Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Unraveling Neuro-Proteogenomic Landscape and Therapeutic Implications for Human Behaviors and Psychiatric Disorders.

Understanding the genetic basis of neuro-related proteins is essential for dissecting the molecular basis of human behavioral traits and the disease etiology of neuropsychiatric disorders. Here, the SCALLOP Consortium conducted a genome-wide association meta-analysis of over 12,500 individuals for 184 neuro-related proteins in human plasma. The analysis identified 117 cis-regulatory protein quantitative trait loci (cis-pQTL) and 166 trans-pQTL. The mapped pQTL capture on average 50% of each protein's heritability. Mendelian randomization analyses revealed multiple proteins showing potential causal effects on neuro-related traits such as sleeping, smoking, feelings, alcohol intake, mental health, and psychiatric disorders. Integrating with established drug information, we validated 13 out of 13 matched combinations of protein targets and diseases or side effects with available drugs, while suggesting hundreds of re-purposing and new therapeutic targets. This consortium effort provides a large-scale proteogenomic resource for biomedical research on human behaviors and other neuro-related phenotypes.

Longitudinal dynamics of clonal hematopoiesis identifies gene-specific fitness effects.

Clonal hematopoiesis of indeterminate potential (CHIP) increases rapidly in prevalence beyond age 60 and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in hematopoietic stem and progenitor cells (HSPCs). Because mutations in HSPCs often drive leukemia, we hypothesized that HSPC fitness substantially contributes to transformation from CHIP to leukemia. HSPC fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. If mutations in different genes lead to distinct fitness advantages, this could enable patient stratification. We quantified the fitness effects of mutations over 12 years in older age using longitudinal sequencing and developed a filtering method that considers individual mutational context alongside mutation co-occurrence to quantify the growth potential of variants within individuals. We found that gene-specific fitness differences can outweigh inter-individual variation and, therefore, could form the basis for personalized clinical management.

Integrative analysis of clinical and epigenetic biomarkers of mortality.

DNA methylation (DNAm) has been reported to be associated with many diseases and with mortality. We hypothesized that the integration of DNAm with clinical risk factors would improve mortality prediction. We performed an epigenome-wide association study of whole blood DNAm in relation to mortality in 15 cohorts (n = 15,013). During a mean follow-up of 10 years, there were 4314 deaths from all causes including 1235 cardiovascular disease (CVD) deaths and 868 cancer deaths. Ancestry-stratified meta-analysis of all-cause mortality identified 163 CpGs in European ancestry (EA) and 17 in African ancestry (AA) participants at p < 1 × 10-7 , of which 41 (EA) and 16 (AA) were also associated with CVD death, and 15 (EA) and 9 (AA) with cancer death. We built DNAm-based prediction models for all-cause mortality that predicted mortality risk after adjusting for clinical risk factors. The mortality prediction model trained by integrating DNAm with clinical risk factors showed an improvement in prediction of cancer death with 5% increase in the C-index in a replication cohort, compared with the model including clinical risk factors alone. Mendelian randomization identified 15 putatively causal CpGs in relation to longevity, CVD, or cancer risk. For example, cg06885782 (in KCNQ4) was positively associated with risk for prostate cancer (Beta = 1.2, PMR  = 4.1 × 10-4 ) and negatively associated with longevity (Beta = -1.9, PMR  = 0.02). Pathway analysis revealed that genes associated with mortality-related CpGs are enriched for immune- and cancer-related pathways. We identified replicable DNAm signatures of mortality and demonstrated the potential utility of CpGs as informative biomarkers for prediction of mortality risk.

DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases.

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Contribution of white matter hyperintensities to ventricular enlargement in older adults.

Lateral ventricles might increase due to generalized tissue loss related to brain atrophy. Alternatively, they may expand into areas of tissue loss related to white matter hyperintensities (WMH). We assessed longitudinal associations between lateral ventricle and WMH volumes, accounting for total brain volume, blood pressure, history of stroke, cardiovascular disease, diabetes and smoking at ages 73, 76 and 79, in participants from the Lothian Birth Cohort 1936, including MRI data from all available time points. Lateral ventricle volume increased steadily with age, WMH volume change was more variable. WMH volume decreased in 20% and increased in remaining subjects. Over 6 years, lateral ventricle volume increased by 3% per year of age, 0.1% per mm Hg increase in blood pressure, 3.2% per 1% decrease of total brain volume, and 4.5% per 1% increase of WMH volume. Over time, lateral ventricle volumes were 19% smaller in women than men. Ventricular and WMH volume changes are modestly associated and independent of general brain atrophy, suggesting that their underlying processes do not fully overlap.

