The Clinical Phenotype and Genetic Analysis of Monogenic Non Syndromic Obesity Caused by MC4R Gene Variation

ABSTRACT

Objective

The objective of this study was to investigate the clinical features and genetic variation of a patient with monogenic nonsyndromic obesity caused by melanocortin 4 receptor (MC4R) gene variation. Additionally, this study aims to provide a reference for the diagnosis of the disease.

Methods

A monogenic non syndromic obese patient who was admitted to Dongying People's Hospital in December 2024 was enrolled in the study. The clinical data and peripheral blood samples of the patient were collected. Whole‐exome sequencing was utilized to identify gene variants. Subsequently, bioinformatic analysis was performed on the candidate variants detected in the patient. The pathogenicity of the variant was evaluated in accordance with the Standards and Guidelines for the Classification of Genetic Variants, which were formulated by the American College of Medical Genetics and Genomics (ACMG). A comprehensive database was meticulously curated to encompass all previously documented monogenic non syndromic cases. A retrospective analysis was then conducted to systematically summarize the phenotypic and pathogenic variation spectrum of the MC4R gene. A comprehensive review of the extant literature on cellular and molecular genetics was conducted, with the objective of elucidating the discrepancies between the mutation location of the MC4R gene and its clinical phenotype.

Results

The results of the patient's case reveal that the subject is a 10‐year‐2‐month‐old female who exhibits the clinical manifestations of severe obesity, hyperinsulinemia, and accelerated puberty development. Whole‐exome sequencing revealed a missense mutation c.185A > G (p.Asn62Ser) in the MC4R gene. According to the ACMG guidelines, the variant was designated as pathogenic (PM2_Supporting + PM3_Supporting + PS4_Supporting + PS3_Moderate + PP1_Strong + PP3_Supporting). A comprehensive literature search yielded a total of 64 children with obesity caused by MC4R mutation. Regardless of the location of the mutation, whether in the transmembrane region or the topological region, no statistically significant differences were observed in age (months), gender, BMI, BMISDS, acanthosis nigricans, hyperinsulin, hyperappetite, and underlying diseases between the two groups. Interaction analysis revealed a significant modification effect of underlying disease status on the association between mutation location and BMI (P for interaction = 0.002–0.069). Among children without underlying diseases, topological region mutations were associated with higher BMI (β = 8.52, 95% CI: −3.20–20.24), whereas among those with underlying diseases, the effect was reversed (β = −9.73, 95% CI: −21.42–1.96), indicating opposite directions of effect across subgroups.

Conclusion

The present study has demonstrated that MC4R gene missense variants are among the most prevalent genetic factors contributing to monogenic nonsyndromic obesity. For children with early‐onset severe obesity, accelerated puberty, and hyperinsulinemia, the consideration of monogenic nonsyndromic obesity is imperative, and genetic testing should be utilized to confirm the diagnosis expeditiously. In the context of pediatric obese patients with underlying diseases, the effect of different mutation positions on BMI varies. These findings also expand the spectrum of MC4R variants.

A novel MC4R mutation (c.185A > G) was identified in a 10‐year‐old girl with severe obesity and hyperinsulinemia. Retrospective analysis of 64 pediatric cases revealed that mutation location influences BMI, modulated by underlying disease status, demonstrating that the genotype–phenotype relationship in MC4R‐associated obesity is clinically context‐dependent.

1. Introduction

Obesity is a multifactorial disease caused by the interaction of genetic susceptibility and environmental factors. Despite the prevalence of polygenic variation, its impact is minimal. Conversely, rare pathogenic variants in a single gene with a high magnitude of effect account for approximately 5% of cases of childhood obesity (El‐Sayed Moustafa and Froguel 2013). On a global scale, childhood obesity has emerged as the most prevalent form of malnutrition, surpassing hunger as the predominant health concern. According to the United Nations International Children's Emergency Fund (UNICEF) 2025 report, the number of obese children worldwide has reached 188 million, surpassing the number of underweight children for the first time in history. In certain Pacific Island countries, including Niue, the prevalence of childhood obesity has reached alarming levels, with rates as high as 38%. In China, the proportion of overweight and obese children and adolescents has also surpassed 30%. Childhood obesity is a grave public health concern, exerting a profound and multifaceted detrimental effect on physical and mental health. This phenomenon is not merely a concern during childhood; it also has ramifications for the quality of life throughout one's lifetime. It is noteworthy that up to 80% of obese adolescents will carry their obesity status into adulthood, thereby significantly increasing their risk of developing various chronic diseases.

