Skip Navigation


Human Molecular Genetics Advance Access originally published online on March 28, 2008
Human Molecular Genetics 2008 17(13):1916-1921; doi:10.1093/hmg/ddn089
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
17/13/1916    most recent
ddn089v2
ddn089v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Benzinou, M.
Right arrow Articles by Froguel, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Benzinou, M.
Right arrow Articles by Froguel, P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2008. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Endocannabinoid receptor 1 gene variations increase risk for obesity and modulate body mass index in European populations

Michael Benzinou1,2, Jean-Claude Chèvre1, Kirsten J. Ward1, Cécile Lecoeur1, Christian Dina1, Stephane Lobbens1, Emmanuelle Durand1, Jérome Delplanque1, Fritz F. Horber3, Barbara Heude4,5, Beverley Balkau4,5, Knut Borch-Johnsen6,7, Torben Jørgensen8, Torben Hansen6, Oluf Pedersen6,7, David Meyre1 and Philippe Froguel1,2,*

1 CNRS 8090-Institute of Biology, Pasteur Institute, Lille, France 2 Genomic Medicine, Hammersmith Hospital, Imperial College, Du Cane Road, London W12 0NN, UK 3 Klinik Lindberg, Schickstrasse 11, Winterthur CH-8400, Switzerland 4 Université Paris Sud, France 5 INSERM U780-IFR69, Villejuif, France 6 Steno Diabetes Center, Niels Steensens Vej 1, NLC2.13, Gentofte DK-2820, Denmark 7 Faculty of Health Science, University of Aarhus, Aarhus, Denmark 8 Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark

* To whom correspondence should be addressed. Tel: +44 2083833989; Fax: +44 2083838577; Email: p.froguel{at}imperial.ac.uk

Received January 4, 2008; Accepted March 16, 2008


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
The therapeutic effects of cannabinoid receptor blockade on obesity-associated phenotypes underline the importance of the endocannabinoid pathway on the energy balance. Using a staged-approach, we examined the contribution of the endocannabinoid receptor 1 gene (CNR1) on obesity and body mass index (BMI) in the European population. With the input of CNR1 exons and 3' and 5' regions sequencing and HapMap database, we selected and genotyped 26 tagging single-nucleotide polymorphisms (SNPs) in 1932 obese cases and 1173 non-obese controls of French European origin. Variants that showed significant associations (P < 0.05) with obesity after correction for multiple testing were further tested in two additional European cohorts including 2645 individuals. For the identification of the potential causal variant(s), we further genotyped SNPs in high linkage disequilibrium (LD) with the obesity-associated variants. Of the 25 successfully genotyped CNR1 SNPs, 12 showed nominal evidence of association with childhood obesity, class I and II and/or class III adult obesity (1.16 < OR < 1.40, 0.00003 < P < 0.04). Intronic SNPs rs806381 and rs2023239, which resisted correction for multiple testing were further associated with higher BMI in both Swiss obese subjects and Danish individuals. The genotyping of all know variants in partial LD (r2 > 0.5) with these two SNPs in the initial case–control study, identified two better associated SNPs (rs6454674 and rs10485170). Our study of 5750 subjects shows that CNR1 variations increase the risk for obesity and modulate BMI in our European population. As CB1 is a drug target for obesity, a pharmacogenetic analysis of the endocannabinoid blockade obesity treatment may be of interest to identify best responders.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
The endocannabinoid pathway is mediated by the binding of 2-arachidonylglycerol (2-AG) and anandamide (AEA) with the cannabinoid receptor (CB1) (1). Animal studies have identified its physiological role in the regulation of energy metabolism (2–5), as well as in food intake and addictive behaviours (6–8). These discoveries have led to the development of CB1 antagonists as potential therapeutic agents for treating obesity (9–12) and metabolic complications (12,13). In humans, CB1 antagonists induce weight loss, reduce food intake (10–13), improve glucose metabolism (10–13) and modulate lipid levels (10–13). These findings show the importance of CB1 in human physiology and qualify CNR1 as a biological candidate for human obesity.

