Introduction

Obesity is a complex endocrine disorder characterized by excessive increase in body weight due to abnormal fat deposition in the adipose tissue, afflicting individuals of all ages and thereby decreasing their life expectancy. It exerts a detrimental impact on practically all physiological systems of the body and is a substantial public health problem [1]. Although exposed to the widespread obesogenic environmental factors, not everyone becomes obese, implying a significant role of the genetic component in the etiology of obesity. Studies have found that BMI heritability is high, spanning from 40 to 70% [2, 3]. The rising incidence of obesity presents a global health problem, as excess weight increases the risk of various diseases, especially chronic diseases like type 2 diabetes, cardiovascular diseases and cancer [4]. Numerous factors, including lifestyle choices, dietary habits, and socioeconomic status, significantly influence the development of obesity. Recent studies conducted by the World Health Organization (WHO) propose that adults up to the age of 64 can sustain a healthy lifestyle by participating in moderate aerobic physical activity for 150–300 min or vigorous activity for 75–150 min daily [5]. Research also indicates that non-vegetarians are more prone to obesity than vegetarians [6]. It has been forecasted that by 2040, a significantly larger segment of the rural as well as urban population will be overweight or obese [7].

The genome-wide association studies (GWAS) have recently revealed that MC4R and LEP are the two important genes involved in the pathophysiology of common obesity. The melanocortin 4 receptor (MC4R) belongs to the G-protein-coupled receptor (GPCR) family found throughout the brain. Its major role is to regulate food intake, maintain energy homeostasis and control body weight [8, 9]. Deficiency in melanocortin receptors can lead to hyperphagia, elevated body fat and hyperinsulinemia (increase in insulin secretion). MC4R is an intronless gene that encodes a 332-amino acid transmembrane protein. It is present on chromosome 18q21.3 and is predominantly expressed in the hypothalamus, whereas leptin is one of the major adipokines, and its primary physiological job is to reduce body fat by lowering food intake and boosting energy expenditure. Leptin functions as a satiety hormone that restricts orexigenic peptides, such as neuropeptide Y and agouti-related protein, which increase hunger, while enhancing anorexigenic peptides, such as alpha-melanocyte stimulating hormone and corticotrophin-releasing hormone, which suppress feeding. Leptin is a 16-kDa hormone with 167 amino acids. It exhibits a globular protein tertiary structure and is located on chromosome 7q32.1 [10].

Several investigators have discovered that polymorphisms near the MC4R and LEP genes are linked to quantitative factors that determine metabolic diseases, as well as alterations in anthropometric parameters and feeding behavior [11]. Several previous investigations involving various ethnic populations (Portuguese, Asian, Czech, Malaysian, Tatar, Chinese and Iranian) found polymorphisms in MC4R (rs571312C/A and rs12970134G/A) to be associated with obesity, proving the connection of MC4R variants with obesity [12,13,14,15,16,17,18,19]. Similarly, many investigators have found both positive as well as contradictory results regarding LEP (rs2167270A/G and rs7799039G/A) gene polymorphisms among different populations [20,21,22,23,24,25]. Thus, it could be predicted that variants near the MC4R and LEP gene may influence the etiology and progression of obesity through direct impacts on anthropometric parameters, lipid profile, cardiovascular activities, metabolic profile of adipose tissue as well as body fat management.

In India, foreign population migration through Punjab has significantly shaped the genetic composition, fostering a distinct gene pool within the region. As a wealthy northwestern state, Punjab grapples with rising obesity rates attributed to high-calorie diets and sedentary lifestyles. Genetics also plays a significant role in the development of complex conditions such as obesity. Single-nucleotide polymorphisms in the leptin–melanocortin pathway genes, MC4R and LEP, are associated with obesity. Inspired by this, we conducted a case–control study in northwest India, genotyping MC4R (rs571312C/A and rs12970134G/A) and LEP (rs2167270A/G and rs7799039G/A) variants to explore their link to obesity risk. This study marks the first investigation of these gene variants concerning obesity in this population. Identifying risk alleles could guide targeted therapies, alleviating the societal and economic burdens of obesity while improving quality of life.

