Predictors of Composite Maternal and Fetal Outcomes among Pregnant Women with Early-Onset Type 2 Diabetes: A Cross-Sectional Study

Abstract

Introduction

The most common form of pregestational diabetes in pregnancy is type 2 diabetes, requiring strict metabolic monitoring owing to the risk of adverse pregnancy outcomes. Our study aimed to identify predictors of composite maternal outcome (CMO) and fetal outcome (CFO) separately in pregnant women with early-onset type 2 diabetes (PwEOT2D).

Methods

The cross-sectional pilot study included 60 PwEOT2D by recording age, socioeconomic determinants, preconception body mass index (pBMI), preconception (pHbA1c) and trimester-specific glycated hemoglobin (HbA1c), gestational weight gain (GWG), and pregnancy outcomes. We defined CMO as at least one of the following: gestational hypertension, preeclampsia, eclampsia, preterm delivery, or emergency section. CFO included at least one of the following: small or large for gestational age, macrosomia, neonatal hypoglycemia, or admission to the neonatal intensive care unit.

Results

CMO was detected in 55% and CFO in 35% of PwEOT2D. The majority of PwEOT2D with CMO lived in suburban areas (73.1%), while those without CMO mostly lived in rural areas (51.9%, p = 0.014). Moreover, PwEOT2D with CMO had comparable pBMI to those without CMO (31.45 ± 6.27 versus 28.99 ± 6.28 kg/m², p = 0.136). However, PwEOT2D with CMO had higher pHbA1c (7.28 ± 0.95 versus 6.46 ± 0.96%, p = 0.002) and first trimester HbA1c (7.24 ± 1.08 versus 6.42 ± 0.97%, p = 0.003). Similarly, PwEOT2D with CFO had higher pHbA1c (7.84 ± 0.95 versus 6.41 ± 0.67%, p < 0.001) and first trimester (7.29 ± 1.07 versus 6.65 ± 1.07%, p = 0.032) and second trimester HbA1c (6.45 ± 0.87 versus 5.96 ± 0.82%, p = 0.038). Additionally, GWG was higher in the second (4.38 ± 2.01 versus 3.33 ± 1.61 kg, p = 0.032) and third trimester (5.66 ± 2.93 versus 3.89 ± 2.61 kg, p = 0.002) compared with those without CMO. Regression analysis identified pHbA₁c, first trimester of pregnancy, and community type as predictors of CMO, while pHbA1c and the occurrence of CMO were predictors of CFO.

Conclusions

Our results imply that preconception and first trimester of pregnancy HbA₁c, as well as community disparities, are predictors of CMO, while the predictors of CFO were only preconception HbA1c and the occurrence of CMO in pregnant women with EOT2D. Therefore, tailoring preventive strategies, followed by achieving and sustaining trimester-specific metabolic control, might improve pregnancy outcomes in women with EOT2D.

Key Summary Points

Why carry out this study?

Type 2 diabetes (T2D) is the most common form of pregestational diabetes in pregnancy, with a growing incidence and lowering age of onset (early-onset T2D).

Pregnancy complicated by pregestational T2D requires a high level of strict monitoring owing to the comparable rate of adverse pregnancy outcomes complicated by pregestational type 1 diabetes (T1D).

What was learned from the study?

In our study, composite maternal outcome (CMO) occurred in almost half of the participants, while composite fetal outcome (CFO) was detected in one-third of pregnant women with early-onset type 2 diabetes.

Preconception glycated hemoglobin (HbA1c) and HbA1c in the first trimester of pregnancy, as well as community type, were identified as predictors of CMO, while preconception HbA1c and the occurrence of CMO itself were predictors of CFO.

Our results imply the importance of not only metabolic control but also social determinants in tailoring strategy for the prevention of CMO and/or CFO among pregnant women with early-onset T2D.

Introduction

Recently, a significant increase in the prevalence of pregestational type 2 diabetes (T2D) has been shown, growing enormously in the last two decades [1, 2].

The dramatic increase in the prevalence of pregestational T2D could potentially be explained by the continuously increasing prevalence of T2D and obesity in the general population, driven by unhealthy diet, sedentary lifestyle, and/or unfavorable social determinants of health [3]. This rise in prevalence has led to a paradigm shift in favor of the importance of T2D in pregnancy, highlighting T2D as the most common pregestational diabetes in pregnancy nowadays [1].

Furthermore, it is important to take into account the contributing effect of the lowering age of onset of T2D, which results in more women of childbearing potential having early-onset type 2 diabetes (EOT2D) [4]. Those with EOT2D in comparison with those diagnosed with T2D after the age of 40 years are more likely to be female, have deprived social determinants, and be obese [4, 5]. Additionally, data from registries show that women with EOT2D may have worse metabolic control owing to the severe metabolic phenotype of early-onset T2D [6, 7]. In this context, it has been shown that early-onset T2D in female individuals is often associated with higher glycated hemoglobin (HbA1c) levels during pregnancy [8].

Owing to the alarming rise in prevalence of pregestational T2D [1], the importance of adverse maternal and fetal outcomes may still be underestimated.

