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Peripheral blood telomerase activity and telomerase reverse transcriptase gene expression in psychiatric disorders: a systematic review and meta-analysis

Published online by Cambridge University Press:  01 September 2025

Johanna Thurin Open the ORCID record for Johanna Thurin [Opens in a new window] ,
Cynthia Marie-Claire Open the ORCID record for Cynthia Marie-Claire [Opens in a new window]  and
Bruno Etain Open the ORCID record for Bruno Etain [Opens in a new window]
Johanna Thurin
Affiliation:
Optimisation Thérapeutique en Neuropharmacologie, Université Paris Cité, INSERM UMRS 1144, Paris, France
Cynthia Marie-Claire
Affiliation:
Optimisation Thérapeutique en Neuropharmacologie, Université Paris Cité, INSERM UMRS 1144, Paris, France
Bruno Etain*
Affiliation:
Optimisation Thérapeutique en Neuropharmacologie, Université Paris Cité, INSERM UMRS 1144, Paris, France Département de Psychiatrie et de Médecine Addictologique, Hôpitaux Lariboisière-Fernand Widal, Université Paris Cité , Paris, France Fondation Fondamental, Créteil, France
*
Corresponding author: Bruno Etain; Email: bruno.etain@inserm.fr

Abstract

Background

Telomere shortening is shared by all psychiatric disorders and is hypothesized as resulting from decreased telomerase activity (TA) or expression of the TERT (Telomerase Reverse Transcriptase) gene.

Methods

A search in four English databases was conducted from inception to November 2024 to evaluate the association between psychiatric disorders and telomerase activity (TA) or TERT gene expression in peripheral blood. We performed two separate meta-analyses to generate pooled effect size (ES) for TA and TERT gene expression, followed by meta-regression.

Results

The systematic review included 16 studies, 14 of which were included in the meta-analyses. When considering all psychiatric disorders, no associations were found for TA (ES = 0.08 [−0.50–0.67], p = 0.78 – I-squared = 95%), nor TERT gene expression (ES = 0.00 [−0.56–0.57], p = 0.99 – I-squared = 91%). However, TA was elevated in mood disorders (ES = 0.61 [0.06–1.16] – p = 0.03), while decreased in non-mood disorders (ES = −0.70 [−1.37 – −0.03] – p = 0.04). ES for TA were larger in mood disorders as compared to other disorders (p = 0.003).

Conclusions

This meta-analysis shows that psychiatric disorders – taken together – are not associated with peripheral blood TA or TERT gene expression. Nevertheless, we find that TA is increased in depressive disorders (unipolar or bipolar), whereas decreased in non-mood psychiatric disorders. The paucity of studies and small sample sizes are important limitations, especially for TERT gene expression. Further research is needed, incorporating a broader spectrum of psychiatric disorders and larger sample sizes.

Keywords

cellular agingpsychiatric disordertelomerasetelomerase activityTERT expression

Information

Type
Review/Meta-analysis
Information
European Psychiatry , Volume 68 , Issue 1 , 2025 , e123
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of European Psychiatric Association

Introduction

Psychiatric disorders are among the top ten leading causes of burden worldwide [Reference Steinmetz, Seeher, Schiess, Nichols, Cao and Servili1]. Individuals diagnosed with psychiatric disorders have 14.66 years-of-potential-life-lost (YPLL) compared to the general population [Reference Chan, Correll, Wong, Chu, Fung and Wong2]. While unnatural causes of death (suicide and drug intoxications) [Reference Plana-Ripoll, Pedersen, Agerbo, Holtz, Erlangsen and Canudas-Romo3] contribute to this premature mortality, psychiatric disorders are also linked to a higher mortality rate due to natural causes such as cardiovascular diseases, diabetes, or cancers [Reference Magas, Norman and Vasan4]. These conditions are recognized as age-related diseases that reflect accelerated cellular aging [Reference Childs, Durik, Baker and van Deursen5]. Twelve biological processes are identified as hallmarks of cellular aging including telomere attrition, mitochondrial dysfunction, cellular senescence, chronic inflammation or stem cell exhaustion [Reference López-Otín, Blasco, Partridge, Serrano and Kroemer6].