Clinically, patients with monogenic obesity develop excessive appetite and overeating symptoms in early childhood, primarily due to mutations in the genes encoding enzymes or receptors in the leptin‐melanocortin (Lep‐melanocortin) pathway, which plays a pivotal role in regulating satiety and maintaining energy balance in the body. The most prevalent of these mutations occurs in the gene encoding the melanocortin‐4 receptor (MC4R) (Farooqi et al. 2003; Farooqi and O'Rahilly 2008). Individuals who carry MC4R mutations are predisposed to develop obesity symptoms as a consequence of unselective overeating, a tendency that is particularly pronounced during early childhood (Farooqi et al. 2003).

The MC4R gene contains seven transmembrane regions, three intracellular loops, and three extracellular loops (Farooqi et al. 2003). Reported mutation sites are distributed in the transmembrane regions and the intracellular and extracellular loops. The mutation, situated in the first transmembrane region of the patient, has been documented in China for the first time. This region has been deemed to be of minimal importance for ligand binding (Yang, Fong, Dickinson, Mao, et al. 2000). However, an analysis of foreign reports and the clinical phenotype of the patient revealed that the mutation located in the first transmembrane region can lead to early‐onset obesity, hyperinsulinemia, and excessive appetite. A comprehensive review of the clinical features associated with monogenic nonsyndromic obesity revealed new clinical phenotypes that were subsequently expanded and refined. These additional features include skin pallor (Neocleous et al. 2016), corneal small fiber degeneration (Gad et al. 2024), and gastrointestinal reflux (De Rosa et al. 2019), among others. The objective of this study was to provide evidence for the pathogenesis, diagnosis, treatment, and genetic counseling of monogenic nonsyndromic obesity variants.

2. Objects and Methods

2.1. Objects

The research subject was a patient diagnosed with monogenic nonsyndromic obesity at Dongying People's Hospital in December 2024. The detailed clinical characteristics and genetic identification of this specific case were previously reported by our team in Chinese (Li et al. 2025). This study was approved by the Ethics Committee of Shandong Provincial Hospital (LCYJ: NO: 2019–147), and patients have signed the informed consent for clinical research. Clinical trial number: Not applicable. Written informed consent was obtained from individual or guardian participants.

The patient was a 10‐year‐old girl who had been experiencing breast development for approximately 1 year and menstruation for 2 months. The patient exhibited a progressive increase in breast size on both sides, accompanied by discomfort, which began 1 year prior. Additionally, pubic hair growth was observed 6 months ago. The patient had been menstruating for a period of time prior to 2 months, exhibiting no symptoms consistent with dysmenorrhea. The initial menstrual cycle lasted approximately 15 days. The subject's typical activity level was moderate. There was no evidence of squatting, and occasional apprehension regarding low temperatures was reported. No symptoms of lethargy, slow reaction, or history of contraceptive misuse were observed. The subject's dietary habits are characterized by a preference for familiar foods, including meat and vegetables, rather than a fastidious approach to nutrition. The patient's height had increased by approximately 6–7 cm over the past year. The patient was admitted to the hospital with a diagnosis of “rapidly progressing puberty” in order to receive further treatment. A physical examination revealed that the patient's body mass index (BMI) was 36.5 kg/m², height was 158.0 cm (+2.84 SD), sitting height was 87.2 cm, fingertip distance was 152 cm, breast development stage was B4, axillae hair was absent, pubic hair development stage was PH2, facial acne was present, posterior neck fat pad was noted, acanthosis nigricans was present in the neck, axillae, and groin regions, and no purple wrinkles were observed on the abdomen. Following admission, the examination was completed, and the biochemical and immune indicators were found to be abnormal as follows: The patient's glycohemoglobin level was 6.4% (reference range: 3.6%–6%), uric acid level was 400.0 μmol/L (reference range: 142.8–339.2 μmol/L), triglyceride level was 2.34 mmol/L (reference range: 0.4–1.7 mmol/L), fasting insulin level was 546 pmol/L (reference range: 17.8–173 pmol/L), and luteinizing hormone, follicle‐stimulating hormone, and estrogen levels were consistent with those observed during puberty. The cortisol and adrenocorticotropic hormone levels exhibited a rhythmic pattern. The results of the complete blood count, urinalysis, stool analysis, liver function tests, electrolyte levels, thyroid function tests, insulin‐like growth factor‐1, and 17‐hydroxyprogesterone were within normal limits. Her bone age was determined to be 13.6 years. Electrocardiogram and pituitary magnetic resonance imaging results were normal.

