Pharmacogenomics of Medication-induced Weight Gain and Anti-Obesity Medications

Abstract

Obesity is a chronic, multifactorial disease associated with a large number of comorbidities. The clinical management of obesity involves a stepwise integrated approach, beginning with behavioral and lifestyle modification, followed by anti-obesity medications, endobariatric procedures, and bariatric surgery. Weight gain and subsequent obesity are common side effects of medications such as prednisone or anti-psychotics. In this era of precision medicine, it is essential to identify patients at highest risk of weight gain as a result of medications use. Pharmacogenomics could play an important role in obesity management by optimizing use of anti-obesity medications as well as minimizing adverse weight gain. This review aims to do a comprehensive analysis of the current literature on the role of pharmacogenomics in obesity and medication induced weight gain. In summary, there are more robust studies of medication associated with weight gain and pharmacogenomics; and more studies are needed to understand the role of pharmacogenomics in anti-obesity medications.

INTRODUCTION

Obesity is a chronic, multifactorial disease, defined as an abnormal or excessive accumulation of body fat that imposes a risk to the health of an individual. It is a major public health concern in the world, especially in the United States, with an enormous socioeconomic burden. The prevalence of obesity in the United States has trended upwards to 39.4% in adults and 18.5% in youth as measured in the National Health and Nutrition Examination Survey (NHANES) 2015– 2016 (3).The WHO global estimates has suggested that obesity prevalence has tripled between 1975–2016 (4). The estimated annual medical cost of obesity in the United States was a staggering 480 billion US dollars in 2018, and it continues to rise (5).

Obesity is an imbalance between energy intake and expenditure. The deleterious effects of obesity are multi-systemic such as insulin resistance, type-2 diabetes mellitus, hypertension, dyslipidemia, cardiovascular disease, stroke, sleep apnea, gall bladder disease, gout, osteoarthritis, and some cancers including colorectal and prostate in men and breast, endometrial, and gall bladder cancer in women (6). The etiology of obesity is a plexus of biological (genetics, brain-gut axis food intake regulation, prenatal determinants, pregnancy and menopause, neuroendocrine conditions, medications, physical disability, gut microbiome, viruses), environmental (food abundance, built environment, socioeconomic status, culture, bias and discrimination, environmental chemicals), and behavioral (excessive calorie intake, eating patterns, sedentary lifestyle, reduced physical activity, insufficient sleep, smoking cessation) factors (7). Medications play a major role in weight gain and the most important classes of drugs commonly associated with weight gain as a side effect include anti-psychotic drugs, anti-depressant drugs, anti-epileptic drugs, beta-blockers, anti-hyperglycaemics, and glucocorticoids (8–11).

An integrated, multidisciplinary, and personalized approach is needed for obesity management. The Practice Guide on Obesity and Weight Management, Education, and Resources (POWER) program, a multidisciplinary, multisocietal effort to introduce a continuum of obesity care, recommends 4 phases for obesity management i.e. 1) multidisciplinary assessment 2) intensive weight loss intervention 3) weight maintenance, and 4) prevention of weight regain (12). In the weight loss intervention, the initial step is to provide all patients with lifestyle modification therapy. If the patient is unable to reach the weight loss goal through lifestyle modification alone, it is recommended a second-level therapy such as medications, devices or surgery. The criteria for anti-obesity medication (AOM) is a BMI greater than 30 kg/m² or a BMI greater than 27 kg/m² plus one obesity-related comorbidity. Currently, the FDA-approved AOM for short term use (up to 12 weeks) include phentermine, benzphentamine, diethylpropion and phendimetrazine, and AOM for long-term use include: phentermine-topiramate extended-release, liraglutide (3mg), orlistat, and bupropion/naltrexone sustained release. Sibutramine,rimonabant, and lorcaserin were withdrawn from the market due to side effects. If the disease is severe and the above management options have failed, bariatric endoscopy or surgery are considered as an option (12, 13).

The use of anti-obesity medications is slowly growing and the current pharmacovigilance reports suggests that there is about ten million prescriptions per year (14). Unfortunately, the response rate to anti-obesity medications is highly variable. On average, only 30% of patients will lose more than 10% of total body weight in one year (15). The heterogeneity in response to AOM may be explained by drug metabolism, absorption, and effect. Thus, the pharmacokinetics and pharmacodynamics of the drug may interact with an individual’s genetic makeup and this interaction with medications is defined as pharmacogenomics. Acknowledging the importance of anti-obesity medications in obesity management and the variability of response and potential role for pharmacogenomics, we decided to perform a literature review of the pharmacogenomics of obesity.

