Association of Early-Term and Full-Term Delivery With Overweight and Obesity Risk in the First Year of Life

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Objective: To determine whether early-term (GA 37-38 weeks) or full-term (GA 39-40 weeks) delivery is associated with early childhood overweight and obesity risk. Design, SETTING, AND PARTICIPANTS: This cohort study included healthy term infants (GA 37-40 weeks) born between January 2011 and July 2022 at two tertiary hospitals in Shandong, China. Infants with congenital malformations or syndromes were excluded. In accordance with local health assessment protocols, length and weight were measured by trained professionals at four time points: 1-3, 4-6, 7-9, and 10-12 months. Data analysis was conducted in September 2024. Exposures: GA was obtained from clinical records. Early-term delivery compared against full-term delivery as the reference group. Main Outcomes and Measures: Body Mass Index (BMI) was calculated, and BMI-for-age Z-scores (BAZ) were derived using the World Health Organization growth standards. Risk of overweight and obesity was defined as BAZ>1. Results: Among 5,092 participants with complete data, 26.6% (n=1,355) were early-term. The prevalence of overweight and obesity risk was significantly higher in early-term infants (27.6%-30.2%) compared to full-term infants (23.6%-26.4%) throughout infancy. Early-term delivery was associated with an increased risk of overweight and obesity from 1-3 months (unadjusted odds ratio [OR], 1.162; 95% confidence interval [CI], 1.001-1.340; P =.038) to 10-12 months of age (unadjusted OR, 1.236; 95% CI, 1.073-1.426; P =.004). This association remained significant after adjusting for mode of delivery, parity, number of fetuses, sex, feeding practices during the first six months, and birth weight. Multiple imputation analysis for missing covariates yielded similar results. Conclusions and Relevance: Early-term infants have a higher risk of overweight and obesity during the first postnatal year. These findings highlight the need for targeted interventions to prevent childhood obesity in this population. Figures Figure 1 Figure 2 Key Points Question: Do early-term infants (GA 37-38 weeks) have a higher risk of overweight and obesity during infancy compared to full-term infants (GA 39-40 weeks)? Findings: In a cohort study of 5,092 term infants, early-term infants exhibited a significantly elevated risk of overweight and obesity from 1-3 months, persisting through infancy. The risk was most pronounced at 10-12 months (unadjusted OR, 1.236; 95% CI, 1.073-1.426; P = .004) Meaning: This study highlights the increased risk of overweight and obesity in early-term infants during the first postnatal year, suggesting the need for targeted interventions to prevent childhood obesity. Introduction Traditionally, term pregnancy is defined as spanning 260 to 294 days since the first day of the last menstrual period 1 . Neonates born before this interval, specifically less than 37 completed weeks of pregnancy, are classified as preterm, while those delivered beyond 42 weeks are designated as post-term. However, in 2013, the American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine introduced new classifications for term pregnancies: early-term (37^0/7 to 38^6/7 weeks), full-term (39^0/7 to 40^6/7 weeks), late-term (41^0/7 to 41^6/7 weeks), and post-term (≥42 weeks) 2 . Accumulating evidence has shown that infants born at 37 to 38 weeks of gestation face higher risks of morbidity and mortality compared to those born at 39 to 41 weeks 3-5 . Moreover, early-term infants exhibit poorer cognitive 6-9 and learning abilities 10-12 , suggesting that the final weeks of gestation are critical for optimal neurodevelopmental outcomes. Research from the National Institute of Child Health and Human Development in the United States has indicated that early-term infants have relatively poorer health outcomes compared to those born after 39 weeks 2,13 . Additionally, emerging studies suggest that early-term infants may follow distinct postnatal growth trajectories compared to full-term infants 14 . Over the past few decades, the prevalence of overweight and obesity among children and adolescents has increased dramatically worldwide, posing a significant public health challenge 15,16 . Overweight and obesity not only affect physical and mental health during childhood but also increase the risk of chronic diseases such as cardiovascular diseases and diabetes in adulthood 17-19 . Emerging evidence suggests that gestational age at birth may be an important early-life factor affecting an individual's future risk of overweight and obesity 20-22 . Specifically, early-term infants experience a shorter in utero developmental period compared to their full-term counterparts. Despite being classified as term, early-term infants may face unique developmental challenges due to this abbreviated period of gestation 23 . This discrepancy in developmental duration may lead to long-term physiological and metabolic consequences, which could be reflected in distinct postnatal growth patterns. While existing literature has extensively explored the disparities in overweight and obesity rates between preterm and term infants 24,25 , the unique characteristics and risks associated with early-term infants remain under-investigated. To address this research gap, the present study will conduct a retrospective analysis of postnatal growth data of early-term and full-term infants during infancy. We aim to elucidate potential differences in growth trajectories, as well as the risk of overweight and obesity, between these two groups. The findings of this study will provide valuable scientific evidence for the optimization of child health management and obesity prevention strategies. Methods Research Design and Participants This multicenter cohort study involved 6,915 healthy term infants born at two tertiary hospitals in Jinan, Shandong Province, between January 2011 and July 2022. Singleton live births with GA ranging from 37^0/7 to 40^6/7 weeks were eligible for inclusion. Infants with congenital malformations, syndromes, or any history of recent or long-term diseases were excluded. Ethical approval was obtained from the Medical Ethics Committee of Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University (NO. 2023-025). Anthropometric Outcomes Based on the local protocols for children's health assessments, infants were scheduled for their initial assessment at 42 days post-birth, followed by routine evaluations every 2 to 3 months. Participants underwent a minimum of four anthropometric measurements during their first year of life, categorized into four stages: 1-3 months, 4-6 months, 7-9 months, and 10-12 months. These measurements, including length and weight, were conducted by trained healthcare professionals using standardized techniques. The Body Mass Index-for-age Z-score (BAZ) was calculated according to the World Health Organization (WHO) child growth standards 26 , utilizing the R software package "anthro". Overweight and obesity risks were defined as BAZ values greater than 1. Covariates In this study, we extracted the following variables from hospital-based case records: maternal age at delivery, mode of delivery, parity, number of fetuses, infant sex, infant feeding practices during first six months, and birth weight. Among the total 6,915 participants, 5,092 had complete datasets for the aforementioned variables, the remaining 1,823 participants had incomplete information for one or more of these covariates. Statistical methods Categorical variables were summarized as frequencies (n) and percentages (%), while continuous variables were reported as means with standard deviations or 95% confidence intervals (CI). Continuous variables were analyzed using t-tests, whereas categorical variables were assessed using chi-square tests. Among the 5,092 subjects with complete datasets for covariables, association between GA and the risk of overweight and obesity at each follow-up stage was analyzed using binary logistic regression. Two models were constructed for this analysis: Model 1 presented the unadjusted associations (crude model), while Model 2 adjusted for potential confounders, including mode of delivery, parity, number of fetuses, infant sex, infant feeding practices during the first six months, and birth weight. Secondary analyses were repeated using multiply imputed missing values for covariates in regression analysis. Variables with missing data included maternal age at delivery (n=361), mode of delivery (n=19), parity (n=672), number of fetuses (n=432), infant feeding during the first six months (n=1070), and birth weight (n=405). Missing values were imputed 20 times via multiple imputation by chained equation (MICE), with the total sample size restored to 6,915. The predictive mean matching (pmm) method was