Association between maternal body composition during pregnancy and birth weight of offspring in pregnant women with gestational diabetes mellitus

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Therefore, this study aims to explore the association between the body composition of GDM patients and offspring birth weight. METHODS: Pregnant women diagnosed with GDM were enrolled and followed until delivery. Maternal body composition was assessed via bioelectrical impedance analysis during pregnancy. Multiple regression analysed associations between maternal body composition and offspring birth weight; restricted cubic spline (RCS) models examined potential nonlinearity. Results: This cohort study involved 929 pregnant women; 32 newborns were macrosomia.After covariate adjustment, offspring birth weight positively correlated with maternal total body water (TBW), intracellular fluid (ICF), extracellular fluid (ECF), fat mass (FM), fat-free mass (FFM), muscle mass (MM), protein, percent protein, minerals, and percent minerals in mothers with GDM. Multiple logistic regression showed that ECF increased the risk of macrosomia in GDM (OR:1.39, 95%CI:1.03-1.90). In the RCS model, n-shaped associations were found between the risk of macrosomia and maternal TBW, ICF, ECF, FFM, MM, protein, and minerals in GDM patients, while no significant association was observed for maternal FM. Conclusions： Although there is a positive correlation between the maternal body composition of pregnant women with GDM and the birth weight of their offspring, the association with the risk of macrosomia is not a simplistic linear relationship. Instead, when the body composition except FM reaches a certain critical threshold, there may be a reduction in the risk of macrosomia observed. gestational diabetes mellitus body composition birth weight macrosomia Figures Figure 1 Introduction Gestational diabetes mellitus (GDM), defined as hyperglycaemia first detected during pregnancy[1], is a prevalent chronic condition during pregnancy that adversely affects the health of millions of women globally[2,3]. It is widely acknowledged that GDM is correlated with a rise in pregnancy complications, as well as with long - term metabolic risks for both the woman and her offspring. According to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria, the global prevalence of GDM is estimated at 14.0%[4]. The age-standardised prevalence of obesity among adult women aged ≥ 20 years increased from 8.8% (95% CrI 8.5–9.1) in 1990 to 18.5% (95% CrI 17.9–19.1) in 2022 globally[5]. The rising prevalence of obesity among women suggests that the incidence of GDM will continue to increase, thereby amplifying the likelihood of adverse birth outcomes in offspring. A recent systematic review and meta-analysis has confirmed that, adjusted for confounders, GDM is a risk factor for both macrosomia and large - for - gestational - age (LGA) infants, regardless of whether insulin therapy is used[6]. Macrosomia and LGA are frequently used to measure large infants[7]. Macrosomia and LGA infants are established risk factors for cesarean delivery, birth trauma, and perinatal complications including shoulder dystocia, brachial plexus injury, neonatal fractures, and perinatal asphyxia[8]. Beyond GDM, macrosomia is linked to a wide range of factors. Genetic factors, environmental conditions, racial backgrounds, pre-pregnancy diabetes, history of macrosomia in previous deliveries, maternal body mass index (BMI), and parity are all associated with the development of macrosomia[9]. Its occurrence can also be caused by factors such as excessive gestational weight gain (GWG), and gestational age, as well as other reproductive-related factors[10]. While the BMI remains widely utilized as a surrogate measure for adiposity assessment in clinical and epidemiological studies, its validity as a reliable marker of body composition remains contested in contemporary research[11]. Body composition analysis provides a direct structural assessment of physiological tissues (muscle, fat, bone), contrasting with BMI’s indirect anthropometric estimation[12]. In the published research, the majority of studies[11,13–15]focus on the association between the body composition of healthy pregnant women and their offspring's birth weight or the risk of macrosomia. Specifically, most of these studies are concerned with the relationship between the maternal fat - free mass (FFM), fat mass (FM), total body water (TBW) and the offspring's birth weight or macrosomia. While perturbations in maternal glucose homeostasis among women with GDM are known to drive significant alterations in body composition, the direct association between maternal body composition parameters in GDM and neonatal birth weight has not been systematically investigated. Relevant simplified models of body composition have been established. Widely - applied models include two - component, three - component, four - component, five - component, and other multi - component models[16]. Among them, the two - component chemical model was established by Behnke et al. in 1942. Based on the differences in the structure and function of human body composition, it divides the body into fat mass and fat - free mass[17]. The application of multi - component models in epidemiological research is relatively complex. Among these models, the five - component model of human body composition, proposed by Wang et al. in 1992, is the most influential. Based on this model, the five fundamental components of human body composition have been identified: FM, extracellular fluid, intracellular fluid, minerals, and protein[18]. Commonly employed methods for body composition assessment comprise anthropometry, densitometry (including air displacement plethysmography and underwater weighing), and hydrometry (encompassing isotope dilution and bioelectrical impedance analysis (BIA))[19]. Currently, BIA is widely utilized in human body composition research owing to its non-invasive, reliable, and rapid clinical assessment advantages[20]. This technique is employed to evaluate body composition parameters, including fat mass, protein content, total body water, and intracellular/extracellular fluid volumes[21]. Therefore, our study is mainly based on the five - component model to investigate the associations between the body composition of pregnant women with GDM measured by BIA and their offspring's birth weight, as well as the risk of macrosomia. Thereby providing a scientific rationale for implementing dynamic body composition monitoring and personalized dietary interventions in this population, which may ultimately contribute to risk stratification and mitigation of macrosomia. Methods Study Design and Participants From January 2023 to July 2024, we recruited 1,065 pregnant women with GDM from the Maternal and Child Health Hospital of Longgang District, Shenzhen, China. We conducted a cohort study to follow the birth outcomes of all pregnancies to assess the association between maternal body composition during pregnancy and birth weight of the offspring. A total of 929 women with GDM and their infants were enrolled in the study if they met the following criteria: (a) a singleton pregnancy; (b) maternal age ≥ 18 years at pregnancy; (c) a gestational age between 37 and 42 weeks at delivery; and (d) maternal body composition measurements obtained during pregnancy. Participants were excluded if they (a) had a pre - pregnancy diagnosis of diabetes mellitus, heart disease, hypertension, psychiatric disorders, or were taking glucose-lowering medications, glucocorticoids, diuretics, antiepileptic medications, etc.; (b) have been diagnosed with pregestational diabetes mellitus (PGDM)[22,23]; (c) were given glucose-lowering medications for poor glycemic control during pregnancy; or (d) had missed visits or incomplete data. The 75 - g oral glucose tolerance test (OGTT) was used as a diagnostic method for GDM in all participants at 24-28 weeks of gestation: the glucose thresholds were 5.1, 10.0, and 8.5 mmol/L for fasting, 1 h, and 2 h after oral glucose intake, respectively, and the diagnosis of GDM was made when glucose values reached or exceeded the above criteria at any one of these time points[24–26]. Measurement of maternal body composition Body composition was assessed using an 8-point tactile electrode BIA apparatus (NQA-Pplus; Sihaihuachen, Beijing, China). The device applies an alternating current ≤ 450 μA at seven discrete frequencies: 1, 5, 50, 100, 250, 500, and 1000 kHz. When testing body composition, the operation procedure of the instrument is strictly followed, allowing pregnant women to empty their bowels, take off their shoes and socks, wear a single garment, spray their hands with quick-drying hand sanitizer, step on their feet and hold the corresponding electrodes in their hands, and carry out measurements. The main indicators include: body weight, total body water (TBW), percent total body water (PTBW), intracellular fluid (ICF), percent intracellular fluid (PICF), extracellular fluid (ECF), percent extracellular fluid (PECF), muscle mass (MM), percent muscle mass (PMM), fat free mass (FFM), percent fat free mass (PFFM), fat mass (FM), percent fat mass (PFM), protein, percent protein, minerals and percent minerals, and the percentage of body composition is obtained by dividing the TBW, ICF, ECF, MM, FFM, FM, protein, and minerals by the body weight respectively. Covariates Upon enrollment, all pregnant women in this cohort study completed a structured questionnaire capturing periconceptional data, including maternal age, pre-pregnancy BMI, family history of diabetes mellitus, adverse pregnancy history, gestational age at delivery (GAD), parity, gravidity, GWG, and gestational age at body composition measurement. The pre-pregnancy BMI of pregnant women was classified into three categories based on Chinese criteria: underweight (< 18.5 kg/m²), normal weight (18.5 - 23.9 kg/m²), and overweight or obese (≥ 24.0 kg/m²). According to the “standard of recommendation for weight gain during pregnancy period” issued by the National Health and Wellness Commission of the People’s Republic of China on July 28, 2022, the pattern of weight gain during pregnancy is classified into three categories: insufficient weight gain, appropriate weight gain, and excessive weight gain (Supplement eTable1). Birth weight was further categorised into low birth weight (< 2500g), normal birth weight (2500 - 3999 g) and macrosomia (defined as ≥ 4000 g)[27]. Statistical Analysis Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables as number (percentage). Univariate linear regression analyses were performed to explore the potential associations between maternal characteristics and offspring birth weight, while univariate logistic regression was used to preliminarily assess their relationships with macrosomia risk. Subsequently, multiple linear regression was employed to assess the associations between offspring birth weight and maternal body composition in GDM, and multiple logistic regression was used to explore the associations between risk of macrosomia and maternal body composition. The multivariate models were adjusted for relevant covariates, which were completely available for the analysis. Possible nonlinear relationships between maternal body composition in GDM and offspring birth