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Abstract Background Plasma growth differentiation factor 15 (GDF15) and N‐terminal proB‐type natriuretic peptide (NT‐proBNP) are cardiovascular biomarkers that associate with a range of diseases. Epigenetic scores (EpiScores) for GDF15 and NT-proBNP may provide new routes for risk stratification. Results In the Generation Scotland cohort (N ≥ 16,963), GDF15 levels were associated with incident dementia, ischaemic stroke and type 2 diabetes, whereas NT-proBNP levels were associated with incident ischaemic heart disease, ischaemic stroke and type 2 diabetes (all PFDR 95%) with levels of GDF15 and NT-proBNP, respectively. EpiScores for GDF15 and NT-proBNP were trained in a subset of the population. The GDF15 EpiScore replicated protein associations with incident dementia, type 2 diabetes and ischaemic stroke in the Generation Scotland test set (hazard ratios (HR) range 1.36–1.41, PFDR

Abstract Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Abstract Background Epigenetic scores (EpiScores), reflecting DNA methylation (DNAm)-based surrogates for complex traits, have been developed for multiple circulating proteins. EpiScores for pro-inflammatory proteins, such as C-reactive protein (DNAm CRP), are associated with brain health and cognition in adults and with inflammatory comorbidities of preterm birth in neonates. Social disadvantage can become embedded in child development through inflammation, and deprivation is overrepresented in preterm infants. We tested the hypotheses that preterm birth and socioeconomic status (SES) are associated with alterations in a set of EpiScores enriched for inflammation-associated proteins. Results In total, 104 protein EpiScores were derived from saliva samples of 332 neonates born at gestational age (GA) 22.14 to 42.14 weeks. Saliva sampling was between 36.57 and 47.14 weeks. Forty-three (41%) EpiScores were associated with low GA at birth (standardised estimates |0.14 to 0.88|, Bonferroni-adjusted p-value

Abstract Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.

Abstract Background Epigenetic Scores (EpiScores) for blood protein levels have been associated with disease outcomes and measures of brain health, highlighting their potential usefulness as clinical biomarkers. They are typically derived via penalised regression, whereby a linear weighted sum of DNA methylation (DNAm) levels at CpG sites are predictive of protein levels. Here, we examine 84 previously published protein EpiScores as possible biomarkers of cross-sectional and longitudinal measures of general cognitive function and brain health, and incident dementia across three independent cohorts. Results Using 84 protein EpiScores as candidate biomarkers, associations with general cognitive function (both cross-sectionally and longitudinally) were tested in three independent cohorts: Generation Scotland (GS), and the Lothian Birth Cohorts of 1921 and 1936 (LBC1921 and LBC1936, respectively). A meta-analysis of general cognitive functioning results in all three cohorts identified 18 EpiScore associations (absolute meta-analytic standardised estimates ranged from 0.03 to 0.14, median of 0.04, P FDR

Abstract DNA methylation is an ideal trait to study the extent of the shared genetic control across ancestries, effectively providing hundreds of thousands of model molecular traits with large QTL effect sizes. We investigate cis DNAm QTLs in three European (n = 3701) and two East Asian (n = 2099) cohorts to quantify the similarities and differences in the genetic architecture across populations. We observe 80,394 associated mQTLs (62.2% of DNAm probes with significant mQTL) to be significant in both ancestries, while 28,925 mQTLs (22.4%) are identified in only a single ancestry. mQTL effect sizes are highly conserved across populations, with differences in mQTL discovery likely due to differences in allele frequency of associated variants and differing linkage disequilibrium between causal variants and assayed SNPs. This study highlights the overall similarity of genetic control across ancestries and the value of ancestral diversity in increasing the power to detect associations and enhancing fine mapping resolution.