applied for continuous covariates, while logistic regression (logreg) was used for dichotomous variables. Each of the 20 imputed datasets was fitted using a generalized linear model (GLM), and the imputation with the best fit was selected. Regression results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A two-tailed P -value less than 0.05 was considered statistically significant. Data analyses were conducted using SPSS Statistics version 27.0 (IBM) and R software (version 4.4.1). Results Basic Features Among the 6,915 infants enrolled in this study, 5,092 had complete datasets for covariates, while the remaining 1,823 infants had one or more missing covariates (Figure 1). There were no significant differences in GA, sex, or birth weight between infants with incomplete covariate data and those with complete data. However, notable differences were observed between these two groups in terms of maternal age at delivery, mode of delivery, parity, number of fetuses, infant feeding practices during the first six months. (eTable 1 in the Supplement) Among the 5,092 participants with complete data, 26.6% (n=1,355) were early-term infants. Table 1 summarizes the maternal and infant characteristics stratified by GA. Compared to full-term infants, early-term infants were more likely to be born to older mothers (30.64 years vs. 29.53 years, P < .001), belong to multiple births (13.7% vs. 4.8%, P < .001), and have higher parity (18.5% vs. 9.0%, P < .001). Additionally, early-term infants were more frequently delivered via cesarean section (56.5% vs. 43.1%, P <.001) and had a higher proportion of male infants (57.0% vs. 52.0%, P = .002). Differences were also noted in birth weight distribution and feeding practices during the first six months of life between early-term and full-term infants. Table 1. Comparison of characteristics between early-term infants and full-term infants Early-term infants Full-term infants Variable n=1355 n=3737 P Value Maternal Mode of delivery Vaginal delivery 589(43.5) 2128(56.9) ＜.001 Cesarean delivery 766(56.5) 1609(43.1) Maternal age at delivery, mean (SD) [range], y 30.64(4.07) [21-46] 29.53(3.46) [20-46] ＜.001 Parity 0 1104(81.5) 3402(91.0) ＜.001 ≥1 251(18.5) 335(9.0) Number of fetuses Single birth 1169(86.3) 3556(95.2) ＜.001 Multiple births 186(13.7) 181(4.8) Infant Sex Male 772(57.0) 1943(52.0) .002 Female 583(43.0) 1794(48.0) a χ2 test was used for categorical variables and 1-factor analysis of variance was used for continuous variables. Feeding practices during the first 6 mo Exclusive breastfeeding 381(28.1) 923(24.7) .002 Mixed feeding 827(61.0) 2476(66.3) Exclusive formula feeding 147(10.8) 338(9.0) Birth weight Low Birth Weight 42(3.1) 6(0.2) <.001 normal 1243(91.7) 3327(89.0) Macrosomia 70(5.2) 404(10.8) Changes in the Proportion of Overweight and Obesity Risk in Early-Term and Full-Term Infants During Infancy Figure 2 illustrates the developmental trajectory of overweight and obesity risk prevalence in early-term and full-term infants throughout infancy. The results indicate that early-term infants consistently faced a higher risk of overweight and obesity from 1-3 months of age onward. Throughout infancy, the proportion of overweight and obesity risk was significantly higher in early-term infants (ranging from 27.6% to 30.2%) compared to full-term infants (ranging from 23.6% to 26.4%). Notably, both early-term and full-term infants exhibited their highest proportions of overweight and obesity risk at 4-6 months of age, with rates of 30.2% and 26.4%, respectively ( P < .001) Association of GA at Birth and the Risk of Overweight and Obesity between Early-Term and Full-Term Infants from Birth to 12 Months of Age Early-term infants exhibited a significantly elevated risk of overweight and obesity throughout infancy compared to full-term infants, with the most pronounced association observed at 10-12 months of age (unadjusted OR, 1.236; 95% CI, 1.073-1.426; P = .004) (Table 2). Univariate analysis revealed that covariates such as mode of delivery, parity, number of fetuses, infant sex, feeding practices during the first six months, and birth weight were significantly correlated with the risk of overweight and obesity at various ages (eTable 2 in the Supplement). To account for potential confounding effects, binary logistic regression models were adjusted for these variables. The association between early-term birth and overweight and obesity risk remained significant after adjustment (Table 2). Furthermore, the risk of overweight and obesity in early-term infants appeared to increase with age. Early-term infants exhibited a significantly higher risk of overweight and obesity throughout infancy compared to full-term infants, with the most pronounced association observed at 10-12 months of age (unadjusted OR 1.236, 95% CI 1.073-1.426, P =0.004) (Table 2). Univariate analyses revealed that covariates such as mode of delivery, parity, number of fetuses, infant sex, feeding practices during the first six months, and birth weight were significantly associated with the risk of overweight and obesity at various ages (eTable 2 in the Supplement). To account for potential confounding effects, binary logistic regression models were adjusted for these variables. The association between early-term birth and overweight and obesity risk remained significant after adjustment (Table 2). Additionally, the risk of overweight and obesity in early-term infants appeared to increase with age. Regression analyses using multiple imputation for missing covariates produced similar results, further supporting the significant association between early-term birth and overweight and obesity risk during the first year of life (Table 3). To exclude the potential influence of birth weight, a sensitivity analysis was conducted among 4,570 children with normal birth weight (2,500 g ≤ birth weight < 4,000 g). The results indicated that early-term infants continued to exhibit a significantly higher risk of overweight and obesity across all four follow-up stages (eTable 4 in the Supplement). Table 2. Association of Gestational Age with the Risk of Overweight and Obesity at Four follow-up Stages among 5092 Participants a Model 1 b Model 2 b Abbreviations: OR, odds ratio; CI, confidence interval. a Data were analyzed using logistic regression. b Model 1 was unadjusted. Model 2 was adjusted for mode of delivery, parity, number of fetuses, sex, feeding practices during the first 6 months, and birth weight. Table 3. Association of Gestational Age with the Risk of Overweight and Obesity at Four Follow-up Stages among 6915 Participants a Model 1 b Model 2 b Abbreviations: OR, odds ratio; CI, confidence interval. a Data were analyzed using logistic regression. b Model 1 was unadjusted. Model 2 was adjusted for mode of delivery, parity, number of fetuses, sex, feeding practices during the first 6 months, and birth weight. Discussion This longitudinal cohort study identified a significant association between GA at birth and overweight and obesity in term infants during early infancy. Specifically, early-term infants (GA 37-38 weeks) exhibited a consistently higher risk of overweight and obesity compared to full-term infants (GA 39-40 weeks) throughout infancy. These findings are consistent with our previous multicenter cross-sectional study involving 7,299 singleton term infants (GA 37-41 weeks), which reported an increased risk of overweight and obesity in early-term infants aged 0-6 months and 6-12 months 27 . However, our results differ from those of Rachel Cooper's longitudinal study 28 , which followed 17,638 individuals (GA range: 28 weeks to >41 weeks) from birth to middle age and found a 'U'-shaped association between GA and BMI among women. In this study, BMI decreased with increasing GA up to 40 weeks but rose again with GA beyond 40 weeks. No significant association was observed in men. The wide range of GA (including preterm, early-term , full-term, late-term and post-term) and the long follow-up period (up to middle age) may have contributed to these differences. Moreover, this study did not specifically address whether BMI reached overweight and obesity thresholds, making it difficult to compare directly with our findings. Similarly, a study from the Medical Center of Soroka University in Israel, which followed up 225,260 term infants (GA >37 weeks) until age 18, reported a higher incidence of overweight and obesity in early-term infants compared to full-term infants 29 . However, this study focused on endocrine and metabolic health during childhood and adolescence rather than infancy. Another large study involving 253,810 mother-child pairs (GA 28-43 weeks) from 16 cohorts across Europe, North America, and Australia revealed a positive association between GA with BMI in early infancy (0.02 SD per week increase in GA) and odds of overweight (adjusted OR 1.02 per week increase in