Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder

Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder | 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 Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder Sergi Mas, Alex Segura, Elena de la Serna, Gisela Sugranyes, Inmaculada Baeza, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2483797/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2023 Read the published version in Translational Psychiatry → Version 1 posted 7 You are reading this latest preprint version Abstract Epigenetic modifications occur sequentially during the lifespan, but their pace can be altered by external stimuli. The onset of schizophrenia and bipolar disorder is critically modulated by stressors that may alter the epigenetic pattern, a putative signature marker of exposure to environmental risk factors. In this study, we estimated the age-related epigenetic modifications to assess the differences between young individuals at familial high risk (FHR) and controls and their association with environmental stressors. The sample included 117 individuals (6-17 years) at FHR (45%) and a control group (55%). Blood and saliva samples were used estimate the epigenetic age with six epigenetic clocks through methylation data. Environmental risk was measured with obstetric complications, socioeconomic statuses and recent stressful life events data. For all epigenetic clocks except Horvath, estimated epigenetic ages were older than the chronological. Stratified analysis showed that Hannum epigenetic clock reported younger epigenetic age only in the FHR group. Epigenetic age was found deaccelerated in the FHR individuals in three epigenetic clocks. No effect of the environmental risk factors on the epigenetic age acceleration could be detected. Epigenetic age asynchronicities were found in the young at high risk, suggesting that offspring of affected parents follow a slower pace of biological aging than the control group. It still remains unclear which environmental stressors orchestrate the changes in the methylation pattern. Further studies are needed to better characterize the molecular impact of environmental stressors before illness onset, which could be critical in the development of tools for personalized psychiatry. Biological sciences/Genetics/Genomics/Personalized medicine Health sciences/Pathogenesis/Clinical genetics epigenetic clock biological age familial high risk environmental risk factor Figures Figure 1 1. Introduction Schizophrenia and bipolar disorder are highly prevalent and impairing conditions that have differential diagnostic criteria. However, their familial aggregation and overlapping clinical and genetic features do not fully correlate with their nosological boundaries, pointing towards a partially shared etiology 1 – 4 . The individuals at familial high risk (FHR) have a 2-to-4-fold increase in the risk of developing a psychiatric disorder, for which the exposure to environmental stressors have a critical role 5 . Schizophrenia and bipolar disorder are associated with a shorter lifespan, which has been linked to age-related biomarkers and physiological conditions such as increased inflammation and oxidative stress, a shorter telomere length and metabolic disruption 6 – 13 , suggesting that patients suffer from the effects of accelerated aging. Epigenetic modifications (changes in chromatin structure, primarily measured by assessing the methylation of CpG dinucleotides) have been closely related to gene expression, driving cell senescence and affecting their function 14 . Methylation patterns change throughout the lifespan, following a specific timing. Epigenetic clocks measure the methylation of specific sets of CpGs for the estimation the epigenetic age in years, a proxy of the biological age of the individual. The epigenetic age has been consistently correlated with chronological age in samples including schizophrenia patients, and yet the latest meta-analysis reports no epigenetic acceleration in the patients 15 . Schizophrenia and bipolar disorder prediction models perform best when including polygenic constructs, multiple environmental factors and their interaction 16 . The characterization of risk factors encompasses multiple sorts of environmental impacts occurring throughout all stages of life 17 , 18 , which can lead to an acceleration the epigenetic age 19 . Obstetric complications, including maternal and perinatal infections that drive immune responses in the offspring, are thought to cause a neurodevelopmental disruption 20 , 21 . Early life adversity (ranging from explicit violence to subtle forms of emotional negligence) has been associated with more severe manifestations of the disorders and suicidal behaviors 22 – 24 . Moreover, recent traumatic events may also be a substantial risk factor for disorder onset 25 , 26 . Young individuals experiencing migration processes, lower socioeconomic statuses and urbanicity – linked to social exclusion and isolation – are also at a higher risk of developing schizophrenia 27 – 29 . In this study, we examined the epigenetic age of a sample consisting of FHR individuals and a control group. Blood and saliva samples were used to estimate their epigenetic age using six epigenetic clocks. We expected that the FHR group would report greater asynchronicities between their epigenetic and chronological age than the control group. Furthermore, we believed that these differences would be associated with the exposure to environmental stressors. 2. Methods The present study is part of the Bipolar and Schizophrenia Young Offspring Study (BASYS), which is a multicenter, longitudinal, naturalistic study that aims to compare the clinical, neuropsychological, neuroimaging, genetic and epigenetic characteristics of the child and adolescent offspring of patients diagnosed with SZ or BD and of a community control group. This study was conducted in the child and adolescent psychiatry units of two hospitals in Spain: the Hospital Clinic in Barcelona and Hospital Gregorio Marañón in Madrid. The methodology as well as the clinical and cognitive characteristics of the sample have been described previously in detail 30 . 2.1 Sample characteristics The individuals at FHR were offspring of patients with schizophrenia or bipolar disorder, recruited by psychiatrists from the adult psychiatry units of both hospitals. The inclusion criteria were: (a) age between 6 and 17 years, and (b) a parent diagnosed with schizophrenia or bipolar disorder. The exclusion criteria were: (a) intellectual disability with an impact on functioning, and (b) significant head injury or a current medical or neurological condition. The only inclusion criterion for the offspring of the community controls was an age between 6 and 17 years, while the exclusion criteria were exactly the same as those for the FHR group plus a family history of psychotic disorders in first- or second-degree relatives. As this study focused on epigenetic data, only the individuals who had provided biological samples for DNA methylation analysis (53 FHR and 64 controls) were assessed. 2.2 Ethical considerations All procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Written informed consent was obtained from one of the parents, having the other parent been informed, together with written assent from the participant if aged 12 and above. 2.3 Clinical and environmental assessment A trained psychiatrist or psychologist performed a mental health assessment of all the parents using the Spanish version of the Structured Clinical Interview for DSM-IV Disorders (SCID-I) 31 , 32 . Parents or primary caregivers were also interviewed about their children. The study participants were assessed directly by trained child psychiatrists or psychologists who were blind to their parental diagnoses, using the Spanish version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime Version (K-SADS-PL) 33 , 34 . Information about obstetric complications was collected using the Lewis-Murray scale 35 . This scale rates 15 obstetric complications as absent or definitely present, while 9 of the exposures can also be rated as equivocally present. For this study, history of obstetric complications was considered positive if at least one complication was definitely present. The socioeconomic status was calculated according to the Hollingshead and Redlich scale 36 . The higher socioeconomic level between each set of parents was considered. The higher socioeconomic level between each set of parents was considered. Lower scores indicate a low socioeconomic status. The occurrence of recent stressful events was determined using the Stressful Life Events Schedule (SLES), child-reported version 37 , 38 . The SLES evaluates the presence/absence of a list of potentially stressful, age-adapted events in the last 12 months and rates their potential impact on a scale of 1 (not at all) to 4 (a lot). The SLES provides two scores: the number of stressful life events (SLEs) in the previous year and the score for the total cumulative impact of the SLEs. 2.4 Biological samples Blood samples were collected in EDTA tubes (K2EDTA BD Vacutainer EDTA tubes; Becton Dickinson, Franklin Lakes, New Jersey, USA) and genomic DNA was extracted with the MagNA Pure LC DNA Isolation Kit III and a MagNA Pure LC system (Roche Diagnostics GmbH, Mannheim, Germany). Saliva samples were collected using the Oragene DNA