Dr. Kung is assistant professor of psychiatry and consultant in psychiatry, and Dr. Li is psychiatry resident, both in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota.

Disclosures: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Simon Kung, MD, Mayo Clinic, 200 First St SW, Rochester, MN 55905; Tel: 507-255-7184; Fax: 507-284-3933; E-mail: kung.simon@mayo.edu.


Abstract

Pharmacogenomic testing is clinically available to assist with medication selection in treatment-resistant depression (TRD). Common tests include the cytochrome P450 (CYP) 2D6 and 2C19 enzymes, the serotonin transporter gene, and the serotonin receptor gene. There are practical recommendations of interventions which can be supported from the literature. Identification of a CYP2D6 poor metabolizer would result in recommending a lower dosage of medications metabolized by CYP2D6, or avoiding the use of CYP2D6 medications. Identification of a serotonin transporter gene short/short genotype suggests more adverse effects, less response, or longer time to respond to selective serotonin reuptake inhibitors (SSRIs), and may warrant focusing treatment with non-SSRIs. Numerous other genotypes have been studied but with mixed implications. The use of pharmacogenomic testing can help the clinician rationalize medication selection and reduce the numerous medication combinations used in TRD. Further research and clinical experience will continue to define the clinical utility of this testing.


Focus Points

• Pharmacogenomic testing can be clinically used in guiding medication selection for treatment-resistant depression.
• Cytochrome P450 metabolizer status can guide whether the clinician uses medications metabolized by a specific pathway or uses different dosing ranges.
• The serotonin transporter gene short/short genotype has been associated with adverse reactions and less response to selective serotonin reuptake inhibitors (SSRIs), thus clinicians might choose a non-SSRI for such patients.
• Further research and clinical practice will help define the utility of pharmacogenomic testing.

 

Introduction

Treatment-resistant depression (TRD) is a common occurrence in clinical practice. Depending on the operational definitions, studied populations and analytic methods used, prevalence ranges from 15% to 80%.1 Results from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study suggest that ~50% of “real world” patients with psychiatric and medical comorbidity who meet criteria for major depressive disorder (MDD) fail to achieve remission, even after four carefully monitored sequenced treatments.2

The most commonly adopted definition of TRD evolved from >15 historic definitions is “major depression with poor response to two adequate trials with different classes of antidepressants, given in an adequate dose for sufficient time.”3 Staging models of TRD reflect the severity of treatment resistance, factoring in the number of failed trials and intensity or optimization of each trial.4

Numerous strategies are used in TRD, including psychotherapy, pharmacotherapy using augmentation strategies, and brain stimulation techniques such as transcranial magnetic stimulation, vagus nerve stimulation, and electroconvulsive therapy. Deep brain stimulation and magnetic seizure therapy are investigational treatments.5 However, the most common treatment for TRD is the selection of alternative antidepressant trials. Algorithms have been developed to guide pharmacotherapy.6

Given the trial-and-error nature of medication treatment for TRD, a method which could decrease the number of trials needed to achieve remission would be valuable. There has been much research into the use of genotyping to predict drug metabolism (pharmacokinetic) and genotyping to determine serotonin gene variants (pharmacodynamic) associated with drug response. Both strategies provide information that can increase the likelihood that a medication trial will be helpful.

This article reviews our current knowledge of pharmacogenomic testing designed to predict antidepressant adverse effects and response. Clinical implications for the care of patients with MDD and TRD are discussed.

 

Pharmacokinetics

Cytochrome P450 (CYP) enzymes are involved with the metabolism of most medications, including antidepressants. Some medications, such as codeine and tamoxifen, are pro-drugs which require activation by CYP enzyme. Several CYP isoenzymes are involved with antidepressant metabolism, mainly the 2D6 and 2C19, and to a lesser extent, 2C9 and 1A2.7 Polymorphisms in the genes that code for these enzymes result in varying drug levels in an individual. The phenotypes typically range from a “poor” metabolizer (PM) with little or no enzyme activity, to an “intermediate” metabolizer with less than normal activity, to the “extensive” normal type, and to the “ultra-rapid” metabolizer (UM) with greatly increased activity. In patients of European ancestry, the distribution for CYP2D6 is ~10% PMs and 2% to 3% UMs. The phenotype frequencies for patients of European ancestry for CYP2C19 are ~3% PMs and 4% UMs. Drug metabolizing enzyme gene polymorphisms play a role in the interethnic variations in drug metabolism given that up to 20% of patients of Asian ancestry are CYP2C19 PMs.8 Generally, poor metabolizers experience more side effects and ultra-rapid metabolizers are less likely to respond to treatment with an antidepressant that is a substrate of the enzyme.

A clinical laboratory test for CYP2D6 genotyping has been available since 2003. Subsequently, clinical laboratory tests for CYP enzymes 2C19, 2C9, and 1A2 have become available. CYP 3A4 is an important enzyme involved in medication metabolism as well, but does not have many polymorphisms of functional significance.9

A current problem is that there is not a single standard for predicting the phenotype based on genotype. Consequently, different laboratories provide differing phenotype interpretations for the same genotype. This problem is compounded because different laboratories analyze for different sets of alleles. Another less problematic issue is that new alleles continue to be identified.10

 

Associations with Plasma Concentrations, Adverse Effects, and Treatment Response

The consequence of the CYP genotype on the pharmacokinetics of many antidepressants has been demonstrated. Desipramine,11 venlafaxine,12,13 nortriptyline,14 doxepin,15 imipramine,16 paroxetine,17 fluvoxamine,18 fluoxetine and paroxetine,19 and amitriptyline and nortriptyline20 have significant correlations between CYP2D6 genotypes and their plasma concentrations. However, the implications of these variable serum concentrations are not completely correlated with side effects or therapeutic response.11,13,21-23

CYP2C19 genotypes have been associated with metabolism of imipramine,24 sertraline,25,26 citalopram/escitalopram,27 and clomipramine.28 A study combining genotypes 2D6, 2C19, and 2C9 found significant influence of the 2D6 genotype, minor influence of the 2C19 genotype, and no influence of the 2C9 genotype on plasma concentrations of citalopram, paroxetine, fluvoxamine, and sertraline.29

Many studies show that poor and intermediate 2D6 metabolizers have been associated with more adverse effects to CYP2D6-dependent antidepressants.30-35 However, in some reports the risk for adverse effects have not reached statistical significance.13,36-38 These negative studies have had issues related to comprehensiveness of genotyping and sample size.

There are mixed reports of CYP2D6 genotyping associations with antidepressant response. UMs have been associated with non-response to antidepressants in several studies.17,31,39 However, in a retrospective study40 of 81 responders and 197 non-responders, CYP2D6 metabolizer status was not associated with either response or remission rates.

 

Practical Recommendations

Pharmacokinetic genotyping provides probabilistic estimates of side effects and efficacy in patients with PM and UM phenotypes. Its usefulness includes guiding certain antidepressant dosage and understanding and avoiding drug-drug interactions (DDIs), especially when 34% of patients in a primary care setting are on an antidepressant and ≥3 medications.41 The current standard clinical practice in using tricyclic antidepressants (TCAs) is to dose until reaching a pre-determined “therapeutic” serum drug level. For newer antidepressants, clinicians sometimes titrate the dose until a patient experiences benefit or uncomfortable side effects. Consequently, patients can be placed on dosages exceeding the manufacturer’s recommended usual dosages. The determination that a patient is an ultra-rapid metabolizer provides a rationale for a patient’s capacity to tolerate higher than recommended doses. Conversely, clinicians should be more cautious with substrate medications if a patient is not able to produce sufficient active enzyme necessary for the metabolism of the drug.

Pharmacokinetic reviews have suggested decreasing by ~50% the dosages of TCAs and risperidone in patients who are CYP2D6 PM, and using higher dosages of a TCA in UM.42-45 More specific dose adjustments have been proposed for the antidepressants imipramine, desipramine, nortriptyline, clomipramine, paroxetine, venlafaxine, amitriptyline, buproprion, citalopram, sertraline, and fluvoxamine, as well as the antipsychotics perphenazine, thioridazine, olanzapine, aripiprazole, haloperidol, and risperidone.44 Another review41 estimates the potential for antidepressants to be the perpetrator of a DDI mediated by effects on CYP2D6 enzymes as substantial (>150%) for paroxetine and fluoxetine; moderate (50% to 150%) for duloxetine; and mild (20% to 50%) for venlafaxine, sertraline, citalopram, and escitalopram.

Fortunately, for the newer antidepressants, clinically significant drug interactions from CYP inhibition are less frequent.46 Psychotropic medications which are not metabolized by CYP2D6 have been developed (eg, desvenlafaxine).

There is one psychotropic medication for which the Food and Drug Administration has made a firm recommendation for genetic testing (HLA-B*1502). Carbamazepine in patients with Asian ancestry with this variant have been shown to be at increased risk of life-threatening skin reactions such as Stevens-Johnson syndrome.47

 

Pharmacodynamics

In addition to CYP enzyme genes, several genes in the serotonin pathway have been studied for their potential role in the susceptibility to depression, adverse effects, and treatment response to psychotropic medications. Commonly studied genes include the 5-HTTLPR promoter region of the serotonin transporter gene (SLC6A4) and the serotonin receptor gene subtypes 5-HT2A and 5-HT2C.

 

Adverse Effects of Psychotropic Medications

Several studies reported that 5-HTTLPR L alleles are associated with fewer selective serotonin reuptake inhibitor (SSRI) side effects.48 In a study49 comparing the SSRI paroxetine versus the non-SSRI mirtazapine, patients with 5-HTTLPR S alleles had worse side effects with paroxetine but tolerated mirtazapine better. A possible interaction of 5-HTTLPR L allele and oral contraceptives associated with sexual side effects has also been reported.50 5-HTTLPR S alleles have also been associated with antidepressant-induced mania.51

The serotonin receptor genes 5-HT2A and 5-HT2C have also been associated with psychotropic adverse effects. Paroxetine side-effect severity and discontinuation was associated with the number of 5-HT2A C alleles.38 Various 5-HT2A polymorphisms have also been associated with fewer SSRI side effects including gastrointestinal side effects52 or increased side effects such as sexual side effects.53 An 5-HT2C polymorphism was reported to be protective against significant antipsychotic-induced weight gain54 and associated with tardive dyskinesia, although the association was not significant.55

 

Response to Treatment

A 2007 meta-analysis of 5-HTTLPR and SSRI treatment reported that the L allele is associated with a better response independent of ethnic differences, and patients with the S/S genotype take >4 weeks to respond and have difficulties reaching remission.56 While there is conflicting data related to the effects of SLC6A4 in patients of African-American or Hispanic ancestry,57,58 an analysis of STAR*D patients restricted to the white non-Hispanic subgroup confirmed an association of SLC6A4 activity level and remission with citalopram.59
 

Ethnic and gender differences can be seen in various reports. A 2009 study60 of Mexican Americans reported a SLC6A4 haplotype associated with remission using desipramine or fluoxetine. Korean patients with the SLC6A4 S/S genotype responded better to mirtazapine compared to those with the L/L or L/S genotype.61 Chinese patients with the L/L genotype experienced better clinical response to SSRIs compared to serotonin norepinephrine reuptake inhibitors.62 Regarding gender, in women with the SLC6A4 S/S genotype, lower efficacy was reported for SSRIs as well as non-SSRIs.63,64
 

Other reports of SLC6A4 associations with antidepressant response are interesting. In geriatric patients, SLC6A4 was reported to interact with serum paroxetine levels to influence antidepressant response.65 In a positron emission tomography imaging study, higher serotonin transporter occupancy was associated with clinical improvement with paroxetine in patients with L/L.66 In patients with S/S genotype, antidepressant augmentation with pindolol and lithium was associated with better response.67,68
 

For 5-HT2A, meta-analysis of antidepressant treatment response showed a contribution to better response with a specific polymorphism, particularly in Asians.52 In the STAR*D data,69 participants who were homozygous for the 5-HT2A A allele of a newly identified variant (rs7997012) had an 18% reduction in absolute risk of having no response to treatment, compared with those homozygous for the other allele. The A allele was over six times more frequent in white than in black participants, and treatment was less effective among black participants.

 

Practical Recommendations

Pharmacodynamic reviews of SLC6A4 suggest that patients with the S/S genotype do not respond as well to SSRI antidepressants, and may experience more side effects.48,52,70 Thus, a practical approach is to use a non-SSRI in a patient who is SLC6A4 S/S or S/L. A decision analytic model of pre-treatment testing for SLC6A4 concluded that such testing would result in more patients experiencing remission earlier in treatment.71
 

Knowledge of 5-HT2A alleles might suggest the clinician try citalopram, or if generalization is possible, an SSRI, in patients who are homozygous for the 5-HT2A A allele.69 If a clinician is making a decision whether to augment an antidepressant with an antipsychotic, results of the 5-HT2C might not support an antipsychotic if the patient has the allele associated with increased weight gain with antipsychotics.

 

Pharmacogenomics in the Perspective of TRD

TRD represents a major public health concern, since it is associated with higher rates of relapse, poorer quality of life, deleterious personal and societal economic ramifications, and increased mortality rates.72,73 In the biopsychosocial model of depression treatment, the biologic standard of care is the medication trial. Numerous algorithms are available for guidance.6,74 Using the example of the Texas Medication Algorithm Project (TMAP), given that each adequate medication trial is ~2 months, and if a patient tries at least 3 SSRIs and 3 non-SSRIs, that would already be 1 year of medication trials. For each antidepressant, augmenting with two different medications such as a mood stabilizer or an antipsychotic for each of the antidepressants tried increases each medication trial by a few more months, and one can appreciate how patients might go through 4 or 5 years of medication trials. By incorporating genotyping results into an algorithm such as TMAP, one should be able to reduce the number of medication trials needed.
 

Genotyping can also explain some of the adverse events associated with medications. Consider the case example of a 58-year-old Caucasian woman with depression who has not responded to citalopram and bupropion. The clinician selects nortriptyline as the next medication trial, and titrates to a therapeutic dose based on serum level. As her depression is not improving, the clinician adds fluoxetine, noting that the combination of an SSRI and a TCA is listed in Stage 3 of the TMAP. Two weeks later, the patient experiences lethargy and unsteadiness, to the point of falling and sustaining a wrist fracture. A nortriptyline serum level shows it is now in the toxic range, and both medications are held. Two weeks later, the patient returns to her baseline state. Genotyping is obtained, and reveals that the patient is an intermediate metabolizer of CYP2D6. The explanation in this situation is that nortriptyline and fluoxetine are both metabolized by CYP2D6, and additionally, fluoxetine is a strong inhibitor of 2D6. The patient was already an intermediate metabolizer, and by inhibiting that state, effectively converted the patient to a poor metabolizer, which resulted in the nortriptyline toxicity and side effects. Adverse effects are common reasons for switching antidepressants, which leads to more medication trials and a sense of medication “resistance.” Understanding and predicting adverse effects can improve the patient’s experience and compliance with medications, leading to a better outcome.
 

A patient’s genetic makeup is only one of the many complex factors involved in his or her response to antidepressants. Other factors include diet, caffeine, nicotine, age, medical illness, and concurrent medications. In addition, appropriate attention should be given to the psychological and social stresses aspects of the patients’ illness. Psychotherapies such as cognitive behavioral therapy and acceptance and commitment therapy can be helpful.75,76 Patients with aversive social contexts for their depression also have consistently lower remission rates, indicating the need for social interventions.77

 

Conclusion

Depression can be difficult to treat, especially with its biopsychosocial contributors. From the biologic perspective, clinicians rely on medication trials which might span several years because of the large number of antidepressants available and the various augmentation strategies. Patients understandably become frustrated with such treatment techniques and look towards methods which might help them identify the optimal medication or combination to treat their depression.
 

There has been much research into whether pharmacogenomic testing might provide sufficient clinical information to guide psychotropic medication choices and thus decrease the trial and error approach of medication management. With regards to pharmacokinetic testing, specifically CYP2D6 and CYP2C19, identifying poor metabolizers in order to help with medication selection and dosage adjustments can be helpful. In patients presenting with numerous side effects, it can also confirm whether a patient is experiencing side effects because of metabolizer status. From the pharmacodynamic perspective, many genes have been studied, with the most common being the serotonin transporter and serotonin receptor genes. Patients of European ancestry with a serotonin transporter gene S/S or S/L genotype seem to not tolerate or not respond as well to SSRIs compared to patients with the L/L genotype. Various serotonin receptor gene alleles have also been associated with increased or decreased response to SSRIs as well as side effects.
 

