The authors have declared that no competing interests exist.
Conceived and designed the experiments: KFC TLH KK. Analyzed the data: KFC TLH RL BK PR. Wrote the paper: KFC TLH KK RL. Participated in the development and writing of this study: KFC RL BK PR TLH.
Obesity-susceptibility loci have been related to adiposity traits in adults and may affect body fat estimates in adolescence. There are indications that different sets of obesity-susceptibility loci influence level of and change in obesity-related traits from adolescence to adulthood.
To investigate whether previously reported obesity-susceptible loci in adults influence adiposity traits in adolescence and change in BMI and waist circumference (WC) from adolescence into young adulthood. We also examined whether physical activity modifies the effects of these genetic loci on adiposity-related traits.
Nine obesity-susceptibility variants were genotyped in 1 643 adolescents (13–19 years old) from the HUNT study, Norway, who were followed-up into young adulthood. Lifestyle was assessed using questionnaires and anthropometric measurements were taken. The effects of genetic variants individually and combined in a genetic predisposition score (GPS) on obesity-related traits were studied cross-sectionally and longitudinally. A modifying effect of physical activity was tested.
The GPS was significantly associated to BMI (B: 0.046 SD/allele [0.020, 0.073], p = 0.001) in adolescence and in young adulthood (B: 0.041 SD/allele [0.015, 0.067], p = 0.002) as it was to waist circumference (WC). The GPS was not associated to change in BMI (p = 0.762) or WC (p = 0.726). We found no significant interaction effect between the GPS and physical activity.
Our observations suggest that obesity-susceptibility loci established in adults affect BMI and WC already in adolescence. However, an association with change in adiposity-related traits from adolescence to adulthood could not be verified for these loci. Neither could an attenuating effect of physical activity on the association between the obesity-susceptibility genes and body fat estimates be revealed.
During the last decades the prevalence of overweight and obesity have increased worldwide in adolescents and adults, which has been largely attributed to lifestyle changes
So far, large-scale meta-analyses of GWAS have identified 32 loci robustly associated with BMI
We genotyped nine loci (near or in
During the last 25 years the Health Study of Nord-Trøndelag, HUNT, a large population- based study (
All students in junior and senior high schools (13–19 years) in the county were invited to participate in Young-HUNT, the adolescent part of HUNT. Young-HUNT1, which formed the basis of the current study, was carried out between 1995 and 1997 and recruited a total of 8 408 adolescents (response rate 83%) who participated by undergoing a health examination and by completing the questionnaires. Our final study-sample included the 1 643 participants who participated both as adolescents in Young-HUNT1 and as young adults (23–29 years) 11 years later in HUNT 3, had complete sets of phenotypic data, good quality genotype data and who were not pregnant at follow-up (
At both time points, all participants completed comprehensive self-administered questionnaires on physical and mental health, somatic complaints and lifestyle and were clinically examined by trained nurses using the same protocols for anthropometric measures. Blood samples were drawn at young adulthood in HUNT3.
Weight and waist circumference were obtained by using standardized weight scales and meter bands. Height was measured by trained nurses using internally standardized measuring tapes. The participants wore light clothing (as T-shirts and trousers) and were barefoot. Height was measured to the nearest centimetre (cm) and weight to the nearest 0.5 kilogram (kg). BMI, as a measure of overall adiposity, was calculated as weight (kg) divided by squared height (m2). Waist circumference, a measure for central adiposity, was measured to the nearest centimetre applying non-stretchable band horizontally at the umbilical level after the participants emptied their lungs, or midway between the last rib and the iliac cristae if the latter was larger.
Overweight (n = 260) and obesity (n = 35) in adolescents was defined by standardising BMI in relation to sex and age using the reference growth charts as proposed by the International Obesity Task Force (IOTF)
Because of the age differences among adolescents and in order to compare data in adolescence and adulthood, we standardized BMI and waist circumference distributions to a mean of 0 and a SD of 1 by calculating age-and-sex-specific z-scores at baseline and sex-specific z-scores at follow-up. BMI change in z-scores was calculated as the difference between the z-scores in adulthood and in adolescence.