Saliva Collection Kit (OG-500, DNA Self-Collection Kit, Genotek, Ottawa, Ontario, Canada) and DNA was extracted according to the manufacturer's instructions. DNA concentration and quality were measured spectrophotometrically using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Epsom, Surrey, UK). DNA methylation β-values were obtained at GenomeScan using the Illumina Infinium MethylationEPIC BeadChip Kit. 2.6 Methylation data collection Raw intensity data (.IDAT) files were received and parallel bioinformatics processes were conducted in-house using the Chip Analysis Methylation Pipeline (ChAMP) Bioconductor package 39 , which were performed separately for the methylation data obtained from blood samples (n = 79) and those obtained from saliva samples (n = 38). Raw .IDAT files were used to load the data into the R environment with the champ.load function, which also enabled the simultaneous undertaking of the probe QC and removal steps. Probes with weak signals (p < 0.01), cross-reactive probes, non-CpG probes, probes with < 3 beads in at least 5% of the samples per probe, probes that bound to SNP sites, and sex chromosomes were all considered problematic for the accurate detection of downstream methylation and were therefore removed. β-values were then normalized using the champ.norm function, specifically with the beta-mixture quantile method (BMIQ function). Next, the singular value decomposition (SVD) method was performed with champ.SVD to assess the amount and significance of the technical batch components in our dataset. Using the champ.runCombat function, combat algorithms were applied to correct for slide and array (significant components detected by the SVD method). 2.7 Epigenetic clock construction The methylclock R package 40 was used to construct six epigenetic clocks. Horvath is a multitusse-based epigenetic clock designed to predict chronological age in individuals along the whole lifespan 41 . Similarly, Hannum and Wu epigenetic clocks estimate the epigenetic patterns linked to chronological age in blood tissues in adults and children, respectively 42 , 43 . PedBE epigenetic clock was constructed for saliva samples in children 44 . Levine epigenetic clock captures the methylation patterns of “phenotypic aging”, mortality and morbidity epigenetic patterns rather than with chronological age 45 . CpGs located in telomeric regions can also be measured to estimate telomere length (TL), a well-established biomarker of health conditions associated with aging. The TL estimation by means of epigenetic markers used in this study was constructed with blood samples of adults 46 . Briefly, from normalized and batch-corrected methylation data, the package extracts the methylation levels of the available CpGs included in each clock. Subsequently, the coefficients obtained through an elastic net in the prediction models of each of the clocks in the original studies are used to predict the epigenetic age. Several studies have demonstrated that the epigenetic clocks are resistant to the CpG site missingness from the MethylationEPIC BeadChip Kit 47 . For each clock, we obtained the epigenetic age in years. The extrinsic epigenetic age acceleration (EEAA) for each clock was obtained after regressing chronological age on the epigenetic age adjusted by seven cell-type proportions known to change throughout the lifespan 48 . 2.8 Statistical analysis All the analyses were performed with R v4.1.2 49 . Multiple testing correction was applied in all the analyses by means of the FDR method, and the threshold of significance of the adjusted p value (p.adj) was set at α < 0.05. Group differences in sociodemographic features and environmental risk factors were calculated by linear mixed-effects models for continuous variables, using family relatedness as a random effect, and by chi square tests for categorical variables. The correlation between epigenetic and chronological age was tested using Pearson’s product-moment correlation. The differences between epigenetic and chronological age was assessed with mixed-effects models, using family relatedness as a random effect and corrected by sex, FHR status and tissue used for methylation. The analyses stratified by tissue were constructed similarly, but only corrected by sex and FHR status. To assess the EEAA difference between the FHR and controls linear mixed-effects models were used, considering the FHR status as a dependent variable, EEAA as fixed effects, family relatedness as a random effect and corrected by sex and tissue used for methylation. The effect of environmental factors (obstetric complications, socioeconomic statuses and recent stressful life events) on the EEAA was assessed using a linear mixed-effects model corrected for sex and the tissue used for the epigenetic characterization, with family relatedness as a random effect. 3. Results 3.1 Sample description One hundred seventeen children and adolescents aged 6–17 years (54.7% females) were included in this study, 53 of whom were at FHR (45.3%) and 64 were controls (54.7%). The main sociodemographic of the study sample and the differences between the FHR individuals and controls are shown in Table 1 . Only the proportion of FHR individuals reporting an obstetric complication was found significantly increased (p.adj = 0.036). Table 1 Summary and group comparison of the sociodemographic and diagnostic features of the sample (n = 117). Significant differences are marked in bold. Feature All (n = 117) FHR (n = 53) Control (n = 64) comparison n(%) or mean(SD) t or χ 2 p.adj Age 11.9(3.2) 11.8(3.1) 12.0(3.2) -0.275 0.784 Sex – female 64(54.7%) 28(52.8%) 36(56.2%) 0.033 0.071 Obstetric complications 30(25.6%) 20(37.7%) 10(15.6%) 6.320 0.036 Socioeconomic status 51.4(12.6) 48.2(14.1) 53.9(10.8) -2.211 0.058 Recent stressful life events - number (z-score) -0.1(0.9) -0.2(0.9) -0.1(0.9) -1.005 0.476 Recent stressful life events - impact (z-score) 0.0(0.9) -0.1(0.9) 0.1(0.9) -0.999 0.384 Familial high risk (FHR) 3.2 Epigenetic age correlation with chronological age The chronological age of the sample was consistently correlated with the estimated epigenetic ages calculated with the epigenetic clocks, and inversely correlated with the estimated TL (p.adj < 0.005 for all analyses). Similar correlations were obtained after the stratification of the sample by tissue used for methylation (p.adj < 0.005 for all analyses) (Table 2 ). Table 2 Correlation between chronologic and epigenetic age measured with 6 epigenetic clocks, measured for the whole sample and for the methylation data extracted from blood and saliva samples separately. Significant correlations are marked in bold. Epigenetic clock All sample Blood methylation Saliva methylation correlation coefficient p.adj correlation coefficient p.adj correlation coefficient p.adj Horvath 0.623 1.12E-12 0.577 5.78E-08 0.726 4.39E-07 Hannum 0.557 4.18E-10 0.637 8.52E-10 0.779 2.17E-08 Levine 0.562 3.83E-10 0.528 7.68E-07 0.737 2.56E-07 PedBE 0.383 7.13E-10 0.529 8.18E-07 0.626 2.97E-05 Wu 0.546 8.33E-10 0.531 7.97E-07 0.634 2.29E-05 TL -0.305 8.85E-04 -0.476 1.21E-05 -0.397 0.014 Telomere length (TL) 3.3 Differences between epigenetic and chronological age The differences between epigenetic and chronological age were assessed for all the epigenetic clocks. The estimated epigenetic age was greater than the chronological age for all the epigenetic clocks in exception of Horvath, that showed a younger epigenetic age than the chronological age (p.adj < 0.05 for all analyses). The mean difference between the epigenetic age was 3.7 years (standard deviation = 3.25 years). The analyses performed separately in the FHR and control subsamples showed similar results (p. adj < 0.05 for all analyses). The only difference was found in the FHR group, in which the Hannum clock estimated an epigenetic mean age 0.192 years younger than the chronological age (Table 3 ). Table 3 Epigenetic and chronological age comparison in the whole sample and in the FHR and control subsamples. Significant differences are marked in bold. Epigenetic clock All sample Blood methylation Saliva methylation correlation coefficient p.adj correlation coefficient p.adj correlation coefficient p.adj Horvath 0.623 1.12E-12 0.577 5.78E-08 0.726 4.39E-07 Hannum 0.557 4.18E-10 0.637 8.52E-10 0.779 2.17E-08 Levine 0.562 3.83E-10 0.528 7.68E-07 0.737 2.56E-07 PedBE 0.383 7.13E-10 0.529 8.18E-07 0.626 2.97E-05 Wu 0.546 8.33E-10 0.531 7.97E-07 0.634 2.29E-05 TL -0.305 8.85E-04 -0.476 1.21E-05 -0.397 0.014 Familial high risk (FHR); telomere length (TL) 3.4 Epigenetic age acceleration and FHR To characterize the differences in epigenetic age acceleration between the FHR and control individuals, the EEAA of the epigenetic clocks were included in the statistical models. The analyses showed that FHR individuals had a deacceleration in the epigenetic ages estimated by three epigenetic clocks. Specifically, the FHR individuals reported epigenetic negative acceleration for the Horvath, Hannum and PedBE epigenetic clocks (p.adj = 0.013, p.adj = 0.020, p.adj = 0.034; respectively) (Fig. 1; Supplementary Table 1 ). 