Explore the vast range of titles available.

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits ( n = 137,588) and multi-modal neuroimaging measures ( n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, p corr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, p corr < 0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, p corr < 0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

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.

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. We explore how multiple aspects of brain structural connectivity can predict general cognitive function and general psychopathology. To do this, we modelled six different structural network weightings obtained from diffusion MRI and tested different machine learning (ML) algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. DL did not improve on prediction accuracies from simpler linear models, indicating that most of the variation between phenotypes in weighted structural connectomes is linear rather than non‐linear in nature. This may have implied that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

Bipolar disorder is a highly heritable condition. First-degree relatives of affected individuals have a more than a ten-fold increased risk of developing bipolar disorder (BD), and a three-fold risk of developing major depressive disorder (MDD) than the general population. It is unclear however whether differences in brain activation reported in BD and MDD are present before the onset of illness. We studied 98 young unaffected individuals at high familial risk of BD and 58 healthy controls using functional Magnetic Resonance Imaging (fMRI) scans and a task involving executive and language processing. Twenty of the high-risk subjects subsequently developed MDD after the baseline fMRI scan. At baseline the high-risk subjects who later developed MDD demonstrated relatively increased activation in the insula cortex, compared to controls and high risk subjects who remained well. In the healthy controls and high-risk group who remained well, this region demonstrated reduced engagement with increasing task difficulty. The high risk subjects who subsequently developed MDD did not demonstrate this normal disengagement. Activation in this region correlated positively with measures of cyclothymia and neuroticism at baseline, but not with measures of depression. These results suggest that increased activation of the insula can differentiate individuals at high-risk of bipolar disorder who later develop MDD from healthy controls and those at familial risk who remain well. These findings offer the potential of future risk stratification in individuals at risk of mood disorder for familial reasons.

'Epigenetic age acceleration' is a valuable biomarker of ageing, predictive of morbidity and mortality, but for which the underlying biological mechanisms are not well established. Two commonly used measures, derived from DNA methylation, are Horvath-based (Horvath-EAA) and Hannum-based (Hannum-EAA) epigenetic age acceleration. We conducted genome-wide association studies of Horvath-EAA and Hannum-EAA in 13,493 unrelated individuals of European ancestry, to elucidate genetic determinants of differential epigenetic ageing. We identified ten independent SNPs associated with Horvath-EAA, five of which are novel. We also report 21 Horvath-EAA-associated genes including several involved in metabolism (NHLRC, TPMT) and immune system pathways (TRIM59, EDARADD). GWAS of Hannum-EAA identified one associated variant (rs1005277), and implicated 12 genes including several involved in innate immune system pathways (UBE2D3, MANBA, TRIM46), with metabolic functions (UBE2D3, MANBA), or linked to lifespan regulation (CISD2). Both measures had nominal inverse genetic correlations with father's age at death, a rough proxy for lifespan. Nominally significant genetic correlations between Hannum-EAA and lifestyle factors including smoking behaviours and education support the hypothesis that Hannum-based epigenetic ageing is sensitive to variations in environment, whereas Horvath-EAA is a more stable cellular ageing process. We identified novel SNPs and genes associated with epigenetic age acceleration, and highlighted differences in the genetic architecture of Horvath-based and Hannum-based epigenetic ageing measures. Understanding the biological mechanisms underlying individual differences in the rate of epigenetic ageing could help explain different trajectories of age-related decline.

