The melanocortin-4 receptor pathway and the emergence of precision medicine in obesity management

Abstract

Over the past few decades, there has been a global surge in the prevalence of obesity, rendering it a globally recognized epidemic. Contrary to simply being a medical condition, obesity is an intricate disease with a multifactorial aetiology. Understanding the precise cause of obesity remains a challenge; nevertheless, there seems to be a complex interplay among biological, psychosocial and behavioural factors. Studies on the genetic factors of obesity have revealed several pathways in the brain that play a crucial role in food intake regulation. The best characterized pathway, thus far, is the leptin–melanocortin pathway, from which disruptions are responsible for the majority of monogenic obesity disorders. The effectiveness of conservative lifestyle interventions in addressing monogenic obesity has been limited. Therefore, it is crucial to complement the management strategy with pharmacological and surgical options. Emphasis has been placed on developing drugs aimed at replacing the absent signals, with the goal of restoring the pathway. In both monogenic and polygenic forms of obesity, outcomes differ across various interventions, likely due to the multifaceted nature of the disease. This underscores the need to explore alternative therapeutic strategies that can mitigate this heterogeneity. Precision medicine can be regarded as a powerful tool that can address this concern, as it values the understanding of the underlying abnormality triggering the disease and provides a tailored treatment accordingly. This would assist in optimizing outcomes of the current therapeutic approaches and even aid in the development of novel treatments capable of more effectively managing the global obesity epidemic.

1 |. INTRODUCTION

Obesity is a chronic, complex, and multifactorial condition and more than two thirds of the population in the United States are affected by overweight or obesity.^1,2 In 2013, the American Medical Association voted to recognize obesity as a disease, conferring legitimacy to the condition and allowing for greater attention and improved management.³ The World Health Organization defines obesity as ‘abnormal or excessive fat accumulation that may impair health’, with its cause stemming from an energy imbalance between consumed calories and expended calories.⁴

While obesity is undeniably a disease on its own, obesity is an independent risk factor for cardiovascular disease.⁵ Additionally, obesity can also exacerbate pre-existing conditions and contribute to the development of new ones; obesity increases the risk of type 2 diabetes mellitus, cardiovascular diseases, hyperlipidaemia, hypertension, and depression, among others, conferring a higher mortality risk to the affected population. Obesity has been ranked as the fifth most common leading cause of death globally and has been shown to be a major economic burden to society, where patients with obesity have increased annual healthcare costs of 36% compared with their average-weight counterparts.⁶ With that in mind, obesity has drawn notable attention within the scientific research field in recent years and has become a major hub of interest for healthcare practitioners.

Obesity remains a complex disease, and studies are being conducted to better understand its intricate epidemiology, explore more efficient treatment modalities, and ultimately overcome the stigma around it.^7,8 The multifactorial nature of this disease is evident with the intricate interplay occurring among genetic, physiological, behavioural, lifestyle, cultural, social, environmental, and metabolic factors, ultimately resulting in a state of positive energy balance.⁹ Recent attention has been directed towards the genetics underlying obesity, which can help elucidate the inherent physiological and molecular mechanisms that control body weight. The melanocortin-4 receptor (MC4R) pathway is a crucial component in the regulation of appetite and energy balance, playing a significant role in the context of obesity.¹⁰ Activation of this pathway leads to a reduction in food intake and an increase in energy expenditure. On the other hand, when the MC4R pathway is disrupted, there is an elevated risk of obesity due to a compromised ability to regulate appetite and energy balance.¹¹

Despite identifying potential causes and modulating factors of obesity, the challenge remains to translate this acquired understanding into effective action. Given that obesity is influenced by a combination of diverse factors, addressing the disease necessitates a comprehensive and multidisciplinary approach. Successful prevention and management strategies encompass dietary and lifestyle interventions, pharmacotherapy, endoscopic devices, and surgery and most recently neuromodulation interventions such as transcranial direct current stimulation and transcranial magnetic stimulation.^12–14

In this review, our objective was to delve into current state-of-the-art of precision medicine for obesity, focusing specifically on MC4R pathway treatment. We aimed to discern the heterogeneity of obesity, the contribution of the MC4R pathway to the heterogeneity associated with the disease, and explore the role of precision medicine in its management. A better insight into this major public health concern will enable us to recognize the primary causes that trigger the disease, offer more individualized weight loss management, and ultimately work towards its prevention.

2 |. CLASSIFICATION OF OBESITY

Despite a commonly agreed-upon definition for obesity, various classifications have been established to categorize patients affected by the disease (Figure 1). Obesity classification models serve as optimizing tools for diagnosing the condition and are based on different traits associated with obesity, such as body anthropometrics, body fat distribution, and energy balance-related tests. These models play a crucial role in precision medicine for obesity as they facilitate the implementation of personalized weight management strategies, tailored to the specific characteristics of the class to which patients belong. Moreover, application of classification models can help optimize treatment strategies and ultimately improve patient care and outcomes.¹⁵ This approach highlights the diverse aetiology and pathophysiology of the disease and how leveraging this diversity can potentially help improve and standardize weight loss results. Body mass index (BMI), waist circumference, and waist-to-hip ratio are the most commonly used methods to diagnose patients with obesity. However, as these measures do not delve deeply into distinguishing between lean and fat mass, they are considered crude proxies of overall adiposity, and hence, the development of novel classification models.

FIGURE 1.

Obesity classification models. ADRB, β-adrenergic receptor; LEPR, Leptin receptor; POMC, Proopiomelanocortin; FTO, fat mass and obesity associated gene; MC4R, melanocortin 4 receptor; UCP, uncoupling protein; WAGR, Wilms tumor, aniridia, genitourinary anomalies, and range of developmental delays.

2.1 |. Anthropometric and body composition

Body mass index is a statistical index that uses weight and height to provide a body fat estimate (BMI = weight [kg]/ height squared [m²]) and it has been widely used due to its strong proven correlation with body fat percentage.^16–18 BMI ranges have been set at 19–24.9 kg/m² for the normal, 25–29.9 kg/m² for the overweight, and ≥ 30 kg/m² for the obese category.¹⁹ Because weight differences among individuals can only be partially explained by variations in body fat, there has been objection to the use of weight or BMI to discriminate between individuals with overweight/obesity and normal weight. One argument would be that athletes having high muscle mass and thus a potentially higher-than-normal BMI would remain within the normal range for body fat percentage.²⁰ Another limitation of this classification is that it overlooks the complications associated with obesity. The correlation between BMI and obesity-related metabolic disorders, such as insulin resistance, is weak across a wide range of BMIs. In fact, a systematic review found that BMI explained only 16% of the variance in insulin resistance.²¹ Despite the elevated risk of comorbidities in individuals with obesity compared to those with normal weight, patients with obesity according to BMI may not necessarily exhibit metabolic complications, thus there is a need to create a classification that would better represent the obesity health risk of patients.

While both BMI and total adiposity are positively correlated with cardiometabolic disease risk at the population level, body fat distribution is better related to obesity-associated complications at the individual level.²² The mechanisms that contribute to inter-individual variations in body fat distribution are intricate and remain to be fully understood. The accumulation of adipose tissue in the upper body is linked to obesity-related health issues and all-cause mortality, while accumulation of adipose tissue in the lower body is associated with a reduction in cardiovascular and metabolic diseases, when accounting for total body fat mass.^23,24 Waist circumference and waist-to-hip ratio have been used to describe body shape-associated phenotypes; these enable a further refinement of the adverse health risk characterized by BMI and should be integrated in clinical practice as a way to counsel patients regarding their higher-risk phenotype of obesity.

