The Pharmacogenomics of Mood Stabilizer Response in Bipolar Disorder
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Bipolar Disorder (BD) is a common psychiatric illness that has a recurring course and 17% - 24% lifetime prevalence of suicide attempts (1). Difficulty in diagnosis and individual variation in response point to a great need for personalized medicine. Studies to date have focused largely on lithium and suggest the idea that different medication responses may reflect different disease mechanisms. An overview of current pharmacogenomic studies will be presented and needs and future directions discussed.
Bipolar Disorder and Its Treatment
Bipolar Disorder (BD) or manic depressive illness is a common debilitating psychiatric disorder with a recurring and relapsing course (2). Patients alternate between mania and depression, and also may suffer from mixed states, in which symptoms of both mania and depression are experienced at the same time. There is a wide range of presentations varying in the frequency and severity of the cycling between these states. Approximately 1% of the population is affected with bipolar disorder and up to 17% of patients will attempt suicide at some point in their lives (1).
The primary goal of treatment is long term stabilization of mood, which often involves the use of a class of medications termed mood stabilizers. The original mood stabilizer was lithium, which has been used for nervous conditions since the 19th century, and was later shown to have more specific efficacy for mania (3). Some anticonvulsants, for example carbamazepine and valproic acid, but not others, such as phenytoin and primidone, were shown to have value as mood stabilizers. More recently, the anticonvulsant lamotrigine was shown to have efficacy in preventing the depressed phase of bipolar disorder (4), and was the first drug to receive a specific FDA indication for bipolar depression. Several newer anticonvulsants including gabapentin, topiramate and levetiracetam have shown efficacy in some case reports, though results of systematic trials remain mixed (5). The appropriate role of antidepressants in treatment is less clear, as studies are conflicted as to the benefit (6). Antidepressant treatment may further incur the risk of inducing mania or rapid cycling, though the reported frequency of this has been inconsistent in the literature (7).
Though psychiatrists are fortunate to have such a varied armamentarium for the management of BD, practical application faces a number of challenges. Foremost among these is the difficulty and delay in accurate diagnosis and appropriate treatment. Herschfield and colleagues used a web based survey among members of a national support group to assess these issues (8). They found that, on average, it took 7 years before a patient with BD was accurately diagnosed. During this time, the patients saw 4 different physicians and received, on average, 3.5 incorrect diagnoses. It is particularly alarming that many of these patients may have been misdiagnosed with unipolar depression and may have been made worse by treatment with an antidepressant without a mood stabilizer. These facts underscore the great clinical need for tests to aid earlier diagnosis and pharmacogenetic tests to expedite medication selection.
Clinical Presentation and Response
Different clinical characteristics have been reported to be associated with patients who respond to different medications (see Table 1) (9-11). Patients who respond to lithium tend to have euphoric mania, a strong family history of BD, good inter-episode recovery and less comorbidity with other psychiatric illness (e.g.,anxiety or substance use disorders), while patients with irritable mania or mixed states may respond better to anticonvulsants (9-10). Lamotrigine is especially useful for bipolar depression and responders differ from lithium responders by both clinical profile and family history (11).
Table 1 – Clinical Features Associated with Response to Different Mood Stabilizers (9-11)
|Family history||+ BD||-||Anxiety, depression, not BD|
|Mixed states and Dysphoric Mania||-||+||+|
|Good Inter-episode Recovery||+||-||-|
|Co-morbid Anxiety Disorders and Substance Use||_||+||++|
These differences in clinical presentation of responders to different mood stabilizers suggest the possibility that bipolar disorder may be comprised of multiple different disease mechanisms. Each patient then may respond best to that medication that modulates and normalizes the pathway that is genetically defective in their form of illness. Though this has been hypothesized for some time, distinguishing these different pathways of disease has proven challenging in the search for disease predisposing genes. Differences in drug response may provide a powerful way to distinguish such different genetic mechanisms of illness and thereby facilitate the identification of disease susceptibility genes (12).
