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The COVID-19 pandemic and mitigation measures are likely to have a marked effect on mental health. It is important to use longitudinal data to improve inferences. To quantify the prevalence of depression, anxiety and mental well-being before and during the COVID-19 pandemic. Also, to identify groups at risk of depression and/or anxiety during the pandemic. Data were from the Avon Longitudinal Study of Parents and Children (ALSPAC) index generation (n = 2850, mean age 28 years) and parent generation (n = 3720, mean age 59 years), and Generation Scotland (n = 4233, mean age 59 years). Depression was measured with the Short Mood and Feelings Questionnaire in ALSPAC and the Patient Health Questionnaire-9 in Generation Scotland. Anxiety and mental well-being were measured with the Generalised Anxiety Disorder Assessment-7 and the Short Warwick Edinburgh Mental Wellbeing Scale. Depression during the pandemic was similar to pre-pandemic levels in the ALSPAC index generation, but those experiencing anxiety had almost doubled, at 24% (95% CI 23-26%) compared with a pre-pandemic level of 13% (95% CI 12-14%). In both studies, anxiety and depression during the pandemic was greater in younger members, women, those with pre-existing mental/physical health conditions and individuals in socioeconomic adversity, even when controlling for pre-pandemic anxiety and depression. These results provide evidence for increased anxiety in young people that is coincident with the pandemic. Specific groups are at elevated risk of depression and anxiety during the COVID-19 pandemic. This is important for planning current mental health provisions and for long-term impact beyond this pandemic.

Canine visceral leishmaniasis (CVL) is a widespread zoonotic disease in Brazil. This study aimed to identify and predict spatial patterns of CVL in an endemic city, Votuporanga, and examine disease associations with key environmental and anthropogenic factors at a fine spatial scale. First, we estimated the spatial clustering of CVL cases relative to non-cases from 8,146 dogs. Second, we assessed CVL density using a Kernel density ratio map. Third, we analyzed associations between disease occurrence and selected variables derived from the Normalized Difference Vegetation Index (NDVI), number of buildings, building area, and street density using binary logistic regression models. Finally, we predicted the spatial odds of CVL using a Generalized Additive Model (GAM) that incorporated the significant covariates. Our results revealed significant clustering of cases up to a range of 1.7 km. Mean NDVI, street density, and sparse vegetation were statistically significant, increasing the odds of CVL by 431%, 109%, and 100%, respectively, per unit change. The predictive performance of the GAM, evaluated through cross-validation, indicated that the model incorporating mean NDVI achieved the best fit, with an area under the receiver operating characteristic (ROC) curve of 0.74 (CI 0.72–0.76). Our findings demonstrate that CVL is widespread across the city, predominantly in urban fringe areas, with nearly 45% of the city classified as having increased odds of CVL (>1). In contrast, the downtown area exhibited lower odds of disease. Furthermore, we identified distinct parasite genotypes across the city, primarily in areas with higher disease odds. Altogether, our results highlight how biological and environmental data can be integrated into mapping to enhance the understanding of the spatial dynamics of disease transmission in urban areas.

Researchers need to be confident about the reliability of epidemiologic studies that quantify medication use through self-report. Some evidence suggests that psychiatric medications are systemically under-reported. Modern record linkage enables validation of self-report with national prescribing data as gold standard. Here, we investigated the validity of medication self-report for multiple medication types. Participants in the Generation Scotland population-based cohort (N = 10,244) recruited 2009–2011 self-reported regular usage of several commonly prescribed medication classes. This was matched against Scottish NHS prescriptions data using 3- and 6-month fixed time windows. Potential predictors of discordant self-report, including general intelligence and psychological distress, were studied via multivariable logistic regression. Antidepressants self-report showed very good agreement (κ = 0.85, [95% confidence interval (CI) 0.84–0.87]), comparable to antihypertensives (κ = 0.90 [CI 0.89–0.91]). Self-report of mood stabilizers showed moderate-poor agreement (κ = 0.42 [CI 0.33–0.50]). Relevant past medical history was the strongest predictor of self-report sensitivity, whereas general intelligence was not predictive. In this large population-based study, we found self-report validity varied among medication classes, with no simple relationship between psychiatric medication and under-reporting. History of indicated illness predicted more accurate self-report, for both psychiatric and nonpsychiatric medications. Although other patient-level factors influenced self-report for some medications, none predicted greater accuracy across all medications studied.

