Antipsychotic Drug Related Weight Gain
Introduction
Section titled “Introduction”Antipsychotic medications are cornerstone treatments for serious mental illnesses such as schizophrenia, bipolar disorder, and severe depression. While highly effective in managing psychiatric symptoms, a significant and prevalent adverse effect associated with their use is weight gain. This side effect can range from moderate to severe, often leading to substantial increases in body mass index (BMI) over the course of treatment.
Biological Basis
Section titled “Biological Basis”The mechanisms underlying antipsychotic-induced weight gain are complex and multifaceted, involving disruptions to various neurobiological and metabolic pathways. Antipsychotic drugs interact with a range of neurotransmitter receptors, including those for dopamine, serotonin, and histamine, which play roles in appetite regulation, satiety, and energy metabolism. For instance, antagonism of histamine H1 receptors and serotonin 5-HT2C receptors is thought to contribute to increased appetite and food intake. Beyond direct effects on appetite, these medications can also alter glucose and lipid metabolism, leading to insulin resistance and dyslipidemia, independent of weight gain.
Genetic predisposition plays a crucial role in an individual’s susceptibility to developing weight gain and other metabolic side effects when treated with antipsychotics.[1] Pharmacogenomic studies have sought to identify specific genetic variants that predict an individual’s response to antipsychotic drugs, including their metabolic side effects.[2]For example, a single nucleotide polymorphism (SNP)rs6741819 in the RNF144 gene has been found to mediate the metabolic side effects of the antipsychotic drug risperidone.[3] Research also investigates genes involved in the major and secondary metabolic pathways of these drugs.[2] as well as common variants at numerous loci that contribute to polygenic dyslipidemia, a condition often linked to weight gain.[4]
Clinical Relevance
Section titled “Clinical Relevance”The clinical implications of antipsychotic-related weight gain are substantial. It significantly increases the risk of developing serious health conditions, including metabolic syndrome, type 2 diabetes mellitus, and cardiovascular disease. These conditions contribute to a higher morbidity and mortality rate among individuals with severe mental illnesses compared to the general population. The distress caused by weight gain can also negatively impact a patient’s quality of life, self-esteem, and adherence to their prescribed medication regimen, potentially leading to symptom relapse. Therefore, managing and mitigating this side effect is a critical aspect of comprehensive psychiatric care.
Social Importance
Section titled “Social Importance”Antipsychotic-induced weight gain carries considerable social importance due to its impact on public health and healthcare systems. The associated physical health complications contribute to increased healthcare costs and resource utilization. Furthermore, the experience of significant weight gain can exacerbate social stigma and discrimination faced by individuals with mental illness, hindering their recovery and reintegration into society. Efforts to understand the genetic and biological underpinnings of this side effect are vital for developing personalized treatment strategies, identifying individuals at higher risk, and ultimately improving patient outcomes and overall well-being.
Limitations
Section titled “Limitations”Research into the genetic underpinnings of antipsychotic drug-related weight gain, like many complex traits, is subject to several methodological and interpretative limitations. These constraints stem from the inherent complexities of study design, the precise characterization of phenotypes, and the diverse genetic and environmental backgrounds of individuals. Acknowledging these limitations is crucial for a balanced understanding of current findings and for guiding future research directions.
Methodological and Statistical Considerations
Section titled “Methodological and Statistical Considerations”The identification of genetic factors contributing to antipsychotic drug-related weight gain is constrained by several methodological and statistical challenges inherent in genetic association studies. Many studies face limitations related to sample size, with some cohorts being considered small for robust genetic evaluations, which can restrict the power to detect true associations and contribute to heterogeneity in findings.[5]This often leads to a risk of effect-size inflation and false positives, where initial findings may show a stronger association that subsequently diminishes or disappears upon replication with additional control data or larger cohorts.[6] Such issues underscore the critical need for larger, well-powered studies and rigorous, independent replication efforts to confirm initial genetic signals and ensure their reliability.
Furthermore, the design and composition of study cohorts introduce additional limitations. The common practice of including unscreened, population-based controls, while expedient, can introduce variability and potential biases into the analysis.[7] Significant differences in population characteristics, such as age at onset or symptom rates, across various study samples, even after attempts to harmonize definitions, can reflect true population variations or sampling biases.[8] These variations can introduce error into the characterization of sub-phenotypes and impact the generalizability and comparability of observed genetic correlations.