Telomeres are non-coding structures located at the ends of chromosomes that gradually shorten with cell division [Reference López-Otín, Blasco, Partridge, Serrano and Kroemer6]. The Hayflick limit refers to the length at which, after repeated cell divisions, telomeres become too short and initiate a DNA damage response, causing genome instability and cellular senescence [Reference Liu, Wang, Wang and Liu7]. The accumulation of senescent cells impairs tissue function, thereby contributing to the onset of age-related diseases [Reference Kuehnemann and Wiley8]. Numerous meta-analyses show that individuals diagnosed with psychiatric disorders have shorter telomere length (TL) compared to the general population. This has been reported for schizophrenia (SCZ) and related disorders [Reference Ayora, Fraguas, Abregú-Crespo, Recio, Blasco and Moises9], Bipolar Disorder (BD) [Reference Huang, Wang, Tseng, Hung and Lin10], Major Depressive Disorders (MDD) [Reference Ridout, Ridout, Price, Sen and Tyrka11], substance use disorders [Reference Navarro-Mateu, Husky, Cayuela-Fuentes, Álvarez, Roca-Vega and Rubio-Aparicio12], post-traumatic stress disorder (PTSD) [Reference Li, Wang, Zhou, Huang and Li13] and anxiety disorders [Reference Malouff and Schutte14]. Moreover, a meta-analysis of 32 studies found shortened TL across all psychiatric disorders [Reference Darrow, Verhoeven, Révész, Lindqvist, Penninx and Delucchi15], with the largest effect sizes being observed for PTSD, followed by MDD and anxiety disorders, then by BD and psychotic disorders. Despite this large evidence of TL shortening in individuals with psychiatric disorders, the underlying molecular mechanisms remain unknown.

Telomerase is a holoenzyme located in the nucleus that elongates the end of telomeres when they become critically short. Telomerase reverse transcriptase (TERT) is the telomerase enzymatic subunit that plays an essential role in its activity [Reference Dratwa, Wysoczańska, Łacina, Kubik and Bogunia-Kubik16]. TL and telomerase levels in cells gradually decrease with age [Reference Guo, Huang, Dou, Yan, Shen and Tang17]. Cells deficient in telomerase display a more rapid reduction in TL and exhibit greater DNA damage compared to those with normal telomerase levels [Reference Roake and Artandi18]. Telomerase has been widely studied in telomere biology disorders where mutations in the TERT gene are known to impair telomerase function, leading to shorter TL [Reference Roake and Artandi18]. In psychiatric research, TL shortening has been hypothesized to be caused by multiple mechanisms. The main mechanism could be an increased telomere attrition due to an excess of oxidative stress [Reference Lin and Epel19]. This phenomenon might be worsened by reduced telomere repair due to down-regulation of the TERT gene expression, which impairs telomerase activity (TA), thus contributing to TL shortening.

To explore the hypothesis of a decreased TA and/or a down regulation of TERT gene expression in psychiatric disorders, we conducted a systematic review of the literature and a meta-analysis with the following aims: 1) to review the case–control studies reporting peripheral blood telomerase activity or TERT gene expression in psychiatric disorders, 2) to undertake pooled analyses to examine the magnitude of any differences in telomerase activity or TERT gene expression between individuals with psychiatric disorders and controls, 3) to use meta-regression analyses to identify any potential confounders (i.e. factors or variables associated with differences between cases and controls for the reported measures).

Materials and methods

The study protocol and the planned meta-analysis were registered in Prospero in October 2024 (reference: CRD42024599331). The project adheres to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) recommendations. A PRISMA checklist is included in Supplementary Appendix 1 and Figure 1 provides a PRISMA flowchart [Reference Liberati, Altman, Tetzlaff, Mulrow, Gøtzsche and Ioannidis20].

Figure 1. Flow chart of the inclusion and exclusion of studies for the systematic review and the meta-analysis.