Pubertal status was assessed using the Pubertal Development Scale (PDS)
Physical activity was assessed by questionnaires as described previously for adolescents
At the time of the design of the study (2009), nine of the currently more than 50 established obesity-susceptibility loci (near
Nine SNPs representing nine obesity-susceptibility loci identified by GWA studies
All markers were genotyped using a Sequenom iPlex assay on a MassARRAY platform (Sequenom, San Diego, CA) at CIGENE, Centre for Integrative Genetics, The Norwegian University of Life Sciences in ÅS, Norway. All variants passed initial quality-control criteria with a call-rate ≥95% and genotype distribution in Hardy-Weinberg equilibrium (P>0.05) (
SNP | Chrom. | Position | Nearest gene | Effect allele | Effect allele | Other allele | References | Call rate | Genotype freq | HWE | |||
frequency | Risk allele homozygous | Heterozygous | Other allele homo | p-value | |||||||||
% | % | % | % | ||||||||||
rs2815752 | 1 | 72585028 | NEGR1 | A | 59.2 | G | 1,6 | 99.7 | 34.5 | 49.3 | 16.2 | 0.525 | |
rs6548238 | 2 | 624905 | TMEM18 | C | 83.7 | T | 1,6 | 99.4 | 69.9 | 27.5 | 2.6 | 0.669 | |
rs10938397 | 4 | 44877284 | GNPDA2 | G | 38.8 | A | 1,6 | 99.4 | 15.2 | 47.3 | 37.6 | 0.765 | |
rs987237 | 6 | 50911009 | TFAP2B | G | 17.7 | A | 9 | 98.4 | 3.3 | 28.8 | 67.9 | 0.745 | |
rs10838738 | 11 | 47619625 | MTCH2 | G | 36.3 | A | 1,6 | 99.6 | 13.9 | 44.7 | 41.4 | 0.221 | |
rs4074134 | 11 | 27603861 | BDNF | G | 81.5 | A | 7,8 | 99.7 | 66.4 | 30.2 | 3.4 | 0.752 | |
rs1121980 | 16 | 52366748 | In FTO | A | 44.4 | G | 1,3 | 99.7 | 18.9 | 51.0 | 30.1 | 0.139 | |
rs17782313 | 18 | 56002077 | MC4R | C | 26.9 | T | 4 | 98.4 | 6.7 | 40.4 | 52.9 | 0.203 | |
rs11084753 | 19 | 39013977 | KCTD15 | G | 69.5 | A | 1,6 | 99.2 | 47.6 | 43.7 | 8.7 | 0.181 |
Article reference: 1) Loos et al., 2009; 2) Hinney et al., 2007; 3) Loos et al., 2008; 4) Willer et al., 2009; 5) Zhao et al., 2009; 6) Thorleifson et al., 2009;
HWE: Hardy-Weinberg equilibrium; Call-rate: rate of successful genotyping. All variants passed initial quality-control criteria with a call-rate ≥95% and genotype distribution were in Hardy-Weinberg equilibrium (P>0.05). The genotype distribution and effect allele frequencies varied from 17.7% for rs987237 to 83.7% for rs654238), which were in consistency with previous reports.
Genotypes were coded 0, 1 or 2 according to the number of BMI-increasing alleles for each SNP, with the BMI-increasing alleles defined by the results reported by previous GWA studies.
Cross-sectional analyses: We tested for association between individual SNPs and the GPS with the z-scores of BMI and WC in adolescence and in young adulthood using linear regression analyses, assuming an additive effect of the BMI-increasing allele. Analyses with BMI and WC in adolescence were adjusted for pubertal development, while age was adjusted for in young adulthood. Analyses with WC as outcome were in addition adjusted for height in both adolescents and adults.
Following regression equations show the use of different adjustments in the various models.
