Multidimensional Epigenetic Clocks Reveal Physiological System-Specific Aging in Schizophrenia

Multidimensional Epigenetic Clocks Reveal Physiological System-Specific Aging in Schizophrenia | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Multidimensional Epigenetic Clocks Reveal Physiological System-Specific Aging in Schizophrenia View ORCID Profile Zachary M. Harvanek , Raghav Sehgal , Daniel Borrus , Jessica Kasamoto , Ahana Priyanka , Ryan Smith , Michael J. Corley , Christiaan H. Vinkers , Marco P. Boks , Varun B. Dwaraka , Jessica Lasky-Su DSc , Albert T. Higgins-Chen doi: https://doi.org/10.1101/2024.10.28.24316295 Zachary M. Harvanek 1 Department of Psychiatry, Yale University , New Haven, CT 2 Yale Stress Center, Yale University , New Haven, CT 3 Connecticut Mental Health Center , New Haven, CT MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Zachary M. Harvanek For correspondence: zachary.harvanek{at}yale.edu a.higginschen{at}yale.edu Raghav Sehgal 1 Department of Psychiatry, Yale University , New Haven, CT 4 Department of Computational Biology and Bioinformatics, Yale University , New Haven, CT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Borrus 1 Department of Psychiatry, Yale University , New Haven, CT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Kasamoto 4 Department of Computational Biology and Bioinformatics, Yale University , New Haven, CT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ahana Priyanka 1 Department of Psychiatry, Yale University , New Haven, CT Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ryan Smith 5 TruDiagnostic, Inc . Lexington, KY Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michael J. Corley 6 Division of Infectious Diseases, Department of Medicine , Weill Cornell Medicine, New York, New York, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Christiaan H. Vinkers 7 Amsterdam UMC location Vrije University Amsterdam, Department of Psychiatry and Anatomy & Neurosciences , Boelelaan 1117, Amsterdam, The Netherlands 8 Amsterdam Public Health (Mental Health program) and Amsterdam Neuroscience (Mood, Anxiety, Psychosis, Stress & Sleep program) research institutes , Amsterdam, the Netherlands ; 9 GGZ inGeest Mental Health Care , Amsterdam, The Netherlands MD PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marco P. Boks 10 Amsterdam UMC location Vrije University Amsterdam, Department of Psychiatry , Boelelaan 1117, Amsterdam, The Netherlands , 11 Dimence Mental Health , Deventer, The Netherlands , 12 University Medical Center Utrecht, Department psychiatry , Utrecht, The Netherlands MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Varun B. Dwaraka 5 TruDiagnostic, Inc . Lexington, KY PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jessica Lasky-Su DSc 13 Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School , Boston, MA, 02115, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Albert T. Higgins-Chen 1 Department of Psychiatry, Yale University , New Haven, CT 14 Department of Pathology, Yale University , New Haven, CT MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: zachary.harvanek{at}yale.edu a.higginschen{at}yale.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Schizophrenia is associated with increased age-related morbidity, mortality, and frailty, which are not entirely explained by behavioral factors. Prior studies using epigenetic clocks have suggested that schizophrenia is associated with accelerated aging, however these studies have primarily used unidimensional clocks that summarize aging as a single “biological age” score. This meta-analysis uses multidimensional epigenetic clocks that split aging into multiple scores to analyze biological aging in schizophrenia. In a meta-analysis of 7 studies with a total sample size of 1,891 patients with schizophrenia and 1,881 controls. we analyzed multidimensional epigenetic clocks, including causality-enriched CausAge clocks, physiological system-specific SystemsAge clocks, RetroelementAge, DNAmEMRAge, and multi omics-informed OMICmAge. Overall SystemsAge, DNAmEMRAge, RetroelementAge, and OMICmAge scores demonstrated increased epigenetic aging in patients with schizophrenia after strict multiple-comparison testing. Ten of the eleven SystemsAge sub-clocks corresponding to different physiological systems demonstrated increased aging, with strongest effects for Heart and Lung systems. OMICmAge DNAm proxies indicated changes in clinical biomarkers as well as novel proteins and metabolites not previously linked to schizophrenia. Most clocks demonstrated age acceleration at the first psychotic episode. Notably, clozapine use was associated with increased Heart and Inflammation aging, which may partially be driven by smoking. Most results survived