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Association between gestational weight change trajectories and perinatal outcomes in twin pregnancies in China

BMC Pregnancy and Childbirth volume 25, Article number: 312 (2025) Cite this article

Abstract

Objective

This study aimed to identify gestational weight change trajectories and examine their association with perinatal outcomes.

Methods

Prenatal and delivery records of 3393 twin pregnancies were obtained from the China–US Collaborative Project for Neural Tube Defects Prevention, a large population-based cohort study. Generalized gestational weight gain (GWG) was calculated by dividing the total GWG by the length of gestation in weeks and multiplying by 37 weeks. Latent class growth modeling (LCGM) was used to identify GWG patterns. Multivariable logistic regression and generalized estimating equations (GEE) were used to analyze the associations between GWG trajectories and perinatal outcomes. The included adverse perinatal outcomes were preterm birth, low birth weight (LBW), small for gestational age (SGA), and large for gestational age (LGA).

Results

The mean ± SD of GWG for underweight women was 17.87 ± 5.67 kg, 16.76 ± 6.22 kg for normal weight, 14.34 ± 6.60 kg for overweight, and 14.27 ± 4.94 kg for obese. Three gestational weight change trajectory groups were identified: low-increase (32.36%), moderate-increase (56.26%), and high-increase (11.38%). Compared to the moderate-increase group, the high-increase group showed a reduced risk of LBW (aOR 0.68, 95%CI 0.56, 0.83), and SGA (aOR 0.49, 95%CI 0.40, 0.60) but an increased risk of LGA (aOR 2.23, 95%CI 1.48, 3.35). No significant change was observed in the risk of preterm birth in the high-increase group. The low-increase group had a higher risk of preterm birth (aOR 1.66 95%CI 1.42, 1.94), LBW (aOR 2.44 95%CI 2.13,2.80), and SGA (aOR 1.32 95%CI 1.16, 1.51), with no significant difference in the risk of LGA (aOR 1.11 95%CI 0.78,1.58).

Conclusions

Distinct patterns of GWG in twin pregnancies are associated with varying risks of adverse perinatal outcomes. These findings highlight the importance of monitoring and managing GWG in twin pregnancies.

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Introduction

Twin pregnancies, which account for approximately 1.49% of all births in China, are associated with an increased risk of adverse perinatal outcomes compared to singleton pregnancies [1]. Nearly 58% of twins are born preterm, and their incidence of being small for gestational age (SGA) is significantly higher compared to singletons, with rates of 37.6% versus 7.0%, respectively [2, 3]. At the same time, mothers carrying twins tend to gain more weight compared to those carrying singleton [4].

Maternal pre-pregnancy body mass index (BMI) and gestational weight gain (GWG) are associated with many adverse pregnancy complications [5, 6]. Overweight and obese women are at an increased risk for common pregnancy complications, including hypertensive disorders, preterm birth, and delivering large for gestational age (LGA) infants [7, 8]. GWG also plays a critical role in determining pregnancy outcomes. Higher GWG in both singleton and twin pregnancies is linked to LGA, while insufficient GWG is associated with preterm birth, low birth weight (LBW) and SGA [9, 10]. Although comprehensive guidelines for GWG stratified by pre-pregnancy BMI have been established for singleton pregnancies [4], evidence-based recommendations for twin pregnancies in China is limited. Currently, the recommendations of Institute of Medicine (IOM) represent a widely accepted standard for GWG. However, the IOM has only issued provisional guidelines for twin pregnancies, based on data from a single study, and has not provided recommendations for GWG across all pre-pregnancy BMI categories [4, 11]. The existing IOM suggest a GWG range of 17–25 kg for normal pre-pregnancy BMI, 14–23 kg for overweight, and 11–19 kg for obesity [4].

Previous research mainly focused on the total GWG, assuming that all women follow the same gain pattern [9, 12]. However, there might be subgroups within the population, having different weight gain trajectories. Understanding the relationship between GWG patterns and perinatal outcomes in twin pregnancies is essential for improving pregnancy management and outcomes [13, 14]. While there is extensive data on the relationship between GWG and pregnancy outcomes in singleton pregnancies [15, 16], the impact of GWG on twin pregnancy outcomes remains less consistent [17, 18]. Moreover, with the unprecedented rise of twin pregnancies, there is a growing need to explore how the GWG affects the risk of common pregnancy complications in twin pregnancies.

