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Epigenetic clock analysis of blood samples in drug-naive first-episode schizophrenia patients



Schizophrenia (SCZ) is a severe and chronic psychiatric disorder with premature age-related physiological changes. However, numerous previous studies examined the epigenetic age acceleration in SCZ patients and yielded inconclusive results. In this study, we propose to explore the epigenetic age acceleration in drug-naive first-episode SCZ (FSCZ) patients and investigate whether epigenetic age acceleration is associated with antipsychotic treatment, psychotic symptoms, cognition, and subcortical volumes.


We assessed the epigenetic age in 38 drug-naive FSCZ patients and 38 healthy controls by using three independent clocks, including Horvath, Hannum and Levine algorithms. The epigenetic age measurements in SCZ patients were repeated after receiving 8 weeks risperidone monotherapy.


Our findings showed significantly positive correlations between epigenetic ages assessed by three clocks and chronological age in both FSCZ patients and healthy controls. Compared with healthy controls, drug-naive FSCZ patients have a significant epigenetic age deceleration in Horvath clock (p = 0.01), but not in Hannum clock (p = 0.07) and Levine clock (p = 0.43). The epigenetic ages of Hannum clock (p = 0.002) and Levine clock (p = 0.01) were significantly accelerated in SCZ patients after 8-week risperidone treatment. However, no significant associations between epigenetic age acceleration and psychotic symptoms, cognitive function, as well as subcortical volumes were observed in FSCZ patients.


These results demonstrate that distinct epigenetic clocks are sensitive to different aspects of aging process. Further investigations with comprehensive epigenetic clock analyses and large samples are required to confirm our findings.

Peer Review reports


Schizophrenia (SCZ) is one of the most disabling illnesses [1], affecting approximately 1% of the general population worldwide. SCZ patients have a higher rate of premature mortality [2], and their life expectancy is roughly 15 years less than healthy individuals [3]. Though suicides and accidents account for a large portion of premature mortality in SCZ, the majority of morbidity is attributed to age-related diseases, such as diabetes [4], cardiovascular disease [5, 6], and cancer [7].

Multiple lines of evidence suggest that patients with SCZ show premature aging characteristics, such as cognitive decline [8, 9], dendritic spine loss [10], cortical atrophy [11], shorted telomere [12], and increased levels of inflammatory factors and oxidative stress [13]. The accelerated aging hypothesis of SCZ has thus been proposed as a cause for the excess mortality in SCZ. This hypothesis proposes SCZ as a syndrome of accelerated aging associated with premature physiological change that increases the risk of aging-related medical conditions and mortality [14]. However, testing this hypothesis is difficult due to the lack of accurate and robust biomarkers for biological age.

The recent development of DNA methylation (DNAm) based epigenetic clocks (also called epigenetic ages) offers a promise for addressing this challenge. The epigenetic age was integrated with DNAm levels at a set of cytosine-phosphate-guanine (CpG) sites using mathematical algorithms, producing an accurate and well-validated measure of chronological age [15]. Epigenetic age acceleration or deceleration is defined as an increase or decrease in epigenetic age compared to chronological age. Accumulating evidence has shown that epigenetic age acceleration is associated with numerous risk factors and outcomes for psychiatric disorders, including adversity exposure [16,17,18], cognitive decline [19,20,21], altered brain structures [22,23,24,25], and depression [26,27,28]. However, previous studies have shown inconsistent results in SCZ. Although no acceleration of epigenetic aging has consistently been reported in blood and brain tissues of patients with SCZ [29, 30], some studies also have detected accelerated or delayed epigenetic aging in patients with SCZ [31,32,33,34,35]. Considering the pathological and clinical heterogeneity of SCZ, confounding variables such as antipsychotic medications and illness duration may partially explain these inconsistent findings.

Here, we investigated the epigenetic age in a cohort of drug-naive FSCZ patients and healthy controls using three independent epigenetic clock approaches, including Horvath [36], Hannum [37] and Levine [38] methods. All drug-naive FSCZ patients received treatment with risperidone monotherapy and were followed up for 8 weeks. Then, we assessed the effect of antipsychotic treatment on the epigenetic aging process in FSCZ and investigated the potential associations of epigenetic age acceleration with psychotic symptoms, cognitive function, as well all subcortical volumes.



