Preterm labor and birth refer to deliveries occurring before 37 weeks of pregnancy (Vogel et al., Reference Vogel, Chawanpaiboon, Moller, Watananirun, Bonet and Lumbiganon2018).About 15 million infants are born prematurely worldwide each year, reflecting a worldwide preterm birth rate of approximately 11% (Walani, Reference Walani2020). Premature birth is the primary cause of death for children under 5 years and is recognized as the leading factor in neonatal mortality (death within the first 28 days of life; Vogel et al., Reference Vogel, Chawanpaiboon, Moller, Watananirun, Bonet and Lumbiganon2018). This condition not only heightens the risk of neonatal respiratory issues, necrotizing enterocolitis, sepsis, neuromotor disorders and sensory impairments (Kramer et al., Reference Kramer, Papageorghiou, Culhane, Bhutta, Goldenberg, Gravett, Iams, Conde-Agudelo, Waller, Barros, Knight and Villar2012), but can also result in neonatal neuromaturation problems, such as cognitive deficits, learning difficulties and executive function challenges (Allen et al., Reference Allen, Cristofalo and Kim2011). Preterm labor and delivery is generally viewed as a complex syndrome with an unclear molecular mechanism. Common causes of preterm birth encompass maternal demographics, pregnancy history, nutritional status, infections, high blood pressure, mental stress, and smoking, among others(Goldenberg et al., Reference Goldenberg, Culhane, Iams and Romero2008).The effector molecules produced by various biological pathways stimulate the structural destruction of the mother-fetus interface, the premature contraction of the myometrium, and ultimately lead to premature labor and delivery (Romero et al., Reference Romero, Dey and Fisher2014).
The age at first birth (AFB) is often used as a key indicator to predict demographic patterns, serving as a dependable measure for assessing intricate reproductive results (Su et al., Reference Su, Xu, Hu, Chang, Wu, Yang and Peng2023). This genetic underpinning of AFB is closely linked to human health and developmental pathways. Earlier research indicates a higher likelihood of premature labor and birth in children of both younger and older parents, as opposed to those with parents of median age (Schummers et al., Reference Schummers, Hacker, Williams, Hutcheon, Vanderweele, McElrath and Hernandez-Diaz2019a). Nevertheless, conventional observational research frequently faces limitations due to confounding variables. We tried to use a novel approach to reveal whether AFB affects preterm labor and delivery. Mendelian randomization (MR) analysis can elucidate the causal relationships between variables and overcomes these limitations by controlling for confounders. This research utilized two-sample Mendelian randomization (MR), drawing on extensive genomewide association study (GWAS) data and using genetic markers as tools. This method seeks to clarify the cause-and-effect relationship between AFB and the occurrence of early labor and birth, establishing a basis for improved forecasting and intervention techniques in handling preterm labor and delivery.
Materials and Methods
Study Design and Data Sources
This study implemented a two-sample MR to examine the causative link between AFB exposure and preterm labor and delivery outcomes. By employing separate GWAS datasets, this approach outperforms single-sample MR in both effectiveness and strength. AFB functioned as the exposure variable, while preterm labor and delivery was the outcome of interest. Single nucleotide polymorphisms (SNPs) served as instrumental variables (IVs) for the analysis, selected according to three key two-sample MR criteria: (1) A robust link between all chosen IVs and AFB exposure (p < 5×10^−8); (2) All selected IVs being independent of confounders impacting both AFB and preterm labor and delivery; (3) The effect of all chosen IVs on preterm labor and delivery occurring solely through AFB, with no other mechanisms involved (Figure 1).
Three key assumptions of Mendelian randomization study.
GWAS summary data for AFB were obtained from the European Bioinformatics Institute database. The AFB data included a 542,901 sample size and 9,702,772 SNPs (Mills et al., Reference Mills, Tropf, Brazel, van Zuydam, Vaez, Pers, Snieder, Perry, Ong, den Hoed, Barban and Day2021). Every individual involved was of European descent, and each one provided informed consent. Summary data on preterm labor and delivery from GWAS were sourced from the FinnGen Consortium (https://r7.finngen.fi/). We used the latest version of the database, which included 5480 cases, 98,626 controls and16,379,340 SNPs.
Selecting Instrument Variables
The MR analysis necessitated rigorous compliance with three key principles: relevance, independence and exclusion restriction. As a result, every IV chosen for additional examination was subjected to rigorous evaluation. To ensure significance and reduce weak IV bias, SNPs strongly linked to AFB exposure (p < 5×10^−8) were selected, excluding those with F-values under 10. The F statistic was determined using the formula F = R^2 × (N-2)/(1-R^2), where R^2 represents the proportion of variance in AFB accounted for by each independent variable. R^2 is calculated as 2 times the product of EAF and (1-EAF) multiplied by beta squared, where beta represents the allelic impact and EAF denotes the frequency of the effect allele. To remove biases due to strong linkage disequilibrium among SNPs, a clumping procedure (r^2 < 0.001, physical distance = 10,000 kb) was used to ensure IV independence. Additionally, palindromic SNPs with intermediate allele frequencies were excluded to align effect alleles between the AFB and preterm labor and delivery datasets.