Therefore, this study aims to comprehensively investigate the intricate associations between gestational weight gain trajectories and adverse perinatal outcomes, with the primary objective of elucidating potential risk factors and providing nuanced insights into maternal-fetal health dynamics.

Methods

Study design and subjects

The data for this study was sourced from a public health campaign conducted in China from 1993 to 1996 (More detailed information about the original design and population has been described previously [19, 20]). The campaign, targeting neural tube defect prevention, provided folic acid supplements and was implemented by the Chinese Ministry of Health across 21 counties in 2 southern provinces (Zhejiang and Jiangsu) and 1 northern province (Hebei). Data were collected using a maternal and child health monitoring system established as part of the public health campaign. This system was designed to record comprehensive perinatal health data and monitor early developmental of children. The monitoring system recorded until 2007, ensuring a large population-based cohort.

The original dataset included records of 15,525 twin pregnancies, allowing for detailed analysis of perinatal outcomes specifically among twin gestations. In this study, 5,534 women with twin pregnancies who enrolled before pregnancy and had the pre-pregnancy weight were selected. Of these women, we excluded 184 (3.32%) with outcomes of selective reduction or stillbirth, 51 (0.92%) with major congenital birth defects, and 1,338 (24.18%) with missing weight and height or abnormal records in the initial phase of selection. Demographic characteristics between the final included participants (n = 3,393) and the excluded pregnancies was summarized in Table(S1). In the second phase of selection, 9 women (0.16%) were excluded, with age < 18 or > 45 years; 170 (3.07%) with abnormal infant birth weight; 41(0.74%) with abnormal weight gain (weight gain < 0 or > 40 kg); 339 (6.13%) with the second weight measurement not recorded in the first trimester; and 9 (0.16%) with a gestational age of < 24 weeks or > 42 weeks. After these exclusions, 3,393 eligible participants were included in the study (Fig. 1).

Fig. 1
figure 1

The flowchart of the enrolled participants

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This study has obtained approval from the Biomedical Ethics Committee of Peking University (Ethics Number: IRB00001052-24076).

Gestational weight gain

Height and weight were measured by trained local health workers. Weight (kg) and height (cm) were recorded while participants wore light indoor clothing without shoes, overcoats, or hats. All measuring instruments were calibrated by local quality and technical supervision experts. Pre-pregnancy weight and height were documented during registration used the same method mentioned above. Weight and height at the first prenatal visit, as well as pre-delivery weight were recorded throughout the course of prenatal care. All women in the final analysis had completed weight change data. The gestational weeks were assessed for their distribution. The median gestational week at the first weight measurement was 9 weeks (interquartile range [IQR]: 7–11 weeks), with a mean of 9.01 weeks (standard deviation [SD]: 2.21 weeks). The distribution was symmetric (skewness: -0.20) and exhibited no extreme kurtosis (kurtosis: 2.34). Additionally, 90% of the measurements were taken between 6 and 12 weeks of gestation. These results indicate that the first weight measurement was sufficiently concentrated within early pregnancy to be treated as a single time point in the analysis. Gestational age was calculated based on the woman’s last normal menstrual period. Total GWG was calculated by subtracting pre-pregnancy weight from the pre-delivery weight. In the current study, the main exposure variable was the standardized GWG equivalent to 37 weeks of gestation, which was calculated by dividing the total GWG by the length of gestation in weeks and multiplying by 37 weeks [21, 22].

Perinatal outcomes

We collected detailed maternal and neonatal data, including maternal height, weight, gestational age at delivery, infant sex, and birth weight. Adverse perinatal outcomes assessed in the study included preterm birth, low birth weight (LBW), small for gestational age (SGA) and large for gestational age (LGA). Preterm birth was defined as delivery before 37 weeks of twin gestation and was further categorized into two clinical subtypes: spontaneous and medically indicated subtypes. Low birth weight was defined as birth weight < 2500 g. SGA (birthweights below the 10th centile for gestational age) and LGA (birthweights greater than the 90th centile for gestational age) were defined according to Chinese standard [23]. At the time of the analysis, there were no widely available, validated twin-specific birthweight percentiles for the Chinese population. Therefore, the definition for SGA and LGA were based on singleton pregnancy. A composite outcome was also defined as the occurrence of any of the following: preterm birth, LBW, SGA, or LGA.