Bonferroni multiple testing correction. These are the first analyses of novel multidimensional clocks in patients with schizophrenia and provide a nuanced view of aging that identifies multiple organ systems at high risk for disease in schizophrenia-related disorders. Question Do novel, multidimensional epigenetic clocks demonstrate accelerated aging in schizophrenia? Findings In this meta-analysis, patients with schizophrenia-spectrum disorders demonstrated evidence of broadly accelerated aging in multiple types of epigenetic clocks. This age acceleration is particularly evident in the Heart and Lung systems and is already evident by the time of the first psychotic episode. Meaning Novel epigenetic clocks may help identify patients with schizophrenia-spectrum disorders at risk for multiple other health comorbidities. Introduction Schizophrenia is a severe psychiatric disorder associated with major reductions in life expectancy of 10-20 years 1 . Much of the increased mortality stems from natural causes 2 , 3 spanning multiple organ systems 4 and multiple physical comorbidities 5 - 8 . Some treatments, especially clozapine, may decrease mortality from both non-natural and natural causes 9 . Increased morbidity and mortality in patients with schizophrenia is associated with accelerated aging, including elevated biomarkers of inflammation, oxidative stress, and age-associated proteins that predict mortality 10 - 12 . Epigenetic clocks are commonly utilized biomarkers that estimate biological age using DNA methylation data 13 - 15 . Studies of schizophrenia using epigenetic clocks have shown mixed results depending on the specific clock used 15 , 16 . Early studies utilizing Horvath’s multi-tissue clock found no changes in epigenetic age in schizophrenia, 14 , 17 likely because the clock’s training included schizophrenia samples and thus the clock is trained to ignore CpGs with altered aging patterns in schizophrenia 16 . More recent work has utilized second- and third-generation epigenetic clocks (GrimAge, PhenoAge, and DunedinPACE), which show increased epigenetic aging in schizophrenia 13 , 18 . These epigenetic clocks better align with epidemiologic data, are more likely to capture causal mechanisms in the aging process, and are less likely to reflect confounding cohort or period effects 19 - 22 . However, traditional epigenetic clocks generally summarize aging as a single number, treating aging as unidimensional. This is problematic for studies of aging and schizophrenia, which are both complex, multi-faceted, heterogeneous phenomena in that they involve many different biological processes. Traditional clocks do not allow more nuanced questions to be asked about aging in schizophrenia. For example, could a subset of epigenetic changes seen in patients with schizophrenia actually reflect adaptive mechanisms that protect individuals with age-related disease? Do some aging processes constitute specific responses to behavioral or environmental stressors? Is the aging process uniformly accelerated in the entire body, or are specific physiologic systems disproportionately affected? To capture multidimensionality in aging, novel epigenetic clocks have recently been developed that report a panel of biological age scores. SystemsAge is composed of 11 systems-based epigenetic clocks, developed by relating blood DNA methylation to 133 clinical and functional biomarkers organized by physiological system and then training mortality predictors for each system. Causality-enriched epigenetic clocks (CausAge) were developed using Mendelian Randomization techniques that identify CpG sites that are potentially causal for age-related traits 23 , including both detrimental (DamAge) and beneficial (AdaptAge) changes. OMICmAge was trained to predict mortality using DNA methylation data integrating 40 epigenetic biomarker proxies of proteins, metabolites, and clinical biomarkers that provide insight into specific biological processes 24 . Other interesting clocks include IntrinClock 25 , intended to capture aging unrelated to changes in cell composition, and RetroelementAge 26 , intended to capture aging at CpGs linked to retroelements. Here, we systematically investigate these new multidimensional epigenetic clocks in 7 datasets of patients with schizophrenia and non-psychiatric controls. We hypothesized that due to increased risk of numerous diseases in schizophrenia 4 , 8 , most physiological systems would demonstrate increased epigenetic aging, but that specific systems would be most altered to reflect particularly high disease risks in schizophrenia (e.g. Lung for pneumonia, COPD, and smoking, or Brain for dementia risk). We use meta-analyses to assess for schizophrenia disease effects across studies, then further assessed for