Markers of kidney function, genetic variation related to cognitive function, and cognitive performance in the UK Biobank.

BACKGROUND: Chronic kidney disease has been linked to worse cognition. However, this association may be dependent on the marker of kidney function used, and studies assessing modification by genetics are lacking. This study examined associations between multiple measures of kidney function and assessed effect modification by a polygenic score for general cognitive function. METHODS: In this cross-sectional study of up to 341,208 European ancestry participants from the UK Biobank study, we examined associations between albuminuria and estimated glomerular filtration rate based on creatinine (eGFRcre) or cystatin C (eGFRcys) with cognitive performance on tests of verbal-numeric reasoning, reaction time and visual memory. Adjustment for confounding factors was performed using multivariate regression and propensity-score matching. Interaction between kidney function markers and a polygenic risk score for general cognitive function was also assessed. RESULTS: Albuminuria was associated with worse performance on tasks of verbal-numeric reasoning (ß(points) = -0.09, p < 0.001), reaction time (ß(milliseconds) = 7.06, p < 0.001) and visual memory (ß(log errors) = 0.013, p = 0.01). A polygenic score for cognitive function modified the association between albuminuria and verbal-numeric reasoning with significantly lower scores in those with albuminuria and a lower polygenic score (p = 0.009). Compared to participants with eGFRcre ≥ 60 ml/min, those with eGFRcre < 60 ml/min had lower verbal-numeric reasoning scores and slower mean reaction times (verbal numeric reasoning ß = -0.11, p < 0.001 and reaction time ß = 6.08, p < 0.001 for eGFRcre < 60 vs eGFRcre ≥ 60). Associations were stronger using cystatin C-based eGFR than creatinine-based eGFR (verbal numeric reasoning ß = -0.21, p < 0.001 and reaction time ß = 11.21, p < 0.001 for eGFRcys < 60 vs eGFRcys ≥ 60). CONCLUSIONS: Increased urine albumin is associated with worse cognition, but this may depend on genetic risk. Cystatin C-based eGFR may better predict cognitive performance than creatinine-based estimates.

Psychological correlates of free colorectal cancer screening uptake in a Scottish sample: a cross-sectional observational study.

OBJECTIVES: Colorectal cancer (CRC) screening uptake in Scotland is 56%. This study examined whether psychological factors were associated with CRC screening uptake. DESIGN: Cross-sectional observational study. SETTING: This study used data from the Healthy AGeing In Scotland (HAGIS) pilot study, a study designed to be representative of Scottish adults aged 50 years and older. PARTICIPANTS: 908 (505 female) Scottish adults aged 50-80 years (mean age=65.85, SD=8.23), who took part in the HAGIS study (2016-2017). PRIMARY AND SECONDARY OUTCOME MEASURES: Self-reported participation in CRC screening was the outcome measure. Logistic regression was used to test whether scores on measures of health literacy, cognitive ability, risk aversion, time preference (eg, present oriented or future oriented) and personality were associated with CRC screening when these psychological factors were entered individually and simultaneously in the same model. RESULTS: Controlling for age, age-squared, sex, living arrangement, and sex*living arrangement, a one-point increase in risk aversion (OR=0.66, 95% CI 0.51 to 0.85) and present orientation (OR=0.86, 95% CI 0.80 to 0.94) was associated with reduced odds of screening. Higher scores on health literacy (OR per one-point increase=1.20, 95% CI 1.09 to 1.31), cognitive ability (OR per SD increase=1.51, 95% CI 1.25 to 1.81) and the intellect personality trait (OR per one-point increase=1.05, 95% CI 1.01 to 1.09) were associated with increased odds of screening. Higher risk aversion was the only psychological variable that was associated with CRC screening participation when all psychological variables were entered in the same model and remained associated with CRC screening when additionally adjusting for deprivation and education. A backward elimination model retained two psychological variables as correlates of CRC screening: risk aversion and cognitive ability. CONCLUSION: Individuals who are more risk averse are less likely to participate in free, home CRC screening.

Circulating Metabolome and White Matter Hyperintensities in Women and Men.