Covariate assessment

Maternal characteristics, including age, occupation, ethnicity, education level, region and gravid were collected from the maternal and child health monitoring system described above. Pre-pregnancy BMI was calculated as dividing pre-pregnancy weight (kg) by the square of height (m2). Pre-pregnancy BMI was categorized into four groups: underweight (< 18.5 kg/m2), normal weight (18.5–24 kg/m2), overweight (24–28 kg/m2) and obese (≥ 28 kg/m2) using the Chinese criteria [24].

Statistics

Descriptive statistics were used to summarize the baseline characteristics of the study population. Continuous variables are presented as means ± standard deviations (SDs), while categorical variables are expressed as ratios (percentages). Between-group comparisons for categorical variables were conducted using the Chi-square test, while continuous variables were compared using the t-test or the Mann-Whitney U test.

Latent class growth model (LCGM) was performed using Stata Plugin “traj” to identify the gestational weight change trajectory patterns [25]. LCGM operates on the assumption that a population consists of distinct groups, each characterized by unique patterns or trajectories over time. It further assumes that individuals within each group follow a consistent trajectory [26]. Trajectory model was determined based on the Bayesian information criterion (BIC), with a lower value indicating better model performance. An average posterior probability of 0.70 or higher for each group is considered as good discrimination in classifying individuals into distinctive groups. The three-class trajectory model was determined and the trajectories of each group were fitted with a polynomial of the highest order of 3, with the smallest group ratio no less than 5% (Table S2). Although Class 2 showed the lowest BIC value, we opted for the three-class model because it provided a more meaningful and clinically relevant distinction between groups. This classification aligns with practical prenatal care experience, where GWG is often categorized into three distinct groups: excessive, insufficient, and adequate. In addition, the three-class model offered better group separation, as indicated by the higher entropy (0.792 vs. 0.740), which may provide a more nuanced and accurate representation of the data. Three trajectory groups were identified and named as low-increase group, moderate-increase group and high-increase group (Table S3).

A multivariable logistic regression was used to analyze the association between gestational weight change trajectory groups and preterm birth, presenting both unadjusted and adjusted results. A generalized estimating equation (GEE) model with a binomial family and a logit link was performed to analyze the association between gestational weight change trajectory groups and perinatal outcomes (LBW, SGA or LGA), controlling for the clustering of twins within a pair. In this analysis, Level 1 represents individual fetuses (first or second in a twin pair), and Level 2 represents the twin pair as the clustering unit. Maternal age, BMI, ethnicity, occupation, education level, gravidity, place of origin and twin gender were adjusted as covariates.

Level 1 (Fetal level): g(πij) = β₀j + β₈(Twin Genderij).

Level 2 (Twin pair level): β₀j = γ₀₀ + γ₁(Agej) + γ₂(BMIj) + γ₃(Ethnicityj) + γ₄(Occupationj) + γ₅(Educationj) + γ₆(Gravidityj) + γ₇(Provincej) + u₀j.

Combined full model: g(πij) = γ₀₀ + γ₁(Agej) + γ₂(BMIj) + γ₃(Ethnicityj) + γ₄(Occupationj) + γ₅(Educationj) + γ₆(Gravidityj) + γ₇(Provincej) + β₈(Twin Genderij) + u₀j Where:

i denotes the individual fetus (Level 1).

j denotes the twin pair (Level 2).

g(·) is the logit link function.

u₀j is the random effect at twin pair level, and u₀j ~ N(0, τ²₀).

The optimal GWG ranges for twin pregnancies, stratified by BMI category, were calculated as the interquartile range (IQR) of total GWG within the group without any composite outcome.

All statistical analyses were performed using Stata, version 18.0 (StataCorp). Statistical significance was defined as a two-tailed p-value < 0.05.

Results

Study population characteristics and optimal GWG

A total of 3,393 women with twin gestations were included in the study. The participants were categorized into four groups based on pre-pregnancy BMI: underweight 682 (20.10%), normal weight 2,537 (74.77%), overweight 156 (4.60%), and obese 18 (0.53%), with a mean (SD) age of 24.63 ± 2.43, 24.53 ± 2.61, 24.78 ± 3.15, and 25.50 ± 3.38 years, respectively. Population characteristics are outlined in Table 1. Mean birth weight was lowest in the underweight group (2,349.07 ± 519.76 g) and highest in the obese group (2,709.72 ± 824.69 g). The incidence of preterm birth was highest in the underweight group (44.13%) and lowest in the overweight group (34.62%). LBW and SGA rates were highest in the underweight group (56.67% and 42.74%, respectively), while LGA incidence was greatest among the obese (11.11%), indicating BMI-related differences in birth outcomes. As shown in Table 1, significant differences were observed among the groups regarding the impact of pre-pregnancy BMI on adverse perinatal outcomes, including preterm birth (p = 0.020), LBW (p < 0.001), SGA (p = 0.011) and LGA (p = 0.034).