Statistical Analysis
The genetic link between AFB and preterm labor and delivery was investigated using five methods: MR-Egger regression, weighted median, inverse-variance weighted (IVW), simple mode, and weighted mode. IVW, given the assumption that all examined SNPs are valid, was expected to yield the most precise estimates and was therefore the main method used in this research. Various evaluations, such as the Cochrane Q test and the analysis of funnel plot symmetry, were employed to confirm the findings. The MR-Egger intercept test and the MR-PRESSO global test were employed to identify multicollinearity, with MR-PRESSO additionally pinpointing and removing outliers to offer refined estimates. A sensitivity analysis, excluding one SNP at a time, evaluated the influence of each SNP on the total association. Data analysis was performed using R software (v4.3.2) with the Two Sample MR package. A p value of less than .05 was considered statistically significant.
Results
Choosing Instrumental Variables
Following the selection of SNPs that showed a significant association (p < 5 × 10^−8; F value > 10) with exposure data and were independent (r2 < 0.001, physical window = 10,000 kb), 67 SNPs were identified as initial IVs, with the minimum F value being 29.691. Following a harmonization review of both the exposed and resultant data, 47 SNPs were confirmed as instrumental variables.
Mendelian Randomization Analysis
The random-effects IVW method was employed to investigate genetic associations with preterm labor and delivery. A notable disparity in the odds ratio (OR) was observed between individuals experiencing preterm labor and delivery and those who did not (p < .001, 95% CI = 0.815 [0.737, 0.900]) (Table 1 and Figure 2 ). In Table 1, IVW was regarded as the major method, and it showed AFB was an independent risk factor for preterm labor and delivery.
The Mendelian randomization results by five methods
Note: MR, Mendelian randomization; AFB, age at first birth; IVW, inverse-variance weighted.
Forest plot of the effect of age at first birth (AFB) on preterm labor and delivery.
The weighted median approach confirmed a genetic link between AFB and the occurrence of preterm labor and delivery (Figure 3).
The scatter plot shows the causal effect of age at first birth (AFB) on preterm labor and delivery.
To detect heterogeneity, Cochrane’s Q test was conducted. The MR Egger approach produced a Q statistic of 47.056 (df = 45, p = .388), indicating no heterogeneity among the genetic instruments. The IVW method’s Q statistic was 47.297 (df = 46, p = .419), indicating no proof of heterogeneity in the link between AFB and preterm labor and delivery. Moreover, the funnel plots demonstrated symmetrical SNP distribution (Figure 4).
Funnel plot of the effect of age at first birth (AFB) on preterm labor and delivery.
The Egger intercept and MR-PRESSO tests showed no evidence of pleiotropy (p = .634), and the MR-PRESSO analysis did not detect any outliers. The leave-one-out evaluation verified that the MR analysis outcomes remained consistent regardless of individual SNPs (Figure 5), indicating that the findings were stable and reliable.
Mendelian randomization (MR) leave-one-out shows the sensitivity analysis of age at first birth (AFB) for preterm labor and delivery.
Discussion
Through a two-sample MR analysis utilizing data from an extensive GWAS, our research identified a negative genetic causal link between AFB and preterm labor and delivery. This suggests that having children at a younger age increases the risk of preterm labor and delivery, whereas postponing childbirth may serve as a protective measure against it. Nonetheless, the pathogenesis of preterm labor and birth may be linked to factors beyond genetics.
Despite numerous socio-demographic, nutritional, medical, obstetric and environmental factors being linked to a higher risk of preterm labor and birth, the exact causes are still not fully comprehended (Schummers et al., Reference Schummers, Hutcheon, Hacker, VanderWeele, Williams, McElrath and Hernandez-Diaz2018). The genetic framework of AFB is significantly associated with health, female reproductive issues, psychiatric conditions and more (Elks et al., Reference Elks, Perry, Sulem, Chasman, Franceschini, He, Lunetta, Visser, Byrne, Cousminer, Gudbjartsson, Esko, Feenstra, Hottenga, Koller, Kutalik, Lin, Mangino, Marongiu and Murray2010; Mehta et al., Reference Mehta, Tropf, Gratten, Bakshi, Zhu, Bacanu, Hemani, Magnusson, Barban, Esko, Metspalu, Snieder, Mowry, Kendler, Yang, Visscher, McGrath, Mills, Wray and Wu2016). A study observed that the detrimental link between having children at a young age, including teenage and early 20s pregnancies, and health in midlife has increased (Wolfe et al., Reference Wolfe, Thomeer and Reczek2023). Research from Korea indicates that women who became mothers either very young or later in life faced higher chances of heart disease and overall mortality compared to those who had children in their mid-20s (Woo et al., Reference Woo, Jae and Park2022). In analyses segmented by race, the heightened risks of preterm labor, delivering small-for-gestational-age infants, neonatal death, and postneonatal death at younger maternal ages were most evident among white and Asian/Pacific Islander women. On the other hand, the dangers for Black and American Indian/Alaska Native women were more significant at advanced ages (Schummers et al., Reference Schummers, Hacker, Williams, Hutcheon, Vanderweele, McElrath and Hernandez-Diaz2019b). Research into genetic causes has determined that increased AFB is linked to a lower likelihood of postpartum depression (Ou et al., Reference Ou, Gao, Wang, Lin and Ye2023). Our study revealed that genetically, higher AFB is associated with a lower likelihood of preterm labor and delivery, with an odds ratio of 0.867. So, we speculate that early AFB may be used as a risk indicator of preterm labor and delivery.