Associations of the individual obesity-susceptibility SNPs and the GPS with adiposity-related traits in adolescence:
Associations of the individual obesity-susceptibility SNPs and the GPS with adiposity-related traits in adulthood:
We used logistic regression to test for associations between the GPS and risk of obesity and overweight in adolescence and in adulthood. Analyses in adolescence were adjusted for sex, age, and pubertal development score and in adulthood for sex and age in the model with BMI as outcome and additionally adjusted for height in the model with waist circumference as outcome.
Longitudinal analyses: We tested the association between each SNP and the GPS with change in BMI and WC from adolescence into young adulthood, using linear regression analyses assuming an additive effect for the BMI-increasing allele. Analyses were adjusted for PDS in adolescence and for age-difference between baseline and follow-up. Change in WC was additional adjusted for adult-height. Associations of the individual obesity-susceptibility SNPs and the GPS with change in adiposity-related traits from adolescence into adulthood:
Interactions with physical activity for both the cross-sectional and longitudinal models were tested by including the cross-product term (physical activity respectively in adolescence and adulthood*SNP and physical activity respectively in adolescence and adulthood* GPS) in the model, in addition to the main effect of the SNP or GPS and physical activity.
Statistical analyses were performed using PLINK and SPSS Statistics, version 18. All tests were performed using the nominal level of significance of p = 0.05.
All participants and guardians of adolescents younger than 16 years signed an informed consent to participation and use of data in research. Participation in the HUNT study was voluntary and approved by the Norwegian Data Inspectorate, the Directorate of Health and recommended by the Regional Committee for Medical Research Ethics (REK-2009/740-2), who also approved the present study. The study has been conducted according to the principles expressed in the Declaration of Helsinki.
Characteristics of the adolescents and young adults (N, mean, SD) stratified by sex are shown in
The genetic predisposition score (GPS, n: 1 634 adolescents) was constructed by summing the effect alleles of each SNP ( = BMI-increasing alleles defined in the original genome-wide association studies). (rs4074134 near
Z-scores BMI | Z-scores WC | |||||||||
SNP | Chrom | Nearest gene | B | CI (95%) | P-value | Literature | B | CI (95%) | P-value | Literature |
rs2815752 | 1 | NEGR1 | −.019 | −.091, .052 | .596 | 0.01 |
−.010 | −.082, .061 | .780 | 0.062 |
rs6548238 | 2 | TMEM18 | .074 | −.020, .168 | .124 | 0.10 |
.071 | −.023, .165 | .137 | 0.068 |
rs10938397 | 4 | GNPDA2 | −.004 | −.075, .067 | .920 | 0.06 |
.019 | −.051, 090 | .591 | 0.041 |
rs987237 | 6 | TFAP2B | .040 | −.050, .130 | .387 | 0.069 |
−.004 | −.094, .086 | .935 | 0.056 |
rs4074134 | 11 | BDNF | .026 | −.063, .114 | .571 | 0.03 |
.021 | −.068, .110 | .642 | 0.034 |
rs10838738 | 11 | MTCH2 | −.021 | −.092, .050 | .569 | 0.03 |
−.030 | −.100, .041 | .411 | 0.001 |
rs1121980 | 16 | inFTO | .057 | −.013, .128 | .110 | 0.06 |
.070 | .000, .140 | .052 | 0.004 |
rs17782313 | 18 | MC4R | .177 | .098, .256 | .0001 | 0.07 |
.155 | .076, .235 | .0001 | −0.006 |
rs11084753 | 19 | KCTD15 | .107 | .030, .183 | .006 | 0.05 |
.053 | −.023, .130 | .172 | 0.009 |
GPS | .046 | .020, .073 | .001 | 0.044 |
.038 | .012, .064 | .005 | 0.025 |
The genetic predisposition score (GPS) is the sum of effect alleles from each of the nine individual SNPs.
Age and sex specific z-scores of BMI and waist circumference in adolescence.
Number of participants: for individual SNPs = 1643 and for GPS = 1634 (those missing more than 3 SNPs excluded).
The linear regression models were adjusted for pubertal maturity regarding BMI and additionally also for height regarding WC, assuming an additive effect. Pregnant participants were excluded.
Chrom: chromosome.
