Prenatal exposure to hyperglycemia and child growth trajectories in the first three years of life: a prospective birth cohort

Prenatal exposure to hyperglycemia and child growth trajectories in the first three years of life: a prospective birth cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prenatal exposure to hyperglycemia and child growth trajectories in the first three years of life: a prospective birth cohort Yiyuan Chen, Xia Chi, Shuting Wu, Jing Wei, Zheng Yong, Hong Lv, and 15 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6858828/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in BMC Medicine → Version 1 posted 9 You are reading this latest preprint version Abstract Background Infants exposed to hyperglycemia in pregnancy (HIP) in utero are known to have higher risks of macrosomia at birth and obesity in adulthood, but little is known about how their growth patterns change over the first 3 years of life. Methods In the prospective Jiangsu Birth Cohort (JBC) Study, 8780 children (23.3% HIP exposed) were included. Linear mixed models were used to evaluate the association between HIP and repeated offspring growth measures. Latent class mixed modeling (LCMM) trajectories were fit for weight-for-age (WAZ), length/height-for-age (LAZ) and weight-for-length z-scores (WFL). Adjusted associations between HIP with trajectory classes were evaluated with modified Poisson regression. Results At birth, children exposed to HIP had a higher risk of LGA. HIP exposure was associated with lower weight-for-age (WAZ, -0.075, 95% CI: -0.117, -0.034), length/height-for-age (LAZ, -0.054, 95% CI: -0.099, -0.009), and weight-for-length z-score (WFL, -0.061, 95% CI: -0.100, -0.022). HIP also correlated with reduced weight and BMI growth velocity at 0–3 and 6–8 months. Three distinct trajectory groups were identified and were labeled as moderate-stable, high-decreasing, and low-increasing group. In adjusted models, children with HIP exposure were more likely to follow the high-decreasing WFL trajectory (aRR = 1.14, 95% CI: 1.01, 1.29). Conclusions HIP exposure is associated with slower growth in early childhood and an increased likelihood of “high-decreasing (HD)” WFL trajectory. Identifying an HD trajectory may be valuable for early risk stratification. cohort study infant growth trajectories hyperglycemia in pregnancy latent class mixed model catch-down growth Figures Figure 1 Figure 2 Background In early gestation, the fetus relies on maternal glucose transferred through the placenta, which leads to a physiological decline in maternal blood glucose levels[ 1 ]. As pregnancy progresses into the second and third trimesters, placental hormones—such as human placental lactogen, cortisol, and progesterone—increase substantially, inducing insulin resistance[ 2 ]. In healthy pregnancies, this can be compensated by increased insulin secretion to maintain glucose homeostasis. However, when this compensatory mechanism is inadequate, maternal hyperglycemia develops. This condition—referred to as hyperglycemia in pregnancy (HIP)—is defined by World Health Organization (WHO), including both gestational diabetes mellitus (GDM) and pre-gestational diabetes (pGDM)[ 3 ]. GDM refers to abnormal blood glucose detected for the first time during pregnancy, but not yet reaching the diagnostic threshold for diabetes, while pGDM includes pre-existing diagnosed diabetes mellitus or blood glucose during pregnancy that meets the diagnostic threshold for adult diabetes mellitus. HIP has become a growing public health challenge worldwide. In terms of time trend, the average incidence rate has been increasing, with obvious regional differences. Globally, it affects approximately 16.7% of pregnancies[ 4 ], whereas in China, the prevalence is even higher—exceeding 21%[ 5 ]. HIP has been linked to large for gestational age (LGA) at birth. The Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study revealed a positive association between maternal glycemic levels and birth weight[ 6 ]. Following these findings, several studies further indicated that children exposed to maternal hyperglycemia are at a higher risk of developing obesity by the age of seven to eleven years[ 7 – 9 ]. How HIP exposure differentially impacts patterns of growth and whether HIP is associated with weight and height changes from birth to 3 years is unknown. Among studies focusing on single time point, a British study examining infant body composition reported that infants exposed to HIP had a 16% higher total adipose tissue volume at 2 months of age compared to controls[ 10 ]. In contrast, an Australian study found that at 18 months, offspring in the HIP group exhibited lower mean values for weight, BMI, head circumference, and upper arm circumference[ 11 ]. In longitudinal studies, an Indian cohort of 630 mother-infant pairs revealed that newborns from the HIP group initially had significantly larger physical measurements than controls. However, these differences diminished over time and were no longer statistically significant by 1 year of age. Interestingly, by 2 years, the HIP offspring again demonstrated significant advantages in measures such as parietal heel length and skinfold thickness[ 12 ]. Similarly, a cohort study involving 6684 mother-child pairs in China found that while higher maternal glycemia levels were associated with increased child BMI at birth, this relationship weakened by the age of two[ 13 ]. A meta-analysis also concluded that higher maternal glucose levels were not associated with offspring BMI z-scores during early childhood; however, they were linked to higher BMI z-scores during school age and later[ 14 ]. Growth trajectories in infancy may be stronger predictors of long-term health outcomes than single cross-sectional measurements[ 15 , 16 ]. but few studies have conducted repeated measurements throughout infancy to capture growth trajectories comprehensively. Therefore, leveraging an advanced analytic technique, latent class mixed modeling (LCMM), we designed a longitudinal cohort study to provide insight about different features of growth from birth to three years. Methods Study design and participants The present study was conducted within the Jiangsu Birth Cohort (JBC) Study, an ongoing prospective cohort study. Detailed research design and information collection have been described previously[ 17 ]. This study was restricted to women who delivered live birth in the Women’s Hospital Affiliated to Nanjing Medical University, Suzhou Hospital Affiliated to Nanjing Medical University and Changzhou Maternity and Child Health Care Hospital Affiliated to Nanjing Medical University. Standardized recruitment procedures were applied across all sites. From 2014 to 2020, 14969 cohort women conceived singletons. After excluded 2463 due to lack of HIP diagnostic data, 19 neonatal deaths and 3707 who received fewer than three anthropometric measurements at 1, 3, 6, 8, 12, 18, 24, 30, and 36 months of age, 8780 mother-infant pairs were ultimately included in the analysis ( Supplementary Fig. 1 ). The study protocol has been approved by the Human Investigation Committees at Nanjing Medical University. All the participants deemed eligible for the JBC provided written consent at recruitment. HIP assessment Detailed clinical diagnosis of HIP were extracted from medical records. As part of routine prenatal care, the oral glucose tolerance test (OGTT) is recommended for all pregnancies[ 3 ]. According to the WHO diagnostic criteria[ 3 ], HIP is diagnosed if one or more of the following conditions are met: 1) fasting blood glucose (FBG) ≥ 5.1 mmol/L; 2) 1-hour plasma glucose ≥ 10.0 mmol/L following a 75g oral glucose load; 3) 2-hour plasma glucose ≥ 8.5 mmol/L following a 75g oral glucose load; or 4) random plasma glucose ≥ 11.1 mmol/L with diabetes symptoms. Among them, those with FBG ≥ 7.0 mmol/L or a 2-hour plasma glucose ≥ 11.1 mmol/L were classified as pGDM, while the remainder were diagnosed with GDM. Outcome variables Offspring birthweights and anthropometric measurements at 1, 3, 6, 8, 18, 24, and 30 months were extracted from medical records When children reached one year and three years of age, the child health care department of the hospital where they were born would offer anthropometric measurements. Undressed weight was recorded using calibrated scales to the nearest 0.1 kg and length was measured using calibrated measuring tapes to the nearest 0.1 cm. Age-and sex- specific z-scores for weight-for-age (WAZ), length/height-for-age (LAZ), and weight-for-length (WFL) were calculated according to the 2006 WHO growth reference standards[ 18 ]. Z-scores higher than 3 or lower than − 3 were reviewed for potential data entry errors. Additionally, unexplained extreme values higher than 5 (WAZ and WFL) or 6 (LAZ); or lower than − 5 (WAZ and WFL) or -6 (LAZ) were excluded from analysis[ 19 ]. Less than 1% of the measurements were excluded To calculate the growth velocity for each indicator, the difference between consecutive measurements was divided by the number of months between them. Covariates Questionnaires were completed through face-to-face interviews at recruitment and follow-up visits. Demographic characteristics, including maternal age at delivery (continuous, years), area of residence (rural or urban), household income ( 20,0000 CNY), maternal and paternal education (< 12 years or ≥ 12 years) and lifestyle habits (including passive smoking or drinking during pregnancy) were obtained. Pre-pregnancy BMI was calculated by dividing pre-pregnancy weight (kg) by height (m) squared. At delivery, mothers’ history of gestation, parity (nulliparous or multiparous), hypertensive disorders in pregnancy, birth weight (continuous, grams), maternal weight gain during pregnancy (continuous, kilograms), gestational age at delivery (continuous, weeks), and sex of newborns (boys or girls) were extracted from the medical record. At one year and three years of age, mothers and children would be invited to complete a structured questionnaire collection data on offsprings’ feeding patterns, sleep patterns and diseases. A directed acyclic graph (DAG) was utilized to represent our priori causal hypotheses regarding the relationships between the HIP and child growth trajectories and to guide the variable selection approach[ 20 ]. Based on the DAG and available data, we finally included