2.2 |. Metabolic status

Metabolic status can provide another classification model, linking obesity to cardiometabolic complications and differentiating between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO).²⁵ Despite the lack of a universally accepted definition for MHO, it is characterized by a BMI high enough to be classified as obesity but with no associated obesity-related metabolic abnormality.²⁵ Three distinguishing clinical features of MHO are: a decreased accumulation of visceral adipose tissue and ectopic fat for equal amount of total adiposity; a diminished degree of both systemic and adipose tissue inflammation; and a preserved insulin sensitivity when compared to MUO.^25,26 The absence of uniform definitions complicates comparisons among studies on metabolic status classification models and prevents the provision of accurate assessments of characteristics within each category. For instance, when comparing gender prevalences between the two categories, a study analysed 10 independent cohorts from different countries in Europe and found that the prevalence of MHO varied from 7% to 28% in women and 2% to 19% in men,²⁷ with one study showing an MHO prevalence of 9% in men compared to 28.4% in women, while another showed similar prevalence in the two sexes, 19% in men and 21% in women. Despite these studies suggesting a potential higher prevalence of MHO among women, certainty cannot be provided.²⁷ Individuals with MHO still necessitate weight-loss interventions because, while their risk of developing cardiometabolic diseases is lower compared to MUO, it remains higher than in metabolically healthy lean individuals.²⁸ MHO should not be viewed as a safe condition; it should instead be used as a guide in obesity management, to provide a more personalized and risk-stratified treatment, thus aligning with the philosophy of precision medicine for obesity.²⁸

2.3 |. Energy balance—biological and behavioural variables

A new classification model for obesity has relied on behavioural and biological testing of the main energy balance components. Energy intake, on the one hand, considers homeostatic (based on the energy requirements during fasting periods) and hedonic (based on the desire to eat driven by pleasurable food properties rather than metabolic needs) eating behaviours; energy expenditure, on the other hand, considers resting energy expenditure, non-exercise physical activity, and thermogenic effect of food and physical activity.²⁹

We have previously suggested a standardized protocol for obesity phenotyping, when we studied key parameters of energy balance regulation.^15,30,31 After overnight fasting, patients undergo a series of tests and procedures; the classification is based on a cutoff of the 25th or 75th percentile of each measurement (applied separately for females and males), yielding distinct obesity phenotypes. Studying these parameters based on gender highlights an important concept in precision medicine for obesity; it emphasizes the necessity of considering various factors, including sex, before reaching a diagnosis or recommending a personalized treatment. Indeed, research has demonstrated that sex can significantly influence food intake regulation parameters.^32,33 One study revealed that sex exerted a substantial impact on satiation, as measured by volume/calories to fullness, even after accounting for other biological factors known to affect these parameters.³²

On the other hand, analysis of energy intake components has resulted in the identification of three distinct obesity phenotypes, stemming from the homeostatic and hedonic behaviours of eating.

2.3.1 |. Homeostatic eating classification

Satiation, defined as calories consumed to reach fullness and terminate a meal, was studied with an ad libitum buffet meal (quantified by kilocalories needed to consume to reach maximal fullness).^15,34 Postprandial satiety, defined as the duration of fullness or return of hunger, was studied using visual analogue scales and gastric emptying of solids (quantified by the half-emptying time, in min).³⁴ Individuals with an abnormal satiation (>894 kcal for females and > 1376 kcal for males) were defined as having a ‘hungry brain’. Those who had an abnormal postprandial satiety (<101 min for females and <86 min for males), were defined as having a ‘hungry gut’.

2.3.2 |. Hedonic eating classification

Current findings indicate that emotion dysregulation and a high level of negative or positive emotions can trigger some people to eat more, contributing to the development of obesity.³⁵ Emotional eating has been defined as an eating behaviour influenced by behaviours, stress, emotions, and individual feelings in relation to eating.³⁶ One possible explanation for the mechanism underlying emotional eating is that it could stem from insufficient interoceptive awareness, where individuals might confuse bodily sensations linked to emotions with the physiological cues regulating feelings of satiety and hunger.³⁷

For our phenotyping method, hedonic eating behaviour was assessed by the Hospital Anxiety and Depression Scale (HADS) and the Three Factor Eating Questionnaire (TFEQ). The HADS is an instrument used to detect states of depression and anxiety, which could be valid measures for the severity of the emotional disorder. The TFEQ is another scoring system that has gained popularity, shortened versions of which have been developed from the original 51-item questionnaire, making it ideal for subjects to complete. It covers scales for emotional eating, uncontrolled eating, and cognitive restraint; all items are coded with either 0 or 1 point, with higher scores indicating stronger characteristic values in the respective domains. Individuals with abnormal behavioural questionnaire results for anxiety (≥7 points on HADS for both genders) were defined as having ‘emotional hunger’.

Other self-report questionnaires have been developed to assess the tendency to emotional eating, including the Dutch Eating Behaviour Questionnaire (DEBQ), the Yale Food Addiction Scale, and the Emotional Eating Scale.^38–40 For instance, the DEBQ is composed of scales for emotional eating (desire to eat under different negative emotions, including stress, depression, and anxiety), external eating (eating regardless of the internal state of hunger or satiety), and restrained eating (intention to restrict food intake to prevent weight gain or promote weight loss).³⁷ The questionnaire comprises 33 questions and participants respond to items using a scale ranging from 1 (never) to 5 (very often), with higher scores indicating greater endorsement of the eating behaviour.⁴¹ It has been used worldwide, having been translated into more than 12 languages, and it provides reliable and valid information across various adult populations.⁴² Studies have demonstrated that individuals who score high on these emotional eating scales exhibit a tendency to consume higher amounts of energy-dense foods following inducement of negative emotions compared to emotionally neutral control situations.⁴³ However, due to the inconsistency of this effect, further research is needed to develop more reliable measures.

Analysis of the energy expenditure component yielded one obesity phenotype, which is studied through resting energy expenditure (REE), reported non-exercise physical activity, and reported exercise. Individuals with an abnormal measured REE of predicted REE based on the Harris-Benedict equation (<96% for females and <94% for males) were defined as ‘slow burn’ phenotype.

