OBJECTIVE

We examined whether the PPARγ2 Ala12 allele influences growth in early life and whether this association is modified by breast-feeding.

RESEARCH DESIGN AND METHODS

This study was embedded in the Generation R Study, a prospective cohort study from early fetal life onward. PPARγ2 was genotyped in DNA obtained from cord blood samples in 3,432 children. Information about breast-feeding was available from questionnaires. Weight, head circumference, and femur length were repeatedly measured in second and third trimesters of pregnancy, at birth, and at the ages of 1.5, 6, 11, 14, and 18 months.

RESULTS

Genotype frequency distribution was 77.6% (Pro12Pro), 20.7% (Pro12Ala), and 1.7% (Ala12Ala). Growth rates in weight from second trimester of pregnancy to 18 months were higher for Pro12Ala and Ala12Ala than for Pro12Pro carriers (differences 1.11 g/week [95% CI 0.47–1.74] and 2.65 g/week [0.45–4.87], respectively). We found an interaction between genotype and breast-feeding duration (P value for interaction <0.0001). In infants who were breast-fed for ≥4 months, PPARγ2 Pro12Ala was not associated with growth rate. When breast-feeding duration was <2 months or 2–4 months, growth rate was higher in Ala12Ala than Pro12Pro carriers (differences 9.80 g/week [3.97–15.63] and 6.32 g/week [−1.04 to 13.68], respectively).

CONCLUSIONS

The PPARγ2 Ala12 allele is associated with an increased growth rate in early life. This effect may be influenced by breast-feeding duration. Further studies should replicate these findings, identify the underlying mechanisms, and assess whether these effects persist into later life.

Previous studies have shown that common polymorphisms of peroxisome proliferator–activated receptor γ2 (PPARγ2) are associated with adipocyte differentiation, lipid metabolism, and insulin sensitivity (1). Recent genome-wide association (GWA) studies found consistent and robust associations of the PPARγ2 Pro12Ala polymorphism (rs1801282) with type 2 diabetes (2,3). Furthermore, several studies have reported increased BMI in PPARγ2 Ala12 carriers (4,6). In a meta-analysis, Masud et al. (4) found that in adults with a BMI >27 kg/m2 carriers of the PPARγ2 Ala12 allele had an increased BMI. Also, in subjects with normal BMI, they found a significant increase in BMI in Ala12Ala carriers versus Pro12Pro carriers. However, a recent GWA study on BMI in more than 80,000 subjects did not identify the PPARγ2 Ala12 allele as a variant associated with BMI in the general adult population (7). Among children, it has been suggested that the PPARγ2 Ala12 allele is associated with BMI, although evidence for this is very limited. In a small study, carriers of the PPARγ2 Ala12 allele were shown to be heavier at age 7 years (5); in a cohort of children aged 1–6 years, an association of PPARγ2 Ala12 allele with increased adiposity was only found in girls aged 3–4 years (6).

Common polymorphisms of PPARγ2 may also explain previously suggested associations of growth in early fetal life and infancy with obesity (8). This association may be explained by early, modest alterations in insulin secretion and sensitivity because insulin is the most important fetal growth hormone (9). The effect of the Ala12 allele on anthropometrics and growth patterns may already be present in fetal life and infancy. In two large studies, no association was found between PPARγ2 and birth weight, although an association with preterm birth has been suggested (10,12). Birth weight alone might be an inappropriate measure of individual growth potential because different fetal growth rates may lead to the same birth weight (13). Moreover, most growth-restricted infants catch up to their own genetically determined growth curve during the first postnatal years (14). The PPARγ2 Ala12 allele has been found to interact with birth weight in determining further growth patterns (15). Also, the effect of PPARγ2 genotype on metabolic phenotype appears to depend on dietary intake (16,18). No previous studies have examined the effect of breast-feeding on the association of PPARγ2 genotype with growth in early life, while breast-feeding is well known to influence early growth and has a protective effect on the risk of obesity in childhood (19,21).

Based on previous findings, we hypothesized that the PPARγ2 Ala12 allele is associated with increased weight gain during early life and that this association might be influenced by breast-feeding duration. We examined in a large prospective birth cohort study from fetal life onward the association of the Pro12Ala polymorphism in the PPARγ2 gene with growth in fetal life and infancy and whether this association may be modified by breast-feeding.

This study was embedded in the Generation R Study, a prospective cohort study from early fetal life onward. This study is designed to identify early environmental and genetic determinants of growth, development, and health from fetal life until young adulthood and has been described previously in detail (22,23). Fetal and postnatal growth and their main determinants were repeatedly measured by physical examinations, fetal ultrasounds, biological samples, and questionnaires. As previously reported, of all eligible children born in the study area, 61% participated in the study (22). The study has been approved by the medical ethics committee of the Erasmus Medical Center, Rotterdam. Written informed consent was obtained from all parents.

