• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of jcemArchiveHomepageTES HomepageSubscriptionsSubmissionAbout
J Clin Endocrinol Metab. Sep 2011; 96(9): E1542–E1549.
Published online Jul 21, 2011. doi:  10.1210/jc.2011-1243
PMCID: PMC3167668

Childhood Environmental and Genetic Predictors of Adulthood Obesity: The Cardiovascular Risk in Young Finns Study

Abstract

Context:

Obesity from childhood to adulthood is associated with adverse health later in life. Increased youth BMI is a risk factor for later obesity, but it is unknown whether identification of other risk factors, including recently discovered genetic markers, would help to identify children at risk of developing adult obesity.

Objectives:

Our objective was to examine the childhood environmental and genetic predictors of adult obesity.

Design, Setting, and Participants:

We followed 2119 individuals of the Cardiovascular Risk in Young Finns Study for up to 27 yr since baseline (1980, age 3–18 yr).

Main Outcome Measure:

We evaluated adult obesity [body mass index (BMI) ≥30 kg/m2].

Results:

The independent predictors (P < 0.05) of adult obesity included childhood BMI, C-reactive protein (CRP), family income (inverse), mother's BMI, and polymorphisms near genes TFAP2B, LRRN6C, and FLJ35579. A risk assessment based on childhood BMI, mother's BMI, and family income was superior in predicting obesity compared with the approach using data only on BMI (C-statistics 0.751 vs. 0.772, P = 0.0015). Inclusion of data on childhood CRP and novel genetic variants for BMI did not incrementally improve C-value (0.779, P = 0.16). A nonlaboratory risk score (childhood BMI, mother's BMI, and family income) predicted adult obesity in all age groups between 3–18 yr (P always <0.001).

Conclusions:

Childhood BMI, CRP, family income (inversely), mother's BMI, and polymorphisms near genes FLJ35779, TFAP2B, and LRRN6C are independently related to adulthood obesity. However, because genetic risk markers and CRP only marginally improve the prediction, our results indicate that children at high risk of adult obesity can be identified using a simple non-laboratory-based risk assessment.

Obesity is a global threat to public health (1). It increases the risk of several conditions, including diabetes, some cancers, and cardiovascular diseases (2, 3). Because the obese have difficulty achieving or maintaining normal weight in the long term, obesity prevention strategies beginning in childhood may be important for reducing obesity-related disorders in adulthood. An effective public health strategy involves the need to recognize youth at risk of becoming obese adults. Obesity is a multifactorial condition that may have several interacting causes including biological, genetic, behavioral, and social factors (4). We have shown that the risk of being obese in adulthood is increased by 3-fold among overweight/obese children and by 4-fold among overweight/obese adolescents (5). Several childhood factors for adult obesity have been identified (4). These include increased childhood body mass index (BMI) (6), parental obesity (7), low childhood physical activity (8), age of maturation (9), birth weight (6), psychological characteristics (such as temperament traits) (10), and low socioeconomic status (11). Some limitations identified in the earlier studies include small study sizes, the inability to comprehensively account for modifying and confounding factors, and lack of long-term follow-up data (4). Furthermore, there are promising novel risk factors for adult obesity that have not been examined using longitudinal data from childhood to adulthood. These include increased inflammation (12) and genetic variation in obesity susceptibility loci (1315).

Current guidelines recommend measuring BMI in childhood to identify those at increased risk of adulthood obesity (16). Whether the prediction could be improved by inclusion of other childhood characteristics is not well known. In this study from the Cardiovascular Risk in Young Finns cohort, we aimed to examine a comprehensive battery of metabolic, inflammatory, behavioral, and environmental factors at age 3–18 and adiposity-related genetic polymorphisms as predictors of adult obesity and to determine what combination of these factors may incrementally improve obesity prediction.