GA) in late infancy 30 . The inconsistencies with our findings may be attributed to the broader GA range and longer time span (1985-2017) of these studies, during which global trends in preterm birth and obesity prevalence may have changed. In addition, the unique cultural 31 , social and economic background of China 32 , including differences in dietary habits, lifestyles, and socioeconomic status, may have contributed to the observed differences in overweight risk between early-term and full-term infants. .Previous studies have shown inconsistent results regarding the association between GA and overweight and obesity risk in children, often involving all live births without focusing specifically on term infants. This study aims to fill this gap by systematically exploring the association between GA at birth and overweight and obesity risk in infancy among term infants. Our findings provide new insights and evidence for the early prevention and intervention strategies targeting infant overweight and obesity. Furthermore, after adjusting for multiple potential confounding factors, we found that the risk of overweight and obesity in early-term infants remained significantly elevated and showed an increasing trend with age. This result strongly suggests that the GA status in term infants may be closely related to the long-term problem of overweight and obesity in children. One possible mechanism underlying this association may be the phenomenon of catch-up growth observed in early-term infants during the first 1-3 months of life. Existing studies have shown that "accelerated" or overly rapid growth in early life has adverse effects on long-term health, particularly increasing the risk of obesity and cardiovascular disease 33-35 . Another potential reason is the significantly higher rate of cesarean section deliveries among early-term infants compared to full-term infants. Previous research has demonstrated that cesarean section delivery is associated with an increased risk of overweight in infants and young children 36-38 . However, in our study, the risk of overweight and obesity in early-term infants persisted and showed an increasing trend with age, even after adjusting for all confounding factors, including delivery method. This indicates that early-term birth has a stable and independent association with the risk of overweight and obesity in infancy. To further explore these potential mechanisms, future studies should adopt a prospective design, combining detailed maternal and infant health data with biomarker measurements. This approach would help elucidate the specific biological pathways underlying the increased risk of overweight and obesity in early-term infants. In 2020, approximately 38.9 million children under the age of five worldwide were at risk of being overweight, with the highest prevalence in Asia (48%), followed by Africa (27%), Latin America and the Caribbean (10%), and Europe (8%) 39 . In China, the prevalence of overweight and obesity risk among infants and young children has significantly increased in recent years, especially in large cities like Beijing 40 , where the incidence rate is relatively high. In our study, the prevalence rates of overweight and obesity risk ranged from 24.6% to 27.4% in term infants. Despite these concerning trends, early intervention measures for overweight and obesity remain limited. Identifing potential early risk factors and developing targeted interventions are therefore crucial. While the association between GA and health outcomes in preterm population has long been a focus of attention—with considerable evidence indicating that preterm infants have an increased risk of overweight and obesity in childhood and adulthood 24,41 —the majority of live births worldwide are term infants, accounting for 85-90% of all births 42 . Therefore, identifying early-life risk factors for overweight and obesity in term infants and developing corresponding interventions may have greater public health value. In this study, early-term infants constituted 26.6% of all term infants, slightly lower than in our previous study (31%) 27 , but consistent with global estimates 43 . Globally, the proportion of early-term infants among singleton live births ranges from 15% to 31%, with most studies reporting a stable range of 26%-29% 44 . This group constitutes a significant portion of the population and is often considered healthy, leading to potential neglect in health monitoring. Our longitudinal cohort study indicated that early-term infants began to show an increased risk of overweight and obesity at 1-3 months of age, with the risk persisting and gradually increasing throughout infancy. Although our study followed participants only until 12 months of age, previous research has shown that overweight during infancy is strongly associated with future overweight, obesity, and metabolic diseases 45-47 . Based on these findings, we propose the following recommendations: First, for early-term infants, enhanced follow-up and close monitoring of growth status are essential to avoid excessive catch-up growth. Lifestyle adjustments, such as appropriate feeding practices and increased activity levels, should be promoted to mitigate the risk of future overweight, obesity, and metabolic diseases. Second, our study provides strong evidence supporting the "39-week rule" introduced by the American College of Obstetricians and Gynecologists in 2009 48 . This rule aims to reduce selective cesarean sections before 39 weeks without medical indications to minimize various health risks faced by infants after birth. Our results further confirm the scientific basis and necessity of this strategy, providing an important reference for clinical practice. The strengths of our study included its multicenter design, large sample size, longitudinal follow-up, and standardized data collection protocols, all of which enhanced the reliability and validity of our findings. The two medical centers involved are located in Jinan, Shandong Province—a developed coastal economic region in China. These hospitals provide comprehensive medical information, ensuring high-quality data collection. The consistent and complete follow-up of participants ensured a reasonable level of population representativeness. The comprehensive dataset allowed us to longitudinally examine the association between GA and the risk of overweight/obesity while accounting for important potential confounding factors such as mode of delivery and birth weight. Collectively, these factors provide robust evidence for a deeper understanding of the early growth patterns and health risks of term infants in economically developed areas of middle-income countries. Limitations This study has several limitations. First, as a retrospective analysis, it is subject to inherent biases, and despite efforts to control for potential confounding factors, their complete elimination is not possible. Second, the study was conducted in only two medical institutions in Jinan, Shandong Province, which may limit the generalizability of the findings to other regions. Lastly, the study did not assess lifestyle factors such as dietary habits and physical activity levels, which could potentially influence the results. Future research should address these limitations by incorporating a broader range of geographic locations and including detailed assessments of lifestyle factors to provide a more comprehensive understanding of the factors contributing to overweight and obesity risk in early-term infants. Conclusions In summary, this longitudinal study demonstrates that early-term infants face a higher risk of overweight and obesity during their first year of life. These findings highlight the importance of recognizing early-term birth as a potential risk factor for childhood overweight and obesity and provide a new perspective for prevention and intervention strategies.. Future research should further investigate the sustained effects of gestational age on childhood overweight and obesity in term infants, as well as elucidate the underlying biological and environmental mechanisms. Additionally, efforts should be made to develop targeted interventions aimed at mitigating the risk of overweight and obeaity in early-term infants, particularly in the context of public health initiatives. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University (NO. 2023-025, 5 July 2023)(Annotation:because of the change of corresponding author units and update on ethics,past approved by the Ethics Committee of the First Affiliated Hospital of Shandong First Medical University(YXLL-KY-2022(017).) Consent for publication Informed consent was obtained from all subjects involved in the study. Availability of data and materials The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no conflict of interest. Funding This work was supported by Natural Science Foundation of Shandong Province (no. ZR2021MH296). Authors' contributions Conceptualization, Y.L.; Methodology, M.G.; Software, L.Z. and M.G.; Validation, Y.L.; Formal Analysis, L.Z. and M.G.; Investigation, L.Z. and M.G.; Data Curation, L.Z., M.G., T.Z., M.