High Education Level Women in China: maternal weight and Pregnancy Risks

doi:10.21203/rs.3.rs-4863121/v1

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High Education Level Women in China: maternal weight and Pregnancy Risks

https://doi.org/10.21203/rs.3.rs-4863121/v1

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Women’s educational interventions affect pregnancy via maternal weight. This retrospective study enrolled 24594 college and above women with singleton live birth. Multivariate logistic regression were performed to evaluate the impact of sociodemographic characteristics on pre-pregnancy body mass index (p-BMI) and gestational weight gain (GWG) values. Both logistic regression and heat maps were utilized to explore the association between the later factors and adverse pregnancy complications. High education women who were older, natives, above college, assisted reproductive technology, and multiparity tended to have higher p-BMI but control their GWG within the lower range. The multivariate analysis revealed that women who were underweight before pregnancy were at high risk of low birth weight (LBW) and small for gestational age (SGA). Overweight was associated with a high risk of hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), macrosomia, and large for gestational age (LGA). Obesity was also associated with an increased risk of preterm birth, GDM, HDP, macrosomia and LGA. Inadequate GWG was associated with a high risk of LBW, SGA, and preterm birth. Excess GWG might be associated with a high risk of macrosomia and LGA. Women with a high educational level common present with a normal BMI before pregnancy and adequate GWG but the percentage of low p-BMI and excess GWG is still high. Abnormal GWG and p-BMI before pregnancy are associated with increased risks of adverse pregnancy outcomes. In particular, more responsive preventive measures and effective management approaches are required for women with a high educational level.

Gestational weight gain

Pre-pregnancy body mass index

high education level pregnancy women

pregnancy outcome

In the super city of China, the average years of education in the population aged ≥ 15 years in 2020 was 11.8 years, and 33.87% of the population has a university degree or higher in our city based on the seventh national population census bulletin of China. According to previous studies conducted on several tertiary hospitals in our city, the percentage of pregnant women with a high education level (college or higher) was 62.2–75.8%[1–3]. Individuals with post-secondary education levels have a substantial advantage in managing one’s short- and long-term health[4, 5]. For example, they focus more on health cognitive and self-care knowledge, better adherence, and compliance to treatment and medical advice and experience less financial constraints on health services. The vast majority of women with a high education level choose not to have children until they finish their education. Thus, they are more likely to be older than those with a low education level, which could be an important cause of the increasing number of women with advanced maternal age. These people usually have mental work, and they need to face a greater pressure at work, which is associated with a significantly increased burden of depressed mood and anxiety. Pregnant women with a high education level can be classified under the paradoxical group.

Although the rate of obesity has been increasing constantly on global scales[6], higher-educated women have a persistently low mean BMI based on a recent study from China with a large sample size[7]. It can be assumed that education level has an effect on maternal weight parameters including pre-pregnancy BMI (p-BMI) and gestational weight gain (GWG). Adverse pregnancy outcome were significantly influenced by all of these parameters[8, 9]. In pregnant women, weight is the most direct and easy-to-use parameter to detect physical fitness. Among the parameters, p-BMI is an important indicator for measuring the basic nutritional status, and GWG can be used to evaluate nutritional status during pregnancy. In comparison with the WHO BMI classification, China's BMI classification has lower cutoff values. Moreover, in October 2022, the optimal GWG of singletons was first recommended in the standard Monitoring and Evaluation of Pregnancy Weight of Chinese Women (WS/T 801–2022) published by the Chinese Nutrition Society[10], which replaced the 2009 Institute of Medicine weight gain recommendations in previous clinical and research work.

High-educated pregnant women are becoming more common, thus the characteristics of weight change based on the Chinese GWG recommendation and BMI criteria should be explored. Therefore, the current study aimed to identify the factors of weight in pregnant women with a high education level. In addition, the association between maternal and fetal outcomes was evaluated, with focus on the essential key points of weight management education and monitoring during pregnancy, which can guide future studies and can help clinicians select better treatment options.

