Literature DB >> 27841944

Programming of Adiposity in Childhood and Adolescence: Associations With Birth Weight and Cord Blood Adipokines.

Joy Simpson1, Andrew D A C Smith2,3, Abigail Fraser2,3, Naveed Sattar4, Robert S Lindsay4, Susan M Ring2,3, Kate Tilling2,3, George Davey Smith2,3, Debbie A Lawlor2,3, Scott M Nelson1.   

Abstract

Context: Exposure to maternal adiposity during pregnancy is associated with higher offspring birth weight and greater adiposity through childhood and adult life. As birth weight reflects the summation of lean and fat mass, the extent to which fat mass at birth tracks into later life is unknown. Objective: To determine whether fat mass at birth is associated with child and adolescent adiposity. Design, Setting, and Participants: UK birth cohort with markers of neonatal fat mass; cord blood leptin, adiponectin, and birth weight and adiposity outcomes at age 9 (n = 2775) and 17 years (n = 2138). Main Outcomes: Offspring body mass index (BMI), waist circumference, dual-energy X-ray absorptiometry-determined fat mass, and obesity at age 9 and 17 years.
Results: Higher cord blood leptin was associated with higher z scores of fat mass [difference in mean per 10 pg/mL: 0.03 standard deviation (SD); 95% confidence interval (CI), 0.00 to 0.06], waist circumference (0.04 SD; 95% CI, 0.00 to 0.07), and BMI (0.04 SD; 95% CI, 0.00 to 0.08) at age 9. However, by age 17 the adjusted results were attenuated to the null. Cord blood adiponectin was not associated with measures of adiposity at age 9. At age 17, cord blood adiponectin was positively associated with fat mass (0.02 SD per 10 μg/mL; 95% CI, 0.02 to 0.03) and waist circumference (0.04 SD per 10 μg/mL; 95% CI, 0.03 to 0.05). Birth weight was positively associated with waist circumference (0.03 SD per 100 g; 95% CI, 0.02 to 0.04) and BMI (0.02 SD per 100 g; 95% CI, 0.00 to 0.03), but not fat mass or odds of obesity. Cord blood leptin and adiponectin were not associated with obesity at either age. Conclusions: Increased cord blood leptin and adiponectin, known surrogates of fetal fat mass, were weakly associated with increased fat mass in late childhood and adolescence, respectively.

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Year:  2017        PMID: 27841944      PMCID: PMC5413167          DOI: 10.1210/jc.2016-2342

Source DB:  PubMed          Journal:  J Clin Endocrinol Metab        ISSN: 0021-972X            Impact factor:   5.958


Exposure to maternal adiposity during pregnancy is associated with higher offspring birth weight and greater adiposity through childhood and adult life (1). Developmental overnutrition has been proposed as a mechanism by which excessive transplacental passage of nutrients facilitates the development of larger babies with greater fat mass. Evidence from within-sibling studies, comparisons of maternal and paternal exposures, and the use of genetic variants as proxies for the maternal exposures support maternal adiposity and developmental overnutrition causing greater adiposity in offspring at birth (2–4). However, whether this causal effect extends to long-term offspring adiposity is unclear. A longer term effect may occur as a result of tracking of birth fatness across the life course. However, because birth weight is unable to distinguish relative contributions of lean vs fat mass (5, 6), few studies to date have been able to determine the extent to which greater fat mass at birth tracks into later life. Umbilical cord blood leptin is widely recognized as an accurate biomarker for neonatal fat mass (7). Maternal exposures, including maternal adiposity, which may cause developmental overnutrition, have been associated with increased cord leptin and neonatal adiposity at birth (8, 9). In animal models, fetal leptin has also been proposed to contribute the long-term programming of hypothalamic feeding circuits, thereby providing a means by which leptin can influence long-term adiposity independent of tracking of adiposity from birth (10). However, use of cord blood leptin in determining whether neonatal fat mass tracks across childhood has been limited (11–14). This primarily reflects the scarcity of large prospective birth cohorts with cord blood samples and detailed measures of offspring adiposity as well as potential confounders. Studies that have made some assessment of this to date have had relatively small sample sizes (N = 56 to 588) (11–14), and we are not aware of any study having followed children beyond age 7 years. These studies have reported inconsistent results, with higher cord leptin associated with both a lower (11) and higher (12) body mass index (BMI) at age 3 years, and a higher BMI at age 7 years (14). Neonatal levels of adiponectin, which has insulin-sensitizing effects in adults, are approximately 4 to 7 times higher than maternal levels. Furthermore, whereas maternal circulating concentrations of adiponectin are inversely associated with BMI, higher levels of cord blood adiponectin are associated with higher birth weight (11, 15). That higher cord blood adiponectin concentrations might reflect increased fat mass in neonates is suggested by mouse studies where overexpression of fetal adiponectin was positively related to the size of fat depots in early life, whereas adiponectin knockout fetuses display lower body weight and lower fat content (16). Given this effect of adiponectin on body composition, specifically its fat deposition–enhancing effect in mice, and the known relationships of leptin in humans to fat mass, we hypothesized that both cord blood leptin and adiponectin would be positively associated with offspring adiposity in prepubertal children and adolescents. The aim of this study was to determine whether cord blood leptin and adiponectin were positively associated with later obesity, BMI, waist circumference, and fat mass and whether this is independent of maternal BMI. For comparison, we also examined associations of birth weight with these outcomes.