Comparable effect sizes in the literature:
den Hoed et al.
Zhao et al.
Willer et al.
In young adulthood, the association of seven and six of the nine obesity susceptibility SNPs with BMI and WC respectively were directionally consistent with results reported in the original GWA studies
Z-scores BMI | Z-scores WC | ||||||||||
SNP | Chrom | Nearest gene | B | CI (95%) | P-value | Literature | B | CI (95%) | P-value | Literature | |
rs2815752 | 1 | NEGR1 | −.028 | −.098, .042 | .436 | 0.024 |
−.007 | −.077, .063 | .845 | 0.022 |
|
rs6548238 | 2 | TMEM18 | .018 | −.075, .125 | .699 | 0.070 |
.017 | −.076, .109 | .726 | 0.050 |
|
rs10938397 | 4 | GNPDA2 | −.001 | −.071, .069 | .976 | 0.045 |
−.013 | −.083, .057 | .719 | 0.039 |
|
rs987237 | 6 | TFAP2B | .022 | −.067, .111 | .624 | − | −.035 | −.124, .054 | .439 | 0.035 |
|
rs4074134 | 11 | BDNF | .054 | −.034, .142 | .226 | 0.055 |
.072 | −.016, .159 | .109 | 0.049 |
|
rs10838738 | 11 | MTCH2 | .048 | −.022, .117 | .182 | 0.021 |
.062 | −.007, .131 | .080 | 0.011 |
|
rs1121980 | 16 | In FTO | .086 | .017, .156 | .015 | 0.086 |
.071 | .002, .140 | .045 | 0.080 |
|
rs17782313 | 18 | MC4R | .103 | .025, .181 | .010 | 0.047 |
.069 | −.009, .147 | .082 | 0.042 |
|
rs11084753 | 19 | KCTD15 | .062 | −.014, .138 | .110 | 0.016 |
.047 | −.028, .123 | .220 | 0.024 |
|
GPS | .041 | .015, .067 | .002 | 0.039 |
.033 | .008, .059 | .011 | 0.033 |
The genetic predisposition score (GPS) is the sum of effect alleles from each of the nine individual SNPs.
Sex specific z-scores of BMI and waist circumference in young adulthood.
Number of participants: for individual SNPs = 1643 and for GPS = 1634 (those missing more than 3 SNPs excluded).
The linear regression models were adjusted for age regarding BMI and additionally also for height regarding WC, assuming an additive effect.
Pregnant participants were excluded.
Chrom: chromosome.
Comparable effect sizes in the literature:
Li et al.
Lindgren et al.
Given the strong prior evidence of association of the test SNPs with measures of obesity, we assessed the significance of associations at a nominal level without accounting multiple testing. If we had adjusted for multiple testing using a Bonferroni correction, associations reaching p<0.0055 would have been considered significant at the 5% α-level. As such, associations between genetic variants near
The GPS showed no significant association with change in body fat estimates measured as the difference in z-score of BMI and WC between adolescence and young adulthood. Of the individual SNPs, only the variant in
Delta BMI |
Delta WC |
|||||||
SNP | Chrom. | Nearest gene | Diff.DeltaZ | CI (95%) | P-value | Diff.DeltaZ | CI (95%) | P-value |
rs2815752 | 1 | NEGR1 | −.009 | −.064, .045 | .734 | .001 | −.066, .067 | .986 |
rs6548238 | 2 | TMEM18 | −.047 | −.119, .025 | .201 | −.051 | −.139, .036 | .250 |
rs10938397 | 4 | GNPDA2 | −.011 | −.066, .043 | .685 | −.041 | −.107, .025 | .224 |
rs987237 | 6 | TFAP2B | −.018 | −.087, .051 | .604 | −.028 | −.112, .057 | .521 |
rs4074134 | 11 | BDNF | .025 | −.043, .093 | .406 | .038 | −.044, .121 | .363 |
rs10838738 | 11 | MTCH2 | .064 | .010, .118 | .020 | .085 | .019, .151 | .011 |
rs1121980 | 16 | In FTO | .026 | −.028, .080 | .343 | −.000 | −.066, .066 | .996 |
rs17782313 | 18 | MC4R | −.048 | −.108, .013 | .125 | −.064 | −.138, .010 | .090 |
rs11084753 | 19 | KCTD15 | −.038 | −.097, .020 | .201 | −.005 | −.077, .067 | .891 |
GPS | −.003 | −.023, .017 | .762 | −.004 | −.029, .020 | .726 |
The genetic predisposition score (GPS) is the sum of effect alleles from each of the nine individual SNPs.