Phenotypic Heterogeneity and Challenges
Section titled “Phenotypic Heterogeneity and Challenges”Accurately assessing antipsychotic drug-related weight gain and its underlying genetic factors is complicated by significant phenotypic heterogeneity and practical challenges. Drug administration protocols can vary substantially across clinical settings and trials, including instances where dosage is non-uniformly altered to achieve a target treatment response.[5] Additional complexities arise from suboptimal drug delivery methods, such as injection site issues, and the challenge of accurately accounting for unknown patient adherence over time to oral medications.[5] These factors contribute to heterogeneity in actual drug exposure, making it difficult to precisely estimate the pharmacokinetic-pharmacodynamic relationship and its correlation with weight outcomes.
Beyond drug-specific factors, the definition and of clinical sub-phenotypes themselves pose limitations. Despite efforts to standardize definitions across studies, observed differences in phenotypic characteristics may reflect true population variations or inconsistencies in techniques.[8] Many studies may only assess a limited number of sub-phenotypes, suggesting that a broader range of clinical variables or clusters of variables, such as specific patterns of treatment response, might offer more robust correlations with common genetic variations relevant to weight gain.[8] This highlights a critical need for more comprehensive and standardized phenotypic characterization to enhance the precision and utility of genotype-phenotype correlation analyses.
Population Diversity and Generalizability
Section titled “Population Diversity and Generalizability”The generalizability of findings regarding antipsychotic drug-related weight gain is significantly influenced by the diversity of study populations and genetic ancestry considerations. While researchers often implement controls for population substructure and adjust for genetic ancestry using cosmopolitan reference panels, many initial genetic analyses are predominantly concentrated on specific, often homogenous, subgroups, such as non-Hispanic White (NHW) individuals.[7] This narrow focus can limit the direct applicability of discovered genetic associations to more diverse populations, where allele frequencies, linkage disequilibrium patterns, and environmental exposures may differ substantially.
Discrepancies observed across different geographic or ethnic cohorts, such as variations in symptom rates or age at onset between samples from different countries, suggest that true differences in patient populations exist, potentially impacting the genetic architecture of drug response.[8] Such population-specific genetic backgrounds or gene-environment interactions may influence the manifestation and severity of drug-induced weight gain. Therefore, broader, multi-ancestry studies are essential to ensure that genetic discoveries are robust, equitable, and clinically relevant across the global population, thereby improving the understanding of genetic contributions to treatment efficacy and adverse effects in a more inclusive manner.
Variants
Section titled “Variants”Genetic variations play a crucial role in an individual’s susceptibility to metabolic changes, including weight gain associated with antipsychotic medications. Several genes involved in protein metabolism, signal transduction, and cellular regulation have been identified as potential contributors to these complex metabolic phenotypes. Understanding how specific variants influence these pathways can shed light on personalized risk profiles for antipsychotic-induced weight gain.
Variants in genes that regulate protein phosphorylation and peptide metabolism are key candidates in metabolic health. ThePEPD gene encodes Peptidase D, an enzyme essential for breaking down prolyl-containing dipeptides, influencing nutrient sensing and overall energy balance; thus, a variant like rs10422861 could alter this metabolic process. Similarly, PTPRD(Protein Tyrosine Phosphatase Receptor Type D) is vital for insulin signaling and neuronal function, where its variantrs3824417 might affect glucose metabolism and appetite regulation.[9] The PRKCZgene, encoding Protein Kinase C Zeta, is a critical component of insulin signaling and glucose uptake pathways, making its variantrs11587831 potentially impactful on insulin sensitivity and weight management. Moreover, thePPP2R5E gene, a regulatory subunit of Protein Phosphatase 2A, is involved in cellular growth and metabolism, and its variant rs972984 may modulate these fundamental processes, contributing to an individual’s metabolic response to medication.