The central nervous system (CNS)¹ plays a crucial role in regulating appetite and energy metabolism. The neuron networks receive and modulate information from energy homeostasis and regulate the body's metabolic actions through humoral mediators and the autonomic nervous system. The melanin concentrating hormone (MCH) is a neuroendocrine factor that has orexigenic effects on appetite, inhibits energy metabolism, and affects behavior (1). MCH receptor 1 (MCHR1) is expressed widely in the CNS (2) but also in adipocytes (3) and skeletal muscle (4). MCHR1 is an exceptional neuroendocrine factor because dysfunction of this receptor leads to leanness, whereas dysfunction in other central factors causes obesity. This makes MCHR1 an interesting candidate target for anti-obesity drugs (5).

MCH-knockout (KO) mice are lean and hypophagic and have a high metabolic rate (6). In contrast, overexpression of MCH in mice leads to obesity, insulin resistance, and hyperplasia of pancreatic islets (7). An increase in insulin resistance is attributable to direct effects of MCH (8). The phenotype of MCHR1-KO mice is similar to that of MCH-KO mice, including higher insulin sensitivity that is not explained by effects on body weight alone (9). However, MCH-KO mice are hypophagic, whereas MCHR1-KO mice are slightly hyperphagic but still lean (10). The leanness of MCHR1-KO mice is explained by high metabolic rate and increased locomotor activity. There is also evidence that MCHR1-KO mice have increased heart rate and altered autonomic tone (11).

Information on MCH and MCHR1 in humans is limited. Obese subjects have higher circulating MCH levels, and fasting also increases peripheral MCH levels, whereas exogenous leptin neutralizes this effect (12). In some populations, single nucleotide polymorphisms (SNPs) of the MCHR1 gene have been suggested to be involved in the polygenic form of human obesity (13, 14). If a genetic variation of MCHR1 causes obesity, the variant receptor should improve the action of MCHR1. In contrast, dysfunction of MCHR1 is expected to result in leanness.

Because animal studies suggest that MCHR1 affects energy and glucose metabolism and knowledge on MCHR1 in humans is limited, we studied the effects of SNPs of MCHR1 on glucose and energy metabolism in 217 middle-age, non-diabetic Finnish subjects who were offspring of subjects with type 2 diabetes (Group 1). In addition, we studied the effects of one prevalent SNP on obesity, glucose, and insulin levels in a population-based study of 1455 unrelated Finnish men, 50 to 70 years of age (Group 2).

We screened six SNPs of MCHR1 in offspring of type 2 diabetic subjects (Group 1). The structure of MCHR1, SNPs of MCHR1, their minor allele frequencies (MAFs), and linkage disequilibrium (LD) statistics are shown in Figure 1. No significant differences in anthropometric measurements or glucose tolerance, energy expenditure, energy partitioning, insulin secretion, insulin sensitivity, or body composition (Group 1) were found. The results by individual SNPs are given in Table 1. We also generated nine haplogenotypes based on four SNPs (rs133070, rs133072, rs133073, and rs133074) of MCHR. The three most common haplogenotypes (in at least 16 subjects) were not associated with any parameters given in Table 1.

Figure 1:.

(A) Gene map shows SNPs genotyped in MCHR1. Exons are marked by black box. Genotyped SNPs are shown with National Center for Biotechnology Information's dbSNP accession numbers. (B) Linkage disequilibrium statistics (D', r²) and the MAFs are shown among the SNPs of MCHR1 (Group 1).

Full figure and legend (105K)

Table 1 - BMI, waist circumference, body composition, whole body glucose uptake, first phase insulin release during an intravenous glucose tolerance test (determined as 0- to 10-minute insulin area under the curve), fasting energy expenditure, and energy expenditure during the hyperinsulinemic clamp according to SNPs of the MCHR1 gene (Group 1).