2.4 |. Obesity-related comorbidity stages

Obesity may also be classified based on obesity-related comorbidity stages.⁴⁴ Despite the fact that excess body fat can have significant implications for overall health, the sole presence of increased body fat does not necessarily predict ill health. There is thus a need to compliment the current obesity classifications with a disease-related staging system that would provide further clinical information in order to better guide treatment; this system would help in the equitable prioritization of patients who would most benefit from aggressive weight management. A four-stage classification was established, in which Stage 0 implies no apparent risk factors, Stage 1 preclinical risk factors, Stage 2 established comorbidities, Stage 3 end-organ damage, and Stage 4 severe disability from comorbidities.⁴⁴

2.5 |. Genetics

In recent decades, multiple strategies have been used to identify the genetic factors contributing to obesity. Genome-wide association studies (GWAS) involve identifying a vast array of genetic markers (single nucleotide polymorphisms [SNPs]) across the entire genome to determine their statistical association with the risk of a disease or specific trait. The study of obesity genetics has resulted in categorizing obesity into three main divisions: monogenic, polygenic, and syndromic forms. Monogenic obesity is caused by a mutation of a single gene. The cases are very severe and occur at an early onset (<10 years of age), and mostly involve mutations in genes in the leptin–melanocortin pathway. While these monogenic disorders are individually rare, collectively at least 10% of children with severe obesity have rare chromosomal abnormalities that drive their condition.⁴⁵ Mutations in the following genes can result in monogenic obesity: Pro-opiomelanocortin (POMC) and Proprotein Convertase Subtilisin/Kexin Type 1 (PCSK1), leptin and leptin receptor, Neuropeptide YY (NPY) gene, ghrelin receptor, MC3R and MC4R, and Sim1 (MC4R will be further detailed in the remainder of our review). As each gene plays a unique role along the brain–gut axis regulating appetite, these mutations would exhibit different physiological abnormalities and could respond differently to interventions.⁴⁶ For instance, POMC1, a gene known for its role in suppressing appetite, generates α-, β-, γ-MSH, and ACTH under the regulation of MC3 and MC4 receptors, which in turn inhibit appetite.⁴⁷ Sim1 is another gene known for its pivotal role in regulating food intake. Its primary function involves neuronal differentiation within the paraventricular nucleus of the hypothalamus, a crucial region for food intake regulation. Additionally, Sim1 activity is associated with a notable decrease in oxytocin levels.⁴⁸ The positive impact of oxytocin on food intake regulation and body composition positions it as a potential therapeutic tool for obesity, a topic that will be addressed later in our review.

For the majority of the population, obesity is considered to be polygenic in nature, with multiple common genetic variants having a small effect on their obesity phenotype, but when combined, contribute to the overall susceptibility of developing obesity.⁴⁹ Variants of polygenic obesity vary significantly from one individual with obesity to another, rendering the study of polygenic obesity more complex compared to monogenic obesity.⁵⁰ Mutations in the following genes can lead to polygenic obesity: β-adrenergic receptor family genes (ADRB1/2/3 receptor gene) and genes in the uncoupling protein (UCP) gene family. The β-adrenergic receptor belongs to the GPCR family and serves as a target for catecholamines, particularly epinephrine, via the sympathetic nervous system.⁵¹ ADRB1 is considered a potential candidate gene for obesity and contributes to catecholamine-induced energy homeostasis; it facilitates energy expenditure and lipolysis in adipose tissues. The UCP gene family includes several members, including UCP1, UCP2 and UCP3. While UCP2 is distributed widely across various tissues, UCP3 is specifically expressed in skeletal muscle and brown adipose tissue.⁵² Both UCP1 and UCP2 play roles in energy metabolism and are identified as potential candidate genes for obesity.

Syndromic obesity is characterized by its occurrence within a broader syndrome that may involve various physical, developmental, or metabolic characteristics, with Prader Willi syndrome being the most common syndromic form of obesity.⁵³ Other examples of syndromic obesity include Fragile X syndrome, Bardet–Biedl syndrome and Alstrom syndrome.

The prevalence of individuals with obesity that has genetic causes is likely to increase due to the wider accessibility to genetic testing and due to new genes being identified from genome sequencing. Identifying a genetic cause of obesity can prove advantageous for genetic counselling and help assess the most effective treatment options.

3 |. GENETICS FACTORS ASSOCIATED WITH OBESITY

The current obesity epidemic is believed to be a product of the interactions between an at-risk genetic profile and an obesogenic environment. It has been shown that the heritability, defined as the proportion of interindividual variation attributable to genetic factors, of BMI is approximately 40%–70%.⁵⁴ While the genome plays an important role in obesity, the relatively rapid increasing rate of obesity prevalence cannot be solely explained by genetic variations, as this process occurs at a significantly slower pace. The role of epigenetics, the study of how behaviour and environment affect gene expression, becomes essential to better explain the highly accelerating rate of obesity.

In this section, we will first highlight the importance of gaining a better understanding of the genetic factors underlying obesity and how the discovery of the leptin–melanocortin pathway has enhanced our comprehension of the aetiology of monogenic obesity. However, it is still worth noting that obesity is predominantly polygenic, involving multiple common genetic variants with modest effects that contribute to people’s susceptibility to weight gain.⁵⁵ We will then examine the interplay among genetics, environment and lifestyle habits and reveal how our obesogenic environment might be enhancing the genetic risk for obesity.⁵⁶

3.1 |. The leptin–melanocortin pathway and appetite control

While the rising prevalence of the obesity epidemic can be partially attributed to various lifestyle and environmental factors, it is important to highlight the genetic component that underlies the variations in body weight.⁵⁷ GWAS have identified over 300 different genetic markers related to obesity,^58–60 with recent advancement raising awareness about the monogenic and polygenic causes of obesity.⁶¹ While polygenic and monogenic obesity are often viewed as distinct conditions, studies have shown that both forms of obesity share genetic and biological foundations, emphasizing the critical involvement of the brain in the regulation of body weight.⁵⁷

Over the past decades, studies have explored mechanisms underlying the regulation of energy homeostasis, with a main focus on the role of the brain leptin–melanocortin pathway in food intake regulation (Figure 2).⁶² The leptin–melanocortin pathway comprises mediators and receptors that regulate the essential steps in food regulation: proopiomelanocortin (POMC), which undergoes post-translational processing to yield α-melanocyte-stimulating hormone (α-MSH), among other various biologically active molecules; the melanocortin receptors, MC1R-MC5R; and endogenous antagonists of these receptors, namely agouti and agouti-related protein (AgRP).⁶²

FIGURE 2.

The leptin–melanocortin pathway and its role in weight regulation. AgRP, agouti-related protein; BDNF, brain-derived neurotrophic factor; GLP-1, glucagon-like peptide; MC4R, melanocortin 4 receptor; NPY, neuropeptide Y; PCSK1, proprotein convertase subtilisin/kexin type 1; POMC/CART, pro-opiomelanocortin/cocaine-and amphetamine-regulated transcript; PYY, peptide YY; SIM1, single-minded homologue 1; α/β-MSH, alpha/beta-melanocyte-stimulating hormone.

Leptin is a satiety hormone secreted mainly by the white adipose tissue.⁶³ In 1994, leptin was identified as the product of the obese (ob) gene through studies involving genetically obese (ob/ob) mice.¹¹ This discovery was pivotal in understanding the regulation of energy balance. Leptin primarily exerts its effects in the brain, where the melanocortin pathway constitutes a significant mechanism of leptin signalling. Leptin has various roles, including regulating food intake, controlling body mass, influencing reproductive functioning, and promoting lipolysis.⁶³ These effects provide an overview of how various pathways regulate the leptin signalling system, ultimately contributing to the maintenance of body mass. Indeed, leptin acts via its trans-membrane receptor, the LEP-R, which is expressed in various locations in the brain; the hypothalamic region is of significant importance, with the arcuate nucleus most closely associated with leptin’s effects on energy homeostasis (Figure 3).⁶⁴ The circulating leptin level is dependent on the chronic level of body adipose mass and current metabolic status, with its concentration decreasing during periods of fasting and energy restriction and increasing during refeeding and overfeeding.⁶⁵

FIGURE 3.

