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Alcohol misuse is common in many societies worldwide and is associated with extensive morbidity and mortality, often leading to alcohol use disorders (AUD) and alcohol-related end-organ damage. The underlying mechanisms contributing to the development of AUD are largely unknown; however, growing evidence suggests that alcohol consumption is strongly associated with alterations in DNA methylation. Identification of alcohol-associated methylomic variation might provide novel insights into pathophysiology and novel treatment targets for AUD. Here we performed the largest single-cohort epigenome-wide association study (EWAS) of alcohol consumption to date (N = 8161) and cross-validated findings in AUD populations with relevant endophenotypes, as well as alcohol-related animal models. Results showed 2504 CpGs significantly associated with alcohol consumption (Bonferroni p value < 6.8 × 10−8) with the five leading probes located in SLC7A11 (p = 7.75 × 10−108), JDP2 (p = 1.44 × 10−56), GAS5 (p = 2.71 × 10−47), TRA2B (p = 3.54 × 10−42), and SLC43A1 (p = 1.18 × 10−40). Genes annotated to associated CpG sites are implicated in liver and brain function, the cellular response to alcohol and alcohol-associated diseases, including hypertension and Alzheimer’s disease. Two-sample Mendelian randomization confirmed the causal relationship of consumption on AUD risk (inverse variance weighted (IVW) p = 5.37 × 10−09). A methylation-based predictor of alcohol consumption was able to discriminate AUD cases in two independent cohorts (p = 6.32 × 10−38 and p = 5.41 × 10−14). The top EWAS probe cg06690548, located in the cystine/glutamate transporter SLC7A11, was replicated in an independent cohort of AUD and control participants (N = 615) and showed strong hypomethylation in AUD (p −17). Decreased CpG methylation at this probe was consistently associated with clinical measures including increased heavy drinking days (p −4), increased liver function enzymes (GGT (p = 1.03 × 10−21), ALT (p = 1.29 × 10−6), and AST (p = 1.97 × 10−8)) in individuals with AUD. Postmortem brain analyses documented increased SLC7A11 expression in the frontal cortex of individuals with AUD and animal models showed marked increased expression in liver, suggesting a mechanism by which alcohol leads to hypomethylation-induced overexpression of SLC7A11. Taken together, our EWAS discovery sample and subsequent validation of the top probe in AUD suggest a strong role of abnormal glutamate signaling mediated by methylomic variation in SLC7A11. Our data are intriguing given the prominent role of glutamate signaling in brain and liver and might provide an important target for therapeutic intervention.

Hypothalamic–pituitary–adrenal (HPA) axis dysregulation has been commonly reported in major depressive disorder (MDD), but with considerable heterogeneity of results; potentially due to the predominant use of acute measures of an inherently variable/phasic system. Chronic longer-term measures of HPA-axis activity have yet to be systematically examined in MDD, particularly in relation to brain phenotypes, and in the context of early-life/contemporaneous stress. Here, we utilise a temporally stable measure of cumulative HPA-axis function (hair glucocorticoids) to investigate associations between cortisol, cortisone and total glucocorticoids with concurrent measures of (i) lifetime-MDD case/control status and current symptom severity, (ii) early/current-life stress and (iii) structural neuroimaging phenotypes, in N = 993 individuals from Generation Scotland (mean age = 59.1 yrs). Increased levels of hair cortisol were significantly associated with reduced global and lobar brain volumes with reductions in the frontal, temporal and cingulate regions (βrange = −0.057 to −0.104, all PFDR βrange = 0.071 to 0.115, all PFDR = β = 0.083, P = 0.017), and with reduced global and regional brain volumes (global: β = −0.059, P = 0.043; nucleus accumbens: β = −0.075, PFDR = 0.044). Associations with total glucocorticoids followed a similar pattern to the cortisol findings. In this large community-based sample, elevated glucocorticoids were significantly associated with MDD, with early, but not later-life stress, and with reduced global and regional brain phenotypes. These findings provide important foundations for future mechanistic studies to formally explore causal relationships between early adversity, chronic rather than acute measures of glucocorticoids, and neurobiological associations relevant to the aetiology of MDD.

