The 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme catalyses the regeneration of active cortisol from inert cortisone and plays a critical role in tissue-specific corticosteroid reactions; therefore, 11β-HSD1 is a key molecule associated with the development of obesity. Despite evidence for its role in obesity, no genetic polymorphisms have been significantly associated with the disease per se.
The aim of this study was to evaluate whether HSD11B1 gene variants, which have never been studied before, are associated with obesity and its related traits, as well as its relation to biomarkers of inflammation, liver damage and cardiovascular disease in a cohort of Spanish children.
We performed a prospective case–control study.
A total of 534 children were examined and classified as being obese (n=292) or normal weight (n=242). Anthropometric and biochemical measurements related to obesity, including inflammation, liver damage and cardiovascular disease, were determined. Genomic DNA was extracted and 10 HSD11B1 gene single-nucleotide polymorphisms (SNPs) were genotyped.
A novel SNP, rs3753519, was strongly associated with obesity and this SNP was the only statistically significant HSD11B1 gene SNP remaining after a Bonferroni correction (odds ratio=1.97 for allelic effect, 95% confidence interval 1.23–3.16; P=0.004 and Bonferroni corrected P=0.046). In addition, this SNP was significantly and positively associated with increased body mass index (BMI), BMI z-score, weight, waist circumference, plasma γ-glutamyl transpeptidase and plasma active plasminogen activator inhibitor 1. The SNP was negatively associated with plasma adiponectin and cortisol after adjusting for sex and age. None of the inflammation biomarkers tested were associated with the risk allele.
These data, which link an HSD11B1 genotype with both disease prevalence and its related phenotypes, strongly support a role for the rs3753519 polymorphism in the pathogenesis of pediatric-onset obesity.
Cortisol, which is the major glucocorticoid in humans, plays an important role in regulating fuel metabolism, energy partitioning and body fat distribution. In addition to the hypothalamic–pituitary–adrenal axis controlling cortisol levels in the blood, intracellular cortisol levels in tissues can be controlled by local enzymes. For example, 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) catalyses the regeneration of active cortisol from inert cortisone, thereby amplifying cortisol levels and glucocorticoid receptor activation in adipose tissue, the liver and other tissue types.1 The 11β-HSD1 is controlled by complex tissue-specific regulation. There is evidence that 11β-HSD1 adjusts local cortisol concentrations independently of plasma cortisol concentrations and this enzyme may be involved in obesity and its related complications.2, 3, 4 Decreased cortisol levels in the liver secondary to reduced 11β-HSD1 expression and activity have been identified;5, 6, 7 in contrast, increased 11β-HSD1 mRNA levels and enzyme activity have been observed in adult adipose tissue8, 9, 10 and in children.11
Controversy exists regarding whether there is a comparable increase in 11β-HSD1 mRNA levels in subcutaneous and omental fat.3 The mechanism of this dysregulation in human obesity remains uncertain; however, studies in animals have demonstrated a role for 11β-HSD1 in obesity. Mice overexpressing 11β-HSD1 in adipocytes demonstrate increased levels of adipose tissue corticosterone (the active metabolite in mice), hyperphagia, greater weight gain (particularly when fed a high-fat diet), increased accumulation of visceral adipose tissue, insulin resistance and increased expression of lipoprotein lipase in omental fat.12 Conversely, 11β-HSD1 inhibition ameliorates the metabolic consequences of obesity, increases insulin sensitivity and reduces blood glucose levels in obese and diabetic mice.13, 14, 15, 16
Despite evidence for a role of 11β-HSD1 in obesity, HSD11B1 gene polymorphisms significantly associated with the disease or its complications have yet to be identified. HSD11B1 gene variants have been reported to be associated with type 2 diabetes17 and hypertension18 but not with obesity per se in adults. A weak association of the rs2236905 HSD11B1 single-nucleotide polymorphism (SNP) with metabolic syndrome in a Japanese population was recently found.19 There is also one study that has reported a positive association of the ins4436A SNP in the HSD11B1 gene with body mass index (BMI) and insulin resistance in obese children.20 Thus, the objective of the current study was to evaluate whether the unstudied variations in the HSD11B1 gene are associated with obesity and features of metabolic syndrome in a cohort of Spanish children.
