DNA Methylation Changes Associated with Antipsychotic Serum Concentrations in Patients with Psychosis.

DNA Methylation Changes Associated with Antipsychotic Serum Concentrations in Patients with Psychosis. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article DNA Methylation Changes Associated with Antipsychotic Serum Concentrations in Patients with Psychosis. Stephanie Le Hellard, Jonelle Villar, Anne-Kristin Stavrum, Letícia Spíndola, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8309238/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Background Antipsychotic drugs (AP) are commonly prescribed for the treatment of psychotic symptoms in schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD). However, despite over 70 years of clinical use, the pharmacological mechanisms underlying AP drug action remain incompletely understood. DNA methylation (DNAm) provides a means to investigate epigenetic changes associated with AP exposure and to explore biological pathways potentially involved in AP pharmacology. This study aims to identify DNAm changes associated with treatment using olanzapine, quetiapine, and risperidone which may suggest shared or drug-specific epigenetic signatures. Methods We analysed genome-wide DNAm levels in the blood of 263 psychiatric patients who were treated with AP monotherapies (n = 136, olanzapine n = 89, quetiapine n = 26, risperidone n = 21) or were medication-free (n = 127). We assessed the correlation between DNAm levels and AP serum concentrations of each drug individually and of those shared between the three drugs. To identify drug-specific effects, we compared DNAm profiles between drugs and DNAm levels of medication-free patients. Results We identified 60 CpGs and seven differentially methylated regions (DMRs) consistently associated with all three AP treatments (experiment wide significant threshold p < 6.1 x 10 − 8 ), involving genes linked to postsynaptic density regulation in glutamatergic neurons ( SHANK2 ), signal transduction ( GNB1 ), synaptic plasticity ( GAS7 ), and neuronal signalling ( FBXW4 and ZNF471 ). No significant DNAm effects were associated with any specific AP monotherapy. None of the identified effects were among DNAm differences reported in a large EWAS between cases with SCZ and controls. These findings contribute to the characterization of the association between AP treatment and DNAm and provide insights into AP molecular mechanisms of action. Biological sciences/Neuroscience/Epigenetics in the nervous system/Epigenetics and behaviour Biological sciences/Drug discovery/Biomarkers/Diagnostic markers Health sciences/Diseases/Psychiatric disorders/Schizophrenia Health sciences/Diseases/Psychiatric disorders/Bipolar disorder Health sciences/Diseases/Psychiatric disorders/Depression Figures Figure 1 Figure 2 Figure 3 Introduction Episodes of psychosis can occur in several mental disorders, including schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorders (MDD). Psychotic episodes are characterized by symptoms such as hallucinations, delusions, disorganized speech, and social withdrawal. Timely and effective treatment of psychotic symptoms reduces the risk of a wide range of adverse health outcomes. Beyond psychiatric symptoms, individuals experiencing psychotic episodes have significantly higher odds of comorbid physical health burdens, independent of their mental disorders, according to the WHO World Health Mental Health Surveys 1 . The standard psychiatric approach to treating these disorders involves the use of antipsychotics (AP), mood stabilizer, and/or antidepressant medications. All APs including second-generation APs (SGAs), such as olanzapine, risperidone, and quetiapine, are typically effective in alleviating positive symptoms (e.g., hallucinations and delusions) during the acute phase of psychosis 2 . However, they have been shown to be less effective in improving negative symptoms and cognitive deficits that may also be symptomatic of psychosis 3 . AP treatment can also lead to adverse effects, such as weight gain and dyslipidemia. These side effects pose challenges for medication compliance, and poor medication adherence increases the risk of recurrent psychotic episodes in long-term care 4 – 7 . Furthermore, poor adherence is associated with an increased risk of early mortality 8 , 9 . To better understand the therapeutic benefits and potential risks of AP, it is essential to characterize their mechanisms of action. Multiple genetic studies have implicated synaptic function, including neuronal synapses, synaptic dysfunction, and neurotransmitter imbalances, in the pathophysiology of psychiatric disorders 10 – 12 . Genome-wide association studies (GWAS), for instance, have identified the dopamine receptor ( DRD2 ) as a key contributor to disease risk and as the primary target of antipsychotic drugs 13 . However, evidence also supports the involvement of broader neurotransmitter disturbances beyond the dopaminergic system, implicating dysregulation of serotonergic, GABAergic, glutamatergic, and muscarinic signaling pathways 14 , 15 . A combination of neurotransmitter disturbances between dopamine and GABA may be predictive of first-episode psychosis 16 , while AP treatment response has been associated with alterations in the relationship between cortical glutamate concentrations and striatal dopamine synthesis capacity 17 . The relationship between genetic risk, AP drug mechanisms, and treatment response is complex. Notably, many genetic risk variants identified through GWAS may act through biological pathways that are distinct from those underlying AP drug action or treatment response 18 . Importantly, the molecular mechanisms underlying AP treatment response remain incompletely understood. DNA methylation (DNAm) is an epigenetic mechanism that regulates gene expression by modulating the binding affinity of transcriptional machinery and through interactions with microRNAs, histone modifications, and chromatin structure. Studies of psychotropic drugs prescribed for the treatment of mood disorders such as antidepressants, lithium, and valproic acid have identified DNAm alterations associated with treatment response 19 , 20 . For example, DNAm profiles obtained from peripheral blood have identified differentially methylated positions (DMPs) in genes implicated in mood regulation and neuroimmune signaling such as BDNF , SLC6A4 , HTR1A , HTR1B , and IL11 21,22 . Differentially methylated regions (DMRs) related to synaptic function and drug metabolism ( SORBS2 and CYP2C18 ) have also been linked to antidepressant response 22 . In patients with BD, DMRs enriched for genes involved in neuronal function have been shown to distinguish excellent lithium responders from non-responders 23 . Moreover, global DNAm levels differ depending on whether lithium is administered as monotherapy or in combination with valproic acid 24 . DNAm studies of AP range from candidate-gene studies to exploratory epigenome-wide methylation studies. A comparative study of olanzapine, risperidone, quetiapine, and mood stabilizers reported hypermethylation in genes involved in glucose uptake ( AKT1 and AKT2 ) associated with AP treatment 25 . In longitudinal studies, risperidone and olanzapine treatment have been associated with normalization of IL-6 methylation levels toward those of controls 26 . Similarly, in a longitudinal study of first-episode psychosis (FEP) patients treated with risperidone, more than 80% of the differential DNAm changes were normalized to control levels 27 . Quetiapine monotherapy has been associated with methylation changes in a network of genes implicated in neurogenesis in patients with BP 20 . In rodents, DNAm alterations in genes related to dopaminergic neurotransmission in the hippocampus have been observed following olanzapine monotherapy 28 , 29 . It is important to note, however, that Epigenome-Wide Association Studies (EWAS) of APs are often limited by small sample sizes, and relatively few human studies of AP monotherapy have been conducted compared with animal or cell models. Previously, we identified DNAm differences associated with SCZ and exposure to environmental risk factors for SCZ 30 – 32 . In this study, we hypothesized that medication effects may contribute to the alterations in DNAm often reported in EWAS of psychiatric disorders. Individual medication effects are particularly challenging to identify due to the common use of polypharmacy, where patients are often treated with a combination of AP, antidepressants, and mood-stabilizing drugs. Consequently, in EWAS, the effects of AP treatment are often addressed by including AP use as a binary or categorical covariate, or more frequently, through surrogate variable correction. However, these approaches may insufficiently capture interindividual variability in drug exposure. To address this limitation, we focused on individuals receiving AP monotherapy and evaluated serum drug concentrations, which more accurately reflect systemic drug exposure than prescribed dosages. This approach allowed us to assess the relationship between DNAm and AP exposure under naturalistic conditions thus reflecting real-world variability in medication adherence. Methods The current study analysed data from a subset of 263 individuals diagnosed with SCZ (n = 119), BP (n = 86), MDD (n = 11), and Other Psychotic Disorders (n = 47) recruited through the Thematically Organised Psychosis (TOP) study cohort in Oslo, Norway. Diagnoses were established using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) with interviews conducted by clinical psychologists or medical doctors with psychiatric training, yielding a DSM-IV diagnostic reliability of 82% 33 . Diagnostic categories included DSM-VI codes for SCZ (295.1-295.9), BP (296.02-296.89), MDD (296.24-296.36, 296.9-296.94), and Other Psychotic Disorders (297.1, 298.8/9). A history of psychosis was obtained from SCID-1 and clinical interviews as previously described 32 . The study was approved by the local Regional Committee for Medical and Health Research Ethics – Region West (REK2009/2485-91) and all participants provided written informed consent. For the analyses in this study, we selected three of the most commonly prescribed atypical antipsychotics in Norway: olanzapine, quetiapine, and risperidone. Pharmacological treatment data were obtained from medical records and patient interviews to select only individuals prescribed these drugs. Antipsychotic serum concentrations were analyzed at the Department of Clinical Pharmacology, St. Olav´s Hospital, Trondheim to confirm adherence and identify non-compliance 34 . Only individuals adhering to antipsychotic monotherapy, as determined by measurable serum concentrations > 0 nmol/L) were selected. While the effects of APs on DNAm may be dose-dependent, recorded dosages may not accurately reflect individual adherence. Therefore, we examined the relationship between measured serum levels and self-reported compliance. We compared the range of the participants´ serum concentrations that represented 100% adherence to the recommended therapeutic ranges 35 . (See Supplementary Materials (SM) Fig. 1 for distribution of serum concentrations and SM Fig. 2 for distribution of serum concentrations versus compliance). Additionally, a medication-free group consisting of individuals with no detectable medication levels were identified (n = 131). The total sample (n = 263) was subsequently grouped for the analyses, with exclusion criteria detailed in the flow diagram (Fig. 1 ). Epigenome-wide DNAm Profiling for EWAS Analyses DNA was extracted from peripheral blood samples at the time of inclusion in the TOP study and was assayed on the Infinium® MethylationEPIC BeadChip v1 (Illumina Inc., CA, USA), which interrogates over 850K CpGs across the genome and quantifies the methylation levels at those sites. The typing was performed at the Life and Brain Genomics GmBH, Bonn, Germany. Pre-processing and Quality Control (QC) of Methylation Data Details of the quality control (QC) and preprocessing of the methylation data have been described previously 31 , 32 . Briefly, the TOP methylation dataset is comprised of three batches collected over three years: 1,000 samples run in 2016, 283 in 2017, and 1082 in 2019. QC and preprocessing of the data were conducted separately for each batch. Probes and samples that failed QC checks, as well as sex chromosome probes, were removed. Functional normalization was applied to reduce non-biological variation and normalize the measure of methylation. The data from the three batches was then merged, and batch effects from identified technical sources were removed with Combat 36 . Overall,1818 samples with self-reported European ancestry were selected. Bioconductor (version 3.1.1) packages minfi 37 , wateRmelon 38 , sva 39 , and ChAMP 40 were utilized for QC and data preprocessing. Additionally, six probes that later failed the meta-analysis were removed. The final data set consisted of 263 samples and 760,662 probes. Blood cell-type proportions for six constituent cell types (monocytes, neutrophils, natural killer cells (NK), CD4 + T, CD8 + T, and B-cells) were estimated from DNAm data using the Houseman algorithm 41 as implemented by the estimateCellCounts2 function in the FlowSorted.Blood.Epic 42 Bioconductor package. The algorithm for Identifying Optimal Libraries (IDOL) was selected as the reference. Given that tobacco smoking is associated with altered white blood cell composition 43 , 44 and subsequent alterations in methylation levels 45 , smoking scores were calculated based on the method described by Elliott et al. 46 to account for any possible effects of smoking. Epigenome-Wide Association Studies Shared DNAm Changes Associated with Olanzapine, Quetiapine and Risperidone Treatment. Samples from patients adhering to monotherapy (n = 136) with serum concentrations > 0 nmol/L were selected with similar ages (median age = 30.0 years) and sex (57% male). An epigenome-wide study was conducted for each monotherapy before performing a meta-analysis of all three: (olanzapine (n = 89), quetiapine (n = 26), and risperidone (n = 21)). The R package limma (version 3.62.2) 47 was used to perform the linear regression with p -values calculated using empirical Bayes moderated t-statistics. An epigenome-wide significance threshold was set at p ≤ 9 × 10 − 8 as recommended by Mansell et al . 