Literature DB >> 19846795

Breast-feeding modulates the influence of the peroxisome proliferator-activated receptor-gamma (PPARG2) Pro12Ala polymorphism on adiposity in adolescents: The Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study.

Caroline Verier1, Aline Meirhaeghe, Szilvia Bokor, Christina Breidenassel, Yannis Manios, Dénes Molnár, Enrique G Artero, Esther Nova, Stefaan De Henauw, Luis A Moreno, Philippe Amouyel, Idoia Labayen, Noemi Bevilacqua, Dominique Turck, Laurent Béghin, Jean Dallongeville, Frédéric Gottrand.   

Abstract

OBJECTIVE: The peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala polymorphism has been associated with a higher BMI and a lower risk of type 2 diabetes in adulthood. The association between adiposity and PPARG variants can be influenced by environmental factors such as early growth, dietary fat, and (as recently shown) breast-feeding. The objectives of this study were to assess 1) the influence of the PPARG2 Pro12Ala polymorphism on adiposity markers in adolescents and 2) a possible modulating effect of breast-feeding on these associations. RESEARCH DESIGN AND METHODS: Data on breast-feeding duration, BMI, and genotypes for the Pro12Ala polymorphism were available for 945 adolescents (mean age 14.7 years). The breast-feeding duration was obtained from parental records. We measured weight, height, waist circumference, and six skinfold thicknesses.
RESULTS: No significant associations between the Pro12Ala polymorphism and any of the above-mentioned anthropometric parameters were found. There were significant interactions between the PPARG2 Pro12Ala polymorphism and breast-feeding with regard to adiposity measurements (all adjusted P < 0.05). Indeed, in children who had not been breast-fed, Ala12 allele carriers had higher adiposity parameters (e.g., Delta BMI +1.88 kg/m(2), adjusted for age, sex, and center, P = 0.007) than Pro12Pro adolescents. In contrast, in breast-fed subjects, there was no significant difference between Ala12 allele carriers and Pro12Pro children in terms of adiposity measurements, whatever the duration of breast-feeding.
CONCLUSIONS: Breast-feeding appears to counter the deleterious effect of the PPARG2 Pro12Ala polymorphism on anthropometric parameters in adolescents.

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Year:  2009        PMID: 19846795      PMCID: PMC2797971          DOI: 10.2337/dc09-1459

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


The peroxisome proliferator–activated receptor-γ (PPARγ) transcription factor is primarily expressed in adipocytes. It is a member of the nuclear hormone receptor family, which influences whole-body energy homeostasis via three main metabolic pathways: adipocyte differentiation, insulin sensitivity, and lipoprotein metabolism. The PPARG gene (located on chromosome 3) gives rise to two different proteins, PPARγ1 and PPARγ2. The PPARγ2 protein is the more abundant isoform in adipose tissue, whereas PPARγ1 is ubiquitous. Of the several variants identified in the PPARG gene, one of the most common (minor allele frequency of ∼10% in Caucasians) is the Pro12Ala (rs1801282) substitution at codon 12 in PPARG2. This polymorphism has been shown to be associated with reduced ability to transactivate responsive promoters and thus with lower PPARγ2 transcriptional activity (1). In adults, the Pro12Ala polymorphism has been associated with higher BMI, waist circumference, and obesity risk (2–4). Even though a recent meta-analysis of genome-wide association studies for BMI failed to find any association between the Pro12Ala polymorphism and childhood or adult obesity (5), another meta-analysis showed that in selected subgroups, such as Caucasians and obese subjects, the Ala12 allele was associated with greater BMI and greater insulin sensitivity (6), suggesting that if this variant does influence obesity predisposition, it may do so through context-dependent mechanisms. This finding illustrates the importance for appropriate stratification of analyses by environmental or other genetic factors when PPARG variants are studied. More consistently, the Pro12Ala polymorphism has been associated with a lower risk of type 2 diabetes in a meta-analysis of genome-wide association studies (7). Data in children are scarcer. In 311 Finnish children aged 7 years, the Ala12 allele was associated with a higher ponderal index at birth and higher waist circumference in adulthood, relative to those for Pro12Pro subjects (8). In Greek girls aged 3–4 years, adiposity was higher in Ala12 allele carriers than in Pro12Pro carriers (9). Eriksson (10) showed that the well-known association existing between low birth weight and insulin resistance later in life was seen only in Pro12Pro individuals. Moreover, Meirhaeghe et al. (11) showed that individuals carrying the Ala12 allele had lower birth weight (due to shorter gestational duration and a higher risk of preterm birth) than Pro12Pro subjects. However, this result was not confirmed in 5,652 individuals from the Northern Finland Birth Cohort of 1966 (12). Labayen et al. (13) showed that low birth weight may program a lower fat-free mass in adolescents carrying the Ala12 allele. Last, certain environmental factors (such as dietary fat and physical activity) interact with the effect of the PPARG polymorphism on adiposity. Mook-Kanamori et al. (14) showed that the growth rate from birth to 18 months of age was higher in Ala12Ala carriers than in Pro12Pro carriers when the duration of breast-feeding was between 0 and 4 months, whereas the Pro12Ala polymorphism was not associated with an early growth rate in infants breast-fed for longer than 4 months. The aims of the present study were to 1) assess the influence of the PPARG2 Pro12Ala polymorphism on BMI, waist circumference, and the sum of six skinfold thicknesses in a sample of 945 European adolescents and 2) test the modulating effect of breast-feeding on these associations.