SEARCH STRATEGIES

A thorough review of articles was done from literature published on PubMed and MEDLINE using keywords-obesity, pharmacogenomics, anti-obesity drugs, phentermine, liraglutide, lorcaserin, bupropion/naltrexone, phentermine/topiramate, orlistat, sibutramine, rimonabant, weight loss, weight gain, antipsychotic drugs, anti-depressants, beta-blockers, glucocorticoids, anti-epileptic drugs, anti-hyperglycemic drugs, sulfonylureas, gene variants, gene polymorphisms.

PHARMACOGENOMICS

KEY DEFINITIONS

It is essential to know some of the basic definitions to have a clear understanding of this review. The term pharmacogenomics reflects an amalgamation of pharmacology (the science of drugs) and genomics (the science of genes and their function). It is the study of the effect of genetic variation on response to a drug. Pharmacogenomics aims to advance the development of safe, effective pharmacotherapy tailored to the genetic makeup of the individual. Figure 1 illustrates pharmacogenomics as genes affecting at various levels such as alteration in drug-metabolizing enzymes (a pharmacokinetic property) or variation in the drug target, for example, receptor or a protein which in turn can lead to inter-individual variation in therapeutic effect and adverse effect (a pharmacodynamics property).

Figure 1.

illustrates pharmacogenomics role in drug metabolism. It reflects pharmacogenomics as gene variations/polymorphisms affecting an individual’s response to a particular drug at various tiers such as an alternation drug-metabolizing enzymes (pharmacokinetics property) or variation in the drug target (pharmacodynamics property)

Genetic polymorphism can be defined as the existence of 2 or more variants of a gene, occurring in a population, with at least 1% frequency of the less common variant (cf mutation). A genotype is the genetic constitution of an individual, either overall or at a specific gene. Phenotype is the observable characteristics of a cell or organism, usually being the result of the product coded by a gene(genotype). Single-nucleotide polymorphism(SNP), a single base pair change in the DNA sequence at a particular point compared with the “common” or “wild-type” sequence. Each SNP receives a unique reference SNP ID number, recored as ‘rs’ number (example: rs25531) (16), An allele can be defined as one of the several variants of a gene, ususally referring to a specific site within the gene. A variant allele is an allele at a particular SNP that is the least frequent in a population. Wild-type allele is the allele at a particular SNP that is the most frequent in the population. Locus/loci is a site on a chromosome at which the gene for a particular trait is located or on a gene at which a particular SNP is located (17).

MEDICATIONS WITH WEIGHT GAIN AS A SIDE EFFECT

Drug induced weight gain is a serious adverse event of some of the commonly used classes of drugs resulting in non-compliance to therapy as well as worsening of comorbidities related to obesity. Psychotropic medications such as anti-psychotics and anti-depressants have long been reported in the literature to cause weight gain and metabolic side-effects (8, 9). Similarly, anti-epileptic drugs such as valproate is also commonly associated with weight gain which constitutes a serious problem in treating patients with epilepsy (11). Steroidal agents such as glucocorticoids and progestin oral contracpetives are commonly associated with weight gain (10, 18). Apart from these drug classes, anti-glycaemic agents and beta-blockers are also known to cause weight gain (19). Table 1 summarizes pharmacogenomics of medications with weight gain as a side effect.

Table 1.

Pharmacogenomics of medications causing weight gain as a side effect

Drug class causing weight gain	Drugs associated with weight gain	Gene	rs#	Outcome	Reference
Anti-psychotics	Olanzapine	5-HTR2C	rs3813929	0% patients with T allele compared to 27% of patients with C Allele >10% increase in BMI over 6 weeks	(21)
			rs518147	5% patients with C allele compared to 32% of patients with C Allele >10% increase in BMI over 6 weeks	(21)
		DRD2	rs2440390	A allele: 2.1 kg greater weight gain over 8 weeks	(61)
	Clozapine	5-HTR2C	rs518147	T allele: 2.04 kg less gain in body weight at 6 months	(62)
		LEP-2548A/G	rs1137101	A allele: 1.4 kg/m2 greater change in BMI	(63)
	Atypical antipsychotics (Olanzapine, clozapine, risperidone)	TNF-α	rs1800629	A/G allele: no significant difference weight gain	(24)
		HTR2C	rs518147	T allele: 2.34 kg greater weight gain at 6 weeks	(22)
		HTR2C	rs518147	T allele: no significant change in BMI after 18 months	(64)
Anti-depressants	SSRIs/SNRIs	COMT	rs4680	G allele : GG genotype gained 4 kg more than AG genotypes at 10 weeks	(26)
		TPH1	rs1800532	A allele : AA genotype were 3.21 times more likely to gain weight compared with CA genotype at 10 weeks	(26)
		HTR2C	rs381929	No significant difference in body weight gain	(26)
		SCLC6A4	rs25531		(26)
Anti-epileptics	Valproate
		LEPR	rs1137101	A allele: 1.01 kg/m2 greater change in BMI at 6 months	(29)
		ANKK1	rs1800497	C allele: 0.65 kg/m2 greater change in BMI at 6 months	(29)
		PRKAA2	rs10789038	G allele: 0.54 kg/m2 greater change in BMI at 6 months	(29)
Anti-diabetics	Rosiglitazone	PLIN	rs894160	G allele: 1.30 kg greater weight gain at 12 weeks	(34)

