacy of acarbose adding to insulin therapy among T1DM patients. Statistical analyses were performed by using Revman 5.3.
Results: A total of 7 RCTs with 517 patients were included. The age of participants was 18 to 65 years, and the intervention duration ranged from 7 to 24 weeks. Compared with placebo, the addition of acarbose resulted in decreased HbA1c (SMD= -0.27%, 95% CI: -1.17%~ -0.63%), mean blood glucose (SMD= -1.88 mmol/L, 95% CI: -2.75~ -1.01 mmol/L), fasting plasma blood glucose (FBG, SMD= -0.98 mmol/L, 95% CI: -3.32~ -0.63 mmol/L), 2h postprandial blood glucose (PPG, SMD= -2.65 mmol/L, 95% CI: -3.58~ -1.71 mmol/L), total daily insulin dose (SMD= -0.34 U, 95% CI: -0.52~ -0.15 U), and improved glucose variability parameters including MAGE (SMD= -1.42 mmol/L, 95% CI: -2.27~ -0.57 mmol/L) and the LAGE (SMD= -1.36 mmol/L, 95% CI: -2.41~ -0.30 mmol/L). The change of body weight and lipid profiles were similar between the two groups. In terms of adverse events, acarbose increased the risk of gastrointestinal complaints (OR= 1.80, 95% CI: 1.52~ 2.13), while the occurrence of hypoglycemia was similar (SMD= -0.93, 95% CI: -2.60~0.74).
Conclusion: The addition of acarbose to insulin could improve overall glucose control in T1DM patients, including HbA1c, mean blood glucose, FBG, PPG, and glucose variability as well. However, gastrointestinal adverse effects should be considered although acarbose did not increase the risk of hypoglycemia in this population.
Disclosure
Z. Liu: None. D. Yang: None. W. Xu: None. J. Lv: None. H. Lin: None. Z. Liu: None. H. Deng: None. J. Yan: None. B. Yao: None.
Funding
National Key Research and Development Project of China (2017YFC1309602)
Diabetic autonomic neuropathy has been proved to be associated with onset of heart failure in patients with diabetes. However, relationship between sudomotor dysfunction, the early manifestation of ...autonomic neuropathy, and cardiac diastolic function has not yet been investigated. This study aimed to explore the association between sudomotor function and cardiac diastolic function in patients with type 2 diabetes mellitus (T2DM).
A total of 63 patients (65% male, 50±13 years old) with T2DM were enrolled and divided into two groups according to mean feet electrochemical skin conductance (ESC) assessed by sudoscan device: sudomotor dysfunction group(SDF group: ESC≤60 μS) and normal sudomotor function group (NSF group: ESC>60 μS). Impaired cardiac diastolic function detected by tissue Doppler imaging echocardiography manifested as elevations of late atrial (A) transmitral peak flow velocity (LgA) and the early (E) transmitral peak flow velocity/early diastolic velocity (e’) (E/e’ratio).
The prevalence of sudomotor dysfunction was 30.2%. In SDF group, LgA and E/e’ ratio were significantly higher than those in NSF group (-0.2±0.2 m/s vs. -0.4±0.3 m/s, P=0.011 and 11.0±3.8 vs. 8.8±2.7, P=0.013, respectively). Pearson correlation analysis revealed that LgA was negatively correlated with mean feet ESC(r=-0.3, P=0.017). In logistic regression analyse, LgA (OR=0.05, 95%CI 0.004-0.585, P=0.017) and E/e‘ ratio (OR=0.79, 95%CI 0.641-0.970, P=0.025) were found to be independently associated with presence of sudomotor dysfunction.
Cardiac diastolic function significantly decreased in type 2 diabetic patients with sudomotor dysfunction.
Disclosure
X. Chen: None. X. Yang: None. W. Xu: None. H. Deng: None. J. Wu: None. J. Yan: None. B. Yao: None.
Funding
Natural Science Foundation of Guangdong Province (2018A030313915); Medical Scientific Research Foundation of Guangdong Province (A2018286)
Aims
To investigate whether intermittently scanned continuous glucose monitoring without alarms (intermittently scanned CGM (isCGM)) improves glycaemic control over capillary blood glucose monitoring ...(BGM) among adult type 1 diabetes mellitus (T1DM) patients with suboptimal control.
Materials and Methods
Adults with T1DM and HbA1c between 7% and 10% were 1:1 randomized to use isCGM or BGM for 24 weeks. The primary outcome was the change in HbA1c levels after intervention. The secondary outcomes were the changes in sensor‐derived metrics.
Results
A total of 104 adults with T1DM (34.2 ± 12.2 years; M/F, 38/66) were randomized to the isCGM group (n = 54) and the BGM group (n = 50). After 24 weeks, HbA1c significantly decreased in the isCGM group (8.1 ± 0.7% to 7.5 ± 1.0%) and the BGM group (8.0 ± 0.8% to 7.7 ± 1.0%) with between‐group differences of 0.3% (95% coefficient intervals, 0.0%–0.6%; P = 0.04). The percentage of HbA1c reduction over 1.0% and 1.5% was significantly higher in the isCGM group with adjusted odds ratios of 2.5 (95% CI: 1.1–5.5; P = 0.03) and 3.2 (95% CI: 1.1–9.0; P = 0.03). Mean time‐in‐range 70–180 mg/dl (TIR) in the isCGM group significantly increased (from 58.5 ± 13.0% to 63.0 ± 12.6%), whereas mean TIR was similar in the BGM group (from 58.0 ± 14.6% to 57.5 ± 14.5%). Time spent in hyperglycemia reduced more in the isCGM group and time spent in hypoglycemia did not change significantly in both groups.
Conclusions
Among adult T1DM patients with suboptimal glycaemic control, compared with BGM, isCGM use resulted in a statistically significant improvement in glycaemic control after 24‐week intervention.
Trial Registration
Clinicaltrials.gov Identifier (NCT03522870)
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