Materials and methods

Study subjects

The present study enrolled a total of 671 participants among which 333 were obese and 338 were healthy controls. All study participants were adults recruited from various districts within the Punjab state. Obesity classification was established according to BMI criteria specific to the Asian and South Asian populations [26]. The inclusion criteria for obese participants were adults aged 18–50 years with a body mass index (BMI) exceeding 25 kg/m2 (mean age: 39.65 ± 10.25 years, mean BMI: 30.37 ± 2.94 kg/m2). Controls consisted of healthy individuals with a BMI ranging from 18.5 to 22.9 kg/m2 (mean age: 37.46 ± 11.56 years, mean BMI: 21.42 ± 1.22 kg/m2). Exclusion criteria encompassed pregnant and lactating women, individuals with significant muscle mass, diabetes, kidney disease, and other cardiovascular conditions. To mitigate selection bias in this present case–control study, controls were gathered randomly from the identical population, matching ages in a 1:1 ratio, thereby enhancing the study's statistical power. The procedures for our case–control study were reviewed and approved by the Guru Nanak Dev University's Ethics Committee (ethical code-1609/HG) in Amritsar, Punjab.

All participants were asked about their background, lifestyle and risk factors. Anthropometric as well as physiometric parameters such as weight, height, hip circumference (HC), waist circumference (WC), skinfold measurements, systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured for all the study subjects. Each participant was asked to sign a written informed consent form. Individuals' height and weight were measured to the nearest 0.5 cm and 0.1 kg, respectively, while wearing lightweight clothes with no shoes. Standard anthropometric techniques were used to measure HC and WC to the closest 0.1 cm with the help of Steel tape (Markson measuring tape). The skinfold measurements were taken with the help of a Lange calliper. The waist-to-hip ratio (WHR) was computed by dividing the waist circumference (cm) by the hip circumference (cm), whereas Steel tape (Markson the waist-to-height ratio (WHtR) was computed by dividing the waist circumference (cm) by the height (m) of the subject. The body mass index (BMI) was obtained by dividing the weight in kilograms by the square of the height in meters. The mercury sphygmomanometer was used to measure systolic and diastolic blood pressure (SBP and DBP) following a 5-min reset period. A sedentary lifestyle was defined as spending n minutes per day on leisure activities (n = 30 min per day for males and 25 min per day for women) [27]. Dietary patterns were examined, distinguishing between those who consumed vegetarian and non-vegetarian diets.

Biochemical analysis

To measure the lipid profile, 5 ml of venous blood was obtained after an overnight fast and serum was separated. Various lipid parameters such as triglycerides (TG), total cholesterol (TC) and high-density lipoproteins (HDL-C) were measured using Erba-Mannheim kits (Trans Sasia Bio-medicals Ltd., Solan, India). The Friedewald formula was used to compute low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL) [28].

Genotyping

The organic approach (Phenol chloroform isoamyl; PCA method) was used to extract the genomic DNA from blood leukocyte pellets. The DNA obtained was quantified and diluted to 50 ng of working concentration before being stored at -20 °C until genotyping. The genotyping of MC4R polymorphisms (rs571312C/A and rs12970134G/A) and LEP polymorphisms (rs2167270A/G and rs7799039G/A) was done by polymerase chain reaction-based restriction fragment length polymorphism method (PCR–RFLP). The annealing temperature and the sequence of primers are given in Table 1.

Table 1 List of primers and restriction enzymes used for genotyping of MC4R (rs571312, rs12970134); LEP (rs2167270, rs7799039)
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The PCR results were validated by running them over a 1.5% agarose gel stained with ethidium bromide (EtBR). The PCR products were then subsequently digested with restriction enzymes. For variant rs571312C/A, enzyme Hpy166II was used to digest the PCR products of 210 bp, whereas, for rs12970134G/A, enzyme DdeI was used to digest PCR products of 124 bp. For variants rs2167270A/G and rs7799039, enzymes HPYCH4III and HhaI were used to digest PCR products of 386 bp and 500 bp, respectively. The digestion was done at 37 °C for 12–14 h. The digested fragments mixture was loaded onto a 2.5% agarose gel stained with 10% ethidium bromide (EtBR). In addition, Sanger Sequencing was performed to corroborate the genetic pattern in 10% of all samples for MC4R (rs571312 and rs12970134) and LEP (rs2167270 and rs7799039) polymorphisms (Figs. 1, 2, 3, and 4).

