research article pharmacogenetics of risperidone and cardiovascular...

Research ArticlePharmacogenetics of Risperidone and Cardiovascular Risk inChildren and Adolescents

Amilton Dos Santos-Juacutenior1 Taciane Barbosa Henriques2

Maricilda Palandi de Mello2 Osmar Henrique Della Torre1 Luacutecia Arisaka Paes1

Adriana Perez Ferreira-Neto1 Letiacutecia Esposito Sewaybricker3 Thiago Salum Fontana1

Eloisa Helena Rubello Valler Celeri1 Gil Guerra-Juacutenior34 and Paulo Dalgalarrondo1

1Department of Psychiatry School of Medical Sciences (FCM) State University of Campinas (Unicamp)13083-887 Campinas SP Brazil2Laboratory of Human Genetics Center for Molecular Biology and Genetic Engineering (CBMEG) Unicamp13083-875 Campinas SP Brazil3Growth and Development Laboratory Center for Investigation in Pediatrics (CIPED) FCM-Unicamp13083-887 Campinas SP Brazil4Department of Pediatrics Pediatric Endocrinology Unit FCM-Unicamp 13083-887 Campinas SP Brazil

Correspondence should be addressed to Osmar Henrique Della Torre drosmarpsiqgmailcom

Received 26 August 2015 Revised 11 December 2015 Accepted 20 December 2015

Academic Editor Javier Salvador

Copyright copy 2016 Amilton Dos Santos-Junior et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Objective To identify the frequency of obesity and metabolic complications in child and adolescent users of risperidone Potentialassociations with clinical parameters and SNPs of the HTR2C DRD2 LEP LEPR MC4R and CYP2D6 genes were analyzedMethods Samples from 120 risperidone users (8ndash20 years old) were collected and SNPs were analyzed alongside assessment ofchronological and bone ages prescribed and weight-adjusted doses use of other psychotropic drugs waist circumference BMI119911-scores blood pressure HOMA-IR index fasting levels of serum glucose insulin cholesterol triglycerides transaminases andleptin Results Thirty-two (267) patients were overweight and 5 (42) obese Hypertension was recorded in 8 patients (67)metabolic syndrome in 6 (5) and increased waist circumference in 20 (167) The HOMA-IR was high for 22 patients (183)while total cholesterol and triglycerides were high in 20 (167) and 41 (342) patients respectively SNP associations were foundfor LEPHTR2C and CYP2D6 with BMI CYP2D6 with blood pressure ALT and HOMA-IRHTR2C and LEPR with leptin levelsMC4R and DRD2 with HOMA-IR HTR2C with WC and LEP with ALT Conclusions Although not higher than in the generalpediatric population a high frequency of patients was overweightobese with abnormalities in metabolic parameters and somepharmacogenetic associations

1 Introduction

Risperidone a serotonin-dopamine antagonist is effectivein preventing delusions and hallucinations in addition toimpulsive and aggressive behavior [1 2] The US Food andDrug Administration has approved the use of risperidone inchildren to treat irritability related to autism (5ndash16 years old)manic or mixed state in bipolar disorder (10ndash17 years old)and schizophrenia (13ndash17 years old) [3] However the off-label use of risperidone to treat a series of other behavioral

symptoms including developmental and disruptive disordersis not uncommon [4]

Although widely prescribed risperidone use has somerisks and few studies have evaluated its pharmacokineticeffects in children and adolescents or adverse drug reactionsin these populations [4] The possibility of weight gaincardiovascular effects and metabolic effects associated withhepatotoxicity insulin and leptin resistance hyperglycemiaand hyperinsulinemia is associated with increased cardiovas-cular and cancer morbimortality in adulthood [5 6]

Hindawi Publishing CorporationInternational Journal of EndocrinologyVolume 2016 Article ID 5872423 10 pageshttpdxdoiorg10115520165872423

2 International Journal of Endocrinology

Regarding the weight gain and metabolic effects inducedby risperidone the HTR2C DRD2 LEP LEPR MC4R andCYP2D6 genes are believed to be involved Considering thatserotonin receptors directly influence the satiety and appetitecontrol systems [6] the X-linkedHTR2C gene is likely to playan important role as it encodes the serotonin 2C receptor [7]

Another gene involved in weight gain is the DRD2gene which encodes the dopamine D2 receptor [8] and isassociated with obesity [9] It has been reported that theavailability of striatal dopamine D2 receptors is significantlyreduced in overweight individuals [9]

The LEP and LEPR genes which encode leptin andits receptor respectively have also been reported to beimportant in antipsychotic-induced weight gain studies [10]Secreted only by the adipose tissue leptin inhibits food intakeand increases energy expenditure [3 11]

Melanocortin-4 Receptor (MC4R) is a transmembrane Gprotein-coupled receptor expressed in the central nervoussystem primarily in the hypothalamus [11 12]MC4R plays acritical role in the regulation of energy homeostasis by leptinand heterozygousmutations in the gene account for 05ndash63of severe or early-onset obesity cases which represent themost frequent cause ofmonogenic human obesity syndromes[6]

The gene coding the P450 2D6 enzyme (CYP2D6) isone of the best studied pharmacogenes [13] Approximately25 of all drugs including risperidone are metabolizedthrough this enzyme which also happens to be one of themost polymorphic enzymes with over 100 known allelicvariants [13] It is now known that polymorphisms in theCYP2D6 gene can result in a range of effects from completeloss of enzyme activity through deletion of the gene (poormetabolizer status) or extensive activity through duplicationof the gene (ultra-rapid metabolizer status) [13] Besides theinvolvement of other genes in the pharmacodynamics ofrisperidone pharmacokinetic studies have hypothesized thatlow activity of the cytochrome complex may be associatedwith an increase in serum risperidone levels which canindirectly lead to weight gain [1 5 14]

The aims of this study were to assess the frequency of obe-sity hypertension metabolic syndrome insulin resistanceand dyslipidemia in a sample of child and adolescent usersof risperidone to evaluate associations with clinical andpharmacological data and with certain polymorphisms of theHTR2C DRD2 LEP LEPRMC4R and CYP2D6 genes

2 Subjects and Methods

21 Study Design and Participants This was a cross-sectionalstudy with a sample composed of patients attending thepsychiatric outpatient service of the hospital of the Universityof Campinas (Unicamp) aged 8ndash20 years old and undergo-ing treatment with risperidone for mental and behavioraldisorders Inclusion criteria were (a) not being overweightor obese (BMI less than +1 standard deviation (SD) for ageand sex) and not having any other illness or use of drugsknown to predispose to obesity or metabolic syndrome at thebeginning of the study (b) not having mental or behavioraldisorders due to physical ailments (c) not being a user of

psychoactive substances (d) not having been diagnosed withsevere intellectual disability (e) no diagnosis of an eatingdisorder and (f) in the case of women not pregnant nursingor using hormonal contraception Only patients who freelygave informed consent (or their guardians) were included inthe study

