Gestational weight gain trajectories and adverse pregnancy outcomes: effect modification by pre-pregnancy BMI

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Methods A prospective cohort study enrolled 1125 pregnant women at Qingdao Women & Children's Hospital (March–December 2023). GWG trajectories were identified using group-based trajectory modelling. Binomial logistic regression assessed associations between pre-pregnancy BMI, GWG trajectories, and APOs, adjusting for 13 covariates across three models. Stratified analyses examined effect modification by pre-pregnancy BMI. Outcomes included cesarean delivery (CD), preterm birth, low birth weight (LBW), macrosomia, small for gestational age (SGA), large for gestational age (LGA), gestational diabetes mellitus (GDM), gestational hypertension (GHp), and preeclampsia. Results Among 833 participants, four GWG trajectories were identified: slow (6.4%), moderate (45.0%), fast (39.5%), and extremely fast (9.1%). Pre-pregnancy overweight/obesity was independently associated with higher risks of CD, GDM, preeclampsia, macrosomia, and LGA (adjusted odds ratios [aORs] 1.72–3.87). For GHp, unadjusted analysis suggested an elevated risk in overweight/obese women (OR 3.28), but the low event rate (2.6%) precluded multivariable adjustment. No associations were observed for preterm birth, LBW, or SGA. Accelerated GWG trajectories (fast and extremely fast) were independently associated with increased risks of macrosomia and LGA (fast: aOR 2.52 and 2.03; extremely fast: aOR 7.51 and 4.85, respectively), and the extremely fast trajectory was also associated with CD (aOR 2.04). Stratified analyses revealed significant effect modification by pre-pregnancy BMI: the adverse effects of accelerated GWG on macrosomia and LGA were confined to women with normal pre-pregnancy weight, with no significant associations observed among overweight/obese women. Conclusions Pre-pregnancy overweight/obesity was independently associated with multiple adverse pregnancy outcomes. Accelerated GWG trajectories also independently increased risks of fetal overgrowth and cesarean delivery. Crucially, the effects of GWG trajectories were significantly modified by pre-pregnancy BMI: rapid weight gain conferred substantial risks only among normal-weight women, with no additional detectable risk among those already overweight/obese. These findings support a stratified approach to weight management based on pre-pregnancy BMI status. Gestational weight gain Body mass index Pregnancy outcomes Effect modification Weight trajectory Cohort study Figures Figure 1 Figure 2 Figure 3 Introduction The prevalence of obesity and excess gestational weight gain (GWG) is increasing in many parts of the world [ 1 ]. Adverse pregnancy outcomes (APOs) are associated with both immediate and long-term health risks [ 2 ]. Previous studies on the relationship between GWG and APOs have primarily focused on total GWG [ 3 – 5 ]. However, this approach overlooks considerable individual variation in the timing and rate of weight gain, which can be captured by examining GWG trajectories. Although it is well established that pre-pregnancy BMI and GWG trajectory are associated with pregnancy outcomes, it remains unclear whether the impact of distinct GWG trajectory patterns on these outcomes differs across maternal pre-pregnancy BMI (ppBMI) categories. Specifically, it is unknown whether adverse GWG trajectories confer additional risks in women already at high risk due to pre-pregnancy overweight/obesity. Conversely, the potential harm of unfavorable GWG trajectories may be underestimated in women with normal pre-pregnancy weight. "Therefore, this study aimed to (1) identify distinct GWG trajectories in a Chinese cohort, (2) examine their independent associations with APOs, and (3) most importantly, investigate whether these associations are modified by ppBMI, specifically testing the hypothesis that the impact of GWG trajectories differs between women with normal weight and those with overweight/obesity." Materials and Methods Study design and data collection This study was conducted from March 2023 to October 2024 in Qingdao Women & Children’s Hospital. We recruited pregnant women who had initiated prenatal care and planned to deliver at the hospital. The inclusion and exclusion criteria are presented in Fig. 1 . At enrollment, baseline demographics and clinical history were collected using a self-administered questionnaire. Additional data on prenatal examination, delivery, and neonatal outcomes were extracted from the hospital's electronic medical records system. The records were then consolidated, and the consistency was verified. Weight was recorded at four time points: pre-pregnancy (self-reported), early pregnancy (12–13 weeks), mid-pregnancy (24–26 weeks), and late pregnancy (measured upon admission for delivery). Definition of relevant indicators BMI = weight (kg)/ height 2 (m 2 ), categorized according to the World Health Organization standards: low weight (< 18.5), normal weight (18.5–24.9), overweight (25-29.9), obesity (≥ 30). GWG was calculated as the difference between weight at each time point and pre-pregnancy weight. APOs include primary outcomes (non-selective cesarean delivery (CD), gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), preterm birth (PB), macrosomia, low birth weight (LBW), small for gestational age (SGA), and large for gestational age (LGA)[ 6 ] and secondary outcomes. Details of these outcomes are provided in the Supplementary Table 1. HDP in this study refer to gestational hypertension (GHp) and preeclampsia[ 7 ]. GDM diagnostic criteria were as follows: oral glucose tolerance test (OGTT) results: Fasting blood glucose ≥ 5.1 mmol/L, blood glucose ≥ 10.0 mmol/L 1 hour after oral 75g sugar powder, and blood glucose ≥ 8.5 mmol/L 2 hours after oral 75g sugar powder[ 8 ]. Statistical analysis Microsoft Office Excel 2019 and IBM SPSS Statistics 23.0 were used to collect and analyze data. Continuous variables were tested for normality. Variables with a normal distribution were presented as mean ± standard deviation and analyzed using one-way ANOVA. In contrast, those with a skewed distribution were presented as median and analyzed using the Kruskal-Wallis test. Categorical variables were presented as frequency (percentage) and analyzed with the chi-square test. To identify distinct GWG trajectories, we applied group-based trajectory modelling (GBTM) using the PROC TRAJ macro in SAS version 9.4. Model selection was guided by the Bayesian information criterion (BIC), with lower (closer to zero) values indicating a better fit. The optimal model was determined based on a combination of criteria, including BIC, average posterior probabilities (AvePP ≥ 0.70), and the requirement that each trajectory group comprise at least 5% of the study population. Binomial logistic regression analyses were conducted to determine the associations of ppBMI and GWG trajectories with APOs. Covariates were selected based on clinical relevance and previously established associations with adverse pregnancy outcomes in the literature [ 9 ]. These included maternal age, gravidity, parity, fetal sex, gestational age, conception type, education, family income, smoking, alcohol consumption, sleep duration, and physical activity during pregnancy. We calculated crude and adjusted odds ratios (ORs and aORs) along with their 95% confidence intervals (CIs). Statistical significance was established when the P value was less than 0.05 for all association analyses. Results Baseline characteristics A total of 833 pregnant women aged 22–50 years (mean 31.7 ± 4.2 years) were included in the final analysis, of whom 98.4% were Han Chinese. According to pre-pregnancy BMI, 8.9% of women were underweight, 69.5% were normal weight, and 21.6% were overweight/obese. The median gravidity was 2 (range: 1–9), and 63.0% of women were nulliparous. The incidence of adverse pregnancy outcomes varied considerably. Cesarean delivery (CD) was the most common outcome (46.3%, n = 386), followed by GDM (9.5%, n = 79), LGA (7.7%, n = 64), macrosomia (7.1%, n = 59), preterm birth (6.0%, n = 50), LBW (4.3%, n = 36), preeclampsia (3.6%, n = 30), GHp (2.6%, n = 22), and SGA (2.3%, n = 19) (Table 1 ). Table 1 Baseline characteristics of the study population by pre-pregnancy BMI categories (N = 833) Characteristics Total (N = 833) Underwt (n = 74) Normal (n = 579) Overwt/obesity (n = 180) P value Demographics Maternal age(years), n (%) 0.044* ~ 25 46 (5.5) 3 (4.1) 26 (4.5) 17 (9.4) ~ 30 284 (34.1) 33 (44.6) 202 (34.9)(34.9%)༈34.9%༉༈34.9%༉ 49 (27.2) ~ 35 351 (42.1) 28 (37.8) 244 (42.1) 79 (43.9) > 35 152 (18.2) 10 (13.5) 107 (18.5) 35 (19.4) Education, n (%) 0.007* High school or