We aimed to develop an efficient, flexible and scalable approach to diagnostic genome-wide sequence analysis of genetically heterogeneous clinical presentations. Here we present G2P ( www.ebi.ac.uk/gene2phenotype ) as an online system to establish, curate and distribute datasets for diagnostic variant filtering via association of allelic requirement and mutational consequence at a defined locus with phenotypic terms, confidence level and evidence links. An extension to Ensembl Variant Effect Predictor (VEP), VEP-G2P was used to filter both disease-associated and control whole exome sequence (WES) with Developmental Disorders G2P (G2P DD ; 2044 entries). VEP-G2P DD shows a sensitivity/precision of 97.3%/33% for de novo and 81.6%/22.7% for inherited pathogenic genotypes respectively. Many of the missing genotypes are likely false-positive pathogenic assignments. The expected number and discriminative features of background genotypes are defined using control WES. Using only human genetic data VEP-G2P performs well compared to other freely-available diagnostic systems and future phenotypic matching capabilities should further enhance performance. Diagnostic filtering is an important step to analyze the functional and clinical significance of the large number of genetic variants identified from next-generation genome sequencing data. Here, the authors develop a flexible and scalable system for diagnostic filtering of genetic variants using G2P with Ensembl VEP.

Chronic pain is highly prevalent and a significant source of disability, yet its genetic and environmental risk factors are poorly understood. Its relationship with major depressive disorder (MDD) is of particular importance. We sought to test the contribution of genetic factors and shared and unique environment to risk of chronic pain and its correlation with MDD in Generation Scotland: Scottish Family Health Study (GS:SFHS). We then sought to replicate any significant findings in the United Kingdom Biobank study. Using family-based mixed-model analyses, we examined the contribution of genetics and shared family environment to chronic pain by spouse, sibling, and household relationships. These analyses were conducted in GS:SFHS (n = 23,960), a family- and population-based study of individuals recruited from the Scottish population through their general practitioners. We then examined and partitioned the correlation between chronic pain and MDD and estimated the contribution of genetic factors and shared environment in GS:SFHS. Finally, we used data from two independent genome-wide association studies to test whether chronic pain has a polygenic architecture and examine whether genomic risk of psychiatric disorder predicted chronic pain and whether genomic risk of chronic pain predicted MDD. These analyses were conducted in GS:SFHS and repeated in UK Biobank, a study of 500,000 from the UK population, of whom 112,151 had genotyping and phenotypic data. Chronic pain is a moderately heritable trait (heritability = 38.4%, 95% CI 33.6% to 43.9%) that is significantly concordant in spouses (variance explained 18.7%, 95% CI 9.5% to 25.1%). Chronic pain is positively correlated with depression (ρ = 0.13, 95% CI 0.11 to 0.15, p = 2.72x10-68) and shows a tendency to cluster within families for genetic reasons (genetic correlation = 0.51, 95%CI 0.40 to 0.62, p = 8.24x10-19). Polygenic risk profiles for pain, generated using independent GWAS data, were associated with chronic pain in both GS:SFHS (maximum β = 6.18x10-2, 95% CI 2.84 x10-2 to 9.35 x10-2, p = 4.3x10-4) and UK Biobank (maximum β = 5.68 x 10-2, 95% CI 4.70x10-2 to 6.65x10-2, p < 3x10-4). Genomic risk of MDD is also significantly associated with chronic pain in both GS:SFHS (maximum β = 6.62x10-2, 95% CI 2.82 x10-2 to 9.76 x10-2, p = 4.3x10-4) and UK Biobank (maximum β = 2.56x10-2, 95% CI 1.62x10-2 to 3.63x10-2, p < 3x10-4). Limitations of the current study include the possibility that spouse effects may be due to assortative mating and the relatively small polygenic risk score effect sizes. Genetic factors, as well as chronic pain in a partner or spouse, contribute substantially to the risk of chronic pain for an individual. Chronic pain is genetically correlated with MDD, has a polygenic architecture, and is associated with polygenic risk of MDD.

