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American Journal of Clinical Nutrition, Vol. 87, No. 6, 1606-1615, June 2008
© 2008 American Society for Nutrition

ORIGINAL RESEARCH COMMUNICATION

Genetic variants of Clock transcription factor are associated with individual susceptibility to obesity1,2,3

Silvia Sookoian, Carolina Gemma, Tomas Fernández Gianotti, Adriana Burgueño, Gustavo Castaño and Carlos Jose Pirola

1 From the Departamento de Genética y Biología Molecular de Enfermedades Complejas, Instituto de Investigaciones Medicas A Lanari, Universidad de Buenos Aires, Buenos Aires, Argentina (SS, CG, TFG, AB, and CJP); CONICET (SS, CG, AB, and CJP); and the Consejo de Investigación GCBA, Buenos Aires, Argentina (SS and GC)

2 Supported by Fundación Alfredo Lanari and grants no. B119 (Universidad de Buenos Aires), PICT 25920 (Agencia Nacional de Promoción Científica y Tecnológica), and PIP 5195 (Consejo Nacional de Investigaciones Científicas y Técnicas). SS, CG, AB and CJP belong to Consejo Nacional de Investigaciones Científicas y Técnicas. SS and TFG are recipients of a Health Ministry Fellowship (Beca Ramón Carrillo-Arturo Oñativia Ministerio de Salud y Ambiente de la Nación) Convocatoria 2006.

3 Address reprint requests and correspondence to CJ Pirola, Instituto de Investigaciones Medicas, A Lanari, Departamento de Genética y Biología Molecular de Enfermedades Complejas, Combatiente de Malvinas 3150, 1427- Ciudad Autónoma de Buenos Aires, Argentina. E-mail: carlospirola{at}ciudad.com.ar or pirola.carlos{at}lanari.fmed.uba.ar.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Background:Altering circadian rhythmicity results in pathophysiologic changes resembling metabolic syndrome and fat accumulation.

Objective:We investigated the role of gene variants and derived haplotypes of the CLOCK transcription factor in obesity and related quantitative metabolic traits.

Design:Lean (n = 715) and overweight or obese (n = 391) unrelated subjects aged 34.4 ± 8.6 y were included in a population-based cross-sectional study. Six tag single-nucleotide polymorphisms (SNPs) with a minor (>10%) allele frequency (rs1554483 C/G; rs11932595 A/G; rs4580704 C/G; rs6843722 A/C; rs6850524 C/G, and rs4864548 A/G) encompassing 117 kb of chromosome 4 and representing 115 polymorphic sites (r2 > 0.8) were genotyped. Association was tested by PLINK and WHAP software, and multiple testing was controlled by permutation test.

Results:The genotype frequencies of 4 tag SNPs—rs1554483, rs6843722, rs6850524, and rs4864548—had significant (empiric P < 0.010, 0.021, 0.021, and 0.010, respectively) associations with overweight or obesity. Haplotype analysis showed that only paired haplotypes, including rs1554483 and rs4864548, had a significant effect on overweight or obesity. Combinations of these SNPs (haplotype block CG and GA) are responsible for the gene effect (GA frequencies: 47% in cases, 41% in controls; empiric P < 0.011). These findings were concurrently observed in a sample of persons from a hospital-based study, and the combined Mantel-Haenszel fixed effect was an odds ratio of 1.82 (95% CI: 1.31, 2.54; P < 0.001) for the paired haplotype, which included CG and GA for rs1554483 and rs4864548.

Conclusions:The present study suggests a putative role of the CLOCK polymorphism and related haplotypes in susceptibility to obesity. The haplotype of rs1554483G and rs4864548A was associated with a 1.8-fold risk of overweight or obesity.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Obesity is a complex, multifactorial chronic disorder that develops from interactive influences of social, behavioral, physiologic, metabolic, cellular, and molecular factors (1). Whereas the exact pathogenesis of the disorder is unknown, body weight is strongly influenced by biological and behavioral factors (2, 3). In fact, regulation of appetite and food intake is influenced by sleep duration, and sleep restriction may favor the development of obesity (4-6). Common genetic effects were observed between insomnia and both sleepiness and obesity, which suggests shared genetic contributions to these conditions (7).

