Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study

Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study View ORCID Profile Kenneth Chi-Yin Wong , Perry Bok-Man Leung , Benedict Ka-Wa Lee , Zoe Zi-Yu Zheng , Emily Man-Wah Tsang , Meng-Hui Liu , Kelly Wing-Kwan Lee , Shi-Tao Rao , Pak-Chung Sham , Simon Sai-Yu Lui , View ORCID Profile Hon-Cheong So doi: https://doi.org/10.1101/2024.09.04.24313052 Kenneth Chi-Yin Wong 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Kenneth Chi-Yin Wong Perry Bok-Man Leung 8 Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR, China 9 Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience , King’s College London, UK Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benedict Ka-Wa Lee 8 Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Zoe Zi-Yu Zheng 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Emily Man-Wah Tsang 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Meng-Hui Liu 12 Department of Epidemiology, University of Florida , Gainesville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kelly Wing-Kwan Lee 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Shi-Tao Rao 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pak-Chung Sham 8 Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR, China 10 Centre for PanorOmic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR, China 11 State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: pcsham{at}hku.hk Simon Sai-Yu Lui 8 Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong , Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: pcsham{at}hku.hk Hon-Cheong So 1 School of Biomedical Sciences, The Chinese University of Hong Kong , Hong Kong SAR, China 2 KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, Kunming Institute of Zoology and The Chinese University of Hong Kong , Hong Kong SAR, China 3 Department of Psychiatry, The Chinese University of Hong Kong , Hong Kong SAR, China 4 CUHK Shenzhen Research Institute , Shenzhen, China 5 Margaret K. L. Cheung Research Centre for Management of Parkinsonism, The Chinese University of Hong Kong , Hong Kong SAR, China 6 Brain and Mind Institute, The Chinese University of Hong Kong , Shatin, Hong Kong SAR, China 7 Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, The Chinese University of Hong Kong , Shatin, Hong Kong SAR, China Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Hon-Cheong So For correspondence: pcsham{at}hku.hk Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Second-generation antipsychotics (SGAs) are widely used to treat schizophrenia (SCZ), but they often induce metabolic side effects, including dyslipidemia and obesity, posing significant clinical challenges. While genetic factors are believed to contribute to the variability of these side effects, pharmacogenetic studies remain limited. This study aimed to identify genetic variants associated with SGA-induced lipid and BMI changes in a Chinese SCZ cohort using genome-wide association studies (GWASs). A naturalistic longitudinal cohort of Chinese SCZ patients receiving SGAs was followed for up to 18.7 years. We analyzed the patients’ genotypes ( N =669), lipid profiles and BMI, utilizing 19 316 prescription records and 3 917 to 7 596 metabolic measurements per outcome. Linear mixed models were used to estimate the random effects of SGAs on lipid profiles and BMI changes for each patient. GWAS and gene set analyses were conducted with false discovery rate (FDR) correction. Two genome-wide significant SNPs were identified under an additive genetic model: rs6532055 in ABCG2 (olanzapine-induced LDL changes) and rs2644520 near SORCS1 (aripiprazole-induced triglyceride changes). Three additional SNPs achieved genome-wide significance under non-additive models: rs115843863 near UPP2 (clozapine-induced HDL changes), rs2514895 near KIRREL3 (paliperidone-induced LDL changes), and rs188405603 in SLC2A9 (quetiapine-induced triglyceride changes). Gene-based analysis revealed six genome-wide significant (p<2.73e-06, Bonferroni correction) genes: ABCG2 , APOA5 , ZPR1 , GCNT4 , MAST2 , and CRTAC1 . Four gene sets were significantly associated with SGA-induced metabolic side effects. This pharmacogenetic GWAS identified several genetic variants associated with metabolic side effects of seven SGAs, potentially informing personalized treatment strategies to minimize metabolic risk in SCZ patients. 1. Introduction Schizophrenia (SCZ) is a severe, chronic mental illness with high heritability and a lifetime prevalence of approximately 1%. The global burden of SCZ has been increasing, with the incidence increasing by 2% annually between 2000 and 2019 1 – 3 . Cardiovascular disease (CVD) is the leading cause of mortality in SCZ patients 4 , and psychosis itself is also a recognized risk factor for dyslipidemia and obesity 5 . Moreover, second-generation antipsychotics (SGAs), the mainstream treatment for SCZ, can adversely affect patients’ lipid profiles, other metabolic parameters, and body mass index (BMI) 6 . Interestingly, the propensity to develop these metabolic side effects varies considerably among individuals. Twin and sibling studies have demonstrated that such interindividual variability may be largely attributable to genetic differences 7 , 8 . However, the underlying genetic mechanisms remain poorly understood. Pharmacogenetics (PGx) examines how genetic variations affect drug metabolism and response, potentially enabling personalized treatment plans with better efficacy and fewer side effects. Over the past two decades, most PGx studies on SGA-induced metabolic side effects have employed candidate gene approaches, focusing primarily on dopamine and serotonin receptor-related genes 9 , 10 . Additionally, variants in cytochrome P450 genes ( CYP1A2 , CYP2C19 , CYP2D6 and CYP3A4 ) have been reported to be associated with antipsychotic serum concentrations 11 , 12 . The advent of genome-wide association studies (GWASs) has largely overcome the limitations of candidate gene approaches, uncovering more variants and genes associated with antipsychotic response 13 – 17 . However, the majority of previous PGx studies have focused mainly on treatment response rather than the metabolic side effects of SGAs. To date, only seven PGx GWASs have been conducted to investigate SGA-induced metabolic side effects 18 – 24 . All but one study focused exclusively on weight gain, with Adkins et al . (2010) investigating PGx on a variety of metabolic side effects across five SGAs and one typical antipsychotic 18 . Additionally, most studies focused on short-term outcomes, with the longest follow-up reported by Adkins et al . (18 months) 18 . Nevertheless, the work of Adkins et al. has several limitations, as acknowledged by the original authors 18 . For example, most participants had prior experience with antipsychotics, and many were concurrently taking other medications, such as antidepressants and mood stabilizers, which could potentially influence the results. Notably, the subjects had received, on average, 14.3 years of antipsychotic medications at baseline. Moreover, DNA collection was conducted after the clinical trial, involving only a subset of participants (51%) who exhibited lower symptom severity and reduced rates of drug and alcohol dependence, potentially introducing bias. With respect to GWAS analysis, Adkins et al . did not employ imputation, which may limit the ability to detect true genetic associations. To address this knowledge gap, our PGx study investigated lipid and BMI changes induced by seven SGAs: olanzapine (OZP), clozapine (CZP), quetiapine (ǪUE), risperidone (RIS), aripiprazole (ARI), amisulpride (AMI) and paliperidone (PAL). We focused on BMI and four lipid measurements, including total cholesterol (TC), high-density cholesterol (HDL), low-density cholesterol (LDL) and triglycerides (TG), as outcomes. Compared with Adkins et al .’s previous study which followed patients for 1.5 years, we utilized a longitudinal cohort with a longer follow-up of up to 5.7 years (median), and our naturalistic dataset had a greater mean number of metabolic measures per subject. The proportion of SGA-naïve patients was also markedly greater at ∼63%. Furthermore, the homogeneity of our Chinese cohort, which was recruited from Hong Kong, China, combined with imputed genotypes based on the ChinaMAP reference panel, enhanced the statistical power of GWAS to detect true signals. Taken together, this sophisticated approach combined with a long follow-up duration and comprehensive medication history and metabolic measures. We aimed to identify novel genetic variants associated with SGA-induced metabolic side effects. Our findings provide insights into the biological mechanism underlying SGA-induced lipid and BMI changes, potentially contributing to more personalized and effective treatments for SCZ patients. 