22 Methods

221 Clinical Evaluation The following data were evaluatedgender (male or female) chronological age bone age (TW220-bone method) [15] period of time of treatment withrisperidone (24months before the study at the most) currentdose of risperidone use of other psychoactive drugs weight(kg measured with a calibrated digital scale balance) height(m measured with a stadiometer) waist circumference (cmmeasured at the level of the umbilical scar) and systolic anddiastolic blood pressure (BP in mmHg measured with pedi-atric cuffs and with values adjusted for the arm diameters)The BMI (kgm2) was calculated and transformed into a 119911-score [16] BMI 119911-scores also called BMI standard deviationscores are clinical measures of relative weight adjusted fora child or an adolescent age and sex [17] Given its age sexBMI and an appropriate reference standard a BMI 119911-score(or its equivalent BMI-for-age percentile) can be determined[17] In this study the external reference used was the oneestablished by theWHO [16 17] Obese patients were definedas having 119911-scores of at least +2 SD and overweight patientsas having a 119911-score of at least +1 SD but less than +2 SD [16]A high waist circumference was defined as ge90th percentilefor age sex and ethnicity [18] Hypertension was defined fol-lowing the National Heart Lung and Blood Institute criteriacorrelated with percentiles specific to sex height and age[19]

222 Laboratory Evaluation Serum glucose total choles-terol LDL HDL triglycerides (enzymatic colorimetricmethod) insulin (immunofluorometric assay) aspartateaminotransferase (AST) alanine transaminase (ALT) (ultra-violet optimized kinetic method) leptin (enzyme-linkedimmunosorbent assay) thyroid-stimulating hormone (TSH)(chemiluminescence) and free thyroxin (T4) (chemilumi-nescence) levels were assessed in the Human PhysiologyLaboratory of University of Campinas clinical hospital after12 hours of fasting Thyroid hormone levels were measuredto exclude patients with hypo- or hyperthyroidism

The relationship between insulin and fasting glucoselevels was used to calculate the Homeostatic Model Assess-ment of Insulin Resistance (HOMA-IR) which is considerednormal when le289 [20] The normal levels for serum lipidswere triglycerides lt75mgdL in children up to nine years ofage andlt90mgdL from ten years onwards HDL cholesterolgt45mgdL LDL cholesterollt110mgdL and total cholesterollt170mgdL [21] The criteria for the diagnosis of diabetesmellitus were fasting blood glucose level ge126mgdL [22]The diagnosis of metabolic syndrome was based upon theInternational Diabetes Federation criteria including bothclinical and laboratory parameters abdominal obesity (waistcircumference ge90th percentile for patients under 16 years

International Journal of Endocrinology 3

old and for those aged 16 years old or more waist circum-ference ge94 cm for males or ge80 cm for females) and twoor more of the other four demanded indexes (triglyceridesge150mgdL HDL-cholesterol lt40mgdL blood pressurege13085 and fasting glycemia ge100mgdL) [23]

Genomic DNA was extracted from total venous bloodby proteinase K lysis (Boehringer Mannheim Germany)The following single nucleotide polymorphisms (SNPs) weredetermined in the HumanGenetics Laboratory of theMolec-ular Biology and Genetic Engineering Center Unicamp byreal-time polymerase chain reaction (PCR) according to theTaqMan allelic discrimination assay (Applied BiosystemsFoster City CA USA) HTR2C SNP rs6318 (c68GgtCpCys23Ser) and rs3813929 (c-759CgtT) LEP SNP rs7799039(-2548AgtG)LEPR SNP rs1137101 (c668AgtG pGln223Arg)DRD2 SNP rs1799978 (c-241AgtG) and rs6277 (C957T)MC4R SNP rs17782313 (31015840 region of the gene) CYP2D6 SNPrs72552269 (c100CgtT pPro34Ser allele CYP2D6lowast10)

The amplification reactionswere performed in a 7500 FastReal-Time PCR System (Applied Biosystems Foster City CAUSA) The total volume of all reactions was 7 120583L containingTaqMan Genotyping PCR Master Mix 2x (35 120583L) SNVGenotyping Assay 40x (0175 120583L) MiliQ water (2325 120583L) and10 ng of each genomic DNA (1 120583L) Data were recorded andanalyzed using 7500 System Sequence Detection Softwarecopy(SDS) The reactions were performed in 96-well opticalplates (01mL MicroAmp Applied Biosystems Foster CityCA USA) and submitted to the following temperatures andcycles 10min denaturation at 95∘C 40 cycles of 15 seconds at95∘C and 1min extension at 60∘C

Ancestry Characteristics of the Patients Racial miscegenationis highly prominent in Brazil because Europeans of white skincolor Africans of black skin color Asians of yellow skin colorand indigenous peoples of Brazil participated in the processof the formation of this country [24] The mixing has beenintense since the beginning of colonization [24] The smallnumber of white women among the Portuguese colonizersled the latter to have relationships with native indigenousand black slaves [24] This mixing gave rise to other groupssuch as ldquomulattosrdquo (the mix of white and black) ldquocaboclordquo orldquomamelucordquo (the mix of white and indigenous peoples) andthe ldquocafuzordquo (the mix of black and indigenous peoples) [24]Pena et al showed recently that in Brazil one cannot predictthe color of persons from their genomic ancestry [24]

As a result miscegenation in Brazil is an aspect that mustalways be considered in any study using racial analysis [25]The analysis adopted by Instituto Brasileiro de Geografia eEstatistica (IBGE) has been considered as official since 1991for racial classification in demographic studies [25] Thisclassification uses self-declaration in the collection of datathat is the person chooses from a list of five racial categories(white black mulatto yellow and indigenous peoples) [25]The IBGE defines individuals who declare themselves asmulatto or black as ldquoblackrdquo [25]

23 Statistical Analysis Statistical analysis was carried outusing SPSS version 22 (IBM Co Armonk NY USA) Chi-square or Fisherrsquos exact test was used to assess the presence

or absence of obesity WC increase hypertension HOMA-IR changes and triglyceride concentrations and cholesterol(total and fractions) in relation to sex and any combinationof these with the concomitant use of other psychoactivedrugs compared to that found in each SNP To assess corre-lations between age BMI BP prescribed dose of risperidonemedication usage time and the continuous distribution ofroutine laboratory assessments the Spearman correlationcoefficient was used To compare clinical and laboratorybiochemical and physiological parameters in relation to thestudied SNPs and nutritional status the Kruskal-Wallis andMann-Whitney tests were applied Significance was consid-ered to be 119901 = 005

3 Results

The sample consisted of 120 participants (817males) with amean age of 13 plusmn 31 years The majority had an externalizingdisorder and received 004 plusmn 003mgkgday of risperidoneAmong the biochemical and hormonal parameters only ASTand leptin levels were statistically different when analyzed bygender AST levels were greater in males (119901 = 0009 Mann-Whitney test) while leptin was higher in females (119901 = 00001Mann-Whitney test)