The circadian variation in metabolic response has also implications for obesity. For instance, the timed seasonal development of obesity in animals may be induced by the biological clock (8). A report by Turek et al (9) showed that mutant mice that are homozygous for circadian locomotor output cycles protein kaput (CLOCK) have a greatly altered diurnal feeding rhythm, are hyperphagic and obese, and develop a metabolic syndrome with hyperleptinemia, hyperlipidemia, hepatic steatosis, hyperglycemia, and hypoinsulinemia.

Although the CLOCK transcription factor is a key component of the molecular circadian clock within pacemaker neurons of the hypothalamic suprachiasmatic nucleus (10), CLOCK also plays an important role in regulating fat and glucose metabolism in peripheral organs such as adipose tissue, muscle and liver (11). It it interesting that results from a whole-genome linkage scan using 380 microsatellite markers to identify genomic regions that may contain quantitative-trait loci for obesity showed that region 4q12 (the chromosome location of the CLOCK gene) may be linked to obesity (12).

Given the above evidence and the results of emerging studies showing that alteration in circadian rhythmicity results in pathophysiological changes resembling metabolic syndrome and fat accumulation, the objective of the present study was to investigate the role of gene variants and their predicted haplotypes of the linkage disequilibrium (LD) block of the gene CLOCK in human overweight or obesity and related quantitative metabolic traits. The present study was performed in samples obtained randomly from the population and in an independent sample of persons ascertained a from clinic.


   SUBJECTS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
Healthy subjects recruited from a factory in the Buenos Aires metropolitan area who underwent an annual health examination were included in a cross-sectional population-based study. Altogether, 1106 men aged 34.4 ± 8.6 y of self-reported European ancestry were included in the present study, in which 715 lean subjects were compared with 391 overweight subjects. A concurrent study was performed in a population of 200 persons aged 52.5 ± 12.6 y (64 healthy subjects and 136 cases) in a hospital-based study from the same geographical area. Subjects were diagnosed with metabolic syndrome according to the Adult Treatment Panel III (13). The subjects' medical history was investigated by using a self-administered questionnaire, and the answers were confirmed by individual interviews conducted by physicians.

After the subjects rested for 5 min in a quiet room, arterial systolic and diastolic blood pressures were measured on the right arm with a standard mercury sphygmomanometer while the subjects were in a sitting position. Health examinations included anthropometric measurements, a questionnaire on health-related behaviors, and biochemical determinations.

Body mass index (BMI; in kg/m2) was calculated and was used as the index for relative weight. In addition, trained staff assessed the waist circumference of subjects in the standing position by measuring midway between the highest point of the iliac crest and the lowest point of the costal margin in the midaxillary line. Hip circumference was measured at the level of the femoral greater trochanter by the same observer. Classification of overweight and obesity was based on the BMI; those with a BMI ≥ 27 were classified as overweight or obese. It has been shown that BMI ≥ 27 can predispose a person to a greater risk of cardiovascular disease and that the risk of death rises more steeply (by 60%) in those with a BMI of >27 than in those with a BMI below this cutoff. Moreover, a BMI > 27 is associated with a greater risk of heart disease, blood pressure, diabetes, hypertension, and elevated cholesterol concentrations in addition to other health risks (14-16). A recent study also showed that a BMI of 27 is associated with the highest sensitivity (68%) and specificity (90%) for a waist circumference of ≥40 inches, which is the measurement used to define the metabolic syndrome according to the National Cholesterol Education Program guidelines (17).

All participants were asked to fast for ≥8 h, and blood was drawn from subjects who had lain in a supine resting position for ≥30 min. Serum insulin, total cholesterol, HDL and LDL cholesterol, triacylglycerols, and plasma glucose were measured by standard clinical laboratory techniques. Homeostatic model assessment (HOMA), calculated as fasting serum insulin (µU/mL) x fasting plasma glucose (mmol/L)/22.5, was used to evaluate an insulin resistance index.

Written informed consent was obtained from all participants in accordance with the procedures approved by the Ethics Committee of our institution. All of the investigations performed in this study were conducted in accordance with the guidelines of the Declaration of Helsinki.