RESEARCH DESIGN AND METHODS

The current report is based on data derived from the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) cross-sectional study, the aim of which was to obtain a broad range of standardized, reliable, and comparable nutrition- and health-related data from a random sample of European adolescents aged 12.5–17.5 years. Data collection took place during 2006 and 2007 in 10 European cities. A detailed description of the HELENA study sampling has been published elsewhere (15). All of the adolescents meeting the general HELENA inclusion criteria and having data for age, sex, and BMI were considered in the final sample (n = 3,546). To investigate biochemical assays and genetic analyses, one-third of the cohort was randomly selected for blood collection (resulting in a total of 1,155 subjects). Of the latter, the 945 adolescents with data on the PPARG2 Pro12Ala polymorphism and BMI and breast-feeding information were included in the present study. After receiving comprehensive information on the study's aims and methods, all adolescents and their parents or guardians signed informed consent forms. The study was performed according to the ethical guidelines of the Edinburgh revision of the 1961 Declaration of Helsinki (2000), good clinical practice, and the legislation on clinical research in each of the participating countries. The protocol was approved by the investigational review boards at the participating university medical centers. The harmonized, standardized anthropometric measurements were strictly monitored. Participants were barefoot and in underwear, and anthropometric measurements were taken by trained researchers. Weight was measured with an electronic set of scales (Type SECA 861; precision 0.05 kg) and height was measured in the Frankfort plane with a height gauge (Type SECA 225; precision 1 mm). Waist circumference was measured with a nonelastic tape (Seca 200; precision 1 mm) to the nearest 0.1 cm. Skinfold thicknesses were measured at the left biceps, triceps, subscapular area, suprailiac area, thigh, and calf with a Holtain caliper (precision 0.2 mm), according to Lohman's anthropometric standardization reference manual. The overall score was calculated by summing the six skinfold thicknesses. Mean skinfolds and circumferences were calculated from three consecutive measurements. Identification of sexual maturation (Tanner and Whitehouse stages I–V) was assessed by a physician. Weight and height at birth and the durations of gestation and breast-feeding were collected via a parental questionnaire. The duration of gestation was stratified into three categories: <35, between 35 and 40, and >40 weeks. The total duration of breast-feeding was recoded from six categories into four: never, <3, 3–5, and ≥6 months. The duration of exclusive breast-feeding (defined by the World Health Organization as no liquid or solid nutrition other than breast milk) was recoded in a similar manner. A uniaxial accelerometer (ActiGraph GT1M, Pensacola, FL; http://www.theactigraph.com) was used to assess physical activity. Adolescents were instructed to place the monitor underneath clothing, at the lower back, using an elastic waistband, and to wear it for 7 consecutive days. They were also instructed to wear the accelerometer during all time awake and only to remove it during water-based activities. At least 3 days of recording with a minimum of 8 h registration/day was set as an inclusion criterion. In this study, the time-sampling interval (epoch) was set at 15 s. A measure of total volume of activity (hereafter called average physical activity) was expressed as the sum of recorded counts per epoch divided by total daily registered time expressed in minutes (counts per minute) (16). The socioeconomic level was assessed in terms of the maternal educational level and was coded into four categories (elementary, lower secondary, higher secondary, or higher education).