A recently published meta-analysis revealed lower risk of preeclampsia, preterm delivery, and macrosomia, but a higher risk of perinatal mortality compared with those with type 1 diabetes (T1D). In addition, a national-based cohort study comparing fetal outcomes in T1D versus T2D highlighted persistent adverse pregnancy outcomes, maternal hyperglycemia, and body mass index (BMI) as key risk factors. Nevertheless, there is limited evidence focusing on the importance of preconception and trimester-specific metabolic control in the context of adverse hyperglycemia-related pregnancy outcomes among women with T2D.

Therefore, the aim of our study was to identify separate predictors of composite maternal outcome (CMO) and composite fetal outcome (CFO) in pregnant women with EOT2D.

Methods

Research Design

The study was designed as cross-sectional and included 60 women with singleton pregnancy and EOT2D who were treated at the Center for Diabetes and Lipid disorders, Clinic for Endocrinology, Diabetes and Metabolic Disease, University Clinical center of Serbia from 1 Jan 2016 to 31 December 2023. The study group was followed at our department from preconception (at least 6 months before) until delivery as a part of routine clinical settings.

Inclusion/Exclusion Criteria

Inclusion criteria were diagnosed EOT2D under the age of 40 years based on the American Diabetes Association diagnostic criteria, with a duration of at least 12 months before pregnancy [11, 12]. Exclusion criteria were type 1 diabetes and gestational diabetes, as well as pregnancy resulting from assisted reproductive technology and pregnant women with EOT2D with missing data or those who were not monitored at our department during the entire follow-up period.

Data Collection

We collected data on age at conception, level of education (primary, secondary, or university), self-perceived economic status (bad, average, good, very good, or excellent), and type of community (urban, suburban, or rural). Furthermore, we analyzed preconception body mass index (pBMI) and gestational weight gain (GWG) in each trimester of pregnancy. Data regarding preconception duration of EOT2D and previous antihyperglycemic treatment as well as antihyperglycemic treatment during pregnancy were obtained from medical records. Simultaneously, we analyzed glycated hemoglobin (HbA1c) since preconception and in each trimester of pregnancy.

Likewise, maternal and fetal outcomes, including gestational hypertension (blood pressure ≥ 140/90 mmHg measured after 20th gestational week), preeclampsia (diagnosed by an obstetrician: blood pressure ≥ 140/90 mmHg with proteinuria (≥ 0.5 g/24 h)), preterm delivery (PD, before 37th completed gestational week), emergency cesarean section, birth weight, newborn large for gestational age (LGA; > 90th percentile according to growth charts for the Serbian population), newborn small for gestational age (SGA; < 10th percentile according to growth charts for the Serbian population), macrosomia (birth weight > 4000 g), neonatal hypoglycemia (glycemia < 1.5 mmol/l or requiring intravenous glucose infusion), and admission to the neonatal intensive care unit (NICU), were collected from the obstetrical medical records [13, 14–15]. Other adverse pregnancy outcomes (miscarriage, congenital anomalies, and intrauterine or perinatal death) were not observed in our cohort. Similar methodologies have been presented elsewhere [16].

Measurements

• Body weight and height were measured at the preconception visit and in each trimester of pregnancy as part of the standard procedure for women with EOT2D and childbearing potential.

• pBMI was calculated according to the equation BMI = weight (kg)/height (m²).

• Gestational weight gain (GWG) was calculated for each trimester of pregnancy separately.

• HbA1c was measured using a commercial test reagent (SEBIA, Lisses, France).

• Data regarding level of education, self-perceived economic status, and type of community were collected at the first pregnancy visit by an experienced interviewer using simple multiple-choice questions as previously described.

Preconception and Pregnancy Diabetes Care

Women with EOT2D, due to their childbearing potential, were informed about the importance of planning pregnancy and the risks of potential adverse outcomes beginning from preconception, at the time of diagnosis and at each pregnancy visit. As per standard procedures, they were advised to take folic acid 5 mg/day during preconception and organogenesis. None of the women with EOT2D took angiotensin-converting enzyme (ACE) inhibitors or statins during preconception or pregnancy.

Study Protocol

Women with EOT2D and childbearing potential were followed at our department from preconception until delivery. During preconception, each woman with EOT2D received advice regarding the importance of optimal glycemic control and body weight, as well as information on stopping or switching certain drugs (i.e., SU, SGLT2inh, ACE inhibitor, statins) owing to safety concerns, as part of standard care. The first pregnancy visit for all women with EOT2D occurred no later than the 12th week of gestation, at which time participants provided informed consent. Additionally, pregnant women with EOT2D were interviewed about their level of education, economic status, and type of community at the first pregnancy visit. Pregnant women with EOT2D were examined every 4 weeks for glycemic control, body weight, and blood pressure. Antihyperglycemic treatment was modified according to recommended glycemic thresholds. All previously described procedures were part of standard care in preconception and pregnancy.

Ethical Approval

This investigation was conducted in agreement with both the Declaration of Helsinki of 1964, as revised in 2013, and national guidelines. All procedures were conducted in accordance with standard clinical settings. All women were informed of the details of the study prior to providing informed consent. The investigation was approved by the Ethics Committee of the Faculty of Medicine, University of Belgrade (25/II-4).

Statistics

Descriptive statistics used to characterize the study sample included means, medians, and standard deviations (SD) for numerical variables, and frequencies and percentages for categorical variables. The Shapiro–Wilk test was used to assess the normality of the data. Associations between categorical variables were analyzed using Pearson’s chi-squared test. For numerical data, the Student’s t-test or the Mann–Whitney U test was used to evaluate differences between the groups under investigation.

