Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content
Download PDF

Association between pre-pregnancy maternal stress and small for gestational age: a population-based retrospective cohort study

BMC Medicine volume 23, Article number: 7 (2025) Cite this article

Abstract

Background

Maternal stress is a potential factor affecting fetal growth, but it is unknown whether it directly affects fetal growth restriction. This study aims to investigate the association between pre-pregnancy maternal stress with small for gestational age (SGA).

Methods

This study used a population-based retrospective cohort analysis to examine the association between pre-pregnancy maternal stress and SGA in offspring. Data were extracted from the National Preconception Health Care Project (NPHCP), conducted between 2010 and 2012, which encompassed preconception health-related information from 572,989 individuals across various regions in China. Logistic regression models were used to assess the associations between pre-pregnancy maternal stress variables and the risk of SGA. In addition, Synthetic Minority Over-sampling Technique (SMOTE) and Propensity Scores (PS) methods were used to enhance the model’s ability to the associations between pre-pregnancy maternal stress and SGA.

Results

Pre-pregnancy maternal stress was significantly associated with an increased the risk of SGA in offspring (OR 1.35, 95% CI 1.20 to 1.51, P < 0.001). Stress related to life and economic factors notably increased the risk of SGA across different socio-economic conditions, whereas stress related to friends did not show a statistically significant association (P > 0.05). Specially, individuals with lower socio-economic status that characterized by below high school education levels (OR = 1.45, 95% CI: 1.23 to 1.70), farmer occupation (OR = 1.33, 95% CI: 1.15 to 1.55, P = 0.002), rural residence (OR = 1.38, 95% CI: 1.22 to 1.56, P < 0.001), and younger age (under 35 years: OR = 1.35, 95% CI: 1.20 to 1.52, P < 0.001) were more susceptible to pre-pregnancy maternal stress, increasing their risk of SGA.

Conclusions

Pre-pregnancy maternal stress was positively associated with an increased risk of SGA in offspring. Individuals with lower socio-economic status were more likely to experience pre-pregnancy maternal stress related to life and economic factors, which in turn contributed to a higher risk of SGA.

Peer Review reports

Background

Small for gestational age (SGA), defined as a birth weight below the 10th percentile, accounts for 14.6% of newborns worldwide [1]. As a significant public health concern, SGA is associated with various adverse health outcomes in offspring [2, 3], including increased risk of perinatal mortality, morbidity and poor neurodevelopment in childhood [4,5,6,7]. In addition to genetic and environmental factors [8,9,10,11], maternal stress has been increasingly recognized as an important risk factor. Previous studies have suggested that prenatal maternal mental disorders and stress may contribute to adverse birth outcomes [12]. For example, perinatal depression has been significantly associated with an increased risk of SGA [13, 14]. Moreover, maternal stress levels may influence fetus growth [15].

Emerging evidence suggests that negative maternal stress during the preconception period may be crucial for abnormal fetus development [16, 17]. Negative stressors, such as life challenges, economic hardships, and psychological distress encountered during pregnancy, can influence physiological functions, including hormonal regulation and immune responses [18, 19]. These stressors can negatively impact the developmental trajectory of the fetus from early pregnancy [20] and persist until birth [21, 22]. To promote optimal pregnancy outcomes and long-term child health, stress management strategies have been emphasized as an integral part of prenatal care. However, evaluating the effect of pre-pregnancy maternal stress on offspring’s growth remains challenging due to the limitations in the follow-up of pregnancy outcomes.

In this study, we utilized data from the National Preconception Health Care Project (NPHCP), which gathered preconception health-related information and pregnancy outcomes across China. This extensive dataset provided an ideal opportunity to assess the association between maternal stress before conception and SGA. Therefore, we conducted a population-based cohort study involving 572,989 pregnancies in China, to investigate the associations between pre-pregnancy maternal stress and offspring SGA, as well as the potential effect modifications by demographic and socio-economic factors on these associations.

Methods

Study design and participants

This study utilized data from the National Preconception Health Care Project (NPHCP) database in China. This public health initiative was conducted from January 2010 to December 2012 across 220 counties in 31 provinces and provincial-level municipalities, in mainland China [23, 24]. The regions, were categorized into the eastern region (including Beijing, Fujian, Guangdong, Jiangsu, Liaoning, Shandong, Shanghai, Tianjin, and Zhejiang), the central region (including Anhui, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangxi, Jilin, and Shanxi), and the western region (including Chongqing, Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Sichuan, Tibet, Yunnan, and Xinjiang). The geographic distribution of individuals in this study is detailed in Fig. 1.

Fig. 1
figure 1

Spatial distribution of sampling spot locations in the study

Full size image

Couples planning for pregnancy were invited to join the NPHCP to receive free preconception reproductive health services. These services were provided by local family planning service agencies and maternal and child care centers, and included health examinations, health education and promotion, health promotion, medical advice, and referrals to physicians or transfers to other healthcare facilities if necessary. After enrollment, participants completed a survey using a standard questionnaire, providing information about their demographic and socio-economic details as well as other psychosocial factors. The detailed design, organization, and implementation of the NPHCP are described in previous publications [25, 26]. Our study mainly focuses on SGA, ultimately including a total of 572,989 pregnancies. Figure 2 illustrates the participant flow in this study. Meanwhile, the proportions of each variable in the included and excluded groups were similar in each group (Additional file 1: Table S1).

Fig. 2
figure 2

Flow gram of participated individuals

Full size image

This study received ethics approval from the Institutional Review Board of the Chinese Association of Maternal and Child Health Studies (IRB201001), and all participants provided written informed consent upon enrollment.

