ABSTRACT
Background
Gestational diabetes is associated with unfavorable body fat distribution in offspring. However, less is known about the effects across the range of maternal gestational glycemia on offspring abdominal adiposity (AA) in infancy and early childhood.
Objectives
This study determined the association between gestational glycemia and offspring AA measured by MRI in the neonatal period and during the preschool years.
Methods
Participants were mother–offspring pairs from the GUSTO (Growing Up in Singapore Towards healthy Outcomes) prospective cohort study. Children who underwent MRI within 2 wk postdelivery (n = 305) and/or at preschool age, 4.5 y (n = 273), and whose mothers had a 2-h 75-g oral-glucose-tolerance test (OGTT) at 26–28 weeks of gestation were included. AA measured by adipose tissue compartment volumes—abdominal superficial (sSAT), deep subcutaneous (dSAT), and internal (IAT) adipose tissue—was quantified from MRI images.
Results
Adjusting for potential confounders including maternal prepregnancy BMI, each 1-mmol/L increase in maternal fasting glucose was associated with higher SD scores for sSAT (0.66; 95% CI: 0.45, 0.86), dSAT (0.65; 95% CI: 0.44, 0.87), and IAT (0.64; 95% CI: 0.42, 0.86) in neonates. Similarly, each 1-mmol/L increase in 2-h OGTT glucose was associated with higher neonatal sSAT (0.11; 95% CI: 0.03, 0.19) and dSAT (0.09; 95% CI: 0.00, 0.17). These associations were stronger in female neonates but only persisted in girls between fasting glucose, and sSAT and dSAT at 4.5 y.
Conclusions
A positive association between maternal glycemia and neonatal AA was observed across the whole range of maternal mid-gestation glucose concentrations. These findings may lend further support to efforts toward optimizing maternal hyperglycemia during pregnancy. The study also provides suggestive evidence on sex differences in the impact of maternal glycemia, which merits further confirmation in other studies.
This trial was registered at clinicaltrials.gov as NCT01174875.
Keywords: maternal glucose, mid-gestation, pediatrics, offspring abdominal adipose tissue compartment, early infancy
Introduction
Greater abdominal adiposity (AA) is an independent risk factor for adverse cardiometabolic outcomes in adulthood (1). As in adults, AA in older children and adolescents appears more strongly related to insulin resistance, cardiovascular, and diabetes risk factors than total body fat (2, 3). Most previous studies used waist circumference (WC) as a surrogate marker of AA (4, 5). South Asians have greater AA and are more insulin resistant than Caucasians of similar BMI (4). This thin-fat phenotype is present already at birth (5). Moreover, studies tracking AA in children observed that children in certain WC and waist-to-hip ratio categories—<25th, 25–75th, and >75th percentile—at 7 y of age remained in the same categories at 15 y of age (6). Concomitantly with the increasing prevalence of childhood obesity and the prevalence of the metabolic syndrome among children and adolescents, it is important to better understand early life factors influencing AA in order to tackle the emerging epidemic of type 2 diabetes, especially in the young.
Animal studies demonstrate that exposure to hyperglycemia in utero results in fetal programming toward a diabetes-prone phenotype in the offspring (7). Infants born to mothers with hyperglycemia are likely to have greater adiposity already at birth independent of infant's birth weight (BW) and maternal obesity. Human epidemiological studies suggest that the association between maternal gestational glucose concentrations and offspring's risk of being large-for-gestational-age and excessive adiposity [>90th percentile of body fat measured by air displacement plethysmography or skinfold thickness (SFT)] is a continuum even for glucose concentrations below diagnostic thresholds for gestational diabetes mellitus (GDM) (8–10). These early-life changes may increase offspring susceptibility to the development of obesity and adverse metabolic risks, and seem to persist from childhood until adulthood (11, 12). There is limited information on the effects of maternal hyperglycemia during pregnancy on offspring AA at birth measured by MRI. Moreover, as far as we know no previous studies have explored these associations longitudinally from early infancy to the preschool years.
Emerging data suggest that the impact of an adverse in utero environment on offspring health may have sex-specific effects, such as maternal overnutrition on offspring adiposity. Potential biological mechanisms include fetal hyperinsulinemia resulting from maternal hyperglycemia which facilitates the conversion of glucose into free fatty acid. This leads to storage of excess glucose as glycogen and fat deposition in the female fetus. On the other hand, in the male fetus, endogenous insulin acts as the dominant growth factor which leads to faster growth in utero than fat deposition (13–15).
However, findings from such studies have been inconsistent and conducted mainly in Western populations (12, 16–21) with sparse data in Asians, a high-risk population for metabolic diseases which forms 60% of the global population. To address these research gaps, this study aimed to determine associations between maternal glycemic status during pregnancy and offspring AA as measured by MRI in the neonatal period and in the preschool years. Further, we investigated whether such associations differed by offspring sex.
Methods
The study was based on mother–offspring pairs from the GUSTO (Growing Up in Singapore Towards healthy Outcomes) study (NCT01174875), a prospective birth cohort study in Singapore (22). Pregnant women aged ≥18 y were recruited between June 2009 and September 2010 during the first trimester of pregnancy (<14 weeks of gestation based on dating ultrasound scans) from 2 public maternity units in Singapore: KK Women's and Children's Hospital and National University Hospital. Pregnant women had homogenous ethnicity, i.e., same ethnicity; Chinese, Malay, or Indian as their partners, and parents on both sides.