Subjects and Methods

Participants

The Cardiovascular Risk in Young Finns Study is an ongoing multicenter follow-up study of atherosclerosis risk factors. The first cross-sectional survey was conducted in 1980, when 3596 individuals aged 3–18 yr participated (17). These participants were randomly chosen from the national register of the study areas. Since 1980, several follow-up studies have been conducted. The latest 21- and 27-yr follow-up surveys were performed in 2001 and 2007 when 2283 (ages 24–39 yr) and 2204 (ages 30–45 yr) of the original participants attended. For this study, the sample comprised 2119 participants that had complete risk factor data available from baseline and adult BMI data available from the 2001 or 2007 surveys (follow-up time 21–27 yr, mean 23.8 yr). Among individuals participating in both follow-ups, data from the latest was used in the analyses.

Participants gave written informed consent, and the study was approved by local ethics committees.

Study variables

Height and weight were measured, and BMI was calculated as BMI = weight in kilograms divided by height in meters squared (2). Obesity was defined as BMI of at least 30 kg/m2. Baseline (1980) variables were obtained as follows. Blood pressure was measured using a standard mercury sphygmomanometer. For the determination of serum lipid levels, venous blood samples were drawn after an overnight fast (17). Serum insulin was measured using a modification of the immunoassay method of Herbert et al. (18). To determine childhood C-reactive protein (CRP) levels, serum samples were taken in 1980 and stored at −20 C. These samples were analyzed in 2005 by an automated analyzer (19). Questionnaires were used to obtain data on diet, physical activity, birth weight (inquired in 1983 and 1986), psychological variables, age at menarche (evaluated in 1980, 1983, and 1986), mother's age (mean 38 ± 8 yr), mother's BMI, father's age (mean 40 ± 9 yr), father's BMI, parental education, and family income. Information on food was obtained with a short 19-item nonquantitative food frequency questionnaire (20). A physical activity index was calculated using data from questionnaires (21). As a measure of childhood temperamental activity, mothers were asked to rate the motor activity of their child on a four-point continuum (10). Aggression-prone negative emotionality was evaluated on the basis of responses to four items ranked on Likert-type scales (10). Emotional deprivation in childhood was assessed as low emotional warmth (a deficient nurturing attitude by the mother toward the child) self-rated by the mothers (22).

Genetic analyses

Genome-wide analysis was performed with Illumina Bead Chip (Human 670K). Complete data were available in 1939 individuals. In the analyses, we used data on single-nucleotide polymorphisms (SNP) that have recently been shown to associate with BMI in a metaanalysis using data on 249,796 individuals, thus providing the largest dataset presently available (15). A genotype risk score was calculated as an arithmetic sum variable of risk alleles in these 31 SNP. The score includes one variant for fat mass and obesity-associated gene FTO (rs1558902). We additionally tested four other FTO variants that have been associated with obesity risk (rs9939609, rs1421085, rs9930506, and rs17817449) (13, 14). The multivariable analyses were performed so that the FTO SNP were included one at a time.

Statistical methods

To study the associations of childhood risk variables with adulthood obesity, we first calculated age- and sex-specific Z-scores for childhood variables. We then examined age- and sex-adjusted odds of adult obesity using logistic regression. Then, a multivariable logistic regression analysis using stepwise modeling was constructed to determine the independent childhood predictors of adult obesity. Because no sex × risk factor interactions were observed in logistic models, the analyses were not stratified by sex. The analyses were repeated using high-risk waist circumference (≥88 cm in females and ≥102 cm in males) as an outcome variable. In addition, similar modeling was performed after including also the genotype data in the analyses. A risk score composed of own BMI, mother's BMI, and family income using effect sizes given in Table 1 was used in age-stratified analyses.

Table 1.
Stepwise multivariable models for associations between childhood risk factors, genotype data, and adulthood obesity (BMI ≥ 30 kg/m2)

Based on power analyses conducted for each SNP, with this sample size, we have at least 80% power at a P value of 0.05 to detect an odds ratio (OR) of 1.4 for obesity, except for two very rare alleles in rs13107325 (n = 1) and rs11847697 (n = 2), where we have 80% power to detect an OR of 2.4 for obesity. The power analysis was conducted using the online power calculator (23), based on a case control allelic test and assuming an additive disease model.