Mood Stabilizer Pathways
Most work done to date on mechanism of action has focused on lithium and identified a plethora of different putative mechanisms which will be reviewed here briefly (for a detailed review see King et al. (13)). Early observations of lithium’s cellular effects suggested that it inhibited adenylate cyclase thereby dampening cAMP signaling (14). Lithium has been reported to affect G protein coupled receptor signaling in several ways (15), and similar effects have been reported for carbamazepine (16). Lithium may also modulate signal transduction through a recently described pathway though β arrestin (17). Another theory involving signal transduction implicates the inositol turnover pathway (18). Lithium inhibits several phosphatases involved in the recycling of IP3 to inositol in the membrane. Depletion of membrane inositol results in a dampening of signaling. This effect on inositol has been shown to be shared by lithium, carbamazepine and valproate (19). Observation of lithium’s dysmorphic effect in drosophila development led to discovery of its inhibitory effect on Wnt signaling and glycogen synthase kinase beta (GSK3B)(20). Valproate has also been shown to inhibit GSK3B. This commonality and the several pathways that converge on GSK3B make it a leading candidate for a common mechanism for mood stabilizers.
Neurotrophins have also been implicated in lithium’s mechanism of action. Brain derived neurotrophic factor (BDNF) expression has been shown to be increased by lithium and selective serotonin reuptake inhibitor antidepressants (SSRI) (21). This increase in expression is necessary for antidepressant action in animal models. BDNF may modulate GSK3B via the tyrosine kinase receptor Trkb (NTRK2).
Glutamate has also been the focus of many studies of mood stabilizers. Mood stabilizers with a more anti-mania effect (e.g. valproate) have been shown to have a different effect on AMPA channel subunit ratios than mood stabilizers with a more anti-depressant effect (e.g. lamotrigine) (22). This is consistent with much literature implicating glutamate and AMPA channels in mood disorders.
Genetic Association Studies of Mood Stabilizer Response
These candidate pathways for mechanism of action have driven most of the candidate gene studies conducted to date on mood stabilizers. Table 2 summarizes results for some of the more frequently examined genes for which replication has been reported (for a detailed review see Smith et al. (23)). Most pharmacogenomic studies to date have examined lithium response and have consisted of candidate gene studies of relatively small samples (100-300). The serotonin transporter (SLC6A4) has been the most extensively studied, largely because it is the site of action of SSRI antidepressants and has been reported to be associated with SSRI response. Overall, the preponderance of data is positive for lithium with 4 out of 6 studies showing poor response associated with the short allele of the HTTLPR promoter repeat variant. However, a recent meta-analysis of SSRI response has cast doubt on that result. Results for GSK3B are mixed with 2 out of 3 studies of the C-50T functional promoter variant showing association of response to the C allele. Another study of a different SNP was negative. Studies of the inositol polyphosphatase gene (INPP1) have been mixed, with suggestive evidence from one small early study, but mixed support from two subsequent larger studies. A number of other genes (Table 2) have been examined in only one study and others only reported to have no association.
Table 2 – Selected Genes Associated with Lithium Response with Reported Replication1
|Gene||Marker||N||Population||Allele associated with better response|
|rs334558 (C-50T)||89||Polish||No association|
|rs3755557 (A-1727T)||134||Brazilian||No association|
|INPP1||A682G, G153T, G348A, C973A||9||Norwegian||C973A|
|SLC6A4||HTTLPR||67||Polish||s/l or l/l|
|HTTLPR||83||Greek||s/s or s/l|
|HTTLPR||111||Polish||s/l or l/l interaction with BDNF|
1For references see McCarthy MJ, et al. (32)
Genes with only negative reports in one or more studies include: HTR2A, HTR2C, CLOCK, COMT, DRD2, DRD3, FYN, GABRA3, GABRA5, GABRB3, GBP3, GRIN2B, MAOA, PDLIM5, SLC6A3, TFAP2A, TRPH1
Genes with only one positive report include: BDNF, DRD1, IMPA1, IMPA2, ND3, NTRK2, NR1D1, PLCG1, PREP, TRPH1, XBP1
One genomewide association study has been conducted by Perlis and colleagues (24). This study examined the STEP-BD sample, which was a large multi-site naturalistic study of the treatment of bipolar disorder in the U.S. This analysis included 431 subjects on lithium for longer than 4 weeks, 274 of whom were on lithium as their sole mood stabilizer. All subjects were genotyped using the Affymetrix 500K chip. No SNP met criteria for genomewide significance; however, five SNPs with low p values did show evidence of replication in an independent sample. These included: syndecan2 precursor (SDC2), AP20 region protein (APRG1), od Oz homolog 4 (ODZ4), a region 3’ of an AMPA glutamate receptor subunit (GRIA2), and a region 3’ of synaptic vesicle protein 2B homolog (SV2B).