Risperidone monotherapy was being used by 32 (267)patients In 49 (409) patients another psychotropic drugwas being used in combination with risperidone two psy-chotropic drugs in 30 (25) patients and three or morepsychiatric drugs in 9 (75) patients The psychiatricdrugs used in association with risperidone were antide-pressants in 53 (602) patients psychostimulants in 27(307) clonidine in 19 patients (216) anticonvulsants in11 (125) lithium carbonate in 6 (68) benzodiazepines in8 (91) nonbenzodiazepine sedative drugs (promethazinemethotrimeprazine or pericyazine) in 7 (8) other atypicalantipsychotics in 6 (68) and biperiden in 1 patient (11)

There was no statistically significant difference inthe occurrence of obesity altered WC hypertension ormetabolic syndrome in relation to sex or whether or notthe user was receiving risperidone monotherapy (Table 1)Metabolic syndrome was present in one (20) obese caseand in five (156) of overweight cases No patient had hypo-or hyperthyroidism

There was no difference between the sexes regardingvalues recorded for HOMA-IR index (119901 = 0122 Fisherrsquosexact test) increased LDL cholesterol (1205942 = 2187 119901 =0139) total cholesterol increase (1205942 = 1295 119901 = 0255)HDL cholesterol reduction (1205942 = 0028 119901 = 0868) orincreased triglycerides (1205942 = 1526 119901 = 0217) There was nostatistical difference between patients receiving risperidonemonotherapy or risperidone in combinationwith other drugsin relation to HOMA-IR index (1205942 = 2339 119901 = 0126)triglyceride levels (1205942 = 0001 119901 = 0977) total cholesterol(1205942 = 0512 119901 = 0474) HDL (1205942 = 1364 119901 = 0243) andLDL (1205942 = 0017 119901 = 0898)

Clinical and laboratory parameters and nutritional statusare shown in Table 2 There was a correlation between thedose of risperidone with dose per kilogram of body weight


Table 1 Frequency of clinical or laboratory abnormalities in the 120child and adolescent users of risperidone assessed by the study

Abnormalities 119899 ()Overweight 32 (267)Obese 5 (42)Diabetes mellitus 0BPlowast 8 (67)Metabolic syndrome 6 (5)Increased waist circumferencesect 20 (167)HOMA-IR changes 22 (183)Total hypercholesterolemia 32 (267)Hypercholesterolemia 29 (242)Hypocholesterolemia 40 (333)Hypertriglyceridemia 41 (342)BP blood pressure HOMA-IR homeostatic model assessment of insulinresistancelowast16 (133) subjects were classified as prehypertensive sect44 (367) subjectswere classified as being between the 75th and 90th percentiles for waistcircumference

(119903 = 0850 119901 = 00001) chronological age (119903 = 0260119901 = 0004) bone age (119903 = 0305 119901 = 0001) and medica-tion usage time (119903 = 0357 119901 = 00001) There waspositive correlation between age and WC SBP and DBPand a negative correlation with AST Positive correlation wasobserved between bone age and WC SBP DBP HOMA-IRindex and negative correlation between bone age and HDLand AST Positive correlations were also seen between doseof risperidone and SBP and DBP (Table 3) with negativecorrelation between the risperidone dose per kilogram ofbody mass and leptin There were no significant correlationsbetween duration of use of risperidone and the clinical andlaboratory parameters evaluated (Table 3) Categorical vari-ables related to clinical and laboratory changes evaluated inrelation to the distribution of the alleles of the evaluated SNPsare shown in Table 4 while comparisons for continuousvariables are shown in Table 5

4 Discussion

The study revealed that the leptin concentrations (Table 4)had significant genetic associations heterozygotes (CT) forSNP rs3813929 in the HTR2C gene had higher serum leptinconcentrations than hemizygous C- or T- (in males) orhomozygous (CC or TT in female patients) This associ-ation however was not sustained when the analysis wasseparated by genderMcCarthy et al have previously reportedan association between leptin concentrations and a differentSNP of the HTR2C gene [26]

In this study SNP rs6318 from the HTR2C gene wasassociated with not being eutrophic when risperidone wasbeing used which was more frequent in carriers of the Gallele although this association was not maintained whenthe analysis was separated by gender In female patients thecondition of being heterozygous (CL) was associated withan increasedWCwhen compared with homozygotes (CC orGG) Lett et al suggest that since previous negative studies

related to this gene have involved long periods of risperidoneusage it is likely that theHTR2C gene is involvedwith adverseeffects that occur at the start of antipsychotic therapy [5]Thisis consistent with the findings of our own study where theaverage time of drug use was more than 15 years [5]

Leptin has beneficial effects on glucose metabolismacting on the hypothalamus with an antidiabetic effect andits increased concentration suggests a resistance to its effects[22] Its association with the HTR2C gene may be linkedto the fact that this gene also has been described as beinginvolved in weight gain induced by antipsychotic drugs [5 610] It is possible that even in patients that have not yet shownweight gain leptin resistance has developed Whether or notthis is the case warrants further investigation

The presence of the T allele of rs17782313 SNP of theMC4R gene was associated with higher HOMA-IR indexlevels The MC4R receptor encoded by the MC4R gene isimportant in the leptin pathway although it is not directlyinvolved [12]Thefindings of our study suggest that theMC4Rgene may indeed play a role in leptin modulation Althoughin this study the MC4R gene was not associated with BMIWiller et al [27] performed a meta-analysis of 15 genome-wide association studies (GWAS) for BMI comprising 32387participants and followed up top signals in 14 additionalcohorts comprising 59082 participants [27] They stronglyconfirmed previous reports of association withMC4R at SNPrs17782313 with a per-allele change in BMI of 020 and anoverall 119901 value of 11 times 10minus20 [27]

The SNP rs7799039 of the LEP gene when the A allelewas absent was associated with a higher frequency of obesityThe presence of the G allele was associated with higher levelsof ALT which is a common finding in obesity-related hepaticsteatosis [28] This finding agrees with the results found byTempleman et al [29] but differs from the negative findingsfrom Fernandez et al [30] and the opposite result found byWu et al [31] in which studies weight gain was describedin those patients with the A allele According to Shen et althese contradictory findingsmay be due to ethnic differenceswith the A allele being a risk factor in Asian populations but aprotective factor in European populations [32] The absenceof the A allele of SNP rs1137101 from the LEPR gene whichencodes the leptin receptor was associatedwith higher serumconcentrations of leptin In the present study it was foundmost elevated in homozygous AA subjects

Considering the CYP2D6 gene the absence of the Callele (TT genotype) was associated with increased weightand the occurrence of hypertension Homozygosity (CCand TT) was associated with a higher HOMA-IR and thepresence of the C allele was associated with higher ALTvalues IndeedCYP2D6 is currently the only gene included inthe pharmacogenomic biomarker frame labeling developedby the FDA for risperidone with attention being given topoor metabolizers [33]