Full table

rs133072 (MAF 0.33), which was in >0.50 LD with other SNPs, thus covering most of the genetic information of other SNPs, was screened in Group 2. In this population, we did not find differences in BMI, waist circumference, or glucose or insulin levels during an oral glucose tolerance test (OGTT) between different genotypes (data not shown).

We failed to show an association of SNPs of MCHR1 with metabolic variables, confirming the findings of previous studies in humans. Although studies with rodents have given encouraging results on the effect of MCHR1 on metabolic variables (9, 10), the results of human studies have been disappointing (13, 14). SNPs of MCHR1 have been found to be associated with obesity in some studies, but the results have not been replicated across different populations.

MCHR1 is expressed in various hypothalamic and thalamic nuclei that are known to affect the body's metabolic homeostasis and also in regions of the brain that are known to regulate higher cognitive and emotional functions, such as the cortex of the cerebrum and the cerebellum, the amygdala, and the hippocampus (2). In addition, MCHR1 is also expressed in peripheral tissues, e.g., in adipocytes (3) and skeletal muscle (4). Wide expression may allow various metabolic and behavioral functions for MCHR1, which, in turn, may interact with other genes and the environment. Therefore, the role of MCHR1 may not be seen in association analyses, which could explain the lack of consistent findings in different populations.

It is also possible that SNPs of MCHR1 might not have significant effects on the function of MCHR1. In fact, no common functional SNPs of MCHR1 have been found in in vitro studies (15). Finally, the function of MCH differs between rodents and humans, and in humans, impaired function of MCHR1 may be compensated by factors that rodents do not have. The actions of MCH are known to be mediated through MCHR1 and MCHR2, but MCHR2 is not functional in rodents. Therefore, rodents could be more vulnerable to impaired function of MCHR1, explaining the positive findings in animal studies.

In conclusion, six SNPs of MCHR1 did not affect metabolic variables in 217 Finnish non-diabetic subjects who were offspring of subjects with type 2 diabetes. More information on the interaction of MCHR1 with other genes and the environment is needed to study the role of MCHR1 in more detail.

Research Methods and Procedures

Subjects

The selection of subjects (Group 1) and the study protocol have been previously published (16). In brief, the subjects were healthy, non-diabetic offspring of patients with type 2 diabetes from an ongoing study. The patients with type 2 diabetes (probands) were randomly selected from diabetic subjects living in the region of the Kuopio University Hospital. Spouses of the probands had to have normal glucose tolerance in an OGTT. A total of 217 offspring from 131 different families (1 to 4 offspring from each family) were included in the study. The study protocol was approved by the Ethics Committee of the University of Kuopio.

Our second study group (Group 2) was drawn from an ongoing population-based study of middle-age and elderly men 50 to 70 years of age. The sample was comprised of 1455 unrelated inhabitants living in Kuopio, eastern Finland. The aim of the study was to investigate genetic and non-genetic determinants of the metabolic syndrome in Finnish men.

Measurements and Metabolic Studies

On the first day, blood pressure was measured with a mercury sphygmomanometer, with the subjects in the sitting position, after a 5-minute rest. Height and weight were measured to the nearest 0.5 cm and 0.1 kg, respectively. BMI was calculated as weight (kilograms) divided by height (meters) squared. Waist (at the midpoint between the lateral iliac crest and lowest rib) was measured to the nearest 0.5 cm. Fasting blood samples were drawn after a 12-hour fast, followed by an OGTT (75 grams of glucose). Subjects with normal glucose tolerance (n = 182), isolated impaired fasting glucose (n = 4), or impaired glucose tolerance (n = 31) (17) were included in further studies. On the second day, after a 12-hour fast, an intravenous glucose tolerance test was performed, followed by the hyperinsulinemic euglycemic clamp and indirect calorimetry, as previously described in detail (16). In the hyperinsulinemic clamp, the mean amount of glucose infused during the last hour was used to calculate the rates of whole body glucose uptake. Furthermore, a computed tomography scan was performed to evaluate the amount of abdominal and subcutaneous fat, as previously described (16).

