
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39312311
MD-D-24-01643
00012
10.1097/MD.0000000000039853
3
5600
Research Article
Observational Study
Influencing factors of glycemic control in singleton pregnancies complicated by gestational diabetes mellitus in western China: A retrospective study
https://orcid.org/0000-0002-3211-3830
Zhang Jiani MD jia_11er@foxmail.com
ab
Mao Chihui MD baekhyunee_up@126.com
ab
Cao Qi MD c-q-cao@foxmail.com
ac
Huang Guiqiong MD 517472357@qq.com
ab
Wang Xiaodong MD ab*
a Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China
b Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, Sichuan, China
c Department of Reproductive Medical Center, West China Second University Hospital, Sichuan University, Chengdu, Sichuan, China.
* Correspondence: Xiaodong Wang, Department of Obstetrics and Gynecology, West China Second University Hospital, Chengdu, Sichuan 610041, China (e-mail: wangxd_scu@sina.com).
20 9 2024
20 9 2024
103 38 e3985314 2 2024
01 6 2024
04 9 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

To investigate the factors influencing glycemic control in gestational diabetes mellitus (GDM) patients and their impacts on pregnancy outcomes, providing insights for GDM management. Pregnant women diagnosed with GDM at a tertiary hospital in western China in 2019. Participants were categorized based on varying levels of glycemic control during pregnancy. A retrospective analysis was conducted, utilizing univariate and multivariate regression analyses, to identify factors influencing glycemic control in GDM patients. Based on various approaches to manage glucose, subjects were categorized into A1 (diet and exercise guidance alone) and A2 (insulin usage) groups. Based on whether glucose levels met the glycemic target in women with GDM, subjects were further divided into satisfactory and unsatisfactory groups. A total of 2621 women meeting the inclusion criteria were enrolled in the study. Independent factors associated with GDM A2 included higher prepregnancy body mass index (odds ratio [OR] = 1.070, 95% confidence interval [CI]: 1.019–1.122, P = .006), a history of GDM (OR = 1.888, 95% CI: 1.052–3.389, P = .033), elevated fasting plasma glucose (FPG) in early pregnancy (OR = 1.828, 95% CI: 1.320–2.532, P < .001), elevated 1-hour postprandial glucose (1-h PG) (OR = 1.126, 95% CI: 1.0091.256, P = .034), and 2-h PG by oral glucose tolerance test (OGTT) (OR = 1.181, 95% CI: 1.046–1.333, P = .007). Higher FPG by OGTT was an independent risk factor for unsatisfactory glycemic control (OR = 1.590, 95% CI: 1.273–1.985, P < .001). Compared with the A1 group, the A2 group has longer hospitalization, higher rates of cesarean section, placenta previa, and neonatal pneumonia (P < .05). Compared with the satisfactory group, the unsatisfactory group has lower gestational age, lower rates of cesarean section and placenta previa, and higher rates of postpartum hemorrhage for mothers; lower length and weight, and higher rates of premature birth, jaundice, hypoglycemia, pneumonia, respiratory distress syndrome, anemia, hospitalization, and hospitalization for more than 15 days in both pediatric unit and neonatal intensive care unit for newborns (P < .05). Elevated prepregnancy body mass index, FPG in early pregnancy, 1-h and 2-h PG during OGTT, and with a history with GDM are independent factors influencing insulin utilization, while elevated 0-h PG is an independent influencing factor of unsatisfactory glycemic control. Poor glycemic control has negative impacts on both maternal and fetal outcomes under 2 classifications.

gestational diabetes mellitus
glycemic control
influencing factors
pregnancy outcomes
Sichuan Province Science and Technology Support Program 10.13039/100012542 2022YF0042 Xiaodong WangOPEN-ACCESSTRUE
SDCT
==== Body
pmcKey point

This study aims to explore the factors influencing glycemic control in patients with GDM and understand the intricate relationship between glycemic control levels and their consequences, to pave the way for more informed clinical interventions and preventive measures.

1. Introduction

Gestational diabetes mellitus (GDM) refers to diabetes that manifests during pregnancy in individuals with normal glucose metabolism prior to conception.[1,2] Reported rates of GDM can be as high as 25%, varying across populations and diagnostic criteria, with overall rates increasing globally.[3] The emergence of GDM poses potential impacts on short-term and long-term outcomes for both the mother and the offspring.[4] Pregnant individuals with GDM face an increased risk of obstetric complications, including gestational hypertension, preeclampsia, polyhydramnios, shoulder dystocia, birth canal injuries, and postpartum hemorrhage.[5] Moreover, they are at an elevated lifetime risk of developing type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases.[6–8] Offspring of GDM-affected individuals are also at a heightened risk for complications, such as macrosomia, fetal growth restriction, neonatal hypoglycemia, and neonatal polycythemia.[4,9] Additionally, there is an increased incidence of long-term risks during the life time of offspring, including abnormal glucose tolerance, obesity, and metabolic disorders.[4,10]

Investigating whether variations in glycemic control among those with GDM impact pregnancy outcomes and understanding how this influence unfolds are crucial. Glycemic treatment target recommendations for women with GDM vary widely internationally in the current, often relying on consensus rather than high-quality trials.[3] In the latest update review in 2023, the effects of different intensities of glycemic control in pregnant women with GDM on maternal and infant health outcomes were assessed, including 4 randomized controlled trials involving 1731 women that took place in Canada,[11] New Zealand,[12] Russia,[13] and the USA.[14] Tighter glycemic control may lead to a potential increase in hypertensive disorders of pregnancy, but it does not seem to significantly affect cesarean section rates or induction of labor rates.[3]

However, the applicability of current results to China remains unknown. In a more nuanced exploration, there are topics lacking thorough investigation in the current, including understanding influencing factors and potential differences of glycemic control based on different classifications, and determining which classification is more advantageous for early prediction,

This study aims to explore the factors influencing glycemic control in patients with GDM and understand the intricate relationship between glycemic control levels and their consequences, to pave the way for more informed clinical interventions and preventive measures.

