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donors such as SNP have been found to stimulate neuronal growth in vitro8 and have been proposed as potential new drugs to treat patients with age-related neurodegenerative disease such as Alzheimer disease.9,10 On the other hand, too much NO could produce neurotoxicity due to accumulation of its toxic metabolite, peroxynitrite.11 The small amount of patients studied is the limitation of this work. Future studies with a larger number of patients are needed. The usual NPS safe dose rate used to treat hypertension is 0.5 to 10 Kg/kg per minute. Therefore, we decide to work with its lowest dose to minimize cardiovascular effects. Indeed, no clear effect of NPS on the vascular system was observed (these results have been discussed elsewhere).1 Further works should investigate the effects of multiple and different doses of SNP and other relevant issues like how frequently do we have to administer SNP to sustain its effect in long-term. To minimize possible learning effects, cognitive tests were performed only on 2 occasions. To avoid confounding factors from demographic and clinical variables, the selection and pairing of subjects were performed carefully and showed no differences between the 2 groups in any of the parameters evaluated (these results have been discussed elsewhere).1 Despite the cognitive deficits being considered as the strongest predictors of long-term functional recovery in patients with schizophrenia, they have been among the symptoms most refractory to the treatment by both second- and first-generation antipsychotics.12,13 Perhaps, the development of drugs that enhance NO levels, such as SNP, could be a productive target to pursue in the development of the next generation of antipsychotic drugs. However, the findings here reported are modest and need to be confirmed for future studies.
data analysis. None of the authors have a conflict of interest to declare. Joao Paulo Maia-de-Oliveira, MD, MSc, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and Department of Clinical Medicine Universidade Federal do Rio Grande do Norte Natal, RN, Brazil [email protected]
Joao Abrao, MD, PhD Paulo R. Evora, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil
Antonio W. Zuardi, MD, PhD Jose A.S. Crippa, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil and National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil
Paulo Belmonte-de-Abreu, MD, PhD National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and University of Rio Grande do Sul Porto Alegre, RS, Brazil
Glen B. Baker, PhD, DSc Serdar M. Dursun, MD, PhD, FRCPC National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil Neurochemical Research Unit Department of Psychiatry University of Alberta Edmonton, Alberta, Canada and Centre for Psychiatric Assessment and Therapeutics Alberta Health Services Alberta Hospital Edmonton Edmonton, Alberta, Canada
Jaime E.C. Hallak, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and Neurochemical Research Unit Department of Psychiatry University of Alberta Edmonton, Alberta, Canada
ACKNOWLEDGMENTS The authors thank their respective universities for the continuous support and Dr Judy Baker for the editorial and secretarial assistance.
AUTHOR DISCLOSURE INFORMATION JPMO has received a grant from Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq). JECH and JASC are recipients of research fellowship awards from CNPq (Brazil). JPMO had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the
Letters to the Editors 4. Gevins A1, Cutillo B. Spatiotemporal dynamics of component processes in human working memory. Electroencephalogr Clin Neurophysiol. 1993;87:128Y143. 5. Bredt DS, Snyder SH. Nitric oxide, a novel neuronal messenger. Neuron. 1992;8:3Y11. 6. Segovia G, Mora F. Role of the nitric oxide in modulating the release of dopamine, glutamate, and GABA in striatum of the freely moving rat. Brain Res Bull. 1998;45:275Y279. 7. Holscher C. Nitric oxide, the enigmatic neuronal messenger: its role in synaptic plasticity. Trends Neurosci. 1997;20:298Y303. 8. Hindley S, Juurlink BH, Gysbers JW, et al. Nitric oxide donors enhance neurotrophin-induced neurite outgrowth through a cGMP-dependent mechanism. J Neurosci Res. 1997;47:427Y439. 9. Paul V, Reddy L, Ekambaram P. A reversal by L-arginine and sodium nitroprusside of ageing-induced memory impairment in rats by increasing nitric oxide concentration in the hippocampus. Indian J Physiol Pharmacol. 2005;49:179Y186. 10. Thatcher GA, Bennett BM, Revnolds JN. Nitric oxide mimetic molecule as therapeutic agents in Alzheimer’s disease. Curr Alzheimer Res. 2005;2:171Y182. 11. Garthwaite J, Boulton CL. Nitric oxide signaling in the nervous system. Annu Rev Physiol. 1995;57:683Y706. 12. Miyamoto S, Miyake N, Jarskog LF, et al. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17:1206Y1227. 13. Velligan DI, Mahurin RK, Diamond PL, et al. The functional significance of symptomatology and cognitive function in schizophrenia. Schizophr Res. 1997;25:21Y31.
Genetic Overlap Between Antipsychotic Response and Susceptibility to Schizophrenia
REFERENCES 1. Hallak JE, Maia-de-Oliveira JP, Abrao J, et al. Rapid improvement of acute schizophrenia symptoms after intravenous sodium nitroprusside. JAMA Psychiatry. 2013;70:668Y676. 2. Liddle PF, Morris DL. Schizophrenic syndromes and frontal lobe performance. Br J Psychiatry. 1991;158:340Y345. 3. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643Y662.