Materials and methods
This study was designed as a case–control multicentre study in children. We recruited 292 (149 male and 143 female) obese children and 242 (135 male and 107 female) normal-weight children. All participants were Caucasian, aged 6–15 years, from two cities in Spain (that is, Cordoba, located in the south, and Santiago de Compostela, located in the north) and from primary care centres and schools. Childhood obesity was defined according to Cole et al.21 The inclusion criteria were (1) the absence of disease related to nutritional status and (2) the absence of endogenous obesity. The exclusion criteria were (1) the presence of disease or undernutrition and (2) the use of medication that alters blood pressure, glucose or lipid metabolism. After the assessments made during the first visit to a school or primary care centre, the parents of children fulfilling the inclusion criteria were invited to bring their children to the pediatric unit of their local hospital for a clinical examination. The parents or guardians were informed about the purpose and procedures of the study before written consent was obtained. All children also provided consent. The protocol was performed in accordance with the Declaration of Helsinki Principles (Edinburgh 2000, revised) and followed the recommendations of both the Good Clinical Practice of the CEE (Document 111/3976/88 July, 1990) and the legal in-force Spanish Regulation that Regulates Clinical Investigation in Human Beings (RD 223/04 about Clinical Assays). The protocol was also approved by the ethics committees at all participating institutions.
Anthropometric and biochemical measurements
Anthropometric measurements were recorded by a single examiner with the children barefoot and in their underwear. Body weight (kg), height (cm) and waist circumference (cm) were measured using standardised procedures, and the BMI was calculated as weight (kg) divided by the square of the height (m2). Obesity was defined according to the BMI using the age- and sex-specific cutoff points proposed by Cole et al.21 (linked to the adult cutoffs of 25 and 30 kg m–2). Blood pressure was measured three times by the same examiner using a mercury sphygmomanometer and following international recommendations. Blood samples were drawn via the antecubital vein after the patient had fasted overnight. Biochemical analyses were performed at the participating University Hospital Laboratories following internationally accepted quality control protocols.22
Other cardiovascular risk and inflammatory biomarkers were analysed using three different LINCOplex kits with the appropriate human monoclonal antibodies (Linco Research, MO, USA) on a Luminex 200 System (Luminex Corporation, Austin, TX, USA). The concentrations of soluble intercellular adhesion molecule 1 (sICAM-1), soluble endothelial selectin (sE-selectin), myeloperoxidase (MPO), matrix metalloproteinase-9 (MMP-9) and total plasminogen activator inhibitor 1 (PAI-1) were measured using the LINCOplex kit with the catalogue number HCVD1-67-AK. Adiponectin, resistin and active PAI-1 were measured using the LINCOplex kit with the catalogue number HADK1-61K-A. Finally, interleukin-6 (IL-6), interleukin-8 (IL-8), tumour necrosis factor-α (TNF-α) and monocyte chemotactic protein-1 (MCP-1) were analysed using the LINCOplex kit with the catalogue number HADK2-61K-B. C-reactive protein (CRP) was determined with a particle-enhanced turbidimetric ultrasensitive immunoassay (Dade Behring Inc., Deerfield, IL, USA).
DNA isolation and genotyping
Genomic DNA was extracted from buffy coats using the QIAamp Blood kit (Qiagen, Valencia, CA, USA). A total of 10 SNPs in the HSD11B1 gene were selected from the HapMap and NCBI (National Center for Biotechnology Information) databases. The inclusion of an SNP in this analysis was based on its location. We first selected every SNP resulting in a missense variation and then selected other SNPs located in the promoter, 3′ untranslated region and 5′ untranslated region with a minor allele frequency >0.05 in the Caucasian population and minimum pairwise linkage disequilibrium of r2=0.8 for the selection of TagSNPs. HSD11B1 is transcribed from two promoters, resulting in two different transcripts (variant one, mRNA NM_005525, and variant two, mRNA NM_181755). Table 1 depicts the gene position in variant one of the 10 SNPs studied.