2.2. Collection of Samples and Extraction of DNA

Two milliliters of peripheral blood were collected in ethylenediaminetetraacetic acid (EDTA) anticoagulant tubes (batch number: 20240904, Weihai Weigao Blood Collection Consumable Co. Ltd). Genomic DNA was extracted using the DP348 centrifugal‐column type RelaxGene Blood DNA System kit (Beijing Tiangen Biochemical Technology Co. Ltd). The standard purity of DNA was between 1.8 and 2.0 at A260/A280, and the concentration was above 30 nanograms per microliter.

2.3. Whole Exome Sequencing

The DNA samples were dispatched to Fuzhou Furui Medical Laboratory for whole‐exome sequencing (WES). The genomic DNA of the submitted samples was fragmented, ligated, amplified, and purified. The DNA library was prepared using a hybrid‐capture method, and human DNA was subsequently detected by means of a high‐throughput sequencing platform.

In the course of the whole‐exome analysis, the exonic and flanking intronic regions (20 bp) of 20,700 genes, as well as the complete mitochondrial genome (16569 bp), were analysed. The sequencing data were aligned to the human genome hg19 (GRCh37) reference sequence, and the coverage of the target region and the quality of the sequencing were evaluated. The NextGENe V2.3.4 software was utilized to align the sequencing data with the UCSC hg19 human reference genome sequence and to identify genetic variants. Quality parameters, including the coverage of the target region and the average sequencing depth, were collected.

2.4. Bioinformatics Analysis

The bioinformatics analysis was conducted using the NextGENe V2.3.4 software and scripts developed by Fuzhou Furui Medical Laboratory. The annotation information included base and amino acid conservation (PhyloP: http://compgen.cshl.edu/phast/phyloP‐tutorial.php), biological function prediction (PolyPhen‐2: http://genetics.bwh.harvard.edu/pph2), MutationTaster 2021 software (https://www.genecascade.org/MutationTaster2021/), and SIFT (https://sift.bii.a‐star.edu.sg/). The frequency if the general population was determined using ExAC (https://gnomad.broadinstitute.org/). The following databases were utilized: /org, 1000 Genomes (https://www.internationalgenome.org), dbSNP (https://www.ncbi.nlm.nih.gov/snp/), a laboratory‐built database, HGMD (https://www.hgmclid.cf.ac.uk/ac/index.php), ClinVar (https://www.ncbi.nlm.nih.gov/nvar/), and OMIM (https://wWw.omim.org/). The following information was included: UniProt (https://www.uniprot.org) was utilized to analyze the conservation of MC4R amino acid sequence variation sites in humans and other species. The Pymol software (https://pymol.org) was utilized to predict the secondary and tertiary structures of the MC4R protein. The pathogenicity of genetic variants was evaluated in accordance with the criteria and guidelines established by the American College of Medical Genetics and Genomics (ACMG).

2.5. Literature Search Methodology

A comprehensive literature search was conducted using the keywords “MC4R gene,” “MC4R gene” and “childhood obesity” in the Chinese medical database CNKI, Wanfang, PubMed, ClinVar, and other relevant databases. By August 2025, a total of 48 articles were retrieved, and the full text of these articles were evaluated. The exclusion criteria included articles that:

• –lacked a detailed description of the clinical phenotype;
• –were not specific to an individual;
• –did not clearly state the exact genetic variant; or
• –excluded the simultaneous combination of other genetic variants The final analysis encompassed 14 articles, which collectively included data from 63 patients. A comprehensive review of the extant literature revealed that the phenotypic characteristics of monogenic nonsyndromic obesity have been systematically classified, common phenotypic abnormalities have been summarized, and newly discovered clinical phenotypes have been described.