Distribution of melanocortin 4 receptor (MC4R), leptin and glucagon-like peptide-1 (GLP-1) receptors within the hypothalamus. AHN, anterior hypothalamus; ARC, arcuate nucleus; DMN, dorsomedial nucleus; LH, lateral hypothalamus; MAM, mammillary body; PH, posterior hypothalamus; PON, preoptic nucleus; PVN, paraventricular nucleus; SCN, suprachiasmatic nucleus; SON, supraoptic nucleus; VMN, ventromedial nucleus.

In the ‘fed state’, leptin, predominantly secreted by the adipose tissue, crosses the blood–brain barrier and binds to receptors on the surface of POMC-producing neurons in the arcuate nucleus of the hypothalamus.¹¹ The processing of POMC to the mature hormone α-MSH then occurs, from where α-MSH binds to MC4R in the paraventricular nucleus. Activation of MC4R results in a variety of effects, including suppression of appetite, decrease in food intake and increase in energy expenditure. In the fed state, as well, leptin binds to AgRP/NPY-releasing neurons in the arcuate nucleus of the hypothalamus, to inhibit the release of AgRP/NPY, whose actions are generally opposite to that of α-MSH. By contrast, in the ‘starved state’, low leptin levels activate AgRP/NPY neurons and inhibit POMC neurons, promoting food intake.⁶⁶ Thus, the leptin–melanocortin pathway illustrates how leptin, a hormone reflective of fat mass, regulates feeding behaviour.

Interestingly, MC4R is expressed not only in various regions of the central nervous system (CNS) but also in the peripheral nervous system (PNS). In the CNS, it is expressed abundantly in the hypothalamus, mainly in the supraoptic and paraventricular nuclei (Figure 3).⁶⁷ In the PNS, MC4R is expressed in enteroendocrine L cells, which receive basolateral regulatory input from enteric neurons and circulatory factors.⁶⁸ Subsequently, in response to α-MSH, MC4R induces the release of peptide YY and glucagon-like peptide-1 (GLP-1), potent satiety factors. GLP-1 receptors are also present in both the CNS and PNS, with various brain regions, including the arcuate and paraventricular nuclei, showing their expression (Figure 3).⁶⁹ The complexity of the leptin–melanocortin pathway not only provides insights into its functioning but also elucidates the potential benefits and adverse effects associated with drugs targeting it, as will be highlighted later in our review. Moreover, mutations in the genes of the leptin–melanocortin pathway cause dysregulation in appetite and food intake, resulting in early-onset, morbid obesity, in both humans and mice. The vast majority of monogenic obesity involves genes falling within this pathway and include the following: Leptin (LEP), Leptin receptor (LEPR), POMC, PC1, MC4R, and MC3R.^57,61

MC4R deficiency represents the most prevalent form of monogenic obesity.⁷⁰ Epidemiological data suggest that variations in MC4R occur in approximately 0.05% of the general population.⁷¹ Moreover, the prevalence of MC4R variants in adults with obesity is approximately 0.2%, while it ranges from 5% to 6% in patients with severe early-onset obesity.^70,72 The prevalence of these mutations varies depending on the ethnic composition of the population being studied.^73–75

The MC4R was first discovered to be related to body weight in 1998, through a frameshift mutation in that gene, and it is predominantly expressed in the brain and neuroendocrine cells of the intestine.^76–78 To note that the MC4R gene regulates functions other than appetite, including activation of the sympathetic system, glucose homeostasis, and gastric motility.¹¹ Importantly, the degree of MC4R dysfunction can predict the amount of food consumed in a meal, thereby contributing to a wider body weight range within the affected population, and potentially elucidating the heterogeneity of treatment response.⁷⁰

Additionally, one of the known effects of α-MSH signalling in the MC4R pathway is that it increases the local dendritic release of oxytocin.⁷⁹ Oxytocin is a hypothalamic neuropeptide that was shown to exert a variety of functions in osmoregulation, reproductive behaviour, prosocial behaviour, and body weight control.⁷⁹ POMC neurons project to nuclei that express Sim1, such as the paraventricular nucleus and the supraoptic nucleus, where oxytocin neurons happen to comprise a large portion of the Sim1 neuron population.⁸⁰ Oxytocin signalling to the nucleus tractus solitarius in the hindbrain appears to simultaneously reduce food intake and increase sympathetic nervous system activity.⁸¹ Overall, oxytocin was shown to affect homeostatic food intake, gastrointestinal satiation signalling, food motivation and feeding reward, and energy expenditure.⁸¹

The rapidly expanding knowledge of molecular pathways underlying genetic obesity promises to offer novel insights into the patho-physiological mechanisms involved in obesity development. Delving deeper into leptin-melanocortin pathway- and MC4R-associated obesity cases will enable us to better comprehend the heterogeneity in outcomes of the various weight loss interventions. This aspect will be highlighted in a later section of our review.

3.2 |. Gene–environment interactions

Recent studies have shown significant associations between obesity phenotypes, genetic variants and obesogenic environmental factors. The term ‘obesogenic environment’ refers to an environment that encourages weight gain and encompasses any factor that contributes to obesity and is not related to genetic variation. Obesogenic environmental factors can interact with genes through epigenetic modifications of the genome, altering gene activity, and shaping an individual’s risk of developing obesity.⁸² Defining individual risk profiles based on a combination of genetic and non-genetic factors can be beneficial for predicting personal risk for obesity and estimating responses to interventions. This is a crucial aspect of the field of precision medicine for obesity, aiming to comprehend the root causes of the disease and tailor treatments accordingly, thereby reducing the heterogeneity of intervention outcomes.⁸³

3.2.1 |. Gene–diet interactions

One of the most important environmental factors that interplays with the genome is the consumed diet. Nutritional genomics are applied to study the role of the nutrients in gene expression (nutrigenomics) and the effect of genetic variation on dietary response (nutrigenetics).⁸⁴ Exploring this field in more depth is essential for understanding how nutrient–gene interactions are affected by and affect the genotype, which would, in turn, enhance our understanding of the varied responses to lifestyle interventions and help in developing personalized dietary approaches. There has been evidence on how certain macronutrients may accelerate the risk of obesity in individuals genetically predisposed to obesity. Genetic predisposition signifies having a high genetic risk score, which was calculated on the basis of the 32 SNPs associated with BMI at a genome-wide significance level.⁸⁵ A study found that four loci (MC4R, GNPDA2, NEGR1 and SEC16B) out of the 32 had significant interactions with fried food consumption (p ≤ 0.05).⁸⁵ Individuals with a higher genetic predisposition to obesity may be more vulnerable to the adverse influence of excessive fried food consumption on adiposity; and overconsumption of fried foods could amplify the genetic effects on adiposity.⁸⁵ Interestingly, the type of diet has been found to be related to changes in the expression of genes associated with obesity. A study investigated the effect of intermittent fasting on fat mass and obesity associated (FTO) gene, which is known to be correlated with BMI, body fat rate, waist circumference, hip circumference, and energy intake.⁸⁶ Intermittent fasting was shown to downregulate the expression of FTO in patients with obesity, partially explaining the favourable metabolic effect of that type of diet. Despite FTO not being directly related to the MC4R pathway, it was the first described obesity-susceptibility gene, with the largest influence on higher BMI, and, like the MC4R gene, it is expressed in the hypothalamus and regulates food intake and energy homeostasis.⁸⁷ Interestingly, several proteins have been found to interact with FTO, with MC4R being the most significant. Additionally, MC4R was found to be co-expressed with FTO in some species.⁸⁶

These results underscore the importance of customizing the diet for individuals genetically predisposed to obesity and to also consider the effect of a diet in shaping a person’s risk of developing obesity.

