Document Type : Original Article
Authors
1 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran;Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Ins
2 Endocrine Research Center, Institute of Endocrinology and Metabolism, Iran University of Medical Sciences (IUMS), Tehran, Iran
3 Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran;Department of Gynecology and Obstetrics, Arash Women’s Ho
4 Department of Gynecology and Obstetrics, Arash Women’s Hospital, Tehran University of Medical Sciences, Tehran, Iran
5 Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
6 5Department of Epidemiology and Reproductive Health, Reproductive Epidemiology Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
7 Department of Endocrinology and Female Infertility, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran;6Department of Andrology, Reproductive Biomedicine Research
Abstract
Keywords
Gestational diabetes mellitus (GDM) is one of the main
obstetrics complications among pregnant women with a
history of fertility problem (
In general terms, GDM is detected at mid-pregnancy
(24th-28th weeks of gestation) by oral glucose tolerance test
(OGTT). Nevertheless, there is evidence suggesting that
GDM occurs in all trimesters of pregnancy (
The national institute for health and care excellence
(NICE) guidelines (2013) recommended determining the
cut-off points for BMI among different populations to help
prevent diabetes and the other chronic conditions (
However, there is no consensus about GDM diagnosis regarding screening time, method and, the optimal cut points. Also, there is no direct evidence concerning the cut-off levels for pre-pregnancy BMI and fasting glucose to predict the risk of developing GDM in infertile women as a high-risk population. Accordingly, the present study was designed to evaluate the predictive values of maternal BMI and FBS to predict GDM risk, and then to determine the cut-points for BMI, FBS, and the combination of two biomarkers (BMI+FBS) for the diagnosis of at-risk pregnant women conceived using ART to target clinical surveillance in a more effective manner.
This nested case-control study was conducted between
October 2016 and June 2017. The data from 270 women
with singleton pregnancies (135 GDM and 135 non-GDM
women) conceived by ART treatment referred to Royan
Institute were studied. ART was defined as being conceived
by intracytoplasmic sperm injection (ICSI) and/
or in vitro fertilization (IVF). Prior to data collection, the
protocol of the study was approved by the institutional
review board and Ethics Committee of Iran University
of Medical Science (Project number: 25469). Clinical
records of the participants were reviewed. Consent form
was obtained and completed by participants. Data on maternal
history and demographic characteristics as well as
the records of the first-trimester para-clinical evaluations
were collected from the documents. The target population
was defined as women with singleton pregnancy via ART
and aged between 20-42 years. The exclusion criteria
were pre-gestational diabetes; chronic diseases (consisted
of hypertension, cardiovascular diseases, untreated thyroid
disease, liver diseases, renal diseases, autoimmune
diseases, and connective tissue disorders); corticosteroids
usage, and incomplete records. Pre-gestational diabetes
was defined when the first-trimester FBS was above 125
mg/dl. GDM was confirmed by an OGTT using 75 g oral
glucose at the first-trimester (for high-risk subjects) or
24-28 weeks of gestation (for non-GDM subjects). The
results of OGTT were interpreted by American diabetes
association (ADA) criteria (
The data pertaining to the characteristics of patients and
infertility treatment cycle were collected as previously
described in details (
Data were analyzed using the statistical package for the social sciences (SPSS) software for Windows (version 20, Chicago, IL, USA). Descriptive data were presented as the mean ± standard deviation (SD) or number (%) where appropriate. The independent sample t test was used to compare quantitative data with normal distribution between the two groups. Chi-square test was applied to compare the qualitative variables. The logistic regression analysis was performed to calculate the relationship between BMI, FBS, and BMI+FBS with the risk of GDM after ART cycles. The result of the analysis was expressed as odds ratio (OR) and 95% confidence intervals (CIs). ORs were presented either as crude or adjusted values for confounding variables (age, gravidity, PCOS diagnosis, and family history of diabetes). The patients with BMI <25 kg/m2 were considered the reference group. The Hosmer-Lemeshow test was used for the goodness of fit in logistic regression models and the Pearson's chi-square was calculated. The Nagelkerke Pseudo-R2 was determined to quantify predictive ability or model performance.
