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Genetic Variance in the Adiponutrin Gene Family and Childhood Obesity

  • Lovisa E. Johansson ,

    lovisa.johansson@med.lu.se

    Affiliation Department of Clinical Sciences Malmö, Lund University, Lund, Sweden

  • Lina M. Johansson,

    Affiliation Department of Clinical Sciences Malmö, Lund University, Lund, Sweden

  • Pernilla Danielsson,

    Affiliation National Childhood Obesity Center, Division of Pediatrics, Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, Stockholm, Sweden

  • Svante Norgren,

    Affiliation National Childhood Obesity Center, Division of Pediatrics, Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, Stockholm, Sweden

  • Stina Johansson,

    Affiliation National Childhood Obesity Center, Division of Pediatrics, Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, Stockholm, Sweden

  • Claude Marcus,

    Affiliation National Childhood Obesity Center, Division of Pediatrics, Department of Clinical Science, Intervention, and Technology, Karolinska Institutet, Stockholm, Sweden

  • Martin Ridderstråle

    Affiliation Department of Clinical Sciences Malmö, Lund University, Lund, Sweden

Genetic Variance in the Adiponutrin Gene Family and Childhood Obesity

  • Lovisa E. Johansson, 
  • Lina M. Johansson, 
  • Pernilla Danielsson, 
  • Svante Norgren, 
  • Stina Johansson, 
  • Claude Marcus, 
  • Martin Ridderstråle
PLOS
x

Abstract

Aim

The adiponutrin gene family consists of five genes (PNPLA1-5) coding for proteins with both lipolytic and lipogenic properties. PNPLA3 has previously been associated with adult obesity. Here we investigated the possible association between genetic variants in these genes and childhood and adolescent obesity.

Methods/Results

Polymorphisms in the five genes of the adiponutrin gene family were selected and genotyped using the Sequenom platform in a childhood and adolescent obesity case-control study. Six variants in PNPLA1 showed association with obesity (rs9380559, rs12212459, rs1467912, rs4713951, rs10947600, and rs12199580, p<0.05 after adjustment for age and gender). Three variants in PNPLA3 showed association with obesity before, but not after, adjustment for age and gender (rs139051, rs12483959, and rs2072907, p>0.05). When analyzing these SNPs in relation to phenotypes, two SNPs in the PNPLA3 gene showed association with insulin sensitivity (rs12483959: β = −0.053, p = 0.016, and rs2072907: β = −0.049, p = 0.024). No associations were seen for PNPLA2, PNPLA4, and PNPLA5.

Conclusions

Genetic variation in the adiponutrin gene family does not seem to contribute strongly to obesity in children and adolescents. PNPLA1 exhibited a modest effect on obesity and PNPLA3 on insulin sensitivity. These data, however, require confirmation in other cohorts and ethnic groups.

Introduction

A new family of genes with conserved patatin and lipase domains has recently been identified and given the name patatin-like phospholipase family [1][3]. The family consists of nine genes, and of these, five genes form a subgroup called the adiponutrin family [3]. This subfamily include patatin-like phospholipase 1 (PNPLA1), adipose triglyceride lipase (ATGL/PNPLA2), adiponutrin (ADPN/PNPLA3), gene sequence 2 (PNPLA4) and GS2-like (PNPLA5). It is believed that members of the adiponutrin family complement the hormone sensitive lipase (HSL) as responsible for adipocyte triacylglycerol lipase activity. Mice lacking HSL display a lean phenotype and accumulate diglycerides suggesting that HSL is the main enzyme for the second step of lipolysis and that other enzymes are responsible for the first step [4][6]. Several studies indicate that the protein encoded by PNPLA2 is one of the enzymes responsible for this first step in lipolysis [3], [7]. Less is known about the function of the other members but data indicates that they retain both lipolytic and lipogenic properties [1][3].

