of the most promising biomarkers of aging that has emerged is the epigenetic clock. Some subset of DNA methylation (DNAm) has been shown to change predictably over the life span (Florath, Butterbach,

Living in a disadvantaged neighborhood is associated with poor health outcomes even after accounting for individual-level socioeconomic factors. The chronic stress of unfavorable neighborhood conditions may lead to dysregulation of the stress reactivity and inflammatory pathways, potentially mediated through epigenetic mechanisms such as DNA methylation. We used multi-level models to examine the relationship between 2 neighborhood conditions and methylation levels of 18 genes related to stress reactivity and inflammation in purified monocytes from 1,226 participants of the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based sample of US adults. Neighborhood socioeconomic disadvantage, a summary of 16 census-based metrics, was associated with DNA methylation [False discovery rate (FDR) q-value ≤ 0.1] in 2 out of 7 stress-related genes evaluated (CRF, SLC6A4) and 2 out of 11 inflammation-related genes (F8, TLR1). Neighborhood social environment, a summary measure of aesthetic quality, safety, and social cohesion, was associated with methylation in 4 of the 7 stress-related genes (AVP, BDNF, FKBP5, SLC6A4) and 7 of the 11 inflammation-related genes (CCL1, CD1D, F8, KLRG1, NLRP12, SLAMF7, TLR1). High socioeconomic disadvantage and worse social environment were primarily associated with increased methylation. In 5 genes with significant associations between neighborhood and methylation (FKBP5, CD1D, F8, KLRG1, NLRP12), methylation was associated with gene expression of at least one transcript. These results demonstrate that multiple dimensions of neighborhood context may influence methylation levels and subsequent gene expression of stress- and inflammation-related genes, even after accounting for individual socioeconomic factors. Further elucidating the molecular mechanisms underlying these relationships will be important for understanding the etiology of health disparities.

SUMMARY Background and Aims To estimate the extent which proton pump inhibitors (PPIs) increase the rate of infections among patients with decompensated cirrhosis. Methods We conducted a retrospective propensity-matched new user design using US Veterans Health Administration data. Only decompensated cirrhotic patients from 2001–2009 were included. New PPI users after decompensation (n=1,268) were 1:1 matched to those who did not initiate gastric acid suppression. Serious infections, defined as infections associated with a hospitalization, were the outcomes. These were separated into acid suppression-related (SBP, bacteremia, C.difficile and pneumonia) and non-acid suppression-related. Time varying Cox models were used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CI) of serious infections. Parallel analyses were conducted with H2 receptor antagonists(H2RA). Results More than half of persons with decompensated cirrhosis were new users of gastric acid suppressants, with most using PPIs (45.6%) compared to H2RAs (5.9%). In the PPI propensity matched analysis, 25.3% developed serious infections and 25.9% developed serious infections in the H2RA analysis. PPI users developed serious infections faster than non-gastric acid suppression users (adjusted HR: 1.66; 95% CI:1.31–2.12). For acid suppression related serious infections, PPI users developed the outcome at a rate 1.75 times faster than non-users (95% CI: 1.32 to 2.34). The H2RA findings were not statistically significant (HR serious infections: 1.59; 95% CI: 0.80–3.18; HR acid suppression related infections: 0.92; 95% CI: 0.31–2.73). Conclusions Among patients with decompensated cirrhosis, PPIs but not H2RAs increase the rate of serious infections.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

DNA methylation (DNAm) clocks are important biomarkers of cellular aging and are associated with a variety of age-related chronic diseases and all-cause mortality. Examining the relationship between education and lifestyle risk factors for age-related diseases and multiple DNAm clocks can increase the understanding of how risk factors contribute to aging at the cellular level. This study explored the association between education or lifestyle risk factors for age-related diseases and the acceleration of four DNAm clocks, including intrinsic (IEAA) and extrinsic epigenetic age acceleration (EEAA), PhenoAge acceleration (PhenoAA), and GrimAge acceleration (GrimAA) in the African American participants of the Genetic Epidemiology Network of Arteriopathy. We performed both cross-sectional and longitudinal analyses. In cross-sectional analyses, gender, education, BMI, smoking, and alcohol consumption were all independently associated with GrimAA, whereas only some of them were associated with other clocks. The effect of smoking and education on GrimAA varied by gender. Longitudinal analyses suggest that age and BMI continued to increase GrimAA, and that age and current smoking continued to increase PhenoAA after controlling DNAm clocks at baseline. In conclusion, education and common lifestyle risk factors were associated with multiple DNAm clocks. However, the association with each risk factor varied by clock, which suggests that different clocks may capture adverse effects from different environmental stimuli.

Excessive alcohol consumption is one of the main causes of death and disability worldwide. Alcohol consumption is a heritable complex trait. We conducted a meta-analysis of genome-wide association studies (GWAS) of gram/day (g/d) alcohol consumption in UK-Biobank, AlcGen and CHARGE+ consortia accumulating 480,842 people of European descent to decipher the genetic architecture of alcohol intake. We identified 46 novel, common loci, and investigated their potential functional significance using magnetic resonance imaging data and gene expression studies. Our results identify genetic pathways associated with alcohol consumption and suggest shared genetic mechanisms with neuropsychiatric disorders including schizophrenia.

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.

Objective Although multiple noncost factors likely influence a patient’s propensity to forego treatment in the face of cost pressures, little is known about how patients’ sociodemographic characteristics, physical and behavioral health comorbidities, and prescription regimens influence cost-related nonadherence (CRN) to medications. We sought to determine both financial and nonfinancial factors associated with CRN in a nationally representative sample of older adults. Methods We used a conceptual model developed by Piette and colleagues that describes financial and nonfinancial factors that could increase someone’s risk of CRN, including income, comorbidities, and medication regimen complexity. We used data from the 2004 wave of the Health and Retirement Study and the 2005 HRS Prescription Drug Study to examine the influence of factors within each of these domains on measures of CRN (including not filling, stopping, or skipping doses) in a nationally representative sample of Americans age 65+ in 2005. Results Of the 3071 respondents who met study criteria, 20% reported some form of CRN in 2005. As in prior studies, indicators of financial stress such as higher out-of-pocket payments for medications and lower net worth were significantly associated with CRN in multivariable analyses. Controlling for these economic pressures, relatively younger respondents (ages 65–74) and depressive symptoms were consistent independent risk factors for CRN. Conclusions Noncost factors influenced patients’ propensity to forego treatment even in the context of cost concerns. Future research encompassing clinician and health system factors should identify additional determinants of CRN beyond patients’ cost pressures.