Exposure variables

Couples who planned to conceive within the 6 months were provided these pre-pregnancy health examinations, and completed a health care questionnaire for the couples as well. This questionnaire concerned about their psychological status. Follow-up was conducted over a 1-year period through telephone calls or by community maternal and child health service personnel to determine if the women were pregnant. To ensure the effective completion of the questionnaire survey, survey administrators were uniformly trained by professional health care personnel.

Social and psychological factors for pre-pregnancy individuals identified common stressors such as life stress, friend stress, and economic stress [24, 27]. In this study, participants were asked about their levels of stress related to their life and social circumstances before pregnancy. The specific survey questions included: (1) Before pregnancy, how often did you feel stressed about life/work?; (2) Before pregnancy, how often did you feel stressed because of relatives/friends/colleagues?; (3) Before pregnancy, how often did you feel stressed due to economic factors? The options for these questions were none, minimal, slight, considerable, and extensive. We subsequently categorized the responses into three levels of stress: almost none (combining none and minimal), sometimes, and considerable (encompassing considerable and extensive). Total pre-pregnancy maternal stress was defined as the occurrence of at least one of the three aforementioned stress types: life stress, friend stress, or economic stress. Therefore, the total pre-pregnancy maternal stress variable was defined as a binary variable (1 = any stressor more than minimal, 0 = no stressor more than minimal). More detailed definitions of exposure variables are provided in the Additional file 1: Table S2.

Outcome measurements

Birth weight was collected based on follow-up by telephone or medical records. The primary outcome was SGA, defined as a birth weight below the 10th percentile for gestational age [28]. The website (http://www.nhc.gov.cn/fzs/s7848/202208/2c1c388fcd0c47c58630e5f971ebb468/files/a92eca2f20584959b2836557a2a96e92.pdf) presents the different percentile weight standards for newborns (P3, P10, P25, P50, P75, P95, P97), calculated according to gestational age (24 to 42 weeks) for both male and female newborns. Additionally, we defined normal birth weight based on 10–90% of birth weight.

Covariates

Maternal age, education level (illiterate, primary school, junior high school, high school/technical secondary school, college/undergraduate, graduate school and above), occupation (farmer, worker, service industry, business, housework, teacher/civil servant/employee), and residential area (urban, rural) were collected via the questionnaire. The covariates were categorized as follows: age (two groups: < 35 years, or ≥ 35 years; three groups: < 30 years, 30–35 years, and ≥ 35 years; four groups: < 25 years, 25–30 years, 30–35 years, and ≥ 35 years); educational level (lower middle school, equal to high school or above); occupation (farmer or non-farmer); residence (urban or rural). Additionally, maternal smoking was categorized as yes or no, second-hand smoking and alcohol consumption during pregnancy were categorized into three groups (often, occasionally, or no). Participants’ medical condition before pregnancy, such as history of diabetes and hypertension (yes or no) were also collected based on medical records. More detailed definitions of covariates are provided in the Additional file 1: Table S2.

Statistical analysis

Participants’ characteristics, including age, pre-pregnancy maternal stress, educational level, occupation and residence, were described using numbers and percentages. The χ2 tests were used to compare the distributions between the SGA and normal birth weight groups. The Cochran-Armitage trend tests were performed to confirm the linear trend of the associations between pre-pregnancy maternal stress variables and SGA (P < 0.001), which justified treating pre-pregnancy maternal stress variables as continuous variables in the models.

Logistic regression models were used to assess the associations between total pre-pregnancy maternal stress variables and the risk of SGA. The models were adjusted for potential confounders including age, educational level, occupation, residence, maternal smoking, second-hand smoking, alcohol consumption, and pre-pregnancy diagnoses of diabetes and hypertension. Multicollinearity test was assessed to identify any significant collinearity between the covariates. Additionally, we conducted multicollinearity assessments for all the statistical models using variance inflation factor (VIF) analysis, and a tolerance value of 0.20 (VIF = 5) was set to be the threshold [29].

In addition, a stratified analysis was performed to explore the potential alleviating effect of socio-economic status on the association between different pre-pregnancy maternal stress and SGA. The stratification variables were age (< 35 years, or ≥ 35 years), educational level (lower middle school, equal to high school or above), occupation (farmer or non-farmer), and residence (urban or rural).

Sensitivity analyses

Sensitivity analyses were then conducted using the balanced dataset, and the results are presented in Additional file 1: Table S3. To address the imbalanced distribution of the SGA across different levels of pre-pregnancy maternal stress, we employed the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance in the dataset [30]. Additionally, we used Propensity Scores (PS) methods to estimate the likelihood of SGA based on their characteristics using observed data and statistical models like logistic regression [31] in Additional file 1: Table S4. These two methods are both described in detail in the Additional file 1: Supplementary Methods [31,32,33,34,35,36,37,38].

Meanwhile, to further assess the robustness of our findings, we conducted sensitivity analyses treating the pre-prenatal stress variables (life stress, friend stress, and economic stress) as ordinal variables (almost none, sometimes, and considerable) in the logistic regression models as presented in Additional file 1: Table S5. The detailed associations between different types of pre-pregnancy maternal stress and SGA across various age groups (three groups: < 30 years, 30–35 years, and ≥ 35 years; four groups: < 25 years, 25–30 years, 30–35 years, and ≥ 35 years) are presented in Additional file 1: Table S6.