48 . Quantile-quantile (qq) plots and the corresponding inflation statistic (lambda ƛ) were evaluated to assess potential effects of technical artifacts (see SM Fig. 3 ). We modeled DNAm levels at each CpG site using the following linear model where β represents beta methylation values at a given CpG site, AP is the serum concentration of the medication, Sex, Age, Smoking score, and Psychiatric Diagnosis are individual level covariates, Technical Batch corresponds to the batch effect from the Ilumina Scanner ID, Blood cell-type corresponds to estimated proportions of major blood cells types (monocytes, neutrophiles, NK, CD4 + T, CD8 + T, and B- cells), and ɛ denotes residual error. Β ~ AP + Sex + Age + Smoking Score + Psychiatric Diagnosis + Technical Batch + Blood Cell-type + ɛ Subsequently, a meta-analysis of the three individual EWAS was conducted using the “metagen()” function from the R package meta (v.8.1-0 ) 49 . A fixed-effects model was used to pool standardized effect estimates across cohorts, based on the assumption that AP effects operate through a shared pharmacological mechanism, primarily via the dopamine D2 receptor ( DRD2 ) binding. P -values reflect two-sided z-tests evaluating whether the pooled effect differs significantly from zero. Inverse-variance weighting was applied using standard errors, and heterogeneity was assessed using the I² statistic 50 . Model selected was guided by theoretical considerations and formally evaluated using the Hausman test 51 , which assess whether the fixed- and random-effects models produce systematically different estimates. Specific DNAm Changes Associated with Olanzapine, Quetiapine and Risperidone Treatment Separately. Given the relatively small number of patients adhering to monotherapy with serum values > 0 nmol/L, binary analyses were also performed, comparing each AP (coded as yes/no) against the other APs, supplemented by a sample of medication-free patients (n = 127) to increase statistical power. The comparison groups were as follows: olanzapine (n = 89) versus “quetiapine + risperidone + medication-free” (n = 174), quetiapine (n = 26) versus “olanzapine + risperidone + medication-free” (n = 237), and risperidone (n = 21) versus “olanzapine + quetiapine + medication-free” (n = 242). Age differences (median age = 29.0 years) between AP subgroups and medication-free individuals, were assessed with an ANOVA test, followed by Kruskal-Wallace rank sum test and Dunn´s post-hoc pairwise comparisons. Differences in sex distribution (57% male) across groups were assessed using Pearson´s chi-square test. P -values were calculated as previously described for the common effects analysis, with QQ-plots presented in SM Fig. 4. We modeled DNAm levels at each CpG site using the following linear model where β represents beta methylation values at a given CpG site, AP is the binary variable Yes/No for a given AP, No for Med-Free, Sex, Age, Smoking score, and Psychiatric Diagnosis are individual level covariates, Technical Batch corresponds to the batch effect from the Ilumina Scanner ID, Blood cell-type corresponds to estimated proportions of major blood cells types (monocytes, neutrophiles, NK, CD4 + T, CD8 + T, and B- cells), and ɛ denotes residual error. β ~ AP vs. (2AP + Med-Free) + Sex + Age + Smoking Score + Psychiatric Diagnosis + Technical Batch Effect + Blood Cell-type + ɛ Differentially Methylated Regions (DMRs) To evaluate the combined effect of DNAm across multiple CpG sites, we conducted a regional analysis using the R package dmrff 52 . The dmrff algorithm integrates groups of CpGs to enhance statistical power in detecting associations, based on the premise that co-methylated CpGs in close proximity exert combined or related biological effects 53 . dmrff identifies candidate DMRs by clustering CPG sites with nominal EWAS p -values < 0.05 within 500 base pairs of each other and sharing a consistent direction of effect. For each candidate region, multiple overlapping subregions are tested, and only the strongest, non-overlapping subregions are retained in the final output. To control for multiple testing, we set the significance threshold to p < 6.134 x 10 − 8 ( 𝜶=0.05/815,168 subregions). Regions with raw p -values below this threshold were considered significant. To enhance the reliability and biological interpretability of the results, we retained only regions composed of at least three CpG sites. Annotation of DMRs was performed using the Illumina Human Methylation EPIC annotation library (ilm10b2.hg19) 54 . However, manual annotation was performed using the UCSC genome browser (GRCH37/hg19) to assign nearest genes to the genomic regions chr1: 1713950–1714271 and chr8: 130696056–130696161, assigned to GNB1 and CCDC26 , respectively (SM Fig. 5 and Fig. 6). Functional Enrichment We investigated functionally enriched Gene Ontology (GO) terms associated with DMPs and DMRs using two complementary Bioconductor packages designed for Illumina methylation arrays: missMethyl (v1.36.0) 55 and methylGSA (v1.24.0) 56 . For the analyses of DMPs, we used the “gometh()” function from missMethyl , which performs a modified hypergeometric test incorporating a probability weighting function (PWF). The PWF adjusts for bias introduced by the varying number of CpG probes per gene, thereby reducing false positives driven by gene size or probe density. In parallel, we used the “methylglm()” function from methylGSA to test for GO term overrepresentation among the 32 annotated DMPs. This method fits a logistic regression model comparing genes associated with significant DMPs to all genes represented on the array, while adjusting for gene-specific CpG coverage. GO terms with FDR-adjusted (Benjamini-Hochberg) p-values < 0.05 were considered significantly enriched. For the analyses of DMRs, we used the “goregion()” function from missMethyl , which maps CpG probes within differentially methylated regions to genes before performing GO enrichment analysis. This method accounts for probe density bias, as implemented in “gometh()”, and reports significant GO terms at FDR < 0.05. GO terms identified by these analyses were further processed using Re duce and V isualize G ene O ntology REVIGO (v1.8.1) 57 for semantic similarity reduction. Using the Homo sapiens reference and default settings, we clustered similar GO terms and selected representative terms summarizing the shared biological themes of each cluster. We also computed composite scores for each term based on REVIGO´s dispensability, frequency, and uniqueness metrics, which were used solely for generating the supplementary visualizations (SM Fig. 5–6 and SM Tables 3–4). Results Cohort Characteristics Demographics of the subset of the TOP cohort selected for the present study are presented in Table 1 . Age differed significantly (Kruskal-Wallis p = 0.036), with the olanzapine group being older on average. In addition, diagnostic distribution differed significantly across treatment groups (χ²(9) = 33.34, p < 0.001) with schizophrenia being the most common diagnosis in all groups and most prevalent in the risperidone group (81%). Table 1 Demographics of the subset of TOP cohort samples. Variable Olanzapine Quetiapine Risperidone Med-Free Overall P-value N = 89 N = 26 N = 21 N = 127 N = 263 Sex p = 0.187 Female 36 (40%) 14 (54%) 5 (24%) 57 (45%) 112 (43%) Male 53 (60%) 12 (46%) 16 (76%) 70 (55%) 151 (57%) Age 32.0 (25.0, 38.0) 25.5 (21.0, 32.0) 29.0 (23.0, 35.0) 27.0 (24.0, 36.0) 29.0 (24.0, 37.0) p = 0.036 Smoking score -5.2 (-7.0, 0.9) -6.0 (-7.1, -4.1) -6.1 (-7.0, -1.5) -4.6 (-7.0, -2.0) -4.9 (-7.0, -1.6) p = 0.293 Diagnosis p < 0.001 BD 19 (21%) 9 (35%) 3 (14%) 55 (43%) 86 (33%) MDD 2 (2.2%) 1 (3.8%) 0 (0%) 8 (6.3%) 11 (4.2%) PSY 15 (17%) 4 (15%) 1 (4.8%) 27 (21%) 47 (18%) SCZ 53 (60%) 12 (46%) 17 (81%) 37 (29%) 119 (45%) Continuous variables are presented as median (Q1, Q3); categorical as n (%). P -values from Fisher’s exact test (Sex), Pearson´s Chi-squared test (Diagnosis), and Kruskal-Wallis test (continuous variables). DNAm Patterns Associated with Olanzapine, Quetiapine, and Risperidone Treatment We performed a meta-analysis of the three EWAS for AP serum (see Methods). The I² value ( p = 0.53) indicated low between-study variability 50 , supporting the use of a fixed-effects model. The Hausman test was significant ( p = 0.001), which may reflect the correlation between study-level effects and predictors in the random-effects model 51 . We identified 60 CpGs associated with AP serum values ( p ≤ 9 × 10 − 8 ) (SM Table 1 ). Amongst the 60 significant CpGs, 58% were located in open sea (inter-CpG island regions), 25% in shore regions (2 kb away from CpG island), 8% in shelf regions (2 kb away from CpG island shores) and 8% in CpG islands. Given the significant difference in median age between individuals treated with olanzapine (32 years) and quetiapine (25.5 years) (SM Table 2 ), we examined whether age was associated with methylation levels at the 60 CpGs identified in the meta-analysis, despite having adjusted for age in the EWAS models. Spearman´s rank correlation coefficients (ρ) between age and methylation levels ranged from − 0.1580 to 0.1406, with no CPGs reaching statistical significance after multiple testing correction. These results indicate that age was not strongly associated with methylation levels at these loci. Table 2 Significant DMRs (n = 7) associated with the common effects of olanzapine, quetiapine, and risperidone (adjusted p [Bonferroni] < 0.05). Model: 𝛽 ~ AP-serum + Sex + Age + Smoking Score + Psychiatric Diagnosis + Technical Batch Effect + Blood Cell counts Genomic location Gene* Effect size SE P -value CpGs in DMR CpGs with EWAS p < 0.05 Chr1: 1713950–1714271 GNB1 0.1558 0.0207 4.87 x 10 –14 4 4 Chr17: 9939895–9940321 GAS7 -0.1193 0.0164 3.44 x 10 –13 5 3 Chr10: 103455113–103455603 FBXW4 -0.1179 0.0164 7.23 x 10 –13 4 2 Chr19: 57019016–57019060 ZNF471 0.1185 0.0190 1.99 x 10 –12 3 2 Chr11: 70672835–70672878 SHANK2 -0.1632 0.0282 7.51 x 10 − 9 5 4 Chr16: 810803–811078 MSLN -0.1505 0.0266 1.48 x 10 − 8 6 4 Chr8: 130696056–130696161 CCDC26 0.0680 0.0125 5.90 x 10 − 8 3 2 Gene names : GNB1 : Guanine Nucleotide Binding Protein (G Protein), Beta GAS7 : Growth Arrest Specific Protein 7 FBXW4 : F-Box And WD Repeat Domain Containing 4 ZNF471 : Zinc Finger Protein 471 SHANK2 : SH3 And Multiple Ankyrin Repeat Domains 2 MSLN : Mesothelin CCDC26 : CCDC26 Long Non-Coding RNA Identification of differentially methylated regions (DMRs), in general, are considered to be a more robust method to identify meaningful and biologically relevant associations than a single point epigenome-wide association study 58 . We identified seven DMRs associated in the