All members of the adiponutrin gene family are highly expressed in the adipose tissue. Expression increases during adipocyte differentiation and is regulated in by nutritional challenges [1][3]. For example, PNPLA3 is downregulated in the adipose tissue of insulin resistant subjects and upregulated in a glucose dependent fashion in response to insulin stimulation [8]. Two studies have demonstrated genetic association between PNPLA2 and PNPLA3 with type 2 diabetes and obesity, respectively [8][10]. Far less is known about the other three members of the family, PNPLA1, PNPLA4 and PNPLA5. The aim of this study was to investigate the genetic relevance of all five genes in the adiponutrin family in the pathogenesis of childhood obesity and insulin resistance.

Results

In total, 61 out of 85 selected SNPs were successfully genotyped using the Sequenom platform in a childhood and adolescent obesity case-control material (Table S1 and Figures S1, S2, S3, S4, S5). Clinical characteristics for this cohort have been presented previously and are summarized in Table 1 [11]. Gender distribution was similar between the obese and non-obese children. By definition, the obese group was younger than the normal weight controls (Table 1). The obese subjects all showed a significant degree of insulin resistance (HOMA-IR: 3.04 [2.11–4.49], n = 297).

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Table 1. Clinical characteristics of the child obesity case-control study.

https://doi.org/10.1371/journal.pone.0005327.t001

Logistic regression identified six variants in PNPLA1 that show association with obesity when adjusting for age and gender (rs9380559, rs12212459, rs1467912, rs4713951, rs10947600 and the coding rs12199580, Table 2 and Table S2). Three variants in PNPLA3 showed association with obesity (rs139051, rs12483959 and rs2072907, Table 2 and Table S2). This association was unaffected by adjustment for gender but attenuated when adjusting for age (data not shown). No variants in the PNPLA2, PNPLA4 and PNPLA5 were associated with obesity in this cohort (Table S2).

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Table 2. Genetic variants in PNPLA1 and PNPLA3 showing significant association with obesity using logistic regression.

https://doi.org/10.1371/journal.pone.0005327.t002

The associated SNPs were further analyzed for association with phenotypes related to obesity in the group of obese children and adolescents (Table 3). Similar data for the control group was not available. PNPLA1 variants rs12212459 and rs1467912 showed association with BMI-standard deviation score (SDS) after adjusting for age and gender (Table 3). Adjusting for insulin resistance defined by HOMA-IR did affect this observation (β = 0.30, p = 0.025 and β = 0.40, p = 0.0029, respectively). PNPLA1 SNP rs10947600 was associated with both body weight (n = 330, GG: 104.3[85.1–124.8] kg, GA: 94.1[78.3–111.9] kg, AA: 94.4[79.3–109.0] kg, β = −3.38, p = 0.018) and glucose levels (n = 292, GG: 4.9[4.7–5.3] mmol/L, GA: 4.9[4.6–5.2] mmol/L, AA: 4.7[4.5–5.1] mmol/L, β = −0.0077, p = 0.032) and PNPLA1 SNP rs12199580 with glucose levels (n = 289, CC: 4.9[4.7–5.3] mmol/L, CA: 5.0[4.6–5.2] mmol/L, AA: 4.7[4.5–5.1] mmol/L, β = −0.0085, p = 0.016).

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Table 3. Obesity-associated variants in PNPLA1 and PNPLA3 and the association with phenotypes using multiple regression analyses.

https://doi.org/10.1371/journal.pone.0005327.t003

The obesity associated PNPLA3 variants rs12483959 and rs2072907 showed association with insulin sensitivity (Table 3) and disposition index (rs12483959: n = 264, GG: 122[66–208], GA: 109[61–214], AA: 74[52–197], β = −0.14, p = 0.017 and rs2072907: n = 265, GG: 121[66–209], GC: 109[58–215], CC: 83[56–192], β = −0.12, p = 0.043). Adjusting for BMI-SDS did not affect the association with insulin sensitivity (β = −0.053, p = 0.015 and β = −0.049, p = 0.022, respectively) or disposition index (β = −0.15, p = 0.012 and β = −0.12, p = 0.031, respectively).