BACKGROUND: White matter hyperintensities (WMH), identified on T2-weighted magnetic resonance images of the human brain as areas of enhanced brightness, are a major risk factor of stroke, dementia, and death. There are no large-scale studies testing associations between WMH and circulating metabolites. METHODS: We studied up to 9290 individuals (50.7% female, average age 61 years) from 15 populations of 8 community-based cohorts. WMH volume was quantified from T2-weighted or fluid-attenuated inversion recovery images or as hypointensities on T1-weighted images. Circulating metabolomic measures were assessed with mass spectrometry and nuclear magnetic resonance spectroscopy. Associations between WMH and metabolomic measures were tested by fitting linear regression models in the pooled sample and in sex-stratified and statin treatment-stratified subsamples. Our basic models were adjusted for age, sex, age×sex, and technical covariates, and our fully adjusted models were also adjusted for statin treatment, hypertension, type 2 diabetes, smoking, body mass index, and estimated glomerular filtration rate. Population-specific results were meta-analyzed using the fixed-effect inverse variance-weighted method. Associations with false discovery rate (FDR)-adjusted P values (PFDR)<0.05 were considered significant. RESULTS: In the meta-analysis of results from the basic models, we identified 30 metabolomic measures associated with WMH (PFDR<0.05), 7 of which remained significant in the fully adjusted models. The most significant association was with higher level of hydroxyphenylpyruvate in men (PFDR.full.adj=1.40×10-7) and in both the pooled sample (PFDR.full.adj=1.66×10-4) and statin-untreated (PFDR.full.adj=1.65×10-6) subsample. In men, hydroxyphenylpyruvate explained 3% to 14% of variance in WMH. In men and the pooled sample, WMH were also associated with lower levels of lysophosphatidylcholines and hydroxysphingomyelins and a larger diameter of low-density lipoprotein particles, likely arising from higher triglyceride to total lipids and lower cholesteryl ester to total lipids ratios within these particles. In women, the only significant association was with higher level of glucuronate (PFDR=0.047). CONCLUSIONS: Circulating metabolomic measures, including multiple lipid measures (eg, lysophosphatidylcholines, hydroxysphingomyelins, low-density lipoprotein size and composition) and nonlipid metabolites (eg, hydroxyphenylpyruvate, glucuronate), associate with WMH in a general population of middle-aged and older adults. Some metabolomic measures show marked sex specificities and explain a sizable proportion of WMH variance.

Epigenetic scores for the circulating proteome as tools for disease prediction.

Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNA methylation (DNAm) signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample (Generation Scotland; n = 9537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore-disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors, and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.

Although our genetic code does not change throughout our lives, our genes can be turned on and off as a result of epigenetics. Epigenetics can track how the environment and even certain behaviors add or remove small chemical markers to the DNA that makes up the genome. The type and location of these markers may affect whether genes are active or silent, this is, whether the protein coded for by that gene is being produced or not. One common epigenetic marker is known as DNA methylation. DNA methylation has been linked to the levels of a range of proteins in our cells and the risk people have of developing chronic diseases. Blood samples can be used to determine the epigenetic markers a person has on their genome and to study the abundance of many proteins. Gadd, Hillary, McCartney, Zaghlool et al. studied the relationships between DNA methylation and the abundance of 953 different proteins in blood samples from individuals in the German KORA cohort and the Scottish Lothian Birth Cohort 1936. They then used machine learning to analyze the relationship between epigenetic markers found in people's blood and the abundance of proteins, obtaining epigenetic scores or 'EpiScores' for each protein. They found 109 proteins for which DNA methylation patterns explained between at least 1% and up to 58% of the variation in protein levels. Integrating the 'EpiScores' with 14 years of medical records for more than 9000 individuals from the Generation Scotland study revealed 130 connections between EpiScores for proteins and a future diagnosis of common adverse health outcomes. These included diabetes, stroke, depression, various cancers, and inflammatory conditions such as rheumatoid arthritis and inflammatory bowel disease. Age-related chronic diseases are a growing issue worldwide and place pressure on healthcare systems. They also severely reduce quality of life for individuals over many years. This work shows how epigenetic scores based on protein levels in the blood could predict a person's risk of several of these diseases. In the case of type 2 diabetes, the EpiScore results replicated previous research linking protein levels in the blood to future diagnosis of diabetes. Protein EpiScores could therefore allow researchers to identify people with the highest risk of disease, making it possible to intervene early and prevent these people from developing chronic conditions as they age.