Analyses were restricted to children from whom DNA was available for PPARγ2 genotyping and with Dutch or other European Caucasian ethnicity as defined by having both parents born in the Netherlands or another European country (n = 3,432). Fetal growth measurements were available in 3,331 and 3,398 children in second and third trimesters, respectively. Of these children, those living outside the study area postnatally (n = 341) were not followed up in infancy, leading to 3,091 subjects for the postnatal growth measurements (Fig. 1). Of all children followed postnatally, data on breast-feeding were missing in 405 subjects, leaving 2,690 children with complete data on postnatal growth and breast-feeding duration. Of all genotyped subjects at baseline, the mean follow-up rate per visit was 77%.

FIG. 1.

Flow diagram indicating number of subjects for each analysis.

FIG. 1.

Flow diagram indicating number of subjects for each analysis.

Genotyping.

DNA was collected from cord blood samples at birth. Cord blood for DNA isolation was available in 59% of all live-born participating children. Missing cord blood samples were mainly due to logistical constraints at the delivery. Genotyping of the PPARγ2 gene Pro12Ala polymorphism (rs1801282) was performed using a Taqman allelic discrimination assay (Applied Biosystems, Foster City, CA) and Abgene QPCR ROX mix (Abgene, Hamburg, Germany). The genotyping reaction was amplified using the GeneAmp PCR system 9600 (95°C [15 min], then 40 cycles of 94°C [15 s] and 60°C [1 min]). Fluorescence was detected on the 7900HT Fast Real-Time PCR System (Applied Biosystems), and individual genotypes were determined using SDS software (version 2.3; Applied Biosystems). Genotyping was successful in 99% of the samples. To confirm the accuracy of the genotyping results, 276 randomly selected samples were genotyped for a second time with the same method. The error rate was 0%.

The genotype distribution (Pro12Pro 77.6%, Pro12Ala 20.7%, Ala12Ala 1.7%; minor allele frequency [Ala] of 22.4%) was similar to that found in previous studies, and the frequency distribution did not deviate from Hardy-Weinberg equilibrium (χ2 = 1.82; P = 0.18) (4).

Fetal growth and birth characteristics.

Fetal ultrasound examinations were carried out during visits to one of the research centers. These fetal ultrasounds were used for establishing gestational age in the first trimester and for assessing fetal growth characteristics in second and third trimesters of pregnancy (24). Fetal growth characteristic measurements used for the present study included head circumference, abdominal circumference, and femur length, measured in second and third trimesters to the nearest millimeter using standardized ultrasound procedures (25). Estimated fetal weight (EFW) was calculated using the formula by Hadlock using head circumference (HC), abdominal circumference (AC), and femur length (FL) (log10 EFW = 1.5662 − 0.0108 [HC] + 0.0468 [AC] + 0.171 [FL] + 0.00034 [HC]2 − 0.003685 [AC × FL]) (26). First trimester ultrasound measures were not included as growth characteristics because these ultrasound examinations were primarily performed to establish gestational age.

Postnatal growth.

Birth weight, date of birth, and sex were obtained from community midwife and hospital registries. Information on head circumference or length at birth was not available. Well-trained staff in community health centers obtained postnatal growth characteristics using standardized procedures. Weight was measured using electronic scales (SECA, Hamburg, Germany). Length was determined in supine position to the nearest millimeter until the age of 6 months using a neonatometer, after which it was measured in upright position (Holtain Limited, Dyfed, U.K.). Head circumference was measured to the nearest millimeter using a standardized tape (SECA). Based on the routine health care program, visits for measurements of these growth characteristics were grouped into five age periods: 1.5 months (range 0–3.99), 6 months (4–9.99), 11 months (10–12.99), 14 months (13–16.99), and 18 months (17–20.99). Postnatally, head circumference was only measured at 1.5, 6, and 14 months.

Breast-feeding.

Information about duration of breast-feeding was obtained by postnatal questionnaires at the ages of 2, 6, and 12 months. This information was combined to form the following categories: 1) breast-fed 0–2 months, 2) 2–4 months, and 3) >4 months.

Covariates.

Information on maternal age, educational level, parity, and weight before pregnancy was obtained by the first questionnaire at enrollment in the study. Maternal height was measured without shoes at our research center, and BMI was calculated as weight divided by the square of height in meters. The occurrence of gestational diabetes was obtained from midwife or obstetric records.

Data analysis.