Search strategy

A systematic strategy was employed, with electronic databases (MEDLINE, EMBASE, PsycINFO, PubMed, and OVID) searched from inception until November 30, 2024. The following MESH terms for psychiatric disorders were used: (bipolar disorder OR mania OR manic-depress* OR affective psychosis OR depression OR unipolar disorder OR psychosis OR schizophrenia OR anxiety OR substance use OR eating disorders OR PTSD OR ADHD (attention deficit hyperactivity disorder)) AND (telomerase activity OR telomerase expression OR TERT expression). The search was repeated using the following MESH terms for anxiety disorders: (panic disorder OR obsessive-compulsive disorder OR phobia OR social phobia OR generalized anxiety disorder) AND (telomerase activity OR telomerase expression OR TERT expression). The search was repeated using the following MESH terms for substance use disorders: (cannabis OR cocaine OR heroin OR psychostimulant OR alcohol) AND (telomerase activity OR telomerase expression OR TERT expression). The search was repeated using the following MESH terms for eating disorders: (anorexia OR bulimia) AND (telomerase activity OR telomerase expression OR TERT expression). Additionally, we investigated journals that have published articles on related topics, conference proceedings, and citations listed in previous review articles and published studies (that are identified through the database searches). Citation lists were examined, and original investigators were contacted if required to ask for raw or additional data.

Selection criteria

Publications were assessed for eligibility for the narrative review and then for the meta-analysis.

Eligibility criteria for the systematic review were:

Inclusion:

  1. a) English language peer-reviewed articles that reported findings from a case–control study of peripheral blood telomerase activity or TERT gene expression,

  2. b) cases met internationally recognized criteria for a clinical diagnosis of psychiatric disorders whatever the psychiatric disorder was (e.g. DSM or ICD criteria or agreed clinical consensus),

  3. c) comparator groups comprised healthy controls (HC), individuals without a current and/or lifetime history of psychiatric disorders, and/or were reported to be mentally healthy (e.g. they screened negative for major mental disorders),

  4. d) telomerase activity and TERT gene expression were measured in peripheral blood using laboratory measures with established evidence of reliability and validity.

Exclusion:

  1. a) articles that failed to report sample mean scores and standard deviations for the measures of telomerase activity or TERT gene expression and/or these data could not be estimated in figures and/or were unavailable from the original investigators,

  2. b) articles that reported any of the measures in brain regions.

Eligibility criteria for the meta-analysis were:

  1. a) met all the inclusion and none of the exclusion criteria for the systematic review,

  2. b) where the original study sample included a range of diagnoses (e.g. BD, schizo-affective disorder, affective psychosis, etc.), the measures in each diagnostic subgroup should be reported or available from the original investigators,

  3. c) where more than one publication arose from one dataset (i.e. overlapping datasets), then measures of TERT gene expression or telomerase activity would be reported for the largest or the most recent study sample only.

Procedure

Data extraction

We first excluded duplicate publications identified through different databases. Two authors (JT and BE) independently screened the remaining titles for potential eligibility. Some articles were excluded at this stage, while others were excluded after review of the abstracts (see Figure 1). Eligible publications were assessed independently by two authors (JT and BE) who extracted core data regarding sample characteristics (diagnosis, mean age, and gender distribution, size of groups), outcomes (telomerase activity or TERT gene expression), and laboratory measures used for the outcomes. Any discrepancies in data extraction were corrected by consensus (JT and BE). Original researchers were contacted as required (e.g. to clarify the independence of samples included in data publications or to obtain additional data) and articles were excluded if clarifications or data were unavailable or there was no response.

Risk of bias (quality) assessment

Quality of included studies was assessed using the Newcastle-Ottawa Quality Assessment Scale for Case Control Studies (NOS). Assessors reviewed and critically appraised each publication independently (total score ranged from 0 to 7) and then recorded a jointly agreed score and quality rating.

Data reporting and statistical analyses

Systematic review

Main characteristics of the samples and main findings from studies were summarized in tabular form and with a written description.

Meta-analyses

We extracted data for means and standard deviations (SD) for each measure (telomerase activity and TERT gene expression). If only medians and inter-quartile ranges were reported, these were transformed into means and SDs using Metaconverter. If data were available only in figures (i.e. without raw data in table or text), we contacted original researchers or used the software Image J to extract the data.

Analyses of pooled data from eligible studies were undertaken using SPSS and the R ‘METAFOR’ package (Meta-Analysis Package for R) [Reference Viechtbauer21]. We undertook a series of pooled analyses (e.g. all studies eligible for meta-analysis for each given outcome) when at least three independent studies reported data for the same outcome. Random effects modelling for pooled effect sizes (ES) were employed [Reference Hedges and Olkin22]. The standardized mean difference (SMD) and 95% confidence intervals (95% CI) were estimated for each study. The SMD was defined as the difference in means between the two groups (cases versus controls) divided by the pooled standard deviation of the measures and was interpreted in a similar manner to Cohen’s d (0.2 = small ES; 0.5 = medium ES; 0.8 = large ES).