Discussion

The genetic analysis using TagSNPs in the five genes included in the adiponutrin gene family revealed that some variants within these genes exert a weak but significant effect on obesity in children and adolescents. In this study the association was limited to the two genes PNPLA1 and PNPLA3. Findings concerning PNPLA3 variants were attenuated when adjusting for age and gender but further analysis indicated that they might influence insulin sensitivity.

Childhood obesity is associated with increased risk of cardiovascular disease [12] and reduced life expectancy [13], [XPATH ERROR: unknown variable "rids-text".]. It is therefore of great importance to study this group in order to identify markers that could recognize individuals predisposed to obesity at an early stage. However, the polygenic nature of obesity makes the search for risk altering genes difficult. Recent studies have identified two strong obesity candidate genes, the fat mass and obesity associated (FTO) and melanocortin 4 receptor (MC4R) [15]18. Genes involved in lipid metabolism such as lipases would be relevant to investigate in relation with obesity since it is a state of excessive storage of lipids. In the obese state, the adipose tissue is less efficient in buffering lipids resulting in increased levels in the circulation. These lipids will then be stored in other tissues thereby promoting development of insulin resistance and possibly type 2 diabetes [19]. Genetic studies of for example the important lipase hormone sensitive lipase (HSL) show significant associations with measures of obesity suggesting that genes coding for proteins with lipase activity are of importance [20][25]. The five adiponutrin gene family members encode proteins that are able to both catalyze the build-up and breakdown of fat thus identifying them as possible candidate genes [1][3]. Data presented here, for the most part, failed to clearly confirm this candidacy. We found borderline association with obesity for PNPLA1 and PNPLA3, but these data would not hold for multiple corrections. Given the hypothesis generating nature of the study it is important to underline that the results should be interpreted with caution and need confirmation elsewhere.

So far little is known concerning genetic variation in the adiponutrin gene family and its possible influence on metabolic disease. Only the PNPLA2 and PNPLA3 have been studied in this context before. For PNPLA2, common variants has been associated with free fatty acid levels, triglyceride levels and type 2 diabetes suggesting that the gene may play an important role for the risk factors associated with obesity rather than obesity per se [9]. Rare mutations in the PNPLA2 gene, resulting in a truncated protein with no capacity to bind to lipid droplets but with an intact patatin domain, has been identified in a subgroup of patients with neutral lipid storage disorder (NLSD) with mild myopathy [26]. NLSD is a disorder characterized by storage of triglyceride-containing cytoplasmic droplets in for example leukocytes, bone marrow, skin and muscle (OMIM #610717). In our study we did not find any association between PNPLA2 and obesity and therefore no further analysis was conducted. As stated in both previous studies regarding PNPLA2, no association was found with obesity and the NLSD patients carrying PNPLA2 mutations were not obese [9], [26].

Genetic variants in PNPLA3 have previously been associated with obesity [8]. In this study we confirm these data but also demonstrate an association with insulin sensitivity. The association with obesity disappears when adjusting for age while that with insulin sensitivity association remains. Data may suggest that variants in PNPLA3 rather affect insulin sensitivity. Although obese adolescents in general are insulin resistant, the degree of obesity is not a major determining factor [27] and together with age, cardiorespiratory fitness, and truncal fat, only 25% of individual variation can be explained [28]. Thus, it is likely that genetic vulnerability is of importance and it is possible that PNPLA3 variation may play a role It has been shown that both genetic variants and insulin resistance regulate adipose PNPLA3 gene expression [8], [29].

Genetic variants in the PNPLA1, PNPLA4 and PNPLA5 genes have not been studied before. We found an association between PNPLA1 and juvenile obesity but no associations were found for PNPLA4 or PNPLA5. These data need to be replicated due to the relatively small study material used in this study.

In conclusion, although members of the adiponutrin gene family are clear candidate genes for obesity we were unable to clearly confirm this candidacy for obesity in children and adolescents. We did find a modest effect of PNPLA1 on obesity and PNPLA3 on insulin sensitivity although these data need confirmatory studies. Furthermore, although PNPLA2, PNPLA4 and PNPLA5 did not show any significant association with obesity and insulin sensitivity, we cannot rule out a possible implication in the pathogenesis due to the low power of this study.