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
To the Editors: xisting pharmacogenetic/genomic (PGt/ PGx) studies suggest that genetic variants associated with adverse effects or treatment response may display larger effect sizes than variants associated with complex disease phenotypes.1 This is demonstrated by the fact that reproducible findings have emerged from PGt studies with relatively small sample sizes, for example, research indicating that warfarin sensitivity can be
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explained in 30% of cases by single nucleotide polymorphisms (SNPs) in CYP2C9 (encoding cytochrome P450 2C9) and VKORC1 (encoding vitamin K reductase complex 1).2 In addition, cutaneous drug reactions such as the Stevens-Johnson syndrome or toxic epidermal necrolysis induced by anticonvulsants are related to a certain type of human leukocyte antigen.3 However, no loci have been found to connect treatment response to antipsychotic or antidepressant agents till date. One reason is that treatment response to psychotropic agents is not explained by genetic factors alone but by the complex relationship between genetic and environmental factors (eg, gene-environmental interaction). In particular, this may be true in case of response to antidepressants because the symptoms of patients with major depression improve even with placebo,4 with an estimated placebo responder rate of 30% to 40%. Another possibility is that the effect size of PGt/PGx studies using psychotropic therapy is much smaller than expected, resulting in a failure to detect predictive variants due to the small sample size. In our previous PGx study of treatment response to risperidone, a secondgeneration antipsychotic indicated for schizophrenia (SCZ), we reported that several SNPs showed a trend toward a significant association with treatment response using 100 K SNPs but failed to detect genome-wide significance.5 In the current study, we investigated the hypothesis that genes related to antipsychotic treatment response partially overlap with genes related to SCZ, particularly under the polygenic architecture, which several studies have detected in the genome-wide association studies for common diseases, such as SCZ.6 In this study, we regenotyped the same samples as before (N = 107, firstepisode and drug-naive schizophrenic patients; 99 samples were completely identical: males, 62; females, 45) using Affymetrix Human Genome-Wide SNP 6.0, containing approximately 1 M SNPs. Standard quality controls were applied (sample call rate, Q95%; SNP drop rate, G5%; Hardy-Weinberg equilibrium, Q10j4; minor allele frequency, Q10%; and population stratification7; Supplemental Fig. 1, Supplemental Digital Content 1, http://links.lww.com/JCP/A264: a total of 531 213 SNPs were analyzed). To extract predictor SNPs for treatment response, we used linear regression analysis under an additive model (independent variable: response rate of total Positive and Negative Syndrome Scale [PANSS] score between the initial and 8-week visits; dependent variables: age, sex, duration of untreated psychosis, and total PANSS score at initial
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visit). Reference alleles were also set to obtain a positive A (coefficient). The association results can be found in Supplemental Figure 2, Supplemental Digital Content 2, http://links.lww.com/JCP/A265 and Supplemental Table 1, Supplemental Digital Content 3, http://links.lww.com/JCP/A266. No SNPs showed significant association with genome-wide significance. To conduct polygenic component analysis as proposed by the International Schizophrenia Consortium,6 independent case-control analysis of a ‘‘target’’ sample was performed. In brief, this comparison aimed to check the enrichment of large numbers of SNPs associated with risperidone response and susceptibility to SCZ (ie, the PGx sample as a ‘‘discovery’’ set). A very liberal threshold for significance was applied (eg, P G 0.5) to gain statistical power and to examine whether common SNPs collectively contribute to a substantial proportion of the genetic overlap. All target case-control samples (for calculation of polygenic score) were genotyped by the same microarray (Affymetrix 6.0 chip; SCZ = 874, control = 962, a part of controls were used in other GenomeWide Association Study8,9 and Hashimoto et al, unpublished observations). Following the International Schizophrenia Consortium method, the data sets were linkage disequilibriumYpruned to set the SNPs in linkage equilibrium (for target samples, r2 = 0.25; window-size 200 SNPs: 82,698 SNPs), and then the polygenic score was calculated by multiplying by the value of A observed in the discovery analysis of the linear regression analysis for risperidone response. Nominally associated SNPs in
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the discovery set were selected using liberal P value thresholds (PT) at PT G 0.5, G 0.4, G 0.3, G 0.2, and G 0.1. Nagelkerke pseudoR2 was calculated by logistic regression analysis using R with covariation for nonmissing SNPs. All participants provided written informed consent after receiving a complete description of the study. The ethics committee of each university and institute participating in this project approved this study. In this polygenic component analysis, we found a cumulative effect of the SNPs related to risperidone response in the target case-control samples. Most of the PTs showed significant enrichment (PT G 0.5, 0.4, and 0.3), and the best P value was detected at PT G 0.5 (P = 3.8 10j3, pseudo-R2 =0.61%; Fig. 1, Supplemental Table 2, Supplemental Digital Content 4, http://links.lww.com/JCP/A267). The score of target controls calculated on the basis of better drug response was higher than that of target SCZ. This implies that patients with SCZ tend to have more alleles, which contribute to poorer drug response to risperidone. To examine the reverse hypothesis, ‘‘does risk for SCZ susceptibility predicts treatment response?’’ another explorative polygenic component analysis (ie, SCZ control analysis as discovery) was conducted, dividing PGx samples into responder and nonresponder defined by PANSS improvement (responder: 30%, 40%, and 50% reductions after 8 weeks treatment). Although the effect size of SCZ is extremely small (odds ratio, ~1.2)10 and likely to fail to capture ‘‘genuine’’ risk alleles in the discovery test due to lack of power, we found trend
FIGURE 1. Polygenic component analysis for the pairs of risperidone response PGx/SCZ susceptibility. Discovery set: risperidone response PGx using linear regression model. Target set: SCZ versus healthy controls. The y-axis indicates Nagelkerke R2, the power of explanation of the model. * 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.