-M.W., H.-J. L., Z.-Y. L. ; Writing–Original Draft Preparation, L.Z. and M.G.; Writing–Review & Editing, Y.L. and L.Z.; Supervision, Y.L.; Project Administration,Y.L.. Acknowledgments We express our gratitude to Professor Hai-Jun Wang at the School of Public Health, Health Science Center of Peking University for her valuable recommendations in the revision of the manuscript.We thank all study participants of the study. References Standard terminology for reporting of reproductive health statistics in the United States. Public Health Rep Sep-Oct. 1988;103(5):464–71. Spong CY. 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Evidence that children born at early term (37–38 6/7 weeks) are at increased risk for diabetes and obesity-related disorders. Am J Obstet Gynecol. 2017;217(5):588 e1-588 e11. 10.1016/j.ajog.2017.07.015 Vinther JL, Cadman T, Avraam D, et al. Gestational age at birth and body size from infancy through adolescence: An individual participant data meta-analysis on 253,810 singletons in 16 birth cohort studies. PLoS Med Jan. 2023;20(1):e1004036. 10.1371/journal.pmed.1004036 . Yuan C, Dong Y, Chen H, et al. Determinants of childhood obesity in China. Lancet Public Health Dec. 2024;9(12):e1105–14. 10.1016/S2468-2667(24)00246-9 . Garcia-Mayor J, Moreno-Llamas A, De la Cruz-Sanchez E. How socioeconomic status affects weight status through health-related lifestyles: a latent class analysis. Eur J Cardiovasc Nurs Oct. 2023;19(7):730–44. 10.1093/eurjcn/zvac101 . Singhal A. Long-Term Adverse Effects of Early Growth Acceleration or Catch-Up Growth. Ann Nutr Metab. 2017;70(3):236–40. 10.1159/000464302 . Zhang DL, Du Q, Djemli A, Julien P, Fraser WD, Luo ZC. Early and Late Postnatal Accelerated Growth Have Distinct Effects on Metabolic Health in Normal Birth Weight Infants. Front Endocrinol (Lausanne). 2017;8:340. 10.3389/fendo.2017.00340 . Rolland-Cachera MF. Rate of growth in early life: a predictor of later health? Adv Exp Med Biol. 2005;569:35–9. 10.1007/1-4020-3535-7_6 . Mueller NT, Zhang M, Hoyo C, Ostbye T, Benjamin-Neelon SE. Does cesarean delivery impact infant weight gain and adiposity over the first year of life? Int J Obes (Lond) Aug. 2019;43(8):1549–55. 10.1038/s41366-018-0239-2 . Pluymen LP, Smit HA, Wijga AH, Gehring U, De Jongste JC, Van Rossem L, Cesarean, Delivery. Overweight throughout Childhood, and Blood Pressure in Adolescence. J Pediatr Dec. 2016;179:111–e1173. 10.1016/j.jpeds.2016.08.059 . Papadopoulou SK, Mentzelou M, Pavlidou E, et al. Caesarean Section Delivery Is Associated with Childhood Overweight and Obesity, Low Childbirth Weight and Postnatal Complications: A Cross-Sectional Study. Medicina (Kaunas). Mar. 2023;27(4). 10.3390/medicina59040664 . Muyulema SL, Carpio-Arias TV, Verdezoto N, et al. Worldwide trends in childhood overweight and obesity over the last 20 years. Clin Nutr ESPEN Feb. 2025;65:453–60. 10.1016/j.clnesp.2024.12.013 . Zhao Y, Wang L, Xue B, Wang Y. Associations between general and central obesity and hypertension among children: The Childhood Obesity Study in China Mega-Cities. Sci Rep Dec. 2017;4(1):16895. 10.1038/s41598-017-16819-y . Lindsay ACAMTMKC, Prematurity. Overweight and Obesity: A Problem That Merits Increased Recognition by Healthcare Practitioners and Researchers. Newborn infant Nurs reviews. 2015;15(4). Ohuma EO, Moller AB, Bradley E, et al. National, regional, and global estimates of preterm birth in 2020, with trends from 2010: a systematic analysis. Lancet Oct. 2023;7(10409):1261–71. 10.1016/S0140-6736(23)00878-4 . Delnord M, Zeitlin J. Epidemiology of late preterm and early term births - An international perspective. Semin Fetal Neonatal Med Feb. 2019;24(1):3–10. 10.1016/j.siny.2018.09.001 . Ahmed AH, Rojjanasrirat W, Breastfeeding Outcomes. Self-Efficacy, and Satisfaction Among Low-Income Women With Late-Preterm, Early-Term, and Full-Term Infants. J Obstet Gynecol Neonatal Nurs Sep. 2021;50(5):583–96. 10.1016/j.jogn.2021.06.010 . Tao MY, Liu X, Chen ZL, et al. Fetal overgrowth and weight trajectories during infancy and adiposity in early childhood. Pediatr Res Apr. 2024;95(5):1372–8. 10.1038/s41390-023-02991-7 . Halilagic A, Moschonis G. The Effect of Growth Rate during Infancy on the Risk of Developing Obesity in Childhood: A Systematic Literature Review. Nutrients Sep. 2021;29(10). 10.3390/nu13103449 . Wang G, Johnson S, Gong Y, et al. Weight Gain in Infancy and Overweight or Obesity in Childhood across the Gestational Spectrum: a Prospective Birth Cohort Study. Sci Rep Jul. 2016;15:6:29867. 10.1038/srep29867 . Cochrane AC, Batson R, Aragon M, et al. Impact of the 39-week rule on adverse pregnancy outcomes: a statewide analysis. Am J Obstet Gynecol MFM Apr. 2023;5(4):100879. 10.1016/j.ajogmf.2023.100879 . Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Posted Version 1 posted 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-6207604","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427736077,"identity":"94253f32-feb7-40af-9ab3-c0aeb61f9f64","order_by":0,"name":"Li Zhang","email":"","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""},{"id":427736078,"identity":"1b694733-57c5-453a-b6c7-522f7be1e7de","order_by":1,"name":"Mai Gao","email":"","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Mai","middleName":"","lastName":"Gao","suffix":""},{"id":427736079,"identity":"a2c1770b-d0bd-4561-8a88-64591562467e","order_by":2,"name":"Ting Zhang","email":"","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Zhang","suffix":""},{"id":427736080,"identity":"c08bcd7c-e1cb-4583-9573-8088b64c7e1b","order_by":3,"name":"Meng-Meng Wei","email":"","orcid":"","institution":"Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meng-Meng","middleName":"","lastName":"Wei","suffix":""},{"id":427736081,"identity":"5170fda0-e47b-4ff2-ac0b-3dd71a88f097","order_by":4,"name":"Hui-Juan Liu","email":"","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Hui-Juan","middleName":"","lastName":"Liu","suffix":""},{"id":427736082,"identity":"2f220d1c-1006-4225-847c-b5157d427e4b","order_by":5,"name":"Zi-Yun Li","email":"","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Zi-Yun","middleName":"","lastName":"Li","suffix":""},{"id":427736083,"identity":"6e1c83ac-b49c-49be-809e-de4dc2051345","order_by":6,"name":"Yan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYBACPmYwxSbHwEysFjaoFmMStEDpxAaiHcbGzmP4ubCNL31+O+/BDww1NtFEOIzHWHrGGbbcDYf5kiUYjqXlErQOqMVAmqcCqAXIkGBsOEyUFuPfPAZs6fLNPMY/iNViBrIlgeEwjxmxtrCVWfOcYTPcANRikUCMX/j5D2++zdt2TF6+/4zxjQ81NoS1MDBwGACJYxB2AmHlIMD+AEjUEKd2FIyCUTAKRiYAABofL24R1QvYAAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-12 01:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6207604/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6207604/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78675004,"identity":"64e128ee-7c70-423b-ac04-a66152e1ffd8","added_by":"auto","created_at":"2025-03-17 13:27:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":73632,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Study Participants\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6207604/v1/c87320d7bbf4298d82101c53.jpg"},{"id":78673879,"identity":"8e5aec3e-5a2c-4d3b-83ea-816650de04fa","added_by":"auto","created_at":"2025-03-17 13:19:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87841,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the Proportion of Overweight and Obesity Risk between Early-Term and Full-Term Infants During Infancy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDevelopmental trajectories of overweight and obesity risk in early-term (37-38 weeks GA) and full-term (39-40 weeks GA) infants during the first year of life. Data are presented as the proportion of infants with a BMI-for-age Z-score (BAZ) \u0026gt; 1, indicating overweight or obesity risk. * The difference is statistically significant, P \u0026lt; 0.05.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6207604/v1/e34b0b028b1f3d4da351da32.png"},{"id":78836159,"identity":"fcbaae41-a01b-498c-9a35-8b9a0aa9a096","added_by":"auto","created_at":"2025-03-19 14:47:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":971367,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6207604/v1/168c8599-c2b5-4b7e-92a6-42180d6b8b48.pdf"},{"id":78676022,"identity":"ee7ef826-2aa2-4185-ba01-1485db764cee","added_by":"auto","created_at":"2025-03-17 13:43:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":83262,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-6207604/v1/86d29a7077d28187d40aa98b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Early-Term and Full-Term Delivery With Overweight and Obesity Risk in the First Year of Life","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u003cstrong\u003eQuestion:\u003c/strong\u003e Do early-term infants (GA 37-38 weeks) have a higher risk of overweight and obesity during infancy compared to full-term infants (GA 39-40 weeks)?