pre-pregnancy BMI, parity, maternal education, maternal age at delivery, passive smoking during pregnancy, area of residence, household income and child sex as minimal adjustment sets ( Supplementary Fig. 2 ), which were adjusted in the main analysis. To account for any residual confounding, sensitivity models were further adjusted for gestational weight gain and delivery method. Statistical methods The study population characteristics were shown with continuous variables being displayed as means ± standard deviations, and categorical variables being presented in numbers (percentage). Linear regressions were used to examine the association between HIP and offspring growth at specified time points. Linear mixed models (LMM) were used to evaluate the association between HIP and repeated offspring growth measures, adjusting for within-individual and between-individual variations[ 21 ]. Afterwards, to identify patterns of child growth trajectories from birth to 3 years old, we used latent class mixed models (LCMM) based on their anthropometries measured at ages of 1, 3, 6, 8, 12, 18, 24, 30, and 36 months. This is a specialized form of finite mixture modelling and is designed to identify latent classes of individuals following similar progressions over time or with age[ 22 ]. The LCMM package in R was used to estimate latent class mixed models in a maximum likelihood framework. Unlike conventional longitudinal regression models, which may overlook the complex and heterogeneous nature of growth, LCMM enables the identification of distinct subgroups allowing for the assumption that growth trajectories are not parallel[ 23 ]. LCMM is a data-driven approach to identify similar patterns of change in longitudinal data. To determine the optimal number of groups, the following criteria should be fitted: (1) a Bayesian information criterion (BIC, requiring data as close to 0 as possible), (2) the threshold of the mean posterior probability of membership (reaching ≥ 0.7 for each trajectory subgroup), and (3) a reasonable distribution of the participants (class size ≥ 2% of the population)[ 24 , 25 ]. To facilitate interpretability, we assigned labels to the trajectories on the basis of their modelled graphic patterns. Associations between HIP with trajectory classes were evaluated with modified Poisson regression. Based on the existing evidence on the sex differentiation in offspring growth[ 26 ], we first conducted sex stratified analysis to explore whether any factor might modify the effect of maternal hyperglycemia. Additionally, given that evidence suggests a link between breastfeeding duration and child growth, we replicated the main analysis stratified by the duration of breastfeeding (less than six months vs. six months or longer) to evaluate the variations on the study’s association[ 27 ], The heterogeneity test was performed to assess the consistency of results among different sub-groups. To address potential bias from metabolic disorders before and during pregnancy, we performed sensitivity analysis by excluding participants with hypertensive disorders during pregnancy (n = 430). Additionally, to account for differences in growth velocity of IVF/ICSI conception and preterm birth, we performed analysis restricted to spontaneously conceived (n = 6952) and term-born infants (n = 8404). All the statistical analysis were conducted in R Software Version 4.3.1 (The R Foundation). A two-sided P value less than 0.05 was considered statistically significant. Results Description of the population Baseline characteristics of the study participants are presented in Table 1 . The mean (SD) BMI of HIP-exposed mothers before pregnancy was 22.6 (3.4), and their mean age at delivery was 31.4 (4.0), compared to 29.7 (3.8) for non-exposed mothers. Additionally, 1699 (82.9%) of HIP-exposed mothers resided in urban or suburban areas. Mothers in the HIP group also had a higher percentage of passive smoking and cesarean deliveries compared to those in the non-exposed group. In the HIP group, 1165 infants (56.9%) were boys, with a mean birth weight of 3372.6±495.5 g. The classification of infant size for gestational age varied by HIP exposure, with a higher percentage of infants exposed to HIP being LGA compared to the non-exposed group (17.2% vs . 11.8%), and a lower percentage being SGA (3.8% vs. 4.2%). Infant anthropometric indexes from birth to 36 months were shown in Supplementary Figure 3 . WAZ, LAZ and WFL showed a rapid increase in the first six months, stabilizing thereafter. Mean weight at birth, one year and three years were 3.36 (0.45), 10.14 (1.16) and 15.06 (1.89), respectively. Body length at one and three years was 76.38 (2.70) and 97.77 (3.64) cm. The sample size at each time points and other details of anthropometric index’s z-scores were displayed in Supplementary Table 1 . The rate of weight gain in the first month of life differed significantly between the groups, with mean values of 1.35±0.46 kg/month and 1.39±0.47 kg/month, respectively ( P =0.013). The rate change in BMI also differed significantly between the groups at 6-8 months of age, with values of -0.08 ± 0.47 kg/m 2 .month in the non-HIP group and -0.11±0.47 kg/m 2 .month in the HIP group ( Supplementary Table 2 ). Association between HIP and anthropometric measures at different time points As shown in Figure 1 , HIP was significantly associated with reduced WAZ, LAZ and WFL during the first three years (WAZ: a β = -0.065 [95% CI: -0.105, -0.026]; LAZ: a β = -0.054 [95% CI: -0.099, -0.009]; WFL: a β = -0.061 95% CI: -0.100, -0.022]). As to birth size, HIP was associated with a higher risk of LGA infants ( Supplementary Table 3 , aRR = 1.17, 95% CI: 1.01, 1.36; P = 0.035). Significant associations between HIP and lower LAZ were observed at 3 months (a β = -0.090, 95% CI: -0.156, -0.025), 6 months (a β =-0.089, 95% CI: -0.153, -0.025), and 8 (a β =-0.081, 95% CI: -0.143, -0.019) months of age. Moreover, HIP was consistently linked to lower WAZ, starting as early as the first month of life. Furthermore, from 8 months onwards, HIP was associated with a decline in WFL (at 8 months: a β =-0.085, 95% CI: -0.145, -0.026; at 12 months: a β =-0.106, 95% CI: -0.158, -0.055; at 18 months: a β =-0.066, 95% CI: -0.116, -0.015; at 24 months: a β =-0.124, 95% CI: -0.175, -0.073; at 30 months: a β =-0.071, 95% CI: -0.130, -0.012; at 36 months: a β =-0.083, 95% CI: -0.136, -0.030), reflecting potential growth challenges relative to weight and length ( Supplementary Table 4 ). In the stratified analysis of infant sex and duration of breastfeeding (less than 6 months versus 6 months or longer), we observed results consistent with the main analysis ( Supplementary Tables 5 and 6 , Supplementary Figures 4 and 5 ). Although the association with WAZ and WFL in girls were not statistically significant at 12 months, the effect size of association still remained. In the sensitivity analysis, when restricted to infants unexposed to maternal hypertensive disorders during pregnancy, those conceived spontaneously, term-born infants, infants born to mothers with a BMI≤24 kg/m², or mothers without pre-pregnancy diabetes, although some changes were observed, the overall results were consistent with our main analysis ( Supplementary Tables 7-11 ; Supplementary Figures 6-10 ). HIP was also significantly associated with reduced weight growth velocity between 0 to 3 ( Table 2 , a β =-0.022, 95% CI: -0.036, -0.008) and 6 to 8 months of age (a β =-0.017, 95% CI: -0.032, -0.002). Similarly, lower BMI growth velocity was observed between 6 to 8 months in the HIP group (a β = -0.042, 95% CI: -0.078, -0.006). Trajectories for anthropometric measures Using LCMM, three distinct trajectory groups were identified for WAZ, LAZ and WFL. Figure 2 illustrates these trajectory groups, which were labeled as “low-increasing”, “moderate-stable”, and “high-decreasing” based on its initial values (low, moderate or high) and trends (increasing, stable or decreasing). Dashed lines around the solid lines represent the confidence intervals for the calculated trajectories. The “moderate-stable” trajectory served as the reference group for all measures, as it included a largest portion of the population and maintained a mean z-score near zero. The BIC values and number of participants for different number of trajectories are presented in Supplementary Table 12 . Association between HIP and growth trajectories Offspring exposed to maternal hyperglycemia exhibited a 14% higher risk of following a high-decreasing WFL trajectory compared to unexposed children (aRR=1.14; 95% CI, 1.01–1.29; P =0.031). No significant associations were observed for other growth indices ( Table 3 ). Similarly, within the low-increasing trajectory group, no significant associations were identified. Association analysis between HIP subtypes and WFL trajectories revealed that GDM was linked to a higher likelihood of infants following a high-decreasing WFL growth trajectory ( Supplementary Table 13 , aRR=1.16; 95% CI, 1.03–1.30; P =0.015). To further explore potential risk groups in smaller samples[25], WFL trajectory patterns were classified into two to five groups, as illustrated in Supplementary Figure 11 . When classified into four groups, a distinct “high-increasing” growth pattern (n=116) emerged. In this group, HIP exposure was significantly associated in the crude model ( Supplementary Table 14 , aRR=1.85; 95% CI, 1.25–2.73; P =0.002). However, this association became marginally significant after adjusting for confounders (aRR=1.56; 95% CI, 0.95–2.54; P =0.076). Discussion In this longitudinal cohort study, infants exposed to HIP in utero had a higher risk of LGA at birth. Interestingly, maternal HIP is significantly associated with lower z-scores for weight, height/length, and WFL in offspring at multiple time points across the first three years of life. Our latent class trajectory analysis showed children exposed to HIP were more likely to follow a 'high-decreasing' WFL growth trajectory. Our findings suggest that although there is a higher prevalence of large-for-gestational-age infants among those exposed to maternal hyperglycemia[6], this association does not persist into infancy. Several studies support this observation. A Danish register-based study, for instance, reported growth deceleration at five and twelve months in children exposed to HIP[28]. Similarly, Pima