The incremental value of adding risk variables to predict adult obesity was examined based on multivariate logistic regression models. The ability of several models to predict obesity risk was estimated using C statistics by calculating the area under the receiver operating characteristic curve (AUC), the net reclassification improvement (NRI) and integrated discrimination index (IDI) (24). For NRI, participants were assigned to one of four categories (<5, 5%–10, 10%–20, and >20%) that reflected their risk of adult obesity based on each model. Model calibration was tested by the Hosmer-Lemeshow χ2 test (24).

Values for triglycerides, insulin, and CRP were log10-transformed before analyses due to skewed distributions. All the analyses were repeated after exclusion of those individuals with childhood CRP higher than 10 mg/liter (n = 44). This exclusion did not affect the main findings. The statistical tests were performed with SAS version 9.2. Statistical significance was inferred at a two-tailed P value <0.05.

Results

Baseline characteristics are shown in Table 2. To determine whether the representativeness of the baseline sample was maintained in the present cohort, baseline characteristics were compared between those who participated and those that did not participate at follow-up. Nonparticipants were younger (10.0 vs. 10.7 yr, P < 0.0001) and more often male (55 vs. 45%, P < 0.0001) than participants. In age- and sex-adjusted analysis, no statistically significant differences were observed for other baseline study variables.

Table 2.
Characteristics of study participants according to sex

Associations between childhood risk factors, genotype data, and adulthood obesity

Age- and sex-adjusted odds between child risk factors and obesity are shown in Table 3. Childhood BMI, insulin, CRP, systolic blood pressure, mother's and father's BMI, birth weight, and aggression-prone negative emotionality were directly associated and high-density lipoprotein-cholesterol, family income, and parental education inversely associated with adulthood obesity (all P < 0.05). As shown in Table 4, the individual SNP near the genes TFAP2B, FLJ35779, FANCL, LRRN6C, and FTO, as well as the genotype risk score, were significantly associated with obesity in age- and sex-adjusted analyses.

Table 3.
Age- and sex-adjusted OR of adult obesity according to childhood risk factors
Table 4.
Age and sex adjusted OR for adult obesity according to genotype data

Of childhood phenotype and environmental factors, the independent predictors of adult obesity included own BMI, CRP, family income (inverse association), and mother's BMI (Table 1). Father's BMI was not included in the model shown in Table 1 because of missing data (n = 1868). When studying the effect of father's BMI in this subcohort, it was independently associated with adult obesity [OR = 1.25; 95% confidence interval (CI) = 1.09–1.43; P = 0.01]. Essentially similar multivariable results were seen using the high-risk waist circumference as an outcome. The only exception was that high insulin (P = 0.02) emerged as an additional risk factor.

A stepwise multivariable analysis was performed including data on all SNP and the significant phenotype and environmental risk variables (Table 1). In this analysis, the SNP near the genes of TFAP2B, LRRN6C, and FLJ35579 remained significantly associated with obesity. When the five different FTO SNP were additionally included in the final multivariable model one at a time, none of them was significantly associated with obesity (P values = 0.12–0.33). In a multivariable regression analysis using continuous BMI data as an outcome, the significant (P < 0.05) predictors of increased adult BMI were childhood BMI, CRP, mother's BMI, family income, and the SNP near the genes of FLJ35579 and SEC16B. Instead, the effects of the SNP near the genes TFAP2B (P = 0.12) and LRRN6C (P = 0.07) were nonsignificant.

Multivariable analysis was rerun after exclusion of mother's BMI. In this model, the results concerning genetic markers were not altered, because SNP near the genes TFAP2B, LRRN6C, and FLJ35579 were the only ones significantly associated with obesity. In addition, to test a possible colinearity between mother's BMI and genetic markers, we calculated correlation coefficients between SNP and mother's BMI. The colinearity was only modest, because the strongest r value was 0.08 (P = 0.0003) observed for FTO SNP rs1558902.