2. Subjects and Methods 2.1 Study population and data collection We recruited SCZ patients from an early psychosis intervention clinic at Castle Peak Hospital Hong Kong between 2009 and 2021 25 . The inclusion criteria were as follows: (1) aged ≥18 years, (2) Chinese ethnicity, (3) ICD-10 diagnosis of SCZ or schizoaffective disorder, (4) treatment with SGAs, and (5) at least one post-SGA measurement of fasting lipids and/or BMI. We excluded patients with preexisting metabolic disorders or those lost to psychiatric follow-up as of March 2021. From 767 eligible patients, we extracted complete medication records, lipid profiles and BMI measurements from initial service contact to the study endpoint. Following local guidelines for monitoring SGA side effects, patients received baseline measures of fasting lipid profiles and BMI before SGA initiation, with annual follow-up measurements while on SGAs. Electronic health records documented the type and dosage of all psychotropic and concomitant medications, including antidepressants and lipid-lowering drugs. 2.2 Genotyping and imputation Blood samples from patients were genotyped using the Illumina Asian Screening Array-24 v1.0. Ǫuality control was performed using PLINK 1.9p, removing genotypes and subjects based on missing data (missing rate > 10%), Hardy‒Weinberg equilibrium (p 0.25). No ethnic outliers were identified through principal component analysis. The genome coordinates were lifted from GRCh37 to GRCh38 using CrossMap v0.6.4. Haplotype phasing and genotype imputation were conducted using Eagle2 and Minimac4, respectively 26 , 27 , with the ChinaMAP phase 1.v1 reference panel (59 010 860 sites from 10 155 Chinese individuals) 28 . This large, ancestrally matched reference panel improved imputation accuracy. The imputed SNPs were removed based on imputation quality (INFO score ≤ 0.3) and minor allele count (MAC ≤ 10). The final dataset comprised 6 992 805 high-quality imputed SNPs. 2.3 Data preprocessing and variable selection Full GWAS data were available for 669 SCZ patients with 19 316 prescription records within 3 months of metabolic measurement. We analyzed TC, HDL, LDL, and TG (all in mmol/L), and BMI (kg/m 2 ) separately. Outliers exceeding six standard deviations from the group mean were removed to avoid a biased effect estimation. As a result, the final analysis included 4 048 TC, 3 917 HDL, 4 035 LDL, 4 034 TG, and 7 596 BMI records. We applied natural log transformation to the data prior to modeling. We selected seven SGAs that had been prescribed to at least 30 patients in our sample, namely CZP, OZP, ARI, AMI, PAL, RIS, and ǪUE ( Table 1 ). The choices of these SGAs, which were taken by a substantial number of subjects, allowed more robust models to be constructed. Long-acting injectable and oral formulations were analyzed equally, after dose conversion using the standard method 29 . Given that some non-SGA psychotropics and statins (lipid-lowering drugs) are commonly prescribed, and might influence patients’ lipid profiles and BMI, we accounted for these concomitant medications (i.e., haloperidol, valproate, lithium, metformin, simvastatin, and atorvastatin) by including them as covariates in our model, as suggested by prior studies 30 – 33 . We also included daily drug dosage (mg) and treatment duration (month) as random-effect covariates, while age, gender and years of education were entered as fixed-effect covariates. View this table: View inline View popup Download powerpoint Table 1 Descriptive statistics of the second-generation antipsychotic prescription records in our cohort. 2.4 Random-effect estimation for SGA-induced lipid/BMI changes We used linear mixed models (LMMs) to estimate the random effects (random slope) of SGAs on lipid/BMI changes. The random effects quantified how each patient’s lipid/BMI changes deviated from the cohort’s mean, serving as a proxy outcome measure for the severity of the metabolic side effects of each patient. A similar approach has been employed in several PGx GWASs 18 , 34 , 35 . In addition, we employed an advanced statistical approach to disentangle the within-subject estimates from the between-subject estimates of SGA random effects 36 – 38 . By focusing on the within-subject effects, we can more accurately estimate the metabolic side effects of SGAs by accounting for unmeasured time-invariant confounders 39 . For each of the seven SGAs (plus one additional model of ‘any SGA use’), five random-effect LMMs were built with corresponding lipid/BMI measures as outcomes, resulting in 40 models. Random effect coefficients were extracted for patients prescribed the corresponding SGAs from these 40 models with their specifications listed in Supplementary Text 1. Following other PGx studies 18 , 34 , 35 , the random-effect coefficients were the primary outcome of the subsequent GWAS and MAGMA analyses, with sample sizes varying across models ( Table 1 ). Details concerning how we identified the best-fitting random-effect LMM models and estimated the within-subject dosage of the SGAs have been described elsewhere 6, 39 . We applied rank-based inverse normal transformation (INT) to the random-effect coefficients 40 to ensure a normal distribution of the outcomes (and residuals) and reduce outlier effects ( Supplementary Figure S1 ). 2.5 Genome-wide association study (GWAS) analysis GWAS association tests between SNP dosages and SGA-induced lipid/BMI changes were conducted using PLINK 2.00a 41 , with gender and the top ten genetic principal components as covariates. The imputed genotypes were converted to PLINK 2 binary formats to retain dosage information, which can improve the statistical power of the association tests. In our primary analyses, we tested additive genetic models using allelic dosage as the predictor. To capture variants with non-additive genetic effects as advised by Guindo-Martínez, Amela 42 , we also performed additional analyses based on dominant, recessive and genotypic (2 degrees of freedom) models. Associations with a p-value (p) < 5e-08 were considered genome-wide (GW) significant 43 , whereas those with p ≥ 5e-08 but a false discovery rate (FDR) < 0.2 were considered ‘suggestive’ associations. A similar approach was also employed by Adkins et al . in their GWAS of the metabolic side effects of antipsychotics 18 . 2.6 MAGMA analyses MAGMA is a powerful approach for testing the associations between a phenotype and SNPs aggregated within an entire gene 43 . We performed gene and gene-set association tests between the imputed genotypes and 40 sets of random-effect coefficients of SGA-induced lipid/BMI changes using MAGMA v1.10. To leverage the strengths and mitigate the weaknesses of each MAGMA model, we built three predefined MAGMA models, including (1) principal component regression, (2) the SNP-wise mean, and (3) the SNP-wise top 1. MAGMA then merged the resulting gene p-values into a single aggregated p-value. This approach has increased the statistical power and sensitivity across various genetic architectures 43 . Such MAGMA models have been detailed in the MAGMA manual. 