Composite maternal outcome (CMO) and composite fetal outcome (CFO) were defined by the authors’ consensus for the purpose of the current analysis. After initial data assessment, we included each maternal or fetal outcome registered in our cohort as a part of CMO or CFO, respectively. CMO was defined as the presence of at least one of the following outcomes: gestational hypertension (GH) and/or preeclampsia (PE) and/or eclampsia (E) and/or preterm delivery (PD) and/or emergency cesarean section (CS), while CFO included at least one of the following: SGA and/or LGA and/or macrosomia and/or neonatal hypoglycemia and/or NICU. Adverse perinatal outcomes not registered in our cohort were not included in the composite outcome analysis. Binary logistic regression analyses were performed to identify factors associated with CMO and CFO.

According to data from literature on the population of pregnant women with T2D, we made a power calculation that indicated a sample size of 300 patients, which could not be reached owing to recruitment issues at this moment and was the reason for conducting cross-sectional pilot study.

The results were expressed as relative risks with corresponding 95% confidence intervals (CI). For all analyses, the level of statistical significance was set at p ≤ 0.05. Data analysis was conducted using R statistical software [17, 18].

Results

Baseline characteristics of pregnant women with EOT2D, along with data regarding antihyperglycemic treatment in pregnancy as well as pregnancy outcome are presented in Table 1 and Fig. 1, respectively.

Table 1. Baseline characteristics of pregnant women with EOT2D

Variable	Total n = 60
Age (years), mean ± SD	34.7 ± 4.6
dT2D (years), mean ± SD	5.3 ± 6.2
p-BMI (kg/m2), mean ± SD	30.3 ± 6.3
Previous GD (n, %)	(19) 31.7
Education level
Primary school (n, %)	(5) 8.3
Secondary school (n, %)	(42) 70
University (n, %)	(13) 21.7
Self-perceived economic status
Bad (n, %)	(4) 6.7
Average (n, %)	(12) 20.0
Good (n, %)	(28) 46.7
Very good (n, %)	(15) 25.0
Excellent (n, %)	(1) 1.7
Community
Urban (n, %)	(14) 23.3
Suburban (n, %)	(20) 33.3
Rural (n, %)	(26) 43.3
P-antihyperglycemic treatment
Metformin (n, %)	(36) 60.0
Combination of OAD (n, %)	(20) 33.3
BOT (n, %)	(3) 5.0
MDI (n, %)	(1) 1.7
pHbA1c (%), mean ± SD	6.9 ± 1.0
First HbA1c (%), mean ± SD	6.8 ± 1.1
Second HbA1c (%), mean ± SD	6.1 ± 0.8
Third HbA1c (%), mean ± SD	5.9 ± 0.8
First GWG (kg), mean ± SD	2.9 ± 1.7
Second GWG (kg), mean ± SD	3.7 ± 1.8
Third GWG (kg), mean ± SD	4.5 ± 2.8
Th in pregnancy
Metformin (n, %)	(9) 15.0
BOT (n, %)	(13) 21.7
MDI (n, %)	(38) 63.3

EOT2D early-onset type 2 diabetes, SD standard deviation, dT2D duration of type 2 diabetes, p-BMI preconception body mass index, GD gestational diabetes, P antihyperglycemic treatment- previous antihyperglycemic treatment, pHbA1c preconception glycated hemoglobin A1c, First HbA1c HbA1c in first trimester, Second HbA1c HbA1c in second trimester, Third HbA1c HbA1c in third trimester, First GWG gestational weight gain during first trimester, Second GWG gestational weight gain during second trimester, Third GWG gestational weight gain during third trimester, Th therapy, OAD oral antihyperglycemic drug, BOT basal oral therapy, MDI multiple daily insulin injections

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Fig. 1

Adverse pregnancy outcomes among women with EOT2D. EOT2D early-onset type 2 diabetes, GH gestational hypertension, PD preterm delivery, CS cesarean section, SGA small for gestational age, LGA large for gestational age, NICU admission in the neonatal intensive care unit

We analyzed anthropometric and sociodemographic characteristics in addition to metabolic parameters of pregnant women with EOT2D on the basis of documented CMO (Table 2).

Table 2. Anthropometric, sociodemographic and metabolic parameters of women with EOT2D in respect to the occurrence of CMO and CFO

