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ORIGINAL ARTICLE
Ahead of print publication  

Association study of Melanocortin-4 Receptor (rs17782313) and PKHD1 (rs2784243) variations and early incidence of obesity at the age of maturity


1 Department of Biology, Science and Research Brand, Islamic Azad University, Tehran, Iran
2 Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran
3 Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences; Metabolomics and Genomics Research Center, Cellular and Molecular Institute Endocrinology and Metabolism Research Institute, Tehran University of Medical Sciences, Tehran, Iran
4 Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran

Date of Submission15-Aug-2022
Date of Acceptance15-Oct-2022
Date of Web Publication25-Nov-2022

Correspondence Address:
Mahsa M Amoli,
Metabolic Disorders Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran
Iran
Mojgan Asadi,
Diabetes Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran
Iran
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aihb.aihb_160_22

  Abstract 


Introduction: Obesity is primarily caused by the dysfunction of the energy homeostasis system. Numerous studies have reported an association between obesity and the rs17782313 variant near the melanocortin-4 receptor (MC4R) gene. In addition, the PKHD1 gene regulates the expression of fibrocystin. This gene is primarily expressed in the kidney and plays a role in fat and glucose metabolism. However, the interaction between PKHD1 polymorphisms and birth weight has not yet been investigated. This study showed the association between the rs17782313 variant near the MRC4 gene and rs2784243 in the PKHD1 gene amongst Iranian cases with obesity before maturity. Methods: One hundred and eleven Iranian patients and 100 healthy individuals aged 5 years and over were selected from the Tehran Moheb-e-Yas Hospital. Polymerase chain reaction-restriction fragment length polymorphism and sequencing methods were used for genotyping the genetic variants. A Chi-square test was applied to determine the association between rs17782313 and food intake and rs2784243 and birth weight. Results: The rs17782313 variant was associated with high food intake (P = 0.04), while the rs2784243 variant was associated with increased birth weight (P = 004). Conclusion: The MC4R rs17782313 and PKHD1 rs2784243 variants may contribute to food intake and early obesity. Moreover, a novel association was suggested between PKHD1 rs2784243 and birth weight.

Keywords: Melanocortin-4 receptor, obesity, PKHD1, rs17782313, rs2784243



How to cite this URL:
Ansari Y, Asadi M, Far IS, Pashaie N, Noroozi N, Amoli MM. Association study of Melanocortin-4 Receptor (rs17782313) and PKHD1 (rs2784243) variations and early incidence of obesity at the age of maturity. Adv Hum Biol [Epub ahead of print] [cited 2022 Dec 2]. Available from: https://www.aihbonline.com/preprintarticle.asp?id=361966




  Introduction Top


The term 'obesity' refers to an excess accumulation of body fat that increases an individual's risk of morbidity and premature death.[1],[2] In the 21st century, childhood obesity is a significant public health concern.[3] Since the 1990s, data from the international study have documented an increase in overweight amongst boys, especially those aged 11, 13 and 15 years.[4] Adolescence is a critical time, during which obesity usually develops. In the United States, 20.5% of adolescents have a body mass index (BMI) above the 95th percentile of their sex-specific BMI for their age.[5] Approximately 80% of adolescents with obesity will continue to suffer from this condition as they grow up.[6] Many long-term and short-term health risks are associated with adolescent obesity, including hyperlipidaemia, type 2 diabetes, hypertension, obstructive sleep apnoea, psychosocial distress and cardiovascular disease in adulthood.[7],[8],[9],[10],[11]