Previously, a study showed modest evidence of a modulating effect of the rs12720071 CNR1 variant on body fat mass and distribution in white European adult men (14). Other studies have also tested the contribution of the CNR1 rs1049353 single-nucleotide polymorphism (SNP) on obesity but found inconsistent results (15–17). To analyze whether CNR1 variations are implicated in human obesity, and in view of the weak linkage disequilibrium (LD) coverage of the previous studies, we chose to investigate the possible role of 26 tagging SNPs, representating 100% of the common variation in a 37.4 kb region encompassing the CNR1 locus (http://www.hapmap.org) in 1932 obese cases and 1173 controls of French origin. We then selected two SNPs displaying P-values that resisted permutation-based correction for multiple testing and analyzed them in two independent European cohorts. Finally, in an attempt to identify putative functional SNP(s), we genotyped in the initial case–control study, all SNPs having a minor allele frequency (MAF) >5% and in LD (r2 > 0.5) with the two obesity-associated tagging SNPs.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
SNP identification and selection
Over 21 kb, including the four known exons described by Zhang et al. (18), the exon–intron boundaries, the intronic SNP-rich regions as well as 5.3 kb upstream and 5.9 kb downstream of CNR1, were sequenced in 24 adult controls and 48 obese children (Supplementary Material, Fig. S1). Thirty-six SNPs with a MAF ≥5% were identified and used to establish an LD map using Haploview. In addition, we ran ‘Tagger’ of the Haploview program with the HapMap phase II data in a 37.4 kb region defined by LD blocks and encompassing the CNR1 locus (chr6:88 904 301–88 941 649 on NCBI Build 36.1). Combining the sequencing results to the HapMap phase II data (http://www.hapmap.org) and for an r2 > 0.8, we selected and genotyped for the case–control association studies, 26 tagging SNPs with a MAF ≥5% that ensure complete coverage of the known haplotype diversity of the locus according to the HapMap II.

Case–control study
Twenty-five of the 26 tagging SNPs that best represent the known haplotype diversity of CNR1 were successfully genotyped in 1932 obese cases and 1173 controls. Results of the case–control analyses are given in Table 1 and in Supplementary Material, Table S1. All SNPs were in Hardy–Weinberg equilibrium (P > 0.05) in the normal, glucose-tolerant non-obese French Caucasian control collection. Twelve SNPs showed nominal evidence of association with childhood obesity, class I and II and/or class III adult obesity [odds ratios (ORs) between 1.16 and 1.40; P = 0.04 to P = 0.00003]. Using spectral decomposition, we estimated the total number of efficient tests at 16 (19,20). The number of SNPs with a P-value <0.05 accounted for nine tests (according to the same method). As an estimate of the concentration of high P-values, we estimated that the probability of 10 significant tests (P < 0.05 level) out of 16 trials was 5.91 x 10–10.


View this table:
[in this window]
[in a new window]

  		Table 1. Association study of two CNR1 variants showing evidence of association with obesity after multiple testing corrections

 
The haplotypes showing the lowest Akaike Information Criterion (AICmin) were tested for association with obesity. None of the major haplotypes (frequency >5%) showed a stronger association with obesity than the single SNPs (Supplementary Material, Table S2).

For the replication cohorts, we selected SNPs with P-values that survived permutation-based correction for multiple testing in the case–control analysis. After this test, the rs806381 SNP (–10908 A>G) was associated with childhood obesity (experiment-wide P-value, eP-values = 0.002) and the rs2023239 SNP (–5489 T>C) with class I and II adult obesity (eP-values = 0.016) (Table 1). To further account for multiple testing, we also performed a False discovery rate (FDR) analysis for the 25 SNPs: the tests that displayed q-value <0.05 occurred for the two variants described above (Table 1).

BMI analysis
The potential effect of rs806381 and rs2023239 on the continuous obesity-related trait, body mass index (BMI), was then tested in two additional independent European cohorts (Table 2). In a Swiss cohort of 865 obese adults, both rs806381 obesity G at-risk allele and rs2023239 obesity T at-risk allele were associated with increased BMI. For the r806381, the GC heterozygous carriers were 1 BMI unit heavier and the GG homozygous carriers ~3 BMI units heavier (P = 0.015) than the individuals with rs806381 CC genotypes. A similar trend was observed for the rs2023239 variant (P = 0.02). Additionally, we studied the effect of two CNR1 SNPs on BMI in a general population of 1780 Danish adults. Data were in agreement with the Swiss results, showing a significant additive effect of the rs806381 G at-risk allele: heterozygous carriers had a higher BMI than the CC homozygotes and a lower BMI when compared with the GG homozygotes (P = 0.0023). For the rs2023239 variant, the obesity T at-risk allele carriers had a significantly higher BMI in the Danish cohort (P = 0.021). We further tested for association of rs806381 and rs2023239 on BMI in the French obese children cohort and in a pooled cohort of French obese adults (class I, II and III obese individuals). While no significant result of the Z-score of BMI was observed in the children cohort, we found that the rs806381 obesity G at-risk allele carriers had a significantly higher BMI when compared with the non carriers in the obese adult cohort (P = 0.016).