meta-analysis shared across the 3 AP serum values (Table 2 and Fig. 2 ). Visualization of the Association Between Significant DMRs and AP Serum Levels For each DMR, we visualized the dose-dependent relationship between AP serum levels and methylation levels. Given that synaptic dysfunction is increasingly recognized as a central mechanism in psychosis and a potential target of antipsychotic action 59 , we specifically visualized DMRs associated with synaptic function and signaling pathways (Fig. 3 ). For the SHANK2 DMR (Fig. 3 A), methylation levels decreased with increased serum values of olanzapine and quetiapine, whereas the direction of effect was opposite for risperidone. For the GNB1 DMR (Fig. 3 B), the methylation levels for all three APs increased with increasing serum values, although minimally for risperidone. For FBXW4 , as shown in Fig. 3 C, all three APs across the range of serum values presented methylation levels similar to unmedicated samples. Noticeable variation in methylation levels, however, was apparent for the FBXW4 DMR. For GAS7 (Fig. 3 D), quetiapine and risperidone had the same direction of effect on the DMR, while minimal difference was seen between olanzapine and medication-free. Visualization for the remaining DMRs can be found in SM Fig. 7. Comparison with DMPs and DMRs associated with SCZ Among the 60 CpGs associated with the shared effects of AP treatment, 24 were present in a recent meta-EWAS comparing the methylation levels of SCZ cases to healthy controls 60 . None of these 24 CpGs showed significant differences in methylation between cases and controls ( p > 0.05). None of the DMRs associated with the shared effects of AP treatment were among the DMRs significantly associated with a SCZ diagnosis. Functional Enrichment and Pathway Analysis Overrepresentation Analysis and Gene Set Enrichment Analysis were employed to GO terms enriched with significant DMPs and DMRs from our EWAS and DMR analyses. At the DMP level, 13 GO terms were significant after Benjamini-Hochberg correction (FDR-adjusted p < 0.05, see SM Table 3 and SM Fig. 8). Those GO terms were reduced to two Biological Process (BP) terms: double-strand break repair via homologous recombination (GO:0000724) and regulation of double-strand break repair (GO:2000779); and four Cellular Component (CC) terms: histone acetyltransferase complex (GO:0000123), nucleosome (GO:0000786), protein-DNA complex (GO:0032993), and acetyltransferase complex (GO:1902493). At the DMR level, no GO terms met the significance threshold for multiple testing correction. However, 55 enriched GO terms had nominal p-values ( p < 0.05), which were related to processes involved in synaptic function, including synaptic receptor adaptor activity (GO:0030160 ), ionotropic glutamate receptor binding (GO:0035255), structural constituent of postsynaptic density (GO:0098919) and SH3 domain binding (GO:0017124) (see complete list SM Table 4 and SM Fig. 9). Specific Treatment Effects of Olanzapine, Quetiapine, and Risperidone Associated with DNAm We also investigated DMPs and DMRs associated with AP-specific associations for olanzapine, quetiapine, and risperidone. Given the limited sample size for the AP EWAS, we conducted an epigenome-wide association study for each AP by comparing it against the other two APs and medication-free samples to increase statistical power. However, none of these AP-specific EWAS identified statistically significant DMPs or DMRs. For reference, we report the CpGs with nominal p -values ( p < 0.05, see SM Table 5–7), and DMRs with the lowest p -value for each AP (see SM Table 8). Discussion A novel aspect of our cross-sectional study was the use of AP serum concentrations to examine the associations of olanzapine, quetiapine, and risperidone with DNAm in individuals with SCZ, BP, and MDD. We identified significant DNAm changes in peripheral blood shared amongst the three APs, however, AP-specific changes did not withstand correction for multiple testing. These findings suggest potential biological shared effects of AP treatment on DNAm. Our discussion focuses primarily on DMRs, as they have been shown to be more statistically robust and biologically relevant 52 . DMRs associated with Shared DNAm Changes Following AP Treatment The seven genes annotated to the DMRs identified in the current study have previously been associated with psychiatric disorders. SHANK2 encodes a molecular scaffolding protein in the postsynaptic density of excitatory glutamatergic neurons 61 . SHANK2 expression, particularly in the prefrontal cortex and hippocampus, plays a critical role in cognitive function, mood regulation, and emotional processing in psychotic disorders and genetic variants of SHANK2 have been identified in psychotic disorders 62 – 65 . Recently, Holmgren et al . 66 reported elevated SHANK2 expression in post-mortem brain tissue from patients with BP compared to controls, although the effects of medication were not controlled. In our study, we controlled for psychiatric diagnosis as a proxy for variations in prescribed medication dosage. For SHANK2 (Fig. 3 A), we observed that methylation levels tended to decrease with increasing serum concentrations of olanzapine and quetiapine, whereas for risperidone, methylation levels increased with increasing serum levels. The DMR identified in an intergenic region on chromosome 1 may regulate GNB1 (guanine nucleotide-binding protein, beta 1) due to genomic proximity (SM Fig. 5). GNB1 encodes the beta subunit of the heterotrimeric G protein complex, which interacts with G protein-coupled receptors (GPCR). The GNB1- encoded 𝜷-subunit regulates signaling in dopaminergic, serotonergic, and adrenergic pathways, which, when dysregulated, negatively impact stress response, mood regulation, psychosis, and reward pathways in addiction 67 . A common direction of effect was observed for serum AP levels on DNAm in the GNB1 DMR (Fig. 3 B). Two DMRs were located in genes involved in the negative regulation of Wnt/β-catenin signaling FBXW4 (F-box and WD Repeat Domain Containing 4) and ZNF471 (zinc finger protein 471) 10 , which is a critical pathway for neurogenesis, synapse formation, neuroinflammation, and oxidative stress. Dysregulation of Wnt/β-catenin signaling has been implicated in psychotic disorders 68 . The mean methylation level of ZNF471 was 20% in medicated patients compared to 0.3% in the unmedicated patients (SM Fig. 7). For FBXW4 , minimal methylation differences were observed between medicated (66%) and unmedicated (63%) (Fig. 3 C). GAS7 (Growth Arrest Specific 7) is a key regulator of dendritic spine growth and is essential for synaptic plasticity and neuronal communication. Reduced GAS7 expression is associated with lower spine density 69 , and alterations in dendritic spine morphology are linked to disrupted synaptic connectivity and cognitive symptoms in psychotic disorders 70 . Multiple studies have reported reduced spine density and progressive brain volume changes associated with prolonged AP treatment 71 . In our study, mean GAS7 methylation levels were 58% in medicated patients versus 66% in unmedicated individuals. As seen in Fig. 3 D, variability in methylation levels was evident, as is often reported in open sea regions 72 . Mesothelin (MSLN) activates PI3K/AKT signaling 73 , a pathway central to glucose metabolism and insulin signaling and increasingly implicated in psychiatric disorders 74 . In treatment-naïve patients with SCZ, MSLN overexpression has been reported to normalize to control levels following AP treatment 75 . In our study, the MSLN DMR overlaps with a previously identified MSLN DMR in brain microglia from patients with mood disorders, although that study did not report medication effects 76 . The seventh DMR we identified was annotated to CCDC26 (coiled-coil domain-containing 26), a long non-coding RNA involved in retinoic acid modulation. Dysregulation of retinoic acid transport, metabolism, and signaling have been implicated in SCZ 77 . DMPs and Enrichment of GO terms Thirteen GO terms associated with the shared AP effects were related to biological processes for DNA repair, including double-strand break (DSB) repair via homologous recombination (GO:0000724) and regulation of DSB repair (GO:2000779). Impaired DSB repair mechanisms, commonly triggered by cellular stress, can contribute to neuronal dysfunction and altered gene expression. DNA damage due to oxidative stress has been implicated in psychosis 78 , while AP treatment may influence DSB repair regulation by modulating oxidative cellular stress responses and/or epigenetic regulation of repair-related genes 79 . Additionally, the histone acetyltransferase complex (GO:0000123), a key cellular component, is an important driver of epigenetic regulation. Altered histone acetylation patterns have been observed in psychosis and are associated with dysregulated gene expression in pathways that are critical for neuronal function and synaptic plasticity 80 . Furthermore, AP treatment effects linked to interactions with histone acetylation complexes have been reported, with some mechanisms appearing specific to certain AP and brain regions 81 . Finally, when comparing the associations of DMPs and DMRs with SCZ, none of the associations identified with the shared effects of AP were among the DNAm differences between cases with SCZ and healthy controls identified in earlier work 60 . These comparisons contribute to the separation of medication effects from diagnosis on differentially methylated loci, and suggest that the DMPs and DMRs identified in our study may not correspond to loci associated with SCZ, while the effects identified in the epigenome-wide association study of SCZ cases and controls were not due to medication. Limitations Sufficient statistical power has been a major challenge for DNAm studies of psychotic disorders and obtaining sufficient sample sizes for monotherapy is challenging due to the widespread practice of polypharmacy in psychiatry 82 , 83 . Although our study included a relatively small sample size, Bayesian statistical analyses provided evidence for shared AP treatment effects on DNAm, whereas the specific-effects model was underpowered. It is important to note that DNAm is tissue specific, and we report methylation changes assayed from peripheral blood that may affect the synaptic function of genes in the human brain. In addition, interindividual differences in DNAm may be influenced by genetic variations specific to pathophysiology rather than AP treatment. Although we adjusted for demographic and environmental risk factors, including age, sex, smoking, psychiatric diagnoses, technical artifacts, and differences in estimated blood cell proportions, we acknowledge that diet, exercise, and psychotherapeutic interventions also have effects on DNAm which we could not adjust for. Samples from healthy controls were not included in this study, as they would not have been exposed to AP. Finally, while we adjusted for sex in our models, we were unable to investigate well-known sex-specific effects associated with either AP 84 or DNAm 60 due to further sub setting and loss of statistical power. Nevertheless, none of the 60 CpGs associated with shared AP treatment effects was identified as sex-specific in the recent epigenome-wide association study of SCZ 60 . Conclusion This study identified seven DMRs associated with DNAm changes following AP treatment that were shared by olanzapine, quetiapine, and risperidone. A novel aspect of our approach was the use of AP serum concentrations to investigate the association between AP exposure and DNAm, a method not previously applied in EWAS of AP. By incorporating serum concentrations, our findings contribute to the growing body of evidence on AP-mediated DNAm changes, particularly in genes associated with synaptic function. Furthermore, our results highlight the importance of investigating how AP modulate DNA repair mechanisms and the epigenetic interplay between DNAm and histone acetylation, potentially advancing our understanding of their molecular mechanisms. Declarations Competing Interests OAA is a consultant to Cortechs.ai and Precision Health, and has received speaker’s honorarium from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion. The other authors declare no competing interests. Acknowledgements We are grateful to all participants in the TOP Study and the Norwegian Centre for Mental Disorders Research (NORMENT) biobank staff responsible for the blood sample collection. We acknowledge the contributions of the clinicians, research assistants, and hospital units involved in the recruitment and assessment of participants. The work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) ( [email protected] ). We thank the Research Council of Norway (RCN) (223273, 273446, 250299 and 300309) and Dr Einar Martens Research Group for Biological Psychiatry for funding, and the Faculty of Medicine at the University of Bergen for the PhD fellowship for JV. References Scott, K. M., Saha, S., Lim, C. C. W., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., et al. 