3.5 Environmental risk effect on the epigenetic age acceleration The effect of obstetric complications, the socioeconomic statuses and recent stressful life events on the EEAA was assessed. No significant effect of the environmental factors was found to be associated with the EEAA (p.adj > 0.05 for all the analyses) (Table 4 ). Table 4 Effects of obstetric complications, socioeconomic status and recent stressful life events on the EEAA. Epigenetic clock EEAA Obstetric complications Socioeconomic status Recent stressful life events (number) Recent stressful life events (impact) t R 2 p.adj t R 2 p.adj t R 2 p.adj t R 2 p.adj Horvath EEAA -0.174 0.000 0.986 -0.343 0.001 1.000 0.039 0.001 0.969 0.312 0.002 1.000 Hannum EEAA 0.951 0.008 1.000 -0.342 0.003 1.000 0.077 0.000 1.000 0.369 0.000 1.000 Levine EEAA 1.179 0.012 1.000 -1.362 0.017 1.000 -0.969 0.010 1.000 -0.715 0.006 1.000 PedBE EEAA 0.262 0.001 1.000 -0.252 0.000 1.000 0.707 0.005 1.000 0.619 0.004 1.000 Wu EEAA 1.546 0.020 1.000 -0.061 0.001 0.993 -0.993 0.009 1.000 -0.587 0.003 1.000 TL EEAA -0.224 0.000 0.988 0.896 0.021 0.994 1.199 0.017 1.000 1.429 0.024 1.000 Extrinsic epigenetic age acceleration (EEAA); telomere length (TL) 4. Discussion The early stages of psychiatric disorders play a critical role in prognosis and outcome. Thus, the identification and characterization of risk factors and their molecular repercussion are key to understanding the mechanisms underlying psychopathology 50 . In this study, we characterized the epigenetic age of the young offspring of patients with schizophrenia and bipolar disorder. Compared to the offspring of control individuals, the FHR individuals reported a deacceleration of their epigenetic age relative to their chronological age for the Horvath, Hannum and PedBE epigenetic clocks and no differences for the Levine, Wu and TL clocks. None of the environmental stressors included could be associated with this phenomenon. Our results suggest that individuals at high risk may present epigenetic decelerated aging, which may conflict with the accelerated aging hypothesis in schizophrenia. Methylation data were extracted from blood and saliva samples. Although Horvath epigenetic clock can estimate the epigenetic age accurately in most tissues, the rest of epigenetic clocks perform best in either blood or saliva samples. The consistent correlation of epigenetic and chronological ages in the whole sample and in both tissues separately implies that in this particular case, variation generated by the DNA source used for methylation does not have a great impact. Yet, all analyses were performed using methylation tissue as a covariate to minimize any possible effect. We found an epigenetic age mean difference of almost 4 years from the chronological age. The analyses including the whole sample reported older epigenetic ages, in exception for the younger epigenetic age estimated with the Horvath epigenetic clock. Intriguingly, the stratification of the sample showed a younger epigenetic age estimated with the Hannum epigenetic clock exclusively in the FHR. These results led to the characterization of the acceleration found in the FHR and control subsamples. Positive acceleration of the epigenetic age reflects premature cellular aging, while negative acceleration – i.e. deacceleration – a denotes a slower pace of cellular aging. We identified epigenetic deacceleration in the FHR individuals for the Horvath, Hannum and PedBE epigenetic clocks. Previous studies in schizophrenia and bipolar disorder adult samples using epigenetic clocks based on age-related methylation markers – i.e. Horvath, Hannum, PedBE and Wu – have found inconsistent results. Four studies reported epigenetic deacceleration for at least one of the clocks included in the analyses 51 – 53 – one only in schizophrenia males treated with clozapine 9 –, four did not detect significant differences for epigenetic age acceleration 9 , 54 – 59 and two detected epigenetic age acceleration only in older bipolar disorder patients 60 and a small acceleration in schizophrenia patients 9 . As for the Levine clock/phenotypic age, accelerated epigenetic aging 9 , 58 and no differences with chronological age 59 have been found, providing no conclusive distinction between chronologic and phenotypic age epigenetic clocks. The epigenetic clock for TL found shorter lengths – thus implying aging acceleration – in schizophrenia patients 9 . Although inconclusive, these findings suggest that different epigenetic clocks could relate to diverse aspects of aging. To our knowledge, this is the first study assessing epigenetic age and its association with environmental stressors in a sample of FHR individuals. The pace of epigenetic aging could be irregular along the lifespan, potentially reporting more pronounced deviations from chronological age in certain stages of life 9 , 60 . Thus, disorder stages – including the ones preceding the onset – have to be considered to establish the epigenetic age dysregulation as a molecular predictor on mental illness. The exposition to environmental stressors has been thoroughly considered as a mediator of biological aging. Lifestyle, diet, substance use, education, economic income, psychosocial stress, disease and many other factors have been found to alter biological aging 61 – 64 . Biological aging has also been found to be affected by the prenatal environment and early life adversity 65 – 74 . Our results could not confirm the association of environmental stress exposure with a dysregulation of time-dependent methylation pattern. Several reasons may explain the lack of association between environmental stressors and the epigenetic age in our sample. The present sample was recruited at a very young age, making it difficult to detect the cumulative impact of environmental stressors. We lacked a measure covering all environmental stressors, and the use of discreet indexes may have limited our capacity to detect small changes. Moreover, our sample had a low frequency of obstetric complications and showed high homogeneity in the sociodemographic status and frequency of recent stressful life events. Further studies with larger samples and more refined tools for prospectively measuring the effect of environmental insults are required to define their role as mediators of epigenetic changes and to understand their contribution to psychopathology. Some limitations of the present work should be taken into consideration. Firstly, the sample size was limited. Therefore, the statistical analysis may have been underpowered to detect small effects. Epigenetic characterization was conducted using heterogeneous biological samples, but parallel data processing was performed for the blood and saliva samples and all the analyses were corrected for tissue type. The study lacked the assessment of alcohol and tobacco use during pregnancy, a putatively confounding variable in epigenetic studies. Participants were recruited based on their family history of either schizophrenia or bipolar disorder, which could have contributed additional heterogeneity in terms of determinants of risk. Finally, the young age of the participants impeded the categorization of the subjects based on their conversion to bipolar disorder or schizophrenia, a presumably more homogeneous phenotype. Nonetheless, the current definition based on familial risk has so far proven to be valid for detecting differences in psychopathology as well as in neuropsychological and brain imaging features 30 , 75 – 81 . Molecular mechanisms driven by epigenetic changes in early stages of life may be critical for the onset of severe mental disorders and their clinical course. The epigenetic age asynchronicities found in the young at high risk provide novel evidence to advance towards the characterization of the molecular signature driven by environmental stressors. The effects of a discordant pace in biological aging could be critical to understand the underlying mechanisms of illness onset and the age-associated conditions detected in schizophrenia and bipolar disorder. Further studies are required to identify the relevance, causality and interaction of internal and external elements that define the clinical manifestation of severe mental disorders to ultimately develop novel tools for personalized psychiatry. Declarations ACKNOWLEDGEMENTS The authors of this report would like to thank the following organizations for their kind support: the Spanish Ministry of Health, Instituto de Salud Carlos III (PI17/00741; PI21/00519), Ajut a la Recerca Pons Bartran (Fundació Clínic Recerca Biomèdica), the Catalonia Government (2017SGR881), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, the Alicia Koplowitz Foundation (2020) and CIBERSAM. IV is supported by the BITRECS project that received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 754550 and from “La Caixa” Foundation, under the agreement LCF/PR/GN18/50310006. CONFLICT OF INTEREST Díaz-Caneja C has received grant support from Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (PI17/00481, PI20/00721, JR19/00024) and honoraria from Exeltis and Angelini The other authors declare no conflict of interest References Berrettini WH. Are schizophrenic and bipolar disorders related? A review of family and molecular studies. Biol Psychiatry 2000; 48 : 531–538. Forstner AJ, Hecker J, Hofmann A, Maaser A, Reinbold CS, Mühleisen TW et al. Identification of shared risk loci and pathways for bipolar disorder and schizophrenia. PLoS One 2017; 12. doi: 10.1371/journal.pone.0171595 . Li W, Zhou F-C, Zhang L, Ng CH, Ungvari GS, Li J et al. Comparison of cognitive dysfunction between schizophrenia and bipolar disorder patients: A meta-analysis of comparative studies. J Affect Disord 2020; 274 : 652–661. Rasic D, Hajek T, Alda M, Uher R. 