Genetic factors play a significant role in obesity, a multifactorial condition.[12] A substantial amount of evidence has been provided in epidemiological and heritability studies suggesting the influence of genetic variables on obesity susceptibility.[13] Based on estimates from twin-, family- and adoption-related studies, it is generally accepted that obesity is inherited between 40% and 70%.[14] Various functional genes associated with energy balance have been investigated in obesity susceptibility.[15] A homeostatic system maintains body weight by controlling energy balance. Energy homeostasis and controlling body weight are crucial functions of the central melanocortin system.[16] In 1998, the melanocortin-4 receptor (MC4R) gene was linked with human weight gain.[17] Scientists have investigated its approach and the different mutations resulting from the disease field.[18] Safe and effective weight loss therapies can be developed by understanding the molecular mechanisms supporting weight regulation.[19] The 7-transmembrane receptor MC4R gene, at chromosome 18q21.3, has 996 base pairs and is primarily distributed in the hypothalamus. It regulates the appetite.[20],[21] There have been several genome-wide association studies (GWASs) that have identified single-nucleotide polymorphisms (SNPs) underlying the MC4R, which is related to typical and monogenic obesity.[22],[23],[24],[25],[26] The MC4R rs17782313 variant was a risk variant in obesity cohorts.[27] The MC4R receptor plays a vital role in regulating food intake. In 2008, Ranadive and Vaisse reported that mutations in the MC4R gene might change the ability of MC4R to bind to α-melanocyte-stimulating hormone or agouti-related peptide, which could, in turn, affect the total amount of energy consumed.[28] The association between obesity and MC4R rs17782313 has been studied in several studies.[29],[30],[31]

PKHD1 encodes a cytoskeletal and membrane-associated binding protein involved in 'positive regulation of cell proliferation' and 'single organism cell-cell adhesion'. PKHD1 mutations cause several phenotypes, including congenital hepatic fibrosis, biliary tract abnormalities and a loss of renal corticomedullary differentiation.[32],[33],[34] Polymorphisms closely associated with PKHD1 are considered genetic markers for metabolic syndrome, obesity and insulin resistance.[35] In addition, the PKHD1 gene may influence drug-induced weight gain.[36] Despite both of these genes contributing modestly to the obesity phenotype, limited studies report the association between the genetic variants of the PKHD1 gene and obesity. There is no documented report regarding the association between MC4R (rs17782313) and PKHD1 (rs2784243) genetic variants and early incidence of obesity at the age of maturity amongst the Iranian cases; hence, we aimed to design such a study.

Methods

Study design and participants

One hundred and eleven patients with premature obesity and 100 normal healthy individuals (control) with no history of obesity in their families were recruited. The patient's samples were collected from cases referred to the Tehran Moheb-e Yas Hospital. The inclusion criteria for the participants were age between 10 and 65 years, with BMI above 30 kg/m2, and no history of metabolic diseases such as hyperthyroidism, Cushing's syndrome, polycystic ovarian syndrome, hypogonadism and acromegaly. The control group had BMI between 18.5 and 24.9 kg/m2. The Ethics Committee approved this study at the Tehran University of Medical Sciences (TUMC), and all the participants signed informed consent before sample collection.

Sample collection

Intravenous blood (5 ml) was collected from participants in EDTA-containing tubes. Samples were transferred to the genetics laboratory of endocrinology and metabolism institute of TUMC and stored at −20°C for DNA extraction and subsequent polymerase chain reaction (PCR)-restriction fragment length polymorphism.

Genomic DNA extraction

According to the manufacturer's instructions, the genomic DNA was extracted using DNSol Midi Kit (Roje Technology). The quality of the extracted DNA was evaluated by a NanoDrop Spectrophotometer machine (BOECO Micro-ultraviolet [UV]-visible spectrophotometer). All samples were amplified using specifically designed primers [Table 1].
Table 1: Primer sequences and the size of their products and temperature

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The total volume of PCR reactions for MC4R and PKHD1 genes was 20 μl and 30 μl, respectively. The following reaction condition was used for the MC4R gene: 11 μl ddH2O, 7 μl Red master mix, 0.5 μl forward primer, 0.5 μl reverse primer and 1 μl DNA. The PKHD1 PCR reaction condition was as follows: 16.5 μl ddH2O, 10.5 μl Red master mix, 0.75 μl forward primer, 0.75 μl reverse primer and 1.5 μl DNA. PCR amplification programme for both genes is shown in [Table 2].
Table 2: Polymerase chain reaction programme for amplification of melanocortin-4 receptor and PKHD1 genes

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Genotyping by polymerase chain reaction-restriction fragment length polymorphism

The BclI restriction enzyme was used to digest the rs17782313 variant in the MC4R gene. The digestion reaction was performed in a total volume of 15 μl containing 5 μl PCR products, 1.5 μl BclI buffer, 0.5 μl BclI enzyme and 8 μl distilled water. Finally, all reactions were incubated at 55°C for 1 h on a thermal block. The enzymatic digestion products were visualised using 3% agarose gel containing SYBR Green and 2 μl loading dye × 6 buffer under UV light. The enzyme recognition sequence and the size of enzymatic digestion products are described in [Table 3].
Table 3: Enzyme recognition sequence and the size of enzymatic digestion products

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Sequencing

The PCR products were sequenced to evaluate the rs2784243 variant in the PKHD1 gene (Niagenenoor Co.). Results were analysed by Finch TV software V. 1.4 and BLAST Align Sequence Nucleotide (http://blast.ncbi.nlm.nih.gov).