Indian children born to mothers with HIP exhibited higher weight-for-age z-scores at birth, but these differences were no longer evident at 1.5 years compared to those born to non-diabetic mothers[29]. Taken together, these results suggest that the long-term impact of maternal glycemia during pregnancy on offspring growth may be age specific, particularly during critical developmental windows such as birth and early infancy. There are several potential explanations for the associations observed in our study. First, during the intrauterine developmental stage, maternal glucose freely crosses the placenta, while insulin cannot. Elevated maternal blood glucose stimulates fetal pancreatic islet cell proliferation and enhances insulin synthesis, resulting in increased fetal insulin and insulin-like growth factor (IGF) secretion[30]. The upregulation of the insulin/IGF axis promotes fetal growth and development[31, 32]. However, once born, infants may be at an increased risk of developing insulin resistance in early infancy, which could contribute to slower growth[33, 34]. Second, evidence also suggests that offspring exposed to maternal hyperglycemia may exhibit smaller kidneys with fewer nephrons and impaired renal endocrine function[35, 36]. This may result in reduced growth hormone activity and slower growth during early childhood[37]. Third, a recent US-based cohort study identified associations between gestational diabetes, breast milk metabolites, and infant growth and body composition[38]. Specifically, three of the nine milk metabolites significantly associated with GDM were also linked to infant growth and body composition measures. Notably, the abundance of 2-hydroxybutyric, a metabolite linked to lipid metabolism, was higher in participants with GDM and negatively associated with the change in infant body fat percentage from 1 to 3 months. This suggests that breastfeeding may be another pathway through which maternal diabetes impairs infant growth. Our findings also suggest potential sex differences in the effects of HIP. Boys in our study appeared more sensitive to HIP exposures, showing stronger negative associations between HIP and both WAZ and WFL compared to girls. Other studies have reported sex-specific effects of HIP on fetal or childhood growth inconsistently. For example, some studies on offspring of mothers with GDM found boys to be at higher risk of obesity in childhood and adolescence compared to girls[39], whereas others suggested that girls could be more vulnerable[40]. A recent study found that sons of women with poorly controlled GDM are characterized by increased and longer-acting activation of the reproductive axis, and faster growth of male genital organs in infancy[41]. The sex differences observed in our study and others may be attributed to postnatal sex steroid production during the so-called mini-puberty[42]. This phase is characterized by a testosterone surge in boys during the first month of life, while girls exhibit higher oestrogen levels, potentially influencing hormone-sensitive metabolic organs differently[43]. Not all infants grow exactly as these LCMM patterns showed but these are identifiable trends. Using LCMM, we saw that infants exposed to HIP were more likely to be in the high-decreasing WFL group across the first three years of life. Growth trajectory curves that follow this downward trend in growth percentiles have been termed catch-down growth in previous literature[44, 45]. Supporting this observation, a recent cohort from Ohio found that infants exposed to HIP accumulated less fat during the first year of life compared to their unexposed counterparts[46]. This growth pattern may reflect a compensatory response to early overnutrition, or it could signal underlying abnormalities in metabolic programming. However, the long-term clinical implications of this pattern remain unclear and warrant further follow-up. We also observed that infants born with lower birth weight exhibit typical catch-up growth, which we labeled as the “low-increasing” pattern. Since excessive catch-up growth is a well-established risk factor for obesity and metabolic disorders later in life[47], these findings warrant the concern over the long-term health influence of prenatal HIP exposure. We hypothesize that the natural biological processes may support self-rehabilitation of prenatal impairments during early postnatal life. However, this intrauterine 'programmed' vulnerability may resurface later in life under the influence of environmental stressors. The differences observed early in life likely to persist or track into later childhood. They warrant enhanced monitoring and intervention in offspring of HIP mothers. The current prospective design and repeated measures of childhood anthropometric measurements provide a unique opportunity to investigate the long-term HIP effects on offspring growth with adjustment for key confounders. To our knowledge, our study has several strengths: 1) the multi-centered prospective cohort design with a relatively large sample size, superior to retrospective studies for investigating the association between HIP and early childhood growth; 2) ten times of dense monitoring of postnatal physical growth ensuring the reliability of trajectory data; 3) comprehensive data collection from interviews and medical records, accounting for multiple confounders; and 4) the use of LCMM to identify subgroup-specific growth patterns, which may be missed in studies that pool heterogeneous groups or focus solely on cross-sectional data. This approach allows us to determine if there are underlying commonalities in growth trends and if HIP is associated with a certain underlying growth trend. However, several limitations must be noted. First, we were unable to distinguish between the effects of type 1 and type 2 diabetes prior to pregnancy. Second, despite adjusting for multiple confounders, there might still be residual confounding factors (e.g., genetics, parental/sibling development, glycemic control). Life-course studies using diverse methodologies are needed[48]. Given our plan to follow these children for an extended period, we aim to further explore the clinical significance of these trajectories in relation to long-term health outcomes. Finally, while LCMM and other trajectory analysis methods provide valuable insights, they cannot perfectly capture individual trajectories, and misclassification remains a possibility. Alternative trajectory modeling approaches are encouraged to validate our findings. Conclusions In conclusion, our findings suggest that maternal HIP is associated with lower z-scores for length, weight and weight-for-length during the first three years of life. Additionally, HIP is linked to reduced weight and BMI growth velocity in early infancy. For the first time, we identify a high-decreasing WFL growth trajectory observed in our sample, which challenges the notion that in utero hyperglycemia exposure has a detrimental effect on obesity beyond infancy. The findings from this study indicate that further research in this area is needed to support females with HIP to better understand and improve both short- and long-term health for infants exposed to HIP in utero, especially those related to catch-down growth. Understanding and tracking such growth patterns during the 0-3-year window may inform timely interventions aimed at optimizing long-term metabolic health outcomes. Abbreviations 95% CI 95% Confidence Interval BMI Body Mass Index DAG Directed Acyclic Graphs FBG Fasting Blood Glucose GDM Gestational Diabetes Mellitus HAPO Hyperglycemia and Adverse Pregnancy Outcomes HCZ Head-Circumference-for-Age Z-score HD High-decreasing HIP Hyperglycemia in Pregnancy IGF Insulin-like Growth Factor JBC Jiangsu Birth Cohort LAZ Length/Height-for-Age Z-score LBW Low Birth Weight LCMM Latent Class Mixed Model LGA Large-for-Gestational-Age LI Low-increasing LMM Linear Mixed Model MS Moderate-stable OGTT Oral Glucose Tolerance Test pGDM Pre-gestational Diabetes Mellitus RR Relative Risk SD Standard Deviation SGA Small-for-Gestational-Age WAZ Weight-for-Age Z-score WFL Weight-for-Length Z-score WHO World Health Organization Declarations Acknowledgements We are grateful to all the families for participating this study, and the whole Jiangsu Birth Cohort. Authors’ contribution ZH and JD initiated, conceived and supervised the study. YC, SW and XC carried out the initial analyses, and critically reviewed and revised the manuscript and involved in the conduct of the study and the analysis and interpretation of the results. JW, ZY, HLv, YD, YL, YJ were involved in study design, conduct of the cohort study, long-term follow-up with YZ, RQ, XX, XL, XH, BX and KZ. YL, YJ, KY designed the data collection instruments, collected data and critically reviewed and revised the manuscript. HM, JD, TJ, ZY and YD proofread the manuscript. ZH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Funding This work was supported by the National Key Research and Development Program of China (Grant No. 2021YFC2700600, 2021YFC2700705), the National Natural Science Foundation of China (Grant No. 82373581, 82103854, 81602927). Data Availability After publication, the data collected for the study (deidentified participant data) could be accessed on reasonable request to the corresponding author. A proposal with detailed description of study objectives and statistical analyses plan will be needed for evaluation of the reasonability of requests. Additional, relevant documents might also be required during the process of evaluation. Declarations Consent for publication Not applicable Competing Interests The authors declare no competing interest. Author details 1 State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, China. 2 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China. 3 Department of Child Health Care, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, Jiangsu, China. 4 Department of Obstetrics and Gynecology, Taizhou People's Hospital, Affiliated to Nanjing Medical University, Taizhou, Jiangsu, China. 5 Department of Maternal, Child and Adolescent Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China 6 State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China. 7 Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China. 8 Department of Child Health, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China. 