2. Method

2.1. Ethics approval and consent to participate

The study was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the West China Second Hospital of Sichuan University (Approval No. 2021 [181]). Due to the retrospective nature of the study, the need for informed consent was waived by the Ethics Committee of the West China Second Hospital of Sichuan University in accordance with the national legislation.

2.2. GDM diagnosis

In accordance with the guidelines outlined in the American Diabetes Association (ADA)’s “Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes (2021),”[15] the standard diagnostic method entails a 75 g oral glucose tolerance test (OGTT) administered between 24 to 28 weeks of gestation. GDM diagnosis is established if the OGTT results meet or exceed any of the following criteria: 0-h PG ≥ 5.1 mmol/L, 1-h PG ≥ 10.0 mmol/L, and/or 2-h PG ≥ 8.5 mmol/L. GDM A1 is defined as glucose can be controlled through diet and exercise guidance alone, while GDM A2 is defined as requiring medication to control glucose.[16] Since metformin pharmacotherapy was not utilized, GDM A2 in this study specifically denoted the use of insulin during pregnancy.

2.3. Sample size

This retrospective study aims to develop a multivariate regression model incorporating 5 to 15 features. Adhering to the Event Per Variable criterion, a sample size exceeding 150 is recommended for binary outcome categories.[17] Considering the current situation in the West China Second Hospital of Sichuan University, the incidence of GDM A1 was approximately 9 times that of GDM A2, and individuals with satisfactory glycemic control were roughly twice as prevalent as those with unsatisfactory glycemic control. Consequently, the total number of participants included in the final analysis should surpass 1500 cases.

2.4. Study participants

This study enrolled pregnant women diagnosed with GDM, who underwent regular antenatal examinations, and delivered at the West China Second Hospital of Sichuan University in 2019. Exclusion criteria encompassed prepregnancy diabetes, age below 18 years old, twin or multiple pregnancies, non-Chinese nationality, non-Han ethnicity, severe dysfunction of vital organs, and hospitalization not related to childbirth, such as therapeutic induced labor. Participants with overt diabetes in pregnancy, defined as hyperglycemia first recognized during pregnancy which meet the thresholds of diabetes in nonpregnant adults, were also excluded.[18]

On the one hand, based on the difficulty of glycemic control during pregnancy in GDM patients, participants were divided into A1 and A2 groups.[19] On the other hand, referring to ADA’s recommendations, glucose targets for GDM patients were fasting plasma glucose (FPG) < 5.3 mmol/L and either 1-h PG < 7.8 mmol/L or 2-h PG < 6.7 mmol/L.[15] In fact, venous blood sampling was performed at least twice during the third trimester of pregnancy to measure glucose levels, in addition to using fingertip blood sampling for glucose monitoring. Participants were categorized as part of the satisfactory group if their glucose values at 32 to 37 weeks of gestation exceeded the standards for glycemic control more than once, while those who did not were classified as part of the unsatisfactory group.

2.5. Observation indicators

The variables examined in this study are outlined in Table S1, Supplemental Digital Content, http://links.lww.com/MD/N655.

2.6. Statistical analysis

Categorical variables were presented as frequencies (percentages [%]) and assessed through χ2 tests. Continuous variables were expressed as mean (standard deviation) and analyzed using Student t tests. Logistic regression models were constructed based on univariate analysis, providing odds ratios (OR) and corresponding 95% confidence intervals (CI). A propensity score matching (PSM) was implemented in a 1:1 ratio, and a reevaluation of pregnancy outcomes was performed to further mitigate the impact of confounding factors. All statistical analyses were conducted using SPSS, with a two-tailed P-value < .05 considered as indicative of statistically significant.

3. Results

3.1. Participants selection

A total of 15,796 inpatients were enrolled from the obstetric medical unit of West China Second University Hospital between January 2019 and December 2019. Among them, 3047 patients diagnosed with GDM, excluding 237 cases with prepregnancy diabetes. Exclusions were made for 317 cases involving twin pregnancies, 10 cases involving multiple pregnancies, 1 cases of non-Chinese ethnicity, 70 cases of non-Han nationality, 92 cases experiencing severe organ dysfunction, and 131 cases not intended for delivery purpose. Ultimately, 2621 patients were included in the final analysis. The participant selection process is illustrated in Figure S1, Supplemental Digital Content, http://links.lww.com/MD/N655.

3.2. Influencing factors of glycemic control

The A2 group were characterized by advanced age (P < .001), elevated prepregnancy weight (P < .001) and prepregnancy body mass index (BMI) (P < .001), higher gravidity (P = .001), higher proportions of individuals with a history of GDM (P = .001), a family history of diabetes (P = .004), and hypertension (P = .027). FPG levels in early pregnancy, and 3 glucose values of OGTT during pregnancy were elevated (all P ≤ .001). Moreover, the A2 group displayed a notable increase in weight gain (P = .009), higher pre-delivery BMI (P = .007), elevated amniotic fluid depth (P = .022), and amniotic fluid index (P = .026).