effects of first-episode psychosis, interactions with age, sex and smoking status, and clozapine use. Methods Selection of Datasets We selected available epigenetic datasets of patients with schizophrenia-spectrum disorders and non-psychiatric controls 27 , 28 . Most datasets utilized the Illumina Infinium 450K BeadChip, though one dataset utilized the Illumina Methylation EPICv1 BeadChip. Individuals for whom chronologic age was not available were excluded from the analyses. Clock Calculation Details for clock calculations can be found in Supplementary Methods. All clocks used in these analyses can be found in Supplementary Table 1. Briefly, clocks were calculated using the methylCIPHER package in R 29 as described by 23 , 30 , 31 or using code provided by the authors 26 , 32 . Cell-type composition estimates Cell-type composition estimates were obtained using EpiDISH 33 . When accounting for cell-type composition, Neutrophils were dropped to avoid overfitting, and proportions of NK cells, B cells, CD4T cells, CD8T cells, Monocytes, and Eosinophils were included. Epigenetic smoking estimates As smoking data was not available for most datasets, we used the GrimAge component DNAmPACKYRS, which is a proxy of smoking pack-years predicts mortality better than self-reported pack-years 20 . Statistical analyses To calculate standardized effect sizes for the effect of schizophrenia, epigenetic ages were first regressed onto age, sex, and any other included covariates for the analyses (e.g., cell-type composition, smoking). These residuals were then scaled such that for the controls, the standard deviation for a given study = 1 and residual mean = 0, and then the final model was a multivariable regression of disease status, age, sex, and any other covariates regressed onto the scaled residuals. To evaluate standardized effect sizes for age-by-disease interaction, the same procedure was used. When examining clozapine effects, a similar procedure was used, limiting analyses to only individuals with schizophrenia and data regarding whether they had taken clozapine or not. Statistical analyses were performed using R version 4.0.2 34 . Meta-analyses were performed using a random-effects model and restricted maximum likelihood estimation in the Metafor package 35 . Weights for studies were calculated as 1/SE 2 . Heatmap and scatter plots were created using ggplot2 36 . Statistics represent nominal values for consistency. However, correction for multiple comparisons via the Bonferroni method adjusts for 19 comparisons to account for all novel clocks examined (the systems-based clocks, the causality-enriched clocks, Retroelement Age, OMICmAge, IntrinClock, and DNAmEMRAge; nominal, unadjusted p of 0.00263 = adjusted p of 0.05). Similar adjustments were applied to earlier generation clocks (accounting for 14 clocks, nominal unadjusted p of 0.00357 = adjusted p of 0.05) and DNAm proxies (accounting for 40 DNAm proxies, nominal unadjusted p of 0.00125 = adjusted p of 0.05) when assessing those findings. Results We identified seven different cohorts of patients ( Table 1 ) composed of 2,210 patients with schizophrenia-spectrum disorders and 1,936 non-psychiatric controls. 549 individuals were excluded due to missing age data. Age distributions for each cohort are in Supplementary Figure 1. Two cohorts (GEO152026 and GEO152027) include individuals with first-episode psychosis (n = 716). Three cohorts (GEO116379, GEO80417, and GEO84727) include information regarding clozapine treatment (number on clozapine = 225, number confirmed not on clozapine = 406). View this table: View inline View popup Download powerpoint Table 1: Characteristics of included Datasets Summary of the seven datasets analyzed here. Age indicates mean +/- SD, range in parentheses. SCZ: Schizophrenia-spectrum disorders. FEP: First-Episode Psychosis. IoPPN: Institute of Psychiatry, Psychology, and Neuroscience Multidimensional Clocks are Altered in Patients with Schizophrenia Meta-analyses demonstrated significantly increased epigenetic age in patients with schizophrenia in the AdaptAge causality-enriched clock, 10 out of 11 systems-based clocks, as well as total SystemsAge, DNAmEMRAge, OMICmAge, and RetroelementAge. Broadly, aging measures increased with chronological age, and differences between control and schizophrenia were evident in individual studies (see figures 1A and 1B for examples). The largest effects were seen for SystemsAge (β = 0.85, p = 1.7E-12), Heart (β = 0.87, p = 2.5E-12), and Lung (β = 0.82, p = 1.8E-6) clocks ( Figure 1C-F ). All results survived Bonferroni multiple testing correction. No significant effect was observed for the Hormone system clock (unadjusted p = 0.84), DamAge (p = 0.29), CausAge (p = 0.19), or IntrinClock (p = 0.15). These