1.Study population

In this retrospective cohort study, data were collected from 1, 2018, to September 30, 2019, in XXX. Pregnant women who received their first antenatal care, underwent subsequent regular antenatal examination, and delivery in our hospital were included in the analysis. The following criteria were used to determine inclusion: (1) women with singleton pregnancy; (2) those with live birth; (3) those with a gestational week of delivery at ≥ 28 weeks; and (4) those with a high education level (college or higher). Meanwhile, the following criteria were used to exclude candidates: women with multiple pregnancy or still birth, foreigners, and those with missing medical data.

2.Data collection

We collected the data of all patients from the electronic medical record system in the hospital. The information on pregnant women included age, height, pregnancy, and childbirth history, educational background, history of smoking and drinking, mode of conception, pre-pregnancy weight, maternal outcomes, and fetal birthweight.

3.Definitions and protocols

During antenatal examinations, each pregnant (lightly dressed) woman was assisted by a professional nurse to measure her weight using the same machine, which consequently records information in the electronic medical record system. Weight measurements were accurate to 0.1 kg. As measured during the first antenatal visit (≤ 12 gestational weeks), p-BMI is calculated as weight in kilograms divided by height in meters squared. The current study showed that pre-pregnancy weight was significantly correlated with the weight recorded at the initial antenatal examination[11]. Based on adult weight determined according to the Chinese health industry standard, BMI was classified as follows: underweight, < 18.5 kg/m²; normal weight, 18.5–24.0 kg/m²; overweight, 24–28.0 kg/m²); and obese, ≥ 28.0 kg/m²)[12].

The total maternal GWG was calculated as the difference between pre-pregnancy weight and delivery weight. According to the Weight Monitoring and Evaluation in the Pregnancy Period of Chinese Women published by the Chinese Nutrition Society in 2021 (T/CNSS-009-2021), the participants were divided into the inadequate, adequate, and excess groups[13].

A woman with gestational hypertension has a blood pressure over 140/90 mmHg after 20 weeks of gestation. The 75-g oral glucose tolerance test was performed on pregnant women during the 24–28 week gestation period. If the pregnant women presented with any of these findings (fasting plasma glucose level ≥ 5.1 mmol/L; 1-h plasma glucose level ≥ 10.0 mmol/L; and 2-h plasma glucose level ≥ 8.5 mmol/L after taking 75 g of oral glucose), they will be diagnosed as gestational diabetes mellitus (GDM).

Small for gestational age (SGA) refers to a newborn with a birth weight less than the 10th percentile of a child of the same gestational age. A large for gestational age (LGA) birth weight is one that exceeds the 90th percentile of a child of the same gestational age[14]. Low birth weight (LBW) was defined as a birth weight of < 2500 g. The term macrosomia is used to describe an infant born weighing more than 4000 grams.

4.Data analysis

The chi-square test was used to compare for categorical variables such as sociodemographic characteristics and outcomes between the different BMI and GWG groups. The cumulative, ordered logistic regression models were used to determine the sociodemographic characteristics affecting pre-pregnancy BMI and GWG. The associations between GWG and BMI and the outcomes were assessed using the multivariate logistic regression models (including cumulative logistic regression and polynomial logistic regression, as appropriate). The results were reported as odds ratios (ORs) and 95% confidence intervals (CIs). In addition, heat maps were used to visually represent the associations between the combined effects of p-BMI and GWG and adverse birth outcomes. An analysis of the data was conducted using SAS software (version 9.4, SAS Institute, Inc., Cary, NC), and a p value of < 0.05 was considered statistically significant.

During the study period, 31824 individuals were identified, and 24594 met the study criteria, as illustrated in Fig. 1 .

1.Sociodemographic characteristics of the BMI groups

Table 1 showed the sociodemographic characteristics of the study cohort stratified according to maternal BMI. Nearly three quarters (71.9%) of pregnant woman with a high education level had a normal weight, and the number of patients who were underweight (13.9%) were comparable to that of with excess weight (overweight and obese: 12.6% and 1.6%, respectively).