In contrast to the findings of this study Melkersson et alhave reported that theCYP2D6 gene is not directly associatedwith a greater frequency of hypertension and values ofHOMA-IR index change [34] However our findings doagree with those of Lane et al in which fewer patients


Table 2 Mean values and SD of clinical and laboratory parameters in relation to total assessments and nutritional status

Parameter Total Eutrophic Overweight Obese119901

(119899 = 83) (119899 = 32) (119899 = 5)

Prescribed dose (mgday) 21 plusmn 13 20 plusmn 13 24 plusmn 13 21 plusmn 14 0250Adjusted dose (mgKg) 004 plusmn 003 005 plusmn 003 004 plusmn 002 003 plusmn 002 0343Time used (months) 259 plusmn 272 247 plusmn 276 290 plusmn 270 274 plusmn 249 0369Age (years) 130 plusmn 31 129 plusmn 29 135 plusmn 36 107 plusmn 31 0176Bone age (years) 130 plusmn 31 130 plusmn 31 133 plusmn 32 109 plusmn 30 0305WC (cm) 754 plusmn 115 708 plusmn 93 86 plusmn 90sect 845 plusmn 92lowast lt0001SBP (mmHg) 1025 plusmn 118 1006 plusmn 103 1066 plusmn 126sect 108 plusmn 217 0080DBP (mmHg) 635 plusmn 113 621 plusmn 111 675 plusmn 108sect 62 plusmn 148 0078Glycemia (mgmL) 852 plusmn 85 842 plusmn 77 867 plusmn 85 936 plusmn 150 0120Insulin (nUImL) 94 plusmn 98 75 plusmn 83 142 plusmn 120sect 101 plusmn 81 0003HOMA-IR index 20 plusmn 23 16 plusmn 21 30 plusmn 25sect 25 plusmn 20 0002Total cholesterol (mgdL) 1562 plusmn 359 1514 plusmn 312 1640 plusmn 437 1846 plusmn 407 0096LDL cholesterol (mgdL) 875 plusmn 291 828 plusmn 262 964 plusmn 332sect 1070 plusmn 311 0045HDL cholesterol (mgdL) 527 plusmn 141 539 plusmn 149 487 plusmn 120 574 plusmn 59 0119Triglycerides (mgdL) 803 plusmn 454 739 plusmn 386 935 plusmn 568 1010 plusmn 557 0081AST (UL) 234 plusmn 100 234 plusmn 109 228 plusmn 67 282 plusmn 124 0475ALT (UL) 184 plusmn 169 165 plusmn 170 211 plusmn 118sect 314 plusmn 330 0001Leptin (pgmL) 260 plusmn 164 192 plusmn 127 408 plusmn 136sect 443 plusmn 85lowast lt0001WC waist circumference SBP systolic blood pressure DBP diastolic blood pressureKruskal-Wallis testsectStatistically significant difference (by Mann-Whitney test) between overweight and normal weightlowastStatistically significant difference (by Mann-Whitney test) between obese and normal weight individuals (119901 = 005)Total prescribed risperidonePrescribed dose of risperidone but adjusted per kg of body weight

using risperidone were CC in genotype than those thatwere CT or TT [35] Correia et al have also reported thatassociations between the CYP2D6 gene and weight gain aremost pronounced among poor risperidonemetabolizers [14]

Many SNP variants of the DRD2 gene have been asso-ciated with weight gain induced by antipsychotics [36 37]In this study there were no specific associations of the tworesearched DRD2 SNPs with weight gain but both wereassociated with HOMA IR To the best of the authorsrsquoknowledge this association has not been previously reported

Among the biochemical andhormonal laboratory param-eters investigated a gender difference was only observed forleptin concentrations and AST values which are physiologi-cally expected to be higher and lower respectively in femalesIn boys leptin concentrations decrease and AST increasesduring puberty due to a greater gain of lean mass over fatFor females the opposite occurs because girls gain body fatduring puberty resulting in increased leptin concentrations[38]

Thirty-seven (309) children and adolescents wereoverweight or obese a higher proportion than the generalBrazilian population in the same age group where 217 ofmale adolescents and 194 of females are overweight [39]At present obesity is viewed as a pandemic that accounts forabout three million deaths annually worldwide [40]

Metabolic syndrome was found in six (5) subjectsproportionally slightly higher than that reported in theThird National Health and Nutrition Examination Survey

(NHANES III) which recorded metabolic syndrome in 42of adolescents aged 12ndash19 years [41] and 92 assessedby a study that also used data from adolescents formNHANES III but with another cut-off [42] While Cook etal (2003) adopted the parameters established by the NationalCholesterol Education Program Adult Treatment Panel III(NCEPATP III) [21 41] the other adapted the definitionaccording to age [21 42] In the present study we used thecriteria defined by IDF [23] Tavares-Giannini et al (2014)conducted a study of obese or overweight adolescents only(119899 = 232) from a public school in Rio de Janeiro [43] andcompared both criteria They found metabolic syndrome in404 and 246 of obese adolescents and 94 and 19of overweight adolescents using the NCEPATPIII and IDFcriteria respectively [21 23 43]These findings show that theIDF criteria as a result of being more restrictive may notconsider the totality of children and adolescents at increasedrisk for developing diabetes and cardiovascular disease [43]Nevertheless in this study occurrence was greater than in thestudy of Tavares-Giannini et al where the same criteria wereused in a population with similar characteristics except forthe use of risperidone [43]

A high frequency of dyslipidemia with both high levelsof total cholesterol and triglyceride was found which isin accordance with the literature [42 44] The occurrenceof dyslipidemia in this sample was higher confirming thepotential influence of risperidone in such changes [42]Besides weight gain child and adolescent risperidone users


Table 3 Values of Spearman correlation test and significance level (119901) between clinical and laboratory parameters with age duration of useand dose of risperidone in the study sample

Parameters ChronologicAge(years)

Bone age(years)

Length of risperidoneuse (months)

Risperidonedose (mg)

Dose (mg) per kilogramof body weight

BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)

Total cholesterol (mgdL) minus0008(0928)

minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)

LDL cholesterol (mgdL) 0057(0537)

minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)

HDL cholesterol (mgdL) minus0171(0062)

minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)

Triglycerides (mgdL) 0126(0170)

0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)

HOMA-IR index 0169(0065)

0185(0043)

0021(0818)

0088(0340)

minus0092(0320)

AST (UL) minus0322(00001)

minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)

Leptin (pgmL) 0113(0217)

0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)

WC waist circumference SBP systolic blood pressure DBP diastolic blood pressure

seem more sensitive than adults to adverse metabolic effectsparticularly lipid abnormalities [45]