Delta BMI and delta WC are differences between sex-specific z-scores in young adulthood and age-and-sex-specific z-scores in adolescence of BMI and WC respectively.
Number of participants: for individual SNPs = 1643 and for GPS = 1634 (those missing more than 3 SNPs excluded).
The linear regression models were adjusted for pubertal development and age-difference between adolescence and adulthood regarding change BMI and additionally also for height regarding change WC, assuming an additive effect. Pregnant participants were excluded.
Chrom: chromosome.
The effect of the GPS was somewhat larger in magnitude in the group adults who exercised less in their leisure time, but not so in adolescents. No significant interactions were found between physical activity with any of the individual SNPs or the GPS neither in the cross-sectional nor the longitudinal models (
Our study, including 1 643 individuals with longitudinal data, showed that a GPS, based on nine established obesity-susceptibility loci, was significantly associated with BMI and WC in adolescence as well as in young adulthood. No significant associations between the GPS and change in BMI or WC between baseline and follow-up could be verified. Four of nine loci reached nominal significant associations when tested individually, whereas most of the associations of the five remaining loci showed directionally consistent associations. None of the interactions between physical activity and the obesity-susceptibility GPS could be statistically significant derived from our data, neither on the level nor the change in body fat estimates from adolescence into young adulthood.
Comparing our findings to those of others is difficult due to differences in age span, and selection of obesity-susceptibility variants. In spite of these differences, our data are in concordance with previous studies
We did not find an association between the GPS and change in body fat estimates from adolescence to adulthood. This might be due to the fact that our study had only one follow-up or did not capture enough time in the life course of the adolescents. Another reason might be that in concordance with Hardy et al. our data suggests that the genetic effects are slightly larger in adolescence and seem to decrease afterwards
Li el al.
Due to the repeated cross sectional design of a total population used in HUNT and a low participation-rate for the age-group 23–29 year old in HUNT3, relatively few of the total number of participants in Young-HUNT 1 were followed-up. Comparisons of participants with non-participants at baseline (Young-HUNT 1) displayed no essential differences in the general characteristics (BMI, WC, gender), suggesting no major selection bias of the follow-up sample.
The main limitation to our study is the rather low number of participants and thus not finding statistical significance might be due to lack of power. Nevertheless, the associations for the four loci that that reach nominal significance are directionally consistent with those reported for theses loci in the original papers
This may be especially prominent when testing interaction effects. Besides, others have pointed out that replication studies may, besides providing insurance from the errors and biases that may be unavoidably afflict any study, also amplify confidence that any associations uncovered reflect processes that are biological interesting, rather than methodological inadequacies
In conclusion, we replicated partly the association between previously published obesity-susceptibility loci and adiposity-related traits in both adolescence and young adulthood. Loci known to affect obesity-susceptibility in adulthood already seem to do so in adolescence. Understanding the different mechanisms behind weight increase from childhood and adolescence to adulthood is important both in a clinical and prevention perspective. Our study might possibly show some indications of effects, which could not all be verified with statistically significance. More large population studies with focus on associations between genes and weight gain over time and possible attenuating effects as physical activity are needed.
(DOCX)
(DOCX)
(DOCX)
(DOCX)
(DOCX)
The present study was supported by a grant of The Norwegian Research Council. The Nord-Trøndelag Health Study (HUNT) is a cooperation between the HUNT Research Center, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), the Norwegian Institute for Public Health and the Nord-Trøndelag County Council. We wish to thank all the participants of the Young-HUNT and HUNT study, whose participation has made this study possible.