2.6. Statistical Analysis

According to UniProtKB and the related literature summary, the MC4R gene has been shown to contain a total of seven transmembrane regions, three intracellular loops, and three extracellular loops. Prior to analysis, the normality of continuous variables (including BMI and BMI SDS) was assessed using the Shapiro–Wilk test, given the relatively small sample size of this study (N = 64). For normally distributed continuous variables, data are presented as the mean ± standard deviation (SD), and group comparisons were performed using the independent samples t‐test. For non‐normally distributed continuous variables, data are presented as the median (interquartile range, IQR), and group comparisons were performed using the Mann–Whitney U test. Categorical variables are presented as frequencies and percentages. Group comparisons were performed using the chi‐square test or Fisher exact test, as appropriate, depending on the expected cell frequencies. Univariate binary logistic regression was performed to evaluate the association between each candidate variable and mutation location (transmembrane region vs. topological region) as the binary outcome variable. Results are expressed as odds ratios (ORs) with 95% confidence intervals (95% CIs). For the interaction analysis, linear regression models were constructed with BMI as the continuous outcome variable and mutation location and underlying disease status as independent variables. Interaction terms were incorporated to assess whether underlying disease status modified the association between mutation location and BMI/BMI SDS. Model I was adjusted for age and sex, and Model I* was further adjusted for the interaction between age and sex. A two‐sided p value of less than 0.05 was considered statistically significant. All statistical analyses were performed using R software (version 4.3.1).

3. Results

3.1. High‐Throughput Sequencing Results

WES revealed that the patient had a de novo NM_005912.3: c.185A > G (p.Asn62Ser) mutation in the MC4R gene, which was identified as the causative agent of monogenic nonsyndromic obesity. The mode of inheritance was autosomal dominant, and the specific sequencing map is shown in Figure 1.

FIGURE 1.

The following presentation offers a visual representation of the genetic sequence of the MC4R gene for the subject under consideration.

3.2. Bio‐Informatics Analysis

In accordance with the recently published ACMG guidelines for variation analysis: The subject was found to carry a heterozygous mutation at the c.185A > G site within the MC4R gene, which resulted in a change from asparagine with serine at the 62 of the protein. The integration of the genome database (gnomAD 2.1.1: https://gnomad.broadinstitute.org/) reveals that the total population of allele frequency variation is currently less than 0.01% (1/251328), indicating an absence of defect detection. The highest observed allele frequency was 0.000033 (1/30616) in the South Asian population (PM2_Supporting). This variant was observed to co‐segregated with the disease in a cohort of nine patients from a single family, comprising five homozygous carriers and four heterozygous carriers. Notably, the individual who was heterozygous for this variant did not manifest any discernible phenotype (Mazur et al. 2022). This variant was identified as heterozygous in a patient with familial early‐onset obesity (Künzel et al. 2025) (PP1_Strong+PM3_Supporting+PS4_Supporting). This variant results in a significant decrease in enzyme activity, with approximately 20% of the wild‐type (Farooqi et al. 2003; Mazur et al. 2022; Künzel et al. 2025) (PS3_Moderate) enzyme activity. The REVEL value was 0.946 (Yeo et al. 2003) (≥ 0.644), suggesting that this mutation has an effect on the function of the MC4R gene (PP3_Supporting). The conservation analysis of the protein encoded by the MC4R gene in the UniProt system demonstrated a high degree of conservation among mammals of different species (Figure 2), suggesting that alterations in the amino acid at this position may potentially impact the function of the protein encoded by the gene. The three‐dimensional structure of the wild‐type MC4R protein was retrieved from the RCSB Protein Data Bank (PDB accession number: 6 W25). The variant structure (p.Asn62Ser) was generated by introducing the corresponding amino acid substitution using the PyMOL mutagenesis wizard (version 3.1.3). Structural comparisons between the wild‐type and variant proteins were performed using PyMOL to assess potential conformational changes induced by the variant as illustrated in Figure 3. Based on the integrated evidence outlined above (PM2_Supporting + PM3_Supporting + PS4_Supporting + PS3_Moderate + PP1_Strong + PP3_Supporting), this variant is classified as Pathogenic in accordance with ACMG guidelines.

FIGURE 2.

A study of the evolutionary relationships among proteins from different species is hereby referred to as “homology analysis.”

FIGURE 3.

The following is a three‐dimensional structural diagram of the Mc4r‐encoded protein.

3.3. The Summary of the Results Found in the Extant Literature

3.3.1. Distribution of Mutation Sites

A total of 64 children with obesity who met the established criteria were included in the systematic literature search. A thorough investigation of gene mutation sites revealed that the number of MC4R gene mutations in children diagnosed with obesity was 39:25 in the transmembrane region and the topological region. The identified mutations primarily included missense, frameshift, deletion, and nonsense mutations. The results of this study are detailed in Table S1 and Figure 4.

