Abstract
Ceramides contribute to the development of type 2 diabetes but it is uncertain whether they predict gestational diabetes (GDM). In this multicentre case–control study including 1040 women ...with GDM and 958 non-diabetic controls, early pregnancy (mean 10.7 gestational weeks) concentrations of four ceramides—Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:0) and Cer(d18:1/24:1)—were determined by a validated mass-spectrometric method from biobanked serum samples. Traditional lipids including total cholesterol, LDL, HDL and triglycerides were measured. Logistic and linear regression and the LASSO logistic regression were used to analyse lipids and clinical risk factors in the prediction of GDM. The concentrations of four targeted ceramides and total cholesterol, LDL and triglycerides were higher and HDL was lower among women with subsequent GDM than among controls. After adjustments, Cer(d18:1/24:0), triglycerides and LDL were independent predictors of GDM, women in their highest quartile had 1.44-fold (95% CI 1.07–1.95), 2.17-fold (95% CI 1.57–3.00) and 1.63-fold (95% CI 1.19–2.24) odds for GDM when compared to their lowest quartiles, respectively. In the LASSO regression modelling ceramides did not appear to markedly improve the predictive performance for GDM alongside with clinical risk factors and triglycerides. However, their adverse alterations highlight the extent of metabolic disturbances involved in GDM.
Mild hyperglycaemia is associated with increased birth weight but association with other neonatal outcomes is controversial. We aimed to study neonatal outcomes in untreated mild hyperglycaemia using ...different oral glucose tolerance test (OGTT) thresholds.
This register-based study included all (n = 4,939) singleton pregnant women participating a 75 g 2-h OGTT in six delivery hospitals in Finland in 2009. Finnish diagnostic cut-offs for GDM were fasting ≥ 5.3, 1 h ≥ 10.0 or 2-h glucose ≥ 8.6 mmol/L. Women who did not meet these criteria but met the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria (fasting 5.1-5.2 mmol/L and/or 2-h glucose 8.5 mmol/L, n = 509) or the National Institute for Health and Clinical Excellence (NICE) criteria (2-h glucose 7.8-8.5 mmol/L, n = 166) were considered as mild untreated hyperglycaemia. Women who met both the Finnish criteria and the IADPSG or the NICE criteria were considered as treated GDM groups (n = 1292 and n = 612, respectively). Controls were normoglycaemic according to all criteria (fasting glucose < 5.1 mmol/L, 1-h glucose < 10.0 mmol/L and 2-h glucose < 8.5 mmol/L, n = 3031). Untreated mild hyperglycemia groups were compared to controls and treated GDM groups. The primary outcome - a composite of adverse neonatal outcomes, including neonatal hypoglycaemia, hyperbilirubinaemia, birth trauma or perinatal mortality - was analysed using multivariate logistic regression.
The risk for the adverse neonatal outcome in untreated mild hyperglycemia was not increased compared to controls (adjusted odds ratio aOR: 1.01, 95% confidence interval CI: 0.71-1.44, using the IADPSG criteria; aOR: 1.05, 95% CI: 0.60-1.85, using the NICE criteria). The risk was lower compared to the treated IADPSG (aOR 0.38, 95% CI 0.27-0.53) or the treated NICE group (aOR 0.32, 95% CI 0.18-0.57).
The risk of adverse neonatal outcomes was not increased in mild untreated hyperglycaemia compared to normoglycaemic controls and was lower than in the treated GDM groups. The OGTT cut-offs of 5.3 mmol/L at fasting and 8.6 mmol/L at 2 h seem to sufficiently identify clinically relevant GDM, without excluding neonates with a risk of adverse outcomes.
Abstract
Background
Gestational diabetes mellitus (GDM) is a common pregnancy-related disorder and a well-known risk factor for adverse pregnancy outcomes. There are conflicting findings on the ...association of GDM with the risk of congenital anomalies (CAs) in offspring. In this study, we aimed to determine study whether maternal GDM is associated with an increased risk of major CAs in offspring.
Methods
This Finnish Gestational Diabetes (FinnGeDi) register-based study included 6,597 women with singleton pregnancies and a diagnosis of GDM and 51,981 singleton controls with no diabetes identified from the Finnish Medical Birth Register (MBR) in 2009. Data from MBR were combined in this study with the Register of Congenital Malformations, which includes the data of CAs. We used logistic regression to calculate odds ratios (OR) for CAs, together with their 95% confidence intervals (CIs), adjusting for maternal age, parity, pre-pregnancy body mass index (BMI), and maternal smoking status.