The response of an individual to antidepressant treatment is not only influenced by the limited number of genes that are currently tested. Genome-wide association studies (GWAS) to investigate the entire genome without focus on a specific hypothesis and genomic area represent a new and promising methodologic strategy. A recent GWAS found remission associated with the number of predicted “response” alleles, and supported that antidepressant response emerges from a multitude of genetic variants.78,79 Further research is predicted to reveal additional clinical applications to guide treatment.  PP
 

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62.    Min W, Li T, Ma X, et al. Monoamine transporter gene polymorphisms affect susceptibility to depression and predict antidepressant response. Psychopharmacology (Berl). 2009;205(3):409-417.
63.    Smits KM, Smits LJ, Peeters FP, et al. The influence of 5-HTTLPR and STin2 polymorphisms in the serotonin transporter gene on treatment effect of selective serotonin reuptake inhibitors in depressive patients. Psychiatr Genet. 2008;18(4):184-190.
64.    Gressier F, Bouaziz E, Verstuyft C, Hardy P, Becquemont L, Corruble E. 5-HTTLPR modulates antidepressant efficacy in depressed women. Psychiatr Genet. 2009;19(4):195-200.
65.    Lotrich FE, Pollock BG, Kirshner M, Ferrell RF, Reynolds Iii CF. Serotonin transporter genotype interacts with paroxetine plasma levels to influence depression treatment response in geriatric patients. J Psychiatry Neurosci. 2008;33(2):123-130.
66.    Ruhe HG, Ooteman W, Booij J, et al. Serotonin transporter gene promoter polymorphisms modify the association between paroxetine serotonin transporter occupancy and clinical response in major depressive disorder. Pharmacogenet Genomics. 2009;19(1):67-76.
67.    Zanardi R, Serretti A, Rossini D, et al. Factors affecting fluvoxamine antidepressant activity: influence of pindolol and 5-HTTLPR in delusional and nondelusional depression. Biol Psychiatry. 2001;50(5):323-330.
68.    Stamm TJ, Adli M, Kirchheiner J, et al. Serotonin transporter gene and response to lithium augmentation in depression. Psychiatr Genet. 2008;18(2):92-97.
69.    McMahon FJ, Buervenich S, Charney D, et al. Variation in the gene encoding the serotonin 2A receptor is associated with outcome of antidepressant treatment. Am J Hum Genet. 2006;78(5):804-814.
70.    Luddington NS, Mandadapu A, Husk M, El-Mallakh RS. Clinical implications of genetic variation in the serotonin transporter promoter region: a review. Prim Care Companion J Clin Psychiatry. 2009;11(3):93-102.
71.    Smits KM, Smits LJ, Schouten JS, Peeters FP, Prins MH. Does pretreatment testing for serotonin transporter polymorphisms lead to earlier effects of drug treatment in patients with major depression? A decision-analytic model. Clin Ther. 2007;29(4):691-702.
72.    Greden JF. The burden of disease for treatment-resistant depression. J Clin Psychiatry. 2001;62 suppl 16:26-31.
73.    Fekadu A, Wooderson SC, Markopoulo K, Donaldson C, Papadopoulos A, Cleare AJ. What happens to patients with treatment-resistant depression? A systematic review of medium to long term outcome studies. J Affect Disord. 2009;116(1-2):4-11.
74.    TMAP. Texas Medication Algorithm Project. Available at: www.dshs.state.tx.us/mhprograms/disclaimer.shtm. Accessed February 11, 2010.
75.    Markowitz JC. Evidence-based psychotherapies for depression. J Occup Environ Med. 2008;50(4):437-440.
76.    Pull CB. Current empirical status of acceptance and commitment therapy. Curr Opin Psychiatry. 2009;22(1):55-60.
77.    Brown GW, Harris TO, Kendrick T, et al. Antidepressants, social adversity and outcome of depression in general practice. J Affect Disord. 2010;121(3):239-246.
78.    Sabbagh A, Darlu P. Data-mining methods as useful tools for predicting individual drug response: application to CYP2D6 data. Hum Hered. 2006;62(3):119-134.
79.    Ising M, Lucae S, Binder EB, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry. 2009;66(9):966-975.

 

Dr. Weiss is head of the Provincial ADHD Program and clinical professor at the University of British Columbia Children’s and Women’s Health Centre in Vancouver.

Disclosure: Dr. Weiss is a consultant to and receives grant support from Eli Lilly, Janssen, Purdue, and Shire. She also receives grant support from the Canadian Institutes of Health Research.

Please direct all correspondence to: Margaret D Weiss MD PhD, Head, Provincial ADHD Program, Clinical Professor, University of British Columbia, Children’s and Women’s Health Centre, Box 178 , 4500 Oak St, Vancouver, BC V7T 2Y2; Tel: 604-875-2010; Fax: 604-875-2099; E-mail: mweiss@cw.bc.ca.


 

All assessments in child psychiatry involve evaluation of particular areas that are not typical in an adult assessment. These include a detailed school history, developmental history, a family interview, and collateral information obtained usually by rating scales from teachers and parents. In certain aspects of adult psychiatry some of these child procedures may also serve to augment the assessment process. Collateral information may be useful in assessment of a patient without insight, such as a patient with hypomania. Rating scales can be useful in identification of severity and follow up of improvement. Broad-based rating scales can be used to assure identification of diagnoses that might be missed by the clinician, or that the patient is reluctant to discuss.

Just as there are procedures that are unique to child psychiatry that may be of benefit to adult psychiatry, there are procedures unique to assessment of attention-deficit/hyperactivity disorder (ADHD) for patients of all ages that may be useful to general child or adult psychiatrists. I will identify the modifications to the assessment process now in place in our Provincial ADHD Program which I think may be of use to those in general practice seeing patients with ADHD, or even to practitioners who are seeing patients with other diagnoses where the same type of issues arise.

In order to improve the efficiency of the assessment process and optimize the time available for discussion of psychosocial care we need to know quickly and as easily as possible Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition1 diagnoses that might have been missed, are comorbid, or represent differential diagnoses. We also need to identify those diagnoses that are apparent to different observers and in different settings. The Kiddie-Schedule for Affective Disorders and Schizophrenia (KSADS) has been used clinically for diagnoses, but the reality of the constrictions on the clinical time we have available is that this is expensive and limited to information from the family. The KSADS and other diagnostic interviews were designed for research. However, the objective of such interviews is as germane in practice as in research. For this reason, the Canadian ADHD Resource Alliance2 has developed a DSM-IV checklist that is completed by the patient and a collateral informant prior to interview. The advantage of this interview is not a substitute to the mental status, but to serve as a guide to the mental status and to assure the clinician remembers what the DSM criteria are as well as to identify important patient/collateral differences.

Since the emergence of the DSM, the diagnostic process has focused heavily on symptoms, and diagnostic criteria. However, patients do not typically present with the chief complaint that they have a DSM disorder. More often, they present with a problem in life functioning where they have difficulty meeting the new expectations of a developmental transition. While Axis V is meant to identify impairment, there is no description of what impairment is—whether it represents absolute impairment or impairment relative to potential—or the settings in which such impairment occurs. Nor on Axis V is there anything like the diagnostic criteria that bring interrater reliability and definition to Axis I.

The reality familiar to all clinicians is that there are patients who have significant diagnoses and function well and patients who have a vague mixture of symptoms from different diagnoses who are severely impaired. For this purpose, prior and post interview from the Weiss Functional Impairment Rating Scale for Self (WFIRS-S: adolescents or adults; Table 1) or WFIR for Parents (Table 2) examines impairment in each of the major domains. Like the Symptom Record, this can be reviewed, discussed with the patient, and used as a cross check on the interview. Within the busy service requirements of the ADHD clinic, psychiatrists do not have the luxury of psychologists to score complex scales. We use the simple rule of rating both symptoms and functional items that are of clinical significance by simply counting those items rated as 2 (pretty much) or 3 (very much) by the patient or informant. While simple, this is much like the Clinical Global Impression–Severity scale in that it gives a precise and clear characterization of the patient’s difficulties.

 

 

 

Some differentials that are more common in ADHD, and therefore a necessary part of the evaluation. However, while they may be more common, they are by no means unique to ADHD. An ADHD assessment requires an evaluation for learning disabilities, sleep, nutrition, bullying, family discipline, and parental frustration. as well as capacity for activities of daily living, school or work success and adaptations, and risk factors such as drug use, driving, or injuries. Evaluation of these differentials and risk factors by self and other report on the Symptom Record and the WFIRS assures that patients receive the clinical attention they deserve.

Perhaps the most important and most often missed aspect of psychiatric assessment is that we are trained to be pathology sensitive. However, from the patient’s point of view identification and reinforcement of strengths and successes sets a tone and models a positive experience. We know very little about what determines long-term outcome apart from obvious advantages such as income, personality, family support, and resilience. However, one aspect of possible prognostic signficance that is likely to be stable over time is the capacity to be compassionate and kind. For example, one of the most widely used child broadband rating scales in the public domain that is age and gender normed, the Strengths and Difficulties Questionnaire,3 has as one of its five subscales “prosocial skills.” Kindness may well be a stable characteristic that when assessed at any age represents a relative strength that can be drawn on to identify to the patient that whatever the symptomatology, her or she is “a good person.” Outcome is not only determined by what is disturbed, but also by what the patient does well. Are they empathic? Do they have a special passion for a skill they do well? Are they psychologically minded?

Apart from inclusion of the whole family, one aspect of child psychiatry that is unique and critical to ADHD is the developmental history. Has their been in utero exposure to nicotine, alcohol, or other drugs? Was their compromise to the newborn during delivery? What was the child’s early temperament? (Temperament tends to be relatively stable, and early memories are of interest.) Were there notable developmental delays, such as clumsiness, indicative of residual developmental coordination disorder? An assessment of ADHD in adults requires the same type of evaluation, since like all neuropsychiatric conditions grown up, early childhood history is critical to establishing a developmental onset of difficulty. Again, while these questions are critical to assessment of ADHD, they may identify early onset prognostic deficits relevant to all adult conditions.

When one asks a child if they have problems paying attention or whether they get into trouble, they often know the answer. When asked if the problem is small, medium, or large, their assessment is also not unlikely to match the results of systematic interview. The point is simple: in adult psychiatry we have the advantage that we interview the patient directly, but nonetheless we may fail to bring into the office those significant others who know the patient in a way he cannot know him or herself. In child psychiatry, we often focus our interview on parent and teacher information. However, the informant who remains critical to an ADHD assessment or any child assessment is the child. A child might say, “I am lazy.” “I only like recess because it is the only part of school that is not boring.” “I have no friends because I am bad.” Assuring that the child remains an important part of the interview provides clues to diagnosis, child insight, and functional impairment. It also tells the clinician the child’s own experience of the impact of his or her disorder on quality of life. Whether this is an assessment of an adult with ADHD and we decide to include the spouse, or an assessment of the child and we interview the caregivers and obtain information from the school, assessment of ADHD is a reminder to all psychiatry that collateral information often brings surprises. In adult psychiatry, where most patients are seen individually for 1 hour, the use of collateral scales has a major role to play that has been under utilized.

The Symptom Record completed by patient and collateral provides a simple, cost effective way to obtain a pathway into the key problems, and an assurance that we won’t miss disorders such as learning problems, sleep, or tics that might otherwise be missed. The Weiss Functional Impairment Rating Scales reminds us that patients came to the interview hoping to be able to do things or meet developmental milestones that have remained closed to them. It reminds the patient and the doctor that even when we get the diagnosis right, if we do not know the problem, the patient will not significantly progress.

ADHD is a neurodevelopmental condition which like many mental health disorders carries through the life cycle, presenting new difficulties as the patient faces new challenges. What we have to learn from assessment of ADHD in adults and children is that a developmental history, collateral information, and assessment of developmental cormorbidities such as sleep or learning have the possibility to deepen and create a better understanding for patients of all ages and all disorders.  PP

 

 

References

1.    Diagnostic and Statistical Manual of Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.
2.    CADDRA. The Canadian Attention Deficit Hyperactivity Disorder Resource Alliance. Available at: www.caddra.ca. Accessed April 12, 2010.
3.    SDQ. Information for researchers and professionals about the Strengths & Difficulties Questionnaires. Available at: www.sdqinfo.com. Accessed April 12, 2010.

 

Dr. Wall is instructor of psychiatry and consultant in child psychiatry and Dr. Swintak is instructor in psychiatry and senior associate consultant in child psychiatry, both in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota. Ms. Oldenkamp is a medical student at the Mayo Medical School in Rochester.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Christopher A. Wall, MD, Instructor of Psychiatry, Consultant–Child Psychiatry, Dept of Psychiatry and Psychology, Mayo Clinic, 200 1st St, SW, Rochester, MN 55905; Tel: 507-284-3352; Fax: 507-533-5353; E-mail: wall.chris@mayo.edu.


Abstract

Historically, clinicians have had few resources beyond empiric tools derived from population-based treatment algorithms and patient/family interviews to inform the “best choice” for psychopharmacologic intervention. Previously unappreciated interindividual variance in activity of cytochrome P450 enzymatic activity can lead to abnormal metabolism of many psychotropics and poor outcomes. Fortunately, advances in our understanding and application of psychiatric pharmacogenomic information have the potential to improve the quality of medical care for children at the level of the individual prescription.


Focus Points

• Advances in pharmacogenomics have the potential to improve the quality of medical care for children at the level of the individual prescription.
• Nearly 80% of all drugs in use today, along with most psychotropics, are metabolized via testable metabolic pathways.
• Children and adolescents with metabolic polymorphisms may be at greater risk for adverse drug events than children with normal metabolism.
• Pediatric psychotropic prescribers must consider treatment-resistant patients as potential abnormal metabolizers.

 

Introduction

A large number of children and adolescents presenting for health care are affected by mental illness and many require psychotropic medications as a component of their overall care.1,2 Despite increasing choices in medication management, many of these patients still experience poor outcomes related to inadequate medication response and significant adverse drug events (ADEs).3 Given ongoing shortages in the specialty of child and adolescent psychiatry,4 the considerable challenge of prescribing psychotropics in the pediatric population is often managed by adult psychiatrists, family physicians, and pediatricians. In considering whether a medication is the “right” one for a given patient, all clinicians must weigh not only issues such as the potential for side effects, family responses to similar psychotropic medications, and the nature and intensity of the patient’s illness, but also psychosocial concerns. This is a process which is complicated by the knowledge that an incorrect choice could result in intolerable side effects, poor efficacy, and ultimately—perhaps most importantly—a negative view towards medication that may have proved helpful. Lack of efficacy and ADEs are frequently cited as reasons for noncompliance in pediatric psychopharmacology.

Currently, children who are treated without the benefit of individualized molecular genotyping have only a 60% chance of successful long-term treatment.5 Fortunately, advances in our understanding and application of individual pharmacogenetic profiles have the potential to improve the quality of medical care for children at the level of the individual prescription.6 Pickar7 has suggested that there is no specialty where the need for pharmacogenetics seems more compelling than for psychiatry. Psychiatric pharmacogenomics is an emerging tool to assist clinicians in developing strategies to personalize treatment and tailor therapy to individual patients, with the goal of optimizing efficacy and safety through better understanding of genetic variability and its influence on drug response. This article provides discussion of the role emerging pharmacogenomic advancement is playing in the clinical practice of individualized psychopharmacology: moving away from “trial and error” prescriptions to individualized prescribing. The article also highlights the growing literature and adoption of pharmacogenomic principles guiding modern psychotropic prescribing practices focusing on the pediatric population.

 

Background of Psychiatric Pharmacogenomics

Psychiatric pharmacogenomics is the study of how gene variations influence the responses of a patient to treatment with psychotropics. The most commonly studied cytochrome P450 (CYP) enzymes include 2D6, 2C19, and 2C9. Polymorphisms and gene duplications in these enzymes account for the most frequent variations in phase I metabolism of drugs since nearly 80% of all drugs in use today, along with most psychotropics (Tables 1 and 2),8 are metabolized via these pathways.9 It should also be noted that genetics may account for 20% to 95% percent of variability in drug disposition and effects.10

 

Historic and current literature divides metabolic phenotypes into four basic categories. These categories presented from least to most efficient metabolism are as follows: poor metabolism (PM; essentially no metabolism at a given enzyme pathway), intermediate metabolism (IM), extensive metabolism (EM; essentially “normal” metabolism), and ultra-rapid metabolism (UM). For the purpose of discussion, this article will highlight safety and efficacy concerns related to the 15% to 25% of pediatric patients that are either PM or UM metabolizers.11

 

Safety and Efficacy in Abnormal Metabolizers

The two primary tenets considered in all pediatric prescriptions are safety and efficacy, and both can be more precisely addressed through pharmacogenomics. “Safety pharmacogenomics” aims to avoid ADEs and side effects by identifying individuals who are likely to have difficulty with certain medications due to either increased activity of an enzymatic pathway (UMs), or lack of activity (PMs). “Efficacy pharmacogenomics” attempts to predict an individual’s likely response to a medication at the outset of treatment.12
 

Interindividual variance of activity of CYP enzymes can lead to abnormal metabolism of most antidepressants (Table 1) and antipsychotics (Table 2). These medications have been associated with a variety of ADEs, ranging from milder side effects, such as activation, irritability, sexual dysfunction, and sedation, to more significant ADEs, such as weight gain, extrapyramidal symptoms, metabolic syndrome, hyperprolactinemia, manic-induction, neuroleptic malignant syndrome, and even suicidality.13 Children and adolescents with polymorphisms leading to abnormal drug metabolism may be at greater risk for some of these ADEs than children with normal metabolism, as medications administered at normal therapeutic doses to poor metabolizers may result in toxicity, and consequently ADEs. Conversely, UMs may not attain therapeutic plasma levels on typical therapeutic doses of medications and the treatment may fail or lead to rapid conversion of prodrug to potentially toxic active metabolites.
 

Table 313-51 includes a list of ADEs that have been linked to abnormal metabolism of psychotropics by at least one study involving abnormal metabolizers. As pharmacogenomic testing is a relatively new technology, not many studies have been performed investigating these links, but identifying the at-risk population in advance could do much to positively affect quality of life, increase compliance with medications, and even circumvent death in rare cases. Pharmacogenomic testing has the potential to offer a more complete, individualized risk profile enabling tailored choices of medication with doses appropriately adjusted for individual metabolism and advanced screening for the propensity of certain undesirable effects.