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Zayats","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tetyana","middleName":"","lastName":"Zayats","suffix":""},{"id":575229852,"identity":"d7aa7102-149d-4f94-a9ec-930518a54196","order_by":5,"name":"Markos Tesfaye","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Markos","middleName":"","lastName":"Tesfaye","suffix":""},{"id":575229853,"identity":"689332c5-f22d-4512-8740-83bd518f27d8","order_by":6,"name":"Thomas Bjella","email":"","orcid":"https://orcid.org/0000-0002-4509-5133","institution":"University of Oslo \u0026 Oslo University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Bjella","suffix":""},{"id":575229854,"identity":"50bcc953-ff92-4aae-a777-80fa5256c602","order_by":7,"name":"Nils Eiel Steen","email":"","orcid":"https://orcid.org/0000-0001-6442-1179","institution":"Oslo University Hospital \u0026 University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"Eiel","lastName":"Steen","suffix":""},{"id":575229855,"identity":"b8513a66-1495-4c31-8850-c4a60a6a5011","order_by":8,"name":"Ingrid Melle","email":"","orcid":"https://orcid.org/0000-0002-9783-548X","institution":"Oslo University Hospital \u0026 Institute of Clinical Medicine, University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Ingrid","middleName":"","lastName":"Melle","suffix":""},{"id":575229856,"identity":"ec78431a-ddba-4e82-8942-8f5d3279e74a","order_by":9,"name":"Ole Andreasson","email":"","orcid":"","institution":"University of Oslo","correspondingAuthor":false,"prefix":"","firstName":"Ole","middleName":"","lastName":"Andreasson","suffix":""},{"id":575229857,"identity":"56e250d0-2e04-40c1-a439-ea385bc2d1b5","order_by":10,"name":"Vidar Steen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Vidar","middleName":"","lastName":"Steen","suffix":""}],"badges":[],"createdAt":"2025-12-08 15:29:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8309238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8309238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100689809,"identity":"1c866f2f-8219-47d2-a4bc-a9e6879bcfbf","added_by":"auto","created_at":"2026-01-20 13:47:44","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187792,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptVillarJD.docx","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/bd4a1fa84195b6dd963fe46b.docx"},{"id":100690253,"identity":"33512fce-c481-4cb0-815f-f84a6f3a35cc","added_by":"auto","created_at":"2026-01-20 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13:52:19","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":912884,"visible":true,"origin":"","legend":"","description":"","filename":"Fig1SampleSelection.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/08f42ed330ad2cb8a9ad8fb3.pdf"},{"id":100689998,"identity":"7dfb7d27-0459-40ca-a5ac-237147e0d832","added_by":"auto","created_at":"2026-01-20 13:48:52","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2003483,"visible":true,"origin":"","legend":"","description":"","filename":"Fig3DMRs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/e1672e2cdae03a191e4ca168.pdf"},{"id":100690035,"identity":"4f170db1-5d18-4485-b18a-322c3668ad1c","added_by":"auto","created_at":"2026-01-20 13:49:39","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1010539,"visible":true,"origin":"","legend":"","description":"","filename":"Fig2DMRQQpanel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/5ca18bedf6491a5a936513b6.pdf"},{"id":100690254,"identity":"f8e0e59e-73d9-40d3-b34d-a585d6ed819e","added_by":"auto","created_at":"2026-01-20 13:52:08","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":205737,"visible":true,"origin":"","legend":"","description":"","filename":"2025TP0028400structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/ec5e05f864d669f85ec010c0.xml"},{"id":100690036,"identity":"2e036e18-2a5e-463a-9850-160963fe2981","added_by":"auto","created_at":"2026-01-20 13:49:40","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":227097,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/b4c793060fb9ab5002b1c4e3.html"},{"id":100690242,"identity":"e80d7658-8ad0-40d9-91d1-2ff59bbe4ffb","added_by":"auto","created_at":"2026-01-20 13:51:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":241163,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of sample selection from a subset of the TOP cohort and subsequent grouping for the Shared Effects (n=136) or Specific Effects (n=263) analyses.\u003c/p\u003e","description":"","filename":"Fig1SampleSelection.png","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/1d4fa46755ea9d90dd960028.png"},{"id":100689988,"identity":"1a4667fc-68ae-4162-b47a-90b365604b3e","added_by":"auto","created_at":"2026-01-20 13:48:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":276689,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic location of DMRs associated with common effects and corresponding \u003cem\u003ep-\u003c/em\u003evalue distribution of DMPs.\u003cem\u003e \u003c/em\u003e(A) The Manhattan plot provides a visual representation of the genomic locations (x-axis) of the seven significant DMRs and corresponding p-values on a negative logarithmic scale (y-axis). The DMRs are represented as darker green lines and included one DMR overlapping approximately with the y-axis. (B) The inflation factor (lambda) approached 1.00 in the qq plot of observed vs. expected \u003cem\u003ep\u003c/em\u003e-values.\u003c/p\u003e","description":"","filename":"Fig2DMRQQpanel.png","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/2081a1f2a495ad05efe138fd.png"},{"id":100690258,"identity":"858bf7a6-4056-4cb9-8f82-844d8911d9c6","added_by":"auto","created_at":"2026-01-20 13:52:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":705592,"visible":true,"origin":"","legend":"\u003cp\u003eDose-dependent relationship between 𝛽-methylation levels (y) and log2-transformed\u003cstrong\u003e \u003c/strong\u003eserum concentrations (nmol/L)\u003cstrong\u003e \u003c/strong\u003e(x) for the DMRs identified in the Shared Effects model. A) \u003cem\u003eSHANK2\u003c/em\u003e, B) \u003cem\u003eGNB1\u003c/em\u003e, C) FBXW4 and D) \u003cem\u003eGAS7. \u003c/em\u003e\u0026nbsp;Blue: olanzapine, Teal green: risperidone, Orange: quetiapine, Red dotted line: medication-free.\u003c/p\u003e","description":"","filename":"Fig3DMRs.png","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/7f75c920dba81f5fb7f50d84.png"},{"id":100697043,"identity":"8177b64d-aa77-4ea4-aa09-eaa21e10a6d0","added_by":"auto","created_at":"2026-01-20 15:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2602964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/27f06d5b-04f6-4d49-90f8-6cd1e888972e.pdf"},{"id":100690008,"identity":"98a0d72c-f8c6-4a3b-81b6-63922fbd65ac","added_by":"auto","created_at":"2026-01-20 13:49:08","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":132277,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SMTablesVillar.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/21d73a50d9f5be22a3322704.xlsx"},{"id":100690105,"identity":"bff5265f-e7d8-48df-9ce2-d8f3cdc720c3","added_by":"auto","created_at":"2026-01-20 13:50:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1582467,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SMFigsVillar.docx","url":"https://assets-eu.researchsquare.com/files/rs-8309238/v1/1810c1391faf7f01fafc48ce.docx"}],"financialInterests":"\u003cb\u003eYes\u003c/b\u003e\nOle A. Andreassen is a consultant to Cortechs.ai and Precision Health, and has received speaker’s honorarium from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion. The other authors declare no competing interests.","formattedTitle":"DNA Methylation Changes Associated with Antipsychotic Serum Concentrations in Patients with Psychosis.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpisodes of psychosis can occur in several mental disorders, including schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorders (MDD). Psychotic episodes are characterized by symptoms such as hallucinations, delusions, disorganized speech, and social withdrawal. Timely and effective treatment of psychotic symptoms reduces the risk of a wide range of adverse health outcomes. Beyond psychiatric symptoms, individuals experiencing psychotic episodes have significantly higher odds of comorbid physical health burdens, independent of their mental disorders, according to the WHO World Health Mental Health Surveys\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe standard psychiatric approach to treating these disorders involves the use of antipsychotics (AP), mood stabilizer, and/or antidepressant medications. All APs including second-generation APs (SGAs), such as olanzapine, risperidone, and quetiapine, are typically effective in alleviating positive symptoms (e.g., hallucinations and delusions) during the acute phase of psychosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, they have been shown to be less effective in improving negative symptoms and cognitive deficits that may also be symptomatic of psychosis\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. AP treatment can also lead to adverse effects, such as weight gain and dyslipidemia. These side effects pose challenges for medication compliance, and poor medication adherence increases the risk of recurrent psychotic episodes in long-term care\u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Furthermore, poor adherence is associated with an increased risk of early mortality\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. To better understand the therapeutic benefits and potential risks of AP, it is essential to characterize their mechanisms of action.\u003c/p\u003e\u003cp\u003eMultiple genetic studies have implicated synaptic function, including neuronal synapses, synaptic dysfunction, and neurotransmitter imbalances, in the pathophysiology of psychiatric disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Genome-wide association studies (GWAS), for instance, have identified the dopamine receptor (\u003cem\u003eDRD2\u003c/em\u003e) as a key contributor to disease risk and as the primary target of antipsychotic drugs\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, evidence also supports the involvement of broader neurotransmitter disturbances beyond the dopaminergic system, implicating dysregulation of serotonergic, GABAergic, glutamatergic, and muscarinic signaling pathways\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. A combination of neurotransmitter disturbances between dopamine and GABA may be predictive of first-episode psychosis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, while AP treatment response has been associated with alterations in the relationship between cortical glutamate concentrations and striatal dopamine synthesis capacity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. The relationship between genetic risk, AP drug mechanisms, and treatment response is complex. Notably, many genetic risk variants identified through GWAS may act through biological pathways that are distinct from those underlying AP drug action or treatment response\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Importantly, the molecular mechanisms underlying AP treatment response remain incompletely understood.