	Women with CMO n = 33	Women without CMO n = 27	p-Value	Women with CFO n = 21	Women without CFO n = 39	p-Value
Age (years), mean ± SD	35.27 ± 4.90	34.00 ± 4.16	0.289	35.52 ± 5.92	34.25 ± 3.70	0.312
dT2D (years) mean ± SD	6.42 ± 7.58	4.00 ± 3.49	0.131	5.52 ± 7.40	5.23 ± 5.49	0.862
pBMI (kg/m2), mean ± SD	31.45 ± 6.27	28.99 ± 6.28	0.136	30.67 ± 6.22	30.17 ± 6.49	0.775
Education level			NA			NA
Primary school (n, %)	(3), 9.1	(2), 7.4		(1), 4.8	(4), 10.3
Secondary school (n, %)	(21), 66.7	(20), 74		(15), 71.4	(27), 69.2
University (n, %)	(8), 24.2	(5), 18.5		(5), 23.8	(8), 20.5
Self-perceived economic status			NA			NA
Bad (n, %)	(2), 6.1	(2), 7.4		(2), 9.5	(2), 5.1
Average (n, %)	(5), 15.2	(7), 25.9		(4), 19.0	(8), 20.5
Good (n, %)	(18), 54.5	(10), 37		(9), 42.9	(19), 48.7
Very good (n, %)	(7), 46.7	(8), 29.6		(5), 23.8	(10), 25.6
Excellent (n, %)	(1), 3.0	(0)		(1), 4.8	(0)
Community			0.014			NA
Urban (n, %)	(8), 24.2	(6), 22.2		(3), 14.3	(11), 28.2
Suburban (n, %)	(19), 73.1	(7), 25.9		(12), 57.1	(14), 35.9
Rural (n, %)	(6), 18.2	(14), 51.9		(6), 30.0	(14), 35.9
P-antihyperglycemic treatment			NA			NA
Metformin (n, %)	(19), 57.6	(17), 63		(14), 66.7	(22), 56.4
Combination of OAD (n, %)	(13), 39.4	(7), 25.9		(5), 23.8	(15), 38.5
BOT (n, %)	(0)	(3), 11.1		(1), 4.8	(2), 5.1
MDI (n, %)	(1), 3	(0)		(1), 4.8	(0)
pHbA1c (%), mean ± SD	7.28 ± 0.95	6.46 ± 0.96	0.002	7.84 ± 0.95	6.41 ± 0.67	< 0.001
First HbA1c (%), mean ± SD	7.24 ± 1.08	6.42 ± 0.97	0.003	7.29 ± 1.07	6.65 ± 1.07	0.032
Second HbA1c (%), mean ± SD	6.26 ± 0.88	5.98 ± 0.83	0.219	6.45 ± 0.87	5.96 ± 0.82	0.038
Third HbA1c (%), mean ± SD	6.07 ± 0.78	5.78 ± 0.88	0.189	6.19 ± 0.79	5.81 ± 0.83	0.095
First GWG (kg), mean ± SD	3.09 ± 1.79	2.85 ± 1.70	0.601	3.04 ± 1.93	2.94 ± 1.65	0.836
Second GWG (kg), mean ± SD	4.06 ± 2.17	3.25 ± 1.12	0.089	4.38 ± 2.01	3.33 ± 1.61	0.032
Third GWG (kg), mean ± SD	5.12 ± 3.45	3.77 ± 1.60	0.068	5.66 ± 2.93	3.89 ± 2.61	0.002

CMO composite maternal outcome, CFO composite fetal outcome, SD standard deviation, dT2D duration of type 2 diabetes, p-BMI preconception body mass index, OAD oral antihyperglycemic drug, BOT basal oral therapy, MDI multiple daily insulin injections, pHbA1c preconception glycated hemoglobin A1c, First HbA1c HbA1c in first trimester, Second HbA1c HbA1c in second trimester, Third HbA1c HbA1c in third trimester, First GWG gestational weight gain during first trimester, Second GWG gestational weight gain during second trimester, Third GWG gestational weight gain during third trimester

Pregnant women with EOT2D and CMO had comparable pBMI to those without CMO. In contrast, education level, self-perceived economic status, and antihyperglycemic treatment at preconception were unable to be analyzed because of the small sample size. However, the majority of pregnant women with EOT2D and CMO lived in suburban communities, while different types of communities (rural) dominated in women without CMO (Table 2).

When we evaluated metabolic parameters, we found significantly higher HbA1c at preconception and first trimester of pregnancy among women with EOT2D and CMO compared with women without CMO. In contrast, HbA1c at the second and third trimester did not differ between two investigated groups. Simultaneously, we consecutively recorded data regarding GWG at each trimester and found comparable GWG at each trimester in both groups (Table 2).

Furthermore, we analyzed anthropometric and sociodemographic characteristics and metabolic parameters of pregnant women and EOT2D with and without CFO. Age, duration of EOT2D, and preconception BMI did not show significant differences between compared groups. In addition, level of education, self-perceived economic status, community level, and antihyperglycemic therapy could not be analyzed owing to limited sample size. Women with EOT2D and CFO had higher HbA1c from preconception throughout each trimester of pregnancy, except for the third trimester, as opposed to women without CFO. GWG was comparable in the first and significantly higher in the second and third trimester in pregnant women with EOT2D and CFO compared with those without CFO. (Table 2).

Approaching co-occurrence of CMO and CFO within our group under investigation, we found that 45.5% of pregnant women with EOT2D and CMO also had CFO. Among those without CMO, 88.9% did not have a CFO (Fig. 2).

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Fig. 2

Co-occurrence of CMO and CFO among pregnant women with EOT2D; *chi-squared test, p < 0.001. CMO composite maternal outcome, CFO composite fetal outcome

Using binary logistic regression analysis with CMO and CFO as response variables, we developed a model and identified key predictors. For CMO, preconception HbA1c, HbA1c during the first and second trimesters of pregnancy, and the type of community were found to be strong predictors of CMO (Table 3). In contrast, preconception HbA1c and the occurrence of CMO itself were singled out as predictors for CFO in pregnant women with EOT2D (Table 4).