The unsatisfactory group exhibited higher prepregnancy BMI (P = .022), higher gravidity (P = .033), a lower proportion of primiparity (P = .020), higher white blood cell count (P = .049), and FPG (P < .001) in early pregnancy and during OGTT. Also, higher pre-delivery BMI (P = .006) and lower diastolic blood pressure (DBP) (P = .028) were displayed in the unsatisfactory group (Tables 1 and 2).

Table 1 Original characteristics of study participants.

Variables	Classification 1	Classification 2	
A1	A2	P	Satisfactory	Unsatisfactory	P	
No.	2353	268		1784	837		
Age, mean (SD), years	32.57 (4.09)	33.69 (4.75)	<.001***	32.69 (4.24)	32.66 (4.04)	.864	
Prepregnancy weight, mean (SD), kg	55.12 (8.03)	57.19 (9.07)	<.001***	55.16 (8.01)	55.80 (8.30)	.063	
Height, mean (SD), cm	159.68 (4.96)	159.54 (4.46)	.622	159.70 (4.90)	159.57 (4.87)	.537	
Prepregnancy BMI, mean (SD), kg/m2	21.60 (2.91)	22.44 (3.23)	<.001***	21.61 (2.89)	21.90 (3.02)	.022*	
Gravidity, n (%)	
=1	755 (32.1)	72 (26.9)	.081	593 (33.2)	233 (27.8)	.006**	
=2	684 (29.1)	60 (22.4)	.022*	504 (28.3)	240 (28.7)	.823	
=3	445 (18.9)	59 (22.0)	.222	323 (18.1)	181 (21.6)	.033*	
≥4	469 (19.9)	77 (28.7)	.001**	364 (20.4)	183 (21.9)	.391	
Primiparity, n (%)	1372 (58.3)	142 (53.0)	.095	1058 (59.3)	456 (54.5)	.020*	
ART application, n (%)	205 (8.7)	31 (11.6)	.122	171 (9.6)	65 (7.8)	.129	
PCOS, n (%)	58 (2.5)	12 (4.5)	.053	47 (2.6)	23 (2.7)	.867	
Chronic HBV infection, n (%)	147 (6.2)	13 (4.9)	.366	111 (6.2)	49 (5.9)	.714	
History of GDM, n (%)	76 (3.2)	19 (7.1)	.001**	56 (3.1)	39 (4.7)	.052	
Family history of diabetes, n (%)	337 (14.3)	56 (20.9)	.004**	256 (14.3)	137 (16.4)	.177	
Family history of hypertension, n (%)	407 (17.3)	61 (22.8)	.027**	314 (17.6)	154 (18.4)	.619	
ART = assisted reproductive technology, BMI = body mass index, GDM = gestational diabetes mellitus, HBV = hepatitis B virus, PCOS = polycystic ovary syndrome, SD = standard deviation.

* P < .05.

** P < .01.

*** P < .001.

Table 2 Indexes of study participants during different periods of pregnancy.