Preparation of genomic DNA from whole blood and genotyping

Blood samples were drawn at school after a 10-h, overnight fast and according to a standardized collection protocol; blood for DNA extraction was collected in EDTA K3 tubes. DNA was extracted from white blood cells with the Puregene kit (Qiagen, Courtaboeuf, France) and stored at −20°C. Genotyping of the Pro12Ala polymorphism was performed on an Illumina system using GoldenGate technology (Illumina, San Diego, CA). The genotyping success rate was 99.4%.

Statistical analyses

Statistical analyses were performed with SAS software (SAS Institute, Cary, NC). Deviation from Hardy-Weinberg equilibrium was tested using the χ2 test (1 degree of freedom). The BMI and the sum of the six skinfolds were normalized by log transformation. We compared groups in terms of genotype and allele distributions by using χ2 tests. Intergroup comparisons of quantitative variables were performed using a general linear model. Reported P values were systematically adjusted for confounding variables. Data on anthropometric phenotypes were adjusted for age, sex, and center. Data on weight and height at birth were adjusted for age, sex, center, and gestational duration. Study center was used as a surrogate estimate of ethnicity. The presence of interaction between polymorphism and breast-feeding for anthropometric variables was tested with a general linear model adjusted for age, sex, and center. The threshold for statistical significance was set to P ≤ 0.05. Power calculations were performed using Quanto v1.2.4 (17).

RESULTS

Within our sample, 81.7% of adolescents had been breast-fed (Table 1). A quarter of the adolescents had finished puberty (28.3%). There were 746 (78.9%) Pro12Pro, 187 (19.8%) Pro12Ala, and 12 (1.3%) Ala12Ala subjects (Ala12 allele frequency = 0.11) in the sample. This distribution respected the Hardy-Weinberg equilibrium in the HELENA study (P = 0.94) and in each center separately (data not shown).
Table 1

Descriptive characteristics of the HELENA study sample

nValue
Neonatal data
    Birth weight (kg)9143.33 ± 0.58
    Birth height (cm)88250.4 ± 3.2
    Duration of total breast-feeding
        Never breast-fed173 (18.3)
        <3 months279 (29.5)
        3–5 months237 (25.1)
        ≥6 months256 (27.1)
    Duration of pregnancy
        <35 weeks49 (5.4)
        35–40 weeks574 (63.6)
        >40 weeks280 (31.0)
Clinical characteristics
    Boys434 (45.9)
    Girls511 (54.1)
    Pubertal status
        Tanner stage 212 (1.4)
        Tanner stage 3/4601 (70.3)
        Tanner stage 5242 (28.3)
    Age (years)94514.7 ± 1.4
    BMI (kg/m2)94521.3 ± 3.8
    Waist circumference (cm)93572.2 ± 9.3
    Sum of 6 skinfolds (mm)88792.0 ± 41.4
    Physical activity (cpm)638434 ± 151

Data are means ± SD or n (%).

Descriptive characteristics of the HELENA study sample Data are means ± SD or n (%). Table 2 presents the association between the PPARG2 Pro12Ala polymorphism and the neonatal characteristics and adiposity measurements. No significant associations were found between the Pro12Ala polymorphism and BMI, waist circumference, and the sum of skinfolds. Accordingly, underweight (n = 61), normal-weight (n = 663), overweight (n = 164), and obese children (n = 57) did not differ significantly in terms of the genotype distribution of the PPARG2 Pro12Ala polymorphism (P = 0.82) (data not shown). However, Ala12Ala subjects had lower weight (P = 0.03) and height (P = 0.02) at birth than subjects carrying the Pro12 allele (Table 2), independently of the duration of gestation. The genotype distribution of the polymorphism did not differ among subjects born before 35 weeks, between 35 and 40 weeks, or after 40 weeks of pregnancy (P = 0.98).
Table 2

Association between the PPARG2 Pro12Ala polymorphism and body composition and neonatal characteristics in the HELENA study