[10] Other variants affect lipid metabolism, cellular integrity, and redox balance, which are all integral to energy homeostasis. The DGKB gene, or Diacylglycerol Kinase Beta, is involved in lipid signaling by phosphorylating diacylglycerol, meaning its variant rs1525085 could influence fat storage and utilization. CAV2(Caveolin 2) contributes to the structure of caveolae, which are critical for lipid transport, insulin signaling, and adipocyte function; therefore, variants such asrs1052990 and rs3779511 may alter these processes and impact weight gain.[11] The NXN gene (Nucleoredoxin), involved in redox regulation, maintains cellular oxidative balance, and its variant rs12942654 could affect metabolic health by influencing cellular stress responses. These genetic differences can modify how the body processes fats and responds to metabolic stressors, including those from antipsychotic drugs.[12] Beyond direct metabolic pathways, variants in genes involved in broader cellular regulation and intergenic regions can also contribute to metabolic risk. The PDE4Bgene regulates intracellular cAMP levels, a key secondary messenger involved in lipolysis and glucose metabolism, whileSGIP1 is linked to signal transduction; the rs11208844 variant in this intergenic region could thus impact energy expenditure and fat accumulation. TheNEK10-SLC4A7 region contains rs77000008 , where SLC4A7 plays a role in cellular pH regulation, a fundamental aspect of cell function that can indirectly affect metabolism. Similarly, the variant rs10114227 in the CDCA7P2-OTX2P1 intergenic region, involving pseudogenes, may influence regulatory elements of nearby functional genes, subtly modulating metabolic processes. Such variations highlight the complex genetic architecture underlying susceptibility to antipsychotic-related weight gain, often involving genes with diverse cellular roles.[13]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10422861 | PEPD | pancreatic fat pad amount type 2 diabetes mellitus monocyte percentage of leukocytes triglyceride neutrophil count |
| rs3824417 | PTPRD | antipsychotic drug related weight gain |
| rs12942654 | NXN | antipsychotic drug related weight gain |
| rs77000008 | NEK10 - SLC4A7 | antipsychotic drug related weight gain |
| rs11208844 | PDE4B - SGIP1 | chronotype antipsychotic drug related weight gain body height |
| rs972984 | PPP2R5E - RPL31P5 | antipsychotic drug related weight gain |
| rs11587831 | PRKCZ | antipsychotic drug related weight gain |
| rs1525085 | DGKB | antipsychotic drug related weight gain |
| rs1052990 rs3779511 | CAV2 | antipsychotic drug related weight gain |
| rs10114227 | CDCA7P2 - OTX2P1 | antipsychotic drug related weight gain |
Biological Background
Section titled “Biological Background”Weight gain associated with antipsychotic drug use is a complex phenomenon influenced by an interplay of genetic predispositions, neuroendocrine signaling, and metabolic processes. Understanding the underlying biological mechanisms that regulate appetite, energy balance, and fat metabolism is crucial for comprehending how these medications can disrupt normal physiological homeostasis, leading to increased body mass. The development of obesity, regardless of its trigger, involves a dysregulation of these tightly controlled biological systems.
Neuroendocrine Regulation of Appetite and Energy Homeostasis
Section titled “Neuroendocrine Regulation of Appetite and Energy Homeostasis”The body’s energy balance, encompassing both energy intake and expenditure, is meticulously regulated by a complex neuroendocrine system, primarily centered in the hypothalamus. This brain region integrates signals from various hormones and nutrients to control feelings of hunger and satiety, thereby influencing feeding behavior and overall energy consumption.[14]Disruptions in this delicate balance can lead to hyperphagia, or excessive eating, and subsequently, weight gain. For instance, the brain-derived neurotrophic factor (BDNF) plays a significant role in this regulatory network; a functional loss of one copy of the BDNFgene has been linked to severe early-onset obesity and hyperphagia, along with impaired cognitive function.[9] Key biomolecules like pro-opiomelanocortin (POMC)-derived peptides are fundamental to the regulation of energy homeostasis, signaling satiety to the brain. Deficiencies in POMCcan directly result in obesity, highlighting its critical role in controlling appetite and body weight.[9]Conversely, gastric inhibitory polypeptide signaling has been identified as a mechanism that can prevent obesity, suggesting its involvement in metabolic regulation.[9] The intricate communication between these peptides, receptors, and neuronal pathways in the hypothalamus dictates an individual’s propensity to gain or lose weight.