	Classification 1	Classification 2	
A1	A2	P	Satisfactory	Unsatisfactory	P	
No.	2353	268		1784	837		
Laboratory indicators in early pregnancy, mean (SD)	
WBC, 109/L	8.51 (1.98)	8.64 (1.87)	.315	8.47 (1.96)	8.64 (1.99)	.049*	
RBC, 1012/L	4.20 (0.55)	4.22 (0.53)	.701	4.20 (0.43)	4.21 (0.74)	.506	
HB, g/L	126.12 (10.43)	125.72 (11.14)	.576	125.97 (10.80)	126.33 (9.81)	.433	
HCT, %	37.22 (2.87)	37.10 (3.43)	.524	37.20 (2.89)	37.23 (3.00)	.846	
PDW, fL	14.24 (3.18)	14.39 (3.56)	.503	14.23 (2.89)	14.31 (3.30)	.582	
MPV, fL	11.31 (1.48)	11.31 (1.35)	.932	11.31 (1.55)	11.29 (1.28)	.785	
ALT, U/L	25.97 (31.73)	24.46 (20.97)	.474	26.38 (34.60)	24.62 (20.36)	.190	
AST, U/L	23.57 (20.15)	22.04 (12.14)	.251	23.61 (21.57)	23.00 (14.06)	.472	
CREA, μmol/L	41.80 (9.62)	41.36 (6.37)	.492	41.85 (6.10)	41.59 (13.96)	.530	
Cys-C, mg/L	0.66 (0.18)	0.67 (0.13)	.474	0.66 (0.17)	0.65 (0.16)	.337	
UA, μmol/L	237.37 (51.22)	242.32 (57.33)	.208	237.72 (51.51)	238.12 (52.35)	.860	
FPG, mmol/L	4.58 (0.46)	4.75 (0.54)	<.001***	4.57 (0.45)	4.65 (0.49)	<.001***	
FER, ng/mL	82.03 (66.01)	86.62 (73.37)	.318	82.24 (67.14)	83.01 (65.99)	.793	
Urine glucose, n (%)	
–	2168 (92.1)	239 (89.2)	.094	1635 (91.6)	772 (92.2)	.609	
+/－–+	93 (4.0)	16 (6.0)	.117	73 (4.1)	36 (4.3)	.803	
2+–4+	92 (3.9)	13 (4.9)	.457	76 (4.3)	29 (3.5)	.306	
Urine ketone, n (%)	
–	1898 (80.7)	224 (83.6)	.249	1448 (81.2)	674 (80.5)	.697	
+/－–+	264 (11.2)	31 (11.6)	.865	191 (10.7)	104 (12.4)	.194	
2+–4+	191 (8.1)	13 (4.9)	.059	145 (8.1)	59 (7.0)	.336	
Glucose values of 75 g OGTT at 24–28 weeks of gestation, mean (SD)	
0-h PG	4.76 (0.98)	5.05 (0.66)	<.001***	4.74 (0.50)	4.85 (0.51)	<.001***	
1-h PG	9.77 (1.41)	10.28 (1.59)	<.001***	9.81 (1.43)	9.84 (1.46)	.603	
2-h PG	8.60 (1.25)	8.94 (1.53)	.001**	8.64 (1.27)	8.63 (1.32)	.880	
Indicators before delivery, mean (SD)	
Weight gain, kg	11.22 (4.22)	10.51 (4.20)	.009**	11.08 (4.18)	11.28 (4.28)	.243	
BMI, kg/m2	26.01 (2.92)	26.58 (3.29)	.007**	25.96 (2.89)	26.31 (3.08)	.006**	
SBP, mm Hg	116.82 (10.95)	117.44 (11.83)	.386	116.86 (11.33)	116.94 (10.43)	.868	
DBP, mm Hg	73.29 (8.53)	74.40 (9.74)	.077	73.66 (8.80)	72.86 (8.36)	.028*	
Fundal height, cm	32.91 (2.10)	32.87 (2.42)	.774	32.95 (2.06)	32.81 (2.28)	.143	
Abdominal circumference, cm	99.53 (6.07)	100.22 (6.73)	.080	99.46 (6.04)	99.90 (6.35)	.092	
A, cm	5.39 (1.45)	5.60 (1.34)	.022*	5.41 (1.44)	5.40 (1.45)	.786	
AFI, cm	13.17 (4.47)	13.86 (4.57)	.026*	13.28 (4.40)	13.14 (4.68)	.475	
S/D	2.24 (0.66)	2.25 (0.36)	.817	2.24 (0.73)	2.23 (0.38)	.561	
0-h PG = fasting blood glucose, 1-h PG = blood glucose level 1 hours after taking dextrose, 2-h PG = blood glucose level 2 hours after taking dextrose, A = amniotic fluid depth, AFI = amniotic fluid index, ALT = alanine aminotransferase;, AST, = aspartate aminotransferase, BMI = body mass index, CREA = creatinine, Cys-C = cystatin C, DBP = diastolic blood pressure, FER = ferritin, FPG = fasting blood glucose, GDM = gestational diabetes mellitus, HB = hemoglobin, HCT = hematocrit, MPV = mean platelet volume, OGTT = oral glucose tolerance test, PDW = platelet distribution width, RBC = red blood cell count, S/D = umbilical artery blood flow., SBP = systolic blood pressure, SD = standard deviation, UA = uric acid, WBC = white blood cell count.

* P < .05.

** P < .01.

*** P < .001.

3.3. The impact of glycemic control on pregnancy outcomes

The A2 group demonstrated prolonged hospitalization days (P < .001), heightened incidences of cesarean section (P = .009), placenta previa (P = .016), and neonatal pneumonia (P = .012).

The unsatisfactory glycemic control had lower gestational age (P < .001), lower incidences of cesarean section (P = .003), placenta previa (P < .001), higher incidence of postpartum hemorrhage (P = .002), lower neonatal length (P = .003) and weight (P = .002), higher incidences of premature birth (P < .001), neonatal jaundice (P < .001), hypoglycemia (P = .006), pneumonia (P < .001), neonatal respiratory distress syndrome (P = .001), anemia (P = .011), hospitalization (P < .001), and hospitalization for more than 15 days in both the pediatric unit (P = .001) and the neonatal intensive care unit (P = .006) (Table 3).

Table 3 The impact of glycemic control levels on pregnancy outcomes in women with GDM.