Our study recruited 38 drug-naive FSCZ patients from the Henan Mental Hospital. Diagnosis of schizophrenia were established by two experienced psychiatrists based on the criteria from the DSM-IV-TR while only individuals with an initial onset of SCZ with no medical history of antipsychotic medication or psychotropic drugs use were selected. A monotherapy session consisting of risperidone for 8 weeks were applied to the FSCZ subjects with a follow-up at the end of treatment. Additionally, thirty-eight healthy volunteers with no previous history of psychiatric or neurological disorders were recruited from the local community to serve as the healthy controls. This study was approved by the Ethics Committee of the Second Xiangya Hospital of Central South University. All the participants provided written informed consent after receiving a complete study description.

Psychopathological and neurocognitive function assessments

The severity of psychotic symptoms of the FSCZ patients at baseline and after 8 weeks of treatment were assessed by using the 30-item Positive and Negative Symptom Scale (PANSS). The improvement of antipsychotic treatment at 8 weeks was evaluated by the percentage change in PANSS as described previously [39].

The cognitive functions of participants were analyzed by utilizing the Digit Span Tests (DST-Forward and DST-Backward), the Verbal Fluency Test (VFT), the Stroop Tests (Stroop-W: words, Stroop-C: colours, and Stroop-I: interference), the Trail Making Test (TMT-part A and TMT-part B), and the Wisconsin Card Sorting Test (WCST-C: categories and WCST-P: perseverative errors, 128 cards). The cognitive function of all participants was evaluated at baseline while the 36 FSCZ patients were reassessed after an eight-week follow-up. Cognitive function improvement in FSCZ patients before and after treatment was determined by the differences in cognitive function scores between 8-week follow-up and baseline.

Imaging data acquisition and processing

All participants were scanned with a Siemens 3.0 T MRI scanner (Verio). Left and right accumbens, amygdala, caudate, hippocampus, pallidum, putamen, thalamus, as well as intracranial volume (ICVs), were obtained from high-resolution T1-weighted structural brain data by using the programme’s segmentation procedure of FreeSurfer software package (v5.3.0, Details on acquisition parameters and image processing are described in our previous study [40].

Genome-wide methylation analysis

Genomic DNA from all participants was extracted from whole blood samples by using the QIAamp DNA Blood Mini Kit (QIAGEN), followed by bisulfite conversion with EZ DNA Methylation™Kit (Zymo Research; Irvine, CA). For each sample, the genome-wide DNA methylation was measured by using the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA), and the β-values of DNA methylation were calculated and normalized by using the GenomeStudio software(v1.9). Full details of the DNAm analysis are described in our previous paper [41].

Epigenetic age estimates

Epigenetic age was calculated using three independent epigenetic clocks: Horvath, Hannum and Levine algorithms. Horvath’s epigenetic age was trained on chronological age by using a total of 353 CpG sites across tissues and cell types [36]. Hannum’s epigenetic age was trained on chronological age based on 71 CpG probes from whole blood samples [37]. Levine algorithm trained on 9 aging biomarkers and chronological age using 513 CpG probes from blood samples, and developed an epigenetic biomarker of aging, called “DNAm PhenoAge” [38]. Horvath’s epigenetic age was estimated with the online DNA Methylation Age Calculator ( Epigenetic ages for Hannum and Levine’s clocks were estimated based on the coefficients of CpG sites listed in Hannum et al’s [37] and Levine et al’s papers [38]. For each epigenetic clock, the epigenetic age acceleration was defined as AgeAccelerationResidual (AgeAccel) and was calculated by using the residual from regressing epigenetic age on chronological age.

Statistical analyses

For continuous variables, Pearson’s or Spearman’s rank correlation analyses were conducted to assess their relationships. Differences in epigenetic age accelerations between drug-naive FSCZ patients and healthy controls at baseline were compared by using multiple linear regression analyses after adjusting for sex, age, and tobacco use. Changes in epigenetic age acceleration in drug-naive FSCZ patients at baseline and after 8-week treatment were tested with Paired samples t-test. All data were analyzed using the Statistical Package for Social Sciences (SPSS, version 18.0).


Descriptive characteristics of the study participants

The cohort of our study consists of 38 drug-naive FSCZ patients (25 male and 13 female; mean age ± SD, 25.00 ± 4.95 years) and 38 healthy controls (25 male and 13 female; 24.76 ± 4.56 years). No differences were observed in chronological age and sex between FSCZ patients and controls (p > 0.05). The demographic and clinical characteristics of the samples are shown in the Supplemental Table 1.