\u003c/p\u003e\u003cp\u003eDNA methylation (DNAm) is an epigenetic mechanism that regulates gene expression by modulating the binding affinity of transcriptional machinery and through interactions with microRNAs, histone modifications, and chromatin structure. Studies of psychotropic drugs prescribed for the treatment of mood disorders such as antidepressants, lithium, and valproic acid have identified DNAm alterations associated with treatment response\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, DNAm profiles obtained from peripheral blood have identified differentially methylated positions (DMPs) in genes implicated in mood regulation and neuroimmune signaling such as \u003cem\u003eBDNF\u003c/em\u003e, \u003cem\u003eSLC6A4\u003c/em\u003e, \u003cem\u003eHTR1A\u003c/em\u003e, \u003cem\u003eHTR1B\u003c/em\u003e, and \u003cem\u003eIL11\u003c/em\u003e\u003csup\u003e21,22\u003c/sup\u003e. Differentially methylated regions (DMRs) related to synaptic function and drug metabolism (\u003cem\u003eSORBS2\u003c/em\u003e and \u003cem\u003eCYP2C18\u003c/em\u003e) have also been linked to antidepressant response\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In patients with BD, DMRs enriched for genes involved in neuronal function have been shown to distinguish excellent lithium responders from non-responders\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moreover, global DNAm levels differ depending on whether lithium is administered as monotherapy or in combination with valproic acid\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDNAm studies of AP range from candidate-gene studies to exploratory epigenome-wide methylation studies. A comparative study of olanzapine, risperidone, quetiapine, and mood stabilizers reported hypermethylation in genes involved in glucose uptake (\u003cem\u003eAKT1\u003c/em\u003e and \u003cem\u003eAKT2\u003c/em\u003e) associated with AP treatment\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. In longitudinal studies, risperidone and olanzapine treatment have been associated with normalization of \u003cem\u003eIL-6\u003c/em\u003e methylation levels toward those of controls\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Similarly, in a longitudinal study of first-episode psychosis (FEP) patients treated with risperidone, more than 80% of the differential DNAm changes were normalized to control levels\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Quetiapine monotherapy has been associated with methylation changes in a network of genes implicated in neurogenesis in patients with BP\u003csup\u003e20\u003c/sup\u003e. In rodents, DNAm alterations in genes related to dopaminergic neurotransmission in the hippocampus have been observed following olanzapine monotherapy\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. It is important to note, however, that Epigenome-Wide Association Studies (EWAS) of APs are often limited by small sample sizes, and relatively few human studies of AP monotherapy have been conducted compared with animal or cell models.\u003c/p\u003e\u003cp\u003ePreviously, we identified DNAm differences associated with SCZ and exposure to environmental risk factors for SCZ\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. In this study, we hypothesized that medication effects may contribute to the alterations in DNAm often reported in EWAS of psychiatric disorders. Individual medication effects are particularly challenging to identify due to the common use of polypharmacy, where patients are often treated with a combination of AP, antidepressants, and mood-stabilizing drugs. Consequently, in EWAS, the effects of AP treatment are often addressed by including AP use as a binary or categorical covariate, or more frequently, through surrogate variable correction. However, these approaches may insufficiently capture interindividual variability in drug exposure. To address this limitation, we focused on individuals receiving AP monotherapy and evaluated serum drug concentrations, which more accurately reflect systemic drug exposure than prescribed dosages. This approach allowed us to assess the relationship between DNAm and AP exposure under naturalistic conditions thus reflecting real-world variability in medication adherence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe current study analysed data from a subset of 263 individuals diagnosed with SCZ (n\u0026thinsp;=\u0026thinsp;119), BP (n\u0026thinsp;=\u0026thinsp;86), MDD (n\u0026thinsp;=\u0026thinsp;11), and Other Psychotic Disorders (n\u0026thinsp;=\u0026thinsp;47) recruited through the Thematically Organised Psychosis (TOP) study cohort in Oslo, Norway. Diagnoses were established using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) with interviews conducted by clinical psychologists or medical doctors with psychiatric training, yielding a DSM-IV diagnostic reliability of 82%\u003csup\u003e33\u003c/sup\u003e. Diagnostic categories included DSM-VI codes for SCZ (295.1-295.9), BP (296.02-296.89), MDD (296.24-296.36, 296.9-296.94), and Other Psychotic Disorders (297.1, 298.8/9). A history of psychosis was obtained from SCID-1 and clinical interviews as previously described\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. The study was approved by the local Regional Committee for Medical and Health Research Ethics \u0026ndash; Region West (REK2009/2485-91) and all participants provided written informed consent.\u003c/p\u003e\u003cp\u003eFor the analyses in this study, we selected three of the most commonly prescribed atypical antipsychotics in Norway: olanzapine, quetiapine, and risperidone. Pharmacological treatment data were obtained from medical records and patient interviews to select only individuals prescribed these drugs. Antipsychotic serum concentrations were analyzed at the Department of Clinical Pharmacology, St. Olav\u0026acute;s Hospital, Trondheim to confirm adherence and identify non-compliance\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Only individuals adhering to antipsychotic monotherapy, as determined by measurable serum concentrations\u0026thinsp;\u0026gt;\u0026thinsp;0 nmol/L) were selected. While the effects of APs on DNAm may be dose-dependent, recorded dosages may not accurately reflect individual adherence. Therefore, we examined the relationship between measured serum levels and self-reported compliance. We compared the range of the participants\u0026acute; serum concentrations that represented 100% adherence to the recommended therapeutic ranges\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. (See Supplementary Materials (SM) Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for distribution of serum concentrations and SM Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for distribution of serum concentrations versus compliance). Additionally, a medication-free group consisting of individuals with no detectable medication levels were identified (n\u0026thinsp;=\u0026thinsp;131). The total sample (n\u0026thinsp;=\u0026thinsp;263) was subsequently grouped for the analyses, with exclusion criteria detailed in the flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEpigenome-wide DNAm Profiling for EWAS Analyses\u003c/h2\u003e\u003cp\u003eDNA was extracted from peripheral blood samples at the time of inclusion in the TOP study and was assayed on the Infinium\u0026reg; MethylationEPIC BeadChip v1 (Illumina Inc., CA, USA), which interrogates over 850K CpGs across the genome and quantifies the methylation levels at those sites. The typing was performed at the Life and Brain Genomics GmBH, Bonn, Germany.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePre-processing and Quality Control (QC) of Methylation Data\u003c/h3\u003e\n\u003cp\u003eDetails of the quality control (QC) and preprocessing of the methylation data have been described previously\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Briefly, the TOP methylation dataset is comprised of three batches collected over three years: 1,000 samples run in 2016, 283 in 2017, and 1082 in 2019. QC and preprocessing of the data were conducted separately for each batch. Probes and samples that failed QC checks, as well as sex chromosome probes, were removed. Functional normalization was applied to reduce non-biological variation and normalize the measure of methylation. The data from the three batches was then merged, and batch effects from identified technical sources were removed with \u003cb\u003eCombat\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Overall,1818 samples with self-reported European ancestry were selected. Bioconductor (version 3.1.1) packages \u003cb\u003eminfi\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, \u003cb\u003ewateRmelon\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, \u003cb\u003esva\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and \u003cb\u003eChAMP\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e were utilized for QC and data preprocessing. Additionally, six probes that later failed the meta-analysis were removed. The final data set consisted of 263 samples and 760,662 probes.\u003c/p\u003e\u003cp\u003eBlood cell-type proportions for six constituent cell types (monocytes, neutrophils, natural killer cells (NK), CD4\u003csup\u003e+\u003c/sup\u003eT, CD8\u003csup\u003e+\u003c/sup\u003eT, and B-cells) were estimated from DNAm data using the Houseman algorithm\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e as implemented by the \u003cem\u003eestimateCellCounts2\u003c/em\u003e function in the \u003cb\u003eFlowSorted.Blood.Epic\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Bioconductor package. The algorithm for Identifying Optimal Libraries (IDOL) was selected as the reference. Given that tobacco smoking is associated with altered white blood cell composition\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and subsequent alterations in methylation levels\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, smoking scores were calculated based on the method described by Elliott \u003cem\u003eet al.\u003c/em\u003e\u003csup\u003e46\u003c/sup\u003e to account for any possible effects of smoking.\u003c/p\u003e\n\u003ch3\u003eEpigenome-Wide Association Studies\u003c/h3\u003e\n\u003cp\u003e\u003cb\u003eShared DNAm Changes Associated with Olanzapine, Quetiapine and Risperidone Treatment.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSamples from patients adhering to monotherapy (n\u0026thinsp;=\u0026thinsp;136) with serum concentrations\u0026thinsp;\u0026gt;\u0026thinsp;0 nmol/L were selected with similar ages (median age\u0026thinsp;=\u0026thinsp;30.0 years) and sex (57% male). An epigenome-wide study was conducted for each monotherapy before performing a meta-analysis of all three: (olanzapine (n\u0026thinsp;=\u0026thinsp;89), quetiapine (n\u0026thinsp;=\u0026thinsp;26), and risperidone (n\u0026thinsp;=\u0026thinsp;21)). The R package \u003cb\u003elimma\u003c/b\u003e (version 3.62.2)\u003csup\u003e47\u003c/sup\u003e was used to perform the linear regression with \u003cem\u003ep\u003c/em\u003e-values calculated using empirical Bayes moderated t-statistics. An epigenome-wide significance threshold was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e as recommended by Mansell \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e48\u003c/sup\u003e. Quantile-quantile (qq) plots and the corresponding inflation statistic (lambda ƛ) were evaluated to assess potential effects of technical artifacts (see SM Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We modeled DNAm levels at each CpG site using the following linear model where β represents beta methylation values at a given CpG site, AP is the serum concentration of the medication, Sex, Age, Smoking score, and Psychiatric Diagnosis are individual level covariates, Technical Batch corresponds to the batch effect from the Ilumina Scanner ID, Blood cell-type corresponds to estimated proportions of major blood cells types (monocytes, neutrophiles, NK, CD4\u0026thinsp;+\u0026thinsp;T, CD8\u0026thinsp;+\u0026thinsp;T, and B- cells), and ɛ denotes residual error.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eΒ\u0026thinsp;~\u0026thinsp;AP\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Smoking Score\u0026thinsp;+\u0026thinsp;Psychiatric Diagnosis\u0026thinsp;+\u0026thinsp;Technical Batch\u0026thinsp;+\u0026thinsp;Blood Cell-type + ɛ\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubsequently, a meta-analysis of the three individual EWAS was conducted using the \u0026ldquo;metagen()\u0026rdquo; function from the R package \u003cb\u003emeta\u003c/b\u003e (v.8.1-0\u003cem\u003e)\u003c/em\u003e\u003csup\u003e49\u003c/sup\u003e. A fixed-effects model was used to pool standardized effect estimates across cohorts, based on the assumption that AP effects operate through a shared pharmacological mechanism, primarily via the dopamine D2 receptor (\u003cem\u003eDRD2\u003c/em\u003e) binding. \u003cem\u003eP\u003c/em\u003e-values reflect two-sided z-tests evaluating whether the pooled effect differs significantly from zero. Inverse-variance weighting was applied using standard errors, and heterogeneity was assessed using the I\u0026sup2; statistic\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Model selected was guided by theoretical considerations and formally evaluated using the Hausman test\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, which assess whether the fixed- and random-effects models produce systematically different estimates.