Research Design and Methods

Study population

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort study investigating the health and development of children (17, 18). The study Web site contains details of all of the data that are available through a fully searchable data dictionary (http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). Ethical approval was obtained from the ALSPAC Law and Ethics Committee and the National Health Service local ethics committees. A total of 14,541 women were initially enrolled, with 5011 mother/offspring pairs having a suitable cord blood sample. A detailed outline of the exclusion criteria for the analysis reported in this study and numbers with missing data are shown in Fig. 1. We included participants when they had (1) attended and completed assessments at either the 9- or 11-year clinic assessment, or (2) attended the 15- or 17-year clinic assessment. The eligible cohort for the current analysis was 2775 mother/offspring pairs at age 9 to 11 years and 2138 mother/offspring pairs at age 15 to 17 years.
Figure 1.

ALSPAC participant flowchart.

ALSPAC participant flowchart.

Cord blood assays

Cord blood samples were collected at the time of delivery, initially stored at 4°C for 0 to 8 days before plasma was separated and then stored at −20°C before being transferred to long-term storage at −80°C. Cord blood leptin and adiponectin were measured using commercially available enzyme-linked immunosorbent assay kits [Quantikine human leptin immunoassay (catalog no. PDLP00), Quantikine human total adiponectin/Acrp30 (catalog no. PDRP300), both R&D Systems, Minneapolis, MN]. Analysis of the cord blood was completed within a maximum of 3 freeze-thaw cycles and remained at −80°C in between thaws. The interassay coefficients of variability were 9.5% for leptin and 3.2% for adiponectin.

Obstetric/perinatal data

Six trained research midwives retrospectively extracted data from obstetric medical records, and error rates were consistently <1%. These data included weight at every antenatal clinic visit (used to determine gestational weight gain), complications during pregnancy (hypertensive or diabetic disorders), and mode of delivery. Gestational age and sex and birth weight of offspring were obtained from hospital records at the time of birth. Maternal age, prepregnancy height and weight, smoking status (defined as never smoked, smoked before but not during pregnancy, and smoked during pregnancy), parity, occupational social class, and highest educational attainment were obtained from questionnaires completed by the mothers in early and advanced stages of pregnancy. Occupation was used to allocate social class groups using the 1991 British Office of Population and Census Statistics classification.

Offspring childhood and adolescent adiposity measurements

Identical protocols were used at all follow-up clinics. At each clinic assessment, participants’ age in months was recorded and their weight and height were measured in light clothing and without shoes. Weight was measured to the nearest 0.1 kg using Tanita scales. Height was measured to the nearest 0.1 cm using a Harpenden stadiometer. Dual-energy X-ray absorptiometry scans were used to measure total fat mass. Waist circumference was measured to the nearest 1 mm at the midpoint between the lower ribs and the pelvic bone with a flexible tape and with the child breathing normally. Offspring obesity was classified using BMI and criteria defined by the International Obesity Task Force (19).