 

Safety and Efficacy Implications in Poor Metabolism

Weight Gain and Metabolic Syndrome

There is no doubt that weight gain can be detrimental to a young person’s physical and mental health and can exacerbate problems with self-esteem during all developmental stages. Obesity, which is common among schizophrenic patients,52 may be further exacerbated by antipsychotics. It has been shown that decreased metabolism due to variations in several CYP genes may contribute to a patient’s risk profile while taking an antipsychotic. For example, decreased metabolism at CYP1A2, which is known to be involved in the metabolism of some antipsychotics, is associated with increased risk for weight gain and a cluster of clinical features including increased visceral adiposity, hyperglycemia, hypertension, and dyslipidemia known as “metabolic syndrome.”53 Prevalence of metabolic syndrome is higher in women than it is in men as demonstrated in the Clinical Antipsychotic Trials of Intervention Effectiveness schizophrenia trial.53 Lower activity of CYP1A2 may also contribute to the risk for metabolic syndrome by leading to increased serum concentrations of antipsychotics at standard doses. Children, especially young females, may be more susceptible to weight gain while on antipsychotics,53-55 and weight gain may lead to noncompliance and subsequent relapse.52-56
 

All of this evidence suggests that pediatric patients are likely to be at increased risk of weight gain and metabolic syndrome if carrying polymorphisms associated with decreased or absent 1A2 activity. Identifying poor metabolizers at this and other genes associated with atypical antipsychotic metabolism could allow a physician to be better informed of all risks when prescribing, and heighten awareness related to early signs of metabolic syndrome or weight gain. This may be especially pertinent to young female patients, who appear to carry the most risk.

 

Extrapyramidal Symptoms

Extrapyramidal symptoms (EPS) are frequent and serious acute adverse reactions to antipsychotics. These symptoms include pseudoparkinsonism, acute dystonia, akathisia, and tardive dyskinesia,34 which may be permanent even after removal of the drug.
 

Several hypotheses and studies indicate that PM at CYP2D6, which metabolizes several of the typical and atypical psychotropics, may increase the risk of developing EPS. Poor CYP2D6 metabolizers are likely to have higher than average plasma concentrations of neuroleptics with an increased risk for developing EPS, including tardive dyskinesia.14,34,57-59 PM or inhibition of CYP2D6 may be linked to the induction of EPS. CYP2D6 in the brain is involved in the metabolism of dopamine and has a possible functional association with the dopamine transporter.59,60 Several selective serotonin reuptake inhibiters and tricyclic antidepressants inhibit CYP2D6, as do a number of non-psychotropic drugs such as quinidine. Methylphenyltetrahydropyridine, a dopamine neurotoxin able to produce Parkinsonism, is metabolized by 2D6 and is also a 2D6 inhibitor. Vandel and colleagues59 concluded that inhibition of CYP2D6 may be involved in the genesis of EPS observed in treatment with 2D6 substrate psychotropics.
 

It follows that poor CYP2D6 metabolizers may be at increased risk for EPS while on certain antidepressants due to high plasma levels.59 Indeed, it has been shown that there is a significant association between EPS and the CYP2D6*4 and CYP2D6*6 polymorphisms that are both associated with the poor metabolizer phenotype.61,62 Furthermore, there may be a relationship between the degree of impaired CYP2D6 activity and the severity of EPS during neuroleptic treatment.34 One study14 demonstrating that the development of EPS or tardive dyskinesia while on antipsychotic medication is significantly more frequent among PMs than among matched IM and EM patients, also found a significantly higher prevalence of noncompliance among the same PM patients. These findings highlight the importance of identifying those at greater risk for experiencing these serious ADEs.

 

Neuroleptic Malignant Syndrome

Neuroleptic malignant syndrome (NMS) is a life-threatening ADE associated with antipsychotics, antidepressants, and other psychotropics. Signs of NMS include hyperthermia, EPS, altered consciousness, fluctuating blood pressure, incontinence, and dyspnea.48,63,64 While some studies were unable to find a significant link between reduced function of CYP2D6 and NMS,65,66 more recent case studies suggest pharmacogenomic factors cannot yet be excluded as risk factors for this serious condition. In two separate case studies, four patients who developed NMS were later determined to have mutations in CYP2D6 conferring the PM phenotype.48 It was concluded that while not all NMS patients have this poor metabolizer phenotype, poor metabolizers at CYP2D6 may be at increased risk for developing NMS.49

 

Hyperprolactinemia

Conventional antipsychotics and certain atypical antipsychotics, such as risperidone, can cause significant elevations in prolactin.53 For risperidone, increases in prolactin levels are dose related.53,67 Though no studies have yet been conducted to show a link between PM phenotypes and ADEs related to hyperprolactinemia, this link remains not only possible, but an important consideration in the pediatric population. Amongst other potential developmental concerns, complications from early hyperprolactinemia may include bone loss, which in turn could lead to significant consequences upon reaching adulthood. Furthermore, if this increase in prolactin is dose related, PMs may have elevated risk as they may experience higher serum concentrations of poorly metabolized medication.

 

Additional Considerations

Prescribers should also bear in mind that over sedation, postural hypotension, and cardiovascular complications may be additional significant concerns in poor metabolizers.68 Likewise, as clinicians follow their natural tendency to optimize dosing in their treatment of psychiatric symptoms, it may be helpful to remember that, in the PM population, so called “somatic symptoms” associated with psychiatric diagnoses (and subsequent treatment) may in fact be medication intolerance exacerbated by dose titration. Without knowledge of the patient’s metabolic phenotype, the clinician must “guess” as to the cause of these symptoms and may incorrectly conclude that the patient is just “anxious” or “dramatic.” Furthermore, the clinician must also wonder whether or not the patient will be able to adequately tolerate the next medication choice.

 

Safety and Efficacy Implications in Ultra-Rapid Metabolism

UMs present their own set of treatment challenges as they may not attain therapeutic plasma levels on normal doses of medications, and thus treatment may have a higher propensity to fail.69 For example, a recent Swedish autopsy study13 found that among those who died of suicide, there was a higher number carrying >2 active CYP2D6 genes (UM phenotype) as compared with those who died of natural causes. Postulated explanations for this finding include accumulation of higher levels of metabolites at a faster rate which is a known risk of UM. This buildup may lead to adverse drug reactions if the metabolite is active or toxic. It could also be argued that in this population, UMs did not reach the desired therapeutic concentration of their prescribed medications and thus had not been treated effectively. This hypothesis is supported by Kawanishi and colleagues43 who found UMs as more likely to fail to respond to antidepressants. The ultra-rapid metabolizers in the study also had the worst scores on the Hamilton Rating Scale for Depression leading the authors to conclude that ultra rapid metabolism may be a risk factor for persistent mood disorders.
 

Case studies in UMs suggest that diphenhydramine may be converted to a compound which causes paradoxical excitation due to the abnormally high CYP2D6 activity.41 More serious consequences might be seen in children treated with other medications like codeine whose ultra-rapid conversion might result in toxic accumulation of morphine leading to death.23 It follows that UMs could be at increased risk of ADEs from higher levels of toxic or active metabolites from psychotropics.

 

Discussion

To date, much of the available literature on pharmacogenomic testing in the pediatric population has focused on the spectrum of efficacy related to cancer treatments.70-75 Impressive results in leukemia remission rates have been described as partly due to advancements in pharmacogenomically derived individualized prescribing practices. Cheok and colleagues70 highlighted the progress made in the treatment of acute lymphoblastic leukemia in children noting the disease as being lethal 4 decades ago to current cure rates exceeding 80%. This progress is largely due to the optimization of existing treatment modalities rather than the discovery of new antileukemic agents. The literature regarding the pharmacogenomics of asthma treatment and research design has also been quite active in the pediatric population in the past few years.76-83 In both cancer and asthma research, there are clear outcomes and endpoints to define treatment response and the role that interindividual variability plays.
 

Historically, the process of initiating psychopharmacologic agents in the child and adolescent population has been empirically based and one in which the clinician considers many variables including age, gender, access to health care, and ability to remain compliant with the proposed treatment. Frequently factored into this consideration are quasi-genetic questions relating to family history of illness as well as family history of medication response. Until very recently, the use of family history has been the only tool available to better understand genetic makeup and its resultant interplay with efficacy and ADEs. In fact, as early as the 19th century, Holmes84 commented that, “All medications are directly harmful; the question is whether they are indirectly beneficial.” Fortunately, unlike in Holmes’ day, we now have the potential capability to resolve that very question; pharmacogenomic testing can help determine in advance whether an individual will respond favorably. Ongoing central nervous system maturation coupled with an increased risk for ADEs makes the utility of this advance most relevant in pediatric psychopharmacology.
 

Though most prescribing in pediatric psychiatry is still off label, treatment algorithms do currently exist for most classes of psychotropics. Unfortunately, none of these algorithms base their recommendations on psychiatric pharmacogenomics. Furthermore, since dosing recommendations are based on “normal” metabolizers, they do not include the estimated 15% to 25% of the population who is either UMs (and therefore at much higher risk for resultant noncompliance due to never reaching therapeutic and/or beneficial levels) or poor with non-compliance resulting from ADEs. These outliers, who frequently end up in treatment-resistant categories of patients, might have entirely different outcomes if medication management were tailored to their genetic—and therefore most fundamental—needs.
 

When considering the “stakes” involved in the early patient-physician-family relationship, it is clear that prescribing with improved confidence, and less risk of ADEs, will pay significant dividends. For example, if a clinician thoughtfully considers not only the symptoms involved in the patient’s illness process, but also the likelihood that the patient will experience difficulties with certain medications, the patient and family cannot help but be appreciative of the efforts involved at defining their particular risks. This transparency of process and subsequent conversations about the role for medications will allow for greater trust and a sense of improved objectivity.
 

Widespread adoption of pharmacogenomic testing will be hampered by several factors including costs, limited sample sizes in research reports, and ingrained practice habits fueled by understandable skepticism and access challenges. Each of these issues will need to be individually addressed and overcome in the foreseeable future. Several academic medical centers are incorporating this form of testing into the comprehensive biopsychosocial workup and results appear promising.11,85 Today, the cost of the genotyping of a single gene varies between $300–$700 depending upon the complexity of the variants that are being identified. Fortunately, panels of informative genes can now be ordered for between $800–$1,500. With the rapid improvement in sequencing technologies that is now occurring, these costs will inevitably decrease in the near future.
 

As psychiatric illnesses are increasingly recognized and treated in the pediatric population, clinicians now have access to an emerging set of pharmacogenomic principles to guide their prescribing practices. The primary principle is to use pharmacogenomic testing to increase the safety of psychotropics. A second principle is to use testing to identify medications that are unlikely to be effective. The ultimate goal of pharmacogenomic testing is to find the “right medication” on the first try. As pharmacogenomic testing becomes more sophisticated, it will be possible to abandon “trial and error” strategies and begin to provide individualized care utilizing metabolic and receptor pharmacogenomics. Using composite data, clinicians will have an unprecedented degree of molecular information available to help them choose effective medication-based treatments while minimizing the potential for ADEs.
 

Conclusion

As clinicians continue to treat pediatric patients with psychotropics, every relevant clinical observation and laboratory assessment should be considered to increase the likelihood of achieving remission of symptoms with minimal ADEs. Reviewing the results of pharmacogenomic testing prior to writing an initial prescription now provides clinicians useful individualized data that can be reviewed with the patient and family to inform them about the role that metabolism may play in treatment response as well as the possibility of ADEs. It is the authors’ belief that pharmacogenomic testing has a significant role in modern psychopharmacologic practice and that the associated expenses are already outweighed by the potential benefits of more individualized prescriptions.  PP

 