We used SPSS to construct forest plots, while publication bias was assessed by visual inspection of the funnel plots and tested using the rank correlation test for funnel plot asymmetry. The I 2 statistic was used to quantify heterogeneity, with the values of 25, 50 and 75% reflecting a small, moderate, or high degree of heterogeneity, respectively [Reference Higgins and Thompson23]. Also, in line with recent suggestions for reporting, we include Cochran’s Q statistics and the corresponding p values.

Meta-regression analyses

Meta-regression analyses were performed using the R METAFOR package, which performs random-effects meta-regression using aggregate-level data. This function uses an iterative method to produce estimates (Beta and SDs) and p-values. Meta-regressions were undertaken for: year of publication, study total sample size, diagnosis (mood disorders vs other psychiatric disorders), age (difference between mean age of cases vs. controls), sex distribution (ratio of percentages of females in cases and controls), cell types (peripheral blood mononuclear cells vs other peripheral blood cells), and study quality.

Results

Literature search

The literature search produced a set of 653 articles (see Figure 1). After de-duplication, the set was reduced to 366 articles. Following review of titles and abstracts, this decreased further to 27 articles, and after full-text assessments, 16 publications met eligibility criteria for the qualitative synthesis. From those, 14 studies were found to be eligible for the meta-analyses, with 10 for telomerase activity [Reference Soeiro-de-Souza, Teixeira, Mateo, Zanetti, Rodrigues and de Paula24–Reference Jergović, Tomičević, Vidović, Bendelja, Savić and Vojvoda30] (Wolkowitz unpublished) and 2 studies were identified as duplicates [Reference Wolkowitz, Epel, Lin, Reus and Rosser31, Reference Chen, Epel, Mellon, Lin, Reus and Rosser32]. The remaining 4 studies were eligible for TERT gene expression [Reference Teyssier, Chauvet-Gelinier, Ragot and Bonin33–Reference Mlakar, Akkouh, Halff, Srivastava, Birkenæs and Ueland36].

Systematic review

Details of each included study are presented in Table 1 and Table 2. Articles were published between 2008–2024, with most (n = 10/16) appearing in the last decade. Only one study in PTSD has not yet been formally published, that is data were reported only as p values in the discussion of the original article [Reference Verhoeven, Yang, Wolkowitz, Bersani, Lindqvist and Mellon38], but investigators agreed to provide the raw data (study further labelled as “Wolkowitz et al. unpublished). These partially published data were considered eligible for the systematic review and the meta-analysis. Most samples (n = 7/16) were from the USA, followed by Europe (n = 6/16). Sample sizes were small, with most studies including less than 100 cases and 100 controls. Only two studies had large sample sizes: Simon et al. [Reference Simon, Walton, Bui, Prescott, Hoge and Keshaviah26] with 166 individuals with MDD and 166 controls (telomerase activity) and Mlakar et al. [Reference Mlakar, Akkouh, Halff, Srivastava, Birkenæs and Ueland36] with 357 individuals with SCZ and 401 controls (TERT gene expression). Studies mainly included individuals diagnosed with MDD (n = 8/16). All control groups had been screened to confirm the absence of psychiatric disorders. One study [Reference Porton, Delisi, Bertisch, Ji, Gordon and Li28] included a mixed control group consisting of healthy controls and unaffected relatives of cases. Most studies included both females and males, except for five studies: 2 in PTSD [Reference Jergović, Tomičević, Vidović, Bendelja, Savić and Vojvoda30] (Wolkowitz et al. unpublished), 1 in BD [Reference Soeiro-de-Souza, Teixeira, Mateo, Zanetti, Rodrigues and de Paula24] and 1 in heroin use disorder that included only males [Reference Cheng, Zeng, Leung, Zhang, Lau and Liu29] and 1 in MDD that included only females [Reference Teyssier, Chauvet-Gelinier, Ragot and Bonin33]. The matching for gender was correct for all studies except one [Reference Soeiro-de-Souza, Teixeira, Mateo, Zanetti, Rodrigues and de Paula24]. The matching for age was correct for most studies except one [Reference Porton, Delisi, Bertisch, Ji, Gordon and Li28]. Twelve studies investigated telomerase activity, while only four investigated TERT gene expression. Regarding telomerase activity, all studies used a similar method, i.e. TRAP (Telomere Repeat Amplification Protocol). Regarding TERT gene expression, 3 out of 4 studies used RT-qPCR, while one used microarrays [Reference Mlakar, Akkouh, Halff, Srivastava, Birkenæs and Ueland36]. Overall, the study quality score was good with 8/16 studies having a score of 6 or 7 (score can range from 0 to 7).