below 118 (14.2) 5 (7.2) 75 (13.0) 38 (20.6) College or above 715 (85.8) 64 (92.8) 504 (87.0) 147 (79.4) Reproductive characteristics Conception type, n (%) 0.017* Natural 761 (91.4) 68 (91.9) 539 (92.9) 154 (86.0) Assisted 72 (8.6) 6 (8.1) 41 (7.1) 25 (14.0) Gravidity, median (range) 2 (1–9) 1 (1–4) 2 (1–5) 3 (1–9) < 0.001* Parity, n (%) 0.098 0 526 (63.1) 55 (74.3) 363 (62.5) 108 (60.3) ≥ 1 307 (36.9) 19 (25.7) 217 (37.5) 71 (39.7) Gestational age at delivery(weeks), mean ± SD 39.1 ± 1.7 39.4 ± 1.1 39.2 ± 1.6 38.9 ± 2.2 0.084 Lifestyle factors Smoking (during pregnancy), n (%) 5 (0.6) 0 5 (0.9) 0 0.329 Alcohol (during pregnancy), n (%) 16 (1.9) 0 14 (2.4) 2 (1.1) 0.239 Physical activity during pregnancy†, n (%) 0.136 Prolonged sitting 708 (88.4) 66 (95.7) 488 (87.9) 154 (87.0) Frequent walking/standing 93 (11.6) 3 (4.3) 67 (12.1) 23 (13.0) Identification of GWG trajectories Using GBTM, we identified four distinct patterns of gestational weight gain based on measurements at early (12–13 weeks), mid- (24–26 weeks), and late pregnancy (delivery) (Fig. 2 ): slow trajectory (6.4%, n = 53): characterized by consistently lower weight gain throughout pregnancy, with weight loss or minimal gain in the first trimester followed by gradual increase thereafter; moderate trajectory (45.0%, n = 375): exhibited steady and progressive weight gain across all three trimesters; fast trajectory (39.5%, n = 329): demonstrated a notably steeper increase from mid-pregnancy onward, with accelerated gain in the second and third trimesters; extremely fast trajectory (9.1%, n = 76): showed the most rapid and sustained weight gain from early pregnancy through delivery, with a nearly linear increasing pattern. The model demonstrated good fit, with all average posterior probabilities exceeding 0.70 and each group comprising at least 5% of the study population. Associations between pre-pregnancy BMI and adverse pregnancy outcomes Table 2 presents the associations between pre-pregnancy BMI and adverse pregnancy outcomes. After adjustment for demographic, obstetric, and lifestyle factors (Model 2), women with pre-pregnancy overweight/obesity had significantly higher risks of CD, GDM, macrosomia, LGA, and preeclampsia compared with normal-weight women. These associations remained robust after further adjustment for GWG trajectories (Model 3), with adjusted odds ratios (aORs) ranging from 1.72 to 3.86 (all p < 0.05; Table 2 ). Table 2 Associations between pre-pregnancy BMI and adverse pregnancy outcomes Outcomes Models Underwt (n = 74) Normal (n = 579) Overwt/obesity (n = 180) OR (95% CI) OR (95% CI) OR (95% CI) Delivery outcomes Cesarean delivery Model 1 0.85 (0.49–1.48) 1.00 (Ref) 1.80 (1.25–2.59) Model 2 0.81 (0.46–1.43) 1.00 (Ref) 1.63 (1.11–2.38) Model 3 0.80 (0.45–1.41) 1.00 (Ref) 1.72 (1.17–2.52) PB* Crude OR 2.05 1.00 (Ref) 0.73 Fetal growth outcomes Macrosomia Model 1 0.55 (0.13–2.42) 1.00 (Ref) 2.70 (1.45–5.03) Model 2 0.59 (0.13–2.58) 1.00 (Ref) 2.93 (1.54–5.58) Model 3 0.53 (0.12–2.38) 1.00 (Ref) 3.87 (1.97–7.63) LGA Model 1 0.75 (0.32–1.69) 1.00 (Ref) 2.18 (1.43–3.34) Model 2 0.72 (0.31–1.64) 1.00 (Ref) 2.08 (1.34–3.24) Model 3 0.69 (0.30–1.60) 1.00 (Ref) 2.54 (1.61–4.02) LBW* Crude OR 1.27 1.00 (Ref) 1.12 SGA* Crude OR 2.40 1.00 (Ref) 1.54 Metabolic outcomes GDM Model 1 0.65 (0.19–2.18) 1.00 (Ref) 2.24 (1.33–3.78) Model 2 0.66 (0.20–2.23) 1.00 (Ref) 2.06 (1.19–3.57) Model 3 0.66 (0.20–2.23) 1.00 (Ref) 2.04 (1.18–3.55) Hypertensive outcomes Preeclampsia Model 1 0.85 (0.11–6.80) 1.00 (Ref) 3.71 (1.58–8.72) Model 2 0.88 (0.11–7.18) 1.00 (Ref) 3.55 (1.44–8.75) Model 3 0.85 (0.10–6.98) 1.00 (Ref) 3.86 (1.55–9.61) GHp* Crude OR 0.91 1.00 (Ref) 3.28 For GHp, the event rate was low (2.6%, n = 22); unadjusted analysis suggested an elevated risk in overweight/obese women (OR 3.28), but multivariable-adjusted estimates could not be reliably obtained due to the insufficient events-per-variable ratio. No significant associations were observed for preterm birth, LBW, or SGA in unadjusted analyses, and multivariable analysis was not feasible for these outcomes given their low incidence (2.3-6.0%, n = 19–50). Underweight women showed no significant differences in any outcomes compared with normal-weight women across all analyses. Associations between GWG trajectories and adverse pregnancy outcomes Table 3 shows the associations between GWG trajectories and adverse pregnancy outcomes. After full adjustment (Model 3), women in the extremely fast GWG trajectory group had significantly higher risks of CD (aOR 2.04, 95% CI 1.16–3.60), macrosomia (aOR 7.51, 95% CI 3.03–18.60), and LGA (aOR 4.85, 95% CI 2.52–9.33) compared with the moderate trajectory group. The fast trajectory group also showed significantly elevated risks of macrosomia (aOR 2.52, 95% CI 1.21–5.26) and LGA (aOR 2.03, 95% CI 1.28–3.22). The slow trajectory group showed no significant associations with any dependent variables. Table 3 Associations between gestational weight gain trajectories and adverse pregnancy outcomes Outcomes Models Slow (n = 53) Moderate (n = 375) Fast (n = 329) Extremely fast (n = 76) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Delivery outcomes Cesarean delivery Model 1 1.51 (0.78–2.91) 1.00 (Ref) 1.20 (0.86–1.66) 2.21 (1.24–3.65) Model 2 1.49 (0.77–2.89) 1.00 (Ref) 1.17 (0.83–1.64) 1.96 (1.11–3.46) Model 3 1.36 (0.69–2.65) 1.00 (Ref) 1.22 (0.86–1.71) 2.04 (1.16–3.60) PB* Crude OR 0.18 1.00 (Ref) 0.91 0.32 Fetal growth outcomes Macroso-mia Model 1 0.67 (0.08–5.41) 1.00 (Ref) 2.10 (1.04–4.27)(1.452–5.028) 6.17 (2.66–14.34) Model 2 0.63 (0.08–5.04) 1.00 (Ref) 2.10 (1.03–4.28) 5.93 (2.49–14.12) Model 3 0.47 (0.06–3.90) 1.00 (Ref) 2.52 (1.21–5.26) 7.51 (3.03–18.60) LGA Model 1 0.93 (0.35–2.52) 1.00 (Ref) 1.70 (1.10–2.63) 4.29 (2.35–7.84) Model 2 0.91 (0.34–2.46) 1.00 (Ref) 2.08 (1.34–3.24) 4.13 (2.17–7.84) Model 3 0.72 (0.26–2.00) 1.00 (Ref) 2.03 (1.28–3.22) 4.85 (2.52–9.33) LBW* Crude OR 0.62 1.00 (Ref) 0.21 0.33 SGA* Crude OR – 1.00 (Ref) 0.49 0.39 Metabolic outcomes GDM Model 1 0.40 (0.09–1.73) 1.00 (Ref) 0.86 (0.51–1.47) 0.64 (0.24–1.74) Model 2 0.40 (0.09–1.78) 1.00 (Ref) 0.95 (0.55–1.64) 0.61 (0.20–1.84) Model 3 0.34 (0.08–1.53) 1.00 (Ref) 1.03 (0.59–1.79)(1.176–3.549) 0.66 (0.22–2.00) Hypertensive outcomes Preecla-mpsia Model 1 0.57 (0.07–4.95) 1.00 (Ref) 0.77 (0.30–2.00) 1.80 (0.52–6.24) Model 2 0.68 (0.08–6.01) 1.00 (Ref) 0.91 (0.33–2.51) 2.83 (0.77–10.45) Model 3 0.52 (0.06–4.85) 1.00 (Ref) 1.10 (0.39–3.12) 3.52 (0.90–13.76) GHp* Crude OR 1.43 1.00 (Ref) 0.88 0.42 No significant associations were observed between GWG trajectories and GDM, preeclampsia, GHp, preterm birth, LBW, or SGA. However, for GHp, preterm birth, LBW, and SGA—all with incidence below 6.0%—the low event rates preclude definitive conclusions, and these null findings should be interpreted with caution. Effect modification by pre-pregnancy BMI: stratified analyses To investigate whether the associations between GWG trajectories and adverse outcomes varied by pre-pregnancy BMI, we conducted stratified analyses among women with normal weight and those with overweight/obesity (Fig. 3 ). Among women with normal pre-pregnancy weight, the risks of adverse outcomes increased progressively with accelerating GWG trajectories. Compared with the moderate trajectory group, women in the extremely fast trajectory group had significantly higher risks of CD (aOR 2.32, 95% CI 1.15–4.68), macrosomia (aOR 9.64, 95% CI 2.76–33.64), and LGA (aOR 6.13, 95% CI 2.76–13.60). The fast trajectory group also showed a significantly elevated risk of LGA (aOR 1.90, 95% CI 1.02–3.52). Strikingly, among women who were overweight or obese before pregnancy, no significant associations were observed between any GWG trajectory group and the risks of CD, macrosomia, or LGA. Although the point estimates for macrosomia and LGA in the fast and extremely fast groups were elevated (aORs approximately 1.5), these findings were not statistically significant. This pattern, where accelerated GWG confers substantial risks only in normal-weight women, represents a key finding. Discussion This prospective cohort study demonstrates that pre-pregnancy BMI and gestational weight gain trajectories exert independent and joint effects on adverse pregnancy outcomes. Pre-pregnancy overweight/obesity was independently associated with higher risks of CD, GDM, macrosomia, LGA, and preeclampsia. Accelerated GWG trajectories were independently associated with increased risks of macrosomia and LGA. Crucially, pre-pregnancy BMI significantly modified these effects. Rapid weight gain was associated with a substantially higher risk of fetal overgrowth and CD only among women with normal pre-pregnancy weight. In contrast, among women with pre-pregnancy