Statistical analysis

SPSS 22 (SPSS Inc., Chicago, IL, USA) for Windows was used to perform the statistical analysis. To assess the association between categorical variables (genotype frequency and odds ratios [ORs]), a Chi-square test was applied. Results are shown as OR with a 95% confidence interval (CI). P < 0.05 was considered statistically significant.


  Results Top


Demographic and clinical profiles

Demographic information consisting of body mass index, age and birth weight in both case and control groups is listed in [Table 4]. Clinical data of the patients are also shown in [Table 5]. A total of 111 obese patients with a mean BMI of 35.5 ± 5.48 kg/m2 were enrolled in this study, including 70 females and 41 males with a mean age of 28.91 ± 17.87 years. The control group included 100 individuals, comprising 53 females and 38 males, with a mean BMI of 23/71 ± 3.91. Of 111 patients, 23 were diabetic, and 77 had a history of obesity before puberty.
Table 4: Demographic profiles of case and control groups

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Table 5: Clinical characteristics of patients with obesity

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Age at onset of obesity

Patient demographic information revealed that the patients aged 5–10 years were more susceptible to obesity. [Table 6] displays the age frequency at the onset of obesity in the patient group.
Table 6: Frequency of age-related obesity

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rs17782313 genotype frequency

[Table 7] shows the genotype frequencies of the polymorphism in patients and normal individuals, with the significance level for each. The comparison of rs17782313 frequency between case and control groups is detailed in [Table 8]. The TT (41.79%) genotype was the most frequent between patients and normal individuals, and the CC (19.9%) genotype was less frequent in the patients and healthy groups. Still, there were no significant differences between these two genotypes (P = 0.1). Moreover, there were no significant CT genotype differences compared to TT + CT genotype frequencies between patients and healthy groups (P = 0.1).
Table 7: Genotype frequency of rs17782313 melanocortin-4 receptor polymorphism in the patients and healthy groups

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Table 8: The comparison of rs17782313 frequency between patient and normal groups

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Furthermore, the CT (50%) genotype was the most frequent in patients with an overeating history, and the CC (13.5%) genotype was less frequent in this group. However, the TT (36.7%) genotype was the most frequent in patients without a history of overeating, and the CT (30%) genotype was less frequent in this group (P = 0.04). Therefore, based on the genotype frequencies of the MC4R rs17782313, this variant was associated with overeating in the patients' group. The association of genotype frequencies of MC4R rs17782313 polymorphism and overeating in patients is presented in [Table 9].
Table 9: The association of genotype frequencies of melanocortin-4 receptor rs17782313 polymorphism and overeating

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rs2784243 genotype

The frequency of PKHD1 rs2784243 genotypes is presented in [Table 10]. The CT (52.1%) and CC (20.8%) genotypes showed the most and less frequent in the control group. The CT (53.3%) and TT (22.2%) genotypes were the most and less frequent amongst obese patients. The Chi-square test showed P = 0.8, suggesting no significant differences between these genotypes in the case and control groups. The frequency of the rs2784243 variant and birth weight is shown in [Table 11]. The highest mean differences in CT genotype compared to TT and CC genotypes were related to CT (3.7 ± 0.51) (P = 0.04). Furthermore, there were no significant mean differences between TT and CC genotypes and birth weight (P = 0.9). Based on data obtained in this study, the PKHD1 rs2784243 variant (CT genotype) was associated with obesity and birth weight.
Table 10: The frequency of PKHD1 rs2784243 polymorphism in patient and control groups

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Table 11: The genotypic mean of PKHD1 polymorphism and birth weight