References

1.    Kessler RC, Wang PS. The descriptive epidemiology of commonly occurring mental disorders in the United States. Annu Rev Public Health. 2008;29:115-129.
2.    McVoy M, Findling R. Child and Adolescent Psychopharmacology Update. Psychiatr Clin North Am. 2009;32(1):111-133.
3.    Bourgeois FT, Mandl KD, Valim C, Shannon MW. Pediatric adverse drug events in the outpatient setting: An 11-year national analysis. Pediatrics. 2009;124(4):e744-e750.
4.    Thomas CR, Holzer CE. The continuing shortage of child and adolescent psychiatrists. J Am Acad Child Adolesc Psychiatry. 2006;45(9):1023-1031.
5.    Mrazek DA. Pharmacogenomics of methylphenidate response: making progress. J Am Acad Child Adolesc Psychiatry. 2009;48(12):1140-1142.
6.    Husain A, Loehle JA, Hein DW. Clinical pharmacogenetics in pediatric patients. Pharmacogenomics. 2007;8(10):1403-1411.
7.    Pickar D. Pharmacogenomics of psychiatric drug treatment. Psychiatr Clin North Am. 2003;26(2):303-321.
8.    Mrazek D. Psychiatric Pharmacogenomics. New York, NY: Oxford University Press; 2010.
9.    Zhou SF, Di YM, Chan E, et al. Clinical pharmacogenetics and potential application in personalized medicine. Curr Drug Metab. 2008;9(8):738-784.
10.    Kalow W, Tang BK, Endrenyi L. Hypothesis: comparisons of inter- and intra-individual variations can substitute for twin studies in drug research. Pharmacogenetics. 1998;8(4):283-289.
11.    Wall CA, Wells L, Mohan A, Odegarden S, Drews M, Mrazek DA. Pharmacogenomics and its emerging role in individualized pediatric psychopharmacology. Poster presented at the 55th Annual Meeting of the American Academy of Child and Adolescent Psychiatry; Chicago, IL; October 28-November 2, 2008.
12.    Roses AD. Pharmacogenetics and drug development: The path to safer and more effective drugs. Nat Rev Genet. 2004;5(9):645-656.
13.    Zackrisson AL, Lindblom B, Ahlner J. High frequency of occurrence of CYP2D6 gene duplication/multiduplication indicating ultrarapid metabolism among suicide cases. Clin Pharmacol Ther. November 11, 2009. [Epub ahead of print].
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15.   Subuh Surja AA, Reynolds KK, Linder MW, El-Mallakh RS. Pharmacogenetic testing of CYP2D6 in patients with aripiprazole-related extrapyramidal symptoms: a case-control study. Per Med. 2008;5(4):361-365.
16.   Scordo MG, Spina E, Romeo P, et al. CYP2D6 genotype and antipsychotic-induced extrapyramidal side effects in schizophrenic patients. Eur J Clin Pharmacol. 2000;56(9-10):679-683.
17.   Inada T, Senoo H, Iijima Y, Yamauchi T, Yagi G. Cytochrome P450 II D6 gene polymorphisms and the neuroleptic-induced extrapyramidal symptoms in Japanese schizophrenic patients. Psychiatr Genet. 2003;13(3):163-168.
18.   Kobylecki CJ, Hansen T, Timm S, et al. The impact of CYP2D6 and CYP2C19 Polymorphisms on suicidal behavior and substance abuse disorder among patients with schizophrenia: a retrospective study. Ther Drug Monit. 2008;30(3):265-270.
19.    Köhnke MD, Griese EU, Stösser D, Gaertner I, Barth G. Cytochrome P450 2D6 deficiency and its clinical relevance in a patient treated with risperidone. Pharmacopsychiatry. 2002;35(3):116-118.
20.  Jaanson P, Marandi T, Kiivet RA, et al. Maintenance therapy with zuclopenthixol decanoate: Associations between plasma concentrations, neurological side effects and CYP2D6 genotype. Psychopharmacology. 2002;162(1):67-73.
21.    Ellingrod VL, Schultz SK, Arndt S. Association between cytochrome P4502D6 (CYP2D6) genotype, antipsychotic exposure, and abnormal involuntary movement scale (AIMS) score. Psychiatr Genet. 2000;10(1):9-11.
22.    Koski A, Sistonen J, Ojanperä I, Gergov M, Vuori E, Sajantila A. CYP2D6 and CYP2C19 genotypes and amitriptyline metabolite ratios in a series of medicolegal autopsies. Forensic Sci Int. 2006;158(2-3):177-183.
23.    Ciszkowski C, Madadi P, Phillips MS, Lauwers AE, Koren G. Codeine, ultrarapid-metabolism genotype, and postoperative death. N Engl J Med. 2009;361(8):827-828.
24.    Stamer UM, Stüber F, Muders T, Musshoff F. Respiratory depression with tramadol in a patient with renal impairment and CYP2D6 gene duplication. Anesth Analg. 2008;107(3):926-929.
25.    He YJ, Brockmöller J, Schmidt H, Roots I, Kirchheiner J. CYP2D6 ultrarapid metabolism and morphine/codeine ratios in blood: was it codeine or heroin? J Anal Toxicol. 2008;32(2):178-182.
26.    Madadi P, Ross CJD, Hayden MR, et al. Pharmacogenetics of neonatal opioid toxicity following maternal use of codeine during breastfeeding: a case-control study. Clin Pharmacol Ther. 2009;85(1):31-35.
27.    Madadi P, Koren G, Cairns J, et al. Safety of codeine during breastfeeding: fatal morphine poisoning in the breastfed neonate of a mother prescribed codeine. Can Fam Physician. 2007;53(1):33-35.
28.    Gasche Y, Daali Y, Fathi M, et al. Codeine intoxication associated with ultrarapid CYP2D6 metabolism. N Engl J Med. 2004;351(27):2827-2831.
29.    Voronov P, Przybylo HJ, Jagannathan N. Apnea in a child after oral codeine: a genetic variant – an ultra-rapid metabolizer. Paediatr Anaesth. 2007;17(7):684-687.
30.    Kirchheiner J, Schmidt H, Tzvetkov M, et al. Pharmacokinetics of codeine and its metabolite morphine in ultra-rapid metabolizers due to CYP2D6 duplication. Pharmacogenomics J. 2007;7(4):257-265.
31.    de Wildt SN, Koren G. Re: Apnea in a child after oral codeine: a genetic variant – an ultra-rapid metabolizer [corrected]. Paediatr Anaesth. 2008 Mar;18(3):273-276. Erratum in: Paediatr Anaesth. 2008;18(5):454.
32.    De Leon J, Dinsmore L, Wedlund P. Adverse drug reactions to oxycodone and hydrocodone in CYP2D6 ultrarapid metabolizers. J Clin Psychopharmacol. 2003;23(4):420-421.
33.    Dalén P, Frengell C, Dahl ML, Sjöqvist F. Quick onset of severe abdominal pain after codeine in an ultrarapid metabolizer of debrisoquine. Ther Drug Monit. 1997;19(5):543-544.
34.    Arthur H, Dahl ML, Siwers B, Sjoqvist F. Polymorphic drug metabolism in schizophrenic patients with tardive dyskinesia. J Clin Psychopharmacol. 1995;15(3):211-216.
35.    Zhou SF. Polymorphism of human cytochrome P450 2D6 and its clinical significance: part II. Clin Pharmacokinet. 2009;48(12):761-804.
36.    Arranz MJ, De Leon J. Pharmacogenetics and pharmacogenomics of schizophrenia: a review of last decade of research. Mol Psychiatry. 2007;12(8):707-747.
37.    Kirchheiner J, Keulen JTHA, Bauer S, Roots I, Brockmöller J. Effects of the CYP2D6 gene duplication on the pharmacokinetics and pharmacodynamics of tramadol. J Clin Psychopharmacol. 2008;28(1):78-83.
38.    Pollock BG, Mulsant BH, Sweet RA, Rosen J, Altieri LP, Perel JM. Prospective cytochrome P450 phenotyping for neuroleptic treatment in dementia. Psychopharmacol Bull. 1995;31(2):327-332.
39.    Meyer JW, Woggon B, Baumann P, Meyer UA. Clinical implications of slow sulphoxidation of thioridazine in a poor metabolizer of the debrisoquine type. Eur J Clin Pharmacol. 1990;39(6):613-614.
40.    Spina E, Ancione M, Di Rosa AE, Meduri M, Caputi AP. Polymorphic debrisoquine oxidation and acute neuroleptic-induced adverse effects. Eur J Clin Pharmacol. 1992;42(3):347-348.
41.    de Leon J, Nikoloff DM. Paradoxical excitation on diphenhydramine may be associated with being a CYP2D6 ultrarapid metabolizer: three case reports. CNS Spectr. 2008;13(2):133-135.
42.    Michelson D, Read HA, Ruff DD, Witcher J, Zhang S, McCracken J. CYP2D6 and clinical response to atomoxetine in children and adolescents with ADHD. J Am Acad Child Adolesc Psychiatry. 2007;46(2):242-251.
43.    Kawanishi C, Lundgren S, Ågren H, Bertilsson L. Increased incidence of CYP2D6 gene duplication in patients with persistent mood disorders: Ultrarapid metabolism of antidepressants as a cause of nonresponse. A pilot study. Eur J Clin Pharmacol. 2004;59(11):803-807.
44.    Gorny M, Röhm S, Läer S, Morali N, Niehues T. Pharmacogenomic adaptation of antiretroviral therapy: overcoming the failure of lopinavir in an African infant with CYP2D6 ultrarapid metabolism. Eur J Clin Pharmacol. 2010;66(1):107-108.
45.   Breil F, Verstuyft C, Orostegui L, et al. Non-response to consecutive antidepressant therapy caused by CYP2D6 ultrarapid metabolizer phenotype. Int J Neuropsychopharmacol. 2008;11(5):727-728.
46.    Ellingrod VL, Miller D, Schultz SK, Wehring H, Arndt S. CYP2D6 polymorphisms and atypical antipsychotic weight gain. Psychiatr Genet. 2002;12(1):55-58.
47.    Lane HY, Liu YC, Huang CL, et al. Risperidone-related weight gain: genetic and nongenetic predictors. J Clin Psychopharmacol. 2006;26(2):128-134.
48.    Kawanishi C, Shimoda Y, Fujimaki J, et al. Mutation involving cytochrome P450IID6 in two Japanese patients with neuroleptic malignant syndrome. J Neurol Sci. 1998;160(1):102-104.
49.    Kato D, Kawanishi C, Kishida I, et al. Effects of CYP2D6 polymorphisms on neuroleptic malignant syndrome. Eur J Clin Pharmacol. 2007;63(11):991-996.
50.    Kato D, Kawanishi C, Kishida I, et al. CYP2D6 gene deletion allele in patients with neuroleptic malignant syndrome: preliminary report. Psychiatry Clin Neurosci. 2005;59(4):504-507.
51.   Tang SW, Helmeste D. Paroxetine. Expert Opin Pharmacother. 2008;9(5):787-794.
52.    Allison DB, Mentore JL, Heo M, et al. Antipsychotic-induced weight gain: a comprehensive research synthesis. Am J Psychiatry. 1999;156(11):1686-1696.
53.    Aichhorn W, Whitworth AB, Weiss EM, Marksteiner J. Second-generation antipsychotics: Is there evidence for sex differences in pharmacokinetic and adverse effect profiles? Drug Saf. 2006;29(7):587-598.
54.    Correll CU. Weight gain and metabolic effects of mood stabilizers and antipsychotics in pediatric bipolar disorder: A systematic review and pooled analysis of short-term trials. J Am Acad Child Adolesc Psychiatry. 2007;46(6):687-700.
55.    Safer DJ. A comparison of risperidone-induced weight gain across the age span. J Clin Psychopharmacol. 2004;24(4):429-436.
56.    Bernstein JG. Induction of obesity by psychotropic drugs. Ann N Y Acad Sci. 1987;499:203-215.
57.    Topic E, Stefanovic M, Ivanisevic AM, Blazinic F, Culav J, Skocilic Z. CYP2D6 genotyping in patients on psychoactive drug therapy. Clin Chem Lab Med. 2000;38(9):921-927.
58.    de Leon J, Susce MT, Pan RM, Fairchild M, Koch WH, Wedlund PJ. The CYP2D6 poor metabolizer phenotype may be associated with risperidone adverse drug reactions and discontinuation. J Clin Psychiatry. 2005;66(1):15-27.
59.    Vandel P, Bonin B, Vandel S, Sechter D, Bizouard P. CYP 2D6 PM phenotype hypothesis of antidepressant extrapyramidal side-effects. Med Hypotheses. 1996;47(6):439-442.
60.    Niznik HB, Tyndale RF, Sallee FR, et al. The dopamine transporter and cytochrome P450IID1 (debrisoquine 4-hydroxylase) in brain: resolution and identification of two distinct [<sup>3</sup>H]GBR-12935 binding proteins. Arch Biochem Biophys. 1990;276(2):424-432.
61.    Crescenti A, Mas S, Gassó P, Parellada E, Bernardo M, Lafuente A. CYP2D6*3, *4, *5 and *6 polymorphisms and antipsychotic-induced extrapyramidal side-effects in patients receiving antipsychotic therapy. Clin Exp Pharmacol Physiol. 2008;35(7):807-811.
62.    Sachse C, Brockmöller J, Bauer S, Roots I. Cytochrome P450 2D6 variants in a Caucasian population: allele frequencies and phenotypic consequences. Am J Hum Genet. 1997;60(2):284-295.
63.    Pope Jr HG, Keck Jr PE, McElroy SL. Frequency and presentation of neuroleptic malignant syndrome in a large psychiatric hospital. Am J Psychiatry. 1986;143(10):1227-1233.
64.    Kawanishi C, Furuno T, Onishi H, et al. Lack of association in Japanese patients between neuroleptic malignant syndrome and a debrisoquine 4-hydroxylase genotype with low enzyme activity. Psychiatr Genet. 2000;10(3):145-147.
65.    Kawanishi C, Hanihara T, Maruyama Y, et al. Neuroleptic malignant syndrome and hydroxylase gene mutations: No association with CYP2D6A or CYP2D6B. Psychiatr Genet. 1997;7(3):127-129.
66.    Iwahashi K, Yoshihara E, Nakamura K, et al. CYP2D6 HhaI genotype and the neuroleptic malignant syndrome. Neuropsychobiology. 1999;39(1):33-37.
67.    Volavka J, Czobor P, Cooper TB, et al. Prolactin levels in schizophrenia and schizoaffective disorder patients treated with clozapine, olanzapine, risperidone, or haloperidol. J Clin Psychiatry. 2004;65(1):57-61.
68.    Dahl ML. Cytochrome P450 phenotyping/genotyping in patients receiving antipsychotics: useful aid to prescribing? Clin Pharmacokinet. 2002;41(7):453-470.
69.    Bertilsson L, Dahl ML, Sjöqvist F, et al. Molecular basis for rational megaprescribing in ultrarapid hydroxylators of debrisoquine. Lancet. 1993;341(8836):63.
70.    Cheok MH, Pottier N, Kager L, Evans WE. Pharmacogenetics in acute lymphoblastic leukemia. Semin Hematol. 2009;46(1):39-51.
71.    Pottier N, Cheok M, Kager L. Antileukemic drug effects in childhood acute lymphoblastic leukemia. Expert Rev Clin Pharmacol. 2008;1(3):401-413.
72.    Ansari M, Krajinovic M. Pharmacogenomics in cancer treatment defining genetic bases for inter-individual differences in responses to chemotherapy. Curr Opin Pediatr. 2007;19(1):15-22.
73.    Ansari M, Krajinovic M. Pharmacogenomics of acute leukemia. Pharmacogenomics. 2007;8(7):817-834.
74.    Cheok MH, Evans WE. Acute lymphoblastic leukaemia: A model for the pharmacogenomics of cancer therapy. Nat Rev Cancer. 2006;6(2):117-129.
75.    Brenner TL, Pui CH, Evans WE. Pharmacogenomics of childhood acute lymphoblastic leukemia. Curr Opin Mol Ther. 2001;3(6):567-578.
76.    Rogers AJ, Tantisira KG, Fuhlbrigge AL, et al. Predictors of poor response during asthma therapy differ with definition of outcome. Pharmacogenomics. 2009;10(8):1231-1242.
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78.    Koster ES, Raaijmakers JA, Koppelman GH, et al. Pharmacogenetics of anti-inflammatory treatment in children with asthma: Rationale and design of the PACMAN cohort. Pharmacogenomics. 2009;10(8):1351-1361.
79.    Warrier MR, Hershey GK. Asthma genetics: personalizing medicine. J Asthma. 2008;45(4):257-264.
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81.    Weiss ST, Litonjua AA, Lange C, et al. Overview of the pharmacogenetics of asthma treatment. Pharmacogenomics J. 2006;6(5):311-326.
82.   Wechsler ME. Managing asthma in the 21st century: the role of pharmacogenetics. Pediatr Ann. 2006;35(9):660-669.
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85.    Oldenkamp C, Wall CA, Mrazek DA. An analysis of the clinical usefulness of psychiatric pharmacogenomic testing in children and adolescents. Poster presented at: the 56th Annual Meeting of the American Academy of Child and Adolescent Psychiatry; October 27-November 1, 2009; Honolulu, HI.

 

Dr. Kennedy is professor in the Department of Psychiatry and Behavioral Sciences at Albert Einstein College of Medicine, and director of the Division of Geriatric Psychiatry at Montefiore Medical Center in the Bronx, New York.

Disclosure: Dr. Kennedy reports no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Gary J. Kennedy, MD, Director, Department of Geriatric Psychiatry, MMC, 111 East 210th St, Klau One, Bronx, NY 10467; Tel: 718-920-4236; Fax: 718-920-6538; E-mail: gjkennedy@msn.com.


 

The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition,1 is scheduled for release in May 2013. In February 2010, the preliminary draft revisions to the current diagnostic criteria became available for review and comment. Although diagnostic biomarkers and neuropsychologic criteria for dementia have yet to emerge, there are conceptual advances evident in the proposed criteria which have substantial implications for treatment. What follows is a discussion of the proposed revisions to help readers who may want to submit comments during the review period.

The DSM-5 proposes four major changes2 for the cognitive disorders listed in the DSM-IV-TR 3 as delirium, dementia, and amnestic disorders. These include dropping the term “dementia” entirely, adding a new diagnostic category titled “minor neurocognitive disorder,” and explicitly categorizing the syndromes of psychosis and depression previously described for Alzheimer’s disease but not mentioned in the DSM-IV. 4 The fourth change is the provision of far greater detail, examples, and assessment techniques for the specific cognitive domains of impairment. Each change reflects advances in neuroscience, neuropsychology, and neuroimaging. However, the first three are also the result of a conceptual process that began more than a century ago.

 

Historic Background

Kraepelin assigned the name “Alzheimer’s disease” to Alzheimer’s initial case reports of marked cognitive deterioration associated with neurofibrillary tangles in 1907 and amyloid plaques in 1911.5 But “Alzheimer’s disease” does not appear in the DSM until the DSM-III.6 Controversy arose over whether Alzheimer’s findings were those of a genuine disease or of normal aging. In addition, there was no consensus about how to classify the illness if it indeed was a disease. In 1951, the United States Public Health Service established a work group to address difficulties perceived in the International Classification of Diseases, Sixth Edition (ICD-6).7 Among the shortcomings were ICD-6’s failure to include chronic brain syndromes (dementias) within the coding rubric of mental disorders along with neurotic and behavioral reactions such as anxiety and depression. This was incongruent with the DSM-I,8 which was published in 1952. In addition, many of the “psychoses associated with organic factors,” meaning dementias, were lumped into a category labeled “psychoses with other demonstrable etiology.”9

Debates about the terminology of cognitive disorders continued through the publication of the DSM-II 10 and the ICD-8.11 Terms such as “brain syndrome” as well as “organic psychoses” and various modifiers were proposed. The term “psychosis” was used to denote severity rather than the presence of hallucinations or delusions. What current practitioners might refer to as vascular dementia would have been classified in the DSM-II as “psychoses associated with organic brain syndromes with cerebral arteriosclerosis (293.0) or other cerebrovascular disturbance (293.1). “Psychoses not attributed to physical conditions” included schizophrenia and manic depressive illness.10 Uncertainty about where to place these conditions and how to describe them reflects their dual identities as both neurologic and psychiatric disorders. This duality is also reflected in the functional versus organic terminology now thought to be out of date as well as inaccurate. Captioning neurodegenerative disorders in which mental rather than motor or perceptual deficits predominate is a historic trend across the DSMs. By transforming the cognitive disorders of the DSM-IV into neurocognitive disorders, the DSM-5 acknowledges the neurologic aspect of the illness without ceding the diagnosis entirely to neurology or primary care. The dementias are mental disorders. Nonetheless, some states bar people with a diagnosis of dementia from receiving services at Medicaid-certified mental health clinics unless there is a specific diagnostic modifier denoting “depression” of “delusions,” or a concurrent major mental disorder such as major depressive disorder (MDD).12

 

Out with the Old, in with the New

Delirium, dementia, amnestic, and other cognitive disorders are subsumed in the DSM-5 under the category of “neurocognitive disorders.” Neurocognitive disorders, in contrast to neurodevelopmental disorders, are acquired and degenerative rather than inborn and apparent in childhood. The term was chosen in part to avoid the stigma associated with dementia when categorizing deficits among younger people with progressive cognitive decline associated with HIV or traumatic brain injury. Neurocognitive disorders are further divided into major and minor. The DSM-IV condition described as “age-related cognitive decline (ARCD)” appearing in “other conditions that may be a focus of clinical attention” would now appear under minor neurocognitive disorder in the DSM-5. This is a decided advance. Terms such as ARCD, cognitive impairment not dementia (CIND), mild cognitive impairment (MCI), amnestic MCI, and non-amnestic MCI—which have variable criteria but are often considered a prodrome of dementia—would now be listed as a minor neurocognitive disorder. More importantly, in contrast to those without detectable impairment, people with CIND exhibit both a greater prevalence of neuropsyschiatric symptoms such as depression as well as functional limitations.13

Minor neurocognitive disorder is analogous to minor depressive disorder, which appears in Appendix B of the DSM-IV along with subsyndromal depressive condition not elsewhere classified (CNEC). Indeed, subsyndromal depressive CNEC is further divided into prodromal depression and subsyndromal, and mixed subsyndromal anxiety-depressive disorder depending on duration, severity, or associated features, respectively. Both MCI and minor depression are similar in that they predict progression to either dementia or MDD. However, many people considered to have MCI or minor depression never develop a major mental illness. The certainty with which we can distinguish a symptom or performance profile which represents a genuine prodrome from periodic variability in cognitive performance or mood remains problematic. Nonetheless, identifying minor neurocognitive disorder as a DSM diagnosis reflects a growing consensus that MCI and CIND are too often the early manifestations of dementia. Additionally, not to have a “minor” diagnostic category would leave investigators and the public with the confusing terminology that followed in the wake of the DSM-IV.