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Aging 2019; 11 : 10031–10051. McEwen LM, O’Donnell KJ, McGill MG, Edgar RD, Jones MJ, MacIsaac JL et al. The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. Proc Natl Acad Sci U S A 2020; 117 : 23329–23335. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging 2018; 10 : 573–591. Lu AT, Seeboth A, Tsai PC, Sun D, Quach A, Reiner AP et al. DNA methylation-based estimator of telomere length. Aging 2019; 11 : 5895–5923. McEwen LM, Jones MJ, Lin DTS, Edgar RD, Husquin LT, MacIsaac JL et al. Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array. Clin Epigenetics 2018; 10. doi: 10.1186/s13148-018-0556-2 . Chen T, Xu J, Liu G, Liu H, Chen M, Qin Y et al. Genetic variants in PTPRD and risk of gestational diabetes mellitus. Oncotarget 2016; 7 : 76101–76107. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2017. https://www.r-project.org/ . Parellada M, Castro-Fornieles J, Gonzalez-Pinto A, Pina-Camacho L, Moreno D, Rapado-Castro M et al. Predictors of functional and clinical outcome in early-onset first-episode psychosis: The child and adolescent first episode of psychosis (CAFEPS) study. Journal of Clinical Psychiatry 2015; 76 : e1441–e1448. Wu X, Ye J, Wang Z, Zhao C. Epigenetic Age Acceleration Was Delayed in Schizophrenia. Schizophr Bull 2021; 47 : 803–811. Okazaki S, Numata S, Otsuka I, Horai T, Kinoshita M, Sora I et al. Decelerated epigenetic aging associated with mood stabilizers in the blood of patients with bipolar disorder. Transl Psychiatry 2020; 10 : 129. Okazaki S, Otsuka I, Numata S, Horai T, Mouri K, Boku S et al. Epigenetic clock analysis of blood samples from Japanese schizophrenia patients. NPJ Schizophr 2019; 5 . doi: 10.1038/s41537-019-0072-1 . Voisey J, Lawford BR, Morris CP, Wockner LF, Noble EP, Young RMD et al. Epigenetic analysis confirms no accelerated brain aging in schizophrenia. NPJ Schizophr 2017; 3 . doi: 10.1038/s41537-017-0026-4 . McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation age is not accelerated in brain or blood of subjects with schizophrenia. Schizophr Res 2018; 196 : 39–44. McKinney BC, Lin H, Ding Y, Lewis DA, Sweet RA. DNA methylation evidence against the accelerated aging hypothesis of schizophrenia. NPJ Schizophr 2017; 3 . doi: 10.1038/s41537-017-0017-5 . Dada O, Adanty C, Dai N, Jeremian R, Alli S, Gerretsen P et al. Biological aging in schizophrenia and psychosis severity: DNA methylation analysis. Psychiatry Res 2021; 296. doi: 10.1016/j.psychres.2020.113646 . Teeuw J, Ori APS, Brouwer RM, de Zwarte SMC, Schnack HG, Hulshoff Pol HE et al. Accelerated aging in the brain, epigenetic aging in blood, and polygenic risk for schizophrenia. Schizophr Res 2021; 231 : 189–197. Kowalec K, Hannon E, Mansell G, Burrage J, Ori APS, Ophoff RA et al. Methylation age acceleration does not predict mortality in schizophrenia. Transl Psychiatry 2019; 9. doi: 10.1038/s41398-019-0489-3 . Fries GR, Bauer IE, Scaini G, Wu MJ, Kazimi IF, Valvassori SS et al. Accelerated epigenetic aging and mitochondrial DNA copy number in bipolar disorder. Transl Psychiatry 2017; 7. doi: 10.1038/s41398-017-0048-8 . Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018; 19 : 371–384. Palma-Gudiel H, Fañanás L, Horvath S, Zannas AS. Psychosocial stress and epigenetic aging . 1st ed. Elsevier Inc., 2020 doi: 10.1016/bs.irn.2019.10.020 . Raffington L, Belsky DW. Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research. Curr Environ Health Rep. 2022. doi: 10.1007/s40572-022-00338-8 . Rosen AD, Robertson KD, Hlady RA, Muench C, Lee J, Philibert R et al. DNA methylation age is accelerated in alcohol dependence. Transl Psychiatry 2018; 8. doi: 10.1038/s41398-018-0233-4 . Dammering F, Martins J, Dittrich K, Czamara D, Rex-Haffner M, Overfeld J et al. The pediatric buccal epigenetic clock identifies significant ageing acceleration in children with internalizing disorder and maltreatment exposure. Neurobiol Stress 2021; 15. doi: 10.1016/j.ynstr.2021.100394 . Koen N, Jones MJ, Nhapi RT, Lake MT, Donald KA, Barnett W et al. Maternal psychosocial risk factors and child gestational epigenetic age in a South African birth cohort study. Transl Psychiatry 2021; 11. doi: 10.1038/s41398-021-01434-3 . van Lieshout RJ, McGowan PO, de Vega WC, Savoy CD, Morrison KM, Saigal S et al. Extremely low birth weight and accelerated biological aging. Pediatrics 2021; 147. doi: 10.1542/peds.2020-001230 . McGill MG, Pokhvisneva I, Clappison AS, McEwen LM, Beijers R, Tollenaar MS et al. Maternal Prenatal Anxiety and the Fetal Origins of Epigenetic Aging. Biol Psychiatry 2022; 91 : 303–312. McKenna BG, Hendrix CL, Brennan PA, Smith AK, Stowe ZN, Newport DJ et al. Maternal Prenatal Depression and Epigenetic Age Deceleration: Testing Potentially Confounding Effects of Prenatal Stress and SSRI Use. Epigenetics 2020;: 1–11. Palma-Gudiel H, Eixarch E, Crispi F, Morán S, Zannas AS, Fañanás L. Prenatal adverse environment is associated with epigenetic age deceleration at birth and hypomethylation at the hypoxia-responsive EP300 gene. Clin Epigenetics 2019; 11 : 1–10. de Prado-Bert P, Ruiz-Arenas C, Vives-Usano M, Andrusaityte S, Cadiou S, Carracedo Á et al. The early-life exposome and epigenetic age acceleration in children. Environ Int 2021; 155. doi: 10.1016/j.envint.2021.106683 . Ross KM, Carroll JE, Horvath S, Hobel CJ, Coussons-Read ME, Dunkel Schetter C. Epigenetic age and pregnancy outcomes: GrimAge acceleration is associated with shorter gestational length and lower birthweight. Clin Epigenetics 2020; 12. doi: 10.1186/s13148-020-00909-2 . Shiau S, Wang L, Liu H, Zheng Y, Drong A, Joyce BT et al. Prenatal gestational diabetes mellitus exposure and accelerated offspring DNA methylation age in early childhood. Epigenetics 2020;: 1–10. Wang J, Zhou WH. Epigenetic clocks in the pediatric population: when and why they tick? Chin Med J (Engl). 2021; 134 : 2901–2910. Camprodon-Boadas P, Rosa-Justicia M, Sugranyes G, Moreno D, Baeza I, Ilzarbe D et al. Cognitive reserve and its correlates in child and adolescent offspring of patients diagnosed with schizophrenia or bipolar disorder. Eur Child Adolesc Psychiatry 2022. doi: 10.1007/s00787-022-01957-0 . de la Serna E, Ilzarbe D, Sugranyes G, Baeza I, Moreno D, Rodríguez-Toscano E et al. Lifetime psychopathology in child and adolescent offspring of parents diagnosed with schizophrenia or bipolar disorder: a 2-year follow-up study. Eur Child Adolesc Psychiatry 2021; 30 : 117–129. de la Serna E, Vila M, Sanchez-Gistau V, Moreno D, Romero S, Sugranyes G et al. Neuropsychological characteristics of child and adolescent offspring of patients with bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2016; 65 : 54–59. de la Serna E, Camprodon-Boadas P, Ilzarbe D, Sugranyes G, Baeza I, Moreno D et al. Neuropsychological development in the child and adolescent offspring of patients diagnosed with schizophrenia or bipolar disorder: A two-year follow-up comparative study. Prog Neuropsychopharmacol Biol Psychiatry 2020; 103 : 109972. Solé-Padullés C, Castro-Fornieles J, de La Serna E, Romero S, Calvo A, Sánchez-Gistau V et al. Altered cortico-striatal connectivity in offspring of schizophrenia patients relative to offspring of bipolar patients and controls. PLoS One 2016; 11. doi: 10.1371/journal.pone.0148045 . Sugranyes G, de La Serna E, Borras R, Sanchez-Gistau V, Pariente JC, Romero S et al. Clinical, Cognitive, and Neuroimaging Evidence of a Neurodevelopmental Continuum in Offspring of Probands with Schizophrenia and Bipolar Disorder. Schizophr Bull 2017; 43 : 1208–1219. Sugranyes G, Solé-Padullés C, de la Serna E, Borras R, Romero S, Sanchez-Gistau V et al. Cortical Morphology Characteristics of Young Offspring of Patients With Schizophrenia or Bipolar Disorder. J Am Acad Child Adolesc Psychiatry 2017; 56 : 79–88. Supplementary Table Supplementary Table 1 is not available with this version Additional Declarations Díaz-Caneja C has received grant support from Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (PI17/00481, PI20/00721, JR19/00024) and honoraria from Exeltis and Angelini The other authors declare no conflict of interest Cite Share Download PDF Status: Published Journal Publication published 08 May, 2023 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 22 Mar, 2023 Review # 1 received at journal 30 Jan, 2023 Reviewer # 1 agreed at journal 19 Jan, 2023 Reviewers invited by journal 19 Jan, 2023 Submission checks completed at journal 16 Jan, 2023 Editor assigned by journal 16 Jan, 2023 First submitted to journal 16 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2483797","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":169247757,"identity":"161826e8-414e-4d50-8b14-f8a6710dd052","order_by":0,"name":"Sergi 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12:40:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2483797/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2483797/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-023-02463-w","type":"published","date":"2023-05-08T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31959154,"identity":"c9f2f466-bc24-4eb3-82f6-311e23ae81a2","added_by":"auto","created_at":"2023-01-23 17:40:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272970,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2483797/v1/6d6dc6ff84cfeed1be50b892.jpg"},{"id":36730340,"identity":"ffa63271-98a5-413b-96bc-e3371b5d3951","added_by":"auto","created_at":"2023-05-09 