Previous reports of altered grey and white matter structure in Major Depressive Disorder (MDD) have been inconsistent. Recent meta-analyses have, however, reported reduced hippocampal grey matter volume in MDD and reduced white matter integrity in several brain regions. The use of different diagnostic criteria, scanners and imaging sequences may, however, obscure further anatomical differences. In this study, we tested for differences in subcortical grey matter volume (n = 1157) and white matter integrity (n = 1089) between depressed individuals and controls in the subset of 8590 UK Biobank Imaging study participants who had undergone depression assessments. Whilst we found no significant differences in subcortical volumes, significant reductions were found in depressed individuals versus controls in global white matter integrity, as measured by fractional anisotropy (FA) (β = −0.182, p = 0.005). We also found reductions in FA in association/commissural fibres (β = −0.184, p corrected  = 0.010) and thalamic radiations (β = −0.159, p corrected  = 0.020). Tract-specific FA reductions were also found in the left superior longitudinal fasciculus (β = −0.194, p corrected  = 0.025), superior thalamic radiation (β = −0.224, p corrected  = 0.009) and forceps major (β = −0.193, p corrected  = 0.025) in depression (all betas standardised). Our findings provide further evidence for disrupted white matter integrity in MDD.

There are striking sex differences in the prevalence and symptomology of Major Depressive Disorder. Here, we conduct the largest sex-stratified genome wide association and genotype-by-sex interaction meta-analyses of Major Depressive Disorder to date (Females: 130,471 cases, 159,521 controls. Males: 64,805 cases, 132,185 controls). We identify 16 and eight independent genome-wide significant variants in females and males, respectively, including one novel variant on the X chromosome. Major Depressive Disorder in females and males shows substantial genetic overlap with a large proportion of variants displaying similar effect sizes across sexes. However, we also provide evidence for a higher burden of genetic risk in females which could be due to female-specific variants. Additionally, sex-specific pleiotropic effects may contribute to the higher prevalence of metabolic symptoms in females with Major Depressive Disorder. These findings underscore the importance of considering sex-specific genetic architectures in the study of health conditions, including Major Depressive Disorder, paving the way for more targeted treatment strategies. Sex differences are well established in the prevalence and symptoms of depression. Here, the authors identify a novel X chromosome variant, greater genetic risk, and stronger links to metabolic traits in females, highlighting the importance of sex-aware approaches.

Background Many people with depression do not respond well to the first antidepressant prescribed. Treatment Resistant Depression (TRD) refers to depression which does not respond to multiple subsequent antidepressant treatments. Identifying TRD in routinely-collected health records is challenging due to limited response-related data. Previous studies have used definitions based on the number of antidepressant switches observed. However, these do not account for other features clinically indicative of treatment resistance, such as augmentation of antidepressants with lithium or antipsychotics and switches between antidepressant classes. This study examined definitions of TRD and their impact on the resulting sample across three cohorts. Methods Across the DataLoch, UK Biobank, and Generation Scotland cohorts, we identified cases of depression from primary and secondary care record codes and extracted antidepressant treatment patterns from dispensing/prescribing records ( N  = 51,283, N  = 10,556, and N  = 649 respectively). We examined 9 TRD definitions that varied along two axes: the minimum number of switches required (1+, 2 + or 3 + switches), and the inclusion of other clinical features (augmentation and one or more between-class switches) as alternative routes to TRD. We contrasted sample size and characteristics between definitions, and examined factors associated with inclusion versus a reference definition of 2 + switches. Results The reference TRD definition included 10% of depression cases in the routine data collection, but substantially fewer cases (4%) in consented cohorts. More inclusive definitions that required fewer switches or included a between-class switch classified more individuals as TRD, but resulted in a proportionally older, more deprived sample with fewer depression-related health record codes, older age of depression onset, lower symptom severity, and greater use of first-line antidepressants. Requiring more switches (3 + switches) classified fewer individuals as TRD, but resulted in a proportionally younger sample, with more depression-related health record codes, younger age of depression onset, and greater use of antidepressants associated with later in the treatment line (e.g., Tricyclics). Definitions including augmentations resulted in a small increase in sample size without notable change in sample characteristics. Conclusions TRD is underrepresented in consented cohort studies. A definition of TRD that includes 2 + antidepressant switches or augmented antidepressant treatment as indicators balances sample size with depression severity, while incorporating features from real-world treatment journeys. Clinical trial number Not applicable.