Statistical analysis

The relationship between exposures (birth weight and cord blood adipokines) and outcomes (BMI, waist circumference, and fat mass at ages 9 to 11 years and 15 to 17 years) was examined by Spearman correlation. Linear (offspring BMI, waist circumference, and fat mass) and logistic (offspring obesity) regression models were used to examine the associations between birth weight and cord blood measures and offspring BMI, waist circumference, fat mass, and obesity at age 9 and 17 years. Offspring waist circumference and fat mass were log transformed to produce approximately normal distributions of regression model residuals. Within-cohort logged fat mass and waist circumference z scores (participant value minus mean for the sex and age category ÷ standard deviation for the sex and age category) were created using 1 year age categories. BMI z scores were created using the UK 1990 British growth reference (20). Birth weight was adjusted for sex, gestational age, and number of offspring (singletons or twins) using nonlinear regression fitting a Gompertz curve. Three incremental analyses were performed to adjust for potential confounders (Supplemental Fig. 1). The basic model (model 1) adjusted for offspring sex and age at outcome measurement alone (and offspring height when fat mass is the outcome). In model 2 we additionally adjusted for maternal confounders (age, smoking, parity, occupational social class, education, and prepregnancy BMI). In the fully adjusted model (model 3) we additionally adjusted for pregnancy characteristics (gestational age at birth, mode of delivery, gestational weight gain, hypertensive and diabetic disorders of pregnancy). In these analyses, because we have scaled the exposures (birth weight, cord blood leptin, and adiponectin) and outcomes (BMI, waist circumference, and fat mass) on their standard deviations, the resultant differences in means from the multivariable linear regression models are equivalent to partial (adjusted) correlation coefficients and can be interpreted in this way. There were small amounts of missing data on some covariables included in the multivariable models (Fig. 1). Twenty imputation data sets were generated by chained equations (21), with all cord exposures, birth weight, the covariates specified for model 3, and the measurements from the 11-year clinic and 15-year clinic informing imputation of missing values in the 9-year clinic and 17-year clinic, respectively (hereafter referred to as 9 and 17 year). The distributions of observed and imputed variables were similar (Supplemental Table 1). In the following sections, we present results from the imputed datasets and, for comparison, present results from those with complete confounders (N = 1041 to 1776) in Supplemental Material (Supplemental Tables 5–8). All statistical analyses were performed using Stata (version 13.0) software (StataCorp, College Station, TX).

Results

Table 1 summarizes the maternal and offspring characteristics for those participants with cord blood measures who completed at least 1 clinic assessment, with Supplemental Table 1 demonstrating the similarity of the observed and imputed data. Supplemental Table 2 shows the Spearman correlation between exposures (birth weight and cord blood adipokines) and outcomes (markers of anthropometry at age 9 and 17 years). Birth weight was positively correlated with cord blood leptin (n = 4751, r = 0.33) and, to a lesser degree, with cord blood adiponectin (n = 4707, r = 0.14). Cord leptin and adiponectin were positively correlated (n = 4962, r = 0.11). Birth weight and leptin also positively correlated with fat mass, BMI, and waist circumference at age 9 and 17 years. There was a weak inverse association between cord adiponectin and waist circumference and BMI at age 9. Among those participants with assessments at both clinics (at age 9 and 17 years), measurements at each clinic were highly correlated (0.74 for BMI, 0.74 for fat mass, and 0.66 for waist circumference).
Table 1.