	Classification 1	Classification 2	
A1	A2	P	Satisfactory	Unsatisfactory	P	
No.	2353	268		1784	837		
Maternal outcomes	
Insulin use, n (%)	0 (0.00)	268 (100.00)	–	189 (10.6)	79 (9.4)	.363	
Gestational age, mean (SD), days	270.81 (12.30)	269.61 (11.92)	.127	271.75 (11.19)	268.45 (14.03)	<.001***	
Hospitalization days, mean (SD)	4.36 (2.76)	5.49 (3.56)	<.001***	4.53 (2.09)	4.37 (2.81)	.173	
Cesarean section, n (%)	1486 (63.2)	191 (71.3)	.009**	1176 (65.9)	501 (59.9)	.003**	
GH, n (%)	48 (2.0)	9 (3.4)	.161	33 (1.8)	24 (2.9)	.096	
PE/E, n (%)	59 (2.5)	9 (3.4)	.406	53 (3.0)	15 (1.8)	.077	
ICP, n (%)	149 (6.3)	9 (3.4)	.053	108 (6.1)	50 (6.0)	.936	
PP, n (%)	93 (4.0)	19 (7.1)	.016*	54 (3.0)	58 (6.9)	<.001***	
Placental abruption, n (%)	34 (1.4)	3.7 (0.7)	.513	27 (1.5)	9 (1.1)	.369	
Polyhydramnios, n (%)	83 (3.5)	13 (4.9)	.275	62 (3.5)	34 (4.1)	.456	
PPH, n (%)	98 (4.2)	13 (4.9)	.279	61 (3.4)	50 (5.0)	.002**	
Fetal and neonatal outcomes	
FGR, n (%)	16 (0.7)	3 (1.1)	.672	16 (0.9)	3 (0.4)	.130	
Fetal distress, n (%)	5 (0.2)	1 (0.4)	1.000	4 (0.2)	2 (0.2)	.941	
Intrauterine infection, n (%)	29 (1.2)	5 (1.9)	.560	19 (1.1)	15 (1.8)	.125	
Newborn length, mean (SD), cm	49.10 (2.56)	49.16 (2.24)	.703	49.21 (2.40)	48.87 (2.77)	.003**	
Newborn weight, mean (SD), kg	3183.31 (502.09)	3189.48 (618.62)	.850	3205.87 (491.61)	3137.15 (491.61)	.002**	
Placental weight, mean (SD), g	566.97 (99.71)	569.82 (108.09)	.661	570.27 (95.26)	567.66 (92.60)	.511	
Apgar-1 ≤ 7 points, n (%)	22 (0.9)	5 (1.9)	.267	16 (0.9)	11 (1.3)	.325	
Apgar-5 ≤ 7 points, n (%)	12 (0.5)	1 (0.4)	1.000	9 (0.5)	4 (0.5)	1.000	
Apgar-10 ≤ 7 points, n (%)	5 (0.2)	1 (0.4)	1.000	4 (0.2)	2 (0.2)	1.000	
Premature, n (%)	230 (9.8)	27 (10.1)	.876	141 (7.9)	116 (13.9)	<.001***	
Macrosomia, n (%)	87 (3.7)	10 (3.7)	.978	73 (4.1)	24 (2.9)	.122	
NNJ, n (%)	238 (10.1)	33 (12.3)	.263	148 (8.3)	123 (14.7)	<.001***	
Hypoglycemia, n (%)	34 (1.4)	4 (1.5)	1.000	18 (1.0)	20 (2.4)	.006**	
Erythrocytosis, n (%)	4 (0.1)	0 (0.0)	1.000	3 (0.2)	0 (0.0)	.235	
Pneumonia, n (%)	84 (3.6)	18 (6.7)	.012*	44 (2.5)	58 (6.9)	<.001***	
NRDS, n (%)	29 (1.2)	3 (1.1)	1.000	13 (0.7)	19 (2.3)	.001**	
Anemia, n (%)	38 (1.6)	4 (1.5)	1.000	21 (1.2)	21 (2.5)	.011**	
Pediatric hospitalization, n (%)	142 (6.0)	18 (6.7)	.659	89 (5.0)	71 (8.5)	<.001***	
NICU, n (%)	114 (4.8)	17 (6.3)	.286	72 (4.0)	59 (7.0)	.001**	
Hospitalization days ≥ 15, n (%)	37 (1.6)	8 (3.0)	.150	20 (1.1)	25 (3.0)	.001**	
NICU hospitalization days ≥ 15, n (%)	30 (1.3)	8 (3.0)	.051	18 (1.0)	20 (2.4)	.006**	
FGR = fetal growth restriction, GDM = gestational diabetes, GH = pregnancy induced hypertension, ICP = intrahepatic cholestasis of pregnancy, NICU = neonatal intensive care unit, NNJ = neonatal jaundice, NRDS = neonatal respiratory distress syndrome, PE/E = preeclampsia and eclampsia, PP = placenta previa, PPH = postpartum hemorrhage, SD = standard deviation.

* P < .05.

** P < .01.

*** P < .001.

Considering the substantial baseline differences between A1 and A2 groups, PSM was employed for further analysis. The A2 group only showed a lower incidence of ICP (P = .003). However, owing to minimal baseline differences between the satisfactory and unsatisfactory groups, and the potential substantial reduction in sample size associated with PSM, we did not employ PSM in subsequent classifications. (Tables S2 and S3, Supplemental Digital Content, http://links.lww.com/MD/N655).

3.4. Multivariate logistic regression of glycemic control (A1 and A2)

In Model 1, adjusting for original characteristics, the results revealed that age (OR = 1.053, 95% CI: 1.019–1.087, P = .002), prepregnancy BMI (1.087, 1.042–1.133, P < .001), and a history of GDM (2.194, 1.287–3.741, P = .004) were independent factors associated with the occurrence of GDM A2.

Building upon Model 1, Model 2 incorporated adjustments for laboratory indicators in early pregnancy. The findings demonstrated that age (1.059, 1.021–1.098, P = .002), prepregnancy BMI (1.067, 1.014–1.122, P = .013), history of GDM (1.992, 1.116–3.556, P = .020), and FPG (2.031, 1.456–2.832, P < .001) in early pregnancy were identified as independent factors.

In Model 3, adjustments were made for original characteristics, FPG in early pregnancy, and glucose values during OGTT. The results indicated that prepregnancy BMI (1.070, 1.019–1.122, P = .006), history of GDM (1.888, 1.052–3.389, P = .033), FPG (1.828, 1.320–2.532, P < .001), 1-h PG (1.126, 1.009–1.256, P = .034), and 2-h PG (1.181, 1.046–1.333, P = .007) were independent factors (Table 4).

Table 4 Multivariate logistic regression analysis of glycemic control (GDM A1 and GDM A2).