1. Antipsychotic Drugs

Anti-psychotic drugs are known to cause significant weight gain. The mechanism behind it involves the interplay of dopaminergic, serotonergic, histaminergic, and adrenergic systems. According to the weight gain profile, they can be grouped into those causing substantial weight gain (clozapine, olanzapine), moderate weight gain (risperidone, sertindole), slight weight gain (Aripiprazole, Iloperidone, amisulpride) and negligible or no weight gain (ziprasidone, quetiapine) (20).

Numerous trials are performed, and robust data has been collected on the impact of genetic profile on the metabolic adverse events of first and second-generation antipsychotics. HTR2C is a commonly studied gene which is located on the X chromosome X. A study aimed at assessing whether there was an association between the rs518147 and rs3813929 polymorphisms of the HTR2C gene and olanzapine-induced weight gain. The study reported a protective role of the −759 T variant allele of the rs3813929 and the −697C variant allele of the rs518147 of the HTR2C gene against anti-psychotics induced substantial weight gain defined as > 10 % increase in BMI (21).

Similar findings were proven in another study involving a prospective cohort of 48 female inpatients with schizophrenia given second-generation antipsychotics risperidone, clozapine, quetiapine, and olanzapine. Weight was measured at the time of admission, and at the end of 6 weeks with weight gain was defined as an increase in weight greater than 7% of the baseline weight. The final results confirmed the protective role of a T allele of rs518147 in antipsychotic induced weight gain (22).

Another longitudinal study was used to investigate the association between LEPR (Leptin Receptor), Q223R (rs1137101), LEP-2548 G/A, and HTR2C-759 C/T(rs518147) polymorphisms and BMI change following antipsychotic drug consumption. The study population included 141 patients, with 58.2 percent males and the majority of the patients were either prescribed olanzapine or clozapine. An interesting finding in this study was the occurrence of an inverse relationship between the associations of LEPR rs1137101 with obesity in males and females. The LEP Q223R genotype is associated with a higher prevalence of obesity in females (p=0.03) compared to the LEPR 223QQ genotype which is associated with a lower prevalence of obesity in females. The stratified analysis for the HTR2C rs518147 polymorphism demonstrated a protective role of −759 T allele against obesity in both males and females but the results did not reach statistical significance (23). A study was performed to establish association of tumor necrosis factor-alpha(TNF-alpha) −308 G>A(rs1800629) polymorphism and weight gain in patients with schizophrenia under long term clozapine, risperidone or olanzapine treatment. Five hundred patients from Yuli Veterans Hospital in Taiwan were enrolled in the study with each receiving a second-generation antipsychotic (clozapine-275, olanzapine-79 and risperidone-146) for at least three months. The body weight gain was determined as greater than 7 % of BMI. The BWG percentages in the individual genotype groups were: GG=4.2±15.7%; GA=5.9±18.2%; AA=−3.0±10.1%. There was no significant association found between the body weight gain and TNF alpha −308 G>A(rs1800629) polymorphisms (24).

Gene polymorphisms in HTR2C rs518147 and LEPR rs1137101 affecting weight gain response to anti-psychotics can be potentially used to predict individuals who are likely to gain weight following the use of antipsychotic medications.

2. Antidepressant drugs

Antidepressant drugs are also responsible for alteration in the metabolic profile of the patients prescribed with them. The older tricyclic antidepressants (TCAs) are more likely to be associated with obesity compared to the newer serotonin reuptake inhibitors (SSRIs) and atypical antidepressants. Mirtazapine can be placed between TCAs and SSRIs. Amongst the SSRIs, paroxetine is more likely to cause weight gain than the other SSRIs. Bupropion and nefazodone are least likely to cause weight gain.

The Clinical Pharmacogenetics Implementation Consortium(CPIC) have already established guidelines on dosing recommendations for SSRIs such as fluvoxamine, paroxetine, citalopram, and sertraline based on its associations with CYP2D6 and CYP2C19 phenotypes. The CYP450 isoforms are major metabolizers for SSRIs, but its direct role in affecting weight gain outcomes has not been studied so far (25).