We explored the differences in gestational age (weeks) and birth weight (SD) between the three genotypes with additive, dominant (PPARγ2 Ala12Ala/Pro12Ala vs. Pro12Pro), and recessive (PPARγ2 Pro12Pro/Pro12Ala vs. Ala12Ala) models using Mann-Whitney U tests and linear regression. To assess longitudinally measured growth characteristics from fetal life to infancy, we performed unbalanced repeated-measures regression analysis with weight, length, and head circumference in fetal life and infancy as outcomes. This regression technique takes the correlation of multiple measurements within one subject into account, assesses both the time-independent and time-dependent effect of PPARγ2 Pro12Ala genotype on growth, and allows for incomplete outcome data (27). The best-fitting model as a function of (gestational) age was constructed using fractional polynomials (28).

To account for differences in growth curves for weight, length, and head circumference in fetal life and infancy, growth models were constructed for three age periods: second trimester to 18 months (overall), second trimester to birth (fetal), and birth to 18 months (infancy). In the fetal and overall models, age was defined as age in weeks after conception. For the infancy model, age represented biological age in weeks, and these models were additionally adjusted for gestational age at birth. In all models, genotype was included as both intercept and interaction with age to account for differences at baseline and in growth rates. The models used are shown in the supplemental material available in an online appendix at http://diabetes.diabetesjournals.org/cgi/content/full/db08-1311/DC1.

Finally, we examined the interaction between genotype and breast-feeding duration and the effect on growth rate. Trend tests were performed by adding both genotype and breast-feeding duration in the model as ordinal variables instead of categorical variables. All models were adjusted only for sex because population genotype distribution is assumed to be unrelated to covariates, and the effect estimates were not materially affected by adjusting for covariates such as maternal age, educational level, prepregnancy, BMI, or parity (29). The occurrence of gestational diabetes in the entire cohort was 0.6%, did not differ between the genotypes, and was, therefore, not included in the analyses. All effect estimates are presented with their 95% CI. Statistical analyses were performed using the Statistical Analysis System, Version 8.2 (SAS, Stata, College Station, TX), including the PROC MIXED module for unbalanced repeated measurements and the Statistical Package of Social Sciences, Version 15.0 for Windows (SPSS, Chicago, IL).

Tables 1 and 2 show the subjects characteristics. In the whole study group, mean birth weight ± SD was 3,512 ± 510 g and median gestational age was 40.3 weeks (95% range 36.7–42.4). The percentages of children born prematurely and with low birth weight were 2.9 and 2.4%, respectively. The median number of measurements (visits) per subject was seven (95% range 3–8 visits). Table 3 shows the differences in birth characteristics between the PPARγ2 Pro12Ala genotypes. No significant differences were found in gestational age and birth weight ±SD. Pro12Ala and Ala12Ala carriers tended to have an increased risk of preterm birth, although these differences were not significant (odds ratios [95% CI] using the Pro12Pro genotype as a reference: 1.51 [0.96–2.38] for Pro12Ala and 2.08 [0.64–6.80] for Ala12Ala).

TABLE 1

Maternal and birth characteristics of 3,432 children

Age (years) 30.8 ± 4.8 
Weight before pregnancy (kg) 66.9 ± 11.8 
Height (cm) 170 ± 6.7 
BMI before pregnancy (kg/m222.9 ± 3.7 
Parity (% nulliparous) 60.1 
Gestational diabetes (%) 0.6 
Placental weight (g) 640 ± 142 
Educational level (%)  
    Primary school 4.5 
    Secondary school 39.1 
    Higher education 56.5 
Birth characteristics  
    Sex (% male) 50.8 
    Gestational age (weeks) 40.3 (36.7–42.4) 
    Birth weight (g) 3512 ± 510 
    Premature (gestational age <37 weeks) (%) 2.9 
    Birth weight <2,500 g (%) 2.4 
    Small for gestational age (below −2 SD) (%) 2.4 
Breast-feeding duration (%)  
    0–2 months 31.3 
    2–4 months 26.4 
    >4 months 42.3 
Number of visits 7 (3–8) 
Age (years) 30.8 ± 4.8 
Weight before pregnancy (kg) 66.9 ± 11.8 
Height (cm) 170 ± 6.7 
BMI before pregnancy (kg/m222.9 ± 3.7 
Parity (% nulliparous) 60.1 
Gestational diabetes (%) 0.6 
Placental weight (g) 640 ± 142 
Educational level (%)  
    Primary school 4.5 
    Secondary school 39.1 
    Higher education 56.5 
Birth characteristics  
    Sex (% male) 50.8 
    Gestational age (weeks) 40.3 (36.7–42.4) 
    Birth weight (g) 3512 ± 510 
    Premature (gestational age <37 weeks) (%) 2.9 
    Birth weight <2,500 g (%) 2.4 
    Small for gestational age (below −2 SD) (%) 2.4 
Breast-feeding duration (%)  
    0–2 months 31.3 
    2–4 months 26.4 
    >4 months 42.3 
Number of visits 7 (3–8) 

Data are means ± SD, median (95% range), or percentages. Of the total group, data were missing on weight before pregnancy (n = 468), height (n = 5), BMI before pregnancy (n = 470), parity (n = 3), gestational diabetes (n = 39), placental weight (n = 792), educational level (n = 45), and breast-feeding duration (n = 742).