Table 3. Binary logistic regression analysis with maternal composite outcome (CMO) as the response variable

Variable	B	Maternal composite outcome
		SE	z	p
pHbA1c	1.49	0.54	2.77	0.005
First HbA1c	1.28	0.60	2.13	0.03
Second HbA1c	0.95	0.63	−1.49	0.13
Rural community	−1.57	1.19	−1.31	0.18
Suburban community	−2.84	1.16	−2.45	0.01

McFadden’s R² for model CMO is 32.24% (Akaike Information Criterion, AIC 67.85, null deviance 82.57, and residual deviance 55.85).

pHbA1c preconception glycated hemoglobin A1c, first HbA1c HbA1c in first trimester, Second HbA1c HbA1c in second trimester

Table 4. Binary logistic regression analysis as fetal composite outcome (CFO) as the response variable

Variable	B	Fetal composite outcome
		SE	z	p
pHbA1c	4.75	1.57	3.02	0.002
CMO	1.77	1.05	1.67	0.09

McFadden’s R² for model CFO is 60.61% (AIC 36.58, null deviance 77.60, and residual deviance 30.58).

pHbA1c preconception glycated hemoglobin A1c, CMO composite maternal outcome

Finally, we performed receiver operating characteristic (ROC) analysis for CMO and CFO. For CMO, our model achieved an area under curve (AUC) of 0.87. Using an optimal threshold of 0.4, it demonstrated a sensitivity of 94% and a specificity of 67% (Fig. 3). McFadden’s R² for the CMO model was 32.24% (null deviance 82.57 and residual deviance 55.85), and AIC was 67.85, indicating a relatively good model fit.

[See PDF for image]

Fig. 3

Receiver operating characteristic (ROC) curves with optimal thresholds for predicting CMO. CMO composite maternal outcome

Simultaneously, our model for CFO achieved an AUC of 0.84. Using an optimal threshold of 0.25, it predicted the outcome with a sensitivity of 73% and a specificity of 85% (Fig. 4). McFadden’s R² for the CFO model was 60.61% (null deviance 77.60 and residual deviance 30.58), and AIC was 36.58, reflecting a strong model fit.

[See PDF for image]

Fig. 4

Receiver operating characteristic (ROC) curves with optimal thresholds for predicting CFO. CFO composite fetal outcome

Discussion

In our study population, a significant number of women with EOT2D experienced CMO or CFO, emphasizing that almost half of women with CMO also had CFO. Interestingly, pregnant women with EOT2D but without CMO were prone not to have newborns with CFO. Analyzing social determinants of pregnant women with EOT2D and CMO, the majority of them lived in suburban areas. Regarding metabolic parameters, pregnant women with EOT2D and CMO had worse HbA1c both in preconception and in the first trimester of pregnancy. Focusing on CFO in pregnant women with EOT2D, we observed unsatisfactory metabolic control from preconception until second trimester and higher GWG in late pregnancy. Furthermore, regression analysis revealed that level of pHbA1c and HbA1c in first in conjunction with second trimester and type of community were predictors of CMO. Moreover, pHbA1c and occurrence of CMO were also identified as predictors of CFO in pregnant women with EOT2D.

Interestingly, it has been shown that pregnancies complicated by T2D may have comparable rates of the most common adverse maternal and fetal pregnancy outcomes as seen in T1D [19], while prior published meta-analyses indicated lower incidence of certain outcomes, except perinatal mortality in pregnancy complicated by T2D.

In our study, almost half of the pregnant women with EOT2D had some form of CMO, CFO, or even both, while the frequency of adverse pregnancy outcomes was almost comparable to the results of previous studies. In contrast, a large UK-based cohort study, which included a more substantial sample size, reported a lower incidence of PD and a higher percentage of LGA babies compared with our findings. While perinatal mortality is a key concern in pregnancies complicated by T2D, our study did not detect any cases, likely owing to the small sample size.

According to our findings, the majority of women with EOT2D and CMO lived in suburban areas, while those without CMO predominantly lived in rural communities. While previous research indicates that unfavorable social determinants may influence adverse pregnancy outcomes [20, 21], we did not conduct a more detailed analysis. It could be speculated whether socioeconomic deprivation predisposes individuals to poor pregnancy outcomes per se or exacerbates them owing to higher metabolic risk.

Moreover, previous studies have shown that preconception obesity is associated with increased rates of GH, PE, E, PD, CS, macrosomia, and LGA in pregnant women with T2D [22, 23]. Other studies suggest that pregnant women with T2D and overweight and obesity are more prone to having only LGA newborns [24, 25]. In our study, preconception obesity did not emerge as an independent predictor in the regression analysis or group analysis, which may reflect the current sample size.

It has been shown that GWG plays an important role in the risk of adverse outcomes in T2D pregnancies. A recent study demonstrated an association between excessive GWG and a higher risk of GH, CS, and macrosomia. Nevertheless, most studies analyze absolute GWG in the context of excessive, recommended, or insufficient throughout pregnancy, without trimester-specific estimation [25, 26–27]. Having this in mind, we aimed to report the trimester-specific GWG from preconception to delivery, with limitations related to potential confounders. In this respect, GWG in the second and third trimesters of pregnancy was higher among pregnant women with EOT2D and CFO.

A prior study showed that the first and the last trimester HbA1c were the main critical factors related to maternal and fetal outcomes in women with T2D [24]. In that context, our results partially differ by indicating preconception HbA1c and first and second trimester HbA1c as predictors of CMO in women with EOT2D. In contrast, women with EOT2D and CFO had continuously worse glycemic control from preconception until the third trimester, which might help assessing the risk at the time of delivery.