Multiple childhood risk factors in predicting obesity

We studied the utility of obesity prediction with several models among individuals with complete data (n = 1939) on BMI, mother's BMI, family income, CRP concentration, and genetic markers (Table 5). A model that included mother's BMI and family income (model 2) performed better than a model including only age, sex, and childhood BMI (model 1). Adding CRP and genetic risk markers (model 3) did not significantly improve AUC or NRI, although such a model provided significant IDI (Table 5).

Table 5.
Comparison of models for prediction of adult obesity (BMI ≥ 30 kg/m2) in 1939 individuals

Nonlaboratory childhood risk score in predicting obesity at different age groups

To examine the strength of association across all ages in childhood, we calculated the odds of adult obesity according to a risk score for each age group. The risk score based on childhood BMI, mother's BMI, and family income was significantly predictive of obesity in all age groups (Fig. 1).

Fig. 1.
OR and 95% CI for childhood risk score (childhood BMI, mother's BMI, and family income) in predicting adulthood obesity in different age groups.

Discussion

In the present analysis from the Young Finns cohort, we observed that the prediction of adulthood obesity can be significantly improved by using data on mother's BMI and socioeconomic status in addition to childhood BMI. Our study also provided important new information on the roles of inflammation (childhood CRP concentration) and novel genetic markers in the development of adult obesity.

Our findings concerning the associations between childhood BMI, socioeconomic position, and parental BMI with later obesity are in line with findings from other cohorts (4, 6, 7). However, previous studies have mainly been conducted in substantially smaller study groups, and/or baseline data on potential confounding and modifying factors have not been as extensive as in the present study. In addition, we examined the effects of several obesity susceptibility gene loci recently associated with BMI (1315). Genetic risk markers near TFAP2B, FLJ35779, and LRRN6C were independently associated with obesity. Taking into account these novel genetic risk markers, however, only marginally improved the prediction of later obesity when considered together with youth BMI, parental BMI, and socioeconomic position. Therefore, our current observations indicate that children at high risk of adult obesity could be identified by applying a simple non-laboratory-based risk assessment. However, it should be used with a caution because all children who will actually become obese as adults cannot be identified. Whether genetic testing is useful in identifying children at risk who do not have other risk factors requires additional studies.

The pathophysiological mechanisms between the genetic variants and obesity are not completely understood. Variants near the FTO gene were among the first genetic markers associated with BMI. We found that FTO variants were associated with adult obesity in models adjusted for age and sex but not in models adjusted for childhood BMI and maternal BMI. The lack of effect in multivariable models may reflect the fact that FTO associates with BMI already in childhood (25). Although FTO was correlated with mother's BMI, the finding cannot be solely explained by the effect of maternal BMI, because FTO SNP were not associated with obesity in an additional multivariable analysis after exclusion of maternal BMI data. FTO may influence weight via effects on energy intake and satiety (26). In a recent study by Church et al. (27), mice with overexpression of FTO had increased food intake resulting in obesity. In the present study, variants near TFAP2B, LRRN6C, and FLJ35779 were independently associated with obesity. Some information exists about the biology of TFAP2B (28). The gene encodes a transcription factor expressed in adipose tissue. Its overexpression leads to increased lipid accumulation (29) and decreased expression of adiponectin (30). Thus, TFAP2B may have an effect on fat accumulation at multiple sites. LRRN6C is a member of the leucine-rich repeat transmembrane protein family. These proteins are involved in innate immunity and nervous system development (31). The mechanism responsible for the relationship between LRRN6C and obesity is unknown. Similarly, nothing is currently known about the mechanism behind the relationship between obesity and FLJ35779 (also called POC5), which codes a protein required in mitosis (32). It is possible that the identified variants near these genes modulate the transcription of some other nearby genes. For example, the rs2112347 (the noncoding variant of the FLJ35779 gene) lies within approximately 400 kb of the 3-hydroxy-3-methylglutaryl-coenzyme A reductase gene, which is intimately involved in cholesterol synthesis and lipid metabolism, thus possibly also affecting obesity.