3.2.2 |. Gene–physical activity interactions

Another important environment factor found to interact with BMI-associated genes is physical activity. A meta-analysis investigated whether physical activity may attenuate the effect of FTO on obesity risk; the study revealed that the influence of the FTO risk allele on BMI was 30% reduced in physically active individuals compared to those who were inactive.⁸⁸ These results emphasize the importance of physical activity in controlling body weight in individuals with a genetic predisposition towards obesity. Whereas increased physical activity attenuated the genetic predisposition to obesity, increased amount of time spent watching television was found to enhance the genetic association with BMI. A UK Biobank study showed that the impact of the genetic risk of obesity on BMI was more pronounced among individuals who watched at least 4 h of television per day compared to those who watched 3 h or less (p_interaction 7 × 10⁻⁵). A study comprising two European prospective population-based cohorts evaluated the interactions of FTO and MC4R polymorphisms with physical activity levels and their effect on BMI and obesity.⁸⁹ Low physical activity accentuated the effect of the FTO polymorphism on BMI increase (p = 0.008) and obesity prevalence (p = 0.01). However, no such significant interactions were found with the MC4R SNP on these outcomes in the same populations (p = 0.09 for BMI increase, and p = 0.23 for obesity).

Studies concerning gene–environment interactions can still be influenced by confounding factors despite efforts to account for them.⁹⁰ In a meta-analysis assessing interactions between physical activity and the influence of FTO on obesity risk, the authors explicitly state that while germline DNA remains stable, physical activity levels may change and could be confounded by other lifestyle and environmental factors correlated with physical activity and body weight, thus affecting the conclusion drawn on the aforementioned interaction.⁸⁸ Although interpreting gene–environment results can be challenging, these studies can still shed light on how certain obesogenic environments may potentially interact with genetic susceptibility to obesity. Insights from gene–lifestyle interactions could help elucidate the mechanisms behind the genetic regulation of obesity, contributing to the development of new diagnostic, preventive and therapeutic approaches to address the current obesity epidemic.

4 |. TREATMENT OF MONOGENIC OBESITY

In the context of precision medicine for obesity, targeting the MC4R pathway involves identifying genetic variations within relevant genes and proposing treatments accordingly to address the abnormal physiological processes triggering obesity. This approach aims to target the specific genetic factors contributing to the disruption of the MC4R pathway, providing a more targeted and personalized strategy for treating obesity. Although there are no consensus guidelines for conducting genetic testing in individuals with early/severe obesity, obtaining insights into the causal factors responsible for obesity can be beneficial in clinical practice for diagnostic and ultimately treatment purposes. While lifestyle modification therapies alone have shown limited long-term efficacy, there is a growing emphasis on exploring pharmacological and surgical interventions as integral components of the management strategy of monogenic obesity.

4.1 |. Lifestyle modification therapies

Studies investigating the effect of lifestyle modifications in patients with monogenic obesity are scarce, due to their limited success in addressing the abnormal physiology characterizing these patients.⁹¹ Children with MC4R mutations were able to lose weight through a lifestyle intervention, similar to the matched children with obesity and without MC4R variation, approximately 0.4 BMI-standard deviation score.⁹² However, 1 year after the end of the intervention, children with MC4R mutations experienced weight recurrence with a return to their initial overweight status, while those not affected by a mutation maintained their weight loss. Another study assessed weight outcomes during a 6-week weight reduction programme, consisting of intensive physical exercise combined with an energy-restricted diet in MC4R-mutation carriers in comparison to non-carriers. The four MC4R mutation carriers were able to lose body weight (ΔZ-BMI: 0.95 ± 0.16 kg/m²), comparable with the weight loss of the 85 non-carriers (ΔZ-BMI: 1.06 ± 0.25 kg/m²).⁹³ A study encompassing 30 children studied the effect of a 1-year lifestyle intervention programme on weight changes in children with MC4R-related obesity.⁹⁴ The intervention included regular visits tackling nutrition, eating behaviours, physical activity, and sleep habit recommendations. After a median of 1 year of treatment, BMI-standard deviation score decreased in non-carriers but not in carriers (p = 0.005). The limited effectiveness of lifestyle interventions in individuals with monogenic obesity stems from the inability of such interventions to precisely target the underlying physiological abnormality triggering obesity, and thus they do not solve the hyperphagia experienced by these individuals. Recent breakthroughs in understanding pathways governing food intake have facilitated the development of drugs capable of targeting the affected step along those pathways, as will be discussed next. Further research is needed to better understand the impact of lifestyle intervention in patients with a genetic cause for their obesity, in order to tailor treatment more effectively.

4.2 |. Pharmacological treatments

Since the discovery of the MC4R pathway, research has mainly been conducted to develop and study the effect of drugs targeting that central pathway. With regard to pharmacological management based on the patient’s genotype, one potential approach involves the administration of recombinant human leptin in patients with leptin deficiency. Interestingly, this marked the inception of the first mechanistically based targeted therapy for obesity.⁹⁵ The effects of leptin replacement therapy are diverse and encompass changes in behavioural, anthropometric, metabolic, endocrine, immunological and neuroimaging factors.⁹⁶ In fact, recombinant leptin reduces food consumption through neural circuits that diminish the perception of food reward and amplify the response to satiety signals, resulting in a sustained loss of weight, primarily stemming from the reduction of fat mass.⁹⁷ The initial findings were recorded in a case report in 1999, describing a 9-year-old female patient who was homozygous for a frameshift mutation in the leptin gene, leading to undetectable serum leptin concentrations. The administration of recombinant leptin over a 12-month period resulted in a weight loss of 16.4 kg, with a 7% decrease in body fat, constituting 95% of the total weight loss.⁹⁸ In a family affected by leptin deficiency, the mean BMI of adult patients decreased by almost half, dropping from 51.2 ± 2.5 kg/m² before treatment to 26.9 ± 2.1 kg/m² after 18 months of leptin treatment.⁹⁶