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for significant enrichment of risk alleles for SCZ in nonresponders (best P = 0.0306, Supplemental Fig. 3, Supplemental Digital Content 5, http://links.lww.com/JCP/A268 and Supplemental Table 3, Supplemental Digital Content 6, http://links.lww.com/JCP/A269) with larger variants explained. However, the target sample was too small (N = 107) to provide conclusive results. In this study, we have shown that the genes related to antipsychotic treatment response partially overlap with those for susceptibility to SCZ, albeit the modest variance explained liability. To the best of our knowledge, this is the first evidence using a molecular genetic approach to elucidate the genetic link between drug response and a complex disorder in psychiatry. It may also provide indirect evidence that the candidate genes for SCZ determined according to the pharmacological evidence by antipsychotic treatment effects are reasonable. AUTHOR DISCLOSURE INFORMATION Dr Ozaki has received research support from Astellas, Eisai, Otsuka, Asahi Kasei Pharma, MSD, GlaxoSmithKline, Kyowa Hakko Kirin, Shionogi, Dainippon Sumitomo, Nihon Medi-Physics, Eli Lilly, Pfizer, Mochida, Meiji Seika Pharma, and Yoshitomi, as well as speaker’s honoraria from AbbVie, Astellas, Otsuka, AbbVie, Eisai, MSD, GlaxoSmithKline, Kyowa Hakko Kirin, Shionogi, Takeda, Novartis Pharma, Eli Lilly, Pfizer, Meiji Seika Pharma, Mochida, and Janssen. Dr Ozaki has served as a consultant to Dainippon Sumitomo. Dr Iwata has received research support or speaker’s honoraria from, or has served as a consultant to, Jansen, GlaxoSmithKline, Eli Lilly, Otsuka, Shionogi, Dainippon Sumitomo, Tanabe Mitsubishi, and Daiichi-Sankyo. For the remaining authors, none were declared. This work is the result of the ‘‘Integrated Research on Neuropsychiatric Disorders’’ carried out under the Strategic Research Program for Brain Sciences by of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan; Takeda Science Foundation; Academic Frontier Project for Private Universities, Comparative Cognitive Science Institutes; Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network and Glia assembly) from the MEXT of Japan; Grant-in-Aids for Scientific Research (B) and for Young Scientists (A) from the MEXT, Japan; Health Labour Sciences Research Grant from the Ministry of Health Labour and Welfare; and SENSHIN Medical Research Foundation, Japan. Supplemental digital content is available for this article. Direct URL citation
Letters to the Editors
appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.psychopharmacology.com).
Kiyoto Kasai, MD, PhD Department of Psychiatry Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
Masatoshi Takeda, MD, PhD Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Masashi Ikeda, MD, PhD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan [email protected]
Jun Nakamura, MD, PhD Department of Psychiatry University of Occupational and Environmental Health Kitakyushu, Fukuoka, Japan
Reiji Yoshimura, MD, PhD Department of Psychiatry University of Occupational and Environmental Health Kitakyushu, Fukuoka, Japan
Norio Ozaki, MD, PhD Department of Psychiatry Nagoya University Graduate School of Medicine Nagoya, Aichi, Japan
Ryota Hashimoto, MD, PhD Molecular Research Center for Children’s Mental Development United Graduate School of Child Development Osaka University Osaka, Japan and Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Nakao Iwata, MD, PhD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan
Kenji Kondo, MD, PhD Takeo Saito, MD Ayu Shimasaki, MD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan
Kazutaka Ohi, MD, PhD Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Mamoru Tochigi, MD, PhD Department of Psychiatry Teikyo University School of Medicine Itabashi, Tokyo, Japan
Yoshiya Kawamura, MD, PhD Department of Psychiatry Sake Seijinkai Hospital Yokohama, Kanagawa, Japan
Nao Nishida, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan and Research Center for Hepatitis and Immunology National Center for Global Health and Medicine Ichikawa, Chiba, Japan
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
Taku Miyagawa, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
Tsukasa Sasaki, MD, PhD Laboratory of Health Education Graduate School of Education The University of Tokyo Bunkyo, Tokyo, Japan
Katsushi Tokunaga, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
REFERENCES 1. Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet. 2010;11:415Y425. 2. Salari K, Watkins H, Ashley EA. Personalized medicine: hope or hype? Eur Heart J. 2012;33:1564Y1570. 3. Ozeki T, Mushiroda T, Yowang A, et al. Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum Mol Genet. 2011;20:1034Y1041. 4. Iovieno N, Papakostas GI. Correlation between different levels of placebo response rate and clinical trial outcome in major depressive disorder: a meta-analysis. J Clin Psychiatry. 2012;73:1300Y1306. 5. Ikeda M, Tomita Y, Mouri A, et al. Identification of novel candidate genes for treatment response to risperidone and susceptibility for schizophrenia: integrated analysis among pharmacogenomics, mouse expression, and genetic case-control association approaches. Biol Psychiatry. 2010;67:263Y269. 6. Purcell SM, Wray NR, Stone JL, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748Y752. 7. Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38: 904Y909. 8. Miyagawa T, Kawashima M, Nishida N, et al. Variant between CPT1B and CHKB associated with susceptibility to narcolepsy. Nat Genet. 2008;40:1324Y1328.
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Letters to the Editors 9. Otowa T, Yoshida E, Sugaya N, et al. Genome-wide association study of panic disorder in the Japanese population. J Hum Genet. 2009;54:122Y126. 10. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43:969Y976.