The receiver operating characteristic (ROC) curve analysis was done by MedCalc statistical software to measure the diagnostic accuracy of BMI, FBS, and BMI+FBS, as well as the optimal cut-point value as predictors for GDM. The DeLong method was used to compare the area under individual and paired ROC curves (AUC). Youden’s index and associated cut-off points were used to measure the overall diagnostic effectiveness. The level of significance was set at P<0.05.
The clinical and biochemical baseline characteristics of participants are presented in Table 1. The mean maternal age was significantly higher in the GDM group (32.15 ± 5.07 vs. 30.28 ± 4.89, P=0.003). There were significant differences in terms of gravidity, pre-pregnancy weight, BMI, history of diabetes in first relative degree, FBS, and PCOS diagnosis between the two groups (P<0.001 for all variables). There were no significant differences between the two groups in terms of parity, systolic and diastolic blood pressure, maternal education and infertility cause. The incidence of overweight (48.9 vs. 32.5%) and obesity (23.7 vs. 10.9%) was significantly higher in the GDM group (P<0.001).
Clinical and biochemical baseline characteristics of women conceived via ART with and without GDM
Variable | Non-GDM group n=135 | GDM groupn=135 | P value | |
---|---|---|---|---|
Maternal age (Y) | 30.28 ± 4.89 | 32.15 ± 5.07 | 0.003 | |
Gravidity (=1, primigravida) | 100 (74.0) | 79 (58.5) | 0.001 | |
Parity (=0, nulliparous) | 116 (85.9) | 114 (84.4) | 0.184 | |
Weight (kg) | 64.34 ± 10.18 | 69.77 ± 10.45 | <0.001 | |
BMI (kg/m2) | 24.57 ± 3.89 | 27.38 ± 3.91 | <0.001 | |
BMI (kg/m2) | <0.001 | |||
<25 | 73 (56.6) | 37 (27.4) | ||
25.0-29.9 | 42 (32.5) | 66 (48.9) | ||
≥30.0 | 14 (10.9) | 32 (23.7) | ||
History of diabetes in first relative degree | 21 (15.5) | 62 (45.9) | <0.001 | |
Maternal education | 0.636 | |||
Lower secondary | 93 ( 68.9) | 95 (70.1) | ||
Upper secondary | 42 (31.1) | 40 (29.9) | ||
FBS (mg/dl) | 80.81 ± 5.45 | 90.66 ± 10.24 | <0.001 | |
PCOS diagnosis | 11 (8.1) | 35 (25.9) | <0.001 | |
Systolic blood pressure (mmHg) | 104.26 ± 8.50 | 106.26 ± 9.77 | 0.078 | |
Diastolic blood pressure (mmHg) | 65.66 ± 7.05 | 66.70 ± 7.58 | 0.248 | |
Infertility cause | 0.714 | |||
Ovulatory factor | 41 (30.3) | 48 (35.8) | ||
Male factor | 65 (48.2) | 59 (44.1) | ||
Tubal factor | 8 (6.0) | 9 (6.7) | ||
Unexplained | 21 (15.5) | 18 (13.4) | ||
Data are presented as mean ± SD or n (%). ART; Assisted reproductive technology, GDM; Gestational diabetes mellitus, BMI; Body mass index, FBS; Fasting blood sugar, and PCOS; Polycystic ovary syndrome.