DNA methylation is a dynamic epigenetic mechanism that occurs at cytosine-phosphate-guanine dinucleotide (CpG) sites. Epigenome-wide association studies (EWAS) investigate the strength of association between methylation at individual CpG sites and health outcomes. Although blood methylation may act as a peripheral marker of common disease states, previous EWAS have typically focused only on individual conditions and have had limited power to discover disease-associated loci. This study examined the association of blood DNA methylation with the prevalence of 14 disease states and the incidence of 19 disease states in a single population of over 18,000 Scottish individuals. DNA methylation was assayed at 752,722 CpG sites in whole-blood samples from 18,413 volunteers in the family-structured, population-based cohort study Generation Scotland (age range 18 to 99 years). EWAS tested for cross-sectional associations between baseline CpG methylation and 14 prevalent disease states, and for longitudinal associations between baseline CpG methylation and 19 incident disease states. Prevalent cases were self-reported on health questionnaires at the baseline. Incident cases were identified using linkage to Scottish primary (Read 2) and secondary (ICD-10) care records, and the censoring date was set to October 2020. The mean time-to-diagnosis ranged from 5.0 years (for chronic pain) to 11.7 years (for Coronavirus Disease 2019 (COVID-19) hospitalisation). The 19 disease states considered in this study were selected if they were present on the World Health Organisation's 10 leading causes of death and disease burden or included in baseline self-report questionnaires. EWAS models were adjusted for age at methylation typing, sex, estimated white blood cell composition, population structure, and 5 common lifestyle risk factors. A structured literature review was also conducted to identify existing EWAS for all 19 disease states tested. The MEDLINE, Embase, Web of Science, and preprint servers were searched to retrieve relevant articles indexed as of March 27, 2023. Fifty-four of approximately 2,000 indexed articles met our inclusion criteria: assayed blood-based DNA methylation, had >20 individuals in each comparison group, and examined one of the 19 conditions considered. First, we assessed whether the associations identified in our study were reported in previous studies. We identified 69 associations between CpGs and the prevalence of 4 conditions, of which 58 were newly described. The conditions were breast cancer, chronic kidney disease, ischemic heart disease, and type 2 diabetes mellitus. We also uncovered 64 CpGs that associated with the incidence of 2 disease states (COPD and type 2 diabetes), of which 56 were not reported in the surveyed literature. Second, we assessed replication across existing studies, which was defined as the reporting of at least 1 common site in >2 studies that examined the same condition. Only 6/19 disease states had evidence of such replication. The limitations of this study include the nonconsideration of medication data and a potential lack of generalizability to individuals that are not of Scottish and European ancestry. We discovered over 100 associations between blood methylation sites and common disease states, independently of major confounding risk factors, and a need for greater standardisation among EWAS on human disease.