Maternal and Offspring Characteristics

Attended at Least 1 Clinic 
Assessment (n = 2955)
N Observations (%)Median (IQR)
Maternal characteristics
 Age291429 (26, 32)
 Smoking
  Never2103 (73.8)
  Before, not during pregnancy212 (7.4)
  During pregnancy533 (18.7)
 BMI258722.2 (20.5, 24.4)
 Parity
  01274 (45.5)
  11011 (36.1)
  2383 (13.7)
  3101 (3.6)
  4+30 (1.1)
 Education
  Left school at 161713 (61.3)
  A level689 (24.8)
  Degree391 (14.0)
 Social class
  I (least disadvantaged)140 (5.9)
  II807 (33.7)
  IIIa1038 (43.5)
  IIIb162 (6.8)
  IV203 (8.5)
  V (most disadvantaged)40 (1.7)
Pregnancy characteristics
 Gestational age at birth, wk291440 (39, 41)
 Model of delivery
  Spontaneous2253 (77.9)
  Breech36 (1.3)
  Cesarean section249 (8.6)
  Forceps167 (5.8)
  Vacuum154 (5.3)
  Other32 (1.1)
  Gestational weight gain, kg266812.5 (9.5, 15.2)
 Hypertension and  pre-eclampsia
  No hypertensive disorders2449 (84.5)
  Hypertension, no pre-eclampsia420 (13.9)
  Hypertension and pre-eclampsia49 (1.7)
 Diabetes
  No glycosuria or diabetes2651 (95.8)
  Existing diabetes10 (0.4)
  Gestational diabetes16 (0.6)
  Glycosuria91 (3.3)
Offspring characteristics
 Sex
  Male1414 (47.9)
  Female1541 (52.2)
 Birth weight, kg28913.5 (3.1, 3.8)
 Cord leptin, pg/mL29526.4 (3.6, 12.1)
 Cord adiponectin, µg/mL292775.7 (53.6, 98.4)
 Height, cmAge 9: 2561140 (136, 144)
Age 11: 2363151 (146, 156)
Age 15: 1816169 (163, 175)
Age 17: 1648170(164, 178)
 Fat mass, kgAge 9: 24607.3 (4.9, 11.2)
Age 11: 232710.0 (6.8, 15.7)
Age 15: 171613.7 (8.6, 20.6)
Age 17: 159416.7 (11.0, 23.5)
 Waist circumference, cmAge 9: 257461.1 (57.4, 66.6)
Age 11: 236266.0 (61.8, 73.5)
Age 15: 147575.4 (71.0, 81.5)
 BMI, kg/m2Age 9: 256017.0 (15.7, 19.1)
Age 11: 235918.4(16.6, 21.0)
Age 15: 181120.7 (19.0, 23.1)
Age 17: 164722.0 (20.2, 24.7)
 ObeseAge 9: 102 (4.0)
Age 11:116 (4.9)
Age 15: 78 (4.3)
Age 17:105 (6.4)
 Age at attendance, yAge 9: 25839.8 (9.6, 10.0)
Age 11: 237811.8 (11.6, 11.8)
Age 15: 183815.4 (15.3, 15.6)
Age 17: 169517.8 (17.6, 17.9)

Figures are numbers (%) unless stated otherwise.

Abbreviation: IQR, interquartile range.

Maternal and Offspring Characteristics Figures are numbers (%) unless stated otherwise. Abbreviation: IQR, interquartile range. Table 2 shows the multivariable associations between cord blood leptin, adiponectin, and birth weight and z scores of offspring fat mass, waist circumference, BMI, and obesity at age 9 years. Cord blood leptin was positively associated with fat mass, waist circumference, and BMI at age 9 (model 1). The effect size was largely attenuated with adjustment for maternal and pregnancy characteristics (Table 2), with the individual univariate association of maternal and pregnancy characteristics on cord leptin, cord adiponectin, and birth weight shown in Supplemental Table 3. A similar but weaker pattern was observed for measures at age 17 where cord leptin was associated with z scores of fat mass, waist circumference, and BMI and with the risk of obesity (Table 3). These associations were similarly attenuated to the null after adjustment for potential confounders.
Table 2.

Associations of Birth Weight and Cord Blood Analyte With Fat Mass, Waist Circumference and BMI

ExposureOutcomeFat Mass z Scorea
Waist Circumference z Score
BMI z Score
Obesity
Coefficient95% CIPCoefficient95% CIPCoefficient95% CIPOR95% CIP
Leptin, per 10 pg/mLModel 10.070.04, 0.10<0.0010.080.05, 0.12<0.0010.110.07, 0.15<0.0011.151.00, 1.310.046
Model 20.040.00, 0.070.0230.050.01, 0.080.0080.060.02, 0.100.0031.000.85, 1.170.993
Model 30.030.00, 0.060.0860.040.00, 0.070.0450.040.00, 0.080.0290.950.81, 1.120.548
Adiponectin, per 10 µg/mLModel 10.00−0.01, 0.010.828−0.01−0.02, 0.000.0720.00−0.02, 0.010.6020.990.94, 1.050.845
Model 20.00−0.01, 0.010.916−0.01−0.02, 0.000.1180.00−0.01, 0.010.8581.000.94, 1.050.874
Model 30.00−0.01, 0.010.875−0.01−0.02, 0.000.1000.00−0.01, 0.010.7670.990.94, 1.050.834
Birth weight, per 100 gbModel 10.010.00, 0.020.0060.030.03, 0.04<0.0010.040.03, 0.05<0.0011.061.02, 1.100.006
Model 20.000.00, 0.010.1920.030.02, 0.04<0.0010.040.03, 0.04<0.0011.030.99, 1.070.193
Model 30.00−0.01, 0.010.7410.020.02, 0.03<0.0010.030.02, 0.04<0.0011.010.96, 1.050.852