Abstract Background Epigenetic scores (EpiScores) can provide biomarkers of lifestyle and disease risk. Projecting new datasets onto a reference panel is challenging due to separation of technical and biological variation with array data. Normalisation can standardise data distributions but may also remove population-level biological variation. Results We compare two birth cohorts (Lothian Birth Cohorts of 1921 and 1936 — nLBC1921 = 387 and nLBC1936 = 498) with blood-based DNA methylation assessed at the same chronological age (79 years) and processed in the same lab but in different years and experimental batches. We examine the effect of 16 normalisation methods on a novel BMI EpiScore (trained in an external cohort, n = 18,413), and Horvath’s pan-tissue DNA methylation age, when the cohorts are normalised separately and together. The BMI EpiScore explains a maximum variance of R 2=24.5% in BMI in LBC1936 (SWAN normalisation). Although there are cross-cohort R 2 differences, the normalisation method makes a minimal difference to within-cohort estimates. Conversely, a range of absolute differences are seen for individual-level EpiScore estimates for BMI and age when cohorts are normalised separately versus together. While within-array methods result in identical EpiScores whether a cohort is normalised on its own or together with the second dataset, a range of differences is observed for between-array methods. Conclusions Normalisation methods returning similar EpiScores, whether cohorts are analysed separately or together, will minimise technical variation when projecting new data onto a reference panel. These methods are important for cases where raw data is unavailable and joint normalisation of cohorts is computationally expensive.

Summary: Background: Smoking impacts DNA methylation, but data are lacking on smoking-related differential methylation by sex or dietary intake, recent smoking cessation (

Abstract 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 (HRGrimAge = 1.47 [1.40, 1.54] with p = 1.08 × 10−52, and HRbAge = 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.

Cunningham-Burley, McCartney, Campbell, Flaig, Orange et al. assess the ethics and potential benefit of secondary data use from newborn blood spot archives for research in a Citizen’s jury that agreed this was in the public interest. They also investigate and demonstrate the feasibility of using the archives for research purposes.

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, blood protein markers of brain health are integrated with omics data to reveal DNA methylation differences that associate with these protein markers.

Chronic inflammation, marked by C-reactive protein, has been associated with changes in methylation, but the causal relationship is unclear. Here, the authors perform a Epigenome-wide association meta-analysis for C-reactive protein levels and find that these methylation changes are likely the consequence of inflammation and could contribute to disease.

Abstract Background Depression is a disabling and highly prevalent condition where genetic and epigenetic, such as DNA methylation (DNAm), differences contribute to disease risk. DNA methylation is influenced by genetic variation but the association between polygenic risk of depression and DNA methylation is unknown. Methods We investigated the association between polygenic risk scores (PRS) for depression and DNAm by conducting a methylome-wide association study (MWAS) in Generation Scotland (N = 8898, mean age = 49.8 years) with replication in the Lothian Birth Cohorts of 1921 and 1936 and adults in the Avon Longitudinal Study of Parents and Children (ALSPAC) (N combined = 2049, 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). Gene ontology analysis was conducted for the cytosine-guanine dinucleotide (CpG) probes significantly associated with depression PRS, followed by Mendelian randomisation (MR) analysis to infer the causal relationship between depression and DNAm. Results Widespread associations (N CpG = 71, p Bonferroni < 0.05, p < 6.3 × 10−8) were found between PRS constructed using genetic risk variants for depression and DNAm in CpG probes that localised to genes involved in immune responses and neural development. The effect sizes for the significant associations were highly correlated between the discovery and replication samples in adults (r = 0.79) and in adolescents (r = 0.82). Gene Ontology analysis showed that significant CpG probes are enriched in immunological processes in the human leukocyte antigen system. Additional MWAS was conducted for each lead genetic risk variant. Over 47.9% of the independent genetic risk variants included in the PRS showed associations with DNAm in CpG probes located in both the same (cis) and distal (trans) locations to the genetic loci (p Bonferroni < 0.045). Subsequent MR analysis showed that there are a greater number of causal effects found from DNAm to depression than vice versa (DNAm to depression: p FDR ranged from 0.024 to 7.45 × 10−30; depression to DNAm: p FDR ranged from 0.028 to 0.003). Conclusions PRS for depression, especially those constructed from genome-wide significant genetic risk variants, showed methylome-wide differences associated with immune responses. Findings from MR analysis provided evidence for causal effect of DNAm to depression.