9 Taizhou People's Hospital, Affiliated to Nanjing Medical University, Taizhou, Jiangsu, China. 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Zhang Y, Chen Z, Cao Z, Zhang Y, Yao C, Qiu L, Li Y, Xu S, Zhou A, Xia W: Associations of maternal glycemia and prepregnancy BMI with early childhood growth: a prospective cohort study . Ann N Y Acad Sci 2020, 1465 (1):89-98. Kawasaki M, Arata N, Miyazaki C, Mori R, Kikuchi T, Ogawa Y, Ota E: Obesity and Abnormal Glucose Tolerance in Offspring of Diabetic Mothers: A Systematic Review and Meta-Analysis . Plos One 2018, 13 (1):e0190676. Balasundaram P, Avulakunta ID: Human Growth and Development . In: StatPearls. edn. Treasure Island (FL): StatPearls Publishing Copyright © 2024, StatPearls Publishing LLC.; 2024. Herle M, Micali N, Abdulkadir M, Loos R, Bryant-Waugh R, Hübel C, Bulik CM, De Stavola BL: Identifying typical trajectories in longitudinal data: modelling strategies and interpretations . Eur J Epidemiol 2020, 35 (3):205-222. Du J, Lin Y, Xia Y, Ma H, Jiang Y, Lu C, Wu W, Chen M, Zhao Y, Dai J et al : Cohort Profile: The Jiangsu Birth Cohort . Int J Epidemiol 2023, 52 (6):e354-e363. Bloem M: The 2006 WHO child growth standards . Bmj 2007, 334 (7596):705-706. Schumacher D: anthro: Computation of the WHO Child Growth Standards . 2023. Shrier I, Platt RW: Reducing bias through directed acyclic graphs . BMC Med Res Methodol 2008, 8 :70. Murphy JI, Weaver NE, Hendricks AE: Accessible analysis of longitudinal data with linear mixed effects models . Dis Model Mech 2022, 15 (5). Proust-Lima C, Séne M, Taylor JM, Jacqmin-Gadda H: Joint latent class models for longitudinal and time-to-event data: a review . Stat Methods Med Res 2014, 23 (1):74-90. Jung T, Wickrama KAS: An Introduction to Latent Class Growth Analysis and Growth Mixture Modeling . Social and Personality Psychology Compass 2008, 2 (1):302-317. Saulnier T, Philipps V, Meissner WG, Rascol O, Pavy-Le Traon A, Foubert-Samier A, Proust-Lima C: Joint models for the longitudinal analysis of measurement scales in the presence of informative dropout . Methods 2022, 203 :142-151. 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Susa JB, Neave C, Sehgal P, Singer DB, Zeller WP, Schwartz R: Chronic hyperinsulinemia in the fetal rhesus monkey. Effects of physiologic hyperinsulinemia on fetal growth and composition . Diabetes 1984, 33 (7):656-660. DiPrisco B, Kumar A, Kalra B, Savjani GV, Michael Z, Farr O, Papathanasiou AE, Christou H, Mantzoros C: Placental proteases PAPP-A and PAPP-A2, the binding proteins they cleave (IGFBP-4 and -5), and IGF-I and IGF-II: Levels in umbilical cord blood and associations with birth weight and length . Metabolism 2019, 100 :153959. Yang MN, Huang R, Zheng T, Dong Y, Wang WJ, Xu YJ, Mehra V, Zhou GD, Liu X, He H et al : Genome-wide placental DNA methylations in fetal overgrowth and associations with leptin, adiponectin and fetal growth factors . Clin Epigenetics 2022, 14 (1):192. Baka S, Malamitsi-Puchner A, Boutsikou T, Boutsikou M, Marmarinos A, Hassiakos D, Gourgiotis D, Briana DD: Cord blood irisin at the extremes of fetal growth . Metabolism 2015, 64 (11):1515-1520. Bocca-Tjeertes IF, Kerstjens JM, Reijneveld SA, Veldman K, Bos AF, de Winter AF: Growth patterns of large for gestational age children up to age 4 years . Pediatrics 2014, 133 (3):e643-649. Cerqueira DM, Hemker SL, Bodnar AJ, Ortiz DM, Oladipupo FO, Mukherjee E, Gong Z, Appolonia C, Muzumdar R, Sims-Lucas S et al : In utero exposure to maternal diabetes impairs nephron progenitor differentiation . Am J Physiol Renal Physiol 2019, 317 (5):F1318-f1330. Brennan S, Kandasamy Y, Rudd DM, Schneider ME, Jones RE, Watson DL: The effect of diabetes during pregnancy on fetal renal parenchymal growth . J Nephrol 2020, 33 (5):1079-1089. Drube J, Wan M, Bonthuis M, Wühl E, Bacchetta J, Santos F, Grenda R, Edefonti A, Harambat J, Shroff R et al : Clinical practice recommendations for growth hormone treatment in children with chronic kidney disease . Nat Rev Nephrol 2019, 15 (9):577-589. Nagel EM, Peña A, Dreyfuss JM, Lock EF, Johnson KE, Lu C, Fields DA, Demerath EW, Isganaitis E: Gestational Diabetes, the Human Milk Metabolome, and Infant Growth and Adiposity . JAMA Netw Open 2024, 7 (12):e2450467. Li S, Zhu Y, Yeung E, Chavarro JE, Yuan C, Field AE, Missmer SA, Mills JL, Hu FB, Zhang C: Offspring risk of obesity in childhood, adolescence and adulthood in relation to gestational diabetes mellitus: a sex-specific association . Int J Epidemiol 2017, 46 (5):1533-1541. Andersson-Hall UK, Järvinen EAJ, Bosaeus MH, Gustavsson CE, Hårsmar EJ, Niklasson CA, Albertsson-Wikland KG, Holmäng AB: Maternal obesity and gestational diabetes mellitus affect body composition through infancy: the PONCH study . Pediatr Res 2019, 85 (3):369-377. Kowalcze K, Burgio S, Ott J, Gullo G, Zaami S, Krysiak R: The Impact of Maternal Gestational Diabetes Mellitus on Minipuberty in Boys . Nutrients 2024, 16 (23). Rohayem J, Alexander EC, Heger S, Nordenström A, Howard SR: Mini-Puberty, Physiological and Disordered: Consequences, and Potential for Therapeutic Replacement . Endocr Rev 2024, 45 (4):460-492. Kiviranta P, Kuiri-Hänninen T, Saari A, Lamidi ML, Dunkel L, Sankilampi U: Transient Postnatal Gonadal Activation and Growth Velocity in Infancy . Pediatrics 2016, 138 (1). Rickman RR, Widen EM, Lane CE, Abrego MR, Nichols AR, Foster SF, Catalano P: Infant body composition trajectories differ by in utero exposure to gestational diabetes mellitus: a prospective cohort from birth to 12 months . Am J Clin Nutr 2025, 121 (1):40-49. Jain V, Kumar B, Khatak S: Catch-up and Catch-down Growth in Term Healthy Indian Infants From Birth to Two Years: A Prospective Cohort Study . Indian Pediatr 2021, 58 (4):325-331. Rickman RR, Widen EM, Lane CE, Abrego MR, Nichols AR, Foster SF, Catalano P: Infant body composition trajectories differ by in utero exposure to gestational diabetes mellitus: a prospective cohort from birth to 12 months . Am J Clin Nutr 2024. Ong KK, Ahmed ML, Emmett PM, Preece MA, Dunger DB: Association between postnatal catch-up growth and obesity in childhood: prospective cohort study . Bmj 2000, 320 (7240):967-971. Tu YK, Tilling K, Sterne JA, Gilthorpe MS: A critical evaluation of statistical approaches to examining the role of growth trajectories in the developmental origins of health and disease . Int J Epidemiol 2013, 42 (5):1327-1339. Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in BMC Medicine → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 18 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviewers invited by journal 19 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 10 Jun, 2025 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. 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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-6858828","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":474981312,"identity":"577a120c-3cd8-48ff-851c-1b1f26dabe36","order_by":0,"name":"Yiyuan Chen","email":"","orcid":"","institution":"State Key Laboratory of Reproductive Medicine, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yiyuan","middleName":"","lastName":"Chen","suffix":""},{"id":474981313,"identity":"59c5281e-f56a-4b01-bdc7-7f85ad7327d4","order_by":1,"name":"Xia Chi","email":"","orcid":"","institution":"Department of Child Health Care, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Chi","suffix":""},{"id":474981314,"identity":"81fef616-7590-456f-93e5-6062b0f1e4b6","order_by":2,"name":"Shuting Wu","email":"","orcid":"","institution":"State Key Laboratory of Reproductive Medicine, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuting","middleName":"","lastName":"Wu","suffix":""},{"id":474981316,"identity":"d533f17c-400d-4314-b77f-0802c4e80311","order_by":3,"name":"Jing Wei","email":"","orcid":"","institution":"Department of Obstetrics and Gynecology, Taizhou People's Hospital, Affiliated to Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wei","suffix":""},{"id":474981318,"identity":"99c2ec6e-6b33-4bbb-b1d6-afb3daedad11","order_by":4,"name":"Zheng 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Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDCCAwwJB4BUAgN7A4MBVIRYLTwHiNcCBgkMEgmoIjgB342Ehwd+7qjN45d8e6DoZhuDHFCE8XMBHi2SNxISDvaeOV4sOTsvwTi3jcEYKMIsPQOPFgOglgO8bccSN9zOMQBpSdxwI4GNmYeAloN/QVpungFrqSdKy2Hethqg4TxgLQkGhLRInnmQcFi27UCxZA/QYTnnJAxnnnnYLI1PC9/xnOSPb9vq8vjZz5gZ55TZyPMdTz74GZ8WBgaeBCBxGMRiA0alBJBmbMCrgYGB/QCQqAOxmB8QUDoKRsEoGAUjFAAAwAZXLInIgvQAAAAASUVORK5CYII=","orcid":"","institution":"Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jiangbo","middleName":"","lastName":"Du","suffix":""},{"id":474981351,"identity":"ab5f0dfa-a7e0-44af-8b40-0daf7a8b3328","order_by":20,"name":"Zhibin Hu","email":"","orcid":"","institution":"State Key Laboratory of Reproductive Medicine, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhibin","middleName":"","lastName":"Hu","suffix":""}],"badges":[],"createdAt":"2025-06-10 04:08:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6858828/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6858828/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-025-04521-0","type":"published","date":"2025-12-29T15:57:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85361577,"identity":"ad698e5d-265c-4df6-85a3-c466eaaa8231","added_by":"auto","created_at":"2025-06-25 06:10:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204905,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between maternal HIP and infant anthropometric z-scores at ten time points from birth to 3 years. β and 95% CI for each anthropometrical measures according to maternal HIP in general linear models. All models were adjusted for pre-pregnancy BMI, parity, maternal education, maternal age at delivery, passive smoking during pregnancy, area of residence, household income and child sex.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HIP, hyperglycemia in pregnancy; BMI, body mass index; CI, confidence interval; LAZ, length/height-for-age z-score; WAZ, weight-for-age z-score; WFL, weight-for-length z-score.