It has been reported that poor preconception HbA1c in pregestational T2D is a predictor of impairment of glycemic control during pregnancy and associated with some of the adverse pregnancy outcomes (PD and lower birth weight) [28]. In our study, preconception glycemic control was important for both CMO and CFO, in contrast to glycemic control during the trimesters. However, we can assume that on the basis of the odds ratio, the value of pHbA1c could be more significant in prediction of CFO than CMO in women with EOT2D.

There is limited evidence regarding trimester-specific effects of hyperglycemia on maternal and fetal outcomes in pregnant women with EOT2D. It is important to emphasize that we estimated metabolic control by trimester, unlike other studies on this topic that most often evaluated the percentage of pregnant women with T2D who achieved target metabolic control [25]. We sought to clarify whether there are differences in the length of exposure to hyperglycemia on adverse maternal and fetal outcomes separately in pregnancies complicated by EOT2D. According to Pederson’s hypothesis, hyperglycemia in pregnancy may have short- and long-term effects on fetal outcomes through “fuel-mediated teratogenesis” [29, 30]. It was previously confirmed that poor glycemic control in the first trimester increases the risk for the development of congenital malformations, while hyperglycemia in late pregnancy leads to unfavorable anthropometric and metabolic outcomes [24, 28, 31]. Bearing in mind that in our study we analyzed not separate fetal entities but CFO, it is not surprising that women with EOT2D and CFO had worse metabolic control throughout the first and second trimesters of pregnancy.

Although it was not possible to analyze the effect of different antihyperglycemic treatment on CMO and CFO due to sample size, more than half of the women with EOT2D were on metformin monotherapy before conception, and over two-thirds of pregnant women required intensification of insulin therapy during pregnancy.

Our study has limitations, including the rather small sample size and study design. Moreover, we acknowledge all the disadvantages related to HbA1c as a metabolic parameter in pregnancy. However, the major strength of our pilot study is the longitudinal follow-up of women with EOT2D from preconception until delivery.

Conclusions

Our results imply preconception and the first trimester of pregnancy as predictors of CMO, as well as community disparities among pregnant women with EOT2D. A partially different pattern was noticed in women with EOT2D and CFO, identifying only preconception HbA1c and, interestingly, the occurrence of CMO as a predictors of CFO. Taking the last finding regarding CMO as a predictor of CFO, this suggests that all mother-directed actions might potentially reduce CFO.

Considering these findings, it is important to emphasize not only the trimester-specific metabolic parameters but also the need to take into consideration social determinants as potential modifiers of pregnancy outcomes in EOT2D. Therefore, tailoring preventive strategies followed by a comprehensive approach may improve both maternal and fetal outcomes, even in pregnancy with EOT2D.

Acknowledgements

We thank the participants of the study.

Authorship

All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.

Author Contributions

Conceptualization was carried out by Aleksandra Z. Jotic, Milica M. Stoiljkovic, Tanja J. Milicic, Katarina S. Lalic, Ljiljana Z. Lukic, and Nebojsa M. Lalić. Methodology was carried out by Aleksandra Z. Jotic, Milica M. Stoiljkovic, Tanja J. Milicic, Marko H. Obradovic, Ljiljana Z. Lukic, Marija V. Macesic, Jelena N. Stanarcic Gajovic, Mina M. Milovancevic, Miroslava G. Gojnic, Djurdja P. Rafailovic, and Nebojsa M. Lalić. Formal analysis and investigation was carried out by Aleksandra Z. Jotic, Milica M. Stoiljkovic, Tanja J. Milicic, Marko H. Obradovic, Ljiljana Z. Lukic, Miroslava G. Gojnic, and Nebojsa M. Lalić. Writing—original draft preparation was carried out by Aleksandra Z. Jotic, Milica M. Stoiljkovic, Katarina S. Lalic, Tanja J. Milicic, Ljiljana Z. Lukic, and Nebojsa M. Lalić. Writing—review and editing: Aleksandra Z. Jotic, Milica M. Stoiljkovic, Tanja J. Milicic, Marko H. Obradovic, and Nebojsa M. Lalic. Funding acquisition was carried out by Nebojsa M. Lalic. Resources were acquired by Aleksandra Z. Jotic, Milica M. Stoiljkovic, Tanja J. Milicic, and Nebojsa M. Lalic. Supervision was carried out by Aleksandra Z. Jotic, Katarina S. Lalic, and Nebojsa M. Lalic.

Funding

The manuscript is funded by project no. 451–03–66/2024–03/200110 from the Ministry of Education, Science and Technological Development, Republic of Serbia.

Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available due to local policy, and therefore, the data will not be deposited.

Declarations

Conflict of Interest

Aleksandra Z. Jotic has nothing to disclose. Milica M. Stoiljkovic has nothing to disclose. Tanja J. Milicic has nothing to disclose. Katarina S. Lalic has nothing to disclose. Ljiljana Z. Lukic has nothing to disclose. Marija V. Macesic has nothing to disclose. Jelena N. Stanarcic Gajovic has nothing to disclose. Mina M. Milovancevic has nothing to disclose. Marko H. Obradovic has nothing to disclose. Miroslava G. Gojnic has nothing to disclose. Djurdja P. Rafailovic has nothing to disclose. Nebojsa M. Lalic has nothing to disclose.