07:09:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":633319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2483797/v1/e376eece-e312-4858-8ecb-f956d6a0d84f.pdf"}],"financialInterests":"\nDíaz-Caneja C has received grant support from Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (PI17/00481, PI20/00721, JR19/00024) and honoraria from Exeltis and Angelini\r\nThe other authors declare no conflict of interest ","formattedTitle":"Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSchizophrenia and bipolar disorder are highly prevalent and impairing conditions that have differential diagnostic criteria. However, their familial aggregation and overlapping clinical and genetic features do not fully correlate with their nosological boundaries, pointing towards a partially shared etiology \u003csup\u003e\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The individuals at familial high risk (FHR) have a 2-to-4-fold increase in the risk of developing a psychiatric disorder, for which the exposure to environmental stressors have a critical role \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSchizophrenia and bipolar disorder are associated with a shorter lifespan, which has been linked to age-related biomarkers and physiological conditions such as increased inflammation and oxidative stress, a shorter telomere length and metabolic disruption \u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10 CR11 CR12\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, suggesting that patients suffer from the effects of accelerated aging. Epigenetic modifications (changes in chromatin structure, primarily measured by assessing the methylation of CpG dinucleotides) have been closely related to gene expression, driving cell senescence and affecting their function \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Methylation patterns change throughout the lifespan, following a specific timing. Epigenetic clocks measure the methylation of specific sets of CpGs for the estimation the epigenetic age in years, a proxy of the biological age of the individual. The epigenetic age has been consistently correlated with chronological age in samples including schizophrenia patients, and yet the latest meta-analysis reports no epigenetic acceleration in the patients \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSchizophrenia and bipolar disorder prediction models perform best when including polygenic constructs, multiple environmental factors and their interaction \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The characterization of risk factors encompasses multiple sorts of environmental impacts occurring throughout all stages of life \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which can lead to an acceleration the epigenetic age \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Obstetric complications, including maternal and perinatal infections that drive immune responses in the offspring, are thought to cause a neurodevelopmental disruption \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Early life adversity (ranging from explicit violence to subtle forms of emotional negligence) has been associated with more severe manifestations of the disorders and suicidal behaviors \u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, recent traumatic events may also be a substantial risk factor for disorder onset \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Young individuals experiencing migration processes, lower socioeconomic statuses and urbanicity \u0026ndash; linked to social exclusion and isolation \u0026ndash; are also at a higher risk of developing schizophrenia \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we examined the epigenetic age of a sample consisting of FHR individuals and a control group. Blood and saliva samples were used to estimate their epigenetic age using six epigenetic clocks. We expected that the FHR group would report greater asynchronicities between their epigenetic and chronological age than the control group. Furthermore, we believed that these differences would be associated with the exposure to environmental stressors.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe present study is part of the Bipolar and Schizophrenia Young Offspring Study (BASYS), which is a multicenter, longitudinal, naturalistic study that aims to compare the clinical, neuropsychological, neuroimaging, genetic and epigenetic characteristics of the child and adolescent offspring of patients diagnosed with SZ or BD and of a community control group. This study was conducted in the child and adolescent psychiatry units of two hospitals in Spain: the Hospital Clinic in Barcelona and Hospital Gregorio Mara\u0026ntilde;\u0026oacute;n in Madrid. The methodology as well as the clinical and cognitive characteristics of the sample have been described previously in detail \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eThe individuals at FHR were offspring of patients with schizophrenia or bipolar disorder, recruited by psychiatrists from the adult psychiatry units of both hospitals. The inclusion criteria were: (a) age between 6 and 17 years, and (b) a parent diagnosed with schizophrenia or bipolar disorder. The exclusion criteria were: (a) intellectual disability with an impact on functioning, and (b) significant head injury or a current medical or neurological condition. The only inclusion criterion for the offspring of the community controls was an age between 6 and 17 years, while the exclusion criteria were exactly the same as those for the FHR group plus a family history of psychotic disorders in first- or second-degree relatives. As this study focused on epigenetic data, only the individuals who had provided biological samples for DNA methylation analysis (53 FHR and 64 controls) were assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ethical considerations\u003c/h2\u003e \u003cp\u003e All procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Written informed consent was obtained from one of the parents, having the other parent been informed, together with written assent from the participant if aged 12 and above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Clinical and environmental assessment\u003c/h2\u003e \u003cp\u003eA trained psychiatrist or psychologist performed a mental health assessment of all the parents using the Spanish version of the Structured Clinical Interview for DSM-IV Disorders (SCID-I) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Parents or primary caregivers were also interviewed about their children. The study participants were assessed directly by trained child psychiatrists or psychologists who were blind to their parental diagnoses, using the Spanish version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children - Present and Lifetime Version (K-SADS-PL) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInformation about obstetric complications was collected using the Lewis-Murray scale \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This scale rates 15 obstetric complications as absent or definitely present, while 9 of the exposures can also be rated as equivocally present. For this study, history of obstetric complications was considered positive if at least one complication was definitely present.\u003c/p\u003e \u003cp\u003eThe socioeconomic status was calculated according to the Hollingshead and Redlich scale \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The higher socioeconomic level between each set of parents was considered. The higher socioeconomic level between each set of parents was considered. Lower scores indicate a low socioeconomic status.\u003c/p\u003e \u003cp\u003eThe occurrence of recent stressful events was determined using the Stressful Life Events Schedule (SLES), child-reported version \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The SLES evaluates the presence/absence of a list of potentially stressful, age-adapted events in the last 12 months and rates their potential impact on a scale of 1 (not at all) to 4 (a lot). The SLES provides two scores: the number of stressful life events (SLEs) in the previous year and the score for the total cumulative impact of the SLEs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Biological samples\u003c/h2\u003e \u003cp\u003eBlood samples were collected in EDTA tubes (K2EDTA BD Vacutainer EDTA tubes; Becton Dickinson, Franklin Lakes, New Jersey, USA) and genomic DNA was extracted with the MagNA Pure LC DNA Isolation Kit III and a MagNA Pure LC system (Roche Diagnostics GmbH, Mannheim, Germany). Saliva samples were collected using the Oragene DNA Saliva Collection Kit (OG-500, DNA Self-Collection Kit, Genotek, Ottawa, Ontario, Canada) and DNA was extracted according to the manufacturer's instructions. DNA concentration and quality were measured spectrophotometrically using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Epsom, Surrey, UK). DNA methylation β-values were obtained at GenomeScan using the Illumina Infinium MethylationEPIC BeadChip Kit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Methylation data collection\u003c/h2\u003e \u003cp\u003eRaw intensity data (.IDAT) files were received and parallel bioinformatics processes were conducted in-house using the Chip Analysis Methylation Pipeline (ChAMP) Bioconductor package \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, which were performed separately for the methylation data obtained from blood samples (n\u0026thinsp;=\u0026thinsp;79) and those obtained from saliva samples (n\u0026thinsp;=\u0026thinsp;38). Raw .IDAT files were used to load the data into the R environment with the \u003cem\u003echamp.load\u003c/em\u003e function, which also enabled the simultaneous undertaking of the probe QC and removal steps. Probes with weak signals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cross-reactive probes, non-CpG probes, probes with \u0026lt;\u0026thinsp;3 beads in at least 5% of the samples per probe, probes that bound to SNP sites, and sex chromosomes were all considered problematic for the accurate detection of downstream methylation and were therefore removed. β-values were then normalized using the \u003cem\u003echamp.norm\u003c/em\u003e function, specifically with the beta-mixture quantile method (BMIQ function). Next, the singular value decomposition (SVD) method was performed with \u003cem\u003echamp.SVD\u003c/em\u003e to assess the amount and significance of the technical batch components in our dataset. Using the \u003cem\u003echamp.runCombat\u003c/em\u003e function, combat algorithms were applied to correct for slide and array (significant components detected by the SVD method).