No patient was found to have glucose levels above126mgdL Lower rates of diabetes than those found in adultstaking antipsychotics is easily explained by the shorter follow-up time of young people continuously using the medicationand by the fact that diabetes mellitus and cardiovascularcomplications are correlated with age and the duration of thepresence of cardiovascular risk factors [45] The HOMA-IRindex was abnormal in 183 of the patients and this is inagreement with other studies that have reported increasedHOMA-IR among children and adolescents using risperi-done [41] This index can reflect not only early pancreaticdamage since the product is composed of glucose andinsulin but also liver damage that may be caused by excesslipids and evaluated using ALT levels [20]

Waist circumference (WC) which was increased in 167of the patients is a good early indicator of metabolic dis-orders It has also been reported even after adjusting forage to be significantly higher in children with abnormalglucose metabolism hyperinsulinemia and high HOMA-IR[46] WC is a simple and sensitive way to determine the riskof developing metabolic syndrome in children treated withsecond-generation antipsychotics [44]

WC and blood pressure and systolic and diastolic levelswere positively correlated with age AST values as expected

[47] correlated negativelywith ageThis is in accordancewiththe physiological increase expected with the growth foreseenin anthropometric age curves for thesemeasurements [18 21]

Higher leptin levels found in children with higher dosesof risperidone per kilogram of body weight may suggestthat these children are in the early stages of developingleptin intolerance Higher HOMA-IR values in patients withincreased bone age point to a potential risk of developing type2 diabetes mellitus

Among the limitations of this study the fact that itwas a transversal study did not allow access to clinical andlaboratory parameters before treatment so it was unable toestablish causal relationships However the design of theSNP portion of the study was not inappropriate since genesdetermine predisposition

Studies of candidate gene analysis are important foradvances in pharmacogenetics Nevertheless newer tech-niques for analyzing the human genome have acceleratedgreatly the knowledge on this field [13] These includegenome-wide association studies and the so-called next-generation sequencing methods which refer to the wholegenome and to the whole exome sequencing [13] Thesetechniques are still less available and more expensive butthey enable precise fast and effective research of multiplepossible SNPs underpinning traits and drug responses oradverse effects [13] However despite all these advantages


Table 4 Absolute and relative frequencies () of alleles of SNPs for the presence or absence of clinical and laboratory changes evaluated asdiscrete variables (119899 = 120)

Genotype 1205942

119901

Obese LEPAA AG GG

Yes mdash mdash 5 (109) 8393 0015sectsectNo 16 (100) 58 (100) 41 (891)

Obese LEPPresence of allele A Absence of allele A

Yes mdash 5 (109) 8393 0007lowastNo 74 (100) 41 (891)

Obese or overweight CYP2D6CC CT TT

Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)

Obese or overweight CYP2D6Presence of allele C Absence of allele C

Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)

Obese or overweight HTR2C rs3813929 (Both genders)Presence of allele G Absence of allele G

Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)

Obese or overweight FemalesPresence of allele G Absence of allele G

Yes 8 (471) mdash 3697 0115lowastNo 9 (529) 5 (100)

Obese or overweight MalesPresence of allele G Absence of allele G

Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)

HOMA-IR DRD2 rs1799978AA AG GG

Changed 12 (132) 10 (345) mdash 6661 0010sectsectNormal 79 (868) 19 (655) mdash

HOMA-IR DRD2 rs6277Presence of allele T Absence of allele T

Changed 6 (109) 16 (246) 3738 0053sectNormal 49 (891) 49 (754)

Hypertension CYP2D6CC CT TT

Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)

Abdominal circumference HTR2C rs6318 (both genders)C CC CG G GG

Increased 1 (38) 3 (429) 16 (184) 6720 0035sectsectNormal 25 (962) 4 (571) 71 (816)Females

CC CG GGIncreased mdash 2 (40) mdash 7480 0024sectsectNormal 5 (100) 3 (60) 12 (100)

MalesC G

Increased 1 (45) 17 (224) 3615 0066lowastNormal 21 (955) 59 (776)sectChi-square (GL = 1) sectsectChi-square (GL = 2) lowastFisherrsquos exact test


Table 5 Average rank (Mann-Whitney test) of continuous variables evaluated in relation to the SNP alleles (119899 = 120)

SNP Parameter 119899 Average Rank 119901

Leptin

HTR2C rs3813929 (both genders) Hemihomozygous 115 5917 0044Heterozygotes 5 9120

HTR2C rs3813929 (females) Heterozygotes 5 910 0704Homozygous 17 1221

HTR2C rs3813929 (males) Hemizygous C 85 4816 0232Hemizygous T 13 5827

LEPR Presence of allele A 93 5704 0043Absence of allele A 27 7241ALT

LEP Absence of allele G 16 4484 0053Presence of allele G 104 6291

CYP2D6 Absence of allele C 113 5860 0016Presence of allele C 7 9114HOMA-IR

MC4R Absence of allele T 7 3514 0047Presence of allele T 113 6207

CYP2D6 Heterozygotes 36 5638 0032Homozygous 84 6227

it should be considered that in none of these methods theconcordance between genotype and phenotype is absolute[13] The phenotypes can also be influenced by conditionssuch as drug-drug interactions age sex and comorbidconditions [13]

Because of logistical limitations this study was unableto collect data such as metabolic rate calorie intake andphysical activity as well as the serum levels of risperidoneand its active metabolite 9-hydroxy-risperidone It is worthnoting that none of the studies reviewed here have assessedthese variables making them worthy of future investigation

Although many of the patients in this study were not onmonotherapy with risperidone and several other psychiatricdrugs can influenceweight gain andmetabolic complicationsmore than a quarter of the sample did not use other drugsand there was no difference in the prevalence of obesityaltered AC hypertension and metabolic syndrome betweengroups

5 Conclusion

In summary the results show that a high frequency of childand adolescent users of risperidone have metabolic disordersandor are overweight despite being given therapeutic dosesconsidered to be suitable for their age There were correla-tions between age and risperidone dose with AC SBP andtriglyceride concentrations and between patientsrsquo age andDBP insulin AST and leptin levels

In the analyzed sample it is clear that many children andadolescents do not use risperidone as a monotherapy whichincreases the risk of adverse effects Some SNPs analyzedwereassociated with adverse effects of risperidone The resultshighlight the importance of clinical metabolic and genetic

factors in adverse responses to a medication widely usedin child and adolescent psychiatry Future pharmacogeneticstudies will help further identify risk factors and facilitateadvances in individualizing antipsychotic treatment and thedevelopment of therapeutic tools with better safety andtolerability profiles

Abbreviations

SNP Single nucleotide polymorphismsHTR2C Serotonin 2C receptorDRD2 Dopamine receptor D2LEP LeptinLEPR Leptin receptorMC4R Melanocortin 4 receptorCYP2D6 Cytochrome P450 family 2 subfamily D

polypeptide 6WC Waist circumferenceBMI Body mass indexBP Blood pressureHOMA-IR Homeostatic model assessment of insulin

resistance indexGWAS Genome-wide association study

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgment

This work is supported by Sao Paulo Research Foundation(FAPESP-Process no 201214005-1)