Based on their crucial role in central body weight regulation, POMC-derived peptides have been studied. Melanocortin receptor agonists have been described since 1980 and years later, a few were approved for several health purposes, including but not limited to body weight regulation (Table 1).¹⁰⁷ A selective MC4R agonist, setmelanotide, was tested in a larger set of patients with defects in the MC4R pathway.¹⁰⁷ In patients with obesity caused by deficiencies in POMC, PCSK1 and LEPR, the normal signalling flow of the MC4R pathway is disrupted, resulting in a shift from the aforementioned regulation of food intake. Subsequently, setmelanotide serves to restore the activity of the MC4R pathway in those patients, by acting as an MC4 receptor agonist, thereby mitigating hunger and facilitating weight loss, primarily through decreased food intake and increased energy expenditure.¹⁰⁸ In a Phase III trial, weight outcomes in response to setmelanotide were assessed in patients having POMC or LEPR deficiency. After a 1-year treatment, patients with POMC deficiency experienced a 25.6% total body weight loss (TBWL), with 80% of patients achieving at least a 10% weight loss.⁴⁶ Patients with LEPR deficiency showed a less pronounced response, losing 12.5% of TBWL, with only 45% of patients achieving at least a 10% weight loss. These results support the significance of tailored treatment for obesity in patients with POMC or LEPR deficiency, aligning with the principles of precision medicine.⁴⁶ The difference in weight loss between groups emphasizes the further need to understand the varied responses that occur in patients with different mutations along the MC4R pathway. One explanation could be that POMC deficiency exerts a direct influence on the production of MC4R ligands, while LEPR deficiency impacts signalling upstream of POMC.⁵⁷ Additionally, leptin activates both POMC and NPY/AgRP neurons in the arcuate nucleus of the hypothalamus, leading to the release of potent neuropeptide modulators that exert opposing effects on feeding and metabolism.¹⁰⁹ The signalling pathway involving LEPR is more complex than that for POMC alone, which may account for the differences in weight outcomes. Furthermore, the efficacy of setmelanotide in MC4R mutation carriers was investigated in a randomized, double-blind, placebo-controlled Phase Ib study.¹⁰² After 28 days of treatment in patients with a heterozygous MC4R variant, setmelanotide-treated individuals lost 3.48 kg (p < 0.0001), despite the placebo-subtracted group mean weight loss differences not reaching statistical difference. These studies serve as compelling examples that underscore the role of precision medicine in managing patients with monogenic obesity. Genetic testing has played a crucial role in identifying the underlying cause of the disease. Importantly, a genetic diagnosis can reduce a patient’s sense of guilt and alleviate social stigma and discrimination. This genetics-based method has enabled the development of a tailored management plan to address the specific cause and has proven to be an effective way to address weight management. It is also worth noting that this approach encourages further research aimed at discovering novel drugs capable of targeting genetic variations, for which a treatment has not yet been identified.

TABLE 1.

Melanocortin-4 receptor agonists in human clinical studies.

Authors	Study design	Population	Treatment	Outcome
Setmelanotide
Clément et al.99	Phase 2 trial	Three patients aged 23, 22, and 14 years with LEPR deficiency	Setmelanotide titrated to an individualized maximum dose of 2 mg, for 26 weeks	Patient 1: weight loss of 28.2 kg Patient 2: weight loss of 12.5 kg Patient 3: did not adhere to treatment and thus did not experience weight loss
Clément et al.46	Single-arm, open-label, multicentre, Phase 3 trials	10 patients (mean age of 18.4) in the POMC trial 11 participants (mean age of 23.7) in the LEPR trial	Setmelanotide titrated from 0.5 mg (<18 years of age) or 1 mg (18 years and older) up to an individualized maximum dose of 2–3 mg based on age and tolerability, for 52 weeks	POMC trial: mean weight loss of −25.6% (p < 0.0001) after 1 year Eight (80%) participants in the POMC trial achieved at least 10% weight loss at approximately 1 year LEPR trial: mean weight loss of −12.5% (p < 0.0001) after 1 year Five (45%) participants in the LEPR trial achieved at least 10% weight loss at approximately 1 year
Kühnen et al.100	Phase 2, non-randomized, open-label pilot study	Two patients aged 21 and 26 years diagnosed with POMC deficiency	Setmelanotide titrated from a starting dose of 0.25 mg in Patient 1 and 0.5 mg in Patient 2 up to a dose of 1.5 mg once daily in both patients Duration of setmelanotide intake: a total of 13 weeks in Patient 1 and 12 weeks in Patient 2	The patients had a weight loss of 51.0 kg in Patient 1 after 42 weeks and 20.5 kg in Patient 2 after 12 weeks
Kühnen et al.101	Phase 2, open-label, long-term extension trial	Two patients aged 21 and 26 years with POMC deficiency	Setmelanotide 2.0 mg daily	The patients had a weight loss of 55.6 kg in Patient 1 after 7.2 years, and 72.6 kg in Patient 2, after 6.8 years
Collet et al.102	Phase 1b, randomized, double-blinded placebo-controlled clinical trial	16 participants (n = 8 with MC4R variants, n = 8 controls with obesity)	Setmelanotide 0.01 mg/kg/day vs. placebo for 28 days	Setmelanotide-treated MC4R variant carriers (n = 6): lost 3.48 kg Placebo-treated MC4R variant carriers (n = 2): lost 0.85 kg Setmelanotide-treated controls (n = 5): lost 3.07 kg Placebo-treated controls (n = 3): gained 0.9 kg
Chen et al.103	Randomized, double-blind, placebo-controlled, crossover study	12 patients with obesity (mean age of 34.9 years)	Setmelanotide (RM-493) (1 mg/24 h) or placebo by continuous subcutaneous infusion over 72 h, followed immediately by crossover to the alternate treatment	RM-493 increased REE vs. placebo by 6.4% (95% CI 0.68%–13.02%), on average by 111 kcal/24 h
MK-0493
Krishna et al.104	Proof-of-concept phase Ila studies (from two multicentre, double-blind, placebo-controlled studies)	Participants aged between 21 and 65 years	A single dose of 400 mg in the fixed-dose study for 12 weeks Titration from 100 mg up to 800 mg in the stepped-titration dosing study for 18 weeks	In the first 12-week study, least-squares mean difference of −0.6 kg (95% Cl −1.5, 0.3 kg) in change in body weight from baseline to Week 12 between the 400-mg MK-0493 group and the placebo group (p = 0.211) In the second 18-week weight loss study, in the all-patients-treated population after 18 weeks of treatment (12 weeks on 800 mg), the least-squares mean difference between the placebo group and the MK-0493 group was −1.2 kg (95% CI −2.6, +0.2 kg; p = 0.099)
MC4-NN2–0453
Royalty et al.105	Randomized, double-blind, placebo-controlled trial	57 patients in the single-dose part of the trial (mean age 34.9 years) 60 patients in the multiple-dose part of the trial (mean age 40.1 years)	Single-dose part of ascending subcutaneous 0.03–1.50 mg/kg doses; and a multiple-dose part of ascending subcutaneous 0.75–3.0 mg/day doses	In both the single-dose and the multiple-dose parts, no change in body weight was observed
Bremelanotide
Spana et al.106	Two Phase 1, single-centre, randomized, double-blind, placebo-controlled trials	Study 1: 30 women aged 18–55 years with a BMI of 30–37 kg/m2 Study 2: 27 women aged 18–45 years with a BMI of 30–40 kg/m2	Study 1: subcutaneous placebo or bremelanotide three times daily for days 1–15 (dose titrated from 3.25 mg/day to 6.5 mg/day) Study 2: three treatment phases with bremelanotide (maximum dose of 4.5 mg /day) or placebo lasting 4 days each and separated by a 5-day washout period	Study 1: reduction in body weight after 16 days with bremelanotide versus placebo (least squares mean difference −1.3 kg [95% CI −1.9 to −0.8 kg]; p < 0.0001) Study 2: reduction of mean body weight occurred in twice daily bremelanotide subjects versus placebo (1.7 vs. 0.9 kg, respectively, p < 0.001)

Abbreviations: BMI, body mass index; CI, confidence interval; LEPR, Leptin receptor; MC4R, melanocortin-4 receptor; POMC, Pro-opiomelanocortin; REE, resting energy expenditure.