View this table:
[in this window]
[in a new window]

  		Table 2. BMI analysis according to CNR1 genotypes in a Swiss obese adult cohort and in a Danish cohort representative of the general population

 
Meta-analysis test
Overall significance was assessed using Fisher’s method, which combines P-values of independent analyses. We used the number of effective tests (19) at each step to correct for multiple testing while accounting for the between SNPs correlations. The meta-analysis combining evidence of association for obesity and higher BMI found for SNP rs806381 and rs2023239, P (df = 6) = 1.1 x 10–6 and P = 4.7 x 10–4, respectively.

Fine mapping of rs2023239 and rs806381 LD blocks
In an attempt to identify the potential causal variant(s), we increased the SNP density of rs2023239 and rs806381 LD blocks. Using phase II genotyping data from the HapMap project (http://www.hapmap.org) in the 37.4 kb region defining the CNR1 locus, we selected 17 SNPs in LD (r2 > 0.5) with rs806381 and rs2023239. The comparison of the fine mapped SNP to the initial LD block representative SNP (rs806381 and rs2023239), (for the same subjects) found one additional variant for each block with increased significance of association with obesity (Fig. 1 and Supplementary Material, Table S3). In the rs2023239 block, in the class I and II obese adult group, the rs10485170 obesity A at-risk allele carriers are at substantially increased risk of being obese [OR = 1.85 (1.41–2.44), P = 0.000008] compared with the rs2023239 T at-risk allele carriers [OR = 1.40 (1.16–1.69), P = 0.0004]. In the rs806381 block, in the childhood obesity group, the risk was also increased for one of the SNPs: rs6454674 OR = 1.52 (1.26–1.84), P = 0.00001 compared with rs806381 OR = 1.39 (1.19–1.63), P = 0.00003 (Fig. 1 and Supplementary Material, Table S3).


Figure 1
View larger version (48K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 1. Structure of the CNR1 gene with the location of 26 common polymorphisms and the LD map. (a) Schematic representation of human CNR1 transcript isoforms (CB1A-E) as described by Zhang PW et al. (18); blue thin lines and bars represent introns and exons (ex1. ex1a. ex2. ex3a. ex3 and ex4) respectively; green CDS box indicates coding region. (b) Bars show sequenced regions in the 37.4 kb studied interval (c) 17 tagging SNPs found using the pairwise method (r2 > 0.8) based on the sequencing data and 9 additional tagging SNPs (*) from HapMap II; SNPs highlighted in bold have a P-value that resisted multiple testing corrections; positions of new markers with no rs number are given relative to first base of ATG start codon. (d) LD plot indicating r2 between the 26 SNPs with a MAF ≥5% found by sequencing. Shades of grey indicate 0 < r < 1. r2 = 0 being white and r2 = 1 being black.

 
To test if these two new SNPs were the best associated SNPs in their respective blocks, we performed a logistic regression analysis including in the model the initial LD block representative (i.e. rs2023239) and the fine mapping SNP (i.e. rs10485170). Interestingly, the rs10485170 was found to be significantly associated with obesity independently of the rs2023239 (P = 0.015), whereas rs2023239 no longer remained associated with obesity with the rs10485170 in the model (P = 0.23). In the logistic regression analysis of the other LD block, we could not discriminate the effect of rs806381 and rs6454674 on obesity.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Although little is known on how the endocannabinoid system contributes to human energy homeostasis (21), data derived from phase III trials of the CB1-receptor inhibitor rimonabant indicate clinical benefits of this antagonist on obesity and associated phenotypes (10–13). Based on these findings, we have tested the contribution of the endocannabinoid receptor 1 gene (CNR1) frequent genetic variation on the risk of obesity and increased BMI. An initial French case–control analysis of 25 tagging SNPs representative of the known haplotype diversity of the CNR1 locus, found a significantly higher number of associated SNPs than expected by chance (5.91 x 10–10). In an attempt to minimize false positives that could result from the number of variants tested, we performed a multiple testing permutation-based correction and selected two variants that were not in LD (r2 < 0.05) for replication. The first replication sample was a Swiss cohort with patients with severe affection status (BMI > 35 kg/m2). With the initial case–control study, the Swiss cohort included a large proportion of cases enriched for family history leading to an overestimation of the general population risk. For this reason, we chose to further test this variant in a general population-based Danish cohort. Despite the dissimilarity of these two independent replication cohorts, we found that both rs2023239 and the rs806381 at-risk allele carriers had a significantly higher BMI. Although the pooled analysis of the three studies remain slightly over the genome-wide level of significance (P = 5.10–7), the consistent associations provide encouraging prospects for subsequent studies.