Genotype and haplotype analysis
The genetic analyses were done on genomic DNA extracted from white blood cells by using a standard method as previously described (18). To assess the contribution of CLOCK gene variants to obesity and related quantitative metabolic traits, we selected tag SNPs (tSNPs) and multimarker predictors as effective surrogates for single untyped SNPs by using the web-based service of the Tagger computer program (aggressive tagging approach) (19) for whites from the Caucasian European Utah dataset (Internet: www.hapmap.org) with a minor allele frequency ≥ 0.10 and a minimum r2 of 0.8. The algorithm used in the Tagger program (Internet: http://www.broad.mit.edu/mpg/tagger/) selects tSNPs to construct single-marker and multimarker tests to capture alleles of interest based on the computed correlation r2 between them (19); multimarker analysis was performed by using HAPLOVIEW software [version 3.32; Whitehead Institute for Biomedical Research, Cambridge, MA; Internet: http://www.broad.mit.edu/mpg/haploview/ (20)].

Genotyping was performed by using a high-throughput genotyping method involving polymerase chain reaction amplification of genomic DNA with 2-tailed allele-specific primers that introduce priming sites for universal energy transfer–labeled primers (PreventionGenetics, Marshfield, WI) as previously described (21). To ensure genotyping quality, we included DNA samples as internal controls, hidden samples of known genotype, and negative controls (water). No genotype with a signal below a negative control was scored. The error analysis was performed by replicating 8 times a blinded sample (that always belongs to the same person) across the templates of the project. On 216 genotypes for the "blinded sample," we had only 1 unmatched genotype (0.46% error).

Haplotype frequencies and LD measures were estimated by using Haploview software (20). PLINK software [version 0.99p; Internet: http://pngu.mgh.harvard.edu/purcell/plink/ (22)] was used for assessing associations between SNPs and affection status and quantitative traits and for testing Hardy-Weinberg equilibrium. SNP haplotype analysis was performed by using both WHAP software [version 2.09; Internet: http://pngu.mgh.harvard.edu/purcell//whap/) (23)] and HAPLOVIEW (20). Control for multiple testing was done by using the maximum of the test statistics permutation testing of individual label to obtain an empirical P value using 10 000 permutations.

A power estimation for the sample of 391 cases and 715 controls was performed for single-point allelic effects, with an odds ratio (OR) of 1.5, at a nominal significance level of 0.008 that corresponds to an empiric P value of 0.05 for HapMap-predicted minor allele frequency of 0.29 (rs4580704) to 0.45 (rs4864548) of a potential susceptibility marker and a prevalence of the disease of 26% (24). This analysis gave us an estimated power of 100% under the additive model.

In addition, because subdivision or recent admixture of populations in case-control association studies may lead to spurious associations between phenotypes and truly unlinked loci, we used a collection of 13 SNPs randomly selected at different loci (located in chromosomes 4, 15, 17, 13, 1, and 3). Next we analyzed the data with STRUCTURE software (version 2.0; Internet: http://pritsch.bsd.chicago.edu/structure.html) (25) and computed the sum of chi-square tests from each locus with the number of df equal to the sum of the number of individual loci (26) to explore a possible stratification in both populations.

Statistical analysis
Quantitative data were expressed as means ± SEs. For univariate analysis, differences between groups were assessed by analysis of variance or Student's t test on log-transformed variables when variable variance was homogenous as assessed by Levene's test. Otherwise, we used the nonparametric Kruskal-Wallis test by ranks. Logistic regression was used for testing of multivariate associations between overweight or obesity and genotypes and haplotypes after adjustment for covariates such as age, sex, and HOMA index after log transformation of the variables. We used CSS/STATISTICA software (version 6.0; StatSoft, Tulsa, OK) to perform these analyses. Results from the different populations were combined by Mantel-Haenszel (MH) meta-analysis. Heterogeneity was evaluated with the Q statistic and the I2 statistic, a transformation of Q that estimates the percentage of the variation in effect sizes that is due to heterogeneity. For the sake of simplicity, P values were rounded to 3 decimals.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Clinical features, anthropometric variables, and laboratory findings of the participants according to overweight or obesity status are shown in Table 1Go for both population-based and hospital-based studies. Overweight or obese subjects are older and had most of the risk factors of the metabolic syndrome: elevated waist circumference and waist-hip ratio, systolic and diastolic blood pressures, fasting glucose and insulin, HOMA index, total cholesterol, triacylglycerols, and uric acid. They also had some features of low-grade inflammation, such as elevated leukocyte count and erythrocyte sedimentation rate.