overweight/obesity—who already exhibited elevated baseline risk—accelerated GWG did not confer any additional detectable risk. This interaction—the core finding—provides a unifying framework for interpreting inconsistent literature and supports a stratified approach to weight management. For low-incidence outcomes (GHp, preterm birth, LBW, SGA), event rates precluded definitive conclusions. The biological pathways underlying these associations likely differ. For pre-pregnancy overweight/obesity, chronic low-grade inflammation [ 10 ] and insulin resistance [ 11 ] establish an adverse metabolic milieu that precedes pregnancy and persists regardless of GWG, explaining its broad impact on both maternal and fetal outcomes. In contrast, the selective association between accelerated GWG and fetal overgrowth aligns with the fetal overnutrition hypothesis [ 12 ]. Excessive maternal weight gain, particularly in the second and third trimesters, increases circulating glucose, free fatty acids, and amino acids, which cross the placenta and stimulate fetal insulin secretion—a key driver of excessive fetal growth [ 13 , 14 ]. The absence of association between GWG trajectories and GDM or hypertensive disorders further supports this mechanistic distinction: accelerated GWG appears to fuel fetal growth directly through increased nutrient supply, rather than by first triggering these pregnancy complications. This suggests that in metabolically healthy women, rapid weight gain may drive fetal overgrowth even in the absence of overt maternal metabolic dysfunction." Our findings may help reconcile inconsistencies in previous studies that reported null or weak associations between GWG and outcomes in general populations. For instance, a 2023 quasi-experimental study found no association between revized GWG guidelines and outcomes among women with obesity [ 15 ], and a large meta-analysis reported that after confounder adjustment, low GWG was no longer associated with SGA [ 16 ]. Our stratified analyses provide a potential explanation for these seemingly inconsistent findings. By pooling normal-weight and overweight/obese women, previous studies may have masked the strong associations present in the normal-weight group with the null findings from their overweight/obese counterparts. This methodological insight—enabled by trajectory-based approaches combined with stratified analysis—highlights the importance of considering effect modification by pre-pregnancy BMI when evaluating GWG-related risks, providing a unifying framework for interpreting seemingly contradictory literature [ 17 , 18 ]. These findings have clear clinical implications. For women with normal pre-pregnancy weight, vigilant monitoring and active management of GWG throughout pregnancy should be prioritized, as this represents their primary modifiable risk factor. For those with pre-pregnancy overweight/obesity, the window of opportunity may begin before conception; pre-conception weight optimization should be emphasized, with realistic counselling that prenatal weight management, while still important, may not fully offset the risks conferred by baseline BMI. This dual approach—pre-conception optimization for high-risk women and prenatal GWG management for low-risk women—offers a personalized strategy for mitigating the growing burden of adverse pregnancy outcomes in the context of the obesity epidemic. Limitations Several limitations should be acknowledged. First, as a single-center study conducted in Qingdao, the generalizability of our findings to other populations may be limited; multi-center validation is warranted. Second, pre-pregnancy weight was self-reported, which may introduce recall bias. However, previous validation studies have demonstrated high correlation between self-reported and measured pre-pregnancy weight, suggesting any bias is likely minimal.[ 19 ] Third, despite comprehensive adjustment for confounders, residual confounding from unmeasured factors (e.g., dietary quality, genetic predisposition) cannot be entirely excluded. Fourth, while our sample size was adequate for trajectory identification, statistical power for stratified analyses—particularly within the overweight/obese subgroup (n = 180)—was limited, as reflected in the wide confidence intervals for some estimates. Therefore, the null findings in this subgroup should be interpreted cautiously; they suggest an absence of evidence for an effect, rather than evidence of absence. Importantly, this limitation does not detract from the robust associations observed in the normal-weight group, which underpin our main conclusion regarding effect modification by pre-pregnancy BMI. Finally, the observational design precludes causal inference. Despite these limitations, key strengths include the prospective design, trajectory-based approach, comprehensive confounder adjustment, and—most critically—the stratified analyses that reveal effect modification by pre-pregnancy BMI, a novel contribution to the literature. Conclusions In conclusion, this study demonstrates that pre-pregnancy BMI and GWG trajectories are both independently associated with adverse pregnancy outcomes. Critically, however, this association was significantly modified by pre-pregnancy BMI. Accelerated GWG confers substantially increased risks of fetal overgrowth and cesarean delivery only among women with normal pre-pregnancy weight, with no additional detectable risk among those already overweight or obese. These findings support a stratified, risk-based approach to weight management: pre-conception weight optimization for high-risk (overweight/obese) women, and prenatal GWG control for low-risk (normal-weight) women. This tailored strategy could help mitigate the growing burden of adverse pregnancy outcomes in the context of the global obesity epidemic. Abbreviations aOR Adjusted odds ratio APOs Adverse pregnancy outcomes BMI Body mass index CD Cesarean delivery CI Confidence interval GBTM Group-based trajectory modelling GDM Gestational diabetes mellitus GHp Gestational hypertension GWG Gestational weight gain HDP Hypertensive disorders of pregnancy LBW Low birth weight LGA Large for gestational age OGTT Oral glucose tolerance test OR Odds ratio PB Preterm birth ppBMI Pre-pregnancy body mass index SGA Small for gestational age SD Standard deviation Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Qingdao Women and Children’s Hospital affiliated with Qingdao University (Approval No. QFELL-KY-2023 03), and written informed consent was obtained from all participants. Consent for publication Not applicable. This manuscript contains no person's data in any form. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding A grant from the Municipal Key Clinical Specialty Programs of Qingdao supported this work. Authors' contributions The individual contributions of authors are specified in the Title Page. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the hospital and all the participating pregnant women. Reporting Guideline This study is reported in accordance with the STROBE statement for cohort studies [20]. References Goldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018;16(1):153. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 26 Mar, 2026 Submission checks completed at journal 26 Mar, 2026 First submitted to journal 26 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9235838","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622780859,"identity":"66b7f874-771d-45aa-9409-92f904cc9e6d","order_by":0,"name":"Lili Xu","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Xu","suffix":""},{"id":622780860,"identity":"0570aaf0-ac16-40e0-ace2-9a636ea291d3","order_by":1,"name":"Peng Sun","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Sun","suffix":""},{"id":622780861,"identity":"561b6136-8505-4fea-8f62-89733ccfcb54","order_by":2,"name":"Junxin Li","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Junxin","middleName":"","lastName":"Li","suffix":""},{"id":622780862,"identity":"30a3993b-a266-454d-b5c4-0357b8efc9f2","order_by":3,"name":"Yanzhen Wan","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Yanzhen","middleName":"","lastName":"Wan","suffix":""},{"id":622780863,"identity":"17b21d10-7647-4936-b2b9-71282025c3c9","order_by":4,"name":"Shan Kang","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Kang","suffix":""},{"id":622780865,"identity":"a16fe984-3a5a-4890-9b5a-22ba941021b6","order_by":5,"name":"Zhifei Wang","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zhifei","middleName":"","lastName":"Wang","suffix":""},{"id":622780867,"identity":"807abfc3-d5bd-4c74-8d62-c6ab6683a29f","order_by":6,"name":"Mingran