Increasing studies have indicated that early AFB is associated with lower socioeconomic status and limited education (Hobcraft & Kiernan, Reference Hobcraft and Kiernan2001). Individuals with a higher AFB often achieve more years of schooling and generally maintain a healthier way of living compared to those from lower socioeconomic backgrounds and with fewer educational qualifications, such as reduced smoking, lower alcohol intake, and maintaining a healthy BMI (18.5–24.9 kg/m2; Brown et al., Reference Brown, Kabir, Clark and Gomersall2016; Lawrence, Reference Lawrence2017). Females who give birth for the first time before 23 or after 40 years old might be at a higher risk of gaining excess body fat (Soria-Contreras et al., Reference Soria-Contreras, Aris, Rifas-Shiman, Perng, Hivert, Chavarro and Oken2023). Furthermore, Tropf et al. (Reference Tropf, Stulp, Barban, Visscher, Yang, Snieder and Mills2015) discovered a genetic positive correlation between AFB and factors such as age at menarche, voice maturation, educational achievement, among others. Conversely, a greater number of alleles linked to higher AFB is associated with a reduced genetic risk of obesity and diabetes (Tropf et al., Reference Tropf, Stulp, Barban, Visscher, Yang, Snieder and Mills2015). Thus, it is plausible to postulate that high education attainment and a healthier lifestyle, mainly considered as social factors, might be a potential mediator on the AFB-preterm labor and delivery pathway.
Research based on the UK Biobank group found a negative relationship between older age at first and last childbirth and esophageal cancer (OC), whereas stillbirths, miscarriages and abortions showed a positive link to OC (Sanikini et al., Reference Sanikini, Muller, Chadeau-Hyam, Murphy, Gunter and Cross2020). The change in physical function with older age may be related to fluctuations in reproductive hormones (Sanikini et al., Reference Sanikini, Muller, Chadeau-Hyam, Murphy, Gunter and Cross2020). Nevertheless, research supports that genetic influences play a significant role, possibly explaining as much as half of the differences in reproductive behaviors, such AFB and the total number of children born (Mills & Tropf, Reference Mills and Tropf2015). This is consistent with the genetic findings of the research, suggesting that AFB could be a potential protective factor against premature labor and birth. Future studies are needed to clarify the underlying mechanisms through reproductive hormones.
There are numerous advantages to this research. Initially, our research is pioneering in examining the causal link between AFB and preterm birth and delivery using an extensive GWAS. The two-sample MR approach can address the shortcomings of certain observational research, including reverse causation, confounding variables and multiple biases. Second, all instrumental variables for the MR analysis underwent thorough screening, ensuring that the minimum F value exceeded 10, thereby confirming the precision of the findings. Ultimately, multiple assessments were conducted to evaluate sensitivity, horizontal pleiotropy and heterogeneity. All these tests showed the stability and reliability of the AFB-preterm labor and delivery association.
However, there are some limitations. Initially, the study involved only individuals of European ancestry, and it is yet to be seen if our results apply to other groups and areas. Pleiotropy was accounted for using MR intercepts and MR-PRESSO global tests, but residual confounding factors might still skew the findings. Second, depending on GWAS meta-studies can hinder detailed analyses by nation, ethnic group or age at initial childbirth, possibly limiting the relevance of the identified AFB impacts to certain demographics. Additionally, due to the current practical situation, we do not have the exact gestational age of preterm birth. It is anticipated that as the scope of GWAS studies broadens, forthcoming research may address this issue.
In conclusion, we discovered a significant adverse genetic link between AFB and preterm labor and delivery. These findings indicate that having a child at a younger age could be a risk factor for premature labor and birth, whereas postponing childbirth might offer protective advantages against these issues. Our research enhances the comprehension of the importance of early childbearing age in relation to preterm labor and birth.
Data availability statement
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
Author contributions
Jinghui Zou: Article conception, data analysis, article writing. Cheng Li: Guidance on software use and data analysis. Hangyu Wu, Aijiao Xue, Lulu Yan: revised the manuscript. Yisheng Zhang: Article proofreading, responsible for the whole article.
Funding statement
This work was supported by the key research and development project of Ningbo (No.2023Z185).
Competing interests
All authors declare that there are no conflicts of interest.