Abstract Background Blood-based markers of cognitive functioning might provide an accessible way to track neurodegeneration years prior to clinical manifestation of cognitive impairment and dementia. Results Using blood-based epigenome-wide analyses of general cognitive function, we show that individual differences in DNA methylation (DNAm) explain 35.0% of the variance in general cognitive function (g). A DNAm predictor explains ~4% of the variance, independently of a polygenic score, in two external cohorts. It also associates with circulating levels of neurology- and inflammation-related proteins, global brain imaging metrics, and regional cortical volumes. Conclusions As sample sizes increase, the ability to assess cognitive function from DNAm data may be informative in settings where cognitive testing is unreliable or unavailable.

Summary: Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

Abstract Measures of information processing speed vary between individuals and decline with age. Studies of aging twins suggest heritability may be as high as 67%. The Illumina HumanExome Bead Chip genotyping array was used to examine the association of rare coding variants with performance on the Digit-Symbol Substitution Test (DSST) in community-dwelling adults participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. DSST scores were available for 30,576 individuals of European ancestry from nine cohorts and for 5758 individuals of African ancestry from four cohorts who were older than 45 years and free of dementia and clinical stroke. Linear regression models adjusted for age and gender were used for analysis of single genetic variants, and the T5, T1, and T01 burden tests that aggregate the number of rare alleles by gene were also applied. Secondary analyses included further adjustment for education. Meta-analyses to combine cohort-specific results were carried out separately for each ancestry group. Variants in RNF19A reached the threshold for statistical significance (p = 2.01 × 10−6) using the T01 test in individuals of European descent. RNF19A belongs to the class of E3 ubiquitin ligases that confer substrate specificity when proteins are ubiquitinated and targeted for degradation through the 26S proteasome. Variants in SLC22A7 and OR51A7 were suggestively associated with DSST scores after adjustment for education for African-American participants and in the European cohorts, respectively. Further functional characterization of its substrates will be required to confirm the role of RNF19A in cognitive function.

Serum urate concentration can be studied in large datasets to find genetic and epigenetic loci that may be related to cardiometabolic traits. Here the authors identify and replicate 100 urate-associated CpGs, which provide insights into urate GWAS loci and shared CpGs of urate and cardiometabolic traits.

Many genetic loci have been identified to be associated with kidney disease, but the molecular mechanisms are not well understood. Here, the authors perform epigenome-wide association studies on kidney function measures to identify epigenetic marks and pathways involved in kidney function.

The Developmental Origins of Health and Disease (DOHaD) theory predicts that prenatal and early life events shape adult health outcomes. Birth weight is a useful indicator of the foetal experience and has been associated with multiple adult health outcomes. DNA methylation (DNAm) is one plausible mechanism behind the relationship of birth weight to adult health. Through data linkage between Generation Scotland and historic Scottish birth cohorts, and birth records held through the NHS Information and Statistics Division, a sample of 1,757 individuals with available birth weight and DNAm data was derived. Epigenome-wide association studies (EWAS) were performed in two independently generated DNAm subgroups (nSet1 = 1,395, nSet2 = 362), relating adult DNAm from whole blood to birth weight. Meta-analysis yielded one genome-wide significant CpG site (p = 5.97x10−9), cg00966482. There was minimal evidence for attenuation of the effect sizes for the lead loci upon adjustment for numerous potential confounder variables (body mass index, educational attainment, and socioeconomic status). Associations between birth weight and epigenetic measures of biological age were also assessed. Associations between lower birth weight and higher Grim Age acceleration (p(FDR) = 3.6x10−3) and shorter DNAm-derived telomere length (p(FDR) = 1.7x10−3) are described, although results for three other epigenetic clocks were null. Our results provide support for an association between birth weight and DNAm both locally at one CpG site, and globally via biological ageing estimates.