Wu","email":"","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Mingran","middleName":"","lastName":"Wu","suffix":""},{"id":622780869,"identity":"53aa993f-de68-4afc-a7de-e7b9b5ec1026","order_by":7,"name":"Jinlian Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYLACCQMGGTYGBsYHDGwgbgJxWniAipkNiNcCBDxAzCZBlBb59t7DLywK7vDwsZ89VvGm7DADP3uOAcPPHbi1GJw5l2YhYfCMh40nL+3mnHOHGSR73hgw9p7Bo0Uix8xAwuAw0C85Zrd52w4zGNzIMWBmbMPjsBkwLfxvzIpBWuwJaWG4kWP8AKwFaB0z2BYJAloMzpwxY4BoeWMsOedcOo/EmWcFB3vxOay9x/izxJ/DcvL9OYYf3pRZy/G3J2988BOfw4DRIS0BY/JAIojhAF4NwEj/+AFJyygYBaNgFIwCDAAAsnpIofB0yZsAAAAASUVORK5CYII=","orcid":"","institution":"Affiliated Women and Children’s Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Jinlian","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2026-03-26 15:11:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9235838/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9235838/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107247059,"identity":"f692c8a2-169f-4e36-b6b8-7b9748dc496c","added_by":"auto","created_at":"2026-04-19 08:11:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57552,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participant selection\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9235838/v1/883a178dc5b825c194a8e605.png"},{"id":107247060,"identity":"df8995f0-0877-47f2-a256-54494b536900","added_by":"auto","created_at":"2026-04-19 08:11:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14661,"visible":true,"origin":"","legend":"\u003cp\u003eGestational weight gain trajectories identified by group-based trajectory modelling. Trajectories were modelled based on weight measurements at early (12-13 weeks), mid- (24-26 weeks), and late pregnancy (delivery). The four trajectories represent distinct patterns of gestational weight gain: slow (6.4%, n = 53), moderate (45.0%, n = 375), fast (39.5%, n = 329), and extremely fast (9.1%, n = 76).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9235838/v1/2d16d45f5ee9e6fab7eac790.png"},{"id":107482872,"identity":"0a60cc71-c335-4e1a-9063-20b263edec0c","added_by":"auto","created_at":"2026-04-22 02:25:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144287,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of gestational weight gain trajectories and adverse outcomes stratified by pre-pregnancy body mass index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e All models were adjusted for maternal age, gravity, parity, newborn sex, gestational age, conception type, education level, family income, smoking, alcohol consumption, sleep duration, and physical activity during pregnancy. Trajectory 2 (moderate increase) served as the reference group.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9235838/v1/e1dbeb312422cda65e68a7e8.jpeg"},{"id":108490928,"identity":"412b4e34-7d39-4ce2-b81f-78d940feb347","added_by":"auto","created_at":"2026-05-05 09:50:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":690448,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9235838/v1/3adb63fd-67f5-4c5b-98ed-7deed6289c4d.pdf"},{"id":107247058,"identity":"4ca22068-e64b-4866-af23-27c2d05135c7","added_by":"auto","created_at":"2026-04-19 08:11:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":20236,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9235838/v1/5ebe2f53b357ec601b8c183f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gestational weight gain trajectories and adverse pregnancy outcomes: effect modification by pre-pregnancy BMI","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe prevalence of obesity and excess gestational weight gain (GWG) is increasing in many parts of the world [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Adverse pregnancy outcomes (APOs) are associated with both immediate and long-term health risks [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Previous studies on the relationship between GWG and APOs have primarily focused on total GWG [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, this approach overlooks considerable individual variation in the timing and rate of weight gain, which can be captured by examining GWG trajectories. Although it is well established that pre-pregnancy BMI and GWG trajectory are associated with pregnancy outcomes, it remains unclear whether the impact of distinct GWG trajectory patterns on these outcomes differs across maternal pre-pregnancy BMI (ppBMI) categories. Specifically, it is unknown whether adverse GWG trajectories confer additional risks in women already at high risk due to pre-pregnancy overweight/obesity. Conversely, the potential harm of unfavorable GWG trajectories may be underestimated in women with normal pre-pregnancy weight. \"Therefore, this study aimed to (1) identify distinct GWG trajectories in a Chinese cohort, (2) examine their independent associations with APOs, and (3) most importantly, investigate whether these associations are modified by ppBMI, specifically testing the hypothesis that the impact of GWG trajectories differs between women with normal weight and those with overweight/obesity.\"\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cp\u003eThis study was conducted from March 2023 to October 2024 in Qingdao Women \u0026amp; Children\u0026rsquo;s Hospital. We recruited pregnant women who had initiated prenatal care and planned to deliver at the hospital. The inclusion and exclusion criteria are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt enrollment, baseline demographics and clinical history were collected using a self-administered questionnaire. Additional data on prenatal examination, delivery, and neonatal outcomes were extracted from the hospital's electronic medical records system. The records were then consolidated, and the consistency was verified. Weight was recorded at four time points: pre-pregnancy (self-reported), early pregnancy (12\u0026ndash;13 weeks), mid-pregnancy (24\u0026ndash;26 weeks), and late pregnancy (measured upon admission for delivery).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of relevant indicators\u003c/h3\u003e\n\u003cp\u003eBMI =\u003cem\u003eweight\u003c/em\u003e (kg)/\u003cem\u003eheight\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e (m\u003csup\u003e2\u003c/sup\u003e), categorized according to the World Health Organization standards: low weight (\u0026lt;\u0026thinsp;18.5), normal weight (18.5\u0026ndash;24.9), overweight (25-29.9), obesity (\u0026ge;\u0026thinsp;30). GWG was calculated as the difference between weight at each time point and pre-pregnancy weight. APOs include primary outcomes (non-selective cesarean delivery (CD), gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), preterm birth (PB), macrosomia, low birth weight (LBW), small for gestational age (SGA), and large for gestational age (LGA)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and secondary outcomes. Details of these outcomes are provided in the Supplementary Table\u0026nbsp;1. HDP in this study refer to gestational hypertension (GHp) and preeclampsia[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. GDM diagnostic criteria were as follows: oral glucose tolerance test (OGTT) results: Fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L, blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;10.0 mmol/L 1 hour after oral 75g sugar powder, and blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;8.5 mmol/L 2 hours after oral 75g sugar powder[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eMicrosoft Office Excel 2019 and IBM SPSS Statistics 23.0 were used to collect and analyze data. Continuous variables were tested for normality. Variables with a normal distribution were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and analyzed using one-way ANOVA. In contrast, those with a skewed distribution were presented as median and analyzed using the Kruskal-Wallis test. Categorical variables were presented as frequency (percentage) and analyzed with the chi-square test.\u003c/p\u003e \u003cp\u003eTo identify distinct GWG trajectories, we applied group-based trajectory modelling (GBTM) using the PROC TRAJ macro in SAS version 9.4. Model selection was guided by the Bayesian information criterion (BIC), with lower (closer to zero) values indicating a better fit. The optimal model was determined based on a combination of criteria, including BIC, average posterior probabilities (AvePP\u0026thinsp;\u0026ge;\u0026thinsp;0.70), and the requirement that each trajectory group comprise at least 5% of the study population.\u003c/p\u003e \u003cp\u003eBinomial logistic regression analyses were conducted to determine the associations of ppBMI and GWG trajectories with APOs. Covariates were selected based on clinical relevance and previously established associations with adverse pregnancy outcomes in the literature [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These included maternal age, gravidity, parity, fetal sex, gestational age, conception type, education, family income, smoking, alcohol consumption, sleep duration, and physical activity during pregnancy. We calculated crude and adjusted odds ratios (ORs and aORs) along with their 95% confidence intervals (CIs).