weight, and between maternal body composition in GDM and risk of macrosomia were examined using restricted cubic spline (RCS) models, with four knots at 5%, 35%, 65%, and 95%. Additionally, covariates in multivariate model were adjusted to refine the analysis. Subgroup analyses stratified by gravidity (1, 2 and ≥ 3), parity (0, 1 and ≥ 2), BMI (< 18.5.0 kg/m², 18.5 - 23.9 kg/m² and ≥ 24.0 kg/m²), GWG (insufficient weight gain, appropriate weight gain and excessive weight gain) and pregnancy period (second trimester and third trimester) were performed to assess the stability of the Multiple linear regression results. Data analyses were performed using R software (version 4.4.1), with the significance level set at P < 0.05. Results Participant characteristics Total of 929 women were enrolled into the study. The characteristics of the participants are presented in Table 1 . Body composition assessments were performed at a mean gestational age of 26.33 weeks. The average maternal age was 31.58 years, the average pre - pregnancy BMI was 22.41, the average gestational week of delivery was 38.96 weeks, and the mean GWG was 11.79 kg. Regarding weight gain during pregnancy, 482 (51.88%) had appropriate weight gain, 157 (16.90%) had insufficient weight gain, and 290 (31.22%) gained excessive weight. Of the participants, 388 (41.77%) had a family history of diabetes mellitus and 130 (13.99%) had a history of adverse pregnancy. According to the pre - pregnancy BMI, 7.8% of the participants were underweight, 64.8% were normal weight, 27.4% were overweight or obese. In the pregnancy outcomes for all participating mothers, there were 32 cases (3.44%) of fetal macrosomia, 23 cases (2.48%) of postpartum hemorrhage, and 310 cases (33.37%) of complications, including a scarred uterus. Table 1 Characteristics of the Participant Enrolled in the Cohort Variables Mean ± SD or N (%) Maternal age (years) 31.58 ± 4.20 Pre-pregnant BMI (kg/m 2 ) 22.41 ± 3.24 Underweight (less than 18.5, %) 73 (7.86) Normal (18.5–23.9, %) 602 (64.80) Overweight and Obese (24.0 or higher, %) 254 (27.34) Birth weight (g) 3225.03 ± 413.93 Gestational age at delivery (wk) 38.96 ± 0.90 Gestational age at body composition assessment (wk) 26.33 ± 2.12 Gestational weight gain (kg) 11.79 ± 4.51 Insufficient weight gain 157 (16.90) Appropriate weight gain 482 (51.88) Excessive weight gain 290 (31.22) Family history of diabetes no 541 (58.23) yes 388 (41.77) History of abnormal pregnancy no 799 (86.01) yes 130 (13.99) Macrosomia no 897 (96.56) yes 32 (3.44) Postpartum hemorrhage no 906 (97.52) yes 23 (2.48) Complication no 619 (66.63) yes 310 (33.37) Mode of delivery Vaginal delivery 577 (62.11) Cesarean delivery 352 (37.89) Gravidity 1 time 323 (34.77) 2 times 302 (32.51) 3 times or more 304 (32.72) Parity 0 time 453 (48.76) 1 time 365 (39.29) 2 times or more 111 (11.95) SD: standard deviation, BMI: body mass index, N: number. Infant’s birth weight Infant’s birth weight ranged from 1550 to 4650 g, with a mean birth weight of 3225.03 g. The majority of newborns (94.00%) were classified as normal birth weight, with 24 cases (2.60%) of low birth weight and 32 cases (3.44%) of macrosomia (Table 1 ). Linear regression analyses revealed significant associations between birth weight and gravidity, parity, maternal age, pre-pregnant BMI, GAD, and GWG (all P < 0.05). However, no significant associations were found between birth weight and family history of diabetes mellitus or history of abnormal pregnancies (Table 2 ). Table 2 Linear regression analysis of infant’s birth weight by maternal characteristics Variables Birth Weight (g) * β (95%CI) P Family history of diabetes no 3232.41 ± 415.06 Reference yes 3214.73 ± 412.66 -17.68 (-71.67 ~ 36.31) 0.521 History of abnormal pregnancy no 3223.14 ± 416.97 Reference yes 3236.62 ± 396.06 13.47 (-63.29 ~ 90.24) 0.731 Gravidity 1 time 3142.65 ± 417.53 Reference 2 times 3240.66 ± 401.53 98.02 (33.80 ~ 162.23) 0.003 3 times or more 3297.02 ± 408.23 154.38 (90.27 ~ 218.49) < .001 Parity 0 time 3161.84 ± 418.62 Reference 1 time 3264.70 ± 407.29 102.86 (46.49 ~ 159.22) < .001 2 times or more 3352.43 ± 372.00 190.59 (105.71 ~ 275.46) < .001 Maternal age - 8.89 (2.58 ~ 15.21) 0.006 Pre-pregnant BMI - 18.45 (10.31 ~ 26.59) < .001 GAD - 120.92 (92.40 ~ 149.44) < .001 GWG - 8.38 (2.49 ~ 14.27) 0.005 BMI: Body Mass Index, GAD: Gestational Age at Delivery, GWG: Gestational Weight Gain. * Birth weight is presented as mean ± standard deviation (SD). Association between birth weight of offspring and maternal body composition in GDM As shown in Table 3 , univariate regression analysis indicated that there was a significant positive correlation between birth weight and maternal body composition indices of TBW, ICF, ECF, FM, PFM, FFM, MM, protein and minerals in GDM patients. However, there was a significant negative correlation between birth weight and PTBW, PICF, PECF, PFFM, PMM, percent protein and percent minerals. The correlation between birth weight and maternal TBW, ICF, ECF, FM, FFM, MM, protein and minerals remained significant and positive after adjustment for covariates. The subgroup analyses showed that the associations between birth weight and maternal body composition for GDM were generally consistent across the different subgroups based on parity, gravidity, BMI, GWG, and pregnancy period at body composition, except for individual subgroups where no associations were found because of insufficient sample size. In addition, no significant interactions were observed in all subgroups (eTable 2 ~ 17 in Supplement). The RCS model was used to simulate the relationship between birth weight and maternal body composition. No significant nonlinear dose-response associations were found between birth weight and maternal body composition at GDM (eFigure in Supplement). Table 3 Relationship of maternal body composition with birth weight in multiple linear regression models Variables Univariate regression analysis Multiple linear regression* β (95%CI) P β (95%CI) P TBW 40.22 (32.51 ~ 47.93) < .001 35.58 (26.81 ~ 44.36) < .001 PTBW -9.96 (-17.00 ~ -2.92) 0.006 5.10 (-4.61 ~ 14.82) 0.304 ICF 62.79 (50.27 ~ 75.31) < .001 54.81 (40.52 ~ 69.10) < .001 PICF -18.04 (-29.41 ~ -6.68) 0.002 3.13 (-12.08 ~ 18.34) 0.687 ECF 106.24 (86.58 ~ 125.89) < .001 94.13 (72.06 ~ 116.19) < .001 PECF -19.42 (-37.23 ~ -1.61) 0.033 24.99 (0.22 ~ 49.76) 0.048 FM 14.24 (9.94 ~ 18.55) < .001 15.72 (7.76 ~ 23.67) < .001 PFM 8.37 (3.23 ~ 13.51) 0.001 -2.57 (-9.78 ~ 4.64) 0.485 FFM 29.53 (23.84 ~ 35.22) < .001 25.98 (19.54 ~ 32.42) < .001 PFFM -7.53 (-12.67 ~ -2.39) 0.004 3.61 (-3.55 ~ 10.77) 0.323 MM 31.17 (25.15 ~ 37.19) < .001 27.52 (20.66 ~ 34.37) < .001 PMM -7.98 (-13.47 ~ -2.49) 0.004 3.45 (-4.09 ~ 11.00) 0.370 Protein 145.28 (116.31 ~ 174.25) < .001 126.81 (93.75 ~ 159.87) < .001 Percent protein -41.72 (-68.01 ~ -15.43) 0.002 7.28 (-27.92 ~ 42.48) 0.685 Minerals 413.59 (334.86 ~ 492.32) < .001 351.20 (268.82 ~ 433.59) < .001 Percent minerals -78.07 (-142.34 ~ -13.79) 0.017 146.77 (47.09 ~ 246.44) 0.004 CI: Confidence Interval, TBW: Total Body Water, PTBW: Percent Total Body Water, ICF: Intracellular Fluid, PICF: Percent Intracellular Fluid, ECF: Extracellular Fluid, PECF: Percent Extracellular Fluid, FM: Fat Mass, PFM: Percent Fat Mass, FFM: Fat-Free Mass, PFFM: Percent Fat-Free Mass, MM: Muscle Mass, PMM: Percent Muscle Mass *Adjusting for maternal age, pre-pregnant BMI, GAD, GWG, gravidity, parity and pregnancy period when body composition was done Association between macrosomia and maternal body composition in GDM In the univariate analysis, statistically significant risk factors for macrosomia were identified (Table 4 ), and these factors, as well as GAD, a variable that has been a risk factor in other studies[ 11 ], were included as covariates in further analyses. Multiple logistic regression showed that ECF increased the risk of macrosomia in GDM (OR:1.39, 95%CI:1.03–1.90, Table 5 ). In the RCS model, we found n-shaped associations between the risk of macrosomia and maternal TBW, ICF, ECF, FFM, MM, protein and minerals in women with GDM (P for overall < 0.05, P for nonlinearity < 0.05, respectively) (Fig. 1 ). No significant nonlinear dose-response associations were found between risk of macrosomia and maternal FM, PTBW, PICF, PECF, PFFM, PMM, percent protein and percent minerals in GDM. ( P for overall > 0.05, P for nonlinearity > 0.05, respectively). Table 4 Risk factors associated with macrosomia Variables β S.E Z P OR (95%CI) Gravidity 1 time 1.00 (Reference) 2 times 1.05 0.53 1.97 0.048 2.86 (1.01 ~ 8.12) 3 times or more 1.12 0.53 2.13 0.033 3.07 (1.09 ~ 8.63) Pre-pregnant BMI 0.10 0.05 2.23 0.026 1.11 (1.01 ~ 1.22) GAD 0.23 0.21 1.10 0.273 1.25 (0.84 ~ 1.88) GWG (kg) 0.09 0.03 2.60 0.009 1.10 (1.02 ~ 1.17) OR: Odds Ratio, CI: Confidence Interval, GAD: Gestational Age at Delivery, GWG: Gestational Weight Gain. Table 5 Relationship of maternal body composition with macrosomia in multiple logistics regression models Variables β S.E Z P OR (95%CI) TBW 0.11 0.06 1.69 0.092 1.12 (0.98 ~ 1.27) PTBW 0.07 0.07 1.07 0.286 1.08 (0.94 ~ 1.24) ICF 0.15 0.11 1.39 0.164 1.16 (0.94 ~ 1.42) PICF 0.06 0.11 0.53 0.593 1.06 (0.85 ~ 1.32) ECF 0.33 0.16 2.12 0.034 1.39 (1.03 ~ 1.90) PECF 0.33 0.18 1.87 0.061 1.39 (0.99 ~ 1.96) FM 0.08 0.05 1.63 0.104 1.08 (0.98 ~ 1.20) PFM 0.01 0.05 0.28 0.777 1.02 (0.92 ~ 1.13) FFM 0.08 0.05 1.65 0.100 1.08 (0.99 ~ 1.19) PFFM 0.05 0.05 1.00 0.315 1.05 (0.95 ~ 1.17) MM 0.08 0.05 1.63 0.104 1.09 (0.98 ~ 1.20) PMM 0.05 0.05 0.96 0.339 1.05 (0.95 ~ 1.17) Protein 0.34 0.24 1.39 0.164 1.40 (0.87 ~ 2.27) Percent Protein 0.14 0.26 0.53 0.596 1.15 (0.69 ~ 1.90) Minerals 0.99 0.60 1.65 0.098 2.70 (0.83 ~ 8.77) Percent minerals 0.97 0.72 1.35 0.178 2.65 (0.64 ~ 10.90) OR: Odds Ratio, CI: Confidence Interval, TBW: Total Body Water, PTBW: Percent Total Body Water, ICF: Intracellular Fluid, PICF: Percent Intracellular Fluid, ECF: Extracellular Fluid, PECF: Percent Extracellular Fluid, FM: Fat Mass, PFM: Percent Fat Mass, FFM: Fat-Free Mass, PFFM: Percent Fat-Free Mass, MM: Muscle Mass, PMM: Percent Muscle Mass Adjust: BMI, GAD, GWG, Gravidity Discussion In this cohort study of Chinese women with singleton pregnancies complicated by GDM, maternal body composition parameters including TBW, ICF, ECF, PECF, FM, FFM, MM, protein, and minerals showed significant positive correlations with neonatal birth weight. Notably, we observed nonlinear n-shaped relationships between specific indices (TBW, ICF, ECF, FFM, MM, protein, and minerals) and macrosomia risk. These associations suggest a dynamic pattern wherein increasing values of these parameters initially correlated with elevated macrosomia risk, followed by paradoxical risk reduction beyond critical thresholds. This biphasic relationship challenges conventional interpretations of linear maternal-fetal growth associations in GDM populations. Body composition assessment during pregnancy conventionally segments the body into FM and FFM compartments[ 19 ]. Consistent with other studies in non-GDM pregnant women, our results showed that birth weight was positively associated with maternal FFM in GDM. A longitudinal study found that maternal FFM assessed via BIA during the second trimester was significantly and independently associated with infant birth weight[ 28 ]. Gernand et al. also reported that maternal