Fig. 1
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Sequencing chromatograms showing the genotypes for variant rs571312 (a) homozygous wild genotype (b) heterozygous genotype (c) homozygous mutant genotype

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Fig. 2
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Sequencing chromatograms showing the genotypes for variant rs12970134 (a) homozygous wild genotype (b) heterozygous genotype (c) homozygous mutant genotype

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Fig. 3
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Sequencing chromatograms showing the genotypes for variant rs2167270 (a) homozygous wild genotype (b) heterozygous genotype (c) homozygous mutant genotype

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Fig. 4
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Sequencing chromatograms showing the genotypes for variant rs7799039 (a) homozygous wild genotype (b) heterozygous genotype (c) homozygous mutant genotype

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Polymorphisms selection

The single-nucleotide polymorphisms near the MC4R (rs571312C/A, rs12970134G/A) and LEP (rs2167270A/G, rs7799039G/A) gene are associated with obesity in many previous studies among different ethnic populations, thereby suggesting a strong impact of MC4R and LEP gene on obesity [14, 29,30,31,32,33,34,35,36]. However, on the contrary, some studies showed no link between these polymorphisms and obesity [22, 37,38,39,40,41]. Due to discrepancies in earlier research findings across various populations, the current study selected four polymorphisms of the MC4R and LEP genes to investigate their association with obesity.

Statistical analysis

The statistical program for social sciences (SPSS) for Windows v. 21.0 was used to analyze the data. The Student's t-test was used to compare continuous variables, and one-way analysis of variance (ANOVA) was used to analyze quantitative data from two groups. The chi-square test (χ2) assessed the allelic and genotypic frequencies between obese cases and nonobese controls. After controlling for numerous confounding factors, logistic regression was used to examine the gene-environment interaction and the association of analyzed variations under assumed genetic models. The interaction between genetic and environmental factors by using software Multifactor dimensionality reduction MDR v 3.0.2.

A linear regression analysis was used to investigate the effect of MC4R and LEP gene variations on obesity-defining features. The genotypes were used to create a linkage disequilibrium (LD) map and haplotype within polymorphisms of the MC4R and LEP gene using Haploview software. As a measure of the connection between the SNPs and the occurrence of obesity, haplotype frequencies, odds ratios (ORs), two-tailed p values, and 95% confidence intervals (CIs) were determined. A two-tailed p value of 0.05 was deemed statistically significant.

Results

Anthropometric, demographic and clinical characteristics in obese versus non-obese subjects

Table 2 summarizes the data for several anthropometric, clinical and biochemical variables within the obese cases and non-obese healthy controls. A statistically significant difference was observed between the obese and non-obese groups. As compared to non-obese subjects, the obese subjects had higher means of weight, BMI, waist circumference (WC), hip circumference (HC), arm circumference (AC), calf circumference (CC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR), triceps skinfold (T-skf), biceps skinfold (B-skf), sub-scapular skinfold (sub-skf), supra-iliac skinfold (sup-skf), sum of skinfolds (sum-skf) (p < 0.0001). The physiometric parameters were also found to be higher in obese individuals than non-obese [systolic blood pressure (SBP): p = 0.0001, diastolic blood pressure (DBP): p = 0.045, mean arterial blood pressure (MBP): p = 0.028, pulse pressure (PP): p = 0.002, pulse rate (PR): p = 0.042]. Among lipid variables except for HDL-C levels, the rest of the variables such as TC, TG and LDL were higher in obese subjects. However, there was no significant variation in the frequencies of height and VLDL-C between obese cases and non-obese controls.

Table 2 Anthropometric, clinical and biochemical characteristics of obese patients and non-obese healthy controls
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Association of MC4R and LEP polymorphisms with obesity under different genetic inheritance model