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6858828/v1/8dc298a72cce39f9e6f4f53e.png"},{"id":85362259,"identity":"399f4037-e7a3-41f3-8ff3-86515a7883ef","added_by":"auto","created_at":"2025-06-25 06:18:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":198778,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopmental trajectories for weight-for-age z-score, length/height-for-age z-score and weight-for-length z-score from birth to 36 months of age in Jiangsu Birth Cohort, Jiangsu, China, recruited from 2014 to 2020. The groups are labeled according to the initial value and following trend (LI: low-increasing; MS: moderate-stable; HD: high-decreasing). The solid line indicates predicted trajectory and the shaded areas around represent the 95% confidence intervals for the calculated trajectories.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6858828/v1/28c33377dcd12908c7569536.png"},{"id":99545533,"identity":"1191c2dc-0a76-4da3-8b72-faaad39ba262","added_by":"auto","created_at":"2026-01-05 16:08:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3088775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6858828/v1/a977c7dd-4684-404e-9892-03f675451654.pdf"},{"id":85361581,"identity":"6287852d-ec2a-472b-a98f-035998fc7139","added_by":"auto","created_at":"2025-06-25 06:10:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2061728,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6858828/v1/b424bbdc1cc4efc3f87f8097.docx"},{"id":85361576,"identity":"34ece2b7-f481-4b38-aca7-1ba263ab92a5","added_by":"auto","created_at":"2025-06-25 06:10:15","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":46910,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6858828/v1/d13646a8ee08648141616a98.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prenatal exposure to hyperglycemia and child growth trajectories in the first three years of life: a prospective birth cohort","fulltext":[{"header":"Background","content":"\u003cp\u003eIn early gestation, the fetus relies on maternal glucose transferred through the placenta, which leads to a physiological decline in maternal blood glucose levels[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As pregnancy progresses into the second and third trimesters, placental hormones\u0026mdash;such as human placental lactogen, cortisol, and progesterone\u0026mdash;increase substantially, inducing insulin resistance[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In healthy pregnancies, this can be compensated by increased insulin secretion to maintain glucose homeostasis. However, when this compensatory mechanism is inadequate, maternal hyperglycemia develops. This condition\u0026mdash;referred to as hyperglycemia in pregnancy (HIP)\u0026mdash;is defined by World Health Organization (WHO), including both gestational diabetes mellitus (GDM) and pre-gestational diabetes (pGDM)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. GDM refers to abnormal blood glucose detected for the first time during pregnancy, but not yet reaching the diagnostic threshold for diabetes, while pGDM includes pre-existing diagnosed diabetes mellitus or blood glucose during pregnancy that meets the diagnostic threshold for adult diabetes mellitus. HIP has become a growing public health challenge worldwide. In terms of time trend, the average incidence rate has been increasing, with obvious regional differences. Globally, it affects approximately 16.7% of pregnancies[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], whereas in China, the prevalence is even higher\u0026mdash;exceeding 21%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHIP has been linked to large for gestational age (LGA) at birth. The Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study revealed a positive association between maternal glycemic levels and birth weight[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Following these findings, several studies further indicated that children exposed to maternal hyperglycemia are at a higher risk of developing obesity by the age of seven to eleven years[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHow HIP exposure differentially impacts patterns of growth and whether HIP is associated with weight and height changes from birth to 3 years is unknown. Among studies focusing on single time point, a British study examining infant body composition reported that infants exposed to HIP had a 16% higher total adipose tissue volume at 2 months of age compared to controls[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, an Australian study found that at 18 months, offspring in the HIP group exhibited lower mean values for weight, BMI, head circumference, and upper arm circumference[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In longitudinal studies, an Indian cohort of 630 mother-infant pairs revealed that newborns from the HIP group initially had significantly larger physical measurements than controls. However, these differences diminished over time and were no longer statistically significant by 1 year of age. Interestingly, by 2 years, the HIP offspring again demonstrated significant advantages in measures such as parietal heel length and skinfold thickness[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Similarly, a cohort study involving 6684 mother-child pairs in China found that while higher maternal glycemia levels were associated with increased child BMI at birth, this relationship weakened by the age of two[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A meta-analysis also concluded that higher maternal glucose levels were not associated with offspring BMI z-scores during early childhood; however, they were linked to higher BMI z-scores during school age and later[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGrowth trajectories in infancy may be stronger predictors of long-term health outcomes than single cross-sectional measurements[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. but few studies have conducted repeated measurements throughout infancy to capture growth trajectories comprehensively. Therefore, leveraging an advanced analytic technique, latent class mixed modeling (LCMM), we designed a longitudinal cohort study to provide insight about different features of growth from birth to three years.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThe present study was conducted within the Jiangsu Birth Cohort (JBC) Study, an ongoing prospective cohort study. Detailed research design and information collection have been described previously[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study was restricted to women who delivered live birth in the Women\u0026rsquo;s Hospital Affiliated to Nanjing Medical University, Suzhou Hospital Affiliated to Nanjing Medical University and Changzhou Maternity and Child Health Care Hospital Affiliated to Nanjing Medical University. Standardized recruitment procedures were applied across all sites. From 2014 to 2020, 14969 cohort women conceived singletons. After excluded 2463 due to lack of HIP diagnostic data, 19 neonatal deaths and 3707 who received fewer than three anthropometric measurements at 1, 3, 6, 8, 12, 18, 24, 30, and 36 months of age, 8780 mother-infant pairs were ultimately included in the analysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). The study protocol has been approved by the Human Investigation Committees at Nanjing Medical University. All the participants deemed eligible for the JBC provided written consent at recruitment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHIP assessment\u003c/h3\u003e\n\u003cp\u003eDetailed clinical diagnosis of HIP were extracted from medical records. As part of routine prenatal care, the oral glucose tolerance test (OGTT) is recommended for all pregnancies[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. According to the WHO diagnostic criteria[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], HIP is diagnosed if one or more of the following conditions are met: 1) fasting blood glucose (FBG)\u0026thinsp;\u0026ge;\u0026thinsp;5.1 mmol/L; 2) 1-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;10.0 mmol/L following a 75g oral glucose load; 3) 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;8.5 mmol/L following a 75g oral glucose load; or 4) random plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L with diabetes symptoms. Among them, those with FBG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L or a 2-hour plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L were classified as pGDM, while the remainder were diagnosed with GDM.\u003c/p\u003e\n\u003ch3\u003eOutcome variables\u003c/h3\u003e\n\u003cp\u003eOffspring birthweights and anthropometric measurements at 1, 3, 6, 8, 18, 24, and 30 months were extracted from medical records When children reached one year and three years of age, the child health care department of the hospital where they were born would offer anthropometric measurements. Undressed weight was recorded using calibrated scales to the nearest 0.1 kg and length was measured using calibrated measuring tapes to the nearest 0.1 cm. Age-and sex- specific z-scores for weight-for-age (WAZ), length/height-for-age (LAZ), and weight-for-length (WFL) were calculated according to the 2006 WHO growth reference standards[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Z-scores higher than 3 or lower than \u0026minus;\u0026thinsp;3 were reviewed for potential data entry errors. Additionally, unexplained extreme values higher than 5 (WAZ and WFL) or 6 (LAZ); or lower than \u0026minus;\u0026thinsp;5 (WAZ and WFL) or -6 (LAZ) were excluded from analysis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Less than 1% of the measurements were excluded To calculate the growth velocity for each indicator, the difference between consecutive measurements was divided by the number of months between them.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eQuestionnaires were completed through face-to-face interviews at recruitment and follow-up visits. Demographic characteristics, including maternal age at delivery (continuous, years), area of residence (rural or urban), household income (\u0026lt;\u0026thinsp;10,0000, 10,0000\u0026ndash;20,0000 and \u0026gt;\u0026thinsp;20,0000 CNY), maternal and paternal education (\u0026lt;\u0026thinsp;12 years or \u0026ge;\u0026thinsp;12 years) and lifestyle habits (including passive smoking or drinking during pregnancy) were obtained. Pre-pregnancy BMI was calculated by dividing pre-pregnancy weight (kg) by height (m) squared. At delivery, mothers\u0026rsquo; history of gestation, parity (nulliparous or multiparous), hypertensive disorders in pregnancy, birth weight (continuous, grams), maternal weight gain during pregnancy (continuous, kilograms), gestational age at delivery (continuous, weeks), and sex of newborns (boys or girls) were extracted from the medical record. At one year and three years of age, mothers and children would be invited to complete a structured questionnaire collection data on offsprings\u0026rsquo; feeding patterns, sleep patterns and diseases.