Recently a group found no evidence of an association of the rs2023239 in German obese children and adolescents (17). Interestingly, this variant that was strongly associated with obesity and BMI in our obese adult case–control and adult replication cohorts (overall P < 10–5) was not found to be associated with childhood obesity in our study group. Although it is too early to conclude an age-dependent effect of CNR1 variants, this result supports the study of our most associated childhood obesity SNPs (rs806381 and rs6454674) in obese children cohorts. Our work also follows the association study of two other CNR1 variants (3813A/G-rs12720071, 4895A/G-rs806368) with obesity and obesity-related traits (14). This study that only tagged 8% of the common variation of the CNR1 locus found an association of rs12720071 with BMI and obesity-related phenotypes (18). The number of variants more significantly associated with obesity in our study and the low level of LD of rs12720071 with the two rs806381 and rs2023239 LD blocks tends to exclude this variant as the causal one.

Presently, there are no features to qualify rs806381 and rs2023239 as functional SNPs, as there are no clear genetic mechanisms to explain how they may alter the function or expression of CNR1. For this reason, we chose to genotype all SNPs in partial LD with these two variants for the identification of potential causal variants. The genotyping of 17 additional variants in our initial case–control groups identified two variants showing increased significance of association with obesity. The comparison of these variants to the two LD block representatives found that the rs10485170 had a significantly stronger effect on obesity than the rs2023239, while we could not discriminate between the effect of the rs806381 and rs6454674 on obesity.

In conclusion, we suggest that the consecutive replications and overall significance of associations with obesity and BMI found for frequent CNR1 variants, in a total of 5750 white European participants, of all ages, in the most complete analysis of the CNR1 locus with obese children and adults, strongly support the contribution of the CNR1 gene to polygenic obesity in European populations. Furthermore, our results raise the important question of whether the pharmacological effect of CNR1 antagonists could differ between patients with different CNR1 genotypes.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Subjects
The study protocols were approved by all local ethics committees and informed consent was obtained from each subject before participation in the studies.

Case–control study
Subjects in the case–control association studies were all French Caucasians recruited through a multimedia campaign run by the Centre National de la Recherche Scientifique (CNRS), Hotel Dieu Hospital, the Pasteur Institute, Lille and the Department of Paediatric Endocrinology of Jeanne de Flandres Hospital.

The case–control association study included three case groups: (i) 615 obese children (BMI above the 97th percentile), (ii) 625 class I and II obese adults (30 kg/m2 ≤ BMI ≤ 40 kg/m2) (22), (iii) 692 class III obese adults (BMI > 40 kg/m2) (22). The case groups were compared to a total of 1173 controls. The control subjects were unrelated adult non-obese French Caucasians pooled from three separate studies: a set of (i) 266 individuals (mean BMI = 23.1 ± 2.16 kg/m2; mean age 42.5 ± 4.5 years; men/women 105/161), (ii) 171 controls (mean BMI = 22.9 ± 2.3 kg/m2; mean age 60.7 ± 11.5 years; men/women 71/100, (iii) 736 individuals (mean BMI = 23.8 ± 1.8 kg/m2; mean age 53.5 ± 5.6 years; men/women 293/443) recruited, respectively, at the CNRS Lille, the ‘Fleurbaix-Laventie Ville Santé’ study (23) and the Epidemiologic Data on the Insulin Resistance Syndrome (DESIR) Study (24). Before being pooled, the three control sets were compared with each other. The Woolf test was applied to assess the genotypic homogeneity among the control studied groups on 2 x 2 x k tables over strata (25,26). This test identified no genotypic heterogeneity between the different groups (Supplementary Material, Table S4).