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSpecific DNAm Changes Associated with Olanzapine, Quetiapine and Risperidone Treatment Separately.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the relatively small number of patients adhering to monotherapy with serum values\u0026thinsp;\u0026gt;\u0026thinsp;0 nmol/L, binary analyses were also performed, comparing each AP (coded as yes/no) against the other APs, supplemented by a sample of medication-free patients (n\u0026thinsp;=\u0026thinsp;127) to increase statistical power. The comparison groups were as follows:\u003c/p\u003e\u003cp\u003eolanzapine (n\u0026thinsp;=\u0026thinsp;89) versus \u0026ldquo;quetiapine\u0026thinsp;+\u0026thinsp;risperidone\u0026thinsp;+\u0026thinsp;medication-free\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;174), quetiapine (n\u0026thinsp;=\u0026thinsp;26) versus \u0026ldquo;olanzapine\u0026thinsp;+\u0026thinsp;risperidone\u0026thinsp;+\u0026thinsp;medication-free\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;237), and risperidone (n\u0026thinsp;=\u0026thinsp;21) versus \u0026ldquo;olanzapine\u0026thinsp;+\u0026thinsp;quetiapine\u0026thinsp;+\u0026thinsp;medication-free\u0026rdquo; (n\u0026thinsp;=\u0026thinsp;242).\u003c/p\u003e\u003cp\u003eAge differences (median age\u0026thinsp;=\u0026thinsp;29.0 years) between AP subgroups and medication-free individuals, were assessed with an ANOVA test, followed by Kruskal-Wallace rank sum test and Dunn\u0026acute;s post-hoc pairwise comparisons. Differences in sex distribution (57% male) across groups were assessed using Pearson\u0026acute;s chi-square test. \u003cem\u003eP\u003c/em\u003e-values were calculated as previously described for the common effects analysis, with QQ-plots presented in SM Fig.\u0026nbsp;4. We modeled DNAm levels at each CpG site using the following linear model where β represents beta methylation values at a given CpG site, AP is the binary variable Yes/No for a given AP, No for Med-Free, Sex, Age, Smoking score, and Psychiatric Diagnosis are individual level covariates, Technical Batch corresponds to the batch effect from the Ilumina Scanner ID, Blood cell-type corresponds to estimated proportions of major blood cells types (monocytes, neutrophiles, NK, CD4\u0026thinsp;+\u0026thinsp;T, CD8\u0026thinsp;+\u0026thinsp;T, and B- cells), and ɛ denotes residual error.\u003c/p\u003e\u003cp\u003e\u003cb\u003eβ\u0026thinsp;~\u0026thinsp;AP vs. (2AP\u0026thinsp;+\u0026thinsp;Med-Free)\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Smoking Score\u0026thinsp;+\u0026thinsp;Psychiatric Diagnosis\u0026thinsp;+\u0026thinsp;Technical Batch Effect\u0026thinsp;+\u0026thinsp;Blood Cell-type + ɛ\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eDifferentially Methylated Regions (DMRs)\u003c/h3\u003e\n\u003cp\u003eTo evaluate the combined effect of DNAm across multiple CpG sites, we conducted a regional analysis using the R package \u003cb\u003edmrff\u003c/b\u003e \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The \u003cb\u003edmrff\u003c/b\u003e algorithm integrates groups of CpGs to enhance statistical power in detecting associations, based on the premise that co-methylated CpGs in close proximity exert combined or related biological effects\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. \u003cb\u003edmrff\u003c/b\u003e identifies candidate DMRs by clustering CPG sites with nominal EWAS \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 within 500 base pairs of each other and sharing a consistent direction of effect. For each candidate region, multiple overlapping subregions are tested, and only the strongest, non-overlapping subregions are retained in the final output. To control for multiple testing, we set the significance threshold to \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;6.134 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e \u003cem\u003e(\u003c/em\u003e\u0026#120630;=0.05/815,168 subregions). Regions with raw \u003cem\u003ep\u003c/em\u003e-values below this threshold were considered significant. To enhance the reliability and biological interpretability of the results, we retained only regions composed of at least three CpG sites.\u003c/p\u003e\u003cp\u003eAnnotation of DMRs was performed using the \u003cb\u003eIllumina Human Methylation EPIC annotation library (ilm10b2.hg19)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. However, manual annotation was performed using the UCSC genome browser (GRCH37/hg19) to assign nearest genes to the genomic regions chr1: 1713950\u0026ndash;1714271 and chr8: 130696056\u0026ndash;130696161, assigned to \u003cem\u003eGNB1\u003c/em\u003e and \u003cem\u003eCCDC26\u003c/em\u003e, respectively (SM Fig.\u0026nbsp;5 and Fig.\u0026nbsp;6).\u003c/p\u003e\n\u003ch3\u003eFunctional Enrichment\u003c/h3\u003e\n\u003cp\u003eWe investigated functionally enriched Gene Ontology (GO) terms associated with DMPs and DMRs using two complementary Bioconductor packages designed for Illumina methylation arrays: \u003cb\u003emissMethyl\u003c/b\u003e (v1.36.0)\u003csup\u003e55\u003c/sup\u003e and \u003cb\u003emethylGSA\u003c/b\u003e (v1.24.0)\u003csup\u003e56\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor the analyses of DMPs, we used the \u0026ldquo;gometh()\u0026rdquo; function from \u003cb\u003emissMethyl\u003c/b\u003e, which performs a modified hypergeometric test incorporating a probability weighting function (PWF). The PWF adjusts for bias introduced by the varying number of CpG probes per gene, thereby reducing false positives driven by gene size or probe density.\u003c/p\u003e\u003cp\u003eIn parallel, we used the \u0026ldquo;methylglm()\u0026rdquo; function from \u003cb\u003emethylGSA\u003c/b\u003e to test for GO term overrepresentation among the 32 annotated DMPs. This method fits a logistic regression model comparing genes associated with significant DMPs to all genes represented on the array, while adjusting for gene-specific CpG coverage. GO terms with FDR-adjusted (Benjamini-Hochberg) p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e\u003cp\u003eFor the analyses of DMRs, we used the \u0026ldquo;goregion()\u0026rdquo; function from \u003cb\u003emissMethyl\u003c/b\u003e, which maps CpG probes within differentially methylated regions to genes before performing GO enrichment analysis. This method accounts for probe density bias, as implemented in \u0026ldquo;gometh()\u0026rdquo;, and reports significant GO terms at FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eGO terms identified by these analyses were further processed using \u003cb\u003eRe\u003c/b\u003educe and \u003cb\u003eV\u003c/b\u003eisualize \u003cb\u003eG\u003c/b\u003eene \u003cb\u003eO\u003c/b\u003entology \u003cb\u003eREVIGO\u003c/b\u003e (v1.8.1)\u003csup\u003e57\u003c/sup\u003e for semantic similarity reduction. Using the \u003cem\u003eHomo sapiens\u003c/em\u003e reference and default settings, we clustered similar GO terms and selected representative terms summarizing the shared biological themes of each cluster. We also computed composite scores for each term based on \u003cb\u003eREVIGO\u0026acute;s\u003c/b\u003e dispensability, frequency, and uniqueness metrics, which were used solely for generating the supplementary visualizations (SM Fig.\u0026nbsp;5\u0026ndash;6 and SM Tables\u0026nbsp;3\u0026ndash;4).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eCohort Characteristics\u003c/h2\u003e\u003cp\u003eDemographics of the subset of the TOP cohort selected for the present study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Age differed significantly (Kruskal-Wallis \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036), with the olanzapine group being older on average. In addition, diagnostic distribution differed significantly across treatment groups (χ\u0026sup2;(9)\u0026thinsp;=\u0026thinsp;33.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) with schizophrenia being the most common diagnosis in all groups and most prevalent in the risperidone group (81%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographics of the subset of TOP cohort samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOlanzapine \u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuetiapine \u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRisperidone \u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMed-Free\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOverall \u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.187\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e70 (55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e151 (57%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.0 (25.0, 38.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.5 (21.0, 32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.0 (23.0, 35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.0 (24.0, 36.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.0 (24.0, 37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.036\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-5.2 (-7.0, 0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-6.0 (-7.1, -4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-6.1 (-7.0, -1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.6 (-7.0, -2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-4.9 (-7.0, -1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.293\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnosis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (14%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86 (33%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMDD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8 (6.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11 (4.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePSY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15 (17%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (4.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27 (21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47 (18%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCZ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53 (60%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (81%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37 (29%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e119 (45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eContinuous variables are presented as median (Q1, Q3); categorical as n (%). \u003cem\u003eP\u003c/em\u003e-values from Fisher\u0026rsquo;s exact test (Sex), Pearson\u0026acute;s Chi-squared test (Diagnosis), and Kruskal-Wallis test (continuous variables).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDNAm Patterns Associated with Olanzapine, Quetiapine, and Risperidone Treatment\u003c/h3\u003e\n\u003cp\u003eWe performed a meta-analysis of the three EWAS for AP serum (see Methods). The I\u0026sup2; value (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53) indicated low between-study variability\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, supporting the use of a fixed-effects model. The Hausman test was significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), which may reflect the correlation between study-level effects and predictors in the random-effects model\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe identified 60 CpGs associated with AP serum values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;9 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) (SM Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Amongst the 60 significant CpGs, 58% were located in open sea (inter-CpG island regions), 25% in shore regions (2 kb away from CpG island), 8% in shelf regions (2 kb away from CpG island shores) and 8% in CpG islands. Given the significant difference in median age between individuals treated with olanzapine (32 years) and quetiapine (25.5 years) (SM Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), we examined whether age was associated with methylation levels at the 60 CpGs identified in the meta-analysis, despite having adjusted for age in the EWAS models. Spearman\u0026acute;s rank correlation coefficients (ρ) between age and methylation levels ranged from \u0026minus;\u0026thinsp;0.1580 to 0.1406, with no CPGs reaching statistical significance after multiple testing correction. These results indicate that age was not strongly associated with methylation levels at these loci.