Results
The risk of major CAs was higher in the GDM-exposed (
n
= 336, 5.09%) than in the non-exposed group (
n
= 2,255, 4.33%) (OR: 1.18, 95% CI: 1.05–1.33,
p
= 0.005). The adjusted OR (aOR) was 1.14 (95% CI: 1.00-1.30,
p
= 0.047). There was a higher overall prevalence of CAs, particularly chromosomal abnormalities (0.52% vs. 0.21%), in the GDM-exposed group (OR: 2.49, 95% Cl: 1.69–3.66,
p
< 0.001). The aOR was 1.93 (95% Cl: 1.25–2.99,
p
= 0.003).
Conclusions
Offspring exposed to GDM have a higher prevalence of major CAs. Of note, risk factors other than GDM, such as older maternal age and a higher pre-pregnancy BMI, diminished the between group differences in the prevalence of major CAs. Nevertheless, our findings suggest that offspring exposed to maternal GDM are more likely to be diagnosed with a chromosomal abnormality, independent of maternal age, parity, pre-pregnancy BMI, and smoking.
The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of ...early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCGβ, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PlGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort.
We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort. Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models.
We found that lower levels of serum PlGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PlGF was lower and hCGβ higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity.
Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts.
International Standard Randomised Controlled Trial number ISRCTN14030412 , Date of registration 6/09/2007, retrospectively registered.
Gestational diabetes mellitus (GDM) is associated with an increased risk of obesity and insulin resistance in offspring later in life, which might be explained by epigenetic changes in response to ...maternal hyperglycemic exposure.
We explored the association between GDM exposure and maternal blood and newborn cord blood methylation in 536 mother-offspring pairs from the prospective FinnGeDi cohort using Illumina MethylationEPIC 850K BeadChip arrays. We assessed two hypotheses. First, we tested for shared maternal and offspring epigenetic effects resulting from GDM exposure. Second, we tested whether GDM exposure and maternal methylation had an epigenetic effect on the offspring.
We did not find any epigenetic marks (differentially methylated CpG probes) with shared and consistent effects between mothers and offspring. After including maternal methylation in the model, we identified a single significant (false discovery rate 1.38 × 10
) CpG at the cg22790973 probe (
associated with GDM. We identified seven additional FDR-significant interactions of maternal methylation and GDM status, with the strongest association at the same cg22790973 probe (
, as well as cg03456133, cg24440941 (
), cg20002843 (
, cg19107264, and cg11493553 located within the
gene and cg17065901 in
both susceptibility genes for type 2 diabetes and BMI, and cg23355087 within the
gene, known to be involved in insulin resistance during pregnancy.
Our study reveals the potential complexity of the epigenetic transmission between mothers with GDM and their offspring, likely determined by not only GDM exposure but also other factors indicated by maternal epigenetic status, such as maternal metabolic history.
Aims
We studied whether androgen excess and low sex hormone‐binding globulin (SHBG) measured in early pregnancy are independently associated with fasting and post‐prandial hyperglycaemia, gestational ...diabetes (GDM), and its severity.
Materials and Methods
This nationwide case–control study included 1045 women with GDM and 963 non‐diabetic pregnant controls. We measured testosterone (T) and SHBG from biobanked serum samples (mean 10.7 gestational weeks) and calculated the free androgen index (FAI). We first studied their associations with GDM and secondly with the type of hyperglycaemia (fasting, 1 and 2 h glucose concentrations during the oral glucose tolerance test), early‐onset GDM (<20 gestational weeks) and the need for anti‐diabetic medication.
Results
After adjustments for gestational weeks at sampling, pre‐pregnancy BMI, and age, women with GDM had 3.7% (95% CI 0.1%–7.3%) lower SHBG levels, 3.1% (95% CI 0.1%–6.2%) higher T levels, and 4.6% (95% CI 1.9%–7.3%) higher FAI levels than controls. SHBG was inversely associated with fasting glucose, whereas higher FAI and T were associated with higher post‐prandial glucose concentrations. Women with early‐onset GDM had 6.7% (95% CI 0.7%–12.7%) lower SHBG levels and women who needed insulin for fasting hyperglycaemia 8.7% (95% CI 1.8%–14.8%) lower SHBG levels than other women with GDM.
Conclusions
Lower SHBG levels were associated especially with early‐onset GDM, higher fasting glucose and insulin treatment, whereas androgen excess was associated with higher post‐prandial glucose values. Thus, a low SHBG level may reflect the degree of existing insulin resistance, while androgen excess might impair post‐prandial insulin secretion.