Genome-wide interaction study with major depression identifies novel variants associated with cognitive function.

Major Depressive Disorder (MDD) often is associated with significant cognitive dysfunction. We conducted a meta-analysis of genome-wide interaction of MDD and cognitive function using data from four large European cohorts in a total of 3510 MDD cases and 6057 controls. In addition, we conducted analyses using polygenic risk scores (PRS) based on data from the Psychiatric Genomics Consortium (PGC) on the traits of MDD, Bipolar disorder (BD), Schizophrenia (SCZ), and mood instability (MIN). Functional exploration contained gene expression analyses and Ingenuity Pathway Analysis (IPA®). We identified a set of significantly interacting single nucleotide polymorphisms (SNPs) between MDD and the genome-wide association study (GWAS) of cognitive domains of executive function, processing speed, and global cognition. Several of these SNPs are located in genes expressed in brain, with important roles such as neuronal development (REST), oligodendrocyte maturation (TNFRSF21), and myelination (ARFGEF1). IPA® identified a set of core genes from our dataset that mapped to a wide range of canonical pathways and biological functions (MPO, FOXO1, PDE3A, TSLP, NLRP9, ADAMTS5, ROBO1, REST). Furthermore, IPA® identified upstream regulator molecules and causal networks impacting on the expression of dataset genes, providing a genetic basis for further clinical exploration (vitamin D receptor, beta-estradiol, tadalafil). PRS of MIN and meta-PRS of MDD, MIN and SCZ were significantly associated with all cognitive domains. Our results suggest several genes involved in physiological processes for the development and maintenance of cognition in MDD, as well as potential novel therapeutic agents that could be explored in patients with MDD associated cognitive dysfunction.

DNA Methylation and Protein Markers of Chronic Inflammation and Their Associations With Brain and Cognitive Aging.

BACKGROUND AND OBJECTIVES: To investigate chronic inflammation in relation to cognitive aging by comparison of an epigenetic and serum biomarker of C-reactive protein and their associations with neuroimaging and cognitive outcomes. METHODS: At baseline, participants (n = 521) were cognitively normal, around 73 years of age (mean 72.4, SD 0.716), and had inflammation, vascular risk (cardiovascular disease history, hypertension, diabetes, smoking, alcohol consumption, body mass index), and neuroimaging (structural and diffusion MRI) data available. Baseline inflammatory status was quantified by a traditional measure of peripheral inflammation-serum C-reactive protein (CRP)-and an epigenetic measure (DNA methylation [DNAm] signature of CRP). Linear models were used to examine the inflammation-brain health associations; mediation analyses were performed to interrogate the relationship between chronic inflammation, brain structure, and cognitive functioning. RESULTS: We demonstrate that DNAm CRP shows significantly (on average 6.4-fold) stronger associations with brain health outcomes than serum CRP. DNAm CRP is associated with total brain volume (ß = -0.197, 95% confidence interval [CI] -0.28 to -0.12, p FDR = 8.42 × 10-6), gray matter volume (ß = -0.200, 95% CI -0.28 to -0.12, p FDR = 1.66 × 10-5), and white matter volume (ß = -0.150, 95% CI -0.23 to -0.07, p FDR = 0.001) and regional brain atrophy. We also find that DNAm CRP has an inverse association with global and domain-specific (speed, visuospatial, and memory) cognitive functioning and that brain structure partially mediates this CRP-cognitive association (up to 29.7%), dependent on lifestyle and health factors. DISCUSSION: These results support the hypothesis that chronic inflammation may contribute to neurodegenerative brain changes that underlie differences in cognitive ability in later life and highlight the potential of DNAm proxies for indexing chronic inflammatory status. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a DNAm signature of CRP levels is more strongly associated with brain health outcomes than serum CRP levels.

Rare and low-frequency exonic variants and gene-by-smoking interactions in pulmonary function.