Accuracy of epigenetic age estimates

As expected, chronological age has strong positive correlations with Horvath’s epigenetic age (rho = 0.79, p = 1.55e− 25), Hannum’s epigenetic age(rho = 0.82, p = 2.14e− 28), and Levine DNAm PhenoAge (rho = 0.79, p = 9.79e− 26) in all subjects. The significant correlations did not change when patients and controls were analyzed separately (Fig. 1A). Compared with chronological age, Horvath and Hannum’s epigenetic ages were overestimated by 4.0 and 6.6 years respectively, and Levine’s DNAm PhenoAge was underestimated by 5.4 years. Comparing AgeAccel among these three epigenetic clocks using all samples, we found a moderate concordance (r = 0.293–0.541), indicating a substantial variation in the epigenetic age acceleration estimated by these three clocks.

Fig. 1
figure 1

Epigenetic age analyses in drug-naive first-episode schizophrenia (SCZ) patients and healthy controls. A Significant Pearson’s correlations between epigenetic age and chronological age in SCZ patients at baseline (red) and after 8 weeks treatment (blue), and healthy controls (black). Each panel of the figure shows scatter plots of epigenetic age (y-axis) against chronological age (x-axis) in whole blood sample measured by Horvath, Hannum, and Levine epigenetic clocks. B Comparison of epigenetic age acceleration between drug-naive first-episode SCZ patients and healthy controls. Between-group difference of epigenetic age acceleration measured by Horvath, Hannum, and Levine epigenetic clocks were tested by regression analyses adjusting for age, sex, and tobacco use. C Antipsychotic effects on epigenetic age acceleration in drug-naive first-episode SCZ patients. The changes of epigenetic age acceleration in drug-naive first-episode SCZ patients before and after 8-week risperidone treatment were tested with Paired samples t-test. FSCZ: drug-naive first-episode schizophrenia, TSCZ: 8-week antipsychotics treated drug-naive first-episode schizophrenia

Comparison of epigenetic age acceleration between FSCZ patients and healthy controls

Multiple regression analyses found a significant difference between FSCZ patients and healthy controls in Horvath’s AgeAccel (β = − 0.28, p = 0.01), but not in Hannum’s AgeAccel (β = − 0.21, p = 0.07) and Levine’s PhenoAgeAccel (β = − 0.09, p = 0.42) (Fig. 1B). The mean Horvath’s AgeAccel in FSCZ patients and controls are − 0.93 and 1.25, indicating a deceleration with 2.18 years in FSCZ patients compared to controls. In addition, multiple regression analyses revealed a sex-effect on Horvath’s AgeAccel (β = − 0.29, p = 0.01) and Horvath’s AgeAccel (β = − 0.25, p = 0.03). No age effect on epigenetic age acceleration was observed for all three epigenetic clocks (p > 0.05).

Antipsychotic effect on epigenetic age acceleration

Paired samples t-test analyses showed significant changes of Hannum’s AgeAccel (t = − 3.30, p = 0.002) and Levine’s PhenoAgeAccel (t = − 2.59, p = 0.01) in FSCZ patients before and after 8-week risperidone treatment (Fig. 1C). The mean Hannum’s AgeAccel and Levine’s PhenoAgeAccel in SCZ patients were − 1.02 and − 0.92 at baseline and 0.53 and 0.94 after treatment, suggesting that Hannum’s and Levine’s epigenetic ages are respectively accelerated with 1.55 and 1.86 years in FSCZ patients after 8-week risperidone treatment. For Horvath’s AgeAccel, an acceleration of 0.61 years was observed in FSCZ patients although the change is not statistically significant (t = − 1.28, p = 0.21). Multiple regression analyses found that there were no significant differences in the epigenetic age acceleration for all three epigenetic clocks (p > 0.05) between FSCZ patients after 8 weeks risperidone treatment and healthy controls.

Association of epigenetic age acceleration with psychotic symptom severity and cognitive function

Multiple linear regression analyses were performed to examine the associations between epigenetic age acceleration and PANSS scores in FSCZ patients at baseline as well as the correlation of baseline epigenetic age acceleration with the percentage change in PANSS after 8-week treatment, adjusting for age, sex, tobacco use, and illness duration. Our result showed that epigenetic age accelerations for all three epigenetic clocks have no significant correlations with PANSS scores and PANSS percentage change values (Table 1).