Trazodone and Cognitive Performance in Alzheimer Disease To the Editors: ognitive impairment is well recognized as a common adverse effect of sleeping pills, such as benzodiazepines1 and ‘‘z-drugs.’’2 There are few studies about the effect of antidepressants (including trazodone) on cognition.3,4 Despite its approval by the Food and Drug Administration to treat depression, insomnia is the most frequent reason for trazodone prescription.5 It exerts hypnotic actions at low doses (25 to 150 mg of dose range) due to blockade of 5-HT2A receptors as well as of H1 histaminic and >1 adrenergic receptors.6 In a recent study, Roth et al7 evaluated the cognitive and psychomotor effects of 50 mg of trazodone for 7 days in 16 primary insomniacs (mean age, 44 years) in a randomized, double-blind, placebo-controlled trial, resulting in small but significant impairment of short-term memory, verbal learning, balance, and arm muscle endurance among users. On the other hand, a double-blind, crossover trial from Rush et al8 compared the acute, subject-rated performance-impairing effects of trazodone (50, 100, 200 mg) and of triazolam (0.125, 0.25, 0.50 mg) with placebo in healthy subjects, which produced significant impairment in learning, recall, and performance skills with triazolam, but not with trazodone. Recently, our group published the results of a randomized, double-blind, placebo-controlled trial to examine possible improvements in sleep parameters in Alzheimer disease (AD) patients admitted with sleep disorders (SDs) after use of trazodone 50 mg.9 Briefly, patients were assessed by wrist actigraphy for a period of 7 to 9 days at baseline (to establish a sleep profile) and for 2 weeks during intervention when receiving 50 mg once daily at 10:00 P.M. (or placebo, in a 1:1 ratio). This trial was registered at clinicaltrials.gov, accession #NCT01142258. The results concerning the primary aim (trazodone effect on SD of AD patients) are presented
C
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therein along with detailed aspects of our methods and procedures. As secondary aim, our hypothesis was that trazodone 50 mg can be used in AD patients with no worsening of cognitive skills, and the scope of the present letter relies on discussing this important finding. With approval of the protocol by the institutional Research Ethics Committee, written informed consent was obtained from all (participants and/or caregiver), including for this complementary investigation. In brief, a neuropsychological inventory was put together to comprise a cast of cognitive assessments based on the criteria of being easy-to-handle and producing useful information by covering a range of cognitive abilities usually impaired in AD patients, so to justify their frequent usage in clinical and research scenarios in view of the author’s experience. These instruments were as follows: Mini Mental State Examination (MMSE)10; Paired Associate Learning Test-Form I (short-term memory) and Paired Associate Learning Test-Form II (long-term memory) of the Wechsler Memory Scale11; and Digit Span Test, Arithmetic, Letter-Number Sequencing, Digit Symbol-Coding and Symbol Search of the Wechsler Adult Intelligence Scale (third edition, WAIS-III).12 Prior and after intervention, all tests were performed by one single trained neuropsychologist, blinded, with assessment and reassessment done in the same interviews in which the actigraphic device was placed or returned, respectively. Tests were applied in the morning (10Y12 hours) after the most recent dose. This routine of trazodone use and reassessments probably reduced biases related to major intersubject variance in bloodstream concentration of the drug, whose elimination half-life in the male and female elderly ranges from 8.2 and 7.1 hours, respectively.13 Thirty-six participants were randomized to either the active treatment (n = 19) or the placebo group (n = 17). One subject of the trazodone group was excluded for not complying with regular use of the prescribed antihypertensive and antiarrhythmic drugs, evolving into heart failure. One patient of the placebo group was also excluded because of an episode of agitation and consequent arm fracture. Complete analyses comprised 34 patients: 18 in active treatment and 16 in placebo. Comparing baseline measures of the 2 groups using the W2 test for categorical variables or the Student t test for continuous variables (Mann-Whitney U test for non-Gaussian distributed traits), no differences could be devised. The mean age of the whole group was 81.0 T 7.5 years, with women comprising 66.7% of the sample. The mean MMSE score was 11.2 T 6.2
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and the most frequent clinical dementia rating scores were 2 and 3. Demographic and descriptive variables (age, sex, marital status, educational level, clinical dementia rating, Cornell depression scale) were the same (P Q 0.05) across intervention groups. None of these variables were reexamined at the end point stage. On what concerns medication use, it is probably worth mentioning that the frequency of users of the most common drugs to treat AD (donepezil, galantamine, rivastigmine, and memantine) as well as of users of antipsychotic drugs was no different across treatment arms. Patients were not allowed to change medications during the study period. Posttreatment scores were herein expressed as the value of absolute, net change in each test, compared using analysis of covariance between treatment arms taking baseline scores as covariables. Our main finding was no differential cognitive performance of trazodone-treated subjects compared to placebo-treated equals, with results shown here (in parenthesis) as the observed net difference followed (in brackets) by the range of its 95% confidence interval and by (after semicolon) the significance level achieved: MMSE (0.1 [j0.9 to 1.1]; P = 0.866), forward/backward digit span task (0.9 [j0.3 to 2.3]; P = 0.150), letter-number sequencing (0.0 [j0.9 to 0.9]; P = 0.958), arithmetic (0.2 [j0.4 to 0.9]; P = 0.453), digit-symbol coding (0.6 [j1.3 to 2.4]; P = 0.528), and symbol search (j0.9 [j2.4 to 0.5]; P = 0.191). The Paired Associate Learning Tests of the Wechsler Memory Scale could not be performed due to the severity of the dementia, and the authors advise other research groups to rule out the instrument whether investigating moderate to severe statuses. All in all, on top of improving sleep of AD patients (as described in our primary report), trazodone demonstrates the property of not impairing (nor improving) cognitive functions. A body of evidence helps advocating in favor of our conclusion. A double-blind, crossover study14 evaluated the use of trazodone 100 mg in healthy adults aged 60 years or older in comparison with amitriptyline and placebo and did not observe effects on either information processing, attention or visual-motor skills. Another study15 evaluating continuous nocturnal doses of mirtazapine 15 mg, trazodone 25 mg, or placebo for 8 days in healthy men showed that trazodone did not impair driving or cognitive skills. In the largest prospective cohort so far (4414 cognitively preserved participants; aged 50 years or older), trazodone was not implicated in decline of psychometric performance.16 Some reasons might explain the disparities between these former (including our)
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.