The genotypic and allele frequency distribution of MC4R rs571312, rs12970134 and LEP rs2167270, rs7799039 polymorphisms in obese cases and non-obese controls is presented in Table 3. To investigate the association between MC4R and LEP gene variants with obesity risk, the data were subjected to logistic regression analysis using several genetic models. For both polymorphisms of MC4R as well as the LEP gene, the differences observed in overall genotype frequencies were statistically significant (p = 0.024 for rs571312 and p = 0.011 for rs12970134 variants; p = 0.002 for both rs2167270 and rs7799039 variants). In both the variants of the MC4R gene, the homozygous recessive AA genotype was more prevalent among obese subjects (18.92%; p = 0.007 in rs571312 and 18.31%; p = 0.011 in rs12970134). The prevalence of wildtype C-allele (rs571312) and G-allele (rs12970134) was higher in the control group than in the cases (66.57% and 65.24%, respectively). There was a significant association of polymorphism rs571312 with the risk of obesity in both dominant and recessive genetic models [OR (95% CI) 1.38 (1.01–1.87); p = 0.042]; [OR (95% CI) 1.69 (1.10–2.59), p = 0.015], respectively. However, the rs12970134 variant showed a significant association with obesity under the recessive genetic model [OR (95% CI) 1.94 (1.24–3.03); p = 0.003].

Table 3 Genotype and allele distribution of MC4R (rs571312, rs12970134) and LEP (rs2167270, rs7799039) polymorphisms in obese patients and non-obese healthy controls
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Among the two LEP variants, the homozygous recessive GG (rs2167270) and AA (rs7799039) genotypes were higher in obese cases (15% and 18%, respectively) as shown in Table 3. Both the polymorphisms showed significant association with obesity risk under the recessive model. Polymorphism rs7799039 conferred twofold higher risk toward the development of obesity [OR (95% CI) 2.26 (1.41–3.60), p = 0.0001]. In obese individuals, variants rs571312 and rs2167270 were in agreement with HWE (p > 0.05), whereas variants rs12970134 and rs7799039 were not in HWE (p < 0.05). Among the control group except rs2167270, all the rest of the polymorphisms were in agreement with HWE.

Haplotype analysis

Tables 4 and 5 present the haplotype distribution and pairwise linkage disequilibrium (LD) among all the studied SNPs of MC4R and LEP genes in obese cases and controls. Based on the genotype distributions of MC4R and LEP gene polymorphisms in obese cases and healthy controls, the LD values between all pairs of polymorphisms were determined using Haploview software (v 4.1) (Fig. 5). In MC4R polymorphisms, the C-G haplotype, carrying both wildtype alleles (rs571312C and rs12970134G), was the most common haplotype among the studied groups and considered as baseline haplotype. After comparing with the baseline haplotype, the AA haplotype comprised of high-risk rs571312A and high-risk rs12970134A dramatically elevated 2 folds risk of obesity [OR (95% CI) 2.61 (1.10–6.20), p = 0.028]. The LD measurements described that the two variants of MC4R were in slight LD among the cases than control groups (D′ = 0.469, r2 = 0.059 for obese cases); (D′ = 0.668, r2 = 0.07 for non-obese controls) (Fig. 5a and b), whereas, in the two polymorphisms of the LEP gene, the total distribution of haplotype frequencies differed markedly between obese cases and non-obese controls (p = 0.0001). The lower-risk haplotype G-A of rs7799039G and rs2167270A was the baseline and most prevalent haplotype among the studied group. When compared to the low-risk baseline G-A haplotype, the high-risk A-G haplotype revealed a sixfold increase in the development of obesity [OR (95% CI) 6.02 (3.39–10.68), p = 0.0001]. Based on the LD metrics, it could be hypothesized that both the SNPs of the LEP gene were in slight LD among obese cases compared to nonobese ones (D′ = 0.231, r2 = 0.052 for obese cases); (D′ = 0.441, r2 = 0.034 for non-obese controls) (Fig. 5c and d) (Tables 4 and 5).

Table 4 Evaluation patterns of LD of MC4R rs571312C/A and rs12970137G/A polymorphisms in obese cases and non-obese controls
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Table 5 Evaluation patterns of LD of LEP rs7799039G/A and rs2167270A/G polymorphisms in obese cases and non-obese controls
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Fig. 5
figure 5

LD plot demonstrating the position of two MC4R and two LEP variants and pairwise D′ values observed in the study population with respect to (a) obese cases of MC4R variants, (b) non-obese controls of MC4R variants, (c) obese cases of LEP variants and (d) non-obese controls of LEP variants

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Interaction analysis

Tables 6 and 7 demonstrate the interaction analysis of various diplotype combinations of the studied SNPs. Table 6 reveals that the combination of the risk genotype AA for rs571312 and AA for rs12970134 exhibited 3.91 (p = 0.024) fold risk toward the development of obesity in the studied population. Apart from this, the combination of CA-AA also conferred twofold risk of obesity (p = 0.029). However, in Table 7, the genotype combinations AA–AA, GG–GG and GG–GA showed a risk toward obesity but among them, the risk genotype combination (GG–AA) conferred a huge risk of about 5 folds toward the development of obesity (p = 0.041).