measures of FFM at approximately 10 weeks of gestation and weight gains from 20 to 32 weeks are independently associated with higher birth weight[ 29 ]. Maternal FFM exhibits a relatively consistent association with offspring birth weight across studies, while the relationship between FM and birth weight remains inconclusive and subject to ongoing debate. Forsum et al. suggested that maternal FM (both pre-pregnancy and at 32 weeks' gestation) and gestational age at birth likely contribute to increased birth weight. Additionally, their analysis indicated that pre-pregnancy percent body fat (PBF) explained 45% of the variation in birth weight[ 30 ]. Our study also found a positive relationship between maternal FM in GDM patients and birth weight. In contrast, Kent et al. demonstrated a positive correlation between birth weight and maternal first-trimester FFM, but found no significant association with FM[ 13 ]. Farah et al. also observed that birth weight correlated with maternal FFM, and not FM at 28 and 37 weeks gestation[ 31 ]. The heterogeneity in maternal FM research outcomes could stem from inter-study disparities in (a) sample size ranges, (b) population composition (GDM-diagnosed versus non-GDM individuals), (c) ethnic backgrounds, and (d) measurement timing of FM during gestation. FFM comprises TBW, protein, and mineral mass[ 19 ]. In the human body, TBW consists of ECF and ICF. ECF, accounting for 1/3 of TBW[ 32 ], mainly includes tissue interstitial fluid, plasma, lymph, cerebrospinal fluid, and so on. In a longitudinal Italian study utilizing single-frequency tetrapolar bioelectric impedance analysis for pregnancy body composition assessment, second-trimester body water was found to predict birth weight [ 28 ]. A study using the deuterium dilution technique to determine body composition also reported a positive correlation between infant birth weight and maternal TBW and FFM, but not FM[ 15 ]. Fluid intake is positively correlated with body water content[ 33 , 34 ]. A Chinese prospective cohort study on fluid intake and hydration status during the third trimester of pregnancy suggests a potential positive linear relationship between the intake of plain water and infant birth weight[ 33 ]. Total fluid intake during the third trimester was inadequate among pregnant women (median = 1574 mL), with only 12.1% meeting China’s recommended adequate intake of 1700 mL/day[ 33 ]. Conversely, the elements that were least frequently mentioned included protein, minerals, and lean mass[ 35 ]. In our study, maternal TBW, ICF, ECF, PECF, MM, protein, minerals, and percent minerals were found to be positively associated with birth weight in pregnant women with GDM. These results indicate that maternal body FFM, including body water, protein and mineral, are of great significance in maintaining birth weight. We should place increased emphasis on dietary intake of water, protein and minerals among GDM patients. Maternal hyperglycemia induces fetal hyperglycemia through facilitated glucose transport mediated by glucose transporter 1 (GLUT1) across the placenta. The resulting fetal hyperglycemia stimulates hyperinsulinemia, which promotes excessive anabolic activity, leading to increased adiposity and accelerated fetal growth, ultimately resulting in macrosomia and LGA[ 8 ]. A retrospective study of 43,020 healthy pregnant women found that body FM, FFM, and muscle mass were associated with an increased risk of macrosomia[ 11 ]. In the studies from China[ 14 ] and Ireland[ 13 ], after adjustment for confounding variables, healthy women in the highest quartile of FFM have a significantly increased risk of macrosomia compared with those in the lowest quartile of FFM. Through logistic regression analysis, our study revealed that only maternal ECF levels in mothers with GDM were significantly and positively associated with an increased risk of macrosomia. We consider whether there is a non-linear relationship between maternal body composition and macrosomia risk. Thus, we employed RCS models to systematically evaluate potential non-linear relationships between maternal body composition and offspring birth weight in women with GDM. TBW, ICF, and ECF each demonstrated a significant N-shaped association with macrosomia risk in GDM. During pregnancy, low plasma volume, ECF, and TBW—with plasma volume serving as a constituent of both ECF and TBW—have been linked to fetal growth restriction[ 36 ]. The expansions in ECF and TBW are driven in part by increases in maternal lean tissue and products of conception, alongside plasma volume augmentation[ 36 ]. The placenta functions to support fetal growth by facilitating the delivery of oxygen and nutrients, as well as the removal of waste products[ 37 ]. Thus, high plasma volume may increase placental blood flow, further promoting excessive nutrient supply by the placenta in individuals with GDM. When plasma volume or TBW, ECF, ICF continue to increase, it may trigger a certain compensatory mechanism, which instead reduces the risk of macrosomia. This aligns with the observed n - shaped associations, indicating that the intricate interplay between these body - composition indices (TBW, ECF, ICF) and plasma - volume - mediated placental function critically influences macrosomia risk in GDM, reflecting how varying maternal body - composition levels affect fetal nutrient supply via placental mechanisms. Consistent with the fluid compartment analyses, the RCS model additionally identified n -shaped relationships for macrosomia risk with maternal FFM, MM, protein, and mineral levels, but not with FM. These maternal body composition measurements in the middle range of the RCS curve are instead associated with the risk of macrosomia in pregnant women with GDM, while those with a low or high maternal body composition have a reduced risk of macrosomia. The observed risk attenuation at higher body composition levels may reflect activation of maternal metabolic compensatory mechanisms. Specifically, exceeding physiological thresholds could trigger adaptive responses that modulate nutrient partitioning between maternal and fetal compartments, potentially counteracting fetal overgrowth. Such nonlinear dynamics emphasize the need to redefine optimal body composition ranges for GDM management, rather than pursuing uniform reductions in maternal nutritional indices. We examined in detail the individuals at both extremes of the RCS curve. Most of them exhibited either a low BMI or a BMI at or above overweight levels. Notably, even among those classified as overweight or obese, more than 50% of their body composition was accounted for by FFM or MM. Experimental evidence from animal models demonstrates that maternal swimming exercise during gestation induces reduced birth weight in mouse offspring, with this weight-modulating effect persisting throughout the first two postnatal months[ 38 ]. In addition, regular exercise during pregnancy is associated with appropriate gestational weight gain and consequent optimal infant birth weight—manifested as reduced incidence of LGA without increased small-for-gestational-age (SGA) risk—thereby potentially lowering offspring susceptibility to major chronic diseases in later life, including cardiovascular disease, obesity, and diabetes[ 39 ]. These findings collectively suggest that regular maternal exercise may reduce macrosomia risk, potentially through enhancing muscle mass to improve metabolic regulation and mitigate excessive fetal nutrient exposure. The n-shaped relationship between maternal mineral content and macrosomia risk in GDM implies threshold effects, wherein optimal mineral levels may reduce macrosomia risk by improving glycemic control. A recent meta-analysis demonstrated that mineral supplementation significantly improves glycemic control, reduces inflammation, and mitigates oxidative stress in GDM[ 40 ]. Calcium, the most abundant mineral in the human body, has been proposed to interact synergistically with vitamin D rather than functioning independently[ 41 ]. These nutrients may influence pregnancy outcomes by modulating skeletal homeostasis, smooth muscle contractility[ 42 ]. A randomized controlled trial (RCT) has shown that supplementation of vitamin D and calcium in patients with GDM exerts beneficial effects on glucose metabolism, lipid metabolism, and oxidative stress[ 43 ]. While this RCT did not examine the impact of combined vitamin D and calcium supplementation on pregnancy outcomes, particularly macrosomia, it can provide certain evidence or conjectures for the nonlinear association between mineral content and the risk of macrosomia in patients with GDM in this study. The strengths of this study include being the first to investigate the association between maternal body composition and birth weight specifically in women with GDM, as well as examining potential non-linear relationships between maternal body composition and the risk of macrosomia. Additionally, the prospective design ensured the availability of sufficiently detailed data on maternal and neonatal characteristics. However, several limitations should be acknowledged. Firstly, a multinational study of 115.6 million live-born neonates across 15 countries (2000–2020) reported a median macrosomia prevalence of 9.6% (IQR: 6.4–13.3%) for birth weight ≥ 4000 g[ 7 ], with a Northwest Ethiopian cohort further demonstrated a fourfold higher risk of fetal macrosomia in GDM pregnancies compared to non-GDM pregnancies[ 44 ]. In contrast, the relatively low prevalence of macrosomia (3.44%) observed in our study may be attributed to two key factors: (1) the exclusion of GDM patients requiring pharmacological treatment due to suboptimal glycemic control, and (2) possible dietary modifications adopted by GDM mothers following their diagnosis and subsequent nutritional counseling. Secondly, our data are solely derived from a single hospital, which may give rise to some confounding biases. Thirdly, given that our sample size is not particularly large and only includes body composition data from the second and third trimesters of pregnancy, we are unable to investigate the association between changes in body composition pre-pregnancy and the birth weight of the offspring. Conclusions Although there is a positive correlation between the maternal body composition of pregnant women with GDM and the birth weight of their offspring, the association with the risk of macrosomia is not a simplistic linear relationship. Instead, when the body composition except FM reaches a certain critical threshold, there may be a paradoxical reduction in the risk of macrosomia observed. Declarations Disclosure of ethical statements: The study protocol adhered to the principles of the Declaration of Helsinki and received ethical approval from the Institutional Ethics Committee of Shenzhen Longgang District Maternal and Child Health Hospital (Approval No. LGFYKYXMLL-2024-119) in September 2024. The ethical approval project identification code is KYXMLL-01-CZGC-14-2-1. Informed Consent Statement : Written informed consent was voluntarily obtained from all participants prior to the commencement of the study. Declaration of Interest statement : The authors declare no conflict of interest. Clinical trial number: not applicable. Funding: This work was supported by the Scientific Research Fund of China Nutrition Society under Grant NO. CNS-YUM2024-121; the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2023A1515030168; the “Master Mentor Plan” of Jinan University, Nutrition Professional Instructor Comprehensive Competency Enhancement Practical Program under Grant NO. YDXS2410; and the 2022 Longgang District Medical and Health Technology Program Project under Grant NO. LGWJ2022-(57). Author Contribution : XY, WLJ, DBJ, CWY and LYX conceived and designed the study; XY, WLJ and DBJ developed the methodology; XY, LJ and ZSY performed formal analysis; XY, LJ, ZSY and LHX curated data; CWY, LYX, ZYF, GFF, XBB, ZHY, LHX, GSX and KMZ conducted investigation; XY and WLJ wrote the original draft; XY, WLJ and DBJ reviewed and edited the manuscript; DBJ and WLJ acquired funding; DBJ, CWY and LYX provided resources; DBJ supervised the research. All authors (XY, WLJ, DBJ, CWY, LYX, LJ, ZSY, LHX, ZYF, GFF, XBB, ZHY, GSX, KMZ) read and approved the final manuscript, agreeing to be accountable for all aspects of the work. Acknowledgement : We are grateful to all relevant staff members, GDM patients, and their children for their contributions to this study. Data Availability : The data that support the findings of this study are available from the corresponding author, Bingjun Deng, upon reasonable request. 