\u003c/p\u003e \u003cp\u003eA directed acyclic graph (DAG) was utilized to represent our priori causal hypotheses regarding the relationships between the HIP and child growth trajectories and to guide the variable selection approach[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Based on the DAG and available data, we finally included pre-pregnancy BMI, parity, maternal education, maternal age at delivery, passive smoking during pregnancy, area of residence, household income and child sex as minimal adjustment sets (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e), which were adjusted in the main analysis. To account for any residual confounding, sensitivity models were further adjusted for gestational weight gain and delivery method.\u003c/p\u003e\n\u003ch3\u003eStatistical methods\u003c/h3\u003e\n\u003cp\u003eThe study population characteristics were shown with continuous variables being displayed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical variables being presented in numbers (percentage). Linear regressions were used to examine the association between HIP and offspring growth at specified time points. Linear mixed models (LMM) were used to evaluate the association between HIP and repeated offspring growth measures, adjusting for within-individual and between-individual variations[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Afterwards, to identify patterns of child growth trajectories from birth to 3 years old, we used latent class mixed models (LCMM) based on their anthropometries measured at ages of 1, 3, 6, 8, 12, 18, 24, 30, and 36 months. This is a specialized form of finite mixture modelling and is designed to identify latent classes of individuals following similar progressions over time or with age[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The LCMM package in R was used to estimate latent class mixed models in a maximum likelihood framework. Unlike conventional longitudinal regression models, which may overlook the complex and heterogeneous nature of growth, LCMM enables the identification of distinct subgroups allowing for the assumption that growth trajectories are not parallel[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. LCMM is a data-driven approach to identify similar patterns of change in longitudinal data. To determine the optimal number of groups, the following criteria should be fitted: (1) a Bayesian information criterion (BIC, requiring data as close to 0 as possible), (2) the threshold of the mean posterior probability of membership (reaching\u0026thinsp;\u0026ge;\u0026thinsp; 0.7 for each trajectory subgroup), and (3) a reasonable distribution of the participants (class size\u0026thinsp;\u0026ge;\u0026thinsp;2% of the population)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. To facilitate interpretability, we assigned labels to the trajectories on the basis of their modelled graphic patterns. Associations between HIP with trajectory classes were evaluated with modified Poisson regression.\u003c/p\u003e \u003cp\u003eBased on the existing evidence on the sex differentiation in offspring growth[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we first conducted sex stratified analysis to explore whether any factor might modify the effect of maternal hyperglycemia. Additionally, given that evidence suggests a link between breastfeeding duration and child growth, we replicated the main analysis stratified by the duration of breastfeeding (less than six months \u003cem\u003evs.\u003c/em\u003e six months or longer) to evaluate the variations on the study\u0026rsquo;s association[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], The heterogeneity test was performed to assess the consistency of results among different sub-groups. To address potential bias from metabolic disorders before and during pregnancy, we performed sensitivity analysis by excluding participants with hypertensive disorders during pregnancy (n\u0026thinsp;=\u0026thinsp;430). Additionally, to account for differences in growth velocity of IVF/ICSI conception and preterm birth, we performed analysis restricted to spontaneously conceived (n\u0026thinsp;=\u0026thinsp;6952) and term-born infants (n\u0026thinsp;=\u0026thinsp;8404).\u003c/p\u003e \u003cp\u003eAll the statistical analysis were conducted in R Software Version 4.3.1 (The R Foundation). A two-sided \u003cem\u003eP\u003c/em\u003e value less than 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eDescription of the population\u003c/h2\u003e\n\u003cp\u003eBaseline characteristics of the study participants are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e. The mean (SD) BMI of HIP-exposed mothers before pregnancy was 22.6 (3.4), and their mean age at delivery was 31.4 (4.0), compared to 29.7 (3.8) for non-exposed mothers. Additionally, 1699 (82.9%) of HIP-exposed mothers resided in urban or suburban areas. Mothers in the HIP group also had a higher percentage of passive smoking and cesarean deliveries compared to those in the non-exposed group. In the HIP group, 1165 infants (56.9%) were boys, with a mean birth weight of 3372.6\u0026plusmn;495.5 g. The classification of infant size for gestational age varied by HIP exposure, with a higher percentage of infants exposed to HIP being LGA compared to the non-exposed group (17.2% \u003cem\u003evs\u003c/em\u003e. 11.8%), and a lower percentage being SGA (3.8% \u003cem\u003evs.\u003c/em\u003e 4.2%). Infant anthropometric indexes from birth to 36 months were shown in \u003cstrong\u003eSupplementary Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e. WAZ, LAZ and WFL showed a rapid increase in the first six months, stabilizing thereafter. Mean weight at birth, one year and three years were 3.36 (0.45), 10.14 (1.16) and 15.06 (1.89), respectively. Body length at one and three years was 76.38 (2.70) and 97.77 (3.64) cm. The sample size at each time points and other details of anthropometric index\u0026rsquo;s z-scores were displayed in \u003cstrong\u003eSupplementary Table\u003c/strong\u003e \u003cstrong\u003e1\u003c/strong\u003e. The rate of weight gain in the first month of life differed significantly between the groups, with mean values of 1.35\u0026plusmn;0.46 kg/month and 1.39\u0026plusmn;0.47 kg/month, respectively (\u003cem\u003eP\u003c/em\u003e=0.013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rate change in BMI also differed significantly between the groups at 6-8 months of age, with values of -0.08 \u0026plusmn; 0.47 kg/m\u003csup\u003e2\u003c/sup\u003e.month in the non-HIP group and -0.11\u0026plusmn;0.47 kg/m\u003csup\u003e2\u003c/sup\u003e.month in the HIP group (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003eAssociation between HIP and anthropometric measures at different time points\u003c/h2\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eFigure 1\u003c/strong\u003e, HIP was significantly associated with reduced WAZ, LAZ and WFL during the first three years (WAZ: a\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.065 [95% CI: -0.105, -0.026]; LAZ: a\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.054 [95% CI: -0.099, -0.009]; WFL: a\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.061 95% CI: -0.100, -0.022]). As to birth size, HIP was associated with a higher risk of LGA infants (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e \u003cstrong\u003e3\u003c/strong\u003e, aRR = 1.17, 95% CI: 1.01, 1.36; \u003cem\u003eP\u003c/em\u003e = 0.035).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificant associations between HIP and lower LAZ were observed at 3 months (a\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.090, 95% CI: -0.156, -0.025), 6 months (a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.089, 95% CI: -0.153, -0.025), and 8 (a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.081, 95% CI: -0.143, -0.019) months of age. Moreover, HIP was consistently linked to lower WAZ, starting as early as the first month of life. Furthermore, from 8 months onwards, HIP was associated with a decline in WFL (at 8 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.085, 95% CI: -0.145, -0.026; at 12 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.106, 95% CI: -0.158, -0.055; at 18 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.066, 95% CI: -0.116, -0.015; at 24 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.124, 95% CI: -0.175, -0.073; at 30 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.071, 95% CI: -0.130, -0.012; at 36 months: a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.083, 95% CI: -0.136, -0.030), reflecting potential growth challenges relative to weight and length (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn the stratified analysis of infant sex and duration of breastfeeding (less than 6 months versus 6 months or longer), we observed results consistent with the main analysis (\u003cstrong\u003eSupplementary Tables\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;5 and 6\u003c/strong\u003e, \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figures 4 and 5\u003c/strong\u003e). Although the association with WAZ and WFL in girls were not statistically significant at 12 months, the effect size of association still remained. In the sensitivity analysis, when restricted to infants unexposed to maternal hypertensive disorders during pregnancy, those conceived spontaneously, term-born infants, infants born to mothers with a BMI\u0026le;24 kg/m\u0026sup2;, or mothers without pre-pregnancy diabetes, although some changes were observed, the overall results were consistent with our main analysis (\u003cstrong\u003eSupplementary Tables\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;7-11\u003c/strong\u003e; \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figures 6-10\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eHIP was also significantly associated with reduced weight growth velocity between 0 to 3 (\u003cstrong\u003eTable 2\u003c/strong\u003e, a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.022, 95% CI: -0.036, -0.008) and 6 to 8 months of age (a\u003cem\u003e\u0026beta;\u003c/em\u003e=-0.017, 95% CI: -0.032, -0.002). Similarly, lower BMI growth velocity was observed between 6 to 8 months in the HIP group (a\u003cem\u003e\u0026beta;\u003c/em\u003e= -0.042, 95% CI: -0.078, -0.006).