findings are summarized in the first column of Figure 1G , and all forest plots can be found in Supplementary Figure 2. When males and females are analyzed separately, we observe similar patterns (Pearson’s r = 0.96 between effect sizes calculated in males and females separately, Figure 2A ). Download figure Open in new tab Figure 1: Patients with Schizophrenia have higher epigenetic age compared to controls We observe significant age acceleration in patients with schizophrenia across multiple datasets. As examples, Total SystemsAge is plotted against chronological age for patients with Schizophrenia (red) and controls (black) for GSE147221 (A) and GSE152026 (B). Meta-analyses demonstrate this age acceleration across studies. As examples, we show Total SystemsAge (C), the Heart Clock (D), and the Lung Clock (E). Forest plots for other clocks can be found in supplementary materials. Body plot (F) demonstrates comparative effect sizes of meta-analysis in Systems-based clocks, with larger text size and bolder color indicating strongest effects. The summary heatmap (G) demonstrates standardized effect size and significance of the association between schizophrenia, schizophrenia-by-age interaction, and clozapine with all novel clocks examined. For forest plots of meta-analysis results, positive numbers indicating epigenetic age is higher in patients with schizophrenia. All p values and 95 th percentile confidence intervals represent nominal significance; accounting for 19 novel clocks, nominal unadjusted p of 0.00263 = Bonferroni adjusted p of 0.05. Download figure Open in new tab Figure 2: Comparison of epigenetic age effect sizes in subgroups based on sex and first episode psychosis (A) Scatter plot demonstrates comparison of effect sizes in men versus women. (B) Scatter plot demonstrates comparison of effect sizes in patients with first-episode psychosis to non-first-episode psychosis cases. Red dashed line is the identity line (x=y). To confirm previous findings 13 , 14 , 17 , 18 using a larger meta-analysis, we repeated our analysis for unidimensional clocks. We replicated previous results demonstrating accelerated aging across most traditional clocks, except the Horvath multi-tissue and Skin and Blood clocks as expected (Supplementary Figure 3). GrimAge (both V1 and V2) showed the strongest standardized effect size of 0.98. The principal component versions of clocks 30 largely recapitulated findings from the original clocks. Multidimensional Clocks are Altered in First-Episode Psychosis less than in Prevalent Schizophrenia We asked whether these differences in epigenetic aging pre-dated psychotic symptoms by examining individuals with first-episode psychosis. None of the causality-enriched clocks demonstrate significant differences in epigenetic age in patients with first-episode psychosis compared to controls. Most of the systems-based clocks continue to show significantly higher epigenetic age at first episode psychosis except the Hormone clock, the Immune clock, and, after Bonferroni correction, the Musculoskeletal clock (Summarized in Figure 1G , “FEP” column). OMICmAge is significantly elevated in first episode psychosis, while RetroelementAge and (after Bonferroni correction) DNAmEMRAge no longer show age acceleration when just considering first-episode psychosis. First-episode psychosis is generally associated with smaller effect sizes than non-FEP, with notable exceptions of RetroelementAge and OMICmAge ( Figure 2B ). We also asked whether effects of schizophrenia on clocks might change with age. Analyses excluded GEO116379 due to a narrow age range, and demonstrated a significant age-by-disease interaction in the Inflammation (p = 0.0020), Heart (p = 3.8E-5), Lung (p = 1.8E-6), and total SystemsAge (9.2E-5) clocks, and nominally significant interactions in the OMICmAge (p = 0.031), and DNAmEMRAge (p = 0.015) clocks (summarized in Figure 1G , “Age-by-Disease Interaction” column). Sensitivity analyses including GEO116379 identify significant interactions in the same clocks. Clozapine is associated with age acceleration across most systems-based clocks We next sought to identify whether specific medications (i.e., Clozapine) might contribute to higher epigenetic age in schizophrenia. Patients with schizophrenia treated with clozapine showed no differences in causality-enriched clocks compared to patients with schizophrenia not treated with clozapine. However, we observed significantly higher epigenetic age in SystemsAge and 7 of 11 systems-based clocks. These effects are apparent in individual studies (See Figure 3A, B for examples) and the largest effects were observed in the total SystemsAge, Inflammation, Heart, and Lung clocks ( Figure 3C-F ). Smaller effects were seen for Metabolic, Immune, Kidney, and