The prevalence of diabetes after gestational diabetes - An Australian perspective

A statistical investigation of parameters associated with low cell-free fetal DNA fraction in maternal plasma for noninvasive prenatal testing

BACKGROUND

Noninvasive prenatal testing (NIPT) is the most common method for prenatal aneuploidy screening. Low fetal fraction (LFF) is the primary reason for NIPT failure. Consequently, factors associated with LFF should be elucidated for optimal clinical implementation of NIPT.

METHODS

In this study, NIPT data from January 2019 to December 2022 from the laboratory records and obstetrical and neonatal data from the electronic medical records were collected and analyzed. Subjects with FF >3.50% were assigned to the control group, subjects with FF <3.50% once were assigned to the LFF group, and subjects with FF <3.50% twice were assigned to the repetitive low fetal fraction (RLFF) group. Factors, including body mass index (BMI), gestational age, maternal age, twin pregnancy, and in vitro fertilization (IVF) known to be associated with LFF were assessed by Kruskal-Wallis H test and logistic regression. Clinical data on first trimester pregnancy-associated plasma protein-A (PAPP-A), beta-human chorionic gonadotropin (β-hCG), gestational age at delivery, birth weight at delivery, and maternal diseases were obtained from the hospital's prenatal and neonatal screening systems (twin pregnancy was not included in the data on gestational age at delivery and the control group did not include data on maternal diseases.), and were analyzed using Kruskal-Wallis H test and Chi-square test.

RESULTS

Among the total of 63,883 subjects, 63,605 subjects were assigned to the control group, 197 subjects were assigned to the LFF group, and 81 subjects were assigned to the RLFF group. The median of BMI in the three groups was 22.43 kg/m2 (control), 25.71 kg/m2 (LFF), and 24.54 kg/m2 (RLFF). The median gestational age in the three groups was 130 days (control), 126 days (LFF), and 122/133 days (RLFF). The median maternal age in the three groups was 29 (control), 29 (LFF), and 33-years-old (RLFF). The proportion of twin pregnancies in the three groups was 3.3% (control), 10.7% (LFF), and 11.7% (RLFF). The proportion of IVF in the three groups was 4.7% (control), 11.7% (LFF), and 21.3% (RLFF). The factors significantly associated with LFF included BMI [2.18, (1.94, 2.45), p < 0.0001], gestational age [0.76, (0.67, 0.87), p < 0.0001], twin pregnancy [1.62, (1.02, 2.52), p = 0.0353], and IVF [2.68, (1.82, 3.86), p < 0.0001]. The factors associated with RLFF included maternal age [1.54, (1.17, 2.05), p = 0.0023] and IVF [2.55, (1.19, 5.54), p = 0.016]. Multiples of the median (MOM) value of β-hCG and pregnant persons' gestational age at delivery were significantly decreased in the LFF and RLFF groups compared to the control group.

CONCLUSION

According to our findings based on the OR value, factors associated strongly with LFF include a high BMI and the use of IVF. Factors associated less strongly with LFF include early gestational age and twin pregnancy, while advanced maternal age and IVF were independent risk factors for a second LFF result.

Primary care management post gestational diabetes in Australia

BACKGROUND

Women with a history of gestational diabetes (GD) have a high risk of developing diabetes and subsequent cardiovascular disease (CVD).

AIM

To assess whether diabetes screening and CVD risk screening occurred in general practice (GP) among postpartum women with GD.

METHODS

This is a retrospective study of clinical record data of women with GD, under active GP management, from the MedicineInsight programme, run by Australia's National Prescribing Service MedicineWise, with GP sites located in Australia from January 2015 to March 2021. Documentation of screening for diabetes, assessment of lipids and measurement of blood pressure (BP) was assessed using proportions and mixed-effects logistic regression with a log follow-up time offset.

RESULTS

There were 10 413 women, with a mean age of 37.9 years (standard deviation, 7.6), from 406 clinics with a mean follow-up of 4.6 years (interquartile range, 1.8-6.2 years) A total of 29.41% (3062/10 413; 95% confidence interval [CI], 28.53-30.28) had not been assessed for diabetes, 37.40% (3894/10 413; 95% CI, 36.47-38.32) were not assessed for lipids and 2.19% (228/10 413; 95% CI, 1.91-2.47) had no BP documented. In total, 51.82% (5396/10 413; 95% CI, 50.86-52.78) were screened for all three (diabetes + lipids + BP) at least once. Obesity, comorbidities and dyslipidaemia were associated with increased likelihood of screening. New diabetes diagnosis was documented in 5.73% (597/10 413; 95% CI, 5.29-6.18) of the cohort.

CONCLUSION

Screening for diabetes and hyperlipidaemia was suboptimal in this high-risk cohort of women with prior GD. Improved messaging that women with a GD diagnosis are at high cardiovascular risk may improve subsequent screening.

The effect of a diabetes prevention program on dietary quality in women with previous gestational diabetes

Background

Women with gestational diabetes have low diet quality. We evaluated the effectiveness of a group-based lifestyle modification program for improvement of dietary quality in women with previous gestational diabetes predominantly within their first postnatal year.