2.7 Post-GWAS annotation LD-clumping was performed using PLINK to identify top SNPs within linkage disequilibrium clusters (with clump-p1=5e-05, clump-p2=0.05, r2=0.6 and window size=250 kb). The top SNPs were annotated using Ensembl Variant Effect Predictor (VEP) v111.0 with VEP cache version 111_GRCh38 44 , including gene information, nearest gene, location, and effect allele frequency in East Asian and European populations. Previous studies reporting GW-significant SNPs within the same genes were annotated based on the GWAS Catalog and Open Target Platform 45 , 46 . To uncover potential hidden associations between the identified genes and annotated enriched terms across multiple datasets and resources, integrative gene set enrichment analyses and visualisation were conducted using the Enrichr-KG platform, incorporating four gene-set libraries 47 : GWAS Catalog (2019), GO_biological Process (2021), DisGeNET, and Human Phenotype Ontology. For each input gene set, the top five enriched terms per library with an FDR < 0.05 were considered significant. A subnetwork linking the input genes to these enriched terms was visualized using the Enrichr-KG platform. 2.8 Fine-mapping with the SuSiE model To identify potential causal SNPs among the GW-significant findings, we employed the SuSiE fine-mapping approach 48 . This method is built on top of variable selection in a linear regression model, fitted by a Bayesian analog of stepwise selection methods. This method reports as many groups of SNPs (credible sets) as the data support, each with the minimum number of SNPs possible. For each credible set, it estimates the posterior distribution of the SNPs’ effect and calculates marginal posterior inclusion probabilities (PIPs) to assess the strength of evidence for each SNP being causal. The region (±1 000 kb) around each GW-significant SNP was taken for fine-mapping using the LD reference panel constructed from the subject’s imputed genotypes. The SuSiE model fit was based on the p-value of the SNPs, with the maximum number of nonzero effects (L) set to 11, where default values were used for the remaining parameters. The casual SNPs of the best credible sets were visualized via region plots. 2.9 Multiple testing correction We employed the FDR approach to balance the risk of false discoveries against potential true findings. We reported the FDR separately for each analysis, which controlled the expected proportion of false discoveries among rejected null hypotheses 49 . In contrast to Bonferroni correction, the FDR is much less affected by the number of tests, because the FDR controls the proportion (instead of the number) of false discoveries 50 , 51 . Intuitively, when controlling the FDR at 0.2 for example, at least 80% of the significant findings would be “true discoveries” on average. Since the FDR controls the proportion of false discoveries, if the FDR is performed separately for each set of analyses, the overall FDR is still generally controlled, especially when the number of hypothesis tests is large, as shown by Efron 51 . 2.10 Ethics statement This study adhered to the ethical principles of the Helsinki Declaration and relevant national and institutional guidelines for human research. Ethical approval was obtained from the New Territories West Cluster Ethics Committee (Approval Numbers: NTWC/CREC/823/10 and NTWC/CREC/1293/14) and the Joint Chinese University of Hong Kong-New Territories East Cluster Clinical Research Ethics Committee (Approval Number: 2016.559). All participants provided written informed consent. The software and tools used in this study are listed in Supplementary Text 2 . 3. Results 3.1 Sample characteristics Our final dataset comprised 625 subjects with lipid profile data and 646 subjects with BMI data after the seven SGAs prescribed to at least 30 patients were selected. Supplementary Table S1 presents the gender ratio, mean age at the first clinical visit, and mean years of follow-up. The longitudinal cohort had a maximum follow-up period of 18.7 years, with a mean follow-ups of 5.7 years (SD = 3.3) for the lipid cohort and 5.5 years (SD = 3.2) for the BMI cohort. We conducted 40 separate GWASs to examine the effects of SNPs on SGA-induced changes in lipids (TC, HDL, LDL, TG) and BMI for seven specific SGAs and any SGA use. The individual GWAS sample sizes ranged from 43 to 567 patients, with mean prescriptions per patient ranging from 1.8 (SD=1.7) to 9.3 (SD=8.8) ( Table 1 ). 3.2 GWAS results 3.2.1 Primary analyses: Additive genetic model In our main GWAS analyses using an additive genetic model, two SNPs reached GW significance (p < 5e-08) ( Table 2 ), whereas eight SNPs achieved an FDR of < 0.2 (Supplementary Table S2 ). The top SNP, rs6532055 (p = 3.13e-09, FDR = 0.022), was associated with olanzapine-induced LDL changes. This SNP is located within an intron of the ABCG2 gene, which is part of the ATP-binding cassette (ABC) family. The second GW-significant SNP, rs2644520 (p = 3.06e-08, FDR = 0.122), was associated with aripiprazole-induced TG changes and located in an intergenic region near SORCS1 , a gene encoding a member of vacuolar protein sorting 10 (VPS10) domain-containing receptor proteins. View this table: View inline View popup Download powerpoint Table 2 The genome-wide significant SNPs (p < 5e-08) associated with the SGA-induced lipid level and BMI changes. The quantile–quantile plots (ǪǪ) plots for the GWASs with GW-significant SNPs are shown at the bottom of Table 2 , and the ǪǪ plots of the GWASs with SNPs achieving an FDR < 0.2 are shown in Supplementary Figure S2 . The ǪǪ plots demonstrate that the p value distributions closely matched the expected p-values under the null hypothesis, with genomic control inflation factor (λ GC at the median) ranging from 0.97 to 1.01 for the GWASs harboring GW-significant SNPs and ranging from 0.94 to 1.01 for the GWASs harboring SNPs with FDR < 0.2, indicating that genomic inflation is unlikely to be a concern. 3.2.2 Additional analyses with non-additive models Further analyses using dominant, recessive and genotypic models revealed three additional GW-significant SNPs ( Table 2 ). The top SNP in the genotypic model, rs115843863 (p = 2.05e-08, FDR = 0.0287), was associated with clozapine-induced HDL changes. The SNP is located in an intergenic region near UPP2 , a gene involved in dCMP and uridine catabolic processes. Another GW-significant SNP in the genotypic model, rs2514895 (p = 4.96e-09, FDR = 0.004), was associated with paliperidone-induced LDL changes and is located near KIRREL3 , a gene encoding a nephrin-like protein expressed in the brain. The last GW-significant SNP rs188405603 (p = 3.52e-08, FDR = 0.065) under the dominant model was associated with quetiapine-induced TG changes and is located within an intron of SLC2A9 , a gene encoding a glucose transporter. 3.2.3 Suggestive associations with FDR < 0.2 Eight SNPs achieved an FDR < 0.2 in the primary GWAS analyses under an additive model (Supplementary Table S2 ). Notably, four SNPs, namely rs7412 (FDR = 0.182), rs2384157 (FDR = 0.195), rs74625905 (FDR = 0.195) and rs56349742 (FDR = 0.195), were associated with olanzapine-induced LDL changes. The well-known LDL-altering SNP rs7412 in APOE is positively associated with olanzapine-induced LDL changes. Four SNPs, namely rs2358259 (FDR = 0.123), rs10174314 (FDR = 0.123), rs117416034 (FDR = 0.123), and rs6424242 (FDR = 0.186), were associated with quetiapine-induced HDL changes. In particular, rs6424242 is located in an upstream region of the SIPA1L2 gene, which has been previously linked to obesity-related traits, response to alcohol consumption, and neuroticism based on Open Targets and the GWAS Catalog 52 – 54 . Another 17 SNPs with FDRs < 0.2 were identified in the additional GWAS analyses under non-additive models. These SNPs are related to the metabolic side effects of clozapine, olanzapine, risperidone, and paliperidone ( Supplementary Table S3 ). The associated genes are implicated in psychiatric disorders, lipid or BMI measurements, or drug responses, including BICD1 and CSMD1 (olanzapine-induced TC changes); GADL1 (risperidone-induced BMI changes); SIPA1L2 (quetiapine-induced HDL changes); and RAB38 , CDH23 , AMPH , FOXN3 , APBB2 , C1R and LRCOL1 (paliperidone-induced LDL changes). Additionally, GWAS analyses were conducted on the heterozygous-only (HETONLY) genetic model, revealing two GW-significant SNPs and six SNPs with suggestive evidence (FDR < 0.2). The detailed results can be found in Supplementary Table S4 , and an overview of all identified genes from different types of analyses is provided in Table 3 . View this table: View inline View popup Download powerpoint Table 3 Genes associated with the SGA-induced lipid/BMI changes in different type of analyses [1,2,3,4] . 