Schizophrenia and Epigenetic Aging Biomarkers: Increased Mortality, Reduced Cancer Risk, and Unique Clozapine Effects

Abstract

Background: Schizophrenia (SZ) is associated with increased all-cause mortality, smoking, and age-associated proteins, yet multiple previous studies found no association between SZ and biological age using Horvath's epigenetic clock, a well-established aging biomarker based on DNA methylation. However, numerous epigenetic clocks that may capture distinct aspects of aging have been developed. This study tested the hypothesis that altered aging in SZ manifests in these other clocks.

Methods: We performed a comprehensive analysis of 14 epigenetic clocks categorized according to what they were trained to predict: chronological age, mortality, mitotic divisions, or telomere length. To understand the etiology of biological age differences, we also examined DNA methylation predictors of smoking, alcohol, body mass index, serum proteins, and cell proportions. We independently analyzed 3 publicly available multiethnic DNA methylation data sets from whole blood, a total of 567 SZ cases and 594 nonpsychiatric controls.

Results: All data sets showed accelerations in SZ for the 3 mortality clocks up to 5 years, driven by smoking and elevated levels of 6 age-associated proteins. The 2 mitotic clocks were decelerated in SZ related to antitumor natural killer and CD8T cells, which may help explain conflicting reports about low cancer rates in epidemiological studies of SZ. One cohort with available medication data showed that clozapine is associated with male-specific decelerations up to 7 years in multiple chronological age clocks.

Conclusions: Our study demonstrates the utility of studying the various epigenetic clocks in tandem and highlights potential mechanisms by which mental illness influences long-term outcomes, including cancer and early mortality.

Keywords: Aging; Cancer; Clozapine; Epigenetics; Schizophrenia; Smoking.

Figure 1.

Aging clocks other than Horvath1 show changes in schizophrenia (SZ) compared to non-psychiatric controls (NPC): (A) The Horvath1 chronological age clock, (B-D) the GrimAge, PhenoAge, and Zhang mortality clocks, (E-F) the MiAge and Yang mitotic clocks, (G) predicted telomere length, (H) predicted smoking pack-years. Here we show results from UCL (NPC, n = 173; SZ, n = 278). The other two data sets showed the same patterns (Figures S2–3). Complete statistics are in Table S4. For each panel, we show chronological age compared to clock score (left), as well as average age accelerations for SZ compared to NPC (right). Age accelerations are defined as residuals from the linear model for NPC (blue line), and therefore average age acceleration for NPC is 0. The first model is adjusted only for age. The second model is adjusted for age and derived cell counts (see Supplementary Methods). Error bars are SEM. More advanced biological age is indicated by positive acceleration values for most clocks, but by negative acceleration values for telomere length. Statistical significance of difference from NPC is shown: <0.2#, <0.05*, <0.01**, <0.001***.

Figure 2.

DNA methylation biomarkers in SZ and NPC: (A-B) Alcohol and BMI, (C-I) serum protein levels, and (J-L) three cell types with altered proportions in SZ. Here we show results from UCL (NPC, n = 173; SZ, n = 278). The other two data sets showed similar patterns (Figures S7–8). Complete statistics are in Table S4.

Figure 3.

Clozapine effects on chronological age clocks in the CF data set are male-specific. Age accelerations are defined as residuals from the linear model for NPC, and therefore average age acceleration for male and female NPCs together is 0. Error bars are SEM. NPC = non-psychiatric controls, SZ = schizophrenia, AP = non-clozapine antipsychotics, CLO = clozapine. Statistical significance of difference from NPC is shown: <0.05*, <0.01**, <0.001***.

Figure 4.

SZ effects on mortality, mitotic, and chronological age clocks are independent. (A) Correlation plot using biweight correlation between all measures for pooled data, adjusted based on study. Age residuals are used for variables that show robust age correlations (epigenetic clocks, proteins, smoking pack-years). Variables are ordered based on hierarchical clustering. Study-specific plots are found in Figure S13. (B-D) First six principal components, only showing variables with loadings higher than expected by chance. (E) PC correlations with SZ and sex for pooled data sets, and with SZ and antipsychotic classes for CF males. CF females are shown in Figure S18. Multiple regression analyses are shown in Table S21.