 

Behavioral Disturbances Categorized

Behavioral disturbances of Alzheimer’s disease are prevalent and add excess disability for the patient and distress for caregivers. Community-based studies13-15 find agitation, apathy, depressed mood, delusions, and less frequently hallucinations to be the most common problems. In the DSM-II, the dementias were categorized as either psychotic or non-psychotic organic brain syndromes. Severity of impairment—not the presence of delusions or hallucinations—determined whether or not the disorder was of psychotic proportions. Hallucinations, delusions, and alterations in mood were considered part of the disorder but no modifying code or category was proposed. With the publication of the DSM-IV-TR, modifying codes had been added to reflect the presence or absence of behavioral disturbance.3 For vascular but not Alzheimer’s dementia, modifying codes included “with delirium,” “with delusions,” and “with depressed mood.” Two syndromes are proposed for the DSM-5,  but specifically for Alzheimer’s disease. The first is a depressive syndrome in which three symptoms of MDD must be present for a minimum of 2 weeks and cause disability or distress. The second is a syndrome of psychosis in which delusions or hallucinations have been present at least intermittently for a minimum of 1 month and cause disability or distress. Alternatively, other behavioral problems such as agitation, aggression, apathy, wandering, disinhibition, or circadian rhythm disturbance might be coded as a fifth digit across any of the neurocognitive disorders. However, with behavioral problems so prevalent and so distressing to caregivers, the work group is wise to flag these matters as requiring additional input. Behavioral disturbance modifiers are necessary in some states for Medicaid-certified mental health clinics to provide services to people with dementia.12
 

Cognitive Domains Mature

The DSM-IV identifies impairments in memory and learning plus one of the following—aphasia, apraxia, agnosia, or executive dysfunction—as criteria for dementia. Deficits must cause social disability to justify the diagnosis. The proposal for the DSM-5 includes domains for “complex attention, executive ability, learning and memory, language, visuoconstructional-perceptual ability, and social cognition.” Each item has a paragraph describing major and minor deficits as well as definitions of the domain and examples of assessment procedures. Equally important are descriptions of how impairment within the domain disrupts behavior and threatens independence. The work group remains uncertain about how to formally portray domain-specific deficits. However, it seems critical to tailor the caregiver approach and structure the environment to take advantage of capacities which are preserved and to compensate for those which are deficient. Achieving the right fit between strengths and vulnerabilities would presumably lessen the patient’s disability, reduce the caregiver’s burden, and minimize the occurrence of behavioral disturbances. This is likely to be the case for people with Alzheimer’s, Parkinson’s, and Lewy Body diseases but may be equally important for veterans of Iraq and Afghanistan returning home with traumatic brain injury. Given the disappointing results from pharmacotherapy for the behavioral disturbances of dementia, a more exacting description of domain-specific deficits could be a major public health advance.16
 

Conclusion

Changes proposed in the DSM-5 related to delirium and dementia highlight the clinical nature of these diagnostic categories. Objective criteria from neuropsychological, imaging, or laboratory assessments are mentioned but neither specified nor required to make the diagnosis. This is a reflection more on the marked variation in neurobiology and cognitive performance between individuals both healthy and otherwise rather than a lack of reliable measures of change within the individual.13,16 The distinction between major and minor neurocognitive disorders also reflects the intense scientific interest in identifying the prodrome for dementia. Given the frequency with which MCI and CIND progress to dementia, failure to include minor neurocognitive disorder would seem a major error. However laudable it may be to avoid the stigma associated with the term “dementia,” substituting “neurocognitve disorder” reminds one of bipolar I (manic) and bipolar II (depressed) of the DSM-IV, which replaced the DSM-II’s “manic depressive illness.” Nonetheless, changes in nomenclature matter. Addition of salient behavioral problems to the diagnostic code regardless of etiology seems clinically sound. The adoption of modifiers to capture even one specific domain of cognitive deficits may lead to improved behavioral approaches. There is bound to be debate about “minor neurocognitive disorder” and the abandonment of “dementia” just as there has been for autism and Asperger’s syndrome.19 However, the clinical utility of noting behavioral disturbance and domain-specific deficits is hard to deny.  PP
 

References

1. Diagnostic and Statistical Manual for Mental Disorders. 5th ed. Washington, DC: American Psychiatric Association; In press.
2. Neurocognitive Disorders Work Group: Jeste D, Blacker D, Blazer D, et al. Draft dated 7 January 2010. Available at: http://www.dsm5.org/ProposedRevisions/Pages/Delirium,Dementia,Amnestic,OtherCognitive.aspx. Accessed April 9, 2010.
3. Diagnostic and Statistical Manual for Mental Disorders. 4th ed, text rev. Washington, DC: American Psychiatric Association; 2000.
4. Diagnostic and Statistical Manual for Mental Disorders. 4th ed. Washington, DC: American Psychiatric Association; 1994.
5. Diagnostic and Statistical Manual for Mental Disorders. 3rd ed. Washington, DC: American Psychiatric Association; 1980.
6. Graeber MB, Kösel S, Egensperger R, Banati RB, Müller U, Bise K, Hoff P, Möller HJ, Fujisawa K, Mehraein P. Rediscovery of the case described by Alois Alzheimer in 1911: historical, histological and molecular genetic analysis. Neurogenetics. 1997;1(1):73-80.
7. International Statistical Classification of Diseases and Related Health Problems. 6th rev. Geneva, Switzerland: World Health Organization; 1948.
8. Diagnostic and Statistical Manual for Mental Disorders. Washington, DC: American Psychiatric Association; 1952.
9. Kramer M. Introduction: the historical background of ICD-8. In: Diagnostic and Statistical Manual for Mental Disorders. 2nd ed. Washington, DC: American Psychiatric Association; 1968:xi-xv.
10. Diagnostic and Statistical Manual for Mental Disorders. 2nd ed. Washington, DC: American Psychiatric Association; 1968.
11. International Statistical Classification of Diseases and Related Health Problems. 8th rev. Geneva, Switzerland: World Health Organization; 1968.
12. Medicaid Requirements for OMH-Licensed Outpatient Programs. NYS Office of Mental Health. January 2004. Available at: www.omh.state.ny.us/omhweb/012104letter/medicaid.htm. Accessed April 16, 2010.
13. Okura T, Plassman BL, Steffens DC, Llewellyn DJ, Potter GG, Langa KM. Prevalence of neurospychiatic symptoms and their association with functional limitation in older adults in the United States: The Aging, Demographics, and Memory Study. J Am Geriatr Soc. 2010;58(2):330-337.
14. Lyketsos CG, Sheppard JM, Steinberg M, et al. Neuropsychiatric disturbance in Alzheimer’s disease clusters into three groups. Int J Geriatr Psychiatry. 2001;16(11):1043-1053.
15. Devanand DP, Jacobs DM, Tang MX, et al. The course of psychopathological features in mild to moderate Alzheimer’s disease. Arch Gen Psychiatry. 1997;54(3):257-263.
16. Holtzer R, Verghese J, Wang C, Hall CB, Lipton RB. Within-person across-neuropsychological test variability and incident dementia. JAMA. 2008;300(7):823-830.
17. Griffith HR, Stewart CC, Stoekel LE, et al. Magnetic resonance imaging volume of the angular gyri predicts financial skill deficits in people with amnestic mild cognitive impairment. J Am Geriatr Soc. 2010;58(2):265-274.
18. Kennedy GJ. From symptom palliation to disease modification: implications for dementia care. Primary Psychiatry. 2007;14(11):30-34.
19. Meiklejohn ST, Brehmer B, Chase I, Greenberg D. To the Editor. A separate label for Asperger’s. New York Times. February 15, 2010:A29.

 

Drs. Hall-Flavin and Schneekloth are assistant professors of psychiatry and consultants in psychiatry and Mr. Allen is research coordinator in psychiatry, all in the Department of Psychiatry and Psychology at the Mayo Clinic in Rochester, Minnesota.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Daniel K. Hall-Flavin, MD, Assistant Professor of Psychiatry, Consultant in Psychiatry, Department of Psychiatry and Psychology, Mayo Clinic, 200 First Street SW, Rochester, MN 55905; Tel: 507-255-7164; Fax: 507-284-3933; E-mail: flavin.daniel@mayo.edu.


Abstract

Significant inter-individual variability exists in antidepressant response, therapeutic dosage, and adverse effect profile. Prolonged times to response or remission represent a period of suffering associated with increased risk for morbidity and mortality. Improving care in depression treatment using a more biologically informed selection of psychopharmacologic agents through genotyping has become a reality in psychiatric practice. Routine genotyping has now become available for gene variations that code for proteins involved in neurotransmission and for drug-metabolizing enzymes involved with the disposition of many pharmacologic agents including antidepressants. Clinical validation and reliability of genotyping, access to testing, uniformity and clarity in test interpretation, and clinician and patient education are critical to this process of innovation diffusion. This article focuses on the introduction of pharmacogenetic testing to the daily practice of psychiatry. Challenges inherent in innovation diffusion in general and in the application of pharmacogenetic testing in particular are addressed. Study data involving the introduction and integration of pharmacogenomic testing into two different types of community psychiatric practice are presented. The article concludes with a discussion of the ethical issues raised in this process and its impact on the physician-patient relationship.


Focus Points

• On average, there exists a 10-year gap between medically relevant bio-technological advances and appropriate application, or translation, of those technologies into routine medical practice.
• Pharmacogenetic testing represents a major advance for translational psychiatry and its goal of advancing personalized medicine.  
• Barriers to change are multifaceted and complex; enhancing the knowledge base of physicians will facilitate the process of clinical acceptance.
• Psychopharmacogenetic testing that leads to a comprehensible report which provides clinical guidance is a new tool that is now available for implementation in the clinical practice of psychiatry.

 

Introduction

It has been over 60 years since antidepressants were introduced into clinical practice, and these medications have become among the most widely prescribed pharmacologic agents used in medicine today. Despite the number of agents available and recent advances in drug design, significant individual variability exists in drug response, therapeutic dosage, and adverse effect profile. Only 35% to 45% of depressed patients have a complete remission of their illness when initially treated with these medications.1 Variation in drug response is complex and is dependent upon numerous factors. These include other pharmaceutical use, age, gender, renal and hepatic function, medical comorbidity, nutritional status, substance use, and genetic factors.2 The selection of an appropriate agent is usually achieved through an informed trial and error process which considers these factors. The time to maximum therapeutic response can extend to 12 weeks. This delayed time to response contributes to the potential for substantial morbidity and mortality associated with depressive illness. The use of pharmacogenomic testing provides a new tool to improve time to response and remission, as well as decrease the likelihood of potential side effects.

Recent developments in pharmacogenomic testing allows for the more efficient and effective treatment of mood disorders that have proven difficult to manage in the clinical setting. Within the past 7 years, routine genotyping has become available to detect genetic variations that code for proteins that influence serotonergic and noradrenergic function, as well as drug-metabolizing enzymes that play a role in the disposition of many psychotropics, including antidepressants.3 Genotyping for the cytochrome P450 (CYP) 2D6, 2C19, and 1A2 drug-metabolizing enzymes, and genotyping of the serotonin transporter gene and the 5-HT2A and 5-HT2C receptors, is now available clinically, and the rationale for testing has been explicitly defined.4 Pharmacogenomic testing can be used to predict potential side effects, receptor sensitivity, and possible drug interactions. In its current iteration it cannot clearly predict response or remission in association with the use of a particular agent, and may not necessarily predict all side effects that a particular patient may experience.

The reliability of the genotyping, access to testing, and the usefulness of the interpretation of test results are critical to the process of innovation diffusion, which involves acceptance, adoption, and appropriate utilization of genomic testing in the clinical setting. It has been estimated that it is typical for a decade to pass between the discovery of applicable technology and its routine application in the clinical setting. This traditional delay in adoption represents a challenge for the implementation of powerful new technologies.

The use of genetic testing to improve the efficacy of psychotropics is a clear example of translational psychiatry. Given the promise of pharmacogenomic testing, it is prudent to analyze the barriers that may affect its adoption.5

Issues related to the introduction of pharmacogenetic testing in clinical practice are likely to result from the extension of testing at academic medical centers to surrounding community medical centers. After a discussion of concepts that are integral to translational medicine, the challenges inherent in implementation science will be discussed. This will be illustrated by a description of a pilot project that was designed to specifically address this process. This study examined the introduction of pharmacogenomic testing into two different community practice settings and documented the lessons learned from this experience.

 

Translational Psychiatry, Personalized Medicine, and Implementation Science

Recent advances in biotechnology, bioinformatics, and studying “real world” patients have improved our understanding of the biological underpinnings of depression as well as the treatment of depression. The sequencing of the human genome was a landmark event which was achieved shortly after the beginning of the new millennium. This was followed by technological advances in gene sequencing and functional genomics, proteomics, metabolomics, and epigenetics. The evolution of functional neuroimaging technology has provided even greater degrees of precision in the definition of biological vulnerabilities. Other advances include the documentation of brain neuroplasticity, an expanding armamentarium of psychopharmacologic agents with ever more specific disease targets, and a greater emphasis on the critical analysis of the extant research regarding treatment efficacy using evidence-based methodology. Additionally, the introduction of more creative research paradigms that involve “real world” patients, who are often not included in traditional research paradigms, adds to the applicability of many current studies.

Coupled with social forces of politics, economics, and cultural expectations, these multiple advances offer the promise of an “upstream shift” in the practice of medicine from primarily a reactive response to a more proactive approach to prevention in combination with informed treatment. Bidirectional communication and effective transmission of technology between researchers and clinicians which this implies is a process that has come to be known as translational medicine.6 Such a process applied to psychiatric patients is appropriately labeled translational psychiatry.

The use of genotypic information to stratify disease and select a therapy that is particularly suited to an individual patient is now described as personalized medicine.7 It is the ultimate goal of personalized medicine to identify individuals who are at-risk for a pathophysiologic process and to prevent the onset of symptoms of that process. As this knowledge base is still not well developed, the current goals include retardation, arrest, or even reversal of pathologic processes. Implementation research is the study of methods used to promote the incorporation of evidence-based research findings into routine practice in order to improve the quality and effectiveness of health services and care.8 The challenge in the implementation of evidence-based innovative technologies is to apply the right technology to the right person in the right way to effect clinical goals which are mutually defined by the physician and patient.

 

Barriers to Effective Implementation

Advancing pharmacogenetic medicine in clinical settings is an iterative process with many challenges. Barriers exist at the interface between research and practice that impede bidirectional discovery and communication. Foremost among these barriers are communication barriers that exist between researchers and clinicians. These communication barriers are influenced by pragmatic, economic, ideologic, informational, and training parameters.9 McGovern and colleagues10 has emphasized the importance of interdisciplinary communication between clinicians, administrators, regulatory agencies, and researchers. To this list, the input of patients should be added.

Bridging this divide calls for innovative and flexible thinking. It ultimately requires clinicians and researchers to participate in a dialogue. This innovation-to-organizational fit is influenced by the forces outlined by McGovern and colleagues.10 Mittman has likened the impact of these dynamic forces upon treatment as pliable bands representing semantics, advocacy, intellectual, regulatory, economic, ideologic, tradition, training, and social forces, which attach to and suspend a concrete block representing current treatment protocols (Willinbring M, personal communication, December, 2007). Ultimately, a transformation in treatment by novel scientific innovation requires a dynamically poised system.

Prochaska and DiClemente11 outlined how clinicians and patients are participating in the process of change. There exists a need for clinician scholars to bridge these gaps with their research colleagues. Similarly, basic scientists need to be rewarded for clinical communications initiatives. Clinicians who are often preoccupied with day to day clinical demands need to be provided with high quality, but concise scientific data in order to effect change. Finally, the use of evidence-based guidelines, identification of appropriate metrics of outcome, and delineation of performance gaps with feedback loops can powerfully improve treatment delivery.

 

Psychopharmacogenetic Testing: Implementation Issues

While psychopharmacogenetic testing is becoming more commonplace in academic and tertiary medical care centers, its use in clinical practice is not yet routine. As with other new technologies, ethical issues are important to consider.5 A recent article utilizing a clinical example from oncology demonstrates differences in patient outcome based upon access to testing. It also identifies disparities in our healthcare systems which negatively impacts access to testing.12

There is no simple pathway that leads from a novel technology to a change in the belief systems of clinicians providing care. This too is an iterative process that has an evolutionary pattern of its own. Important issues such as quantification of validity, establishment of regulatory policy, and insuring reimbursement must be resolved in order to provide these services.13-21

Key issues are provided in the Table. Responses to these challenges are underway. Research funded by the Pharmacogenetics Research Network of the National Institute of General Medical Sciences continues to define pharmacogenetic practices for specific disease treatment. Improved communications and cooperation between stakeholders at various levels with the support of public policy are leading to improved validation of research findings, the development of quality cost-effectiveness measures, the evolution of clinical guidelines for the application of testing in clinical practice, and the creation of appropriate incentives for use in clinical practice.

One objective of this article is to focus on innovation diffusion at the level of clinical practice. Specifically, the authors discuss the introduction of psychopharmacogenetic testing into two community practices. This discussion focuses on those issues which most directly face the community clinician. A report22 issued by the Consortium on Pharmacogenetics in the United Kingdom stated that:

     “Perhaps the greatest single factor affecting the penetration of pharmacogenomics into clinical practice and the pace at which it will occur will be the knowledge and acceptance of physicians. Studies indicated that many physicians lack basic knowledge of genetics and also frequently fail to take into account available information about drugs.”22

It is clear from empirical studies that effective behavioral change in established medical practices will require an enhancing of the knowledge base of physicians.23 However, more will be required than introducing new information. Making behavioral change in any clinical setting requires at least three cognitive steps. First, there must be a willingness to acknowledge that a problem or situation exists which can be improved. Second, there must be an awareness of the means to make the improvement. Third, one must believe that the individual or system can effect this change. Addressing these issues will require educational efforts targeted at physicians and patients. It will require the incorporation of guidelines for testing and interpretation as well as appropriate research incentives for testing. Addressing the time pressures facing primary practitioners will require a simplification of the means of transmission of this information. One option would be involvement of a focused liaison team from an academic institution which could present on-site information and evaluate outcomes of the introduction of testing. This team could also monitor related quality outcomes including patient satisfaction and quality of life.