Genome-wide association studies have identified numerous common genetic variants associated with spirometric measures of pulmonary function, including forced expiratory volume in one second (FEV1), forced vital capacity, and their ratio. However, variants with lower minor allele frequencies are less explored. We conducted a large-scale gene-smoking interaction meta-analysis on exonic rare and low-frequency variants involving 44,429 individuals of European ancestry in the discovery stage and sought replication in the UK BiLEVE study with 45,133 European ancestry samples and UK Biobank study with 59,478 samples. We leveraged data on cigarette smoking, the major environmental risk factor for reduced lung function, by testing gene-by-smoking interaction effects only and simultaneously testing the genetic main effects and interaction effects. The most statistically significant signal that replicated was a previously reported low-frequency signal in GPR126, distinct from common variant associations in this gene. Although only nominal replication was obtained for a top rare variant signal rs142935352 in one of the two studies, interaction and joint tests for current smoking and PDE3B were significantly associated with FEV1. This study investigates the utility of assessing gene-by-smoking interactions and underscores their effects on potential pulmonary function.

Meta-analysis of epigenome-wide association studies of carotid intima-media thickness.

Common carotid intima-media thickness (cIMT) is an index of subclinical atherosclerosis that is associated with ischemic stroke and coronary artery disease (CAD). We undertook a cross-sectional epigenome-wide association study (EWAS) of measures of cIMT in 6400 individuals. Mendelian randomization analysis was applied to investigate the potential causal role of DNA methylation in the link between atherosclerotic cardiovascular risk factors and cIMT or clinical cardiovascular disease. The CpG site cg05575921 was associated with cIMT (beta = -0.0264, p value = 3.5 × 10-8) in the discovery panel and was replicated in replication panel (beta = -0.07, p value = 0.005). This CpG is located at chr5:81649347 in the intron 3 of the aryl hydrocarbon receptor repressor gene (AHRR). Our results indicate that DNA methylation at cg05575921 might be in the pathway between smoking, cIMT and stroke. Moreover, in a region-based analysis, 34 differentially methylated regions (DMRs) were identified of which a DMR upstream of ALOX12 showed the strongest association with cIMT (p value = 1.4 × 10-13). In conclusion, our study suggests that DNA methylation may play a role in the link between cardiovascular risk factors, cIMT and clinical cardiovascular disease.

Apolipoprotein E e4 allele status and later-life depression in the Lothian Birth Cohort 1936.

BACKGROUND: Previous results have been mixed regarding the role of the apolipoprotein E e4 (APOE e4) allele in later-life depression: some studies note that carriers experience greater symptoms and increased risk while others find no such association. However, there are few prospective, population-based studies of the APOE e4-depression association and fewer that examine depressive symptom trajectory and depression risk longitudinally. We examined the association between APOE e4 allele status and longitudinal change in depressive symptoms and depression risk in later-life, over a 12-year follow-up period. METHODS: We used data from 690 participants of the Lothian Birth Cohort 1936 who took part in the Scottish Mental Survey 1947 (aged 11) and were followed-up in later-life over five waves from 2004 to 2019 (aged 70-82). We used APOE e4 allele status to predict longitudinal change in depressive symptom scores and risk of depression (defined by a symptom score threshold or use of depression-related medication). Models were adjusted for sex, childhood cognitive ability, childhood social class, education, adult social class, smoking status and functional limitations at baseline. RESULTS: Depressive symptom scores increased with age. Once adjusted for covariates, APOE e4 allele status did not significantly predict symptom score trajectories or depression risk. Greater functional limitations at baseline significantly predicted poorer symptom score trajectories and increased depression risk (defined by medications). APOE e4 allele status did not significantly moderate the contribution of sex, education or functional limitations. CONCLUSIONS: There was no evidence that APOE e4 carriers experience an increased risk for later-life depression.

DNA methylation of blood cells is associated with prevalent type 2 diabetes in a meta-analysis of four European cohorts.

BACKGROUND: Type 2 diabetes (T2D) is a heterogeneous disease with well-known genetic and environmental risk factors contributing to its prevalence. Epigenetic mechanisms related to changes in DNA methylation (DNAm), may also contribute to T2D risk, but larger studies are required to discover novel markers, and to confirm existing ones. RESULTS: We performed a large meta-analysis of individual epigenome-wide association studies (EWAS) of prevalent T2D conducted in four European studies using peripheral blood DNAm. Analysis of differentially methylated regions (DMR) was also undertaken, based on the meta-analysis results. We found three novel CpGs associated with prevalent T2D in Europeans at cg00144180 (HDAC4), cg16765088 (near SYNM) and cg24704287 (near MIR23A) and confirmed three CpGs previously identified (mapping to TXNIP, ABCG1 and CPT1A). We also identified 77 T2D associated DMRs, most of them hypomethylated in T2D cases versus controls. In adjusted regressions among diabetic-free participants in ALSPAC, we found that all six CpGs identified in the meta-EWAS were associated with white cell-types. We estimated that these six CpGs captured 11% of the variation in T2D, which was similar to the variation explained by the model including only the common risk factors of BMI, sex, age and smoking (R2 = 10.6%). CONCLUSIONS: This study identifies novel loci associated with T2D in Europeans. We also demonstrate associations of the same loci with other traits. Future studies should investigate if our findings are generalizable in non-European populations, and potential roles of these epigenetic markers in T2D etiology or in determining long term consequences of T2D.

