Background
Bipolar disorder (BD) remains an interesting candidate for neurobiological analyses owing to its heterogenous presentation and both genetic and environmental risk factors (Ludwig and Dwivedi
2016). While genome-wide association studies (GWAS) in BD have identified dozens of associated variants, they have explained only a small fraction of overall disease liability (Stahl et al.
2019). Therefore, the last decade has seen a shift towards investigating the complex interplay between genetic and environmental risk factors (Sharma et al.
2016). Advances in technologies have supported high-throughput investigations of biological markers representative of environmental modulation of the genome. These biomarkers hold promise for stratifying symptom-based phenotypes and assessing the prognosis of individual patients (Kobeissy et al.
2012). Moreover, these biomarkers could contribute to a more accurate multi-level diagnostic framework which relies on biological measures to supplement clinical ratings of symptoms (Meana and Mollinedo-Gajate
2017).
BD is a chronic, disabling, and severe mental illness characterized by recurrent depressive and manic episodes, somatic and psychiatric comorbidities, and functional impairments (Goodwin and Jamison
2007). Considering the high global burden and lifetime prevalence of bipolar spectrum disorders, estimated at approximately 2.4% (Rowland and Marwaha
2018), there is a need to better understand the factors affecting its onset and course. The significance of environment, especially childhood trauma and stressful life events on the trajectories of affective disorders, including vulnerability, onset, relapse and occurrence, has been well established (Aldinger and Schulze
2017; Lex et al.
2017; Johnson
2005; Alloy et al.
2005; Paykel
2003). However, little is known about the mechanisms involved in the consequences of such life events.
Recently, emphasis has been placed on the potential role of epigenetic variation in the etiopathogenesis of BD (Li et al.
2015). Epigenetics is an adaptive mechanism which can modulate the stress response through subtle gene expression modifications (Aas et al.
2016). In particular, DNA methylation (DNAm), the addition of a methyl group to DNA, primarily at cytosine-guanine dinucleotides (CpG), may pose a “mechanism by which life-experiences become ‘embedded’ in the genome” (Marzi et al.
2018).
Increasing evidence from both animal and human data supports the epigenetic programming of genes in response to trauma and chronic stress. Consistent findings have linked prenatal (Monk et al.
2012; Weaver et al.
2004) and early-life adversities to epigenetic modifications of genes, especially those involved in the hypothalamic–pituitary–adrenal (HPA) axis (Kular and Kular
2018; McGowan et al.
2009; Vinkers et al.
2015; Jaworska-Andryszewska and Rybakowski
2019). While several studies have shown methylation changes associated with trauma during the adult period, few studies have investigated non-traumatic chronic stress (Matosin et al.
2017) or acute stressful life events. Candidate gene approaches in the general population have reported differential methylation of CpGs in the vicinity of
SLC6A4 (Alasaari et al.
2012),
TH (Myaki et al.
2015), and
BDNF (Song et al.
2014) in association with sustained work-related stress. One study, which examined
LINE-
1 as a proxy for global methylation, found no signification associations with chronic lifestyle stress (Duman and Canli
2015). To the best of our knowledge, not a single study has explored epigenome-wide signatures of DNAm in relation to acute, non-traumatic stress in humans. With regards to BD, studies have investigated methylation differences as both trait and state markers of the disorder in several promoter regions including
SLC6A4,
PPIEL31,
BDNF,
HCG9,
KCNQ3,
5HTR1A and
GPR24 (Ludwig and Dwivedi
2016; Fries et al.
2016; Pishva et al.
2014). Interestingly, evidence supports altered DNAm profiles for high-risk affected and even unaffected offspring of individuals with BD in comparison to low risk controls. Moreover, there seems to be a unique rate of change in DNAm over time for high risk individuals (Duffy et al.
2019). However, despite findings of differential epigenetic profiles, results have been inconsistent and there remains a need for genome-wide methylation studies, especially ones longitudinal in design.
This study aims to gain a better understanding of the role of epigenetic modifications, specifically DNAm, in relation to stress during the course of BD. Using repeated measures over a 1-year period, we explored the relationship between DNAm and stressful life events in chronic BD patients. We took a two-pronged approach, first by interrogating CpGs in the vicinity of candidate genes previously implicated in the stress response and, second, by conducting an exploratory epigenome-wide analysis. Furthermore, we determined whether changes in symptom and stress measures over time were associated with a DNAm-based age estimate and epigenetic aging.
Discussion
To the best of our knowledge, our study is the first to investigate epigenome-wide methylation changes over time in BD patients. Moreover, it is the first to explore methylation changes related to non-traumatic stressful life events on an epigenome-wide scale. Although no locus withstood correction for multiple testing, our suggestive findings and secondary analyses provide limited evidence supporting a role of DNAm in association with non-traumatic life events in chronic BD patients.
We identified a single, suggestively significant, CpG site associated with total LEQ scores, mapping to
POU6F2, which has been associated with several psychiatric traits as well as intelligence and educational attainment. More specifically, genome-wide association studies have identified
POU6F2 risk variants associated with psychological distress (Koshimizu et al.
2019), feeling emotionally hurt (Nagel et al.
2018), schizophrenia (Goes et al.
2015), autism (Anney et al.
2010), educational attainment (Lee et al.
2018; Okbay et al.