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Wei Y, Yang H, Zhu W, et al. International Association of Diabetes and Pregnancy Study Group criteria is suitable for gestational diabetes mellitus diagnosis: further evidence from China. Chin Med J (Engl) 2014;127:3553–6. World Health Organization. International Classification of Diseases, eleventh revision (ICD-11). Geneva: World Health Organization; 2022. Ghezzi F, Franchi M, Balestreri D, et al. Bioelectrical impedance analysis during pregnancy and neonatal birth weight. European Journal of Obstetrics & Gynecology and Reproductive Biology 2001;98:171–6. https://doi.org/10.1016/S0301-2115(01)00330-X. Gernand AD, Christian P, Paul RR, et al. Maternal weight and body composition during pregnancy are associated with placental and birth weight in rural Bangladesh. J Nutr 2012;142:2010–6. https://doi.org/10.3945/jn.112.163634. Forsum E, Löf M, Olausson H, et al. Maternal body composition in relation to infant birth weight and subcutaneous adipose tissue. Br J Nutr 2006;96:408–14. https://doi.org/10.1079/bjn20061828. Farah N, Stuart B, Donnelly V, et al. The influence of maternal body composition on birth weight. European Journal of Obstetrics & Gynecology and Reproductive Biology 2011;157:14–7. https://doi.org/10.1016/j.ejogrb.2010.12.047. Wang Y, Luo B-R. The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study. Medicine (Baltimore) 2019;98:e17576. https://doi.org/10.1097/MD.0000000000017576. Song Y, Zhang F, Wang X, et al. A Study of Fluid Intake, Hydration Status, and Body Composition of Pregnant Women in Their Third Trimester, and Relationships with Their Infant’s Birth Weight in China: A Prospective Cohort Study. Nutrients 2024;16:972. https://doi.org/10.3390/nu16070972. Laja García AI, Moráis-Moreno C, Samaniego-Vaesken M de L, et al. Influence of Water Intake and Balance on Body Composition in Healthy Young Adults from Spain. Nutrients 2019;11:1923. https://doi.org/10.3390/nu11081923. Guzman-Ortiz E, Bueno-Hernandez N, Melendez-Mier G, et al. Quantitative systematic review: Methods used for the in vivo measurement of body composition in pregnancy. Journal of Advanced Nursing 2021;77:537–49. https://doi.org/10.1111/jan.14594. Gernand AD, Christian P, Schulze KJ, et al. Maternal nutritional status in early pregnancy is associated with body water and plasma volume changes in a pregnancy cohort in rural Bangladesh. J Nutr 2012;142:1109–15. https://doi.org/10.3945/jn.111.155978. Cindrova-Davies T, Sferruzzi-Perri AN. Human placental development and function. Semin Cell Dev Biol 2022;131:66–77. https://doi.org/10.1016/j.semcdb.2022.03.039. Wasinski F, Bacurau RFP, Estrela GR, et al. Exercise during pregnancy protects adult mouse offspring from diet-induced obesity. Nutr Metab (Lond) 2015;12:56. https://doi.org/10.1186/s12986-015-0052-z. Vargas-Terrones M, Nagpal TS, Barakat R. Impact of exercise during pregnancy on gestational weight gain and birth weight: an overview. Braz J Phys Ther 2019;23:164–9. https://doi.org/10.1016/j.bjpt.2018.11.012. Li D, Cai Z, Pan Z, et al. The effects of vitamin and mineral supplementation on women with gestational diabetes mellitus. BMC Endocr Disord 2021;21:106. https://doi.org/10.1186/s12902-021-00712-x. Asemi Z, Foroozanfard F, Hashemi T, et al. Calcium plus vitamin D supplementation affects glucose metabolism and lipid concentrations in overweight and obese vitamin D deficient women with polycystic ovary syndrome. Clinical Nutrition 2015;34:586–92. https://doi.org/10.1016/j.clnu.2014.09.015. Merewood A, Mehta SD, Chen TC, et al. Association between vitamin D deficiency and primary cesarean section. J Clin Endocrinol Metab 2009;94:940–5. https://doi.org/10.1210/jc.2008-1217. Gunasegaran P, Tahmina S, Daniel M, et al. Role of vitamin D-calcium supplementation on metabolic profile and oxidative stress in gestational diabetes mellitus: A randomized controlled trial. J Obstet Gynaecol Res 2021;47:1016–22. https://doi.org/10.1111/jog.14629. Muche AA, Olayemi OO, Gete YK. Gestational diabetes mellitus increased the risk of adverse neonatal outcomes: A prospective cohort study in Northwest Ethiopia. Midwifery 2020;87:102713. https://doi.org/10.1016/j.midw.2020.102713. Additional Declarations No competing interests reported. Supplementary Files Supplement.docx Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Journal of Diabetes & Metabolic Disorders → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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City","correspondingAuthor":true,"prefix":"","firstName":"Bingjun","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2025-07-01 10:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7019486/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7019486/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s40200-026-01902-x","type":"published","date":"2026-02-17T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86784426,"identity":"d567557c-ec6d-4209-bcaf-7bd0383478a3","added_by":"auto","created_at":"2025-07-15 13:56:30","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176671,"visible":true,"origin":"","legend":"\u003cp\u003eDose-Response Association of Maternal Body Composition with the Risk of Macrosomia in Offspring. Panels A to G represent the following components with their statistical significance: \u003cstrong\u003eA\u003c/strong\u003e: Total body water (TBW) (P for overall = 0.019, P for nonlinearity = 0.027); \u003cstrong\u003eB\u003c/strong\u003e: Intracellular fluid (ICF) (P for overall = 0.030, P for nonlinearity = 0.032); \u003cstrong\u003eC\u003c/strong\u003e: Extracellular fluid (ECF) (P for overall = 0.011, P for nonlinearity = 0.026); \u003cstrong\u003eD\u003c/strong\u003e: Fat-free mass (FFM) (P for overall = 0.021, P for nonlinearity = 0.028); \u003cstrong\u003eE\u003c/strong\u003e: Muscle mass (MM) (P for overall = 0.021, P for nonlinearity = 0.028); \u003cstrong\u003eF\u003c/strong\u003e: Protein (P for overall = 0.031, P for nonlinearity = 0.033); \u003cstrong\u003eG\u003c/strong\u003e: Minerals (P for overall = 0.034, P for nonlinearity = 0.049). Curves depict adjusted odds ratios (ORs; solid lines) with 95% confidence intervals (shaded areas), modeled using restricted cubic splines.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7019486/v1/862408f3ead56556050f05ec.jpeg"},{"id":103251719,"identity":"3731985e-9f87-4f94-bff5-99d79d728188","added_by":"auto","created_at":"2026-02-23 16:11:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1377490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7019486/v1/98b5f033-101a-4532-b476-d3db17f7aca5.pdf"},{"id":86784430,"identity":"289508b9-97b2-48f5-b011-dc307de8bfea","added_by":"auto","created_at":"2025-07-15 13:56:30","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":164607,"visible":true,"origin":"","legend":"","description":"","filename":"Supplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-7019486/v1/c1e768dafd7cb865ee8867d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between maternal body composition during pregnancy and birth weight of offspring in pregnant women with gestational diabetes mellitus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM), defined as hyperglycaemia first detected during pregnancy[1], is a prevalent chronic condition during pregnancy that adversely affects the health of millions of women globally[2,3]. It is widely acknowledged that GDM is correlated with a rise in pregnancy complications, as well as with long - term metabolic risks for both the woman and her offspring. According to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria, the global prevalence of GDM is estimated at 14.0%[4]. The age-standardised prevalence of obesity among adult women aged \u0026ge; 20 years increased from 8.8% (95% CrI 8.5\u0026ndash;9.1) in 1990 to 18.5% (95% CrI 17.9\u0026ndash;19.1) in 2022 globally[5].\u0026nbsp;The rising prevalence of obesity among women suggests that the incidence of GDM will continue to increase, thereby amplifying the likelihood of adverse birth outcomes in offspring. A recent systematic review and meta-analysis has confirmed that, adjusted for confounders, GDM is a risk factor for both macrosomia and large - for - gestational - age (LGA) infants, regardless of whether insulin therapy is used[6]. Macrosomia and LGA are frequently used to measure large infants[7]. Macrosomia and LGA infants are established risk factors for cesarean delivery, birth trauma, and perinatal complications including shoulder dystocia, brachial plexus injury, neonatal fractures, and perinatal asphyxia[8].\u003c/p\u003e\n\u003cp\u003eBeyond GDM, macrosomia is linked to a wide range of factors. Genetic factors, environmental conditions, racial backgrounds, pre-pregnancy diabetes, history of macrosomia in previous deliveries, maternal body mass index (BMI), and parity are all associated with the development of macrosomia[9]. Its occurrence can also be caused by factors such as excessive gestational weight gain (GWG), and gestational age, as well as other reproductive-related factors[10]. While the BMI remains widely utilized as a surrogate measure for adiposity assessment in clinical and epidemiological studies, its validity as a reliable marker of body composition remains contested in contemporary research[11]. Body composition analysis provides a direct structural assessment of physiological tissues (muscle, fat, bone), contrasting with BMI\u0026rsquo;s indirect anthropometric estimation[12]. In the published research, the majority of studies[11,13\u0026ndash;15]focus on the association between the body composition of healthy pregnant women and their offspring\u0026apos;s birth weight or the risk of macrosomia. Specifically, most of these studies are concerned with the relationship between the maternal fat - free mass (FFM), fat mass (FM), total body water (TBW) and the offspring\u0026apos;s birth weight or macrosomia. While perturbations in maternal glucose homeostasis among women with GDM are known to drive significant alterations in body composition, the direct association between maternal body composition parameters in GDM and neonatal birth weight has not been systematically investigated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRelevant simplified models of body composition have been established. Widely - applied models include two - component, three - component, four - component, five - component, and other multi - component models[16]. Among them, the two - component chemical model was established by Behnke et al. in 1942. Based on the differences in the structure and function of human body composition, it divides the body into fat mass and fat - free mass[17].