Genotyping was performed using the Illumina GoldenGate protocol (Illumina, San Diego, CA, USA) in 96-well Sentrix arrays. Per assay, 250 ng of sample DNA was used. The genotyping of the 10 SNPs resulted in a genotype success rate of >95%, except for rs846906 (75%), which was excluded from the downstream analyses. The Hardy–Weinberg equilibrium for each SNP was examined with Fisher's exact test using PLINK version 1.07 software (available at http://pngu.mgh.harvard.edu/~purcell/plink). The Hardy–Weinberg equilibrium P-values were >0.05 in the case and control subjects for all SNPs (Table 1). The allelic frequencies of the SNPs observed in this study were similar to those reported in the HapMap for Caucasians (data not shown).
All statistical analyses were performed using either PLINK or SPSS (Statistical Package for the Social Sciences, version 15.0.1, Chicago, IL, USA). All continuous variables were expressed as the mean±s.e.m. The normal distribution of the clinical parameter data was assessed with the Kolmogorov–Smirnov test. The insulin, the homeostatic model assessment for insulin resistance (HOMA-IR), total cholesterol, MMP-9 and total PAI-1 values were logarithmically transformed to approximate normal distributions. The homogeneity of variances was estimated using the Levene's test. The continuous variables in the obese and normal-weight children were compared using Student's t-test for unpaired samples. The genotypic relative risk was assessed by comparing the obese with the control group and calculating the odds ratio and 95% confidence interval (95% CI) using logistic regression analysis under the additive model implemented in PLINK after adjusting for age and sex. Additional association analyses were performed independently of the recruitment city, and the homogeneity of both populations was assessed by a meta-analysis using the PLINK software. Linear regressions for the entire population or for each case and control group were performed using an additive model to estimate the associations of each SNP with the phenotypic parameters related to obesity and biomarkers of inflammation, liver damage and cardiovascular disease.
Table 2 displays the clinical characteristics of both the obese and control subjects. Weight, height, BMI, BMI z-score and waist circumference were significantly higher in the obese compared with the normal-weight children. Other biomarkers related to metabolic syndrome were different between both groups. Systolic and diastolic blood pressure, plasma triacylglycerols, apolipoprotein B, insulin and HOMA-IR were higher in the obese children. The plasma total cholesterol, high-density lipoprotein-cholesterol and apolipoprotein A-1 were all lower in the normal-weight children. Fasting plasma glucose and low-density lipoprotein-cholesterol concentrations did not differ between the two groups. Fasting plasma concentrations of resistin and leptin were significantly higher in the obese than in the normal-weight subjects; in contrast, reduced adiponectin and cortisol levels were observed in the obese children. The liver enzymes γ-glutamyl transpeptidase (GGT) and alanine transaminase (ALT) concentrations were significantly higher in the obese children; however, aspartate transaminase (AST) levels were lower in the obese population.
Cardiovascular risk and inflammatory biomarkers were different between the two groups (Table 3). The sICAM-1, sE-selection, MPO and active PAI-1 levels were all significantly higher in the obese compared with the normal-weight group. Similarly, plasma levels of CRP, IL-6, IL-8 and TNF-α were higher in the obese group; however, MCP-1 and MMP-9 levels were the same in both groups.
Association of HSD11B1 gene SNPs with obesity
The results of the association analyses of the nine SNPs are displayed in Table 4. In our study, five of the nine SNPs were nominally associated with obesity in children after adjusting for age and sex using an additive model; however, rs3753519 was the SNP most strongly associated with obesity, and it was the only statistically significant after Bonferroni correction (odds ratio=1.97 for allelic effect, 95% CI 1.23–3.16; P=0.004 and Bonferroni corrected P=0.045). A meta-analysis was performed by combining the results from the two recruitment cities. There was a high level of homogeneity between these two populations for rs3753519 (Q Cochrane's statistic, P=0.882). A consistent association of this SNP with obesity was identified after the meta-analysis (odds ratio=2.05, P=0.003).
Association of rs3753519 with obesity-related traits
We further examined the associations of rs3753519 with obesity-related quantitative traits (Table 5). This SNP was significantly (P<0.05) associated with increased BMI, BMI z-score, weight, waist circumference and decreased adiponectin and cortisol levels after adjusting for sex and age when linear regression analyses were performed on the entire population. Children carrying allele A had increases in their BMI, weight and waist circumference of 2.4 kg m–2 (95% CI 1.21–3.64), 5 kg (95% CI 1.93–8.15) and 5.1 cm (95% CI 0.36–9.92) per allele, respectively. In contrast, reductions in plasma adiponectin 2.62 mg l–1 (95% CI −5.15 to −0.09) and cortisol 42.8 nmol l–1 (95% CI −77.24 to −8.52) concentrations were found per risk allele. However, when linear regression analyses were performed separately per case or on the control children, only the association of the risk allele with BMI remained significant in the case group (P=0.010) β 1.12 (95% CI 0.25–1.99) after adjusting for age and sex. Following a Bonferroni correction, only the associations of rs3753519 with weight, BMI and BMI z-score remained significant (Table 5). No significant association of this SNP was linked to any other quantitative trait of obesity in either the case or control groups.