\u003c/p\u003e \u003cp\u003eStatistical significance was established when the \u003cem\u003eP\u003c/em\u003e value was less than 0.05 for all association analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eA total of 833 pregnant women aged 22\u0026ndash;50 years (mean 31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2 years) were included in the final analysis, of whom 98.4% were Han Chinese. According to pre-pregnancy BMI, 8.9% of women were underweight, 69.5% were normal weight, and 21.6% were overweight/obese. The median gravidity was 2 (range: 1\u0026ndash;9), and 63.0% of women were nulliparous.\u003c/p\u003e \u003cp\u003eThe incidence of adverse pregnancy outcomes varied considerably. Cesarean delivery (CD) was the most common outcome (46.3%, n\u0026thinsp;=\u0026thinsp;386), followed by GDM (9.5%, n\u0026thinsp;=\u0026thinsp;79), LGA (7.7%, n\u0026thinsp;=\u0026thinsp;64), macrosomia (7.1%, n\u0026thinsp;=\u0026thinsp;59), preterm birth (6.0%, n\u0026thinsp;=\u0026thinsp;50), LBW (4.3%, n\u0026thinsp;=\u0026thinsp;36), preeclampsia (3.6%, n\u0026thinsp;=\u0026thinsp;30), GHp (2.6%, n\u0026thinsp;=\u0026thinsp;22), and SGA (2.3%, n\u0026thinsp;=\u0026thinsp;19) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of the study population by pre-pregnancy BMI categories (N\u0026thinsp;=\u0026thinsp;833)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;833)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderwt (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverwt/obesity\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eMaternal age(years), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.044*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (34.9)(34.9%)༈34.9%༉༈34.9%༉\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49 (27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (37.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e244 (42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e107 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e715 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64 (92.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e504 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReproductive characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eConception type, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e761 (91.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e539 (92.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154 (86.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssisted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25 (14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity, median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eParity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e526 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e307 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at delivery(weeks),\u003c/p\u003e \u003cp\u003emean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (during pregnancy), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol (during pregnancy), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003ePhysical activity during pregnancy\u0026dagger;, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProlonged sitting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e708 (88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66 (95.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e154 (87.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequent walking/standing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of GWG trajectories\u003c/h2\u003e \u003cp\u003eUsing GBTM, we identified four distinct patterns of gestational weight gain based on measurements at early (12\u0026ndash;13 weeks), mid- (24\u0026ndash;26 weeks), and late pregnancy (delivery) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): slow trajectory (6.4%, n\u0026thinsp;=\u0026thinsp;53): characterized by consistently lower weight gain throughout pregnancy, with weight loss or minimal gain in the first trimester followed by gradual increase thereafter; moderate trajectory (45.0%, n\u0026thinsp;=\u0026thinsp;375): exhibited steady and progressive weight gain across all three trimesters; fast trajectory (39.5%, n\u0026thinsp;=\u0026thinsp;329): demonstrated a notably steeper increase from mid-pregnancy onward, with accelerated gain in the second and third trimesters; extremely fast trajectory (9.1%, n\u0026thinsp;=\u0026thinsp;76): showed the most rapid and sustained weight gain from early pregnancy through delivery, with a nearly linear increasing pattern. The model demonstrated good fit, with all average posterior probabilities exceeding 0.70 and each group comprising at least 5% of the study population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociations between pre-pregnancy BMI and adverse pregnancy outcomes\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the associations between pre-pregnancy BMI and adverse pregnancy outcomes. After adjustment for demographic, obstetric, and lifestyle factors (Model 2), women with pre-pregnancy overweight/obesity had significantly higher risks of CD, GDM, macrosomia, LGA, and preeclampsia compared with normal-weight women. These associations remained robust after further adjustment for GWG trajectories (Model 3), with adjusted odds ratios (aORs) ranging from 1.72 to 3.86 (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between pre-pregnancy BMI and adverse pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnderwt (n\u0026thinsp;=\u0026thinsp;74)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNormal (n\u0026thinsp;=\u0026thinsp;579)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverwt/obesity (n\u0026thinsp;=\u0026thinsp;180)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDelivery outcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCesarean delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.49\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.80 (1.25\u0026ndash;2.59)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81 (0.46\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.63 (1.11\u0026ndash;2.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.45\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.72 (1.17\u0026ndash;2.52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal growth outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMacrosomia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55 (0.13\u0026ndash;2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.70 (1.45\u0026ndash;5.03)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59 (0.13\u0026ndash;2.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.93 (1.54\u0026ndash;5.58)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53 (0.12\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.87 (1.97\u0026ndash;7.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.75 (0.32\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.18 (1.43\u0026ndash;3.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.31\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.08 (1.34\u0026ndash;3.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69 (0.30\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.54 (1.61\u0026ndash;4.02)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBW*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGA*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.65 (0.19\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.24 (1.33\u0026ndash;3.78)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.20\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.06 (1.19\u0026ndash;3.57)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.66 (0.20\u0026ndash;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.04 (1.18\u0026ndash;3.55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertensive outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePreeclampsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.11\u0026ndash;6.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.71 (1.58\u0026ndash;8.72)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88 (0.11\u0026ndash;7.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.55 (1.44\u0026ndash;8.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.10\u0026ndash;6.