Highlights • First study of both serum and DNAm CRP associations with depression/neuroimaging. • Serum CRP is associated with somatic symptoms and reduced entorhinal cortex thickness. • DNAm CRP is associated with widespread reductions in white matter integrity. • Evidence for central effects of peripheral inflammation from serum and DNAm markers., Inflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear, partly due to complexities around the use of acute/phasic inflammatory biomarkers. Here, we report the first large-scale study of both serological and methylomic signatures of CRP (considered to represent acute and chronic measures of inflammation respectively) and their associations with depression status/symptoms, and structural neuroimaging phenotypes (T1 and diffusion MRI) in a large community-based sample (Generation Scotland; NMDD cases = 271, Ncontrols = 609). Serum CRP was associated with overall MDD severity, and specifically with current somatic symptoms- general interest (β = 0.145, PFDR = 6 × 10−4) and energy levels (β = 0.101, PFDR = 0.027), along with reduced entorhinal cortex thickness (β = −0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and widespread reductions in integrity of 16/24 white matter tracts (with greatest regional effects in the external and internal capsules, βFA= −0.12 to −0.14). In general, the methylation-based measures showed stronger associations with imaging metrics than serum-based CRP measures (βaverage = −0.15 versus βaverage = 0.01 respectively). These findings provide evidence for central effects of peripheral inflammation from both serological and epigenetic markers of inflammation, including in brain regions previously implicated in depression. This suggests that these imaging measures may be involved in the relationship between peripheral inflammation and somatic/depressive symptoms. Notably, greater effects on brain morphology were seen for methylation-based rather than serum-based measures of inflammation, indicating the importance of such measures for future studies.

Variation in DNA methylation (DNAm) is associated with lifestyle factors such as smoking and body mass index (BMI) but there has been little research exploring its ability to identify individuals with major depressive disorder (MDD). Using penalised regression on genome-wide CpG methylation, we tested whether DNAm risk scores (MRS), trained on 1223 MDD cases and 1824 controls, could discriminate between cases (n = 363) and controls (n = 1417) in an independent sample, comparing their predictive accuracy to polygenic risk scores (PRS). The MRS explained 1.75% of the variance in MDD (β = 0.338, p = 1.17 × 10−7) and remained associated after adjustment for lifestyle factors (β = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (β = 0.384, p = 4.69 × 10−9) the MRS remained associated with MDD (β = 0.327, p = 5.66 × 10−7). The MRS was also associated with incident cases of MDD who were well at recruitment but went on to develop MDD at a later assessment (β = 0.193, p = 0.016, R2 = 0.52%). Heritability analyses found additive genetic effects explained 22% of variance in the MRS, with a further 19% explained by pedigree-associated genetic effects and 16% by the shared couple environment. Smoking status was also strongly associated with MRS (β = 0.440, p ≤ 2 × 10−16). After removing smokers from the training set, the MRS strongly associated with BMI (β = 0.053, p = 0.021). We tested the association of MRS with 61 behavioural phenotypes and found that whilst PRS were associated with psychosocial and mental health phenotypes, MRS were more strongly associated with lifestyle and sociodemographic factors. DNAm-based risk scores of MDD significantly discriminated MDD cases from controls in an independent dataset and may represent an archive of exposures to lifestyle factors that are relevant to the prediction of MDD.

BackgroundDepression is a disabling and highly prevalent condition where genetic and epigenetic differences, such as DNA methylation (DNAm), contribute to prediction of disease liability.MethodWe investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N=8,898, mean age=49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in Avon Longitudinal Study of Parents and Children (ALSPAC) (Ncombined=2,049, mean age=79.1, 69.6 and 47.2 years, respectively). We also conducted a replication MWAS in the ALSPAC children (N=423, mean age=17.1 years).ResultWide-spread associations were found between PRS constructed using genetic risk variants for depression and DNAm in cytosine-guanine dinucleotide (CpG) probes that localised to genes involved in immune responses and neural development (NCpG=599, pBonferroni−8). The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r=0.83) and in adolescents (r=0.76). Additional analysis on the methylome-wide associations was conducted for each lead genetic risk variant. Over 40% of the independent genetic risk variants showed associations with CpG probe DNAm located in both the same (cis) and distal probes (trans) to the genetic loci (pBonferroniFDRFDR ranged from 0.045 to 2.06×10−120; depression to DNAm: pFDR ranged from 0.046 to 2.1×10−23).ConclusionPolygenic risk scores for depression, especially those constructed from genome-wide significant genetic risk variants, showed epigenome-wide methylation association differences in the methylome associated with immune responses and brain development. We also found evidence from Mendelian randomisation evidence that DNAm may be causal to depression, as well as a causal consequence of depression.