Table 1 No correlations of epigenetic aging acceleration estimated by three clocks with the psychosis symptom severity and symptom improvement in patients with drug-naive first-episode SCZ

For cognitive function, multiple linear regression analyses revealed no significant relationship between epigenetic age acceleration and cognitive function in all participants at baseline (Table 2). After 8-week treatment, epigenetic age accelerations for all three epigenetic clocks at baseline are negatively correlated with the improvement of Stroop-W in FSCZ patients (p < 0.05). Furthermore, Hannum’s AgeAccel and Levine’s PhenoAgeAccel at baseline have significantly negative correlations with the improvement of Stroop-C in FSCZ patients (p < 0.05). However, no significant results remain after Bonferroni correction (Table 3).

Table 2 No correlations of epigenetic aging acceleration estimated by three clocks with the cognitive function in drug-naive first-episode SCZ and healthy controls
Table 3 Association of epigenetic aging acceleration estimated by three clocks with the cognitive function improvement in drug-naive first-episode SCZ after 8 weeks resperidone treatment

Association of epigenetic age acceleration with subcortical volumes

At baseline, epigenetic age acceleration has no significant associations with the volume of 14 subcortical regions in all participants, adjusting for age, sex, tobacco use, and ICV (Table 4). For FSCZ patients, multiple linear regression analyses showed that baseline Horvath’s AgeAccel has a significantly negative correlation with the change of right accumben volume (β = − 0.43, p = 0.01), but not remained after Bonferroni correction (Table 5).

Table 4 No correlations of epigenetic aging acceleration estimated by three clocks with the subcortical volume in drug-naive first-episode SCZ and healthy controls
Table 5 Association of epigenetic aging acceleration estimated by three clocks with the subcortical volume changes in drug-naive first-episode SCZ after 8 weeks resperidone treatment


This study investigated the epigenetic age and the effect of antipsychotic treatment on the epigenetic aging process in drug-naive FSCZ patients. We measured the epigenetic age acceleration by using three independent algorithms, including Horvath, Hannum and Levine’s epigenetic clocks. Our findings demonstrated a significant epigenetic age deceleration in Horvath’s epigenetic clock among drug-naive FSCZ patients relative to controls. In addition, we found that epigenetic aging of Hannum and Levine clocks was significantly accelerated in patients with FSCZ after 8-week risperidone treatment.

This study showed that epigenetic age acceleration is delayed in patients with drug-naive FSCZ against the accelerated aging hypothesis of SCZ. Our finding is consistent with two recent studies [33, 42]. Talarico et al. found longer telomere length and decreased epigenetic age in drug-naive FSCZ patients [42]. Wu et al. revealed an epigenetic age deceleration in SCZ patients by using the largest size of methylation datasets from 1211 brain tissues and 2333 whole blood samples [33]. These results support the hypothesis that SCZ may be a neurodevelopmental disorder [43]. Intriguingly, Wu et al. also found that some CpG sites of epigenetic clocks are differentially methylated in SCZ patients [33]. Among these differentially methylated CpG sites (DMPs), 70–80% were located within the promoter regions. Furthermore, genes regulated by these DMPs displayed differential expression in SCZ patients and involved in the SCZ-related biological processes, such as immune dysregulation and neurological dysplasia [33]. These findings suggest that epigenetic clocks might be mediated by the dysregulation of pathophysiological processes in SCZ [33], which may be a potential cause of the epigenetic age deceleration underlying the development of SCZ. However, some previous studies have found that patients with SCZ had an accelerated epigenetic age or no difference in epigenetic age compared with healthy controls [29,30,31,32, 44]. Multiple clinical variables, such as trauma history, sex, and antipsychotic treatment have been associated with epigenetic aging processes [18, 35, 44], which may account for these inconsistent results and therefore should be taken into account in the future studies.