Ethical Approval

This investigation was performed in agreement with both the Declaration of Helsinki of 1964, as revised in 2013, and its later amendments. All women were informed of the details of the study prior to giving informed consent for participation. The investigation was approved by the Ethics Committee of the Faculty of Medicine, University of Belgrade (25/II-4).

References

1. Feig, DS. Epidemiology and therapeutic strategies for women with preexisting diabetes in pregnancy: how far have we come? The 2021 Norbert Freinkel Award Lecture. Diabetes Care; 2022; 45, 11 pp. 2484-2491. [DOI: https://dx.doi.org/10.2337/dci21-0027] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37579297]

2. Albrecht, SS; Kuklina, EV; Bansil, P et al. Diabetes trends among delivery hospitalizations in the U.S., 1994–2004. Diabetes Care; 2010; 33, pp. 768-773. [DOI: https://dx.doi.org/10.2337/dc09-1801] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20067968][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2845025]

3. Chivese, T; Hoegfeldt, CA; Werfalli, M; Yuen, L; Sun, H; Karuranga, S; Li, N; Gupta, A; Immanuel, J; Divakar, H et al. IDF Diabetes Atlas: the prevalence of pre-existing diabetes in pregnancy—A systematic review and meta-analysis of studies published during 2010–2020. Diabetes Res Clin Pract; 2022; 183, [DOI: https://dx.doi.org/10.1016/j.diabres.2021.109049] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34883190]109049.

4. Lascar, N; Brown, J; Pattison, H; Barnett, AH; Bailey, CJ; Bellary, S. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol; 2018; 6, pp. 69-80. [DOI: https://dx.doi.org/10.1016/S2213-8587(17)30186-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28847479]

5. Murphy, HR; Moses, RG. Pregnancy outcomes of young women with type 2 diabetes: poor care and inadequate attention to glycemia. Diabetes Care.; 2022; 45, 5 pp. 1046-1048. [DOI: https://dx.doi.org/10.2337/dci21-0059] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35561136]

6. International Diabetes Federation. IDF Diabetes Atlas. 9th ed. Brussels, International Diabetes Federation, 2019. Available from https://www.diabetesatlas.org. Accessed 01 Nov 2024.

7. Lascar, N; Brown, J; Pattison, H; Barnett, AH; Bailey, CJ; Bellary, S. Type 2 diabetes in adolescents and young adults. Lancet Diabetes Endocrinol; 2018; 6, pp. 69-80. [DOI: https://dx.doi.org/10.1016/S2213-8587(17)30186-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28847479]

8. Murphy, HR; Howgate, C; O’Keefe, J National Pregnancy in Diabetes (NPID) advisory groupet al. Characteristics and outcomes of pregnant women with type 1 or type 2 diabetes: a 5-year national population-based cohort study. Lancet Diabetes Endocrinol; 2021; 9, pp. 153-164. [DOI: https://dx.doi.org/10.1016/S2213-8587(20)30406-X] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33516295]

9. TODAY Study Group. Pregnancy outcomes in young women with youth-onset type 2 diabetes followed in the TODAY study. Diabetes Care; 2022; 45, pp. 1038-1045. [DOI: https://dx.doi.org/10.2337/dc21-1071]

10. Klingensmith, GJ; Pyle, L; Nadeau, KJ TODAY Study Groupet al. Pregnancy outcomes in youth with type 2 diabetes: the TODAY study experience. Diabetes Care; 2016; 39, pp. 122-129.1:CAS:528:DC%2BC28XitVOis7nE [DOI: https://dx.doi.org/10.2337/dc15-1206] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26628417]

11. Strati, M; Moustaki, M; Psaltopoulou, T et al. Early-onset type 2 diabetes mellitus: an update. Endocrine; 2024; 85, pp. 965-978.1:CAS:528:DC%2BB2cXls1KmtrY%3D [DOI: https://dx.doi.org/10.1007/s12020-024-03772-w] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38472622][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11316703]

12. American Diabetes Association Professional Practice Committee; 2. Diagnosis and classification of diabetes: standards of care in diabetes—2024. Diabetes Care. 2024; 47(Supplement_1): S20–S42

13. ACOG Practice Bulletin No. 202: gestational hypertension and preeclampsia. Obstet Gynecol. 2019;133(1):1

14. Kiserud, T; Piaggio, G; Carroli, G; Widmer, M; Carvalho, J; Neerup, JL et al. The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight. PLoS Med; 2017; 14, 1 [DOI: https://dx.doi.org/10.1371/journal.pmed.1002220] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28118360][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5261648]e1002220.

15. Giouleka, S; Gkiouleka, M; Tsakiridis, I; Daniilidou, A; Mamopoulos, A; Athanasiadis, A; Dagklis, T. Diagnosis and management of neonatal hypoglycemia: a comprehensive review of guidelines. Children (Basel); 2023; 10, 7 1220. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/37508719]

16. Jotic, AZ; Stoiljkovic, MM; Milicic, TJ et al. Prevalence and metabolic predictors for early diagnosed prediabetes in women with previous gestational diabetes: observational cohort study. Diabetes Ther; 2021; 12, pp. 2691-2700.1:CAS:528:DC%2BB3MXitVajs7%2FP [DOI: https://dx.doi.org/10.1007/s13300-021-01144-5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34458964][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8479028]