\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u0026nbsp;\u003c/strong\u003eIn a cohort study of 5,092 term infants, early-term infants exhibited a significantly elevated risk of overweight and obesity from 1-3 months, persisting through infancy. The risk was most pronounced at 10-12 months (unadjusted OR, 1.236; 95% CI, 1.073-1.426; \u003cem\u003eP\u003c/em\u003e = .004)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeaning:\u0026nbsp;\u003c/strong\u003eThis study highlights the increased risk of overweight and obesity in early-term infants during the first postnatal year, suggesting the need for targeted interventions to prevent childhood obesity.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eTraditionally, term pregnancy is defined as spanning 260 to 294 days since the first day of the last menstrual period\u003csup\u003e1\u003c/sup\u003e. Neonates born before this interval, specifically less than 37 completed weeks of pregnancy, are classified as preterm, while those delivered beyond 42 weeks are designated as post-term. However, in 2013, the American College of Obstetricians and Gynecologists and the Society for Maternal-Fetal Medicine introduced new classifications for term pregnancies: early-term (37^0/7 to 38^6/7 weeks), full-term (39^0/7 to 40^6/7 weeks), late-term (41^0/7 to 41^6/7 weeks), and post-term (≥42 weeks)\u003csup\u003e2\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccumulating evidence has shown that infants born at 37 to 38 weeks of gestation face higher risks of morbidity and mortality compared to those born at 39 to 41 weeks\u003csup\u003e3-5\u003c/sup\u003e. Moreover, early-term infants exhibit poorer cognitive\u003csup\u003e6-9\u003c/sup\u003e and learning abilities\u003csup\u003e10-12\u003c/sup\u003e, suggesting that the final weeks of gestation are critical for optimal neurodevelopmental outcomes. Research from the National Institute of Child Health and Human Development in the United States has indicated that early-term infants have relatively poorer health outcomes compared to those born after 39 weeks\u003csup\u003e2,13\u003c/sup\u003e. Additionally, emerging studies suggest that early-term infants may follow distinct postnatal growth trajectories compared to full-term infants\u003csup\u003e14\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOver the past few decades, the prevalence of overweight and obesity among children and adolescents has increased dramatically worldwide, posing a significant public health challenge\u003csup\u003e15,16\u003c/sup\u003e. Overweight and obesity not only affect physical and mental health during childhood but also increase the risk of chronic diseases such as cardiovascular diseases and diabetes in adulthood\u003csup\u003e17-19\u003c/sup\u003e. Emerging evidence suggests that gestational age at birth may be an important early-life factor affecting an individual's future risk of overweight and obesity\u003csup\u003e20-22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSpecifically, early-term infants experience a shorter in utero developmental period compared to their full-term counterparts. Despite being classified as term, early-term infants may face unique developmental challenges due to this abbreviated period of gestation\u003csup\u003e23\u003c/sup\u003e. This discrepancy in developmental duration may lead to long-term physiological and metabolic consequences, which could be reflected in distinct postnatal growth patterns. While existing literature has extensively explored the disparities in overweight and obesity rates between preterm and term infants\u003csup\u003e24,25\u003c/sup\u003e, the unique characteristics and risks associated with early-term infants remain under-investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address this research gap, the present study will conduct a retrospective analysis of postnatal growth data of early-term and full-term infants during infancy. We aim to elucidate potential differences in growth trajectories, as well as the risk of overweight and obesity, between these two groups. The findings of this study will provide valuable scientific evidence for the optimization of child health management and obesity prevention strategies.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eResearch Design and Participants\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis multicenter cohort study involved 6,915 healthy term infants born at two tertiary hospitals in Jinan, Shandong Province, between January 2011 and July 2022. Singleton live births with GA ranging from 37^0/7 to 40^6/7 weeks were eligible for inclusion. Infants with congenital malformations, syndromes, or any history of recent or long-term diseases were excluded. Ethical approval was obtained from the Medical Ethics Committee of Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University (NO. 2023-025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnthropometric Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the local protocols for children's health assessments, infants were scheduled for their initial assessment at 42 days post-birth, followed by routine evaluations every 2 to 3 months. Participants underwent a minimum of four anthropometric measurements during their first year of life, categorized into four stages: 1-3 months, 4-6 months, 7-9 months, and 10-12 months. These measurements, including length and weight, were conducted by trained healthcare professionals using standardized techniques. The Body Mass Index-for-age Z-score (BAZ) was calculated according to the World Health Organization (WHO) child growth standards\u003csup\u003e26\u003c/sup\u003e, utilizing the R software package \"anthro\". Overweight and obesity risks were defined as BAZ values greater than 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we extracted the following variables from hospital-based case records: maternal age at delivery, mode of delivery, parity, number of fetuses, infant sex, infant feeding practices during first six months, and birth weight. Among the total 6,915 participants, 5,092 had complete datasets for the aforementioned variables, the remaining 1,823 participants had incomplete information for one or more of these covariates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were summarized as frequencies (n) and percentages (%), while continuous variables were reported as means with standard deviations or 95% confidence intervals (CI). Continuous variables were analyzed using t-tests, whereas categorical variables were assessed using chi-square tests.\u003c/p\u003e\n\u003cp\u003eAmong the 5,092 subjects with complete datasets for covariables, association between GA and the risk of overweight and obesity at each follow-up stage was analyzed using binary logistic regression. Two models were constructed for this analysis: Model 1 presented the unadjusted associations (crude model), while Model 2 adjusted for potential confounders, including mode of delivery, parity, number of fetuses, infant sex, infant feeding practices during the first six months, and birth weight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSecondary analyses were repeated using multiply imputed missing values for covariates in regression analysis. Variables with missing data included maternal age at delivery (n=361), mode of delivery (n=19), parity (n=672), number of fetuses (n=432), infant feeding during the first six months (n=1070), and birth weight (n=405). Missing values were imputed 20 times via multiple imputation by chained equation (MICE), with the total sample size restored to 6,915. The predictive mean matching (pmm) method was applied for continuous covariates, while logistic regression (logreg) was used for dichotomous variables. Each of the 20 imputed datasets was fitted using a generalized linear model (GLM), and the imputation with the best fit was selected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegression results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). A two-tailed\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value less than 0.05 was considered statistically significant. Data analyses were conducted using SPSS Statistics version 27.0 (IBM) and R software (version 4.4.1).