\u0026nbsp;The application of multi - component models in epidemiological research is relatively complex. Among these models, the five - component model of human body composition, proposed by Wang et al. in 1992, is the most influential. Based on this model, the five fundamental components of human body composition have been identified: FM, extracellular fluid, intracellular fluid, minerals, and protein[18].\u003c/p\u003e\n\u003cp\u003eCommonly employed methods for body composition assessment comprise anthropometry, densitometry (including air displacement plethysmography and underwater weighing), and hydrometry (encompassing isotope dilution and bioelectrical impedance analysis (BIA))[19]. Currently, BIA is widely utilized in human body composition research owing to its non-invasive, reliable, and rapid clinical assessment advantages[20]. This technique is employed to evaluate body composition parameters, including fat mass, protein content, total body water, and intracellular/extracellular fluid volumes[21]. Therefore, our study is mainly based on the five - component model to investigate the associations between the body composition of pregnant women with GDM measured by BIA and their offspring\u0026apos;s birth weight, as well as the risk of macrosomia. Thereby providing a scientific rationale for implementing dynamic body composition monitoring and personalized dietary interventions in this population, which may ultimately contribute to risk stratification and mitigation of macrosomia.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\n\u003cp\u003eFrom January 2023 to July 2024, we recruited 1,065 pregnant women with GDM from the Maternal and Child Health Hospital of Longgang District, Shenzhen, China. We conducted a cohort study to follow the birth outcomes of all pregnancies to assess the association between maternal body composition during pregnancy and birth weight of the offspring. A total of 929 women with GDM and their infants were enrolled in the study if they met the following criteria: (a) a singleton pregnancy; (b) maternal age \u0026ge; 18 years at pregnancy; (c) a gestational age between 37 and 42 weeks at delivery; and (d) maternal body composition measurements obtained during pregnancy. Participants were excluded if they (a) had a pre - pregnancy diagnosis of diabetes mellitus, heart disease, hypertension, psychiatric disorders, or were taking glucose-lowering medications, glucocorticoids, diuretics, antiepileptic medications, etc.; (b) have been diagnosed with pregestational diabetes mellitus (PGDM)[22,23]; (c) were given glucose-lowering medications for poor glycemic control during pregnancy; or (d) had missed visits or incomplete data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe 75 - g oral glucose tolerance test (OGTT) was used as a diagnostic method for GDM in all participants at 24-28 weeks of gestation: the glucose thresholds were 5.1, 10.0, and 8.5 mmol/L for fasting, 1 h, and 2 h after oral glucose intake, respectively, and the diagnosis of GDM was made when glucose values reached or exceeded the above criteria at any one of these time points[24\u0026ndash;26]. \u003c/p\u003e\n\u003ch2\u003eMeasurement of maternal body composition\u003c/h2\u003e\n\u003cp\u003eBody composition was assessed using an 8-point tactile electrode BIA apparatus (NQA-Pplus; Sihaihuachen, Beijing, China). The device applies an alternating current \u0026le; 450 \u0026mu;A at seven discrete frequencies: 1, 5, 50, 100, 250, 500, and 1000 kHz. When testing body composition, the operation procedure of the instrument is strictly followed, allowing pregnant women to empty their bowels, take off their shoes and socks, wear a single garment, spray their hands with quick-drying hand sanitizer, step on their feet and hold the corresponding electrodes in their hands, and carry out measurements. The main indicators include: body weight, total body water (TBW), percent total body water (PTBW), intracellular fluid (ICF), percent intracellular fluid (PICF), extracellular fluid (ECF), percent extracellular fluid (PECF), muscle mass (MM), percent muscle mass (PMM), fat free mass (FFM), percent fat free mass (PFFM), fat mass (FM), percent fat mass (PFM), protein, percent protein, minerals and percent minerals, and the percentage of body composition is obtained by dividing the TBW, ICF, ECF, MM, FFM, FM, protein, and minerals by the body weight respectively.\u003c/p\u003e\n\u003ch2\u003eCovariates\u003c/h2\u003e\n\u003cp\u003eUpon enrollment, all pregnant women in this cohort study completed a structured questionnaire capturing periconceptional data, including maternal age, pre-pregnancy BMI, family history of diabetes mellitus, adverse pregnancy history, gestational age at delivery (GAD), parity, gravidity, GWG, and gestational age at body composition measurement. The pre-pregnancy BMI of pregnant women was classified into three categories based on Chinese criteria: underweight (\u0026lt; 18.5 kg/m\u0026sup2;), normal weight (18.5 - 23.9 kg/m\u0026sup2;), and overweight or obese (\u0026ge; 24.0 kg/m\u0026sup2;). According to the \u0026ldquo;standard of recommendation for weight gain during pregnancy period\u0026rdquo; issued by the National Health and Wellness Commission of the People\u0026rsquo;s Republic of China on July 28, 2022, the pattern of weight gain during pregnancy is classified into three categories: insufficient weight gain, appropriate weight gain, and excessive weight gain (Supplement eTable1). Birth weight was further categorised into low birth weight (\u0026lt; 2500g), normal birth weight (2500 - 3999 g) and macrosomia (defined as \u0026ge; 4000 g)[27].\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eContinuous variables were expressed as mean \u0026plusmn; standard deviation (SD), and categorical variables as number (percentage). Univariate linear regression analyses were performed to explore the potential associations between maternal characteristics and offspring birth weight, while univariate logistic regression was used to preliminarily assess their relationships with macrosomia risk. Subsequently, multiple linear regression was employed to assess the associations between offspring birth weight and maternal body composition in GDM, and multiple logistic regression was used to explore the associations between risk of macrosomia and maternal body composition. The multivariate models were adjusted for relevant covariates, which were completely available for the analysis. Possible nonlinear relationships between maternal body composition in GDM and offspring birth weight, and between maternal body composition in GDM and risk of macrosomia were examined using restricted cubic spline (RCS) models, with four knots at 5%, 35%, 65%, and 95%. Additionally, covariates in multivariate model were adjusted to refine the analysis. Subgroup analyses stratified by gravidity (1, 2 and \u0026ge; 3), parity (0, 1 and \u0026ge; 2), BMI (\u0026lt; 18.5.0 kg/m\u0026sup2;, 18.5 - 23.9 kg/m\u0026sup2; and \u0026ge; 24.0 kg/m\u0026sup2;), GWG (insufficient weight gain, appropriate weight gain and excessive weight gain) and pregnancy period (second trimester and third trimester) were performed to assess the stability of the Multiple linear regression results. Data analyses were performed using R software (version 4.4.1), with the significance level set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal of 929 women were enrolled into the study. The characteristics of the participants are presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Body composition assessments were performed at a mean gestational age of 26.33 weeks. The average maternal age was 31.58 years, the average pre - pregnancy BMI was 22.41, the average gestational week of delivery was 38.96 weeks, and the mean GWG was 11.79 kg. Regarding weight gain during pregnancy, 482 (51.88%) had appropriate weight gain, 157 (16.90%) had insufficient weight gain, and 290 (31.22%) gained excessive weight. Of the participants, 388 (41.77%) had a family history of diabetes mellitus and 130 (13.99%) had a history of adverse pregnancy. According to the pre - pregnancy BMI, 7.8% of the participants were underweight, 64.8% were normal weight, 27.4% were overweight or obese. In the pregnancy outcomes for all participating mothers, there were 32 cases (3.44%) of fetal macrosomia, 23 cases (2.48%) of postpartum hemorrhage, and 310 cases (33.37%) of complications, including a scarred uterus.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the Participant Enrolled in the Cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or N (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnant BMI (kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e \u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight (less than 18.5, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal (18.5\u0026ndash;23.9, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e602 (64.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight and Obese (24.0 or higher, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e254 (27.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBirth weight (g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3225.03\u0026thinsp;\u0026plusmn;\u0026thinsp;413.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational age at delivery (wk)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational age at body composition assessment (wk)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGestational weight gain (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.79\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsufficient weight gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (16.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAppropriate weight gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482 (51.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcessive weight gain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290 (31.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily history of diabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e541 (58.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e388 (41.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of abnormal pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e799 (86.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130 (13.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMacrosomia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e897 (96.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePostpartum hemorrhage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906 (97.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eComplication\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e619 (66.