3.2.4 Fine-mapping results The fine-mapping results for the five GW-significant SNPs are visualized in region plots ( Figure 1 ). The top GW-significant SNPs associated with olanzapine-induced LDL changes and aripiprazole-induced TG changes were proposed to be causal (PIP = 1.0). However, the remaining three top GW-significant SNPs were not considered causal, as shown by their low PIP values. Another SNP, rs73968514 (PIP = 1.0), was identified as potentially causal for clozapine-induced HDL changes (PIP = 1.0), replacing the original GWAS hit rs115843863. Both rs2441693 and another SNP rs2441693, with the same p-value, were identified as potentially causal for paliperidone-induced LDL effects (PIP = 0.5 each). Finally, instead of the observed GWAS hit rs188405603, fine-mapping evidence suggested that rs77140241 was the real causal variant for quetiapine-induced TG changes (PIP = 1.0, p = 9.5e-08, FDR = 0.065). Download figure Open in new tab Figure 1 Region plots of the five GW-significant SNPs fine-mapped via the R package suiseR. The fine-mapping results of the five GW-significant SNPs are visualized in the region plots above. The lead variant, marked in yellow, is the SNP with the lowest p values after the LD-clump, whereas the causal variant is marked by a red cross. Other SNPs are coloured according to their decreasing R-square values with the lead variant. The credible set containing SNP(s) nearest to the lead variant with the smallest p values is chosen as causal SNP(s). The subplot at the bottom in each region plot shows the PIP values for the SNPs in all creditable sets, with the causal SNPs in the optimum credible set outlined by a circle. 3.3 MAGMA analysis results 3.3.1 Gene-level analysis Six genes reached the GW significance threshold of p < 2.73e-06 after Bonferroni correction (α = 0.05/18 288 genes tested) in the gene-level analysis( Table 4 ), with their corresponding ǪǪ plots from the gene-level analysis shown in Supplementary Figure S3 . All GW-significant genes also had an FDR < 0.05. Diseases or traits associated with these genes were annotated using the Open Target Platform 55 , which we also highlighted here. The top gene ABCG2 (p = 8.26e-09, FDR = 1.51e-04) was associated with olanzapine-induced LDL changes; this gene is related to gout, urate measurement, and BMI based on information from the Open Target Platform. APOA5 (p = 3.45e-08, FDR = 6.31e-04) and ZPR1 (p = 1.80e-06, FDR = 0.016) were associated with SGA-induced TG changes; these genes were related to TG, HDL, and LDL levels and metabolic syndrome. GCNT4 (p = 3.17e-07, FDR = 5.12e-03) and MAST2 (p = 4.79e-07, FDR = 8.62e-03) were associated with quetiapine-induced TG and risperidone-induced LDL changes respectively. CRTAC1 (p = 2.273e-06, FDR = 0.042) was associated with olanzapine-induced HDL changes. Based on the evidence from the Open Target Platform 55 , GCNT4 and MAST2 are related to neurodegenerative disease and measurements of erythrocyte count, BMI, LDL and TC 56 – 60 ; whereas CRTAC1 is related to body fat percentage and measurements of HDL and TG 60 , 61 . View this table: View inline View popup Download powerpoint Table 4 Significant gene associations with SGA-induced lipid level and BMI changes identified in MAGMA gene-level analyses (p < 2.73e-06) Gene set enrichment analysis, incorporating these six significant genes along with those associated with the five GW-significant SNPs identified in the primary GWAS analyses, was performed using the Enrichr-KG platform. The subnetwork of gene and enriched terms are illustrated in Figure 2 , with corresponding enrichment p-values and FDRs listed in Supplementary Table S5. Download figure Open in new tab Figure 2 Subnetwork of top genes linked to enriched terms This subnetwork illustrates the top five enriched terms (FDRs < 0.05) for each library, connected to the 11 genes significantly associated with the SGA-induced metabolic side effects, as identified through the primary GWAS and gene-level MAGMA analyses. 3.3.2 Gene set analysis Fourteen gene sets were nominally associated with SGA-induced metabolic changes. After FDR correction (FDR < 0.05), four gene sets remained significant ( Supplementary Table S6 ). The top gene set, skeletal muscle satellite cell diflerentiation (p Bonferroni = 1.29e-05, FDR = 4.4e-04), was associated with SGA-induced TG changes 62 . The mRNA editing (p Bonferroni = 2.20e-05, FDR = 4.4e-04) gene set was associated with clozapine-induced LDL changes. The gene sets ER ubiquitin ligase complex (p Bonferroni = 0.004, FDR = 0.04) and Saccadic smooth pursuit (p Bonferroni = 0.004, FDR = 0.04) were associated with clozapine-induced BMI and amisulpride-induced BMI changes respectively. 4. Discussion This study is one of the largest longitudinal PGx GWAS investigations, identifying the genetic variants associated with the lipid and BMI changes induced by seven commonly used SGAs in a Chinese SCZ cohort. Our investigation included 19 316 prescription records and 3 917 to 7 596 metabolic measurements for each outcome, with a median follow-up duration of 5.7 years (SD=3.3, max=18.7), surpassing the duration of comparable GWASs 18 , 20 – 22 . The study design incorporates several key strengths that enhance the robustness and applicability of our findings. Notably, our cohort recruited from the early psychosis intervention clinic comprised a high proportion of antipsychotic-naïve patients (approximately 63%) at baseline; as such, confounding by previous medications was reduced and likely lower than many other comparable studies, including Adkins et al . 18 . Furthermore, our focus on a homogeneous ethnic Chinese sample provides valuable insights specific to this population, which has been underrepresented in previous studies. This is particularly important given the known differences in allele frequency and LD patterns between East Asian (EAS) and European (EUR) populations 63 – 66 , as evidenced in Table 2 . We employed a sophisticated analytical approach using within-subject random effects of SGA-induced lipid/BMI changes. This method substantially reduces the risk of confounding by indication/contraindication 67 , enabling a more accurate assessment of the SGA-induced metabolic side effects. To further mitigate potential confounding effects of other medications, we included lipid-lowering drugs as covariates in the GWAS phenotype estimations. Notably, the mean age of our cohort at the first clinical visit (28.3 years, SD = 9.8) was lower than that reported in a similar GWAS study (mean=40.9 years old, SD=11) 18 , reducing the influence of age-related metabolic changes on our findings. Our findings might have important clinical implications and contribute to understanding the biological mechanisms underlying SGA-induced metabolic changes. The interindividual variations in PGx may explain the high variability in CVD risk in SCZ patients taking SGAs. Notably, we identified several GW-significant SNPs (rs6532055 in ABCG2, rs2644520 near SORCS1 , rs115843863 near UPP2 , rs2514895 near KIRREL3 , and rs1884050603 in SLC2A9) and several loci with suggestive evidence (FDR < 0.2). These discoveries may enable more accurate predictions of the risk of metabolic side effects and pave ways for future personalized prescriptions to patients. The top GW-significant SNP rs6532055 is located in ABCG2 , which encodes a translocation protein involved in the efflux of antipsychotics across cellular membranes, including the blood‒brain barrier 68 , 69 . Its association with olanzapine-induced LDL changes suggests a potential role in antipsychotic pharmacokinetics and lipid metabolism. Notably, ABCG2 has also been reported to be associated with the degree of LDL reduction in response to rosuvastatin 70 , 71 . Another top gene identified was SORCS1 which was associated with aripiprazole-induced triglyceride changes. SORCS1 encodes a member of the VPS10 domain-containing receptor protein family and is strongly expressed in the central nervous system 72 . These receptors bind neuropeptides and facilitate intracellular trafficking. SORCS1 has been implicated in insulin regulation and type 2 diabetes risk in both animal and clinical studies 73 – 75 . Notably, several studies have shown that increased TG levels are associated with increased type 2 diabetes risk and impaired fasting glucose 76 – 78 . Its role in energy balance further supports its potential involvement in antipsychotic-induced metabolic alterations. 