Replication cohorts
Associations with BMI have been tested in 865 class II and III (BMI ≥ 35 kg/m2) unrelated Caucasian subjects from Switzerland (27) (mean BMI = 43.4 ± 7.1 kg/m2; mean age 42.8 ± 10.7 years; men/women 667/198) and in a random sample of 1780 middle-aged individuals recruited from the Danish general population (Inter99 cohort) (mean BMI = 26 ± 4.4 kg/m2; mean age 45.9 ± 7.9 years; men/women 914/866) (Supplementary Material, Table S5).

Sequencing and SNP genotyping
Sequencing was performed using the automated ABI Prism 3730 DNA sequencer in combination with the Big Dye Terminator Cycle Sequencing Ready Reaction Kit (Applied Biosystems). Tagging SNPs were genotyped with SNplex, Taqman (Applied Biosystems) or Lightyper (Roche Diagnostics) technologies. As a standard laboratory quality control measure, a random 10% of DNA samples were systematically re-genotyped to ensure minimal genotyping error. Concordance rate was comprised between 99 and 100% for the 25 SNPs.

Statistical analysis
Case–control analyses
Allele frequencies in cases and controls were compared using the {chi}2 test integrated in Cocaphase (28). ORs and P-values were from logistic regression, adjusted for age and gender, in the class I and II and class III adult obesity case–control study. For the childhood obesity case–control, the test was adjusted for gender. To test the association between case–control data and the combination of CNR1 SNPs, we selected haplotypes with frequencies of at least 5% and showing the lowest AICmin. These models were tested using Cocaphase.

BMI analysis
Associations between BMI and the SNPs were analyzed using SPSS software (version 14.0.2, SPSS Inc., Chicago, IL, USA). For the BMI analysis, we used a general linear model taking into account gender and age.

Fisher’s test
To produce an overall significance of increased allelic frequency in obese individuals, we combined the P-value from the case–control study (all obese versus controls) with the P-values of the replication studies using Fisher’s method in which the twice the negative sum of the natural log of n P-values follows {chi}2 distribution with 2n degrees of freedom.

Multiple testing
Multiple testing issues in the case–control comparisons were addressed using permutation analysis that takes into consideration the inter-SNPs correlations. For the study of 25 SNPs in three cohorts, we performed 1000 permutations of case labels for each case–control analysis, obtaining three empirical P-values per SNP. These P-values were then corrected for the number of cohorts to obtain an eP-value for each SNP.

FDR analysis was also performed for the case–control comparisons: in this study the cut-off for significance was the P-value = 0.0006 with an FDR q-value < 0.05.

For the BMI analysis, the uncorrected P-values are presented and discussed in the main text. In consideration of the number of statistical tests carried out, a Bonferroni correction was applied and the significant corrected P-values (P ≤ 0.05) in the Table 2 are identified by an asterisk (*).

Fine mapping
To determine the best associated SNPs in each respective block, we performed a logistic regression test including in the model, the gender, the initial LD block representative and the ‘fine mapping’ SNP.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
FUNDING
 REFERENCES
 
Supplementary Material is available at HMG Online.


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
REFERENCES
 
This work was funded by the French MINEFI—Ministère de l’Industrie et des Finances—through the project ‘Integrated Genomic approaches for the molecular dissection of Cannabinoid receptor 1 signalling and of CB1 antagonist effects in Obesity and in the Metabolic Syndrome’. The Danish part of the project was supported by grants from the Lundbeck Foundation Centre of Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care, the Danish Health Research Council and The European Union (HEPADIP, grant no. LSHM-CT-2005-018734).


   ACKNOWLEDGEMENTS
 
We are indebted to all families and other individuals who participated to this study. We thank Olfert Landt at Tib-Molbiol (www.tib-molbiol.com) for technical assistance, David-Alexandre Trégouët from INSERM, UMR S 525, Paris, F-75634 France for his statistical advice and Christophe Wachter for his advice on the in silico analysis. We would like to thank Natascha Potoczna and Barbara Heude for providing us DNA and quantitative data on the Swiss, Fleurbaix and Danish cohorts, respectively. We would also like to thank Philippe Boutin for his help in the management of the project, Alex I.F. Blakemore for her help on the interpretation of the data and Vincent Vatin for his technical support.