Table 1

Comparison and Multivariate analysis of socio-demographic characteristics in the four p-BMI groups
Characteristic	P-BMI category ( N = 24594 )					Univariate analysis		Multivariate analysis
Characteristic	Underweight (n = 3419)		Normal weight (n = 17685)	Overweight (n = 3095)	Obese (n = 395)	P-value		OR (95% CI)
Age						< 0.001
< 25	117(3.4)	267(1.5)		37(1.2)	8(2.0)		1
25–30	1588(46.5)	6129(34.6)		815(26.3)	110(27.8)		1.49 (1.21, 1.83)*
30–35	1289(37.7)	7635(43.2)		1398(45.2)	167(42.3)		2.18 (1.77, 2.69)*
≥ 35	425(12.4)	3654(20.7)		845(27.3)	110(27.8)		2.79 (2.24, 3.46)*
Conception mode						< 0.001
spontaneous	3234 (94.6)	16265(92.0)		2700(87.2)	334(84.6)			1
ART	185(5.4)	1420(8.0)		395(12.8)	61(15.4)		1.59 (1.44, 1.76)*
Parity						< 0.001
primipara	2700(79.0)	12604(71.3)		2065(66.7)	273(69.1)			1
multipara	719(21.0)	5081(28.7)		1030(33.3)	122(30.9)		1.16 (1.08 ,1.24)*
Race						0.454
Han	3372(98.6)	17393(98.4)		3041(98.3)	391(99.0)
Others	47(1.4)	292(1.6)		54(1.7)	4(1.0)
Region						< 0.001
Local	2184(63.9)	11886(67.2)		2213(71.5)	299(75.7)			1
others	1235(36.1)	5799(32.8)		882(28.5)	96(24.3)		0.85 (0.80, 0.90)*
Smoking						0.127
Yes	25(0.7)	88(0.5)		20(0.7)	0
No	3394(99.3)	17597(99.5)		3075(99.3)	395(100.0)
Drinking						0.454
Yes	82(2.4)	389(2.2)		63(2.0)	5(1.3)
No	3337(97.6)	17296(97.8)		3032(98.0)	390(98.7)
Education level						< 0.001
college	2655(77.7)	13353(75.5)		2535(81.9)	327(82.8)			1
college above	764(22.3)	4332(24.5)		560(18.1)	68(17.2)		0.83 (0.77, 0.88)*
BMI: body mass index; ART: assisted reproductive technology * P<0.05 (multivariate analysis)

Patients aged over 35 years old in the underweight subgroup had the lowest rate of accepted ART and locals. Meanwhile, obese individuals had the highest rate of accepted ART and locals. The four BMI groups significantly differed in terms of parity and education level. The history of smoking and drinking, as well as race, showed no significant differences. After correction via multivariate logistic regression, results showed that patients aged 25–30 years (OR: 1.49, P < 0.001), 30–35 years (OR:2.18, P < 0.001), and ≥ 35 years old (OR: 2.79, P < 0.001) had a higher BMI prior to pregnancy than those aged < 25 years (Table 1). Compared with pregnant women with college education, those with above college education (OR: 0.83, P < 0.001) were more likely to keep p-BMI within the low range. From the viewpoint of region, non-locals (OR: 0.85, P < 0.001) could have a lower BMI before pregnancy than locals. Moreover, individuals who accepted ART (OR: 1.59, P < 0.001) or those with multiparity (OR: 1.16, P < 0.001) had a higher p-BMI.

2.Sociodemographic characteristics of the GWG groups

Table 2 depicts the sociodemographic characteristics of the cohort stratified according to GWG. Approximately half of pregnant woman with a high education level had an adequate GWG. Further, 37% and 12.5% were divided in the excessive and inadequate groups, respectively.