For this study, N= 2775. Model 1 was adjusted for offspring sex and age at measurement. Model 2 was adjusted for offspring sex, age at measurement, and maternal confounders (age, smoking, parity, occupational social class, education, and prepregnancy BMI). Model 3 was adjusted for offspring sex, age at measurement, and maternal confounders plus pregnancy confounders (gestational age at birth, mode of delivery, gestational weight gain, hypertensive disorders, and diabetic disorders of pregnancy).

Abbreviations: CI, confidence interval; OR, odds ratio.

Fat mass adjusted for height.

Birth weight adjusted for sex, gestational age, and singleton/twin pregnancy.

Table 3.

Associations of Birth Weight and Cord Blood Analyte With Fat Mass, Waist Circumference (at Age 15 Years), BMI

ExposureOutcomeFat Mass z Scorea
Waist Circumference z Score
BMI z Score
Obesity
Coefficient95% CIPCoefficient95% CIPCoefficient95% CIPOR95% CIP
Leptin, per 10 pg/mLModel 10.070.03, 0.11<0.0010.060.02, 0.100.0030.090.04, 0.14<0.0011.130.99, 1.280.060
Model 20.02−0.02, 0.060.2630.01−0.03, 0.050.5450.03−0.02, 0.070.2720.960.83. 1.120.629
Model 30.02−0.02, 0.050.4440.01−0.03, 0.050.5980.02−0.03, 0.060.4810.950.81, 1.110.497
Adiponectin, per 10 µg/mLModel 10.010.00, 0.030.0340.010.00, 0.030.0330.01−0.01, 0.020.2451.030.98, 1.080.238
Model 20.020.00, 0.030.0060.020.00, 0.030.0080.010.00, 0.030.0761.040.99, 1.100.660
Model 30.020.00, 0.030.0070.020.00, 0.030.0080.010.00, 0.030.0801.050.99, 1.100.613
Birth weight, per 100 gbModel 10.020.02, 0.03<0.0010.040.03, 0.05<0.0010.040.03, 0.05<0.0011.051.02, 1.090.004
Model 20.010.00, 0.020.0100.030.02, 0.04<0.0010.020.01, 0.03<0.0011.020.98, 1.060.241
Model 30.010.00, 0.020.0980.030.02, 0.04<0.0010.020.01, 0.03<0.0011.010.97, 1.050.516

For this study, N = 2138. Model 1 was adjusted for offspring sex and age at measurement. Model 2 was adjusted for offspring sex, age at measurement, and maternal confounders (age, smoking, parity, occupational social class, education, and prepregnancy BMI). Model 3 was adjusted for offspring sex, age at measurement, and maternal confounders plus pregnancy confounders (gestational age at birth, mode of delivery, gestational weight gain, hypertensive disorders, and diabetic disorders of pregnancy).

Abbreviations: CI, confidence interval; OR, odds ratio.

Fat mass adjusted for height.

Birth weight adjusted for sex, gestational age, and singleton/twin pregnancy.

