Table 3.
Indicators Evaluating GV in GDM Researches
| References | Country | Population | Study Design | Definition of Glycemic Variability | Exposure | Outcomes | Results |
|---|---|---|---|---|---|---|---|
| Dalfra 201133 | Italy | N=48,31GDM,17NDP | Prospective observational study | CONGA,IQR,SD,MAGE | Insulin vs diet | LGA | Patients on insulin had significantly higher glycemic variability compared to those on dietary restriction[MAGE: 3.5 mmol/L (63.3 mg/dL) vs 2.1 mmol/L (38 mg/dL); P = 0.012] |
| Dalfra 201320 | Italy | N=30,20GDM,10NDP | Prospective observational study | CONGA,IQR,SD,MAGE | CGM use | Correlations between indicators of glucose variability and mean glucose value and HbA1c | GDM had significant correlations between indicators of glycemic variability(MAGE and IQR r = 0.84, P < 0.001; MAGE and CONGA r = 0.54,P = 0.03) |
| Graham R 201917 | UK | N=162,162GDM | Prospective observational study | SD,CV,MBG | LGA vs.Non-LGA | Glycemic variability | Mean glucose was significantly higher in women who delivered an LGA infant (6.2 vs.5.8 mmol/L, P = 0.025).There were no significant differences in glucose variability measures (P > 0.05) |
| Panyakat 201818 | Thailand | N=55,55GDM | Prospective observational study | SD,CV%,MBG,MAGE | CGM use | Fetal birth weight and adverse outcomes | No statistically significant correlation was identified between glycemic variability in third trimester and birth weight percentiles, or between third trimester CGMS parameters and pregnancy outcomes in the study. |
| Yu 201419 | China | N=340,190GDM with SMBG; 150GDM with CGM | Prospective observational study | MBG,SD,MAGE | Variables in the first week vs.Variables in the fifth week | Maternal complications and neonatal outcomes | The MAGE was associated with birth weight (P<0.001), and it was an independent factor for preeclampsia (odds ratio, 3.66;95% confidence interval 2.16–6.20) and composite neonatal outcome (odds ratio, 1.34; 95% con fidence interval 1.01–1.77). |
| Nigam 201934 | India | N=62,29GDM,33NDP | Prospective observational study | MBG,IQR | CGM use | Glycemic variability and ambulatory glucose profile | Glycaemic variability as measured by interquartile range was higher in GDM pregnancies |
| Mazze 201231 | USA | N=76,25GDM,51NDP | Prospective observational study | IQR | CGM use | Glycemic variability, ambulatory glucose profile, hypoglycemia | CGM confirmed that diurnal glucose patterns differ throughout the day by 20% when pregnant and nonpregnant states are compared |
| Wang 200036 | China | N=96,48 NDP with previous GDM, 48 NDP without previous GDM | Prospective observational study | MBG,SDBG,MODD,MAGE | With previous GDM vs.without previous GDM | Glycemic variability | The pGDM group had a greater MBG (p = 0.004), SDBG (p = 0.000), MODD (p = 0.002), MAGE(p = 0.000) |
| Su 201332 | China | N=50,30GDM,20NDP | Prospective observational study | MBG,SDBG,MODD,MAGE | CGM use | Glycemic variability and its association with B cell function | MODD and SDBG value of GDM group were all higher than those of NDP groups (p<0.05) |
| Alfadhli 201658 | Saudi Arabia | N = 130,62 GDM with SMBG; 68 GDM with CGM | Prospective open label randomized controlled study | SD,MBG | CGM vs SMBG use | Pregnancy outcomes and glucose variability | There was significant improvement in the parameters of glucose variability on the last day of sensor application; both mean glucose and the SD of mean glycaemia were reduced significantly; P = 0.016 and P = 0.034, respectively. |
| Cypryk 200635 | Poland | N=19,12GDM,7NDP | Prospective observational study | MBG | CGM vs SMBG use | Glycemic control | There was no significant differences between groups for mean 24 h glycaemia, mean glucose level during the night, and duration of glycaemia below 3.3 mmol/L or above 6.7 mmol/L, regardless of whether CGM or SMBG was used to measure the parameters |
| Wei 201657 | China | N = 120,62 GDM with SMBG; 58 GDM with CGM | Prospective observational,open-label randomized controlled trial | MBG,SDBG,MODD,MAGE,SD,PPGE | CGM vs SMBG use | Maternal complications,neonatal outcomes and glycemic variability | There were no significant differences in prenatal or obstetric outcomes,between the CGMS and SMBG groups. |
| Pintaudi 201847 | Italy | N=12,12GDM | Case-control study | SD,MAGE | Myo-inositol and folic acid vs folic acid | Glycemic variability | Myo-inositol is effective in re ducing glucose variability in women with GDM |
| Carreiro 201650 | Brazil | N=33,22GDM,11NDP | Prospective observational study | IQR,SD | Dietary counseling | Glycemic variability | Dietary counseling was able to keep glucose levels to those of healthy patients |
| Rasmussen 202051 | Denmark | N=12,12GDM | Randomized Crossover Study | MAGE,CV,MBG | A high-carbohydrate-morning-intake vs.low-carbohydrate-morning-intake | Glycemic variability and glucose control | There was significantly higher MAGE (p = 0.004) and CV (p = 0.01) when comparing HCM with LCM. |
| Kizirian 201752 | Australia | N=17,17GDM | Crossover study | SD,MAGE | A high glycemic load diet vs a low glycemic load diet | Glycemic variability | Glycemic variability was significantly lower on the low GL day, as demonstrated by a lower average SD (p<0.001) |
Abbreviations: GDM, gestational diabetes mellitus; GV, glycemic variability; NDP, non-diabetic pregnancies; CGM, continuous glucose monitoring; SMBG, self-monitoring of blood glucose; MAGE, mean amplitude of glycemic excursion; CV, coefficient of variation; MBG, mean of daily continuous 24-hour blood glucose; SDBG, standard deviation of blood glucose; MODD, mean of daily difference; PPGE, postprandial glucose excursion; CONGA, continuous overlapping net glycemic action; IQR, inter-quartile range.