79 . In our additional analyses ( Table 2 ), UPP2 was linked to clozapine-induced HDL changes which encode uridine phosphorylase 2. Notably, several studies have revealed an association between uridine metabolism with lipid metabolism and glucose homeostasis 80 – 82 . It has been demonstrated that increasing endogenous hepatic uridine levels by inhibiting uridine phosphorylase 2 may reduce drug-induced liver lipid accumulation 82 , 83 , although long-term uridine consumption might promote liver lipid accumulation and exacerbate glucose intolerance 82 . KIRREL3 encodes a synaptic cell adhesion molecule essential for the formation of target-specific synapses and is expressed in fetal and adult brain tissues. This gene was associated with paliperidone-induced LDL changes in our study and is known primarily for its role in neurological and cognitive disorders, neuroticism, and autism spectrum disorders 84 – 87 . While its role in lipid metabolism remains to be investigated, this finding suggests a potential novel link between neuronal function and metabolic regulation. SLC2A9 encodes glucose transporter 9 (GLUT9), a protein involved in reabsorbing or excreting urate and glucose in kidney proximal tubules. This gene has been strongly associated with uric acid levels and gout in numerous studies 88 – 91 . SLC2A9 was identified to be linked to TG changes in our sample; notably, studies have revealed a significant positive association between TG and urate levels 92 – 94 . Recently, a GWAS from Ǫatar revealed the association of SLC2A9 with LDL levels 95 . Additionally, we identified eight SNPs with suggestive evidence (FDR < 0.2) in our primary analyses under an additive model, including SNPs located in or near APOE , MBL2 , MZT1 , LOC105373454 , CDCA7 , DDX1 , CD34 , and SIPA1L2 (Supplementary Table S2 ). Many of these genes are associated with lipid levels, diabetes, CVD, urate levels or other metabolic measurements based on data from the Open Target Platform 45 . These SNPs may also play a role in SGA-induced lipid/BMI changes, but further studies are needed. Similar evidence was found for the 17 suggestive SNPs (FDR < 0.2) identified in our additional analyses using non-additive genetic models ( Supplementary Table S3 ). Our MAGMA gene-level analyses identified six GW-significant genes associated with SGA-induced lipid/BMI changes ( Table 4 ). Notably, ABCG2 was identified via both GWAS and MAGMA analyses, providing further support for its potential role in olanzapine-induced LDL changes. Our study has several limitations. First, only seven SGAs were included, although these are probably among the most commonly prescribed. Future research should aim to expand the scope to include a broader range of SGAs. Second, although our sample sizes (ranging from 43 to 307 participants) are relatively large compared with those of similar GWASs 19 – 22 , 96 , a larger cohort would further increase the statistical power and robustness of our findings. Third, potential residual confounding can also be a problem affecting the estimation of the metabolic side effects of SGAs, which may in turn affect the estimation of the genetic influence on these side effects. While we have applied sophisticated methods such as within-subject random effects estimates and controlled for other concomitant medications, there may be unmeasured confounders that could impact our results. Finally, lifestyle factors such as diet, exercise, alcohol consumption and tobacco smoking were not measured and may be included in future studies. Despite these limitations, our study provides a solid foundation for future research in this critical area of psychopharmacology. Addressing these limitations in subsequent investigations will further refine our understanding of the genetic basis of SGA-induced metabolic side effects. Our study provides valuable insights into the pharmacogenetics of SGA-induced metabolic changes in a Chinese SCZ cohort. The identified genetic markers not only enhance our understanding of the biological mechanisms underlying these metabolic changes but also hold promise for developing more tailored and safer treatment strategies for individuals with SCZ. However, further studies and replication are needed before these genetic findings can be applied in clinical practice. Data Availability All data produced in the present study are available upon reasonable request to the authors Conflict of interest None Funding Statement This work was supported by the Health and Medical Research Fund (HCS, grant number 06170506). The views expressed are those of the authors and not necessarily those of the Health Bureau of HKSAR. This work was also partially supported by the KIZ-CUHK Joint Laboratory of Bioresources and Molecular Research of Common Diseases, the Hong Kong Branch of the Chinese Academy of Sciences Center for Excellence in Animal Evolution and Genetics, the Lo-Kwee Seong Biomedical Research Fund from CUHK, and a National Natural Science Foundation of China grant (HCS, grant number 81971706); the HKU Seed Fund for Basic Research for New Staff (SSYL, grant number 202009185071); the HKU Enhanced Start-up Fund for New Staff (SSYL); Suen Chi-Sun Endowed Professorship in Clinical Science at The University of Hong Kong (PCS). Availability of Data and Materials The data that support the findings of this study are available upon request from the corresponding authors, Hon-Cheong SO and Simon Sai-Yu LUI. The data are not publicly available because they contain information that could compromise the privacy of research participants. Acknowledgments We thank all collaborators who were involved in patient recruitment and preparation of data, including Ms Hera Yeung, Ms Kirby Tsang, Dr Wong Ting Yat, Dr Karen Ho, Dr Karen Hung, Dr Eric Cheung and Dr KM Cheng. The authors made use of Claude to edit this article for grammatical accuracy, and the original draft was written by the authors without the help of Claude. Claude 3.5 Sonnet was accessed from https://claude.ai and used without modification in June 2024. References 1. ↵ Radojčić MR , Pierce M , Hope H , Senior M , Taxiarchi VP , Trefan L et al. Trends in antipsychotic prescribing to children and adolescents in England: cohort study using 2000–19 primary care data . The Lancet Psychiatry 2023 ; 10 ( 2 ): 119 – 128 . OpenUrl 2. Shoham N , Cooper C , Lewis G , Bebbington P , McManus S . Temporal trends in psychotic symptoms: repeated cross-sectional surveys of the population in England 2000–14 . Schizophrenia research 2021 ; 228 : 97 – 102 . OpenUrl CrossRef 3. ↵ McGrath J , Saha S , Chant D , Welham J . Schizophrenia: a concise overview of incidence, prevalence, and mortality . Epidemiologic reviews 2008 ; 30 ( 1 ): 67 – 76 . OpenUrl CrossRef PubMed Web of Science 4. ↵ Correll CU , Solmi M , Veronese N , Bortolato B , Rosson S , Santonastaso P et al. Prevalence, incidence and mortality from cardiovascular disease in patients with pooled and specific severe mental illness: a large-scale meta-analysis of 3,211,768 patients and 113,383,368 controls . World Psychiatry 2017 ; 16 ( 2 ): 163 – 180 . OpenUrl CrossRef PubMed 5. ↵ Papanastasiou E . The prevalence and mechanisms of metabolic syndrome in schizophrenia: a review . Therapeutic advances in psychopharmacology 2013 ; 3 ( 1 ): 33 – 51 . OpenUrl CrossRef PubMed 6. ↵ Wong KC-Y , Leung PB-M , Lee BK-W , Sham P-C , Lui SS-Y , So H-C . Long-term metabolic side effects of second-generation antipsychotics in Chinese patients with schizophrenia: A within-subject approach with modelling of dosage effects . Asian Journal of Psychiatry 2024 ; 100 : 104172 . 