Table 1. Telomerase activity

Abbreviations: MDD: Major Depressive Disorder, PTSD: Post-Traumatic Stress Disorder, BD: Bipolar Disorder, SZ: Schizophrenia; N: Number, DSM: Diagnostic and Statistical Manual of Mental Disorders, ICD: International Classification of Diseases, NA: Not Available, USA: United States of America, yo: year old, TRAP: Telomerase Repeated Amplification Protocol, HC: Healthy Controls, PBMC: Peripheral blood mononuclear cell

Table 2. TERT gene expression

Abbreviations: MDD: major depressive disorder, BD: bipolar disorder, SZ: schizophrenia; N: number, DSM: diagnostic and Statistical Manual of Mental Disorders, ICD: International Classification of Diseases, NA: not available, USA: United States of America, yo: year old, RT-qPCR: quantitative reverse transcription polymerase chain reaction, HC: healthy controls, PBMC: peripheral blood mononuclear cell, TERT: telomerase reverse transcriptase, TERC: telomerase RNA component.

Meta-analysis

We undertook two separate meta-analyses, one for telomerase activity and one for TERT gene expression.

Telomerase activity

The meta-analysis was based on 10 studies, including a total of 510 cases and 521 controls. We stratified the analyses on the type of psychiatric disorders (individuals with MDD and depressed individuals with BD versus other psychiatric disorders). As shown in Figure 2, when all psychiatric disorders were considered, the pooled ES was 0.08 [−0.50–0.67], p = 0.78. A significant heterogeneity was detected between studies (I-squared = 0.95, p < 0.001). Funnel plots are provided in Supplementary Figure S1. Rank correlation tests for funnel plot asymmetry did not reveal evidence of publication bias (Kendall’s tau = −0.02 p = 1.00).

Figure 2. Forest plot of cases versus controls comparison for telomerase activity.

ID: name of first author of the article, BD: Bipolar Disorder, Heroin: Heroin use disorder, MDD: Major Depressive Disorder, PTSD: Post-Traumatic Stress Disorder, SZ: Schizophrenia.

Analyses stratified on the types of psychiatric disorders (mood disorders versus others) yielded an ES of 0.61 [0.06–1.16] (p = 0.03) for mood disorders and an ES of −0.70 [−1.37 to −0.03] (p = 0.04) for other psychiatric disorders.

According to meta-regression analyses, larger ES were observed in studies involving individuals with mood disorders versus other types of psychiatric disorders (p = 0.003), but also in more recent studies (p = 0.01). Total number of included participants (p = 0.86), case–control gender or age differences (respectively p = 0.67 and p = 0.56), analysis in PBMCs rather than other peripheral blood cells (p = 0.55) and quality of the studies (p = 0.13) did not influence ES. Meta-regression plots are presented for the year of publication and types of psychiatric disorders in Supplementary Figures S2 and S3.

TERT gene expression

The meta-analysis was based on four studies, including a total of 492 cases and 537 controls. Since one study included the same cases versus controls [Reference Köse Çinar34] – but at different time points (during mania then in remission), we first performed a meta-analysis including only the data for the individuals in remission versus controls. As shown in Figure 3, the pooled ES was 0.00 [−0.56–0.57], p = 0.99. A significant heterogeneity was detected between studies (I-squared = 0.91, p < 0.001). Funnel plots are provided in Supplementary Figure S4. Rank correlation tests for funnel plot asymmetry did not reveal any evidence of publication bias (Kendall’s tau = 0.33 p = 0.75).

Figure 3. Forest plot of cases versus controls comparison for telomerase gene expression.