Conflict of Interest statement: none declared.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 

  1. Hillard C.J. Biochemistry and pharmacology of the endocannabinoids arachidonylethanolamide and 2-arachidonylglycerol. Prostaglandins Other Lipid Mediat (2000) 61:3–18.[CrossRef][Web of Science][Medline]

  2. Cota D., Marsicano G., Tschop M., Grubler Y., Flachskamm C., Schubert M., Auer D., Yassouridis A., Thone-Reineke C., Ortmann S., et al. The endogenous cannabinoid system affects energy balance via central orexigenic drive and peripheral lipogenesis. J. Clin. Invest (2003) 112:423–431.[CrossRef][Web of Science][Medline]

  3. Di Marzo V., Goparaju S.K., Wang L., Liu J., Batkai S., Jarai Z., Fezza F., Miura G.I., Palmiter R.D., Sugiura T., et al. Leptin-regulated endocannabinoids are involved in maintaining food intake. Nature (2001) 410:822–825.[CrossRef][Medline]

  4. Ravinet Trillou C., Arnone M., Delgorge C., Gonalons N., Keane P., Maffrand J.P., Soubrie P. Anti-obesity effect of SR141716, a CB1 receptor antagonist, in diet-induced obese mice. Am. J. Physiol. Regul. Integr. Comp. Physiol (2003) 284:R345–R353.[Abstract/Free Full Text]

  5. Ravinet Trillou C., Delgorge C., Menet C., Arnone M., Soubrie P. CB1 cannabinoid receptor knockout in mice leads to leanness, resistance to diet-induced obesity and enhanced leptin sensitivity. Int. J. Obes. Relat. Metab. Disord (2004) 28:640–648.[CrossRef][Web of Science][Medline]

  6. Ledent C., Valverde O., Cossu G., Petitet F., Aubert J.F., Beslot F., Bohme G.A., Imperato A., Pedrazzini T., Roques B.P., et al. Unresponsiveness to cannabinoids and reduced addictive effects of opiates in CB1 receptor knockout mice. Science (1999) 283:401–404.[Abstract/Free Full Text]

  7. Bari M., Paradisi A., Pasquariello N., Maccarrone M. Cholesterol-dependent modulation of type 1 cannabinoid receptors in nerve cells. J. Neurosci. Res (2005) 81:275–283.[CrossRef][Web of Science][Medline]

  8. Soria G., Mendizabal V., Tourino C., Robledo P., Ledent C., Parmentier M., Maldonado R., Valverde O. Lack of CB1 cannabinoid receptor impairs cocaine self-administration. Neuropsychopharmacology (2005) 30:1670–1680.[CrossRef][Web of Science][Medline]

  9. Pagotto U., Vicennati V., Pasquali R. The endocannabinoid system and the treatment of obesity. Ann. Med (2005) 37:270–275.[CrossRef][Web of Science][Medline]

  10. Pi-Sunyer F.X., Aronne L.J., Heshmati H.M., Devin J., Rosenstock J. Effect of rimonabant, a cannabinoid-1 receptor blocker, on weight and cardiometabolic risk factors in overweight or obese patients: RIO-North America: a randomized controlled trial. Jama (2006) 295:761–775.[Abstract/Free Full Text]

  11. Van Gaal L.F., Rissanen A.M., Scheen A.J., Ziegler O., Rossner S. Effects of the cannabinoid-1 receptor blocker rimonabant on weight reduction and cardiovascular risk factors in overweight patients: 1-year experience from the RIO-Europe study. Lancet (2005) 365:1389–1397.[CrossRef][Web of Science][Medline]

  12. Scheen A.J., Finer N., Hollander P., Jensen M.D., Van Gaal L.F. Efficacy and tolerability of rimonabant in overweight or obese patients with type 2 diabetes: a randomised controlled study. Lancet (2006) 368:1660–1672.[CrossRef][Web of Science][Medline]

  13. Despres J.P., Golay A., Sjostrom L. Effects of rimonabant on metabolic risk factors in overweight patients with dyslipidemia. N. Engl. J. Med (2005) 353:2121–2134.[Abstract/Free Full Text]