Laboratory Determinations

Blood glucose was measured by the glucose oxidase method (Glucose & Lactate Analyzer 2300 Stat Plus; Yellow Springs Instrument Co., Inc., Yellow Springs, OH) and plasma insulin by radioimmunoassay (Phadeseph Insulin RIA 100; Pharmacia Diagnostics, Uppsala, Sweden).

DNA Analysis

We screened two promoter [ rs133067 (T/C), rs133070 (A/G)] , two exon 1 [ rs133072 (G/A), rs133073 (T/C)] , one exon 2 [ rs133074 (C/T)] , and one 3' flanking region [ rs133076 (T/C)] polymorphisms of MCHR1 using the TaqMan Allelic Discrimination Assays (Applied Biosystems, Foster City, CA). Genotyping reaction was amplified on a GeneAmp PCR system 2700 (95 °C for 10 minutes, followed by 40 cycles of 95 °C for 15 seconds and 60 °C for 1 minute), and fluorescence was detected on an ABI Prism 7000 Sequence Detection System (Applied Biosystems). The primer and probe sequences are available from the authors on request. Selection of the SNPs was based on an earlier association study (13) and the genotype data of Utah residents with ancestors from northern and western Europe available on the HapMap project's website (18) (HapMap Public Release 20). Tagger software, available at http://www.broad.mit.edu/mpg/tagger/ (19), was used in the selection of SNPs to properly cover the entire region of the MCHR1 gene locus (3.2 kb upstream, 3.6 kb of the MCHR1 gene, and 3.2 kb downstream). Two of the SNPs (rs133070 and rs133072) are not available in the HapMap project database. LD statistics were calculated and haplotype blocks were visualized using the Haploview software available at http://www.broad.mit.edu/mpg/haploview/ (20).

Statistical Analysis

Data analyses were performed with the SPSS 11.0 for Windows programs (SPSS, Chicago, IL). The results for continuous variables are given as means standard deviation. Variables with skewed distribution (glucose, insulin, subcutaneus and intra-abdominal fat) were logarithmically transformed for statistical analyses. The incremental area under the insulin curve during an intravenous glucose tolerance test was calculated by the trapezoidal method. The differences between the two groups were assessed by the ANOVA for continuous variables and by the ² test for non-continuous variables. Linear mixed model analysis was applied to adjust for confounding factors. For mixed model analysis, we included the pedigree (coded as a family number) as a random factor, the MCHR1 genotype and sex as fixed factors, and BMI and age as covariates. If the p value for the covariance parameter for the random effect was >0.1, the pedigree membership was excluded from the model, and the analysis of covariance was used for additional adjustment. Haplotype estimation was performed by using the SNPHAP, available at http://www-gene.cimr. cam.ac.uk/clayton/software/. We estimated the power to detect minimal statistically significant differences (power = 0.8 and p < 0.05) for variables given in Table 1 under the dominant model with Java applets for power and sample size, available at http://www.cs.uiowa.edu/rlenth/Power/. Assuming the genotype frequencies (0.40, 0.45, and 0.15), we were able to detect a 3.7% to 14.2% difference under the dominant model in Group 1 (lowest for energy expenditure and highest for whole body glucose uptake) depending on the standard deviation of the variable. In Group 2, the corresponding percentages for waist and BMI were 1.6% and 2.1% , respectively. Therefore, we had sufficient power to detect clinically meaningful differences with respect to all major variables in the study under the dominant model.

Notes

¹ Nonstandard abbreviations: CNS, central nervous system; MCH, melanin concentrating hormone; MCHR1, melanin concentrating hormone receptor-1; KO, knockout; SNP, single nucleotide polymorphism; MAF, minor allele frequency; LD, linkage disequilibrium; OGGT, oral glucose tolerance test.

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Acknowledgments

This study was financially supported by grants from the Academy of Finland (to M. L.), the EVO Fund of the Kuopio University Hospital (5194), and the European Union (EUGENE2, LSHM-CT-2004-512013).