Background Genome-wide DNA methylation (DNAm) profiling has allowed for the development of molecular predictors for a multitude of traits and diseases. Such predictors may be more accurate than the self-reported phenotypes and could have clinical applications. Results Here, penalized regression models are used to develop DNAm predictors for ten modifiable health and lifestyle factors in a cohort of 5087 individuals. Using an independent test cohort comprising 895 individuals, the proportion of phenotypic variance explained in each trait is examined for DNAm-based and genetic predictors. Receiver operator characteristic curves are generated to investigate the predictive performance of DNAm-based predictors, using dichotomized phenotypes. The relationship between DNAm scores and all-cause mortality ( n  = 212 events) is assessed via Cox proportional hazards models. DNAm predictors for smoking, alcohol, education, and waist-to-hip ratio are shown to predict mortality in multivariate models. The predictors show moderate discrimination of obesity, alcohol consumption, and HDL cholesterol. There is excellent discrimination of current smoking status, poorer discrimination of college-educated individuals and those with high total cholesterol, LDL with remnant cholesterol, and total:HDL cholesterol ratios. Conclusions DNAm predictors correlate with lifestyle factors that are associated with health and mortality. They may supplement DNAm-based predictors of age to identify the lifestyle profiles of individuals and predict disease risk.

Background Epigenetic clocks can track both chronological age (cAge) and biological age (bAge). The latter is typically defined by physiological biomarkers and risk of adverse health outcomes, including all-cause mortality. As cohort sample sizes increase, estimates of cAge and bAge become more precise. Here, we aim to develop accurate epigenetic predictors of cAge and bAge, whilst improving our understanding of their epigenomic architecture. Methods First, we perform large-scale ( N  = 18,413) epigenome-wide association studies (EWAS) of chronological age and all-cause mortality. Next, to create a cAge predictor, we use methylation data from 24,674 participants from the Generation Scotland study, the Lothian Birth Cohorts (LBC) of 1921 and 1936, and 8 other cohorts with publicly available data. In addition, we train a predictor of time to all-cause mortality as a proxy for bAge using the Generation Scotland cohort (1214 observed deaths). For this purpose, we use epigenetic surrogates (EpiScores) for 109 plasma proteins and the 8 component parts of GrimAge, one of the current best epigenetic predictors of survival. We test this bAge predictor in four external cohorts (LBC1921, LBC1936, the Framingham Heart Study and the Women’s Health Initiative study). Results Through the inclusion of linear and non-linear age-CpG associations from the EWAS, feature pre-selection in advance of elastic net regression, and a leave-one-cohort-out (LOCO) cross-validation framework, we obtain cAge prediction with a median absolute error equal to 2.3 years. Our bAge predictor was found to slightly outperform GrimAge in terms of the strength of its association to survival (HR GrimAge  = 1.47 [1.40, 1.54] with p  = 1.08 × 10 −52 , and HR bAge  = 1.52 [1.44, 1.59] with p  = 2.20 × 10 −60 ). Finally, we introduce MethylBrowsR, an online tool to visualise epigenome-wide CpG-age associations. Conclusions The integration of multiple large datasets, EpiScores, non-linear DNAm effects, and new approaches to feature selection has facilitated improvements to the blood-based epigenetic prediction of biological and chronological age.

Epigenetic Clocks have been trained to predict chronological age, healthspan and lifespan. Such clocks are often analysed in relation to disease outcomes – typically using small datasets and a limited number of clocks. Here, we present a large-scale ( n  = 18,859), unbiased comparison of 14 widely used clocks as predictors of 174 incident disease outcomes and all-cause mortality over 10-years of follow up. Second- and third-generation clocks significantly outperform first-generation clocks, which have limited applications in disease settings. Of the 176 Bonferroni significant (P < 0.05/174) associations from fully-adjusted Cox regression models controlling for lifestyle and socioeconomic measures, there are 27 diseases (including primary lung cancer and diabetes) where the hazard ratio for the clock exceeds the clock’s association with all-cause mortality. Furthermore, for 32 of the 176 findings, adding the clock to a null classification model with traditional risk factors significantly increases the classification accuracy by >1%. However, there is minimal evidence for interactions between the clocks and sex or smoking (ever/never) status. Second- and third-generation epigenetic clocks show promise for disease risk prediction, particularly in relation to respiratory and liver-based conditions. Epigenetic clocks estimate biological age and health risks. Here, the authors compare 14 clocks in 18,859 individuals, showing second-generation clocks better predict disease incidence and mortality, particularly for respiratory and liver-related conditions.

'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.