Crude and adjusted odds ratios of BMI categories and FBS for development of GDM
Variable | OR crude(95% CI) | OR adjusted(95% CI) (Model 1) | OR adjusted (95% CI)(Model 2) | OR adjusted (95% CI)(Model 3) | |
---|---|---|---|---|---|
BMI ( Kg/m2) | |||||
<25 | Reference | Reference | Reference | Reference | |
25-29.9 | 3.10 (1.78,5.39) | 2.26 (1.10,4.6) | 2.79 (1.37,5.68) | 3.27 (1.61,6.66) | |
≥30.0 | 4.51 (2.15,9.47) | 2.27 (0.649,7.96) | 3.58 (1.05,12.20) | 5.14 (1.53,17.26) | |
Nagelkerke R² | 0.118 | 0.126 | 0.119 | 0.116 | |
Hosmer and Lemeshow Test | |||||
Chi-square | 1 | 2.003 | 4.08 | 4.16 | |
P value* | 0.01 | 0.981 | 0.855 | 0.842 | |
FBS (mg/dl) | 1.171 (1.12-1.20) | 1.56 (1.28-1.90) | 1.71 (1.41-2.07) | 1.4 (1.26-1.56) | |
Nagelkerke R² | 0.364 | 0.400 | 0.429 | 0.422 | |
Hosmer and Lemeshow TestChi-square | 15.46 | 11.87 | 12.613 | 12.67 | |
P value* | 0.051 | 0.157 | 0.126 | 0.124 | |
BMI; Body mass index, FBS; Fasting blood sugar, CI; Confidence interval, GDM; Gestational diabetes mellitus, OR; Odds ratio, and PCOS; Polycystic ovary syndrome. Data are presented as OR (95% CI), Model 1; Adjusted by age and gravidity, Model 2; Adjusted by age, gravidity and PCOS diagnosis, Model 3; Adjusted by age, gravidity, PCOS diagnosis and family history of diabetes, and *; The P value is related to the Hosmer and Lemeshow test- which is not significant- it shows goodness of fitting the model.
Logistic regression analysis illustrated that both FBS
level and BMI were significant and independent risk factors
for development of GDM after adjustment for confounding
variables (age, gravidity, PCOS and family history
of diabetes). The results of logistic regression showed
that overweight and obese women had a 3.27-fold [adjusted
OR (a-OR) 3.27, 95% CI, (1.61, 6.66), P<0.002]
and 5.14-fold [aOR 5.14, 95% CI, (1.53, 17.26), P<0.002]
higher odds for GDM than that of normal weight women,
respectively. There was an approximately 17% increase
in the odds of developing GDM with each 1 mg/dl increase
in FBS level [OR 1.17, 95% CI, 1.17 (1.12-1.20),
P<0.001] (
Our result presents that Nagelkerke Pseudo-R2 are 0.118 (BMI) and 0.364 (FBS). It means that our model is stable and can predict the results. The Chi-square of Hosmer- Lemeshow test is the interpreter the goodness of fit test for logistic regression and shows this model is fit for our data.
The ROC curves illustrate the ability of FBS, BMI,
and BMI+FBS to predict GDM development (
Receiver operating characteristics (ROC) curve analysis for the ability of the first-trimester fasting blood sugar (FBS), pre-pregnancy body mass index (BMI), and BMI+FBS to predict gestational diabetes mellitus (GDM) in women conceived via assisted reproductive technology (ART).
The values of BMI, FBS, and BMI+FBS for the prediction of GDM and overall diagnostic effectiveness of each factor were presented in Table 3. On the basis of the ROC curves, the best cut-off point for FBS was 84.5 mg/dl, with a sensitivity of 72.9% (95% CI: 64.5-80.3) and specificity of 74.4% (95% CI: 66.0-81.7). Regarding BMI, the best cut-off point was obtained as 25.4 kg/m2 with a sensitivity of 68.9% (95% CI: 60.4-76.6), specificity of 62.8% (95% CI: 53.8-71.1). The combination of two biomarkers (BMI+FBS) has a better AUC value (0.83). The best cut- off point for BMI+FBS was 111.2 with a sensitivity of 70.7% (95% CI: 62.2-78.2) and specificity of 80.6% (95% CI: 72.7-87.0), separately.