 

Implementation of Psychopharmacogenomic Testing in Clinical Psychiatric Practice: A Pilot Project

A study designed to introduce pharmacogenomic testing into two clinical psychiatric practices has been initiated and is currently in progress with ongoing data collection. This testing utilizes a panel that includes five genes: three cytochrome P450 drug-metabolizing genes, as well as the serotonin transporter and serotonin receptors 2A genes. Results of the panel are summarized in a format designed to provide clinicians with useful clinical information. In the consent process what testing can and cannot provide at the present time is reviewed with patients and physician alike. It is important to note that such testing cannot clearly predict response or remission, and may not fully predict an individual’s psychotropic or other medication side-effect profile. Rather, it does provide information that may guide a physician’s choice of psychotropic agent that is likely to be tolerated by the patient and that would minimize the potential of adverse drug interaction and extended trial-and-error clinical attempts to find “the right drug.”

The two clinical practices chosen for this pilot study are structurally quite different. They serve patients from two different psychosocial and ethnic backgrounds. One practice primarily provides psychopharmacologic intervention. The second practice integrates medication management with psychotherapy in an ethnically diverse population. Continuity with practitioners is a core value in each program. At both institutions, testing is offered as an initial study arm examining “practice as usual.” Testing is conducted at the end of an 8-week period of standard treatment. The second phase introduces testing at the time of study entry and includes rapid feedback to both physicians and patients within 48 hours of specimen collection. Data points are then monitored to measure the potential impact of testing on practice, with attention given to the frequency of side effects experienced, need to change medications, usefulness of the interpretive report, time to response and remission, and impact on the utilization of resources both within the practice and associated settings such as the hospital emergency room or hospital. Perceptions of physicians and patients are measured. Variables include medication changes, number of visits to emergency rooms, and days in the hospital. Physician and patient satisfaction is also being documented.

A high level of physician satisfaction with the interpretive report is critical for the incorporation of this technology into clinical practice. A copy of this report is shown in the Figure. The report also includes specific genotyping results, an interpretation of these results, and practically categorized information on drug-drug interactions including drugs known to increase and decrease specific enzyme activity. The clinical usefulness of the report in patient education, guidance of medication choice, development of potential side effects and risk/benefit assessments, improvement in the rapport with patients, and confidence in medication choice by both physician and patient will be analyzed. Patient satisfaction evaluation includes assessing the quality of the explanation of the interpretive report, the ease of understanding of report findings, and the perception of benefit from this report in treatment. Overall satisfaction ratings for the report and the clinical visit are also being assessed.

A key to the overall success of clinical implementation is that medical directors at each practice are stakeholders in the process. These clinical leaders must be well-educated in the scientific rationale and supportive of the clinical objective of offering more personalized care for individual patients. The first practice consists primarily of psychiatrists offering brief counseling in conjunction with pharmacotherapy. In this group there is general acceptance among the physicians of the potential benefit of testing. This may be offset by limitations in training, time pressures, competing priorities, and difficulties inherent in making the cognitive changes necessary to incorporate a new concept into their practices. In this setting, patients themselves appear to be a more positive force for change as they expressed interest in testing as a means of dealing with the chronic frustration in the management of their depressive symptoms. However, it is critical to keep patients grounded in what the testing can and cannot offer. Both patients and physicians informally report finding the ease of the reporting process quite helpful in promoting elements of the healing relationship.

There has been some anxiety on the part of non-physician practitioners which have raised concerns about biological reductionism and the implications of genomic technology on their future practice opportunities. Educational research designed to define the role of these clinicians should be a high priority. The relationships between therapists and patients should be investigated in future study in a manner which would challenge Cartesian dualism. Pelletier and Dorval24 summarized some of these challenges in an article on the impact of translational psychiatry in the field of psychology.

 

Translational Psychiatry and the Physician-Patient Relationship

Ultimately, one of the most critical factors in the introduction of a new technology that may have an impact on the practice of medicine is the effect that the technology has on the physician-patient relationship. Traditionally, this relationship has accepted a Cartesian reductionism that views the body as a machine and the physician as a technician whose job it is to repair that machine. However, in recent years this way of thinking has given way to the more complex notion that the doctor-patient relationship is in its essence one of healing. In the philosophical model of medicine advanced by Pelligrino and Thomasma,25 the “center of medicine” is a relationship that has the central purpose of healing. Technical competence, including incorporation of appropriate new technologies, is not denied in this model because “the act of medical profession is inauthentic and a lie unless it fulfills the expectation of technical competence…however…Competence must itself be shaped by the end of a medical act, a right and good healing action for the patient.”

Scott and colleagues26 have built upon this foundation to describe the Healing Relationship Model. In this model, healing is defined as “being cured when possible, reducing suffering when cure is not possible, and finding meaning beyond the illness experience.” Critical to this relationship are mutual respect (valuing), a recognition of the inherent asymmetry of the relationship (appreciating power), and continuity (abiding). On the part of the patient three relational factors are critical. They include trust (a willingness to be vulnerable), hope (that some future beyond the present suffering is possible), and a sense of being known. (Parenthetically, the word “patient” is etymologically traced to the Latin verb patior, to suffer.) On the clinician’s side of this relational equation are four essential clinical competencies: self-confidence, emotional self-management, mindfulness, and clinical knowledge. Of particular import to the discussion of pharmacogenetic testing is what this latter competency implies: the store of knowledge of empirical medicine, and the ability to synthesize and tailor that knowledge for the benefit of a particular individual. These factors influence the bidirectional accuracy and flow of information between physician and patient, helping to ensure a cooperative spirit with mutually agreed upon treatment goals and components. Examples of this cooperation include receptivity to medication use and compliance. Other discussions of the physician-patient relationship have centered on the four pillars of ethical reasoning, which include beneficence, autonomy, non-maleficence, and justice. One could argue the forces of translational medicine have the potential to enrich the physician-patient relationship and move clinical practice beyond reactivity to a hybrid of reactivity and proactivity.

 

Conclusion

It is imprudent to allow a 10-year gap between research discovery and practice implementation. Pharmacogenetic testing represents a major advance for translational psychiatry and its goal of advancing personalized medicine. There is a need to proceed judiciously and focus on barriers to change that need to be addressed. The authors summarized challenges to a timelier implementation of personalized medicine with particular reference to psychopharmacogenetic testing. Enhancing the knowledge base of physicians will facilitate the process of clinical acceptance. The authors discussed efforts to address translational challenges. Their initial impressions offer a snapshot of key practical issues which occur in a “real world” setting. Psychopharmacogenetic testing that leads to a comprehensible report which provides clinical guidance is a new tool that is now available for implementation in the clinical practice of psychiatry.  PP

 

References

1.    Kemp AH, Gordon E, Rush AJ, Williams LM. Improving the prediction of treatment response in depression: integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures. CNS Spectr. 2008;13(12):1066-1086.
2.    Bondy B. Pharmacogenomics in depression and antidepressants. Dialogues Clin Neurosci. 2005;7(3):223-230.
3.    de Leon J, Armstrong SC, Cozza KL. Clinical guidelines for psychiatrists for the use of pharmacogenetic testing for CYP450 2D6 and CYP450 2C19. Psychosomatics. 2006;47(1):75-85.
4.    Mrazek DA. Psychiatric Pharmacogenomics. New York, NY: Oxford University Press; 2010.
5.    Williams-Jones B, Corrigan OP. Rhetoric and hype: where’s the ‘ethics’ in pharmacogenomics? Am J Pharmacogenomics. 2003;3(6):375-383.
6.    Mankoff SP, Brander C, Ferrone S, Marincola FM. Lost in translation: obstacles to translational medicine. J Transl Med. 2004;2(1):14.
7.    Piquette-Miller M, Grant DM. The art and science of personalized medicine. Clin Pharmacol Ther. 2007;81(3):311-315.
8.    Madon T, Hofman KJ, Kupfer L, Glass RI. Public health. Implementation science. Science. 2007;318(5857):1728-1729.
9.    Stetler CB, Mittman BS, Francis J. Overview of the VA Quality Enhancement Research Initiative (QUERI) and QUERI theme articles: QUERI Series. Implement Sci. 2008;3:8.
10.  McGovern MP, Fox TS, Xie H, Drake RE. A survey of clinical practices and readiness to adopt evidence-based practices: Dissemination research in an addiction treatment system. J Subst Abuse Treat. 2004;26(4):305-312.
11.    Prochaska J, DiClemente CC. Toward a comprehensive model of change. In: Miller WR, Heather N, eds. Treating Addictive Behaviors: Processes of Change. New York, NY: Plenum Press; 1986:3-27.
12.    Griggs JJ. Personalized medicine: a perk of privilege? Clin Pharmacol Ther. 2009;86(1):21-23.
13.    Kirchheiner J, Bertilsson L, Bruus H, Wolff A, Roots I, Bauer M. Individualized medicine – implementation of pharmacogenetic diagnostics in antidepressant drug treatment of major depressive disorders. Pharmacopsychiatry. 2003;36 suppl 3:S235-243.
14.    Oscarson M. Pharmacogenetics of drug metabolising enzymes: importance for personalised medicine. Clin Chem Lab Med. 2003;41(4):573-580.
15.    Abrahams E, Ginsburg GS, Silver M. The Personalized Medicine Coalition: goals and strategies. Am J Pharmacogenomics. 2005;5(6):345-355.
16.    Manolopoulos VG. Pharmacogenomics and adverse drug reactions in diagnostic and clinical practice. Clin Chem Lab Med. 2007;45(7):801-814.
17.    Perlis RH. Pharmacogenetic studies of antidepressant response: how far from the clinic? Psychiatric Clinics of North America. 2007;30(1):125-138.
18.    Parkinson DR, Ziegler J. Educating for personalized medicine: a perspective from oncology. Clin Pharmacol Ther. 2009;86(1):23-25.
19.    Lin KM, Perlis RH, Wan YJ. Pharmacogenomic strategy for individualizing antidepressant therapy. Dialogues Clin Neurosci. 2008;10(4):401-408.
20.    Leeder JS, Spielberg SP. Personalized medicine: reality and reality checks. Ann Pharmacother. 2009;43(5):963-966.
21.    Ikediobi ON, Shin J, Nussbaum RL, et al. Addressing the challenges of the clinical application of pharmacogenetic testing. Clin Pharmacol Ther. 2009;86(1):28-31.
22.    Buchanan A, McPherson E, Brody B, et al. Pharmacogenetics: Ethical and Regulatory Issues in Research and Clinical Practice. Report of the Consortium on Pharmacogenetics, Findings and Recommendations; 2002.
23.    Mrazek M, Koenig B, Skime M, et al. Assessing attitudes about genetic testing as a component of continuing medical education. Acad Psychiatry. 2007;31(6):447-451.
24.    Pelletier S, Dorval M. Predictive genetic testing raises new professional challenges for psychologists. Canadian Psychology. 2004;45(1):16-30.
25.    Pellegrino ED, Thomasma DC. A Philosophical Basis of Medical Practice: Toward a Philosophy and Ethic of the Healing Professions. New York, NY: Oxford University Press; 1981.
26.    Scott JG, Scott RG, Miller WL, Stange KC, Crabtree BF. Healing relationships and the existential philosophy of Martin Buber. Philos Ethics Humanit Med. 2009;4:11.

 

Dr. Bastiaens is Clinical Associate Professor of Psychiatry at the University of Pittsburgh in Pennsylvania.

Disclosure: Dr. Bastiaens reports no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Leo Bastiaens, MD, Clinical Associate Professor of Psychiatry, University of Pittsburgh, 33 Sunnyhill Drive, Pittsburgh, PA 15228; Tel: 412-343-6871; Fax: 724-335-2867; E-mail: bastiaensl@aol.com.


 

Abstract

Introduction: Experts’ recommendations to implement measurement-based treatment in the community raise many questions about the feasibility and effectiveness of this practice.
Methods: This study used a case-controlled retrospective chart review of 60 patients with chronic schizophrenia to investigate the impact of an abbreviated symptom rating scale (Positive and Negative Symptom Scale [PANSS]) on several outcome measures during 1 year.
Results: Compared to treatment-as-usual, the use of the abbreviated PANSS had no impact on numerous outcome measures, such as the number of prescribed medications, number of hospitalizations, or patient’s global level of functioning.
Conclusion: Measurement-based practice in community clinics may not be effective unless accompanied by other changes, such as the ability of psychiatrists to spend more time with their patients.


Focus Points

• Based on large-scale treatment studies, such as the Clinical Antipsychotic Trials for Interventions Effectiveness, experts are recommending the implementation of measurement-based practice in the community.
• This recommendation raises questions about the feasibility and effectiveness of this practice.
• This study used an abbreviated Positive and Negative Symptom Scale in the care of 60 patients with schizophrenia during 1 year.
• The use of the rating scale did not appear to influence outcome measures, such as level of functioning, number of hospitalizations, or number of prescribed medications.
• Measurement-based practice in the community may need to be accompanied by other changes to become effective.

 

Introduction

Recently, large-scale treatment studies in depression (Sequenced Treatment Alternatives to Relieve Depression), bipolar disorder (Systematic Treatment Enhancement Program for Bipolar Disorder), and schizophrenia (Clinical Antipsychotic Trials for Interventions Effectiveness) have been completed. Many reports, detailing their results, have been published.1-3 At times, it has been difficult to translate findings from these projects into concrete guidelines for practitioners. However, as the result of these studies, one of the most frequent recommendations made is the need to bring measurement-based practice into the real world.4 Basing treatment decisions on actual symptom rating scales, rather than global impressions, is believed to be more efficient and effective.

A general recommendation to use measurement-based practice in the real world, based upon the above studies, provokes several questions. Are these studies truly representative of the real world? Is it feasible to employ similar instruments in busy community clinics? Is measurement-based practice effective for all the different diseases studied?

Because the answer to these questions is not yet available, it is problematic that some intermediaries, for example insurance companies, have started to put pressure on psychiatrists to use algorithms and rating scales in community clinics,5 without taking the realities of these clinics into account. These include providing care for a large number of severely ill patients, who have significant psychosocial adversity; infrequent 10–15-minute follow-up visits; few support staff; and little time to document encounters. Most importantly, it is not clear if patients are better served with the use of specific symptom rating scales. This article reports on the use of a rating scale during the treatment of patients with schizophrenia in a community clinic.

The largest source of information regarding the implementation of measurements during clinical care of schizophrenia comes from the Texas Medication Algorithm Project.6 The specifics of this state-wide project are well known and are available online.7 Research coordinators, available on site, administered the rating scales and provided feedback to the treating physicians, who followed the antipsychotic algorithm. Published results8 appear to be quite modest; the group of patients with schizophrenia who received the measurement-based practice did not differ on positive and negative symptoms after 12 months, compared to the treatment-as-usual group, although a sustained improvement in cognitive functioning was noted.

Other attempts have been made to introduce evidence-based decisions into routine clinical care.9,10 Clearly, much effort is involved in the realization of these practice improvements.10 While fidelity to the originally developed algorithm is important,11 it is problematic that many algorithms do not discuss very specific guidelines, such as response criteria.12 Thus, for individual practitioners in the community, questions regarding feasibility and effectiveness remain.

The use of a rating scale in the treatment of patients with schizophrenia in a community mental health clinic is documented in this article. It was a quality improvement project in the care of chronic patients. A retrospective case-controlled chart review was used to examine the impact of an abbreviated version of the Positive and Negative Symptom Scale (PANSS)13 on several outcomes, such as the amount of medications used in the treatment, number of hospitalizations, and patient’s level of functioning. If measurement-based practice, that is feasible within the constraints of a community clinic, is effective in schizophrenia, one would expect certain outcomes such as fewer hospitalizations, fewer medications used, and better level of functioning.

 

Methods

Patients with chronic schizophrenia or schizoaffective disorder, who were followed in the same clinic between March 2007 and February 2009, were eligible to be included. Patients were seen by a psychiatrist every 2–3 months, in brief medication management visits. Since March 2008, all patients, during every psychiatric visit, were evaluated with a much abbreviated version of the PANSS. Three positive PANSS items (hallucinatory behavior, delusions, conceptual disorganization) and three negative PANSS items (blunted affect, poor rapport, emotional withdrawal) were rated on a scale from one to seven (1=absent, 2=minimal, 3=mild, 4=moderate, 5=moderately severe, 6=severe, 7=extreme).

The positive items represent the most common positive symptoms in schizophrenia (hallucinations, delusions, and thought disorder). The negative items were chosen based on their observability within the patient-physician encounter.

Pharmacotherapy was based on clinical grounds, incorporating all relevant information provided during the visit by patient, family, and case managers. Since March 2008, the abbreviated PANSS was part of this clinical process. No specific guidelines were used to change the pharmacotherapeutic regimen based on the abbreviated PANSS rating. Rather, the use of the rating scale was meant to provide a more comprehensive assessment of pertinent positive and negative symptoms, compared to a treatment-as-usual situation. Also, no specific medication algorithm was used, since all subjects were chronic patients who had an extensive past history of multiple antipsychotic trials.

The study employed a retrospective chart review and compared, for each patient, the period of March 2007 until February 2008 (year 1) when no PANSS ratings were performed, with the period of March 2008 until February 2009 (year 2) when the abbreviated PANSS ratings were performed during every visit. The following data were extracted from the clinic’s electronic record system: age; gender; race; Global Assessment of Functioning (GAF) scale and number of total psychiatric medications (antipsychotics, antidepressants, benzodiazepines, etc.) in the beginning of year 1 and year 2, and at the end of year 2; number of changes in all psychiatric medications made during the two periods (dose and actual medication changes); number of visits in each period; number of hospitalizations in each period; other treatments added and/or deleted in each period (case management, partial programs, etc); and the abbreviated PANSS scores in the beginning and end of year 2. Students’ t-tests for continuous measures and Chi Square tests for categorical measures were used.

 

Results

Sixty patients (30 males, 30 females) were included. Fifty-two patients were white and the remaining eight were black. Their average age was 49.2±11.1 years. All patients suffered from schizophrenia or schizoaffective disorder and had been followed for many years in the clinic.