Further studies are needed to elucidate the mechanisms contributing to weight loss with setmelanotide. One potential mechanism is a chaperone effect, whereby agonists can rescue mutant forms of the MC4R by restoring their expression at the cell surface.¹¹⁰ Gaining a deeper understanding of the mechanisms through which MC4R operates has emphasized the intricacies of its function. Various related G-signalling molecules become activated upon MC4R stimulation. Moreover, β-arrestin has been identified as playing a role in receptor internalization and desensitization, further contributing to the complexity of MC4R function regulation.¹⁰⁷ This knowledge could, on one hand, help explain how various mutations of the MC4R can result in distinct obesity-related characteristics and, consequently, explain the varied treatment responses. On the other hand, it could be utilized in the future to develop new treatment approaches for patients with monogenic obesity, with the goal of optimizing their weight loss outcomes.

Despite MC4R being considered a target for weight regulation, the receptor controls multiple pathways that include, in addition to energy homeostasis, cardiovascular function, glucose and lipid homeostasis, and sexual response.¹¹¹ Initially, MC4R agonists were tested in clinical trials for the treatment of skin disorders (afamelanotide for adults with erythropoietic protoporphyria) and sexual dysfunction (bremelanotide, currently only approved for the treatment of hypoactive sexual desire disorder in women). Furthermore, an MC4R agonist, LY2112688, was developed with the aim of controlling body weight. Adverse effects associated with LY2112688 included the classic symptoms of increased blood pressure, yawning, stretching, and penile erection, which are associated with activation of the central MC4R.¹¹² The cardiovascular effects, including increased blood pressure, observed with these and other first-generation MC4R agonists led to the termination of these studies and to the development of agonists more selective to the MC4R, thereby reducing adverse events. Subsequently, setmelanotide was studied for weight loss and the reported adverse effects were minor, including nausea and/or vomiting, skin hyperpigmentation (due to MC1R activation), penile erection, and injection site reactions.⁵⁷ Importantly, no reported cardiovascular side effects were reported. By contrast, blood pressure was found to decrease in parallel to body weight loss with setmelanotide use.¹⁰⁷ These results were consistent with studies conducted in non-human primate models.¹¹³ LY211268 was associated with increases in blood pressure and heart rate along with a modest decrease in food intake. On the other hand, RM-493 (currently referred to as setmelanotide) was linked to a significant improvement in food intake regulation and improved metabolic parameters, without any increases in blood pressure or heart rate.¹¹³ A possible explanation for the cardiovascular safety profile of setmelanotide, in comparison to previous MC4R agonists, could be the different penetration in the central nervous system of the different tested compounds, whereby they may couple through different G-protein signalling pathways.¹⁰² Overall, setmelanotide is a well-tolerated drug among patients with monogenic obesity, resulting in persistent weight loss without concomitant adverse effects on cardiovascular function.

Besides the genetic-tailored treatments for patients with an MC4R mutation, one study investigated the role of GLP-1 receptor agonists in 14 patients with obesity and an MC4R mutation, as compared to 28 non-mutated patients with obesity.¹¹⁴ GLP-1 receptor agonists have been shown to exert part of their mechanism in the hypothalamus, where they specifically stimulate the electrical activity of hypothalamic POMC neurons (Figure 4). The results showed an equivalent weight loss between groups after 16 weeks of treatment, with patients with the mutation losing 6.8 kg, as compared to 6.1 kg for those without the mutation. There is no additional information regarding the potential enduring effects of GLP-1 agonist treatment in individuals with monogenic obesity. However, these current data indicate a maintained efficacy of GLP-1 agonists for genetic obesity characterized by reduced MC4R signalling. It is important to note that MC4R mutations are only one aspect of the many mutations occurring along the MC4R pathway. Therefore, it would be interesting to study the effects of GLP-1 and other US Food and Drug Administration (FDA)-approved antiobesity medications in patients with monogenic obesity caused by the other aforementioned mutations.

FIGURE 4.

Central mechanisms of antiobesity medications targeting the melanocortin 4 receptor (MC4R) pathway within the hypothalamus. AGRP, agouti-related protein; ARC, arcuate nucleus; GIP, gastric inhibitory polypeptide; GLP-1, glucagon-like peptide-1; MC4R, melanocortin 4 receptor; NPY, neuropeptide Y; POMC, pro-opiomelanocortin; PVN, paraventricular nucleus; RA, receptor agonist; SON, supraoptic nucleus.

Studies investigating the effects of oxytocin treatment on appetite regulation and body weight have been conducted, demonstrating results of weight loss and reduced caloric intake.^115,116 In a randomized pilot clinical trial, subjects with overweight and obesity underwent 4-week oxytocin treatment and experienced a decrease in body weight of 4.6 kg, whereas the placebo failed to produce a therapeutic effect against obesity.¹¹⁵ Extending the duration of oxytocin treatment to 8 weeks amplified its therapeutic impact, revealing that compared to their pretreatment baseline levels, patients exhibited a reduction in body weight of 8.9 kg (p < 0.001). To the best of our knowledge, there is no known evidence-based treatment for patients with SIM-1 or other forms monogenic obesity. Oxytocin has been shown to improve eating behaviours and habits and to reduce BMI in patients with craniopharyngioma, with reduced levels of oxytocin; this suggest a potential therapeutic implication in patients with monogenic obesity characterized by abnormal oxytocin levels.¹¹⁷ Further studies are required in order to disclose the effect of oxytocin therapy on appetite and to delve deeper into the attributes of novel oxytocin mimetic peptides in individuals impacted by monogenic obesity. The detailed MC4R pathway, with its several receptors and intermediates involved in regulating food intake, serves as a critical target for antiobesity medications. FDA-approved weight loss medications have demonstrated effects through this pathway (Figure 4). For instance, liraglutide activates POMC neurons directly at the level of the cell body by binding to the GLP-1 receptor. In naltrexone-bupropion treatment, there is stimulation of POMC neurons with bupropion, with this stimulation augmented by blocking of the autoinhibitory mechanism of POMC by naltrexone.

4.3 |. Bariatric surgery

Bariatric surgery remains the most effective therapy for patients with common, non-monogenic obesity. Understanding the genetic profile of patients before deciding on a bariatric procedure has been a topic of interest in recent years. This approach allows for the selection of patients who would benefit from the procedure and also helps establish realistic outcome expectations. There was a focus on patients carrying an MC4R mutation, with one study that investigated outcomes of adjustable gastric banding finding that total weight loss was 18% lower in carriers compared with non-carriers (p = 0.003). By contrast, six other studies showed no differences in outcomes after adjustable gastric banding, Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy compared with non-carriers.¹¹⁸ Additionally, a study compared outcomes between MC4R mutation carriers and non-carriers for both RYGB and sleeve gastrectomy: no differences were observed after RYGB after up to 2 years of follow-up, however, sleeve gastrectomy was less effective in MC4R mutation carriers compared with the rest of the cohort.¹¹⁹ Exploring changes in the levels of molecules involved in the leptin–melanocortin pathway before and after bariatric procedures may potentially explain the differences in outcomes in patients carrying mutations along that pathway.¹²⁰ Another study examined the results of bariatric surgery conducted in eight individuals with monogenic obesity, characterized by bi-allelic variants in the genes LEPR, POMC and MC4R.¹²¹ Initially, all patients experienced weight loss post-surgery (median maximum reduction of body weight of −21.5 kg), which was followed by significant weight regain (median weight regain of 24.1 kg), after a maximum follow-up duration of 19 years post-surgery. The study suggested incorporating genetic testing into the preoperative protocols prior to bariatric surgery, especially for patients with severe early-onset obesity. With a broader overview of mutations affecting components along the MC4R pathway, a study included 50 carriers, who were matched based on sex, age, BMI, and years since surgery with 100 non-carriers.^122,123 The study showed that individuals with heterozygous variants experienced progressive and significant weight regain in the mid and long term following RYGB. Similarly, in a case–control study analysing outcomes of transoral outlet reduction due to weight regain after RYGB, carriers exhibited decreased weight loss after undergoing this procedure.¹²⁴ Further studies are needed to enhance our understanding of the underlying pathophysiology contributing to variations, if any, in outcomes among patients with genetic obesity, enabling informed treatment decisions and establishment of standardized outcomes.