Methods

Women were randomised to intervention (n = 284) or usual care (n = 289). Dietary data was collected at baseline and twelve months using a food frequency questionnaire and recoded into the Australian Recommended Food Score (ARFS). Mixed model analyses investigated the intervention effect on ARFS (per-protocol-set (PPS) excluded women without the minimum intervention exposure).

Results

Baseline mean total ARFS was low (31.8 ± 8.9, maximum score = 74) and no significant changes were seen in total ARFS (Cohen’s D = − 0.06). 2% reduction in alcohol for intervention (0.05, 0.26) compared with − 1% for usual care (Odds ratio: 0.68; 95%CI 0.46, 0.99). Dairy ARFS sub-category significantly improved (low fat/saturated fat foods) in the intervention group over time compared with usual care for the PPS analysis (dairy + 0.28 in intervention (95%CI 0.08, 0.48) compared with + 0.02 in usual care (95%CI -0.14, 0.18) (group-by-treatment interaction p = 0.05, Cohen’s D = 0.14)).

Conclusions

Engaging with the intervention improved aspects of diet quality that aligned with minimum intervention exposure, but the total diet quality remains low. Further research is needed to improve diabetes prevention program engagement.

Trial registration

Australian New Zealand Clinical Trials Registry ANZCTRN12610000338066, April 2010.

GooD4Mum: A general practice-based quality improvement collaborative for diabetes prevention in women with previous gestational diabetes

AIMS

Gestational diabetes (GDM) and Type 2 diabetes pose tremendous health and economic burdens as worldwide incidence increases. Primary care-based systematic diabetes screening and prevention programs could be effective in women with previous GDM. GooD4Mum aimed to determine whether a Quality Improvement Collaborative (QIC) would improve postpartum diabetes screening and prevention planning in women with previous GDM in general practice.

METHODS

Fifteen general practices within Victoria (Australia) participated in a 12-month QIC, consisting of baseline and four quarterly audits, guideline-led workshops and Plan-Do-Study-Act feedback cycles after each audit. The primary outcome measures were the proportion of women on local GDM registers completing a diabetes screening test and a diabetes prevention planning consultation within the previous 15 months.

RESULTS

Diabetes screening increased with rates more than doubled from 26% to 61% and postpartum screening increased from 43%-60%. Diabetes prevention planning consultations did not show the same level of increase (0%-10%). The recording of body mass index improved overall (51%-69%) but the number of women with normal body mass index did not.

CONCLUSIONS

GooD4Mum supported increased diabetes screening and the monitoring of high risk women with previous GDM in general practice.

Exposure to heavy metals during pregnancy related to gestational diabetes mellitus in diabetes-free mothers

Evidence is cumulating on the adverse health effects of environmental exposures on health of the fetus and the childbearing mothers. Among mother's conditions, gestational diabetes mellitus has been considered rarely in spite of its importance for both mother and child. We determined the role of maternal exposure to lead (Pb), cadmium (Cd) and manganese (Mn) to gestational diabetes mellitus (GDM) on diagnosed GDM and impaired glucose tolerance (IGT) in diabetes-free mothers from the French EDEN mother-child cohort. 623 pregnant women without pre-existing diabetes were included in the study. GDM and IGT were diagnosed by a gynecologist during consultations after blood analysis. Pb, Cd and Mn were measured in second-trimester blood samples. Associations between ln-transformed concentrations of metals and GDM and IGT respectively were examined using multiple logistic regression analysis adjusted for potential confounders. The prevalences of GDM and IGT were 7.1% and 10.1% respectively. After adjustment for confounders, Cd was statistically related to having had a diagnosis of GDM or IGT (Adjusted Odds-Ratio (AOR): 1.61, 1.05-2.48), and Pb to GDM at borderline significance (AOR: 1.65, 0.82-3.34). Our findings add to the growing evidence supporting the role of maternal exposure to heavy toxic metals that persist longtime in the environment as a risk factor for GDM.

Ethnic Disparities in Gestational Diabetes

PURPOSE OF REVIEW

Ethnicity has long been described as a major risk factor for the development of gestational diabetes mellitus (GDM), and it is widely recognised that women from ethnicities other than Europids are at higher risk of developing GDM. There are also described differences between ethnicities in key GDM pregnancy outcomes. This review describes some of the factors that relate to the ethnic disparities in GDM.

RECENT FINDINGS

The global prevalence of GDM has been steadily increasing and estimated to be 16.2% from the International Diabetes Federation extrapolation. Reported prevalence rates may understate the true prevalence, due to factors of access and attitudes to GDM diagnosis and screening in low resource settings for foreign-born women and indigenous populations. Other factors may relate to genes associated with specific ethnicities, obesity, body composition and gestational weight gain. Various factors such as access to screening, body composition, genetics and gestational weight gain may result in ethnic disparities in the prevalence and outcomes of GDM.