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Volume 35, Number 1, February 2015
donors such as SNP have been found to stimulate neuronal growth in vitro8 and have been proposed as potential new drugs to treat patients with age-related neurodegenerative disease such as Alzheimer disease.9,10 On the other hand, too much NO could produce neurotoxicity due to accumulation of its toxic metabolite, peroxynitrite.11 The small amount of patients studied is the limitation of this work. Future studies with a larger number of patients are needed. The usual NPS safe dose rate used to treat hypertension is 0.5 to 10 Kg/kg per minute. Therefore, we decide to work with its lowest dose to minimize cardiovascular effects. Indeed, no clear effect of NPS on the vascular system was observed (these results have been discussed elsewhere).1 Further works should investigate the effects of multiple and different doses of SNP and other relevant issues like how frequently do we have to administer SNP to sustain its effect in long-term. To minimize possible learning effects, cognitive tests were performed only on 2 occasions. To avoid confounding factors from demographic and clinical variables, the selection and pairing of subjects were performed carefully and showed no differences between the 2 groups in any of the parameters evaluated (these results have been discussed elsewhere).1 Despite the cognitive deficits being considered as the strongest predictors of long-term functional recovery in patients with schizophrenia, they have been among the symptoms most refractory to the treatment by both second- and first-generation antipsychotics.12,13 Perhaps, the development of drugs that enhance NO levels, such as SNP, could be a productive target to pursue in the development of the next generation of antipsychotic drugs. However, the findings here reported are modest and need to be confirmed for future studies.
data analysis. None of the authors have a conflict of interest to declare. Joao Paulo Maia-de-Oliveira, MD, MSc, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and Department of Clinical Medicine Universidade Federal do Rio Grande do Norte Natal, RN, Brazil [email protected]
Joao Abrao, MD, PhD Paulo R. Evora, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil
Antonio W. Zuardi, MD, PhD Jose A.S. Crippa, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil and National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil
Paulo Belmonte-de-Abreu, MD, PhD National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and University of Rio Grande do Sul Porto Alegre, RS, Brazil
Glen B. Baker, PhD, DSc Serdar M. Dursun, MD, PhD, FRCPC National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil Neurochemical Research Unit Department of Psychiatry University of Alberta Edmonton, Alberta, Canada and Centre for Psychiatric Assessment and Therapeutics Alberta Health Services Alberta Hospital Edmonton Edmonton, Alberta, Canada
Jaime E.C. Hallak, MD, PhD University of Sao Paulo Ribeirao Preto, SP, Brazil National Institute of Science and Technology in Translational Medicine Ribeirao Preto, Brazil and Neurochemical Research Unit Department of Psychiatry University of Alberta Edmonton, Alberta, Canada
ACKNOWLEDGMENTS The authors thank their respective universities for the continuous support and Dr Judy Baker for the editorial and secretarial assistance.
AUTHOR DISCLOSURE INFORMATION JPMO has received a grant from Conselho Nacional de Desenvolvimento Cientı´fico e Tecnolo´gico (CNPq). JECH and JASC are recipients of research fellowship awards from CNPq (Brazil). JPMO had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the
Letters to the Editors 4. Gevins A1, Cutillo B. Spatiotemporal dynamics of component processes in human working memory. Electroencephalogr Clin Neurophysiol. 1993;87:128Y143. 5. Bredt DS, Snyder SH. Nitric oxide, a novel neuronal messenger. Neuron. 1992;8:3Y11. 6. Segovia G, Mora F. Role of the nitric oxide in modulating the release of dopamine, glutamate, and GABA in striatum of the freely moving rat. Brain Res Bull. 1998;45:275Y279. 7. Holscher C. Nitric oxide, the enigmatic neuronal messenger: its role in synaptic plasticity. Trends Neurosci. 1997;20:298Y303. 8. Hindley S, Juurlink BH, Gysbers JW, et al. Nitric oxide donors enhance neurotrophin-induced neurite outgrowth through a cGMP-dependent mechanism. J Neurosci Res. 1997;47:427Y439. 9. Paul V, Reddy L, Ekambaram P. A reversal by L-arginine and sodium nitroprusside of ageing-induced memory impairment in rats by increasing nitric oxide concentration in the hippocampus. Indian J Physiol Pharmacol. 2005;49:179Y186. 10. Thatcher GA, Bennett BM, Revnolds JN. Nitric oxide mimetic molecule as therapeutic agents in Alzheimer’s disease. Curr Alzheimer Res. 2005;2:171Y182. 11. Garthwaite J, Boulton CL. Nitric oxide signaling in the nervous system. Annu Rev Physiol. 1995;57:683Y706. 12. Miyamoto S, Miyake N, Jarskog LF, et al. Pharmacological treatment of schizophrenia: a critical review of the pharmacology and clinical effects of current and future therapeutic agents. Mol Psychiatry. 2012;17:1206Y1227. 13. Velligan DI, Mahurin RK, Diamond PL, et al. The functional significance of symptomatology and cognitive function in schizophrenia. Schizophr Res. 1997;25:21Y31.
Genetic Overlap Between Antipsychotic Response and Susceptibility to Schizophrenia
REFERENCES 1. Hallak JE, Maia-de-Oliveira JP, Abrao J, et al. Rapid improvement of acute schizophrenia symptoms after intravenous sodium nitroprusside. JAMA Psychiatry. 2013;70:668Y676. 2. Liddle PF, Morris DL. Schizophrenic syndromes and frontal lobe performance. Br J Psychiatry. 1991;158:340Y345. 3. Stroop JR. Studies of interference in serial verbal reactions. J Exp Psychol. 1935;18:643Y662.
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
To the Editors: xisting pharmacogenetic/genomic (PGt/ PGx) studies suggest that genetic variants associated with adverse effects or treatment response may display larger effect sizes than variants associated with complex disease phenotypes.1 This is demonstrated by the fact that reproducible findings have emerged from PGt studies with relatively small sample sizes, for example, research indicating that warfarin sensitivity can be
E
www.psychopharmacology.com
Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.