2016) and intelligence (Hill et al.
2018; Davies et al.
2018). Additionally, in a longitudinal investigation of DNAm changes preceding adolescent psychotic experiences, DNAm of the CpG site cg11604728 (
POU6F2) measured at age 15–17 was among the top 20 CpG sites indicative of psychotic experiences at age 18 (Roberts et al.
2019). Furthermore,
POU6F2 is highly expressed in the brain with the highest expression found in the frontal cortex (Additional file
1: Fig. S13) and methylation of our suggestive CpG site in blood is correlated with methylation in brain tissue across multiple brain regions. Interestingly, another of our top 20 CpG sites (cg26822318) falls in proximity to the
FER1L6 gene, of which a variant (rs4870888) has been associated with suicide attempts in a meta-analysis of major depressive disorder, schizophrenia and BD (Mullins et al.
2019). Furthermore, another GWAS reported a
FER1L6 variant (rs10481151) suggestively associated with cognitive performance (Need et al.
2009).
At the current sample size, our study provides only minimal evidence supporting an association between methylation of individual CpGs and non-traumatic, recent stressful life events in BD. These findings, however, corroborate other reports of a limited role of DNAm with non-traumatic stress (Marzi et al.
2018). Noteworthy, a recent study reported hypermethylation of
KITLG associated with childhood trauma in healthy controls (
n = 91) but not in bipolar patients (
n = 50) (He et al.
2018). Although the mechanistic role of DNAm in the phenotypic expression of early life adversities is well established in the literature, other mechanisms may be responsible in adulthood and in association with subsequent events. This notion aligns with theories such as Post’s kindling hypothesis and the decay model which suggest a higher impact of life events on first episode than on subsequent episodes in BD (Aldinger and Schulze
2017; Kemner et al.
2015; Hillegers et al.
2004). Furthermore, it must be considered whether positive epigenetic associations with life events could be disorder-specific, genotype-dependent, associated with specific trauma exposure, age groups, sex and/or tissues measured (Marzi et al.
2018; Vinkers et al.
2015; Uddin et al.
2010; Boks et al.
2015; Smith et al.
2011; Mehta et al.
2017). While there is no gold standard for life stress measurements, differences in how to quantify stress may also have a major effect on findings (Johnson
2005; Bender and Alloy
2011; Monroe
2008; Dohrenwend
2010; Brown and Harris
2012).
The main strength of our study is its longitudinal design, allowing for repeated measures within individuals and to investigate methylation changes over time and in relation to symptomatology and stressful life events. To the best of our knowledge, this is the first study to collect repeated epigenome-wide methylation measures in bipolar patients. Furthermore, our study paid attention to critical confounding factors which often lead to spurious findings. For example, the use of methylation-based smoking scores better controls for the extent of smoking throughout the lifetime than the use of self-reported smoking measures (Elliott et al.
2014; Shenker et al.
2013). Finally, in contrast to most other studies, we have included an exploratory epigenome-wide approach.
Despite the strengths of our study, several limitations need to be addressed. First, our study was limited by our small sample size which makes identifying subtle differences in methylation difficult. Taking power into consideration, and as an attempt to address the inherent multiple testing problem associated with EWAS, we limited our EWAS to only the most variable CpG sites according to MAD scores. While the fact that not a single site-specific association in DNAm survived correction for multiple testing could reflect the limited statistical power of our small sample, it may also be related to an overly conservative multiple testing correction considering the lack of variability in methylation at many CpGs and spatial correlation of methylation with nearby sites (Walker et al.
2016; Lunnon et al.
2015). A recent study estimated there are approximately 530,000 independent tests in a whole blood EPIC array DNAm study. Accordingly, they proposed a corrected significance threshold of 9.42 × 10
−8 to be used as a standard threshold for future EWAS based on the EPIC array (Mansell et al.
2019). Furthermore, the study introduced a freely available online tool which allows users to perform power calculations to guide sample sizes, accounting for the individual properties of each DNAm site and using their empirically derived significance threshold. According to their tool, an effect size of just 1% difference between cases and controls would require a sample of 1000 participants, for only a third of methylation sites to have > 80% power. We observed an effect size below 5% in our study (based on median split) for our most significantly associated site, indicating that our study is nevertheless underpowered. Future studies should take advantage of this tool to assess, a priori, required sample sizes according to their expected effect sizes. Furthermore, complementary systems biology approaches such as weighted gene co-methylation network analysis (WGCNA) could be beneficial for studies with limited sample sizes, providing more insight into the functional role of altered DNAm (Langfelder and Horvath
2008).
Another limitation is in relation to the fact that our sample represents a cohort of chronic BD patients which likely influenced our investigation of epigenetic aging related to symptom ratings over time. The chronicity of patients may also confound our findings with regards to the heterogenous treatments patients have received over the years. To acknowledge this critical factor, we conducted a sensitivity analysis excluding those subjects not taking psychotropic drugs at the time of testing, however, this also did not lead to significant results. One must also consider the possible recall and desirability biases associated with self-rating questionnaires like the LEQ and CTS. Lastly, little is known about the temporal stability of epigenetic markers (Byun et al.
2012; Talens et al.
2010). We cannot be sure whether the time interval of 1 year was too long or short to observe dramatic methylation changes or at what time window following exposure to stressful life events one might observe changed methylation profiles.
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