Materials and Methods

Study subjects

We studied 466 obese children and adolescents referred to the National Childhood Obesity Centre at Karolinska University Hospital and Karolinska Institute and 491 non-obese adolescents recruited from 17 upper secondary schools around Stockholm (Table 1) [11]. For the obese children we had available blood samples, growth charts, clinical journal notes, medical examination and laboratory reports as well as questionnaires completed by the parents of the children at enrolment. The lean adolescents were asked through the school nurse if they wanted to participate in the study. Blood was collected and every adolescent completed a questionnaire concerning ethnicity, health and the use of medical drugs. Subjects with overweight/obesity or chronic diseases were excluded from the control group.

Height (Ulmer Stadiometer, Ulm, Germany), and weight (Vetek TI-1200, Väddö, Sweden) were measured with subjects in light clothing and body mass index (BMI; kg/m2) calculated. Body weight and height was measured at the first visit to the nearest 0.1 kg and 1 cm, respectively. All subjects in the obese group were obese according to international age and sex adjusted standards [30]. Values of a BMI standard deviation score (BMI-SDS) was calculated from weight, height, age and gender based on a French material from 1982 [31]. All subjects gave their written informed consent and the Regional Committee of Ethics, Stockholm, approved the study. The study was conducted according to the principles of the Helsinki Declaration.

Laboratory analysis

Blood samples from the obese children for measurement of glucose, (glucose-6-phosphate dehydrogenase method, Kebo Lab, Stockholm, Sweden), insulin (Pharmacia Diagnostics AB, Uppsala, Sweden), HDL-cholesterol and triglycerides (Boehringer, Mannheim, Germany) were obtained after an over-night fast. Analyses were performed at a certified laboratory (Department of Clinical Chemistry, Karolinska University Hospital). The control subjects were not fasting when the blood samples were obtained. Therefore insulin and glucose were measured only in the obese cohort. Insulin resistance was estimated by homeostasis model of assessment (HOMA-IR) [32]. Insulin sensitivity index representing the effect of insulin to catalyze the clearance of glucose from plasma after an intravenous glucose load were calculated using the Bergman minimal model approach [11], [33]. Acute insulin response reflects the first phase of endogenous secretion in response to glucose infusion and was calculated as area under the curve during the first 10 minutes [11], [34]. Genomic DNA was prepared by standard methods. DNA was extracted from whole blood by using QiaGen MaxiPrep (QiaGen, Germany) at the DNA/RNA Genotyping Lab, SWEGENE Resource Center for Profiling Polygenic Disease, Lund University, Malmö University Hospital, Malmö, Sweden.

Genotyping

SNPs were selected by using data from the HapMap consortium for each of the sequences coding for the selected five genes including an extra 5000 bases upstream and downstream [35]. TagSNPs were then selected using Tagger in the Haploview program for all five genes [36]. Additional coding SNPs were selected from the National Center for Biotechnology Information (NCBI) SNP database (http://www.ncbi.nlm.nih.gov/SNP/). In total, 85 SNPs passed the assay design and were genotyped using the Sequenom platform (MALDI-TOF) at the DNA/RNA Genotyping Lab, SWEGENE Resource Center for Profiling Polygenic Disease, Lund University, Malmö University Hospital, Malmö, Sweden. 24 SNPs failed genotyping and Hardy Weinberg equilibrium. These SNPs were removed from further analysis leaving a total of 61 SNPs for analysis (Table S1 and Figures S1, S2, S3, S4, S5). A selection of SNPs were re-analyzed in a subset of 283 patients using the TaqMan allelic discrimination method on the ABI 7900HT according to manufacturers' recommendations (Applied Biosystems). Success rate was 98.6%.