Details of the study have been presented previously (23). In total, 333 healthy neonates born at ≥34 weeks of gestation with BW ≥ 2000 g had abdominal adipose tissue compartment (AAT) volumes quantified using MRI images performed within 2 wk after delivery. Nineteen neonates born at <37 completed weeks of gestation and 9 neonates of mothers with no oral-glucose-tolerance test (OGTT) performed were excluded (Supplemental Figure 1). A total of 305 mother–neonate pairs remained for analysis in this study. Eight neonates in this study were born from in vitro fertilization pregnancy. One hundred and nineteen children who attended the neonatal MRI visit came back for 4.5-y MRI visits. Further, an additional 154 children born at term with available maternal OGTT participated in this visit. A total of 273 children had AAT volumes quantified at preschool age, 4.5 y (Supplemental Table 1).
Ethics
This study was approved by the Institutional Review Board of the Singapore National Healthcare Group and the Central Institutional Review Board of SingHealth. Parents of the neonates gave voluntary written informed consent.
Maternal and infant characteristics
Demographic data, lifestyle, obstetric, and medical history of the pregnant women were collected at antenatal visits at 11–12 and 26–28 weeks of gestation using interviewer-administered questionnaires. Self-reported prepregnancy weights of mothers were recorded.
Maternal OGTT
Pregnant women underwent a 2-h 75-g OGTT at 26–28 weeks of gestation. Plasma glucose concentrations were measured by a glucose oxidase method using the Advia 2400 Chemistry system (Siemens Medical Solutions Diagnostics) and Beckman LX20 Pro analyzer (Beckman Coulter). GDM was diagnosed using 1999 WHO criteria: ≥7.0 mmol/L for fasting plasma glucose (FPG) concentrations or ≥7.8 mmol/L for 2-h plasma glucose (2hPG) concentrations (24). Mothers diagnosed as having GDM (n = 46) were treated according to clinical protocols at KK Women's and Children's Hospital and National University Hospital. Of 46 GDM mothers, 37 (80.4%) were treated with diet modification and 6 (13.0%) were on insulin. Information on treatment of GDM for 3 mothers (6.5%) was not available.
MRI acquisition and quantification of AATs
MRI of neonates was performed by a GE Signa HDxt 1.5T MR scanner (GE Healthcare) within 2 wk after delivery. AAT volumes were quantified from images from neonatal MRI scans. The details have been described previously (23). The AAT was categorized into superficial (sSAT), deep subcutaneous (dSAT), and internal (IAT) adipose tissue. Neonatal MRI images were initially processed by an in-house semiautomated quantitative analysis algorithm using MATLAB 7.13 software (MathWorks Inc.) followed by manual optimization by 2 trained analysts who were blinded to all subject information. The mean interobserver CVs were 1.6% for sSAT, 3.2% for dSAT, and 2.1% for IAT. The mean intraobserver CVs were 0.9%, 2.1%, and 4.0% for sSAT, dSAT, and IAT, respectively.
MRI was also performed when children were ∼4.5 y using a 3T MR scanner (Siemens Skyra, VE11A). Axial images with a 5-mm slice thickness and in-plane resolution of 0.94 × 0.94 mm were acquired using a water-suppressed half-Fourier-acquired single-shot turbo spin echo sequence (repetition time = 1000 ms, echo time = 95 ms) from the top of the liver to the top of the sacrum to be consistent with the definition of the abdominal region for the neonatal MRI. Abdominal subcutaneous adipose tissue (SAT) and IAT compartments were segmented from the abdominal MRI images using a fully automated graph theoretic segmentation algorithm (25, 26) developed in MATLAB R2016 software (MathWorks Inc.). The SAT compartment was then subclassified into dSAT and sSAT by a boundary being manually drawn along the fascial plane by a trained analyst who was blinded to participant information. The volumes of each fat compartment were computed by summing the voxels and multiplying by the image resolution. The details of the MRI scans and the image analysis for 4.5-y-old children have been described elsewhere (26). The MATLAB software used for image analyses at 4.5 y was an upgraded version of the one used at the neonatal period. This upgraded MATLAB software was applied to MRI images of all children at the 4.5-y follow-up visit.
Statistical analysis
Multivariable regression analyses were used to examine the associations of maternal glycemia with the offspring's sSAT, dSAT, and IAT. Maternal glucose concentrations were considered as both continuous (FPG or 2hPG) and categorical (GDM status) variables. The primary outcome variables, AAT compartment volumes, were converted to SD scores for better comparison of magnitude of differences among the 3 compartments and at the 2 different time points. Covariates were controlled for based on prior knowledge from the literature about factors that might confound the associations between maternal glycemia and offspring adiposity. For the neonatal period, ethnicity, maternal educational attainment, tobacco exposure during pregnancy (27), parity, maternal age at recruitment, prepregnancy BMI, neonatal sex, and neonatal age on MRI day were adjusted in the regression analysis. Maternal gestational weight gains and gestational age (GA) at delivery were also adjusted in the regression models as sensitivity analyses to test the robustness of the associations. At 4.5 y, models were adjusted for ethnicity, maternal educational attainment, maternal age at recruitment, child's sex, and child's age on MRI day. BW was also included as a covariate for taking into account the adiposity of the child at birth. Because BW is highly correlated with maternal BMI and also on the causal pathway between maternal BMI and childhood adiposity, maternal height was included in the models to account for maternal size. Because the interactions were significant between FPG and sex for 2 of the 3 AAT compartments, stratified analyses were performed. Interactions between maternal prepregnancy BMI groups (28) and glycemia were also tested to determine if the associations between maternal glycemia and offspring AAT outcomes were modified by the amount of maternal adiposity. All statistical analyses used SPSS Statistics for Windows, version 21.0 (IBM Corp.).