5 |. TREATMENT OF POLYGENIC OR COMMON OBESITY

The genetic susceptibility to obesity is heterogeneous.¹²⁵ As previously discussed, a subset of individuals experiences obesity as a consequence of a single gene. Although the identification of genes responsible for monogenic obesity has greatly advanced our understanding of the molecular basis of the condition, it is worth noting that monogenic cases constitute less than 5% of all obesity cases.¹²⁶ For the majority of individuals with obesity, no such monogenic mutation can be identified, which implies that most of the genetic predisposition to obesity has a polygenic basis. A polygenic variant exerts a modest effect on the obesity phenotype; it is only when combined with other predisposing variants that a significant phenotypic effect manifests.¹²⁵

Due to the multifactorial nature of obesity, it would not be surprising to observe heterogeneity in outcomes of the currently available weight loss interventions. This variability is evident across all types of treatment, including lifestyle, pharmacological and surgical/procedural approaches.^127–130 The reasons underlying this heterogeneity have not been thoroughly investigated to date, but some studies have been trying to understand why some individuals may respond more favourably than others and find predictors for response.^29,131–133

To effectively address the heterogeneity and enhance the predictability of outcomes, it would be beneficial to classify patients based on common obesity-related traits and tailor treatment accordingly. The first section of our review highlighted the different approaches used to classify patients with obesity; while each provides clinical benefits and reflects a crucial aspect of the disease, clinical phenotyping based on energy balance components best conveys the complexity of obesity pathogenesis. By identifying unique patient quantitative traits based on pathophysiological and behavioural variables associated with obesity, obesity treatment could shift from a ‘one-size-fits-all’ approach to a more personalized one. In fact, we have demonstrated that a phenotype-lifestyle intervention resulted in significantly better weight loss outcomes at 12 weeks, when compared to a standard lifestyle intervention (TBWL: 7.4 kg vs. 4.3 kg, difference: 3.1 kg; p = 0.004). Additionally, the proportion of patients that achieved a weight loss of at least 10% at 12 months was higher in the phenotype-lifestyle intervention group when compared to the standard of care group (26% vs. 11%). In terms of pharmacological treatment, we have also shown that our phenotype-guided antiobesity medication selection provided better weight loss outcomes at 12 months when compared to the standardized approach (TBWL%: 15.9% vs. 9.0%, difference: −6.9%; p < 0.001). When taking a closer look at the phenotypes independently, the abnormal satiation phenotype, characterized by a larger energy intake, is assigned phentermine-topiramate extended release in our phenotype-guided antiobesity medication approach based on drug mechanism reasoning. Energy intake is quantified by the number of calories needed to achieve satiation in an ad libitum meal. We have shown that, in a 2-week randomized placebo-controlled clinical trial, phentermine-topiramate extended release led to a significant reduction in the number of calories to achieve satiation during an ad libitum meal (mean difference: 206 kcal) when compared to the placebo group (p = 0.03). By studying the energy components in individuals with obesity and categorizing them into specific phenotypes, we can gain insights into the underlying abnormalities contributing to their obesity. Furthermore, this approach enables us to partially address and minimize the variability in outcomes.

Nevertheless, we also believe that other traits of obesity could be linked to the heterogeneity in intervention outcomes. It is thus crucial to explore the potential role of genetics and adopt a multi-omics-based approach to identify additional phenotypes and blur the gaps between individual responses.

We have demonstrated that in monogenic obesity, identifying the underlying genetic mutation can enable the development of targeted treatments and lead to positive outcomes. Although more complex, identifying the diverse genetic contributions leading to polygenic obesity would undoubtedly assist us in a similar personalized-treatment approach. Despite monogenic and polygenic obesity being portrayed as distinct entities, they have common ground.^57,134 In fact, recombinant leptin and setmelanotide, used in the management of monogenic obesity, have also been studied in common obesity, and yielded variable results; this suggests that some SNPs may share a mutual mechanistic gene of action.^102,135 In order to address that issue, polygenic risk scores for obesity have been created that aggregate the effects of many obesity-associated genetic variants across the human genome into a single score; the higher the score, the higher the genetic susceptibility to develop obesity.⁵⁵ This tool can identify individuals at high risk, enabling the delivery of timely interventions to prevent, or at least contain, excess weight gain, through personalized recommendations.⁵⁵

In a study comprising more than 300 000 adults, 97 loci were found to be associated with BMI, many of which were consistently reported to be related to obesity across ethnicities.¹³⁶ Polygenic risk score models with 12, 20, 32, 56 and 97 of the aforementioned BMI-associated loci have been tested, and use of these found stronger effects on anthropometric measures.¹³⁷ Analysis of the obesity-related pathway and gene set, based on these loci, has already provided new insights into the biology that underlies body weight regulation. Although these models are limited by the heritability trait of the condition, they can nonetheless assist in implementing more targeted strategies for the prevention and treatment of obesity, aligning with the core principles of precision medicine.¹³⁸ Moreover, as studies have been attempting to identify factors associated with better weight loss outcomes, these genetic risk score models may serve as potential tools to assess the best responders to weight loss interventions. Genetic risk scores constructed based on BMI-associated SNPs were found to predict weight loss trajectory following bariatric surgery, underscoring the importance of genetic factors in determining weight loss outcomes.^139,140

6 |. CONCLUSION

While obesity has been a longstanding challenge for humanity, it has reached epidemic proportions only recently. Addressing and reversing the obesity epidemic has become a worldwide priority, and gaining a thorough understanding of the underlying complexity of the disease is considered a reasonable initial step. Studies on genetics have not only allowed us to gain insights into the pathophysiology that could trigger obesity but have also led to the development of new targeted treatments.

Specifically, the MC4R pathway has garnered significant attention as several mutations along this pathway have been found to be associated with obesity. Treatment approaches used to target the affected molecular pathway have shown great results, proving how interwinding genetics with precision medicine could improve weight and overall health-related outcomes. Nevertheless, there is still much to uncover in the field of obesity, and additional refined treatments must be developed. Interdisciplinary collaboration between geneticists, clinicians, and public health experts is essential to determine treatment strategies and assess the benefits and risks of proposed interventions. Consideration of genetic testing could be an initial step in management, specifically in patients with early-onset severe and/or familial obesity.

Further research remains necessary to delve deeper into the MC4R and other obesity-associated pathways and to better comprehend the variability in outcomes resulting from various interventions in patients with obesity. Precision medicine can stand as a valuable tool that can assist in mitigating the steeply rising prevalence of obesity.

DATA AVAILABILITY STATEMENT

The investigators will share deidentified data that underlies the results reported in this article after deidentification upon request by bona fide researchers who provide a methodologically appropriate proposal. Proposals should be directed to acosta.andres@mayo.edu. To gain access, data requestors will need to sign a data access agreement.