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e310 (33.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMode of delivery\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVaginal delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e577 (62.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCesarean delivery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352 (37.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (34.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302 (32.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 times or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e304 (32.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e453 (48.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e365 (39.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (11.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSD: standard deviation, BMI: body mass index, N: number.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInfant\u0026rsquo;s birth weight\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfant\u0026rsquo;s birth weight ranged from 1550 to 4650 g, with a mean birth weight of 3225.03 g. The majority of newborns (94.00%) were classified as normal birth weight, with 24 cases (2.60%) of low birth weight and 32 cases (3.44%) of macrosomia (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Linear regression analyses revealed significant associations between birth weight and gravidity, parity, maternal age, pre-pregnant BMI, GAD, and GWG (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant associations were found between birth weight and family history of diabetes mellitus or history of abnormal pregnancies (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLinear regression analysis of infant\u0026rsquo;s birth weight by maternal characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBirth Weight (g) *\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFamily history of diabetes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3232.41\u0026thinsp;\u003cstrong\u003e\u0026plusmn;\u003c/strong\u003e\u0026thinsp;415.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3214.73\u0026thinsp;\u0026plusmn;\u0026thinsp;412.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-17.68 (-71.67\u0026thinsp;~\u0026thinsp;36.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistory of abnormal pregnancy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3223.14\u0026thinsp;\u0026plusmn;\u0026thinsp;416.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3236.62\u0026thinsp;\u0026plusmn;\u0026thinsp;396.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.47 (-63.29\u0026thinsp;~\u0026thinsp;90.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGravidity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3142.65\u0026thinsp;\u0026plusmn;\u0026thinsp;417.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3240.66\u0026thinsp;\u0026plusmn;\u0026thinsp;401.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98.02 (33.80\u0026thinsp;~\u0026thinsp;162.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 times or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3297.02\u0026thinsp;\u0026plusmn;\u0026thinsp;408.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154.38 (90.27\u0026thinsp;~\u0026thinsp;218.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eParity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3161.84\u0026thinsp;\u0026plusmn;\u0026thinsp;418.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3264.70\u0026thinsp;\u0026plusmn;\u0026thinsp;407.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.86 (46.49\u0026thinsp;~\u0026thinsp;159.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3352.43\u0026thinsp;\u0026plusmn;\u0026thinsp;372.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e190.59 (105.71\u0026thinsp;~\u0026thinsp;275.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.89 (2.58\u0026thinsp;~\u0026thinsp;15.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnant BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.45 (10.31\u0026thinsp;~\u0026thinsp;26.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.92 (92.40\u0026thinsp;~\u0026thinsp;149.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.38 (2.49\u0026thinsp;~\u0026thinsp;14.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBMI: Body Mass Index, GAD: Gestational Age at Delivery, GWG: Gestational Weight Gain.\u003c/p\u003e\n \u003cp\u003e* Birth weight is presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between birth weight of offspring and maternal body composition in GDM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, univariate regression analysis indicated that there was a significant positive correlation between birth weight and maternal body composition indices of TBW, ICF, ECF, FM, PFM, FFM, MM, protein and minerals in GDM patients. However, there was a significant negative correlation between birth weight and PTBW, PICF, PECF, PFFM, PMM, percent protein and percent minerals. The correlation between birth weight and maternal TBW, ICF, ECF, FM, FFM, MM, protein and minerals remained significant and positive after adjustment for covariates.\u003c/p\u003e\n\u003cp\u003eThe subgroup analyses showed that the associations between birth weight and maternal body composition for GDM were generally consistent across the different subgroups based on parity, gravidity, BMI, GWG, and pregnancy period at body composition, except for individual subgroups where no associations were found because of insufficient sample size. In addition, no significant interactions were observed in all subgroups (eTable 2\u0026thinsp;~\u0026thinsp;17 in Supplement).\u003c/p\u003e\n\u003cp\u003eThe RCS model was used to simulate the relationship between birth weight and maternal body composition. No significant nonlinear dose-response associations were found between birth weight and maternal body composition at GDM (eFigure in Supplement).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship of maternal body composition with birth weight in multiple linear regression models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate regression analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultiple linear regression*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.22 (32.51\u0026thinsp;~\u0026thinsp;47.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.58 (26.81\u0026thinsp;~\u0026thinsp;44.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.96 (-17.00 ~ -2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.10 (-4.61\u0026thinsp;~\u0026thinsp;14.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.79 (50.27\u0026thinsp;~\u0026thinsp;75.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.81 (40.52\u0026thinsp;~\u0026thinsp;69.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-18.04 (-29.41 ~ -6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13 (-12.08\u0026thinsp;~\u0026thinsp;18.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106.24 (86.58\u0026thinsp;~\u0026thinsp;125.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.13 (72.06\u0026thinsp;~\u0026thinsp;116.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePECF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-19.42 (-37.23 ~ -1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.99 (0.22\u0026thinsp;~\u0026thinsp;49.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.24 (9.94\u0026thinsp;~\u0026thinsp;18.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.72 (7.76\u0026thinsp;~\u0026thinsp;23.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.37 (3.23\u0026thinsp;~\u0026thinsp;13.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.57 (-9.78\u0026thinsp;~\u0026thinsp;4.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.53 (23.84\u0026thinsp;~\u0026thinsp;35.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.98 (19.54\u0026thinsp;~\u0026thinsp;32.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.53 (-12.67 ~ -2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61 (-3.55\u0026thinsp;~\u0026thinsp;10.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.17 (25.15\u0026thinsp;~\u0026thinsp;37.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.52 (20.66\u0026thinsp;~\u0026thinsp;34.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.98 (-13.47 ~ -2.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45 (-4.09\u0026thinsp;~\u0026thinsp;11.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145.28 (116.31\u0026thinsp;~\u0026thinsp;174.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.81 (93.75\u0026thinsp;~\u0026thinsp;159.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-41.72 (-68.01 ~ -15.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.28 (-27.92\u0026thinsp;~\u0026thinsp;42.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e413.59 (334.86\u0026thinsp;~\u0026thinsp;492.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351.20 (268.82\u0026thinsp;~\u0026thinsp;433.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent minerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-78.07 (-142.34 ~ -13.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146.77 (47.09\u0026thinsp;~\u0026thinsp;246.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eCI: Confidence Interval, TBW: Total Body Water, PTBW: Percent Total Body Water, ICF: Intracellular Fluid, PICF: Percent Intracellular Fluid, ECF: Extracellular Fluid, PECF: Percent Extracellular Fluid, FM: Fat Mass, PFM: Percent Fat Mass, FFM: Fat-Free Mass, PFFM: Percent Fat-Free Mass, MM: Muscle Mass, PMM: Percent Muscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e*Adjusting for maternal age, pre-pregnant BMI, GAD, GWG, gravidity, parity and pregnancy period when body composition was done\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation between macrosomia and maternal body composition in GDM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the univariate analysis, statistically significant risk factors for macrosomia were identified (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e), and these factors, as well as GAD, a variable that has been a risk factor in other studies[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e], were included as covariates in further analyses. Multiple logistic regression showed that ECF increased the risk of macrosomia in GDM (OR:1.39, 95%CI:1.03\u0026ndash;1.90, Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In the RCS model, we found n-shaped associations between the risk of macrosomia and maternal TBW, ICF, ECF, FFM, MM, protein and minerals in women with GDM (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.05, P for nonlinearity\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant nonlinear dose-response associations were found between risk of macrosomia and maternal FM, PTBW, PICF, PECF, PFFM, PMM, percent protein and percent minerals in GDM. (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026gt;\u0026thinsp;0.05, \u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;\u0026gt;\u0026thinsp;0.05, respectively).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRisk factors associated with macrosomia\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGravidity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00 (Reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 times\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.86 (1.01\u0026thinsp;~\u0026thinsp;8.