Statistical analysis

Logistic regression with age and gender as covariates were used for estimating the genotype association. Linear multiple regressions were performed in order to test for SNP effects on obesity and insulin resistance (HOMA-IR) as quantitative traits. All traits were log transformed for normal distribution. These analyses were adjusted for age and gender. Also, the obesity analysis was adjusted for insulin resistance and vice versa insulin resistance analysis for obesity. All p-values are based on additive models for the genetic variants. Data are presented as median with interquartile range within brackets [25th–75th] or odds ratio (OR) with 95% confidence interval (CI). All statistical calculations were performed using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/index.shtml) [37]. Furthermore, the power to detect an additive OR of 1.2 in this material when the minor allele frequency (MAF) is 0.05 is 17% and 53% for a MAF of 0.5 when α is set at 0.05.

Supporting Information

Table S1.

Hardy Weinberg equilibrium (HWE) for genetic variants analyzed in the Adiponutrin gene family.

https://doi.org/10.1371/journal.pone.0005327.s001

(0.12 MB DOC)

Table S2.

Variants in the five genes in the Adiponutrin gene family and the association with obesity using logistic regression analyses.

https://doi.org/10.1371/journal.pone.0005327.s002

(0.12 MB DOC)

Figure S1.

Graphical overview of the patatin-like phospholipase 1 (PNPLA1) gene and linkage disequilibrium obtained from HapMap (http://www.hapmap.org/). SNPs successfully genotyped by MALDI-TOF MS are marked with a black square.

https://doi.org/10.1371/journal.pone.0005327.s003

(0.62 MB PNG)

Figure S2.

Graphical overview of the patatin-like phospholipase 2 (PNPLA2) gene and linkage disequilibrium obtained from HapMap (http://www.hapmap.org/). SNPs successfully genotyped by MALDI-TOF MS are marked with a black square. One SNP, the rs1138693, is not included since it was not present in the HapMap database at the time of data extraction. It was included in the study because it is a coding SNP.

https://doi.org/10.1371/journal.pone.0005327.s004

(0.01 MB PNG)

Figure S3.

Graphical overview of the patatin-like phospholipase 3 (PNPLA3) gene and linkage disequilibrium obtained from HapMap (http://www.hapmap.org/). SNPs successfully genotyped by MALDI-TOF MS are marked with a black square.

https://doi.org/10.1371/journal.pone.0005327.s005

(0.26 MB PNG)

Figure S4.

Graphical overview of the patatin-like phospholipase 4 (PNPLA4) gene and linkage disequilibrium obtained from HapMap (http://www.hapmap.org/). SNPs successfully genotyped by MALDI-TOF MS are marked with a black square.

https://doi.org/10.1371/journal.pone.0005327.s006

(0.06 MB PNG)

Figure S5.

Graphical overview of the patatin-like phospholipase 5 (PNPLA5) gene and linkage disequilibrium obtained from HapMap (http://www.hapmap.org/). SNPs successfully genotyped by MALDI-TOF MS are marked with a black square.

https://doi.org/10.1371/journal.pone.0005327.s007

(0.17 MB PNG)

Author Contributions

Conceived and designed the experiments: LEJ CM MR. Performed the experiments: LEJ LMJ. Analyzed the data: LEJ. Contributed reagents/materials/analysis tools: PD SN SJ CM MR. Wrote the paper: LEJ CM MR.