Furthermore, a structural equation model was performed to identify the path relationship among education levels, occupation, urban and rural, life stress, friend stress, economic stress, and SGA. Detailed methods and results of the structural equation modeling are presented in the Additional file 1: Supplementary Methods [39,40,41]. Significant demographic characteristics, including age, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension, were used as covariates. The model was examined and then modified [40, 41]. The final model (Additional file 1: Fig. S1) exhibited excellent model-fitting indices: RMSEA = 0.034, GFI = 0.985, CFI = 0.954. The mediating effect of socio-economic status between pre-prenatal maternal stress and SGA and 95% confidence intervals is presented in Additional file 1: Table S7.

The spatial distribution of sampling locations was mapped using the ArcGIS (version 10.8). All data analyses were performed using R software (version 4.3.0, Comprehensive R Archive Network). A two-sided P < 0.05 was considered as statistically significant.

Results

Characteristics of the study participants

Demographic characteristics of 572,989 individuals are presented in Table 1. Compared to participants in the normal birth weight group, those in the SGA group were less likely to report ‘almost none’ life stress (82.6% vs 89.4%) and more likely to report ‘sometimes’ (15.2% vs 9.7%) and ‘considerable’ (2.2% vs 0.9%) life stress. A similar pattern was observed for economic stress. Participants in the SGA group were less likely to report ‘almost none’ economic stress (83.6% vs 88.4%) and more likely to report ‘sometimes’ (13.7% vs 10.5%) and ‘considerable’ (2.7% vs 1.1%). However, the two groups showed comparable distributions for friend stress (P = 0.793).

Table 1 Characteristics of participants, N (%)
Full size table

Association between total pre-pregnancy maternal stress and SGA by socio-economic status

Association between pre-pregnancy maternal stress and SGA, stratified by socio-economic status, are shown in Table 2. Overall, pre-pregnancy maternal stress was associated with increased odds of SGA (OR 1.35, 95% CI 1.20 to 1.51, P < 0.001). Specifically, the association was significant in women under the age of 35 years (OR 1.35, 95% CI 1.20 to 1.52, P < 0.001), but not in those aged 35 years or older (OR 1.28, 95% CI 0.85 to 1.95, P = 0.241). The association was significant in rural areas (OR 1.38, 95% CI: 1.22 to 1.56, P < 0.001), while no significant association was observed in urban areas (OR 1.06, 95% CI: 0.76 to 1.47, P = 0.734).

Table 2 Associations between total pre-prenatal maternal stress and SGA by socio-economic status
Full size table

Association between different pre-pregnancy maternal stress types and SGA by socio-economic status

The alleviating effect of socio-economic status on the relationship between pre-pregnancy maternal stress and SGA is presented in Fig. 3. Overall, pre-pregnancy life stress (OR 1.44, 95% CI 1.30 to 1.59, P < 0.001) and economic stress (OR 1.32, 95% CI 1.19 to 1.46, P < 0.001) were associated with increased risk of SGA. In addition, friend stress did not show a statistically significant association (OR 0.91, 95% CI 0.70 to 1.19, P = 0.499).

Fig. 3
figure 3

Association between different pre-pregnancy maternal stress and SGA by socio-economic status. Note: a, Adjusted model was adjusted for age, education levels, occupation, urban and rural, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension; b, Adjusted model was adjusted for education levels, occupation, urban and rural, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension; c, Adjusted model was adjusted for age, occupation, urban and rural, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension; d, Adjusted model was adjusted for age, education levels, urban and rural, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension; e, Adjusted model was adjusted for age, education levels, occupation, maternal smoking, secondhand smoking, drinking, historical diabetes, and historical hypertension

Full size image

In addition, SMOTE and PS methods were used to enhance the model’s ability to account for the association between pre-pregnancy maternal stress and SGA, and similar results were obtained. The results consistently showed that both life stress (OR 1.43, 95% CI 1.42 to 1.45) and economic stress (OR 1.28, 95% CI 1.26 to 1.29) were associated with an increased risk of SGA using SMOTE methods (Additional file 1: Table S3). What is more, life stress (OR 1.62, 95% CI 1.45 to 1.82) and economic stress (OR 1.66, 95% CI 1.48 to 1.86) were associated with an increased risk of SGA using PS methods (Additional file 1: Table S4).

Life stress and economic stress were both significantly associated with an increased risk of SGA in individuals under 35 years, with odds ratios of 1.43 (95% CI 1.29 to 1.60, P < 0.001) and 1.33 (95% CI 1.20 to 1.48, P < 0.001), respectively. No significant association was found for participants aged 35 years or older. There was no significant association between friend stress and SGA across different education levels and occupations (P > 0.05). In rural areas, both life stress and economic stress were associated with an increased risk of SGA, with odds ratios of 1.50 (95% CI 1.34 to 1.67, P < 0.001) and 1.38 (95% CI 1.24 to 1.54, P < 0.001), respectively. However, no significant association was observed in urban areas. Additionally, friend stress did not demonstrate a statistically significant association with SGA across any socio-economic status categories, including age groups, education levels, occupation, and residence (P > 0.05).

Additionally, we observed dose–effect associations between pre-pregnancy maternal stress and SGA, with the risk of SGA increasing as level of stress increased (Additional file 1: Table S5). The detailed associations between different types of pre-pregnancy stress and SGA across various age groups are presented in Additional file 1: Table S6.