Creating and Validating a DNA Methylation-Based Proxy for Interleukin-6.

BACKGROUND: Studies evaluating the relationship between chronic inflammation and cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals' persisting levels of inflammation. DNA methylation (DNAm) has shown utility in indexing environmental exposures and could be leveraged to provide proxy signatures of chronic inflammation. METHOD: We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 875 older adults (Lothian Birth Cohort 1936; mean age: 70 years) to develop a DNAm-based predictor. The predictor was tested in an independent cohort (Generation Scotland; N = 7028 [417 with measured IL-6], mean age: 51 years). RESULTS: A weighted score from 35 CpG sites optimally predicted IL-6 in the independent test set (Generation Scotland; R2 = 4.4%, p = 2.1 × 10-5). In the independent test cohort, both measured IL-6 and the DNAm proxy increased with age (serum IL-6: n = 417, ß = 0.02, SE = 0.004, p = 1.3 × 10-7; DNAm IL-6 score: N = 7028, ß = 0.02, SE = 0.0009, p < 2 × 10-16). Serum IL-6 did not associate with cognitive ability (n = 417, ß = -0.06, SE = 0.05, p = .19); however, an inverse association was identified between the DNAm score and cognitive functioning (N = 7028, ß = -0.16, SE = 0.02, pFDR < 2 × 10-16). CONCLUSIONS: These results suggest methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for further insight into the relationship between inflammation and pertinent health outcomes.

Life Course Air Pollution Exposure and Cognitive Decline: Modelled Historical Air Pollution Data and the Lothian Birth Cohort 1936.

BACKGROUND: Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data. OBJECTIVE: To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels. METHODS: Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking. RESULTS: Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11-70 years (ß = -0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70-79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05). CONCLUSION: The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health.

Genetic and shared couple environmental contributions to smoking and alcohol use in the UK population.

Alcohol use and smoking are leading causes of death and disability worldwide. Both genetic and environmental factors have been shown to influence individual differences in the use of these substances. In the present study we tested whether genetic factors, modelled alongside common family environment, explained phenotypic variance in alcohol use and smoking behaviour in the Generation Scotland (GS) family sample of up to 19,377 individuals. SNP and pedigree-associated effects combined explained between 18 and 41% of the variance in substance use. Shared couple effects explained a significant amount of variance across all substance use traits, particularly alcohol intake, for which 38% of the phenotypic variance was explained. We tested whether the within-couple substance use associations were due to assortative mating by testing the association between partner polygenic risk scores in 34,987 couple pairs from the UK Biobank (UKB). No significant association between partner polygenic risk scores were observed. Associations between an individual's alcohol PRS (b = 0.05, S.E. = 0.006, p < 2 × 10-16) and smoking status PRS (b = 0.05, S.E. = 0.005, p < 2 × 10-16) were found with their partner's phenotype. In support of this, G carriers of a functional ADH1B polymorphism (rs1229984), known to be associated with greater alcohol intake, were found to consume less alcohol if they had a partner who carried an A allele at this SNP. Together these results show that the shared couple environment contributes significantly to patterns of substance use. It is unclear whether this is due to shared environmental factors, assortative mating, or indirect genetic effects. Future studies would benefit from longitudinal data and larger sample sizes to assess this further.

Epigenetic prediction of major depressive disorder.

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 (ß = 0.338, p = 1.17 × 10-7) and remained associated after adjustment for lifestyle factors (ß = 0.219, p = 0.001, R2 = 0.68%). When modelled alongside PRS (ß = 0.384, p = 4.69 × 10-9) the MRS remained associated with MDD (ß = 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 (ß = 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 (ß = 0.440, p ≤ 2 × 10-16). After removing smokers from the training set, the MRS strongly associated with BMI (ß = 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.