Serum CRP levels correlate strongly with BMI (33), but it is unclear whether CRP has a causal role in obesity. Such a role has been recently suggested in a study that found an association between a genetic variant in the CRP gene and fat mass (34). In line, we observed that childhood CRP levels were independent predictors of adult obesity. Previously in this cohort, a correlation of r = 0.17 was reported between childhood CRP and BMI levels (18). Therefore, it is possible that part of the association between childhood CRP and adult obesity is because some individuals with elevated baseline CRP levels were already obese as children. In other studies, it has been shown that CRP levels are higher in the offspring of obese parents (35). In addition, high CRP levels predict weight change (36) and adiposity-related conditions including diabetes (37) and the metabolic syndrome (38). These observations suggest that a proinflammatory state may not only be a consequence of obesity but potentially also a precursor of adiposity-related conditions. In addition, two recent studies using genetic data have supported the concept that obesity and inflammation may share a common genetic and/or pathophysiological basis. In a genome-wide association study (39), it was observed that nine genetic variants within the leptin receptor gene reached genome-wide significance for an association with CRP levels. In addition, Fisher et al. (40) found that a FTO polymorphism (rs9939609) contributed to the variation in plasma CRP levels independently of obesity indices. In our study, despite the fact that childhood CRP concentration predicted adult obesity in multivariable models, the inclusion of CRP did not significantly improve the prediction power of a model that included childhood BMI, mother's BMI, and family income.

We found that a simple risk score based on nonlaboratory childhood risk factors (high BMI, high mother's BMI, and low socioeconomic status) was superior to a model including only BMI values in predicting obesity. Importantly, the risk score predicted adult obesity in all age groups (i.e. 3, 6, 9, 12, 15, and 18 yr old) of the present cohort. In a recent statement by U.S. Preventive Services Task Force, a grade B recommendation was given that clinicians should screen children aged 6 yr and older for obesity (16). The screening was recommended to be performed based on BMI measurements. Our results suggest that the identification of children at high risk could be improved by additionally taking into account parental BMI and indices of socioeconomic status such as family income. Our data also suggest that these measurements are predictive of later obesity already at the age of 3 yr.

Strengths and limitations

We had a large, randomly selected, cohort of young men and women prospectively followed for up to 27 yr since childhood. Extensive data were available on several possible childhood determinants of obesity and genetic determinants of obesity that could be comprehensively taken into account in multivariable models to limit the effects of any bias in the data analysis.

Limitations of this study include the loss of original participants during the long-term follow-up. However, although the nonparticipants were younger and more often male than the participants, the baseline risk factors were similar between participants and nonparticipants in age- and sex-adjusted analyses, such that the study cohort appears to be representative of the original population. Because genetic data were available only among those individuals participating in the adult follow-up, we could not test whether participants and nonparticipants of the study differ concerning genetic factors. Because our study cohort was racially homogeneous, the generalizability of our results is limited to white Caucasians. Childhood CRP levels were analyzed from samples stored for 25 yr at −20 C. Albeit this may be associated with some deterioration of CRP, we have shown a strong correlation of CRP levels before and after storage at this temperature (19). Because we tested previously identified genetic variants, replication of the results was not attempted in independent samples. Therefore, confirmation of these findings in other populations would be important to evaluate their implications in obesity prevention initiatives.

Conclusions

In summary, we found that childhood BMI, CRP, family income (inversely) mother's BMI, and polymorphisms near genes FLJ35779, TFAP2B, and LRRN6C were independently related with obesity 21–27 yr later in adulthood. Including information on genetic variants and inflammation (CRP) in the prediction models, however, only marginally improved the predictive power. Newly identified genetic variants may give insights into the biology of obesity but seem not to add much to obesity prediction. This is expected given the low attributable risk of these genes. Similar observations with novel genetic risk factors have been made for cardiovascular endpoints (41). Therefore, from the perspective of health policy, the most important finding was that three easily measurable nonlaboratory risk factors (high own BMI, high mother's BMI, and low socioeconomic status) can be used to identify children and adolescents between ages 3 and 18 yr who are at substantially increased risk of developing adult obesity. A simple risk score based on these three risk factors was superior in predicting adulthood obesity compared with the currently recommended approach of using data only on childhood BMI (16). This obesity prediction model based on nonlaboratory factors could be easily obtained in a clinical setting.