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Journal of Clinical Psychopharmacology
Letters to the Editors
explained in 30% of cases by single nucleotide polymorphisms (SNPs) in CYP2C9 (encoding cytochrome P450 2C9) and VKORC1 (encoding vitamin K reductase complex 1).2 In addition, cutaneous drug reactions such as the Stevens-Johnson syndrome or toxic epidermal necrolysis induced by anticonvulsants are related to a certain type of human leukocyte antigen.3 However, no loci have been found to connect treatment response to antipsychotic or antidepressant agents till date. One reason is that treatment response to psychotropic agents is not explained by genetic factors alone but by the complex relationship between genetic and environmental factors (eg, gene-environmental interaction). In particular, this may be true in case of response to antidepressants because the symptoms of patients with major depression improve even with placebo,4 with an estimated placebo responder rate of 30% to 40%. Another possibility is that the effect size of PGt/PGx studies using psychotropic therapy is much smaller than expected, resulting in a failure to detect predictive variants due to the small sample size. In our previous PGx study of treatment response to risperidone, a secondgeneration antipsychotic indicated for schizophrenia (SCZ), we reported that several SNPs showed a trend toward a significant association with treatment response using 100 K SNPs but failed to detect genome-wide significance.5 In the current study, we investigated the hypothesis that genes related to antipsychotic treatment response partially overlap with genes related to SCZ, particularly under the polygenic architecture, which several studies have detected in the genome-wide association studies for common diseases, such as SCZ.6 In this study, we regenotyped the same samples as before (N = 107, firstepisode and drug-naive schizophrenic patients; 99 samples were completely identical: males, 62; females, 45) using Affymetrix Human Genome-Wide SNP 6.0, containing approximately 1 M SNPs. Standard quality controls were applied (sample call rate, Q95%; SNP drop rate, G5%; Hardy-Weinberg equilibrium, Q10j4; minor allele frequency, Q10%; and population stratification7; Supplemental Fig. 1, Supplemental Digital Content 1, http://links.lww.com/JCP/A264: a total of 531 213 SNPs were analyzed). To extract predictor SNPs for treatment response, we used linear regression analysis under an additive model (independent variable: response rate of total Positive and Negative Syndrome Scale [PANSS] score between the initial and 8-week visits; dependent variables: age, sex, duration of untreated psychosis, and total PANSS score at initial
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visit). Reference alleles were also set to obtain a positive A (coefficient). The association results can be found in Supplemental Figure 2, Supplemental Digital Content 2, http://links.lww.com/JCP/A265 and Supplemental Table 1, Supplemental Digital Content 3, http://links.lww.com/JCP/A266. No SNPs showed significant association with genome-wide significance. To conduct polygenic component analysis as proposed by the International Schizophrenia Consortium,6 independent case-control analysis of a ‘‘target’’ sample was performed. In brief, this comparison aimed to check the enrichment of large numbers of SNPs associated with risperidone response and susceptibility to SCZ (ie, the PGx sample as a ‘‘discovery’’ set). A very liberal threshold for significance was applied (eg, P G 0.5) to gain statistical power and to examine whether common SNPs collectively contribute to a substantial proportion of the genetic overlap. All target case-control samples (for calculation of polygenic score) were genotyped by the same microarray (Affymetrix 6.0 chip; SCZ = 874, control = 962, a part of controls were used in other GenomeWide Association Study8,9 and Hashimoto et al, unpublished observations). Following the International Schizophrenia Consortium method, the data sets were linkage disequilibriumYpruned to set the SNPs in linkage equilibrium (for target samples, r2 = 0.25; window-size 200 SNPs: 82,698 SNPs), and then the polygenic score was calculated by multiplying by the value of A observed in the discovery analysis of the linear regression analysis for risperidone response. Nominally associated SNPs in
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the discovery set were selected using liberal P value thresholds (PT) at PT G 0.5, G 0.4, G 0.3, G 0.2, and G 0.1. Nagelkerke pseudoR2 was calculated by logistic regression analysis using R with covariation for nonmissing SNPs. All participants provided written informed consent after receiving a complete description of the study. The ethics committee of each university and institute participating in this project approved this study. In this polygenic component analysis, we found a cumulative effect of the SNPs related to risperidone response in the target case-control samples. Most of the PTs showed significant enrichment (PT G 0.5, 0.4, and 0.3), and the best P value was detected at PT G 0.5 (P = 3.8 10j3, pseudo-R2 =0.61%; Fig. 1, Supplemental Table 2, Supplemental Digital Content 4, http://links.lww.com/JCP/A267). The score of target controls calculated on the basis of better drug response was higher than that of target SCZ. This implies that patients with SCZ tend to have more alleles, which contribute to poorer drug response to risperidone. To examine the reverse hypothesis, ‘‘does risk for SCZ susceptibility predicts treatment response?’’ another explorative polygenic component analysis (ie, SCZ control analysis as discovery) was conducted, dividing PGx samples into responder and nonresponder defined by PANSS improvement (responder: 30%, 40%, and 50% reductions after 8 weeks treatment). Although the effect size of SCZ is extremely small (odds ratio, ~1.2)10 and likely to fail to capture ‘‘genuine’’ risk alleles in the discovery test due to lack of power, we found trend
FIGURE 1. Polygenic component analysis for the pairs of risperidone response PGx/SCZ susceptibility. Discovery set: risperidone response PGx using linear regression model. Target set: SCZ versus healthy controls. The y-axis indicates Nagelkerke R2, the power of explanation of the model. * 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.