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Abstract Background DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. Methods The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. Results We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. Conclusions We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context.

Abstract Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

Abstract Background The apolipoprotein E (APOE) ε4 allele is the strongest genetic risk factor for late onset Alzheimer’s disease, whilst the ε2 allele confers protection. Previous studies report differential DNA methylation of APOE between ε4 and ε2 carriers, but associations with epigenome-wide methylation have not previously been characterised. Methods Using the EPIC array, we investigated epigenome-wide differences in whole blood DNA methylation patterns between Alzheimer’s disease-free APOE ε4 (n = 2469) and ε2 (n = 1118) carriers from the two largest single-cohort DNA methylation samples profiled to date. Using a discovery, replication and meta-analysis study design, methylation differences were identified using epigenome-wide association analysis and differentially methylated region (DMR) approaches. Results were explored using pathway and methylation quantitative trait loci (meQTL) analyses. Results We obtained replicated evidence for DNA methylation differences in a ~ 169 kb region, which encompasses part of APOE and several upstream genes. Meta-analytic approaches identified DNA methylation differences outside of APOE: differentially methylated positions were identified in DHCR24, LDLR and ABCG1 (2.59 × 10−100 ≤ P ≤ 2.44 × 10−8) and DMRs were identified in SREBF2 and LDLR (1.63 × 10−4 ≤ P ≤ 3.01 × 10−2). Pathway and meQTL analyses implicated lipid-related processes and high-density lipoprotein cholesterol was identified as a partial mediator of the methylation differences in ABCG1 and DHCR24. Conclusions APOE ε4 vs. ε2 carrier status is associated with epigenome-wide methylation differences in the blood. The loci identified are located in trans as well as cis to APOE and implicate genes involved in lipid homeostasis.

Despite the identification of genetic risk loci for late-onset Alzheimer’s disease (LOAD), the genetic architecture and prediction remains unclear. Here, the authors use genetic risk scores for prediction of LOAD across three datasets and show evidence suggesting oligogenic variant architecture for this disease.

Abstract Background The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets. Methods In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches). Results We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn’s disease. Conclusions Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

Linking epigenetic marks to clinical outcomes promises insight into the underlying processes. Here, the authors introduce a statistical approach to estimate associations between a phenotype and all epigenetic probes jointly, and to estimate the proportion of variation captured by epigenetic effects.

Abstract Background Large numbers of autosomal sites are found differentially methylated in the aging genome. Due to analytical difficulties in dealing with sex differences in X-chromosome content and X-inactivation (XCI) in females, this has not been explored for the X chromosome. Methods Using data from middle age to elderly individuals (age 55+ years) from two Danish cohorts of monozygotic twins and the Scottish Lothian Birth Cohort 1921, we conducted an X-chromosome-wide analysis of age-associated DNA methylation patterns with consideration of stably inferred XCI status. Results Through analysing and comparing sex-specific X-linked DNA methylation changes over age late in life, we identified 123, 293 and 55 CpG sites significant (FDR

Late-life cognitive dysfunction is common, but the biological substrates are largely unknown. Here, the authors examined a panel of 90 neurology-related protein biomarkers and show that plasma levels of 22 of these proteins are associated with general fluid cognitive ability in later life.

Abstract Background Previous studies have demonstrated an association between DNA methylation-based measures of accelerated ageing and age-related health outcomes and mortality. As a disease closely associated with advancing age, we hypothesized that DNA methylation-based measures of accelerated ageing might be associated with risk for dementia. This study therefore aimed to examine the association between four recognised measures of age acceleration and subsequent dementia. Methods Study subjects (n = 488) were members of the Lothian Birth Cohort 1921. Dementia case ascertainment used data from death certificates, electronic hospital records, and clinical reviews. Venous blood samples were taken at baseline, at age 79 years. DNA methylation and measures of epigenetic age were calculated in accordance with Horvath’s epigenetic clock tutorial, using the online calculator ( https://dnamage.genetics.ucla.edu/ ). From these values, four measures of accelerated ageing were calculated: extrinsic epigenetic age acceleration (EEAA), intrinsic epigenetic age acceleration (IEAA), AgeAccelPheno and AgeAccelGrim. Competing risk regression models – with death as a competing risk – were performed to examine the association between each measure of accelerated ageing and incident dementia. APOE ɛ4 status, sex, age, smoking status, history of cardiovascular disease, cerebrovascular disease, hypertension, and diabetes were included as covariates. Results None of the multivariate models revealed a positive association between increased epigenetic age acceleration and dementia risk. Across all included models, never-smoking increased risk for dementia (HR 1.69 [1.06, 2.71], p = 0.03), and having no APOE ɛ4 alleles reduced risk for dementia (HR 0.44 [0.29, 0.67], p