The values of BMI, FBS and BMI+FBS for the prediction of GDM and their overall diagnostic effectiveness
ROC index | BMI | FBS | BMI+FBS |
---|---|---|---|
AUC | 0.69 | 0.79 | 0.83 |
95% CI of AUC | 0.63-0.76 | 0.74-0.85 | 0.78-0.88 |
P value* | <0.0001 | <0.0001 | <0.0001 |
Youden index J | 0.304 | 0.473 | 0.513 |
Cut-off criterion | 25.4 | 84.5 | 111.2 |
Sensitivity (%) | 68.8 | 72.9 | 70.7 |
95% CI of sensitivity | 60.4-76.6 | 64.5-80.3 | 62.2-78.2 |
Specificity | 62.79 | 74.42 | 80.62 |
95% CI of specificity | 53.8-71.1 | 66.0-81.7 | 72.7-87.0 |
Positive likelihood ratio | 1.85 | 2.85 | 3.65 |
Negative likelihood ratio | 0.5 | 0.36 | 0.36 |
BMI; Body mass index, FBS; Fasting blood sugar, GDM; Gestational diabetes mellitus, ROC; Receiver operating characteristic, AUC; Under individual ROC curves, CI; Confidence interval, and *; P<0.05 was significant.
The AUC of three ROC curves is compared in Table 4. The results indicate that there are significant differences among pairwise groups. The combination of BMI+FBS significantly improves the predictive ability of FBS or BMI alone for GDM development.
The pairwise comparison of the area under the ROC curves between BMI, FBS, and BMI+FBS
Variable | BMI vs. FBS | BMI vs. BMI+FBS | FBS vs. BMI+FBS |
---|---|---|---|
Difference between areas | 0.09 | 0.13 | 0.03 |
Standard error | 0.041 | 0.031 | 0.013 |
95% confidence interval | 0.016-0.18 | 0.070-0.19 | 0.0069-0.060 |
z statistic | 2.35 | 4.20 | 2.46 |
Significance level | P=0.02* | P<0.0001* | P=0.01* |
ROC; Receiver operating characteristic, BMI; Body mass index, FBS; Fasting blood sugar, *; P<0.05 was significant.
In the present study, the predictive values of first-trimester FBS, pre-pregnancy BMI, and the combination of two biomarkers for the development of GDM in pregnant women after ART treatment were determined. The results of this study demonstrated that overweight and obese women had approximately 3 and 5 folds increase in the odds of developing GDM, respectively. The cut-off point of 84.5 mg/dl for FBS had a sensitivity of 72.9% and specificity 74.4%, while the cut-off point of 25.4 kg/m2 for BMI had a sensitivity of 68.8% and specificity of 62.8%. However, the combination of BMI and FBS significantly improves the predictive ability for GDM development (BMI+FBS cut point: 111.2 with 70.7% sensitivity, 80.6% specificity).
Current evidence indicates that obesity has a negative
effect on female reproductive health including ovulatory
dysfunction, infertility problems, and poorer outcomes
after infertility treatment. Moreover, obesity is associated
with impaired ovarian responsiveness to IVF treatment,
a lower rate of oocyte fertilization, poor embryo quality,
and higher abortion rates (
In our study, a higher incidence of overweight (48.9
vs.32.5%) and obesity (23.7 vs.10.9%) was observed in
GDM compared to that of the non-GDM group in the
ART population. Provost et al. (
Our data show a significant association between BMI
and GDM in the ART population. Overweight and obese
women had approximately 3 and 5 folds increase in odds
of GDM, compared with normal BMI women. Consistent
with our findings, Torloni et al. (
Anyway, there is controversy on GDM definition,
screening time and method, and threshold values. Previously,
a risk factor for GDM was defined as obese women
who have BMI above 30 kg/m2 (
Recently, Cai et al. (
As there is controversy about GDM screening and diagnosis,
recent studies focused on the first-trimester prediction
of GDM based on the maternal and clinical characteristics
(
On the basis of the authors’ knowledge, this is the first report in ART subjects. The current research has some limitations. We did not evaluate infertility-related factors (hormonal and environmental factors), habits, physical activity, pre-pregnancy waist and hip circumferences, and the dietary regimen of the participants.
Pre-pregnancy BMI and the first-trimester FBS are independent predictors of GDM in pregnant women conceived by ART. The co-occurrence of high FBS and obesity increases the risk of GDM dramatically in pregnant women conceived by ART.