FIGURE 4.

Schematic diagram of currently reported MC4R gene and amino acid variants.

3.3.2. MC4R Gene Mutation

In the 64 patients with MC4R gene variants, no matter the mutation site being located in the transmembrane or topological region, there were no statistically significant differences between the two groups in terms of age (months), sex, BMI, BMISDS, presence or absence of acanthosis nigricans, presence or absence of hyperinsulinemia, presence or absence of hyperphagia, and presence or absence of underlying diseases, as shown in Table 1.

TABLE 1.

The clinical characteristics of the population that was included in the study.

SITE	Transmembrane	Topology	p
N	39	25
Age(mouths)	86.04 ± 59.57	118.42 ± 67.21	0.12
BMI	34.14 ± 9.54	36.61 ± 12.12	0.62
BMISDS	3.70 ± 1.15	4.19 ± 2.30	0.82
SEX			0.83
Female	11 (50.00%)	7 (53.85%)
Male	11 (50.00%)	6 (46.15%)
Acanthosis nigricans			0.46
No	32 (82.05%)	23 (92.00%)
Yes	7 (17.95%)	2 (8.00%)
Hyperinsulinism			0.74
No	31 (79.49%)	19 (76.00%)
Yes	8 (20.51%)	6 (24.00%)
Excessive appetite			0.94
No	33 (84.62%)	21 (84.00%)
Yes	6 (15.38%)	4 (16.00%)
Associated underlying diseases			0.88
No	29 (74.36%)	19 (76.00%)
Yes	10 (25.64%)	6 (24.00%)

Univariate logistic regression analysis was used to compare the mutation sites in the transmembrane region and the topological region. The analysis revealed no significant differences in age (months), gender, BMI, BMISDS, acanthosis nigricans, hyperinsulinemia, excessive appetite, and underlying diseases as shown in Table S2.

Interaction analysis demonstrated that the presence of underlying diseases significantly modified the association between mutation location and BMI (Crude: p = 0.069; Model I: p = 0.002; Model I*: p = 0.006). Among children without underlying diseases, those with mutations located in topological regions had higher BMI compared to those with mutations in transmembrane regions (adjusted β = 8.52, 95% CI: −3.20–20.24), suggesting that topological region mutations were associated with higher BMI. Conversely, among children with underlying diseases, those with mutations in topological regions had lower BMI compared to those with mutations in transmembrane regions (adjusted β = −9.73, 95% CI: −21.42–1.96), indicating a completely opposite direction of effect. These findings were consistent across the crude model and models adjusted for age and sex, suggesting that the interaction effect is reasonably robust.

It should be noted that, despite the statistically significant interaction, subgroup sample sizes were small (ranging from 4 to 13), and none of the within‐subgroup effects reached statistical significance. These findings should therefore be considered exploratory and warrant confirmation in larger, independent cohorts.

A comprehensive review of the extant literature revealed that the combined underlying diseases of the included population primarily included dyslipidemia, corneal small fiber degeneration, autism spectrum disorder, gallstones, hypothyroidism, type 2 diabetes, and hypertension as shown in Table 2.

TABLE 2.

The multiple regression analysis of two groups.

Site	Associated underlying diseases	N	Crude β (95% CI) p	ModelI β (95% CI) p	ModelI*β (95% CI) p
Transmembrane	No	13	Ref.	Ref.	Ref.
Topological	No	4	9.82 (−1.54, 21.19) 0.10	8.52 (−3.20, 20.24) 0.17	7.24 (−5.19, 19.67) 0.27
Transmembrane	Yes	8	3.12 (−5.81, 12.05) 0.49	6.33 (−2.11, 14.76) 0.16	2.92 (−14.24, 20.07) 0.74
Topological	Yes	5	−1.27 (−11.73, 9.19) 0.81	−9.73 (−21.42, 1.96) 0.12	−11.95 (−32.16, 8.27) 0.26
P for interaction			0.069	0.002	0.006

Note: Model I:Adjustments were made for age and sex. Model I*:Adjustments were made for age、sex and the interaction terms for age, sex.