\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 6,915 infants enrolled in this study, 5,092 had complete datasets for covariates, while the remaining 1,823 infants had one or more missing covariates (Figure 1). There were no significant differences in GA, sex, or birth weight between infants with incomplete covariate data and those with complete data. However, notable differences were observed between these two groups in terms of maternal age at delivery, mode of delivery, parity, number of fetuses, infant feeding practices during the first six months. (eTable 1 in the Supplement)\u003c/p\u003e\n\u003cp\u003eAmong the 5,092 participants with complete data, 26.6% (n=1,355) were early-term infants. Table 1 summarizes the maternal and infant characteristics stratified by GA. Compared to full-term infants, early-term infants were more likely to be born to older mothers (30.64 years vs. 29.53 years, \u003cem\u003eP\u003c/em\u003e\u0026lt; .001), belong to multiple births (13.7% vs. 4.8%, \u003cem\u003eP\u003c/em\u003e\u0026lt; .001), and have higher parity (18.5% vs. 9.0%, \u003cem\u003eP\u003c/em\u003e\u0026lt; .001). Additionally, early-term infants were more frequently delivered via cesarean section (56.5% vs. 43.1%, \u003cem\u003eP\u003c/em\u003e\u0026lt;.001) and had a higher proportion of male infants (57.0% vs. 52.0%, \u003cem\u003eP\u003c/em\u003e= .002). Differences were also noted in birth weight distribution and feeding practices during the first six months of life between early-term and full-term infants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Comparison of characteristics between early-term infants and full-term infants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 210px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eEarly-term infants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eFull-term infants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=1355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003en=3737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMode of delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e589(43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2128(56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCesarean delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e766(56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1609(43.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eMaternal age at delivery,\u0026nbsp;\u003cbr\u003e\u0026nbsp;mean (SD) [range], y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e30.64(4.07)\u003cbr\u003e\u0026nbsp;[21-46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.53(3.46)\u003cbr\u003e\u0026nbsp;[20-46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1104(81.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3402(91.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026ge;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e251(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e335(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eNumber of fetuses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1169(86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3556(95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e＜.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMultiple births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e186(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e181(4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e772(57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1943(52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e583(43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1794(48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\"\u003e\n \u003cp\u003ea \u0026chi;2 test was used for categorical variables and 1-factor analysis of variance was used for continuous variables.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eFeeding practices during the first 6 mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExclusive breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e381(28.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e923(24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMixed feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e827(61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2476(66.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eExclusive formula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e147(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003eBirth weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow Birth Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42(3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003enormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1243(91.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3327(89.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMacrosomia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e404(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in the Proportion of Overweight and Obesity Risk in Early-Term and Full-Term Infants During Infancy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the developmental trajectory of overweight and obesity risk prevalence in early-term and full-term infants throughout infancy. The results indicate that early-term infants consistently faced a higher risk of overweight and obesity from 1-3 months of age onward. Throughout infancy, the proportion of overweight and obesity risk was significantly higher in early-term infants (ranging from 27.6% to 30.2%) compared to full-term infants (ranging from 23.6% to 26.4%). Notably, both early-term and full-term infants exhibited their highest proportions of overweight and obesity risk at 4-6 months of age, with rates of 30.2% and 26.4%, respectively (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; .001)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of GA at Birth and the Risk of Overweight and Obesity between Early-Term and Full-Term Infants from Birth to 12 Months of Age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEarly-term infants exhibited a significantly elevated risk of overweight and obesity throughout infancy compared to full-term infants, with the most pronounced association observed at 10-12 months of age (unadjusted OR, 1.236; 95% CI, 1.073-1.426; \u003cem\u003eP\u003c/em\u003e = .004) (Table 2). Univariate analysis revealed that covariates such as mode of delivery, parity, number of fetuses, infant sex, feeding practices during the first six months, and birth weight were significantly correlated with the risk of overweight and obesity at various ages (eTable 2 in the Supplement). To account for potential confounding effects, binary logistic regression models were adjusted for these variables. The association between early-term birth and overweight and obesity risk remained significant after adjustment (Table 2). Furthermore, the risk of overweight and obesity in early-term infants appeared to increase with age. Early-term infants exhibited a significantly higher risk of overweight and obesity throughout infancy compared to full-term infants, with the most pronounced association observed at 10-12 months of age (unadjusted OR 1.236, 95% CI 1.073-1.426, \u003cem\u003eP\u003c/em\u003e=0.004) (Table 2). Univariate analyses revealed that covariates such as mode of delivery, parity, number of fetuses, infant sex, feeding practices during the first six months, and birth weight were significantly associated with the risk of overweight and obesity at various ages (eTable 2 in the Supplement). To account for potential confounding effects, binary logistic regression models were adjusted for these variables. The association between early-term birth and overweight and obesity risk remained significant after adjustment (Table 2). Additionally, the risk of overweight and obesity in early-term infants appeared to increase with age.\u003c/p\u003e\n\u003cp\u003eRegression analyses using multiple imputation for missing covariates produced similar results, further supporting the significant association between early-term birth and overweight and obesity risk during the first year of life (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo exclude the potential influence of birth weight, a sensitivity analysis was conducted among 4,570 children with normal birth weight (2,500 g \u0026le; birth weight \u0026lt; 4,000 g). The results indicated that early-term infants continued to exhibit a significantly higher risk of overweight and obesity across all four follow-up stages (eTable 4 in the Supplement).\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2. Association of Gestational Age with the Risk of Overweight and Obesity at Four follow-up Stages among 5092 Participants\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"821\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 334px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 821px;\"\u003e\n \u003cp\u003e\u003cimg width=\"805\" 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\" alt=\"IMG_256\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 821px;\"\u003e\n \u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval.\u003cbr\u003e\u003csup\u003ea\u003c/sup\u003e Data were analyzed using logistic regression.