Results
Supplemental Figure 1 shows the flowchart of this study. Table 1 shows characteristics of mothers and offspring divided by sex. A total of 305 mother–neonate pairs were included in this study: 135 Chinese (44.3%), 115 Malay (37.7%), and 55 Indian (18.0%) mothers. In general, mothers of male and female neonates had similar characteristics except that a greater proportion of mothers of female neonates were nulliparous. There were 163 males (53.4%) and 142 females (46.6%). Female neonates had greater sSAT and dSAT despite having lower BW and shorter birth length. They were born at marginally longer GA than male neonates. Characteristics of mothers and children who participated in 4.5-y visits between boys and girls were similar to those of children who attended during the neonatal period (Table 1). Most of the characteristics of children who attended both neonatal and 4.5-y MRI visits were also similar to those of children who attended during the neonatal period. However, a greater proportion of mothers of boys had higher educational attainment and girls had greater AAT in all 3 compartments. Supplemental Table 1 presents characteristics of participants compared with nonparticipants in this study. In this study, there were relatively fewer Chinese and more Malay mothers than among those who did not participate. A lower proportion of mothers in this study had university or higher education and no tobacco exposure. They were marginally younger, had higher prepregnancy BMI, higher FPG, and lower 2hPG than nonparticipants. Neonates in this study had higher BW (80 g) and marginally longer GA than neonates who did not participate in the study.
TABLE 1.
Maternal and offspring characteristics among participants in the GUSTO (Growing Up in Singapore Towards healthy Outcomes) study stratified by offspring sex, at the neonatal period and preschool age1
| Neonatal period | Preschool age | |||||||
|---|---|---|---|---|---|---|---|---|
| All (n = 305) | Male (n = 163) | Female (n = 142) | P | All (n = 273) | Boys (n = 124) | Girls (n = 149) | P | |
| Maternal characteristics | ||||||||
| Ethnicity | 0.523 | 0.548 | ||||||
| Chinese | 135 (44.3) | 70 (42.9) | 65 (45.8) | 139 (50.9) | 63 (50.8) | 76 (51.0) | ||
| Malay | 115 (37.7) | 66 (40.5) | 49 (34.5) | 81 (29.7) | 40 (32.3) | 41 (27.5) | ||
| Indian | 55 (18.0) | 27 (16.6) | 28 (19.7) | 53 (19.4) | 21 (16.9) | 32 (21.5) | ||
| Mother highest education groups | 0.386 | 0.322 | ||||||
| Below secondary | 129 (43.0) | 63 (39.4) | 66 (47.1) | 102 (37.8) | 44 (36.4) | 58 (38.9) | ||
| GCE, ITE, diploma | 111 (37.0) | 64 (40.0) | 47 (33.6) | 93 (34.4) | 38 (31.4) | 55 (36.9) | ||
| University and above | 60 (20.0) | 33 (20.6) | 27 (19.3) | 75 (27.8) | 39 (32.2) | 36 (24.2) | ||
| Parity | 0.023 | 0.033 | ||||||
| Nulliparous | 123 (40.3) | 56 (34.4) | 67 (47.2) | 103 (37.7) | 38 (30.6) | 65 (43.6) | ||
| Multiparous | 182 (59.7) | 107 (65.6) | 75 (52.8) | 170 (62.3) | 86 (69.4) | 84 (56.4) | ||
| Maternal tobacco exposure | 1.000 | 0.714 | ||||||
| No exposure | 113 (39.6) | 61 (39.6) | 52 (39.7) | 133 (52.0) | 62 (53.0) | 71 (51.1) | ||
| Exposed with cotinine concentration < level of detection | 90 (31.6) | 49 (31.8) | 41 (31.3) | 74 (28.9) | 35 (29.9) | 39 (28.2) | ||
| Exposed with cotinine concentration < 14 ng/mL | 71 (24.9) | 38 (24.7) | 33 (25.2) | 41 (16.0) | 18 (15.4) | 23 (16.6) | ||
| Exposed with cotinine concentration > 14 ng/mL | 11 (3.9) | 6 (3.9) | 5 (3.8) | 8 (3.1) | 2 (1.7) | 6 (3.1) | ||
| Mother age, y | 30 ± 5 | 30 ± 5 | 29 ± 6 | 0.407 | 31 ± 5 | 30 ± 5 | 31 ± 5 | 0.457 |
| Mother prepregnancy BMI, kg/m2 | 23.3 ± 5.1 | 23.0 ± 4.7 | 23.7 ± 5.6 | 0.310 | 23.2 ± 4.6 | 22.6 ± 4.2 | 23.6 ± 4.8 | 0.083 |
| Fasting plasma glucose, mmol/L | 4.4 ± 0.6 | 4.4 ± 0.6 | 4.4 ± 0.6 | 0.625 | 4.3 ± 0.4 | 4.3 ± 0.4 | 4.3 ± 0.4 | 0.802 |