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Epigenetic clock construction\u003c/h2\u003e \u003cp\u003eThe \u003cem\u003emethylclock\u003c/em\u003e R package \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e was used to construct six epigenetic clocks. Horvath is a multitusse-based epigenetic clock designed to predict chronological age in individuals along the whole lifespan \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Similarly, Hannum and Wu epigenetic clocks estimate the epigenetic patterns linked to chronological age in blood tissues in adults and children, respectively \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. PedBE epigenetic clock was constructed for saliva samples in children \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Levine epigenetic clock captures the methylation patterns of \u0026ldquo;phenotypic aging\u0026rdquo;, mortality and morbidity epigenetic patterns rather than with chronological age \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. CpGs located in telomeric regions can also be measured to estimate telomere length (TL), a well-established biomarker of health conditions associated with aging. The TL estimation by means of epigenetic markers used in this study was constructed with blood samples of adults \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBriefly, from normalized and batch-corrected methylation data, the package extracts the methylation levels of the available CpGs included in each clock. Subsequently, the coefficients obtained through an elastic net in the prediction models of each of the clocks in the original studies are used to predict the epigenetic age. Several studies have demonstrated that the epigenetic clocks are resistant to the CpG site missingness from the MethylationEPIC BeadChip Kit \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. For each clock, we obtained the epigenetic age in years. The extrinsic epigenetic age acceleration (EEAA) for each clock was obtained after regressing chronological age on the epigenetic age adjusted by seven cell-type proportions known to change throughout the lifespan \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the analyses were performed with R v4.1.2 \u003csup\u003e49\u003c/sup\u003e. Multiple testing correction was applied in all the analyses by means of the FDR method, and the threshold of significance of the adjusted p value (p.adj) was set at α\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eGroup differences in sociodemographic features and environmental risk factors were calculated by linear mixed-effects models for continuous variables, using family relatedness as a random effect, and by chi square tests for categorical variables.\u003c/p\u003e \u003cp\u003eThe correlation between epigenetic and chronological age was tested using Pearson\u0026rsquo;s product-moment correlation. The differences between epigenetic and chronological age was assessed with mixed-effects models, using family relatedness as a random effect and corrected by sex, FHR status and tissue used for methylation. The analyses stratified by tissue were constructed similarly, but only corrected by sex and FHR status.\u003c/p\u003e \u003cp\u003eTo assess the EEAA difference between the FHR and controls linear mixed-effects models were used, considering the FHR status as a dependent variable, EEAA as fixed effects, family relatedness as a random effect and corrected by sex and tissue used for methylation. The effect of environmental factors (obstetric complications, socioeconomic statuses and recent stressful life events) on the EEAA was assessed using a linear mixed-effects model corrected for sex and the tissue used for the epigenetic characterization, with family relatedness as a random effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample description\u003c/h2\u003e \u003cp\u003eOne hundred seventeen children and adolescents aged 6\u0026ndash;17 years (54.7% females) were included in this study, 53 of whom were at FHR (45.3%) and 64 were controls (54.7%). The main sociodemographic of the study sample and the differences between the FHR individuals and controls are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Only the proportion of FHR individuals reporting an obstetric complication was found significantly increased (p.adj\u0026thinsp;=\u0026thinsp;0.036).\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\u003eSummary and group comparison of the sociodemographic and diagnostic features of the sample (n\u0026thinsp;=\u0026thinsp;117). Significant differences are marked in bold.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFeature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll (n\u0026thinsp;=\u0026thinsp;117)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFHR (n\u0026thinsp;=\u0026thinsp;53)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;64)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003ecomparison\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003en(%) or mean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et or χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.9(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.8(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.0(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex \u0026ndash; female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(54.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28(52.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36(56.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObstetric complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20(37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10(15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.4(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.2(14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.9(10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent stressful life events - number (z-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.2(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecent stressful life events - impact (z-score)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1(0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eFamilial high risk (FHR)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Epigenetic age correlation with chronological age\u003c/h2\u003e \u003cp\u003eThe chronological age of the sample was consistently correlated with the estimated epigenetic ages calculated with the epigenetic clocks, and inversely correlated with the estimated TL (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for all analyses). Similar correlations were obtained after the stratification of the sample by tissue used for methylation (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.005 for all analyses) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eCorrelation between chronologic and epigenetic age measured with 6 epigenetic clocks, measured for the whole sample and for the methylation data extracted from blood and saliva samples separately. Significant correlations are marked in bold.