\u003cbr\u003e\u003csup\u003eb\u003c/sup\u003e Model 1 was unadjusted. Model 2 was adjusted for mode of delivery, parity, number of fetuses, sex, feeding practices during the first 6 months, and birth weight.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Association of Gestational Age with the Risk of Overweight and Obesity at Four Follow-up Stages among 6915 Participants\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"821\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 351px;\"\u003e\n \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 821px;\"\u003e\n \u003cp\u003e\u003cimg 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\" alt=\"IMG_256\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 821px;\"\u003e\n \u003cp\u003eAbbreviations: OR, odds ratio; CI, confidence interval.\u003cbr\u003e\u003csup\u003ea\u003c/sup\u003e Data were analyzed using logistic regression.\u003cbr\u003e\u003csup\u003eb\u003c/sup\u003e Model 1 was unadjusted. Model 2 was adjusted for mode of delivery, parity, number of fetuses, sex, feeding practices during the first 6 months, and birth weight.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis longitudinal cohort study identified a significant association between GA at birth and overweight and obesity in term infants during early infancy. Specifically, early-term infants (GA 37-38 weeks) exhibited a consistently higher risk of overweight and obesity compared to full-term infants (GA 39-40 weeks) throughout infancy. These findings are consistent with our previous multicenter cross-sectional study involving 7,299 singleton term infants (GA 37-41 weeks), which reported an increased risk of overweight and obesity in early-term infants aged 0-6 months and 6-12 months\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, our results differ from those of Rachel Cooper's longitudinal study\u003csup\u003e28\u003c/sup\u003e, which followed 17,638 individuals (GA range: 28 weeks to \u0026gt;41 weeks) from birth to middle age and found a 'U'-shaped association between GA and BMI among women. In this study, BMI decreased with increasing GA up to 40 weeks but rose again with GA beyond 40 weeks. No significant association was observed in men. The wide range of GA (including preterm, early-term , full-term, late-term and post-term) and the long follow-up period (up to middle age) may have contributed to these differences. Moreover, this study did not specifically address whether BMI reached overweight and obesity thresholds, making it difficult to compare directly with our findings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, a study from the Medical Center of Soroka University in Israel, which followed up 225,260 term infants (GA \u0026gt;37 weeks) until age 18, reported a higher incidence of overweight and obesity in early-term infants compared to full-term infants\u003csup\u003e29\u003c/sup\u003e. However, this study focused on endocrine and metabolic health during childhood and adolescence rather than infancy. Another large study involving 253,810 mother-child pairs (GA 28-43 weeks) from 16 cohorts across Europe, North America, and Australia revealed a positive association between GA with BMI in early infancy (0.02 SD per week increase in GA) and odds of overweight (adjusted OR 1.02 per week increase in GA) in late infancy\u003csup\u003e30\u003c/sup\u003e. The inconsistencies with our findings may be attributed to the broader GA range and longer time span (1985-2017) of these studies, during which global trends in preterm birth and obesity prevalence may have changed. In addition, the unique cultural\u003csup\u003e31\u003c/sup\u003e, social and economic background of China\u003csup\u003e32\u003c/sup\u003e, including differences in dietary habits, lifestyles, and socioeconomic status, may have contributed to the observed differences in overweight risk between early-term and full-term infants.\u003c/p\u003e\n\u003cp\u003e.Previous studies have shown inconsistent results regarding the association between GA and overweight and obesity risk in children, often involving all live births without focusing specifically on term infants. This study aims to fill this gap by systematically exploring the association between GA at birth and overweight and obesity risk in infancy among term infants. Our findings provide new insights and evidence for the early prevention and intervention strategies targeting infant overweight and obesity. Furthermore, after adjusting for multiple potential confounding factors, we found that the risk of overweight and obesity in early-term infants remained significantly elevated and showed an increasing trend with age. This result strongly suggests that the GA status in term infants may be closely related to the long-term problem of overweight and obesity in children.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOne possible mechanism underlying this association may be the phenomenon of catch-up growth observed in early-term infants during the first 1-3 months of life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExisting studies have shown that \"accelerated\" or overly rapid growth in early life has adverse effects on long-term health, particularly increasing the risk of obesity and cardiovascular disease\u003csup\u003e33-35\u003c/sup\u003e. Another potential reason is the significantly higher rate of cesarean section deliveries among early-term infants compared to full-term infants. Previous research has demonstrated that cesarean section delivery is associated with an increased risk of overweight in infants and young children\u003csup\u003e36-38\u003c/sup\u003e. However, in our study, the risk of overweight and obesity in early-term infants persisted and showed an increasing trend with age, even after adjusting for all confounding factors, including delivery method. This indicates that early-term birth has a stable and independent association with the risk of overweight and obesity in infancy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo further explore these potential mechanisms, future studies should adopt a prospective design, combining detailed maternal and infant health data with biomarker measurements. This approach would help elucidate the specific biological pathways underlying the increased risk of overweight and obesity in early-term infants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn 2020, approximately 38.9 million children under the age of five worldwide were at risk of being overweight, with the highest prevalence in Asia (48%), followed by Africa (27%), Latin America and the Caribbean (10%), and Europe (8%)\u003csup\u003e39\u003c/sup\u003e. In China, the prevalence of overweight and obesity risk among infants and young children has significantly increased in recent years, especially in large cities like Beijing\u003csup\u003e40\u003c/sup\u003e, where the incidence rate is relatively high. In our study, the prevalence rates of overweight and obesity risk ranged from 24.6% to 27.4% in term infants. Despite these concerning trends, early intervention measures for overweight and obesity remain limited. Identifing potential early risk factors and developing targeted interventions are therefore crucial.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the association between GA and health outcomes in preterm population has long been a focus of attention—with considerable evidence indicating that preterm infants have an increased risk of overweight and obesity in childhood and adulthood\u003csup\u003e24,41\u003c/sup\u003e—the majority of live births worldwide are term infants, accounting for 85-90% of all births\u003csup\u003e42\u003c/sup\u003e. Therefore, identifying early-life risk factors for overweight and obesity in term infants and developing corresponding interventions may have greater public health value.\u003c/p\u003e\n\u003cp\u003eIn this study, early-term infants constituted 26.6% of all term infants, slightly lower than in our previous study (31%)\u003csup\u003e27\u003c/sup\u003e, but consistent with global estimates\u003csup\u003e43\u003c/sup\u003e. Globally, the proportion of early-term infants among singleton live births ranges from 15% to 31%, with most studies reporting a stable range of 26%-29%\u003csup\u003e44\u003c/sup\u003e. This group constitutes a significant portion of the population and is often considered healthy, leading to potential neglect in health monitoring. Our longitudinal cohort study indicated that early-term infants began to show an increased risk of overweight and obesity at 1-3 months of age, with the risk persisting and gradually increasing throughout infancy. Although our study followed participants only until 12 months of age, previous research has shown that overweight during infancy is strongly associated with future overweight, obesity, and metabolic diseases\u003csup\u003e45-47\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on these findings, we propose the following recommendations: First, for early-term infants, enhanced follow-up and close monitoring of growth status are essential to avoid excessive catch-up growth. Lifestyle adjustments, such as appropriate feeding practices and increased activity levels, should be promoted to mitigate the risk of future overweight, obesity, and metabolic diseases. Second, our study provides strong evidence supporting the \"39-week rule\" introduced by the American College of Obstetricians and Gynecologists in 2009\u003csup\u003e48\u003c/sup\u003e. This rule aims to reduce selective cesarean sections before 39 weeks without medical indications to minimize various health risks faced by infants after birth. Our results further confirm the scientific basis and necessity of this strategy, providing an important reference for clinical practice.