Pro12ProPro12AlaAla12AlaP*P*
X/Ala12 vs. Pro12ProAla12Ala vs. X/Pro12
n74618712
BMI (kg/m2)21.3 ± 3.621.4 ± 4.320.2 ± 2.50.550.980.29
Waist circumference (cm)72.1 ± 9.272.8 ± 10.069.8 ± 7.50.500.780.29
Sum of 6 skinfolds (mm)92.2 ± 41.192.5 ± 43.573.2 ± 24.00.520.790.31
Birth weight (kg)3.33 ± 0.573.34 ± 0.572.90 ± 1.080.100.430.03
    n68917710
Birth height (cm)50.4 ± 3.150.4 ± 2.747.7 ± 6.10.070.430.02
    n66617110
Duration of gestation
    <35 weeks38 (0.78)10 (0.20)1 (0.02)
    35–40 weeks454 (0.79)113 (0.20)7 (0.01)0.98
    >40 weeks219 (0.78)58 (0.21)3 (0.01)

Data are means ± SD or n (frequency).

*Adjusted for age, sex. and center.

†Adjusted for age, sex, center, and gestational duration.

Association between the PPARG2 Pro12Ala polymorphism and body composition and neonatal characteristics in the HELENA study Data are means ± SD or n (frequency). *Adjusted for age, sex. and center. †Adjusted for age, sex, center, and gestational duration. After checking that the distribution of the Pro12Ala polymorphism was similar in all four breast-feeding categories (never breast-fed, <3 months, 3–5 months, and ≥6 months) (P = 0.73), breast-feeding was introduced into the analysis. We detected significant interactions between the Pro12Ala polymorphism and breast-feeding, when considering BMI (adjusted for age, sex, and center, P = 0.004), waist circumference (adjusted P = 0.03), or skinfolds (adjusted P = 0.03). Indeed, in children who had not been breast-fed (n = 173), Ala12 allele carriers had higher BMI (+1.88 kg/m2, adjusted P = 0.007) (Fig. 1A), higher waist circumference (+3.8 cm, adjusted P = 0.02) (Fig. 1B), and higher skinfold thicknesses (+16.3 mm, adjusted P = 0.03) (Fig. 1C) than Pro12Pro subjects. This association was not altered by further adjustment for maternal educational level, Tanner and Whitehouse stage, average physical activity level, birth weight, or duration of gestation (data not shown). In contrast, in children who had been breast-fed, there was no significant difference in adiposity measurements between Ala12 allele carriers and Pro12Pro subjects, whatever the duration of breast-feeding. It is noteworthy that our analyses yielded similar results when we used the duration of exclusive breast-feeding (data not shown). Furthermore, there were no significant interactions with sex (P > 0.90), and the associations were similar in boys and girls (data not shown).
Figure 1

A: Mean BMI as a function of the breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). **P = 0.007 (adjusted for age, sex, and center). B: Mean waist circumference as a function of breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). *P = 0.02 (adjusted for age, sex, and center). C: Mean sum of skinfolds as a function of breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). *P = 0.03 (adjusted for age, sex, and center).

A: Mean BMI as a function of the breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). **P = 0.007 (adjusted for age, sex, and center). B: Mean waist circumference as a function of breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). *P = 0.02 (adjusted for age, sex, and center). C: Mean sum of skinfolds as a function of breast-feeding duration in PPARG2 Pro12Pro (■) vs. Ala12 allele carriers (□). *P = 0.03 (adjusted for age, sex, and center). We performed power calculations in the whole sample (n = 945) using a dominant or a recessive model, and in the non–breast-fed children subsample (n = 173) using a dominant model only (Table 3). As an example, the whole sample had sufficient power (>80%) to identify significant effect sizes of at least 0.75 kg/m2 for BMI, 2.1 cm for waist circumference, 9.6 mm for skinfold thicknesses, 140 g for birth weight, and 0.7 cm for birth height using a minor allele frequency of 0.11 under a dominant model.
Table 3

Power calculation for the PPARG2 Pro12Ala polymorphism effects

Mean Δ using a dominant modelMean Δ using a recessive model
In the HELENA study (n = 945)
    BMI (kg/m2)0.753.15
    Waist circumference (cm)2.17.7
    Sum of 6 skinfolds (mm)9.636
    Birth weight (kg)0.140.50
    Birth height (cm)0.72.8
In non–breast-fed children (n = 173)
    BMI (kg/m2)2.1NC
    Waist circumference (cm)5.0NC
    Sum of 6 skinfolds (mm)23.1NC

NC, not calculated.

Power calculation for the PPARG2 Pro12Ala polymorphism effects NC, not calculated.