TABLE 2

Fetal and postnatal growth characteristics of 3,432 children

Second trimester  
    Gestational age at visit (weeks) 20.5 (18.6–23.0) 
    Head circumference (cm) 18.0 ± 1.4 
    Femur length (mm) 33.3 ± 3.4 
    Estimated fetal weight (g) 380 ± 87 
Third trimester  
    Gestational age at visit (weeks) 30.4 (28.5–32.7) 
    Head circumference (cm) 28.6 ± 1.2 
    Femur length (mm) 57.4 ± 2.8 
    Estimated fetal weight (g) 1,628 ± 249 
Birth  
    Gestational age (weeks) 40.3 (36.7–42.4) 
    Weight (g) 3,512 ± 510 
1.5 months  
    Age at visit (months) 1.3 (0.9–3.0) 
    Head circumference (cm) 38.3 ± 1.5 
    Length (cm) 55.8 ± 2.8 
    Weight (g) 4,788 ± 728 
6 months  
    Age at visit (months) 6.1 (4.5–7.7) 
    Head circumference (cm) 43.5 ± 1.3 
    Length (cm) 67.7 ± 2.6 
    Weight (g) 7,798 ± 862 
11 months  
    Age at visit (months) 11.0 (10.1–12.5) 
    Length (cm) 74.4 ± 2.5 
    Weight (g) 9,644 ± 999 
14 months  
    Age at visit (months) 14.2 (13.5–15.7) 
    Head circumference (cm) 47.2 ± 1.3 
    Length (cm) 78.4 ± 2.7 
    Weight (g) 10,532 ± 1,089 
18 months  
    Age at visit (months) 18.3 (17.3–20.4) 
    Length (cm) 82.4 ± 3.0 
    Weight (g) 11,556 ± 1,214 
Second trimester  
    Gestational age at visit (weeks) 20.5 (18.6–23.0) 
    Head circumference (cm) 18.0 ± 1.4 
    Femur length (mm) 33.3 ± 3.4 
    Estimated fetal weight (g) 380 ± 87 
Third trimester  
    Gestational age at visit (weeks) 30.4 (28.5–32.7) 
    Head circumference (cm) 28.6 ± 1.2 
    Femur length (mm) 57.4 ± 2.8 
    Estimated fetal weight (g) 1,628 ± 249 
Birth  
    Gestational age (weeks) 40.3 (36.7–42.4) 
    Weight (g) 3,512 ± 510 
1.5 months  
    Age at visit (months) 1.3 (0.9–3.0) 
    Head circumference (cm) 38.3 ± 1.5 
    Length (cm) 55.8 ± 2.8 
    Weight (g) 4,788 ± 728 
6 months  
    Age at visit (months) 6.1 (4.5–7.7) 
    Head circumference (cm) 43.5 ± 1.3 
    Length (cm) 67.7 ± 2.6 
    Weight (g) 7,798 ± 862 
11 months  
    Age at visit (months) 11.0 (10.1–12.5) 
    Length (cm) 74.4 ± 2.5 
    Weight (g) 9,644 ± 999 
14 months  
    Age at visit (months) 14.2 (13.5–15.7) 
    Head circumference (cm) 47.2 ± 1.3 
    Length (cm) 78.4 ± 2.7 
    Weight (g) 10,532 ± 1,089 
18 months  
    Age at visit (months) 18.3 (17.3–20.4) 
    Length (cm) 82.4 ± 3.0 
    Weight (g) 11,556 ± 1,214 

Data are means ± SD or median (95% range).