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSignificant DMRs (n\u0026thinsp;=\u0026thinsp;7) associated with the common effects of olanzapine, quetiapine, and risperidone (adjusted \u003cem\u003ep\u003c/em\u003e [Bonferroni]\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eModel: \u0026#120573; ~ AP-serum\u0026thinsp;+\u0026thinsp;Sex\u0026thinsp;+\u0026thinsp;Age\u0026thinsp;+\u0026thinsp;Smoking Score\u0026thinsp;+\u0026thinsp;Psychiatric Diagnosis\u0026thinsp;+\u0026thinsp;Technical Batch Effect\u0026thinsp;+\u0026thinsp;Blood Cell counts\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eGenomic location\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGene*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEffect size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCpGs in DMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eCpGs with EWAS p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr1:\u003c/p\u003e\u003cp\u003e1713950\u0026ndash;1714271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGNB1\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.87 x 10\u003csup\u003e\u0026ndash;14\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr17:\u003c/p\u003e\u003cp\u003e9939895\u0026ndash;9940321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eGAS7\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.44 x 10\u003csup\u003e\u0026ndash;13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr10:\u003c/p\u003e\u003cp\u003e103455113\u0026ndash;103455603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFBXW4\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.23 x 10\u003csup\u003e\u0026ndash;13\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr19:\u003c/p\u003e\u003cp\u003e57019016\u0026ndash;57019060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eZNF471\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.99 x 10\u003csup\u003e\u0026ndash;12\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr11:\u003c/p\u003e\u003cp\u003e70672835\u0026ndash;70672878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eSHANK2\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1632\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.51 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr16:\u003c/p\u003e\u003cp\u003e810803\u0026ndash;811078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMSLN\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.1505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.48 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eChr8:\u003c/p\u003e\u003cp\u003e130696056\u0026ndash;130696161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eCCDC26\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.90 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGene names\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e\u003cem\u003eGNB1\u003c/em\u003e: Guanine Nucleotide Binding Protein (G Protein), Beta\u003c/p\u003e\u003cp\u003e\u003cem\u003eGAS7\u003c/em\u003e: Growth Arrest Specific Protein 7\u003c/p\u003e\u003cp\u003e\u003cem\u003eFBXW4\u003c/em\u003e: F-Box And WD Repeat Domain Containing 4\u003c/p\u003e\u003cp\u003e\u003cem\u003eZNF471\u003c/em\u003e: Zinc Finger Protein 471\u003c/p\u003e\u003cp\u003e\u003cem\u003eSHANK2\u003c/em\u003e: SH3 And Multiple Ankyrin Repeat Domains 2\u003c/p\u003e\u003cp\u003e\u003cem\u003eMSLN\u003c/em\u003e: Mesothelin\u003c/p\u003e\u003cp\u003e\u003cem\u003eCCDC26\u003c/em\u003e: CCDC26 Long Non-Coding RNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIdentification of differentially methylated regions (DMRs), in general, are considered to be a more robust method to identify meaningful and biologically relevant associations than a single point epigenome-wide association study\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. We identified seven DMRs associated in the meta-analysis shared across the 3 AP serum values (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eVisualization of the Association Between Significant DMRs and AP Serum Levels\u003c/h2\u003e\u003cp\u003eFor each DMR, we visualized the dose-dependent relationship between AP serum levels and methylation levels. Given that synaptic dysfunction is increasingly recognized as a central mechanism in psychosis and a potential target of antipsychotic action\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, we specifically visualized DMRs associated with synaptic function and signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For the \u003cem\u003eSHANK2\u003c/em\u003e DMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), methylation levels decreased with increased serum values of olanzapine and quetiapine, whereas the direction of effect was opposite for risperidone. For the \u003cem\u003eGNB1\u003c/em\u003e DMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), the methylation levels for all three APs increased with increasing serum values, although minimally for risperidone. For \u003cem\u003eFBXW4\u003c/em\u003e, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, all three APs across the range of serum values presented methylation levels similar to unmedicated samples. Noticeable variation in methylation levels, however, was apparent for the \u003cem\u003eFBXW4\u003c/em\u003e DMR. For \u003cem\u003eGAS7\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), quetiapine and risperidone had the same direction of effect on the DMR, while minimal difference was seen between olanzapine and medication-free. Visualization for the remaining DMRs can be found in SM Fig.\u0026nbsp;7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eComparison with DMPs and DMRs associated with SCZ\u003c/h2\u003e\u003cp\u003eAmong the 60 CpGs associated with the shared effects of AP treatment, 24 were present in a recent meta-EWAS comparing the methylation levels of SCZ cases to healthy controls\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. None of these 24 CpGs showed significant differences in methylation between cases and controls (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). None of the DMRs associated with the shared effects of AP treatment were among the DMRs significantly associated with a SCZ diagnosis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFunctional Enrichment and Pathway Analysis\u003c/h2\u003e\u003cp\u003eOverrepresentation Analysis and Gene Set Enrichment Analysis were employed to GO terms enriched with significant DMPs and DMRs from our EWAS and DMR analyses.\u003c/p\u003e\u003cp\u003eAt the DMP level, 13 GO terms were significant after Benjamini-Hochberg correction (FDR-adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, see SM Table\u0026nbsp;3 and SM Fig.\u0026nbsp;8). Those GO terms were reduced to two Biological Process (BP) terms: \u003cem\u003edouble-strand break repair via homologous recombination\u003c/em\u003e (GO:0000724) and \u003cem\u003eregulation of double-strand break repair\u003c/em\u003e (GO:2000779); and four Cellular Component (CC) terms: \u003cem\u003ehistone acetyltransferase complex\u003c/em\u003e (GO:0000123), \u003cem\u003enucleosome\u003c/em\u003e (GO:0000786), \u003cem\u003eprotein-DNA complex\u003c/em\u003e (GO:0032993), and \u003cem\u003eacetyltransferase complex\u003c/em\u003e (GO:1902493).\u003c/p\u003e\u003cp\u003eAt the DMR level, no GO terms met the significance threshold for multiple testing correction. However, 55 enriched GO terms had nominal p-values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), which were related to processes involved in synaptic function, including \u003cem\u003esynaptic receptor adaptor activity\u003c/em\u003e (GO:0030160\u003cem\u003e), ionotropic glutamate receptor binding\u003c/em\u003e (GO:0035255), \u003cem\u003estructural constituent of postsynaptic density\u003c/em\u003e (GO:0098919) and \u003cem\u003eSH3 domain binding\u003c/em\u003e (GO:0017124) (see complete list SM Table\u0026nbsp;4 and SM Fig.\u0026nbsp;9).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSpecific Treatment Effects of Olanzapine, Quetiapine, and Risperidone Associated with DNAm\u003c/h2\u003e\u003cp\u003eWe also investigated DMPs and DMRs associated with AP-specific associations for olanzapine, quetiapine, and risperidone. Given the limited sample size for the AP EWAS, we conducted an epigenome-wide association study for each AP by comparing it against the other two APs and medication-free samples to increase statistical power. However, none of these AP-specific EWAS identified statistically significant DMPs or DMRs. For reference, we report the CpGs with nominal \u003cem\u003ep\u003c/em\u003e-values (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, see SM Table\u0026nbsp;5\u0026ndash;7), and DMRs with the lowest \u003cem\u003ep\u003c/em\u003e-value for each AP (see SM Table\u0026nbsp;8).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eA novel aspect of our cross-sectional study was the use of AP serum concentrations to examine the associations of olanzapine, quetiapine, and risperidone with DNAm in individuals with SCZ, BP, and MDD. We identified significant DNAm changes in peripheral blood shared amongst the three APs, however, AP-specific changes did not withstand correction for multiple testing. These findings suggest potential biological shared effects of AP treatment on DNAm. Our discussion focuses primarily on DMRs, as they have been shown to be more statistically robust and biologically relevant \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eDMRs associated with Shared DNAm Changes Following AP Treatment\u003c/h2\u003e\u003cp\u003eThe seven genes annotated to the DMRs identified in the current study have previously been associated with psychiatric disorders. \u003cem\u003eSHANK2\u003c/em\u003e encodes a molecular scaffolding protein in the postsynaptic density of excitatory glutamatergic neurons\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eSHANK2\u003c/em\u003e expression, particularly in the prefrontal cortex and hippocampus, plays a critical role in cognitive function, mood regulation, and emotional processing in psychotic disorders and genetic variants of \u003cem\u003eSHANK2\u003c/em\u003e have been identified in psychotic disorders\u003csup\u003e\u003cspan additionalcitationids=\"CR63 CR64\" citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Recently, Holmgren \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e66\u003c/sup\u003e reported elevated \u003cem\u003eSHANK2\u003c/em\u003e expression in post-mortem brain tissue from patients with BP compared to controls, although the effects of medication were not controlled. In our study, we controlled for psychiatric diagnosis as a proxy for variations in prescribed medication dosage. For \u003cem\u003eSHANK2\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), we observed that methylation levels tended to decrease with increasing serum concentrations of olanzapine and quetiapine, whereas for risperidone, methylation levels increased with increasing serum levels.\u003c/p\u003e\u003cp\u003eThe DMR identified in an intergenic region on chromosome 1 may regulate \u003cem\u003eGNB1\u003c/em\u003e (guanine nucleotide-binding protein, beta 1) due to genomic proximity (SM Fig.\u0026nbsp;5). \u003cem\u003eGNB1\u003c/em\u003e encodes the beta subunit of the heterotrimeric G protein complex, which interacts with G protein-coupled receptors (GPCR). The \u003cem\u003eGNB1-\u003c/em\u003eencoded \u0026#120631;-subunit regulates signaling in dopaminergic, serotonergic, and adrenergic pathways, which, when dysregulated, negatively impact stress response, mood regulation, psychosis, and reward pathways in addiction\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. A common direction of effect was observed for serum AP levels on DNAm in the \u003cem\u003eGNB1\u003c/em\u003e DMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003eTwo DMRs were located in genes involved in the negative regulation of Wnt/β-catenin signaling \u003cem\u003eFBXW4\u003c/em\u003e (F-box and WD Repeat Domain Containing 4) and \u003cem\u003eZNF471\u003c/em\u003e (zinc finger protein 471)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, which is a critical pathway for neurogenesis, synapse formation, neuroinflammation, and oxidative stress. Dysregulation of Wnt/β-catenin signaling has been implicated in psychotic disorders\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. The mean methylation level of \u003cem\u003eZNF471\u003c/em\u003e was 20% in medicated patients compared to 0.3% in the unmedicated patients (SM Fig.