Acknowledgments

This work was supported by the Academy of Finland (Grants 117797, 121584, and 126925), the Social Insurance Institution of Finland, Tampere and Turku University Hospital Medical Funds, Juho Vainio Foundation, Paavo Nurmi Foundation, the Orion-Farmos Research Foundation, and the Finnish Foundation of Cardiovascular Research. M.Ki. is supported by the BUPA Foundation Specialist Research Grant, UK, and the Heart, Lung, and Blood Institute (R01HL036310-20A2), National Institutes of Health. The sponsors had no role in preparing the manuscript.

Disclosure Summary: The authors do not have a conflict of interest.

Footnotes

Abbreviations:

AUC
Area under the receiver operating characteristic curve
BMI
body mass index
CI
confidence interval
CRP
C-reactive protein
IDI
integrated discrimination index
NRI
net reclassification improvement
OR
odds ratio
SNP
single-nucleotide polymorphism.

References

1. Daniels SR, Jacobson MS, McCrindle BW, Eckel RH, Sanner BM. 2009. American Heart Association Childhood Obesity Research Summit: executive summary. Circulation 119:2114–2123 [PubMed]
2. Flegal KM, Graubard BI, Williamson DF, Gail MH. 2007. Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA 298:2028–2037 [PubMed]
3. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. 2010. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med 362:485–493 [PMC free article] [PubMed]
4. Parsons TJ, Power C, Logan S, Summerbell CD. 1999. Childhood predictors of adult obesity: a systematic review. Int J Obes Relat Metad Disord 23:S1–S107 [PubMed]
5. Juonala M, Raitakari M, S A Viikari J, Raitakari OT. 2006. Obesity in youth is not an independent predictor of carotid IMT in adulthood. The Cardiovascular Risk in Young Finns Study. Atherosclerosis 185:388–393 [PubMed]
6. Eriksson J, Forsen T, Osmond C, Barker D. 2003. Obesity from cradle to grave. Int J Obes Relat Metad Disord 27:722–727 [PubMed]
7. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. 1997. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med 337:869–873 [PubMed]
8. Kimm SY, Glynn NW, Obarzanek E, Kriska AM, Daniels SR, Barton BA, Liu K. 2005. Relation between the changes in physical activity and body-mass index during adolescence: a multicentre longitudinal study. Lancet 366:301–307 [PubMed]
9. van Lenthe FJ, Kemper CG, van Mechelen W. 1996. Rapid maturation in adolescence results in greater obesity in adulthood: the Amsterdam Growth and Health Study. Am J Clin Nutr 64:18–24 [PubMed]
10. Pulkki-Råback L, Elovainio M, Kivimäki M, Raitakari OT, Keltikangas-Järvinen L. 2005. Temperament in childhood predicts body mass in adulthood: the Cardiovascular Risk in Young Finns Study. Health Psychol 24:307–315 [PubMed]
11. Power C, Matthews S. 1997. Origins of health inequalities in a national population sample. Lancet 350:1584–1589 [PubMed]
12. Mattsson N, Rönnemaa T, Juonala M, Viikari JS, Raitakari OT. 2008. Childhood predictors of the metabolic syndrome in adulthood. The Cardiovascular Risk in Young Finns Study. Ann Med 40:542–552 [PubMed]
13. Dina C, Meyre D, Gallina S, Durand E, Körner A, Jacobson P, Carlsson LM, Kiess W, Vatin V, Lecoeur C, Delplanque J, Vaillant E, Pattou F, Ruiz J, Weill J, Levy-Marchal C, Horber F, Potoczna N, Hercberg S, Le Stunff C, Bougnères P, Kovacs P, Marre M, Balkau B, Cauchi S, et al. 2007. Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet 39:724–726 [PubMed]
14. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, et al. 2007. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316:889–894 [PMC free article] [PubMed]
15. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Mägi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segrè AV, Estrada K, et al. 2010. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42:937–948 [PMC free article] [PubMed]
16. US Preventive Services Task Force, Barton M. 2010. Screening for obesity in children and adolescents: US preventive services task force recommendation statement. Pediatrics 125:361–367 [PubMed]
17. Raitakari OT, Juonala M, Rönnemaa T, Keltikangas-Järvinen L, Räsänen L, Pietikäinen M, Hutri-Kähönen N, Taittonen L, Jokinen E, Marniemi J, Jula A, Telama R, Kähönen M, Lehtimäki T, Akerblom HK, Viikari JS. 2008. Cohort profile: The Cardiovascular Risk in Young Finns Study. Int J Epidemiol 37:1220–1226 [PubMed]
18. Herbert V, Lau KS, Gottlieb CW, Bleicher SJ. 1965. Coated charcoal immunoassay of insulin. J Clin Endocrinol Metab 25:1375–1384 [PubMed]
19. Juonala M, Viikari JS, Rönnemaa T, Taittonen L, Marniemi J, Raitakari OT. 2006. Childhood C-reactive protein in predicting CRP and carotid intima-media thickness in adulthood: the Cardiovascular Risk in Young Finns Study. Arterioscler Thromb Vasc Biol 26:1883–1888 [PubMed]
20. Juonala M, Viikari JSA, Kähönen M, Taittonen L, Laitinen T, Hutri-Kähönen N, Lehtimäki T, Jula A, Pietikäinen M, Jokinen E, Telama R, Räsänen L, Mikkilä V, Helenius H, Kivimäki M, Raitakari OT. 2010. Life-time risk factors and progression of carotid atherosclerosis in young adults. The Cardiovascular Risk in Young Finns Study. Eur Heart J 31:1745–1751 [PubMed]
21. Telama R, Viikari J, Välimäki I, Siren-Tiusanen H, Akerblom HK, Uhari M, Dahl M, Pesonen E, Lähde PL, Pietikäinen M. 1985. Atherosclerosis precursors in Finnish children and adolescents. X. Leisure-time physical activity. Acta Paediatr Scand Suppl 318:169–180 [PubMed]
22. Hintsanen M, Kivimäki M, Hintsa T, Theorell T, Elovainio M, Raitakari OT, Viikari JS, Keltikangas-Järvinen L. 2010. A prospective cohort study of deficient maternal nurturing attitudes predicting adulthood work stress independent of adulthood hostility and depressive symptoms. Stress 13:425–434 [PubMed]
23. Purcell S, Cherny SS, Sham PC. 2003. Genetic Power Calculator: design of linkage and association genetic mapping studies of complex traits. Bioinformatics 19:149–150 [PubMed]
24. Pencina MJ, D'Agostino RB, Sr, D'Agostino RB, Jr, Vasan RS. 2008. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27:157–172; discussion 207–212 [PubMed]
25. Hakanen M, Raitakari OT, Lehtimäki T, Peltonen N, Pahkala K, Sillanmäki L, Lagström H, Viikari J, Simell O, Rönnemaa T. 2009. FTO genotype is associated with body mass index after the age of seven years but not with energy intake or leisure-time physical activity. J Clin Endocrinol Metab 94:1281–1287 [PubMed]
26. Hetherington MM, Cecil JE. 2010. Gene-environment interactions in obesity. Forum Nutr 63:195–203 [PubMed]
27. Church C, Moir L, McMurray F, Girard C, Banks GT, Teboul L, Wells S, Brüning JC, Nolan PM, Ashcroft FM, Cox RD. 2010. Overexpression of Fto leads to increased food intake and results in obesity. Nat Genet 42:1086–1092 [PMC free article] [PubMed]
28. Lindgren CM, Heid IM, Randall JC, Lamina C, Steinthorsdottir V, Qi L, Speliotes EK, Thorleifsson G, Willer CJ, Herrera BM, Jackson AU, Lim N, Scheet P, Soranzo N, Amin N, Aulchenko YS, Chambers JC, Drong A, Luan J, Lyon HN, Rivadeneira F, Sanna S, Timpson NJ, Zillikens MC, Zhao JH, et al. 2009. Genome-wide association scan meta-analysis identifies three Loci influencing adiposity and fat distribution. Plos Genet 5:e1000508. [PMC free article] [PubMed]