Brain that did not pass Bonferroni correction. No differences were observed in DNAmEMRAge, OMICmAge, IntrinClock, or RetroelementAge with clozapine treatment (Summarized in figure 1G , “Clozapine” column, remaining forest plots in Supplementary Figure 4). Download figure Open in new tab Figure 3: Clozapine treatment is associated with accelerated aging in the total SystemsAge, inflammation, heart, and lung clocks Clozapine treatment is associated with age acceleration in multiple systems-based clocks. As examples, Total SystesmsAge is plotted against chronological age for patients with schizophrenia on clozapine (red) versus patients with schizophrenia not on clozapine (black) for GSE80417 (A) and GSE84727 (B). Meta-analyses demonstrate clozapine-associated age acceleration in multiple clocks, including total SystemsAge (C) and the Inflammation (D), Heart (E), and Lung (F) clocks. We do observe significantly higher values of DNAmPACKYR (an epigenetic marker of smoking) in patients on clozapine (G). (H) Scatter plot demonstrates that smoking broadly reduces effect sizes of clozapine. Red dashed line is the identity line (x=y). For forest plots of meta-analysis results, positive numbers indicating epigenetic age is higher in patients with schizophrenia on clozapine compared to patients with schizophrenia not on clozapine. All p values and 95 th percentile confidence intervals represent nominal significance; accounting for 19 novel clocks, nominal unadjusted p of 0.00263 = Bonferroni adjusted p of 0.05. As we observed evidence of higher smoking rates in patients with clozapine in our study ( Figure 3G ), we next asked whether smoking accounted for these differences. Most systems showed weaker effects after accounting for smoking, with Lung showing the greatest attenuation as expected. After accounting for smoking, only the Inflammation, Heart, and Total SystemsAge clocks remain nominally significantly elevated with clozapine use ( Figure 3H ). Neither Cell-type composition nor Smoking account for the differences in Systems-Based Clocks in patients with Schizophrenia We examined potential drivers of differences in epigenetic age, including cell-type composition 37 and smoking. In general, effect sizes were lower after adjusting for cell-type composition, and AdaptAge no longer passed Bonferroni correction (p = 0.016). The conclusions from the Systems-based clocks, RetroelementAge, DNAmEMRAge, IntrinClock, and OMICmAge were unchanged after accounting for cell-type proportions (Summarized in Figure 1G , “SCZ with cell prop.” column). When accounting for smoking, AdaptAge again shows a nominally significant association with schizophrenia (p = 0.011). Of the systems-based clocks, the Lung clock experienced the greatest decrease, with a 8.6-fold reduction of effect size, though remained significantly associated with schizophrenia (p = 0.00044). The Immune systems clock was only nominally associated with schizophrenia after correcting for smoking (p = 0.0092). All other systems remained robustly elevated in schizophrenia after correcting for smoking. RetroelementAge (p = 0.017), DNAmEMRAge (p = 0.0086), and OMICmAge (p = 0.039) showed nominal significance after accounting for smoking (Summarized in Figure 1G , “SCZ with smoking” column). SystemsAge Identifies Deficits in Distinct Physiologic Systems when compared to biochemical, imaging, and physical assessments We next compared conclusions from SystemsAge to a recent study examining the health of multiple physiologic systems in neuropsychiatric disorders utilized brain imaging, physical assessment, and biochemical assays 38 . We identified little statistical correlation between these methods in terms of the effects on organ systems in schizophrenia when considering either overall estimated effect size ( Figure 4A , p = 0.49, R 2 = 0.083) or rank order ( Figure 4B , p = 0.65, R 2 = 0.036). Notably, patients with schizophrenia are more likely than controls to have increased epigenetic aging in multiple systems-based clocks ( Figure 4C , p < 1E-15). Download figure Open in new tab Figure 4: The association between epigenetic aging and schizophrenia represents a distinct phenotype from prior body health scores (A) Scatter plot of SystemsAge standardized effect sizes versus prior reported body health scores. Red line is the identity line (x=y). (B)Scatter plot using rank-order of SystemsAge versus prior reported body health scores. Red dashed line is the identity line (x=y). (C) Histogram of # of highly accelerated systems-based clocks (age > 1SD from 0) by disease status. individuals with schizophrenia (red) or controls (black). Specific DNAm Proxies are altered in Schizophrenia OMICmAge is constructed from multiple DNAm proxies for clinical biomarkers, proteins, and