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  		TABLE 1 Clinical and biochemical characteristics of the subjects in the population-based study and in the hospital-based study1

 
Clock gene variants
To minimize the burden of genotyping the multiple variants present in the candidate gene CLOCK, we selected 6 tagSNPs showing a minor allele frequency > 10% (ie, rs1554483 C/G, rs11932595 A/G, rs4580704 C/G, rs6843722 A/C, rs6850524 G/C, and rs4864548 G/A) encompassing 117 kb in chromosome 4 (56 139 000–56 256 000). The 6 tagSNPs represent 115 polymorphic sites with an r2 >0.8 considering the HapMap project data (Table 2Go). The basic LD plot between the studied SNPs is shown in Figure 1Go. The Tagger algorithm showed the previously mentioned single-marker tags and 8 multimarker tags.


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  		TABLE 2 Tag single-nucleotide polymorphisms (SNPs) of the CLOCK gene genotyped in the study1

 

Figure 1
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  		FIGURE 1. Linkage disequilibrium (LD) plot across the CLOCK gene. SNP, single-nucleotide polymorphism. The horizontal white bar depicts the 117-kb DNA segment of chromosome 4q12 analyzed in the sample. The 6 tagSNP locations are indicated by hatch marks. An LD plot is depicted in the bottom part of the figure: each diamond represents the magnitude of LD for a single pair of markers. Black indicates strong LD (r2 = 1.0); white indicates no LD (r2 = 0); and the gray tones indicate intermediate LD. The numbers inside the diamonds stand for r2 values x 100.

 
No marker showed a departure from Hardy-Weinberg equilibrium (P > 0.1), which indicated robust genotyping performance (data not shown). The genotyping success rate was 99.5% for rs1554483, 99.4% for rs11932595, 99% for rs4580704, 99% for rs6843722, 96.3% for rs6850524, and 98.6% for rs4864548.

In univariate analysis, after the multiple comparison correction by permutation tests, the genotype frequencies of 4 tSNPs in lean and overweight or obese persons showed significant differences. For rs1554483, rs6843722, rs6850524, and rs4864548, the empiric P values were <0.010, 0.021, 0.021, and 0.010, respectively. It is interesting that we were able to show that the results remained significant when currently recommended cutoffs for overweight (BMI ≥ 25) and obesity (BMI ≥ 30) were used, at least for rs1554483 and rs4864548 (Table 3Go).


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  		TABLE 3 Association of CLOCK tag single-nucleotide polymorphisms (SNPs) with overweight and obesity defined by the currently used BMI cutoffs (25 and 30, respectively) in a population-based study1

 
Genotype counts in lean and obese persons for each tSNP are shown in Table 4Go. No significant association was observed between any quantitative metabolic trait (total cholesterol, HDL and LDL cholesterol, triacylglycerols, HOMA, fasting glucose, and fasting insulin) and the tSNPs analyzed (data not shown). Logistic regression analysis, adjusted for age and HOMA index, showed a still significant effect of 3 tSNPs (ie, rs1554483, P < 0.02; rs6850524, P < 0.01; and rs4864548, P < 0.04) on the risk of overweight or obesity.


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  		TABLE 4 Genotype distribution of CLOCK tag single-nucleotide polymorphisms (SNPs) according to overweight or obesity in population-based and hospital-based studies1

 
Using the WHAP software, we further evaluated the LD pattern for the previously mentioned tSNPs. The estimation of haplotypes from genotype data in our population showed 6 possible combinations covering 99.1% of the common haplotypes with a frequency of >0.01 (Table 5Go). Among all of the haplotypes, the GACCGA haplotype alone explains much of the overall association at the locus (empiric P < 0.045).


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  		TABLE 5 Estimated haplotype frequency distributions of CLOCK gene single-nucleotide polymorphisms (SNPs) in lean and overweight or obese subjects1

 
In an attempt to dissect the association signal, we performed the analysis removing individual markers one by one, and we observed that CLOCK gene variant haplotype frequencies of rs1554483C/rs6843722A/rs4864548G and rs1554483G/rs6843722C/rs4864548A in obese persons differed significantly from those in lean subjects (cases, 53% versus controls, 59%; empiric P < 0.009; cases, 43% versus controls, 39%; empiric P < 0.026, respectively). Dropping out rs6843722 showed that this SNP has no independent effect. Haplotype analysis consistently showed that only paired haplotypes (all possible haplotypes of SNPs pairs), which included rs1554483 and rs4864548, showed a significant effect on obesity or overweight (data not shown).

