A systematic review and meta-analysis of epigenetic clocks in schizophrenia

Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies

BACKGROUND

Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging.

METHODS

We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years.

RESULTS

We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication.

CONCLUSIONS

Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.

Immunophenotypes in psychosis: is it a premature inflamm-aging disorder?

Immunopsychiatric field has rapidly accumulated evidence demonstrating the involvement of both innate and adaptive immune components in psychotic disorders such as schizophrenia. Nevertheless, researchers are facing dilemmas of discrepant findings of immunophenotypes both outside and inside the brains of psychotic patients, as discovered by recent meta-analyses. These discrepancies make interpretations and interrogations on their roles in psychosis remain vague and even controversial, regarding whether certain immune cells are more activated or less so, and whether they are causal or consequential, or beneficial or harmful for psychosis. Addressing these issues for psychosis is not at all trivial, as immune cells either outside or inside the brain are an enormously heterogeneous and plastic cell population, falling into a vast range of lineages and subgroups, and functioning differently and malleably in context-dependent manners. This review aims to overview the currently known immunophenotypes of patients with psychosis, and provocatively suggest the premature immune "burnout" or inflamm-aging initiated since organ development as a potential primary mechanism behind these immunophenotypes and the pathogenesis of psychotic disorders.

Polygenic risk for schizophrenia, social dispositions, and pace of epigenetic aging: Results from the Young Finns Study

Schizophrenia is often regarded as a disorder of premature aging. We investigated (a) whether polygenic risk for schizophrenia (PRSsch ) relates to pace of epigenetic aging and (b) whether personal dispositions toward active and emotionally close relationships protect against accelerated epigenetic aging in individuals with high PRSsch . The sample came from the population-based Young Finns Study (n = 1348). Epigenetic aging was measured with DNA methylation aging algorithms such as AgeAccelHannum , EEAAHannum , IEAAHannum , IEAAHorvath , AgeAccelHorvath , AgeAccelPheno , AgeAccelGrim , and DunedinPACE. A PRSsch was calculated using summary statistics from the most comprehensive genome-wide association study of schizophrenia to date. Social dispositions were assessed in terms of extraversion, sociability, reward dependence, cooperativeness, and attachment security. We found that PRSsch did not have a statistically significant effect on any studied indicator of epigenetic aging. Instead, PRSsch had a significant interaction with reward dependence (p = 0.001-0.004), cooperation (p = 0.009-0.020), extraversion (p = 0.019-0.041), sociability (p = 0.003-0.016), and attachment security (p = 0.007-0.014) in predicting AgeAccelHannum , EEAAHannum , or IEAAHannum . Specifically, participants with high PRSsch appeared to display accelerated epigenetic aging at higher (vs. lower) levels of extraversion, sociability, attachment security, reward dependence, and cooperativeness. A rather opposite pattern was evident for those with low PRSsch . No such interactions were evident when predicting the other indicators of epigenetic aging. In conclusion, against our hypothesis, frequent social interactions may relate to accelerated epigenetic aging in individuals at risk for psychosis. We speculate that this may be explained by social-cognitive impairments (perceiving social situations as overwhelming or excessively arousing) or ending up in less supportive or deviant social groups.

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

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. Epigenetic age was correlated with chronological age. FHR individuals showed epigenetic age deacceleration of Horvath and Hannum epigenetic clocks compared to controls. No effect of the environmental risk factors on the epigenetic age acceleration could be detected. Epigenetic age acceleration adjusted by cell counts showed that the FHR group was deaccelerated also with the PedBE epigenetic clock. 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.

Decoding the role of transcriptomic clocks in the human prefrontal cortex

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.