TABLE 3

Birth characteristics according to PPARγ2 Pro12Ala genotypes in 3,432 children

Pro12Pro (n = 2,664)Pro12Ala (n = 710)Ala12Ala (n = 58)P values
AdditiveDominantRecessive
Gestational age (weeks) 40.3 (36.9–42.4) 40.3 (36.4–42.4) 40.4 (32.3–42.9) 0.35 0.69 0.91 
    <37 weeks 2.6 (68) 3.8 (27) 5.2 (3)    
    >37 weeks 97.4 (2,596) 96.2 (683) 94.8 (55)    
Birth weight 0.04 ± 0.98 0.04 ± 1.03 0.28 ± 0.97 0.37 0.66 0.07 
    <−2 SD 2.2 (59) 3.4 (24) 0.0 (0)    
    >−2 SD 97.8 (2,605) 96.6 (686) 100.0 (58)    
Pro12Pro (n = 2,664)Pro12Ala (n = 710)Ala12Ala (n = 58)P values
AdditiveDominantRecessive
Gestational age (weeks) 40.3 (36.9–42.4) 40.3 (36.4–42.4) 40.4 (32.3–42.9) 0.35 0.69 0.91 
    <37 weeks 2.6 (68) 3.8 (27) 5.2 (3)    
    >37 weeks 97.4 (2,596) 96.2 (683) 94.8 (55)    
Birth weight 0.04 ± 0.98 0.04 ± 1.03 0.28 ± 0.97 0.37 0.66 0.07 
    <−2 SD 2.2 (59) 3.4 (24) 0.0 (0)    
    >−2 SD 97.8 (2,605) 96.6 (686) 100.0 (58)    

Data are means ± SD or median (95% range) for continuous variables and (%) n for dichotomous variables. Differences were tested using Mann-Whitney U-test or linear regression. Dominant model: PPARγ2 Ala12Ala/Pro12Ala vs. Pro12Pro. Recessive model: PPARγ2 Pro12Pro/Pro12Ala vs. Ala12Ala.

Table 4 shows the differences in growth rates between the PPARγ2 genotypes using Pro12Pro as the reference group. In fetal life, PPARγ2 Ala12Ala carriers tended to show an increased growth rate in weight compared with the Pro12Pro carriers. Postnatally, both the Pro12Ala and Ala12Ala carriers have an increased growth rate in weight compared with Pro12Pro carriers, although this was not significant in the latter. Furthermore, we observed an allele dose effect for each additional Ala12 allele (P = 0.0092). For weight gain over the entire period from the second trimester to 18 months, PPARγ2 Pro12Pro carriers had a significantly lower growth rate for weight than the other genotypes, and there was a significant trend for each additional Ala12 allele (P < 0.0001). Figure 2 shows the estimated weight differences in grams between the genotype groups. The estimated differences at 18 months compared with the Pro12Pro group were 99 and 291 g for the Pro12Ala and Ala12Ala carriers, respectively. Prenatal head circumference growth rate was significantly lower in Ala12Ala compared with Pro12Pro carriers. No other significant differences were found in growth rate of head circumference or length.

TABLE 4

Differences in growth rate for weight by PPARγ2 Pro12Ala genotypes using repeated measures regression analysis

Difference in weight gain from second trimester to birth (g/week) (n = 3,432)Difference in weight gain from birth to 18 months (g/week) (n = 3,091)Difference in weight gain from second trimester to 18 months (g/week) (n = 3,091)
Pro12Pro Reference Reference Reference 
Pro12Ala 0.50 (−0.96 to 1.98) 1.06 (0.02–2.10) 1.11 (0.47–1.74) 
    P 0.4988 0.0454 0.0007 
Ala12Ala 4.02 (−0.59 to 8.63) 3.52 (−0.05 to 7.10) 2.65 (0.45–4.87) 
    P 0.0876 0.0535 0.0185 
P for trend 0.1575 0.0092 <0.0001 
Difference in weight gain from second trimester to birth (g/week) (n = 3,432)Difference in weight gain from birth to 18 months (g/week) (n = 3,091)Difference in weight gain from second trimester to 18 months (g/week) (n = 3,091)
Pro12Pro Reference Reference Reference 
Pro12Ala 0.50 (−0.96 to 1.98) 1.06 (0.02–2.10) 1.11 (0.47–1.74) 
    P 0.4988 0.0454 0.0007 
Ala12Ala 4.02 (−0.59 to 8.63) 3.52 (−0.05 to 7.10) 2.65 (0.45–4.87) 
    P 0.0876 0.0535 0.0185 
P for trend 0.1575 0.0092 <0.0001 
Difference in head circumference gain from second trimester to 1.5 months (mm × 10−1/week) (n = 3,432)Difference in head circumference gain from 1.5 to 14 months (mm × 10−1/week) (n = 3,091)Difference in head circumference gain from second trimester to 14 months (mm × 10−1/week) (n = 3,091)
Pro12Pro Reference Reference Reference 
Pro12Ala −0.16 (−0.51 to 0.18) 0.01 (−0.01 to 0.03) 0.05 (−0.09 to 0.19) 
    P 0.3617 0.1587 0.4725 
Ala12Ala −1.40 (−2.56, −0.23) 0.02 (−0.05, 0.10) −0.48 (−1.01, 0.02) 
    P 0.0186 0.5104 0.0643 
P for trend 0.0566 0.1293 0.8368 
Difference in head circumference gain from second trimester to 1.5 months (mm × 10−1/week) (n = 3,432)Difference in head circumference gain from 1.5 to 14 months (mm × 10−1/week) (n = 3,091)Difference in head circumference gain from second trimester to 14 months (mm × 10−1/week) (n = 3,091)
Pro12Pro Reference Reference Reference 
Pro12Ala −0.16 (−0.51 to 0.18) 0.01 (−0.01 to 0.03) 0.05 (−0.09 to 0.19) 
    P 0.3617 0.1587 0.4725 
Ala12Ala −1.40 (−2.56, −0.23) 0.02 (−0.05, 0.10) −0.48 (−1.01, 0.02) 
    P 0.0186 0.5104 0.0643 
P for trend 0.0566 0.1293 0.8368 
Difference in length gain from second trimester to 1.5 months (mm × 10−1/week) (n = N/A)Difference in length gain from 1.5 to 18 months (mm × 10−1/week) (n = 3,091)Difference in length gain from second trimester to 14 months (mm × 10−1/week) (n = N/A)
Pro12Pro N/A Reference N/A 
Pro12Ala N/A −0.11 (−0.37 to 0.14) N/A 
    P  0.3864  
Ala12Ala N/A 0.43 (−0.44 to 1.31) N/A 
    P  0.3299  
P for trend  0.4429  
Difference in length gain from second trimester to 1.5 months (mm × 10−1/week) (n = N/A)Difference in length gain from 1.5 to 18 months (mm × 10−1/week) (n = 3,091)Difference in length gain from second trimester to 14 months (mm × 10−1/week) (n = N/A)
Pro12Pro N/A Reference N/A 
Pro12Ala N/A −0.11 (−0.37 to 0.14) N/A 
    P  0.3864  
Ala12Ala N/A 0.43 (−0.44 to 1.31) N/A 
    P  0.3299  
P for trend  0.4429  