Few studies have evaluated the role of several gene polymorphisms affecting weight gain response to anti-depressants. A study examined the role of HTR2C, TPH1, and COMT genes as well as the SLC6A4 rs25531 in body weight gain during anti-depressive treatment with different classes of drugs such as SSRIs, SNRIs and combination of SSRI with SNRI. The study included a total of 301 patients out of which only 165 subjects were included in the analysis. The results showed that COMT rs6480 significantly contributed to weight outcomes. The AG genotype were less likely (OR=0.402, 95% CI=0.18–0.91) to gain more than 4 kg weight during treatment than A homozygotes. The GG genotype were four times (95% CI=1.64–9.75) more likely to gain more than 4 kg of weight during treatment than heterozygotes. Moreover, TPH1 rs1800532 also contributed to weight outcome(p=0.039). Upon correcting for multiple factors, only COMT rs4680 remained significant. Moreover, the genotypes HTR2C rs3813929 and SLC6A4 rs25531 did not significantly contribute to weight outcome (26).

Currently, the COMT rs6480 polymorphism is not clinically used to predict weight gain outcomes of antidepressants. Lack of robust evidence in the literature warrants further research in pharmacogenomics of SSRIs-induced weight gain.3. Antiepileptic drugs

Antiepileptic medications have an impact on weight. A report studied weight changes associated with antiepileptic drug therapy, and it was found that levetiracetam and valproic acid cause significant relative and absolute weight gain, carbamazepine and lamotrigine caused nonsignificant weight gain, and topiramate was associated with significant weight loss (27).

The most common anti-epileptic drug studied for the association of gene variation with weight gain as a side effect is valproate. A prospective study recruited 225 epileptic patients treated with valproate from the Chinese Han population to investigate the association of SNPs in CD36 andperoxisome proliferator-activated receptor gamma(PPAR gamma)on valproate-induced obesity. Total four SNPs, two in CD36 (rs1194197, rs7807607) and two in PPAR gamma (rs10865710, rs2920502) were studied. The statistical interpretation from the data was that the CD36 rs1194197 C allele and rs7807607 T allele (OR=0.31; 95% CI=0.13–0.72; p=0.009 and OR= 0.38; 95%CI=0.18–0.83; p=0.02, respectively) were found to play a protective role in valproate-induced obesity. The PPAR gamma rs10865710 C allele carriers were found less likely to have valproate-induced obesity compared with G allele carriers (OR=0.04; 95%CI=0.01–0.12; p< 0.001) (28). Another study examined the correlation of 19 SNPs in 11 genes on weight gain caused by valproate over 6 months in 212 epilepsy patients treated with valproate. The observations were three polymorphisms—LEPR rs1137101, rs1800497 Ankyrin Repeat And Kinase Domain Containing 1(ANKK1), and rs10789038 Protein Kinase AMP-Activated Catalytic Subunit Alpha 2(PRKAA2) were associated with a BMI increase within six months after initiation of valproate treatment (p<0.001, p=0.017, and p=0.020, respectively). Carriers of the A allele of LEPR rs1137101 gained significantly more weight compared to those with the GG genotype and carriers of the C allele of ANK1 rs1800497 gained more weight compared than that with wild genotype. Similarly, the G allele carriers of PRKAA2 rs10789038 had greater BMI change than the AA genotype (29). Apart from these prospective studies, a retrospective longitudinal study was conducted in 85 young epileptic patients treated with valproate and 93 treated with carbamazepine to study the impact of CYP2C19 polymorphisms. The final outcome was a greater incidence of weight gain in valproate-treated female patients with one or two loss of function CYP2C19 alleles than in females without the loss of function of CYP2C19 alleles (30).

4. Glucocorticoids

Glucocorticoids have a multi-systemic side effect profile, and one of the most common side effects is weight gain. Systemic steroids have a greater potential to cause weight gain compared to the topical preparations. A systematic review concluded that oral corticosteroids in the short term do not significantly affect body weight, appetite, energy intake, or body composition; however, long term consumption of oral corticosteroids significantly affected these parameters (10).

The literature on the correlation of glucocorticoid-induced weight gain with gene polymorphisms is scant. In one report, 68 patients with adrenal insufficiency treated with glucocorticoids were included in the study and the genotypes investigated were active SNPs of the Hydroxysteroid 11-Beta Dehydrogenase 1(HSD11B1) and Glucocorticoid Receptor(GR) genes. The GR Bcl1 polymorphisms showed a significant association with both BMI and body weight. Homozygous carriers of BcII had significantly higher BMI compared to the heterozygous carriers (p=0.007). The rs4844880 polymorphism of the HSD11B1 gene exerted a significant impact on BMI and weight gain compared to the non-carriers (17.5 ± 9.87 and 4.05 ± 9.95 kg, respectively; p=0.02) (31).