Table 6 Frequency distribution of MC4R rs571312 and rs12970134 polymorphisms and interaction analysis of various diplotypes combination in obese cases and non-obese healthy controls
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Table 7 Frequency distribution of LEP rs2167270 and rs7799039 polymorphisms and interaction analysis of various diplotypes combination in obese cases and non-obese healthy controls
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Linear regression analysis

Table 8 reveals the impact of the investigated polymorphisms on the numerous anthropometric and biochemical parameters among obese cases. The evaluation was done by linear regression analysis under the additive genetic model of inheritance that was adjusted for gender, diet and lifestyle. The MC4R gene variant rs12970134 showed a statistically significant association with WC (β = 0.557, p = 0.017) and WHtR (β = 0.612, p = 0.009) which depicted an increased trend in the model. On the other hand, the presence of LEP rs7799039 polymorphism significantly contributed to a 2.24% and 4.15% rise in total cholesterol (TC) and triglycerides (TG) levels among obese subjects (β = 2.246, p = 0.0001; β = 4.154, p = 0.0001, respectively). These findings indicated an increasing trend of obesity-related parameters with each addition of a minor allele of the corresponding variant. However, the MC4R polymorphism rs571312 and the LEP polymorphism rs2167270 had no significant association with any of the investigated anthropometric and biochemical parameters.

Table 8 Linear regression analysis: impact of studied polymorphisms on anthropometric and biochemical parameters associated with obese cases under additive model
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Gene-environment interaction analysis

The present work analyzed the interaction of 7 factors consisting of 4 single-nucleotide polymorphisms (rs571312, r12970134, rs2167270, rs7799039) and 3 obesity-related parameters (WHR, lifestyle and diet) in this population (Table 9). It was observed that except for the 6-locus model (G1 G2 G3 G4 E1 E2), the cross-validation consistencies (CVC) for the rest of the models were maximal (10/10). However, the accuracy of the test sample was greatest in the 5-locus interaction model (G1, G2, G3, G4, E2) (82.1%) and the accuracy of the training sample (TrBA) was highest in the 6-locus interaction model (G1, G2, G3, G4, E1, E2) (88%). At the 5% level, all the interaction models were found to be significant. The testing balance accuracy (TBA) evaluates the frequency of accurately classifying individuals concerning their case–control status. A TBA score exceeding 55% is deemed satisfactory. In this analysis, the 5-locus interaction model (G1, G2, G3, G4, E2) exhibited the highest TBA of 82.1%. This indicates that the combined interaction of these four genetic variations and the lifestyle factor collectively produced the most effective model for predicting obesity progression. The findings underscore the significant influence of lifestyle factor on the interplay between these genetic variants in obesity development.

Table 9 Gene-environment interaction model identified by Multifactor Dimensionality Reduction method for obese cases and controls
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The dendrogram given in Fig. 6 represents that the clusters, i.e., G2–G4, had a stronger synergistic effect in predicting the susceptibility toward the obesity risk than G1–G3. Both these clusters also showed a lesser degree of interaction with one other. Furthermore, the 3 environmental factors (E1, E2, E3) demonstrated midway interactions between synergy and redundancy with all the polymorphisms in the development of obesity. In Fig. 7, it was observed that E2 (Lifestyle factor) played a prominent role by contributing the highest entropy, accounting for 19.7%, to the progression of obesity independently. The entropy model showed a strong synergistic interaction between G2 and G4 (15.97%) regarding obesity risk. G1 demonstrated moderate synergistic interaction with G4 (5.30%), G3 (7.51%) and G2 (3.50%) concerning the risk of developing obesity, whereas E1, E2 and E3 showed a midway interaction between synergy and redundancy with each other and with the rest of the genetic markers.