References

[1] T Cabaleiro D Ochoa R Lopez-Rodrıguez et al ldquoEffect ofpolymorphisms on the pharmacokinetics pharmacodynamicsand safety of risperidone in healthy volunteersrdquo Human Psy-chopharmacology vol 29 no 5 pp 459ndash469 2014

[2] S Stahl Stahlrsquos Essential Psychopharmacology NeuroscientificBasis and Practical Applications Cambridge University PressNew York NY USA 3rd edition 2008

[3] FDADrug Approved for Two Psychiatric Conditions in Childrenand Adolescents FDA Silver Spring Md USA 2007

[4] C McKinney and K Renk ldquoAtypical antipsychotic medicationsin the management of disruptive behaviors in children safetyguidelines and recommendationsrdquo Clinical Psychology Reviewvol 31 no 3 pp 465ndash471 2011

[5] T A P Lett T J MWallace N I Chowdhury A K Tiwari J LKennedy andD J Muller ldquoPharmacogenetics of antipsychotic-induced weight gain review and clinical implicationsrdquoMolecu-lar Psychiatry vol 17 no 3 pp 242ndash266 2012

[6] G P Reynolds ldquoPharmacogenetic aspects of antipsychoticdrug-induced weight gainmdasha critical reviewrdquo Clinical Psy-chopharmacology and Neuroscience vol 10 no 2 pp 71ndash772012

[7] P J Hoekstra P W Troost B E Lahuis et al ldquoRisperidone-induced weight gain in referred children with autism spectrumdisorders is associated with a common polymorphism in the5-hydroxytryptamine 2C receptor generdquo Journal of Child andAdolescent Psychopharmacology vol 20 no 6 pp 473ndash4772010

[8] D J Muller C C Zai M Sicard et al ldquoSystematic analysisof dopamine receptor genes (DRD1-DRD5) in antipsychotic-induced weight gainrdquo Pharmacogenomics Journal vol 12 no 2pp 156ndash164 2012

[9] G-J Wang N D Volkow J Logan et al ldquoBrain dopamine andobesityrdquoThe Lancet vol 357 no 9253 pp 354ndash357 2001

[10] R Perez-Iglesias I Mata J A Amado et al ldquoEffect of FTOSH2B1 LEP and LEPR polymorphisms on weight gain asso-ciated with antipsychotic treatmentrdquo Journal of Clinical Psy-chopharmacology vol 30 no 6 pp 661ndash666 2010

[11] G Grunberger A Garber and J Mechanick ldquoObesity man-agement applying clinical trial data to clinical carerdquo EndocrinePractice vol 20 supplement 2 pp 6ndash19 2014

[12] J F List and J F Habener ldquoDefective melanocortin 4 receptorsin hyperphagia and morbid obesityrdquo The New England Journalof Medicine vol 348 no 12 pp 1160ndash1163 2003

[13] S D S Maggo R L Savage and M A Kennedy ldquoImpact ofnewgenomic technologies onunderstanding adverse drug reac-tionsrdquo Clinical Pharmacokinetics pp 1ndash18 2015

[14] C T Correia J P Almeida P E Santos et al ldquoPharmacogenet-ics of risperidone therapy in autism association analysis of eightcandidate genes with drug efficacy and adverse drug reactionsrdquoPharmacogenomics Journal vol 10 no 5 pp 418ndash430 2010

[15] J Tanner and R Whitehouse Assessment of Skeletal Maturityand Prediction of Adult Height (TW2 Method) AcademicLondon UK 1975

[16] WHOmdashWorld Health Organization Growth Reference 5ndash19Years 2007 httpwwwwhointgrowthrefwho2007 bmi forageenindexhtml

[17] A Must and S E Anderson ldquoBody mass index in children andadolescents considerations for population-based applicationsrdquoInternational Journal of Obesity vol 30 no 4 pp 590ndash5942006

[18] J R Fernandez D T Redden A Pietrobelli and D BAllison ldquoWaist circumference percentiles in nationally repre-sentative samples of African-American European-Americanand Mexican-American children and adolescentsrdquo Journal ofPediatrics vol 145 no 4 pp 439ndash444 2004

[19] National High Blood Pressure Education Program WorkingGroup on High Blood Pressure in Children and AdolescentsldquoThe fourth report on the diagnosis evaluation and treatmentof high blood pressure in children and adolescentsrdquo Pediatricsvol 114 no 2 supplement 4 pp 555ndash576 2004

[20] C Aradillas-Garcıa M Rodrıguez-Moran M E Garay-SevillaJMMalacara R A Rascon-Pacheco and FGuerrero-RomeroldquoDistribution of the homeostasis model assessment of insulinresistance in Mexican children and adolescentsrdquo EuropeanJournal of Endocrinology vol 166 no 2 pp 301ndash306 2012

[21] JMDe Jesus ldquoExpert panel on integrated guidelines for cardio-vascular health and risk reduction in children and adolescentssummary reportrdquo Pediatrics vol 128 supplement 5 pp S213ndashS256 2011

[22] World Health Organization Definition Diagnosis and Clas-sification of Diabetes Mellitus and Its Complications Part 1Diagnosis and Classification of Diabetes Mellitus World HealthOrganization Geneva Switzerland 1999

[23] IDF International Diabetes Federation ldquoThe definition ofmetabolic syndrome in childrenrdquo httpwwwidforgwebdatadocsMets definition childrenpdf

[24] S D J Pena L Bastos-Rodrigues J R Pimenta and S P Byd-lowski ldquoDNA tests probe the genomic ancestry of BraziliansrdquoBrazilian Journal of Medical and Biological Research vol 42 no10 pp 870ndash876 2009

[25] Ministerio da Educacao and Instituto Nacional de Estudos ePesquisas Educacionais Mostre Sua Raca Declare Sua CorDiretoria de Estatısticas da Educacao Basica Brasılia Brazil2005

[26] S McCarthy S Mottagui-Tabar Y Mizuno et al ldquoComplexHTR2C linkage disequilibrium and promoter associations withbody mass index and serum leptinrdquo Human Genetics vol 117no 6 pp 545ndash557 2005

[27] C J Willer E K Speliotes R J F Loos et al ldquoSix new lociassociated with body mass index highlight a neuronal influenceon body weight regulationrdquo Nature Genetics vol 41 no 1 pp25ndash34 2009

[28] M Botros and K A Sikaris ldquoThe de ritis ratio the test of timerdquoClinical Biochemist Reviews vol 34 no 3 pp 117ndash130 2013

[29] L A Templeman G P Reynolds B Arranz and L SanldquoPolymorphisms of the 5-HT2C receptor and leptin genesare associated with antipsychotic drug-induced weight gain inCaucasian subjects with a first-episode psychosisrdquo Pharmacoge-netics and Genomics vol 15 no 4 pp 195ndash200 2005

[30] E Fernandez E Carrizo V Fernandez et al ldquoPolymorphismsof the LEP- and LEPR genes metabolic profile after prolongedclozapine administration and response to the antidiabetic met-forminrdquo Schizophrenia Research vol 121 no 1ndash3 pp 213ndash2172010

