Continuous glucose monitoring in pregnancy: new frontiers in clinical applications and research

GLU: a software package for analysing continuously measured glucose levels in epidemiology

Continuous glucose monitors (CGM) record interstitial glucose levels 'continuously', producing a sequence of measurements for each participant (e.g. the average glucose level every 5 min over several days, both day and night). To analyse these data, researchers tend to derive summary variables such as the area under the curve (AUC), to then use in subsequent analyses. To date, a lack of consistency and transparency of precise definitions used for these summary variables has hindered interpretation, replication and comparison of results across studies. We present GLU, an open-source software package for deriving a consistent set of summary variables from CGM data. GLU performs quality control of each CGM sample (e.g. addressing missing data), derives a diverse set of summary variables (e.g. AUC and proportion of time spent in hypo-, normo- and hyper- glycaemic levels) covering six broad domains, and outputs these (with quality control information) to the user. GLU is implemented in R and is available on GitHub at https://github.com/MRCIEU/GLU. Git tag v0.2 corresponds to the version presented here.

Glycemic profile of women with normoglycemia and gestational diabetes mellitus during early pregnancy using continuous glucose monitoring system

AIM

We studied women between 8 and 20 weeks of gestation with the aim of evaluating and comparing those having normoglycemia and GDM according to WHO 2013 criteria.

METHODS

In this cross-sectional study (2017-2019), eligible pregnant women underwent a 75-g OGTT, followed by placement of a CGMS.

RESULTS

Women (n = 96, 58 with normoglycemia and 38 with GDM) were enrolled at 14.0 ± 3.2 weeks of gestation. Mean preprandial, 1-h and 2-h postprandial and peak glucose values were significantly higher in women with GDM. Peak glucose value was achieved 60.0 ± 12.3 and 64.3 ± 11.6 min after meal in the normoglycemia and GDM group, respectively. 24-h mean glucose (5.8 ± 0.6 vs. 5.3 ± 0.4 mmol/L), mean daytime glucose (6.0 ± 0.6 vs. 5.5 ± 0.4 mmol/L) and mean nocturnal glucose (5.4 ± 0.7 vs. 5.0 0 ± 0.5 mmol/L) were significantly higher in women with GDM. Total time spent in range was significantly lower in the GDM group compared to the normoglycemia group (92.1 vs. 98.2%).

CONCLUSIONS

This study highlights differences in glycemic patterns between women with normoglycemia and GDM in the context of a South Asian population where burden of GDM is high but good quality data in early pregnancy are limited.

Serum albumin-adjusted glycated albumin is a better indicator of glycaemic control in diabetic patients with end-stage renal disease not on haemodialysis

Backgrounds

Diabetic patients with end-stage renal disease who are not on haemodialysis show low concentrations of HbA1c and glycated albumin due to renal anaemia and proteinuria, respectively. In the present study, we examined whether serum albumin-adjusted glycated albumin could accurately reflect glycaemic control in these patients.

Methods

To examine the correlation between glycated albumin and serum albumin (Study 1), 49 diabetic patients with end-stage renal disease not on haemodialysis were used. To evaluate the association between the glycaemic control indicators and the glycaemic control state (Study 2), 30 diabetic patients with end-stage renal disease were enrolled. The estimated HbA1c and the estimated glycated albumin concentrations were calculated based on the mean blood glucose concentrations obtained from the diurnal variation. The adjusted glycated albumin concentrations were calculated from the regression formula between the serum albumin and glycated albumin obtained from Study 1.

Results

No significant correlation was found between the measured HbA1c and estimated HbA1c concentrations. The estimated HbA1c (inversely) and measured HbA1c/estimated HbA1c ratio (positively), but not measured HbA1c, showed a significant correlation with Hb concentrations. The estimated glycated albumin was positively associated with the measured glycated albumin and adjusted glycated albumin concentrations. Although measured glycated albumin/estimated glycated albumin ratio was positively correlated with serum albumin, there was no significant association between the adjusted glycated albumin/estimated glycated albumin ratio and serum albumin, Hb and estimated glomerular filtration rate.

Conclusions

We found for the first time that the adjustment of glycated albumin by serum albumin could be useful to determine glycaemic control in diabetic patients with end-stage renal disease not on haemodialysis. These findings suggest that adjusted glycated albumin might be a better indicator of glycaemic control than measured HbA1c and measured glycated albumin in these patients.