Data are regression coefficients (95% CI) and reflect the difference in growth rate. Models are adjusted for sex of the child. Analyses focused on growth during infancy are additionally adjusted for gestational age at birth. Estimates based on repeated-measures regression analysis. N = 3,432 for growth from second trimester to birth/1.5 months (Pro12Pro, n = 2664; Pro12Ala, n = 710; Ala12Ala, n = 58); N = 3,091 for postnatal growth and growth over the entire period (Pro12Pro, n = 2440; Pro12Ala, n = 644; Ala12Ala, n = 47).

FIG. 2.

Differences in weight between fetal PPARγ2 Pro12Ala genotypes using Pro12Pro as the reference in 3,091 children. Values reflect the difference in weight and are based on repeated-measures regression analysis, using the following model: Weight (g) = β0 + β1 × age + β2 × age2 + β3 × genotype + β4 × genotype × age + β5 × sex.

FIG. 2.

Differences in weight between fetal PPARγ2 Pro12Ala genotypes using Pro12Pro as the reference in 3,091 children. Values reflect the difference in weight and are based on repeated-measures regression analysis, using the following model: Weight (g) = β0 + β1 × age + β2 × age2 + β3 × genotype + β4 × genotype × age + β5 × sex.

We found a significant interaction between genotype and breast-feeding duration on growth rate in weight (P for interaction <0.0001). Figure 3 shows the differences in estimated growth rates between the genotypes in the three breast-feeding groups. When breast-feeding duration was <2 months or 2–4 months, growth rate was higher in Ala12Ala than Pro12Pro carriers (differences 9.80 g/week [95%CI 3.97–15.63] and 6.32 g/week [−1.04 to 13.68], respectively). No associations of PPARγ2 with growth rate were found in children who were breast-fed >4 months. Table 5 shows the trends within each genotype and breast-feeding group. The maximum difference in growth rate was found between PPARγ2 Ala12Ala carriers who were breast-fed <2 months and Pro12Pro carriers who were breast-fed >4 months (difference 12.62 g/week (6.80–18.44)). Similar effects were found in subjects who were breast-fed for 2–4 months, and we found an allele dose effect for each additional Ala12 allele in these subjects (P = 0.0264). All effect estimates did not materially change after restricting analyses to term-born subjects (>37 weeks of gestation) or after adjusting for covariates, such as maternal age, educational level, prepregnancy, BMI, or parity (data not shown). No interactions between genotype and breast-feeding on length or head circumference growth rate were observed (data not shown).

FIG. 3.

Differences in postnatal weight between fetal PPARγ2 Pro12Ala genotypes stratified by duration of breast-feeding in 2,690 children. Values are regression coefficients (95% CI) and reflect the difference in weight gain (g/week). Model is adjusted for gestational age at birth and sex of the child. Estimates based on repeated-measures regression analysis.

FIG. 3.