Genetic Influences on Body Mass and Metabolic Pathways
Section titled “Genetic Influences on Body Mass and Metabolic Pathways”An individual’s genetic makeup significantly contributes to their susceptibility to obesity and variations in body mass index (BMI). Numerous genes have been identified through genome-wide association studies (GWAS) as being associated with obesity-related traits. For example, theFTOgene is a prominent genetic locus whose variants are strongly linked to obesity and BMI.[11] Research suggests that FTOprimarily influences BMI by affecting energy intake, rather than altering energy expenditure, indicating its role in neuronal functions related to hunger control.[14] Beyond FTO, a multitude of other genes contributes to the polygenic architecture of obesity. These include genes involved in lipid metabolism, such asLPL(lipoprotein lipase),APOA2, APOA4, and APOA5, which are critical for processing dietary fats.[11] Genes like MC4R (melanocortin-4 receptor) and LEPR(leptin receptor) are integral to central appetite regulation, whilePPARG(peroxisome proliferator-activated receptor gamma) is a key transcription factor in adipogenesis and insulin sensitivity.[11]The collective impact of variants in these genes can predispose individuals to dysregulated metabolism and increased body weight.
Cellular and Molecular Mechanisms of Nutrient Sensing and Fat Metabolism
Section titled “Cellular and Molecular Mechanisms of Nutrient Sensing and Fat Metabolism”At the cellular level, weight gain is a consequence of an imbalance where energy intake consistently exceeds energy expenditure, leading to the storage of excess energy, primarily as fat in adipose tissue. This process involves complex molecular and cellular pathways that govern nutrient sensing, lipid synthesis, and breakdown. Genes such asUCP1, UCP2, and UCP3(uncoupling proteins) are involved in thermogenesis and energy expenditure, influencing how efficiently the body burns calories.[11]Variations in these genes can affect an individual’s basal metabolic rate and their susceptibility to weight gain.
Other critical enzymes and proteins like SCD (stearoyl-CoA desaturase) play a role in fatty acid synthesis, while PLIN (perilipin) is involved in regulating lipid droplet metabolism within adipocytes.[11] The interplay of these molecular components determines the efficiency of fat storage and mobilization. When these pathways are altered, either by genetic predisposition or external factors, the body’s ability to maintain a healthy weight is compromised, leading to increased adiposity.
Systemic Metabolic Dysregulation and Adiposity
Section titled “Systemic Metabolic Dysregulation and Adiposity”Antipsychotic drug-related weight gain involves a systemic disruption of metabolic homeostasis, leading to increased adiposity and a higher risk of metabolic syndrome. This dysregulation is not confined to a single organ but involves interactions across various tissues, including the brain, adipose tissue, liver, and pancreas. The altered hypothalamic signaling, as seen with genes likeFTO impacting hunger control, can lead to increased caloric intake, which is a primary driver of systemic weight gain.[14]The cumulative effect of genetic predispositions and altered neuroendocrine signals can manifest as increased visceral fat accumulation, insulin resistance, and dyslipidemia. Genes likeIL6 and TNF (pro-inflammatory cytokines) and RETN(resistin) are implicated in inflammatory responses and insulin sensitivity, which are often disturbed in obesity.[11]The disruption of these systemic regulatory networks ultimately leads to a persistent positive energy balance, promoting the expansion of adipose tissue and the development of clinical obesity.
Genetic Predisposition to Antipsychotic-Induced Weight Gain
Section titled “Genetic Predisposition to Antipsychotic-Induced Weight Gain”Antipsychotic medications, particularly second-generation agents, are frequently associated with significant metabolic side effects, including substantial weight gain, which can impact patient adherence and long-term health. Individual genetic variations play a crucial role in determining a patient’s susceptibility to these adverse drug reactions. A genome-wide pharmacogenomic study by.[1] specifically investigated metabolic side effects, including weight gain, associated with antipsychotic drugs, underscoring the complex, polygenic nature of this clinically important trait. Identifying these genetic markers is essential for predicting which patients are at highest risk for developing antipsychotic-induced weight gain.
Pharmacokinetic and Pharmacodynamic Modulators of Metabolic Risk
Section titled “Pharmacokinetic and Pharmacodynamic Modulators of Metabolic Risk”Genetic variations in drug-metabolizing enzymes and transporters significantly influence the pharmacokinetic profiles of antipsychotics, thereby affecting an individual’s susceptibility to weight gain. Polymorphisms in cytochrome P450 (CYP450) enzymes, phase II enzymes, and drug transporters can alter the absorption, distribution, metabolism, and excretion of these medications.[15] Such variations can lead to altered systemic drug concentrations, potentially increasing exposure to the drug’s effects on metabolic pathways and contributing to the severity of weight gain. For instance, individuals with slower metabolism may experience higher drug levels, intensifying adverse effects.