Fig. 6
figure 6

The dendrogram representing the nature of interaction between the 4 single-nucleotide polymorphisms (rs571312, rs12970134, rs2167270 and rs7799039) and environment factors for obese cases and controls. Color scheme used—red = high synergistic interaction (synergy), orange = lesser degree of interaction, green = weak interaction, blue = no interaction (redundancy), gold = midway interaction between synergy and redundancy

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Fig. 7
figure 7

Interaction entropy model analyzed by multifactor dimensionality reduction (MDR) for obese cases and controls

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Discussion

Obesity stands out as a paramount public health concern due to its strong associations with cancer, type 2 diabetes, and cardiovascular diseases [42,43,44]. Unravelling the intricate mechanisms underlying obesity and its comorbidities reveals a substantial contribution from genetic factors, as evidenced by emerging research. In our investigation, we investigated the association between genetic variants in the MC4R and LEP genes and obesity risk through a case–control study encompassing 671 individuals (333 cases and 338 controls) in Punjab, a northwest state of India. Our study revealed a significant correlation between the genotypes MC4R rs571312AA, rs12970134AA, and LEP rs2167270GG, rs7799039AA with an elevated risk of obesity and aberrant lipid profiles within this northwest Indian population. Individuals carrying the rs2167270GG and rs7799039AA genotypes exhibited a twofold higher susceptibility to obesity compared to non-carriers, underscoring the potential significance of these LEP gene variants in obesity etiology within this population. Similarly, carriers of both polymorphisms in the MC4R gene (rs571312AA and rs12970134AA) faced a 1.8-fold increased risk of obesity.

These findings align harmoniously with a plethora of prior studies, corroborating the association of risk with the minor alleles of the aforementioned polymorphisms [25, 36, 45,46,47]. For instance, Hong et al. [48] demonstrated a 1.43-fold increase in obesity risk associated with the rs571312 variant, while Huang et al. [45] reported a nearly threefold risk linked to rs12970134. Likewise, Dasgupta et al. [25] observed significant elevations in obesity risk among south Indian cohorts, with a 3.2-fold increase tied to rs2167270 and a 1.8-fold increase associated with rs7799039.

Employing a gene candidate strategy in the obese cohort revealed potential contributions of MC4R rs12970134 and LEP rs7799039 polymorphisms to heightened susceptibility to obesity, as evidenced by increases in obesity-related traits including waist circumference (WC), waist-to-height ratio (WHtR), total cholesterol (TC), and triglycerides (TG). These findings were consistent with prior investigations conducted across diverse populations, encompassing Portuguese, Indian, Iranian, Tunisian, and Moroccan cohorts [12, 13, 19, 34, 49, 50]. Linear regression analysis further validated these associations, with MC4R rs12970134 demonstrating elevated WC and WHtR, while LEP rs7799039 was associated with significant elevations in TC and TG levels among obese individuals, suggesting that these variants are pertinent markers for obesity-related indices in this population. However, conflicting results exist, as evidenced by studies such as those conducted by Kochetova et al. [17], Constantin et al. [51], Vogel et al. [52] and Mohamed et al. [53] which reported no significant associations between these genetic polymorphisms and obesity-related traits (p > 0.05).

In the hypothalamus, the interplay between leptin and melanocortin receptors constitutes the fundamental components of the leptin–melanocortin circuit, crucial for regulating energy intake and expenditure. Notably, genes within this pathway, particularly MC4R and LEP, serve as key players in the modulation of energy homeostasis and have been identified as primary determinants of monogenic obesity [54]. Previous investigations have elucidated that polymorphisms in the MC4R gene can influence the receptor's functionality by impeding the establishment of binding sites on the cell surface. Such genetic variations may disrupt the standard inhibitory activity of MC4R in the hypothalamus, consequently leading to perturbations in energy balance and contributing to the development of obesity. Likewise, within the hypothalamus, leptin functions to curb food intake while simultaneously boosting energy expenditure. Polymorphisms such as rs2167270 and rs7799039, located in the 5' untranslated region and promoter region of the leptin gene, respectively, have been implicated in modulating serum lipid levels and altering leptin gene expression [29, 30]. Aboelros et al. [55] suggested that the rs7799039 variant may underlie the aberrant metabolism of insulin and leptin hormones, potentially resulting in resistance to their physiological effects.