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care. Author summary Healthcare systems around the world are struggling to accommodate high numbers of the most severely ill patients with COVID-19. Moreover, the pandemic creates a pressing need to accelerate clinical trials investigating potential new therapeutics. While various biomarkers can discriminate and predict the future course of disease for patients of different disease severity, prognosis remains difficult for patient groups with similar disease severity, e.g. patients requiring intensive care. Established risk assessments in intensive care medicine such as the SOFA or APACHE II show only limited reliability in predicting future disease outcomes for COVID-19. In this study we hypothesized that the plasma proteome, which reflects the complete set of proteins that are expressed by an organism and are present in the blood, and which is known to comprehensively capture the host response to COVID-19, can be leveraged to allow for prediction of survival in the most critically ill patients with COVID-19. Here, we found 14 proteins, which over time changed in opposite directions for patients who survive compared to patients who do not survive on intensive care. Using a machine learning model which combines the measurements of multiple proteins, we were able to accurately predict survival in critically ill patients with COVID-19 from single blood samples, weeks before the outcome, substantially outperforming established risk predictors.

Abstract Background Previous studies on the relationship between anticholinergic drugs and dementia have reported heterogeneous results. This variability could be due to different anticholinergic scales and differential effects of distinct classes of drugs. Methods Using Cox proportional hazards models, we computed the association between annual anticholinergic burden (AChB) and the risk of dementia in UK Biobank with linked general practitioner prescription records between the years 2000 and 2015 (n = 171,775). Results AChB according to most anticholinergic scales (standardized odds ratio range: 1.027–1.125) and the slope of the AChB trajectory (hazard ratio = 1.094; 95% confidence interval: 1.068–1.119) were predictive of dementia. However, the association between AChB and dementia held only for some classes of drugs, especially antidepressants, antiepileptics, and antidiuretics. Discussion The heterogeneity in previous findings may partially be due to different effects for different classes of drugs. Future studies should establish differences in more detail and further examine the practicality of a general measure of AChB relating to the risk of dementia.

Abstract 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

CovidLife is a longitudinal observational study designed to investigate the impact of the COVID-19 pandemic on mental health, well-being and behaviour in adults living in the UK. In total, 18,518 participants (mean age = 56.43, SD = 14.35) completed the first CovidLife questionnaire (CovidLife1) between April and June 2020. To date, participants have completed two follow-up assessments. CovidLife2 took place between July and August 2020 (n = 11,319), and CovidLife3 took place in February 2021 (n = 10,386). A range of social and psychological measures were administered at each wave including assessments of anxiety, depression, well-being, loneliness and isolation. Information on sociodemographic, health, and economic circumstances was also collected. Questions also assessed information on COVID-19 infections and symptoms, compliance to COVID-19 restrictions, and opinions on the UK and Scottish Governments’ handling of the pandemic. CovidLife includes a subsample of 4,847 participants from the Generation Scotland cohort (N~24,000, collected 2006-2011); a well-characterised cohort of families in Scotland with pre-pandemic data on mental health, physical health, lifestyle, and socioeconomic factors, along with biochemical and genomic data derived from biological samples. These participants also consented to their study data being linked to Scottish health records. CovidLife and Generation Scotland data can be accessed and used by external researchers following approval from the Generation Scotland Access Committee. CovidLife can be used to investigate mental health, well-being and behaviour during COVID-19; how these vary according to sociodemographic, health and economic circumstances; and how these change over time. The Generation Scotland subsample with pre-pandemic data and linkage to health records can be used to investigate the predictors of health and well-being during COVID-19 and the future health consequences of the COVID-19 pandemic.