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  Discussion Top


There is no clear explanation for the association between MC4R polymorphisms and obesity susceptibility.[37] Energy metabolism is controlled by MC4R protein, which is abundant in the central nervous system. Researchers found that MC4R can control food choice, intake and energy expenditure through a distinct pathway.[38] In the present work, we have assessed the association of MC4R (rs17782313) with food intake and early obesity amongst children and adults. There has been reported variation in minor allele frequency of MC4R rs17782313 in several populations, ranging from 14% in Asians to 28% in Europeans. However, according to our results, the Iranian people have a frequency of 19.9%. Several studies have explored the effect of dietary factors and MC4R gene variants on obesity and other metabolic traits,[39],[40] but only a few have reported significant results.[41]

MC4R is unique, because it harbours variants with different effect sizes, and its minor risk alleles increase and decrease BMI across other populations.[42],[43],[44] Our results indicated that the heterozygote CT (50%) genotype is significantly associated with overeating in obese patients compared to the homozygote CC (13.5%) genotype (P = 0.04). However, the TT (36.7%) genotype has significantly lower eating habits. However, the results were not statistically significant. Although 74.8% of the studied population had a family history of obesity, data indicated that rs17782313 is only associated with overeating.

Similarly, significant evidence suggests that obesity associated with the rs17782313 can develop through a high food intake. Conversely, human studies indicate contradictory associations between energy and dietary intakes and this variant.[45],[46],[47],[48] Various factors including dietary assessment instruments, differences in environmental and genetic influences and the possibility of underreporting dietary intake can result in dissimilarities.[49],[50],[51] According to Qi et al., a large cohort study found that an rs17782313 variant was associated with higher dietary fat and total energy intakes.[48] However, other human studies have found inconsistent results.[46] The MC4R polymorphism is associated with the risk of obesity, according to a 2012 meta-analysis by Xi et al.[25]

In contrast, Young et al. discovered that the rs17782313 variant protects against obesity at a population level.[52] According to Xi et al., a significant association between the minor C allele and obesity risk was only found amongst children with sedentary lifestyles.[53] A meta-analysis by Dastgheib et al. illustrated a significant association between MC4R polymorphism and the risk of obesity in children.[52] Despite the absence of a clear explanation of how MC4R rs17782313 influences obesity and its associated metabolic phenotypes, there is a speculation that this variant could play a significant role in appetite control and eating behaviours.[54]

The PKHD1 gene regulates fibrocystin expression. It is not well understood how fibrocystin works, but it may act as a receptor and facilitate adhesion, repulsion and proliferation. PKHD1 plays a role in fat and glucose metabolism, despite being primarily expressed in the kidney.[36] We have also investigated the association between PKHD1 rs2784243 polymorphism and obesity. This work indicated that the CT genotype showed the highest mean differences between TT and CC genotypes which were statistically significant, suggesting this genotype is associated with the age at onset of obesity. Previous studies reported that PKHD1 might influence drug-induced weight gain, for instance, due to olanzapine.[36] In genome-wide association analysis, Riveros-Mckay et al. identified an association between BMI and variants of PKHD1 (rs10456655) in the UK Biobank (UKBB) BMI dataset,[55] where an earlier proxy (rs2579994) has been associated with waist and hip circumference.[56] Conversely, Aasbrenn et al. reported that PKHD1 SNPs are related to surgery-induced weight loss in small-scale GWASs.[57],[58] In another cohort study, PKHD1 SNPs were associated with weight loss after surgery.[59] Several limitations of the present study should be considered when interpreting the results. As the first consideration, a relatively small sample size could reduce statistical power. Therefore, our results should be cautiously viewed and replicated in more extensive longitudinal studies. Second, the under-reporting of dietary intake by obese participants may lead to null results, because under-reporting may bias the study results.[60]


  Conclusion Top


The current study revealed a statistically significant association between overeating and birth weight and the MC4R rs17782313 and PKHD1 rs2784243, respectively. Furthermore, our results showed that inherent genetic factors are a leading cause of childhood obesity. In addition, obesity was observed mainly in female patients, indicating environmental factors, hormones, low physical activity and other factors in obesity. Moreover, our results showed a novel association between the rs2784243 variant and birth weight. A functional study is needed to confirm these findings to determine definitive genetic determinants of food intake. Future research may improve interventions, especially for those with genetic predispositions to obesity.

Ethical approval

The Ethics Committee approved the study at the Tehran University of Medical Sciences.

Informed consent

Informed consent was obtained from the study participants.

Acknowledgement

The authors would like to express their gratitude to all participants enrolled in this project.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7], [Table 8], [Table 9], [Table 10], [Table 11]



 

 
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