Predictive variables	Model 1	Model 2	Model 3	
OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	
Original characteristics	
Age, years	1.053 (1.019–1.087)	.002**	1.059 (1.021–1.098)	.002**	1.033 (0.996–1.070)	.082	
Prepregnancy BMI, kg/m2	1.087 (1.042–1.133)	<.001***	1.067 (1.014–1.122)	.013*	1.070 (1.019–1.122)	.006***	
Primiparity	0.994 (0.727–1.358)	.970	0.965 (0.675–1.379)	.844	0.886 (0.624–1.258)	.499	
History of GDM	2.194 (1.287–3.741)	.004**	1.992 (1.116–3.556)	.020*	1.888 (1.052–3.389)	.033*	
Family history of diabetes	1.374 (0.984–1.918)	.062	1.399 (0.972–2.014)	.071	1.348 (0.940–1.933)	.105	
Family history of hypertension	1.186 (0.859–1.638)	.299	1.073 (0.747–1.541)	.703	1.113 (0.778–1.593)	.558	
Laboratory indicators in early pregnancy	
FPG, mmol/L	–	–	2.031 (1.456–2.832)	<.001***	1.828 (1.320–2.532)	<.001***	
WBC, 109/L	–	–	1.030 (0.954–1.111)	.449	–	–	
RBC, 1012/L	–	–	0.926 (0.602–1.425)	.727	–	–	
HB, g/L	–	–	0.988 (0.953–1.024)	.512	–	–	
HCT, %	–	–	1.008 (0.871–1.165)	.917	–	–	
ALT, U/L	–	–	0.999 (0.985–1.013)	.889	–	–	
AST, U/L	–	–	0.988 (0.960–1.016)	.396	–	–	
UA, μmol/L	–	–	1.002 (0.999–1.005)	.214	–	–	
FER, ng/mL	–	–	1.001 (0.999–1.003)	.415	–	–	
Urine glucose (+)	–	–	1.391 (0.891–2.174)	.147	–	–	
Glucose values of 75 g OGTT at 24–28 weeks of gestation	
0-h PG, mmol/L	–	–	–	–	1.107 (0.987–1.242)	.084	
1-h PG, mmol/L	–	–	–	–	1.126 (1.009–1.256)	.034*	
2-h PG, mmol/L	–	–	–	–	1.181 (1.046–1.333)	.007***	
0-h PG = fasting blood glucose, 1-h PG = blood glucose level 1 hours after taking dextrose, 2-h PG = blood glucose level 2 hours after taking dextrose, ALT = alanine aminotransferase, AST = aspartate aminotransferase, BMI = body mass index, CI = confidence interval, FER = ferritin, FPG = fasting blood glucose, GDM = gestational diabetes mellitus, HB = hemoglobin, HCT = hematocrit, OGTT = oral glucose tolerance test, OR = odds ratio, RBC = red blood cell count, UA = uric acid, WBC = white blood cell count.

* P < .05.

** P < .01.

*** P < .001.

3.5. Multivariate logistic regression of glycemic control (satisfactory and unsatisfactory)

In Model 1, with adjustments for original characteristics and laboratory indicators in early pregnancy, the results indicated that FPG (1.349, 1.107–1.644, P = .003) and white blood cell count (1.049, 1.002–1.098, P = .040) were independent factors associated with unsatisfactory glycemic control.

Expanding upon Model 1, Model 2 included adjustments for OGTT results. The findings revealed that only FPG during OGTT emerged as an independent factor (1.546, 1.235–1.935, P < .001).

In Model 3, which accounted for original characteristics, FPG in early pregnancy, and OGTT results, the analysis demonstrated that only FPG during OGTT remained an independent factor (1.590, 1.273–1.985, P < .001) (Table 5).

Table 5 Multivariate logistic regression analysis of glycemic control (satisfactory and unsatisfactory).

Predictive variables	Model 1	Model 2	Model 3	
OR (95% CI)	P	OR (95% CI)	P	OR (95% CI)	P	
Original characteristics	
Prepregnancy BMI, kg/m2	1.017 (0.986–1.050)	.281	1.014 (0.982–1.048)	.396	1.017 (0.985–1.050)	.307	
Primigravidity	0.819 (0.646–1.038)	.098	0.887 (0.696–1.129)	.330	0.901 (0.708–1.147)	.398	
Primiparity	0.912 (0.733–1.134)	.406	0.848 (0.678–1.060)	.148	0.867 (0.694–1.082)	.207	
Laboratory indicators in early pregnancy	
FPG, mmol/L	1.349 (1.107–1.644)	.003**	1.022 (0.812–1.288)	.851	1.012 (0.803–1.274)	.922	
WBC, 109/L	1.049 (1.002–1.098)	.040*	1.039 (0.990–1.090)	.119	–	–	
Glucose values of 75 g OGTT at 24 to 28 weeks of gestation	
0-h PG, mmol/L	–	–	1.546 (1.235–1.935)	<.001***	1.590 (1.273–1.985)	<.001***	
1-h PG, mmol/L	–	–	1.004 (0.937–1.074)	.920	1.007 (0.941–1.078)	.842	
2-h PG, mmol/L	–	–	1.044 (0.965–1.128)	.282	1.043 (0.965–1.127)	.288	
0-h PG = fasting blood glucose, 1-h PG = blood glucose level 1 hours after taking dextrose, 2-h PG = blood glucose level 2 hours after taking dextrose, BMI = body mass index, CI = confidence interval, FPG = fasting blood glucose, GDM = gestational diabetes mellitus, OGTT = oral glucose tolerance test, OR = odds ratio, WBC = white blood cell count.

* P < .05.