7. ↵ Choong E , Quteineh L , Cardinaux J-R , Gholam-Rezaee M , Vandenberghe F , Dobrinas M et al. Influence of CRTC1 polymorphisms on body mass index and fat mass in psychiatric patients and the general adult population . JAMA psychiatry 2013 ; 70 ( 10 ): 1011 – 1019 . OpenUrl 8. ↵ Gebhardt S , Theisen F , Haberhausen M , Heinzel-Gutenbrunner M , Wehmeier P , Krieg JC et al. Body weight gain induced by atypical antipsychotics: an extension of the monocygotic twin and sib pair study . Journal of clinical pharmacy and therapeutics 2010 ; 35 ( 2 ): 207 – 211 . OpenUrl CrossRef PubMed 9. ↵ Ryu S , Cho EY , Park T , Oh S , Jang W-S , Kim S-K et al. − 759 C/T polymorphism of 5-HT2C receptor gene and early phase weight gain associated with antipsychotic drug treatment . Progress in Neuro-Psychopharmacology and Biological Psychiatry 2007 ; 31 ( 3 ): 673 – 677 . OpenUrl CrossRef PubMed 10. ↵ Balt S , Galloway G , Baggott M , Schwartz Z , Mendelson J . Mechanisms and genetics of antipsychotic-associated weight gain . Clinical Pharmacology & Therapeutics 2011 ; 90 ( 1 ): 179 – 183 . OpenUrl 11. ↵ Czerwensky F , Leucht S , Steimer W . CYP1A2* 1D and* 1F polymorphisms have a significant impact on olanzapine serum concentrations . Therapeutic drug monitoring 2015 ; 37 ( 2 ): 152 – 160 . OpenUrl 12. ↵ van der Weide K , van der Weide J . The influence of the CYP3A4* 22 polymorphism and CYP2D6 polymorphisms on serum concentrations of aripiprazole, haloperidol, pimozide, and risperidone in psychiatric patients . Journal of clinical psychopharmacology 2015 ; 35 ( 3 ): 228 – 236 . OpenUrl 13. ↵ Yu H , Yan H , Wang L , Li J , Tan L , Deng W et al. Five novel loci associated with antipsychotic treatment response in patients with schizophrenia: a genome-wide association study . The Lancet Psychiatry 2018 ; 5 ( 4 ): 327 – 338 . OpenUrl 14. Wang D , Fu D-J , Wu X , Shapiro A , Favis R , Savitz A et al. Large-scale candidate gene study to identify genetic risk factors predictive of paliperidone treatment response in patients with schizophrenia . Pharmacogenetics and Genomics 2015 ; 25 ( 4 ): 173 – 185 . OpenUrl 15. Li Q , Wineinger NE , Fu D-J , Libiger O , Alphs L , Savitz A et al. Genome-wide association study of paliperidone efficacy . Pharmacogenetics and genomics 2017 ; 27 ( 1 ): 7 – 18 . OpenUrl 16. Ovenden ES , Drögemöller BI , van der Merwe L , Chiliza B , Asmal L , Emsley RA et al. Fine-mapping of antipsychotic response genome-wide association studies reveals novel regulatory mechanisms . Pharmacogenomics 2017 ; 18 ( 2 ): 105 – 120 . OpenUrl 17. ↵ Li J , Yoshikawa A , Brennan MD , Ramsey TL , Meltzer HY . Genetic predictors of antipsychotic response to lurasidone identified in a genome wide association study and by schizophrenia risk genes . Schizophrenia research 2018 ; 192 : 194 – 204 . OpenUrl CrossRef 18. ↵ Adkins DE , Åberg K , McClay JL , Bukszár J , Zhao Z , Jia P et al. Genomewide pharmacogenomic study of metabolic side effects to antipsychotic drugs . Molecular Psychiatry 2011 ; 16 ( 3 ): 321 – 332 . OpenUrl CrossRef PubMed Web of Science 19. ↵ Yu H , Wang L , Lv L , Ma C , Du B , Lu T et al. Genome-Wide Association Study Suggested the PTPRD Polymorphisms Were Associated With Weight Gain Effects of Atypical Antipsychotic Medications . Schizophrenia Bulletin 2015 ; 42 ( 3 ): 814 – 823 . OpenUrl 20. ↵ Sjaarda J , Delacrétaz A , Dubath C , Laaboub N , Piras M , Grosu C et al. Identification of four novel loci associated with psychotropic drug-induced weight gain in a Swiss psychiatric longitudinal study: A GWAS analysis . Molecular Psychiatry 2023 ; 28 ( 6 ): 2320 – 2327 . OpenUrl 21. ter Hark SE , Jamain S , Schijven D , Lin BD , Bakker MK , Boland-Auge A et al. A new genetic locus for antipsychotic-induced weight gain: A genome-wide study of first-episode psychosis patients using amisulpride (from the OPTiMiSE cohort) . Journal of Psychopharmacology 2020 ; 34 ( 5 ): 524 – 531 . OpenUrl 22. ↵ Liao Y , Yu H , Zhang Y , Lu Z , Sun Y , Guo L et al. Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation . Molecular Psychiatry 2024 . 23. Brandl E , Tiwari A , Zai C , Nurmi E , Chowdhury N , Arenovich T et al. Genome-wide association study on antipsychotic-induced weight gain in the CATIE sample . The pharmacogenomics journal 2016 ; 16 ( 4 ): 352 – 356 . OpenUrl 24. ↵ Malhotra AK , Correll CU , Chowdhury NI , Müller DJ , Gregersen PK , Lee AT et al. Association between common variants near the melanocortin 4 receptor gene and severe antipsychotic drug–induced weight gain . Archives of general psychiatry 2012 ; 69 ( 9 ): 904 – 912 . OpenUrl CrossRef PubMed 25. ↵ Lui SS , Sham P , Chan RC , Cheung EF . A family study of endophenotypes for psychosis within an early intervention programme in Hong Kong: Rationale and preliminary findings . Chinese Science Bulletin 2011 ; 56 : 3394 – 3397 . OpenUrl 26. ↵ Das S , Forer L , Schönherr S , Sidore C , Locke AE , Kwong A et al. Next-generation genotype imputation service and methods . Nature Genetics 2016 ; 48 ( 10 ): 1284 – 1287 . OpenUrl CrossRef PubMed 27. ↵ Loh P-R , Danecek P , Palamara PF , Fuchsberger C , A Reshef Y , K Finucane H et al. Reference-based phasing using the Haplotype Reference Consortium panel . Nature Genetics 2016 ; 48 ( 11 ): 1443 – 1448 . OpenUrl CrossRef PubMed 28. ↵ Li L , Huang P , Sun X , Wang S , Xu M , Liu S et al. The ChinaMAP reference panel for the accurate genotype imputation in Chinese populations . Cell Research 2021 ; 31 ( 12 ): 1308 – 1310 . OpenUrl 29. ↵ VandenBerg AM . An update on recently approved long-acting injectable second-generation antipsychotics: Knowns and unknowns regarding their use . Mental Health Clinician 2022 ; 12 ( 5 ): 270 – 281 . OpenUrl 30. ↵ Chávez-Castillo M , Ortega Á , Nava M , Fuenmayor J , Lameda V , Velasco M et al. Metabolic risk in depression and treatment with selective serotonin reuptake inhibitors: are the metabolic syndrome and an increase in cardiovascular risk unavoidable . Vessel Plus 2018 ; 2 ( 6 ): 2574 – 1209 .2018. OpenUrl 31. Kamarck TW , Muldoon MF , Manuck SB , Haskett RF , Cheong J , Flory JD et al. Citalopram improves metabolic risk factors among high hostile adults: results of a placebo-controlled intervention . Psychoneuroendocrinology 2011 ; 36 ( 7 ): 1070 – 1079 . OpenUrl 32. Rosenblat JD , McIntyre RS . Pharmacological approaches to minimizing cardiometabolic side effects of mood stabilizing medications . Current Treatment Options in Psychiatry 2017 ; 4 : 319 – 332 . OpenUrl 33. ↵ Fiorentino N , Soddu A , Solomita B , Rosato G , Franza F , Tavormina G . Metabolic Alterations and Drug Interactions: The Role of the Association between Antipsychotics/Mood Stabilizers and Cognitive Deficits . Psychiatria Danubina 2022 ; 34 ( Suppl 8 ): 100 – 104 . OpenUrl 34. ↵ Smith RL , O’Connell K , Athanasiu L , Djurovic S , Kringen MK , Andreassen OA et al. Identification of a novel polymorphism associated with reduced clozapine concentration in schizophrenia patients—a genome-wide association study adjusting for smoking habits . Translational Psychiatry 2020 ; 10 ( 1 ): 198 . OpenUrl 35. ↵ Pardiñas AF , Nalmpanti M , Pocklington AJ , Legge SE , Medway C , King A et al. Pharmacogenomic variants and drug interactions identified through the genetic analysis of clozapine metabolism . American Journal of Psychiatry 2019 ; 176 ( 6 ): 477 – 486 . OpenUrl CrossRef 36. ↵ Allen E , Knopp K , Rhoades G , Stanley S , Markman H . Between- and within-subject associations of PTSD symptom clusters and marital functioning in military couples . Journal of Family Psychology 2018 ; 32 ( 1 ): 134 – 144 . OpenUrl 37. Schirmbeck F , Konijn M , Hoetjes V , Vermeulen J , Zink M , Dekker J et al. Stressful experiences affect the course of co-occurring obsessive-compulsive and psychotic symptoms: A focus on within-subject processes . Schizophrenia Research 2020 ; 216 : 69 – 76 . OpenUrl 38. ↵ Schirmbeck F , Konijn M , Hoetjes V , Zink M , de Haan L , For Genetic R et al. Obsessive– compulsive symptoms in psychotic disorders: longitudinal associations of symptom clusters on between- and within-subject levels . European Archives of Psychiatry and Clinical Neuroscience 2019 ; 269 ( 2 ): 245 – 255 . OpenUrl 39. ↵ Hamaker EL. Why researchers should think “within-person”: A paradigmatic rationale . 