Greater research is needed to address the gene variants affecting weight gain due to glucocorticoids as it is prescribed for a multitude of conditions, and a prior pharmacogenomic profile can help prevent this adverse event.

5. Anti-hyperglycemic agents

Amongst anti-diabetic agents, sulfonylureas and thiazolidinedione are most commonly associated with weight gain(32). Currently, no studies are available in the literature that establishes the direct effect of gene polymorphism on weight gain due to sulfonylureas; however, some studies see the role of these gene SNPs in treatment response to sulfonylurea. One such study was done to find out the influence of polymorphisms Gly972Arg, SNP43, and Pro12Ala, of the genes Insulin Receptor Substrate 1(IRS1), Calpain 10(CAPN10), PPARG2 on treatment response to sulfonylurea and metformin. The outcome suggested that polymorphism SNP43 may influence response to treatment with sulfonylurea and metformin (33). In the thiazolidinedione group, a study was performed aiming to examine the effects of perilipin (PLIN) gene polymorphisms on weight gain with rosiglitazone (4 mg/day) treatment for 12 weeks in 160 of type 2 diabetic patients. Four polymorphisms at the PLIN locus were genotyped- PLIN 6209TC (intron 2), PLIN 11482G A (intron 6)(rs894160), PLIN 13041AG (exon 8), and PLIN 14995AT (exon 9). Significant weight gain was observed with the PLIN rs894160 polymorphism with an additive dose-response relationship between the number of A allele and the degree of weight gain (GG, 1.33±1.59 kg; GA, 0.85±1.89 kg; and AA, 0.03±1.46 kg;p=0.010) (34).

6. Beta-Blockers

Beta-blockers have been associated with weight gain and counteract the cachexia induced by chronic heart failure. In a report assessing 293 patients with chronic heart failure on beta-blockade therapy and its association with weight gain, an increase of 0.9±7.0 kg (p=0.03) weight gain was observed with beta-blockers (35).

Data is present on the effect of various beta-adrenergic receptor gene polymorphism on weight changes, but no direct evidence supports the impact of gene variants on weight gain as an adverse event of this drug. A study investigated beta1AR polymorphisms-beta(1)Ser49Gly, beta(1)Arg389Gly, beta(2)Arg16Gly, beta(2)Gln27Glu and beta(3)Trp64Arg in 188 type-2 diabetic patients. Beta (1) Ser49Gly was found to be significantly associated with obesity. The beta1Gly genotype group had higher BMI compared to the Ser49 / Ser49 genotype groups. (24.7±3.7 vs. 23.4±3.3 kg/m²; p=0.031) (36).

PHARMACOGENOMICS AND ANTI-OBESITY MEDICATIONS

Currently, four FDA approved medications are available for long-term management of obesity, which include phentermine-topiramate extended release, liraglutide, orlistat, bupropion-naltrexone, and lorcaserin. Further, there are four additional drugs approved for short term use (up to 12 weeks) and include phentermine, benzphentamine, diethylpropion, and phendimetrazine (14). The mechanism of action of anti-obesity medications is varied and has been extensively reviewed elsewhere (37). Here, we focused on the pharmacogenomics of anti-obesity medications (Table 2).

Table 2.

Pharmacogenomics of anti-obesity medications

Anti-Obesity Drugs	Gene	rs#	Outcome	Reference
Liraglutide	GLP-1 R	rs6923761	A allele- 1.8 kg more weight loss at 14 weeks	(42)
		rs6923761	A allele- delay in GE (adverse event)	(44)
		rs10305420	T allele- 5.7 kg less weight loss at 12 weeks	(43)
Topiramate	GRIK1	rs2832407	No effect on weight loss outcomes	(65)
	INSR	rs4804428rs2396185 rs10419421	T-C-A allele- 2.1 % greater body weight loss % at 24 weeks	(38)
Orlistat	GNB3	rs5443	T allele- 14.5% less fat mass reduction at 12 weeks	(46)
Bupropion	DRD2 Taq1	rs1800497	No polymorphisms predicted weight change during treatment with bupropion at 12 months	(48)
	DRD2–141	rs1799732
	C957T	rs6277
	COMT	rs4818
	SLC6A3
Sibutramine	GNB3	rs5443	C allele- 7.2 kg greater weight loss	(51)
	ADIPOQ	rs266729	C allele- No difference in weight loss outcomes at 12 weeks	(52)
	UCP2	rs28359178	G allele- No difference in weight loss outcomes at 12 weeks	(53)
	SLC6A4		S allele- 2.2 kg greater weight loss at 12 weeks	(66)
Metformin	SLC22A1	rs1867351 rs12208357 rs683369 rs3413415 rs2282143 rs34205214 rs34130495 rs628031 rs34888879 rs72552763 rs35270274 rs41267797 rs78899680	No difference in weight loss outcomes at 6 months; and 1.2% reduction in truck fat	(59)