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e3.86 (1.55\u0026ndash;9.61)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHp*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor GHp, the event rate was low (2.6%, n\u0026thinsp;=\u0026thinsp;22); unadjusted analysis suggested an elevated risk in overweight/obese women (OR 3.28), but multivariable-adjusted estimates could not be reliably obtained due to the insufficient events-per-variable ratio. No significant associations were observed for preterm birth, LBW, or SGA in unadjusted analyses, and multivariable analysis was not feasible for these outcomes given their low incidence (2.3-6.0%, n\u0026thinsp;=\u0026thinsp;19\u0026ndash;50). Underweight women showed no significant differences in any outcomes compared with normal-weight women across all analyses.\u003c/p\u003e\n\u003ch3\u003eAssociations between GWG trajectories and adverse pregnancy outcomes\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the associations between GWG trajectories and adverse pregnancy outcomes. After full adjustment (Model 3), women in the extremely fast GWG trajectory group had significantly higher risks of CD (aOR 2.04, 95% CI 1.16\u0026ndash;3.60), macrosomia (aOR 7.51, 95% CI 3.03\u0026ndash;18.60), and LGA (aOR 4.85, 95% CI 2.52\u0026ndash;9.33) compared with the moderate trajectory group. The fast trajectory group also showed significantly elevated risks of macrosomia (aOR 2.52, 95% CI 1.21\u0026ndash;5.26) and LGA (aOR 2.03, 95% CI 1.28\u0026ndash;3.22). The slow trajectory group showed no significant associations with any dependent variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between gestational weight gain trajectories and adverse pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcomes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate (n\u0026thinsp;=\u0026thinsp;375)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFast\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;329)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExtremely fast\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eDelivery outcomes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eCesarean delivery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.51 (0.78\u0026ndash;2.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.20 (0.86\u0026ndash;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.21 (1.24\u0026ndash;3.65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (0.77\u0026ndash;2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.17 (0.83\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.96 (1.11\u0026ndash;3.46)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (0.69\u0026ndash;2.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.22 (0.86\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2.04 (1.16\u0026ndash;3.60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePB*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFetal growth outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMacroso-mia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67 (0.08\u0026ndash;5.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.10 (1.04\u0026ndash;4.27)(1.452\u0026ndash;5.028)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6.17 (2.66\u0026ndash;14.34)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63 (0.08\u0026ndash;5.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.10 (1.03\u0026ndash;4.28)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e5.93 (2.49\u0026ndash;14.12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.06\u0026ndash;3.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.52 (1.21\u0026ndash;5.26)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e7.51 (3.03\u0026ndash;18.60)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eLGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.35\u0026ndash;2.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.70 (1.10\u0026ndash;2.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.29 (2.35\u0026ndash;7.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.34\u0026ndash;2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.08 (1.34\u0026ndash;3.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.13 (2.17\u0026ndash;7.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.72 (0.26\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.03 (1.28\u0026ndash;3.22)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e4.85 (2.52\u0026ndash;9.33)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLBW*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSGA*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.09\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (0.51\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64 (0.24\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40 (0.09\u0026ndash;1.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95 (0.55\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.61 (0.20\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34 (0.08\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.59\u0026ndash;1.79)(1.176\u0026ndash;3.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.66 (0.22\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertensive outcomes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePreecla-mpsia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57 (0.07\u0026ndash;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77 (0.30\u0026ndash;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80 (0.52\u0026ndash;6.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.68 (0.08\u0026ndash;6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91 (0.33\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.83 (0.77\u0026ndash;10.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52 (0.06\u0026ndash;4.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.39\u0026ndash;3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.52 (0.90\u0026ndash;13.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGHp*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrude OR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo significant associations were observed between GWG trajectories and GDM, preeclampsia, GHp, preterm birth, LBW, or SGA. However, for GHp, preterm birth, LBW, and SGA\u0026mdash;all with incidence below 6.0%\u0026mdash;the low event rates preclude definitive conclusions, and these null findings should be interpreted with caution.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffect modification by pre-pregnancy BMI: stratified analyses\u003c/h2\u003e \u003cp\u003eTo investigate whether the associations between GWG trajectories and adverse outcomes varied by pre-pregnancy BMI, we conducted stratified analyses among women with normal weight and those with overweight/obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAmong women with normal pre-pregnancy weight, the risks of adverse outcomes increased progressively with accelerating GWG trajectories. Compared with the moderate trajectory group, women in the extremely fast trajectory group had significantly higher risks of CD (aOR 2.32, 95% CI 1.15\u0026ndash;4.68), macrosomia (aOR 9.64, 95% CI 2.76\u0026ndash;33.64), and LGA (aOR 6.13, 95% CI 2.76\u0026ndash;13.60). The fast trajectory group also showed a significantly elevated risk of LGA (aOR 1.90, 95% CI 1.02\u0026ndash;3.52).\u003c/p\u003e \u003cp\u003eStrikingly, among women who were overweight or obese before pregnancy, no significant associations were observed between any GWG trajectory group and the risks of CD, macrosomia, or LGA. Although the point estimates for macrosomia and LGA in the fast and extremely fast groups were elevated (aORs approximately 1.5), these findings were not statistically significant.\u003c/p\u003e \u003cp\u003eThis pattern, where accelerated GWG confers substantial risks only in normal-weight women, represents a key finding.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective cohort study demonstrates that pre-pregnancy BMI and gestational weight gain trajectories exert independent and joint effects on adverse pregnancy outcomes. Pre-pregnancy overweight/obesity was independently associated with higher risks of CD, GDM, macrosomia, LGA, and preeclampsia. Accelerated GWG trajectories were independently associated with increased risks of macrosomia and LGA.\u003c/p\u003e \u003cp\u003eCrucially, pre-pregnancy BMI significantly modified these effects. Rapid weight gain was associated with a substantially higher risk of fetal overgrowth and CD only among women with normal pre-pregnancy weight. In contrast, among women with pre-pregnancy overweight/obesity\u0026mdash;who already exhibited elevated baseline risk\u0026mdash;accelerated GWG did not confer any additional detectable risk. This interaction\u0026mdash;the core finding\u0026mdash;provides a unifying framework for interpreting inconsistent literature and supports a stratified approach to weight management. For low-incidence outcomes (GHp, preterm birth, LBW, SGA), event rates precluded definitive conclusions.