Table 2

Comparison and multivariate analysis of socio-demographic characteristics in different GWG groups
Characteristic	Gestational weight gain category			Univariate analysis	Multivariate analysis
Characteristic	Inadequate (n = 3065)	Adequate (n = 12423)	Excessive (n = 9106)	P-value	OR (95% CI)
Age				< 0.001
< 25	41(1.3)	179(1.4)	209(2.3)		1
25–30	881(28.8)	4130(33.3)	3631(39.9)		0.83 (0.68, 0.99)*
30–35	1316(42.9)	5464(44.0)	3709(40.7)		0.66 (0.55, 0.80)*
≥ 35	827(27.0)	2650(21.3)	1557(17.1)		0.54 (0.45, 0.66)*
Conception mode				< 0.001
spontaneous	2748 (89.7)	11336(91.2)	8449(92.8)		1
ART	317(10.3)	1087(8.8)	657(7.2)		0.83 (0.75, 0.90)*
Parity				< 0.001
primipara	2113(68.9)	8718(70.2)	6811(74.8)		1
multipara	952(31.1)	3705(29.8)	2295(25.2)		0.92 (0.87, 0.98)*
Race				0.576
Han	3013(98.3)	12215(98.3)	8969(98.5)
others	52(1.7)	208(1.7)	137(1.5)
Region				< .0001
local	2140(69.8)	8430(67.9)	6012(66.0)		1
others	925(30.2)	3993(32.1)	3094(34.0)		1.07 (1.02, 1.13)*
Education level				< 0.001
college	2265 (73.9)	9325(75.1)	7280(80.0)		1
college above	800(26.1)	3098(24.9)	1826(20.0)		0.79 (0.75, 0.84)*
Smoking				0.775
Yes	15(0.5)	65(0.5)	53(0.6)
No	3050(99.5)	12358(99.5)	9053(99.4)
Drinking				0.849
Yes	63(2.1)	273(2.2)	203(2.2)
No	3002(97.9)	12150(97.8)	8903(97.8)
GWG: gestational weight gain; ART: assisted reproductive technology
* P<0.05 (multivariate analysis)

GWG groups differed significantly in age, mode of conception, parity, region, and education level, and but not in race and smoking and drinking history. Among the three groups, Excess GWG group had the highest percentage of women with spontaneous conception, those who were primipara, and non-locals. Moreover, the excessive GWG group had the lowest proportion of women with educational level that is above college or university. After correction via multivariate logistic regression, results showed that women aged 25–30 years (OR: 0.83, P = 0.044), 30–35 years (OR: 0.66, P < 0.001), and ≥ 35 years (OR: 0.54, P < 0.001) had a lower weight during pregnancy than those aged < 25 years (Table 2). In term of education, compared with pregnant women with college education, those with a higher education level (OR: 0.79, P < 0.001) were more likely to control their GWG weight within a lower range. In terms of region, non-locals (OR: 1.07, P = 0.009) were more likely to present with a higher GWG weight than locals. Women who received ART (OR: 0.83, P < 0.001) and those with multiparity (OR: 0.92, P = 0.008) were more likely to manage their GWG weight within a lower level.

3.Maternal and neonatal outcomes of the BMI and GWG groups

Table 3 presents the maternal and neonatal outcomes of the cohort stratified according to BMI. Women with a high education level in the normal weight group had the highest educational level, and those who were obese had the highest rate of cesarean delivery, preterm birth (< 37 weeks), HDP, and GDM. From the viewpoint of neonatal outcomes, the obese group had the highest rate of LBW and macrosomia. The underweight group had the highest rate of SGA and the lowest rate of LGA. The overweight group had the highest Apgar score (≥ 7), and the underweight group had the lowest Apgar score (<7).