The current study demonstrated a significant effect of antipsychotic treatment on the Hannum and Levine clock, but not on the Horvath clock. Since each epigenetic clock is developed with different algorithms and captures distinct features of biological aging [15], these inconclusive findings among three epigenetic clocks are understandable. Horvath clock was developed with DNAm datasets from multiple tissues and development stages, measuring cellular aging independent of cell-type compositions [36]. Hannum clock was trained on blood samples, capturing more cell-extrinsic aging with moderate correlation with cell compositions [37]. Whereas the Levine clock was trained on the older adult population incorporating biological measures and capturing phenotypic age [38]. As Horvath clock is independent of cell-type compositions and cell-extrinsic biological measures [36], we supposed that the antipsychotic effect on accelerated epigenetic age in SCZ patients may be caused by the alterations of blood cell composition and other aging-related blood biomarkers. In line with our supposition, Talarico et al. recently found an accelerated epigenetic age in FSCZ patients after 10 weeks of treatment with risperidone. However, this result was not observed after adjusting for blood cell composition [42]. Furthermore, amounting evidence also has shown that antipsychotic treatment may influence the blood cell type compositions or other blood-based biomarkers [45, 46], which further support that the effects of antipsychotic treatment on epigenetic age may be biased by the blood cell composition. However, we cannot rule out the possibility that risperidone may accelerate epigenetic age through other biological processes. For example, risperidone treatment may change the methylation level of some CpG sites among epigenetic clocks, but this effect might be insufficient to be detected in the current study because of the short-term treatment.

Although SCZ-related genes were found to be regulated by the epigenetic clock and displayed abnormal expression in SCZ patients [33], the mechanism of epigenetic aging process implicated in the SCZ pathogenesis remains unclear. A recent study demonstrated a significant cross-sectional association between epigenetic age acceleration and psychosis severity that was measured by the Symptom Checklist 90 (SCL-90) psychotic domain [47]. Unlike this association finding, our analyses showed that epigenetic aging acceleration from all three epigenetic clocks was neither correlated with psychotic symptom severity (PANSS scores) nor symptom improvement (the percentage change on PANSS scores). In addition to the difference in symptom measures, the medicinal uses and substantially clinical heterogeneity in SCZ patients may partly account for this discrepancy.

We found no significant associations between epigenetic age acceleration and cognitive function in all participants at baseline. Our longitudinal analyses showed that baseline epigenetic age acceleration has an inverse association with the improvement of cognitive functions in some measures. However, this significant finding is not retained after multiple test corrections. This is not surprising given that the statistical power of our study is limited by the small sample size and a short-term follow-up. A recent large cross-sectional study investigated the association between two measures of epigenetic age acceleration (Horvath and Hannum) and three different neuropsychological tests in 4827 middle-aged participants from two independent cohorts, and only found a significant inverse association between Hannum AgeAccel and Word Fluency Test scores [48]. Another twin cohort study examined both cross-sectional and longitudinal associations (11.5-year) between four epigenetic clocks using Horvath, Hannum, Levine, and Grim algorithms and cognitive function using Trail Making Test (TMT) [20]. This twin study found that Horvath AgeAccel were correlated with cognitive decline longitudinally, but no association between epigenetic age acceleration and cognitive function at baseline [20]. Differences in the measures of epigenetic clocks and cognitive function in previous studies may account for the inconsistent results.

Only a few studies have investigated the relationship between epigenetic age acceleration and subcortical volume. A previous longitudinal study of 46 adolescent girls found Horvath AgeAccel related to reduced left hippocampal volume (4 years follow up) [23]. Similarly, two recent cross-sectional studies found a significant cross-sectional association between Hannum AgeAccel and reduced hippocampal volume, but this finding was not observed in Horvath’s clock [22, 25]. In our study, we failed to find any significant relationships between epigenetic age acceleration and subcortical volumes. This may also be related to different measures of epigenetic clocks and our small sample size as the cause of the discrepancy in cognitive function. Large replicate data with well-developed epigenetic clocks will be needed to draw a conclusion.

This study has several strengths. First, we investigated the epigenetic age acceleration in drug-naive FSCZ patients with a similar illness duration to exclude the potential confounding effects of medication and the pathophysiological process of SCZ on our results. Second, we analyzed the effect of medication on the epigenetic aging process in FSCZ through a longitudinal design of the cohort. Third, we implemented multiple epigenetic clocks that are designed with different algorithms and capture distinct epigenetic aging features, enabling a deeper understanding of various aging processes in SCZ. However, a major limitation of the present study should be noted. Our study has a relatively small sample size, which may limit the detective power of our study, accounting for the inconsistent findings from distinct epigenetic clocks analyses.