** P < .01.

*** P < .001.

4. Discussion

Based on difficulty of glycemic control, as determined by the utilization of insulin was used during pregnancy[19] and adhering to the glycemic control targets outlined by ADA,[15] this study including 2621 women performed a parallel classification analysis. Elevated prepregnancy BMI, FPG in early pregnancy, 1-h and 2-h PG during OGTT, and with a history with GDM are independent factors influencing insulin utilization, while elevated 0-h PG is an independent influencing factor of unsatisfactory glycemic control. Compared with the A1 group, the A2 group has longer hospitalization, higher rates of cesarean section, placenta previa, and neonatal pneumonia (P < .05). Compared with the satisfactory group, the unsatisfactory group has lower gestational age, lower rates of cesarean section and placenta previa, and higher rates of postpartum hemorrhage for mothers; lower length and weight, and higher rates of premature birth, jaundice, hypoglycemia, pneumonia, respiratory distress syndrome, anemia, hospitalization, and hospitalization for more than 15 days in both pediatric unit and neonatal intensive care unit for newborns.

The selection of indicators plays a crucial role in determining the generalizability of research findings. In a study conducted by Daniel PJ et al, the relationship between cardiovascular biomarkers and glucose regulation was investigated, with assessment based on third trimester HbA1c levels.[20] However, these indicators were not routinely examined in western China. Our study opted baseline indicators, a choice more conducive to generalization within primary health care systems with limited costs. We found that prepregnancy BMI, history of GDM, FPG in early pregnancy, and 1-h PG and 2-h PG during OGTT was closely related to insulin use during pregnancy. Notably, only FPG during OGTT emerged as an independent influencing factor contributing to failure in achieving glycemic targets.

A correlation between glycemic control and pregnancy outcomes has been demonstrated to some extent.[21,22] Early identification of unsatisfactory glycemic control allows for proactive regulation of glycemic levels, which is crucial for improving maternal and neonatal outcomes. However, large-scale studies are still warranted to comprehensively investigate this association. This study offers significant strengths, including a large sample size, dual classification, detailed outcome presentation, and adjustment for multiple confounding factors. Our findings suggested that under the classification 1, pregnancy outcomes did not exhibit significant differences, which was similar to the study of Koren et al.[23] There were minor differences in general clinical characteristics between the 2 groups under the classification 2; therefore, PSM was not performed in this classification.

It is worth noting that diverse glycemic targets can influence study outcomes. Hofer OJ, et al proposed stringent tight glycemic targets ranging between ≤5.0 and 5.1 mmol/L for FPG and ≤6.7 and 7.4 mmol/L postprandial in the included trials. Less-tight targets for glycemic control ranged between <5.3 and 5.8 mmol/L for FPG and <7.8 and 8.0 mmol/L postprandial.[3] The evidence suggested a possible increase in hypertensive disorders of pregnancy with tighter glycemic control.[3] In contrast, our study specifically focused on insulin utilization and ruled that patients with GDM maintained glucose levels at FPG ≤5.3 mmol/L, and 2-h postprandial PG ≤ 6.7 mmol/L, and showed no statistical difference in preeclampsia and eclampsia between groups.

Yefet et al proposed that well-controlled glucose was correlated with a long-term reduction in maternal cardiovascular risk.[6] In our study, although DBP revealed no significant difference in classification 2, the unsatisfactory group demonstrated lower DBP (P = .028). The question of whether greater pulse pressure differences are associated with underlying cardiovascular disease remains a topic worthy of long-term follow-up. As for outcomes for offspring, González-Quintero et al highlighted that over one-third of infants in the poorly glycemic controlled group tested positive for the composite variable, comprising macrosomia, large for gestational age, hypoglycemia, jaundice, and stillbirth, which was similar to the results of our study.[24]

The description of pregnancy outcomes in this study concluded with the completion of delivery, but further exploration is warranted in long-term follow-ups. This should encompass postpartum metabolic changes in mothers and the growth and development of newborns, taking into account social contributors and circadian rhythm.[25,26] Moreover, the study’s handling of outcome variables separately could benefit from considering adverse pregnancy outcomes as a composite variable, defining it as the occurrence of at least one adverse pregnancy outcome.

Acknowledgments

The authors express gratitude to all research staff involved in data collection and analysis, as well as the participants who actively participated in the study. All individuals who made significant contributions to this research are acknowledged as authors and meet the criteria for authorship.

Author contributions

Conceptualization: Jiani Zhang, Chihui Mao, Qi Cao, Xiaodong Wang.

Data curation: Jiani Zhang, Chihui Mao, Guiqiong Huang.

Formal analysis: Jiani Zhang, Chihui Mao, Qi Cao.

Funding acquisition: Xiaodong Wang.

Methodology: Jiani Zhang, Qi Cao, Guiqiong Huang.

Supervision: Xiaodong Wang.

Writing – original draft: Jiani Zhang.

Writing – review & editing: Xiaodong Wang.

Supplementary Material

Abbreviations:

ADA American Diabetes Association

BMI body mass index

CI confidence interval

DBP diastolic blood pressure

FPG fasting plasma glucose

GDM gestational diabetes mellitus

OGTT oral glucose tolerance test

OR odds ratio

PSM propensity score matching

This study was supported by the Science Foundation of science and technology program key project of Sichuan Province (grant number 2022YF0042).

The study was in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the West China Second Hospital of Sichuan University [Approval No. 2021 (181)]. Due to the retrospective nature of the study, the need for informed consent was waived by the Ethics Committee of the West China Second Hospital of Sichuan University in accordance with the national legislation.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental Digital Content is available for this article.