Abstract Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.

Abstract Background DNA methylation changes with age. Chronological age predictors built from DNA methylation are termed ‘epigenetic clocks’. The deviation of predicted age from the actual age (‘age acceleration residual’, AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association. Methods In this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues. Results We found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91–1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79–1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor. Conclusions This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

Plasma levels of neurological proteins have the potential to serve as biomarkers for neurological conditions. Here, Hillary et al. perform genome- and epigenome-wide association studies for 92 neurological proteins and identify 41 genomic loci for 33 proteins and 26 CpG sites for 9 proteins.

Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.

Background: The causes of poor respiratory function and COPD are incompletely understood, but it is clear that genes and the environment play a role. As DNA methylation is under both genetic and environmental control, we hypothesised that investigation of differential methylation associated with these phenotypes would permit mechanistic insights, and improve prediction of COPD. We investigated genome-wide differential DNA methylation patterns using the recently released 850 K Illumina EPIC array. This is the largest single population, whole-genome epigenetic study to date. Methods: Epigenome-wide association studies (EWASs) of respiratory function and COPD were performed in peripheral blood samples from the Generation Scotland: Scottish Family Health Study (GS:SFHS) cohort (n = 3781; 274 COPD cases and 2919 controls). In independent COPD incidence data (n = 149), significantly differentially methylated sites (DMSs; p

Abstract Background Multiple epigenome-wide association studies have been performed to identify DNA methylation patterns regulated by aging or correlated with risk of death. However, the inter-relatedness of the epigenetic basis of aging and mortality has not been well investigated. Methods Using genome-wide DNA methylation data from the Lothian Birth Cohorts, we conducted a genome-wide association analysis of all-cause mortality and compared this with age-associated methylation patterns reported on the same samples. Results Survival analysis using the Cox regression model identified 2552 CpG sites with genome-wide significance (false discovery rate

Carotid intima-media thickness (cIMT) and plaque are associated with subclinical atherosclerosis and coronary heart disease (CHD). Here, the authors identify and prioritize genetic loci for cIMT and plaque by GWAS and colocalization approaches and further demonstrate genetic correlation with CHD and stroke.

Abstract Background Epigenetic age acceleration (an older methylation age compared to chronological age) correlates strongly with various age-related morbidities and mortality. Chronic systemic inflammation is thought to be a hallmark of ageing, but the relationship between an increased epigenetic age and this likely key phenotype of ageing has not yet been extensively investigated. Methods We modelled the trajectories of the inflammatory biomarkers C-reactive protein (CRP; measured using both a high- and low-sensitivity assay) and interleukin-6 (IL-6) over the eighth decade in the Lothian Birth Cohort 1936. Using linear mixed models, we investigated the association between CRP and immune cell profiles imputed from the methylation data and examined the cross-sectional and longitudinal association between the inflammatory biomarkers and two measures of epigenetic age acceleration, derived from the Horvath and Hannum epigenetic clocks. Results We found that low-sensitivity CRP declined, high-sensitivity CRP did not change, and IL-6 increased over time within the cohort. CRP levels inversely associated with CD8+T cells and CD4+T cells and positively associated with senescent CD8+T cells, plasmablasts and granulocytes. Cross-sectionally, the Hannum, but not the Horvath, measure of age acceleration was positively associated with each of the inflammatory biomarkers, including a restricted measure of CRP (≤ 10 mg/L) likely reflecting levels relevant to chronic inflammation. Conclusions We found a divergent relationship between inflammation and immune system parameters in older age. We additionally report the Hannum measure of epigenetic age acceleration associated with an elevated inflammatory profile cross-sectionally, but not longitudinally.