| 2-h OGTT glucose, mmol/L | 6.3 ± 1.5 | 6.4 ± 1.6 | 6.2 ± 1.4 | 0.316 | 6.3 ± 1.3 | 6.3 ± 1.3 | 6.4 ± 1.4 | 0.589 |
| Offspring characteristics | ||||||||
| Macrosomia | 0.127 | 1.000 | ||||||
| No | 298 (97.7) | 157 (96.3) | 141 (99.3) | 271 (99.3) | 123 (99.2) | 148 (99.3) | ||
| Yes | 7 (2.3) | 6 (3.7) | 1 (0.7) | 2 (0.7) | 1 (0.8) | 1 (0.7) | ||
| Size at birth | 0.917 | 0.153 | ||||||
| Large-for-gestational-age | 52 (17.0) | 28 (17.2) | 24 (16.9) | 44 (16.1) | 18 (14.5) | 26 (17.4) | ||
| Small-for-gestational-age | 36.0 (11.8) | 18 (11.0) | 18 (12.7) | 27 (9.9) | 8 (6.5) | 19 (12.8) | ||
| Birth weight, kg | 3.1 ± 0.4 | 3.2 ± 0.4 | 3.1 ± 0.4 | 0.023 | 3.1 ± 0.4 | 3.2 ± 0.3 | 3.1 ± 0.4 | 0.105 |
| Birth length, cm | 48.6 ± 2.0 | 48.8 ± 2.0 | 48.3 ± 2.1 | 0.032 | 48.6 ± 1.9 | 48.8 ± 1.8 | 48.5 ± 1.9 | 0.295 |
| Gestational age, wk | 38.9 ± 1.0 | 38.8 ± 1.0 | 39.1 ± 1.0 | 0.035 | 39.0 ± 1.0 | 38.8 ± 1.0 | 39.1 ± 1.0 | 0.003 |
| Age on the day of MRI at early infancy, d | 10 ± 3 | 10 ± 3 | 10 ± 3 | 0.408 | — | — | — | |
| Age on the day of MRI at preschool age, y | — | — | — | 4.6 ± 0.1 | 4.6 ± 0.1 | 4.6 ± 0.1 | 0.413 | |
| Weight at age 4.5 y MRI, kg | — | — | — | — | 17.6 ± 3.0 | 17.7 ± 3.0 | 17.5 ± 3.1 | 0.550 |
| Height at age 4.5 y MRI, cm | — | — | — | — | 105.5 ± 4.1 | 105.6 ± 4.5 | 105.4 ± 4.1 | 0.714 |
| BMI at age 4.5 y MRI, kg/m2 | — | — | — | — | 15.7 ± 1.8 | 15.8 ± 1.8 | 15.7 ± 1.9 | 0.546 |
| sSAT, mL | 79.40 ± 21.67 | 75.67 ± 19.11 | 83.68 ± 23.63 | 0.001 | 337.77 ± 160.84 | 310.35 ± 127.09† | 377.84 ± 221.19† | <0.001 |
| dSAT, mL | 13.66 ± 5.64 | 12.78 ± 5.15 | 14.67 ± 6.02 | 0.003 | 111.25 ± 105.69 | 85.23 ± 81.36† | 136.49 ± 131.70† | <0.001 |
| IAT, mL | 23.10 ± 7.51 | 23.13 ± 7.69 | 23.06 ± 7.33 | 0.934 | 177.32 ± 73.99 | 185.60 ± 171.53† | 171.53 ± 71.18† | 0.161 |
Values are n (%) for categorical variables or mean ± SD for continuous variables unless otherwise stated. P values are based on between-group comparisons of offspring sex using ANOVA for continuous variables and χ2-test for categorical variables. †Data shown are median (IQR) and P values were based on the nonparametric Mann–Whitney U test. Abbreviations: dSAT, abdominal deep subcutaneous adipose tissue; GCE, General Certificate of Education; IAT, abdominal internal adipose tissue; ITE: Institute of Technical Education; OGTT, oral-glucose-tolerance test; sSAT, abdominal superficial subcutaneous adipose tissue.
Maternal glycemic status and AA in early infancy
Table 2 shows the positive associations between maternal glycemia as a continuous variable and neonatal AA. Maternal FPG and 2hPG were positively associated with AA in the neonates. Each 1-mmol/L increment in FPG was associated with increases in sSAT, dSAT, and IAT: differences in SD scores, β (95% CI), of 0.66 (0.45, 0.86), 0.65 (0.44, 0.87), and 0.64 (0.42, 0.86), respectively. Similarly, each 1-mmol/L increment in 2hPG was associated with greater sSAT and dSAT, by 0.11 (0.03, 0.19) and 0.09 (0.00, 0.17), respectively (Table 2). We also observed that the aforementioned associations varied by offspring sex (Table 2). Interaction terms between maternal glucose and sex were significant for 4 of the 6 associations examined between FPG/2hPG and sSAT/dSAT/IAT (Table 2). The interaction (maternal glycemia and sex) coefficients in the regression models showed the slope difference in these associations between the offspring sexes; the SD slope differences (95% CIs) in the associations between maternal FPG and neonatal AAT volumes between male and female neonates were −0.59 (−0.99, −0.19) for sSAT, −0.45 (−0.88, −0.03) for dSAT, and −0.33 (−0.78, 0.11) for IAT; they were −0.23 (−0.38, −0.08) for sSAT, −0.15 (−0.31, 0.02) for dSAT, and −0.20 (−0.36, −0.04) for IAT for the associations between maternal 2hPG and neonatal AAT volumes (Figure 1).
TABLE 2.