CONCLUSIONS

In the present study, the PPARG2 Ala12 allele was associated with higher adiposity indexes (BMI, waist circumference, and the sum of skinfolds) in children who had not been breast-fed. However, this association was not seen in children who had been breast-fed (even for a short period). Our results are in agreement with those of Mook-Kanamori et al. (14), who showed that the Ala12 allele was associated with increased weight gain in early infancy in non–breast-fed children (14). We observed similar findings for BMI, waist circumference, and skinfolds, even later in life (i.e., adolescence). This result supports the hypothesis whereby breast-feeding has a beneficial effect on the obesity risk later in life in a genetically predisposed group. Our study illustrates an association between an environmental factor (breast-feeding) and the phenotypic expression of a gene (modulation of anthropometric parameters by PPARG) and thus suggests that phenotypes modulated by PPARG2 polymorphisms can be influenced by gene-environment interactions early in life. Barker (18) has explained the impact of pre- and postnatal nutrition later in life by the theory of “nutritional programming”: what is beneficial in utero and during the postnatal period in cases of undernutrition could become deleterious in the event of an excessive nutritional environment (i.e., metabolic diseases). The exact mechanisms involved in this type of phenomenon are still subject to speculation; they may begin to operate during fetal life and continue until the early neonatal period. A recent meta-analysis performed by the World Health Organization, including 33 studies, concluded that breast-fed individuals were less likely to be overweight and/or obese in childhood and adolescence (19). Some studies but not all showed a dose-response effect, with a more pronounced effect associated with a long duration of breast-feeding (20). The reason for the absence of a dose-response effect on the PPARG2 Ala12 allele in our study is unclear; one possible explanation is that the programming effect of breast-feeding is more strongly influenced by gene × nutrient interactions at an early age rather than a quantitative process linked to the duration of the exposition. A number of mechanisms can potentially explain how breast-feeding could counterbalance the deleterious effect of the Ala12 allele in adolescents. It has been shown that the association between dietary fat and BMI is influenced by PPARG2 genotypes. Memisoglu et al. (21) found that monounsaturated fat–rich diets were inversely associated with BMI in Ala12 allele carriers, but the authors did not find any association in Pro12Pro women. Similarly, Luan et al. (22) showed that for a diet with a low polyunsaturated-to-saturated fat ratio, Ala12 allele carriers had a greater BMI than Pro12Pro carriers. Considering that breast milk constitutes a diet with specific fat intake (with a higher proportion of polyunsaturated fatty acids than formula milk [23]), our results seem to be in line with those reported by Luan et al. (22), albeit their study was conducted in adults. Moreover, one potential hypothesis is that breast milk or breast-feeding supplies factors such as prostaglandin J2 (24), a natural PPARγ ligand. The decrease in PPARγ2 transcriptional activity observed in Ala12 allele carriers could be, therefore, compensated for by breast milk. The latter also contains a number of adipokines. It is known that PPARγ agonists (such as the thiazolidinediones) can downregulate leptin expression (25); however, the presence of this compound in breast and/or formula milk has yet to be established and would require further investigation. We also showed in the present study that Ala12Ala subjects had a lower body weight (−430 g) and height (−2.7 cm) at birth than subjects carrying the Pro12 allele, independently of the duration of gestation. Although these results need to be considered with caution (as they concern only 12 homozygote children) and replicated, they are in line with previous data. Indeed, in two Irish population samples, we have previously shown that the PPARG2 Ala12 allele was associated with lower birth weight (primarily caused by shorter gestational duration) (11). The present study has certain limitations. First, the duration of gestation was coded into three categories rather than being specified in weeks and was obtained from questionnaires filled out by the parents (rather than from a national health registry). Therefore, the accuracy of the data on gestational duration needs to be considered with circumspection. Second, the “being small for gestational age” phenotype could not be assessed. However, because the duration of gestation did not influence the effect of the PPARG2 polymorphism in the present study, we believe that this factor did not bias our results. Likewise, we lacked information on singleton or multiple pregnancies, which have different growth patterns. Other factors (such as parental weight status, food preferences, or smoking status) known to influence the effect of breast-feeding on the subject's subsequent BMI could not be assessed in our study. However, the main factors known to influence fat mass were available and did not alter the observed associations when used as confounders. Third, study center was used as a surrogate estimate of ethnicity, which is not ideal and may induce misclassification. Last, the subgroup of non–breast-fed children was relatively small (n = 173), which might make it prone to identification of false-positive associations. However, we feel confident of our data as they are in line with the data of Mook-Kanamori et al. (14). In summary, our results suggest that breast-feeding can counterbalance the deleterious impact of the PPARG2 Pro12Ala polymorphism on adiposity in adolescents. These findings confirm the importance of taking account of gene-environment interactions in association studies and the possible effect of early, diet-based prevention programs in population subgroups. At a time when the prevalence of obesity in children and adolescents continues to increase, our results may constitute a new argument in favor of the public health benefits of breast-feeding.
  23 in total