1. Phentermine-Topiramate extended release

Phentermine has been approved for short term management (up to 3 months) of obesity and its combination with topiramate is approved for long term use. Phentermine is a trace amino acid receptor agonist (TAAR-1) that acts as a releasing agent of norepinephrine and dopamine. The increase in the biogenic amines in the hypothalamus mediates appetite suppression. The effects of pharmacogenomics profile on phentermine have not been studied thus far despite being the most widely prescribed medication for short term management of weight and that it has been in the market for more than 6 decades.

Topiramate, initially an anti-epileptic drug, potentiates the action of GABAergic neuron. The exact mechanism by which topiramate induces weight loss is not completely understood. However, clinical response to topiramate in individuals with obesity is highly variable (38). Variants in the insulin receptor gene (INSR) has been associated with differential weigh loss in those treated with topiramate. In a clinical study of 445 subjects with obesity, carriers of rs4804428 (T), rs2396185 (C), and rs10419421 (A) in INSR lost 9.1% of their baseline body weight compared with non-carriers (n=316), who lost 7.0% of their baseline body weight (38).

2. Liraglutide

Liraglutide is a glucagon-like peptide receptor (GLP-1R) agonist that causes weight loss centrally through inducing satiety and peripherally by delaying gastric emptying (39, 40). The drug is subcutaneously (s.c.) administered. The safety and efficacy of liraglutide have been evaluated in various trials. In a randomized, double-blinded, placebo-controlled comparing weight loss and cardiovascular outcomes following administration of 3 mg s.c. liraglutide vs. 1.8 mg s.c. liraglutide vs. placebo in 846 overweight and individuals with obesity, the total body weight loss percentage at the end of 56 weeks was −6 vs. −4.7 vs. −2 (p<0.001), respectively (41).

Few studies have been performed to establish the role of pharmacogenomics in treatment response to liraglutide. However, variants in GLP-1R have been associated with differential response. In a prospective study, 90 patients with type 2 diabetes mellitus and overweight were selected and initiated with the progressive treatment of liraglutide at the dose of 1.8 mg/day subcutaneously. Of these participants, 51 of them had the genotype GG, and 39 had the GA/AA genotype of the GLP-1R SNP rs6923761. Parameters evaluated at the end of the study were BMI, weight, and a decrease in fat mass. The decrease was higher in A allele carriers, BMI (−0.59±2.5 kg/m² vs. −1.69±3.9 kg/m²; p=0.05), weight (−2.78±2.8 kg vs. −4.52±4.6 kg; p=0.05) and fat mass (−0.59±2.5 kg vs. −1.69±3.9 kg; p=0.05) (42). Another report that studied the variants in the GLP-1R included the SNPs rs10305420 and rs6923761. The report involved 57 females with obesity diagnosed with polycystic ovarian syndrome(PCOS) who were assigned 1.2 mg, four times daily liraglutide subcutaneously for 12 weeks. The results were measured in terms of strong responders who lost 5 % or more of the initial body weight. Carriers of at least one T allele of rs10305420 had poor treatment response compared to two C alleles (OR=0.27, 95% CI=0.09–0.85, p=0.025).Carriers of at least one A allele of rs6923761 allele tended to have stronger treatment response compared to carriers of G alleles (OR=3.06, 95% CI=0.96–9.74, p=0.05) (43).

A correlation between the genetic variants of GLP-1 R and Transcription Factor 7 Like 2 (TCF7L2) to delayed gastric emptying and weight loss in individuals with obesity was established using data from two randomized, double-blinded placebo-controlled trials. The data reflected that within the GLP-1R (rs6923761), patients carrying the minor allele who received exenatide or liraglutide had larger mean retardation in gastric emptying relative to baseline (117.9±27.5 minutes and 128.9±38.3 minutes, respectively), compared to those with the wild type allele G (GG), [98.5±30.4 minutes and 61.4±21.4 minutes, respectively (p=0.11)]. However, for the TCF7L2 (rs7903146) genotype (major vs. minor) changes in GE at five weeks (p=0.93) and weight loss at five weeks (p=0.72) were not different (44). There is a consensus among all studies that patients with A allele carriers of GLP-1R SNP (rs6923761) are better treatment responders to liraglutide.