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 times or more\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.07 (1.09\u0026thinsp;~\u0026thinsp;8.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-pregnant BMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11 (1.01\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25 (0.84\u0026thinsp;~\u0026thinsp;1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGWG (kg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (1.02\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval, GAD: Gestational Age at Delivery, GWG: Gestational Weight Gain.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab5\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship of maternal body composition with macrosomia in multiple logistics regression models\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12 (0.98\u0026thinsp;~\u0026thinsp;1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.94\u0026thinsp;~\u0026thinsp;1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.94\u0026thinsp;~\u0026thinsp;1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePICF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (0.85\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eECF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39 (1.03\u0026thinsp;~\u0026thinsp;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePECF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39 (0.99\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.98\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02 (0.92\u0026thinsp;~\u0026thinsp;1.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08 (0.99\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePFFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.95\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09 (0.98\u0026thinsp;~\u0026thinsp;1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05 (0.95\u0026thinsp;~\u0026thinsp;1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.87\u0026thinsp;~\u0026thinsp;2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent Protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15 (0.69\u0026thinsp;~\u0026thinsp;1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.70 (0.83\u0026thinsp;~\u0026thinsp;8.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePercent minerals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.65 (0.64\u0026thinsp;~\u0026thinsp;10.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval, TBW: Total Body Water, PTBW: Percent Total Body Water, ICF: Intracellular Fluid, PICF: Percent Intracellular Fluid, ECF: Extracellular Fluid, PECF: Percent Extracellular Fluid, FM: Fat Mass, PFM: Percent Fat Mass, FFM: Fat-Free Mass, PFFM: Percent Fat-Free Mass, MM: Muscle Mass, PMM: Percent Muscle Mass\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eAdjust: BMI, GAD, GWG, Gravidity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"},{"header":"Discussion","content":"\u003cp\u003eIn this cohort study of Chinese women with singleton pregnancies complicated by GDM, maternal body composition parameters including TBW, ICF, ECF, PECF, FM, FFM, MM, protein, and minerals showed significant positive correlations with neonatal birth weight. Notably, we observed nonlinear n-shaped relationships between specific indices (TBW, ICF, ECF, FFM, MM, protein, and minerals) and macrosomia risk. These associations suggest a dynamic pattern wherein increasing values of these parameters initially correlated with elevated macrosomia risk, followed by paradoxical risk reduction beyond critical thresholds. This biphasic relationship challenges conventional interpretations of linear maternal-fetal growth associations in GDM populations.\u003c/p\u003e\u003cp\u003eBody composition assessment during pregnancy conventionally segments the body into FM and FFM compartments[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consistent with other studies in non-GDM pregnant women, our results showed that birth weight was positively associated with maternal FFM in GDM. A longitudinal study found that maternal FFM assessed via BIA during the second trimester was significantly and independently associated with infant birth weight[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Gernand et al. also reported that maternal measures of FFM at approximately 10 weeks of gestation and weight gains from 20 to 32 weeks are independently associated with higher birth weight[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Maternal FFM exhibits a relatively consistent association with offspring birth weight across studies, while the relationship between FM and birth weight remains inconclusive and subject to ongoing debate. Forsum et al. suggested that maternal FM (both pre-pregnancy and at 32 weeks' gestation) and gestational age at birth likely contribute to increased birth weight. Additionally, their analysis indicated that pre-pregnancy percent body fat (PBF) explained 45% of the variation in birth weight[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our study also found a positive relationship between maternal FM in GDM patients and birth weight. In contrast, Kent et al. demonstrated a positive correlation between birth weight and maternal first-trimester FFM, but found no significant association with FM[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Farah et al. also observed that birth weight correlated with maternal FFM, and not FM at 28 and 37 weeks gestation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The heterogeneity in maternal FM research outcomes could stem from inter-study disparities in (a) sample size ranges, (b) population composition (GDM-diagnosed versus non-GDM individuals), (c) ethnic backgrounds, and (d) measurement timing of FM during gestation.\u003c/p\u003e\u003cp\u003eFFM comprises TBW, protein, and mineral mass[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the human body, TBW consists of ECF and ICF. ECF, accounting for 1/3 of TBW[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], mainly includes tissue interstitial fluid, plasma, lymph, cerebrospinal fluid, and so on. In a longitudinal Italian study utilizing single-frequency tetrapolar bioelectric impedance analysis for pregnancy body composition assessment, second-trimester body water was found to predict birth weight [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A study using the deuterium dilution technique to determine body composition also reported a positive correlation between infant birth weight and maternal TBW and FFM, but not FM[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Fluid intake is positively correlated with body water content[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A Chinese prospective cohort study on fluid intake and hydration status during the third trimester of pregnancy suggests a potential positive linear relationship between the intake of plain water and infant birth weight[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Total fluid intake during the third trimester was inadequate among pregnant women (median\u0026thinsp;=\u0026thinsp;1574 mL), with only 12.1% meeting China\u0026rsquo;s recommended adequate intake of 1700 mL/day[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Conversely, the elements that were least frequently mentioned included protein, minerals, and lean mass[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In our study, maternal TBW, ICF, ECF, PECF, MM, protein, minerals, and percent minerals were found to be positively associated with birth weight in pregnant women with GDM. These results indicate that maternal body FFM, including body water, protein and mineral, are of great significance in maintaining birth weight. We should place increased emphasis on dietary intake of water, protein and minerals among GDM patients.\u003c/p\u003e\u003cp\u003eMaternal hyperglycemia induces fetal hyperglycemia through facilitated glucose transport mediated by glucose transporter 1 (GLUT1) across the placenta. The resulting fetal hyperglycemia stimulates hyperinsulinemia, which promotes excessive anabolic activity, leading to increased adiposity and accelerated fetal growth, ultimately resulting in macrosomia and LGA[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A retrospective study of 43,020 healthy pregnant women found that body FM, FFM, and muscle mass were associated with an increased risk of macrosomia[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the studies from China[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Ireland[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], after adjustment for confounding variables, healthy women in the highest quartile of FFM have a significantly increased risk of macrosomia compared with those in the lowest quartile of FFM. Through logistic regression analysis, our study revealed that only maternal ECF levels in mothers with GDM were significantly and positively associated with an increased risk of macrosomia. We consider whether there is a non-linear relationship between maternal body composition and macrosomia risk. Thus, we employed RCS models to systematically evaluate potential non-linear relationships between maternal body composition and offspring birth weight in women with GDM. TBW, ICF, and ECF each demonstrated a significant N-shaped association with macrosomia risk in GDM. During pregnancy, low plasma volume, ECF, and TBW\u0026mdash;with plasma volume serving as a constituent of both ECF and TBW\u0026mdash;have been linked to fetal growth restriction[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The expansions in ECF and TBW are driven in part by increases in maternal lean tissue and products of conception, alongside plasma volume augmentation[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The placenta functions to support fetal growth by facilitating the delivery of oxygen and nutrients, as well as the removal of waste products[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Thus, high plasma volume may increase placental blood flow, further promoting excessive nutrient supply by the placenta in individuals with GDM. When plasma volume or TBW, ECF, ICF continue to increase, it may trigger a certain compensatory mechanism, which instead reduces the risk of macrosomia. This aligns with the observed n - shaped associations, indicating that the intricate interplay between these body - composition indices (TBW, ECF, ICF) and plasma - volume - mediated placental function critically influences macrosomia risk in GDM, reflecting how varying maternal body - composition levels affect fetal nutrient supply via placental mechanisms.\u003c/p\u003e\u003cp\u003eConsistent with the fluid compartment analyses, the RCS model additionally identified n -shaped relationships for macrosomia risk with maternal FFM, MM, protein, and mineral levels, but not with FM. These maternal body composition measurements in the middle range of the RCS curve are instead associated with the risk of macrosomia in pregnant women with GDM, while those with a low or high maternal body composition have a reduced risk of macrosomia. The observed risk attenuation at higher body composition levels may reflect activation of maternal metabolic compensatory mechanisms. Specifically, exceeding physiological thresholds could trigger adaptive responses that modulate nutrient partitioning between maternal and fetal compartments, potentially counteracting fetal overgrowth. Such nonlinear dynamics emphasize the need to redefine optimal body composition ranges for GDM management, rather than pursuing uniform reductions in maternal nutritional indices.