There has been a substantial amount of research reporting the neuroanatomical associations of psychotic symptoms in people with schizophrenia. Comparatively little attention has been paid to the neuroimaging correlates of subclinical psychotic symptoms, so-called “psychotic-like experiences” (PLEs), within large healthy populations. PLEs are relatively common in the general population (7–13%), can be distressing and negatively affect health. This study therefore examined gray and white matter associations of four different PLEs (auditory or visual PLEs, and delusional ideas about conspiracies or communications) in subjects of the UK Biobank study with neuroimaging data (N = 21,390, mean age = 63 years). We tested for associations between any PLE (N = 768) and individual PLEs with gray and white matter brain structures, controlling for sex, age, intracranial volume, scanning site, and position in the scanner. Individuals that reported having experienced auditory hallucinations (N = 272) were found to have smaller volumes of the caudate, putamen, and accumbens (β = −0.115–0.134, pcorrected = 0.048–0.036), and reduced temporal lobe volume (β = −0.017, pcorrected = 0.047) compared to those that did not. People who indicated that they had ever believed in unreal conspiracies (N = 111) had a larger volume of the left amygdala (β = 0.023, pcorrected = 0.038). Individuals that reported a history of visual PLEs (N = 435) were found to have reduced white matter microstructure of the forceps major (β = −0.029, pcorrected = 0.009), an effect that was more marked in participants who reported PLEs as distressing. These associations were not accounted for by diagnoses of psychotic or depressive illness, nor the known risk factors for psychotic symptoms of childhood adversity or cannabis use. These findings suggest altered regional gray matter volumes and white matter microstructure in association with PLEs in the general population. They further suggest that these alterations may appear more frequently with the presentation of different psychotic symptoms in the absence of clinically diagnosed psychotic disorders.

BackgroundDepression is assessed in many different ways, with large population studies often relying on minimal phenotyping approaches. Genetic results suggest that more formal clinical diagnoses and simpler self-report measures of depression show some core similarities, but also important differences. It is not yet clear whether this is also the case for neuroimaging measures.MethodsWe studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging (T1 and DTI) and depression data. Depression phenotypes included a minmal single-item self-report measure, an intermediate symptom-based measure of ‘probable’ depression, and a more clinically robust measure based on DSM-IV criteria. We tested i) associations between brain structural measures and each depression phenotype, and ii) the effects of depression phenotype on these associations.ResultsSmall depression-brain structure associations (β < 0.1) were significant after FDR correction for many global and regional metrics for all three phenotypes. The most consistent imaging associations across depression phenotypes were for measures of white matter integrity. There were small but significant effects of phenotype definition primarily for cortical thickness, which showed stronger negative associations with Self-reported Depression than the symptom-based measures.ConclusionSimilar to previous genetic studies, we found some consistent associations indicating a core component of depression across phenotypes, and some additional associations that were phenotype-specific. Although these specific results did not relate to depth of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research.

BackgroundInflammatory processes are implicated in the aetiology of Major Depressive Disorder (MDD); however, the relationship between peripheral inflammation, brain structure and depression remains unclear. This study investigates associations between depression, structural neuroimaging measures and two measures of inflammation: serum-based C-reactive protein (CRP) and a methylation-based score for CRP (DNAm CRP) in a large community-based sample.MethodsSerum CRP and DNAm CRP were assessed for participants in Generation Scotland (NMDD cases = 240, Ncontrols = 558) alongside structural brain scans (T1 and diffusion MRI). Linear regression was used to investigate associations between (i) both CRP measures and depressive symptoms, (ii) both CRP measures and structural imaging variables and (iii) inflammation x MDD interaction effects (for both CRP measures) with imaging measures.ResultsIncreased serum CRP was significantly associated with overall MDD symptom severity and specifically with somatic symptoms-general interest (β= 0.145, PFDR = 6×10-4) and energy levels (β= 0.101, PFDR = 0.027) and also reduced entorhinal cortex thickness (β= – 0.095, PFDR = 0.037). DNAm CRP was significantly associated with reduced global grey matter/cortical volume and reduced integrity of 16 white matter tracts and showed larger effect sizes (βaverage = −0.15) compared to serum CRP across all measures (βaverage = 0.01).ConclusionsAcute measures of CRP were related to current depression symptoms, specifically somatic symptoms, whereas methylation signatures of inflammation demonstrated greater differences in global and regional brain structure. This study highlights the utility of combining serological and methylation markers to study chronic inflammation effects on the brain in psychiatric disorders.