3. Orlistat

Orlistat acts by inhibiting gastric and pancreatic lipase and henceforth inhibiting the breakdown of intestinal triglycerides into fatty acids and monoglycerides for absorption by the intestinal mucosa. A 4 year, double-blinded placebo-controlled study looked at the effect of orlistat on various parameters and included 3,305 patients with obesity with BMI>30 who were subjected to lifestyle changes plus either orlistat or placebo. The mean weight loss at the 4-year endpoint was 5.8 kg with orlistat compared to 3.00 kg with placebo and was statistically significant (p<0.05), proving its strong clinical utility in weight loss management (45).

Variability in weight loss response to orlistat alone and its association with gene polymorphisms is not studied so far; however, there is one clinical trial that investigaed genetic variations affecting response to the additive effect of orlistat on sibutramine therapy for weight loss. The 12-week trial evaluated variation in rs5443 in the Guanine nucleotide-binding protein beta polypeptide 3 (GNB3) and drug response. After the intervention, fat mass proportion in total weight loss was significantly lower in subjects with a T allele than in those without a T allele than in those without a T allele(p=0.034) suggesting blunted fat mass reduction in females with obesity with T allele carriers (46).

4. Bupropion-Naltrexone

Bupropion potentiates dopaminergic and noradrenergic neurotransmission via inhibiting the dopamine and norepinephrine transporter, respectively, at the neuronal endplate. Naltrexone and its active metabolite 6B-naltrexol is an antagonist of the μ-opiod receptor. Bupropion, in combination with naltrexone as a sustained-release preparation, is approved and proven to cause significant weight loss likely by reducing the desire to eat and food cravings (47). Very limited information is available on pharmacogenomics of weight loss response to bupropion. The studies available have evaluated only gene variations affecting bupropion for smoking cessation and alcohol consumption. A trial studied the impact of 5 candidate genes on weight gain response to bupropion vs. placebo in smoking cessation. Variants in 5 candidate genes were studied and included D2 dopamine receptor gene(DRD2) Taq1 (rs1800497), DRD2–141 (rs1799732), C957T (rs6277), Catechol-O-methyltransferase(COMT) (rs4818), andSolute Carrier Family 6 Member 3(SLC6A3)]. Weight was recorded at baseline, end of 6 months, and 12 months. The results did not support the role of these genotypes in weight changes and treatment response (48).

Abundant evidence is available on gene polymorphism correlation with naltrexone on response to its different indications such as pain management, analgesia, opioid intoxication, alcohol dependence, but no data is collected on gene variants affecting weight loss response to bupropion/naltrexone extended-release preparation which warrants need for further studies.

5. Lorcaserin

Lorcaserin is a serotonin receptor(5-HT2C-R) agonist that activates the Proopiomelanocortin(POMC) pathway in the brain which promotes satiation. A multicenter randomized double-blinded trial, including 3182 subjects, were randomized to receive either placebo or lorcaserin. The primary endpoints in the study were weight loss at one year and two years. Patients in the lorcaserin group lost an average of 5.81 ± 0.16% of the baseline body weight, as compared with 2.16 ± 0.14% in the placebo group (p<0.001) indicating it to be an effective drug (49).

Plenty of studies are done on the gene 5HTR2C and its variants on the weight outcomes following anti-psychotic and anti-depressant medications. However, research on the influence of gene polymorphisms in weight loss response to lorcaserin is scant, and the 5HTR2C gene holds excellent potential as a candidate gene for future research for alternative 5HTR2c agonists, since lorcaserin was recently withdrawn from the market.

6. . Sibutramine

While subutramine is no longer in the market, the pharmacogenomics studies of sibutramine were useful and might be of value to be replicated in other anti-obesity medications. Sibutramine influences both noradrenergic and serotonergic pathways within the hypothalamus, inhibiting the release of both noradrenaline and serotonin from the hypothalamic neurons. It has a dual physiological action, reducing the food intake by enhancing satiation and reducing the decline in metabolic rate that occurs with weight loss (50).