Table 3

Comparison of maternal and neonatal outcomes in different p-BMI groups
Characteristic	P-BMI category					P-value
Characteristic	Underweight (n = 3419)		Normal weight (n = 17685)	Overweight (n = 3095)	Obese (n = 395)	P-value
Gestational weeks						0.023
<37	128 (3.7)		718(4.1)	137(4.4)	27(6.8)
≥37	3291(96.3)		16967(95.9)	2958(95.6)	368(93.2)
Delivery mode						< 0.001
vaginal birth	2077(60.7)		9395(53.1)	1265(40.9)	131(33.2)
Caesarean section	1179(34.5)		7436(42.1)	1720(55.6)	248(62.8)
Forceps or Vacuum delivery		163(4.8)	854(4.8)	110(3.5)	16(4.0)
HDP						< 0.001
Yes	43(1.3)		507(2.9)	294(9.5)	90(22.8)
No	3376(98.7)		17178(97.1)	2801(90.5)	305(77.2)
GDM						< .0001
Yes	312(9.1)		2360(13.3)	771(24.9)	139(35.2)
No	3107(90.9)		15325(86.7)	2324(75.1)	256(64.8)
Birth weight						< 0.001
Low birth weight	93(2.7)		348(2.0)	60(1.9)	13(3.3)
Normal birth weight	3263(95.4)		16415(92.8)	2718(87.8)	334(84.6)
Macrosomia	63(1.9)		922(5.2)	317(10.3)	48(12.1)
Fetal birthweight						< 0.001
SGA	141(4.1)		429(2.4)	42(1.4)	6(1.5)
Normal	3053(89.3)		15083(85.3)	243(77.6)	274(69.4)
LGA	225(6.6)		2173(12.3)	650(21.0)	115(29.1)
PROM						0.020
Yes	769(22.2)		4144(23.4)	652(21.1)	86(21.8)
No	2659(77.8)		13541(76.6)	2443(78.9)	309(78.2)
Apgar						0.002
≤7	33(1.0)		230(1.3)	62(2.0)	4(1.0)
>7	3386(99.0)		17455(98.7)	3033(98.0)	391(99.0)
BMI: body mass index; HDP: hypertensive disorders of pregnancy; GDM: gestational diabetes mellitus; SGA: small for gestational age; LGA: large for gestational age; PROM: premature rupture of membranes

A comparison of the maternal and neonatal outcomes of the study cohort stratified by GWG shown in Table 4. The inadequate group had the highest rate of preterm birth, and the obese group had the highest rate of cesarean delivery and HDP. The inadequate group had the highest rate of GDM, and the excessive group had the lowest rate of GDM. In terms of neonatal outcomes, In the inadequate group, LBW and SGA were the most prevalent, while macrosomia and LGA were more prevalent in the excessive group. The three groups significantly differed in terms of the rate of PROM and Apgar score.

Table 4

Comparison of maternal and neonatal outcomes in the three GWG groups
	Gestational weight gain category			P-value
	Inadequate (n = 3065)	Adequate (n = 12423)	Excessive (n = 9106)	P-value
Gestational weeks				< 0.001
<37	215(7.0)	550(4.4)	245(2.7)
≥37	2850(93.0)	11873(95.6)	8861(97.3)
Delivery mode				< 0.001
vaginal birth	1686(55.0)	6634(53.4)	4548(50.0)
Caesarean section	1239(40.4)	5229(42.1)	4115(45.2)
Forceps or Vacuum delivery	140(4.6)	560(4.5)	443(4.8)
HDP				< 0.001
Yes	128(4.2)	367(3.0)	439(4.8)
No	2937(95.8)	12056(97.0)	8667(95.2)
GDM				< .0001
Yes	1023(33.4)	1876(15.1)	683(7.5)
No	2042(66.6)	10547(84.9)	8423(92.5)
Birth weight				< 0.001
Low birth weight	142 (4.6)	260(2.1)	112(1.2)
Normal birth weight	2859(93.0)	11707(94.2)	8174(89.8)
Macrosomia	74(2.4)	456(3.7)	820(9.0)
Fetal birthweight				< 0.001
SGA	137(4.5)	337(2.7)	144(1.6)
Normal	2703(88.2)	10755(86.6)	7355(80.8)
LGA	225(7.3)	1331(10.7)	1607(17.6)
PROM				0.242
Yes	673(22.0)	2838(22.8)	2131(23.4)
No	2392(78.0)	9585(77.2)	6975(76.6)
Apgar				0.681
≤7	36(1.2)	167(1.3)	126(1.4)
>7	3029(98.8)	12256(98.7)	8980(98.6)
BMI: body mass index; HDP: hypertensive disorders of pregnancy; GDM: gestational diabetes mellitus; SGA: small for gestational age; LGA: large for gestational age; PROM: premature rupture of membranes