Changes in SD scores of AAT volumes per 1-mmol/L increment in maternal glucose concentrations in mid-gestation at the neonatal period in mother–neonate pairs1
| FPG, mmol/L | 2hPG, mmol/L | |||||
|---|---|---|---|---|---|---|
| sSAT | dSAT | IAT | sSAT | dSAT | IAT | |
| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | |
| All (n = 305) | 0.66 (0.45, 0.86) | 0.65 (0.44, 0.87) | 0.64 (0.42, 0.86) | 0.11 (0.03, 0.19) | 0.09 (0.00, 0.17) | 0.07 (−0.02, 0.15) |
| P value2 | <0.001 | <0.001 | <0.001 | 0.009 | 0.042 | 0.135 |
| Male (n = 163) | 0.23 (−0.03, 0.49) | 0.25 (−0.06, 0.57) | 0.38 (0.02, 0.74) | 0.01 (−0.10, 0.07) | 0.01 (−0.10, 0.09) | 0.05 (−0.16, 0.06) |
| P value3 | 0.088 | 0.110 | 0.036 | 0.736 | 0.885 | 0.350 |
| Female (n = 142) | 0.93 (0.62, 1.23) | 0.89 (0.59, 1.19) | 0.79 (0.52, 1.06) | 0.27 (0.12, 0.41) | 0.20 (0.05, 0.34) | 0.20 (0.07, 0.33) |
| P value3 | <0.001 | <0.001 | <0.001 | <0.001 | 0.008 | 0.003 |
| P-interaction | 0.004 | 0.037 | 0.139 | 0.003 | 0.075 | 0.017 |
β coefficients shown are differences in SD scores (95% CIs) of AAT volumes (mL) with each 1-mmol/L increment in maternal glucose concentrations: FPG or 2hPG. P values were determined with the use of multivariable regression models. AAT, abdominal adipose tissue compartment; dSAT, abdominal deep subcutaneous adipose tissue; FPG, fasting plasma glucose; IAT, abdominal internal adipose tissue; sSAT, abdominal superficial subcutaneous adipose tissue; 2hPG, 2-h plasma glucose.
Models adjusted for ethnicity, parity, maternal age, maternal education, tobacco exposure, prepregnancy BMI, age on MRI day, and neonatal sex.
Models adjusted for ethnicity, parity, maternal age, maternal education, tobacco exposure, prepregnancy BMI, and age on MRI day.
FIGURE 1.
Scatterplots of abdominal adipose tissue compartment volumes in relation to maternal mid-gestation glucose concentrations stratified by sex. (A) Associations between maternal FPG concentrations and neonatal adipose tissue compartment volumes stratified by sex. P values for interaction between maternal mid-gestation FPG concentrations and sex on neonatal adipose tissue compartment volumes were 0.004, 0.037, and 0.003 for sSAT, dSAT, and IAT, respectively. The slope differences SD scores (95% CIs) are −0.59 (−0.99, −0.19) for sSAT, −0.45 (−0.88, −0.03) for dSAT, and −0.33 (−0.78, 0.11) for IAT. (B) Associations between maternal 2-h OGTT glucose concentrations and neonatal adipose tissue compartment volumes stratified by sex. P values for interaction between maternal mid-gestation 2-h OGTT glucose concentrations and sex on neonatal adipose tissue compartment volumes were 0.003, 0.075, and 0.017 for sSAT, dSAT, and IAT, respectively. The slope differences SD scores (95% CIs) are −0.23 (−0.38, −0.08) for sSAT, −0.15 (−0.31, 0.02) for dSAT, and −0.20 (−0.36, −0.04) SD for IAT. dSAT, abdominal deep subcutaneous adipose tissue; FPG, fasting plasma glucose; IAT, abdominal internal adipose tissue; OGTT, oral-glucose-tolerance test; sSAT, abdominal superficial subcutaneous adipose tissue.
Stronger associations were seen in female neonates; each 1-mmol/L increase in FPG was associated with higher AAT SD scores, by 0.93 (0.62, 1.23) in sSAT, 0.89 (0.59, 1.19) in dSAT, and 0.79 (0.52, 1.06) in IAT. In male neonates, the association between FPG and IAT was significant but weaker than the associations in female neonates (Table 2). Likewise, higher 2hPG was associated with greater AAT SDs only in female neonates; each 1-mmol/L increase in 2hPG was associated with increases of 0.27 (0.12, 0.41), 0.20 (0.05, 0.34), and 0.20 (0.07, 0.33) in sSAT, dSAT, and IAT, respectively. Associations between 2hPG and AAT appeared to be weaker than those between FPG and AAT. By contrast, among male neonates, no significant associations were observed between 2hPG and neonatal AAT volumes. Female neonates appeared to be more sensitive to maternal glycemia than did male neonates (Figure 1).
The aforementioned associations became stronger when all models (for both FPG and 2hPG) were further adjusted for maternal gestational weight gain groups and GA at delivery (Supplemental Table 2). Also, the association between 2hPG and IAT for the whole cohort became significant (Supplemental Table 2).
There were no interactions between maternal prepregnancy BMI groups and glycemia (both FPG and 2hPG) on associations between maternal glycemia and neonatal AAT (all P values ≥ 0.05).
Maternal glycemic status and AA at preschool age (4.5 y)
There was a substantial increase in AAT in the preschool years compared with the neonatal period (n = 119). The mean ± SD magnitude of change in AAT was 4.4 ± 2.8-fold for sSAT, 11.5 ± 11.9-fold for dSAT, and 8.2 ± 4.6-fold for IAT. Compared with a 4.7 ± 1.0-fold change in weight, the increase in dSAT and IAT was disproportional over the first 4.5 y. In the early neonatal period, visceral adipose tissue (VAT) or intraperitoneal fat within the IAT such as mesenteric fat (fat around the intestines, liver, and pancreas) was minimal compared with retro-peritoneal fat. However, there was a substantial increase in intra-abdominal VAT within the IAT at 4.5 y (Figure 2).
FIGURE 2.
Original abdominal water-suppressed MRI images (left column) and segmented images (right column) at the neonatal period and preschool years. In the segmented images, each abdominal adipose tissue compartment is color-coded: red denotes the superficial subcutaneous tissue, green denotes the deep subcutaneous tissue, and blue denotes the IAT at neonatal period and VAT at preschool years. There was a substantial increase in abdominal adipose tissue for all 3 compartments during the preschool years compared with the neonatal period. At the neonatal period (A, B), the intra-peritoneal fat within the IAT such as mesenteric fat (fat around the intestines, liver, and pancreas) was minimal compared with retro-peritoneal fat but it increased substantially during the preschool years (C, D); it is indicated by a yellow arrow. IAT, abdominal internal adipose tissue; VAT, visceral adipose tissue.