References

  1. 1. Jenkins CM, Mancuso DJ, Yan W, Sims HF, Gibson B, et al. (2004) Identification, cloning, expression, and purification of three novel human calcium-independent phospholipase A2 family members possessing triacylglycerol lipase and acylglycerol transacylase activities. J Biol Chem 279: 48968–48975.CM JenkinsDJ MancusoW. YanHF SimsB. Gibson2004Identification, cloning, expression, and purification of three novel human calcium-independent phospholipase A2 family members possessing triacylglycerol lipase and acylglycerol transacylase activities.J Biol Chem2794896848975
  2. 2. Lake AC, Sun Y, Li JL, Kim JE, Johnson JW, et al. (2005) Expression, regulation, and triglyceride hydrolase activity of Adiponutrin family members. J Lipid Res 46: 2477–2487.AC LakeY. SunJL LiJE KimJW Johnson2005Expression, regulation, and triglyceride hydrolase activity of Adiponutrin family members.J Lipid Res4624772487
  3. 3. Wilson PA, Gardner SD, Lambie NM, Commans SA, Crowther DJ (2006) Characterization of the human patatin-like phospholipase family. J Lipid Res 47: 1940–1949.PA WilsonSD GardnerNM LambieSA CommansDJ Crowther2006Characterization of the human patatin-like phospholipase family.J Lipid Res4719401949
  4. 4. Osuga J, Ishibashi S, Oka T, Yagyu H, Tozawa R, et al. (2000) Targeted disruption of hormone-sensitive lipase results in male sterility and adipocyte hypertrophy, but not in obesity. Proc Natl Acad Sci U S A 97: 787–792.J. OsugaS. IshibashiT. OkaH. YagyuR. Tozawa2000Targeted disruption of hormone-sensitive lipase results in male sterility and adipocyte hypertrophy, but not in obesity.Proc Natl Acad Sci U S A97787792
  5. 5. Mulder H, Sorhede-Winzell M, Contreras JA, Fex M, Strom K, et al. (2003) Hormone-sensitive lipase null mice exhibit signs of impaired insulin sensitivity whereas insulin secretion is intact. J Biol Chem 278: 36380–36388.H. MulderM. Sorhede-WinzellJA ContrerasM. FexK. Strom2003Hormone-sensitive lipase null mice exhibit signs of impaired insulin sensitivity whereas insulin secretion is intact.J Biol Chem2783638036388
  6. 6. Wang SP, Laurin N, Himms-Hagen J, Rudnicki MA, Levy E, et al. (2001) The adipose tissue phenotype of hormone-sensitive lipase deficiency in mice. Obes Res 9: 119–128.SP WangN. LaurinJ. Himms-HagenMA RudnickiE. Levy2001The adipose tissue phenotype of hormone-sensitive lipase deficiency in mice.Obes Res9119128
  7. 7. Wolf G (2005) The mechanism and regulation of fat mobilization from adipose tissue: desnutrin, a newly discovered lipolytic enzyme. Nutr Rev 63: 166–170.G. Wolf2005The mechanism and regulation of fat mobilization from adipose tissue: desnutrin, a newly discovered lipolytic enzyme.Nutr Rev63166170
  8. 8. Johansson LE, Hoffstedt J, Parikh H, Carlsson E, Wabitsch M, et al. (2006) Variation in the adiponutrin gene influences its expression and associates with obesity. Diabetes 55: 826–833.LE JohanssonJ. HoffstedtH. ParikhE. CarlssonM. Wabitsch2006Variation in the adiponutrin gene influences its expression and associates with obesity.Diabetes55826833
  9. 9. Schoenborn V, Heid IM, Vollmert C, Lingenhel A, Adams TD, et al. (2006) The ATGL gene is associated with free fatty acids, triglycerides, and type 2 diabetes. Diabetes 55: 1270–1275.V. SchoenbornIM HeidC. VollmertA. LingenhelTD Adams2006The ATGL gene is associated with free fatty acids, triglycerides, and type 2 diabetes.Diabetes5512701275
  10. 10. Johansson LE, Lindblad U, Larsson CA, Rastam L, Ridderstrale M (2008) Polymorphisms in the adiponutrin gene are associated with increased insulin secretion and obesity. Eur J Endocrinol 159: 577–583.LE JohanssonU. LindbladCA LarssonL. RastamM. Ridderstrale2008Polymorphisms in the adiponutrin gene are associated with increased insulin secretion and obesity.Eur J Endocrinol159577583