Journal of Clinical Psychopharmacology
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for significant enrichment of risk alleles for SCZ in nonresponders (best P = 0.0306, Supplemental Fig. 3, Supplemental Digital Content 5, http://links.lww.com/JCP/A268 and Supplemental Table 3, Supplemental Digital Content 6, http://links.lww.com/JCP/A269) with larger variants explained. However, the target sample was too small (N = 107) to provide conclusive results. In this study, we have shown that the genes related to antipsychotic treatment response partially overlap with those for susceptibility to SCZ, albeit the modest variance explained liability. To the best of our knowledge, this is the first evidence using a molecular genetic approach to elucidate the genetic link between drug response and a complex disorder in psychiatry. It may also provide indirect evidence that the candidate genes for SCZ determined according to the pharmacological evidence by antipsychotic treatment effects are reasonable. AUTHOR DISCLOSURE INFORMATION Dr Ozaki has received research support from Astellas, Eisai, Otsuka, Asahi Kasei Pharma, MSD, GlaxoSmithKline, Kyowa Hakko Kirin, Shionogi, Dainippon Sumitomo, Nihon Medi-Physics, Eli Lilly, Pfizer, Mochida, Meiji Seika Pharma, and Yoshitomi, as well as speaker’s honoraria from AbbVie, Astellas, Otsuka, AbbVie, Eisai, MSD, GlaxoSmithKline, Kyowa Hakko Kirin, Shionogi, Takeda, Novartis Pharma, Eli Lilly, Pfizer, Meiji Seika Pharma, Mochida, and Janssen. Dr Ozaki has served as a consultant to Dainippon Sumitomo. Dr Iwata has received research support or speaker’s honoraria from, or has served as a consultant to, Jansen, GlaxoSmithKline, Eli Lilly, Otsuka, Shionogi, Dainippon Sumitomo, Tanabe Mitsubishi, and Daiichi-Sankyo. For the remaining authors, none were declared. This work is the result of the ‘‘Integrated Research on Neuropsychiatric Disorders’’ carried out under the Strategic Research Program for Brain Sciences by of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan; Takeda Science Foundation; Academic Frontier Project for Private Universities, Comparative Cognitive Science Institutes; Grant-in-Aid for Scientific Research on Innovative Areas (Comprehensive Brain Science Network and Glia assembly) from the MEXT of Japan; Grant-in-Aids for Scientific Research (B) and for Young Scientists (A) from the MEXT, Japan; Health Labour Sciences Research Grant from the Ministry of Health Labour and Welfare; and SENSHIN Medical Research Foundation, Japan. Supplemental digital content is available for this article. Direct URL citation
Letters to the Editors
appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.psychopharmacology.com).
Kiyoto Kasai, MD, PhD Department of Psychiatry Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
Masatoshi Takeda, MD, PhD Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Masashi Ikeda, MD, PhD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan [email protected]
Jun Nakamura, MD, PhD Department of Psychiatry University of Occupational and Environmental Health Kitakyushu, Fukuoka, Japan
Reiji Yoshimura, MD, PhD Department of Psychiatry University of Occupational and Environmental Health Kitakyushu, Fukuoka, Japan
Norio Ozaki, MD, PhD Department of Psychiatry Nagoya University Graduate School of Medicine Nagoya, Aichi, Japan
Ryota Hashimoto, MD, PhD Molecular Research Center for Children’s Mental Development United Graduate School of Child Development Osaka University Osaka, Japan and Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Nakao Iwata, MD, PhD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan
Kenji Kondo, MD, PhD Takeo Saito, MD Ayu Shimasaki, MD Department of Psychiatry Fujita Health University School of Medicine Toyoake, Aichi, Japan
Kazutaka Ohi, MD, PhD Department of Psychiatry Graduate School of Medicine Osaka University Osaka, Japan
Mamoru Tochigi, MD, PhD Department of Psychiatry Teikyo University School of Medicine Itabashi, Tokyo, Japan
Yoshiya Kawamura, MD, PhD Department of Psychiatry Sake Seijinkai Hospital Yokohama, Kanagawa, Japan
Nao Nishida, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan and Research Center for Hepatitis and Immunology National Center for Global Health and Medicine Ichikawa, Chiba, Japan
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
Taku Miyagawa, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
Tsukasa Sasaki, MD, PhD Laboratory of Health Education Graduate School of Education The University of Tokyo Bunkyo, Tokyo, Japan
Katsushi Tokunaga, PhD Department of Human Genetics Graduate School of Medicine the University of Tokyo Bunkyo, Tokyo, Japan
REFERENCES 1. Cirulli ET, Goldstein DB. Uncovering the roles of rare variants in common disease through whole-genome sequencing. Nat Rev Genet. 2010;11:415Y425. 2. Salari K, Watkins H, Ashley EA. Personalized medicine: hope or hype? Eur Heart J. 2012;33:1564Y1570. 3. Ozeki T, Mushiroda T, Yowang A, et al. Genome-wide association study identifies HLA-A*3101 allele as a genetic risk factor for carbamazepine-induced cutaneous adverse drug reactions in Japanese population. Hum Mol Genet. 2011;20:1034Y1041. 4. Iovieno N, Papakostas GI. Correlation between different levels of placebo response rate and clinical trial outcome in major depressive disorder: a meta-analysis. J Clin Psychiatry. 2012;73:1300Y1306. 5. Ikeda M, Tomita Y, Mouri A, et al. Identification of novel candidate genes for treatment response to risperidone and susceptibility for schizophrenia: integrated analysis among pharmacogenomics, mouse expression, and genetic case-control association approaches. Biol Psychiatry. 2010;67:263Y269. 6. Purcell SM, Wray NR, Stone JL, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460:748Y752. 7. Price AL, Patterson NJ, Plenge RM, et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38: 904Y909. 8. Miyagawa T, Kawashima M, Nishida N, et al. Variant between CPT1B and CHKB associated with susceptibility to narcolepsy. Nat Genet. 2008;40:1324Y1328.
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Letters to the Editors 9. Otowa T, Yoshida E, Sugaya N, et al. Genome-wide association study of panic disorder in the Japanese population. J Hum Genet. 2009;54:122Y126. 10. Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium. Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 2011;43:969Y976.