Adjusting for ethnicity, maternal age, maternal education, maternal height, BW, neonatal sex, and neonatal age on MRI day, maternal FPG and 2hPG showed no association with a child's AAT volumes (Table 3). However, interaction terms were significant between maternal FPG and sex on all three AAT compartment volumes of the child at 4.5 y. Therefore, stratified analyses were performed and the associations were present only in girls between FPG and SAT; per 1-mmol/L increase in maternal glucose, SD scores (95% CIs) were 0.47 (0.04, 0.89) and 0.47 (0.04, 0.90) higher for sSAT and dSAT, respectively (Table 3). However, no associations were observed between 2hPG and AA of preschool children among girls.
TABLE 3.
Changes in SD scores of AAT volumes per 1-mmol/L increment in maternal glucose concentrations during the preschool years in mother–child pairs1
| FPG, mmol/L | 2hPG, mmol/L | |||||
|---|---|---|---|---|---|---|
| sSAT | dSAT | IAT | sSAT | dSAT | IAT | |
| β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | β (95% CI) | |
| All (n = 273) | 0.22 (−0.07, 0.51) | 0.20 (−0.09, 0.49) | 0.13 (−0.17, 0.42) | 0.03 (−0.06, 0.12) | 0.03 (−0.06, 0.17) | 0.00 (−0.09, 0.09) |
| P value2 | 0.140 | 0.178 | 0.393 | 0.558 | 0.542 | 0.963 |
| Male (n = 124) | −0.11 (−0.50, 0.27) | −0.16 (−0.55, 0.22) | −0.14 (−0.52, 0.24) | −0.04 (−0.15, 0.08) | −0.04 (−0.16, 0.07) | −0.02 (−0.13, 0.10) |
| P value3 | 0.563 | 0.397 | 0.480 | 0.514 | 0.448 | 0.743 |
| Female (n = 149) | 0.47 (0.04, 0.89) | 0.47 (0.04, 0.90) | 0.33 (−0.11, 0.76) | 0.08 (−0.06, 0.21) | 0.08 (−0.05, 0.22) | 0.03 (−0.10, 0.17) |
| P value3 | 0.031 | 0.032 | 0.138 | 0.258 | 0.226 | 0.648 |
| P-interaction | 0.021 | 0.012 | 0.016 | 0.138 | 0.114 | 0.314 |
β coefficients shown are differences in SD scores (95% CIs) of AAT volumes (mL) with each 1-mmol/L increment in maternal glucose concentrations: FPG or 2hPG. P values were determined with the use of multivariable regression models. AAT, abdominal adipose tissue compartment; dSAT, abdominal deep subcutaneous adipose tissue; FPG, fasting plasma glucose; IAT, abdominal internal adipose tissue; sSAT, abdominal superficial subcutaneous adipose tissue; 2hPG, 2-h plasma glucose.
Models adjusted for ethnicity, maternal age, maternal education, maternal height, age on MRI day, birth weight, and neonatal sex.
Models adjusted for ethnicity, maternal age, maternal education, maternal height, age on MRI day, and birth weight.
Maternal GDM status and AA
Maternal GDM status was characterized based on WHO 1999 criteria. Supplemental Table 3 shows characteristics of participants by GDM status. A higher proportion of mothers with GDM had lower tobacco exposure. They were older and had higher BMI and higher glucose concentrations (both FPG and 2hPG). AAT volumes were not different across the whole cohort. Mean ± SD BW and the proportion of neonates with macrosomia in GDM compared with non-GDM mothers were similar, i.e., 3.14 ± 0.39 compared with 3.18 ± 0.50 kg and 2.3% compared with 2.2%, respectively. However, sex modified the associations between maternal GDM status and child's AAT (P-interaction = 0.011 for both sSAT and IAT). In the analyses stratified by sex of children, associations between sSAT and IAT in the neonatal period were significantly greater for female neonates who were born from GDM pregnancies than for those born from pregnancies uncomplicated by GDM; differences in SD scores (95% CIs) of AAT were 0.78 (0.11, 1.45) and 0.76 (0.17, 1.35), respectively. The differences were greater when models were further adjusted for maternal gestational weight gain and GA at delivery; SD score differences (95% CIs) were 0.97 (0.28, 1.67) for sSAT and 0.86 (0.23, 1.48) for IAT. There was no association between maternal GDM status and AAT when children were of preschool age.
Discussion
This prospective multiethnic Asian study with a large number of longitudinal MRI measurements at the neonatal period and preschool age reports on associations between maternal gestational glycemia and offspring AA. A positive association between maternal gestational glycemia and AAT of neonates measured by MRI was observed during the first 2 wk of life. These associations were continuous across the whole range of both FPG and 2hPG concentrations and independent of maternal prepregnancy BMI. Female neonates were particularly susceptible to maternal glycemia and more prone to have higher AAT, and this persisted in girls for sSAT and dSAT during the preschool years independently of maternal size and child's BW. sSAT is suggested to be metabolically different in Asians (29). Ethnic differences in adipose tissue partitioning were observed in a study on adipose tissue volumes measured by MRI comparing British and Indian neonates. Indian neonates had greater AAT in all 3 compartments and significantly lower nonabdominal SAT (such as SAT from the head, chest, pelvis, and extremities) at birth than had British neonates. dSAT has been studied only recently. dSAT is suggested to be strongly related to insulin resistance and cardiometabolic risk factors in a similar manner to VAT (30–32).