Finally, multivariate analysis was conducted to evaluate the association of p-BMI and GWG (below and above the range of optimal GWG) with obstetric complications (Table 5). The risk of HDP and GDM in the overweight and obese group increased. However, the risk of HDP and GDM in the underweight subgroup decreased. The risk of preterm birth in the obese group increased. Pregnant women who were underweight before pregnancy had a decreased risk of PORM and an increased risk of LBW. Those who were underweight before pregnancy were more likely to had LBW and SGA babies. However, but overweight increased the risk of LBW. Overweight and obese women were more likely to give birth to babies with macrosomia and LGA. Meanwhile, the underweight group had a decreased risk of macrosomia and LGA. The overweight group had a risk of having a low Apgar score (≥ 7). Interventional management with diet and exercise in patients with GDM had an impact on total pregnancy weight gain, and pregnancy swelling affected pregnancy weight in pregnant with HDP. Therefore, the association between GWG and GDM or HDP was not investigated in this study. The inadequate group had an increased risk of preterm birth and SGA and a decreased risk of macrosomia and LGA. The excessive group had an increased risk of macrosomia and LGA and a decreased risk of LBW, preterm birth, and SGA.

Table 5

Multivariate analysis of the effect of p-BMI and GWG on maternal and infant complications
Outcomes	P-BMI category			Gestational weight gain category
Outcomes	Underweight vs normal	Overweight vs normal	Obese vs normal	Inadequate vs adequate	Excessive vs adequate
GDM
OR	0.71 (0.63, 0.81)	2.01 (1.83, 2.21)	3.33 (2.69, 4.13)	-	-
P-value	< 0.001	< 0.001	< 0.001	-	-
HDP
OR	0.44 (0.32, 0.60)	3.37 (2.90, 3.92)	9.42 (7.32, 12.13)	-	-
P-value	< 0.001	< 0.001	< 0.001	-	-
Low birth weight
OR	1.41 (1.12, 1.78)	0.96 (0.72, 1.26)	1.64 (0.93, 2.87)	2.28 (1.85, 2.80)	0.58 (0.46, 0.72)
P-value	0.004	0.751	0.087	< 0.001	< 0.001
Macrosomia
OR	0.34 (0.26, 0.43)	2.06 (1.80, 2.36)	2.49 (1.82, 3.39)	0.64 (0.50, 0.82)	2.61 (2.32, 2.94)
P-value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
SGA
OR	1.71 (1.41, 2.08)	0.56 (0.40, 0.77)	0.62 (0.28, 1.40)	1.70 (1.39, 2.09)	0.57 (0.46, 0.69)
P-value	< 0.001	< 0.001	0.251	< 0.001	< 0.001
LGA
OR	0.53(0.46, 0.61)	1.81 (1.64, 1.99)	2.80(2.24, 3.50)	0.63 (0.55, 0.74)	1.86 (1.72, 2.01)
P-value	< 0.001	< 0.001	< 0.001	< 0.001	< 0.001
Preterm birth
OR	0.94 (0.78, 1.14)	1.05 (0.87, 1.27)	1.66 (1.11, 2.47)	1.61 (1.37, 1.90)	0.60 (0.51, 0.69)
P-value	0.530	0.591	0.013	< 0.001	< 0.001
PROM
OR	0.91 (0.83, 0.99)	0.90 (0.82, 0.99)	0.94 (0.74, 1.20)	0.97 (0.88, 1.07)	1.02 (0.96, 1.09)
P-value	0.029	0.031	0.632	0.513	0.565
Apgar ≤ 7
OR	0.77 (0.53, 1.11)	1.53 (1.15, 2.03)	0.77 (0.28, 2.07)	0.86 (0.60, 1.23)	1.06 (0.84, 1.34)
P-value	0.162	0.004	0.601	0.408	0.633
adjusted for age, setting, education level; BMI: body mass index; GWG: gestational weight gain; HDP: hypertensive disorders of pregnancy; GDM: gestational diabetes mellitus; SGA: small for gestational age; LGA: large for gestational age; PROM: premature rupture of membranes