Trazodone and Cognitive Performance in Alzheimer Disease To the Editors: ognitive impairment is well recognized as a common adverse effect of sleeping pills, such as benzodiazepines1 and ‘‘z-drugs.’’2 There are few studies about the effect of antidepressants (including trazodone) on cognition.3,4 Despite its approval by the Food and Drug Administration to treat depression, insomnia is the most frequent reason for trazodone prescription.5 It exerts hypnotic actions at low doses (25 to 150 mg of dose range) due to blockade of 5-HT2A receptors as well as of H1 histaminic and >1 adrenergic receptors.6 In a recent study, Roth et al7 evaluated the cognitive and psychomotor effects of 50 mg of trazodone for 7 days in 16 primary insomniacs (mean age, 44 years) in a randomized, double-blind, placebo-controlled trial, resulting in small but significant impairment of short-term memory, verbal learning, balance, and arm muscle endurance among users. On the other hand, a double-blind, crossover trial from Rush et al8 compared the acute, subject-rated performance-impairing effects of trazodone (50, 100, 200 mg) and of triazolam (0.125, 0.25, 0.50 mg) with placebo in healthy subjects, which produced significant impairment in learning, recall, and performance skills with triazolam, but not with trazodone. Recently, our group published the results of a randomized, double-blind, placebo-controlled trial to examine possible improvements in sleep parameters in Alzheimer disease (AD) patients admitted with sleep disorders (SDs) after use of trazodone 50 mg.9 Briefly, patients were assessed by wrist actigraphy for a period of 7 to 9 days at baseline (to establish a sleep profile) and for 2 weeks during intervention when receiving 50 mg once daily at 10:00 P.M. (or placebo, in a 1:1 ratio). This trial was registered at clinicaltrials.gov, accession #NCT01142258. The results concerning the primary aim (trazodone effect on SD of AD patients) are presented
C
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therein along with detailed aspects of our methods and procedures. As secondary aim, our hypothesis was that trazodone 50 mg can be used in AD patients with no worsening of cognitive skills, and the scope of the present letter relies on discussing this important finding. With approval of the protocol by the institutional Research Ethics Committee, written informed consent was obtained from all (participants and/or caregiver), including for this complementary investigation. In brief, a neuropsychological inventory was put together to comprise a cast of cognitive assessments based on the criteria of being easy-to-handle and producing useful information by covering a range of cognitive abilities usually impaired in AD patients, so to justify their frequent usage in clinical and research scenarios in view of the author’s experience. These instruments were as follows: Mini Mental State Examination (MMSE)10; Paired Associate Learning Test-Form I (short-term memory) and Paired Associate Learning Test-Form II (long-term memory) of the Wechsler Memory Scale11; and Digit Span Test, Arithmetic, Letter-Number Sequencing, Digit Symbol-Coding and Symbol Search of the Wechsler Adult Intelligence Scale (third edition, WAIS-III).12 Prior and after intervention, all tests were performed by one single trained neuropsychologist, blinded, with assessment and reassessment done in the same interviews in which the actigraphic device was placed or returned, respectively. Tests were applied in the morning (10Y12 hours) after the most recent dose. This routine of trazodone use and reassessments probably reduced biases related to major intersubject variance in bloodstream concentration of the drug, whose elimination half-life in the male and female elderly ranges from 8.2 and 7.1 hours, respectively.13 Thirty-six participants were randomized to either the active treatment (n = 19) or the placebo group (n = 17). One subject of the trazodone group was excluded for not complying with regular use of the prescribed antihypertensive and antiarrhythmic drugs, evolving into heart failure. One patient of the placebo group was also excluded because of an episode of agitation and consequent arm fracture. Complete analyses comprised 34 patients: 18 in active treatment and 16 in placebo. Comparing baseline measures of the 2 groups using the W2 test for categorical variables or the Student t test for continuous variables (Mann-Whitney U test for non-Gaussian distributed traits), no differences could be devised. The mean age of the whole group was 81.0 T 7.5 years, with women comprising 66.7% of the sample. The mean MMSE score was 11.2 T 6.2
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and the most frequent clinical dementia rating scores were 2 and 3. Demographic and descriptive variables (age, sex, marital status, educational level, clinical dementia rating, Cornell depression scale) were the same (P Q 0.05) across intervention groups. None of these variables were reexamined at the end point stage. On what concerns medication use, it is probably worth mentioning that the frequency of users of the most common drugs to treat AD (donepezil, galantamine, rivastigmine, and memantine) as well as of users of antipsychotic drugs was no different across treatment arms. Patients were not allowed to change medications during the study period. Posttreatment scores were herein expressed as the value of absolute, net change in each test, compared using analysis of covariance between treatment arms taking baseline scores as covariables. Our main finding was no differential cognitive performance of trazodone-treated subjects compared to placebo-treated equals, with results shown here (in parenthesis) as the observed net difference followed (in brackets) by the range of its 95% confidence interval and by (after semicolon) the significance level achieved: MMSE (0.1 [j0.9 to 1.1]; P = 0.866), forward/backward digit span task (0.9 [j0.3 to 2.3]; P = 0.150), letter-number sequencing (0.0 [j0.9 to 0.9]; P = 0.958), arithmetic (0.2 [j0.4 to 0.9]; P = 0.453), digit-symbol coding (0.6 [j1.3 to 2.4]; P = 0.528), and symbol search (j0.9 [j2.4 to 0.5]; P = 0.191). The Paired Associate Learning Tests of the Wechsler Memory Scale could not be performed due to the severity of the dementia, and the authors advise other research groups to rule out the instrument whether investigating moderate to severe statuses. All in all, on top of improving sleep of AD patients (as described in our primary report), trazodone demonstrates the property of not impairing (nor improving) cognitive functions. A body of evidence helps advocating in favor of our conclusion. A double-blind, crossover study14 evaluated the use of trazodone 100 mg in healthy adults aged 60 years or older in comparison with amitriptyline and placebo and did not observe effects on either information processing, attention or visual-motor skills. Another study15 evaluating continuous nocturnal doses of mirtazapine 15 mg, trazodone 25 mg, or placebo for 8 days in healthy men showed that trazodone did not impair driving or cognitive skills. In the largest prospective cohort so far (4414 cognitively preserved participants; aged 50 years or older), trazodone was not implicated in decline of psychometric performance.16 Some reasons might explain the disparities between these former (including our)
* 2015 Wolters Kluwer Health, Inc. All rights reserved.
Copyright © 2014 Wolters Kluwer Health, Inc. All rights reserved.