ID: name of first author of the article, BD: Bipolar Disorder, MDD: Major Depressive Disorder, SZ: Schizophrenia.

For Köse Çinar et al. 2018, only data for BD cases in remission versus controls are presented.

The meta-analyses were repeated, including only the data from the same study [Reference Köse Çinar34] but for the individuals during mania versus controls. This analysis led to similar results with an ES of 0.03 [−0.47–0.53] (p = 0.91) (data not shown in detail).

No meta-regression analysis for TERT gene expression was performed, given the small number of studies.

Discussion

Telomere shortening is a phenomenon shared by most psychiatric disorders, leading to the hypothesis of alterations of telomerase activity (TA) and/or TERT expression. To the best of our knowledge, this is the first meta-analysis that investigates the association between peripheral blood telomerase activity and TERT gene expression levels across psychiatric disorders compared with healthy controls. We showed that peripheral blood TA and TERT gene expression do not differ between individuals with psychiatric disorders compared to healthy controls. However, stratification of the analyses shows an increase of TA in mood disorders (MDD and BD), and a decrease of TA in non-mood disorders (PTSD, SCZ, heroin use disorder).

We identified important limitations in our systematic review that might explain the results when considering all psychiatric disorders. First, the search retrieved a limited number of studies, with some duplicates or overlapping samples for TA and even fewer studies for TERT expression. Second, most studies – except for two – included small sample sizes. These two observations may have limited the ability to detect any differences between cases and controls. Third, all (except one) studies used a cross-sectional design, meaning that the data were collected at a given point in time. This limitation prevents a deeper understanding of how TERT gene expression and TA may fluctuate over time or depend on symptom levels, but also prevents observing their variations over the progression of chronic disorders.

Of particular interest is the difference in effect sizes for telomerase activity between non-mood disorders and mood disorders. In non-mood disorders, the hypothesis of an underlying mechanism linking shorter telomere length to lower telomerase activity (TA) may be valid, since TA was decreased in these conditions. However, this observation may be driven by the study in heroin-abstinent individuals [Reference Cheng, Zeng, Leung, Zhang, Lau and Liu29], which reported the lowest effect size. For instance, it has been shown that chronic stress can lead to a secondary adaptation that suppresses TERT expression and TA [Reference de Punder, Heim, Wadhwa and Entringer39], which could be the case for non-mood disorders. Furthermore, in a recent longitudinal study, chronic stress predicts lower Mitochondrial Health Index (a composite marker integrating mitochondrial energy-transformation capacity and content) that in turn predicts decreases in telomerase activity and lower TL [Reference Guillen-Parra, Lin, Prather, Wolkowitz, Picard and Epel40].

For mood disorders, the results are in the opposite direction, that is increased TA both in MDD and BD (all samples were collected during a depressive episode) as compared to controls. Studies in MDD mostly include drug-naïve individuals exhibiting severe depressive symptoms at inclusion. Based on the previously mentioned mechanistic hypothesis, these individuals would be expected to demonstrate low TA levels. Our findings are therefore counter intuitive. Equally unexpected is the observation that some of these studies reported a positive correlation between TA and the severity of depressive symptoms [Reference Walia, Sidana, Arun, Kaur and Sharma27, Reference Wolkowitz, Epel, Lin, Reus and Rosser31, Reference Bürhan-Çavuşoğlu, İscan, Güneş, Atabey and Alkın37]. Since most studies (except one) are cross-sectional, a one-point measure may reflect or overlook a secondary adaptation. For example, acute stress can induce telomere attrition by increasing reactive oxygen species (ROS), which in turn leads to elevated TERT expression and TA, which might be the case during a depressive episode. However, when stress becomes chronic, a secondary adaptation suppresses TERT expression and TA [Reference de Punder, Heim, Wadhwa and Entringer39] which might explain the opposite observation in non-mood disorders.

The difference observed for TA in mood and non-mood disorders may reflect differences in treatment regimens. Indeed, most studies examining mood disorders included drug-naïve participants. In studies investigating TA in non-mood disorders, medication regimens were heterogenous (not reported, naive patients, or treated patients). Therefore, no meta-regression analysis was feasible to assess the impact of the different pharmacological interventions on TA. In a recently published review, we have shown that very few studies assessed the effect of psychiatric medications on TA in longitudinal designs [Reference Thurin, Etain, Bellivier and Marie-Claire41]. We identified only two studies in MDD, both showing no significant change of TA pre-vs post-antidepressants, and only one study in BD showing no significant change of TA pre-vs post-lithium. Therefore, future studies should accurately describe the medication regimens at inclusion and further explore any potential associations with TA.