Background Advanced age is associated with cognitive and physical decline and is a major risk factor for a multitude of disorders. There is also a gap in life expectancy between males and females. DNA methylation differences have been shown to be associated with both age and sex. Here, we investigate age-by-sex differences in blood-based DNA methylation in an unrelated cohort of 2586 individuals between the ages of 18 and 87 years, with replication in a further 4450 individuals between the ages of 18 and 93 years. Methods Linear regression models were applied, with stringent genome-wide significance thresholds ( p  < 3.6 × 10 −8 ) used in both the discovery and replication data. A second, highly conservative mixed linear model method that better controls the false-positive rate was also applied, using the same genome-wide significance thresholds. Results Using the linear regression method, 52 autosomal and 597 X-linked CpG sites, mapping to 251 unique genes, replicated with concordant effect size directions in the age-by-sex interaction analysis. The site with the greatest difference mapped to GAGE10 , an X-linked gene. Here, DNA methylation levels remained stable across the male adult age range (DNA methylation by age r  = 0.02) but decreased across female adult age range (DNA methylation by age r  = − 0.61). One site (cg23722529) with a significant age-by-sex interaction also had a quantitative trait locus (rs17321482) that is a genome-wide significant variant for prostate cancer. The mixed linear model method identified 11 CpG sites associated with the age-by-sex interaction. Conclusion The majority of differences in age-associated DNA methylation trajectories between sexes are present on the X chromosome. Several of these differences occur within genes that have been implicated in sexually dimorphic traits.

Recruitment to population-based health studies remains challenging, with difficulties meeting target participant numbers, biosample returns, and achieving a representative sample. Few studies provide evaluations of traditional and web-based recruitment methods particularly for studies with broad inclusion criteria and extended recruitment periods. Generation Scotland (GS) is a family-based cohort study that initiated a new wave of recruitment in 2022 using web-based data collection and remote saliva sampling (for genotyping). Here, we provide an overview of recruitment strategies used by GS over the first 18 months of new recruitment, highlighting which proved most effective and cost-efficient in order to inform future research. This study evaluated recruitment strategies using four main outcomes: (1) absolute recruitment numbers, (2) sociodemographic representativeness, (3) biosample return rate, and (4) cost per participant. Between May 2022 and December 2023, recruitment was undertaken via snowball recruitment (through friends and family of existing volunteers), invitations to those who participated in a previous survey (CovidLife: the GS COVID-19 impact survey), and Scotland-wide recruitment through social media (including sponsored Meta-advertisements), news media, and TV advertisement. The method of recruitment was self-reported in the baseline questionnaire. We present absolute recruitment numbers and sociodemographic characteristics by recruitment method and evaluate the saliva sample return rate by recruitment strategy using chi-square tests. The overall cost and cost per participant were calculated for each method. In total, 7889 new participants joined the cohort over this period. Recruitment sources by contribution were social media (n=2436, 30.9%), survey responder invitations (n=2049, 26.0%), TV advertising (n=367, 17.3%), snowball (n=891, 11.3%), news media (n=747, 9.5%), and other methods or unknown (n=399, 5.0%). More females signed up than males (5570/7889, 70.5% female). To date, 83.5% (6543/7836) of participants returned their postal saliva sample, which also varied by demographic factors (3485/3851, 90.5% older than 60 years vs 471/662, 71.1% aged 16-34 years). Average cost per participant across all recruitment strategies was £13.52 (US $16.82). Previous survey recontacting was the most cost-effective (£0.37 [US $0.46]), followed by social media (£14.78 [US $18.39]), while TV advertisement recruitment was the most expensive per recruit (£33.67 [US $41.89]). This study highlights both the challenges and the opportunities in large web-based cohort recruitment. Overall, social media advertising has been the most cost-effective and easily sustained strategy for recruitment over the reported recruitment period. We note that different strategies resulted in successful recruitment over varying timescales (eg, consistent sustained recruitment for social media and large spikes for news media and TV advertising), which may be informative for future studies with different requirements of recruitment periods. Limitations include self-reported methods of recruitment and difficulties in evaluating multilayered recruitment. Overall, these data demonstrate the potential cost requirements and effectiveness of different strategies that could be applied to future research studies.