\u003c/p\u003e\u003cp\u003eWe examined in detail the individuals at both extremes of the RCS curve. Most of them exhibited either a low BMI or a BMI at or above overweight levels. Notably, even among those classified as overweight or obese, more than 50% of their body composition was accounted for by FFM or MM. Experimental evidence from animal models demonstrates that maternal swimming exercise during gestation induces reduced birth weight in mouse offspring, with this weight-modulating effect persisting throughout the first two postnatal months[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, regular exercise during pregnancy is associated with appropriate gestational weight gain and consequent optimal infant birth weight\u0026mdash;manifested as reduced incidence of LGA without increased small-for-gestational-age (SGA) risk\u0026mdash;thereby potentially lowering offspring susceptibility to major chronic diseases in later life, including cardiovascular disease, obesity, and diabetes[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These findings collectively suggest that regular maternal exercise may reduce macrosomia risk, potentially through enhancing muscle mass to improve metabolic regulation and mitigate excessive fetal nutrient exposure.\u003c/p\u003e\u003cp\u003eThe n-shaped relationship between maternal mineral content and macrosomia risk in GDM implies threshold effects, wherein optimal mineral levels may reduce macrosomia risk by improving glycemic control. A recent meta-analysis demonstrated that mineral supplementation significantly improves glycemic control, reduces inflammation, and mitigates oxidative stress in GDM[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Calcium, the most abundant mineral in the human body, has been proposed to interact synergistically with vitamin D rather than functioning independently[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. These nutrients may influence pregnancy outcomes by modulating skeletal homeostasis, smooth muscle contractility[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. A randomized controlled trial (RCT) has shown that supplementation of vitamin D and calcium in patients with GDM exerts beneficial effects on glucose metabolism, lipid metabolism, and oxidative stress[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. While this RCT did not examine the impact of combined vitamin D and calcium supplementation on pregnancy outcomes, particularly macrosomia, it can provide certain evidence or conjectures for the nonlinear association between mineral content and the risk of macrosomia in patients with GDM in this study.\u003c/p\u003e\u003cp\u003eThe strengths of this study include being the first to investigate the association between maternal body composition and birth weight specifically in women with GDM, as well as examining potential non-linear relationships between maternal body composition and the risk of macrosomia. Additionally, the prospective design ensured the availability of sufficiently detailed data on maternal and neonatal characteristics. However, several limitations should be acknowledged. Firstly, a multinational study of 115.6\u0026nbsp;million live-born neonates across 15 countries (2000\u0026ndash;2020) reported a median macrosomia prevalence of 9.6% (IQR: 6.4\u0026ndash;13.3%) for birth weight\u0026thinsp;\u0026ge;\u0026thinsp;4000 g[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], with a Northwest Ethiopian cohort further demonstrated a fourfold higher risk of fetal macrosomia in GDM pregnancies compared to non-GDM pregnancies[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In contrast, the relatively low prevalence of macrosomia (3.44%) observed in our study may be attributed to two key factors: (1) the exclusion of GDM patients requiring pharmacological treatment due to suboptimal glycemic control, and (2) possible dietary modifications adopted by GDM mothers following their diagnosis and subsequent nutritional counseling. Secondly, our data are solely derived from a single hospital, which may give rise to some confounding biases. Thirdly, given that our sample size is not particularly large and only includes body composition data from the second and third trimesters of pregnancy, we are unable to investigate the association between changes in body composition pre-pregnancy and the birth weight of the offspring.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAlthough there is a positive correlation between the maternal body composition of pregnant women with GDM and the birth weight of their offspring, the association with the risk of macrosomia is not a simplistic linear relationship. Instead, when the body composition except FM reaches a certain critical threshold, there may be a paradoxical reduction in the risk of macrosomia observed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure of ethical statements:\u003c/strong\u003e The study protocol adhered to the principles of the Declaration of Helsinki and received ethical approval from the Institutional Ethics Committee of Shenzhen Longgang District Maternal and Child Health Hospital (Approval No. LGFYKYXMLL-2024-119) in September 2024. The ethical approval project identification code is KYXMLL-01-CZGC-14-2-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eStatement\u003c/strong\u003e: Written informed consent was voluntarily obtained from all participants prior to the commencement of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003estatement\u003c/strong\u003e: The authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Scientific Research Fund of China Nutrition Society under Grant NO. CNS-YUM2024-121; the Guangdong Basic and Applied Basic Research Foundation under Grant NO. 2023A1515030168; the \u0026ldquo;Master Mentor Plan\u0026rdquo; of Jinan University, Nutrition Professional Instructor Comprehensive Competency Enhancement Practical Program under Grant NO. YDXS2410; and the 2022 Longgang District Medical and Health Technology Program Project under Grant NO. LGWJ2022-(57).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution :\u003c/strong\u003eXY, WLJ, DBJ, CWY and LYX conceived and designed the study; XY, WLJ and DBJ developed the methodology; XY, LJ and ZSY performed formal analysis; XY, LJ, ZSY and LHX curated data; CWY, LYX, ZYF, GFF, XBB, ZHY, LHX, GSX and KMZ conducted investigation; XY and WLJ wrote the original draft; XY, WLJ and DBJ reviewed and edited the manuscript; DBJ and WLJ acquired funding; DBJ, CWY and LYX provided resources; DBJ supervised the research. All authors (XY, WLJ, DBJ, CWY, LYX, LJ, ZSY, LHX, ZYF, GFF, XBB, ZHY, GSX, KMZ) read and approved the final manuscript, agreeing to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement :\u0026nbsp;\u003c/strong\u003eWe are grateful to all relevant staff members, GDM patients, and their children for their contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability :\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author, Bingjun Deng, upon reasonable request. The data are not publicly available due to their containing information that could compromise the privacy of research participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHartling L, Dryden DM, Guthrie A, et al. 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J Obstet Gynaecol Res 2021;47:1016\u0026ndash;22. https://doi.org/10.1111/jog.14629.\u003c/li\u003e\n\u003cli\u003eMuche AA, Olayemi OO, Gete YK. Gestational diabetes mellitus increased the risk of adverse neonatal outcomes: A prospective cohort study in Northwest Ethiopia. Midwifery 2020;87:102713. https://doi.org/10.1016/j.midw.2020.102713.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gestational diabetes mellitus, body composition, birth weight, macrosomia","lastPublishedDoi":"10.21203/rs.3.rs-7019486/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7019486/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eThe relationship between maternal body composition and offspring birth weight remains controversial, and limited research has been conducted in gestational diabetes mellitus (GDM) patients. Therefore, this study aims to explore the association between the body composition of GDM patients and offspring birth weight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003ePregnant women diagnosed with GDM were enrolled and followed until delivery. Maternal body composition was assessed via bioelectrical impedance analysis during pregnancy. Multiple regression analysed associations between maternal body composition and offspring birth weight; restricted cubic spline (RCS) models examined potential nonlinearity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThis cohort study involved 929 pregnant women; 32 newborns were macrosomia.After covariate adjustment, offspring birth weight positively correlated with maternal total body water (TBW), intracellular fluid (ICF), extracellular fluid (ECF), fat mass (FM), fat-free mass (FFM), muscle mass (MM), protein, percent protein, minerals, and percent minerals in mothers with GDM. Multiple logistic regression showed that ECF increased the risk of macrosomia in GDM (OR:1.39, 95%CI:1.03-1.90). In the RCS model, n-shaped associations were found between the risk of macrosomia and maternal TBW, ICF, ECF, FFM, MM, protein, and minerals in GDM patients, while no significant association was observed for maternal FM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions：\u003c/strong\u003eAlthough there is a positive correlation between the maternal body composition of pregnant women with GDM and the birth weight of their offspring, the association with the risk of macrosomia is not a simplistic linear relationship. Instead, when the body composition except FM reaches a certain critical threshold, there may be a reduction in the risk of macrosomia observed.\u003c/p\u003e","manuscriptTitle":"Association between maternal body composition during pregnancy and birth weight of offspring in pregnant women with gestational diabetes mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-15 13:56:25","doi":"10.21203/rs.3.rs-7019486/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c311aa09-e180-4c10-8f0d-91af2d9646fa","owner":[],"postedDate":"July 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:08:18+00:00","versionOfRecord":{"articleIdentity":"rs-7019486","link":"https://doi.org/10.1007/s40200-026-01902-x","journal":{"identity":"journal-of-diabetes-and-metabolic-disorders","isVorOnly":false,"title":"Journal of Diabetes \u0026 Metabolic Disorders"},"publishedOn":"2026-02-17 15:57:55","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-07-15 13:56:25","video":"","vorDoi":"10.1007/s40200-026-01902-x","vorDoiUrl":"https://doi.org/10.1007/s40200-026-01902-x","workflowStages":[]},"version":"v1","identity":"rs-7019486","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7019486","identity":"rs-7019486","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}