\u003c/p\u003e\n\u003ch2\u003eTrajectories for anthropometric measures\u003c/h2\u003e\n\u003cp\u003eUsing LCMM, three distinct trajectory groups were identified for WAZ, LAZ and WFL. \u003cstrong\u003eFigure 2\u003c/strong\u003e illustrates these trajectory groups, which were labeled as \u0026ldquo;low-increasing\u0026rdquo;, \u0026ldquo;moderate-stable\u0026rdquo;, and \u0026ldquo;high-decreasing\u0026rdquo; based on its initial values (low, moderate or high) and trends (increasing, stable or decreasing). Dashed lines around the solid lines represent the confidence intervals for the calculated trajectories. The \u0026ldquo;moderate-stable\u0026rdquo; trajectory served as the reference group for all measures, as it included a largest portion of the population and maintained a mean z-score near zero. The BIC values and number of participants for different number of trajectories are presented in \u003cstrong\u003eSupplementary Table 12\u003c/strong\u003e.\u003c/p\u003e\n\u003ch2\u003eAssociation between HIP and growth trajectories\u003c/h2\u003e\n\u003cp\u003eOffspring exposed to maternal hyperglycemia exhibited a 14% higher risk of following a high-decreasing WFL trajectory compared to unexposed children (aRR=1.14; 95% CI, 1.01\u0026ndash;1.29; \u003cem\u003eP\u003c/em\u003e=0.031). No significant associations were observed for other growth indices (\u003cstrong\u003eTable 3\u003c/strong\u003e). Similarly, within the low-increasing trajectory group, no significant associations were identified. Association analysis between HIP subtypes and WFL trajectories revealed that GDM was linked to a higher likelihood of infants following a high-decreasing WFL growth trajectory (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e \u003cstrong\u003e13\u003c/strong\u003e, aRR=1.16; 95% CI, 1.03\u0026ndash;1.30; \u003cem\u003eP\u003c/em\u003e=0.015).\u003c/p\u003e\n\u003cp\u003eTo further explore potential risk groups in smaller samples[25], WFL trajectory patterns were classified into two to five groups, as illustrated in \u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Figure 11\u003c/strong\u003e. When classified into four groups, a distinct \u0026ldquo;high-increasing\u0026rdquo; growth pattern (n=116) emerged. In this group, HIP exposure was significantly associated in the crude model (\u003cstrong\u003eSupplementary Table\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;14\u003c/strong\u003e, aRR=1.85; 95% CI, 1.25\u0026ndash;2.73; \u003cem\u003eP\u003c/em\u003e=0.002). However, this association became marginally significant after adjusting for confounders (aRR=1.56; 95% CI, 0.95\u0026ndash;2.54; \u003cem\u003eP\u003c/em\u003e=0.076).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this longitudinal cohort study, infants exposed to HIP in utero had a higher risk of LGA at birth. Interestingly, maternal HIP is significantly associated with lower z-scores for weight, height/length, and WFL in offspring at multiple time points across the first three years of life. Our latent class trajectory analysis showed children exposed to HIP were more likely to follow a \u0026apos;high-decreasing\u0026apos; WFL growth trajectory.\u003c/p\u003e\n\u003cp\u003eOur findings suggest that although there is a higher prevalence of large-for-gestational-age infants among those exposed to maternal hyperglycemia[6], this association does not persist into infancy. Several studies support this observation. A Danish register-based study, for instance, reported growth deceleration at five and twelve months in children exposed to HIP[28]. Similarly, Pima Indian children born to mothers with HIP exhibited higher weight-for-age z-scores at birth, but these differences were no longer evident at 1.5 years compared to those born to non-diabetic mothers[29]. Taken together, these results suggest that the long-term impact of maternal glycemia during pregnancy on offspring growth may be age specific, particularly during critical developmental windows such as birth and early infancy.\u003c/p\u003e\n\u003cp\u003eThere are several potential explanations for the associations observed in our study. First, during the intrauterine developmental stage, maternal glucose freely crosses the placenta, while insulin cannot. Elevated maternal blood glucose stimulates fetal pancreatic islet cell proliferation and enhances insulin synthesis, resulting in increased fetal insulin and insulin-like growth factor (IGF) secretion[30]. The upregulation of the insulin/IGF axis promotes fetal growth and development[31, 32]. However, once born, infants may be at an increased risk of developing insulin resistance in early infancy, which could contribute to slower growth[33, 34]. Second, evidence also suggests that offspring exposed to maternal hyperglycemia may exhibit smaller kidneys with fewer nephrons and impaired renal endocrine function[35, 36]. This may result in reduced growth hormone activity and slower growth during early childhood[37]. Third, a recent US-based cohort study identified associations between gestational diabetes, breast milk metabolites, and infant growth and body composition[38]. Specifically, three of the nine milk metabolites significantly associated with GDM were also linked to infant growth and body composition measures. Notably, the abundance of 2-hydroxybutyric, a metabolite linked to lipid metabolism, was higher in participants with GDM and negatively associated with the change in infant body fat percentage from 1 to 3 months. This suggests that breastfeeding may be another pathway through which maternal diabetes impairs infant growth.\u003c/p\u003e\n\u003cp\u003eOur findings also suggest potential sex differences in the effects of HIP. Boys in our study appeared more sensitive to HIP exposures, showing stronger negative associations between HIP and both WAZ and WFL compared to girls. Other studies have reported sex-specific effects of HIP on fetal or childhood growth inconsistently. For example, some studies on offspring of mothers with GDM found boys to be at higher risk of obesity in childhood and adolescence compared to girls[39], whereas others suggested that girls could be more vulnerable[40]. A recent study found that sons of women with poorly controlled GDM are characterized by increased and longer-acting activation of the reproductive axis, and faster growth of male genital organs in infancy[41]. The sex differences observed in our study and others may be attributed to postnatal sex steroid production during the so-called mini-puberty[42]. This phase is characterized by a testosterone surge in boys during the first month of life, while girls exhibit higher oestrogen levels, potentially influencing hormone-sensitive metabolic organs differently[43].\u003c/p\u003e\n\u003cp\u003eNot all infants grow exactly as these LCMM patterns showed but these are identifiable trends. Using LCMM, we saw that infants exposed to HIP were more likely to be in the high-decreasing WFL group across the first three years of life. Growth trajectory curves that follow this downward trend in growth percentiles have been termed catch-down growth in previous literature[44, 45]. Supporting this observation, a recent cohort from Ohio found that infants exposed to HIP accumulated less fat during the first year of life compared to their unexposed counterparts[46]. This growth pattern may reflect a compensatory response to early overnutrition, or it could signal underlying abnormalities in metabolic programming. However, the long-term clinical implications of this pattern remain unclear and warrant further follow-up. We also observed that infants born with lower birth weight exhibit typical catch-up growth, which we labeled as the \u0026ldquo;low-increasing\u0026rdquo; pattern. Since excessive catch-up growth is a well-established risk factor for obesity and metabolic disorders later in life[47], these findings warrant the concern over the long-term health influence of prenatal HIP exposure. We hypothesize that the natural biological processes may support self-rehabilitation of prenatal impairments during early postnatal life. However, this intrauterine \u0026apos;programmed\u0026apos; vulnerability may resurface later in life under the influence of environmental stressors. The differences observed early in life likely to persist or track into later childhood. They warrant enhanced monitoring and intervention in offspring of HIP mothers.\u003c/p\u003e\n\u003cp\u003eThe current prospective design and repeated measures of childhood anthropometric measurements provide a unique opportunity to investigate the long-term HIP effects on offspring growth with adjustment for key confounders. To our knowledge, our study has several strengths: 1) the multi-centered prospective cohort design with a relatively large sample size, superior to retrospective studies for investigating the association between HIP and early childhood growth; 2) ten times of dense monitoring of postnatal physical growth ensuring the reliability of trajectory data; 3)\u0026nbsp;comprehensive data collection from interviews and medical records, accounting for multiple confounders; and 4) the use of LCMM to identify subgroup-specific growth patterns, which may be missed in studies that pool heterogeneous groups or focus solely on cross-sectional data. This approach allows us to determine if there are underlying commonalities in growth trends and if HIP is associated with a certain underlying growth trend.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations must be noted. First, we were unable to distinguish between the effects of type 1 and type 2 diabetes prior to pregnancy. Second, despite adjusting for multiple confounders, there might still be residual confounding factors (e.g., genetics, parental/sibling development, glycemic control). Life-course studies using diverse methodologies are needed[48]. Given our plan to follow these children for an extended period, we aim to further explore the clinical significance of these trajectories in relation to long-term health outcomes. Finally, while LCMM and other trajectory analysis methods provide valuable insights, they cannot perfectly capture individual trajectories, and misclassification remains a possibility. Alternative trajectory modeling approaches are encouraged to validate our findings.