  14. Russo P., Strazzullo P., Cappuccio F.P., Tregouet D.A., Lauria F., Loguercio M., Barba G., Versiero M., Siani A. Genetic variations at the endocannabinoid type 1 receptor gene (CNR1) are associated with obesity phenotypes in men. J. Clin. Endocrinol. Metab (2007) 92:2382–2386.[Abstract/Free Full Text]

  15. Peeters A., Beckers S., Mertens I., Van Hul W., Van Gaal L. The G1422A variant of the cannabinoid receptor gene (CNR1) is associated with abdominal adiposity in obese men. Endocrine (2007) 31:138–141.[CrossRef][Web of Science][Medline]

  16. Aberle J., Fedderwitz I., Klages N., George E., Beil F.U. Genetic variation in two proteins of the endocannabinoid system and their influence on body mass index and metabolism under low fat diet. Horm. Metab. Res (2007) 39:395–397.[CrossRef][Web of Science][Medline]

  17. Muller T.D., Reichwald K., Wermter A.K., Bronner G., Nguyen T.T., Friedel S., Koberwitz K., Engeli S., Lichtner P., Meitinger T., et al. No evidence for an involvement of variants in the cannabinoid receptor gene (CNR1) in obesity in German children and adolescents. Mol. Genet. Metab (2007) 90:429–434.[CrossRef][Web of Science][Medline]

  18. Zhang P.W., Ishiguro H., Ohtsuki T., Hess J., Carillo F., Walther D., Onaivi E.S., Arinami T., Uhl G.R. Human cannabinoid receptor 1: 5' exons, candidate regulatory regions, polymorphisms, haplotypes and association with polysubstance abuse. Mol. Psychiatry (2004) 9:916–931.[CrossRef][Web of Science][Medline]

  19. Nyholt D.R. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am. J. Hum. Genet (2004) 74:765–769.[CrossRef][Web of Science][Medline]

  20. Li J., Ji L. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity (2005) 95:221–227.[CrossRef][Web of Science][Medline]

  21. Horvath T.L. The unfolding cannabinoid story on energy homeostasis: central or peripheral site of action? Int. J. Obes. (Lond) (2006) 30(Suppl 1):S30–S32.[CrossRef]

  22. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ. Tech. Rep. Ser (2000) 894:i–xii. 1–253.[Medline]

  23. Lafay L., Basdevant A., Charles M.A., Vray M., Balkau B., Borys J.M., Eschwege E., Romon M. Determinants and nature of dietary underreporting in a free-living population: the Fleurbaix Laventie Ville Sante (FLVS) Study. Int. J. Obes. Relat. Metab. Disord (1997) 21:567–573.[CrossRef][Web of Science][Medline]

  24. Jaziri R., Lobbens S., Aubert R., Pean F., Lahmidi S., Vaxillaire M., Porchay I., Bellili N., Tichet J., Balkau B., et al. The PPARG Pro12Ala polymorphism is associated with a decreased risk of developing hyperglycemia over 6 years and combines with the effect of the APM1 G-11391A single nucleotide polymorphism: the Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) study. Diabetes (2006) 55:1157–1162.[Abstract/Free Full Text]

  25. Woolf B. On estimating the relation between blood group and disease. Ann. Hum. Genet (1955) 19:251–253.[Medline]

  26. Agresti A. Exact inference for categorical data: recent advances and continuing controversies. Stat. Med (2001) 20:2709–2722.[CrossRef][Web of Science][Medline]

  27. Branson R., Potoczna N., Kral J.G., Lentes K.U., Hoehe M.R., Horber F.F. Binge eating as a major phenotype of melanocortin 4 receptor gene mutations. N. Engl. J. Med (2003) 348:1096–1103.[Abstract/Free Full Text]

  28. Dudbridge F. Pedigree disequilibrium tests for multilocus haplotypes. Genet. Epidemiol (2003) 25:115–121.[CrossRef][Web of Science][Medline]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 J. Clin. Endocrinol. Metab.Home page
A. I. F. Blakemore and P. Froguel
Is Obesity Our Genetic Legacy?
J. Clin. Endocrinol. Metab., November 1, 2008; 93(11_Supplement_1): s51 - s56.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Supplementary Data
Right arrow All Versions of this Article:
17/13/1916    most recent
ddn089v2
ddn089v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Benzinou, M.
Right arrow Articles by Froguel, P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Benzinou, M.
Right arrow Articles by Froguel, P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?