This study provided evidence for the epigenetic age deceleration of Horvath clock in drug-naive FSCZ patients. Importantly, this longitudinal study demonstrated a potential effect of antipsychotic treatment on the epigenetic aging process in SCZ patients. Nevertheless, the inconclusive findings from three epigenetic clocks indicated that distinct epigenetic clocks are sensitive to different aspects of aging process. Large scale studies with long-term longitudinal designs and comprehensive epigenetic clock analyses are needed to confirm our findings and further advance our understanding of epigenetic aging progression in SCZ patients.

Availability of data and materials

The datasets used and analysed during the current study available from the corresponding author on reasonable request.





First-episode schizophrenia


DNA methylation


Positive and Negative Symptom Scale


Digit Span Tests


Verbal Fluency Test


the Stroop Words Tests


the Stroop Colours Tests


the Stroop Interference Tests


Trail Making Test


the Wisconsin Card Sorting Test-categories


the Wisconsin Card Sorting Test-perseverative errors


Age Acceleration Residual


  1. Collaborators GDaIIaP. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet. 2017;390(10100):1211–59.

    Article  Google Scholar 

  2. Olfson M, Gerhard T, Huang C, Crystal S, Stroup TS. Premature mortality among adults with schizophrenia in the United States. JAMA Psychiatry. 2015;72(12):1172–81.

    Article  Google Scholar 

  3. Hjorthøj C, Stürup AE, McGrath JJ, Nordentoft M. Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis. Lancet Psychiatry. 2017;4(4):295–301.

    Article  Google Scholar 

  4. Chan JKN, Wong CSM, Or PCF, Chen EYH, Chang WC. Risk of mortality and complications in patients with schizophrenia and diabetes mellitus: population-based cohort study. Br J Psychiatry. 2021;219(1):375–82.

    Article  Google Scholar 

  5. Laursen TM, Munk-Olsen T, Vestergaard M. Life expectancy and cardiovascular mortality in persons with schizophrenia. Curr Opin Psychiatry. 2012;25(2):83–8.

    Article  Google Scholar 

  6. Nielsen RE, Banner J, Jensen SE. Cardiovascular disease in patients with severe mental illness. Nat Rev Cardiol. 2021;18(2):136–45.

    Article  Google Scholar 

  7. Tran E, Rouillon F, Loze JY, Casadebaig F, Philippe A, Vitry F, et al. Cancer mortality in patients with schizophrenia: an 11-year prospective cohort study. Cancer. 2009;115(15):3555–62.

    Article  Google Scholar 

  8. Kahn RS, Keefe RS. Schizophrenia is a cognitive illness: time for a change in focus. JAMA Psychiatry. 2013;70(10):1107–12.

    Article  Google Scholar 

  9. Zanelli J, Mollon J, Sandin S, Morgan C, Dazzan P, Pilecka I, et al. Cognitive change in schizophrenia and other psychoses in the decade following the first episode. Am J Psychiatry. 2019;176(10):811–9.

    Article  Google Scholar 

  10. Moyer CE, Shelton MA, Sweet RA. Dendritic spine alterations in schizophrenia. Neurosci Lett. 2015;601:46–53.

    Article  CAS  Google Scholar 

  11. van Haren NE, Schnack HG, Cahn W, van den Heuvel MP, Lepage C, Collins L, et al. Changes in cortical thickness during the course of illness in schizophrenia. Arch Gen Psychiatry. 2011;68(9):871–80.

    Article  Google Scholar 

  12. Rao S, Kota LN, Li Z, Yao Y, Tang J, Mao C, et al. Accelerated leukocyte telomere erosion in schizophrenia: evidence from the present study and a meta-analysis. J Psychiatr Res. 2016;79:50–6.

    Article  Google Scholar 

  13. Fraguas D, Díaz-Caneja CM, Ayora M, Hernández-Álvarez F, Rodríguez-Quiroga A, Recio S, et al. Oxidative stress and inflammation in first-episode psychosis: a systematic review and Meta-analysis. Schizophr Bull. 2019;45(4):742–51.

    Article  Google Scholar 

  14. Kirkpatrick B, Messias E, Harvey PD, Fernandez-Egea E, Bowie CR. Is schizophrenia a syndrome of accelerated aging? Schizophr Bull. 2008;34(6):1024–32.

    Article  Google Scholar 

  15. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–84.