1.  No evidence that established type 2 diabetes susceptibility variants in the PPARG and KCNJ11 genes have pleiotropic effects on early growth.

Authors:  A J Bennett; U Sovio; A Ruokonen; H Martikainen; A Pouta; A-L Hartikainen; S Franks; P Elliott; M-R Järvelin; M I McCarthy
Journal:  Diabetologia       Date:  2007-11-10       Impact factor: 10.122

2.  Functional antagonism between CCAAT/Enhancer binding protein-alpha and peroxisome proliferator-activated receptor-gamma on the leptin promoter.

Authors:  A N Hollenberg; V S Susulic; J P Madura; B Zhang; D E Moller; P Tontonoz; P Sarraf; B M Spiegelman; B B Lowell
Journal:  J Biol Chem       Date:  1997-02-21       Impact factor: 5.157

3.  Evidence for gene-nutrient interaction at the PPARgamma locus.

Authors:  J Luan; P O Browne; A H Harding; D J Halsall; S O'Rahilly; V K Chatterjee; N J Wareham
Journal:  Diabetes       Date:  2001-03       Impact factor: 9.461

4.  The Pro12Ala variant of peroxisome proliferator-activated receptor-gamma2 (PPAR-gamma2) is associated with measures of obesity in Mexican Americans.

Authors:  S A Cole; B D Mitchell; W C Hsueh; P Pineda; B A Beamer; A R Shuldiner; A G Comuzzie; J Blangero; J E Hixson
Journal:  Int J Obes Relat Metab Disord       Date:  2000-04

5.  Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with Pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals.

Authors:  Anke Tönjes; Markus Scholz; Markus Loeffler; Michael Stumvoll
Journal:  Diabetes Care       Date:  2006-11       Impact factor: 19.112

6.  Interaction between a peroxisome proliferator-activated receptor gamma gene polymorphism and dietary fat intake in relation to body mass.

Authors:  Asli Memisoglu; Frank B Hu; Susan E Hankinson; JoAnn E Manson; Immaculata De Vivo; Walter C Willett; David J Hunter
Journal:  Hum Mol Genet       Date:  2003-09-23       Impact factor: 6.150

7.  Effect of the Ala12 allele in the PPARgamma-2 gene on the relationship between birth weight and body composition in adolescents: the AVENA study.

Authors:  Idoia Labayen; Luis A Moreno; Amelia Marti; Domingo González-Lamuño; Julia Wärnberg; Francisco B Ortega; Gloria Bueno; Esther Nova; Jonatan R Ruiz; Jesús M Garagorri; J Alfredo Martínez; Miguel García-Fuentes; Manuel Bueno
Journal:  Pediatr Res       Date:  2007-11       Impact factor: 3.756

8.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes.

