The birth weight of newborn and the incidence of macrosomia are both increasing along with economic improvement recently. In this study, prevalence of macrosomia was 6.13%, which was close to 5.96% reported in the tertiary hospitals and lower than that in the total prevalence (7.069%) in beijng [18]. This is comparable to the United states (7.8%) and requires intervention [19].

Macrosomia is closely related to a variety of maternal factors, such as genetic factors, environment, and healthy condition. Previous studies have shown that prepregnancy BMI, excessive weight gain during pregnancy, and preexisting GDM/DM are independent risk factors for macrosomia. GDM/DM, overweight/obesity and excess gestational weight gain have more metabolic characteristics such as increased insulin resistance, hyperglycemia, and hyperinsulinemia, which play important roles in macrosomia [20,21,22,23] This study found that prepregnancy BMI, weight gain during pregnancy, the proportion of preexisting GDM/DM in the macrosomia group were significantly higher than those in the non-macrosomia group (P < 0.05). The result of multivariate logistic regression showed the risk of macrosomia were significantly and independently associated with prepregnancy overweight /obesity, the history of GDM/DM and weight gain during pregnancy, which was consistent with the above findings.

A multi-center, cross-sectional survey involving 101,723 singleton term infants showed that risk of macrosomia was positively associated with maternal age, parity [24]. In this study, the mean age and the frequency of pregnant women ≥35-year-old in the macrosomia group were higher than those in the non- macrosomia group, but the difference was not statistically significant. And there was no association between age and macrosomia by univariate logistic regression analysis (P > 0.05). Multiparas in the macrosomia group was observed more than that in the non- macrosomia group, and the risk of macrosomia in multiparas was 1.8 times higher than that in primiparas(P < 0.05). These results were not completely consistent with the previous studies.

Data from a prospective cohort study including 54,371 singleton pregnancies at 12 centers in the US showed that women who had delivered a macrosomic newborn in the past had a higher risk to have another macrosomia in the subsequent pregnancy with the recurrence rate of 23.2% (95% CI: 21.2–25.2%), and the number of prior macrosomic infants was positively associated with the risk of recurrent macrosomia [25]. Previous delivery of macrosomia was the single strongest individual risk factor for macrosomia controlling for prepregnancy BMI, excess weight gain, DM/GDM and other risk factors [22, 23]. In this study, previous delivery of macrosomia was also the most predominant risk factor for macrosomia in multivariate logistic regression analysis (OR 29.021, P < 0.05). Although this factor can not be controlled, it is helpful in screening for macrosomia.

The blood lipid level of pregnant women is one of the important factors affecting the birth weight of newborns. Maternal TG and TC can be taken up by the placenta, metabolized and transported to the fetus in various forms for providing energy for the fetus and helping the fetus to build cell membrane [26]. Elevation of these two lipids in a certain range during pregnancy are beneficial to the development and growth of the fetus. However, excessive lipid can cause fetal overgrowth [27]. Previous studies showed that maternal TG level in the first term of pregnancy was positively associated with higher birth weight, and an independent predictor of neonatal birth weight, but maternal TC level was not associated with birth weight [27, 28]. Whereas, Khaire A, et al. reported a positive correlation between maternal TC level and neonatal birth weight [26]. In this study, we found that the levels of maternal TC, TG in the first trimester in the macrosomia group were significantly higher than those in the non-macrosomia group (P < 0.05). The results of univariate logistic regression analysis showed the levels of maternal TC, TG in the first trimester were related to the occurrence of macrosomia. However, after adjusting for prepregnancy BMI, gestational weight gain, parity, history of macrosomia delivery, history of GDM/DM, HbA1c, and CRP, maternal TC level remained an independent risk factors of macrosomia in multivariate logistic regression analysis. Whereas, TG was not an independent risk factor for macrosomia.

HbA1c is a sensitive indicator reflecting glucose metabolism during pregnancy. Hatice Kansu-Celik, et al. have found a positive correlation between first-trimester HbA1c level and birth weight of newborns [29]. Similar to previous studies, we found that maternal HbA1c level in the first trimester was positively correlated with macrosomia and an independent risk factor for macrosomia, which can be used to predict macrosomia.

Previous studies have shown that increased CRP levels are associated with insulin resistance, maternal dysglycemia and GDM [30], which may cause macrosomia. In this study, first-trimester CRP levels were higher in the macrosomia group and related to macrosomia in univariate logistic regression analysis (P < 0.05). However, the result of multivariate logistic regression analysis showed that CRP was not an independent risk factor for macrosomia (P > 0.05), which was not consistent with the above findings.

Given the increased morbidity and mortality for infants and mothers caused by macrosomia, predicting macrosomia and taking effective interventions in early pregnancy were both crucial. Before 1976, fetal weight was estimated mainly by measuring the height of uterus and abdominal circumference of pregnant women. This method was easy and rapid, but it’s accuracy could be affected by many factors, such as uterine tension, amniotic fluid volume, fetal position and abdominal wall thickness. In recent 40 years, with the extensive use of ultrasound examination in obstetrics, ultrasonography has been currently an important measure to predict fetal weight in many countries. Sonographic estimated fetal weight (EFW) uses 2-dimensional ultrasound imaging to record fetal biometric parameters (such as abdominal circumference, head circumference, femur length, and biparietal diameter), and then a formula was entered to estimate fetal weight [31]. Compared with maternal physical examination, ultrasonographyis more accurate in assessing normal fetal weight. Whereas its sensitivity and specificity of evaluating macrosomia were only 0.56 (95% CI 0.49–0.61) [32] Although 3-dimensional (3D) ultrasound and magnetic resonance imaging (MRI) have been used to estimate fetal macrosomia, there is still lack of evidence to conclude that 3D ultrasound and MRI, which are more expensive, are superior to 2D ultrasound [33, 34]. Thus they are not widely used in clinical practice. Furthermore, these methods for predicting macrosomia are all carried out in the third trimester. Therefore, it requires to develop a simple, cheap and accurate method to predict the risk of macrosomia in early pregnancy.

Nomogram model has been widely used in clinical cohort studies due to its high accuracy, efficiency and stability. In this study, we constructed a nomogram model based on the risk factors of macrosomia including maternal BMI before pregnancy, parity, a prior macrosomic newborn, preexisting GDM/DM, the levels of HbA1 and TC in the first trimester. The AUC of the nomogram model indicated an overall predictive performance of 0.807 (95% CI: 0.755–0.859) in the internal validation, which showed good predictive ability. The sensitivity, specificity, positive predictive value, and negative predictive value were 0.716, 0.777, 0.174, and 0.977, respectively. Compared with traditional predictive models [35, 36], the nomogram model enables visual and personalized prediction, which can help obstetricians to more easily access the risk of macrosomia according to the score of each pregnant woman, then provide personalized healthcare service. Besides, the nomogram model can help pregnant women understand personal risk of macrosomia easily and clearly, which can enable pregnant women to cooperate with the intervention and achieve better clinical effects. In addition, the predictive model of this study is established based on maternal general characteristics before pregnancy and the clinical data in early pregnancy, which can be used to screen pregnant women for macrosomia in the early pregnancy stage so that effective intervention and treatment can be earlier implemented for these gestational women. More importantly, the variables of the screening tool are easily available, which is suitable for the wide promotion and application in clinical practice.

There were some limitations in the current study. The data was from a single center, the sample size was small, and the model was only internally validated. In addition, other demographic parameters, such as socioeconomic statu and genetic potential, were not considered, which were also associated with the macrosomia. Therefore, multi-center, larger sample studies with more parameters and further external validation should be carried out to explore the generalizability of this predictive model.

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