Association of rs3753519 with biomarkers of inflammation, cardiovascular risk and liver damage
To investigate the effect of the risk allele A of rs3753519 on the plasma biomarkers of inflammation and other obesity complications, such as cardiovascular risk and non-alcoholic fatty liver disease, additional linear regression analyses were performed (Table 6). In the entire population, none of the inflammation biomarkers (IL-6, IL-8, TNF-α, MCP-1 and CRP) displayed an association with the risk allele after adjusting for sex and age, nor were any of the biomarkers significant when the case and control groups were analysed separately.
The liver enzyme GGT, which is a marker of non-alcoholic fatty liver disease, was significantly increased in the children carrying allele A (1.03 U l−1 (95% CI 0.03–2.02) P=0.040 per allele). In contrast, transaminase concentrations (ALT and AST) did not show any association with the risk allele. Among the analysed biomarkers for cardiovascular risk (sICAM-1, sE-selectin, MPO, MMP-9 and active and total PAI-1), only the concentration of active PAI-1 was associated with allele A in the studied population (P=0.007) β 2.44 μg l–1 (95% CI 0.25–1.99); however, this significance was not upheld after a Bonferroni correction (Table 6). These associations were not observed when the linear regression analyses were conducted separately on the control or case groups (data not shown).
In this study, we identified a strong association of the rs3753519 HSD11B1 SNP with obesity in Spanish children. The risk of obesity in children carrying the minor allele, ‘allele A’, was nearly double versus non-carriers. Furthermore, after adjusting for sex and age, the presence of this variant in our cohort was associated with increased BMI, BMI z-score, weight and waist circumference and decreased plasma fasting adiponectin levels. These data, which link the HSD11B1 genotype with both disease prevalence and its related phenotypes, strongly support the role of the HSD11B1 gene in the pathogenesis of obesity.
Despite observing a significant association of the allelic risk presence with common obesity-related phenotypes (BMI, weight, waist circumference and adiponectin) in the entire population, we failed to show these associations when the analyses were performed separately in the control and case groups (except for BMI in the case group). This may have been because of the relatively small sample size and narrow range of the tested quantitative traits in both the case and control groups. Nevertheless, the robust effect of the rs3753519 HSD11B1 variant in obesity was evident, demonstrating a significant association with BMI in the obese group despite the relatively small sample size. However, additional association studies with a larger independent Caucasian population are necessary to validate our current findings and investigate the associations of this SNP with obesity-related quantitative traits.
The regulation of HSD11B1 expression is highly tissue specific and involves the use of two alternative promoters: a distal promoter, P1, and a proximal promoter, P2.23 In a recent study, transcription from P2 predominated in human liver, lung and subcutaneous adipose tissues, whereas transcription from P1 predominated in human tumour cell lines.24 rs3753519 is located in the P2 promoter exactly 2679 nucleotides 5′ to the transcriptional start codon; therefore, this SNP may affect the transcription rate of the HSD11B1 gene. Although the functional consequences of this polymorphism were assessed using several different web tools for expression quantitative trait loci and transcription factor binding sites, there were no positive results (data not shown). Polymorphisms in the P2 promoter region (rs846910) and an intronic enhancer (rs12086634) have been associated with type 2 diabetes and/or hypertension in three different populations, although not with obesity per se.17, 18, 25 The G allele of rs12086634 has been associated with lower 11β-HSD1 transcriptional activity in vitro;26 however, no functional consequences of the allelic variation rs846910 have been found in vitro.27 Another polymorphism in the P2 promoter located two nucleotides 5′ to the translation initiation site (rs13306421) has shown functional consequences on 11β-HSD1 enzyme activity in vitro by producing higher enzyme expression and activity levels;27 however, the presence of this variant has not been detected in the Caucasian population, and its association with disease has not been published.