Out of numerous polymorphisms studied so far, one of the important ones is GNB3 C825T polymorphism. In a retrospective study, patients who had participated in a multicenter double-blinded placebo-controlled trial were selected. The trial consisted of 348 subjects with 174 participants in the sibutramine group and 174 in the placebo group. Only 111 participants could be traced, and blood samples and buccal samples were obtained from them. The samples were genotyped for GNB3 C825T (rs5443) polymorphism. The results demonstrated a strong effect of sibutramine in individuals with CC genotype (p=0.003). This group lost an additional 7.2 ± 2.2 compared to 4.1 ± 2.0 kg in the subjects with the TT/TC genotype (p=0.0013) (51). Another interesting gene that is important to note is the ADIPOQ gene. Adiponectin is an adipose-derived plasma protein known for modulating insulin sensitivity and glucose homeostasis and is encoded by the adiponectin CQI and collagen domain-containing (ADIPOQ) gene located on chromosome 3q27. The study consisted of 131 individuals from the Taiwanese population in a randomized clinical trial, including 87 in the sibutramine group and 44 in the placebo group. The first endpoint was body fat loss percentage, and the second endpoint was weight loss compared to baseline at the end of 12 weeks. The samples were genotyped for ADIPOQ rs266729. The strong effect of sibutramine on percent body fat loss was indicated for subjects with the CC genotype (4.6±0.5 vs. 1.9±0.3%; p=0.001). On the contrary, sibutramine had no significant effect on percent body fat loss in subjects with the GG and GC genotypes (p=0.383 and p=0.814, respectively) (52). With a similar study design by the same author in the same Taiwanese population, a different genotype was studied. This time a common SNP, in the uncoupling protein 2 (UCP2) gene −866G/A (rs659366), was under investigation. Data concluded that sibutramine had a strong effect on weight loss and body fat percentage in AA+GA genotype groups (p< 0.001) compared to the GG genotype which had no significant effect on weight loss and body fat percentage in response to sibutramine (53). The GNB3 C825T(rs5443) polymorphism and ADIPOQ rs266729 gene variations are the key players in determining weight loss outcomes with sibutramine.

7. Metformin:

In the recent years epidemiological and preclinical studies have shown the favorable effect of metformin in body weight loss and metformin is usually used as an off label medication for obesity (54). The Metformin Study Group showed a decrease in 3.8 kg of body weight in the metformin group compared with sulfonylureas, at 29 week (55). The BIGRO study group examined weight loss treatment with metformin 850 mg BID for 1 year and found a 2 kg weight loss compared to controls (56). And the Diabetes Prevention Study (DPP), the largest study to show the weight benefits of metformin, showed that patients at high risk for Type 2 diabetes randomized to metformin experienced a 2.1 Kg weight loss (57). Even though the evidence suggests an effect of metformin in weight loss, there is a considerable inconsistency regarding this outcome.

The interpatient variability to metformin response in terms of weight reduction has been attributed to several factors including genetic polymorphisms of metformin transporters, such as the organic cation transporter member 1 (OCT1, encoded by SLC22A1), the plasma membrane monoamine transporter (PMAT), and the multidrug and toxic compound extrusion proteins (MATEs)(58). Waio Johnn Sam et al. found that SLC22A1 polymorphism detected in children with obesity did not significantly affect the 6 months response to metformin in terms of body weight; however, SLC22A1 variant carriers had smaller reductions in percentage of total trunk fat after metformin therapy (59). The genetic variation of the metformin transporters OCT1, MATE1, MATE2‐K has been also studied in women with polycystic ovarian syndrome, nevertheless, none of the polymorphism significantly affected the clinical response to metformin to weight loss, lipid profile or insulin sensitivity. However, these previous findings should be interpreted carefully due to the small sample size and the short duration of both studies (60).

CONCLUSION

Further research on gene polymorphisms associated with anti-obesity drugs can play a pivotal role in understanding the variability in response to anti-obesity drugs. Incorporating those genetic variants from the evidence into the commercially available pharmacogenomics testing can help individualize obesity treatment. Translating the current knowledge and evidence of weight gain and pharmacogenomics as a common side effect of certain medication into clinical practice may help achieve better patient outcomes and prevent weight gain as an adverse event of a drug. Knowing the pharmacogenomic profile of a person can guide the provider, which drug would be more likely to cause weight gain as a side effect and hence help in making the right choice of drug for that patient.

STUDY IMPORTANCE QUESTIONS.

1. What major reviews have already been published on this subject?

1. There are few reviews in the literature on pharmacogenomics and obesity drug therapy.

   1. Precision Obesity Treatments Including Pharmacogenetic and Nutrigenetic Approaches (1)
   2. Pharmacogenetics of Obesity Drug Therapy(2)

2. What are the new findings in your manuscript?

1. In this review, we cover both aspects of pharmacogenomics in obesity.

   1. The role of pharmacogenomics of commonly prescribed drugs with weight gain as side effects. and
   2. The role of pharmacogenomics in anti-obesity medications and weight loss.

3. How might your results change the direction of research or the focus of clinical practice?

1. Our review in pharmacogenomics for obesity will guide providers and obesity experts to consider pharmacogenomics studies to prevent or stop weight gain from commonly prescribed medications and to enhance weight loss from antiobesity medications.