17. R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R

18. Foundation for Statistical Computing, Vienna, Austria. Available from www.R-project.org/. Accessed 03 Dec 2024.

19. Robin, X; Turck, N; Hainard, A; Tiberti, N; Lisacek, F; Sanchez, JC; Müller, CM. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics; 2011; 12, 77. [DOI: https://dx.doi.org/10.1186/1471-2105-12-77] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21414208][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3068975]

20. Balsells, M; García-Patterson, A; Gich, I; Corcoy Servei, R; Nutrició, EM. Maternaland fetal outcome in women with type 2 versus type 1 diabetes mellitus: a systematic review and metaanalysis. J Clin Endocrinol Metab; 2009; 94, pp. 4284-4291.1:CAS:528:DC%2BD1MXhsVejtLbP [DOI: https://dx.doi.org/10.1210/jc.2009-1231] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19808847]

21. Murphy, HR; Steel, SA; Roland, JM et al. Obstetric and perinatal outcomes in pregnancies complicated by type 1 and type 2 diabetes: influences of glycaemic control, obesity and social disadvantage. Diabet Med; 2011; 28, pp. 1060-1067.1:CAS:528:DC%2BC3MXht1Gmt7%2FM [DOI: https://dx.doi.org/10.1111/j.1464-5491.2011.03333.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21843303][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3322333]

22. Field, C; Grobman, WA; Yee, LM; Johnson, J; Wu, J; McNeil, B; Mercer, B; Simhan, H; Reddy, U; Silver, RM; Parry, S; Saade, G; Chung, J; Wapner, R; Lynch, CD; Venkatesh, KK. Community-level social determinants of health and pregestational and gestational diabetes. Am J Obstet Gynecol MFM; 2024; 6, 2 [DOI: https://dx.doi.org/10.1016/j.ajogmf.2023.101249] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38070680]101249.

23. Murphy, HR; Howgate, C; O'Keefe, J; Myers, J; Morgan, M; Coleman, MA; Jolly, M; Valabhji, J; Scott, EM; Knighton, P; Young, B; Lewis-Barned, N National Pregnancy in Diabetes (NPID) advisory group. Characteristics and outcomes of pregnant women with type 1 or type 2 diabetes: a 5-year national population-based cohort study. Lancet Diabetes Endocrinol.; 2021; 9, 3 pp. 153-164. [DOI: https://dx.doi.org/10.1016/S2213-8587(20)30406-X] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33516295]

24. Ali, DS; Davern, R; Rutter, E; Coveney, C; Devine, H; Walsh, JM; Higgins, M; Hatunic, M. Pre-gestational diabetes and pregnancy outcomes. Diabetes Ther; 2020; 11, pp. 2873-2885.1:CAS:528:DC%2BB3cXhvF2iu73F [DOI: https://dx.doi.org/10.1007/s13300-020-00932-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33010001][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644712]

25. Bashir, M; Dabbous, Z; Baagar, K; Elkhatib, F; Ibrahim, A; Brich, SA; Abdel-Rahman, ME; Konje, JC; Abou-Samra, AB. Type 2 diabetes mellitus in pregnancy: the impact of maternal weight and early glycaemic control on outcomes. Eur J Obstet Gynecol Reprod Biol; 2019; 233, pp. 53-57. [DOI: https://dx.doi.org/10.1016/j.ejogrb.2018.12.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30572188]

26. Xie, X; Liu, J; García-Patterson, A; Chico, A; Mateu-Salat, M; Amigó, J; Adelantado, JM; Corcoy, R. Gestational weight gain and pregnancy outcomes in women with type 1 and type 2 diabetes mellitus. Acta Diabetol; 2023; 60, 5 pp. 621-629. [DOI: https://dx.doi.org/10.1007/s00592-023-02031-0] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36707439]

27. Parellada, CB; Ásbjörnsdóttir, B; Ringholm, L et al. Fetal growth in relation to gestational weight gain in women with type 2 diabetes: an observational study. Diabet Med; 2014; 31, pp. 1681-1689.1:STN:280:DC%2BC2cbns1yltw%3D%3D [DOI: https://dx.doi.org/10.1111/dme.12558] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25081349][PubMedCentral: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4257095]

28. Yee, LM; Cheng, YW; Inturrisi, M; Caughey, AB. Effect of gestational weight gain on perinatal outcomes in women with type 2 diabetes mellitus using the 2009 Institute of Medicine guidelines. Am J Obstet Gynecol; 2011; 205, 3 pp. 257.e1-6. [DOI: https://dx.doi.org/10.1016/j.ajog.2011.06.028] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/22071055]

29. Kapur, A; McIntyre, HD; Hod, M. Type 2 diabetes in pregnancy. Endocrinol Metab Clin North Am; 2019; 48, 3 pp. 511-531. [DOI: https://dx.doi.org/10.1016/j.ecl.2019.05.009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31345520]

30. Pedersen J. Diabetes and pregnancy: blood sugar of newborn infants (Ph.D. Thesis) Danish Science Press; Copenhagen. 1952. p. 230

31. Pedersen J. The pregnant diabetic and her newborn: problems and management. William & Wilkins; Baltimore. 1967. pp. 128–37.

32. Feig, DS; Palda, VA. Type 2 diabetes in pregnancy: a growing concern. Lancet; 2002; 359, pp. 1690-1692. [DOI: https://dx.doi.org/10.1016/S0140-6736(02)08599-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/12020549]