DNA methylation is an important biological process that involves the reversible addition of chemical tags called methyl groups to DNA and affects whether genes are active or inactive. Individual methylation profiles are determined by both genetic and environmental influences. Inter-individual variation in DNA methylation profiles can be exploited to estimate or predict a wide variety of human characteristics and disease risk profiles. Indeed, a number of methylation-based predictors of human traits have been developed and linked to important health outcomes. However, there is an unmet need to communicate the applicability and limitations of state-of-the-art methylation-based predictors to the wider community. To address this need, we have created a secure, web-based interactive platform called ‘MethylDetectR’ which automates the calculation of estimated values or scores for a variety of human traits using blood methylation data. These traits include age, lifestyle traits and high-density lipoprotein cholesterol. Methylation-based predictors often return scores on arbitrary scales. To provide meaning to these scores, users can interactively view how estimated trait scores for a given individual compare against other individuals in the sample. Users can optionally upload binary phenotypes and investigate how estimated traits vary according to case vs. control status for these phenotypes. Users can also view how different methylation-based predictors correlate with one another, and with phenotypic values for corresponding traits in a large reference sample (n = 4,450; Generation Scotland). The ‘MethylDetectR’ platform allows for the fast and secure calculation of DNA methylation-derived estimates for several human traits. This platform also helps to show the correlations between methylation-based scores and corresponding traits at the level of a sample, report estimated health profiles at an individual level, demonstrate how scores relate to important binary outcomes of interest and highlight the current limitations of molecular health predictors.

Abstract Introduction This study aims to first discover plasma proteomic biomarkers relating to neurodegeneration (N) and vascular (V) damage in cognitively normal individuals and second to discover proteins mediating sex‐related difference in N and V pathology. Methods Five thousand and thirty‐two plasma proteins were measured in 1061 cognitively normal individuals (628 females and 433 males), nearly 90% of whom had magnetic resonance imaging measures of hippocampal volume (as N) and white matter hyperintensities (as V). Results Differential protein expression analysis and co‐expression network analysis revealed different proteins and modules associated with N and V, respectively. Furthermore, causal mediation analysis revealed four proteins mediated sex‐related difference in N and one protein mediated such difference in V damage. Discussion Once validated, the identified proteins could help to select cognitively normal individuals with N and V pathology for Alzheimer's disease clinical trials and provide targets for further mechanistic studies on brain sex differences, leading to sex‐specific therapeutic strategies.

Abstract Introduction Genetic variation in the apolipoprotein E (APOE) gene is associated with Alzheimer's disease (AD) and risk factors for cardiovascular disease (CVD). DNA methylationat APOE has been associated with altered cognition and AD. It is unclear if epigenetic marks could be used for predicting future disease. Methods We assessed blood‐based DNA methylation at 13 CpGs in the APOE gene in 5828 participants from the Generation Scotland (GS) cohort. Using linear mixed models regression, we examined the relationships among APOE methylation, cognition, cholesterol, the family history of AD and the risk for CVD. Results DNA methylation at two CpGs was associated with the ratio of total cholesterol and HDL cholesterol, but not with cognition, family history of AD, or the risk of CVD. Discussion APOE methylation is associated with the levels of blood cholesterol, but there is no evidence for the utility of APOE methylation as a biomarker for predicting AD or CVD.

Abstract Introduction Dementia pathogenesis begins years before clinical symptom onset, necessitating the understanding of premorbid risk mechanisms. Here we investigated potential pathogenic mechanisms by assessing DNA methylation associations with dementia risk factors in Alzheimer's disease (AD)–free participants. Methods Associations between dementia risk measures (family history, AD genetic risk score [GRS], and dementia risk scores [combining lifestyle, demographic, and genetic factors]) and whole‐blood DNA methylation were assessed in discovery and replication samples (n = ~400 to ~5000) from Generation Scotland. Results AD genetic risk and two dementia risk scores were associated with differential methylation. The GRS associated predominantly with methylation differences in cis but also identified a genomic region implicated in Parkinson disease. Loci associated with dementia risk scores were enriched for those previously associated with body mass index and alcohol consumption. Discussion Dementia risk measures show widespread association with blood‐based methylation, generating several hypotheses for assessment by future studies.