Table 1 Maternal and neonatal characteristics by pre-pregnancy BMI category in twin pregnancies
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The mean GWG varied across BMI categories, with higher weight gain observed in lower pre-pregnancy BMI categories (Table 1). GWG was 17.87 ± 5.67 kg in the underweight group, similar at 16.76 ± 6.22 kg in the normal weight group, but decreased in the overweight and obesity groups, at 14.34 ± 6.60 kg and 14.27 ± 4.94 kg, respectively. In the normal weight group, 52.58% (1,334/2,537) of individuals gained weight below the IOM guidelines, compared to 51.28% (80/156) in the overweight group and 33.33% (6/18)in the obese group, while 9.70% (246/2,537)of individuals in the normal weight group above the IOM guidelines, a proportion that increased to 16.67% (3/18)in the obese group.

A total of 1,277 individuals with a normal composite outcome were categorized as the low-risk group, comprising 206 underweight, 999 normal weight, 66 overweight, and 6 obese individuals. The optimal GWG ranges after 37 weeks of gestation obtained by the IQR of total GWG were 16.4–24.1 kg for underweight, 14.5–22.0 kg for normal weight, 10.0–19.3 kg for overweight. The obese group is too small to determine the optimal GWG range. The optimal ranges in this study for all BMI categories were below the IOM recommendation (Table 2).

Table 2 Comparison of GWG IQR with IOM recommendations
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Gestational weight trajectory

Using the LCGM method, three gestational weight change trajectory groups were identified: the low-increase group, comprising 32.36% of participants; the moderate-increase group, comprising 56.26%; and the high-increase group, comprising 11.38% (Fig. 2). The moderate-increase trajectory group represented the largest group (n = 1,909), with the majority having a normal pre-pregnancy BMI (73.97%) and the highest proportion meeting the IOM recommendations (n = 961, 50.34%) (Table 3). The high-increase trajectory group had the largest proportion (60.62%) exceeding the IOM guidelines, while the low-increase group had the largest proportion (84.34%) falling below the IOM guidelines (Table 3). In the low-increase and moderate-increase groups, a higher proportion of individuals meet the adequate weight gain in the IQR range compared to the IOM recommendation (Table 3). The low-increase group had the highest incidence of spontaneous preterm birth, LBW, and SGA, whereas LGA were most prevalent in the high-increase group (Table S4).

Fig. 2
figure 2

Gestational weight change trajectory groups of the study participants

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Table 3 Basic information of participants based on gestational weight change trajectory groups
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Total GWG showed wide variation within each pre-pregnancy BMI category and across different trajectory groups (Fig. 3). In the normal weight-low-increase group, 99.88% participants fell below the IOM recommendations. As pre-pregnancy BMI increased, the proportion falling below the IOM recommendations decreased, from 98.68% in the overweight-low-increase group to 85.71% in the obese-low-increase group. In the moderate-increase group with different pre-pregnancy BMIs, the proportion meeting the IOM recommendations was the highest. A gain over the upper limit was observed in 210 (75.27%) and 14 (87.50%) participants in the normal weight and overweight high-increase group, respectively.

Fig. 3
figure 3

Total gestational weight gain by pre-pregnancy BMI category among participants. Total weight gain is shown in 2 kg blocks. The red dash line indicates the lower and upper limits for total gestational weight gain in twins according to IOM guidelines. The blue line indicates the lower and upper limits according to IQR

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The three trajectory groups exhibited small variations during the first trimester. The low-increase group experienced a slight weight loss, the moderate-increase group showed almost no change between pre-pregnancy and first-trimester weight, while the high-increase group had an average weight gain of 4.76 kg during this period.

Association between gestational weight change trajectories and adverse perinatal outcomes

Compared to the moderate-increase group (reference), the high-increase group showed a reduced risk of LBW, and SGA, but an increased risk of LGA (Table 4). The risk of preterm birth was not associated with the high-increase group. The low-increase group showed an increased risk of preterm birth, LBW, and SGA, compared to the reference moderate-increase group, with no difference in the risk of LGA. After excluding pre-pregnancy BMI as a covariate, the odds ratios (ORs) for several outcomes showed slight changes but the overall associations remained unchanged (Table S5).