    Article  CAS  Google Scholar 

  16. Sumner JA, Colich NL, Uddin M, Armstrong D, McLaughlin KA. Early experiences of threat, but not deprivation, are associated with accelerated biological aging in children and adolescents. Biol Psychiatry. 2019;85(3):268–78.

    Article  Google Scholar 

  17. Copeland WE, Shanahan L, McGinnis EW, Aberg KA, van den Oord E. Early adversities accelerate epigenetic aging into adulthood: a 10-year, within-subject analysis. J Child Psychol Psychiatry. 2022;63(11):1308–15.

    Article  Google Scholar 

  18. Zannas AS, Arloth J, Carrillo-Roa T, Iurato S, Röh S, Ressler KJ, et al. Lifetime stress accelerates epigenetic aging in an urban, African American cohort: relevance of glucocorticoid signaling. Genome Biol. 2015;16:266.

    Article  Google Scholar 

  19. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian birth cohort 1936. Int J Epidemiol. 2015;44(4):1388–96.

    Article  Google Scholar 

  20. Vaccarino V, Huang M, Wang Z, Hui Q, Shah AJ, Goldberg J, et al. Epigenetic age acceleration and cognitive decline: a twin study. J Gerontol A Biol Sci Med Sci. 2021;76(10):1854–63.

    Article  CAS  Google Scholar 

  21. Beydoun MA, Shaked D, Tajuddin SM, Weiss J, Evans MK, Zonderman AB. Accelerated epigenetic age and cognitive decline among urban-dwelling adults. Neurology. 2020;94(6):e613–25.

    Article  Google Scholar 

  22. Hoare J, Stein DJ, Heany SJ, Fouche JP, Phillips N, Er S, et al. Accelerated epigenetic aging in adolescents from low-income households is associated with altered development of brain structures. Metab Brain Dis. 2020;35(8):1287–98.

    Article  CAS  Google Scholar 

  23. Davis EG, Humphreys KL, McEwen LM, Sacchet MD, Camacho MC, MacIsaac JL, et al. Accelerated DNA methylation age in adolescent girls: associations with elevated diurnal cortisol and reduced hippocampal volume. Transl Psychiatry. 2017;7(8):e1223.

    Article  CAS  Google Scholar 

  24. Cheong Y, Nishitani S, Yu J, Habata K, Kamiya T, Shiotsu D, et al. The effects of epigenetic age and its acceleration on surface area, cortical thickness, and volume in young adults. Cereb Cortex. 2022;32(24):5654–63.

    Article  Google Scholar 

  25. Milicic L, Vacher M, Porter T, Doré V, Burnham SC, Bourgeat P, et al. Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume. Geroscience. 2022;44(3):1807–23.

    Article  Google Scholar 

  26. Han LKM, Aghajani M, Clark SL, Chan RF, Hattab MW, Shabalin AA, et al. Epigenetic aging in major depressive disorder. Am J Psychiatry. 2018;175(8):774–82.

    Article  Google Scholar 

  27. Klopack ET, Crimmins EM, Cole SW, Seeman TE, Carroll JE. Accelerated epigenetic aging mediates link between adverse childhood experiences and depressive symptoms in older adults: results from the health and retirement study. SSM Popul Health. 2022;17:101071.

    Article  Google Scholar 

  28. Protsenko E, Yang R, Nier B, Reus V, Hammamieh R, Rampersaud R, et al. “GrimAge,” an epigenetic predictor of mortality, is accelerated in major depressive disorder. Transl Psychiatry. 2021;11(1):193.

    Article  CAS  Google Scholar 

  29. Voisey J, Lawford BR, Morris CP, Wockner LF, Noble EP, Young RM, et al. Epigenetic analysis confirms no accelerated brain aging in schizophrenia. NPJ Schizophr. 2017;3(1):26.

    Article  Google Scholar 

  30. 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.

    Article  Google Scholar 

  31. 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(1):4.

    Article  Google Scholar 

  32. Jeremian R, Bani-Fatemi A, Strauss JS, Tasmim S, Dada O, Graff-Guerrero A, et al. Investigation of accelerated epigenetic aging in individuals suffering from schizophrenia in the context of lifetime suicide attempt. Schizophr Res. 2019;243:222–4.

    Article  Google Scholar 

  33. Wu X, Ye J, Wang Z, Zhao C. Epigenetic age acceleration was delayed in schizophrenia. Schizophr Bull. 2021;47(3):803–11.

    Article  Google Scholar 

  34. 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;231:189–97.