We investigated the combined effects of GWG and p-BMI on birth outcomes. Figure 2 presents the heat maps for the combined association of p-BMI (x-axis) (by and total GWG (y-axis) with the incidence of LGA (%) and SGA (%) (z-axis; the red color representing a higher incidence and the blue color indicating a lower incidence). The P-BMI was divided into the ≤ 18, 18–20, 20–22, 22–24, 24–26, 26–28, and ≥ 28 kg/m² groups. The total GWG was divided into the eight ≤ 3, 3–6, 6–9, 9–12, 12–15, 15–18, 15–21, and ≥ 21 kg group.

Over the period 1975–2014, the global prevalence of underweight in women decreased from 14.6–9.7%. But an increase in obesity prevalence among women from 6.4–14.9%[15]. Between 2010 and 2014, the proportion of reproductive-age women who were underweight in China was 7.8%, and the proportion of overweight/obesity was 24.8%[16]. The Monitoring of the Nutrition and Health Status of the Chinese Population (2010–2013) showed that according to the recommended GWG by the Institute of Medicine, the proportions of women with insufficient, appropriate, and excessive GWG were 27.2%, 36.2%, and 36.6%, respectively[17]. Globally, obesity is more prevalent among less educated women than among highly educated women[18], which was consistent with our results. Knowledgeable women can better control their weight with diverse healthy lifestyle habits that can prevent excessive weight before pregnancy. One study has shown that the scores based on the Chinese Healthy Eating Index were positively associated with education and urbanization levels[19]. In addition, Darmon et al. revealed that high-quality and healthy diets are more common in affluent and better educated people[20]. Women with a high education level were knowledgeable about the adequate utilization of maternal healthcare services, which could improve the health risks of pregnancy complications[21–24]. However, there are two issues among women with a high education level. One is that they may have excessive control, thereby leading to the increased prevalence of underweight before pregnancy. In a previous study, it was found that women in the Asia-Pacific region felt that they were overweight and exercised more to lose weight than women in four other regions[25]. This may be attributed to women's social value tied to their thinness, as internalized by society[26]. Second, it is noted that the rate of excessive GWG in highly educated pregnant women was not optimistic. Despite the higher health awareness of pregnant women with a high education level, evidence showed that Chinese pregnant women generally consume too much energy-rich food, mainly from excessive fat intake[27]. Further clinical studies should be performed to address the abovementioned issues.

This was the first large-scale retrospective study focusing on pregnant women with a high education level. Interestingly, we found that women who were older, our city natives, those with ART conception, and those with multiparity were more likely to have a higher p-BMI and to control their GWG weight within a lower range. Previous research had shown that socioeconomic status is associated with early and late childbearing. In particular, women with a low socioeconomic status entered parenthood earlier compared with women with a high socioeconomic status[28]. Compared with nonlocals, locals in our city face less socioeconomic pressure, such as home ownership, school attendance of children, and companionship to parents. Hence, our city natives chose late childbearing. Education popularized led the phenomenon of late marriage and later childbearing among women[29]. Due to longer of educational attainment and less socioeconomic pressure, women get pregnant at an older age, and there was a progressive increase in BMI with aging[30]. Meanwhile, older women and those who were knowledgeable were more likely to choose a regular and healthy life. Moreover, they exhibit good habits and perform physical exercise, and they have good state of the economy. Thus, they can better manage their weight. In addition, multiparity was a risk factor of p-BMI, which was similar to that the study of Hill et al. that performed a meta-analysis and systematic review[31]. Every time a woman gives birth to a baby, her average postpartum weight increases by 1 kg, which is higher than the weight increase commonly associated with age[32]. However, compared with women with primiparity, those who have had childbearing experiences may have a greater understanding of pregnancy health care and be more aware of nutrition and exercise during pregnancy[33].