At the beginning of year 1, 16 patients were taking clozapine, 49 were taking another atypical, and 6 patients were taking a typical antipsychotic. Three patients were not taking any antipsychotic, 14 patients were taking a combination of antipsychotics, and six patients were on long-acting injectables. Many patients were taking other psychiatric medications as well.

In the first year, patients had an average of 4.8±2.4 visits. They started out taking an average of 2.8±1.5 psychiatric medications and ended the first year on an average of 2.9±1.6 psychiatric medications. Medications were changed 2.5±3.8 times. An average of 0.17±0.4 other treatments was added, while 0.15±0.4 treatments were deleted. The GAF decreased slightly from 47.4±8.3 to 47±9.4. Four patients were hospitalized during the first year.

In the second year, patients had 4.6±2.2 visits. The amount of prescribed psychiatric medications did not change significantly from 2.9±1.6 to 2.8±1.5 (P=.773). Medication changes happened 2.3±3.0 times in the second year. An average of 0.4±1.4 other treatments was added, while 0.2±0.6 treatments were deleted. The GAF increased slightly from 47.0±9.4 to 49.5±8.3 (P=.133). Six patients were hospitalized in the second year (two patients were admitted twice; P>.5). Antipsychotic poly-pharmacy did not change.

The abbreviated PANSS rating scale decreased from 12.2±5.1 to 9.8±3.4 (P=.003) during the second year.

None of the differences in primary outcomes, level of functioning, number of medications, or hospitalizations between year 1 and year 2 were statistically significant. The Figure visualizes several of the outcome measures.

 

Discussion

In this 2-year follow-up of 60 patients with schizophrenia or schizoaffective disorder, the use of a specific rating scale to measure symptomatology, added to the ongoing pre-existing clinical care, did not appear to make a difference in certain outcome measures, such as the number of hospitalizations, intensity of treatment, utilization of pharmacotherapy, or level of functioning.

The results need to be viewed in light of the study’s limitations: small sample size, naturalistic design, abbreviated rating scale, and relatively brief period of follow-up. However, this study is probably representative of the manner in which patients with schizophrenia are treated in the community in the United States.

Some characteristics of the patients and the study may have limited the possibility to find an impact of this measurement-based practice. Patients were relatively stable. This was evidenced by their high GAF score and the low number of medications used in their treatment. Their stability may have limited the impact of measuring their symptomatology to guide changes in their treatment.

The abbreviated rating scale may not have captured enough symptoms or more crucial symptoms to show an impact on patients’ level of functioning. However, longer rating scales may not be feasible on a routine basis in a community clinic. The fact that no guidelines were used on how to adjust medications, based on the score of the rating scale, may have limited the psychiatrist’s ability to intervene appropriately. However, it is not clear what guidelines would be used in a very chronic patient population with multiple past antipsychotic trials.

A major limitation to clinical care and the use of more sophisticated measurements is the limited time available, in community clinics, for psychiatrists to spend with their patients (in the Texas Medication Algorithm Project, physicians were supported by on-site research coordinators). In this regard, the use of an abbreviated measurement may not add any value to the patient’s care since no time is available to adequately investigate the meaning of the changes in the rating scale. Also, follow-up, in terms of frequency and length of psychiatric visits, may not be adequate. This may explain the slight increase in hospitalizations and other treatments in the second year of this study, possibly in response to changes in the abbreviated PANSS, while medications, prescribed by a psychiatrist, did not change.

 

Conclusion

Thus, it appears that making a recommendation for measurement-based treatment of schizophrenia in the community needs to be preceded by investigations into the necessary changes that need to accompany this practice.14,15 These changes could include training physicians in the use of validated rating scales, recommending specific treatment changes (optimization, switching, augmentation) based upon specific response criteria, developing guidelines for patients who do not fit into existing algorithms (eg, patients who refuse treatment with clozapine, patients on long-acting injectable antipsychotics with insufficient response), and developing regional networks to standardize treatments (so that patients who go from one treatment setting to another receive the same algorithmic treatment). In order for patients to benefit, more frequent visits, longer visits, and higher reimbursement for psychiatric services, may be necessary as well.  PP

 

References

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6.    Chiles J, Miller A, Crismon M, et al. The Texas medication algorithm project: development and implementation of the schizophrenia algorithm. Psychiatr Serv. 1999;50(1):69-74.
7.    Texas Department of State Health Services. TMAP Table of Contents. Available at: www.dshs.state.tx.us/mhprograms/TMAPtoc.shtm. Accesed January 27, 2010.
8.    Miller A, Crismon M, Rush J, et al. The Texas medication algorithm project: clinical results for schizophrenia. Schiz Bull. 2004;30(3):627-647.
9.    Chuang W, Crismon M. Evaluation of a schizophrenia medication algorithm in a state hospital. Am J Health – Syst Pharm. 2003;60(7):1459-1467.
10.    Dasori A, Chiles J, Swenson-Britt E. Best practices: implementing best-practice guidelines for schizophrenia in a public-sector institution. Psychiatr Serv. 2000;51(8):972-979.
11.    Drake R, Goldman H, Leff H, et al. Implementing evidence-based practices in routine mental health service settings. Psychiatr Serv. 2001;52(2):179-182.
12.    Buckley P, Miller A, Chiles J, et al. Implementing effectiveness research and improving care for schizophrenia in real-world settings. Am J Managed Care. 1999;5(SP):47-56.
13.    Kay S, Fiszbein A, Opler L. The positive and negative syndrome scale (PANSS) for schizophrenia. Schiz Bull. 1987;13(2):261-276.
14.    March J, Silva S, Compton S, et al. The case for practical clinical trials in psychiatry. Am J Psychiatry. 2005;162(5):836-846.
15.    Mellman T, Miller A, Weissman E, et al. Evidence-based pharmacologic treatment for people with severe mental illness: a focus on guidelines and algorithms. Psychiatr Serv. 2001;52(5):619-625.

 

 

 

Dr. Sharma is professor and head of psychiatry at Indira Gandhi Medical College & Hospital in Shimla, Himachal Pradesh, India. Mr. Thakur is surgeon at Civil Hospital Rampur in Shimla.

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.

Please direct all correspondence to: Ravi C. Sharma, MD, Professor & Head, Department of Psychiatry, Indira Gandhi Medical College & Hospital, Shimla (171001), Himachal Pradesh, India.
Tel: 91-177-2844644; Fax: 91-177-2658339; E-mail: ravi82000@yahoo.com.


 

Abstract

Acute urinary retention as a conversion symptom has received little attention in the literature and has been mostly considered as a diagnosis per exclusion. This is a case report of 20-year-old female who presented with acute retention of urine as a conversion symptom with strong psychological antecedents; she recovered completely by removing secondary gain, giving suggestions, and undergoing family counselling.

 

Introduction

There is a scarcity of information in the literature about the cause and management of acute urinary retention in females in comparison to males.1 The causes of acute urinary retention can be divided into four etiologic groups: obstructive, neurologic, pharmacologic, and psychogenic.2 Females presenting with urinary retention in the absence of any identifiable neuro-anatomic cause for their symptom pose a diagnostic and management challenge and may be dismissed as psychogenic cases.3 The present case report highlights the importance of identifying and resolving psychological factors leading to acute urinary retention in a young female.
 

Case report

A 20-year-old unmarried female, student of class 12, presented in the Surgical Out Patient Department (OPD) of our hospital with the complaint of acute retention of urine. She gave a history of intermittent catheterization at the local primary health center thrice over the past 5 days after which she was referred to our hospital. There was no history of similar complaints in the past. Examination of the patient was unremarkable except for the palpable bladder. The patient was catheterized and investigated for retention of urine and urinary tract infection. Her urine routine as well as microscopic and culture examination were normal. Plain X-ray of the abdomen region, ultrasound of the abdomen, and pelvic organs were also normal. The patient was put on empirical treatment in the form of tablet ofloxacin 200 mg BID and hyoscine butyl bromide 10 mg TID, and was given a catheter-free trial which proved futile because she again developed retention. Psychiatric opinion was sought as the patient was not responding to the treatment, and urodynamic evaluation was planned.
 

Psychiatric evaluation revealed that the female who came from a rural nuclear family had been facing severe psychosocial stress due to her father who frequently used to quarrel with his wife and scold the patient quite often after consuming alcohol. Just a day prior to the onset of her symptoms, the patient’s father had created a ruckus in the house and had physically assaulted the patient. The patient was an average student and was described to be sincere, sensitive, and passive by nature. The initial mental state examination was unremarkable; however, on subsequent exploration the patient was found to be preoccupied with her ongoing family stress but was oblivious to her physical symptom (la belle indifference). In the absence of any evident physical cause for urinary retention and strong temporal association of the symptom with the family stress, the patient was diagnosed as a case of “Conversion Reaction” as per the International Classification of Diseases, Tenth Revision,4 criteria.
 

The secondary gain the patient was receiving from her relatives and medical professionals was minimized and she was given strong suggestions. She was also prescribed fluoxetine 20 mg/day along with alprazolam 0.25 mg at bed time. Her parents were counselled in detail and role of stress in the genesis of this symptom was explained. The father of the patient was enrolled for further evaluation and management of alcohol dependence in the psychiatry OPD. The patient’s catheter was removed the third day; she started passing urine normally and was discharged on the above medications with advice to follow up after 7 days in the psychiatry OPD. On follow-up visit the patient reported that she did not take any medications at home and was completely asymptomatic. The patient had been maintaining well even after 3 months of discharge when she last came for follow-up.

 

Conclusion

Urinary dysfunction due to psychogenic causes like conversion reaction and anxiety has been reported both in males and females.5 Psychogenic urinary retention has been described more frequently in young adult females with history of childhood enuresis and disturbed social backgrounds. Such patients have been frequently diagnosed as “hysteric,” with their symptom representing a displacement of unacceptable sexual wishes and impulse.6
 

In one study,7 psychogenic factors have been cited as the second important cause of retention of urine in females. The role of psychological disturbances in the genesis of acute and chronic urinary retention in females has also been reported by other authors.8-10 In the patient presented, there was no evidence of any physical or organic cause to explain her retention of urine, but there was a definite evidence of a family stressor preceding the development of this retention. Furthermore, the symptom resolved completely after appropriate suggestions, cutting down secondary gain, and family counseling. Therefore, the retention of urine in the present case qualifies to be labelled as conversion symptom.
 

The present case highlights the need for looking in to and resolving precipitating and or perpetuating psychological stressors also as a cause of acute urinary retention, especially in young females, before subjecting them to unnecessary urodynamic investigations and repeated catheterizations.  PP

 

References

1.    Barone JG, Berger Y. Acute urinary retention in females. Int Urogynecol J. 1993;4(3):152-156.
2.    Vander Linden EF, Venema PL. Acute urinary retention in women. Ned Tijdschr Geneeskd. 1998;142(28):1603-1606.
3.    Kavia RB, Datta SN, Dasgupta R, et al. Urinary retention in women: its causes and management. BJU Int. 2006;97(2):281-287.
4.    International Classification of Diseases. 10th rev. Geneva, Switzerland: World Health Organization; 1992.
5.    Sakakibara R, Uchiyama T, Awa Y, et al. Psychogenic urinary dysfunction: a uro-neurological assessment. Neurourol Urodynam. 2007;26(4):516-524.
6.    Bird JR. Psychogenic urinary retention. Psychother Psychosom. 1980;34(1):45-51.
7.    Kumar A, Banerjee GK, Goel MC, et al. Non-neurogenic, non-organic urinary retention in female: An indication for urodynamic evaluation. Indian Jl of Urol. 1996;12(2):55-59.
8.    Wheeler JS, Walter JS. Urinary retention in females: a review. Int Urogynecol J. 1992;3(2):137-142.
9.    Mosli HA, Farsi HM, Rimawi MH, et al. Retention of urine in females: causes and management. East Afr Med J. 1991 68(8):617-623.
10.    Sagar RS, Ahuja N. Psychogenic urinary retention. Am J Psychiatry. 1988;145(9):1176-1177.

 

Dr. Sussman is editor of Primary Psychiatry as well as Associate Dean for Post-Graduate Programs and professor of psychiatry at the New York University School of Medicine in New York City.

Dr. Sussman reports no affiliation with or financial interest in any organization that may pose a conflict of interest.

Email questions or comments to ns@mblcommunications.com


 

Most of the articles in this issue of Primary Psychiatry address different ways to diagnose mental disorders and their manifestations. Given the reliance on diagnostic criteria and rating scales, our understanding of what clinical entities represent are constantly evolving. It is important that we keep current about any data that improve our efforts to understand the disorder at hand.

It is well known that patients with panic disorder are frequent visitors to emergency departments, usually with fears they are having a heart attack. Geneviève Belleville, PhD, and colleagues describe how the characteristics of patients with panic disorder in an emergency room differ from patients seen in psychiatric settings with respect to panic symptoms, comorbid psychiatric disorders, and psychological correlates of panic disorder. They assessed >2,000 patients seen either in an emergency department or anxiety disorder clinics. The authors report that men were more likely than women to go to an emergency room. Those in the emergency room sample were also more likely to have recently experienced suicidal ideation. Of interest was the finding that patients from the emergency department had less severe panic symptoms, but had higher rates of psychiatric comorbidity, most notably other anxiety disorders and major depressive disorder. Other differences between the groups are discussed in the article.

As a reminder, the American Psychiatric Association (APA) has just released the draft disorders and disorder criteria that have been proposed by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) Work Groups.1 As part of the development process of the DSM-5, set for publication in May 2013, the preliminary draft revisions to the current diagnostic criteria for psychiatric diagnoses are now available for public review and comment. The draft criteria are listed in Table 1.

 

Another anxiety disorder addressed in this issue is obsessive-compulsive disorder (OCD). Ashish Aggarwal, MD, and colleagues provide a case report of obsessive-compulsive symptoms following administration of clozapine. There have been numerous reports of OCD emerging or becoming exacerbated during the treatment of schizophrenia with atypical antipsychotics. In the reported case, these symptoms were dose related. The authors discuss possible explanations for this phenomenon. Incidentally, the APA work group is recommending that this OCD be included under a grouping of anxiety and obsessive-compulsive spectrum disorders, with the diagnostic criteria listed in Table 2.

 

The common dilemma of how to treat anxiety and insomnia in patients with chronic alcohol use disorders is addressed by Aazaz U. Haq, MD. Using an evidence-based approach, he describes many pharmacologic strategies that rely on off-label use of various agents and advocates concurrent use of cognitive behavioral therapy.

David Goodman, MD, and colleagues report on interpreting attention-deficit/hyperactivity disorder rating scale scores. The article supports the evidence that improvement on a rating scale translates into clinically significant symptom reduction. Conversely, Leo Baestiaens, MD, notes that measurement-based approaches to patient care that rely on validated rating scales may in fact be less helpful than believed. Addressing the care of patients with schizoprenia, he argues that professionals interact more with their patients and spend more time with them. This, of course, would require higher reimbursement rates.

In a case report, Ravi C. Sharma, MD, and Rajeshwar S. Thakur, MS, offer a reminder that conversion symptoms do indeed still occur. They report the case of a woman with acute urinary retention manifesting as a conversion symptom.

Finally, I want to share with you a communication I received from one of our readers about a December 2009 article by Galit Ben-Amitay, and colleagues2 about the psychiatric assessment of children with poor verbal capacities using a sandplay technique. Erno Daniel, MD, PhD, at the Sansum Clinic in Santa Barbara, CA wrote:

“An interesting offshoot of the study you reported could be the following. When my children were young, we built a sandcastle on the beach. When we tired of playing with it, we sat away from it in the sand doing other things. A little child came by. As he approached the sandcastle, it occurred to me that he had several choices: 1. Sit and play with it. 2. Add on to the sandcastle and make it better to suit his own imagination. 3. Kick it to bits and walk away.

The latter is what happened. It occurred to me that the ‘sandcastle test’ may have predictive correlates with future behavior: fit-in personality versus creative/progressive personality versus destructive personality. I would welcome a study to see if such is true.”  PP

 

References

1.     Proposed Draft Revisions to DSM Disorders and Criteria. Available at: www.dsm5.org/Pages/Default.aspx. Accessed February 17, 2010.
2.    Ben-Amitay G, Lahav R, Toren P. Psychiatric assessment of children with poor verbal capacities using a sandplay technique. Primary Psychiatry. 2009;16(12):38-44.

 

Dr. Aggarwal is senior resident, Dr. D.D. Sharma is assistant professor, Dr. R.C. Sharma is professor and head, and Dr. Kumar is associate professor, all in the Department of Psychiatry at Indira Gandhi Medical College in Shimla, India.
 

Disclosure: The authors report no affiliation with or financial interest in any organization that may pose a conflict of interest.
 

Please direct all correspondence to: Ashish Aggarwal, MD, Senior Resident, Department of Psychiatry, Indira Gandhi Medical College, Shimla-171001, Himachal Pradesh, India; Tel: +91-0-9218832616; Fax: 91-177-2658339; E-mail: drashish1980@gmail.com.


 

Abstract

Clozapine is an atypical antipsychotic used for treatment-resistant schizophrenia. It has also been used for other disorders, such as obsessive-compulsive disorder, especially as an augmenting agent. Paradoxically, there are few reports of clozapine-induced obsessive-compulsive symptoms (OCS). The authors report on a patient with schizophrenia who developed OCS twice when clozapine was initiated, and responded positively to lowering the dose of clozapine.