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches., Previous case‐control classification studies of depression have reported promising accuracies up to 80% and above, but investigated relatively small samples with clinically‐ascertained cases. In the current study we explore multiple classification approaches with brain structural and white matter integrity measures, and achieve up to 75% predictive classification accuracy in a small sample with clinical interview‐based diagnoses (N 700 cases, UK Biobank cohort), and also in larger samples with remitted MDD (STRADL) and lifetime‐experienced MDD (UK Biobank). These results indicate that high predictive case‐control classification accuracies may not immediately translate to larger samples with broader diagnostic criteria for depression.

Background Depression is the leading cause of disability worldwide with > 50% of cases emerging before the age of 25 years. Large-scale neuroimaging studies in depression implicate robust structural brain differences in the disorder. However, most studies have been conducted in adults and therefore, the temporal origins of depression-related imaging features remain largely unknown. This has important implications for understanding aetiology and informing timings of potential intervention. Methods Here, we examine associations between brain structure (cortical metrics and white matter microstructural integrity) and depression ratings (from caregiver and child), in a large sample (N = 8634) of early adolescents (9 to 11 years old) from the US-based, Adolescent Brain and Cognitive Development (ABCD) Study®. Data was collected from 2016 to 2018. Findings We report significantly decreased global cortical and white matter metrics, and regionally in frontal, limbic and temporal areas in adolescent depression (Cohen's d = -0⋅018 to -0⋅041, β = -0·019 to -0⋅057). Further, we report consistently stronger imaging associations for caregiver-reported compared to child-reported depression ratings. Divergences between reports (caregiver vs child) were found to significantly relate to negative socio-environmental factors (e.g., family conflict, absolute β = 0⋅048 to 0⋅169). Interpretation Depression ratings in early adolescence were associated with similar imaging findings to those seen in adult depression samples, suggesting neuroanatomical abnormalities may be present early in the disease course, arguing for the importance of early intervention. Associations between socio-environmental factors and reporter discrepancy warrant further consideration, both in the wider context of the assessment of adolescent psychopathology, and in relation to their role in aetiology. Funding Wellcome Trust (References: 104036/Z/14/Z and 220857/Z/20/Z) and the Medical Research Council (MRC, Reference: MC_PC_17209).

ObjectiveDNA methylation (DNAm) is associated with environmental risk factors for major depressive disorder (MDD) but has not yet been tested for its ability to discriminate individuals with MDD from unaffected individuals.MethodsUsing penalized regression based on genome-wide CpG methylation, we trained a DNAm risk score of MDD (DNAm-RS) in 1,223 cases and 1,824 controls and tested in a second independent sample of 363 prevalent cases and 1,417 controls. Using DNA from 1,607 unaffected individuals, we tested whether DNAm-RS could discriminate the 190 incident cases of lifetime MDD from the 1,417 individuals who remained unaffected at follow-up.ResultsA weighted linear combination of 196 CpG sites were derived from the training sample to form a DNAm-RS. The DNAm-RS explained 1.75% of the variance in MDD risk in an independent case-control sample and significantly predicted future incident episodes of MDD at follow up (R2=0.52%). DNAm-RS and MDD polygenic risk scores together additively explained 3.99% of the variance in prevalent MDD. The DNAm-RS was also significantly associated with lifestyle factors associated with MDD, including smoking status (β=0.440, p=−16) and alcohol use (β=0.092, p=9.85×10−5). The DNAm-RS remained significantly associated with MDD after adjustment for these environmental factors (independent association: β=0.338, p=1.17×10−7 association post-adjustment: β=0.081, p=0.0006).ConclusionsA novel risk score of MDD based on DNAm data significantly discriminated MDD cases from controls in an independent dataset, and controls who would subsequently develop MDD from those who remained unaffected. DNAm-RS captured the effects of exposure to key lifestyle risk factors for MDD, revealing a potential role in risk stratification.