A structural equation model was performed to identify the path relationship among education levels, occupation, urban and rural, life stress, friend stress, economic stress, and SGA (Additional file 1: Fig. S1). The result revealed that the association between socio-economic status and pre-prenatal maternal stress was 0.049 (95% CI = [0.048, 0.050]), and the association between socio-economic status and SGA was 0.005 (95% CI = [0.004, 0.006]). Meanwhile, socio-economic status also plays a partial mediating role between pre-prenatal maternal stress and SGA (β = 0.069, P = 0.029, 95% CI = [0.041, 0.098]) (Additional file 1: Table S7).

Discussion

In this population-based retrospective cohort study involving over 572,989 individuals across various ages and social economic status in China, we identified associations between pre-pregnancy maternal stress and increased risks of SGA. Specifically, individuals under the age of 35 years, with lower education levels, occupied as farmers, and residing in rural areas were particularly vulnerable to the adverse effects of pre-pregnancy life and economic stress on SGA. The findings contributed to the real-world empirical evidence of the negative impacts of maternal stress prior to pregnancy on neonatal health.

Our study firstly found that pre-pregnancy maternal life stress and economic stress are associated with increased risks of SGA. These findings were consistent with results from a randomized trial that utilized mindfulness-based stress reduction techniques to address stress in pregnant women at high risk for small-for-gestational-age infants [42]. Life stress, derived from personal and relational difficulties, and economic stress related to financial insecurity and poverty, may induce physiological changes. Associations between maternal stress during pregnancy and adverse birth outcomes, such as SGA, have been indicated in previous studies [43, 44]. The preconception period is increasingly recognized as an important determinant of birth outcome. Stress experienced before pregnancy may affect physiological systems, including hormonal balances and immune function, which could influence conception and early embryonic development [45, 46]. Despite this, evidence on the effects of pre-pregnancy maternal stress on SGA remains limited, particularly in Low- and Middle-income Countries (LMICs) where populations often experience relatively lower social economic status. Specifically, such stressors can activate the maternal hypothalamic–pituitary–adrenal (HPA) axis, leading to increased secretion of corticotropin-releasing hormone (CRH). Elevated CRH levels may affect placental function and restrict fetal growth, potentially resulting in SGA [47, 48]. Further mechanism studies are required.

Interestingly, our study did not find significant associations between friend stress and SGA. This distinction highlights the varying effects of different types of stress. One possible explanation could be that, compared to interpersonal stress related to friendships, life and economic stress involves more persistent and pervasive challenges such as employment, social security, and financial stability [49]. These stressors are typically chronic and can provoke sustained physiological responses, including prolonged activation of HPA axis and elevated cortisol levels [50, 51]. These findings suggest that public health interventions aimed at reducing pre-pregnancy maternal stress might benefit from prioritizing life and economic stressors, given their substantial impact on risk of offspring SGA.

Furthermore, the effects of pre-pregnancy maternal stress on SGA may vary across different age and socio-economic groups. Previous research has established that lower socio-economic status (SES) is a significant determinant of SGA [52, 53]. Individuals from lower SES backgrounds often face restricted access to healthcare services, increased exposure to chronic stressors, and limited availability of stress management resources [54, 55], which may amplify the adverse effects of pre-pregnancy maternal stress. Our study underscores the substantial role of SES as a modifier in the relationship between pre-pregnancy maternal stress and SGA. Specifically, we observed that individuals under 35 years of age, those with lower education levels, those self-identified as farmers, and residents of rural areas were more susceptible to the adverse effects of pre-pregnancy life stress on SGA. Younger individuals may have less established support networks and less experience in managing stress [56]. Biological differences, such as variations in hormonal regulation and immune responses to stress, might also contribute to their increased vulnerability [57, 58]. Lower educational levels may impair the ability to access, understand, and utilize healthcare information effectively, as education often correlates with health literacy, resource access, and navigation of healthcare systems [59]. Specially, our results indicate that the association between pre-prenatal maternal stress and SGA is not homogeneous across all socioeconomic status. Meanwhile, individuals who were farmers and living in rural areas, may experience limited accessibility to healthcare services and greater exposure to environmental hazards, exacerbating the negative effects of life stress on offspring SGA [60]. This could be reflective of specific environmental and economic pressures prevalent in these communities. The differential impact of pre-prenatal maternal stress on SGA across socio-economic groups underscores the importance of targeted interventions. Health policies aimed at reducing pregnancy-related stress should consider socio-economic disparities, focusing on providing additional support where it is most needed.

In this study, we observed a lower prevalence of SGA compared to global estimates. SGA remains a critical global health issue, with an approximate prevalence rate of 6% [61]. A significant challenge encountered was the imbalance in SGA samples, which may lead to biased results and increased risk of false positives where the model identifies an effect or association that does not actually exist [62]. This limitation underscores the importance of employing robust statistical techniques to ensure accuracy and reliability of findings. The SMOTE adopted in this study was proved to be an effective strategy for mitigating the limitations of sample imbalance [30, 63]. By increasing the number of minority class samples, SMOTE enhanced the model’s ability to detect genuine patterns and associations between pre-pregnancy maternal stress and SGA.

The present study has several strengths. First, the large sample size, which included participants from a broad range of socio-economic backgrounds and across wide geographic areas in mainland China, provides robust statistical power and enhances the generalizability of our findings to diverse populations. Second, our study’s focus on various types of stress during the pre-pregnancy period adds insights on the impact of preconception maternal stress on SGA. Our study had several limitations. First, the study did not include the information of pregnancy complications (such as gestational diabetes, gestational hypertension) or genetic factors, which may also contribute to SGA in offspring. Second, we did not systematically explore individual access to socio-economic resources and healthcare services, which limits our ability to examine how socio-economic status modifies the relationship between pre-pregnancy maternal stress and SGA. Third, incorporating standardized scales to evaluate pre-pregnancy mental health would provide additional insights into its role in adverse SGA. Fourth, the imbalanced distributions of SGA and stressors might potentially result in false positive findings. Nevertheless, we adopted the SMOTE to enhance the model’s ability to detect genuine patterns and associations between pre-pregnancy maternal stress and SGA. Fifth, this study focused on exploring the association between pre-pregnancy maternal stress with SGA, and the stress status during pregnancy has not been evaluated. Further study concerning the effect of changed maternal stress on pregnancy outcomes is required.