Beyond pharmacokinetics, genetic variants in drug target receptors and signaling pathways contribute to antipsychotic-induced weight gain through pharmacodynamic mechanisms. Polymorphisms in various receptor genes, such as those for serotonin (e.g., 5-HT2C receptor) or histamine (e.g., H1 receptor), or in genes encoding proteins involved in downstream signaling pathways, can modify how an individual’s body responds to antipsychotic-mediated blockade or agonism. These genetic differences can influence appetite regulation, satiety signals, energy expenditure, and glucose-lipid metabolism, directly impacting the propensity for clinically significant weight gain.
Clinical Implications for Personalized Treatment Strategies
Section titled “Clinical Implications for Personalized Treatment Strategies”The identification of genetic predictors for antipsychotic-induced weight gain holds significant promise for advancing personalized prescribing practices in psychiatry. Understanding a patient’s genetic profile could enable clinicians to make more informed decisions regarding drug selection, allowing them to choose antipsychotics with a lower metabolic risk for genetically susceptible individuals. Furthermore, pharmacogenetic insights could guide individualized dosing recommendations or prompt early implementation of targeted lifestyle interventions and metabolic monitoring, thereby mitigating the severity of this common adverse effect. While research continues to identify potential associations, the routine clinical implementation of these findings requires further validation, the establishment of robust clinical guidelines, and consideration of their overall utility in diverse patient populations.
Frequently Asked Questions About Antipsychotic Drug Related Weight Gain
Section titled “Frequently Asked Questions About Antipsychotic Drug Related Weight Gain”These questions address the most important and specific aspects of antipsychotic drug related weight gain based on current genetic research.
1. Why do I gain so much weight on my medicine when others don’t?
Section titled “1. Why do I gain so much weight on my medicine when others don’t?”Your individual genetic makeup can significantly influence how susceptible you are to weight gain from antipsychotic medications. While these drugs affect appetite and metabolism for many, some people have specific genetic variations that make them more sensitive to these side effects. For example, a variant in the RNF144 gene has been linked to metabolic side effects from certain antipsychotics like risperidone. This means your body might process the drug or regulate appetite differently than someone else’s.
2. My family is heavy; will I also struggle with weight on this medicine?
Section titled “2. My family is heavy; will I also struggle with weight on this medicine?”Yes, a family history of weight struggles can increase your risk, especially when taking antipsychotics. Genetic predisposition is a significant factor in how your body responds to these medications. If your family tends to gain weight easily, you might inherit similar genetic variations that make you more susceptible to the metabolic changes induced by the drugs, like altered glucose and lipid metabolism. This means you might need more proactive strategies to manage your weight.
3. Why does my diet feel useless with this medication?
Section titled “3. Why does my diet feel useless with this medication?”It’s not just you; these medications can significantly alter your body’s metabolism and appetite regulation. Antipsychotics can block certain receptors, like histamine H1 and serotonin 5-HT2C, which directly increase your hunger and food cravings. They can also disrupt your glucose and lipid metabolism, leading to insulin resistance, making it harder to lose weight even with a strict diet. This means your body is working against you in ways a typical diet might not account for.
4. Am I imagining these intense food cravings from my pills?
Section titled “4. Am I imagining these intense food cravings from my pills?”No, you’re definitely not imagining them. Many antipsychotic medications block specific brain receptors, such as histamine H1 and serotonin 5-HT2C receptors. This blockade directly leads to increased appetite and intense food cravings, making you feel much hungrier than usual. These cravings are a real biological effect of the medication.
5. Is my medicine messing with my body’s natural metabolism?
Section titled “5. Is my medicine messing with my body’s natural metabolism?”Yes, your medicine can indeed mess with your natural metabolism. Antipsychotics are known to alter how your body processes glucose (sugar) and lipids (fats), leading to conditions like insulin resistance and dyslipidemia. These metabolic changes can occur even before significant weight gain, making it harder for your body to manage energy effectively. This disruption contributes significantly to weight gain and other health risks.
