Retrospective study the clinical data of pregnant women at Zhuzhou Central Hospital of Zhuzhou from January 2019 to December 2021. A total of 10,086 newborns, including 175 newborns with birth defects. The collected data included various factors such as age, height, pre-pregnancy weight, GWG, parity, pregnancy history, pre-pregnancy and pregnancy medication usage, and adverse habits such as smoking and alcohol consumption.

The inclusion criteria were as follows: (1) having undergone a normal pre-pregnancy health checkup, with no pre-pregnancy medication usage or adverse habits; (2) availability of complete prenatal examination data; (3) no family history of psychiatric illness, dementia, birth defects, hypertension, diabetes, or other hereditary diseases; (4) no history of illnesses such as heart disease, tuberculosis, liver disease, kidney disease, chronic hypertension, anemia, blood disorders, psychiatric illness, diabetes, or thyroid dysfunction; (5) residing in the city for more than a year; and (6) willingness to cooperate with clinical data collection.

The exclusion criteria were: (1) residing in the city for less than a year; (2) abnormal results of pre-pregnancy health checkup, pre-pregnancy medication usage, or adverse habits; (3) incomplete prenatal examination data; (4) presence of relevant family history and/or past medical history; and (5) availability of convenient and safe non-invasive methods for monitoring gestational weight gain. This study was approved by the ethics committee of the hospital.

The clinical data of the pregnant women were collected. The collected information included the following:

(1) Basic information of pregnant women: this included their age, height, pre-pregnancy weight, GWG, premarital examination results, adherence to a healthy diet, maintenance of a regular lifestyle, and parity.

(2) Maternal health conditions: details about the women’s health were recorded, including their pre-pregnancy and pregnancy medication history, instances of pregnancy-related complications, any adverse pregnancy history, and a history of psychiatric and neurological abnormalities.

(3) Maternal adverse habits: information regarding habits such as smoking, alcohol consumption, and drug use.

(4) Basic information about pregnant women before delivery: information regarding weight, uterine height, and abdominal circumference.

(5) Labor process and newborn information: comprehensive information about the labor process and the newborns.

The criteria for inclusion in the control group were as follows: when diagnosing a newborn with a birth defect during childbirth, two healthy infants were randomly selected as controls. These control infants had to meet the following conditions: pregnant women with age difference of no more than 2 years, and they should be from the same geographical area and have the same gender, and delivery time of no more than 3 moths compare with matched birth defect cases. Moreover, the selected controls had to have no obvious adverse pregnancy outcomes. During the study period, a total of 175 cases of birth defects were identified, and throng control case matching (1:2) selected as controls. 350 cases of pregnant women without birth defects were randomly selected as the control group by control case matching (1:2). Matching factors include maternal age, pre-pregnancy BMI, delivery time of pregnant women and birth weight of newborn.

The diagnostic criteria for identifying birth defects involved a comprehensive approach. Clinical monitoring was conducted according to the requirements of the “China Birth Defects Monitoring Program” and the 23 categories of birth defect diagnostic criteria outlined in the “China Birth Defect Working Manual” published by the National Defects Monitoring Center.

The control group were selected by case control matching (1:2) [12, 13]. Data analysis was conducted using SPSS 26.0 software (IBM, Armonk, NY, USA). Normally distributed continuous data were represented as the mean $\pm$ standard deviation ($\overline{x}$$\pm$ s). Categorical data were presented as frequencies (n), and data comparisons were conducted using chi-squared tests or the Monte Carlo method. A p-value 0.05 was considered statistically significant. Two-sided tests were performed, with a significance level set at $\alpha$ = 0.05.

A total of 525 parturients were included in the study. Their mean age was 30.3 $\pm$ 4.16 years, with 478 (91.05%) participants being younger than 35 years and 47 (8.95%) being 35 years or older. Among the participants, 83 had low pre-pregnancy weight, 352 had normal weight, 77 were classified as overweight, and 13 were categorized as obese. Regarding GWG, 48 participants had gained inadequate weight, 205 had gained appropriate weight, and 272 had gained excessive weight. The distribution of primiparous, second-parous, and multiparous women was 165, 188, and 172 cases, respectively. There were 224 cases of primiparity, 276 cases of second parity, and 25 cases of third or higher parity. Additionally, 167 patients had experienced pregnancy-related complications, whereas 358 did not experience any complications. Among the participants, 464 did not use any medication during pregnancy, while 61 did. Postpartum checkups revealed that 157 participants showed normal results, whereas 346 participants showed abnormal findings (Table 2).

Among the 175 cases of birth defects, there were 21 different types. The top three types were circulatory system 114 (65.14%) cases, musculoskeletal system 34 (19.43%), urinary system 15 (8.57%). These defects were categorized based on the system involved, as follows: circulatory system defects, including congenital heart disease and vascular malformations; musculoskeletal system defects, including polydactyly/syndactyly, clubfoot, and umbilical hernia; urinary system defects, including congenital inguinal hernia and hypospadias; digestive system defects, including anal atresia, esophageal atresia, and small bowel atresia; and other system defects, including cleft lip/palate, sacral agenesis, and cerebral ventricle malformation. The specific data are presented in Table 3.