Conclusions

Out retrospective cohort study of 572,989 individuals found that pre-pregnancy maternal stress was associated with an increased risk of SGA and socio-economic status significantly modified this association. Specifically, individuals under the age of 35 years, with lower education levels, self-identified as farmers and living in rural areas were more susceptible to the pre-pregnancy life and economic stress on SGA. Our findings highlight the need for public health interventions targeting stress level before pregnancy among younger women from lower socio-economic background, to reduce the risk of offspring SGA. Further research on the effects of pre-pregnancy maternal stress on SGA is crucial for developing public health strategies aimed at improving birth outcomes through enhancing maternal psychosocial health, especially for those facing socio-economic disadvantages.

Data availability

Individual participant data from our study will not be publicly available. For information on data access policies and procedures, please contact the corresponding authors.

Abbreviations

CI:

Confidence interval

CRH:

Corticotropin-releasing hormone

HPA:

Hypothalamic-pituitary-adrenal

LMICs:

Low- and middle-income countries

NPHCP:

National Preconception Health Care Project

OR:

Odds ratio

PS:

Propensity Scores

SES:

Socio-economic status

SGA:

Small for gestational age

SMOTE:

Synthetic Minority Over-sampling Technique

VIF:

Variance inflation factor

References

  1. Blencowe H, Krasevec J, de Onis M, Black RE, An X, Stevens GA, Borghi E, Hayashi C, Estevez D, Cegolon L, et al. National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849–60.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lawn JE, Gravett MG, Nunes TM, Rubens CE, Stanton C. Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data. BMC Pregnancy Childbirth. 2010;10 Suppl 1(Suppl 1):S1.

    Article  PubMed  Google Scholar 

  3. McConnell MA, Rokicki S, Ayers S, Allouch F, Perreault N, Gourevitch RA, Martin MW, Zhou RA, Zera C, Hacker MR, et al. Effect of an Intensive Nurse Home Visiting Program on Adverse Birth Outcomes in a Medicaid-Eligible Population: A Randomized Clinical Trial. JAMA. 2022;328(1):27–37.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Sacchi C, Marino C, Nosarti C, Vieno A, Visentin S, Simonelli A. Association of Intrauterine Growth Restriction and Small for Gestational Age Status With Childhood Cognitive Outcomes: A Systematic Review and Meta-analysis. JAMA Pediatr. 2020;174(8):772–81.

    Article  PubMed  Google Scholar 

  5. Morriss FH Jr, Saha S, Bell EF, Colaizy TT, Stoll BJ, Hintz SR, Shankaran S, Vohr BR, Hamrick SE, Pappas A, et al. Surgery and neurodevelopmental outcome of very low-birth-weight infants. JAMA Pediatr. 2014;168(8):746–54.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Goldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, Li N, Hu G, Corrado F, Rode L, et al. Association of Gestational Weight Gain With Maternal and Infant Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017;317(21):2207–25.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Rinke CM. Infant mortality and the low-birth-weight infant. JAMA. 1985;253(6):826.

    Article  PubMed  Google Scholar 

  8. Bekkar B, Pacheco S, Basu R, DeNicola N. Association of Air Pollution and Heat Exposure With Preterm Birth, Low Birth Weight, and Stillbirth in the US: A Systematic Review. JAMA Netw Open. 2020;3(6): e208243.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Choi A, Noh Y, Yon DK, Shin JY. Maternal Proton Pump Inhibitor Use During Pregnancy and Risk of Low Birth Weight in Offspring in Korea, 2008–2019. JAMA Netw Open. 2023;6(4): e237962.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Nosarti C. Social Relationships, Preterm Birth or Low Birth Weight, and the Brain. JAMA Netw Open. 2019;2(7): e196960.

    Article  PubMed  Google Scholar 

  11. Tong M, Xu H, Wang R, Liu H, Li J, Li P, Qiu X, Gong J, Shang J, Zhu T, et al. Estimating birthweight reduction attributable to maternal ozone exposure in low- and middle-income countries. Sci Adv. 2023;9(49):eadh4363.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Helle N, Barkmann C, Ehrhardt S, Bindt C. Postpartum posttraumatic and acute stress in mothers and fathers of infants with very low birth weight: Cross-sectional results from a controlled multicenter cohort study. J Affect Disord. 2018;235:467–73.

    Article  PubMed  Google Scholar 

  13. Reynolds RM, Pesonen AK, O’Reilly JR, Tuovinen S, Lahti M, Kajantie E, Villa PM, Laivuori H, Hämäläinen E, Seckl JR, et al. Maternal depressive symptoms throughout pregnancy are associated with increased placental glucocorticoid sensitivity. Psychol Med. 2015;45(10):2023–30.