metabolites, which can lend potential mechanistic insight. We next asked whether these DNAm proxies showed association with schizophrenia. Of the 40 DNAm proxies examined, 19 were nominally associated with schizophrenia (nominal p ≤ 0.05), 10 of which pass Bonferroni correction (nominal p ≤ 0.00125; Figure 5A ). Broadly similar patterns were observed in men and women. Remarkably, 12 DNAm proxies were significant in the first-episode psychosis datasets, 3 of which met Bonferroni: ITIH3, RNAS1, and Mimecan. In the non-first episode psychosis datasets, the majority of DNAm proxies showed at least nominal significance. Correction for cell-type proportions and smoking generally blunted effect sizes. Of the 40 DNAm proxies, 6 showed at least nominally significant associations in all models (the full sample, male/female subsets, FEP/no-FEP subsets, after adjusting for cell-type proportions, and after adjusting for smoking): Vanillactate, H2B1C, Mimecan, RNAS1, Gluconate, and ITIH3. No DNAm proxies passed Bonferroni correction for age-by-schizophrenia interactions or treatment with clozapine. Download figure Open in new tab Figure 5: DNAm Proxies show significant associations with schizophrenia (A) Heatmap demonstrating effect size and significance of the association between schizophrenia, schizophrenia-by-age interaction, and clozapine with DNAm Proxies from OMICmAge. Accounting for 40 DNAm proxies, nominal unadjusted p of 0.00125 = Bonferroni adjusted p of 0.05. Forest plots can be found in supplementary materials. Discussion Here, we show that schizophrenia is associated with broad accelerated aging in multidimensional epigenetic clocks. These clocks provide a far more nuanced picture of aging through subscores that each capture different aspects of aging. These subscores reveal schizophrenia is characterized by adaptive age-related changes, accelerated aging in most physiological systems, many age-related metabolites and proteins, as well as retroelements. Critically, the large sample size and meta-analytic design of this study allowed us to obtain robust results that generally pass strict Bonferroni multiple testing correction even when examining numerous clocks simultaneously. Sensitivity analyses indicated the observed effects are generally independent of sex and robust correction for cell-type composition and smoking. Interestingly, smoking, clozapine, and first-episode psychosis each affected specific clock subscores, highlighting the utility of multidimensional clocks in disentangling the effects of different clinical variables on aging. Importantly, the multidimensional clocks correlate with the known epidemiology of schizophrenia. Large studies including meta-analyses have demonstrated that individuals with schizophrenia have increased disease and mortality rates from natural causes covering every physiological system 4 , 8 . Accordingly, 10/11 of the SystemsAge subscores are increased in schizophrenia. Because each SystemsAge subscore has specific associations with outcomes related to that system 31 (e.g. lung cancer for Lung, cognition for Brain, diabetes for Musculoskeletal and Metabolic), SystemsAge could help explain the increased disease and mortality risks in schizophrenia. The only score not increased in schizophrenia is Hormone, which was previously reported to be most related to thyroid disease and cancer risk 31 . Accordingly, thyroid disease is not elevated in schizophrenia 8 , and cancer shows the smallest increased risk in Correll et al 4 . The lack of age acceleration in the hormone subscore strengthens the case for the use of multi-dimensional clocks, as it suggests they maintain specificity even in diseases such as schizophrenia with significant comorbidities. As expected, patients with schizophrenia are more likely to show multi-system accelerated aging, consistent with previous results showing a 69% increased risk of multimorbidity in schizophrenia 39 . Importantly, SystemsAge can better capture the heterogeneity of risk in schizophrenia and thus could identify which diseases individuals with schizophrenia are at greatest risk for. A recent study by Tian et al. developed system scores using clinical data and examined changes in schizophrenia 38 . Interestingly, while we found that Heart and Lung were the scores with greatest increase in schizophrenia, Tian et al. found nearly no change in their Cardiovascular score and a modest change in the Pulmonary score. The Cardiovascular score from Tian et al. utilized heart rate, blood pressure, and arterial stiffness, while the SystemsAge Heart score includes DNAm proxies of BMI, smoking, blood pressure, clinical history, and mortality-associated proteins. Clinical experience is more consistent with markedly increased heart and