2012 . 40. ↵ McCaw ZR , Lane JM , Saxena R , Redline S , Lin X . Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies . Biometrics 2020 ; 76 ( 4 ): 1262 – 1272 . OpenUrl CrossRef 41. ↵ Purcell S , Neale B , Todd-Brown K , Thomas L , Ferreira MA , Bender D et al. PLINK: a tool set for whole-genome association and population-based linkage analyses . The American journal of human genetics 2007 ; 81 ( 3 ): 559 – 575 . OpenUrl CrossRef PubMed 42. ↵ Guindo-Martínez M , Amela R , Bonàs-Guarch S , Puiggròs M , Salvoro C , Miguel-Escalada I et al. The impact of non-additive genetic associations on age-related complex diseases . Nature Communications 2021 ; 12 ( 1 ): 2436 . OpenUrl 43. ↵ de Leeuw CA , Mooij JM , Heskes T , Posthuma D . MAGMA: generalized gene-set analysis of GWAS data . PLoS computational biology 2015 ; 11 ( 4 ): e1004219 . OpenUrl 44. ↵ McLaren W , Gil L , Hunt SE , Riat HS , Ritchie GR , Thormann A et al. The ensembl variant effect predictor . Genome biology 2016 ; 17 : 1 – 14 . OpenUrl CrossRef PubMed 45. ↵ Ochoa D , Hercules A , Carmona M , Suveges D , Baker J , Malangone C et al. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt . Nucleic acids research 2023 ; 51 ( D1 ): D1353 – D1359 . OpenUrl CrossRef 46. ↵ Sollis E , Mosaku A , Abid A , Buniello A , Cerezo M , Gil L et al. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource . Nucleic acids research 2023 ; 51 ( D1 ): D977 – D985 . OpenUrl CrossRef PubMed 47. ↵ Evangelista JE , Xie Z , Marino GB , Nguyen N , Clarke DJ , Ma’ayan A . Enrichr-KG: bridging enrichment analysis across multiple libraries . Nucleic acids research 2023 ; 51 ( W1 ): W168 – W179 . OpenUrl 48. ↵ Wang G , Sarkar A , Carbonetto P , Stephens M . A simple new approach to variable selection in regression, with application to genetic fine mapping . Journal of the Royal Statistical Society Series B: Statistical Methodology 2020 ; 82 ( 5 ): 1273 – 1300 . OpenUrl 49. ↵ Benjamini Y , Hochberg Y . Controlling the false discovery rate: a practical and powerful approach to multiple testing . Journal of the Royal statistical society: series B (Methodological ) 1995 ; 57 ( 1 ): 289 – 300 . OpenUrl CrossRef PubMed Web of Science 50. ↵ Fernando R , Nettleton D , Southey B , Dekkers J , Rothschild M , Soller M . Controlling the proportion of false positives in multiple dependent tests . Genetics 2004 ; 166 ( 1 ): 611 – 619 . OpenUrl Abstract / FREE Full Text 51. ↵ Efron B. Simultaneous inference: When should hypothesis testing problems be combined? 2008 . 52. ↵ Quillen EE , Chen XD , Almasy L , Yang F , He H , Li X et al. ALDH2 is associated to alcohol dependence and is the major genetic determinant of “daily maximum drinks” in a GWAS study of an isolated rural Chinese sample . American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 2014 ; 165 ( 2 ): 103 – 110 . OpenUrl CrossRef 53. Cai N , Revez JA , Adams MJ , Andlauer TF , Breen G , Byrne EM et al. Minimal phenotyping yields genome-wide association signals of low specificity for major depression . Nature genetics 2020 ; 52 ( 4 ): 437 – 447 . OpenUrl CrossRef 54. ↵ Comuzzie AG , Cole SA , Laston SL , Voruganti VS , Haack K , Gibbs RA et al. Novel genetic loci identified for the pathophysiology of childhood obesity in the Hispanic population . PloS one 2012 ; 7 ( 12 ): e51954 . OpenUrl CrossRef PubMed 55. ↵ Ochoa D , Hercules A , Carmona M , Suveges D , Baker J , Malangone C et al. The next-generation Open Targets Platform: reimagined, redesigned, rebuilt . Nucleic Acids Research 2022 ; 51 ( D1 ): D1353 – D1359 . OpenUrl 56. ↵ Tian R , Abarientos A , Hong J , Hashemi SH , Yan R , Dräger N et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis . Nature neuroscience 2021 ; 24 ( 7 ): 1020 – 1034 . OpenUrl CrossRef PubMed 57. Leng K , Rose IV , Kim H , Xia W , Romero-Fernandez W , Rooney B et al. CRISPRi screens in human iPSC-derived astrocytes elucidate regulators of distinct inflammatory reactive states . Nature neuroscience 2022 ; 25 ( 11 ): 1528 – 1542 . OpenUrl CrossRef 58. Zhu Z , Guo Y , Shi H , Liu C-L , Panganiban RA , Chung W et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank . Journal of Allergy and Clinical Immunology 2020 ; 145 ( 2 ): 537 – 549 . OpenUrl CrossRef PubMed 59. Ligthart S , Vaez A , Hsu Y-H , Consortium IWGotC , PMI-WG-XCP , Study LC et al. Bivariate genome-wide association study identifies novel pleiotropic loci for lipids and inflammation . BMC genomics 2016 ; 17 : 1 – 10 . OpenUrl CrossRef PubMed 60. ↵ Sakaue S , Kanai M , Tanigawa Y , Karjalainen J , Kurki M , Koshiba S et al. A cross-population atlas of genetic associations for 220 human phenotypes . Nature genetics 2021 ; 53 ( 10 ): 1415 – 1424 . OpenUrl CrossRef PubMed 61. ↵ Martin S , Cule M , Basty N , Tyrrell J , Beaumont RN , Wood AR et al. Genetic evidence for different adiposity phenotypes and their opposing influences on ectopic fat and risk of cardiometabolic disease . Diabetes 2021 ; 70 ( 8 ): 1843 – 1856 . OpenUrl Abstract / FREE Full Text 62. ↵ Scimè A , Rudnicki MA . Anabolic potential and regulation of the skeletal muscle satellite cell populations . Current Opinion in Clinical Nutrition & Metabolic Care 2006 ; 9 ( 3 ): 214 – 219 . OpenUrl 63. ↵ Meyer JM , Rosenblatt LC , Kim E , Baker RA , Whitehead R . The moderating impact of ethnicity on metabolic outcomes during treatment with olanzapine and aripiprazole in patients with schizophrenia . Journal of Clinical Psychiatry 2009 ; 70 ( 3 ): 318 . OpenUrl 64. Ader M , Garvey WT , Phillips LS , Nemeroff CB , Gharabawi G , Mahmoud R et al. Ethnic heterogeneity in glucoregulatory function during treatment with atypical antipsychotics in patients with schizophrenia . Journal of psychiatric research 2008 ; 42 ( 13 ): 1076 – 1085 . OpenUrl CrossRef PubMed Web of Science 65. Yang S , Yu B , Yu W , Dai S , Feng C , Shao Y et al. Development and validation of an age-sex-ethnicity-specific metabolic syndrome score in the Chinese adults . Nature Communications 2023 ; 14 ( 1 ): 6988 . OpenUrl 66. ↵ Heymsfield SB , Peterson CM , Thomas DM , Heo M , Schuna J. Jr . Why are there race/ethnic differences in adult body mass index–adiposity relationships? A quantitative critical review . Obesity reviews 2016 ; 17 ( 3 ): 262 – 275 . OpenUrl 67. ↵ Twisk JWR , de Vente W . Hybrid models were found to be very elegant to disentangle longitudinal within- and between-subject relationships . Journal of Clinical Epidemiology 2019 ; 107 : 66 – 70 . OpenUrl 68. ↵ Nasyrova RF , Shnayder NA , Osipova SM , Khasanova AK , Efremov IS , Al-Zamil M et al. Genetic Predictors of Antipsychotic Efflux Impairment via Blood-Brain Barrier: Role of Transport Proteins . Genes 2023 ; 14 ( 5 ): 1085 . OpenUrl 69. ↵ Bohn K , Lange A , Chmielewski J , Hrycyna CA . Dual modulation of human P-glycoprotein and ABCG2 with prodrug dimers of the atypical antipsychotic agent paliperidone in a model of the blood–brain barrier . Molecular Pharmaceutics 2017 ; 14 ( 4 ): 1107 – 1119 . OpenUrl 70. ↵ Chasman DI , Giulianini F , MacFadyen J , Barratt BJ , Nyberg F , Ridker PM . Genetic determinants of statin-induced low-density lipoprotein cholesterol reduction: the Justification for the Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) trial . Circulation: Cardiovascular Genetics 2012 ; 5 ( 2 ): 257 – 264 . OpenUrl Abstract / FREE Full Text 71. ↵ Alrajeh K , Roman YM . The frequency of rs2231142 in ABCG2 among Asian subgroups: implications for personalized rosuvastatin dosing . Pharmacogenomics 2023 ; 24 ( 1 ): 15 – 26 . OpenUrl CrossRef 72. ↵ Zhang Y , Iwasaki H , Wang H , Kudo T , Kalka TB , Hennet T et al. Cloning and Characterization of a New Human UDP-N-Acetyl-α-d-galactosamine: PolypeptideN-Acetylgalactosaminyltransferase, Designated pp-GalNAc-T13, That Is Specifically Expressed in Neurons and Synthesizes GalNAc α-Serine/Threonine Antigen . Journal of Biological Chemistry 2003 ; 278 ( 1 ): 573 – 584 . OpenUrl Abstract / FREE Full Text 73. ↵ Clee SM , Yandell BS , Schueler KM , Rabaglia ME , Richards OC , Raines SM et al. Positional cloning of Sorcs1, a type 2 diabetes quantitative trait locus . Nature Genetics 2006 ; 38 ( 6 ): 688 – 693 . OpenUrl CrossRef PubMed Web of Science 74. Goodarzi MO , Lehman DM , Taylor KD , Guo X , Cui J , Quinones MJ et al. SORCS1: a novel human type 2 diabetes susceptibility gene suggested by the mouse . diabetes 2007 ; 56 ( 7 ): 1922 – 1929 . OpenUrl Abstract / FREE Full Text 75. ↵ Florez JC , Manning AK , Dupuis J , McAteer J , Irenze K , Gianniny L et al. A 100K genome-wide association scan for diabetes and related traits in the Framingham Heart Study: replication and integration with other genome-wide datasets . Diabetes 2007 ; 56 ( 12 ): 3063 – 3074 . OpenUrl Abstract / FREE Full Text 76. ↵ Lin SX , Berlin I , Younge R , Jin Z , Sibley CT , Schreiner P et al. Does elevated plasma triglyceride level independently predict impaired fasting glucose? The Multi-Ethnic Study of Atherosclerosis (MESA) . Diabetes care 2013 ; 36 ( 2 ): 342 – 347 . OpenUrl Abstract / FREE Full Text 77. Beshara A , Cohen E , Goldberg E , Lilos P , Garty M , Krause I . Triglyceride levels and risk of type 2 diabetes mellitus: a longitudinal large study . Journal of Investigative Medicine 2016 ; 64 ( 2 ): 383 – 387 . OpenUrl Abstract / FREE Full Text 78. ↵ Dotevall A , Johansson S , Wilhelmsen L , Rosengren A . Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women. A prospective 18-year follow-up of the BEDA* study . Diabetic Medicine 2004 ; 21 ( 6 ): 615 – 622 . OpenUrl CrossRef PubMed Web of Science 79. ↵ Subkhangulova A , Malik AR , Hermey G , Popp O , Dittmar G , Rathjen T et al. SORCS 1 and SORCS 3 control energy balance and orexigenic peptide production . EMBO reports 2018 ; 19 ( 4 ): e44810 . OpenUrl Abstract / FREE Full Text 80. ↵ Le TT , Ziemba A , Urasaki Y , Hayes E , Brotman S , Pizzorno G . Disruption of uridine homeostasis links liver pyrimidine metabolism to lipid accumulation [S] . Journal of lipid research 2013 ; 54 ( 4 ): 1044 – 1057 . OpenUrl Abstract / FREE Full Text 81. Le TT , Urasaki Y , Pizzorno G . Uridine prevents fenofibrate-induced fatty liver . PLoS One 2014 ; 9 ( 1 ): e87179 . OpenUrl CrossRef PubMed 82. ↵ Urasaki Y , Pizzorno G , Le TT . Chronic uridine administration induces fatty liver and pre-diabetic conditions in mice . Plos one 2016 ; 11 ( 1 ): e0146994 . OpenUrl CrossRef PubMed 83. ↵ Zhang Y , Repa JJ , Inoue Y , Hayhurst GP , Gonzalez FJ , Mangelsdorf DJ . Identification of a liver-specific uridine phosphorylase that is regulated by multiple lipid-sensing nuclear receptors . Molecular Endocrinology 2004 ; 18 ( 4 ): 851 – 862 . OpenUrl CrossRef PubMed 84. ↵ Hill WD , Weiss A , Liewald DC , Davies G , Porteous DJ , Hayward C et al. Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life . Molecular psychiatry 2020 ; 25 ( 11 ): 3034 – 3052 . OpenUrl CrossRef 85. Nagel M , Jansen PR , Stringer S , Watanabe K , De Leeuw CA , Bryois J et al. Meta-analysis of genome-wide association studies for neuroticism in 449,484 individuals identifies novel genetic loci and pathways . Nature genetics 2018 ; 50 ( 7 ): 920 – 927 . OpenUrl CrossRef PubMed 86. Guo H , Duyzend MH , Coe BP , Baker C , Hoekzema K , Gerdts J et al. Genome sequencing identifies multiple deleterious variants in autism patients with more severe phenotypes . Genetics in Medicine 2019 ; 21 ( 7 ): 1611 – 1620 . OpenUrl CrossRef PubMed 87. ↵ Bhalla K , Luo Y , Buchan T , Beachem MA , Guzauskas GF , Ladd S et al. Alterations in CDH15 and KIRREL3 in patients with mild to severe intellectual disability . The American Journal of Human Genetics 2008 ; 83 ( 6 ): 703 – 713 . OpenUrl CrossRef PubMed Web of Science 88. ↵ Döring A , Gieger C , Mehta D , Gohlke H , Prokisch H , Coassin S et al. SLC2A9 influences uric acid concentrations with pronounced sex-specific effects . Nature genetics 2008 ; 40 ( 4 ): 430 – 436 . OpenUrl CrossRef PubMed Web of Science 89. Wallace C , Newhouse SJ , Braund P , Zhang F , Tobin M , Falchi M et al. Genome-wide association study identifies genes for biomarkers of cardiovascular disease: serum urate and dyslipidemia . The American Journal of Human Genetics 2008 ; 82 ( 1 ): 139 – 149 . OpenUrl CrossRef PubMed Web of Science 90. Tin A , Marten J , Halperin Kuhns VL , Li Y , Wuttke M , Kirsten H et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels . Nature genetics 2019 ; 51 ( 10 ): 1459 – 1474 . OpenUrl PubMed 91. ↵ Vitart V , Rudan I , Hayward C , Gray NK , Floyd J , Palmer CN et al. SLC2A9 is a newly identified urate transporter influencing serum urate concentration, urate excretion and gout . Nature genetics 2008 ; 40 ( 4 ): 437 – 442 . OpenUrl CrossRef PubMed Web of Science 92. ↵ Zhang Y , Wei F , Chen C , Cai C , Zhang K , Sun N et al. Higher triglyceride level predicts hyperuricemia: a prospective study of 6-year follow-up . Journal of clinical lipidology 2018 ; 12 ( 1 ): 185 – 192 . OpenUrl 93. Giacomello A , Di Sciascio N , Quaratino CP . Relation between serum triglyceride level, serum urate concentration, and fractional urate excretion . Metabolism 1997 ; 46 ( 9 ): 1085 – 1089 . OpenUrl CrossRef PubMed 94. ↵ Zheng R , Ren P , Chen Q , Yang T , Chen C , Mao Y . Serum uric acid levels and risk of incident hypertriglyceridemia: a longitudinal population-based epidemiological study . Annals of Clinical & Laboratory Science 2017 ; 47 ( 5 ): 586 – 591 . OpenUrl Abstract / FREE Full Text 95. ↵ Umlai U-KI , Toor SM , Al-Sarraj YA , Mohammed S , Al Hail MS , Ullah E , et al. Identification of a Novel SLC2A9 Gene Association with LDL-C levels and Evaluation of Polygenic Scores in a Multi-Ancestry Genome Wide Association Study . medRxiv 2024: 2024 .2007. 2004.24309936. 96. ↵ Zhang J-P , Lencz T , Zhang RX , Nitta M , Maayan L , John M et al. Pharmacogenetic Associations of Antipsychotic Drug-Related Weight Gain: A Systematic Review and Meta-analysis . Schizophrenia Bulletin 2016 ; 42 ( 6 ): 1418 – 1437 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted September 04, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study Kenneth Chi-Yin Wong , Perry Bok-Man Leung , Benedict Ka-Wa Lee , Zoe Zi-Yu Zheng , Emily Man-Wah Tsang , Meng-Hui Liu , Kelly Wing-Kwan Lee , Shi-Tao Rao , Pak-Chung Sham , Simon Sai-Yu Lui , Hon-Cheong So medRxiv 2024.09.04.24313052; doi: https://doi.org/10.1101/2024.09.04.24313052 Share This Article: Copy Citation Tools Pharmacogenetic Study of Antipsychotic-Induced Lipid and BMI Changes in Chinese Schizophrenia Patients: A Genome-Wide Association Study Kenneth Chi-Yin Wong , Perry Bok-Man Leung , Benedict Ka-Wa Lee , Zoe Zi-Yu Zheng , Emily Man-Wah Tsang , Meng-Hui Liu , Kelly Wing-Kwan Lee , Shi-Tao Rao , Pak-Chung Sham , Simon Sai-Yu Lui , Hon-Cheong So medRxiv 2024.09.04.24313052; doi: https://doi.org/10.1101/2024.09.04.24313052 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (303) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6620) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4557) Health Policy (1372) Health Systems and Quality Improvement (1615) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6634) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a033ed0d6decad07',t:'MTc4MDA0MTIxMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();