The continuous positive association observed between maternal glycemia and neonatal AAT in this study is generally consistent with previous findings based on BW and SFT measurements in the GUSTO cohort (8), as well as with findings in the HAPO (Hyperglycemia and Adverse Pregnancy Outcome) study (9), which both demonstrated that each 1-SD increase in maternal mid-gestation glycemia was associated with increasing odds of excessive neonatal adiposity defined by large-for-gestational-age, >90th percentile of sum of SFT, or percentage fat (8, 9). Recently, the HAPO Follow-up Study (HAPO-FUS) reported that exposure to higher concentrations of glucose in utero is independently associated with childhood adiposity at 10–14 y; being overweight/obese according to age- and sex-specific cutoffs based on International Obesity Task Force criteria; or being >85th percentile for SFT, WC, and percentage body fat measured by air displacement plethysmography (10). However, mothers diagnosed with GDM received treatment in the present study, unlike HAPO-FUS in which mothers did not receive treatment. These findings of continuous associations may have clinical implications not only in controversies on the best cutoffs for screening maternal glycemia but also on future metabolic health of the offspring.
We observed stronger associations between maternal glycemia and neonatal AA among female neonates. However, the sex difference in associations between maternal glycemia and offspring total adiposity in previous studies seemed to be distinct between Western and Asian populations. In Western populations, such associations were observed primarily in boys at early infancy throughout adolescence (16, 18, 21, 33) although 1 Norwegian study showed that maternal FPG at 30–32 weeks of gestation was significantly associated with BW among girls only (34). A prospective study in Australia reported that maternal FPG was the major predictor of adiposity as measured by air displacement plethysmography in male neonates of GDM mothers but had little effect in female neonates (16). Similarly, the association of GDM with offspring obesity risk from late childhood through early adulthood was observed among boys in a large prospective study of 15,009 US individuals (21). By contrast, previous studies in Asian cohorts showed associations of maternal GDM on offspring adiposity in girls of preschool/school age. In a study in Indian children, female offspring of diabetic mothers had larger SFT than offspring of non-GDM mothers at 5 and 9.5 y of age (19, 20). Maternal hyperglycemia in pregnancy was independently associated with offspring's risk of abnormal glucose tolerance, obesity, and higher blood pressure at 7 y of age only in girls in a total of 970 Chinese mothers in Hong Kong (12). Our findings support and strengthen the previous findings in Asians.
The significant and positive associations of maternal FPG with offspring sSAT and IAT during the preschool years persisted for girls despite their having similar weight to boys, and independently of maternal size. This may reflect at least in part a contribution of maternal glycemia to long-term health consequences among the offspring in addition to potential environmental influence of an obesogenic environment. With the disproportional increase in dSAT and substantial increase in VAT at 4.5 y which are metabolically active, our findings may reflect an unfavorable metabolic phenotype of the offspring, especially among Asian girls.
Previous studies have shown the association between maternal glycemia and offspring total adiposity, and also reported sex differences. However, a unique strength of our study is the use of AAT measurement by MRI in a large number of neonates with a follow-up MRI. MRI is the most accurate and only available method without radiation to quantify AA (29). In addition, GUSTO is a prospective study of Asian mothers and their offspring, including 3 Asian ethnic populations (Chinese, Malay, Indian) which all are at high risk of type 2 diabetes. We also explored sex differences in AA in association with maternal glycemia, which is little studied. The timing of MRI scans for neonates was within 2 wk after delivery, thus the observation would largely reflect the developmental influences on the offspring before any environmental exposures. Similarly, the variation in postnatal environmental influences among children was relatively less at the preschool age of 4.5 y. Several potential limitations merit discussion. First, only a subset of eligible children whose parents gave consent for their children's MRI were included in this study, therefore care should be taken when generalizing our findings. Future studies are warranted to confirm these findings in larger study populations. Secondly, the MATLAB software used at 4.5-y analyses was an upgraded version of the one used at the neonatal period. However, because this same upgraded MATLAB software was applied to the MRI images of all children at the follow-up 4.5-y visit, the upgrade cannot explain the observed associations between maternal glycemia and offspring AA. Lastly, as with any observational study, we cannot exclude the possibility of bias due to residual confounding although we carefully controlled for a number of major potential confounders. However, our findings, in line with those from others, indicated that the association between maternal glycemia and offspring AA is a continuum.
Taken together, our findings reinforce the impact of maternal glycemia, even below the threshold for the diagnosis of GDM, on offspring AA during fetal development. These findings lend further support to efforts toward optimizing mothers’ glycemic status during pregnancy. The suggestive evidence on sex differences in the impact of maternal glycemia merits further confirmation in other studies.
Supplementary Material
Acknowledgments
The authors’ responsibilities were as follows. All authors read and approved the final manuscript.—Y-SC, PDG, KMG, and YSL: conceptualized and designed the study; MTT: supervised the collection of data, performed the image analysis of neonates, analyzed the data, and wrote the manuscript; SAS, NM, and SSV: performed the image analysis of preschool children; MVF: supervised the image acquisition and image analysis; CZ, JGE, and YSL: provided statistical advice and made critical revisions to the manuscript for important intellectual content; KMG, S-YC, FY, KHT, LPCS, S-ES, and IMA: contributed to discussion and reviewed the manuscript; MTT and YSL: had primary responsibility for the final content. KMG, PDG, and Y-SC are part of an academic consortium that has received research funding and have received reimbursement for speaking at conferences sponsored by companies selling nutritional products. KMG is supported by the UK Medical Research Council (MC_UU_12011/4), the National Institute for Health Research (NIHR Senior Investigator (NF-SI-0515-10042), NIHR Southampton 1000DaysPlus Global Nutrition Research Group (17/63/154) and NIHR Southampton Biomedical Research Centre (IS-BRC-1215-20004)), the European Union (Erasmus+ Programme Early Nutrition eAcademy Southeast Asia-573651-EPP-1-2016-1-DE-EPPKA2-CBHE-JP and ImpENSA 598488-EPP-1-2018-1-DE-EPPKA2-CBHE-JP) and the British Heart Foundation (RG/15/17/3174). All other authors report no conflicts of interest.