\u003c/p\u003e"},{"header":" Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggest that maternal HIP is associated with lower z-scores for length, weight and weight-for-length during the first three years of life. Additionally, HIP is linked to reduced weight and BMI growth velocity in early infancy. For the first time, we identify a high-decreasing WFL growth trajectory observed in our sample, which challenges the notion that in utero hyperglycemia exposure has a detrimental effect on obesity beyond infancy. The findings from this study indicate that further research in this area is needed to support females with HIP to better understand and improve both short- and long-term health for infants exposed to HIP in utero, especially those related to catch-down growth. Understanding and tracking such growth patterns during the 0-3-year window may inform timely interventions aimed at optimizing long-term metabolic health outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95% Confidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDirected Acyclic Graphs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFasting Blood Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHAPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHyperglycemia and Adverse Pregnancy Outcomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHCZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHead-Circumference-for-Age Z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh-decreasing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHyperglycemia in Pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIGF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eInsulin-like Growth Factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eJBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eJiangsu Birth Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLength/Height-for-Age Z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow Birth Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLCMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLatent Class Mixed Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLarge-for-Gestational-Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow-increasing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLinear Mixed Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eModerate-stable\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOral Glucose Tolerance Test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003epGDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePre-gestational Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRelative Risk\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStandard Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSmall-for-Gestational-Age\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWAZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight-for-Age Z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWFL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWeight-for-Length Z-score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the families for participating this study, and the whole Jiangsu Birth Cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contribution\u003c/p\u003e\n\u003cp\u003eZH and JD initiated, conceived and supervised the study. YC, SW and XC carried out the initial analyses, and critically reviewed and revised the manuscript and involved in the conduct of the study and the analysis and interpretation of the results. JW, ZY, HLv, YD, YL, YJ were involved in study design, conduct of the cohort study, long-term follow-up with YZ, RQ, XX, XL, XH, BX and KZ. YL, YJ, KY designed the data collection instruments, collected data and critically reviewed and revised the manuscript. HM, JD, TJ, ZY and YD proofread the manuscript. ZH is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All authors read and approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key Research and Development Program of China (Grant No. 2021YFC2700600, 2021YFC2700705), the National Natural Science Foundation of China (Grant No. 82373581, 82103854, 81602927).\u003c/p\u003e\n\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eAfter publication, the data collected for the study (deidentified participant data) could be accessed on reasonable request to the corresponding author. A proposal with detailed description of study objectives and statistical analyses plan will be needed for evaluation of the reasonability of requests. Additional, relevant documents might also be required during the process of evaluation.\u003c/p\u003e\n\u003cp\u003eDeclarations\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003cp\u003eAuthor details\u003c/p\u003e\n\u003cp\u003e1 State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e2 Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e3 Department of Child Health Care, Women\u0026apos;s Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e4 Department of Obstetrics and Gynecology, Taizhou People\u0026apos;s Hospital, Affiliated to Nanjing Medical University, Taizhou, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e5 Department of Maternal, Child and Adolescent Health, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China\u003c/p\u003e\n\u003cp\u003e6 State Key Laboratory of Reproductive Medicine (Suzhou Centre), The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e7 Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e8 Department of Child Health, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.\u003c/p\u003e\n\u003cp\u003e9 Taizhou People\u0026apos;s Hospital, Affiliated to Nanjing Medical University, Taizhou, Jiangsu, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAye I, Aiken CE, Charnock-Jones DS, Smith GCS: \u003cstrong\u003ePlacental energy metabolism in health and disease-significance of development and implications for preeclampsia\u003c/strong\u003e. \u003cem\u003eAm J Obstet Gynecol\u0026nbsp;\u003c/em\u003e2022, \u003cstrong\u003e226\u003c/strong\u003e(2s):S928-s944.\u003c/li\u003e\n \u003cli\u003eBleicher SJ, O\u0026apos;Sullivan JB, Freinkel N: \u003cstrong\u003eCARBOHYDRATE METABOLISM IN PREGNANCY. 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\u003cstrong\u003e42\u003c/strong\u003e(5):1327-1339.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cohort study, infant growth trajectories, hyperglycemia in pregnancy, latent class mixed model, catch-down growth","lastPublishedDoi":"10.21203/rs.3.rs-6858828/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6858828/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eInfants exposed to hyperglycemia in pregnancy (HIP) in utero are known to have higher risks of macrosomia at birth and obesity in adulthood, but little is known about how their growth patterns change over the first 3 years of life.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn the prospective Jiangsu Birth Cohort (JBC) Study, 8780 children (23.3% HIP exposed) were included. Linear mixed models were used to evaluate the association between HIP and repeated offspring growth measures. Latent class mixed modeling (LCMM) trajectories were fit for weight-for-age (WAZ), length/height-for-age (LAZ) and weight-for-length z-scores (WFL). Adjusted associations between HIP with trajectory classes were evaluated with modified Poisson regression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAt birth, children exposed to HIP had a higher risk of LGA. HIP exposure was associated with lower weight-for-age (WAZ, -0.075, 95% CI: -0.117, -0.034), length/height-for-age (LAZ, -0.054, 95% CI: -0.099, -0.009), and weight-for-length z-score (WFL, -0.061, 95% CI: -0.100, -0.022). HIP also correlated with reduced weight and BMI growth velocity at 0\u0026ndash;3 and 6\u0026ndash;8 months. Three distinct trajectory groups were identified and were labeled as moderate-stable, high-decreasing, and low-increasing group. In adjusted models, children with HIP exposure were more likely to follow the high-decreasing WFL trajectory (aRR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 1.01, 1.29).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHIP exposure is associated with slower growth in early childhood and an increased likelihood of \u0026ldquo;high-decreasing (HD)\u0026rdquo; WFL trajectory. Identifying an HD trajectory may be valuable for early risk stratification.\u003c/p\u003e","manuscriptTitle":"Prenatal exposure to hyperglycemia and child growth trajectories in the first three years of life: a prospective birth cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 06:10:10","doi":"10.21203/rs.3.rs-6858828/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-28T14:33:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T15:13:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-03T08:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123912057205326030205845805044958039739","date":"2025-07-03T02:39:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136545379892780041358608099006184117350","date":"2025-06-23T07:30:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-19T06:55:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T05:39:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T05:32:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2025-06-10T04:06:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"909da843-f081-4003-b447-352f1c5c49b3","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-05T16:06:41+00:00","versionOfRecord":{"articleIdentity":"rs-6858828","link":"https://doi.org/10.1186/s12916-025-04521-0","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2025-12-29 15:57:35","publishedOnDateReadable":"December 29th, 2025"},"versionCreatedAt":"2025-06-25 06:10:10","video":"","vorDoi":"10.1186/s12916-025-04521-0","vorDoiUrl":"https://doi.org/10.1186/s12916-025-04521-0","workflowStages":[]},"version":"v1","identity":"rs-6858828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6858828","identity":"rs-6858828","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}