\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEpigenetic clock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBlood methylation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSaliva methylation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorvath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.12E-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.78E-08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.39E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHannum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.18E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.52E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.17E-08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.83E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.68E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.56E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.13E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.18E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.97E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.33E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.97E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.29E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.85E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.21E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eTelomere length (TL)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differences between epigenetic and chronological age\u003c/h2\u003e \u003cp\u003eThe differences between epigenetic and chronological age were assessed for all the epigenetic clocks. The estimated epigenetic age was greater than the chronological age for all the epigenetic clocks in exception of Horvath, that showed a younger epigenetic age than the chronological age (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses). The mean difference between the epigenetic age was 3.7 years (standard deviation\u0026thinsp;=\u0026thinsp;3.25 years). The analyses performed separately in the FHR and control subsamples showed similar results (p. adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all analyses). The only difference was found in the FHR group, in which the Hannum clock estimated an epigenetic mean age 0.192 years younger than the chronological age (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEpigenetic and chronological age comparison in the whole sample and in the FHR and control subsamples. Significant differences are marked in bold.\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEpigenetic clock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll sample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eBlood methylation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eSaliva methylation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorvath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.12E-12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5.78E-08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4.39E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHannum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4.18E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.52E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.17E-08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.83E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.68E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.56E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedBE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e7.13E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e8.18E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.97E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.33E-10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.97E-07\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e2.29E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.85E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.21E-05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFamilial high risk (FHR); telomere length (TL)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Epigenetic age acceleration and FHR\u003c/h2\u003e \u003cp\u003eTo characterize the differences in epigenetic age acceleration between the FHR and control individuals, the EEAA of the epigenetic clocks were included in the statistical models. The analyses showed that FHR individuals had a deacceleration in the epigenetic ages estimated by three epigenetic clocks. Specifically, the FHR individuals reported epigenetic negative acceleration for the Horvath, Hannum and PedBE epigenetic clocks (p.adj\u0026thinsp;=\u0026thinsp;0.013, p.adj\u0026thinsp;=\u0026thinsp;0.020, p.adj\u0026thinsp;=\u0026thinsp;0.034; respectively) (Fig.\u0026nbsp;1; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Environmental risk effect on the epigenetic age acceleration\u003c/h2\u003e \u003cp\u003eThe effect of obstetric complications, the socioeconomic statuses and recent stressful life events on the EEAA was assessed. No significant effect of the environmental factors was found to be associated with the EEAA (p.adj\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all the analyses) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffects of obstetric complications, socioeconomic status and recent stressful life events on the EEAA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEpigenetic clock EEAA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eObstetric complications\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSocioeconomic status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eRecent stressful life events (number)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eRecent stressful life events (impact)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eR\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003ep.adj\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHorvath\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHannum\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevine\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePedBE\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWu\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTL\u003csub\u003eEEAA\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003eExtrinsic epigenetic age acceleration (EEAA); telomere length (TL)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe early stages of psychiatric disorders play a critical role in prognosis and outcome. Thus, the identification and characterization of risk factors and their molecular repercussion are key to understanding the mechanisms underlying psychopathology \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. In this study, we characterized the epigenetic age of the young offspring of patients with schizophrenia and bipolar disorder. Compared to the offspring of control individuals, the FHR individuals reported a deacceleration of their epigenetic age relative to their chronological age for the Horvath, Hannum and PedBE epigenetic clocks and no differences for the Levine, Wu and TL clocks. None of the environmental stressors included could be associated with this phenomenon. Our results suggest that individuals at high risk may present epigenetic decelerated aging, which may conflict with the accelerated aging hypothesis in schizophrenia.\u003c/p\u003e \u003cp\u003eMethylation data were extracted from blood and saliva samples. Although Horvath epigenetic clock can estimate the epigenetic age accurately in most tissues, the rest of epigenetic clocks perform best in either blood or saliva samples. The consistent correlation of epigenetic and chronological ages in the whole sample and in both tissues separately implies that in this particular case, variation generated by the DNA source used for methylation does not have a great impact. Yet, all analyses were performed using methylation tissue as a covariate to minimize any possible effect.\u003c/p\u003e \u003cp\u003eWe found an epigenetic age mean difference of almost 4 years from the chronological age. The analyses including the whole sample reported older epigenetic ages, in exception for the younger epigenetic age estimated with the Horvath epigenetic clock. Intriguingly, the stratification of the sample showed a younger epigenetic age estimated with the Hannum epigenetic clock exclusively in the FHR. These results led to the characterization of the acceleration found in the FHR and control subsamples. Positive acceleration of the epigenetic age reflects premature cellular aging, while negative acceleration \u0026ndash; i.e. deacceleration \u0026ndash; a denotes a slower pace of cellular aging. We identified epigenetic deacceleration in the FHR individuals for the Horvath, Hannum and PedBE epigenetic clocks.\u003c/p\u003e \u003cp\u003ePrevious studies in schizophrenia and bipolar disorder adult samples using epigenetic clocks based on age-related methylation markers \u0026ndash; i.e. Horvath, Hannum, PedBE and Wu \u0026ndash; have found inconsistent results. Four studies reported epigenetic deacceleration for at least one of the clocks included in the analyses \u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e \u0026ndash; one only in schizophrenia males treated with clozapine \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e \u0026ndash;, four did not detect significant differences for epigenetic age acceleration \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR55 CR56 CR57 CR58\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e and two detected epigenetic age acceleration only in older bipolar disorder patients \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e and a small acceleration in schizophrenia patients \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. As for the Levine clock/phenotypic age, accelerated epigenetic aging \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and no differences with chronological age \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e have been found, providing no conclusive distinction between chronologic and phenotypic age epigenetic clocks. The epigenetic clock for TL found shorter lengths \u0026ndash; thus implying aging acceleration \u0026ndash; in schizophrenia patients \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Although inconclusive, these findings suggest that different epigenetic clocks could relate to diverse aspects of aging. To our knowledge, this is the first study assessing epigenetic age and its association with environmental stressors in a sample of FHR individuals. The pace of epigenetic aging could be irregular along the lifespan, potentially reporting more pronounced deviations from chronological age in certain stages of life \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Thus, disorder stages \u0026ndash; including the ones preceding the onset \u0026ndash; have to be considered to establish the epigenetic age dysregulation as a molecular predictor on mental illness.