  11. 11. Jacobsson JA, Danielsson P, Svensson V, Klovins J, Gyllensten U, et al. (2008) Major gender difference in association of FTO gene variant among severely obese children with obesity and obesity related phenotypes. Biochem Biophys Res Commun 368: 476–482.JA JacobssonP. DanielssonV. SvenssonJ. KlovinsU. Gyllensten2008Major gender difference in association of FTO gene variant among severely obese children with obesity and obesity related phenotypes.Biochem Biophys Res Commun368476482
  12. 12. Baker JL, Olsen LW, Sorensen TI (2007) Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med 357: 2329–2337.JL BakerLW OlsenTI Sorensen2007Childhood body-mass index and the risk of coronary heart disease in adulthood.N Engl J Med35723292337
  13. 13. Fontaine KR, Redden DT, Wang C, Westfall AO, Allison DB (2003) Years of life lost due to obesity. JAMA 289: 187–193.KR FontaineDT ReddenC. WangAO WestfallDB Allison2003Years of life lost due to obesity.JAMA289187193
  14. 14. Mossberg HO (1989) 40-year follow-up of overweight children. Lancet 2: 491–493.HO Mossberg198940-year follow-up of overweight children.Lancet2491493
  15. 15. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, et al. (2008) Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet 40: 768–775.RJ LoosCM LindgrenS. LiE. WheelerJH Zhao2008Common variants near MC4R are associated with fat mass, weight and risk of obesity.Nat Genet40768775
  16. 16. Chambers JC, Elliott P, Zabaneh D, Zhang W, Li Y, et al. (2008) Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat Genet 40: 716–718.JC ChambersP. ElliottD. ZabanehW. ZhangY. Li2008Common genetic variation near MC4R is associated with waist circumference and insulin resistance.Nat Genet40716718
  17. 17. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, 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.TM FraylingNJ TimpsonMN WeedonE. ZegginiRM Freathy2007A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity.Science316889894
  18. 18. Dina C, Meyre D, Gallina S, Durand E, Korner A, et al. (2007) Variation in FTO contributes to childhood obesity and severe adult obesity. Nat Genet 39: 724–726.C. DinaD. MeyreS. GallinaE. DurandA. Korner2007Variation in FTO contributes to childhood obesity and severe adult obesity.Nat Genet39724726
  19. 19. Frayn KN (2002) Adipose tissue as a buffer for daily lipid flux. Diabetologia 45: 1201–1210.KN Frayn2002Adipose tissue as a buffer for daily lipid flux.Diabetologia4512011210
  20. 20. Klannemark M, Orho M, Langin D, Laurell H, Holm C, et al. (1998) The putative role of the hormone-sensitive lipase gene in the pathogenesis of Type II diabetes mellitus and abdominal obesity. Diabetologia 41: 1516–1522.M. KlannemarkM. OrhoD. LanginH. LaurellC. Holm1998The putative role of the hormone-sensitive lipase gene in the pathogenesis of Type II diabetes mellitus and abdominal obesity.Diabetologia4115161522
  21. 21. Magre J, Laurell H, Fizames C, Antoine PJ, Dib C, et al. (1998) Human hormone-sensitive lipase: genetic mapping, identification of a new dinucleotide repeat, and association with obesity and NIDDM. Diabetes 47: 284–286.J. MagreH. LaurellC. FizamesPJ AntoineC. Dib1998Human hormone-sensitive lipase: genetic mapping, identification of a new dinucleotide repeat, and association with obesity and NIDDM.Diabetes47284286
  22. 22. Qi L, Shen H, Larson I, Barnard JR, Schaefer EJ, et al. (2004) Genetic variation at the hormone sensitive lipase: gender-specific association with plasma lipid and glucose concentrations. Clin Genet 65: 93–100.L. QiH. ShenI. LarsonJR BarnardEJ Schaefer2004Genetic variation at the hormone sensitive lipase: gender-specific association with plasma lipid and glucose concentrations.Clin Genet6593100
  23. 23. Lavebratt C, Ryden M, Schalling M, Sengul S, Ahlberg S, et al. (2002) The hormone-sensitive lipase i6 gene polymorphism and body fat accumulation. Eur J Clin Invest 32: 938–942.C. LavebrattM. RydenM. SchallingS. SengulS. Ahlberg2002The hormone-sensitive lipase i6 gene polymorphism and body fat accumulation.Eur J Clin Invest32938942