    Article  PubMed  Google Scholar 

  14. Yang Q, Borges MC, Sanderson E, Magnus MC, Kilpi F, Collings PJ, Soares AL, West J, Magnus P, Wright J, et al. Associations between insomnia and pregnancy and perinatal outcomes: Evidence from mendelian randomization and multivariable regression analyses. PLoS Med. 2022;19(9): e1004090.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Osorio JS. Gut health, stress, and immunity in neonatal dairy calves: the host side of host-pathogen interactions. J Anim Sci Biotechnol. 2020;11(1):105.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Borba VV, Zandman-Goddard G, Shoenfeld Y. Exacerbations of autoimmune diseases during pregnancy and postpartum. Best Pract Res Clin Endocrinol Metab. 2019;33(6): 101321.

    Article  PubMed  Google Scholar 

  17. Bais B, Zarchev MG, Luik AI, van Rossem L, Steegers-Theunissen RPM. Maternal Sleep Problems in the Periconceptional Period and the Impact on Health of Mother and Offspring: A Systematic Review. Clocks Sleep. 2022;4(4):688–713.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Colicino E, Cowell W, Pedretti NF, Joshi A, Petrick L, Niedzwiecki M, Wright RO, Wright RJ. Maternal steroids during pregnancy and their associations with exposure to lifetime stressful life events, prenatal stress appraisal and psychological functioning. Psychoneuroendocrinology. 2023;158: 106395.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Tung I, Krafty RT, Delcourt ML, Melhem NM, Jennings JR, Keenan K, Hipwell AE. Cardiac vagal control in response to acute stress during pregnancy: Associations with life stress and emotional support. Psychophysiology. 2021;58(6): e13808.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Martín-Calvo N, Goni L, Tur JA, Martínez JA. Low birth weight and small for gestational age are associated with complications of childhood and adolescence obesity: Systematic review and meta-analysis. Obes Rev. 2022;23(Suppl 1): e13380.

    Article  PubMed  Google Scholar 

  21. Phillips DI, Jones A, Goulden PA. Birth weight, stress, and the metabolic syndrome in adult life. Ann N Y Acad Sci. 2006;1083:28–36.

    Article  PubMed  Google Scholar 

  22. McCormick MC. The contribution of low birth weight to infant mortality and childhood morbidity. N Engl J Med. 1985;312(2):82–90.

    Article  PubMed  Google Scholar 

  23. Liu J, Zhang S, Liu M, Wang Q, Shen H, Zhang Y. Maternal pre-pregnancy infection with hepatitis B virus and the risk of preterm birth: a population-based cohort study. Lancet Glob Health. 2017;5(6):e624–32.

    Article  PubMed  Google Scholar 

  24. Zhou Q, Wang Q, Shen H, Zhang Y, Zhang S, Li X, Acharya G. Seroprevalence of Cytomegalovirus and Associated Factors Among Preconception Women: A Cross-Sectional Nationwide Study in China. Front Public Health. 2021;9: 631411.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang S, Wang Q, Shen H. Design of the national free proception health examination project in China. Zhonghua Yi Xue Za Zhi. 2015;95(3):162–5.

    PubMed  Google Scholar 

  26. Liu J, Zhang S, Wang Q, Shen H, Zhang M, Zhang Y, Yan D, Liu M. Seroepidemiology of hepatitis B virus infection in 2 million men aged 21–49 years in rural China: a population-based, cross-sectional study. Lancet Infect Dis. 2016;16(1):80–6.

    Article  PubMed  Google Scholar 

  27. Zhou Q, Zhang S, Wang Q, Shen H, Zhang Y, Tian W, Li X. Association between preconception paternal smoking and birth defects in offspring: evidence from the database of the National Free Preconception Health Examination Project in China. BJOG. 2020;127(11):1358–64.

    Article  PubMed  Google Scholar 

  28. Gardosi J, Madurasinghe V, Williams M, Malik A, Francis A. Maternal and fetal risk factors for stillbirth: population based study. BMJ. 2013;346: f108.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Marcoulides KM, Raykov T. Evaluation of Variance Inflation Factors in Regression Models Using Latent Variable Modeling Methods. Educ Psychol Meas. 2019;79(5):874–82.

    Article  PubMed  Google Scholar 

  30. Blagus R, Lusa L. SMOTE for high-dimensional class-imbalanced data. BMC Bioinformatics. 2013;14:106.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners. BMJ. 2019;367: l5657.

    Article  PubMed  Google Scholar 

  32. Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150–61.

    Article  PubMed  Google Scholar 

  33. Dablain D, Krawczyk B, Chawla NV. DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Trans Neural Netw Learn Syst. 2023;34(9):6390–404.

    Article  PubMed  Google Scholar 

  34. Glynn RJ, Schneeweiss S, Stürmer T. Indications for propensity scores and review of their use in pharmacoepidemiology. Basic Clin Pharmacol Toxicol. 2006;98(3):253–9.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Korycki U, Krawczyk B. Concept Drift Detection from Multi-Class Imbalanced Data Streams. In: 37th IEEE International Conference on Data Engineering (ICDE 2021): 1068-79.

  36. Tang Y, Zhang YQ, Chawla NV, Krasser S: SVMs Modeling for Highly Imbalanced Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 2009.

  37. Yi X, Xu Y, Hu Q, Krishnamoorthy S, Li W, Tang Z: ASN-SMOTE: a synthetic minority oversampling method with adaptive qualified synthesizer selection. Complex & Intelligent Systems:1–26.

  38. Yoshida K, Hernández-Díaz S, Solomon DH, Jackson JW, Gagne JJ, Glynn RJ, Franklin JM. Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching. Epidemiology. 2017;28(3):387–95.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hayes A. Introduction to mediation, moderation, and conditional process analysis. J Educ Meas. 2013;51(3):335–7.