\u003c/p\u003e \u003cp\u003eThe exposition to environmental stressors has been thoroughly considered as a mediator of biological aging. Lifestyle, diet, substance use, education, economic income, psychosocial stress, disease and many other factors have been found to alter biological aging \u003csup\u003e\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Biological aging has also been found to be affected by the prenatal environment and early life adversity \u003csup\u003e\u003cspan additionalcitationids=\"CR66 CR67 CR68 CR69 CR70 CR71 CR72 CR73\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Our results could not confirm the association of environmental stress exposure with a dysregulation of time-dependent methylation pattern. Several reasons may explain the lack of association between environmental stressors and the epigenetic age in our sample. The present sample was recruited at a very young age, making it difficult to detect the cumulative impact of environmental stressors. We lacked a measure covering all environmental stressors, and the use of discreet indexes may have limited our capacity to detect small changes. Moreover, our sample had a low frequency of obstetric complications and showed high homogeneity in the sociodemographic status and frequency of recent stressful life events. Further studies with larger samples and more refined tools for prospectively measuring the effect of environmental insults are required to define their role as mediators of epigenetic changes and to understand their contribution to psychopathology.\u003c/p\u003e \u003cp\u003eSome limitations of the present work should be taken into consideration. Firstly, the sample size was limited. Therefore, the statistical analysis may have been underpowered to detect small effects. Epigenetic characterization was conducted using heterogeneous biological samples, but parallel data processing was performed for the blood and saliva samples and all the analyses were corrected for tissue type. The study lacked the assessment of alcohol and tobacco use during pregnancy, a putatively confounding variable in epigenetic studies. Participants were recruited based on their family history of either schizophrenia or bipolar disorder, which could have contributed additional heterogeneity in terms of determinants of risk. Finally, the young age of the participants impeded the categorization of the subjects based on their conversion to bipolar disorder or schizophrenia, a presumably more homogeneous phenotype. Nonetheless, the current definition based on familial risk has so far proven to be valid for detecting differences in psychopathology as well as in neuropsychological and brain imaging features \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan additionalcitationids=\"CR76 CR77 CR78 CR79 CR80\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMolecular mechanisms driven by epigenetic changes in early stages of life may be critical for the onset of severe mental disorders and their clinical course. The epigenetic age asynchronicities found in the young at high risk provide novel evidence to advance towards the characterization of the molecular signature driven by environmental stressors. The effects of a discordant pace in biological aging could be critical to understand the underlying mechanisms of illness onset and the age-associated conditions detected in schizophrenia and bipolar disorder. Further studies are required to identify the relevance, causality and interaction of internal and external elements that define the clinical manifestation of severe mental disorders to ultimately develop novel tools for personalized psychiatry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this report would like to thank the following organizations for their kind support: the Spanish Ministry of Health, Instituto de Salud Carlos III (PI17/00741; PI21/00519), Ajut a la Recerca Pons Bartran (Fundaci\u0026oacute; Cl\u0026iacute;nic Recerca Biom\u0026egrave;dica), the Catalonia Government (2017SGR881), co-financed by ERDF Funds from the European Commission, \u0026ldquo;A way of making Europe\u0026rdquo;, the Alicia Koplowitz Foundation (2020) and CIBERSAM.\u003c/p\u003e\n\u003cp\u003eIV is supported by the BITRECS project that received funding from the European Union\u0026rsquo;s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 754550 and from \u0026ldquo;La Caixa\u0026rdquo; Foundation, under the agreement LCF/PR/GN18/50310006.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD\u0026iacute;az-Caneja C has received grant support from Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation (PI17/00481, PI20/00721, JR19/00024) and honoraria from Exeltis and Angelini\u003c/p\u003e\n\u003cp\u003eThe other authors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eBerrettini WH. 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J Am Acad Child Adolesc Psychiatry 2017; \u003cstrong\u003e56\u003c/strong\u003e: 79\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Table","content":"\u003cp\u003eSupplementary Table 1 is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"epigenetic clock, biological age, familial high risk, environmental risk factor","lastPublishedDoi":"10.21203/rs.3.rs-2483797/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2483797/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpigenetic modifications occur sequentially during the lifespan, but their pace can be altered by external stimuli. The onset of schizophrenia and bipolar disorder is critically modulated by stressors that may alter the epigenetic pattern, a putative signature marker of exposure to environmental risk factors. In this study, we estimated the age-related epigenetic modifications to assess the differences between young individuals at familial high risk (FHR) and controls and their association with environmental stressors.\u003c/p\u003e\n\u003cp\u003eThe sample included 117 individuals (6-17 years) at FHR (45%) and a control group (55%). Blood and saliva samples were used estimate the epigenetic age with six epigenetic clocks through methylation data. Environmental risk was measured with obstetric complications, socioeconomic statuses and recent stressful life events data.\u003c/p\u003e\n\u003cp\u003eFor all epigenetic clocks except Horvath, estimated epigenetic ages were older than the chronological. Stratified analysis showed that Hannum epigenetic clock reported younger epigenetic age only in the FHR group. Epigenetic age was found deaccelerated in the FHR individuals in three epigenetic clocks. No effect of the environmental risk factors on the epigenetic age acceleration could be detected.\u003c/p\u003e\n\u003cp\u003eEpigenetic age asynchronicities were found in the young at high risk, suggesting that offspring of affected parents follow a slower pace of biological aging than the control group. It still remains unclear which environmental stressors orchestrate the changes in the methylation pattern. Further studies are needed to better characterize the molecular impact of environmental stressors before illness onset, which could be critical in the development of tools for personalized psychiatry.\u003c/p\u003e","manuscriptTitle":"Epigenetic age deacceleration in youth at familial risk for schizophrenia and bipolar disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-23 17:40:00","doi":"10.21203/rs.3.rs-2483797/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2023-03-22T10:58:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2023-01-31T02:16:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2023-01-20T01:12:18+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2023-01-19T22:40:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-01-16T17:58:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-01-16T12:35:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2023-01-16T12:35:45+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":"daa4e530-33cc-4d72-9867-ec331a572043","owner":[],"postedDate":"January 23rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":18557420,"name":"Biological sciences/Genetics/Genomics/Personalized medicine"},{"id":18557421,"name":"Health sciences/Pathogenesis/Clinical genetics"}],"tags":[],"updatedAt":"2023-05-09T07:09:38+00:00","versionOfRecord":{"articleIdentity":"rs-2483797","link":"https://doi.org/10.1038/s41398-023-02463-w","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2023-05-08 04:00:00","publishedOnDateReadable":"May 8th, 2023"},"versionCreatedAt":"2023-01-23 17:40:00","video":"","vorDoi":"10.1038/s41398-023-02463-w","vorDoiUrl":"https://doi.org/10.1038/s41398-023-02463-w","workflowStages":[]},"version":"v1","identity":"rs-2483797","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2483797","identity":"rs-2483797","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}