lung aging in schizophrenia, given the high rates of smoking 40 , known adverse effects associated with antipsychotic medications 41 (weight gain, myocardial infarction, pneumonia), and elevated mortality risk due to pneumonia (RR 7), any respiratory cause (RR 3.75) and cardio-cerebrovascular causes (RR 3.47) 4 . Given the discrepant results from the two methods, it will be interesting to determine if combining clinical and laboratory-based risk factors with epigenetic scores may provide complementary information on risks of comorbidities in patients with schizophrenia. Increased epigenetic age was noted at first-episode psychosis, suggesting many changes are detectable early in the disease. However, effect sizes were smaller in first-episode psychosis compared to prevalent schizophrenia for nearly all clocks. Interestingly, only a subset of clocks accelerated over time as suggested by age-by-disease interactions - these were the Heart, Lung, Inflammation, full SystemsAge, OMICmAge, and DNAmEMRAge clocks. It is possible changes in these systems reflect the cumulative effects of factors associated with psychosis (e.g., smoking, stress) and treatment of psychosis (e.g., atypical antipsychotics). This suggests that older individuals with schizophrenia may be particularly vulnerable to a cardiovascular, pulmonary, and inflammatory age-related diseases. Clozapine has unique treatment benefits but also greater metabolic side effects and can induce serious cardiac, hematologic, and neurological adverse events. We find that clozapine treatment is associated with acceleration in SystemsAge, multiple SystemsAge subclocks especially Inflammation and Heart but also in Metabolic, Kidney, Immune, and Brain, as well as in multiple hematologic markers of OMICmAge. These changes may reflect the side effect profile of clozapine, and indeed longitudinal studies have shown that clozapine can directly impact the methylome 42 . However, there are other explanations: treatment-resistant individuals, in whom clozapine is primarily used, may represent a distinct clinical population with unique risks 43 . Notably, prior meta-analyses have suggested that clozapine is associated with decreased mortality in patients with schizophrenia, despite its known effects on cardiometabolic risk factors 4 . Further longitudinal studies are needed to determine potential causal relationships between clozapine, epigenetic aging, and mortality. Our analysis of OMICmAge reveals novel insights into clinical biomarkers, proteins and metabolites altered in schizophrenia. Previously, we showed that DNAm proxies of serum B2M, Cystatin C, GDF-15, TIMP-1, ADM, and PAI-1 (components of GrimAge) are elevated in schizophrenia, which matches the literature concerning these proteins 13 . This suggests DNAm proxies can be used for discovery. In some cases, the DNAm proxies may even be more useful. Prior results showed stronger associations with mortality for a DNAm proxy of smoking pack-years compared to self-reported smoking pack-years 20 , and stronger associations with brain health outcomes for a DNAm proxy of CRP compared to directly measured CRP 44 . OMICmAge predicted changes in multiple clinical biomarkers which were often sex-specific. While both men and women demonstrated higher RDW and lower liver albumin, men showed associations with decreased hemoglobin and hematocrit, and women showed elevated hemoglobin A1C. These results are generally consistent with anemia, malnutrition, other comorbidities, and medication effects in schizophrenia 45 , 46 , although further explorations into whether the sex-specific differences in the DNAm proxies seen here translate clinically are necessary. Notably, higher RDW and lower albumin have been associated with increased mortality in the general population in both NHANES and the UK Biobank 47 . IGFBP2 was found to be increased in both men and women, (although not in the full sample), consistent with prior literature 48 . IGFBP-2 may play a role in increased metabolic risk and altered synaptic plasticity in schizophrenia or treatment 48 , 49 . Potentially novel proteins and metabolites that could play a role in features of schizophrenia include (4-hydroxy)phenylacetylglutamine (heart disease 50 ), vanillactate (heart disease 51 ), carboxypeptidase B2 (thromboembolic disease 52 ), histone H2B type 1-K (cellular senescence 53 ), mimecan (food intake 54 ), and chordin-like protein 1 (cognitive decline 55 , adipogenesis 56 ). Notably, directionality is not necessarily consistent with prior literature – for example we found increased DNAm-predicted cystine though a prior study found reduced directly measured cystine 57 , suggesting a complex relationship between cystine, DNAm and schizophrenia. These metabolites may also play