4. Discussion

Monogenic nonsyndromic obesity is primarily attributable to genetic variations within the leptin‐melanocortin pathway and semaphorin 3 (SEMA3) pathway (Pigeyre et al. 2016; van der Klaauw et al. 2019). Mutations in the MC4R gene have been demonstrated to induce aberrations within the signaling pathway (Kühnen et al. 2019). It has been determined to be one of the primary contributors to monogenic nonsyndromic obesity. Leptin (LEP) is a pivotal signaling molecule that modulates energy balance. Following its release from adipose tissue, the substance traverses the blood–brain barrier, subsequently exerting its effects on the central nervous system. The following mechanisms comprise the core of the system: The binding of the hormone leptin to its receptor, known as the leptin receptor (LEPR), located in the arcuate nucleus of the hypothalamus, activates pro‐opiomelanocortin (POMC) neurons. This activation has been observed to result in the inhibition of neuropeptide Y and agouti‐related protein (NPY/AgRP) neurons. The subsequent section will address the processes involved in hormone processing and signal transmission. The activation of POMC neurons results in the cleavage of POMC to produce α‐melanocyte‐stimulating hormone (α‐MSH) and β‐melanocyte‐stimulating hormone (β‐MSH), a process that is facilitated by prohormone convertase (PC) (1/3). These hormones subsequently act on the paraventricular nucleus of the hypothalamus, binding to melanocortin‐3 and 4 receptors (MC3R/MC4R), thereby triggering physiological effects, suppressing appetite, and enhancing energy expenditure. (3) Antagonistic mechanism and energy balance: AgRP functions as a natural antagonist of MC3R/MC4R by competitively binding to its receptors, thereby reversing the regulation of the MC3R/MC4R pathway. This, in turn, promotes feeding behavior and reduces metabolic rate. This pathway has been demonstrated to play a pivotal role in the regulation of appetite and energy metabolism through the “Leptin‐POMC‐MC4R” axis. Dysregulation of this pathway has been identified as a contributing factor to metabolic diseases, including obesity.

The MC4R gene is located on chromosome 18q21.32 in the paraventricular nucleus of the hypothalamus and encodes mainly G protein‐coupled receptors. MC4R functions as an “endogenous agonist” in the leptin‐melanocortin energy balance pathway, thereby inhibiting appetite and increasing energy metabolism when combined with α‐MSH and β‐MSH. It has also been demonstrated to antagonize the action of AgRP, thereby increasing appetite and decreasing energy metabolism (Krashes et al. 2016). Consequently, mutations in the MC4R gene are a prevalent etiology of monogenic nonsyndromic obesity. Giles et al. (Yeo et al. 2003) conducted a study of the functional study of the MC4R gene mutant receptor and investigated the ability of the p.Asn62Ser mutant receptor to produce cAMP in response to increasing α‐MSH concentrations in transiently infected HEK293 cells. The binding of the p. Asn62Ser mutant receptor to a radiolabeled NDP‐MSH tracer in HEK293 cells that had been previously infected was also studied. The partially active missense mutation, p. Asn62ser, demonstrated a modest response to the cAMP response to α‐MSH. Furthermore, cells that had been infected with the mutant receptors exhibited a capacity to bind the tracer at levels of approximately 20% and 30% of those observed in wild‐type cells, respectively. Experimental findings demonstrated that the substitution of Asn62 with Ser significantly compromised the affinity for NDP‐MSH, resulting in a 17‐fold reduction in AgRP affinity. Therefore, the diminished ligand response of p. Asn62ser can be ascribed to a substantial decrease in ligand binding capacity. Consequently, mutations in the MC4R gene are a prevalent etiology of monogenic nonsyndromic obesity. The initial association of MC4R with monogenic nonsyndromic obesity was documented in 1998, when the presence of frameshift mutations in the MC4R gene was first identified in individuals with extreme obesity (Saeed et al. 2012; Liu et al. 2017; Bruschetta et al. 2018; Iepsen et al. 2018). The majority of mutations in the MC4R gene are missense mutations (Iepsen et al. 2018), which are inherited in both dominant and recessive forms. Individuals who carry heterozygous mutations tend to have a body mass index (BMI) that is overweight but not obese (Pigeyre et al. 2016). In contrast, patients with recessive homozygous mutations exhibit characteristics associated with early‐onset obesity (Almeida et al. 2018). Farooqi et al. (Farooqi et al. 2003) conducted a study in which they sequenced 500 patients with obesity and identified 29 patients with both heterozygous and homozygous mutations in the MC4R gene. It has been established that individuals carrying MC4R mutations may exhibit significant obesity, marked hyperphagia, augmented lean body mass, accelerated linear growth, and substantial hyperinsulinemia. The clinical manifestation of homozygous mutations is reported to be more pronounced than that of heterozygous mutations (Farooqi et al. 2003). Individuals with MC4R mutations are characterized by severe obesity and an early age of disease onset, and they are present in approximately 1%–6% of patients with extreme obesity (Qiu and Guo 2009). In addition to hyperphagia and obesity, patients with a MC4R gene mutation do not exhibit the same constellation of endocrine and metabolic abnormalities, thyroid, adrenal, and reproductive dysfunctions seen in other types of obesity caused by single‐gene mutations. According to the findings of early foreign studies (Farooqi et al. 2003; Yeo et al. 2003), the clinical phenotype of MC4R p.Asn62Ser was characterized by early‐onset obesity, hyperinsulinemia, and hyperphagia. The patient in this study exhibited clinical manifestations consistent with the patient in the present study, including severe obesity, severe hyperinsulinemia, and increased appetite. However, the patient exhibited a precipitous rise in height over a brief period, accompanied by the onset of menses due to the accelerated progression of puberty. It is imperative to consider the patient's severe obesity, which has been demonstrated to increase ceramide levels in the hypothalamus. This, in turn, has been shown to lead to precocious puberty by modulating sympathetic nerves and stimulating the ovaries (Geller et al. 2004).