Differences in postnatal weight between fetal PPARγ2 Pro12Ala genotypes stratified by duration of breast-feeding in 2,690 children. Values are regression coefficients (95% CI) and reflect the difference in weight gain (g/week). Model is adjusted for gestational age at birth and sex of the child. Estimates based on repeated-measures regression analysis.

TABLE 5

Interaction between fetal PPARγ2 Pro12Ala genotype and breast-feeding in relation to growth rate (g/week) between birth and 18 months in 2,690 children

Breast-feeding duration
0–2 months (n = 843)2–4 months (n = 710)>4 months (n = 1,137)P for trend
Pro12Pro (N = 2,079) 2.82 (1.78–3.85) 1.37 (0.28–2.46) 0 (ref.) <0.0001 
    n 663 547 869  
    P <0.0001 0.0135   
Pro12Ala (N = 570) 3.34 (1.64–5.05) 3.05 (1.27–4.84) 0.17 (−1.26 to 1.59) 0.0013 
    n 166 155 249  
    P 0.0001 0.0008 0.8170  
Ala12Ala (N = 41) 12.62 (6.80–18.44) 7.69 (0.35–15.03) −1.21 (−5.99 to 3.56) 0.0003 
 14 19  
 <0.0001 0.0399 0.6183  
P for trend 0.0432 0.0264 0.9555 interaction <0.0001 
Breast-feeding duration
0–2 months (n = 843)2–4 months (n = 710)>4 months (n = 1,137)P for trend
Pro12Pro (N = 2,079) 2.82 (1.78–3.85) 1.37 (0.28–2.46) 0 (ref.) <0.0001 
    n 663 547 869  
    P <0.0001 0.0135   
Pro12Ala (N = 570) 3.34 (1.64–5.05) 3.05 (1.27–4.84) 0.17 (−1.26 to 1.59) 0.0013 
    n 166 155 249  
    P 0.0001 0.0008 0.8170  
Ala12Ala (N = 41) 12.62 (6.80–18.44) 7.69 (0.35–15.03) −1.21 (−5.99 to 3.56) 0.0003 
 14 19  
 <0.0001 0.0399 0.6183  
P for trend 0.0432 0.0264 0.9555 interaction <0.0001 

Data are regression coefficients (95% CI) unless otherwise indicated and reflect the difference in weight gain from birth to 18 months (g/week). Model is adjusted for gestational age at birth and sex of the child. Estimates based on repeated-measures regression analysis.

In this study, we show that the Ala allele of the PPARγ2 Pro12Ala gene polymorphism (rs1801282) is associated with an increased growth rate in early life. Between the second trimester of pregnancy and 18 months, children with Pro12Ala and Ala12Ala had higher growth rates in weight than Pro12Pro subjects. Furthermore, our results suggest that the effect of PPARγ2 gene polymorphism on growth in infancy depends on breast-feeding duration.

To our knowledge, this study is the first prospective cohort study that examines the association of this PPARγ2 Pro12Ala gene polymorphism with growth from fetal life until infancy. DNA for genotyping was available in 59% of all subjects. Missing cord blood and DNA was mainly caused by logistical constraints at delivery. Children who were not genotyped had a shorter gestational age (P < 0.001) and were lighter at birth (P < 0.001) than subjects who were genotyped. Of all genotyped subjects at baseline, the mean follow-up rate per visit was 77%. Response rates were lowest at the age of 18 months. This is mainly due to the general lower response rate at this age in the routine child care system. In our data, no differences in genotype frequency or birth characteristics were observed between children with and without postnatal growth measurements. Furthermore, similar results were observed when analyses were restricted to children aged 11 or 14 months and when breast-feeding duration was dichotomized into shorter or longer than 3 months (data not shown). Our effect estimates would be biased if the associations between genotypes and growth characteristics differed between those with and without postnatal growth data available. This seems unlikely. Finally, similar to other studies, the number of PPARγ2 Ala12Ala carriers was small, especially after stratifying for breast-feeding duration (4). Despite the low number of subjects in this group, we found significant effects in Ala12Ala carriers. Furthermore, we found effects in Pro12Ala carriers and significant trend effects for each additional Ala12 allele.

Several studies have found associations between this PPARγ2 Pro12Ala gene polymorphism and body composition (4,30,31). A meta-analysis demonstrated that the Ala12Ala is associated with higher BMI in adulthood (4). However, in a recent very large GWA study, the PPARγ2 Ala12 allele was not identified as a BMI variant in adulthood (7). A number of studies have suggested that the PPARγ2 Ala12 allele may be associated with adiposity during childhood, although evidence of this is limited (5,6,15). In a small study, Pihlajamäki et al. (5) showed that children with the Ala12 allele were heavier at age 7 years. Also, in a cohort of children from 1–6 years of age, an association of the PPARγ2 Ala12 allele with increased adiposity was only found in girls aged 3–4 years (6). Two large birth cohort studies found no association between this PPARγ2 polymorphism and birth weight (10,11). Eriksson et al. (32) described a gene–birth weight interaction in adults, where individuals with the Ala12 allele and a lower birth weight were at risk of increased lipid levels.