6. Could a DNA test tell me if I’ll gain weight on this drug?
Section titled “6. Could a DNA test tell me if I’ll gain weight on this drug?”Potentially, yes. Pharmacogenomic studies are actively identifying specific genetic markers that predict an individual’s susceptibility to antipsychotic-induced weight gain. For instance, a variant like rs6741819 in the RNF144 gene has been linked to metabolic side effects from drugs like risperidone. While not yet routine for all drugs, such tests could eventually help your doctor choose medications with fewer side effects for you.
7. My sibling takes the same drug, why am I gaining more?
Section titled “7. My sibling takes the same drug, why am I gaining more?”Even with the same medication, your individual genetic makeup can lead to different responses than your sibling’s. You and your sibling might have different genetic variations that influence how your body processes the drug or regulates appetite and metabolism. These subtle genetic differences can significantly impact your susceptibility to weight gain, making one person gain more than another.
8. Will this weight gain from my medicine cause other diseases?
Section titled “8. Will this weight gain from my medicine cause other diseases?”Unfortunately, yes, significant weight gain from antipsychotic medication substantially increases your risk for other serious health problems. This includes developing metabolic syndrome, type 2 diabetes, and cardiovascular disease. Managing this weight gain is crucial because these conditions can lead to higher morbidity and mortality.
9. Can I really fight my genes to avoid weight gain?
Section titled “9. Can I really fight my genes to avoid weight gain?”While genetic predisposition plays a crucial role in your susceptibility, it’s not a destiny you can’t influence. Your genes might make you more prone to weight gain, but lifestyle factors like diet and exercise can significantly mitigate these effects. Working closely with your doctor to manage your medication and adopt healthy habits is vital. It’s about proactive management and finding strategies that work best for your unique genetic profile.
10. Does my ethnic background change my weight gain risk?
Section titled “10. Does my ethnic background change my weight gain risk?”Yes, research suggests that different ethnic backgrounds can influence your risk of weight gain from antipsychotics. Genetic variations linked to metabolism and drug response can differ across populations. This means your ancestry might contribute to how susceptible you are to these side effects, highlighting the need for personalized approaches in medicine.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
Section titled “References”[1] Adkins, D. E., Aberg, K., McClay, J. L., Bukszar, J., Zhao, Z., et al. “Genomewide pharmacogenomic study of metabolic side effects to antipsychotic drugs.” Molecular psychiatry, vol. 16, 2011, pp. 321–332.
[2] McClay, J. L., et al. “Genome-wide pharmacogenomic study of neurocognition as an indicator of antipsychotic treatment response in schizophrenia.”Neuropsychopharmacology, 2011.
[3] Xu, C., et al. “BCL9 and C9orf5 are associated with negative symptoms in schizophrenia: meta-analysis of two genome-wide association studies.”PLoS One, vol. 8, no. 1, 2013, e51674.
[4] Kathiresan, S., et al. “Common variants at 30 loci contribute to polygenic dyslipidemia.” Nat Genet, 2008.
[5] Li, Q., et al. “Genome-wide association study of paliperidone efficacy.” Pharmacogenetics and Genomics, 2016.
[6] Abraham, R., et al. “A genome-wide association study for late-onset Alzheimer’s disease using DNA pooling.”BMC Medical Genomics, 2008.
[7] Hollingworth, P., et al. “Genome-wide association study of Alzheimer’s disease with psychotic symptoms.”Molecular Psychiatry, 2011.
[8] Belmonte Mahon, P., et al. “Genome-wide association analysis of age at onset and psychotic symptoms in bipolar disorder.” American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 2011.
[9] Speliotes EK et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):901-908.
[10] Fox CS et al. Genome-wide association to body mass index and waist circumference: the Framingham Heart Study 100K project. BMC Med Genet. 2007;8:55.
[11] Scuteri A et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3(7):e115.
[12] Wallace C et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia. Am J Hum Genet. 2008;82(1):139-149.
[13] Velez Edwards DR et al. Gene-environment interactions and obesity traits among postmenopausal African-American and Hispanic women in the Women’s Health Initiative SHARe Study. Hum Genet. 2013;132(2):161-174.
[14] Liu JZ. et al. “Genome-wide association study of height and body mass index in Australian twin families.”Twin Res Hum Genet, 2010.
[15] Arranz, M. J., and J. de Leon. “Pharmacogenomics and pharmacogenetics of schizophrenia: a critical review of the last decade.”Pharmacogenomics, vol. 8, 2007, pp. 1543–1558.