The MC4R gene contains a total of seven transmembrane regions, three intracellular loops (Farooqi et al. 2003), and three extracellular loops. The reported mutation sites are distributed across the transmembrane region and the intracellular and extracellular loops. The mutation located in the first transmembrane region of the patient was reported for the first time in China. Prior to this report, the mutation was considered to be of minimal importance with regard to ligand binding (Yang, Fong, Dickinson, et al. 2000). However, subsequent analysis of foreign reports and the clinical phenotype of this patient revealed that the mutation in the first transmembrane region can result in early‐onset obesity, hyperinsulinemia, and excessive appetite. This patient also exhibited precocity, characterized by accelerated puberty, which further enriched the clinical phenotype of the mutation site located in the first transmembrane region. This observation further corroborated the notion that the binding of this region with ligands is of greater significance. The present study represents an exploratory analysis of genotype–phenotype correlations in pediatric MC4R‐associated obesity. While a statistically significant interaction between mutation location and underlying disease status on BMI was identified (P for interaction = 0.002–0.069), several important limitations must be acknowledged. First, subgroup sample sizes were relatively small (ranging from 4 to 13 cases per subgroup), which substantially limits the statistical power of the interaction analysis. Second, within‐subgroup differences in BMI, BMI SDS, hyperinsulinemia, and hyperphagia did not reach statistical significance individually. Third, the overall cohort was derived from published literature rather than from a prospectively collected dataset, which may introduce selection bias. Therefore, all observations regarding phenotypic variation according to mutation location should be considered hypothesis‐generating rather than confirmatory, and future studies with larger, prospectively collected and well‐characterized patient cohorts are warranted to validate these preliminary findings and elucidate the clinical significance of MC4R mutation location in phenotypic expression.

The MC4R mutation p.V1031 has been demonstrated to exert a substantial protective effect on obesity, while concurrently exhibiting an inverse association with its development (Geller et al. 2004). This observation suggests the potential for mutations within the MC4R gene to induce a shift in MC4R function, either enhancing or diminishing its activity. Beyond the fundamental management of lifestyle factors, the implementation of targeted pharmacotherapy has demonstrated notable efficacy in patients afflicted with monogenic obesity due to MC4R gene mutations. According to Kuhnen's Group (Kühnen et al. 2019), setmelanotide, a novel pharmaceutical agent, exhibits structural similarity to the molecular properties of α‐MSH. It has been observed to specifically activate the melanocortin 4 receptor (MC4R) and to directly bypass the traditional leptin signaling pathway in obese patients with defects in the MC4R, LEPR, and POMC genes. Direct targeting of MC4R receptor activation has been demonstrated to result in effective long‐term weight regulation, thereby providing further evidence that monogenic obesity can be treated precisely with targeted drugs. Kuhnen et al. (Kühnen et al. 2016) employed MC4R agonists to achieve successful treatment outcomes in two patients with extreme obesity and homozygous POMC mutations, who experienced a weight reduction of over 30% over a period of 10 months. This study demonstrates the potential of MC4R agonists as a targeted therapeutic approach for monogenic nonsyndromic obesity, including obesity associated with POMC, PCSK1, and LEPR deficiency. Glucagon‐like peptide‐1 receptor agonist (GLP‐1A) has been shown to induce weight loss by reducing appetite, a process that is independent of the MC4R pathway. This property of GLP‐1A makes it a promising candidate for the treatment of obesity caused by MC4R deficiency. Another clinical study further validated the therapeutic potential of GLP‐1A for monogenic obesity by comparative analysis, a finding that has been corroborated by related studies. The study population comprised 14 obese subjects with MC4R variants and 28 control subjects without MC4R variants. The GLP‐1A agent, such as liraglutide, was utilized in both groups, and the efficacy of weight reduction was found to be highly significant and consistent. Conversely, the absence of MC4R signaling indicates that this class of drugs may achieve weight loss through alternative metabolic pathways (Iepsen et al. 2018).