Though lithium has been the focus of most pharmacogenetic studies of mood stabilizers, some work has addressed the several anticonvulsants used for bipolar disorder. Kim and colleagues have reported an association between allelic variants in the XBP1 gene and response to valproate (25).
Bioavailability and Adverse Events
HLA-B*15:02 has been associated with carbamazepine-induced Stevens Johnson’s Syndrome in Asians, leading the FDA to make labeling changes for this drug (26). Similarly, vulnerability to Stevens Johnson’s Syndrome induced by lamotrigine has been associated to HLA type in Han Chinese. The A allele of the G1247A SNP in multidrug resistance protein 2 (MRP2) has been associated with adverse neurological events in carbamazepine treatment (27). Studies of epilepsy treatment have pointed to the importance of genes influencing the blood brain barrier such as the multi-drug resistance 1 gene (MDR1, ABCB1)(28). Topiramate induced side effects have been associated to a SNP in the GRIK1 gene that codes for the GluR5 glutamate receptor (29). Polymorphisms in CYP2C9 may influence the metabolism of valproate, and variation in the EPHX1 gene may influence elimination of carbamazepine-10,11-epoxide and in turn be associated with adverse effects (30). Lithium and the other anticonvulsants with efficacy in bipolar disorder are largely eliminated by glucuronidation or are renally excreted.
Future Directions in Personalized Treatment of Mood Disorders
As with other areas of medicine, the need for personalization of clinical practice is clear, as is the contribution of pharmacogenomics to that practice. Studies of mood stabilizers used in bipolar disorder are at an early stage but already show promise. Several new projects and technologies are now on the horizon that promise advances in this area. A chronic problem in studies to date has been small sample size. Recently, the ConLiGen consortium under the leadership of Dr. Thomas Schulze has assembled a set of over 1,300 bipolar subjects whose response to lithium has been assessed (31). Another limitation has been the lack of modern clinical trial methodology. Most studies have employed retrospective assessment of response, which is notoriously vulnerable to limitations in patient memory and medical records. Psychiatric medical records rarely quantify response, which leads to a largely qualitative assessment, rather than the quantitative assessment from rating scales that is standard in prospective trials. Further, most patients in retrospective studies are on multiple medications with inconsistent adherence, and lack control of medication selection, making clear conclusions more difficult. Though STEP-BD was prospective, its focus was on naturalistic observation which limited its ability to control some confounds. Another limitation is that most clinical pharmacogenetic studies to date have only examined lithium and there is a relative paucity of data regarding the anticonvulsants used in bipolar disorder.
Recently, as part of the Pharmacogenomics Research Network (PGRN), these authors and their collaborators have begun a prospective multi-site pharmacogenetic trial of lithium and valproate. As part of the Pharmacogenomics of Bipolar Disorder (PGBD) study, over 700 bipolar subjects will be stabilized prospectively on lithium monotherapy and followed over 2 years. Lithium failures will be crossed over to valproate, and response will be quantified as the time to relapse into a mania, depression or mixed episode. Ultimately, DNAs will be genotyped for GWAS or sequenced in order to identify gene response correlations.
The delay and difficulty in making accurate mood disorder diagnoses, along with the high inter-individual variation in medication response make for a pressing need for personalized medicine for mood disorders. These factors may lead to years of suffering due to inadequate or inappropriate treatment, and suicides that could have been prevented. Studies in this area today have mainly surveyed some logical candidate genes in small samples of subjects, but may have identified some gene effects that will prove to be reliable and replicable. GWAS and whole genome sequencing technology, along with several new studies underway generating large samples, will provide powerful tools for finding genes. Pharmacogenetic genes and disease susceptibility genes will likely in part overlap, and these discoveries will work hand in hand to distinguish different pathophysiological mechanisms and the medications that normalize them.
This work was supported by grants to JRK from NIMH (MH68503, MH078151, MH92758) and the Department of Veterans Affairs.
Conflict of Interest Statement
Dr. Kelsoe is a consultant for Psynomics and Astra-Zeneca. The terms of this arrangement have been reviewed and approved by UCSD in accordance with its conflict of interest policies. Dr. McCarthy and Ms. Leckband have nothing to disclose.
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