In terms of maternal and fetal outcomes, we found that inadequate GWG, but not excessive GWG, was a risk factor of premature delivery (OR: 1.61 vs 0.60, p < 0.05). A meta-analysis based on 1 309 136 women[34] and a study in a third-tier city in China[35] had a higher risk of insufficient GWG (OR: 1.70 and 1.92, respectively) and a high risk of excess GWG for premature delivery (OR: 0.77 and 0.81, respectively). Having better access to health information and better medical compliance may help decrease the risk of preterm delivery among women with a high education level. However, a study on Chinese urban women has found a U-shaped association between GWG rate and preterm birth[36]. In particular, underweight before pregnancy was a risk factor of SGA and LBW, which was consistent with previous research results in Chinese women[37–40]. However, the risk may be relatively lower in women with a high education level. Interestingly, compared with the results of a meta-analysis on Chinese women[41], people with a low BMI had a lower risk of LBW and SGA. However, people with a higher BMI had an increased risk of LGA and macrosomia. In addition, this study innovatively used heat maps to graphically reflect the combined effect of changes in p-BMI and GWG on LGA and SGA in populations with a high education level. Results showed that p-BMI and GWG had similar effects on fetal weight. It can be used as a reference for clinical work to evaluate the risk of SGA and LGA in an individual with a high education level. Taken together, we hypothesized that p-BMI and GWG can be a predictor of birth outcomes. Diet control, nutrition education, and moderate exercise are essential for gestational weight management among pregnant women with excessive GWG and p-BMI.

The current study had several strengths. That it, it included a large sample size from various centers with standardized health care and complete medical. To the best of our knowledge, this study first investigated the combined effects of p-BMI and GWG according to the Chinese BMI standard and optimal GWG on birth outcomes in singletons from women with a high education level. However, our study also had several limitations. The participants were commonly from our city. Thus, a large multi-center research design could be adopted in the future. This study was retrospective in nature. Although the confounding factors were controlled, the presence of residual confounding could not be ruled out. In addition, information on social state and family incomes was not available, which might affect pregnancy outcomes and could be further improved.

With societal progression, the pregnant group is undergoing possible structural changes. One of the trends is the increase in the proportion of women with a high educational level, which was exactly the target group of this study. In our city, weight before and during pregnancy were significantly correlated with age, conception mode, region, long educational period, and nulliparity. Even though women with a high educational level are knowledgeable and health literate, they still experienced two major challenges, which are underweight before pregnancy and excessive weight gain during pregnancy. Our study showed the impact of maternal weight knowledge based on the Chinese-specific BMI categories and GWG standard on pregnancy outcomes. In clinical practice, new weight management methods (e.g., use of wearable devices for daily monitoring, consultation with online nutritionists for dietary counseling and intervention) can be explored to reduce maternal–infant complications. Finally, heat maps were adopted to identify the combined impact of pre-BMI and GWG for the risk of LGA and SGA, which was more convenient for clinicians to predict individual risks.

Ethics approval: Our research strictly adheres to the Declaration of Helsinki. The life, health, privacy and dignity of the subjects are protected from the procedures of trial design, subject inclusion, data collection, data analysis, and result presentation in our research. This work was approved by research ethics committee of International Peace Maternity and Child Health Hospital. We confirmed that informed consent was waived by the Ethics Committee of International Peace Maternity and Child Health Hospital due to the review of the study.

Consent to Participate: Not applicable.

Conflicts of Interest Statement: The authors declare that they have no competing interests.

Availability of data and materials: The authors confirm that the data supporting the findings of this study are available within the article.

Acknowledgements: None.

Funding: None.

Authors' contributions: WW.C , J.G and F.Q designed of the work; S.G, J,G and F.Q analysis the data, M.L. L.C. and XY.W collected data; J.G, S.G and XY.W have drafted the work or substantively revised it.

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High Education Level Women in China: maternal weight and Pregnancy Risks

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Abstract

Figures

Introduction

Methods

1.Study population

2.Data collection

3.Definitions and protocols

4.Data analysis

Results

1.Sociodemographic characteristics of the BMI groups

2.Sociodemographic characteristics of the GWG groups

3.Maternal and neonatal outcomes of the BMI and GWG groups

Discussion

Declarations

References

Additional Declarations

Status:

Version 1