Differences in age, pre-pregnancy BMI, GWG, parity, number of pregnancies, pregnancy-related complications, perinatal examinations, medication during pregnancy, smoking history, and alcohol consumption history were compared between the two groups (Table 4). We found that age ($\ge$35 years), medication use during pregnancy, and perinatal TORCH (Toxoplass, Other (Syphilis, Hepatitis B), Rubivirus, Cytomegalovirus, Herpesvirus) showed statistically significant differences between the two groups (p 0.000, odds ratio (OR) (95% confidence interval (CI)), 3.66 (1.97, 6.81), p 0.000, OR (95% CI), 16.02 (7.67, 33.47), p 0.000, OR (95% CI), 283.74 (128.36, 627.21), respectively), indicating their independent roles as risk factors for causing birth defects. However, no statistically significant differences were observed in terms of weight gain, parity, number of pregnancies, pregnancy-related complications, smoking history, and alcohol consumption history (p $>$ 0.05). However, 2 number of births have was significantly difference (p = 0.002, OR (95% CI), 0.55 (0.37, 0.80). Notably, in the classification of pre-pregnancy BMI, obesity exhibited statistically significant abnormalities between the two groups (p = 0.01), whereas low body weight and overweight showed no statistically significant abnormalities.

We conducted a correlation analysis of GWG in the 175 cases of birth defects. The distribution of inadequate, appropriate, and excessive GWG cases was 17, 61, and 97, respectively, constituting proportions of 9.71%, 34.86%, and 55.43%, respectively (Table 5). Despite the lack of statistical significance in the comparison of GWG between the control group and the birth defect group (p 0.05, Table 3), based on the proportions of GWG within the birth defect cases, we considered both insufficient and excessive GWG as potential risk factors for birth defects.

The fetal outcomes were categorized into six groups, and the frequencies of gestational BMI and weight gain groups were compared using the Monte Carlo method. The p-values for both the pre-pregnancy BMI group and the GWG group were $>$0.05, indicating no statistically significant differences (Table 6).

In our study, we found that a significant increase in birth defects occurred when the age was over 35 years old and perinatal TORCH were positive. It has been confirmed that advanced maternal age and TORCH were identified as risk factors for birth defects [15, 16]. For TORCH, approximately 75% of those infected in utero will be asymptomatic at birth, but as they grow, they will be at significant risk for developing motor dysfunction, cerebellar dysfunction, microcephaly, seizures, chorioretinitis, intellectual disabilities, and sensorineural hearing loss [17].

About pregnancy medication, there are 68 cases with clearly documented medication names and courses, it was observed that 48 participants took prenatal nutrients like iron, calcium, vitamins, and amino acids, whereas 20 participants took progesterone for miscarriage prevention, insulin for blood sugar control, or antihypertensive medications. Karcz et al. [18] suggest that maternal nutrient deficiencies might be present during pregnancy and that supplementing adequate nutrients can promote maternal and infant health. Similarly, Hansu et al. [19] suggest that supplementing vitamins and minerals during different pregnancy stages can better maintain maternal and infant health. For supplementing iron, calcium, vitamins, and amino acids during pregnancy, we recommend supplementing with nutrients. However, during pregnancy, we recommend avoiding medication during pregnancy. Anderson et al. [20] compared women who were exposed to antidepressants during early pregnancy with those who were not and found an association between antidepressant use in early pregnancy and specific birth defects such as congenital heart defects. Huybrechts et al. [21] found that the use of hydroxychloroquine in the early stages of pregnancy slightly increased the risk of cleft lip and urinary system defects.

In our study, we found that obesity accounted for 5.14% in the birth defect group, compared to 1.14% in the control group. The results suggest that pre-pregnancy obesity is a risk factor for birth defects. However, pre-pregnancy underweight and overweight do not appear to influence the occurrence of birth defects. Research has indicated that pre-pregnancy obesity could impair embryonic development and the health of the offspring [22]. Vena et al. [23] conducted a systematic review on the relationship between maternal weight and neural tube defects, revealing a significantly higher risk of neural tube defects in fetuses of obese mothers. However, there is no difference in being overweight or underweight [23]. Additionally, when analyzing congenital heart diseases, Persson et al. [24] identified maternal overweight and obesity as high-risk factors. Another cross-sectional study on birth defects also found that overweight and obesity were risk factors [25]. Zhang et al. [26] discovered that compared to women with normal weight, the risk of spina bifida significantly increased for both mothers and offspring in obese pregnant women, whereas the risk of anencephaly in offspring significantly increased for underweight pregnant women. However, some researchers did not find a link between maternal pre-pregnancy obesity and the risk of hypospadias or cryptorchidism in male newborns [27].

Considering the results of this study and related literature, BMI, particularly obesity, is identified as a risk factor for birth defects. Nevertheless, BMI is not a universal risk factor for all types of birth defects. Interestingly, in this study, the proportions of insufficient, appropriate, and excessive GWG gain were 9.71%, 34.86%, and 55.43%, respectively. Based on the proportion of GWG in birth defect cases, insufficient or excessive gestational weight gain is considered a potential risk factor for birth defects. Severe insufficient GWG is associated with a higher risk of low birth weight, preterm birth, growth retardation, and microcephaly, whereas excessive GWG is linked to a higher risk of macrosomia and large-for-gestational-age babies, highlighting the close relationship between GWG and fetal birth weight [28, 29]. Perumal et al. [28] discussed the correlation between GWG and birth defects in 7561 Tanzanian women, suggesting the necessity of interventions for inadequate or excessive GWG to prevent adverse neonatal outcomes. Moreover, some studies have found correlations of decreased pre-pregnancy BMI and insufficient GWG with the severity of clinical features of optic nerve development, especially bilateral diseases and severe brain anomalies [30].

Although BMI and gestational weight gain did not show statistically significant differences between the control group and the birth defect group in this study, might be attributed to the relatively small sample size and potential influence of confounding biases. Further analysis with larger sample sizes is needed to confirm the impact of pre-pregnancy BMI and GWG on fetal birth defects. Nevertheless, trends in the data still suggest that weight management is crucial for women of reproductive age and pregnant women, as it holds significant implications for reducing adverse pregnancy outcomes and improving the overall quality of the population.