Focus Points

• Clozapine is the drug of choice for patients with treatment-resistant schizophrenia.
• Obsessive-compulsive symptoms (OCS) can occur during the course of schizophrenia.
• Clozapine can cause OCS in patients with schizophrenia.
• It is important to clarify the relation of OCS with antipsychotic medications, including clozapine, in patients with schizophrenia.
• Clozapine-induced OCS needs to be managed either by lowering the dose or adding an anti-obsessive medication like serotonin reuptake inhibitors. 

 

Introduction

Clozapine is an atypical antipsychotic used for managing patients with treatment-resistant schizophrenia. Unlike other antipsychotics, it has potent antagonistic activity at the serotonin (5-HT)2 receptor and has less affinity for dopamine-2 receptors. Development of obsessive-compulsive symptoms (OCS) with atypical antipsychotics, including clozapine, have been reported in the literature.1,2 On the contrary, these atypical antipsychotics, including clozapine, have been used for the management of treatment-resistant obsessive-compulsive disorder (OCD).3,4 A large retrospective review5 did not find any worsening or emergence of OCS/OCD with clozapine treatment. The authors report on a patient with schizophrenia who developed OCS twice when clozapine was initiated, and responded positively to lowering the dose of clozapine.

 

Case Report

Mr. P, a 20-year-old unmarried male presented with a history of violent aggressive behavior, smiling and muttering to himself, suspiciousness, decreased sleep, and decrease in self care for the last 2 years. There was no significant past, family, or personal history. He was well adjusted premorbidly. He was diagnosed as a case of undifferentiated schizophrenia, as per the International Statistical Classification of Diseases and Health Related Problems, Tenth Revision.6 The patient was receiving treatment for the last 1.5 years but tended to be non-compliant. Currently, the patient had exacerbated his illness after he stopped treatment for 2 months. No records of his treatment were currently available. This time, he was started on olanzapine 10 mg/day increased to 20 mg/day, along with chlorpromazine 200 mg/day. There was no improvement at all for a period of ~3 weeks.

In view of marked aggression, adamant behavior, and hostility, the patient was started on clozapine 25 mg/day gradually increased to 250 mg/day over a period of 2 weeks; simultaneously, other drugs were gradually tapered over a period of 1 week. After ~5 days of clozapine 250 mg, it was observed that, in addition to previous symptoms, the patient also started doing things repeatedly. He would repeatedly touch some objects such as glass and doors and would get irritable if asked not to do so. He would lie down on the floor and, after a few minutes, would get up and repeat the cycle 4–5 times before finally lying on the bed. On asking, he would report that he felt compelled to do it again and again, although he knew that these actions were irrational and that he was doing this himself, without any external influence. The Yale-Brown Obsessive Compulsive Scale score was 20. Review of the patient’s treatment record revealed that ~8 months ago, he was started on clozapine, in view of non-response to haloperidol and risperidone in adequate doses for an adequate time period. The patient started developing similar OCS at that time when he was on clozapine 250 mg/day; however, thinking it to be related to psychosis, the dose of clozapine was increased further up to 400 mg/day, leading to further exacerbation of OCS. Capsule fluoxetine up to 40 mg/day was added to the treatment without much relief in the symptoms. Since the patient developed marked sedation and increased OCS, clozapine was stopped and the patient was started on trifluperazine and amisulpride at that time. The OCS remitted within a period of ~3 weeks.

In view of appearance of OCS at clozapine 250 mg/day on both occasions, it was decided to decrease the dose of clozapine to 200 mg/day, and amisulpride up to 400 mg/day was added to the treatment. The patient’s OCS decreased and subsided after ~1 week of decreasing the dose.

 

Discussion

In this case, during both instances, the symptoms developed when a dose of clozapine >200 mg and abated on decreasing the dose. Alhough during the first scenario fluoxetine was also added, it did not lead to significant improvement in symptoms and the OCS improved after stopping clozapine. Thus, in this patient, OCS were definitely related to clozapine and were a dose-related phenomenon. This case differs from earlier reported cases of clozapine-induced OCS because the patient developed OCS after a short period of clozapine treatment (<3 weeks) and the patient developed OCS at clozapine 250 mg/day.
 

Previous reports of clozapine-induced OCS have been at comparatively higher doses of clozapine (300–900 mg/day)2,7,8 and after a long period of clozapine treatment (ranging from 10 weeks to 2 years).2,7-9 In adition, this patient did not have any OCS prior to clozapine treatment, though there have been reports of clozapine exacerbating already existing OCS in schizophrenia.2
 

A recent study10 of OCD in clozapine-treated patients with schizophrenia or schizoaffective disorder revealed a prevalence of 24% of clinically significant OCS. However, the temporal relationship between the onset of obsessional and schizophrenic symptoms and clozapine treatment was not established. Approximtely 50% of these patients had OCS prior to clozapine treatment.
 

Multiple hypotheses have been offered to explain the development of OCS during antipsychotic treatment. For clozapine-induced OCS, both 5-HT2A and 5-HT2C receptor antagonisms11 have been postulated to play a role in the generation of OCS. Other mechanisms that have been reported are the role of dopamine in the pathogenesis of OCS and the serotonergic modulation of dopaminergic system.12,13
 

It is important to clarify the relationship with antipsychotics while evaluating a patient of schizophrenia presenting with OCS. This is of importance for the management of such patients. Cases of spontaneous self-remission of clozapine-induced OCS within 1–3 weeks have also been described in the literature.14 Other options for clozapine-induced OCS include lowering the dose of clozapine as in this case, or adding a serotonin reuptake inhibitor. Switching to another antipsychotic might also be an option, but one should be careful for the exacerbation of psychosis as clozapine is generally used for treatment-resistant patients.

 

Conclusion

Given the paradoxical efficacy of clozapine in resistant cases with OCD, the overlapping neurobiology of OCD and psychosis, and the increasing use of clozapine for the management of treatment-resistant patients with schizophrenia, it is recommended that one should be vigilant and cautious while using clozapine. In addition, proper treatment history and delineation of symptoms in relation to drugs is important for correct management of patients and also to avoid polypharmacy.  PP
 

 

References

1.    Khullar A, Chue P, Tibbo P. Quetiapine and obsessive compulsive symptoms (OCS): case report and review of atypical antipsychotics induced OCS. J Psychiatry Neurosci. 2001;26(1):55-59.
2.    Chong SA, Wong KE. Clozapine and obsessive compulsive symptoms in schizophrenia. Hong Kong Journal of Psychiatry. 1996;6(1):45-47.
3.    Young CR, Bostic JQ, McDonald CL. Clozapine and refractory obsessive compulsive disorder. A case report. J Clin Psychopharmacol. 1994;14(3):209-211.
4.    Keuneman RJ, Pokos V, Weerasundera R, et al. Antipsychotic treatment in obsessive compulsive disorder: a literature review. Aust N Z J Psychiatry. 2005;39(5):336-343.
5.    Ghaemi SN, Zarate CA, Popli AP, Pillay SS, Cole JO. Is there a relationship between clozapine and obsessive-compulsive disorder?: a retrospective chart review. Compr Psychiatry. 1995;36(4):267-270.
6.    International Statistical Classification of Diseases and Health Related Problems. 10th rev. 2nd ed. Geneva, Switzerland: World Health Organization; 2004.
7.    Patel B, Tandon R. Development of obsessive compulsive symptoms during clozapine treatment. Am J Psychiatry. 1993;150(5):836.
8.    Rahman MS, Grace JJ, Pato MT, Priest B. Sertraline in the treatment of clozapine-induced obsessive-compulsive behavior. Am J Psychiatry. 1998;155(11):1629-1630.
9.    Levkovitch Y, Kronnenberg Y, Gaoni B. Can clozapine trigger OCD? J Am Acad Child Adoles Psychiatry. 1995;34(3):263.
10.    Mukhopadhaya K, Krishnaiah R, Taye T, et al. Obsessive-compulsive disorder in UK clozapine-treated schizophrenia and schizoaffective disorder: a cause for clinical concern. J Psychopharmacol. 2009;23(1):6-13
11.    Dursun SM, Reveley MA. Obsessive-compulsive symptoms and clozapine. Br J Psychiatry. 1994;165(2):267-268.
12.    Goodman WK, McDougle CJ, Price LH, et al. Beyond the serotonin hypothesis: a role for dopamine in some forms of obsessive-compulsive disorder? J Clin Psychiatry. 1990;51(suppl):36-43.
13.    Dewey SL, Smith GS, Logan J, et al. Serotonergic modulation of striatal dopamine measured with positron emission tomography (PET) and in vivo microdialysis. J Neurosci. 1995;15(1 pt 2):821-829.
14.    Patil, VJ. Development of transient obsessive-compulsive symptoms during treatment with clozapine. Am J Psychiatry. 1992;149(2):272.

 

Dr. Kennedy is professor in the Department of Psychiatry and Behavioral Sciences at Albert Einstein College of Medicine, and director of the Division of Geriatric Psychiatry at Montefiore Medical Center in the Bronx, New York.

Disclosure: Dr. Kennedy has received grant support from Forest.

Please direct all correspondence to: Gary J. Kennedy, MD, Director, Division of Geriatric Psychiatry, Montefiore Medical Center, 111 East 210th St, Bronx, NY 10467; Tel: 718-920-4236; Fax: 718-920-6538; E-mail: gjkennedy@msn.com.


 

The identification of genetic risk factors for the familial dementias has been a productive area of scientific study, but the clinical impact for the far more common sporadic dementias has been modest at best. As a result, interest in the characterization of biomedical and psychosocial protective factors is intense as evidenced by the April 2010 National Institutes of Health (NIH) consensus conference on Preventing Alzheimer’s Disease and Cognitive Decline. If genetic polymorphisms associated with exceptional longevity are associated with lessened incidence of dementia, their characterization may suggest novel pharmacologic interventions to prevent Alzheimer’s disease. 

 

Introduction

The most common heritable dementias, familial Alzheimer’s disease and Huntington’s disease, exhibit an early age of onset and have a well described genetic profile. Genetic testing can inform family members of their risk status with near certainty. However, the search for genetic risk in the more common later-onset sporadic Alzheimer’s disease has had little clinical impact. Moreover, pharmacologic strategies to counter cholinergic deficits, cerebral amyloidosis, and neurofibrillary tangles—the major neuropathologic manifestations of Alzheimer’s dementia—have yet to show genuine disease-modifying effects. Failure to find a breakthrough in treatment has lead to intense interest in prevention as evidenced by the April 26–28, 2010 NIH consensus conference on “Preventing Alzheimer’s Disease and Cognitive Decline”.1 Risk factors for vascular disease are often cited as risk factors for Alzheimer’s disease such that a heart-healthy diet and lifestyle are advocated by the Alzheimer’s Association as reasonable steps to reduce one’s chances of developing dementia.2 In addition, studies of exceptional longevity suggest that polymorphisms involved in lipid transport may also provide protection against Alzheimer’s disease.

 

Longevity Genes and Heart Disease

Apolipoprotein (APOE) and cholesterol ester transfer protein (CETP) are both involved in central nervous system cholesterol homeostasis. The APOE ε4 allele is associated with late onset sporadic Alzheimer’s disease while the APOE ε2 allele is associated with increased life span as well as reduced risk of heart disease. A functional single-nucleotide polymorphism (SNP) substitution of valine for isoleucine at codon 405 in the CETP gene has been associated with reduced CETP serum activity and an increase in high-density lipoprotein, both of which are thought to convey protection against heart disease. Additionally, like the APOE ε2 allele, the valine CETP SNP is associated with exceptional longevity. Thus, APOE ε2 and CETP V405 may be called “longevity genes”,3 but the mechanism with which they provide benefits is unclear.

 

Longevity Genes and Dementia     

In addition to conferring benefits for increased life span, evidence suggests that that they also protect against cognitive decline and dementia. Most recently, investigators with the Einstein Aging Study4 examined the genotypes of 523 community residents ≥70 years of age who were dementia free at baseline. The mean age was 87 years, 69% were white, 25.6% were African American and 5.4% were of other ethnicity. Those who were either homozygous for the CETP valine SNP made up 66% of the group. Those homozygous or heterozygous for APOE ε4 numbered 23%. There were 40 people who developed dementia over the period of observation. Valine CETP homozygotes but not heterozygotes experienced a relative 51% less decline in memory compared to the isoleucine homozygotic reference group after adjusting for gender, race/ethnicity, education, medical comorbidities, and APOE status. After controlling for these same variables, the hazard ratios for any dementia and for Alzheimer’s disease specifically were less among both valine homo- and heterozygotes compared to the isoleucine homozygotic group. However, the results were statistically significant only among the valine homozygotes. Importantly, the protective effect remained after adjusting for APOE status.

 

The Cholesterol Hypothesis

Carter has suggested that there is a convergence of polymorphic genes implicated in Alzheimer’s disease, including those associated with the amyloid precursor protein, cholesterol, lipoproteins, and atherosclerosis.5 Cholesterol and its transport system have also been associated with amyloid production as well as tau hyperphosphorylation and neurofibrillary tangles.6 Thus, both of the signature pathologic findings of Alzheimer’s disease are related in some way to cholesterol homeostasis. 

Moreover, a number of retrospective and case control studies comparing individuals prescribed statins for hypercholesterolemia have detected a small but statistically reliable protective effect against Alzheimer’s disease.6 Statins have anti-inflammatory effects and reliably prevent cardiovascular disease and stroke which has a direct impact on dementia.7 Yet, despite the hypothetical appeal of cholesterol as a target for intervention, large-scale prospective studies of two statins, simvastatin and pravastatin, failed to prevent dementia. In both studies, total cholesterol and LDL cholesterol were significantly and substantially decreased compared to placebo. But there were no differences in cognitive performance over time or in the incidence of dementia.8 However, both studies were designed to examine cardiovascular events rather than dementia as the primary outcome. The sample sizes and periods of observation may not have been sufficient to detect protection against dementia.7 In his 2008 Public Policy forum for the Alzheimer’s Association, Dekosky9 described the challenge of finding a protective effect of any medication against Alzheimer’s disease. The requisite sample size would approach 3,000 individuals and require a 5-year period of observation in order to detect a difference between drug and placebo. In contrast, the Cholesterol Lowering Agent to Slow Progression of Alzheimer’s disease study [CLASP] included 400 people with mild to moderate Alzheimer’s disease randomized to receive placebo or simvastatin. People with vascular disease and those whose cholesterol level met criteria for lipid-lowering medications were excluded. Change measured by the cognitive portion of the Alzheimer’s Disease Assessment Scale is the primary outcome. The CLASP study10,11 is the only double-blind, randomized controlled trial specifically designed to detect reduced cognitive decline among people with Alzheimer’s disease who would not have been prescribed a statin for cardiovascular indications. Prior studies have examined whether the cerebral cholesterol shuttle plays a role in initiating dementia. CLASP, if positive, will determine whether it sustains the disease.

 

Conclusion

Studies of longevity genes such as CETP and APOE add to the argument that aggressively targeting cardiovascular risk factors may be the most effective public health approach against Alzheimer’s disease at present. Cardiovascular mortality declined substantially between 1970 and 2000 representing nearly 800,000 lives saved from heart disease.9 If this trend continues and if the CLASP study is positive, the threatened pandemic of disability due to dementia may well be abated. Use of the current Food and Drug Administration-approved medications to combat the symptoms of dementia combined with lipid-modifying agents could then push the disability of Alzheimer’s disease to the end of the naturally occurring life span. The personal and societal benefit would then be similar to that observed for interventions which postpone the disability of diabetes. If genetic polymorphisms associated with exceptional longevity are associated with lessened incidence of dementia, their characterization may suggest novel pharmacologic interventions to prevent Alzheimer’s disease as well. PP

 

References

1.     NIH State-of-the-Science Conference Preventing Alzheimer’s Disease and Cognitive Decline. Available at: http://consensus.nih.gov/2010/alz.htm. Accessed February 2, 2010.
2.    alz.org. Brain Health. Available at: www.alz.org/we_can_help_brain_health_maintain_your_brain.asp. Accessed February 2, 2010.
3.    Barzilai N, Atzmon C, Schecter C, et al. Unique lipoprotein phenotype and genotype in humans with exceptional longevity. JAMA. 2003;290(15):2030-2040.
4.    Sanders AE, Wang C, Katz M, et al. Association of a functional polymorphism in the cholesteryl ester transfer protein (CETP) gene with memory decline and incidence of dementia. JAMA. 2010;303(2):150-158.
5.`Carter CJ. Convergence of genes implicated in Alzheimer’s disease on the cerebral cholesterol shuttle: APP, cholesterol, lipoproteins, and atherosclerosis. Neurochem Int. 2007;50(1):12-38.
6.    Kandiah N, Feldman HH Therapeutic potential of statins in Alzheimer’s disease. J Neurol Sci. 2009;283(1-2):230-234.
7.    Haan MN. Review: statins do not protect against development of dementia. Evidence Based Mental Health. 2009;12(4):114.
8.    McGuinness B, Craig D, Bullock R, Passmore P. Statins for prevention of dementia. Cochrane Database Syst Rev. 2009;(2):CD003160.
9.    DeKosky ST. Alzheimer’s Disease: Current and Future Research. Available at: www.alz.org/publicpolicyforum/08/downloads/Dekosky_Slides.pdf. Accessed February 2, 2010.
10.    Sano M. Multi-centre, randomised, double-blind, placebo controlled trial of simvastatin to slow the progression of Alzheimer’s disease. Alzheimer’s & Dementia. 2008;4(4 suppl 1):T200.
11. CLASP. Cholesterol lowering agent to slow progression of Alzheimer’s disease study. Clinical Trials.gov, National Institutes of Health/National Library of Medicine Web site. Available at: www.clinicaltrials.gov/show/NCT00053599. Accessed February 2, 2010.