It is not surprising that further analysis indicated that only combinations of rs1554483 and rs4864548 (haplotype block CG and GA) are mostly responsible for the gene effect (CG frequencies: cases, 53% versus controls, 59%; GA frequencies: cases, 47% versus controls, 41%; empiric P = 0.0102). In addition, we found that subjects carrying the haplotype of rs1554483-G and rs4864548-A are 1.5 times as likely to be overweight or obese (OR: 1.50; 95% CI: 1.03, 2.18; P < 0.033, after adjustment for age and HOMA index) as are those with other haplotypes.

Finally, we were able to validate the results of the population-based study in the concurrent sample of persons ascertained from a hospital-based study, because we found a significant effect on overweight or obesity for rs1554483 (empiric P < 0.005), rs6843722 (empiric P < 0.005), rs6850524 (empiric P < 0.015), and rs4864548 (empiric P = 0.005). These associations persisted after control for age and sex by logistic regression (rs1554483, P < 0.001; rs6843722, P < 0.001; rs6850524, P < 0.002; and rs4864548, P < 0.001).

Genotype counts in cases and controls for each tSNP are shown in Table 4Go. Moreover, the haplotype block CG and GA for rs1554483 and rs4864548, respectively, showed a significant effect (empiric P < 0.001) on obesity as compared with the lean control group (CG frequencies: cases, 44% and controls, 63%; GA frequencies: cases, 56% and controls, 37%). The combined MH fixed effect for both populations was an OR of 1.82 (95% CI: 1.31, 2.54; P < 0.001). The test for heterogeneity in combined MH was significant for rs1554483, rs6843722, and rs4864548. The MH effects for the individual SNPs according to genotypes are shown in Table 4Go.

Association results for the 6 single-marker and 8 multimarker tests from the 2 populations were combined by MH fixed effect and are presented in Table 6Go. The probabilistic estimate of each multimarker haplotype was compared individually against the others by using the HAPLOVIEW software.


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  		TABLE 6 Association study of single-tag single-nucleotide polymorphism (SNP) and multimarker tagSNP tests (haplotypes) in CLOCK with overweight or obesity in the combined group1

 
We also analyzed BMI values as a continuous trait in both populations, and we observed that mean BMI values for each genotype were significantly different across genotypic groups—in particular, for rs1554483 and rs4864548—in both studies (Table 7Go). In addition, rs4580704 genotypes showed significant differences in the population-based study and rs6843722 genotypes showed significant differences in the hospital-based study. Information about waist circumference in both populations according to genotypes is shown in Table 7Go. We observed that mean waist circumference values for each genotype showed significant differences across genotypic groups—in particular, for rs1554483 and rs4864548 in the population-based sample.


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  		TABLE 7 Analysis of the continuous traits BMI and waist circumference values for each genotype in both populations

 
We found no evidence of stratification in our samples because cases and controls had similar Q values and were assigned with a similar distance to clusters by the STRUCTURE software program (data not shown) with no further improvement in the fitting model when adding up to 4 clusters (the ln of likelihood was maximum for K = 1). Moreover, the sum of the chi-square test of individual loci indicated a nonsignificant difference in allele distributions corresponding to 13 loci in cases and controls (sum chi-square test, 14.35; df, 13; P = 0.35).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
We examined the association of obesity with gene variants and haplotypes of the LD block of the CLOCK transcription factor in a sample of 1106 unrelated men of self-reported European ancestry. In this study, 4 of 6 tSNPs (ie, rs1554483, rs6843722, rs6850524 and rs4864548) were significantly associated with overweight or obesity after control for multiple testing by permutation test. In addition, we were able to show significant differences in BMIs across genotypic groups. When, trying to determine which of the associated variants contains the true signal, we observed that haplotype frequencies of rs1554483C/rs6843722A/rs48645448G and rs1554484G/rs6843722C/rs48645448A in obese subjects differed significantly from those in lean subjects. The haplotype of rs1554483-G and rs4864548-A was associated with a 1.50-fold risk of overweight or obesity. We observed comparable results in an independent group of subjects from a second sample.