  24. 24. Hoffstedt J, Arner P, Schalling M, Pedersen NL, Sengul S, et al. (2001) A common hormone-sensitive lipase i6 gene polymorphism is associated with decreased human adipocyte lipolytic function. Diabetes 50: 2410–2413.J. HoffstedtP. ArnerM. SchallingNL PedersenS. Sengul2001A common hormone-sensitive lipase i6 gene polymorphism is associated with decreased human adipocyte lipolytic function.Diabetes5024102413
  25. 25. Garenc C, Perusse L, Chagnon YC, Rankinen T, Gagnon J, et al. (2002) The hormone-sensitive lipase gene and body composition: the HERITAGE Family Study. Int J Obes Relat Metab Disord 26: 220–227.C. GarencL. PerusseYC ChagnonT. RankinenJ. Gagnon2002The hormone-sensitive lipase gene and body composition: the HERITAGE Family Study.Int J Obes Relat Metab Disord26220227
  26. 26. Fischer J, Lefevre C, Morava E, Mussini JM, Laforet P, et al. (2007) The gene encoding adipose triglyceride lipase (PNPLA2) is mutated in neutral lipid storage disease with myopathy. Nat Genet 39: 28–30.J. FischerC. LefevreE. MoravaJM MussiniP. Laforet2007The gene encoding adipose triglyceride lipase (PNPLA2) is mutated in neutral lipid storage disease with myopathy.Nat Genet392830
  27. 27. Rössner SM, Neovius M, Montgomery SM, Marcus C, Norgren S (2008) Alternative Methods of Insulin Sensitivity Assessment in Obese Children and Adolescents Diabetes Care. 2008 31: 802–804.SM RössnerM. NeoviusSM MontgomeryC. MarcusS. Norgren2008Alternative Methods of Insulin Sensitivity Assessment in Obese Children and Adolescents Diabetes Care.200831802804
  28. 28. Morinder G, Larsson UE, Norgren S, Marcus C (2009) Insulin sensitivity, VO2max and body composition in severely obese Swedish children and adolescents. Acta Paediatr 98: 132–138.G. MorinderUE LarssonS. NorgrenC. Marcus2009Insulin sensitivity, VO2max and body composition in severely obese Swedish children and adolescents.Acta Paediatr98132138
  29. 29. Baulande S, Lasnier F, Lucas M, Pairault J (2001) Adiponutrin, a transmembrane protein corresponding to a novel dietary- and obesity-linked mRNA specifically expressed in the adipose lineage. J Biol Chem 276: 33336–33344.S. BaulandeF. LasnierM. LucasJ. Pairault2001Adiponutrin, a transmembrane protein corresponding to a novel dietary- and obesity-linked mRNA specifically expressed in the adipose lineage.J Biol Chem2763333633344
  30. 30. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320: 1240–1243.TJ ColeMC BellizziKM FlegalWH Dietz2000Establishing a standard definition for child overweight and obesity worldwide: international survey.BMJ32012401243
  31. 31. Rolland-Cachera MF, Sempe M, Guilloud-Bataille M, Patois E, Pequignot-Guggenbuhl F, et al. (1982) Adiposity indices in children. Am J Clin Nutr 36: 178–184.MF Rolland-CacheraM. SempeM. Guilloud-BatailleE. PatoisF. Pequignot-Guggenbuhl1982Adiposity indices in children.Am J Clin Nutr36178184
  32. 32. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412–419.DR MatthewsJP HoskerAS RudenskiBA NaylorDF Treacher1985Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.Diabetologia28412419
  33. 33. Bergman RN (2005) Minimal model: perspective from 2005. Horm Res 64: (Suppl 3)8–15.RN Bergman2005Minimal model: perspective from 2005.Horm Res64(Suppl 3)815
  34. 34. Bergman RN (1989) Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-model approach. Diabetes 38: 1512–1527.RN Bergman1989Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-model approach.Diabetes3815121527
  35. 35. (2003) The International HapMap Project. Nature 426: 789–796.2003The International HapMap Project.Nature426789796
  36. 36. Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21: 263–265.JC BarrettB. FryJ. MallerMJ Daly2005Haploview: analysis and visualization of LD and haplotype maps.Bioinformatics21263265
  37. 37. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81: 559–575.S. PurcellB. NealeK. Todd-BrownL. ThomasMA Ferreira2007PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet81559575