We thank the GUSTO study group. The GUSTO study group includes Allan Sheppard, Amutha Chinnadurai, Anne Eng Neo Goh, Anne Rifkin-Graboi, Anqi Qiu, Arijit Biswas, Bee Wah Lee, Birit FP Broekman, Boon Long Quah, Borys Shuter, Chai Kiat Chng, Cheryl Ngo, Choon Looi Bong, Christiani Jeyakumar Henry, Claudia Chi, Cornelia Yin Ing Chee, Yam Thiam Daniel Goh, Doris Fok, E Shyong Tai, Elaine Quah Phaik Ling, Elaine Tham, Evelyn Chung Ning Law, Evelyn Xiu Ling Loo, Fabian Yap, Falk Mueller-Riemenschneider, George Seow Heong Yeo, Helen Chen, Heng Hao Tan, Hugo PS van Bever, Iliana Magiati, Inez Bik Yun Wong, Ivy Yee-Man Lau, Izzuddin Bin Mohd Aris, Jeevesh Kapur, Jenny L Richmond, Jerry Kok Yen Chan, Joanna D Holbrook, Joanne Yoong, Joao N Ferreira, Jonathan Tze Liang Choo, Jonathan Y Bernard, Joshua J Gooley, Keith M Godfrey, Kenneth Kwek, Kok Hian Tan, Krishnamoorthy Niduvaje, Kuan Jin Lee, Leher Singh, Lieng Hsi Ling, Lin Su, Ling-Wei Chen, Lourdes Mary Daniel, Lynette P Shek, Marielle V Fortier, Mark Hanson, Mary Foong-Fong Chong, Mary Rauff, Mei Chien Chua, Melvin Khee-Shing Leow, Michael Meaney, Mya Thway Tint, Neerja Karnani, Ngee Lek, Oon Hoe Teoh, Paulin Tay Straughan, PC Wong, Peter D Gluckman, Pratibha Agarwal, Queenie Ling Jun Li, Rob M van Dam, Salome A Rebello, Seang-Mei Saw, See Ling Loy, Seng Bin Ang, Shang Chee Chong, Sharon Ng, Shiao-Yng Chan, Shirong Cai, Shu-E Soh, Sok Bee Lim, S Sendhil Velan, Stella Tsotsi, Chin-Ying Stephen Hsu, Sue Anne Toh, Swee Chye Quek, Victor Samuel Rajadurai, Walter Stunkel, Wayne Cutfield, Wee Meng Han, Wei Pang, Yap-Seng Chong, Yin Bun Cheung, Yiong Huak Chan, and Yung Seng Lee.
Notes
Supported by Singapore National Research Foundation grants NMRC/TCR/004-NUS/2008 and NMRC/TCR/012-NUHS/2014 (to YS-C) under its Translational and Clinical Research Flagship Programme, administered by the Singapore Ministry of Health's National Medical Research Council. Additional funding is provided by the Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore.
Supplemental Figure 1 and Supplemental Tables 1–3 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.
CZ and YSL contributed equally to this work.
Abbreviations used: AA, abdominal adiposity; AAT, abdominal adipose tissue compartment; BW, birth weight; dSAT, abdominal deep subcutaneous adipose tissue; FPG, fasting plasma glucose; GA, gestational age; GDM, gestational diabetes mellitus; GUSTO, Growing Up in Singapore Towards healthy Outcomes; HAPO, Hyperglycemia and Adverse Pregnancy Outcome; HAPO-FUS, Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study; IAT, abdominal internal adipose tissue; OGTT, oral-glucose-tolerance test; SAT, abdominal subcutaneous adipose tissue; SFT, skinfold thickness; sSAT, abdominal superficial subcutaneous adipose tissue; WC, waist circumference; VAT: visceral adipose tissue; 2hPG, 2-h plasma glucose.
Contributor Information
Mya-Thway Tint, Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Suresh A Sadananthan, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Shu-E Soh, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Izzuddin M Aris, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Navin Michael, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Kok H Tan, Department of Maternal Fetal Medicine, KK Women's and Children's Hospital, Singapore; Academic Medicine, Duke–National University of Singapore Graduate Medical School, Singapore.
Lynette P C Shek, Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
Fabian Yap, Department of Pediatric Endocrinology, KK Women's and Children's Hospital, Singapore; Duke–National University of Singapore Graduate Medical School, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Peter D Gluckman, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Liggins Institute, University of Auckland, Auckland, New Zealand.
Yap-Seng Chong, Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Keith M Godfrey, Medical Research Council Lifecourse Epidemiology Unit, Univeristyof Southhampton, Southampton, United Kingdom; National Institute for Health Research Southampton Biomedical Research Centre, University Hospital Southampton, NHS Foundation Trust, Southhampton, United Kingdom.
S Sendhil Velan, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Singapore Bioimaging Consortium, Agency for Science, Technology and Research (A*STAR), Singapore.
Shiao-Yng Chan, Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore.
Johan G Eriksson, Department of Obstetrics & Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland; Folkhälsan Research Center, Helsinki, Finland.
Marielle V Fortier, Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore.
Cuilin Zhang, Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Rockville, MD, USA.
Yung S Lee, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Division of Pediatric Endocrinology, Department of Pediatrics, Khoo Teck Puat—National University Children's Medical Institute, National University Health System, Singapore.
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