    Google Scholar 

  40. Morin AJS, Marsh HW, Nagengast B: Exploratory structural equation modeling. In: Structural equation modeling: A second course, 2nd ed. edn. Charlotte, NC, US: IAP Information Age Publishing; 2013: 395–436.

  41. Ortega FB, Lavie CJ, Blair SN. Obesity and Cardiovascular Disease. Circ Res. 2016;118(11):1752–70.

    Article  PubMed  Google Scholar 

  42. Crovetto F, Crispi F, Casas R, Martín-Asuero A, Borràs R, Vieta E, Estruch R, Gratacós E. Effects of Mediterranean Diet or Mindfulness-Based Stress Reduction on Prevention of Small-for-Gestational Age Birth Weights in Newborns Born to At-Risk Pregnant Individuals: The IMPACT BCN Randomized Clinical Trial. JAMA. 2021;326(21):2150–60.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Christian LM. Psychoneuroimmunology in pregnancy: immune pathways linking stress with maternal health, adverse birth outcomes, and fetal development. Neurosci Biobehav Rev. 2012;36(1):350–61.

    Article  PubMed  Google Scholar 

  44. Eick SM, Eatman JA, Chandler M, Brooks NR: Reproductive and Social Policies, Sociopolitical Stress, and Implications for Maternal and Child Health Equity. Curr Environ Health Rep 2024.

  45. Lindsay KL, Buss C, Wadhwa PD, Entringer S. The Interplay between Maternal Nutrition and Stress during Pregnancy: Issues and Considerations. Ann Nutr Metab. 2017;70(3):191–200.

    Article  PubMed  Google Scholar 

  46. Burton GJ, Jauniaux E. Pathophysiology of placental-derived fetal growth restriction. Am J Obstet Gynecol. 2018;218(2s):S745-s761.

    Article  PubMed  Google Scholar 

  47. Shriyan P, Sudhir P, van Schayck OCP, Babu GR. Association of high cortisol levels in pregnancy and altered fetal growth Results from the MAASTHI, a prospective cohort study. Bengaluru Lancet Reg Health Southeast Asia. 2023;14:100196.

    Article  PubMed  Google Scholar 

  48. Su Y, D’Arcy C, Meng X. Research Review: Developmental origins of depression - a systematic review and meta-analysis. J Child Psychol Psychiatry. 2021;62(9):1050–66.

    Article  PubMed  Google Scholar 

  49. Parnia A, Siddiqi A. Socioeconomic disparities in smoking are partially explained by chronic financial stress: marginal structural model of older US adults. J Epidemiol Community Health. 2020;74(3):248–54.

    Article  PubMed  Google Scholar 

  50. Wan Y, Zhang Z, Lin D, Wang X, Huang T, Su J, Zhang J, Li J, Wang Y. Characterization of CRH-Binding Protein (CRHBP) in Chickens: Molecular Cloning, Tissue Distribution and Investigation of Its Role as a Negative Feedback Regulator within the Hypothalamus-Pituitary-Adrenal Axis. Genes (Basel). 2022;13(10):1680.

    Article  PubMed  Google Scholar 

  51. Fernandes SB, Patil ND, Meriaux S, Theresine M, Muller CP, Leenen FAD, Elwenspoek MMC, Zimmer J, Turner JD. Unbiased Screening Identifies Functional Differences in NK Cells After Early Life Psychosocial Stress. Front Immunol. 2021;12: 674532.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Dwarkanath P, Vasudevan A, Thomas T, Anand SS, Desai D, Gupta M, Menezes G, Kurpad AV, Srinivasan K. Socio-economic, environmental and nutritional characteristics of urban and rural South Indian women in early pregnancy: findings from the South Asian Birth Cohort (START). Public Health Nutr. 2018;21(8):1554–64.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Sindiani A, Awadallah E, Alshdaifat E, Melhem S, Kheirallah K. The relationship between maternal health and neonatal low birth weight in Amman, Jordan: a case-control study. J Med Life. 2023;16(2):290–8.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Sow M, Raynault MF, De Spiegelaere M. Associations between socioeconomic status and pregnancy outcomes: a greater magnitude of inequalities in perinatal health in Montreal than in Brussels. BMC Public Health. 2022;22(1):829.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Tu S, Wang AL, Tan MZ, Lu JH, He JR, Shen SY, Wei DM, Lu MS, Au Yeung SL, Xia HM, et al. Family socioeconomic position and abnormal birth weight: evidence from a Chinese birth cohort. World J Pediatr. 2019;15(5):483–91.

    Article  PubMed  Google Scholar 

  56. Lau Y, Yin L. Maternal, obstetric variables, perceived stress and health-related quality of life among pregnant women in Macao. China Midwifery. 2011;27(5):668–73.

    Article  PubMed  Google Scholar 

  57. Kim AW, Said Mohamed R, Norris SA, Richter LM, Kuzawa CW. Psychological legacies of intergenerational trauma under South African apartheid: Prenatal stress predicts greater vulnerability to the psychological impacts of future stress exposure during late adolescence and early adulthood in Soweto. South Africa J Child Psychol Psychiatry. 2023;64(1):110–24.