[31] RWu J Zhao P Shao JOu andMChang ldquoGenetic predictorsof antipsychotic-induced weight gain a case-matched multi-gene studyrdquo Zhong Nan Da Xue Xue Bao Yi Xue Ban vol 36no 8 pp 720ndash723 2011

[32] J Shen W Ge J Zhang H J Zhu and Y Fang ldquoLeptinminus2548ga gene polymorphism in association with antipsy-chotic-induced weight gain a meta-analysis studyrdquo PsychiatriaDanubina vol 26 no 2 pp 145ndash151 2014


[33] US Food and Drug Administration ldquoTable of pharmacoge-nomic biomarkers in drug labelsrdquo httpwwwfdagovdrugsscienceresearchresearchareaspharmacogeneticsucm083378Htm

[34] K I Melkersson M G Scordo A Gunes and M-L DahlldquoImpact of CYP1A2 and CYP2D6 polymorphisms on drugmetabolism and on insulin and lipid elevations and insulinresistance in clozapine-treated patientsrdquo Journal of ClinicalPsychiatry vol 68 no 5 pp 697ndash704 2007

[35] H-Y Lane Y-C Liu C-L Huang et al ldquoRisperidone-relatedweight gain genetic and nongenetic predictorsrdquo Journal ofClinical Psychopharmacology vol 26 no 2 pp 128ndash134 2006

[36] T J Wallace C C Zai E J Brandl and D J Muller ldquoRole of5-HT2C receptor gene variants in antipsychotic-induced weightgainrdquo Pharmacogenomics and Personalized Medicine vol 4 pp83ndash93 2011

[37] J-P Zhang andAKMalhotra ldquoPharmacogenetics and antipsy-chotics therapeutic efficacy and side effects predictionrdquo ExpertOpinion on Drug Metabolism and Toxicology vol 7 no 1 pp9ndash37 2011

[38] T B Meira F L de Moraes and M T S Bohme ldquoRelacoesentre leptina puberdade e exercıcio no sexo femininordquo RevistaBrasileira de Medicina do Esporte vol 15 no 4 pp 306ndash3102009

[39] IBGEmdashInstituto Brasileiro de Geografia e Estatıstica ldquoPesquisade orcamentos familiares 2008-2009rdquo inAntropometria e EstadoNutricional de Criancas Adolescentes e Adultos no Brasil p 130Instituto Brasileiro de Geografia e Estatıstica Rio de JaneiroBrazil 2010

[40] M M Finucane G A Stevens M J Cowan et al ldquoNationalregional and global trends in body-mass index since 1980systematic analysis of health examination surveys and epi-demiological studies with 960 country-years and 9sdot1 millionparticipantsrdquoThe Lancet vol 377 no 9765 pp 557ndash567 2011

[41] S CookMWeitzman P Auinger M Nguyen andWH DietzldquoPrevalence of ametabolic syndrome phenotype in adolescentsfindings from the Third National Health and Nutrition Exami-nation Survey 1988ndash1994rdquo Archives of Pediatrics and AdolescentMedicine vol 157 no 8 pp 821ndash827 2003

[42] S D De Ferranti K Gauvreau D S Ludwig E J NeufeldJ W Newburger and N Rifai ldquoPrevalence of the metabolicsyndrome in American adolescents findings from the ThirdNational Health and Nutrition Examination Surveyrdquo Circula-tion vol 110 no 16 pp 2494ndash2497 2004

[43] D Tavares-Giannini M C Caetano-Kuschnir and M SzkloldquoMetabolic syndrome in overweight and obese adolescentsa comparison of two different diagnostic criteriardquo Annals ofNutrition and Metabolism vol 64 no 1 pp 71ndash79 2014

[44] C Panagiotopoulos R Ronsley B Kuzeljevic and J DavidsonldquoWaist circumference is a sensitive screening tool for assessmentof metabolic syndrome risk in children treated with second-generation antipsychoticsrdquo Canadian Journal of Psychiatry vol57 no 1 pp 34ndash44 2012

[45] L Maayan and C U Correll ldquoWeight gain and metabolic risksassociated with antipsychotic medications in children and ado-lescentsrdquo Journal of Child and Adolescent Psychopharmacologyvol 21 no 6 pp 517ndash535 2011

[46] C A Calarge G Nicol J A Schlechte and T L Burns ldquoCar-diometabolic outcomes in children and adolescents followingdiscontinuation of long-term risperidone treatmentrdquo Journal ofChild and Adolescent Psychopharmacology vol 24 no 3 pp120ndash129 2014

[47] V Motta Clinical Biochemistry for LaboratorymdashPrinciples andInterpretations MedBook Rio de Janeiro Brazil 5th edition2009

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014


MEDIATORSINFLAMMATION

of


Behavioural Neurology

EndocrinologyInternational Journal of



Disease Markers


BioMed Research International

OncologyJournal of



Oxidative Medicine and Cellular Longevity


PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of



Computational and Mathematical Methods in Medicine

OphthalmologyJournal of


Diabetes ResearchJournal of



Research and TreatmentAIDS


Gastroenterology Research and Practice


Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom


Regarding the weight gain and metabolic effects inducedby risperidone the HTR2C DRD2 LEP LEPR MC4R andCYP2D6 genes are believed to be involved Considering thatserotonin receptors directly influence the satiety and appetitecontrol systems [6] the X-linkedHTR2C gene is likely to playan important role as it encodes the serotonin 2C receptor [7]

Another gene involved in weight gain is the DRD2gene which encodes the dopamine D2 receptor [8] and isassociated with obesity [9] It has been reported that theavailability of striatal dopamine D2 receptors is significantlyreduced in overweight individuals [9]

The LEP and LEPR genes which encode leptin andits receptor respectively have also been reported to beimportant in antipsychotic-induced weight gain studies [10]Secreted only by the adipose tissue leptin inhibits food intakeand increases energy expenditure [3 11]

Melanocortin-4 Receptor (MC4R) is a transmembrane Gprotein-coupled receptor expressed in the central nervoussystem primarily in the hypothalamus [11 12]MC4R plays acritical role in the regulation of energy homeostasis by leptinand heterozygousmutations in the gene account for 05ndash63of severe or early-onset obesity cases which represent themost frequent cause ofmonogenic human obesity syndromes[6]

The gene coding the P450 2D6 enzyme (CYP2D6) isone of the best studied pharmacogenes [13] Approximately25 of all drugs including risperidone are metabolizedthrough this enzyme which also happens to be one of themost polymorphic enzymes with over 100 known allelicvariants [13] It is now known that polymorphisms in theCYP2D6 gene can result in a range of effects from completeloss of enzyme activity through deletion of the gene (poormetabolizer status) or extensive activity through duplicationof the gene (ultra-rapid metabolizer status) [13] Besides theinvolvement of other genes in the pharmacodynamics ofrisperidone pharmacokinetic studies have hypothesized thatlow activity of the cytochrome complex may be associatedwith an increase in serum risperidone levels which canindirectly lead to weight gain [1 5 14]