No other studies have examined the effect of this polymorphism on growth in fetal life and infancy in a large prospective cohort. Most studies on weight and body composition were performed retrospectively on cross-sectional data. We believe that if this PPARγ2 polymorphism is truly associated with growth in early life, associations with longitudinally measured growth patterns might be expected to be stronger than those with only one or two growth measurements. Our study showed that carriers of at least one PPARγ2 Ala12 allele had an increased growth rate in weight until the age of 18 months. The postnatal effect on growth rate appeared to be dependent on duration of breast-feeding. No association was found between genotype and growth rate in children who had received at ≤4 months of breast-feeding. Among children who were breast-fed for <4 months, a significant positive effect on growth rate of up to almost 10 g/week was found in the PPARγ2 Ala12 allele carriers. The size of the effect was inversely related to breast-feeding duration. These findings may indicate a possible gene-nutrition interaction with regards to growth rate.

There have been a limited number of previous studies that also have suggested a gene-nutrition interaction concerning this PPARγ2 polymorphism. In a large cohort, Memisoglu et al. (33) found that the relationship between dietary fat intake and BMI was dependent on PPARγ2 genotype. In this study, dietary fat intake was strongly associated with an increased risk of obesity among Pro12Pro carriers, although no association was reported among carriers of the Ala12 allele. Luan et al. (17) found that dietary fat intake (expressed as polyunsaturated-to-saturated fat ratio) was not associated with BMI or fasting insulin levels in Pro12Pro carriers but was inversely related in these outcomes in Ala12 allele carriers. Other studies, however, were not able to replicate these results (18,33). Our results would be in line with the findings of Luan et al. (34) based on the assumption that breast milk contains more polyunsaturated fatty acids than formula. Longer breast-feeding duration could lead to a higher polyunsaturated fat intake and, subsequently, to a relatively lower growth rate among Ala12Ala carriers who were breast-fed compared with those who were formula fed. Breast-feeding has also been indicated to have a protective effect on the risk of obesity, although the effect appears to be limited (19,21). Our results suggest that this protective effect might be modified by PPARγ2, because we found the highest growth rates in children who were never breast-fed or were breast-fed for <4 months and were Ala12 allele carriers. Furthermore, rapid weight gain in the first months of life is associated with increased risk of obesity in childhood (35). Based on the current study, it could be hypothesized that the interaction between PPARγ2 and breast-feeding plays an important role in this association.

From the current data, however, it remains unclear whether breast-feeding reduces the risk of an increased growth rate in PPARγ2 Ala12 allele carriers or formula feeding increases that same risk. The associations may be explained by either growth-stimulating or metabolism-inhibiting activities among Ala12 allele carriers. A previous study has shown that the PPARγ2 Ala12 allele is associated with a moderate reduction in PPARγ2 transcriptional activity (36). However, this study also demonstrated decreased BMI in Ala12 allele carriers, a finding that has been not confirmed by a large meta-analysis (4,36). Mouse models showed that mice with reduced PPARγ2 activity seem to be resistant to high-fat diet–induced obesity, but not high-carbohydrate diet–induced obesity (37). The current study did not allow us to measure calorie intake of breast-fed or formula-fed children. Calorie intake in breast-feeding has been shown to be dependent on maternal factors, such as body composition and nutritional status (38). All these factors indicate that the underlying mechanism in which PPARγ2 Pro12Ala interacts with early dietary intake is highly complex and emphasizes the necessity for further genetic association and functional studies.

In summary, our results suggest that this PPARγ2 polymorphism influences growth rate from early fetal life to infancy. This effect on growth rate is restricted to infants who were breast-fed for <4 months. Studies in larger cohorts with a longer follow-up period will allow us to examine whether these effects persist throughout childhood. Additionally, systematic searches for common genetic variants by means of GWA studies may enable us to obtain a more complete understanding of what genes are involved in growth in fetal life and infancy and how they interact with the environment.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service of Rotterdam, the Rotterdam Homecare Foundation, and the Stichting Trombosedienst and Artsenlaboratorium Rijnmond (STAR) Rotterdam. The first phase of the Generation R Study is made possible by financial support from the Erasmus Medical Center, the Erasmus University Rotterdam, and the Netherlands Organization for Health Research and Development (ZonMw).

No potential conflicts of interest relevant to this article were reported.

We acknowledge the contribution of general practitioners, hospitals, midwives, and pharmacies in Rotterdam, the Netherlands.

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Supplementary data