Background: Multiple studies have made robust associations between differential DNA methylation and exposure to cigarette smoke. But whether a DNA methylation phenotype is established immediately upon exposure, or only after prolonged exposure is less well–established. Here, we assess DNA methylation patterns from peripheral blood samples in current smokers in response to dose and duration of exposure, along with the effects of smoking cessation on DNA methylation in former smokers. Methods: Dimensionality reduction was applied to DNA methylation data at 90 previously identified smoking–associated CpG sites for over 4900 individuals in the Generation Scotland cohort. K–means clustering was performed to identify clusters associated with current and never smoker status based on these methylation patterns. Cluster assignments were assessed with respect to duration of exposure in current smokers (years as a smoker), time since smoking cessation in former smokers (years), and dose (cigarettes per day). Findings: Two clusters were specified, corresponding to never smokers (97·5% of whom were assigned to Cluster 1) and current smokers (81·1% of whom were assigned to Cluster 2). The exposure time point from which >50% of current smokers were assigned to the smoker–enriched cluster varied between 5 and 9 years in heavier smokers and between 15 and 19 years in lighter smokers. Low–dose former smokers were more likely to be assigned to the never smoker–enriched cluster in the first year following cessation. In contrast, a period of at least two years was required before the majority of former high–dose smokers were assigned to the never smoker–enriched cluster. Interpretation: Our findings suggest that smoking–associated DNA methylation changes are a result of prolonged exposure to cigarette smoke, and can be reversed following cessation. The length of time in which these signatures are established and recovered is dose dependent. Should DNA methylation–based signatures of smoking status be predictive of smoking–related health outcomes, our findings may provide an additional criterion on which to stratify risk. Keywords: DNA methylation, Epigenetics, Smoking, Epidemiology

Age-related changes in DNA methylation were observed in cross-sectional studies, but longitudinal evidence is still limited. Here, we aimed to characterize longitudinal age-related methylation patterns using 1011 blood samples collected from 385 Swedish twins (age at entry: mean 69 and standard deviation 9.7, 73 monozygotic and 96 dizygotic pairs) up to five times (mean 2.6) over 20 years (mean 8.7). We identified 1316 age-associated methylation sites (P

Abstract 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.

Abstract Background The biological mechanisms underlying frailty in older people are poorly understood. There is some evidence to suggest that DNA methylation patterns may be altered in frail individuals. Methods Participants were 791 people aged 70 years from the Lothian Birth Cohort 1936. DNA methylation was measured in whole blood. Biological age was estimated using two measures of DNA methylation-based age acceleration-extrinsic and intrinsic epigenetic age acceleration. We carried out an epigenome-wide association study of physical frailty, as defined by the Fried phenotype. Multinomial logistic regression was used to calculate relative risk ratios for being physically frail or pre-frail according to epigenetic age acceleration. Results There was a single significant (P = 1.16 × 10–7) association in the epigenome-wide association study comparing frail versus not frail. The same CpG was not significant when comparing pre-frail versus not frail. Greater extrinsic epigenetic age acceleration was associated with an increased risk of being physically frail, but not of being pre-frail. For a year increase in extrinsic epigenetic age acceleration, age- and sex-adjusted relative risk ratios (95% CI) for being physically frail or pre-frail were 1.06 (1.02, 1.10) and 1.02 (1.00, 1.04), respectively. After further adjustment for smoking and chronic disease, the association with physical frailty remained significant. Intrinsic epigenetic age acceleration was not associated with physical frailty status. Conclusions People who are biologically older, as indexed by greater extrinsic epigenetic age acceleration, are more likely to be physically frail. Future research will need to investigate whether epigenetic age acceleration plays a causal role in the onset of physical frailty.