29. Tao Y, Maegawa H, Ugi S, Ikeda K, Nagai Y, Egawa K, Nakamura T, Tsukada S, Nishio Y, Maeda S, Kashiwagi A. 2006. The transcription factor AP-2β causes cell enlargement and insulin resistance in 3T3-L1 adipocytes. Endocrinology 147:1685–1696 [PubMed]
30. Ikeda K, Maegawa H, Ugi S, Tao Y, Nishio Y, Tsukada S, Maeda S, Kashiwagi A. 2006. Transcription factor activating enhancer-binding protein-2β. A negative regulator of adiponectin gene expression. J Biol Chem 281:31245–31253 [PubMed]
31. Dolan J, Walshe K, Alsbury S, Hokamp K, O'Keeffe S, Okafuji T, Miller SF, Tear G, Mitchell KJ. 2007. The extracellular leucine-rich repeat superfamily: a comparative survey and analysis of evolutionary relationships and expression patterns. BMC Genomics 14:320. [PMC free article] [PubMed]
32. Azimzadeh J, Hergert P, Delouvée A, Euteneuer U, Formstecher E, Khodjakov A, Bornens M. 2009. hPOC5 is a centrin-binding protein required for assembly of full-length centrioles. J Cell Biol 185:101–114 [PMC free article] [PubMed]
33. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. 1999. Elevated C-reactive protein levels in overweight and obese adults. JAMA 282:2131–2135 [PubMed]
34. Bochud M, Marquant F, Marques-Vidal PM, Vollenweider P, Beckmann JS, Mooser V, Paccaud F, Rousson V. 2009. Association between C-reactive protein and adiposity in women. J Clin Endocrinol Metab 94:3969–3977 [PubMed]
35. Lieb W, Pencina MJ, Lanier KJ, Tofler GH, Levy D, Fox CS, Wang TJ, D'Agostino RB, Sr, Vasan RS. 2009. Association of parental obesity with concentrations of select systemic biomarkers in nonobese offspring: the Framingham Heart Study. Diabetes 58:134–137 [PMC free article] [PubMed]
36. Barzilay JI, Forsberg C, Heckbert SR, Cushman M, Newman AB. 2006. The association of markers of inflammation with weight change in older adults: the Cardiovascular Health Study. Int J Obes (Lond) 30:1362–1367 [PubMed]
37. Pradhan AD, Manson JE, Rifai N, Buring JE, Ridker PM. 2001. C-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitus. JAMA 286:327–334 [PubMed]
38. Hanley AJ, Wiliams K, Festa A, Wagenknecht LE, D'Agostino RB, Jr, Haffner SM. 2005. Liver markers and development of the metabolic syndrome: the Insulin Resistance Atherosclerosis Study. Diabetes 54:3140–3147 [PubMed]
39. Ridker PM, Pare G, Parker A, Zee RY, Danik JS, Buring JE, Kwiatkowski D, Cook NR, Miletich JP, Chasman DI. 2008. Loci related to metabolic-syndrome pathways including LEPR, HNF1A, IL6R, and GCKR associate with plasma C-reactive protein: the Women's Genome Health Study. Am J Hum Genet 82:1185–1192 [PMC free article] [PubMed]
40. Fisher E, Schulze MB, Stefan N, Häring HU, Döring F, Joost HG, Al-Hasani H, Boeing H, Pischon T. 2009. Association of the FTO rs9939609 single nucleotide polymorphism with C-reactive protein levels. Obesity 17:330–334 [PubMed]
41. Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, Guiducci C, Perola M, Jula A, Sinisalo J, Lokki ML, Nieminen MS, Melander O, Salomaa V, Peltonen L, Kathiresan S. 2010. A multilocus genetic risk score for coronary heart disease: case-control and prospective cohort analyses. Lancet 376:1393–1400 [PMC free article] [PubMed]

Articles from The Journal of Clinical Endocrinology and Metabolism are provided here courtesy of The Endocrine Society
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...