Combining both studies by the MH approach (n = 1306), we observed an even stronger association with overweight or obesity (fixed-effect model, OR: 1.82; 95% CI: 1.31, 2.54; P < 0.001). A note of caution should be added, because we observed heterogeneity for some SNPs (3 of 6). However, it is worth mentioning that we may expect some degree of biological variability between populations, particularly because of natural heterogeneity among persons, owing to differences in their physiologic stages and sex, among other factors. Nevertheless, the effects remain in the same direction in both populations.

A limitation of our study should be noted, especially with respect to the partial dependence of the results on the definition of obesity. Nevertheless, we were able to show that the results remained significant even when currently used cutoffs for overweight and obesity are applied, at least for rs1554483 and rs48644. Furthermore, these 2 SNPs are associated with different BMIs in both the population-based and hospital-based samples and with different waist circumferences in the population-based sample. Although population stratification can lead to false-positive association results, we examined the issue of population stratification by using multilocus genotype data. No evidence of stratification difference was observed in our populations.

Our study is an extension of an initial estimate of the association of the CLOCK variants with the previously mentioned phenotype (28). By the time the present report was undergoing review, another study was reported that replicated the association of the CLOCK variants with obesity, at least with a haplotype containing the rs4864548 in the promoter region of the gene, in a smaller sample of adults from a family-based study (29). In addition, a recent report showed that several CLOCK genes, such as Bmal1, Per2, and Cry1, are expressed in both subcutaneous and visceral fat, and these CLOCK gene expressions were related to the features of the metabolic syndrome (30). The effects of circadian gene networks on obesity- and metabolic syndrome–associated phenotypes extend beyond the CLOCK gene; for instance, it was recently shown that Rev-erb alpha also regulates lipid metabolism, adipogenesis, and vascular inflammation and also cross-talks with several other nuclear receptors involved in energy homeostasis and circadian rhythm (31, 32).

Although the molecular mechanisms underlying the observed association are unknown, several lines of evidence support the connection between the CLOCK variants and overweight or obesity. For instance, obesity has been associated with dysregulated circadian expression profiles of leptin, adiponectin, and other fat-derived cytokines (33). In addition, it was recently shown that Clock is involved in obesity-induced disordered fibrinolysis in ob/ob mice by regulating plasminogen activator inhibitor type 1 gene expression in a tissue-dependent manner (34). Another mouse model has shown relations between circadian mechanism dysfunction and glucose homeostasis regulation because inactivation of the known clock components Bmal1 and CLOCK suppresses the diurnal variation in glucose and triacylglycerols (35).

Finally, new evidence that CLOCK mutant mice are hyperphagic and obese (9) suggests a previously unrecognized link between molecular controls of circadian rhythm and energy homeostasis (10). In fact, CLOCK mutant mice fed either a regular or high-fat diet showed significant increases in energy intake and body weight (9) and had lower orexin and ghrelin transcription levels than did wild-type mice. The variants that we observed in association with obesity were all located in different introns of CLOCK. To further investigate whether these tSNPs—and also the ones that were tagged in high LD—may have potential functional significance, we used both PupaSNP (Spanish National Genotyping Center, Barcelona, Spain: Internet: http://www.cegen.org) and FASTSNP (Institute of Biomedical Sciences and Institute of Information Science, Academia Sinica, Taipei, Taiwan) programs (data not shown). The analysis retrieved showed that the previously mentioned tSNPs or their tagged SNPs could affect DNA triplexes in the gene sequences, which have been suggested to be regulatory regions for control of gene expression (36). Although possible functional implications of the overweight- or obesity-associated SNPs deserve further investigation, it is tempting to speculate that the variants we are showing may affect regulatory regions for controlling gene expression by playing a role as genetic modifiers. In conclusion, the present study suggests a potential role of the CLOCK polymorphisms and their derived haplotypes in greater susceptibility to overweight or obesity. We hope that the present study can serve as a primer; further research is needed to extend the current findings showing the intimate mechanism by which CLOCK variants may lead to the mentioned phenotype.


   ACKNOWLEDGMENTS
 
The authors' responsibilities were as follows—SS: study design, data collection, analysis and interpretation of data, and writing of the manuscript; CG: sample collection and preparation; TFG: sample preparation and biochemical determinations; AB: sample collection and preparation; GC: data collection and assisting patients; and CJP: study design, analysis and interpretation of data, writing of the manuscript, and leadership for the project. None of the authors had a personal or financial conflict of interest exist.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 SUBJECTS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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Received for publication October 2, 2007. Accepted for publication March 2, 2008.





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