1. Introduction

A macrosomia, also known as a large for gestational age infant, refers to a fetus with a birth weight of 4000 g or more at any gestational age.[1] The occurrence of macrosomia leads to an increase in short-term and long-term complications for both mother and infant, including a higher risk of difficult delivery, birth injury, postpartum hemorrhage, and cesarean section, as well as an increased risk of cardiovascular disease and metabolic disorders in the offspring later in life.[2,3] The incidence of macrosomia in China is approximately 7%, while it is around 15.1% in other countries.[1] To reduce the incidence of macrosomia and prevent the potential long-term health risks, this study aims to investigate the incidence and influencing factors of macrosomia in the Beijing area.

2. Materials and methods 2.1. Data source

The study included mothers and newborns who had established their maternal and child health records in the Beijing Maternal and Child Health Network Information System at community health service centers affiliated with Haidian District, Beijing, and who had given birth between January 1, 2019 and December 31, 2019 with the exclusion criteria of ① the pregnancy outcomes were non singleton and stillbirth, and ② the gestational age at delivery was <28 weeks. A total of 26,379 parturients, and their newborns were finally included in this study. This study was approved by the Medical Ethics Committee of the Haidian District Maternal and Child Health Care Hospital (No. 2020-18).

2.2. Study content

This study employed a retrospective cohort design and included variables such as maternal age, ethnicity, education level, pre-pregnancy body mass index (BMI), parity, folic acid supplementation, gestational diabetes mellitus (GDM), gestational hypertension, hypothyroidism during pregnancy (including subclinical hypothyroidism), hyperthyroidism during pregnancy, and newborn gender.

According to the Chinese adult BMI classification, pre-pregnancy BMI was stratified into 4 categories: underweight (BMI < 18.5 kg/m2), normal weight (BMI 18.5–23.9 kg/m2), overweight (BMI 24.0–27.9 kg/m2), and obese (BMI ≥ 28.0 kg/m2).[4] Diagnostic criteria for gestational diabetes mellitus, hypertensive disorders complicating pregnancy, and thyroid diseases during pregnancy were based on the 9th edition of Obstetrics and Gynecology.[1]

2.3. Statistical analysis

Statistical analysis was performed using SPSS 24.0 (SPSS, Inc., Chicago, IL), and the metrology data were analyzed using x¯ ±s, expressed as number and percentage of cases for count data, were used for univariate analysis (P < .05) were considered significant by χ2 test, and unconditional multiple logistic regression analysis was used for multivariate analysis.

Less than 5% of data are missing completely at random. We therefore dropped them. As a rule of thumb, if <5% of the observations are missing, the missing data can simply be deleted without any significant ramifications.

3. Result 3.1. Basic information of study subjects

A total of 26,379 parturients were included in this survey, with a minimum age of 17 years and maximum 52 years, with a mean age of (30.96 ± 4.167) years; Educational level was community college degree and above, accounting for 89.6% (23,633/26,379). The average weight of newborns (3323.5 ± 441.6) g, minimum 1000 g, and maximum 5250 g, of which 5.8% (1522/26,379) were macrosomia and 94.2% (24,857/ 26,379) were non-macrosomia.

3.2. Univariate analysis of impact of macrosomia

Based on univariate analysis, maternal age, pre-pregnancy BMI, education level, parity, hypothyroidism during pregnancy, and infant gender were identified as influencing factors for macrosomia (P < .05). In addition, there was a positive linear correlation between the rate of macrosomia and the BMI before pregnancy (P < .05). See Table 1.

Table 1 - Occurrence of macrosomia in different factors [n (%)]. Project Macrosomia Nonmacrosomia χ 2 P Age 9.631 .022  <25 49 (4.5) 1050 (95.5)  25–29 510 (5.4) 8966 (94.6)  30–34 644 (6.2) 9765 (93.8)  ≥35 319 (5.9) 5076 (94.1) Qualification 14.188 .000  College degree and above 1 320 (5.6) 22,313 (94.4)  High school education and less 202 (7.4) 2544 (92.6) Pre-pregnancy BMI 173.861 .000  <18.5 60 (2.2) 2641 (97.8)  18.5–23.9 1 019 (5.4) 17,900 (94.6)  24–27.9 346 (9.1) 3468 (90.9)  ≥28 97 (10.3) 848 (89.7) Folic acid 0.000 .996  Yes 1 380 (5.8) 22,537 (94.2)  No 142 (5.8) 2320 (94.2) Parity 49.684 .000  Primipara 816 (5.0) 15,571 (95.0)  Multiparous 706 (7.1) 9286 (92.9) GDM 0.344 .558  Yes 192 (6.0) 3010 (94.0)  No 1330 (5.7) 21,847 (94.3) HDCP 2.594 .107  Yes 19 (4.1) 450 (95.9)  No 1503 (5.8) 24,407 (94.2) Hypothyroidism  Yes 84 (4.4) 1835 (95.6) 7.381 .007  No 1 438 (5.9) 23,022 (94.1) Hyperthyroidism 0.025 .874  Yes 12 (6.0) 187 (94.0)  No 1 510 (5.8) 24,670 (94.2) Nation 0.427 .513  Han nationality 1 421 (5.7) 23,297 (94.3)  Non-han nationality 100 (6.1) 1529 (93.9) Infant sex 121.121 .000  Male 990 (7.3) 12,558 (92.7)  Female 532 (4.1) 12,299 (95.9)
3.3. Multivariate analysis of factors influencing macrosomia

Based on the univariate analysis, a logistic regression model was established to conduct multivariate analysis. The variables with statistically significant differences in the univariate analysis, including age, prepregnancy BMI, education, parity, hypothyroidism during pregnancy, and infant gender, were taken as independent variables, and whether macrosomia occurred was taken as the dependent variable. The non-conditional logistic regression analysis was performed using a stepwise backward method, with a significance level of 0.05 for variable entry and 0.10 for variable removal. Five factors were included in the final equation. The results of the multivariate analysis showed that maternal age ≥ 35 years, high school education or lower, pre-pregnancy BMI, hypothyroidism during pregnancy, male infant, and parity were all influencing factors for macrosomia (P < .05). See Table 2.