The aims of this study were to assess the frequency of obe-sity hypertension metabolic syndrome insulin resistanceand dyslipidemia in a sample of child and adolescent usersof risperidone to evaluate associations with clinical andpharmacological data and with certain polymorphisms of theHTR2C DRD2 LEP LEPRMC4R and CYP2D6 genes

2 Subjects and Methods

21 Study Design and Participants This was a cross-sectionalstudy with a sample composed of patients attending thepsychiatric outpatient service of the hospital of the Universityof Campinas (Unicamp) aged 8ndash20 years old and undergo-ing treatment with risperidone for mental and behavioraldisorders Inclusion criteria were (a) not being overweightor obese (BMI less than +1 standard deviation (SD) for ageand sex) and not having any other illness or use of drugsknown to predispose to obesity or metabolic syndrome at thebeginning of the study (b) not having mental or behavioraldisorders due to physical ailments (c) not being a user of

psychoactive substances (d) not having been diagnosed withsevere intellectual disability (e) no diagnosis of an eatingdisorder and (f) in the case of women not pregnant nursingor using hormonal contraception Only patients who freelygave informed consent (or their guardians) were included inthe study

22 Methods

221 Clinical Evaluation The following data were evaluatedgender (male or female) chronological age bone age (TW220-bone method) [15] period of time of treatment withrisperidone (24months before the study at the most) currentdose of risperidone use of other psychoactive drugs weight(kg measured with a calibrated digital scale balance) height(m measured with a stadiometer) waist circumference (cmmeasured at the level of the umbilical scar) and systolic anddiastolic blood pressure (BP in mmHg measured with pedi-atric cuffs and with values adjusted for the arm diameters)The BMI (kgm2) was calculated and transformed into a 119911-score [16] BMI 119911-scores also called BMI standard deviationscores are clinical measures of relative weight adjusted fora child or an adolescent age and sex [17] Given its age sexBMI and an appropriate reference standard a BMI 119911-score(or its equivalent BMI-for-age percentile) can be determined[17] In this study the external reference used was the oneestablished by theWHO [16 17] Obese patients were definedas having 119911-scores of at least +2 SD and overweight patientsas having a 119911-score of at least +1 SD but less than +2 SD [16]A high waist circumference was defined as ge90th percentilefor age sex and ethnicity [18] Hypertension was defined fol-lowing the National Heart Lung and Blood Institute criteriacorrelated with percentiles specific to sex height and age[19]

222 Laboratory Evaluation Serum glucose total choles-terol LDL HDL triglycerides (enzymatic colorimetricmethod) insulin (immunofluorometric assay) aspartateaminotransferase (AST) alanine transaminase (ALT) (ultra-violet optimized kinetic method) leptin (enzyme-linkedimmunosorbent assay) thyroid-stimulating hormone (TSH)(chemiluminescence) and free thyroxin (T4) (chemilumi-nescence) levels were assessed in the Human PhysiologyLaboratory of University of Campinas clinical hospital after12 hours of fasting Thyroid hormone levels were measuredto exclude patients with hypo- or hyperthyroidism

The relationship between insulin and fasting glucoselevels was used to calculate the Homeostatic Model Assess-ment of Insulin Resistance (HOMA-IR) which is considerednormal when le289 [20] The normal levels for serum lipidswere triglycerides lt75mgdL in children up to nine years ofage andlt90mgdL from ten years onwards HDL cholesterolgt45mgdL LDL cholesterollt110mgdL and total cholesterollt170mgdL [21] The criteria for the diagnosis of diabetesmellitus were fasting blood glucose level ge126mgdL [22]The diagnosis of metabolic syndrome was based upon theInternational Diabetes Federation criteria including bothclinical and laboratory parameters abdominal obesity (waistcircumference ge90th percentile for patients under 16 years









3 Results










4 Discussion












(119899 = 83) (119899 = 32) (119899 = 5)












Bone age(years)




BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)


minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)


minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)


minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)


0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)


0185(0043)

0021(0818)

0088(0340)

minus0092(0320)


minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)


0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)












Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
























3 Results










4 Discussion












(119899 = 83) (119899 = 32) (119899 = 5)












Bone age(years)




BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)


minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)


minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)


minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)


0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)


0185(0043)

0021(0818)

0088(0340)

minus0092(0320)


minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)


0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)












Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of




















4 Discussion












(119899 = 83) (119899 = 32) (119899 = 5)












Bone age(years)




BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)


minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)


minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)


minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)


0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)


0185(0043)

0021(0818)

0088(0340)

minus0092(0320)


minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)


0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)












Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















(119899 = 83) (119899 = 32) (119899 = 5)












Bone age(years)




BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)


minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)


minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)


minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)


0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)


0185(0043)

0021(0818)

0088(0340)

minus0092(0320)


minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)


0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)












Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















Bone age(years)




BMI minus0015(0872)

0017(0851)

0026(0781)

0090(0327)

minus0217(0017)

WC (cm) 0395(00001)

0409(00001)

0030(0746)

0217(0017)

minus0151(0100)

SBP (mmHg) 0487(00001)

0501(00001)

0202(0027)

0203(0027)

minus0080(0386)

DBP (mmHg) 0335(00001)

0308(0001)

0176(0055)

0126(0169)

minus0041(0658)


minus0051(0580)

minus0068(0462)

0119(0194)

0075(0415)


minus0007(0940)

minus0037(0687)

0075(0414)

0008(0932)


minus0183(0046)

minus0138(0133)

0024(0791)

0120(0193)


0147(0108)

minus0023(0806)

0115(0213)

minus025(0784)


0185(0043)

0021(0818)

0088(0340)

minus0092(0320)


minus0283(0002)

0009(0925)

minus0070(0450)

0043(0644)

ALT (UL) 0019(0840)

0017(0858)

minus0023(0801)

0100(0277)

minus0013(0889)


0087(0345)

minus0045(0624)

minus0012(0898)

minus0257(0005)












Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of


















Genotype 1205942

119901

Obese LEPAA AG GG





Yes 25 (325) 7 (194) 5 (714) 7695 0021sectsectNo 52 (675) 29 (806) 2 (286)


Yes 32 (283) 5 (714) 5744 0028lowastNo 81 (717) 2 (286)


Yes 33 (351) 4 (154) 3714 0054sectNo 61 (649) 22 (846)




Yes 25 (325) 4 (19) 1426 0289sectNo 52 (675) 17 (81)






Yes 3 (39) 3 (83) 2 (286) 6509 0039sectsectNo 74 (961) 33 (917) 5 (714)




MalesC G





Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of



















Leptin












5 Conclusion




Abbreviations






Acknowledgment



References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of

















References






















































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of





































of






Disease Markers



OncologyJournal of





PPAR Research



Journal of

ObesityJournal of
















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