(1) Hyperglycemia and oral pathology accelerate each other in diabetes. We evaluated whether gestational diabetes mellitus (GDM) is associated with self-reported increased oral health care needs and ...oral symptoms, including third molar symptoms, during pregnancy. (2) Pregnant women with (
= 1030) and without GDM (
= 935) were recruited in this multicenter Finnish Gestational Diabetes study in 2009-2012. Of the women with GDM, 196 (19.0%) receiving pharmacological treatment, 797 (77.0%) receiving diet treatment and 233 (23.0%) with recurrent GDM were analyzed separately. Oral health was assessed using structured questionnaires and analyzed by multivariable logistic regression adjusted for background risk factors. (3) Women with GDM were more likely to report a higher need for oral care than controls (31.1% vs. 24.5%; odds ratio (OR) 1.39; 95% confidence interval (CI) 1.14-1.69), particularly women with recurrent GDM (38.1% vs. 24.5%; OR 1.90; 95% CI 1.40-2.58). Women with pharmacologically treated GDM (46.9%) more often had third molar symptoms than controls (36.1%; OR 1.57; 95% CI 1.15-2.15) than women with diet-treated GDM (38.0%; OR 1.47; 95% CI 1.07-2.02). (4) GDM is associated with perceived oral care needs. Third molar symptoms were associated with pharmacologically treated GDM.
•Elevated first-trimester maternal serum inhibin-A predicted pre-eclampsia.•The performance of inhibin-A further improved when combined with maternal risk factors.•PAPP-A2 is a potential biomarker of ...pre-eclampsia at 26–28 weeks of gestation.
Maternal serum inhibin-A, pregnancy associated plasma protein-A (PAPP-A) and PAPP-A2 together with placental growth factor (PlGF), maternal risk factors and uterine artery pulsatility index (UtA PI) were analysed to study their ability to predict pre-eclampsia (PE).
Serial serum samples for the nested case-control study were collected prospectively at 12–14, 18–20 and 26–28 weeks of gestation from 11 women who later developed early-onset PE (EO PE, diagnosis < 34 + 0 weeks of gestation), 34 women who developed late-onset PE (LO PE, diagnosis ≥ 34 + 0 weeks) and 89 controls.
Gestational age -adjusted multiples of the median (MoM) values were calculated for biomarker concentrations. Multivariate regression analyses were performed to combine first trimester biomarkers, previously reported results on PlGF, maternal risk factors and UtA PI. Area under curve (AUC) values and 95% confidence intervals (CIs) for the prediction of PE and its subtypes were calculated.
A high first trimester inhibin-A predicted PE (AUC 0.618, 95%CI, 0.513–0.724), whereas PAPP-A and PlGF predicted only EO PE (0.701, 0.562–0.840 and 0.798, 0.686–0.909, respectively). At 26–28 weeks PAPP-A2 and inhibin-A predicted all PE subtypes. In the multivariate setting inhibin-A combined with maternal pre-pregnancy body mass index, prior PE and mean UtA PI predicted PE (0.811,0.726–0.896) and LO PE (0.824, 0.733–0.914).
At first trimester inhibin-A show potential ability to predict not only EO PE but also LO PE whereas PlGF and PAPP-A predict only EO PE. At late second trimester inhibin-A and PAPP-A2 might be useful for short-term prediction of PE.
To study whether maternal serum hyperglycosylated human chorionic gonadotropin (hCG-h) improves first trimester prediction of pre-eclampsia when combined with placental growth factor (PlGF), ...pregnancy-associated plasma protein-A (PAPP-A) and maternal risk factors.
Gestational-age-adjusted concentrations of hCG, hCG-h, PlGF and PAPP-A were analysed in serum samples by time-resolved immunofluorometric assays at 8-13 weeks of gestation. The case-control study included 98 women who developed pre-eclampsia, 25 who developed gestational hypertension, 41 normotensive women with small-for-gestational-age (SGA) infants and 177 controls.
Of 98 women with pre-eclampsia, 24 women developed preterm pre-eclampsia (diagnosis < 37 weeks of gestation) and 13 of them had early-onset pre-eclampsia (diagnosis < 34 weeks of gestation). They had lower concentrations of PlGF, PAPP-A and proportion of hCG-h to hCG (%hCG-h) than controls. In receiver-operating characteristics (ROC) curve analysis, the area under the curve (AUC) for the combination of PlGF, PAPP-A, %hCG-h, nulliparity and mean arterial blood pressure was 0.805 (95% confidence interval, CI, 0.699-0.912) for preterm pre-eclampsia and 0.870 (95% CI 0.750-0.988) for early-onset pre-eclampsia. Without %hCG-h the AUC values were 0.756 (95% CI 0.651-0.861) and 0.810 (95% CI 0.682-0.938) respectively. For prediction of gestational hypertension, the AUC for %hCG-h was 0.708 (95% CI 0.608-0.808), but for other markers the AUC values were not significant. None of the AUC values were significant for the prediction of SGA infants in normotensive women.
First trimester maternal serum %hCG-h tended to improve prediction of preterm and early-onset pre-eclampsia when combined with PlGF, PAPP-A and maternal risk factors.
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