roles in schizophrenia risk – we identified increased DNAm-predicted ITIH3 in all our models, including those just examining first episode psychosis. Previous genome wide studies have implicated multiple SNPs within ITIH3 in schizophrenia. While one is a missense variant, another SNP is in an intron and does not seem to affect ITIH3 expression 58 , 59 . The biology of these proteins and metabolites in schizophrenia represent fertile areas for future investigation. Limitations of our study include its cross-sectional nature and limited datasets for first-episode psychosis and clozapine. The absence of patients with schizophrenia without medication prevents analyses of general anti-psychotic treatment and epigenetic aging. Future longitudinal and interventional studies with more detailed phenotypic data will be needed to determine whether antipsychotics or other factors associated with schizophrenia contribute to accelerated aging. Conclusion In this meta-analysis, we identify a rich tapestry of accelerated epigenetic aging in schizophrenia-spectrum disorders. Specific physiological systems are particularly affected, changes can be either damaging or adaptive changes, and many age-related metabolites, proteins, and retroelements are affected. These findings are robust after strict multiple testing correction and correcting for covariates. Factors such as smoking, first-episode psychosis, and clozapine have effects on particular subsets of clocks. These clocks may complement clinical data in identifying and preventing aging health risks in patients in schizophrenia. Conflicts of Interest A.H.C. and R. Sehgal are named as inventors of SystemsAge on a patent application. A.H.C. has received consulting fees from TruDiagnostic and FOXO Biosciences for work unrelated to this publication. R. Sehgal has received consulting fees from the TruDiagnostic, LongevityTech.fund, Healthy Longevity Clinic and Cambrian BioPharma for work unrelated to this publication. J.L.S. is a scientific advisor to Precion Inc. and TruDiagnostic Inc. V.B.D. and R. Smith are employees of TruDiagnostic Inc. J.L.S., V.B.D., and R. Smith developed OMICmAge. The other authors do not declare any conflicts of interest. Data availability All data is available on NCBI GEO ( https://www.ncbi.nlm.nih.gov/geo/ ) and datasets are listed in Table 1 . Information on clozapine status can be found in their respective papers 13 , 60 . Code availability Code to calculate all clocks except for OMICmAge and DNAmEMRAge are accessible at https://github.com/HigginsChenLab/methylCIPHER . Code to calculate OMICmAge, DNAmEMRAge and associated algorithms will be accessible via TruDiagnostic’s DNAm Analysis Software after publication. You can request access to the software at https://www.trudiagnostic.com/softwarerequest . Author contributions Z.M.H. and A.H.C. designed the study and drafted the initial manuscript. Z.M.H. performed the analyses. R.S. contributed body plots and code for analyses. Z.M.H., A.H.C., R.S., D.B., J.K., and A.P. contributed to data cleaning and clock calculation. A.H.C. conceived the project and provided supervision. C.H.V. and M.P.B. provided data and conceptual input. M.J.C., V.B.D., J.L.S., and R.S. provided clock code and insights. All authors reviewed and contributed to the manuscript. Acknowledgements/Funding This work was supported by the Yale Physician Scientist Development Award (Z.M.H.) and CTSA (Z.M.H.; NIH UL1 TR001863), grants from the National Institute on Aging (A.H.C.; 1R01AG065403), the National Heart Lung Institute (J.L.S.; R01HL123915, R01HL155742, and 1R01HL169300), the National Institute of Diabetes and Digestive and Kidney Diseases (Z.M.H.; K23DK136932), and the Impetus Grants (R. Sehgal). Additionally, Dr. Harvanek is a Pepper Scholar with support from the Claude D. Pepper Older Americans Independence Center at Yale School of Medicine (P30AG021342). Dr. Higgins-Chen was supported by a Pilot Grant from the Claude D. Pepper Older Americans Independence Center at Yale School of Medicine (P30AG021342). Footnotes The major revision was in changing from a fixed-effects model to a random-effects model for the meta-analyses in response to feedback on our original manuscript. Additionally, figures were updated to decrease clutter and improve clarity, and text was edited throughout the manuscript. References 1. ↵ Hennekens CH , Hennekens AR , Hollar D , Casey DE . Schizophrenia and increased risks of cardiovascular disease . Am Heart J . Dec 2005 ; 150 ( 6 ): 1115 – 21 . doi: 10.1016/j.ahj.2005.02.007 OpenUrl CrossRef PubMed Web of Science 2. ↵ Saha S , Chant D , McGrath J . A systematic review of mortality in schizophrenia: is the differential mortality gap worsening over time? 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