    Article  CAS  Google Scholar 

  35. Ori AP, Loohuis LMO, Guintivano J, Hannon E, Dempster E, Clair DS, et al. Epigenetic age is accelerated in schizophrenia with age-and sex-specific effects and associated with polygenic disease risk. bioRxiv. 2021:727859.

  36. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115.

    Article  Google Scholar 

  37. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–67.

    Article  CAS  Google Scholar 

  38. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY). 2018;10(4):573–91.

    Article  Google Scholar 

  39. Obermeier M, Mayr A, Schennach-Wolff R, Seemüller F, Möller HJ, Riedel M. Should the PANSS be rescaled? Schizophr Bull. 2010;36(3):455–60.

    Article  Google Scholar 

  40. Zong X, He C, Huang X, Xiao J, Li L, Li M, et al. Predictive biomarkers for antipsychotic treatment response in early phase of schizophrenia: multi-Omic measures linking subcortical covariant network, transcriptomic signatures, and peripheral epigenetics. Front Neurosci. 2022;16:853186.

    Article  Google Scholar 

  41. Zong X, Zhang Q, He C, Huang X, Zhang J, Wang G, et al. DNA methylation basis in the effect of white matter integrity deficits on cognitive impairments and psychopathological symptoms in drug-naive first-episode schizophrenia. Front Psychiatry. 2021;12:777407.

    Article  Google Scholar 

  42. Talarico F, Xavier G, Ota VK, Spindola LM, Maurya PK, Tempaku PF, et al. Aging biological markers in a cohort of antipsychotic-naïve first-episode psychosis patients. Psychoneuroendocrinology. 2021;132:105350.

    Article  CAS  Google Scholar 

  43. Birnbaum R, Weinberger DR. Genetic insights into the neurodevelopmental origins of schizophrenia. Nat Rev Neurosci. 2017;18(12):727–40.

    Article  CAS  Google Scholar 

  44. Higgins-Chen AT, Boks MP, Vinkers CH, Kahn RS, Levine ME. Schizophrenia and epigenetic aging biomarkers: increased mortality, reduced Cancer risk, and unique clozapine effects. Biol Psychiatry. 2020;88(3):224–35.

    Article  CAS  Google Scholar 

  45. Miller BJ, Gassama B, Sebastian D, Buckley P, Mellor A. Meta-analysis of lymphocytes in schizophrenia: clinical status and antipsychotic effects. Biol Psychiatry. 2013;73(10):993–9.

    Article  CAS  Google Scholar 

  46. Steiner J, Frodl T, Schiltz K, Dobrowolny H, Jacobs R, Fernandes BS, et al. Innate immune cells and C-reactive protein in acute first-episode psychosis and schizophrenia: relationship to psychopathology and treatment. Schizophr Bull. 2020;46(2):363–73.

    Google Scholar 

  47. 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;296:113646.

    Article  CAS  Google Scholar 

  48. Bressler J, Marioni RE, Walker RM, Xia R, Gottesman RF, Windham BG, et al. Epigenetic age acceleration and cognitive function in African American adults in midlife: the atherosclerosis risk in communities study. J Gerontol A Biol Sci Med Sci. 2020;75(3):473–80.

    Article  Google Scholar 

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We would like to thank all patients who participated in our study.


This work was supported by the National Natural Science Foundation of China (Grant No. 81871056, 82101576). The funding sources had no role in study design, data collection, analysis and interpretation of data, decision to publish or preparation of the manuscript.

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Authors and Affiliations



ZL, XZ, MH and XC contributed to the study design. XZ, YH, JT and MH contributed to the data collection. ZL, XZ, DL and MH contributed to the data analysis and interpretation. ZL and DL wrote and revised the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Maolin Hu or Xiaogang Chen.

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Ethics approval and consent to participate

This study was approved by the the Medical Ethics Committee of the Second Xiangya Hospital of Central South University. We confrm that all participants were given information about the study and signed informed consent. All study procedures were conducted in accordance with the principles of the Declaration of Helsinki.

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Not applicable.

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All authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Supplemental Table 1.

Demographics for Schizophrenia Patients and Healthy Controls.

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Li, Z., Zong, X., Li, D. et al. Epigenetic clock analysis of blood samples in drug-naive first-episode schizophrenia patients. BMC Psychiatry 23, 45 (2023).

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