Authors:  Eleftheria Zeggini; Laura J Scott; Richa Saxena; Benjamin F Voight; Jonathan L Marchini; Tianle Hu; Paul I W de Bakker; Gonçalo R Abecasis; Peter Almgren; Gitte Andersen; Kristin Ardlie; Kristina Bengtsson Boström; Richard N Bergman; Lori L Bonnycastle; Knut Borch-Johnsen; Noël P Burtt; Hong Chen; Peter S Chines; Mark J Daly; Parimal Deodhar; Chia-Jen Ding; Alex S F Doney; William L Duren; Katherine S Elliott; Michael R Erdos; Timothy M Frayling; Rachel M Freathy; Lauren Gianniny; Harald Grallert; Niels Grarup; Christopher J Groves; Candace Guiducci; Torben Hansen; Christian Herder; Graham A Hitman; Thomas E Hughes; Bo Isomaa; Anne U Jackson; Torben Jørgensen; Augustine Kong; Kari Kubalanza; Finny G Kuruvilla; Johanna Kuusisto; Claudia Langenberg; Hana Lango; Torsten Lauritzen; Yun Li; Cecilia M Lindgren; Valeriya Lyssenko; Amanda F Marvelle; Christa Meisinger; Kristian Midthjell; Karen L Mohlke; Mario A Morken; Andrew D Morris; Narisu Narisu; Peter Nilsson; Katharine R Owen; Colin N A Palmer; Felicity Payne; John R B Perry; Elin Pettersen; Carl Platou; Inga Prokopenko; Lu Qi; Li Qin; Nigel W Rayner; Matthew Rees; Jeffrey J Roix; Anelli Sandbaek; Beverley Shields; Marketa Sjögren; Valgerdur Steinthorsdottir; Heather M Stringham; Amy J Swift; Gudmar Thorleifsson; Unnur Thorsteinsdottir; Nicholas J Timpson; Tiinamaija Tuomi; Jaakko Tuomilehto; Mark Walker; Richard M Watanabe; Michael N Weedon; Cristen J Willer; Thomas Illig; Kristian Hveem; Frank B Hu; Markku Laakso; Kari Stefansson; Oluf Pedersen; Nicholas J Wareham; Inês Barroso; Andrew T Hattersley; Francis S Collins; Leif Groop; Mark I McCarthy; Michael Boehnke; David Altshuler
Journal:  Nat Genet       Date:  2008-03-30       Impact factor: 38.330

9.  Breast-feeding modifies the association of PPARgamma2 polymorphism Pro12Ala with growth in early life: the Generation R Study.

Authors:  Dennis O Mook-Kanamori; Eric A P Steegers; Andre G Uitterlinden; Henriëtte A Moll; Cornelia M van Duijn; Albert Hofman; Vincent W V Jaddoe
Journal:  Diabetes       Date:  2009-02-02       Impact factor: 9.461

10.  Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

Authors:  Cristen J Willer; Elizabeth K Speliotes; Ruth J F Loos; Shengxu Li; Cecilia M Lindgren; Iris M Heid; Sonja I Berndt; Amanda L Elliott; Anne U Jackson; Claudia Lamina; Guillaume Lettre; Noha Lim; Helen N Lyon; Steven A McCarroll; Konstantinos Papadakis; Lu Qi; Joshua C Randall; Rosa Maria Roccasecca; Serena Sanna; Paul Scheet; Michael N Weedon; Eleanor Wheeler; Jing Hua Zhao; Leonie C Jacobs; Inga Prokopenko; Nicole Soranzo; Toshiko Tanaka; Nicholas J Timpson; Peter Almgren; Amanda Bennett; Richard N Bergman; Sheila A Bingham; Lori L Bonnycastle; Morris Brown; Noël P Burtt; Peter Chines; Lachlan Coin; Francis S Collins; John M Connell; Cyrus Cooper; George Davey Smith; Elaine M Dennison; Parimal Deodhar; Paul Elliott; Michael R Erdos; Karol Estrada; David M Evans; Lauren Gianniny; Christian Gieger; Christopher J Gillson; Candace Guiducci; Rachel Hackett; David Hadley; Alistair S Hall; Aki S Havulinna; Johannes Hebebrand; Albert Hofman; Bo Isomaa; Kevin B Jacobs; Toby Johnson; Pekka Jousilahti; Zorica Jovanovic; Kay-Tee Khaw; Peter Kraft; Mikko Kuokkanen; Johanna Kuusisto; Jaana Laitinen; Edward G Lakatta; Jian'an Luan; Robert N Luben; Massimo Mangino; Wendy L McArdle; Thomas Meitinger; Antonella Mulas; Patricia B Munroe; Narisu Narisu; Andrew R Ness; Kate Northstone; Stephen O'Rahilly; Carolin Purmann; Matthew G Rees; Martin Ridderstråle; Susan M Ring; Fernando Rivadeneira; Aimo Ruokonen; Manjinder S Sandhu; Jouko Saramies; Laura J Scott; Angelo Scuteri; Kaisa Silander; Matthew A Sims; Kijoung Song; Jonathan Stephens; Suzanne Stevens; Heather M Stringham; Y C Loraine Tung; Timo T Valle; Cornelia M Van Duijn; Karani S Vimaleswaran; Peter Vollenweider; Gerard Waeber; Chris Wallace; Richard M Watanabe; Dawn M Waterworth; Nicholas Watkins; Jacqueline C M Witteman; Eleftheria Zeggini; Guangju Zhai; M Carola Zillikens; David Altshuler; Mark J Caulfield; Stephen J Chanock; I Sadaf Farooqi; Luigi Ferrucci; Jack M Guralnik; Andrew T Hattersley; Frank B Hu; Marjo-Riitta Jarvelin; Markku Laakso; Vincent Mooser; Ken K Ong; Willem H Ouwehand; Veikko Salomaa; Nilesh J Samani; Timothy D Spector; Tiinamaija Tuomi; Jaakko Tuomilehto; Manuela Uda; André G Uitterlinden; Nicholas J Wareham; Panagiotis Deloukas; Timothy M Frayling; Leif C Groop; Richard B Hayes; David J Hunter; Karen L Mohlke; Leena Peltonen; David Schlessinger; David P Strachan; H-Erich Wichmann; Mark I McCarthy; Michael Boehnke; Inês Barroso; Gonçalo R Abecasis; Joel N Hirschhorn
Journal:  Nat Genet       Date:  2008-12-14       Impact factor: 38.330