How to cite this article: Zhang J, Mao C, Cao Q, Huang G, Wang X. Influencing factors of glycemic control in singleton pregnancies complicated by gestational diabetes mellitus in western China: A retrospective study. Medicine 2024;103:38(e39853).
==== Refs
References

[1] Draznin B Aroda VR Bakris G . 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care. 2022;45 (Supplement_1 ):S17–38.34964875
[2] American Diabetes Association Professional Practice Committee. 15. Management of diabetes in pregnancy: standards of medical care in diabetes-2022. Diabetes Care. 2022;45 (Suppl 1 ):S232–S43.34964864
[3] Hofer OJ Martis R Alsweiler J Crowther CA . Different intensities of glycaemic control for women with gestational diabetes mellitus. Cochrane Database Syst Rev. 2023;2023 :Cd011624.
[4] Moon JH Jang HC . Gestational diabetes mellitus: diagnostic approaches and maternal-offspring complications. Diabetes Metab J. 2022;46 :3–14.35135076
[5] Xiong X Saunders LD Wang FL Demianczuk NN . Gestational diabetes mellitus: prevalence, risk factors, maternal and infant outcomes. Int J Gynaecol Obstet. 2001;75 :221–8.11728481
[6] Yefet E Schwartz N Sliman B Ishay A Nachum Z . Good glycemic control of gestational diabetes mellitus is associated with the attenuation of future maternal cardiovascular risk: a retrospective cohort study. Cardiovasc Diabetol. 2019;18 :75.31167664
[7] Vounzoulaki E Khunti K Abner SC Tan BK Davies MJ Gillies CL . Progression to Type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ. 2020;369 :m1361.32404325
[8] Pathirana MM Lassi ZS Roberts CT Andraweera PH . Cardiovascular risk factors in offspring exposed to gestational diabetes mellitus in utero: systematic review and meta-analysis. J Dev Orig Health Dis. 2020;11 :599–616.31902382
[9] Cao Y Yang Y Liu L Ma J . Analysis of risk factors of neonatal hypoglycemia and its correlation with blood glucose control of gestational diabetes mellitus: a retrospective study. Medicine (Baltimore). 2023;102 :e34619.37657063
[10] Wroblewska-Seniuk K Wender-Ozegowska E Szczapa J . Long-term effects of diabetes during pregnancy on the offspring. Pediatr Diabetes. 2009;10 :432–40.19476566
[11] Snyder J Morin I Meltzer S Nadeau J . Gestational diabetes and glycaemic control: a randomized clinical trial. Am J Obstet Gynecol. 1998;178 (1 Pt 2 ):S55.
[12] Crowther CA Samuel D Hughes R Tran T Brown J Alsweiler JM . Tighter or less tight glycaemic targets for women with gestational diabetes mellitus for reducing maternal and perinatal morbidity: a stepped-wedge, cluster-randomised trial. PLoS Med. 2022;19 :e1004087.36074760
[13] Popova P Vasilyeva L Tkachuck A . A randomised, controlled study of different glycaemic targets during gestational diabetes treatment: effect on the level of adipokines in cord blood and ANGPTL4 expression in human umbilical vein endothelial cells. Int J Endocrinol. 2018;2018 :6481658.29861725
[14] Scifres CM Mead-Harvey C Nadeau H . Intensive glycemic control in gestational diabetes mellitus: a randomized controlled clinical feasibility trial. Am J Obstet Gynecol MFM. 2019;1 :100050.33345840
[15] American Diabetes Association. 14. Management of diabetes in pregnancy: standards of medical care in diabetes-2021. Diabetes Care. 2021;44 (Suppl 1 ):S200–s10.33298425
[16] White P . Pregnancy Complicating Diabetes. Am J Med. 1949;7 :609–16.15396063
[17] Riley RD Ensor J Snell KIE . Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368 :m441.32188600
[18] Goyal A Gupta Y Tandon N . Overt diabetes in pregnancy. Diabetes Ther. 2022;13 :589–600.35107789
[19] American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2021. Diabetes Care. 2021;44 (Suppl 1 ):S15–s33.33298413
[20] Jacobsen DP Røysland R Strand H . Cardiovascular biomarkers in pregnancy with diabetes and associations to glucose control. Acta Diabetol. 2022;59 :1229–36.35796791
[21] Nicolosi BF Vernini JM Costa RA . Maternal factors associated with hyperglycemia in pregnancy and perinatal outcomes: a brazilian reference center cohort study. Diabetol Metab Syndr. 2020;12 :49.32518595
[22] Pénager C Bardet P Timsit J Lepercq J . Determinants of the persistency of macrosomia and shoulder dystocia despite treatment of gestational diabetes mellitus. Heliyon. 2020;6 :e03756.32346630
[23] Koren R Hochman Y Koren S Ziv-Baran T Wiener Y . Insulin treatment of patients with gestational diabetes: does dosage play a role? J Matern Fetal Neonatal Med. 2020;35 :914–20.32126857
[24] González-Quintero VH Istwan NB Rhea DJ . The impact of glycemic control on neonatal outcome in singleton pregnancies complicated by gestational diabetes. Diabetes Care. 2007;30 :467–70.17327306
[25] Colicchia LC Parviainen K Chang JC . Social contributors to glycemic control in gestational diabetes mellitus. Obstet Gynecol. 2016;128 :1333–9.27824747
[26] Weschenfelder F Lohse K Lehmann T Schleußner E Groten T . Circadian rhythm and gestational diabetes: working conditions, sleeping habits and lifestyle influence insulin dependency during pregnancy. Acta Diabetol. 2021;58 :1177–86.33837820