Associations of Birth Weight and Cord Blood Analyte With Fat Mass, Waist Circumference and BMI For this study, N= 2775. Model 1 was adjusted for offspring sex and age at measurement. Model 2 was adjusted for offspring sex, age at measurement, and maternal confounders (age, smoking, parity, occupational social class, education, and prepregnancy BMI). Model 3 was adjusted for offspring sex, age at measurement, and maternal confounders plus pregnancy confounders (gestational age at birth, mode of delivery, gestational weight gain, hypertensive disorders, and diabetic disorders of pregnancy). Abbreviations: CI, confidence interval; OR, odds ratio. Fat mass adjusted for height. Birth weight adjusted for sex, gestational age, and singleton/twin pregnancy. Associations of Birth Weight and Cord Blood Analyte With Fat Mass, Waist Circumference (at Age 15 Years), BMI For this study, N = 2138. Model 1 was adjusted for offspring sex and age at measurement. Model 2 was adjusted for offspring sex, age at measurement, and maternal confounders (age, smoking, parity, occupational social class, education, and prepregnancy BMI). Model 3 was adjusted for offspring sex, age at measurement, and maternal confounders plus pregnancy confounders (gestational age at birth, mode of delivery, gestational weight gain, hypertensive disorders, and diabetic disorders of pregnancy). Abbreviations: CI, confidence interval; OR, odds ratio. Fat mass adjusted for height. Birth weight adjusted for sex, gestational age, and singleton/twin pregnancy. Cord blood adiponectin was not associated with any measures of adiposity at age 9 years (Table 2). At age 17 years, cord blood adiponectin was positively associated with fat mass and waist circumference, with the effect size strengthened after adjustment for maternal and pregnancy characteristics (Table 3). Birth weight was positively associated with fat mass, waist circumference, and BMI at age 9 years and 17 years and showed a weak relationship with obesity in both age groups (Tables 2 and 3). After adjustment for maternal and pregnancy characteristics, increasing birth weight remained associated with greater waist circumference and BMI, with the association with fat mass and obesity attenuated to the null. Results did not differ substantially when absolute measures of adiposity at age 9 years (Supplemental Table 4) or age 17 years were considered (Supplemental Table 5). Results were similar for nonimputed analyses but with wider confidence intervals (Supplemental Tables 6–9).

Discussion

In this prospective birth cohort study, cord leptin, a marker of neonatal fat mass, exhibited relatively weak relationships with later measures of adiposity. These were largely attenuated by adjustment for maternal factors, particularly in later childhood. In contrast, adiponectin exhibited no relationship with measures of fat mass at age 9 years and showed a weak relationship with fat mass and waist circumference at age 15 years. Neither cord leptin nor adiponectin was associated with the risk of being classed as obese in late childhood or adolescence. Taken together, this would suggest that neonatal fat mass per se has a limited contribution in determining fat mass in adolescence. To date, birth weight, as a proxy for intrauterine growth, and its relationship to adult BMI have been extensively studied. Similar to our findings, studies principally demonstrate a positive association between birth weight and childhood and adult fat mass, BMI, and waist circumference (22). To try to examine whether birth weight is simply acting as a surrogate for neonatal fat mass, we previously used the Ponderal index (birth weight/length3), a measure of fatness, and demonstrated positive associations with lean body mass, total body fat, and the fat-to-lean mass ratio at age 9 years (23). Although this suggests that neonatal fat mass is related to later adiposity, the Ponderal index is a relatively poor measure of neonatal total body fat (24). To extend and improve on this work, the present study used cord blood leptin, a strong correlate of neonatal fat mass as assessed by skinfolds or total body electrical conductivity (25), and adiponectin, which in mouse studies is suggested to be a further positive correlate of fat mass (16). That cord blood leptin was positively associated with several adiposity measures, and specifically fat mass z score at age 9 years, suggests that there is either accretion of adipose tissue during intrauterine life that is maintained throughout childhood, the propensity to develop fat mass may be maintained, or there is a direct effect on the programming of hypothalamic feeding circuits. However, given our observed effect size, the contribution of neonatal fat to later fat mass is likely to be small. For example, a 10 pg/mL increase in cord leptin would be associated with a BMI increase from 22 to 22.1 kg/m2 at age 9 years. In accordance with some (26–28) but not all (29, 30) previous studies, we observed that adiponectin was weakly positively correlated with birth weight and cord leptin. We found some evidence for weak associations of cord blood adiponectin with adiposity at the older age (15–17) but none that this was mediated by increased (and persistent) fat mass through childhood. Why adiponectin is not related to adiposity outcomes in earlier childhood, as leptin is, is not clear. Perhaps these associations emerge after puberty, which has a major impact on body composition and adipocyte number (31). It is also possible that given the multiple tests performed, some associations are due to chance, and we would caution against assuming these associations are real without further replication. As previously shown in this cohort (32), we observed consistent positive associations of birth weight with later BMI and waist circumference in both early childhood and adolescence, although null associations (coefficients equal to 0) were found for fat mass at both ages. Our study has several strengths, including its size, duration of follow-up, and the availability of data on a range of maternal, pregnancy, and social factors to facilitate a robust analysis. This is also 1 of the very few studies with dual-energy X-ray absorptiometry measurements of body composition at different time points, thereby overcoming the potential increase in overall mass attributed to the expected increase in bone density that results from increased adiposity. We do, however, acknowledge some limitations. The number of children who were overweight or obese was smaller than many contemporary populations. That birth weight and cord measures were not associated with the risk of being obese may reflect this. Another limitation of the study is the loss to follow-up. Our results may be biased if associations were substantially different among excluded participants due to conditioning on the variables in the model. We acknowledge that engaged participants may exhibit different characteristics at birth beyond gestational age and birth weight that are representative of the whole cohort, and also for the 2 outcomes. Replication of our analyses in additional birth cohorts with different metabolic risk profiles would strengthen our findings. Cord blood sample degradation may have contributed to variability, but leptin and adiponectin do appear to be stable with long-term storage (33–38). This is in stark contrast to C-peptide, the preferred index of fetal glucose exposure, which we were unable to measure accurately due to degradation with long-term storage, a phenomenon previously reported by others (39). In conclusion, we found that cord blood leptin and adiponectin, known surrogates of fetal fat mass, were weakly positively associated with some measures of fat mass in late childhood and adolescence. That these associations were robust to a wide range of confounders that may reflect intrauterine, maternal, and shared environmental exposures suggests that neonatal fat mass may track into later life. However, we acknowledge replication of our findings in cohorts with a different risk profile is critical, and that the magnitude of the observed associations is small, potentially limiting the impact that neonatal life adiposity has on later outcomes.
  39 in total