Most importantly, the maintenance of telomeres is a complex biological mechanism and the sole study of TA or TERT protein may be too restrictive. The three primary multiprotein complexes central to this process are the Shelterin complex, the CST complex, and the telomerase complex (with five other proteins than TERT and one non-coding RNA), along with numerous accessory proteins. In telomere biology disorders, research has identified 16 genes encoding proteins that regulate telomere homeostasis. Alterations in any of these genes can lead to excessive telomere shortening and dysfunction [Reference Revy, Kannengiesser and Bertuch42]. Supporting the hypothesis that other proteins than TERT may play a role in TL shortening in psychiatric disorders, previous studies have identified two of these proteins as potentially implicated. One study identified a downregulation of the CST protein CTC1 in individuals with schizophrenia as compared to controls through whole-genome expression profiling [Reference Gardiner, Cairns, Liu, Beveridge, Carr and Kelly43]. Another study analyzed the expression of 29 genes involved in telomere homeostasis and aging and observed a downregulation of POT1 in individuals with bipolar disorder with shorter telomeres, compared to age-matched cases with normal telomere length [Reference Spano, Marie-Claire, Godin, Lebras, Courtin and Laplanche44]. As a constituent of the Shelterin complex, POT1 plays a pivotal role in the recruitment of telomerase and the CST complex, both of which are critical for the elongation of telomeres [Reference Sekne, Ghanim, van Roon and Nguyen45, Reference Cai, Takai, Zaug, Dilgen, Cech and Walz46]. These two studies provide new insights into the understanding of TL in psychiatric disorders that go beyond the sole investigation of TA or TERT gene expression [Reference Thurin, Etain, Bellivier and Marie-Claire41].

Conclusion

This meta-analysis shows that psychiatric disorders – when considered independently of diagnoses – are not associated with peripheral blood telomerase activity nor TERT gene expression. However, we suggest that telomerase activity is increased in depressive unipolar or bipolar disorders, while it is decreased in non-mood psychiatric disorders. The paucity of studies and small sample sizes are important limitations of the available literature, particularly for TERT expression. Future studies are therefore required, which would include larger samples and a wider range of psychiatric disorders. Although telomerase and its catalytic subunit TERT have been suggested to play a central role in TL shortening in psychiatric disorders, more comprehensive investigations of other biological pathways involved in telomere homeostasis are essential to understand the mechanisms at stake.

Supplementary material

The supplementary material for this article can be found at http://doi.org/10.1192/j.eurpsy.2025.10099.

Data availability statement

Data extracted from included studies and data used for all analyses are available upon request to the authors.

Acknowledgements

We acknowledge authors who agreed to provide raw or additional data from their original studies.

Author contribution

Johanna Thurin: Investigation - Writing – original draft, Writing – review & editing. Cynthia Marie-Claire: Supervision - Writing – review & editing. Bruno Etain: Supervision - Methodology - Writing – original draft, Writing – review & editing.

Financial support

This work was supported by INSERM (Institut National de la Santé et de la Recherche Médicale). C. Marie-Claire is supported by the Centre National de la Recherche Scientifique.

Competing interests

The authors declare that they have no conflicts of interest.

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Figure 0

Figure 1. Flow chart of the inclusion and exclusion of studies for the systematic review and the meta-analysis.

Figure 1

Table 1. Telomerase activity

Figure 2

Table 2. TERT gene expression

Figure 3

Figure 2. Forest plot of cases versus controls comparison for telomerase activity.ID: name of first author of the article, BD: Bipolar Disorder, Heroin: Heroin use disorder, MDD: Major Depressive Disorder, PTSD: Post-Traumatic Stress Disorder, SZ: Schizophrenia.

Figure 4

Figure 3. Forest plot of cases versus controls comparison for telomerase gene expression.ID: name of first author of the article, BD: Bipolar Disorder, MDD: Major Depressive Disorder, SZ: Schizophrenia.For Köse Çinar et al. 2018, only data for BD cases in remission versus controls are presented.

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