Table 4 The associations of gestational weight change trajectory groups with the incidence of adverse perinatal outcomes in twin pregnancies
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Subgroup analysis by BMI category regarding the relationship of GWG trajectories and perinatal outcomes was performed. Compared to the moderate-increase group, the low-increase group showed higher risks for preterm birth and LBW across both underweight and normal weight categories. In the high-increase group, a reduced risk of preterm birth, LBW and SGA was observed in underweight women, while a reduced risk of LBW and SGA was observed in the normal weight women (Table S6).

Discussion

Using longitudinal data from a large cohort in China from 1993 to 2007, we identified three distinct patterns of GWG and analyzed their associations with adverse perinatal outcomes including preterm birth, LBW, SGA and LGA. Our result indicated that the risk of adverse perinatal outcomes varies by GWG patterns. The optimal GWG in this study was below the IOM recommendations across all BMI categories, with ranges of 16.4–24.1 kg for underweight, 14.5–22.0 kg for normal weight, and 10.0–19.3 kg for overweight.

This study utilized data from 1993 to 2007, a period when living conditions, healthcare systems, and societal norms in China were markedly different from today [27]. At that time, nutritional awareness and prenatal care services were less developed, leading to distinct pregnancy outcomes compared to current cohorts [28]. These historical factors likely influenced the GWG patterns observed in our study. For example, the prevalence of overweight and obesity was considerably lower during the study period due to the country’s earlier economic stage. This contrasts with recent years, where higher obesity rates and altered dietary patterns have shifted GWG trends [29]. In this study, the GWG of most cases was lower than IOM recommendations. However, this study provides important GWG references for underweight pregnant women, particularly in cases where existing guidelines may not fully apply to certain subgroups.

Three distinct gestational weight change trajectory groups were identified: low-increase (average GWG of 10.34 kg), moderate-increase (average GWG of 18.45 kg), and high-increase (average GWG of 27.55 kg). Women who exceeded the IOM recommendations were more likely to belong to the high-increase group, whereas those with a pre-pregnancy BMI classified as overweight or obese were more likely to fall into the low-increase group, similar to previous finding in singleton pregnancy [30]. Our findings suggest that the low-increase trajectory was associated with a higher risk of preterm birth, LBW, and SGA, while the high-increase trajectory was linked to an increased risk of LGA, but a reduced risk of LBW, and SGA. These results align with previous studies on twin pregnancies, which have shown that excessive GWG is associated with an increased risk of LGA and reduced risk of SGA [18, 31, 32]. However, the inverse association of GWG with SGA is inconsistent with several twin studies [17, 33]. It is difficult to compare our findings to these twin studies considering we focused on gestational weight change trajectories. Besides, we used the standardized GWG to minimize the effect of gestational weeks, considering many studies only use the total GWG as variable.

Emerging evidence suggests that weight change in the first trimester may play a significant role in fetal development and the long-term health of the child [34, 35]. Weight gain during the first trimester is generally minimal in singleton pregnancies, typically ranging from 0.5 to 2 kg across all BMI categories recommended by the IOM [4]. However, our study observed distinct patterns of gestational weight change during this period. In our study, the low-increase group experienced slight weight loss during the first trimester, the moderate-increase group showed little difference between pre-pregnancy weight and first-trimester weight, while the high-increase group had an average weight gain of 4.76 kg during this period. Many studies rely on self-reported pre-pregnancy weight when early first-trimester weight measurements are unavailable, often disregarding the significance of weight gain during this critical period [36]. Nichols et al. has reported three distinct GWG trajectories in twin pregnancies with similar GWG at 10 weeks of gestation [37]. However, their study did not specifically address the role of weight gain during the first trimester. Ashtree et al. investigated GWG trajectories and focused on patterns occurring after the first trimester [38].

Pre-pregnancy BMI is a known factor influencing both GWG and pregnancy outcomes [39,40,41]. In our study, women in the underweight group had the highest incidence of preterm birth, LBW, and SGA, while those in the obese group had the highest incidence of LGA. A meta-analysis highlighted that pre-pregnancy BMI has a stronger association with adverse pregnancy complications compared to GWG in singleton pregnancy [16]. In our study, the incidence of LBW, SGA, and LGA varied significantly across BMI range. Consistent with the IOM guidelines [4], this study showed that women with highest pre-pregnancy BMI had the lowest GWG. For underweight women, they were likely falling below the IOM recommendations, while women in the obese group were more likely to exceed the IOM recommendations. Adherence to the IOM recommendations for GWG is low, with approximately 36.22% (982/2711) of women meeting the guidelines in our study. Previous studies have reported that only about 40% of women with singleton pregnancies in China achieve adequate GWG as recommended by the IOM [42, 43]. The IQR of total GWG in this study was below the IOM recommendations across BMI categories. These findings suggest that the IOM recommendations may not be fully suitable for the Chinese population.