1.  Estimating the long-term effects of storage at -70 degrees C on cholesterol, triglyceride, and HDL-cholesterol measurements in stored sera.

Authors:  W J Shih; P S Bachorik; J A Haga; G L Myers; E A Stein
Journal:  Clin Chem       Date:  2000-03       Impact factor: 8.327

2.  Evaluation of Freeze Thaw Cycles on stored plasma in the Biobank of the Norwegian Mother and Child Cohort Study.

Authors:  Liv Paltiel; Kjersti S Rønningen; Helle M Meltzer; Susan V Baker; Jane A Hoppin
Journal:  Cell Preserv Technol       Date:  2008-09-01

3.  Adiponectin in human cord blood: relation to fetal birth weight and gender.

Authors:  Eyal Sivan; Shali Mazaki-Tovi; Clara Pariente; Yael Efraty; Eyal Schiff; Rina Hemi; Hannah Kanety
Journal:  J Clin Endocrinol Metab       Date:  2003-12       Impact factor: 5.958

Review 4.  Maternal adiposity--a determinant of perinatal and offspring outcomes?

Authors:  Debbie A Lawlor; Caroline Relton; Naveed Sattar; Scott M Nelson
Journal:  Nat Rev Endocrinol       Date:  2012-09-25       Impact factor: 43.330

5.  Establishing a standard definition for child overweight and obesity worldwide: international survey.

Authors:  T J Cole; M C Bellizzi; K M Flegal; W H Dietz
Journal:  BMJ       Date:  2000-05-06

Review 6.  Early markers of adult obesity: a review.

Authors:  T D Brisbois; A P Farmer; L J McCargar
Journal:  Obes Rev       Date:  2011-12-16       Impact factor: 9.213

7.  Cohort Profile: the 'children of the 90s'--the index offspring of the Avon Longitudinal Study of Parents and Children.

Authors:  Andy Boyd; Jean Golding; John Macleod; Debbie A Lawlor; Abigail Fraser; John Henderson; Lynn Molloy; Andy Ness; Susan Ring; George Davey Smith
Journal:  Int J Epidemiol       Date:  2012-04-16       Impact factor: 7.196

8.  Adiponectin enhances mouse fetal fat deposition.

Authors:  Liping Qiao; Hyung Sun Yoo; Alysha Madon; Brice Kinney; William W Hay; Jianhua Shao
Journal:  Diabetes       Date:  2012-08-07       Impact factor: 9.461

9.  Associations of size at birth and dual-energy X-ray absorptiometry measures of lean and fat mass at 9 to 10 y of age.