\u003c/p\u003e \u003cp\u003eThe biological pathways underlying these associations likely differ. For pre-pregnancy overweight/obesity, chronic low-grade inflammation [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and insulin resistance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] establish an adverse metabolic milieu that precedes pregnancy and persists regardless of GWG, explaining its broad impact on both maternal and fetal outcomes. In contrast, the selective association between accelerated GWG and fetal overgrowth aligns with the fetal overnutrition hypothesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Excessive maternal weight gain, particularly in the second and third trimesters, increases circulating glucose, free fatty acids, and amino acids, which cross the placenta and stimulate fetal insulin secretion\u0026mdash;a key driver of excessive fetal growth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The absence of association between GWG trajectories and GDM or hypertensive disorders further supports this mechanistic distinction: accelerated GWG appears to fuel fetal growth directly through increased nutrient supply, rather than by first triggering these pregnancy complications. This suggests that in metabolically healthy women, rapid weight gain may drive fetal overgrowth even in the absence of overt maternal metabolic dysfunction.\"\u003c/p\u003e \u003cp\u003eOur findings may help reconcile inconsistencies in previous studies that reported null or weak associations between GWG and outcomes in general populations. For instance, a 2023 quasi-experimental study found no association between revized GWG guidelines and outcomes among women with obesity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and a large meta-analysis reported that after confounder adjustment, low GWG was no longer associated with SGA [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our stratified analyses provide a potential explanation for these seemingly inconsistent findings. By pooling normal-weight and overweight/obese women, previous studies may have masked the strong associations present in the normal-weight group with the null findings from their overweight/obese counterparts. This methodological insight\u0026mdash;enabled by trajectory-based approaches combined with stratified analysis\u0026mdash;highlights the importance of considering effect modification by pre-pregnancy BMI when evaluating GWG-related risks, providing a unifying framework for interpreting seemingly contradictory literature [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings have clear clinical implications. For women with normal pre-pregnancy weight, vigilant monitoring and active management of GWG throughout pregnancy should be prioritized, as this represents their primary modifiable risk factor. For those with pre-pregnancy overweight/obesity, the window of opportunity may begin before conception; pre-conception weight optimization should be emphasized, with realistic counselling that prenatal weight management, while still important, may not fully offset the risks conferred by baseline BMI. This dual approach\u0026mdash;pre-conception optimization for high-risk women and prenatal GWG management for low-risk women\u0026mdash;offers a personalized strategy for mitigating the growing burden of adverse pregnancy outcomes in the context of the obesity epidemic.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, as a single-center study conducted in Qingdao, the generalizability of our findings to other populations may be limited; multi-center validation is warranted.\u003c/p\u003e \u003cp\u003eSecond, pre-pregnancy weight was self-reported, which may introduce recall bias. However, previous validation studies have demonstrated high correlation between self-reported and measured pre-pregnancy weight, suggesting any bias is likely minimal.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThird, despite comprehensive adjustment for confounders, residual confounding from unmeasured factors (e.g., dietary quality, genetic predisposition) cannot be entirely excluded.\u003c/p\u003e \u003cp\u003eFourth, while our sample size was adequate for trajectory identification, statistical power for stratified analyses\u0026mdash;particularly within the overweight/obese subgroup (n\u0026thinsp;=\u0026thinsp;180)\u0026mdash;was limited, as reflected in the wide confidence intervals for some estimates. Therefore, the null findings in this subgroup should be interpreted cautiously; they suggest an absence of evidence for an effect, rather than evidence of absence. Importantly, this limitation does not detract from the robust associations observed in the normal-weight group, which underpin our main conclusion regarding effect modification by pre-pregnancy BMI.\u003c/p\u003e \u003cp\u003eFinally, the observational design precludes causal inference.\u003c/p\u003e \u003cp\u003eDespite these limitations, key strengths include the prospective design, trajectory-based approach, comprehensive confounder adjustment, and\u0026mdash;most critically\u0026mdash;the stratified analyses that reveal effect modification by pre-pregnancy BMI, a novel contribution to the literature.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study demonstrates that pre-pregnancy BMI and GWG trajectories are both independently associated with adverse pregnancy outcomes. Critically, however, this association was significantly modified by pre-pregnancy BMI. Accelerated GWG confers substantially increased risks of fetal overgrowth and cesarean delivery only among women with normal pre-pregnancy weight, with no additional detectable risk among those already overweight or obese.\u003c/p\u003e \u003cp\u003eThese findings support a stratified, risk-based approach to weight management: pre-conception weight optimization for high-risk (overweight/obese) women, and prenatal GWG control for low-risk (normal-weight) women. This tailored strategy could help mitigate the growing burden of adverse pregnancy outcomes in the context of the global obesity epidemic.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eaOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted odds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPOs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdverse pregnancy outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCesarean delivery\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGBTM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGroup-based trajectory modelling\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGHp\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational hypertension\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational weight gain\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypertensive disorders of pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLBW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow birth weight\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLarge for gestational age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOGTT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOral glucose tolerance test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePreterm birth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eppBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePre-pregnancy body mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSGA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall for gestational age\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of \u0026nbsp; Qingdao Women and Children\u0026rsquo;s Hospital affiliated with Qingdao University (Approval No. QFELL-KY-2023 03), and written informed consent was obtained from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript contains no person\u0026apos;s data in any form.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA grant from the Municipal Key Clinical Specialty Programs of Qingdao supported this work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe individual contributions of authors are specified in the Title Page. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the hospital and all the participating pregnant women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReporting Guideline\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is reported in accordance with the STROBE statement for cohort studies [20].