Our study has several strengths. The data were derived from a large population-based study, with systematically recorded exposures and outcomes, ensuring comprehensive data collection and minimizing the risk of selection bias. The study population predominantly consisted of women of Han ethnicity, providing a genetically homogeneous background that helped to minimize residual confounding. An advanced method, LCGM was used to identify GWG patterns and its associations with perinatal outcomes. The LCGM method advanced the research on GWG in this population, by uncovering distinct subgroups with unique weight gain trajectories, providing a deeper understanding of the variability in GWG patterns. We had sufficient data to provide the optimal GWG for the underweight and normal weight women considering our population characteristics.

Limitations

This study had some limitations to consider. First, the data used in this study were collected between 1993 and 2007 in China, which may limit the generalizability of the findings to more recent cohorts. Second, the data only documented three weight measurements: pre-pregnancy, first trimester, and weight before delivery. Additional data from the second and third trimesters would provide more comprehensive insights. Moreover, the missing weight and height or abnormal records in the initial phase of selection is high as the study required at least three- time measurements. This requirement may have introduced selection bias. Then, using standardized GWG and assuming linear weight gain throughout pregnancy may also introduce bias, as GWG is slower in the first and late third trimesters. Lastly, the database lacked the information on chorionicity. In the absence of this data, we used neonatal sex combination as a proxy for chorionicity, which is considered suboptimal in such study [44].

Conclusion

This study identified distinct gestational weight change trajectories in twin pregnancies and their associations with adverse perinatal outcomes. The findings suggest that the low-increase group was associated with a higher risk of preterm birth, LBW, and SGA, while the high-increase group was linked to a higher risk of LGA but a reduced risk of LBW, and SGA.

Data availability

Datasets in this study are available from the corresponding author, upon reasonable requests.

Abbreviations

LCGM:

Latent class growth modeling

GWG:

Gestational weight gain

GEE:

Generalized estimating equations

LBW:

Low birth weight

SGA:

Small for gestational age

LGA:

Large for gestational age

BMI:

Body mass index

IOM:

Institute of Medicine

BIC:

Bayesian information criterion

IQR:

Interquartile range

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Acknowledgements

The authors thank all of the volunteers and staff involved in this research.

Funding

This research was funded by National Key Research and Development Program of China (2023YFC2705900).

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Authors and Affiliations

  1. Institute of Reproductive and Child Health, Key Laboratory of Reproductive Health, Peking University, National Health Commission of the People’s Republic of China, Beijing, 100191, China

    Mengmeng Li, Yali Zhang, Weixian Gao & Zhiwen Li

  2. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, 38 Xueyuan Road, Beijing, 100191, China

    Mengmeng Li, Wubulitalifu Dawuti & Tao Huang

  3. Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, 100191, China

    Tianchen Wu

  4. Department of Epidemiology and Biostatistics, School of Public Health, Xinjiang medical University, Urumqi, China

    Xiaoli Tian

  5. Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China

    Tao Huang

  6. Center for Intelligent Public Health, Academy for Artificial Intelligence, Peking University, Beijing, China

    Tao Huang

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  1. Mengmeng Li
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  4. Xiaoli Tian
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  7. Tao Huang
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Contributions

Conceptualization: TH, ZL. Data curation: ML, WD, XT. Formal analysis: ML. Funding acquisition: ZL. Investigation: TH, ZL. Methodology: TW, YZ, WG. Writing—original draft: ML. Writing—review and editing: TH, ZL. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Tao Huang or Zhiwen Li.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Biomedical Ethics Committee of Peking University (Ethics Number: IRB00001052-24076) and fulfils the ethical principles of the Declaration of Helsinki. All the participants provided written informed consent.

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All the participants consent for the publication.

Competing interests

The authors declare no competing interests.

Conflict of interest

All authors approved the final manuscript and declared no competing interests.

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Li, M., Dawuti, W., Wu, T. et al. Association between gestational weight change trajectories and perinatal outcomes in twin pregnancies in China. BMC Pregnancy Childbirth 25, 312 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12884-025-07414-5

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Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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