Table 2 - Logistic regression analysis for impact of macrosomia. Variable β SE Wald P OR 95% CI Age  25–29 (reference)  <25 −0.210 0.157 1.793 .181 0.811 0.596–1.102  30–34 0.032 0.064 0.242 .623 1.032 0.910–1.170  ≥35 −0.197 0.082 5.788 .016 0.821 0.700–0.964 Qualification  College degree and above (reference)  High school education and less 0.175 0.082 4.521 .033 1.192 1.014–1.401 Prepregnancy BMI  18.5–23.9  <18.5 −0.908 0.135 45.245 .000 0.403 0.310–0.526  24–27.9 0.538 0.066 66.964 .000 1.713 1.506–1.949  ≥28 0.689 0.113 37.121 .000 1.991 1.595–2.484 Parity 0.317 0.060 27.742 .000 1.373 1.220–1.546 Hypothyroidism −0.301 0.116 6.763 .009 0.740 0.590–0.929 Infant sex 0.608 0.055 119.882 .000 1.836 1.647–2.047
4. Discussion

Macrosomia is one of the most common complications of pregnancy. The present study found more male than female infants with macrosomia, which is consistent with related studies,[5,6] and the risk of macrosomia was 1.836 times higher in male than in female in this study. Maternal advanced age (≥35 years) was 0.821 times that of 25–29 years, and was a protective factor for macrosomia, while there was no significant difference in other age groups compared to the 25–29 years group. Multiparous deliveries have a higher risk of macrosomia,[7] with this study revealing a 1.373 times greater risk of macrosomia in multiparous deliveries compared to primiparous deliveries. The risk of macrosomia is higher at low education delivery.[8]

BMI is positively correlated with body fat content to reflect the degree of obesity, and pre-pregnancy BMI represents the maternal pre-pregnancy nutritional status and plays a key role in fetal growth,[9] this study found that the incidence of macrosomia was positively and linearly correlated with pre-pregnancy BMI (P < .05), that is, a higher pre-pregnancy BMI was associated with a higher incidence of macrosomia, which was consistent with the findings of Xiaofang Liu et al.[10–12] The rate of macrosomia was significantly higher in the high BMI group than in the normal BMI group, and the rate of macrosomia was significantly higher in the normal BMI group than in the low BMI group.[13] Studies have shown that obesity, diabetes, and other metabolic syndromes may begin with abnormal intrauterine growth, and that giving scientifically sound dietary and exercise instructions to women with pre-pregnancy BMI who are overweight or obese and whose BMI is in the normal range before pregnancy can reduce the incidence of adverse obstetric events[14] and reduce the birth of macrosomia and provide a good foundation for offspring physical health.[15,16]

Previous studies have suggested that GDM is a risk factor for macrosomia,[1,17–19] and the results of this study show that GDM is not a risk factor for macrosomia, which is consistent with the Juan J[20,21] study. This may be attributed to the early detection of gestational diabetes through regular screening of fasting blood glucose and glucose tolerance during mid-pregnancy in Beijing’s maternity hospitals, as well as the provision of tailored dietary and exercise guidance and the use of insulin to control blood glucose levels among gestational diabetes patients.[21] Additionally, the abundance of medical resources, high level of medical care, and high level of health knowledge and compliance among pregnant women in Beijing have significantly reduced the likelihood of delivering a large baby among gestational diabetes patients through scientific prenatal interventions.[22]

Pregnant women often experience physiological changes in hormones and metabolism, and thyroid dysfunction is the second most common endocrine disorder that affects pregnant women,[23] This study found that hypothyroidism during pregnancy is a protective factor for macrosomia. This may be due to the low basal metabolic rate of pregnant women with hypothyroidism, relatively low energy intake during pregnancy, and insufficient blood supply and oxygen to the uterus and placenta, which affects fetal growth and development, thereby preventing the birth of macrosomic infants.[24]

With the improvement of people’s physical living standards, the transition of Fertility Policies and fertility concepts, and the enhancement of healthcare levels, in economically developed regions like Beijing, where the North China is located, pre-pregnancy overweight or obesity, male fetuses, multiparity, and low education level are risk factors for delivering macrosomic infants. Efforts should be made to strengthen the prevention and education on macrosomia, correct the misconception that bigger fetuses are better, and provide easy-to-understand health education materials for the public. Women should give birth within the optimal reproductive age range and be advised to plan their pregnancy at the right time. Adequate nutrition during pregnancy is essential but overnutrition should be avoided. For women with pre-pregnancy overweight or obesity, scientific and reasonable dietary and exercise guidance should be provided before pregnancy to keep their BMI within the normal range and prevent macrosomia, thereby reducing the burden of macrosomia on perinatal and long-term health economics.

5. Limitations

The limitations of this study are: firstly, the data source is from the Maternal and Child Health Information System, which may not provide a comprehensive analysis of the factors influencing macrosomia, and the analysis may not be thorough enough. Secondly, the data is only from the Haidian district, which may not be representative of the entire Beijing region, and future studies could expand to multiple centers.

Author contributions

Conceptualization: Kangna Mao, Liqun Chi.

Formal analysis: Kangna Mao, Shanshan Li.

Investigation: Kangna Mao.

Writing – original draft: Kangna Mao, Yuan Gao.

Writing – review & editing: Yuan Gao, Liqun Chi.

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