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoldstein RF, Abell SK, Ranasinha S, Misso ML, Boyle JA, Harrison CL, et al. Gestational weight gain across continents and ethnicity: systematic review and meta-analysis of maternal and infant outcomes in more than one million women. BMC Med. 2018;16(1):153.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFleming TP, Watkins AJ, Velazquez MA, Mathers JC, Prentice AM, Stephenson J, et al. Origins of lifetime health around the time of conception: causes and consequences. Lancet. 2018;391(10132):1842\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi H, Lim JY, Lim NK, Ryu HM, Kwak DW, Chung JH, et al. Impact of pre-pregnancy body mass index and gestational weight gain on the risk of maternal and infant pregnancy complications in Korean women. Int J Obes (Lond). 2022;46(1):59\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouhana S, Hallit S, Nicolas G. The association of maternal pre-pregnancy Body Mass Index and gestational weight gain with pregnancy and neonatal outcomes. Ir J Med Sci. 2024;193(1):303\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y, Shen Z, Zhan Y, Wang Y, Ma S, Zhang S, et al. Effects of pre-pregnancy body mass index and gestational weight gain on maternal and infant complications. BMC Pregnancy Childbirth. 2020;20(1):390.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Miao C, Xu L, Gao H, Bai M, Liu W, et al. Maternal pre-pregnancy body mass index, gestational weight gain trajectory, and risk of adverse perinatal outcomes. Int J Gynaecol Obstet. 2022;157(3):723\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHypertensive Disorders in Pregnancy Group SoOaG, Chinese Medical Association. Guidelines for Diagnosis and Management of Hypertensive Disorders in Pregnancy. Chinese Journal of Obstetrics and Gynecology 2020;55(04):227\u0026ndash;238.DOI:210.3760/cma.j.cn112141-20200114-20200039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldstein RF, Abell SK, Ranasinha S, Misso M, Boyle JA, Black MH, et al. Association of Gestational Weight Gain With Maternal and Infant Outcomes: A Systematic Review and Meta-analysis. JAMA. 2017;317(21):2207\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCatalano PM, Shankar K. Obesity and pregnancy: mechanisms of short term and long term adverse consequences for mother and child. BMJ. 2017;356:j1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu R, Wang F, Li L, Wang Q. The Double-Edged Sword of Gestational Insulin Resistance: Navigating Maternal Adaptation and Its Risks for Pregnancy and Offspring Health. Obes Rev 2025:e70048.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDabelea D, Crume T. Maternal environment and the transgenerational cycle of obesity and diabetes. Diabetes. 2011;60(7):1849\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHomko CJ, Sivan E, Reece EA, Boden G. Fuel metabolism during pregnancy. Semin Reprod Endocrinol. 1999;17(2):119\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalazar-Petres ER, Sferruzzi-Perri AN. Pregnancy-induced changes in β-cell function: what are the key players? J Physiol. 2022;600(5):1089\u0026ndash;117.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollin DF, Pulvera R, Hamad R. The effect of the 2009 revised U.S. guidelines for gestational weight gain on maternal and infant health: a quasi-experimental study. BMC Pregnancy Childbirth. 2023;23(1):118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMustafa HJ, Seif K, Javinani A, Aghajani F, Orlinsky R, Alvarez MV, et al. Gestational weight gain below instead of within the guidelines per class of maternal obesity: a systematic review and meta-analysis of obstetrical and neonatal outcomes. Am J Obstet Gynecol MFM. 2022;4(5):100682.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoerman E, Santos S, Inskip H, Amiano P, Barros H, Charles MA, et al. Association of Gestational Weight Gain With Adverse Maternal and Infant Outcomes. JAMA. 2019;321(17):1702\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornton YS. Pregnancy outcomes with weight gain above or below the 2009 Institute of Medicine guidelines. Obstet Gynecol. 2013;122(3):696.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin D, Chung H, Weatherspoon L, Song WO. Validity of prepregnancy weight status estimated from self-reported height and weight. Matern Child Health J. 2014;18(7):1667\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. PLoS Med. 2007;4(10):e296.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational weight gain, Body mass index, Pregnancy outcomes, Effect modification, Weight trajectory, Cohort study","lastPublishedDoi":"10.21203/rs.3.rs-9235838/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9235838/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe increasing prevalence of obesity and excessive gestational weight gain (GWG) highlights the need to clarify their independent and joint associations with adverse pregnancy outcomes (APOs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective cohort study enrolled 1125 pregnant women at Qingdao Women \u0026amp; Children's Hospital (March\u0026ndash;December 2023). GWG trajectories were identified using group-based trajectory modelling. Binomial logistic regression assessed associations between pre-pregnancy BMI, GWG trajectories, and APOs, adjusting for 13 covariates across three models. Stratified analyses examined effect modification by pre-pregnancy BMI. Outcomes included cesarean delivery (CD), preterm birth, low birth weight (LBW), macrosomia, small for gestational age (SGA), large for gestational age (LGA), gestational diabetes mellitus (GDM), gestational hypertension (GHp), and preeclampsia.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 833 participants, four GWG trajectories were identified: slow (6.4%), moderate (45.0%), fast (39.5%), and extremely fast (9.1%). Pre-pregnancy overweight/obesity was independently associated with higher risks of CD, GDM, preeclampsia, macrosomia, and LGA (adjusted odds ratios [aORs] 1.72\u0026ndash;3.87). For GHp, unadjusted analysis suggested an elevated risk in overweight/obese women (OR 3.28), but the low event rate (2.6%) precluded multivariable adjustment. No associations were observed for preterm birth, LBW, or SGA. Accelerated GWG trajectories (fast and extremely fast) were independently associated with increased risks of macrosomia and LGA (fast: aOR 2.52 and 2.03; extremely fast: aOR 7.51 and 4.85, respectively), and the extremely fast trajectory was also associated with CD (aOR 2.04). Stratified analyses revealed significant effect modification by pre-pregnancy BMI: the adverse effects of accelerated GWG on macrosomia and LGA were confined to women with normal pre-pregnancy weight, with no significant associations observed among overweight/obese women.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePre-pregnancy overweight/obesity was independently associated with multiple adverse pregnancy outcomes. Accelerated GWG trajectories also independently increased risks of fetal overgrowth and cesarean delivery. Crucially, the effects of GWG trajectories were significantly modified by pre-pregnancy BMI: rapid weight gain conferred substantial risks only among normal-weight women, with no additional detectable risk among those already overweight/obese. These findings support a stratified approach to weight management based on pre-pregnancy BMI status.\u003c/p\u003e","manuscriptTitle":"Gestational weight gain trajectories and adverse pregnancy outcomes: effect modification by pre-pregnancy BMI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 08:11:40","doi":"10.21203/rs.3.rs-9235838/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"204823264812096445756260225794262249122","date":"2026-05-16T14:50:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223803449231921123053190960495559732855","date":"2026-05-12T01:36:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T02:25:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T05:33:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T00:52:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T00:51:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-03-26T14:54:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3c1334e3-e323-44bc-a311-38d9e5a2b06c","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"204823264812096445756260225794262249122","date":"2026-05-16T14:50:39+00:00","index":154,"fulltext":""},{"type":"reviewerAgreed","content":"223803449231921123053190960495559732855","date":"2026-05-12T01:36:43+00:00","index":149,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T08:11:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 08:11:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9235838","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9235838","identity":"rs-9235838","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}