Following an examination of the relationship between LEP single-nucleotide polymorphisms (SNPs) and their haplotypes, we noted that the combination of rs7799039A and rs2167270G haplotypes was associated with a sixfold increase in the risk of obesity compared to other haplotype combinations. Concurrently, during the construction of haplotypes for MC4R gene polymorphisms, we identified that the rs571312A–rs12970134A haplotype exhibited a twofold positive association with obesity risk in this population. All four LEP polymorphisms, as well as the MC4R gene, demonstrated slight linkage disequilibrium in the cases group compared to the control group (D′ = 0.231, r2 = 0.052; D′ = 0.441, r2 = 0.034; D′ = 0.469, r2 = 0.059; D′ = 0.668, r2 = 0.07) respectively.

Consistent with our findings, Dasgupta et al. [25] demonstrated a significant association between the AAC haplotype combination involving rs7799039, rs2167270, and rs4731426 with obesity in the South Indian population, characterized by strong linkage disequilibrium (LD) (D′ > 0.8). Shamsuddin et al. (2018) reported that the AAG haplotype combination, harboring risk alleles of rs7799039G/A, H1328080, and 2167270A/G, conferred an eightfold risk for obesity development [31]. Additionally, Wei et al. [18] identified that the CCA haplotype, comprising rs17782313, rs476828, and rs12970134, was associated with increased morbid obesity in the Chinese population. However, in contrast, Bouafi et al. [50] found no association between LEP gene polymorphism haplotypes and obesity risk in the Moroccan population. Moreover, Apalasamy et al. [16] and Chehadeh et al. [36] did not find any association between MC4R haplotypes in the Malaysian and United Arab Emirates (UAE) populations, respectively. The findings of interaction association between the diplotypes of the studied polymorphisms also point toward a possible role of MC4R (rs571312C/A- rs12970134G/A) and LEP (rs2167270A/G- rs7799039G/A polymorphisms in obesity and metabolic changes. Upon analysis of the genetic-environmental interaction, it was observed that lifestyle factors independently contributed the highest entropy of 19.70% to the progression of obesity. Furthermore, it was revealed that lifestyle factors exhibited strong interactions with all genetic polymorphisms of the MC4R and LEP genes in the 5-locus model. This interaction demonstrated an 82.1% testing balance accuracy, indicating a higher likelihood of predicting obesity development. Several studies, including the present one, have shown that lifestyle choices can alter the impact of genetic variations that predispose individuals to obesity. For instance, research conducted by Qi et al. [56] on nurses’ health or health professionals in the United States found that excessive television viewing amplified the influence of a genetic risk score comprising 32 genetic variations on BMI. Therefore, conducting a thorough assessment of genetic influence and its interaction with the gene-environment interface could enhance our ability to identify specific genetic variations associated with diseases.

Limitations

The present research's sample size, especially when stratified by BMI, was not sufficient to draw definitive conclusions. Hence, it is crucial to replicate these results in future investigations with larger sample sizes to confirm their validity. Serum levels of MC4R and LEP were not assessed in this study, consequently precluding the ability to conduct genotype–phenotype correlation studies. Another constraint was the absence of data on dietary intake or caloric consumption, which could serve as a significant confounding factor. The sample lacks national representation, with notable implications primarily relevant to northwest Indian ethnic groups. This raises doubts about the findings' relevance to other ethnicities. Further research involving diverse groups is necessary.

Conclusion

The current study unveils a distinct correlation between four polymorphisms of the MC4R and LEP genes and obesity risk in the northwest Indian population. Notably, carrying risk alleles for both MC4R and LEP variants increased the risk of obesity by twofold and sixfold, respectively. Furthermore, the research highlights how various combinations of diplotypes in MC4R and LEP genes collectively influence obesity predisposition. Associations were also observed between MC4R rs12970134 and LEP rs7799039 variants with elevated lipid profiles and waist-related traits. Recognizing these genetic links aids in predicting metabolic risks, guiding early interventions to manage lipids and central adiposity, thereby reducing the risk of metabolic syndrome and cardiovascular diseases. The interplay between genetic polymorphisms and environmental factors accentuates their combined impact on predicting obesity risk. Lifestyle factors were particularly potent in shaping the effects of genetic variants on obesity within the studied population. These findings are significant as obesity rates continue to rise steadily in India, particularly in Punjab. Therefore, evaluating the collective influence of genetic polymorphisms can expedite the identification of individuals at risk, empowering them to improve their lifestyles and fostering a healthier, disease-free existence. Thus, uncovering and understanding the mechanisms involving genes in the leptin–melanocortin pathway and obesity will facilitate the development of rational strategies and personalized disease management.