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  8 in total

Review 1.  Nutrition and lifestyle in european adolescents: the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study.

Authors:  Luis A Moreno; Frédéric Gottrand; Inge Huybrechts; Jonatan R Ruiz; Marcela González-Gross; Stefaan DeHenauw
Journal:  Adv Nutr       Date:  2014-09       Impact factor: 8.701

Review 2.  Epigenetic effects of human breast milk.

Authors:  Elvira Verduci; Giuseppe Banderali; Salvatore Barberi; Giovanni Radaelli; Alessandra Lops; Federica Betti; Enrica Riva; Marcello Giovannini
Journal:  Nutrients       Date:  2014-04-24       Impact factor: 5.717

Review 3.  Epigenetic Matters: The Link between Early Nutrition, Microbiome, and Long-term Health Development.

Authors:  Flavia Indrio; Silvia Martini; Ruggiero Francavilla; Luigi Corvaglia; Fernanda Cristofori; Salvatore Andrea Mastrolia; Josef Neu; Samuli Rautava; Giovanna Russo Spena; Francesco Raimondi; Giuseppe Loverro
Journal:  Front Pediatr       Date:  2017-08-22       Impact factor: 3.418

4.  Phenotype and genotype predictors of BMI variability among European adults.

Authors:  Leticia Goni; Marta García-Granero; Fermín I Milagro; Marta Cuervo; J Alfredo Martínez
Journal:  Nutr Diabetes       Date:  2018-05-24       Impact factor: 5.097

5.  Early life course risk factors for childhood obesity: the IDEFICS case-control study.

Authors:  Karin Bammann; Jenny Peplies; Stefaan De Henauw; Monica Hunsberger; Denes Molnar; Luis A Moreno; Michael Tornaritis; Toomas Veidebaum; Wolfgang Ahrens; Alfonso Siani
Journal:  PLoS One       Date:  2014-02-13       Impact factor: 3.240

6.  Relationship between exclusive breast feeding and cardiorespiratory fitness in children and adolescents: a protocol for a systematic review and meta-analysis.

Authors:  Carlos Berlanga-Macías; Diana P Pozuelo-Carrascosa; Celia Álvarez-Bueno; Jose Alberto Martínez-Hortelano; Miriam Garrido-Miguel; Vicente Martínez-Vizcaíno
Journal:  BMJ Open       Date:  2018-10-31       Impact factor: 2.692

7.  Evidence from 3-month-old infants shows that a combination of postnatal feeding and exposures in utero shape lipid metabolism.

Authors:  Samuel Furse; Stuart G Snowden; Laurentya Olga; Philippa Prentice; Ken K Ong; Ieuan A Hughes; Carlo L Acerini; David B Dunger; Albert Koulman
Journal:  Sci Rep       Date:  2019-10-04       Impact factor: 4.379

Review 8.  Personalized Nutrition Approach in Pregnancy and Early Life to Tackle Childhood and Adult Non-Communicable Diseases.

Authors:  Shaikha Alabduljabbar; Sara Al Zaidan; Arun Prasath Lakshmanan; Annalisa Terranegra
Journal:  Life (Basel)       Date:  2021-05-24
  8 in total

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