\u003c/p\u003e\n\u003cp\u003eThe strengths of our study included its multicenter design, large sample size, longitudinal follow-up, and standardized data collection protocols, all of which enhanced the reliability and validity of our findings. The two medical centers involved are located in Jinan, Shandong Province—a developed coastal economic region in China. These hospitals provide comprehensive medical information, ensuring high-quality data collection. The consistent and complete follow-up of participants ensured a reasonable level of population representativeness. The comprehensive dataset allowed us to longitudinally examine the association between GA and the risk of overweight/obesity while accounting for important potential confounding factors such as mode of delivery and birth weight. Collectively, these factors provide robust evidence for a deeper understanding of the early growth patterns and health risks of term infants in economically developed areas of middle-income countries.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations. First, as a retrospective analysis, it is subject to inherent biases, and despite efforts to control for potential confounding factors, their complete elimination is not possible. Second, the study was conducted in only two medical institutions in Jinan, Shandong Province, which may limit the generalizability of the findings to other regions. Lastly, the study did not assess lifestyle factors such as dietary habits and physical activity levels, which could potentially influence the results. Future research should address these limitations by incorporating a broader range of geographic locations and including detailed assessments of lifestyle factors to provide a more comprehensive understanding of the factors contributing to overweight and obesity risk in early-term infants.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this longitudinal study demonstrates that early-term infants face a higher risk of overweight and obesity during their first year of life. These findings highlight the importance of recognizing early-term birth as a potential risk factor for childhood overweight and obesity and provide a new perspective for prevention and intervention strategies.. Future research should further investigate the sustained effects of gestational age on childhood overweight and obesity in term infants, as well as elucidate the underlying biological and environmental mechanisms. Additionally, efforts should be made to develop targeted interventions aimed at mitigating the risk of overweight and obeaity in early-term infants, particularly in the context of public health initiatives.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and approved by the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthics Committee of the Shandong Provincial Maternal and Child Health Care Hospital Affiliated to Qingdao University (NO. 2023-025, 5 July 2023)(Annotation:because of the change of corresponding author units and update on ethics,past approved by the Ethics Committee of the First Affiliated Hospital of Shandong First Medical University(YXLL-KY-2022(017).)\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ecorresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Natural Science Foundation of Shandong Province (no. ZR2021MH296).\u003c/p\u003e\n\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, Y.L.; Methodology, M.G.; Software, L.Z. and M.G.; Validation, Y.L.; Formal Analysis, L.Z. and M.G.; Investigation, L.Z. and M.G.; Data Curation, L.Z., M.G., T.Z., M.-M.W., H.-J. L., Z.-Y. L. ; Writing–Original Draft Preparation, L.Z. and M.G.; Writing–Review \u0026amp; Editing, Y.L. and L.Z.; Supervision, Y.L.; Project Administration,Y.L..\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to Professor Hai-Jun Wang at the School of Public Health, Health\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eScience Center of Peking University for her valuable recommendations in the revision of the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003emanuscript.We thank all study participants of the study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStandard terminology for reporting of reproductive health statistics in the United States. Public Health Rep Sep-Oct. 1988;103(5):464\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpong CY. Defining term pregnancy: recommendations from the Defining Term Pregnancy Workgroup. 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Am J Obstet Gynecol MFM Apr. 2023;5(4):100879. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ajogmf.2023.100879\u003c/span\u003e\u003cspan address=\"10.1016/j.ajogmf.2023.100879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6207604/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6207604/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eImportance:\u003c/strong\u003e The association between gestational age (GA) and overweight/obesity risk has been studied in preterm infants, but remains underexplored in term infants, who account for the majority of live births.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo determine whether early-term (GA 37-38 weeks) or full-term (GA 39-40 weeks) delivery is associated with early childhood overweight and obesity risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign, SETTING, AND PARTICIPANTS: \u003c/strong\u003eThis cohort study included healthy term infants (GA 37-40 weeks) born between January 2011 and July 2022 at two tertiary hospitals in Shandong, China. Infants with congenital malformations or syndromes were excluded. In accordance with local health assessment protocols, length and weight were measured by trained professionals at four time points: 1-3, 4-6, 7-9, and 10-12 months. Data analysis was conducted in September 2024.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposures:\u003c/strong\u003e GA was obtained from clinical records. Early-term delivery compared against full-term delivery as the reference group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Outcomes and Measures:\u003c/strong\u003e Body Mass Index (BMI) was calculated, and BMI-for-age Z-scores (BAZ) were derived using the World Health Organization growth standards. Risk of overweight and obesity was defined as BAZ\u0026gt;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among 5,092 participants with complete data, 26.6% (n=1,355) were early-term. The prevalence of overweight and obesity risk was significantly higher in early-term infants (27.6%-30.2%) compared to full-term infants (23.6%-26.4%) throughout infancy. Early-term delivery was associated with an increased risk of overweight and obesity from 1-3 months (unadjusted odds ratio [OR], 1.162; 95% confidence interval [CI], 1.001-1.340; \u003cem\u003eP\u003c/em\u003e=.038) to 10-12 months of age (unadjusted OR, 1.236; 95% CI, 1.073-1.426; \u003cem\u003eP\u003c/em\u003e=.004). This association remained significant after adjusting for mode of delivery, parity, number of fetuses, sex, feeding practices during the first six months, and birth weight. Multiple imputation analysis for missing covariates yielded similar results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and Relevance:\u003c/strong\u003eEarly-term infants have a higher risk of overweight and obesity during the first postnatal year. These findings highlight the need for targeted interventions to prevent childhood obesity in this population.\u003c/p\u003e","manuscriptTitle":"Association of Early-Term and Full-Term Delivery With Overweight and Obesity Risk in the First Year of Life","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 13:19:05","doi":"10.21203/rs.3.rs-6207604/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"03daf98c-edcc-4b33-9f66-1c731f55b075","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-19T14:39:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 13:19:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6207604","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6207604","identity":"rs-6207604","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}