In the present study, the previously reported association of rs846910 with diabetes and hypertension-related phenotypes was not found, and its association with obesity was not significant despite being in linkage disequilibrium with the polymorphism rs3753519 (D’=1; r2=0.495 calculated with Haploview 4.2 software available at www.broad.mit.edu/mpg/haploview/). The reason for this lack of a clear association between 11β-HSD1 genetic variability and obesity has yet to be reported. Despite evidence of its effect in animal and human obesity, this lack of association may be because neither of the SNPs investigated were functional, and the weak associations found with obesity-related phenotypes could be attributable to a linkage disequilibrium with a functional locus.
The reduction in plasma cortisol concentrations associated with the presence of rs3753519 risk allele A, which was identified in the present study, may indicate an effect of this variant on gene transcription and enzyme activity. Lower hepatic 11β-HSD1 activity (assessed by the urinary tetrahydrocortisol + 5α-tetrahydrocortisol/tetrahydrocortisone ratio) has been reported in obese children28 and is supported by the finding that the activation of oral cortisone to cortisol is impaired in obesity.5 However, there is no consensus on plasma cortisol levels in obese subjects. It should be noted that the interpretation of plasma cortisol values is complicated by cortisol being secreted episodically;29 thus, a single spot value may be unrepresentative. This problem can be obviated by measuring the 24-h mean plasma cortisol concentrations or 24-h integrated plasma concentration.30 Only studies using one of these parameters should be accepted for the critical evaluation of plasma cortisol concentrations in obesity. Decreased plasma cortisol concentrations have been found in obese individuals using these parameters, and this is likely because of the subnormal responsiveness to feedback stimulation in the hypothalamic–pituitary–adrenal axis.30 Although the method of measuring plasma cortisol concentrations used in this study is a limitation, the plasma cortisol concentrations were analysed under the same conditions in all subjects (that is, in the morning at 0800–0830 h after an overnight fast). Therefore, the decreased cortisol concentrations found in obese children could support this hypothesis.
Independent of plasma cortisol concentration, tissue glucocorticoid dysregulation has been described in common obesity. Decreased cortisol levels in the liver secondary to reduced 11β-HSD1 activity5, 6, 7 and increased 11β-HSD1 mRNA levels and activity in adipose tissue have been reported.6, 7, 8, 9, 10 However, the underlying mechanisms for the emerging tissue-specific expression patterns are poorly understood. Recently, it has been proposed that several key regulators of lipid metabolism and inflammation, including peroxisome proliferator-activated receptor α and γ, liver X receptor and CCAAT/enhancer-binding proteins, participate in regulating HSD11B1 expression.2 In this study, no associations between the HSD11B1 variant and circulating lipids or inflammation biomarkers were identified. Therefore, although tissue concentrations should be assessed, these results could rule out this hypothesis, at least in children. The association of rs3753519 with increased concentrations of the liver enzyme GGT, which is a classical marker of fatty liver disease, as well as decreased concentrations of adiponectin, which is a marker of adipose tissue alteration, and increased concentrations of active PAI-1, a marker of cardiovascular disease, could imply a possible role for this variant in the aetiology of obesity. We conclude that the rs3753519 gene polymorphism in HSD11B1 is associated with susceptibility to pediatric-onset obesity and its complications; however, this finding should be replicated in an independent cohort.
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We thank the children and parents who participated in the study. This work was supported by the Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnológica (I+D+I), Instituto de Salud Carlos III-Fondo de Investigación Sanitaria (PI020826, PI051968), the Consejería de Innovación y Ciencia, Junta de Andalucía (P06-CTS 2203) and the Ministerio de Universidades y Tecnología, Instituto de Salud Carlos III, Fondo de Investigaciones Sanitarias, Redes temáticas de investigación cooperativa RETIC (Red SAMID RD08/0072/0028).
The authors declare no conflict of interest.
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Cite this article
Olza, J., Gil-Campos, M., Leis, R. et al. A gene variant of 11β-hydroxysteroid dehydrogenase type 1 is associated with obesity in children. Int J Obes 36, 1558–1563 (2012). https://doi.org/10.1038/ijo.2012.4
- 11β-hydroxysteroid dehydrogenase type 1
- genetic polymorphisms
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