Due to the patient's young age, there is no indication for GLP‐1A and related targeted drugs. Currently, the patient is undergoing lifestyle and dietary interventions for weight management. In light of the multidisciplinary consultation on obesity, the exercise prescription should be grounded in aerobic exercise, such as water exercise and slow walking, ensuring a minimum of 30–60 min per day. The experimental group performed progressive load adjustment in combination with strength and function training on two to three days per week. Nutritional prescriptions with high protein and fiber content should be prioritized, and the use of compound carbohydrates should be substituted with refined carbohydrates to meet the demands of growth and development. This approach should be informed by considerations of nutritional supply regulation and satiety. Three months later, the patient exhibited a weight reduction of 3.4 kg. The non‐drug intervention of this patient establishes a survival intervention model for monogenic non syndromal obesity patients during the drug gap, and provides an evidence‐based management template for children with similar conditions worldwide.

Obesity can be defined as the result of a positive energy balance. The biological functions of monogenic nonsyndromic obesity‐related genes, such as LEP, LEPR, MC4R, MC3R, POMC, BDNF, SIM1, etc., leading to early‐onset obesity have been elucidated. Monogenic obesity, precipitated by mutations in the MC4R gene, can present with severe obesity and hyperinsulinemia. Concurrent studies have demonstrated that GLP‐1A and MC4R agonist α‐MSH analogues can promote weight reduction in patients, thereby offering further clinical evidence to support the precise diagnosis and treatment of obesity. The interaction between genetic and environmental factors plays an important role in the development of obesity. In the case of obesity resulting from environmental factors, a combination of lifestyle modification and pharmacotherapy has been demonstrated to be efficacious in achieving weight reduction. Further exploration is necessary to provide clinical evidence for the precise prevention, diagnosis, and intervention of obesity caused by genetic factors.

Author Contributions

Xin Li: conceptualization, data curation, formal analysis, funding acquisition, methodology, project administration, supervision, visualization, writing – review and editing. Wentao Yang: conceptualization, funding acquisition, investigation, methodology, project administration, writing – original draft. Xin Li: writing – review and editing. Shuping Wang: investigation, writing – review and editing. Xiaotian Wang: funding acquisition, writing – review and editing.

Funding

This research was supported by Dongying Natural Science Joint Fund for High‐Quality Development of Health (Grant No. 2025ZRWS068, 2024ZRWS031), Shandong Provincial Medical and Health Science and Technology Development Plan (Grant No. 202103060606, 2018WS520), Chronic Disease Management Research Project of National Health Commission Capacity Building and Continuing Education Center (Grant No. GWJJMB202510024041). We are grateful for their financial support.

Ethics Statement

The research protocol received ethical approval from the Ethics Committee of the Shandong Provincial Hospital (Institutional Review Board Approval No: 2019–147). All study procedures rigorously complied with the ethical guidelines of the Declaration of Helsinki (https://www.wma.net/policies‐post/wma‐declaration‐of‐helsinki/). The studies were conducted in accordance with local legislation and institutional requirements. Written informed consent for participation in this study was provided by the child's parents/legal guardians.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: Clinical characteristics and structural domain distribution of reported MC4R variants residing in different regions of the MC4R protein in pediatric patients with monogenic nonsyndromic obesity.

Table S2: the univariate regression analysis of two group.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.