\u0026nbsp;7). For \u003cem\u003eFBXW4\u003c/em\u003e, minimal methylation differences were observed between medicated (66%) and unmedicated (63%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003cem\u003eGAS7\u003c/em\u003e (Growth Arrest Specific 7) is a key regulator of dendritic spine growth and is essential for synaptic plasticity and neuronal communication. Reduced \u003cem\u003eGAS7\u003c/em\u003e expression is associated with lower spine density\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, and alterations in dendritic spine morphology are linked to disrupted synaptic connectivity and cognitive symptoms in psychotic disorders\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Multiple studies have reported reduced spine density and progressive brain volume changes associated with prolonged AP treatment\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. In our study, mean \u003cem\u003eGAS7\u003c/em\u003e methylation levels were 58% in medicated patients versus 66% in unmedicated individuals. As seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, variability in methylation levels was evident, as is often reported in open sea regions\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMesothelin \u003cem\u003e(MSLN)\u003c/em\u003e activates PI3K/AKT signaling\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, a pathway central to glucose metabolism and insulin signaling and increasingly implicated in psychiatric disorders\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. In treatment-na\u0026iuml;ve patients with SCZ, \u003cem\u003eMSLN\u003c/em\u003e overexpression has been reported to normalize to control levels following AP treatment\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. In our study, the \u003cem\u003eMSLN\u003c/em\u003e DMR overlaps with a previously identified \u003cem\u003eMSLN\u003c/em\u003e DMR in brain microglia from patients with mood disorders, although that study did not report medication effects\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe seventh DMR we identified was annotated to \u003cem\u003eCCDC26\u003c/em\u003e (coiled-coil domain-containing 26), a long non-coding RNA involved in retinoic acid modulation. Dysregulation of retinoic acid transport, metabolism, and signaling have been implicated in SCZ\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eDMPs and Enrichment of GO terms\u003c/h2\u003e\u003cp\u003eThirteen GO terms associated with the shared AP effects were related to biological processes for DNA repair, including \u003cem\u003edouble-strand break (DSB) repair via homologous recombination\u003c/em\u003e (GO:0000724) and regulation \u003cem\u003eof DSB repair\u003c/em\u003e (GO:2000779). Impaired DSB repair mechanisms, commonly triggered by cellular stress, can contribute to neuronal dysfunction and altered gene expression. DNA damage due to oxidative stress has been implicated in psychosis\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, while AP treatment may influence DSB repair regulation by modulating oxidative cellular stress responses and/or epigenetic regulation of repair-related genes\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdditionally, the \u003cem\u003ehistone acetyltransferase complex\u003c/em\u003e (GO:0000123), a key cellular component, is an important driver of epigenetic regulation. Altered histone acetylation patterns have been observed in psychosis and are associated with dysregulated gene expression in pathways that are critical for neuronal function and synaptic plasticity\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. Furthermore, AP treatment effects linked to interactions with histone acetylation complexes have been reported, with some mechanisms appearing specific to certain AP and brain regions\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFinally, when comparing the associations of DMPs and DMRs with SCZ, none of the associations identified with the shared effects of AP were among the DNAm differences between cases with SCZ and healthy controls identified in earlier work\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. These comparisons contribute to the separation of medication effects from diagnosis on differentially methylated loci, and suggest that the DMPs and DMRs identified in our study may not correspond to loci associated with SCZ, while the effects identified in the epigenome-wide association study of SCZ cases and controls were not due to medication.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eSufficient statistical power has been a major challenge for DNAm studies of psychotic disorders and obtaining sufficient sample sizes for monotherapy is challenging due to the widespread practice of polypharmacy in psychiatry\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Although our study included a relatively small sample size, Bayesian statistical analyses provided evidence for shared AP treatment effects on DNAm, whereas the specific-effects model was underpowered.\u003c/p\u003e\u003cp\u003eIt is important to note that DNAm is tissue specific, and we report methylation changes assayed from peripheral blood that may affect the synaptic function of genes in the human brain. In addition, interindividual differences in DNAm may be influenced by genetic variations specific to pathophysiology rather than AP treatment. Although we adjusted for demographic and environmental risk factors, including age, sex, smoking, psychiatric diagnoses, technical artifacts, and differences in estimated blood cell proportions, we acknowledge that diet, exercise, and psychotherapeutic interventions also have effects on DNAm which we could not adjust for.\u003c/p\u003e\u003cp\u003eSamples from healthy controls were not included in this study, as they would not have been exposed to AP. Finally, while we adjusted for sex in our models, we were unable to investigate well-known sex-specific effects associated with either AP \u003csup\u003e84\u003c/sup\u003e or DNAm \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e due to further sub setting and loss of statistical power. Nevertheless, none of the 60 CpGs associated with shared AP treatment effects was identified as sex-specific in the recent epigenome-wide association study of SCZ\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified seven DMRs associated with DNAm changes following AP treatment that were shared by olanzapine, quetiapine, and risperidone. A novel aspect of our approach was the use of AP serum concentrations to investigate the association between AP exposure and DNAm, a method not previously applied in EWAS of AP. By incorporating serum concentrations, our findings contribute to the growing body of evidence on AP-mediated DNAm changes, particularly in genes associated with synaptic function. Furthermore, our results highlight the importance of investigating how AP modulate DNA repair mechanisms and the epigenetic interplay between DNAm and histone acetylation, potentially advancing our understanding of their molecular mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eOAA is a consultant to Cortechs.ai and Precision Health, and has received speaker\u0026rsquo;s honorarium from Lundbeck, Janssen, Otsuka, Lilly, and Sunovion. The other authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe are grateful to all participants in the TOP Study and the Norwegian Centre for Mental Disorders Research (NORMENT) biobank staff responsible for the blood sample collection. We acknowledge the contributions of the clinicians, research assistants, and hospital units involved in the recruitment and assessment of participants. The work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) ([email protected]). We thank the Research Council of Norway (RCN) (223273, 273446, 250299 and 300309) and Dr Einar Martens Research Group for Biological Psychiatry for funding, and the Faculty of Medicine at the University of Bergen for the PhD fellowship for JV.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScott, K. M., Saha, S., Lim, C. C. W., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., \u003cem\u003eet al.\u003c/em\u003e Psychotic experiences and general medical conditions: a cross-national analysis based on 28 002 respondents from 16 countries in the WHO World Mental Health Surveys. \u003cem\u003ePsychol. 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Psychiatry\u003c/em\u003e 12, 650904 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8309238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8309238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAntipsychotic drugs (AP) are commonly prescribed for the treatment of psychotic symptoms in schizophrenia (SCZ), bipolar disorder (BP), and major depressive disorder (MDD). However, despite over 70 years of clinical use, the pharmacological mechanisms underlying AP drug action remain incompletely understood. DNA methylation (DNAm) provides a means to investigate epigenetic changes associated with AP exposure and to explore biological pathways potentially involved in AP pharmacology. This study aims to identify DNAm changes associated with treatment using olanzapine, quetiapine, and risperidone which may suggest shared or drug-specific epigenetic signatures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed genome-wide DNAm levels in the blood of 263 psychiatric patients who were treated with AP monotherapies (n = 136, olanzapine n = 89, quetiapine n = 26, risperidone n = 21) or were medication-free (n = 127). We assessed the correlation between DNAm levels and AP serum concentrations of each drug individually and of those shared between the three drugs. To identify drug-specific effects, we compared DNAm profiles between drugs and DNAm levels of medication-free patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified 60 CpGs and seven differentially methylated regions (DMRs) consistently associated with all three AP treatments (experiment wide significant threshold \u003cem\u003ep\u003c/em\u003e \u0026lt; 6.1 x 10\u003csup\u003e− 8\u003c/sup\u003e), involving genes linked to postsynaptic density regulation in glutamatergic neurons (\u003cem\u003eSHANK2\u003c/em\u003e), signal transduction (\u003cem\u003eGNB1\u003c/em\u003e), synaptic plasticity (\u003cem\u003eGAS7\u003c/em\u003e), and neuronal signalling (\u003cem\u003eFBXW4\u003c/em\u003e and \u003cem\u003eZNF471\u003c/em\u003e). No significant DNAm effects were associated with any specific AP monotherapy. None of the identified effects were among DNAm differences reported in a large EWAS between cases with SCZ and controls. These findings contribute to the characterization of the association between AP treatment and DNAm and provide insights into AP molecular mechanisms of action.\u003c/p\u003e","manuscriptTitle":"DNA Methylation Changes Associated with Antipsychotic Serum Concentrations in Patients with Psychosis.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:35:16","doi":"10.21203/rs.3.rs-8309238/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-04-09T09:37:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-03-25T22:40:29+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-10T12:52:24+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-03T15:42:32+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-01-21T17:07:28+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-15T18:53:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T15:36:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-09T15:28:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2025-12-08T15:23:22+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"01a3f33d-42a3-44b9-9bc0-008df85b8de1","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":61207644,"name":"Biological sciences/Neuroscience/Epigenetics in the nervous system/Epigenetics and behaviour"},{"id":61207645,"name":"Biological sciences/Drug discovery/Biomarkers/Diagnostic markers"},{"id":61207646,"name":"Health sciences/Diseases/Psychiatric disorders/Schizophrenia"},{"id":61207647,"name":"Health sciences/Diseases/Psychiatric disorders/Bipolar disorder"},{"id":61207648,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"}],"tags":[],"updatedAt":"2026-04-09T09:53:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-20 11:35:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8309238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8309238","identity":"rs-8309238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}