Authors:  Imogen S Rogers; Andy R Ness; Colin D Steer; Jonathan C K Wells; Pauline M Emmett; John R Reilly; Jon Tobias; George Davey Smith
Journal:  Am J Clin Nutr       Date:  2006-10       Impact factor: 7.045

10.  Differential associations of leptin with adiposity across early childhood.

Authors:  Caroline E Boeke; Christos S Mantzoros; Michael D Hughes; Sheryl L Rifas-Shiman; Eduardo Villamor; Chloe A Zera; Matthew W Gillman
Journal:  Obesity (Silver Spring)       Date:  2013-05-25       Impact factor: 5.002

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  24 in total

1.  Associations of cord blood leptin and adiponectin with children's cognitive abilities.

Authors:  Nan Li; Tye E Arbuckle; Gina Muckle; Bruce P Lanphear; Michel Boivin; Aimin Chen; Linda Dodds; William D Fraser; Emmanuel Ouellet; Jean R Séguin; Maria P Velez; Kimberly Yolton; Joseph M Braun
Journal:  Psychoneuroendocrinology       Date:  2018-10-25       Impact factor: 4.905

2.  Leptin trajectories from birth to mid-childhood and cardio-metabolic health in early adolescence.

Authors:  Ling-Jun Li; Sheryl L Rifas-Shiman; Izzuddin M Aris; Christos Mantzoros; Marie-France Hivert; Emily Oken
Journal:  Metabolism       Date:  2018-11-07       Impact factor: 8.694

3.  Newborn adipokines and early childhood growth.

Authors:  E H Yeung; R Sundaram; Y Xie; D A Lawrence
Journal:  Pediatr Obes       Date:  2018-05-20       Impact factor: 4.000

4.  Early-life exposure to traffic-related air pollution and child anthropometry.

Authors:  Clara G Sears; Catrina Mueller-Leonhard; Gregory A Wellenius; Aimin Chen; Patrick Ryan; Bruce P Lanphear; Joseph M Braun
Journal:  Environ Epidemiol       Date:  2019-10

5.  Adiponectin and vitamin D-binding protein are independently associated at birth in both mothers and neonates.

Authors:  Spyridon N Karras; Stergios A Polyzos; Danforth A Newton; Carol L Wagner; Bruce W Hollis; Jody van den Ouweland; Erdinc Dursun; Duygu Gezen-Ak; Kalliopi Kotsa; Cedric Annweiler; Declan P Naughton
Journal:  Endocrine       Date:  2017-11-18       Impact factor: 3.633

6.  Associations of maternal and cord blood adipokines with offspring adiposity in Project Viva: is there an interaction with child age?

Authors:  L-J Li; S L Rifas-Shiman; I M Aris; J G Young; C Mantzoros; M-F Hivert; E Oken
Journal:  Int J Obes (Lond)       Date:  2017-10-13       Impact factor: 5.095

7.  Vitamin A and E Nutritional Status in Relation to Leptin, Adiponectin, IGF-I and IGF-II in Early Life - a Birth Cohort Study.

Authors:  Qinwen Du; Zhong-Cheng Luo; Anne Monique Nuyt; Francois Audibert; Pierre Julien; Shu-Qin Wei; Dan-Li Zhang; William Fraser; Emile Levy
Journal:  Sci Rep       Date:  2018-01-08       Impact factor: 4.379

8.  Maternal Circulating Placental Growth Factor and Neonatal Metabolic Health Biomarkers in Small for Gestational Age Infants.

Authors:  Hua He; Anne Monique Nuyt; Zhong-Cheng Luo; Francois Audibert; Lise Dubois; Shu-Qin Wei; Haim A Abenhaim; Emmanuel Bujold; Isabelle Marc; Pierre Julien; William D Fraser
Journal:  Front Endocrinol (Lausanne)       Date:  2018-04-25       Impact factor: 5.555

Review 9.  High Fat Programming and Cardiovascular Disease.

Authors:  Marlon E Cerf
Journal:  Medicina (Kaunas)       Date:  2018-11-13       Impact factor: 2.430

10.  The protective effect of neonatal oral administration of oleanolic acid against the subsequent development of fructose-induced metabolic dysfunction in male and female rats.

Authors:  Trevor T Nyakudya; Emmanuel Mukwevho; Kennedy H Erlwanger
Journal:  Nutr Metab (Lond)       Date:  2018-11-20       Impact factor: 4.169

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