    Article  PubMed  Google Scholar 

  58. Chen HC, Spiers JG, Lerskiatiphanich T, Parker SE, Lavidis NA, Fung JN, Woodruff TM, Lee JD. Complement C5a Receptor Signaling Alters Stress Responsiveness and Modulates Microglia Following Chronic Stress Exposure. Biol Psychiatry Glob Open Sci. 2024;4(3): 100306.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Geleta TA, Deriba BS. Exploring Enablers and Barriers to Utilize Printed Information, Education, and Communication Materials Among Healthcare Providers of North Shoa Zone, Oromia. Ethiopia Risk Manag Healthc Policy. 2022;15:1303–13.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Kim KM, Farkas DK, Wong V, Hjorth CF, Horváth-Puhó E, Cahan E, Cohen E, Shah NR, Sørensen HT, Milstein A. Association between maternal employment status and presence of children with major congenital anomalies in Denmark. BMC Public Health. 2024;24(1):715.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ohuma EO, Moller AB, Bradley E, Chakwera S, Hussain-Alkhateeb L, Lewin A, Okwaraji YB, Mahanani WR, Johansson EW, Lavin T, et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. Lancet. 2023;402(10409):1261–71.

    Article  PubMed  Google Scholar 

  62. Kroeger CM, Ejima K, Hannon BA, Halliday TM, McComb B, Teran-Garcia M, Dawson JA, King DB, Brown AW, Allison DB. Persistent confusion in nutrition and obesity research about the validity of classic nonparametric tests in the presence of heteroscedasticity: evidence of the problem and valid alternatives. Am J Clin Nutr. 2021;113(3):517–24.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Adebanji AO, Asare C, Gyamerah SA. Predictive analysis on the factors associated with birth Outcomes: A machine learning perspective. Int J Med Inform. 2024;189: 105529.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was part of the Chinese Association of Maternal and Child Health Studies (AMCHS-2014-4). We extend our gratitude to the health workers in the 220 counties across 31 provinces for their invaluable collaboration and efforts as part of the National Preconception Health Care Project. We are grateful to Professor Baosheng Liang (Department of Biostatistics, School of Public Health, Peking University) for his invaluable statistical suggestions on the data analyses. The authors declare that they have no competing interests.

Funding

This study was supported by the National Key Research and Development Program (2021YFC2701600, 2021YFC2701601, 2021YFC2701603), National Natural Science Foundation of China (82371698), Shenzhen Key Laboratory of Maternal and Child Health and Diseases (ZDSYS20230626091559006), Science Foundation of Shanghai (21ZR1410600), Clinical Research Plan of SHDC (SHDC2024CRI077), Research on the Construction of a New Public Health Science System and Talent Training Model (No. 201920102401), Shenzhen Medical Research Special Fund Project (C2401035), Sanming Project of Medicine in Shenzhen (No.SZSM202211032, No.SZSM202311005), and the Fundamental Research Funds for the Central Universities (3332024187).

Author information

Author notes

  1. Manman Chen, Qiongjie Zhou and Yuanyuan Li contributed equally to this work.

Authors and Affiliations

  1. School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Manman Chen & Anying Bai

  2. Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, Guangdong, China

    Manman Chen & Xiaotian Li

  3. Department of Obstetrics, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

    Qiongjie Zhou, Fangyi Ruan, Yandan Liu & Xiaotian Li

  4. National Research Institute for Family Planning, Beijing, China

    Yuanyuan Li

  5. School of Health Policy and Management, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Qu Lu & Yu Jiang

Authors
  1. Manman Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qiongjie Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yuanyuan Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Qu Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Anying Bai
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Fangyi Ruan
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Yandan Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yu Jiang
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Xiaotian Li
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Prof Y. J. and X. L. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. M. C., Q. Z., and Y. L1 (Yuanyuan Li). conducted and are responsible for the data analysis. Concept and design: M. C., Q. Z., Y. L1 (Yuanyuan Li)., Y. J., X. L.. Acquisition, analysis, or interpretation of data: M. C., Q. Z., Y. L1 (Yuanyuan Li)., Q. L., A. B., F. R., Y. L2 (Yandan Liu).. Drafting of the manuscript: M. C., Q. Z., Y. L1 (Yuanyuan Li).. Critical revision of the manuscript for important intellectual content: Q. L., A. B., F. R., Y. L2 (Yandan Liu)., Y. J., X. L.. Supervision: Y. J., X. L..

Corresponding authors

Correspondence to Yu Jiang or Xiaotian Li.

Ethics declarations

Ethics approval and consent to participate

This study received ethics approval from the Institutional Review Board of the Chinese Association of Maternal and Child Health Studies (IRB201001), and all participants provided written informed consent upon enrollment.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12916_2024_3837_MOESM1_ESM.docx

Additional file 1: Supplementary methods, Table S1-S7 and Figure S1. Supplementary methods – Detailed information on synthetic minority oversampling technique (SMOTE) methods, propensity scores (PS) methods, and structural equation modeling. Table S1 – Included and excluded demographic characteristics. Table S2 – Definitions of the main exposures, outcomes and covariates. Table S3 – Comparison of the association between different pre-pregnancy maternal stress and SGA by original sample and using SMOTE methods. Table S4 – Comparison of the association between different pre-pregnancy maternal stress and SGA by original sample and using PS methods. Table S5 – Associations between pre-pregnancy maternal stress levels (categorical variable) and SGA by socio-economic status. Table S6 – Associations between pre-pregnancy maternal stress and SGA by different age groups. Table S7 – The mediating effect of socio-economic status between pre-prenatal maternal stress and SGA and 95% confidence intervals. Fig S1 – Structural equation modeling of the factors affecting SGA.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, M., Zhou, Q., Li, Y. et al. Association between pre-pregnancy maternal stress and small for gestational age: a population-based retrospective cohort study. BMC Med 23, 7 (2025). https://doi.org/10.1186/s12916-024-03837-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-024-03837-7

Keywords

BMC Medicine

ISSN: 1741-7015

Contact us